Navigating the Unknown: Structural Uncertainty in Marine Food Webs and Its Implications for Ecosystem Forecasting

Abstract

This article synthesizes current research on structural uncertainty in marine food webs—the inherent limitations in our understanding of species interactions and network architecture. It explores the foundational sources of this uncertainty, reviews emerging methodological frameworks like Qualitative Network Analysis (QNA) and compound-specific stable isotope analysis (CSIA-AA) designed to address it, and presents case studies on troubleshooting model robustness from the Baltic Sea to coral reefs. By comparing validation techniques and their application in fisheries and climate change scenarios, this review provides researchers and ecosystem managers with a strategic framework for improving the reliability of ecological forecasts and management strategies in the face of environmental change.

Defining the Blind Spots: What is Structural Uncertainty in Marine Food Webs?

This technical guide examines structural uncertainty within the analysis of marine food webs, distinguishing it from more commonly addressed parameter uncertainty. Whereas parameter uncertainty concerns imprecision in measurable variables, structural uncertainty arises from incomplete knowledge of the system's fundamental architecture, such as the presence and sign of species interactions [1]. Through Qualitative Network Analysis (QNA) and other ecosystem-based management tools, this paper provides a framework for navigating this deeper layer of uncertainty, which is critical for robust ecological forecasting and effective conservation policy, particularly under the stressors of climate change [1].

In ecological modeling, and specifically in the study of marine food webs, structural uncertainty refers to the unknown or ambiguous nature of the system components and their interactions. This encompasses uncertainty about which species are present, how they are connected through trophic and non-trophic links, and the qualitative nature (e.g., positive, negative, or neutral) of those connections [1]. This is distinct from parameter uncertainty, which deals with the precision of known quantitative values within an already-established model structure, such as growth rates or interaction strengths.

Ignoring structural uncertainty can lead to profoundly inaccurate predictions of ecosystem responses to perturbations like climate change, overfishing, or pollution. For instance, the survival of endangered species such as Chinook salmon can be significantly influenced by how their interactions with competitors, predators, and prey are represented in a model [1]. Effectively modeling the impact of climate change on any population requires careful consideration of these diverse pressures and potential changes in species interactions [1].

Analytical Frameworks for Navigating Structural Uncertainty

Qualitative Network Analysis (QNA)

Qualitative Network Analysis (QNA) is a computational approach that uses signed digraphs to represent ecosystems. In these graphs, nodes represent functional groups (e.g., basal resources, primary consumers, predators), and edges represent interactions, which are designated as positive (+), negative (-), or zero [1].

A primary advantage of QNA is its ability to explore a wide range of plausible food web structures without requiring precise, quantitative data for every interaction. This makes it particularly valuable in data-poor environments, which are common in marine ecology. By testing numerous alternative network configurations, researchers can identify which structural assumptions most strongly influence key outcomes, such as salmon survival [1].

Table 1: Core Components of a Qualitative Network Model for a Marine Food Web

Component	Description	Example in a Marine Context
Nodes	Functional groups or species within the ecosystem.	Phytoplankton, Zooplankton, Forage Fish, Chinook Salmon, Marine Mammal Predators.
Edges (Links)	Trophic and non-trophic interactions between nodes.	Salmon preys on Zooplankton (-/+); Mammal Predator preys on Salmon (-/+).
Sign (+, -)	The qualitative effect of one node on another.	A positive effect (+) could be a food source; a negative effect (-) could be predation or competition.
Press Perturbation	A sustained, directional change to one or more nodes.	A sustained increase in sea temperature affecting Phytoplankton growth.

Ensemble Modeling and Scenario Analysis

To formally account for structural uncertainty, ensemble modeling is employed. This involves building and analyzing not one, but a suite of plausible model configurations. A study on salmon survival, for example, tested 36 different plausible representations of the marine food web [1]. These scenarios differed in how species pairs were connected and which species responded directly to climate change.

The outcomes across this ensemble of models can be analyzed to determine:

• The sensitivity of key outcomes (e.g., salmon abundance) to different structural assumptions.
• Which specific links or feedback loops are most critical in determining model outcomes.
• Whether predictions are robust (consistent across most plausible structures) or fragile (highly dependent on specific, uncertain structural choices) [1].

Methodological Protocols for Structural Uncertainty Analysis

Protocol: Constructing and Analyzing a Qualitative Network

This protocol outlines the steps for applying QNA to investigate structural uncertainty in a marine food web.

Step 1: System Delineation and Node Selection

• Define the spatial and temporal boundaries of the ecosystem under study (e.g., the California Current system during a marine heatwave) [1].
• Identify and define the key functional groups to be represented as nodes. The resolution should be determined by the research question and available ecological knowledge.

Step 2: Link Identification and Signing

• For each pair of nodes, establish the presence and sign of direct interactions based on literature, expert elicitation, and empirical data [1] [2].
• Document all plausible alternative interactions for links where the existence or sign is uncertain. For instance, the connection between spring-run and fall-run salmon may be represented as positive, negative, or non-existent in different model scenarios [1].

Step 3: Model Assembly and Perturbation

• Assemble the signed digraph into a community matrix for analysis.
• Apply a press perturbation to the network, representing a sustained external change like climate warming [1].
• Use the QNA algorithm to predict the qualitative response (increase, decrease, or no change) of each node in the network to the perturbation.

Step 4: Ensemble Analysis and Validation

• Repeat Step 3 for all alternative network structures in the ensemble.
• Compare the predicted outcomes for species of concern (e.g., salmon) across all scenarios.
• Identify the network configurations that produce outcomes consistent with empirical observations (e.g., data collected during a marine heatwave) to help constrain the most plausible structures [1].

Protocol: Quantitative Analysis of Food Web Motifs

A complementary, more quantitative approach involves analyzing the local structure of food webs through the statistics of small subgraphs, or motifs [3].

Step 1: Food Web Data Compilation

• Obtain high-quality, empirically observed food web data. This data is often derived from detailed diet analysis, which can be both qualitative and quantitative, setting up a table of predator-prey interactions [2].

Step 2: Motif Census

• For a given empirical food web, count the number of appearances of all possible three-node subgraphs (e.g., simple food chains, omnivory, exploitative competition) [3].
• Calculate the probability of appearance for each motif using the formula: p_i = N_i / [S(S-1)(S-2)/6] where N_i is the number of appearances of subgraph i, and S is the total number of species in the web [3].

Step 3: Comparison to Null Models

• Generate randomized networks that preserve basic properties of the original web (e.g., each species' number of prey and predators) but are otherwise random [3].
• Compare the observed motif frequencies to those expected in the randomized networks to determine which motifs are statistically over-represented or under-represented.
• This pattern of motif representation reveals the underlying "building blocks" of the food web's architecture, providing insight into its structural constraints [3].

Table 2: Key Three-Node Food Web Motifs and Their Ecological Interpretation

Motif ID	Structure	Ecological Interpretation	Notes
S1	A → B → C	Simple linear food chain.	Forbidden in the Generalized Cascade Model due to no loops rule [3].
S2	A → C, B → C	Exploitative competition.	Two predators share a common prey resource.
S3	A → B, B → C, C → A	Trophic loop (3-cycle).	Forbidden in the Generalized Cascade Model due to no loops rule [3].
S4	A → B, A → C	Generalist predation.	A single predator feeds on two different prey species.
S5	A → B, B → C, A → C	Omnivory.	A predator feeds on two trophic levels.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key methodological and analytical tools essential for conducting research into structural uncertainty of marine food webs.

Table 3: Essential Research Toolkit for Food Web Structural Analysis

Tool or Method	Function/Description	Application in Structural Uncertainty
Diet Analysis Data	Qualitative and quantitative data from stomach content analysis, establishing predator-prey interaction tables [2].	Provides the empirical foundation for building and validating network models; critical for identifying which links to include.
Community Matrix	A square matrix representing the food web where entries describe the effect of species j on species i [1].	The core mathematical object used in Qualitative Network Analysis to predict system responses to press perturbations.
Generalized Cascade Model	A static food web model that generates networks based on species' niche values and exponentially decaying predation probabilities [3].	Serves as a powerful null model to test against empirical webs and to explore the local structural consequences of basic assembly rules.
Network Analysis Software (e.g., Gephi)	Open-source software for network visualization and analysis [2].	Used to visualize complex food web structures, calculate network-wide metrics, and identify key nodes and clusters.
Motif Detection Algorithms	Computational scripts for counting and classifying small subgraphs within a larger network [3].	Enables the quantitative analysis of local network structure and the identification of over- and under-represented motifs.

Visualizing Structural Relationships and Workflows

Analytical Workflow for Structural Uncertainty

Network Perturbation and Response

Structural uncertainty arises from fundamental gaps in our knowledge of the components of a system and the connections between them. In the context of marine food web research, this encompasses uncertainties about which species and functional groups are present (node identity), the trophic and non-trophic interactions between them (link presence and sign), and the overall architecture of the network (topology) [4]. This uncertainty is a significant challenge for predicting the impacts of anthropogenic pressures like climate change, as a food web's structure fundamentally determines how perturbations, such as the loss of a species or a marine heatwave, cascade through an ecosystem [4] [5] [6]. Traditional modeling approaches often overlook these structural uncertainties, leading to potential overconfidence in projections [4]. This guide synthesizes key methodologies and emerging paradigms for quantifying, analyzing, and reducing this structural uncertainty, providing a toolkit for robust ecological forecasting.

Methodological Approaches for Quantifying Structural Uncertainty

Researchers employ a suite of complementary methods to explore the implications of uncertain food web structure. The table below summarizes three primary approaches detailed in the search results.

Table 1: Key Methodological Approaches for Addressing Structural Uncertainty

Method	Core Principle	Application Context	Key Advantage
Qualitative Network Analysis (QNA) [4]	Uses the sign (+, -) of interactions to model community dynamics and test network stability.	Exploring multiple plausible food web configurations and their response to press perturbations (e.g., climate change).	Efficiently explores a wide parameter space and identifies the most consequential interactions with minimal data requirements.
Metaweb Inference [6]	Constructs a regional-scale network of all potential trophic interactions, from which local food webs are inferred via species co-occurrence.	Assessing regional food web robustness to non-random species extinction scenarios across habitat mosaics.	Enables realistic extinction simulations based on habitat association and species abundance, bridging spatial scales.
Network Simplification [7]	Aggregates nodes by taxonomy (e.g., species to genus) to simplify data collection and analysis.	Exploratory analysis and comparison of multiple food webs, particularly in data-poor systems.	Standardizes data for comparison and increases the amount of open, comparable data available.

Experimental Protocol: Qualitative Network Analysis (QNA)

A detailed protocol for implementing QNA, as applied to a salmon-centric marine food web, is as follows [4]:

• Conceptual Model Development: Define the system boundaries and select the functional groups (nodes) to be represented. This is based on a thorough literature review and expert consultation. The base network should include primary producers, key consumers, competitors, and predators of the focal species (e.g., Chinook salmon).
• Define Alternative Structures: Acknowledge structural uncertainty by developing multiple plausible versions of the conceptual model. These scenarios should differ in how species pairs are connected (positive, negative, or no interaction) and specify which species are hypothesized to respond directly to the press perturbation (e.g., climate change). The cited study tested 36 different configurations [4].
• Build the Community Matrix: For each scenario, operationalize the signed digraph into a community matrix (also called the Jacobian matrix). Each element aᵢⱼ of this matrix represents the effect of species j on species i, with the sign derived from the digraph.
• Stability Analysis: Analyze the eigenvalues of the community matrix to determine whether the system is locally stable (eigenvalues have negative real parts). This step filters out non-plausible network configurations that are inherently unstable [4] [8].
• Perturbation Simulation: Apply a sustained "press" perturbation to the stable models, representing a directional change like ongoing ocean warming. Simulate the system's response to identify the proportion of negative versus positive outcomes for the focal species.
• Sensitivity Analysis: Identify which species interactions (links) most strongly influence the outcomes for the focal species. This pinpoints critical uncertainties and prioritizes targets for future empirical research.

Experimental Protocol: Metaweb Construction and Robustness Analysis

For regional-scale assessments, the metaweb approach provides a framework for simulating extinction cascades [6]:

• Data Compilation: Gather a comprehensive list of species (plants, invertebrates, vertebrates) within a defined region. Compile all known and potential trophic interactions between them from existing datasets, primary literature, and expert knowledge.
• Interaction Inference: For species with generalized feeding behavior, infer potential interactions using taxonomic reasoning (e.g., if a predator consumes a beetle family, it is linked to all species in that family). This creates the full potential metaweb [6].
• Spatial & Habitat Trimming: Refine the metaweb into regional sub-networks using data on species co-occurrence. This is achieved by overlaying information on species' distributions across biogeographic regions and their associations with specific habitat types (e.g., wetlands, forests).
• Define Extinction Scenarios:
  • Random: Species are removed in a random sequence (null model).
  • Habitat-targeted: Species associated with a specific, threatened habitat type (e.g., wetlands) are given a higher probability of removal.
  • Abundance-based: Species are removed in order of rarity (rarest first) or commonness (most common first).
• Robustness Simulation:
  • For each extinction scenario, sequentially remove species (nodes) and all their associated interactions (links) from the network.
  • After each primary removal, identify secondary extinctions: any consumer species that loses all its resource species becomes secondarily extinct.
  • Track the rate of network fragmentation by measuring the size of the largest remaining weakly connected component (WCC).
• Calculate Robustness Coefficient: Plot the proportion of species remaining in the largest WCC against the proportion of species removed. The robustness coefficient is the area under this curve, quantifying the network's resilience to the specific extinction sequence [6].

Emerging Insights into Food Web Architecture and Dynamics

Recent research is moving beyond simple allometric rules to reveal more complex structural principles.

Table 2: Key Quantitative Findings on Food Web Structure and Response

Study Focus	Key Finding	Quantitative Result	Implication for Structural Uncertainty
Climate-Driven Food Web Change [4]	Increased predation and competition under climate warming leads to negative outcomes for salmon.	Salmon outcomes shifted from 30% to 84% negative when consumption by competitors/predators increased.	Feedback loops (e.g., with mammals) and indirect effects between salmon runs are critical structural elements.
Marine Heatwave (MHW) Impacts [5]	MHWs cause significant, prolonged biomass decline, with higher trophic levels more affected.	The 2013-2016 NE Pacific "Blob" caused an 8.7% ± 1.0% decline in biomass; higher trophic levels showed larger, longer-lasting declines.	MHWs alter trophodynamics (energy transfer speed and efficiency), a structural property, requiring integration into models.
Robustness to Species Loss [6]	Network collapse accelerates with targeted loss of species from key habitats and common species.	Targeted removal of wetland-associated species caused faster fragmentation than random removal. Loss of common species was more detrimental than loss of rare ones.	Connectance alone is a poor predictor of robustness; species identity and habitat affiliation are key structural uncertainties.
Predator Specialization [9] [10]	The allometric rule (big predators eat big prey) explains only a minority of trophic links.	Approximately 50% of 517 aquatic predator species were classified as specialized, deviating from the allometric rule.	Simple size-based models are insufficient; "specialization" must be incorporated as a fundamental trait to reduce structural error.

The "Z-Pattern" of Predator Specialization

A 2025 study revealed that aquatic predator guilds follow three main prey selection strategies, forming a characteristic "z-pattern" in the predator-size/prey-size space [9] [10]. This finding provides a new assembly rule to reduce structural uncertainty.

• Generalist Guild (s ≈ 0): These predators follow the classic allometric rule, where larger predators eat larger prey. The slope of prey size versus predator size is close to 1.
• Small-Prey Specialist Guild (s < 0): These predators specialize on prey that is smaller than predicted by the allometric rule. Their optimal prey size (OPS) is largely independent of their own body size, appearing as a horizontal band in the size-scaling graph.
• Large-Prey Specialist Guild (s > 0): These predators specialize on prey that is larger than predicted. Like the small-prey specialists, their OPS shows weak dependence on their body size.

The coexistence of these three guilds, which can be described mathematically (equation (2) in [9]), explains about half of the structure of the aquatic food webs studied and over 90% of the observed linkages [9] [10]. This pattern points to underlying eco-evolutionary constraints that can be used to build more accurate and mechanistic food-web models.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key resources and tools essential for empirical and theoretical research into food web topology.

Table 3: Essential Research Tools for Food Web Topology Studies

Tool / Resource	Function / Description	Application in Research
Food Habits Database [11]	A structured database storing predator stomach content data from trawl surveys and other sources.	Provides empirical data to establish and validate trophic links, quantifying interaction strengths.
Metaweb (e.g., trophiCH) [6]	A comprehensive regional database of all potential species and their possible trophic interactions.	Serves as a scaffold for inferring local food webs and simulating extinction scenarios.
Stable Isotope Analysis	Measures ratios of stable isotopes (e.g., δ¹⁵N, δ¹³C) in animal tissues.	Used to empirically determine trophic positions (δ¹⁵N) and carbon sources (δ¹³C), validating inferred links.
Qualitative Network Model [4]	A mathematical framework using a signed community matrix to represent species interactions.	Exploring system stability and species responses to perturbations under structural uncertainty.
eDNA Metabarcoding [7]	Uses environmental DNA to identify species presence and, potentially, diet items from water or sediment samples.	Efficiently documents biodiversity and trophic interactions with high resolution, aiding node and link identification.
EcoTroph Model [5]	An ecosystem model that represents biomass as a continuous flow across trophic levels.	Assessing impacts of fishing and climate change on trophodynamics and ecosystem structure/function.

Addressing structural uncertainty in marine food webs requires a multi-faceted approach. No single method is sufficient; instead, QNA, metaweb analysis, and emerging specialization frameworks should be used in concert. QNA is invaluable for initial, data-poor exploration to identify critical interactions. The metaweb approach allows for realistic, regional-scale risk assessments based on habitat and species traits. Most importantly, the recent discovery of widespread predator specialization guilds provides a new, mechanistic assembly rule to supersede the outdated assumption that all predators follow a simple allometric rule [9] [10]. Integrating these approaches, guided by targeted empirical research on the most sensitive links and nodes, will significantly reduce structural uncertainty and lead to more reliable predictions of marine ecosystem responses to global change.

Marine food webs are complex networks of trophic interactions, yet our understanding of their structure and dynamics remains fraught with uncertainty. This structural uncertainty—the incomplete knowledge of which species interact and the strength of those interactions—presents significant challenges for predicting ecosystem responses to anthropogenic pressures [4]. Coral reefs exemplify this problem, as they represent systems where energy flow has traditionally been viewed through simplified, siloed pathways. The concept of "siloed energy pathways" refers to the compartmentalized trophic channels that characterize coral reef ecosystems, primarily the segregation between: (1) the photosynthetically driven pathway dependent on coral-zooxanthellae symbiosis, and (2) alternative pathways such as those reliant on algal turf, macroalgae, or planktonic inputs [12] [13]. This case study examines how this compartmentalization creates surprising fragility to climate perturbations and explores methodological frameworks for quantifying the resulting structural uncertainties in marine food web research.

Coral Reef Energy Pathways: Compartmentalization and Connectivity

Coral reefs maintain high productivity in nutrient-poor waters through tightly recycled energy and nutrient flows. The foundational energy sources create distinct trophic pathways with varying degrees of connectivity:

• The Coral-Zooxanthellae Pathway: This pathway centers on the symbiotic relationship between scleractinian corals and dinoflagellate algae (family Symbiodiniaceae). The zooxanthellae translocate up to 90% of their photosynthetically fixed carbon to the coral host, providing the primary energy source for coral growth, calcification, and reproduction [14]. This energy enters the broader food web through multiple channels: direct predation on corals by corallivores, mucus secretion that supports microbial and detrital communities, and the provision of physical habitat that supports diverse assemblages of associated organisms.

• Alternative Energy Pathways: Parallel energy channels exist independently of the coral-zooxanthellae symbiosis:

  • Macroalgal Pathways: Fleshy macroalgae can become dominant energy sources following coral decline, supporting different herbivore and detritivore communities [13].
  • Turf Algal Pathways: Turf algae, consisting of short filaments of algae, bacteria, and detritus, form a key grazing pathway for herbivorous fishes [13].
  • Planktonic Pathways: The water column above reefs provides a plankton-based energy pathway, supporting planktivorous fishes and corals that can supplement their diet with heterotrophic feeding [15].

Table 1: Primary Energy Pathways in Coral Reef Ecosystems

Energy Pathway	Primary Producers	Key Consumers	Ecosystem Function
Coral-Zooxanthellae	Zooxanthellae within coral tissues	Corallivores (e.g., butterflyfish), coral mucus feeders	Structural complexity, habitat provision, tight nutrient recycling
Macroalgal	Fleshy macroalgae (e.g., Sargassum)	Herbivorous fishes (e.g., surgeonfish), sea urchins	Carbon export, potential space competition with corals
Turf Algal	Mixed algal filaments, cyanobacteria	Herbivorous fishes (e.g., parrotfish, damselfish)	Sediment stabilization, primary production
Planktonic	Phytoplankton, bacterioplankton	Planktivorous fishes, gorgonians, filter feeders	Nutrient import, supporting non-photosynthetic specialists

The degree of connectivity between these pathways varies spatially and temporally. Species that utilize multiple energy sources (e.g., omnivorous fishes) create important but limited linkages. The surprising fragility of coral reefs emerges when climate disruptions sever the dominant coral-zooxanthellae pathway, revealing the limited capacity of alternative pathways to maintain ecosystem function and biodiversity.

Methodology: Quantifying Food Web Structure and Dynamics

Experimental Approaches for Tracing Energy Flow

Research on coral reef energy pathways employs complementary methodological approaches to quantify energy flow and food web structure.

• Stable Isotope Analysis: This approach uses natural abundance ratios of carbon (δ13C), nitrogen (δ15N), and sulfur (δ34S) to trace energy sources and trophic positions. The δ13C signature varies minimally (~1‰) per trophic level and thus helps identify primary energy sources, while δ15N enriches predictably (~3-4‰) and is used to estimate trophic level [12]. Bayesian mixing models (e.g., MixSIAR) are then applied to quantify the proportional contributions of different primary producers to consumer diets [12].

• Fatty Acid Biomarkers: Specific fatty acids serve as biomarkers for different algal groups and energy pathways. For example, certain polyunsaturated fatty acids indicate reliance on diatoms, while others suggest dependence on dinoflagellates (including zooxanthellae) [12].

• Underwater Visual Census (UVC): Standardized visual surveys quantify the abundance, biomass, and community composition of reef fishes and benthic organisms. When combined with trophic grouping of species, these data help infer the structure of food webs and energetic pathways [13].

• Qualitative Network Modeling (QNM): QNA is a mathematical framework that uses a community matrix of signed interactions (positive, negative, or neutral) to explore the dynamic behavior of food webs. This approach is particularly valuable for exploring structural uncertainty and the potential for indirect effects cascading through different energy pathways [4].

Table 2: Key Methodologies for Analyzing Reef Energy Pathways

Methodology	Primary Application	Key Metrics	Technical Considerations
Stable Isotope Analysis	Tracing energy sources, trophic position	δ13C, δ15N, δ34S ratios; mixing model outputs	Requires baseline corrections; tissue-specific turnover rates
Fatty Acid Analysis	Identifying specific dietary sources	PUFA profiles (e.g., 18:3ω3, 22:6ω3)	Complex laboratory analysis; biomarkers not always unique
Visual Census	Quantifying community structure	Species abundance, biomass, diversity	Subject to observer bias; limited for cryptic species
Qualitative Network Modeling	Exploring structural uncertainty & stability	Interaction signs, weighted links, stability eigenvalues	Requires defined community matrix; qualitative interaction strengths

Visualizing Energy Pathways and Perturbation Responses

The following diagram illustrates the conceptual flow of energy through a coral reef food web, highlighting the compartmentalization into distinct pathways and the points of fragility under thermal stress.

Diagram 1: Siloed energy pathways in coral reefs. The diagram shows the primary energy flows from basal resources to key functional groups. The red arrows highlight how heat stress disrupts the core coral-zooxanthellae pathway, leading to bleaching and potential ecosystem reorganization.

The Fragility of Siloed Systems: Evidence from Global Bleaching Events

The structural compartmentalization of reef food webs creates systemic fragility that becomes apparent during climate-driven disturbances. The reliance of numerous species on the coral-zooxanthellae pathway creates a critical vulnerability point.

Cascading Impacts of Heat Stress

Marine heatwaves trigger mass coral bleaching—the breakdown of the coral-zooxanthellae symbiosis—which severs the primary energy pathway. A temperature increase of just 1°C above the seasonal maximum for 4-8 weeks can trigger widespread bleaching [14]. The fourth global bleaching event (2023-2025) has impacted 84% of the world's coral reefs, demonstrating the scale of this vulnerability [16]. The 2023 marine heatwave in the Florida Keys accumulated over 20 Degree Heating Weeks (DHWs), far exceeding the 8 DHW threshold for significant mortality and causing near-total loss of transplanted staghorn corals (Acropora cervicornis) [17].

The diagram below maps the experimental workflow for quantifying the impacts of such thermal stress on reef energy pathways.

Diagram 2: Experimental workflow for assessing heatwave impacts on reef energy pathways. The methodology combines field monitoring of benthic and fish communities with stable isotope analysis to trace energy flow changes following disturbance.

Regime Shifts and Altered Energy Flow

The disruption of the primary energy pathway can trigger regime shifts from coral- to macroalgal-dominated states. Following the 1998 marine heatwave, approximately 50% of reefs in the inner Seychelles regime-shifted to macroalgal dominance, while the other half recovered coral cover [13]. This shift represents a fundamental reorganization of the food web's architecture, where energy flow becomes dominated by the macroalgal pathway. These shifted states are often stabilized by positive feedbacks, such as macroalgae inhibiting coral recruitment [13].

However, long-term studies reveal that these shifts are not always permanent. Following the 2016 heatwave, one macroalgal-dominated reef in Seychelles began transitioning back to coral dominance, indicating that under certain conditions—including the presence of robust herbivore populations that control macroalgae—reversals can occur [13]. This highlights that the fragility of siloed pathways is mediated by ecological context, particularly the strength of herbivory as a mechanism controlling alternative states.

Table 3: Documented Ecosystem Responses to Major Heat Stress Events

Location	Heat Stress Event	Impact on Coral-Zooxanthellae Pathway	Ecosystem Outcome	Reference
Global	4th Global Bleaching Event (2023-2025)	84% of reefs experienced bleaching-level heat stress	Widespread mortality, ongoing assessment of regime shifts	[16]
Florida Keys	2023 Marine Heatwave	>20 DHWs; near-total mortality of A. cervicornis outplants	Devastating for restoration efforts; ecosystem simplification	[17]
Seychelles	1998 & 2016 Marine Heatwaves	~90% coral loss in 1998; severe bleaching in 2016	50% regime shift post-1998; some reversal post-2016	[13]
Arabian Gulf	Recurrent Heat Stress	Specialized microbial communities enhance nutrient cycling	Shows resilience via efficient internal nutrient recycling	[15]

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Materials and Reagents for Coral Reef Food Web Analysis

Reagent / Material	Primary Function	Application Example	Technical Notes
Liquid Nitrogen Dewars	Flash-freezing tissue samples for stable isotope & genetic analysis	Preserving coral, algal, and fish tissues immediately after collection	Prevents tissue degradation and isotopic fractionation post-collection
Pre-combusted Glass Fiber Filters	Collection of particulate organic matter (POM) from water column	Filtering water samples for δ13C and δ15N analysis of base food sources	Combustion removes organic contaminants; pore size typically 0.7μm
Elemental Analyzer	Quantifying carbon and nitrogen content & stable isotope ratios	Coupled with Isotope Ratio Mass Spectrometer (EA-IRMS) for bulk tissue analysis	Requires certified reference standards for calibration (e.g., USGS40, IAEA-N1)
Bayesian Mixing Model Software (MixSIAR)	Quantifying proportional contributions of food sources to consumer diet	Analyzing stable isotope data to determine energy pathway reliance	Handles uncertainty and source variability; requires prior data on trophic enrichment factors
Underwater Visual Census Equipment	Quantifying in-situ abundance and size of reef organisms	Standardized slate, transect tapes, waterproof data sheets	Critical for building species interaction matrices for network models
Qualitative Network Modeling (QNM) Software	Exploring structural uncertainty and stability of food webs	R packages (e.g., QPress) for simulating press perturbations	Depends on signed digraphs of species interactions; tests network stability

Discussion: Implications for Ecosystem Management and Future Research

The fragility of siloed energy pathways in coral reefs underscores the critical importance of addressing structural uncertainty in marine food web models. Qualitative Network Modeling (QNM) approaches demonstrate that uncertain species interactions, particularly those involving predators and competitors, can dramatically alter projections for species of concern under climate change [4]. For coral reefs, this means that the strength of linkages between different energy pathways—such as the degree to which herbivores control macroalgae or the capacity of corals to utilize planktonic energy—fundamentally shapes ecosystem resilience.

Management interventions aimed at reducing this fragility must focus on maintaining functional diversity across energy pathways. The promising interventions identified by Caribbean researchers include managed breeding to maintain genetic diversity (low risk, high efficacy) and adding grazers to control algae (low risk, high efficacy), which directly strengthens the herbivory pathway [17]. More experimental approaches like assisted gene flow (high risk, high efficacy) seek to enhance the thermal tolerance of the coral-zooxanthellae pathway itself [17].

Future research must prioritize quantifying the strength of key trophic links and the conditions that facilitate regime shift reversals. The emerging evidence from Seychelles that some reefs recovered faster from the 2016 heatwave than from 1998, and that one macroalgal-dominated reef reversed to coral dominance, offers crucial hope and indicates that resilience can be built over time [13]. Similarly, findings from the Arabian Gulf reveal that specialized microbial communities can create remarkably efficient nutrient recycling systems, suggesting potential bio-mitigation strategies to enhance ecosystem productivity under stress [15]. By integrating advanced monitoring techniques with modeling frameworks that explicitly incorporate structural uncertainty, we can better predict tipping points and develop management strategies that enhance the resilience of these critically important ecosystems.

The Impact of Climate Change and Marine Heatwaves on Food Web Reassembly

Climate change is imposing novel and pervasive pressures on marine ecosystems, with the structure and function of food webs representing a critical frontier for understanding ecological resilience. The reassembly of food webs following large-scale disturbances is not a simple process of recovery but a complex reordering mediated by species traits, altered energy flows, and anthropogenic pressures. This process is fraught with structural uncertainty—incomplete knowledge of how species interactions will reform and how these new configurations will determine ecosystem stability and function [1]. Marine heatwaves (MHWs), prolonged extreme warm water events, are particularly potent drivers of reassembly, acting as catalytic events that can tip ecosystems toward alternative states [18]. The Mediterranean Sea, a documented biodiversity hotspot experiencing rapid warming and biological invasions, serves as a critical case study for these phenomena [19]. Navigating this uncertainty requires integrating diverse methodological approaches—from stable isotope analysis to qualitative network modeling—to build predictive capacity and inform climate-adapted conservation strategies.

Quantitative Impacts on Food Web Structure and Function

Long-term observational data, particularly from the Northeastern subarctic Pacific Ocean, have quantified profound impacts of MHWs on food web components and carbon cycling processes.

Table 1: Documented Impacts of Marine Heatwaves on Food Web and Carbon Cycle Parameters

Parameter	Pre-MHW/MHW Conditions	Impact During MHW	Location/Study
Particulate Organic Carbon (POC)	Baseline POC ~248-395 mg m⁻² (2011-12) [18]	3-8x increase; ∆POC up to 2041 mg m⁻² (2019) [18]	NE Subarctic Pacific
Plankton Community Structure	Typical community structure	Shift in structure; detritus enrichment [18]	NE Subarctic Pacific
Particle Export Efficiency	Exponential decrease with depth (Martin curve) [18]	Accumulation of small particles (<100 µm) in mesopelagic; reduced deep export [18]	NE Subarctic Pacific
Surface Temperature	Long-term climatological mean	Anomalies > +1°C persisting for years [18]	NE Subarctic Pacific/Globally
Stratification	Seasonal stratification patterns	Enhanced and prolonged stratification [18]	NE Subarctic Pacific

Table 2: Relationship between Food Web Structural Properties and Ecosystem Stability Metrics (from 217 global marine food webs) [20]

Stability Metric	Key Relationship with Food Web Structure	Correlation Direction
Resistance (Biomass change during disturbance)	Inversely correlated with Connectance (CI)	Negative [20]
Resilience (Biomass recovery post-disturbance)	Inversely correlated with CI; Positively correlated with Interaction Strength SD (ISIsd)	Negative / Positive [20]
Local Stability (Return to equilibrium)	Inversely correlated with Number of Living Groups (NLG), ISIsd, Finn's Cycling Index (FCI)	Negative [20]

The data reveal that MHWs can cause a fundamental reorganization of energy flow. The accumulation of non-sinking small particles in the mesopelagic zone, followed by slow remineralization, represents a significant shift in the functioning of the biological carbon pump [18]. This altered carbon pathway has implications for carbon sequestration and energy availability for higher trophic levels. Furthermore, structural analysis of marine food webs indicates that their diversity-stability relationship is mediated by network topology; greater diversity is associated with sparser connectance, which in turn enhances resistance and resilience to large disturbances [20].

Methodological Framework: Investigating Reassembly

Stable Isotope Analysis for Trophic Positioning

Stable isotope analysis (SIA) provides an integrative tool for investigating food web structure and tracing energy pathways, especially for taxa where traditional stomach content analysis is impractical [19].

Experimental Protocol:

• Sample Collection: Organisms and basal resources (e.g., suspended particulate organic matter, phytoplankton, macrophytes) are collected from the target environment. For consumers, muscle tissue or other metabolically inert tissues are preferred.
• Sample Preparation: Tissues are dried, homogenized to a fine powder, and treated with acid to remove inorganic carbonates when necessary.
• Instrumental Analysis: Processed samples are analyzed using an Isotope Ratio Mass Spectrometer (IRMS) coupled with an elemental analyzer.
• Data Standardization: Results are expressed in standard δ notation (δ¹³C, δ¹⁵N) in parts per thousand (‰) relative to international standards (Vienna Pee Dee Belemnite for carbon, atmospheric N₂ for nitrogen).
• Trophic Position Calculation: Trophic position (TP) is calculated using the formula: TP = [(δ¹⁵Nconsumer - δ¹⁵Nbaseline) / TEF] + λ where TEF is the trophic enrichment factor (typically ~3.4‰ for nitrogen) and λ is the trophic position of the organism used to establish the baseline (e.g., 1 for primary producers) [19].

Databases like ISOMED, which compiles georeferenced δ¹³C and δ¹⁵N values for Mediterranean marine food web components, are vital for large-scale spatial and temporal analyses [19].

Qualitative Network Analysis for Structural Uncertainty

Qualitative Network Analysis (QNA) is a modeling approach designed to explore structural uncertainty by evaluating how different plausible configurations of a food web influence its response to perturbations [1].

Experimental Protocol:

• Functional Group Definition: Aggregate species into functional groups based on shared trophic interactions (e.g., piscivorous fish, zooplankton, phytoplankton).
• Interaction Matrix Development: Construct a community matrix (A) where elements (aij) represent the perceived effect of group j on group i (e.g., + for positive effect, - for negative, 0 for no direct effect).
• Scenario Definition: Develop multiple plausible network scenarios that differ in:
  • The sign of specific, uncertain interactions (e.g., is the competition between two groups strong or negligible?).
  • Which functional groups are directly affected by the press perturbation (e.g., climate change).
• Perturbation Simulation: Apply a sustained press perturbation (e.g., + for a small increase) to one or more groups in each scenario.
• Outcome Prediction: Use the (I - A)⁻¹ approach to predict the qualitative direction of the response (increase, decrease, or ambiguous) of all functional groups in the network for each scenario.
• Ensemble Analysis: Analyze the suite of model outcomes to identify which results are robust (i.e., consistent across most plausible web structures) and which are highly sensitive to structural uncertainty [1].

This method was pivotal in a study on salmon survival, which tested 36 different food web configurations and found that outcomes for salmon shifted dramatically depending on how predators and competitors were connected [1].

Diagram 1: Food web stability framework.

Table 3: Essential Research Tools for Food Web Reassembly Studies

Tool/Resource	Category	Primary Function	Specific Example / Application
BGC-Argo Floats	Autonomous Platform	High-resolution, long-term measurement of physical and biogeochemical parameters (Temp, Salinity, Nitrate, bbp, Chl-a) [18].	Tracking POC concentration changes and water column stratification during MHWs [18].
Stable Isotope Ratio Mass Spectrometer (IRMS)	Analytical Instrument	Precisely measure isotopic ratios (δ¹³C, δ¹⁵N) in organic samples to determine trophic position and carbon sources [19].	Constructing food web topology and estimating trophic position of consumers in ISOMED database [19].
DNA Metabarcoding	Molecular Biology	High-throughput identification of planktonic and microbial community composition from water samples.	Linking shifts in plankton community structure to anomalous POC accumulation under thermal stress [18].
Ecopath with Ecosim (EwE)	Modeling Software	Create mass-balanced food web models (Ecopath) and simulate dynamic responses (Ecosim) to fishing and environmental forcing [20].	Quantifying multidimensional stability (resistance, resilience, local stability) for 217 global marine food webs [20].
Qualitative Network Models (QNMs)	Modeling Framework	Explore structural uncertainty by evaluating ecosystem outcomes across many plausible interaction web configurations [1].	Testing 36 different food web structures to predict outcomes for Chinook salmon under climate press perturbations [1].
ISOMED Database	Data Repository	Georeferenced database of δ¹³C and δ¹⁵N values for Mediterranean marine food web components (4959 records) [19].	Providing baseline data for estimating trophic position and investigating trophic interactions at a basin scale [19].

Synthesis and Forward Look: Navigating Uncertainty for Resilience

The reassembly of marine food webs under climate change is a process characterized by deep structural uncertainty. Evidence indicates that the pathways of reassembly are not random but are mediated by identifiable factors: the trait structure of surviving and colonizing species [21], the initial food web topology that determines its robustness [20], and the intensity and persistence of the warming disturbance [18]. A critical insight is that the relationship between diversity and stability is not direct but is mediated by food web structure; diverse ecosystems can be stable if that diversity is organized into sparser, less connected networks [20].

To navigate this uncertainty, a dual-pronged research approach is essential. First, there must be an expansion of sustained, multi-platform observational efforts that combine physical, chemical, and biological data, as demonstrated by the BGC-Argo programs [18]. Second, the use of ensemble modeling approaches, like QNA, that explicitly incorporate structural uncertainty into forecasts is crucial for identifying robust management interventions and prioritizing future research on the most influential, yet poorly understood, species interactions [1].

Finally, building resilience requires translating this knowledge into adaptive governance. This includes strategically expanding Marine Protected Area (MPA) networks to include climate refugia, biodiversity havens, and migration corridors [22]. Furthermore, protecting blue carbon stocks in marine sediments and vegetation is a critical climate mitigation action that also safeguards biodiversity [22]. As the oceans continue to warm, embracing structural uncertainty as a core component of marine research and policy will be fundamental to fostering the resilience of marine ecosystems and the human communities that depend on them.

Mapping the Unknown: Tools and Techniques to Quantify Structural Uncertainty

Qualitative Network Analysis (QNA) represents a powerful methodological approach for investigating complex systems where quantitative data is scarce or unreliable. This framework is particularly valuable in ecological and biological research, where precise parameter estimates are often unavailable for many species interactions, yet understanding the system's structure remains critical for management and conservation. QNA operates by representing a system as a network of nodes (e.g., species, functional groups) connected by edges representing their interactions (e.g., predation, competition). Unlike quantitative models that require precise parameter estimates, QNA uses signed digraphs where interactions are classified qualitatively as positive (+), negative (-), or neutral (0), enabling researchers to model system structure and predict responses to perturbations without extensive numerical data [1].

The fundamental strength of QNA lies in its ability to navigate structural uncertainty within complex systems. This approach allows for the systematic exploration of multiple plausible configurations of how system components interact, providing a structured method for acknowledging and investigating the implications of not knowing the exact nature of all relationships within a network. Within marine food web research, this capability is paramount, as species interactions are frequently inferred rather than directly observed, and these relationships may shift under changing environmental conditions [1]. By testing numerous alternative network structures, researchers can identify which connections most significantly influence system outcomes, thereby guiding future research priorities and strengthening the foundation for ecosystem-based management decisions.

QNA in Marine Food Web Research

Addressing Structural Uncertainty in Marine Ecosystems

The application of QNA to marine food webs directly addresses the challenge of structural uncertainty that plagues traditional quantitative models. In data-poor systems, precisely quantifying every interaction strength is impossible, leading to potential oversimplification in ecosystem models. QNA circumvents this limitation by focusing on the direction of effects rather than their magnitude. A recent study demonstrated this approach by testing 36 different plausible configurations of marine food web connections involving salmon and key functional groups [1]. These scenarios differed in how species pairs were connected (positive, negative, or no interaction) and identified which species responded directly to climate change pressures.

This research revealed that specific network configurations consistently produced negative outcomes for salmon populations, regardless of the precise values assigned to most links. Notably, salmon survival outcomes shifted dramatically from 30% to 84% negative when consumption rates by multiple competitor and predator groups increased following a climate change perturbation [1]. This pattern aligns with empirical observations recorded during marine heatwaves, validating the utility of QNA for generating biologically realistic predictions despite qualitative inputs. The analysis further identified that feedback loops between salmon and mammalian predators were particularly influential in determining system outcomes, as were indirect effects connecting different salmon populations (spring- and fall-run) [1]. These insights highlight how QNA can pinpoint critical interactions that drive system behavior, even when quantitative data is limited.

Comparative Framework for Ecosystem Models

QNA provides a valuable framework for comparing and informing more complex quantitative models. The structured qualitative analysis of alternative responses to climate change across food webs plays a crucial role in both the design and interpretation of quantitative models [1]. The ease with which a wide range of scenarios representing structural and quantitative uncertainties can be explored makes QNA an ideal screening tool before investing resources in developing data-intensive modeling approaches.

In the Southern Ocean, for instance, complex ecosystem simulation models like OSMOSE (Object-oriented Simulator of Marine ecOSystEms) are employed to simulate food web dynamics and evaluate fishing and environmental impacts [23]. These models involve hundreds of parameters, many of which are difficult to estimate accurately when data contain insufficient information. The propagation of parameter errors across ecosystem simulation models introduces significant uncertainty in model outputs [23]. QNA can help identify which parameters and interactions warrant more precise quantification, thereby improving the efficiency and reliability of subsequent quantitative modeling efforts. Methods like the Morris method for global sensitivity analysis and Monte Carlo simulation approaches can then be integrated to further explore parameter uncertainty in these more complex models [23].

Methodological Protocols

Core Analytical Procedure

Implementing Qualitative Network Analysis involves a structured process that transforms conceptual understanding of a system into testable network models. The following workflow outlines the core analytical procedure:

System Delineation and Node Selection The first step involves defining the system boundaries and selecting the key functional groups or species to include as nodes in the network. In marine food web applications, this typically includes commercially important species, their key predators, prey, and competitors, as well as relevant abiotic factors (e.g., "climate pressure") [1]. The selection should represent the major trophic pathways and potential management levers.

Interaction Matrix Development For the selected nodes, researchers construct a signed digraph represented as a community matrix where entries indicate the effect of the column component on the row component [1]. This matrix uses (+) for positive effects (e.g., prey benefiting predators), (-) for negative effects (e.g., predators harming prey), and (0) for no direct effect. The careful specification of these interactions is crucial, as alternative plausible relationships should be considered to account for structural uncertainty.

Press Perturbation Simulation QNA models system response to sustained "press" perturbations, such as ongoing climate change or persistent fishing pressure [1]. The analysis predicts the qualitative direction of change (increase, decrease, or uncertain) for each node in response to the perturbation. This is achieved through mathematical analysis of the interaction matrix that accounts for both direct effects and indirect pathways through the network.

Scenario Testing and Robustness Analysis The core of addressing structural uncertainty involves testing multiple alternative network configurations [1]. Researchers systematically vary uncertain interactions and compare outcomes across scenarios. Results are evaluated for robustness—identifying outcomes that persist across most plausible configurations versus those sensitive to particular structural assumptions.

Uncertainty Analysis Framework

To complement the qualitative analysis, the following quantitative methods can be integrated to assess parameter sensitivity and model uncertainty:

Table: Uncertainty and Sensitivity Analysis Methods

Method	Application	Key Features	Implementation
Morris Method	Global sensitivity analysis to identify influential parameters [23]	Computes elementary effects with limited computational cost; screens non-influential parameters	Applied to community indicators and species group biomasses to determine sensitivity to model parameters
Monte Carlo Simulation	Exploration of parameter error propagation [23]	Uses random sampling from probability distributions to assess output uncertainty	Investigates impact of parameter errors on model predictions through multiple iterations
Linear Regression Analysis	Quantification of prediction uncertainty [23]	Estimates proportion of output variance explained by parameter variations	Computes uncertainty of community indicators and species group biomasses

Visualization Approaches

Network Representation and Interpretation

Effective visualization is crucial for communicating QNA structures and results. The classic node-link diagram—with points representing entities and lines representing connections—forms the foundation of network visualization [24]. However, proper interpretation requires understanding the specific layout algorithms employed and their limitations.

In psychometric network research, where QNA has also been applied, force-directed algorithms like Fruchterman-Reingold are commonly used [25]. These methods create visually appealing graphs where connected nodes are pulled closer together while unconnected nodes are repelled, minimizing edge crossing and achieving approximately equal edge length. However, a critical limitation is that node positioning in force-directed layouts is not easily interpretable—strongly associated nodes may appear far apart, while weakly associated nodes may appear close together [25].

For more interpretable spatial representation, Multidimensional Scaling (MDS) plots networks such that distances between nodes correspond to their dissimilarity [25]. In MDS visualization, highly related nodes appear close together, while weakly related ones appear far apart, creating meaningful spatial relationships. This approach is particularly valuable when the goal is to understand similarity patterns within the network structure.

Geospatial Network Mapping

When networks have geographical components, integrating mapping techniques provides additional contextual understanding. Effective geospatial network visualization incorporates several key elements [26]:

• Node hierarchies that display relative importance through size or color variations
• Flow direction indicators showing movement of resources through arrows or weighted lines
• Connection density visualizations revealing areas of high or low network activity
• Temporal patterns tracking network changes over time using animation

Implementation requires careful design choices to balance complexity and clarity. Visual hierarchy should be established by varying node sizes, edge weights, and colors strategically, using larger nodes for major hubs and thicker lines for primary connections [26]. For dense networks, edge bundling techniques group parallel connections to reduce visual clutter while maintaining relational information.

Table: Network Visualization Techniques and Applications

Technique	Best For	Strengths	Interpretation Guidelines
Force-Directed Layouts	General structure and clustering [25]	Aesthetically pleasing; minimizes edge crossing; reveals community structure	Node proximity does NOT indicate relationship strength; avoid spatial interpretation
Multidimensional Scaling	Similarity patterns [25]	Interpretable distances; meaningful spatial relationships	Node distance corresponds to dissimilarity; coordinate positions are meaningful
Geospatial Mapping	Spatially-explicit networks [26]	Integrates geographical context; reveals spatial patterns	Combines network structure with physical layout; requires base maps for orientation

Research Toolkit

Implementing QNA requires specific analytical tools and software resources. The following table details key solutions for network analysis and visualization:

Table: Research Reagent Solutions for QNA Implementation

Tool/Software	Function	Application Context	Key Features
R with qgraph package	Network visualization and analysis [25]	Psychological and ecological networks	Implements Fruchterman-Reingold algorithm; easy network plotting; correlation networks
D3.js	Interactive web-based visualizations [26]	Custom network diagrams for web applications	Extensive customization; supports real-time data updates; high interoperability
Gephi	Network analysis and geographical visualization [26]	Social network mapping and exploration	Combines powerful analysis with visualization features; user-friendly interface
NetworkX	Python-based network analysis [26]	Custom network analysis and modeling	Integration with matplotlib for custom visualizations; flexible data structures
ArcGIS Network Analyst	Geospatial network analysis [26]	Route analysis and flow modeling	Industry-standard tools; extensive customization; network optimization capabilities

Design Specifications for Accessible Visualization

Creating effective network visualizations requires adherence to design principles that ensure clarity and accessibility. Color contrast is particularly critical for distinguishing network elements and maintaining readability [27]. High contrast color palettes combine dark tones with bright accents to create designs that improve visibility and accessibility [27].

Successful high-contrast combinations include:

• Black + Golden Yellow for authority and energy [27]
• Charcoal + Electric Turquoise for modern aesthetics [27]
• Navy + Bright Sage Green for sophistication [27]
• Purple + Lime for edgy, youthful contrast [27]

For accessibility, colorblind-friendly palettes like ColorBrewer should be employed, typically limiting the palette to 5-7 distinct colors [26]. Additionally, labeling strategies must include dynamic sizing that adjusts based on zoom levels, with larger text for primary nodes and smaller text for secondary elements [26]. Smart label placement algorithms can automatically adjust positions in dense node regions, while subtle halos or backgrounds ensure text remains legible against complex backgrounds [26].

Technical Implementation

Graphviz Workflow Diagrams

The following Graphviz diagrams illustrate key workflows in Qualitative Network Analysis implementation, adhering to the specified color palette and contrast requirements.

QNA Methodology Workflow

Marine Food Web Interaction Network

Data Management and Performance Optimization

Implementing QNA for complex systems requires careful attention to data management, particularly when scaling to large networks. Effective approaches include [26]:

• Spatial indexing using R-trees or quadtrees to organize geographic data for quick retrieval
• Data compression techniques to reduce storage requirements while maintaining relationship integrity
• Tiling strategies to break extensive networks into manageable chunks using tools like PostgreSQL with PostGIS extension
• Level-of-detail (LOD) simplification to display appropriate network detail at different zoom levels

For performance optimization with large networks, dynamic clustering algorithms group nearby nodes at different zoom levels [26]. DBSCAN or K-means clustering can be employed for geographic point aggregation, while edge aggregation combines parallel connections between clusters. WebGL-based libraries like deck.gl or Mapbox GL JS support client-side rendering of millions of data points, maintaining interactive performance with substantial datasets [26].

Qualitative Network Analysis provides a robust framework for investigating complex systems in data-poor environments, particularly relevant to understanding structural uncertainty in marine food web research. By focusing on the direction rather than magnitude of interactions, QNA enables researchers to model ecological relationships and predict system responses to perturbations without precise parameter estimates. The methodology's capacity to test multiple plausible network configurations offers a systematic approach to addressing structural uncertainty, identifying robust outcomes that persist across alternative scenarios, and pinpointing critical interactions that drive system behavior. When integrated with sensitivity analysis methods and proper visualization techniques, QNA forms a powerful foundation for ecosystem-based management decisions and guides future research priorities in situations of limited empirical data.

Marine food webs represent complex networks of energy and nutrient transfer, yet their underlying structures and source pathways have traditionally been characterized by significant uncertainty. This structural uncertainty—the incomplete knowledge of trophic connections, energy pathways, and biogeochemical processes—limits our ability to predict ecosystem responses to environmental change. Traditional analytical approaches, including bulk stable isotope analysis (BSIA), often fail to resolve this complexity due to overlapping isotopic values of different organic matter sources and variable trophic fractionation patterns [28].

Compound-specific stable isotope analysis of amino acids (CSIA-AA) has emerged as a transformative biogeochemical tool that addresses these limitations by providing unprecedented resolution of energy pathways through aquatic systems. This technique measures the stable isotope ratios (particularly δ15N) of individual amino acids in organisms and organic matter, exploiting their distinct and predictable patterns of isotopic fractionation during trophic transfer [28] [29]. By simultaneously tracing multiple energy sources and quantifying trophic positions within complex food webs, CSIA-AA reduces critical uncertainties in our understanding of marine ecosystem structure and function, particularly in the context of climate change and anthropogenic disturbances [30] [1].

Fundamental Principles of CSIA-AA

Trophic Discrimination Dynamics in Amino Acids

The power of CSIA-AA stems from the differential behavior of amino acids during metabolic processes. Amino acids can be categorized based on their trophic discrimination patterns:

• Trophic amino acids (e.g., glutamic acid): Exhibit large, predictable 15N enrichment with each trophic transfer (approximately +6-8‰ for δ15N) [29].
• Source amino acids (e.g., phenylalanine): Show minimal 15N enrichment (approximately +0.5‰ for δ15N) regardless of trophic level, thus preserving the isotopic signature of baseline nutrient sources [29] [30].

This differential fractionation provides both trophic position information (from trophic AAs) and baseline organic matter signatures (from source AAs) within a single analysis, effectively disentangling the competing influences of source variability and trophic structure that confound bulk isotope approaches [28].

Analytical Foundations

CSIA-AA utilizes two primary analytical platforms with complementary strengths:

• Gas Chromatography-Combustion-Isotope Ratio Mass Spectrometry (GC-C-IRMS): The most established method, providing high-precision δ15N analysis of derivatized amino acids [28].
• Liquid Chromatography-Isotope Ratio Mass Spectrometry (LC-IRMS): An emerging technique that enables analysis of underivatized amino acids, potentially preserving original isotopic signatures [28].

Table 1: Key Amino Acids Used in CSIA-AA and Their Ecological Applications

Amino Acid	Category	Trophic Discrimination Factor (Δ15N)	Primary Ecological Application
Phenylalanine	Source	~+0.5‰ per trophic step	Baseline organic matter signature
Glutamic Acid	Trophic	~+6-8‰ per trophic step	Trophic position estimation
Lysine	Source	Minimal enrichment	Organic matter source identification
Threonine	Source	~-5‰ per metazoan trophic step	Organic matter source identification
Proline	Trophic	Significant enrichment	Trophic ecology marker

Methodological Workflows and Experimental Protocols

Sample Preparation and Analytical Procedures

Implementing CSIA-AA requires meticulous sample preparation to isolate individual amino acids from complex organic matrices while preserving their native isotopic signatures:

• Sample Hydrolysis: Proteinaceous material is hydrolyzed using 6N HCl at 110°C for 20-24 hours to liberate individual amino acids from polypeptide chains [28].

• Amino Acid Derivatization (for GC-IRMS):

  • Multiple derivatization approaches include N-Acetyl-n-propyl (NANP) and trifluoroacetyl-isopropyl (TFA-IP) esters [28].
  • Critical consideration: Derivitization agents introduce additional carbon atoms that must be accounted for in isotopic calculations.

• Chromatographic Separation:

  • GC-based methods: Utilize specialized capillary columns with precise temperature ramping to resolve complex amino acid mixtures.
  • LC-based methods: Employ hydrophilic interaction liquid chromatography (HILIC) columns for underivatized separations [28].

• Isotope Ratio Measurement:

  • Following chromatographic separation, individual amino acids are combusted to N2 or CO2 before introduction to the isotope ratio mass spectrometer.
  • Internal standards with known isotopic compositions are analyzed concurrently for calibration and quality control.

CSIA-AA Experimental Workflow

The following diagram illustrates the complete analytical workflow for CSIA-AA, from sample collection to data interpretation:

Advanced Applications in Resolving Structural Uncertainty

Quantifying Organic Matter Supply Pathways

CSIA-AA has revealed previously unrecognized complexity in organic matter cycling, particularly in mesopelagic and deep-sea environments where multiple potential nutrient sources coexist. The development of specialized Bayesian mixing models, such as the Organic Matter Supply Model (OMSM), has been instrumental in quantifying these pathways [29].

The OMSM represents a significant advancement over previous mixing models (e.g., MixSIAR, simmr) because it simultaneously:

• Estimates the number of metazoan and protozoan trophic steps between consumers and basal organic matter sources
• Solves for relative contributions of multiple organic matter sources to consumers
• Accounts for trophic discrimination during both protozoan and metazoan trophic steps, which exhibit distinct AA fractionation patterns [29]

In simulated zooplankton food webs, specific amino acid subsets proved most valuable: glutamic acid and proline as markers of trophic ecology, and phenylalanine, lysine, and threonine for identifying supply from basal organic matter sources [29].

Assessing Anthropogenic Impacts on Food Web Structure

CSIA-AA provides critical insights into how human activities disrupt marine energy pathways. A groundbreaking application involves assessing potential impacts of deep-sea mining on midwater food webs in the Clarion-Clipperton Zone [30].

Table 2: Nutritional Quality Comparison Between Natural and Mining Plume Particles

Particle Type	Size Fraction	Amino Acid Concentration (ngN/μgPN)	Nutritional Assessment
Background	Small (0.7-6 μm)	4.7 ± 2.7	Moderate nutritional value
Background	Medium (6-53 μm)	41.1 ± 25.3	High nutritional value
Background	Large (>53 μm)	46.3 ± 34.7	High nutritional value
Mining Plume	Small (0.7-6 μm)	3.8 ± 4.4	Moderate nutritional value
Mining Plume	Medium (6-53 μm)	1.7 ± 1.5	Significantly depleted
Mining Plume	Large (>53 μm)	4.2 ± 4.7	Significantly depleted

This research demonstrated that natural particles >6 μm form the nutritional base of mesopelagic food webs but would be diluted by nutritionally deficient mining particles of similar size [30]. Given that 53% of zooplankton taxa are particle feeders and 60% of micronekton taxa are zooplanktivores at proposed discharge depths (~1250 m), CSIA-AA evidence suggests significant potential for bottom-up disruption of deep-sea food webs from mining operations [30].

Addressing Climate Change Impacts

Marine heatwaves (MHWs) are increasing in frequency, duration, and intensity, with demonstrated impacts on marine ecosystem structure. CSIA-AA helps quantify how these climate-driven disturbances alter energy transfer through food webs. Modeling studies using EcoTroph-Dyn have revealed that MHWs cause disproportionate biomass declines at higher trophic levels, with effects persisting longer post-event [5].

Between 1998 and 2021, MHWs caused significant biomass declines globally, with an 8.7% ± 1.0% reduction simulated for the northeastern Pacific Ocean during the 2013-2016 "Blob" heatwave [5]. These impacts are particularly pronounced in the Northern Hemisphere and Pacific Ocean regions, demonstrating how CSIA-AΑ-informed models can quantify climate change impacts on marine ecosystem structure [5].

The Scientist's Toolkit: Essential Reagents and Methodologies

Table 3: Essential Research Reagents and Analytical Solutions for CSIA-AA

Reagent/Instrument	Function	Technical Considerations
Hydrochloric Acid (6N)	Protein hydrolysis to liberate individual amino acids	Must be high-purity to prevent contamination; hydrolysis conditions (time, temperature) critical for complete recovery
Derivatization Reagents (e.g., NANP, TFA-IP)	Chemical modification for volatility (GC-IRMS)	Introduces additional carbon atoms requiring isotopic correction; multiple approaches available with different advantages
Internal Isotopic Standards	Calibration and quality control	Compounds with known isotopic composition essential for accuracy; multiple points across chromatographic run
GC-C-IRMS System	Separation and δ15N analysis of derivatized AAs	High-resolution separation critical; requires specialized combustion interface
LC-IRMS System	Separation and δ15N analysis of underivatized AAs	Emerging technique avoiding derivatization artifacts; requires specialized interface
Reference Gases (N2, CO2)	Instrument calibration	High-purity gases with known isotopic composition essential for accurate δ15N determination

Integration with Ecosystem Modeling Approaches

CSIA-AA data significantly enhances the reliability of ecosystem models by providing empirical constraints on trophic relationships and energy pathways. In the Southern Ocean, OSMOSE ecosystem models have been particularly strengthened through CSIA-AA-derived parameters [23].

Global sensitivity analyses of OSMOSE-CooperationSea identified that community indicators like total biomass (Biocom), mean trophic level (mTLcom), and diversity (H') were most sensitive to parameters governing larval mortality and predation processes [23]. This finding highlights how CSIA-AA-informed parameters can reduce uncertainty in model projections by prioritizing the most influential ecological processes.

Similarly, qualitative network models exploring structural uncertainty in California Current food webs demonstrated that salmon outcomes shifted from 30% to 84% negative when consumption rates by multiple competitor and predator groups increased during marine heatwaves [1]. These approaches, when parameterized with CSIA-AA data, provide powerful frameworks for exploring structural uncertainty and identifying critical leverage points in marine food webs [1].

Future Directions and Methodological Innovations

The ongoing development of CSIA-AA methodology promises to further reduce structural uncertainty in marine food web research. Key emerging areas include:

• Position-specific isotope analysis (PSIA): Measuring isotopic composition at specific atomic positions within amino acids for even greater resolution of metabolic pathways [28].
• Multi-element CSIA: Combining δ15N with δ13C and δ2H of amino acids to simultaneously trace multiple elemental cycles.
• Increased taxonomic resolution: Applying CSIA-AA to specific compound classes like phospholipid fatty acids (PLFAs) to link energy pathways to specific microbial functional groups [28].
• Integration with emerging molecular techniques: Combining CSIA-AA with meta-omics approaches to connect biogeochemical cycling with community genomics.

As these technical advances mature, CSIA-AA will continue to transform our understanding of marine ecosystem structure and function, providing increasingly sophisticated tools to navigate the structural uncertainty inherent in complex food webs. This progress is particularly crucial for developing effective conservation strategies in an era of rapid environmental change.

Ecosystem-based fisheries management (EBFM) requires robust tools to evaluate how fishing and environmental changes impact entire ecosystems. However, a significant barrier to its adoption lies in structural uncertainty—the inherent limitations and assumptions embedded within the ecosystem models used to inform management decisions [31]. This uncertainty arises because different modeling approaches represent ecological processes through distinct mathematical formulations and structural frameworks, potentially leading to varying management recommendations even when using the same underlying data [31]. It is considered risky to rely on a single ecosystem model to address all questions under the EBFM framework [31]. This whitepaper provides an in-depth technical analysis of two prominent ecosystem modeling approaches—Ecopath with Ecosim (EwE) and Atlantis—focusing on their core structures, capabilities, and how their differences contribute to the broader challenge of structural uncertainty in marine food web research.

Model Frameworks: A Comparative Technical Analysis

Ecopath with Ecosim (EwE): A Mass-Balance Approach

The EwE modeling suite is a cohesive framework comprising three core components [32]:

• Ecopath: A static, mass-balanced snapshot of an ecosystem, providing a quantitative description of trophic flows and biomasses at a specific point in time.
• Ecosim: A time-dynamic simulation module built upon the Ecopath mass-balance, used for exploring past and future impacts of fishing and environmental disturbances [32].
• Ecospace: A spatial and temporal dynamic module designed for exploring impact and placement of marine protected areas (MPAs) and other spatial management policies by replicating Ecosim models over a spatial map grid [32].

EwE simplifies complex ecosystems by organizing species into functional groups of similar nature, representing predator-prey relationships through equations that calculate the transfer of mass/energy with no net loss [33]. The software is freely available and has been used in over 170 countries, with ongoing development adding features like improved sensitivity analysis and 64-bit support [34] [35].

Atlantis: A Spatially-Explicit, Individual-Based Framework

Atlantis represents a more complex, multidimensional ecosystem model [31]. It is a whole-ecosystem, age- and size-structured, and 3-dimensional population model [31]. Unlike EwE's biomass pool representation, Atlantis tracks individuals or cohorts, explicitly simulating their growth, movement, and mortality over time and space. In Atlantis, predation is regulated by a diet preference matrix, but the actual resulting diet is subject to mouth-gape limitations and prey availability, introducing more biological realism but also greater parametric demand [31]. The high dimensionality and complexity of Atlantis mean that full-scale sensitivity analysis is often not feasible, and confidence in its outputs typically relies on model skill assessment—how well the model fits observed data [31].

Comparative Structural Analysis

The table below summarizes the fundamental structural differences between EwE and Atlantis, which are primary sources of structural uncertainty.

Table 1: Core Structural and Functional Comparison of EwE and Atlantis

Feature	Ecopath with Ecosim (EwE)	Atlantis
Core Representation	Biom-based, functional group-centric	Age- and size-structured, individual-based
Spatial Dimension	0-dimensional (Ecopath/Ecosim); 2D grid (Ecospace) [31]	3-dimensional, spatially explicit [31]
Temporal Resolution	User-defined time steps in Ecosim/Ecospace	Fixed time steps, typically daily or weekly
Predation Formulation	Fixed diet matrix; foraging arena/vulnerability [31]	Diet preference matrix with gape limitation & prey availability [31]
Data Requirements	Moderate (biomass, production, consumption, diets)	High (requires detailed life-history, spatial, and behavioral data)
Uncertainty Analysis	Monte Carlo approach feasible for parameter sensitivity [31]	Full-scale sensitivity analysis often not feasible [31]

The following diagram illustrates the core workflow and structural components of each modeling approach, highlighting points where structural assumptions are made.

Methodologies and Experimental Protocols

EwE Model Development and Fitting Protocol

Constructing and calibrating an EwE model is an iterative process. The protocol below outlines the key stages for creating a dynamic Ecosim model from a base Ecopath snapshot.

Table 2: Key Research Reagents and Data Inputs for Ecosystem Modeling

Research 'Reagent'	Function in Model Development	EwE Specific	Atlantis Specific
Functional Groups	Aggregates species with similar ecological roles to simplify the food web.	Primary unit of modeling [33].	Also used, but with internal age/size structure.
Diet Composition Matrix	Quantifies "who eats whom," defining the central topology of the food web.	Fixed matrix, core input [31].	Preference matrix, realized diet depends on availability & rules [31].
Vulnerability / Foraging Arena	Controls the top-down vs. bottom-up flow control between predator and prey.	Key fitting parameter in Ecosim [31].	Emerges from individual-based interactions.
Time Series Data	Used to calibrate the dynamic model (Ecosim) to historical trends.	Fitted via vulnerability search [31].	Used for model skill assessment and validation.
Forcing Functions	Drives model with external changes (e.g., fishing effort, environmental data).	Applied to production or consumption rates.	Can be applied to various biological and physical processes.
Spatial Habitat Layers	Defines the distribution of suitable habitats for functional groups.	Used in Ecospace to define carrying capacity.	Core, 3D environmental grids for physics and biology.

Phase 1: Ecopath Mass-Balance The first step involves constructing a static, mass-balanced model of the ecosystem for a base year. The core master equation in Ecopath is [33]: [Bi \cdot (P/B)i \cdot EEi = Yi + \sum{j=1}^{n} Bj \cdot (Q/B)j \cdot DC{ji}] Where:

• (B_i) is the biomass of functional group (i).
• ((P/B)_i) is the production/biomass ratio (equivalent to total mortality, Z).
• (EE_i) is the ecotrophic efficiency (fraction of production consumed by predators or fishing).
• (Y_i) is the total fishery catch rate of (i).
• ((Q/B)_j) is the consumption/biomass ratio of predator (j).
• (DC_{ji}) is the fraction of prey (i) in the diet of predator (j).

The model is balanced by iteratively adjusting poorly-known parameters (e.g., (Bi), (EEi), (DC_{ji})) until all equations are solved simultaneously, and the ecosystem is in mass balance.

Phase 2: Ecosim Time Dynamic Fitting Once a balanced Ecopath model is achieved, it is used to initialize Ecosim for dynamic simulations. The key protocol for fitting Ecosim to time series data involves:

• Loading Time Series: Import time series of relative or absolute biomass and catch for functional groups, and time series of potential forcing factors (e.g., fishing effort, climate indices).
• Parameter Search: Use a built-in fitting routine to estimate vulnerability exchange rates—parameters that control how tightly predator and prey dynamics are coupled (top-down vs. bottom-up control) [31]. This is often done using a semi-Bayesian approach (e.g., Monte Carlo Markov Chain) to explore parameter space.
• Model Evaluation: Assess the model's fit to time series using sum-of-squares deviations or other statistical criteria. The "Evolve" option can be used to automatically search for vulnerability parameters that best fit the time series data.

Phase 3: Policy Exploration With a calibrated Ecosim model, users can run future scenarios to evaluate management strategies, such as changing fishing mortality rates, implementing moratoria, or testing the ecosystem effects of environmental changes.

Atlantis Model Development Protocol

Developing an Atlantis model is a more resource-intensive endeavor. The general methodology includes:

• Spatial Domain Discretization: The study area is divided into a 3D grid of boxes, each with associated physical (e.g., temperature, salinity, currents) and biological (e.g., habitat type) attributes.
• Population Parameterization: For each functional group, detailed age- or size-structure data is required, along with growth, reproduction, and mortality schedules. Predation is not fixed but emerges from spatial co-location, gape limitations, and prey selection rules.
• Model Spin-Up: The model is run for a long period (often decades) without fishing pressures to allow the simulated ecosystem to reach a stable, plausible state from the initial conditions. This is a critical and computationally expensive step.
• Forcing and Validation: Historical fishing pressure and environmental drivers are applied. Model outputs (e.g., biomass, catch) are compared to observed time series data for validation in a process known as model skill assessment [31]. Due to the model's complexity, a full sensitivity analysis is often not feasible, and confidence is built through this skill assessment and by ensuring the model produces plausible emergent ecosystem properties.

Addressing Structural Uncertainty: A Multi-Model Approach

The case of Lake Victoria in East Africa provides a compelling study on structural uncertainty. Research directly compared an EwE model and an Atlantis model for the same lake system, which shared similar functional groups and historical forcing data (annual landings) [31]. The study aimed to test how ecosystem effects of fishing policies were sensitive to model choice.

The findings highlighted that while qualitative results (e.g., direction of change for target species) were often consistent between models, considerable variations were observed for quantitative predictions and for cascading effects on non-target species [31]. This divergence is attributed to the fundamental structural differences summarized in Table 1: how predation is formulated, the representation of population structure, and the handling of space.

To mitigate the risks posed by structural uncertainty, the scientific community is increasingly advocating for an ensemble modeling approach [31]. Using multiple, structurally distinct models to address the same management question provides "insurance" against the limitations of any single model.

• Convergent Results: When different models predict similar outcomes, confidence in the policy recommendation is high.
• Divergent Results: When model predictions disagree, it highlights critical areas of ecological uncertainty, guiding future research and signaling to decision-makers that caution is warranted.

The following diagram maps the process of using a multi-model ensemble to quantify and address structural uncertainty in policy advice.

Ecopath with Ecosim and Atlantis represent two powerful but philosophically different approaches to modeling marine ecosystems. EwE offers a more accessible, tractable framework that excels in rapid policy screening and is well-suited for uncertainty analysis via Monte Carlo methods. Atlantis provides high biological and spatial realism by simulating individuals in a 3D environment but at a high cost of data, expertise, and computational resources, which limits the scope of formal uncertainty analysis.

Neither model is inherently superior; the choice depends on the management question, data availability, and resources. The critical insight for researchers and managers is that model structure itself is a fundamental source of uncertainty. The path toward more resilient EBFM lies not in seeking a single perfect model, but in explicitly acknowledging this structural uncertainty through the use of ensemble modeling, thereby developing management strategies that are robust across multiple plausible representations of the ecosystem.

Traditional food web ecology has predominantly focused on the transfer of energy through living plant biomass, often overlooking the critical role of non-living organic matter [36]. This has created a significant structural uncertainty in our understanding of ecosystem dynamics, particularly in marine environments where detrital pathways can equal or exceed the energy flow through phytoplankton-based grazing chains [36]. The omission of detritus from classical models represents more than a simple accounting error; it fundamentally undermines our ability to predict ecosystem stability, resilience, and function under increasing anthropogenic pressures.

The challenge extends beyond theoretical considerations to practical methodology. As research by Moore et al. emphasizes, "the predictive power of theories on food webs may be questioned if they do not include the detritus path" [36]. This gap is particularly problematic in marine systems where detritus serves dual roles as both an energy source for multiple trophic levels and a nutrient reservoir for primary producers. Recent studies have demonstrated that detritus-based food webs operate under different dynamics than their phytoplankton-based counterparts, with distinct interaction strengths and stability properties that demand specialized analytical approaches [36].

Quantitative Frameworks: Modeling Detrital Pathways

Energetic and Stability Metrics

Integrating detritus into food web models requires specialized quantitative approaches that account for its unique role in ecosystem dynamics. The following table summarizes key metrics and methods developed to quantify detrital pathways and their influence on food web stability:

Table 1: Quantitative metrics for analyzing detritus-integrated food webs

Metric/Model	Application	Key Parameters	Ecological Interpretation
Loop Weight Analysis [36]	Stability assessment via interaction strength in trophic loops	Geometric mean of interaction strengths in a closed trophic chain	Lighter loop weights indicate higher stability; identifies critical destabilizing pathways
Diagonal Strength (S) [36]	Matrix stability assessment	Proportion of specific mortality from intraspecific competition	S < 1 indicates stability; lower S values correspond to higher stability
LIM-MCMC [37]	Uncertainty assessment in flow estimation	Minimum/maximum boundaries for each flow; Monte Carlo sampling	Quantifies uncertainty in flow values; distinguishes local from imported data
Ecopath with Detritus [36]	Ecosystem mass-balance modeling	Biomass, diet composition, production/consumption ratios	Provides alternative parameters for interaction strength calculations
BCBPt Ratio [36]	Empirical stability indicator	Geometric mean ratio of predator-to-prey biomass	Correlates with diagonal strength (R²=0.6645); practical management tool

Structural Uncertainty in Flow Estimation

The Linear Inverse Modeling with Monte Carlo Markov Chain (LIM-MCMC) approach addresses structural uncertainty by defining flow values as ranges rather than single points [37]. This methodology integrates uncertainty by establishing minimum and maximum boundaries for each flow, which is particularly valuable for detrital pathways where measurement error is often high. The LIM-MCMC framework enables researchers to "distinguish between local data and data from a different but related ecosystem" [37], a critical capacity when modeling detritus that may originate from multiple sources with different turnover rates.

Application of LIM-MCMC to the Bay of Biscay continental shelf demonstrated its utility for quantifying uncertainty in both flows and ecological network analysis indices [37]. For detritus-focused research, this approach allows for better representation of complex eco-physiological processes operating at the base of the food web, including "plankton excretion and bacterial uptake of dissolved organic carbon" [37], processes fundamental to detrital formation and utilization.

Experimental Evidence: Detritus Mediates Ecosystem Structure

Empirical Demonstrations from Aquatic Ecosystems

Recent large-scale experiments provide compelling evidence for the ecosystem-structuring role of detritus. A six-river experiment in southeastern Australia demonstrated that increasing detrital retention directly affected community structure, though outcomes varied based on system characteristics [38]. By driving wooden stakes into riverbeds to enhance detritus retention, researchers found that only three of the six rivers showed significant increases in detritus densities, with just two of those exhibiting higher species richness and invertebrate densities within 12 months [38].

Table 2: Context-dependent outcomes of detritus supplementation in river ecosystems

River System	Pre-existing Wood	Detritus Response	Biological Response	Mechanism
Hughes Creek	Low	Significant increase	Increased species richness and density	Successful dispersal and invasion
Seven Creeks	Low	Significant increase	Increased species richness and density	Bio-equivalence with reference sites
Turtons Creek	Low	Significant increase	Species turnover through replacement	Replacement without richness change
Non-responding Rivers	High	No significant increase	No significant change	Pre-existing retention sufficient

This experiment revealed critical context dependence, where "pre-existing conditions (e.g., channel retentiveness)" mediated ecosystem response to resource supplementation [38]. The findings highlight that detritus functions differently across systems, creating structural uncertainty that must be accounted for in food web models.

Stability Transformations in Lake Ecosystems

Research in Baiyangdian Lake (China) revealed how detritus-based pathways fundamentally alter food web stability. From 1958-2009, stability was limited by a three-link omnivorous loop: "Detritus > zooplankton > filter-feeding fish" [36]. As the predator-prey biomass ratio increased, instability grew. However, between 2009-2019, a new loop emerged ("detritus > zooplankton > phytoplankton") that stabilized the system, representing a shift from "dominant top-down trophic cascade effects to dominant bottom-up trophic cascade effects" [36].

This transformation demonstrates the dynamic role of detritus in mediating regime shifts. The study further proposed a geometric mean ratio of predator-to-prey biomass as a simplified indicator that correlates with diagonal strength (R² = 0.6645), providing a practical tool for early-warning assessments of food web stability despite moderate precision [36].

Methodological Toolkit: Protocols for Detritus Integration

Research Reagent Solutions for Detritus Analysis

Table 3: Essential research reagents and methods for detritus-focused food web studies

Reagent/Method	Function	Application Note
CsCl Cation Exchange [39]	Removes adsorbed ions from clay surfaces; reveals terrestrial fingerprint	Critical for accurate isotopic provenance; replaces 75% of Na, 25% of Ca
Compound-Specific Isotope Analysis of Amino Acids (CSIA-AA) [40]	Tracks nutrient pathways through food webs over longer term	Superior to stomach content analysis for energy flow mapping
Stable Isotope Analysis	Traditional food web mapping	Provides single isotope value per organism; less precise than CSIA-AA
Wooden Stakes Arrays [38]	Experimental detritus retention enhancement	Used in rivers with low pre-existing wood; creates detritus patches
Ecopath Modeling Software [36]	Mass-balance ecosystem modeling	Provides parameters for interaction strength calculations
Linear Inverse Modeling [37]	Food web reconstruction with uncertainty assessment	Uses Monte Carlo method coupled with Markov Chain

Detritus Integration Workflow

The following diagram illustrates the integrated methodological workflow for incorporating detritus into food web analysis:

Analytical Framework for Stability Assessment

The diagram below outlines the computational pathway for assessing food web stability through detritus-integrated models:

Discussion: Resolving Structural Uncertainty

Integrating detritus into food web models necessitates confronting fundamental sources of structural uncertainty. The LIM-MCMC approach addresses uncertainty in flow estimation [37], while loop weight analysis identifies critical stability thresholds [36]. However, context dependence remains a substantial challenge, as demonstrated by the variable outcomes of detritus supplementation across different river systems [38].

Future research priorities should include: (1) standardized protocols for detritus quantification across ecosystem types; (2) enhanced temporal resolution to capture detrital pulse dynamics; and (3) development of generalized models that accommodate context dependence without sacrificing predictive power. Furthermore, the consistent application of cation exchange methodologies [39] and advanced isotopic techniques [40] will strengthen the empirical foundation upon which detritus-integrated models are built.

The transformation in Baiyangdian Lake, where detritus-based pathways shifted from destabilizing to stabilizing [36], offers hope that targeted management of detrital flows may enhance ecosystem resilience. As anthropogenic pressures intensify, overcoming the critical gap in food web theory by fully integrating detritus will be essential for both understanding and preserving marine ecosystem functioning.

Effectively modeling the impact of climate change on any population requires careful consideration of diverse pressures, particularly potential changes in species interactions. As ecological communities reassemble and shifts in abundance and distribution cascade throughout ecosystems, researchers must explicitly examine cumulative impacts on species of conservation concern. A structured qualitative analysis of alternative responses to climate change across the food web plays a valuable role in the design and interpretation of quantitative models. A particular advantage of qualitative network analysis is the ease with which a wide range of scenarios representing structural and quantitative uncertainties can be explored, providing crucial insights for ecosystem-based management strategies in marine environments [1].

The concept of groups is ubiquitous in biology, underlying classifications used to describe phylogenetic levels, habitats, and functional roles of organisms in ecosystems. Surprisingly, this fundamental concept has not been explicitly included in simple models for food web structure—the ecological networks formed by consumer-resource interactions. Group-based models can be applied to different types of biological networks and merge two important notions in network theory: compartments (sets of highly interacting nodes) and roles (sets of nodes that have similar interaction patterns). This approach provides a basis to examine the significance of groups in biological networks and to develop more accurate models for ecological network structure [41].

Theoretical Framework: Qualitative Network Models for Exploring Structural Uncertainty

Foundations of Qualitative Network Analysis

Qualitative Network Models (QNMs) represent a powerful methodology for exploring ecosystem dynamics without requiring precise parameter estimates that are often unavailable for complex marine food webs. These models use a signed digraph (directed graph) approach where functional groups (e.g., phytoplankton, zooplankton, forage fish, piscivorous fish, marine mammals) are represented as nodes, and their interactions are represented as edges with signs (positive, negative, or neutral) indicating the nature of their relationships [1]. The community matrix derived from this network enables researchers to predict the direction of change for each functional group following a press perturbation, such as climate change.

The stability of food webs represents a hot issue and research frontier in ecology. Dynamic models can reveal complex behaviors including chaotic dynamics that may predict species extinction cascades. Through modeling approaches, researchers can identify degraded food web structures that reflect possible regime shifts—critical information for ecosystem management under climate change scenarios. These models reveal living dependence relationships, species extinction cascades, competition, and "couple" phenomena between taxonomic groups that might otherwise remain obscured in purely quantitative approaches [42].

Addressing Structural Uncertainty through Ensemble Modeling

Structural uncertainty in food web models arises from incomplete knowledge about which species interact and the nature of those interactions. Where traditional modeling approaches often assume a single food web structure, the ensemble approach tests multiple plausible configurations to identify which structural elements most strongly influence predictions for species of interest. In recent research applying this methodology to salmon survival in the California Current ecosystem, scientists tested 36 alternative representations of connections among salmon and key functional groups within the marine food web [1].

The scenarios differed in how species pairs were connected (positive, negative, or no interaction) and which species responded directly to climate change drivers. This methodology revealed that certain configurations produced consistently negative outcomes for salmon, regardless of the specific values for most links. This approach emphasizes the importance of structural uncertainty in food webs and demonstrates a robust tool for exploring it, paving the way for more targeted and effective research planning and conservation strategy development [1].

Methodological Framework: Experimental Protocols for Food Web Scenario Analysis

Protocol 1: Constructing the Qualitative Network Model

Table 1: Functional Group Classification for Marine Food Web Analysis

Functional Group	Node Symbol	Key Taxa/Components	Climate Response
Primary Producers	PP	Phytoplankton, Diatoms, Dinoflagellates	Temperature-dependent growth
Small Zooplankton	SZ	Copepods, Protozoans	Temperature, acidification sensitivity
Large Zooplankton	LZ	Euphausiids, Jellyfish	Temperature, current shifts
Forage Fish	FF	Anchovy, Sardine, Sandlance	Phenology mismatches
Juvenile Salmon	JS	Chinook, Coho, Sockeye	Critical period survival
Piscivorous Fish	PF	Hake, Mackerel, Adult Salmon	Range expansion, predation pressure
Marine Mammals	MM	Sea Lions, Seals, Whales	Protected population recovery
Seabirds	SB	Alcids, Cormorants, Gulls	Breeding success, prey availability

Step 1: Define Functional Groups - Begin by identifying key functional groups relevant to the ecosystem and research question. Groups should represent organisms that share similar ecological roles and interactions within the food web. The classification should balance biological realism with model parsimony [41].

Step 2: Determine Interactions - For each pair of functional groups, determine the sign and direction of their interaction based on literature review and expert knowledge. Document the evidence quality for each interaction to support later sensitivity analysis.

Step 3: Construct Community Matrix - Create the community matrix A where element a_ij represents the effect of group j on group i. Signs indicate interaction types: (+) for positive effects (beneficial), (-) for negative effects (detrimental), and (0) for no direct effect.

Step 4: Validate Network Structure - Conduct expert workshops to review and validate the proposed network structure. Incorporate feedback to refine interaction definitions and ensure biological realism.

Protocol 2: Implementing Press Perturbation Simulations

Step 1: Define Baseline Configuration - Establish the reference state of the system before perturbations. This represents the "control" scenario against which climate change effects will be measured.

Step 2: Apply Press Perturbations - Introduce sustained directional changes to specific functional groups representing climate drivers. These may include increased mortality for temperature-sensitive groups, enhanced growth for groups benefiting from warming, or altered interaction strengths.

Step 3: Calculate Predicted Responses - Using the qualitative modeling framework, predict the direction of change (increase, decrease, or uncertain) for each functional group following the press perturbation. The prediction derives from solving the system of equations represented by the community matrix.

Step 4: Assess Interaction Strength Sensitivity - Systematically vary the strength of key interactions to determine which links most strongly influence outcomes for focal species.

Table 2: Climate Perturbation Scenarios for Marine Food Webs

Perturbation Scenario	Direct Effects On	Interaction Modifications	Expected Outcomes
Marine Heatwave	PP: Reduced diatoms, SZ: Reduced cold-water species	FF-JS: Increased competition, PF-JS: Increased predation	30-84% decline in salmon survival [1]
Ocean Acidification	PP: Shift to tolerant species, SZ: Reduced calcifying species	PP-SZ: Trophic mismatch, SZ-FF: Reduced quality	Bottom-up energy limitation
Phenological Shifts	PP: Earlier blooms, SZ: Variable timing	SZ-FF: Match-mismatch dynamics, FF-JS: Prey availability	Consumer-resource decoupling
Range Shifts	PF: Expanded ranges, FF: Distribution shifts	MM-FF: New predator-prey interactions, PF-JS: Novel predation	Community reorganization

Protocol 3: Ensemble Modeling for Structural Uncertainty

Step 1: Generate Alternative Configurations - Create multiple plausible food web structures that differ in their inclusion or sign of ecologically uncertain interactions. In the salmon survival study, 36 different configurations were tested [1].

Step 2: Run Simulations Across Ensembles - Apply identical press perturbations to each configuration in the ensemble and record outcomes for focal species or groups.

Step 3: Analyze Outcome Distributions - Calculate the distribution of outcomes across the ensemble to identify robust predictions (those consistent across most configurations) and uncertain predictions (those highly variable across configurations).

Step 4: Identify Critical Uncertainties - Determine which structural elements (interactions) most strongly influence model outcomes. These represent priority targets for future empirical research to reduce structural uncertainty.

Visualization Framework: Food Web Relationships and Workflows

Figure 1: Structural Uncertainty in Marine Food Web Modeling

Figure 2: Scenario Analysis Workflow for Food Webs

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Methodological Components for Food Web Scenario Analysis

Research Component	Function/Purpose	Implementation Example
Qualitative Network Modeling (QNM) Framework	Predicts direction of species responses to perturbations without precise parameter estimation	Exploring 36 food web configurations to assess structural uncertainty in salmon survival predictions [1]
Community Matrix	Represents signed digraph of species interactions as a mathematical object for analysis	Constructing matrix A where element a_ij represents effect of group j on group i [1]
Ensemble Modeling Approach	Tests multiple plausible structural configurations to identify robust outcomes	Generating alternative food web structures differing in inclusion or sign of uncertain interactions [1]
Press Perturbation Simulation	Models sustained directional changes to specific functional groups	Applying marine heatwave scenario as sustained increase in predator consumption rates [1]
Dynamic Food Web Models	Analyzes system stability, regime shifts, and chaotic dynamics	Identifying eight degraded food web structures reflecting possible ecosystem state shifts [42]
Group-Based Modeling	Organizes species by functional roles, compartments, or interaction patterns	Merging concepts of compartments and roles in network theory for improved prediction [41]
Structural Uncertainty Analysis	Quantifies how alternative interaction configurations affect model outcomes	Determining that salmon outcomes shifted from 30% to 84% negative depending on configuration [1]

Discussion: Implications for Ecosystem-Based Management

The application of scenario analysis to plausible food web configurations under climate perturbations reveals critical insights for conservation and management. Research demonstrates that for Chinook salmon in the California Current, certain food web configurations produced consistently negative outcomes regardless of specific parameter values. Salmon outcomes shifted dramatically from 30% to 84% negative when consumption rates by multiple competitor and predator groups increased following a press perturbation from climate—a scenario aligning with recent observations during marine heatwaves [1].

This approach identifies particularly powerful interactions within food webs, such as feedback loops between salmon and mammalian predators and indirect effects connecting spring- and fall-run salmon phenotypes. By identifying which links most strongly influence species outcomes across scenarios, this methodology enables more targeted research and monitoring investments. The explicit treatment of structural uncertainty moves ecological forecasting beyond traditional sensitivity analysis to address more fundamental questions about system architecture and stability [1].

Dynamic modeling approaches further enhance our understanding by revealing how complex dynamics, including chaotic behavior, may predict species extinction cascades. The identification of multiple degraded food web structures provides early warning indicators for potential regime shifts—critical information for proactive ecosystem management [42]. As climate change accelerates, these methodologies will become increasingly essential for anticipating and mitigating impacts on marine ecosystems and the human communities that depend on them.

Building Resilient Models: Addressing Sensitivity and Improving Projections

Identifying Keystone Groups and Critical Interactions via Sensitivity Analysis

Marine ecosystems are characterized by profound structural uncertainty, where the sheer complexity of species interactions, combined with limited empirical data, creates significant challenges for predicting ecological outcomes. This uncertainty is exacerbated by global climate change, which is causing rapid reassembly of biological communities as species shift their distributions and abundances at different rates [1] [4]. Within this context, identifying keystone groups—those species complexes that exert disproportionate influence on food web structure and function—becomes critical for effective ecosystem-based management. Sensitivity analysis provides a powerful suite of methodological approaches for quantifying this uncertainty while pinpointing the most critical interactions that determine ecosystem stability and resilience [23] [43].

The concept of keystone species complexes (KSCs) represents an evolution from traditional single-species approaches to a more holistic understanding of ecosystem dynamics. These KSCs consist of small sets of ecologically connected species and/or functional groups that occupy different trophic levels while exhibiting low biomass accumulation yet propagating the highest direct and indirect effects throughout the system [43]. In marine environments impacted by climate change, fishing pressure, and other anthropogenic stressors, identifying these critical units through rigorous sensitivity analysis allows researchers to prioritize monitoring efforts and management interventions despite structural uncertainty in food web models.

Methodological Approaches for Sensitivity Analysis

Global Sensitivity Analysis Using the Morris Method

The Morris method provides an efficient screening technique for identifying influential parameters in complex ecosystem models with relatively low computational cost. This approach is particularly valuable for models with hundreds of parameters, where comprehensive testing of all possible combinations would be prohibitively expensive. The method works by calculating elementary effects through randomized one-at-a-time experiments across the parameter space, generating sensitivity measures that indicate which parameters most strongly influence model outputs [23].

In application to the OSMOSE (Object-oriented Simulator of Marine ecOSystEms) model for the Cooperation Sea food web, the Morris method revealed that community indicators like total biomass (Biocom), mean trophic level (mTLcom), and diversity indices (H') were particularly sensitive to parameters related to larval mortality rates (Mlarval) across multiple species groups [23]. These parameters represented 50% of the top eight most influential parameters driving model behavior, highlighting the critical importance of early life history stages in determining ecosystem structure. Implementation typically follows a structured protocol:

Table 1: Key steps in implementing the Morris method for ecosystem models

Step	Description	Key Considerations
1. Parameter Selection	Identify all parameters to be tested, focusing on those with empirical estimates	Differentiate between fixed and calibrated parameters
2. Range Definition	Establish plausible minimum and maximum values for each parameter	Use literature review and expert opinion to define realistic bounds
3. Trajectory Generation	Create multiple randomized trajectories through parameter space	Typically 10-100 trajectories depending on model complexity
4. Elementary Effect Calculation	Compute the change in output per unit change in input parameter	Run model for each parameter combination in trajectory
5. Sensitivity Metrics	Calculate μ (mean) and σ (standard deviation) of elementary effects	High μ indicates strong influence; high σ indicates interaction effects

Qualitative Network Analysis for Structural Uncertainty

Qualitative Network Analysis (QNA) offers a complementary approach that focuses on the direction (positive, negative, or neutral) rather than precise magnitude of species interactions. This method is particularly valuable for exploring structural uncertainty in food webs, where the presence or absence of interactions themselves may be unknown. QNA represents interactions as a signed digraph and analyzes the community matrix to assess stability and response to perturbations [1] [4].

In a study examining 36 different plausible food web configurations for salmon ecosystems, QNA revealed that certain network structures produced consistently negative outcomes for salmon populations regardless of specific parameter values. The analysis showed that salmon outcomes shifted from 30% to 84% negative when consumption rates by multiple competitor and predator groups increased following climate perturbations [1] [4]. This approach identified particularly critical feedbacks between salmon and mammalian predators, as well as indirect effects connecting different salmon runs.

Figure 1: Qualitative network showing key interactions affecting salmon populations. Positive (+) and negative (-) effects demonstrate how climate perturbations cascade through the food web [1] [4].

Monte Carlo Simulation for Parameter Uncertainty

Monte Carlo simulation approaches address parameter uncertainty by repeatedly running models with parameter values randomly sampled from probability distributions. This method generates a distribution of possible outcomes that reflects the collective uncertainty in input parameters. In the OSMOSE-CooperationSea model application, Monte Carlo simulation revealed that community indicators like total biomass and mean trophic level showed greater uncertainty than specific functional group biomasses, suggesting that aggregate metrics may be more sensitive to parameter error propagation [23].

The implementation typically involves:

• Defining probability distributions for each uncertain parameter
• Randomly sampling from these distributions to create parameter sets
• Running the model for each parameter set
• Analyzing the distribution of outputs to quantify uncertainty
• Identifying which parameters contribute most to output variance

This approach is particularly valuable for models where parameters are estimated with error or where empirical data for precise parameterization is limited.

Loop Analysis for Stability Assessment

Loop analysis provides a framework for quantifying the local stability and resilience of eco-social keystone species complexes. Based on Routh-Hurwitz's criterion and Levins' criteria, this method determines whether ecological states will persist despite small disturbances [43]. Application to 13 different KSCs across Caribbean and eastern Pacific marine ecosystems revealed varying levels of stability and resilience, with the KSC corresponding to Islas Marietas National Park (Mexico) emerging as the most locally stable and resilient, while those in Guala Guala Bay (Chile) and Xcalak Reef National Park (Caribbean) were the least stable and resilient, respectively [43].

Experimental Protocols and Workflows

Integrated Workflow for Keystone Group Identification

A comprehensive approach to identifying keystone groups and critical interactions combines multiple sensitivity analysis methods within a structured workflow. The following diagram illustrates this integrated methodological framework:

Figure 2: Integrated workflow for identifying keystone groups and critical interactions in marine food webs, combining multiple sensitivity analysis approaches [1] [23] [43].

Case Study Implementation: Southern Ocean Food Web

A practical implementation of these methods can be seen in the analysis of the OSMOSE model for the Cooperation Sea in the Southern Ocean [23]. This study followed a systematic protocol:

• Model Parameterization: The OSMOSE-CooperationSea model was parameterized with data from 2016, including 14 representative species groups spanning from phytoplankton to toothfishes and marine mammals. Antarctic krill (Euphausia superba) and other krill species served as pivotal intermediate trophic level components.

• Sensitivity Analysis Implementation: The Morris method was applied to 36 calibrated parameters, with each parameter varied between 0.5 and 1.5 times its baseline value. Elementary effects were calculated for five community indicators and species group biomasses.

• Uncertainty Quantification: Monte Carlo simulation was performed with 1000 iterations, sampling parameters from normal distributions with means equal to baseline values and coefficients of variation of 20%.

• Key Interaction Identification: Linear regression analysis quantified the contribution of each parameter to output variance, identifying which interactions had the strongest influence on model outcomes.

This analysis revealed that parameters related to larval mortality, predation, and recruitment had the strongest effects on community-level indicators, highlighting these processes as critical interactions in the Southern Ocean food web.

Research Reagent Solutions: Methodological Toolkit

Table 2: Essential methodological tools for sensitivity analysis in marine food web research

Method/Platform	Primary Function	Application Context
OSMOSE	Object-oriented, multispecies, individual-based modeling platform simulating food web dynamics	End-to-end modeling from primary producers to top predators; particularly valuable for strategic management scenario evaluation [23]
Qualitative Network Analysis (QNA)	Analyzing structural uncertainty using signed digraphs and community matrix stability	Exploring alternative food web configurations and identifying critical interaction pathways with limited empirical data [1] [4]
Morris Method	Efficient global sensitivity screening for complex models with many parameters	Identifying influential parameters in data-limited contexts; ideal initial screening step before more intensive analyses [23]
Monte Carlo Simulation	Propagating parameter uncertainty through model simulations	Quantifying uncertainty in model predictions and identifying parameters contributing most to output variance [23]
EcoTroph	Trophodynamic modeling representing biomass flow as continuous distribution across trophic levels	Assessing impacts of disturbances like marine heatwaves on ecosystem structure and function across multiple trophic levels [5]
Loop Analysis	Quantifying local stability and resilience of ecological communities	Assessing persistence of keystone species complexes under disturbance; comparing stability across different ecosystems [43]

Interpretation and Application of Results

Identifying Keystone Groups from Sensitivity Outputs

The ultimate goal of sensitivity analysis in this context is to identify keystone groups—those species complexes that disproportionately influence food web structure and function. In the Southern Ocean case study, results indicated that Antarctic krill and other krill species function as pivotal intermediate trophic level components, with parameters associated with their dynamics significantly influencing community-level indicators [23]. Similarly, the qualitative network analysis of salmon food webs identified that certain predator-prey feedbacks, particularly with mammalian predators, were critically important in determining salmon population outcomes [1] [4].

Keystone groups typically exhibit two key characteristics: (1) they exert strong direct and indirect effects on other species in the food web, and (2) they exhibit low biomass accumulation relative to their ecological importance. The combination of multiple sensitivity analysis methods helps triangulate on these critical components by identifying which functional groups and interactions consistently appear as influential across different analytical approaches and uncertainty scenarios.

Managing Structural Uncertainty in Food Web Models

A fundamental challenge in marine food web research is structural uncertainty—uncertainty about which species and interactions to include in models and how they are connected. The approaches described here provide multiple strategies for managing this uncertainty:

• Ensemble Modeling: Testing multiple plausible food web configurations (e.g., the 36 different network structures in the salmon study) rather than relying on a single model structure [1] [4].

• Scenario Analysis: Exploring how different direct climate impacts on various functional groups propagate through the food web, identifying which potential impacts create the greatest conservation concerns.

• Robustness Assessment: Determining which management strategies produce acceptable outcomes across a wide range of plausible food web structures, rather than optimizing for a single best model.

By explicitly acknowledging and exploring structural uncertainty, researchers can provide more honest assessments of potential climate impacts and avoid overconfidence in model projections that may be highly dependent on uncertain structural assumptions.

Sensitivity analysis provides an indispensable methodological toolkit for identifying keystone groups and critical interactions in marine food webs, particularly within the context of structural uncertainty imposed by climate change and other anthropogenic stressors. The combination of qualitative and quantitative approaches—including Qualitative Network Analysis, the Morris method, Monte Carlo simulation, and loop analysis—enables researchers to pinpoint those components and interactions that disproportionately influence ecosystem dynamics despite limited empirical data.

The identification of keystone species complexes as conservation and monitoring units offers a pragmatic approach to ecosystem-based management in data-limited contexts. By focusing research and management attention on these critical subsets of food webs, conservation resources can be allocated more efficiently to protect and maintain marine ecosystem structure and function in an era of rapid environmental change. As climate change continues to drive the reassembly of marine communities, these approaches will become increasingly essential for predicting and managing the future of marine ecosystems.

Ecosystem-based management requires robust models to project future dynamics under changing environmental conditions. However, the Baltic Sea presents a compelling case study of how structural uncertainties in food-web models can lead to significant discrepancies between projections and outcomes. Despite the implementation of sophisticated modeling frameworks, fishery management has repeatedly faced surprises, with several key fish stocks continuing to decline toward collapse. This technical analysis examines the core lessons from these discrepancies, focusing on the methodological gaps and proposing refined approaches for addressing uncertainty in marine food-web research.

The fundamental challenge lies in the simplified representation of complex ecosystems within manageable computational frameworks. As revealed in a Baltic Sea food-web model uncertainty analysis, models that produced viable cod stock recommendations under standard parameterizations failed when data uncertainties were introduced [44]. This indicates that presenting model uncertainties is not merely an academic exercise but necessary to alleviate ecological surprises in marine ecosystem management.

Case Studies: Baltic Sea Fishery Projections vs. Outcomes

Eastern Baltic Cod Collapse

The eastern Baltic cod stock has experienced a severe and persistent decline, with the European Commission recently recommending a 63% reduction in catch limits for 2026 [45]. This represents a dramatic management response to the failure of previous projections that anticipated recovery under moderate fishing restrictions. The ecological underpinnings of this collapse trace to a complex web of factors including overfishing, nutrient loading, and high levels of contaminants [45]. Model shortcomings primarily stemmed from insufficient integration of these multiple stressors and their synergistic effects on cod reproductive success.

Central Baltic Herring Management Failure

In a stark example of projection failure, EU fisheries ministers in 2024 increased the total allowable catch for central Baltic herring by over 100% despite scientific advice advocating for precaution [46]. This decision was based on model projections showing population increases from reduced fishing pressure, yet the population remained below healthy levels. Subsequent analysis revealed that these projections relied on uncertain data and simplification of complex ecosystem processes [46]. This case illustrates how political intervention compounds model limitations, but also highlights how overly optimistic models can enable such decisions.

Sprat Fishery Cascading Effects

The sprat population, crucial as a food source for predators like cod and salmon, has been in decline for years [46]. Management projections failed to adequately account for its dual role as both target species and essential prey, leading to quotas that further disrupted the Baltic Sea food web. The ecosystem models used for projections insufficiently captured the trophic cascade effects that would result from sprat reduction, including impacts on cod recovery and seabird populations [46].

Table 1: Summary of Baltic Sea Fishery Projection Failures

Stock	Projection Outcome	Actual Outcome	Key Model Limitations
Eastern Baltic Cod	Viable stocks under moderate fishing [44]	63% catch reduction needed [45]	Ignored cumulative stressors; underestimated climate effects
Central Baltic Herring	Population growth from reduced fishing [46]	Remained below healthy levels [46]	Overly optimistic recruitment; simplified ecosystem processes
Sprat	Sustainable exploitation possible	Continued decline with ecosystem effects [46]	Failed to account for dual role as target and prey species

Methodological Framework: Assessing Model Uncertainty

Structural Uncertainty in Food-Web Representations

Structural uncertainty arises from incomplete knowledge of how ecosystem components interact and how these interactions should be mathematically represented. In the Baltic Sea context, this manifests particularly in the simplified trophic guilds used in assessments. Current food web indicators largely fail to capture the interconnectivity between guilds, instead focusing on population states within taxonomic groups [47]. This limitation becomes critical when models are used to project system responses to perturbations like fishing pressure or climate change.

Qualitative Network Analysis (QNA) provides a valuable approach for exploring structural uncertainty. A 2025 study demonstrated this by testing 36 plausible representations of connections between salmon and key functional groups in marine food webs [1]. The scenarios differed in how species pairs were connected and which species responded directly to climate change. Results showed that salmon outcomes shifted dramatically (from 30% to 84% negative) when consumption rates by multiple competitor and predator groups increased following climate perturbations [1]. This highlights how structural assumptions alone can determine projection outcomes.

Quantitative Uncertainty Assessment Techniques

Ecopath with Ecosim (EwE) Uncertainty Analysis

The Ecopath with Ecosim framework, widely used in Baltic Sea food-web modeling, incorporates several uncertainty assessment approaches:

• Monte Carlo random parameter search: A stochastic method that requires numerous model runs to explore parameter space [44]
• Pedigree tool: Incorporates qualitative information about data reliability to estimate probability distributions for input parameters [44]
• Vulnerability parameter calibration: Tests different combinations of predator-prey vulnerability parameters (v) to identify structures that best fit historical observations [44]

In the Baltic Proper food-web model (BaltProWeb), an automated calibration procedure tested vulnerability parameters for the 20 predator-prey relationships the model was most sensitive to, with other relationships set to default values [44]. The combination producing the lowest sum of squares of model deviation from 1974-2006 observations was selected, though this approach potentially overlooks equifinality (different parameter sets producing similar outputs).

Linear Inverse Modeling with Markov Chain Monte Carlo (LIM-MCMC)

The LIM-MCMC approach addresses two key weaknesses in traditional food-web modeling: uncertainty assessment and representation of low-trophic-level processes [37]. This method defines minimum and maximum boundaries for each flow in the food web, then uses Markov Chain Monte Carlo sampling to generate probability distributions for these flows and derived ecosystem indices.

Application to the Bay of Biscay demonstrated how this approach quantifies uncertainty in both flows and ecosystem indices [37]. The method is particularly valuable for incorporating microbial processes and providing a more holistic understanding of ecosystem structure from prokaryotes to top predators.

Global Sensitivity Analysis with Morris Method

Global sensitivity analysis identifies key model parameters with relatively low computational cost compared to comprehensive stochastic methods. In an OSMOSE model of the Cooperation Sea, the Morris method identified that community indicators were particularly sensitive to parameters related to larval mortality and predation effects [23]. This approach helps prioritize parameters for careful estimation and provides guidance for efficient model calibration.

Table 2: Methodological Approaches for Addressing Uncertainty in Marine Food-Web Models

Method	Key Features	Applications	Limitations
Ecopath with Ecosim (EwE)	Mass-balance foundation; time-dynamic simulation; vulnerability parameters [44]	Baltic Proper food-web; fishery scenario projections [44]	Limited functionality with age-structured groups; computational demands [44]
Qualitative Network Analysis (QNA)	Explores structural uncertainty; tests alternative interaction scenarios [1]	Salmon survival under climate change; food-web reassembly [1]	Qualitative nature; limited quantitative predictions
LIM-MCMC	Quantifies uncertainty in flows; incorporates microbial processes [37]	Bay of Biscay food-web status; Good Environmental Status assessment [37]	Complex implementation; requires expert knowledge
Morris Method	Global sensitivity analysis; identifies influential parameters [23]	OSMOSE model parameterization; uncertainty prioritization [23]	Screens parameters but doesn't quantify uncertainty

Experimental Protocol for Model Uncertainty Assessment

Based on the synthesized literature, the following protocol provides a standardized approach for assessing structural uncertainty in food-web models:

Step 1: Key Group Identification

• Calculate Relative Total Impact (RTI) indices using Mixed Trophic Impact (MTI) analysis [44]
• Identify groups with disproportionate ecosystem influence through direct and indirect effect analysis [44]

Step 2: Parameter Uncertainty Estimation

• Define probability distributions for input parameters using pedigree criteria based on data quality [44]
• For EwE models, focus particularly on biomass (B), production/biomass (P/B), and consumption/biomass (Q/B) ratios of key groups [44]

Step 3: Structural Uncertainty Exploration

• Develop multiple model structures representing alternative hypotheses about key interactions [1]
• Use Qualitative Network Analysis to test different interaction signs (positive, negative, neutral) [1]
• For EwE models, test different vulnerability (v) parameter configurations beyond the default value of 2 [44]

Step 4: Sensitivity Analysis Implementation

• Apply the Morris method to identify most influential parameters [23]
• Conduct Monte Carlo simulations to explore parameter error propagation [23]
• Perform linear regression analysis to quantify uncertainty in output indicators [23]

Step 5: Scenario Testing Under Different Conditions

• Run calibrated models under various climate, fishing, and nutrient loading scenarios [44]
• Compare uncertainty ranges across scenarios to identify conditions where projections are most unreliable [44]

The following diagram illustrates the relationship between uncertainty sources, assessment methods, and management outcomes in Baltic Sea food-web modeling:

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Methodological Tools for Food-Web Uncertainty Research

Tool/Platform	Primary Function	Uncertainty Assessment Features	Application Context
Ecopath with Ecosim (EwE)	Mass-balance and dynamic food-web modeling	Pedigree tool; Monte Carlo routine; vulnerability calibration [44]	Whole ecosystem modeling; fishery scenario projections [44]
Linear Inverse Modeling (LIM)	Food-web flow estimation under constraints	Markov Chain Monte Carlo integration for uncertainty bounds [37]	Microbial loop incorporation; Good Environmental Status assessment [37]
Qualitative Network Analysis	Exploring structural uncertainty	Scenario testing with alternative interaction signs [1]	Climate impact studies; research prioritization [1]
OSMOSE	Individual-based multispecies modeling	Morris method screening; Monte Carlo simulation [23]	End-to-end ecosystem simulation; strategic management advice [23]

The repeated failures of Baltic Sea fishery projections underscore the critical importance of comprehensively addressing structural uncertainty in food-web models. The Baltic Sea case demonstrates that without proper uncertainty assessment, even sophisticated models can produce dangerously optimistic projections that undermine ecosystem recovery efforts. The methodological framework presented here provides a pathway toward more honest and robust projections that explicitly acknowledge and quantify their limitations.

Future directions must include better integration of modeling approaches, such as combining the qualitative insights from Network Analysis with quantitative Ecopath or OSMOSE models. Furthermore, the implementation of ensemble modeling approaches that explicitly compare outcomes across different model structures would help bracket the range of plausible futures. Most importantly, uncertainty assessment must transition from an academic exercise to a core component of the scientific advice provided to policymakers, ensuring that management decisions are robust to the inevitable limitations in our understanding of complex marine food webs.

Structural uncertainty presents a significant challenge in predicting the responses of complex ecological systems like marine food webs to environmental pressures. This whitepaper explores ensemble modeling as a robust methodological framework for quantifying and reducing the single-model bias inherent in traditional ecological forecasting. Through a case study centered on Northeast Pacific Ocean food webs and Chinook salmon survival, we demonstrate how combining multiple plausible model structures provides more reliable assessments of ecosystem response to climate perturbations. Our analysis reveals that specific structural assumptions, particularly regarding predator-prey feedback mechanisms, can dramatically alter conservation outcomes—highlighting the critical importance of explicitly addressing structural uncertainty in environmental decision-making.

Ecosystem models are essential tools for forecasting the impacts of global change on marine resources. However, their predictive accuracy is often limited by structural uncertainty—the inherent ambiguity in how system components and their interactions are represented mathematically. In marine food webs, this uncertainty manifests in several dimensions: the precise sign and strength of species interactions, the functional responses of predators to shifting prey abundances, and the direct versus indirect effects of climatic drivers on different trophic levels. Relying on a single, seemingly best model ignores this inherent ambiguity and can lead to severely biased predictions with significant consequences for conservation and management.

The problem is particularly acute in systems facing rapid climate change. As Gomes et al. (2024) documented in the Northeast Pacific Ocean, marine heatwaves trigger complex, cascading disruptions throughout the food web, fundamentally altering energy pathways [48]. These disruptions benefit some species (e.g., gelatinous zooplankton like pyrosomes) while threatening others, with impacts that are "not necessarily straightforward and linear." Such ecosystem reorganization challenges the structural assumptions of existing models, demanding approaches that can accommodate multiple plausible configurations of the future state.

Ensemble modeling directly addresses this challenge by combining projections from multiple competing models, each representing a different hypothesis about system structure. This paper provides a technical framework for implementing ensemble modeling to reduce single-model bias, using research on marine food webs as our guiding context.

Ensemble Modeling: Conceptual Framework and Methodology

Core Principles of Ensemble Modeling

Ensemble modeling operates on the principle that no single model can perfectly capture the full complexity of an ecological system. Instead of seeking one true model, the approach acknowledges multiple plausible representations of system structure. The core objectives are:

• Quantifying Structural Uncertainty: Explicitly representing the range of possible system behaviors arising from different, equally defensible model structures.
• Reducing Single-Model Bias: Mitigating the risk of over-reliance on any particular set of structural assumptions by averaging across multiple models.
• Identifying Robust Outcomes: Distinguishing predictions that are consistent across many model structures from those that are highly sensitive to structural choices.

The theoretical foundation lies in model averaging, where predictions from individual models are combined—often with weights reflecting each model's empirical support or predictive performance—to produce a consensus forecast that is typically more accurate and reliable than any single component model.

Technical Implementation: A Workflow for Qualitative Network Models

Qualitative Network Models (QNMs) offer a powerful, accessible framework for implementing ensemble modeling in complex food webs where precise quantitative parameters are unknown. The following workflow, adapted for analyzing structural uncertainty, is visualized in Figure 1.

Figure 1. Ensemble modeling workflow for food web analysis.

Step 1: System Definition and Functional Group Delineation Define the spatial and temporal boundaries of the system (e.g., Northern California Current). Identify key species or functional groups for inclusion, typically focusing on commercially important species, keystone predators, basal resources, and groups known to be sensitive to the environmental driver of interest.

Step 2: Structural Scenario Development Formulate multiple alternative hypotheses about how functional groups interact. Structural scenarios differ in:

• Interaction Sign: Whether a link between species is positive (+, commensal/mutualistic), negative (-, predatory/competitive), or zero (no direct interaction).
• Direct Climate Responses: Which functional groups are directly affected by the press perturbation (e.g., climate change), and whether that effect is positive or negative.

Step 3: Community Matrix Construction For each structural scenario, build a community matrix A where element a_ij represents the sign of the effect of species j on species i. The diagonal is typically set to positive one, representing self-limiting populations.

Step 4: Ensemble Simulation and Analysis

• Apply a press perturbation representing the environmental driver (e.g., sustained increased temperature) to each model in the ensemble.
• Simulate the system's response using qualitative analysis to predict the direction of change (increase, decrease, or ambiguous) for each functional group.
• Aggregate outcomes across the entire ensemble of models to quantify the distribution of potential responses.

Step 5: Robustness Assessment and Sensitivity Analysis

• Calculate the proportion of models predicting a positive, negative, or ambiguous response for each species.
• Outcomes consistent across a high percentage (e.g., >80%) of model structures are considered robust.
• Perform sensitivity analysis to identify which structural assumptions have the greatest influence on key outcomes.

Case Study: Ensemble Modeling of Salmon Survival in the California Current

Experimental Background and Objectives

Chinook salmon (Oncorhynchus tshawytscha) are an economically and culturally vital species in the Northeast Pacific that have experienced significant population declines. A primary research and management challenge is predicting how salmon populations will respond to climate change, given their position within a complex and changing marine food web. A study employing ensemble modeling with QNMs tested 36 different plausible structures of the California Current marine food web to assess the robustness of projections for salmon survival under climate perturbation [1].

The model ensemble included key functional groups relevant to salmon: Chinook salmon (differentiating spring and fall runs), marine mammals, piscivorous fish, planktivorous fish, gelatinous zooplankton, and benthic invertebrates, among others. The structural uncertainties explored included:

• Varying interaction signs between competitor and predator groups.
• Different assumptions about which species groups respond directly to climate warming.
• Alternative strengths of feedback between salmon runs and mammalian predators.

Summarized Quantitative Outcomes

The ensemble analysis revealed that model outcomes for salmon were highly sensitive to specific structural assumptions. The quantitative results across the 36 model scenarios are summarized in Table 1.

Table 1: Summary of salmon outcome scenarios across 36 model structures. Data derived from [1].

Scenario Configuration	Proportion of Models Predicting Negative Outcome for Salmon	Key Factors Influencing Outcome
Baseline Assumptions	30%	Varying weak-to-moderate interaction links
Increased consumption by multiple competitors & predators	84%	Strengthened negative links from predator/competitor groups
Critical Structural Uncertainty	54 percentage point swing	Feedback between salmon and mammalian predators

The results demonstrate a dramatic swing in projected salmon outcomes—from a 30% to an 84% chance of negative population responses—based solely on changing assumptions about predator and competitor interactions, without altering any quantitative parameters [1]. This underscores the profound influence of structural choices.

Protocol for Qualitative Network Analysis

Research Question: How does structural uncertainty in the marine food web influence robust predictions of Chinook salmon population responses to climate warming?

Methodology Details:

• Model Structure: Qualitative Network Models (QNMs) represented via signed digraphs and community matrices.
• Ensemble Design: 36 unique model structures generated by combinatorially altering the sign (+, -, 0) of key predator-prey and competitive interaction links.
• Perturbation Simulation: A sustained "press" perturbation representing increased ocean temperature was applied. The direct effects of this press were also varied across scenarios (e.g., directly benefiting some groups like gelatinous zooplankton while harming others).
• Analysis: The qualitative response (direction of change) of each functional group, including salmon, was predicted for each model using the community matrix and press perturbation analysis techniques.
• Robustness Metric: The percentage of the 36 models predicting a negative response for salmon was calculated for different structural scenarios.

The Scientist's Toolkit: Essential Reagents and Research Solutions

Implementing ensemble modeling for food web analysis requires a combination of conceptual frameworks, software tools, and data resources. Table 2 details key components of the research toolkit.

Table 2: Research Reagent Solutions for Ensemble Food Web Modeling.

Item	Function in Research	Specification / Notes
Qualitative Network Model (QNM)	Conceptual framework representing species interactions as a signed digraph (positive, negative, neutral).	Foundation for simulating press perturbations and predicting direction of change in species abundances [1].
Community Matrix	The mathematical implementation of a QNM; a square matrix where elements describe the sign of interactions between species pairs.	Used for stability analysis and predicting system response to perturbations.
Ensemble Model Structure Library	A defined set of alternative model structures representing competing hypotheses about food web connectivity.	Critical for quantifying structural uncertainty; the case study used 36 distinct configurations [1].
Press Perturbation Simulation	A methodological technique applying a sustained, directional change to the model system (e.g., permanent increase in temperature).	Mimics long-term environmental stressors like climate change.
End-to-End Ecosystem Model	A holistic, quantitative model that integrates physics, lower trophic levels, and higher trophic levels (e.g., NOAA's Ecopath with Ecosim).	Used for ground-truthing and providing baseline parameter estimates; Gomes et al. updated one with new data following marine heatwaves [48].
Time-Series Data	Long-term, multidisciplinary ecological data (e.g., species abundance, temperature, diet).	Essential for validating model projections and identifying regime shifts, as used in the Northeast Pacific study [48].

Discussion and Implications for Research and Management

Interpretation of Findings

The case study clearly demonstrates that structural uncertainty is not merely a theoretical concern but a substantive factor influencing conservation forecasts. The >50 percentage point difference in negative outcomes for salmon, contingent solely on how predator-prey interactions were structured, reveals that predictions from any single model should be viewed with extreme caution. This finding aligns with observations from the Northeast Pacific, where marine heatwaves caused pyrosomes (gelatinous zooplankton) to proliferate, creating an "energy sink" that diverted resources away from higher trophic levels [48]. This real-world phenomenon would be captured only in model structures that include this specific competitive pathway and climate response.

Furthermore, the analysis identified that feedback loops between salmon and mammalian predators were particularly critical in determining system trajectories [1]. This type of insight is invaluable, as it directs future empirical research toward quantifying these specific interactions, thereby reducing the most influential uncertainties.

Advantages and Limitations of the Ensemble Approach

Advantages:

• Bias Reduction: Explicitly accounts for and reduces the risk of overconfidence from single-model bias.
• Hypothesis Prioritization: Identifies which structural uncertainties most strongly influence key outcomes, guiding targeted data collection.
• Robust Decision-Making: Provides managers with a clearer understanding of which predictions are reliable across many scenarios and which are highly uncertain.

Limitations and Considerations:

• Computational Intensity: Analyzing a large ensemble of models can be computationally demanding, though QNMs are relatively lightweight compared to fully quantitative models.
• Interpretive Complexity: Presenting a distribution of outcomes, rather than a single prediction, can be challenging for communicating with stakeholders.
• Ensemble Design Bias: The range of outcomes is constrained by the imagination of the modelers in designing the ensemble of possible structures. Critical interactions may be omitted entirely.

Best Practices and Recommendations

• Structured Scenario Development: Use formal expert elicitation processes to ensure the ensemble of model structures encompasses a wide yet plausible range of ecological hypotheses.
• Iterative Modeling: Treat ensemble modeling as an iterative process. Initial results should inform future research, which in turn updates and refines the model ensemble and associated weights.
• Clear Communication: Develop clear visualizations, such as the ones in this paper, to communicate the distribution of outcomes and the concept of robust predictions to diverse audiences.
• Integration with Quantitative Methods: Where possible, use QNMs to screen critical uncertainties and guide the development of more complex, parameterized quantitative simulation models.

Ensemble modeling represents a paradigm shift in how ecologists approach the prediction of complex system behavior. By systematically combining multiple models to account for structural uncertainty, this methodology provides a more honest and robust assessment of potential futures than can be achieved by any single model. The application to marine food webs, as illustrated, proves that the choice of model structure can be as consequential as the parameter values within it. For researchers, drug development professionals, and policymakers facing deep uncertainties, adopting ensemble approaches is no longer a luxury but a necessity for building resilient strategies in a changing world.

The Role of Trophic Cascades and Feedback Loops in Model Stability

Trophic cascades, the powerful indirect interactions where impacts at one trophic level control the abundance or behavior of others, represent a fundamental challenge for modeling marine ecosystem stability [49]. These cascades, combined with complex feedback loops, create nonlinear dynamics that can trigger abrupt regime shifts between alternative stable states [50]. Understanding these mechanisms is critical for predicting ecosystem responses to anthropogenic pressures. This technical guide examines how these ecological processes introduce structural uncertainty into food web models, explores methodologies for their detection, and provides a framework for incorporating these dynamics into stable ecosystem models to inform marine management and conservation strategies.

Theoretical Foundations of Cascades and Feedback Loops

Trophic Cascade Mechanisms

Trophic cascades manifest through two primary mechanisms that directly influence model stability:

• Density-Mediated Indirect Interactions (DMIIs): Occur when changes in predator abundance directly alter prey density through consumption. Experimental demonstration shows triggerfish predation reduced pencil urchin densities 24-fold within 21 hours, directly releasing algae from grazing pressure [51].
• Behaviorally-Mediated Indirect Interactions (BMIIs): Occur when prey alter their behavior in response to predation risk. The same experiments documented that per capita interference effects from hogfish and top predators modified triggerfish-urchin interaction strength through behavioral modifications [51].

The strength of these cascading effects varies significantly with the trophic level of targeted species. Harvesting high-trophic-level species can trigger strong top-down cascades, while targeting low-trophic-level species risks cutting energy supply to higher levels [52]. This creates inherent instability in models that fail to account for vertical network structure.

Stabilizing Feedback Loops and Alternative States

Feedback mechanisms between predator and prey populations can stabilize ecosystems in contrasting states, creating hysteresis that prevents recovery even after original conditions are restored [50]. Two primary mechanisms underlie these dynamics:

• Cultivation-Depensation: Piscivores at high abundance "cultivate" favorable environments by controlling prey fish populations through predation, reducing competition for their juveniles. At low abundances, released prey fish populations suppress piscivore recovery through competition or predation on early life stages [50].
• Overcompensation: Heavy predation on prey fish releases survivors from intraspecific competition, enhancing growth, condition, and fecundity. This increased production of vulnerable prey biomass supports higher predator growth. When predators are scarce, high intraspecific competition creates smaller, less nutritious prey that cannot support predator recovery [50].

Table 1: Characteristics of Feedback Mechanisms Creating Alternative Stable States

Mechanism	Dominating Interactions	Stabilizing Feedback Loop	Evidence in Marine Systems
Competitive Cultivation-Depensation	Prey compete with juvenile predators	Predators reduce prey, decreasing competition for juveniles	Baltic Sea cod, Eastern Scotian Shelf [50]
Predatory Cultivation-Depensation	Prey predate on juvenile predators	Predators reduce prey, decreasing predation on juveniles	North Sea, Baltic Sea cod [50]
Abundance Overcompensation	Predation releases prey competition	Predation increases vulnerable prey biomass production	NW Atlantic cod stocks [50]
Condition Overcompensation	Predation improves prey condition	Predation creates higher-quality prey	Baltic Sea food webs [50]

Structural Uncertainty in Marine Food Web Research

Structural uncertainty arises from incomplete knowledge of food web architecture and interaction strengths, fundamentally impacting model stability predictions:

• Compartmentalization vs. Connectivity: Coral reef food webs demonstrate surprising compartmentalization, with predator species relying on distinct "silos" of production from specific primary producers (phytoplankton, macroalgae, or coral) despite opportunity for broader feeding [40]. This challenges assumptions of high connectivity and functional redundancy in diverse ecosystems.
• Interaction Strength Uncertainty: Research on salmon survival demonstrates profound outcome variability (30% to 84% negative outcomes) based solely on how species pairs are connected (positive, negative, or no interaction) in qualitative network models [1]. This highlights how uncertain interaction types constitute a major structural uncertainty.
• Climate-Driven Structural Shifts: Marine heatwaves (MHWs) deeply alter food web structure and function, causing disproportionate biomass declines at higher trophic levels that persist longer than impacts on lower levels [5]. These structural alterations create fundamental stability challenges for existing models.

Methodologies for Addressing Structural Uncertainty

• Qualitative Network Analysis: A structured approach for testing multiple plausible food web configurations that differ in how species pairs are connected. This method identified that feedbacks between salmon and mammalian predators were particularly important for outcomes [1].
• Dynamic Ecosystem Modeling: The EcoTroph-Dyn approach represents ecosystem dynamics with biomass flowing continuously from primary producers to top predators, revealing that MHWs caused an 8.7% biomass decline in the northeastern Pacific (2013-2016) [5].
• Complex Food Web Simulations: Dynamic models coupling complex food web dynamics with economic fisheries models systematically compare fishing scenarios across 800 food webs comprising 130 species each [52].

Diagram 1: Structural uncertainty sources and impacts.

Experimental and Modeling Approaches

Experimental Protocols for Detection

Open Experimental Design for Trophic Cascades

Objective: To detect trophic cascades in diverse food webs while accounting for natural predator-prey interactions and behavioral modifications.

Methodology (as demonstrated in Galápagos rocky subtidal):

• Field Setup: Establish experiments where grazers (sea urchins) are restricted by fences yet remain vulnerable to natural assemblages of predatory fish [51].
• Predator Access: Utilize "fence" treatments that allow unconfined predatory fish to move freely, interacting with tethered prey and each other [51].
• Control: Compare against exclusion treatments that prevent predator access.
• Monitoring: Employ time-lapse photography at 1-2 minute intervals to document predation events, species identity, and behavioral interactions [51].
• Prey Selection: Use multiple prey species (e.g., pencil urchins Eucidaris galapagensis and green urchins Lytechinus semituberculatus) to test for species identity effects [51].

Key Measurements:

• Prey survivorship rates across treatments
• Predator identity and attack frequency from imagery
• Diel variation in prey behavior and abundance
• Ultimate impact on primary producer (algae) percent cover

Detecting Alternative Stable State Mechanisms

Objective: To distinguish between cultivation-depensation and overcompensation mechanisms preventing predator recovery.

Data Requirements:

• Individual Performance Metrics: Body growth at different life stages, fecundity, condition factor [50]
• Population Data: Abundance time series across multiple trophic levels
• Prey Availability: Size-structure, abundance, and condition of prey populations [50]

Analytical Framework:

• If early life stage growth is impeded while adult performance is unaffected → Evidence for competitive cultivation-depensation [50]
• If piscivorous life stage growth is impeded while early stages are unaffected → Evidence for overcompensation mechanisms [50]
• Combine with prey population data to distinguish abundance vs. condition overcompensation [50]

Modeling Approaches for Stability Analysis

Complex Food Web Simulations with Economic Feedback

Model Structure:

• Simulate 800 complex food webs comprising 130 species each [52]
• Integrate three open-access fisheries with economic market feedbacks [52]
• Compare six fishing scenarios based on economic and network criteria [52]

Stability Metrics:

• Ecological Stability: Species persistence and food-web biomass change relative to pristine communities [52]
• Economic Viability: Sustained total revenue, sustained total biomass catch, and number of sustained fisheries [52]

Dynamic Trophodynamic Modeling

EcoTroph-Dyn Framework:

• Represents biomass as continuous flow across trophic levels from primary producers (TL=1) to top predators [5]
• Uses trophic spectra with trophic class width of 0.1 for computational efficiency [5]
• Incorporates temperature-dependent effects on biomass transfer efficiency and flow kinetics [5]

MHW Impact Analysis:

• Compares simulations with and without marine heatwave events using observed temperature and NPP data (1998-2021) [5]
• Quantifies MHW-induced biomass declines by trophic level and their persistence [5]

Table 2: Quantitative Findings from Food Web Stability Research

Study Focus	Methodology	Key Quantitative Findings	Stability Implications
Fishing Scenarios [52]	800 complex food web simulations	Similar-trophic-level fisheries maintained all 3 fisheries in 49% of simulations vs. 0-16% for other scenarios	Mid-trophic targeting enhances stability
Marine Heatwaves [5]	Global EcoTroph-Dyn modeling 1998-2021	8.7% ±1.0% MHW-induced biomass decline in NE Pacific 2013-2016; higher TLs showed larger, longer-lasting declines	Climate extremes disproportionately affect top predators
Trophic Cascades [51]	Open experimental design	Triggerfish predation caused 24-fold reduction in pencil urchin density in 21 hours; cascade to algae confirmed	Strong species identity effects cascade strength
Microplastic Transfer [53]	Ecotracer module calibration	Small benthic/pelagic consumers show highest microplastic concentrations; evidence of biomagnification in secondary consumers	Pollutants follow energetic pathways, affecting stability

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Methodologies and Analytical Tools for Food Web Stability Research

Tool/Methodology	Function	Application Example
Compound-Specific Stable Isotope Analysis of Amino Acids (CSIA-AA)	Traces nutrient pathways from specific primary producers to predators	Revealed siloed energy pathways in coral reef snappers [40]
Qualitative Network Models	Tests structural uncertainty by exploring alternative interaction scenarios	Identified critical feedbacks affecting salmon survival under climate change [1]
EcoTroph-Dyn Modeling	Simulates biomass flows through trophic levels under dynamic conditions	Quantified MHW impacts on global marine biomass distribution [5]
Time-Lapse Predation Monitoring	Documents species-specific predation rates and behavior in field conditions	Identified triggerfish as keystone predators in Galápagos trophic cascade [51]
Microplastic Ecotracer Module	Traces contaminant movement through food webs	Identified bioaccumulation patterns in Black Sea food web [53]
Complex Food Web Fisheries Model	Integrates ecological dynamics with economic feedbacks	Tested sustainability of multispecies fishing strategies [52]

Diagram 2: Methodological workflow for stability research.

Trophic cascades and feedback loops introduce fundamental structural uncertainties that challenge the stability of marine food web models. Experimental evidence demonstrates these processes can trigger abrupt regime shifts stabilized by alternative state dynamics [50], while modern tracing techniques reveal food web compartmentalization that contradicts assumptions of high connectivity [40]. Climate extremes like marine heatwaves disproportionately impact higher trophic levels, creating persistent biomass declines that alter ecosystem structure [5]. Successful management requires modeling approaches that explicitly incorporate these nonlinear dynamics, with empirical studies indicating that targeting similar mid-trophic levels in multispecies fisheries provides the most stable outcomes for both ecological and economic objectives [52]. Future research must continue to integrate multiple methodological approaches—from qualitative network models that test structural uncertainty to dynamic trophodynamic simulations—to better predict stability thresholds in an era of rapid environmental change.

Managing Computational Cost vs. Realism in Complex Ecosystem Simulations

Ecosystem models are indispensable tools for advancing Ecosystem-Based Management (EBM), enabling scientists and managers to test hypotheses about management options, species interactions, and climate adaptation strategies [54]. However, a fundamental tension exists between the pursuit of biological realism and the practical constraints of computational cost, data availability, and scientific capacity. This challenge is particularly acute in marine food web research, where structural uncertainty—uncertainty about the model structure itself, including which species and interactions to include—can significantly impact predictions and management advice.

The selection of an ecosystem model is a critical decision that involves balancing these competing demands. On one end of the spectrum, highly complex, quantitative end-to-end models (e.g., Ecopath with Ecosim, Atlantis) capture detailed dynamics but require extensive data and computational resources, often taking years to develop [54]. On the other end, qualitative models (e.g., Qualitative Network Models, Bayesian Belief Networks) offer a less data-intensive and faster alternative, accommodating different knowledge types, including Local Ecological Knowledge (LEK), but provide less precise, numerical outputs [54] [55]. Finding the optimal balance, or "sweet spot," of model complexity is therefore essential for generating robust scientific advice while making efficient use of limited resources [54].

A Spectrum of Modeling Approaches: From Qualitative Conceptualization to Quantitative Dynamic Simulation

Ecosystem modeling approaches can be categorized based on their complexity, data requirements, and primary outputs. The table below summarizes the key characteristics of prevalent model types used in marine research.

Table 1: Comparison of Ecosystem Modeling Approaches for Marine Food Web Research

Model Type	Key Characteristics	Data Requirements	Computational Cost	Primary Outputs	Ideal Use Cases
Qualitative Network Models (QNMs)	Signed digraphs representing positive/negative interactions; focus on general trends [54].	Low; relies on interaction networks (diet matrices, expert knowledge) [54].	Low	Qualitative predictions of biomass change (increase, decrease, no change) [54].	Rapid prototyping, data-poor scenarios, integrating LEK, initial hypothesis testing [54] [55].
EcoTroph	Represents biomass as a continuous flow across trophic levels; steady-state or dynamic (EcoTroph-Dyn) [5].	Medium; requires biomass and production/biomass ratios by trophic level [5].	Medium	Trophic spectra, biomass trends, energy flow kinetics [5].	Analyzing fishing or climate impacts on trophic structure and energy transfer [5].
Ecopath with Ecosim (EwE)/Rpath	Mass-balanced static model (Ecopath) with dynamic simulation capability (Ecosim) [54].	High; requires biomass, production, consumption, and diet data for all functional groups [54].	High (especially for Ecosim)	Biomass time series, fisheries yields, ecosystem indicators [54].	Tactical management strategy evaluation, exploring climate and fishing scenarios [54] [18].
End-to-End Models (e.g., Atlantis)	Spatially explicit, integrates physics, biogeochemistry, and ecology across all trophic levels [54].	Very High; requires extensive physiological, behavioral, and environmental data.	Very High	System-wide responses, emergent properties, complex interaction effects.	Strategic, long-term scenario analysis for entire coupled human-natural systems.

Finding the "Sweet Spot": Guidance from Comparative Studies

The critical question for researchers is not which model is "best," but which offers the optimal complexity for a specific research question. A 2025 study systematically compared quantitative models (Rpath) against qualitative models (QNMs) of varying complexity to determine the conditions under which they provide corresponding results [54].

Experimental Protocol for Model Comparison

• Model Development: An existing complex Ecopath model of the western Scotian Shelf was simplified to 28 functional groups (WSS28) to serve as a baseline quantitative model [54].
• Complexity Gradient: The quantitative model was translated into a suite of six qualitative models. Complexity was systematically reduced by eliminating linkages between model elements based on diet-composition strength thresholds (e.g., QNM10 removed linkages between -0.10 and +0.10) [54].
• Perturbation Experiments: A series of positive and negative biomass perturbations were applied to functional groups at different trophic levels across all models.
• Analysis of Correspondence: The directional outcomes (increase, decrease, no change) from the qualitative models were compared against the range of outcomes from the quantitative model's Bayesian uncertainty routine (Ecosense) [54].

Key Findings on Model Performance

• Trophic Level Matters: The performance of qualitative models was highly dependent on the trophic level of the perturbed group. When perturbations were applied to lower trophic levels, higher-complexity qualitative models (with more linkages retained) performed closer to the quantitative model [54].
• The Case for Simplicity: For scenarios involving perturbations to mid-trophic level groups, lower-complexity models were recommended to avoid spurious conclusions from weak linkages [54].
• The "Sweet Spot": There is no universal ideal complexity. The appropriate level is a function of the research question, specifically the ecosystem components one aims to perturb. Utilizing multiple models to identify consistent, strongest impacts is recommended over relying on a single model outcome [54].

The following diagram visualizes the decision process for selecting model complexity based on research goals and data constraints.

Practical Applications: Case Studies in Marine Ecosystem Simulation

Case Study 1: Simulating Global Impacts of Marine Heatwaves with EcoTroph-Dyn

Objective: To disentangle the ecological impacts of acute Marine Heatwaves (MHWs) from those of slow-onset climate change on a global scale [5].

Methodology:

• Model: The dynamic EcoTroph-Dyn model was applied at a spatial resolution of 1°x1° and a temporal resolution of 15 days [5].
• Input Data: The model used daily satellite-derived sea surface temperature and monthly Net Primary Production (NPP) data from 1998 to 2021 [5].
• Experimental Protocol:
  • The model was run with actual temperature and NPP data to simulate real-world conditions.
  • A counterfactual simulation was run using temperature and NPP time series where MHW signals had been statistically removed.
  • The differences in simulated biomass by trophic level between the two scenarios were attributed to the additive effect of MHWs [5].

Key Results: The study revealed a significant MHW-induced decline in global biomass from 1998 to 2021, with pronounced effects in the Northern Hemisphere and Pacific Ocean. Notably, high-trophic-level biomass experienced larger and longer-lasting declines than lower levels, highlighting a legacy effect of extreme events on predators [5].

Case Study 2: Integrating Local Knowledge for Data-Poor Fisheries Management

Objective: To assess ecosystem effects of fishing in data-poor small-scale fisheries by combining quantitative science-based models and qualitative LEK-based models [55].

Methodology:

• Framework: An extended framework was applied to three small-scale finfish fisheries in northwestern Mexico.
• Protocol for Qualitative Models (used when no quantitative model existed):
  • LEK Integration: Local fishers contributed to building a conceptual model of the ecosystem, identifying key species and their interactions.
  • Species Removal Simulation: The model simulated the removal of target species.
  • Impact Analysis: Ecosystem impacts were assessed using topological indicators (e.g., changes in connectivity, cycles) derived from the network structure, as traditional ecological indicators could not be calculated [55].
• Protocol for Quantitative Models:
  • Historical Calibration: Models were fitted to historical catch time series to dynamically assess past impacts.
  • Driver Attribution: Changes in ecosystems were parsed between the effects of fishing effort and underlying environmental drivers like primary productivity [55].

Key Results: The approach demonstrated that both model types could provide strategic management advice. Topological indicators from qualitative models showed similar trends to ecological indicators from quantitative models, validating their use in data-poor conditions and underscoring the value of fishers' knowledge in co-management processes [55].

The Scientist's Toolkit: Essential Reagents for Ecosystem Simulation

Table 2: Key Research Reagent Solutions for Ecosystem Modeling

Tool or Resource	Function in Ecosystem Simulation	Application Example
Rpath Package (R)	Implements the Ecopath with Ecosim methodology within the R environment, facilitating reproducibility and integration with Bayesian uncertainty routines (Ecosense) [54].	Used to convert a complex Ecopath model into a simplified 28-functional-group model and generate plausible parameter sets for perturbation analysis [54].
QPress Package (R)	A stochastic package for analyzing signed digraphs (Qualitative Network Models) and simulating their response to perturbations [54].	Used to run multiple stability tests on qualitative models to determine the probable direction of change of model elements after a perturbation [54].
BGC-Argo Float Data	Provides high-resolution, autonomous water column profiles of physical and biogeochemical parameters (e.g., temperature, nitrate, optical backscatter as a POC proxy) [18].	Provided the core observational data to validate and quantify the impacts of MHWs on particulate organic carbon and plankton community structure [18].
Local Ecological Knowledge (LEK)	Qualitative information from resource users on species interactions, phenology, and ecosystem changes, used to fill scientific data gaps [55].	Co-developed qualitative ecosystem models with fishers to assess the potential impacts of species removal in data-poor fisheries [55].
EcoTroph-Dyn Model	A dynamic trophodynamic model that represents biomass as a continuous flow up the food web, allowing simulation of climate impacts on energy transfer efficiency and kinetics [5].	Used to hindcast the global biomass declines caused by marine heatwaves between 1998 and 2021, isolating their effect from long-term climate change [5].

Navigating the trade-off between computational cost and realism is a central challenge in marine ecosystem modeling. Evidence shows that the most complex model is not always the most appropriate. The choice must be strategically aligned with the research question, data availability, and the specific component of the ecosystem under investigation. Quantitative models remain powerful for tactical advice when data is sufficient, but qualitative models offer a valid, efficient, and inclusive alternative for strategic guidance in data-poor contexts. As the field progresses, the ability to thoughtfully match model complexity to the problem at hand—guided by frameworks and comparative studies—will be crucial for reducing structural uncertainty and providing robust science to support the sustainable management of marine ecosystems.

Benchmarking Knowledge: Validating and Comparing Food Web Structures

Within the study of complex ecosystems, significant structural uncertainty often surrounds the accurate representation of marine food webs. This technical guide details a framework for vetting the plausibility of ecological network models by integrating the analysis of loop weights and eigenvalues. These quantitative measures provide a robust methodology for assessing model stability, thereby addressing core uncertainties in predicting ecosystem responses to perturbations. The protocols and analytical workflows presented herein are designed to enable researchers to rigorously test and refine structural food web models against empirical data, moving beyond purely descriptive topology to dynamic stability assessment.

Marine food web research is fundamentally challenged by structural uncertainty—the incomplete or inaccurate representation of trophic interactions within ecological models. Simple models, such as the generalized cascade model, have been successful in capturing the global and local topological structure of empirical food webs [3]. These models rely on two fundamental mechanisms: (i) species' niche values form a totally ordered set, and (ii) each species has a specific, exponentially decaying probability of preying on a given fraction of species with lower niche values [3]. However, accurately reproducing topology does not guarantee that a model will correctly predict the system's dynamic behavior or stability.

Assessing Good Environmental Status (GES), particularly for marine descriptors like food web stability, requires simulation-based approaches that can navigate this structural uncertainty [56]. The stability of an ecosystem—its ability to return to equilibrium after a disturbance—is not directly observable from static network maps alone. It must be inferred through mathematical analysis of the dynamic properties encoded within the network structure. This guide bridges this gap by providing a rigorous methodology to use loop weights and eigenvalue spectra to vet whether a proposed food web model possesses the stability characteristics expected in a real, persistent ecosystem.

Theoretical Foundations: From Network Structure to Dynamic Stability

Local Motifs and Global Stability

The local structure of a food web, characterized by the prevalence of small subgraphs or motifs, forms the building blocks of its global dynamics. In food webs, common three-species motifs include simple food chains, omnivory, and exploitative competition [3]. The generalized cascade model, for instance, allows for the analytical calculation of the probability of these motifs appearing, which in turn influences the distribution of feedback loops within the system [3]. The collective strength of these loops, quantified through their weights, contributes directly to the determination of the system's eigenvalues and thus its stability.

Eigenvalues as Stability Indicators

In a dynamic systems framework, a food web is represented by a Jacobian matrix, which linearizes the interactions around an equilibrium point. The entries of this matrix represent the per-capita effect of one species on the growth rate of another. The stability of the equilibrium is determined by the eigenvalues of this Jacobian matrix. Specifically, the real parts of all eigenvalues must be negative for the equilibrium to be locally stable. The dominant eigenvalue (the one with the largest real part) dictates the rate of return to equilibrium following a small perturbation. Analyzing the eigenvalue spectrum of a model's Jacobian is therefore a direct method for vetting its dynamic plausibility.

Quantitative Methodologies and Protocols

Protocol 1: Calculating Loop Weights in Food Web Models

Objective: To quantify the cumulative interaction strength of all feedback loops of a given length within a proposed food web model.

• Construct the Interaction Matrix (A): From your food web model (e.g., a generalized cascade model output), create an S x S matrix A, where S is the number of species.
• Define Interaction Strengths: Populate the matrix elements a_ij. For simple topological analysis, this can be a binary matrix (0 for no interaction, 1 for a trophic link). For more nuanced dynamic analysis, assign interaction strengths based on allometric scaling, bioenergetic models, or empirical data.
• Calculate Loop Weights: The total weight of all loops of length L can be approximated by the trace of the matrix raised to the power L. Use the following formula: Loop Weight (L) ≈ Trace(A^L) The trace operation sums the eigenvalues, and the eigenvalues of A^L are the eigenvalues of A raised to the power L. This connects directly to the system's eigenvalues.

Table 1: Loop Weight Signatures for Different Three-Node Motifs (Binary Interactions)

Motif Type	Subgraph ID [3]	Loop Weight (L=3)	Ecological Interpretation
Simple Food Chain	S1	0	Acyclic, no feedback loop.
Omnivory	S2	0	Acyclic, no feedback loop.
Trophic Loop	S3	6	Strong, direct feedback between three species.
Exploitative Competition	S4	0	Acyclic, no feedback loop.
Generalist Predation	S5	0	Acyclic, no feedback loop.

Protocol 2: Eigenvalue Analysis for Stability Assessment

Objective: To compute the eigenvalues of a food web's Jacobian matrix and assess the model's local stability.

• Formulate the Jacobian Matrix (J): Define the community matrix J where the element J_ij represents the effect of species j on the growth rate of species i near equilibrium. This often requires defining a population dynamics model (e.g., Lotka-Volterra type) for the food web.
• Parameterize the Model: Assign values to the interaction parameters. This can be done using allometric scaling relationships, thermodynamic constraints, or by sampling from plausible biological ranges to account for uncertainty.
• Compute Eigenvalues: Use numerical linear algebra packages (e.g., in R or Python) to calculate all eigenvalues of the Jacobian matrix.
• Determine Stability: The system is locally stable if the real part of the dominant eigenvalue (λ_max) is negative. The magnitude of the real part indicates the recovery rate.

Table 2: Interpretation of Eigenvalue Analysis Results

Result	Real Part of λ_max	Imaginary Part	Stability Interpretation
Stable	< 0	Any	System returns to equilibrium after perturbation.
Unstable	> 0	~0	System diverges from equilibrium (collapse).
Unstable Oscillations	> 0	Large ≠	Growing oscillations (population cycles).
Neutral	= 0	Any	System does not return or diverge (critically stable).

Protocol 3: Vetting Model Plausibility via Stability-Centric Randomizations

Objective: To test if a proposed model's stability properties are non-random and ecologically plausible.

• Generate Randomized Ensembles: Create a large number of randomized versions of your candidate food web model. Crucially, these randomizations should preserve key biological constraints, such as the number of prey and predators for each species (as done in [3]).
• Perform Stability Analysis: Calculate the dominant eigenvalue for each randomized network in the ensemble.
• Compare Distributions: Statistically compare the dominant eigenvalue of your candidate model against the distribution of eigenvalues from the randomized ensemble.
  • A model with a significantly more negative dominant eigenvalue than the random expectation can be considered stabilized.
  • A model with a dominant eigenvalue close to or more positive than the random mean may be structurally implausible for a real, persistent ecosystem.

Table 3: Key Research Reagent Solutions for Food Web Stability Analysis

Item / Resource	Function / Description	Application in Protocol
Generalized Cascade Model Code	Algorithm to generate model food webs based on niche values and beta-distributed predation probabilities [3].	Protocol 1: Generating the initial network topology for analysis.
Empirical Food Web Database	Curated datasets of documented trophic interactions (e.g., from aquatic, estuarine environments) [3].	Protocol 1 & 2: Ground-truthing model structure and parameter ranges.
Linear Algebra Software Library (e.g., NumPy, LAPACK)	A computational library for performing efficient matrix operations and eigenvalue decomposition.	Protocol 2: Core computation of eigenvalues from the Jacobian matrix.
Allometric Scaling Parameters	Constants and equations linking body size to metabolic rates and interaction strengths.	Protocol 2: Parameterizing the Jacobian matrix with biologically realistic values.
Network Randomization Algorithm	Code to generate null models that preserve specific network properties (e.g., degree sequence).	Protocol 3: Creating biologically-informed null ensembles for comparison.

Workflow Visualization: From Model Construction to Plausibility Assessment

The following diagram illustrates the integrated workflow for vetting model plausibility, connecting the protocols defined in Section 3.

Addressing structural uncertainty in marine food web models requires moving from static pattern description to dynamic stability assessment. The methodology outlined here—combining the analysis of loop weights, which capture the architecture of feedback within the web, with the computation of eigenvalues, which directly determine dynamic stability—provides a powerful and mathematically rigorous framework for researchers. By vetting models against the stability criterion, scientists can discard structurally implausible networks and focus on those that are both topologically realistic and capable of exhibiting the persistent dynamics characteristic of natural ecosystems. This approach is vital for simulating and assessing the stability of marine food webs to inform management strategies and the determination of Good Environmental Status in the face of environmental change [56].

Structural uncertainty—the incomplete knowledge of how species are connected and how these interactions may change—presents a fundamental challenge in predicting the responses of marine ecosystems to global change. Effectively modeling the impact of climate change on any population requires careful consideration of diverse pressures and potential changes in species interactions [1]. This uncertainty is compounded when comparing disparate marine ecosystems, where differences in functional redundancy, energy pathways, and physical constraints create distinct vulnerabilities. While traditional food web studies often focused on pelagic systems, creating a knowledge gap and disconnection from benthic research [57], recent approaches are developing frameworks to explore this structural uncertainty explicitly [1].

This technical guide provides a comparative framework for analyzing food web structure and function across the pelagic-benthic and tropical-polar divides, with explicit consideration of methodological approaches to address structural uncertainty. We synthesize current understanding of how these ecosystems vary in their network topology, response mechanisms, and resilience characteristics, providing researchers with protocols for cross-system comparisons under changing climate conditions.

Core Concepts and Definitions

Fundamental Ecosystem Compartments

• Pelagic ecosystems: Refer to the water column environment, divided vertically into distinct zones (e.g., epipelagic, mesopelagic). These systems are characterized by phytoplankton-based primary production and dominant grazing chains [58].
• Benthic ecosystems: Encompass the seafloor environment, including sedimentary habitats, rocky substrates, and coral reefs. These systems rely heavily on sedimentation of organic matter from the pelagic zone and exhibit higher degrees of omnivory and connectivity than pelagic systems [57].
• Tropical systems: Typically found between 23.5°N and 23.5°S, characterized by high annual temperatures, relatively stable environmental conditions, and high biodiversity with significant functional redundancy.
• Polar systems: Located poleward of 60° latitude, characterized by low temperatures, extreme seasonality in light availability, and seasonal sea ice dynamics that structure entire food webs [58].

Key Functional Properties

Food quality represents a critical concept in comparative food web studies, defined as the degree to which the quantity and composition of accessible food fulfill consumer nutritional needs [57]. This cannot be fully disentangled from quantity, as large amounts of low-quality food may be as useful as small quantities of highly nutritious food. Key parameters include biopolymeric carbon, essential fatty acids, essential amino acids, and semi-essential carotenoids [57].

Benthic-pelagic coupling describes the processes connecting water column and seafloor ecosystems, including both passive processes (e.g., sedimentation) and active processes (e.g., predation, vertical migration) [59]. This coupling represents the integration of resources by consumers from different habitats and varies significantly across environmental gradients [59].

Methodological Framework for Cross-Ecosystem Comparison

Approaches to Address Structural Uncertainty

Qualitative Network Models (QNMs) provide a structured approach for analyzing alternative responses to climate change across food webs when quantitative data are limited. These models test multiple plausible representations of connections among species and functional groups, differing in how species pairs are connected (positive, negative, or no interaction) and which species respond directly to environmental drivers [1]. A key advantage is the ease with which a wide range of scenarios representing structural and quantitative uncertainties can be explored [1].

Ensemble modeling techniques address structural uncertainty by running multiple model configurations and comparing outcomes. Research on salmon survival demonstrates the value of this approach, where testing 36 alternative food web configurations revealed that certain structures produced consistently negative outcomes for salmon, regardless of specific parameter values [1].

Empirical Approaches for Food Web Structure Analysis

Table 1: Methodological Approaches for Food Web Analysis

Method	Application	Structural Uncertainty Considerations
Stable Isotope Analysis (δ13C, δ15N)	Tracing energy pathways, trophic positions [59]	Accounts for multiple basal carbon sources; requires careful source characterization
Stomach Content Analysis	Short-term feeding patterns, prey identification [59]	Provides high taxonomic resolution but snapshot view of diet
Bayesian Mixing Models (MixSIAR, SIMMr)	Quantifying resource contributions to diet [59]	Explicitly incorporates uncertainty in source values and discrimination factors
Ecopath with Ecosim (EwE)	Mass-balance ecosystem modeling [5]	Sensitive to group aggregation schemes and diet matrix assumptions
EcoTroph Modeling	Continuous biomass spectrum analysis [5]	Represents biomass flows along trophic gradient rather than discrete groups

Experimental Design for Cross-System Comparisons

When comparing food webs across ecosystems, standardized sampling protocols are essential:

• Simultaneous measurement of food quantity and quality parameters: Include Total Organic Carbon (TOC), total nitrogen, C:N ratios, pigments (chlorophyll a, chlorophyll a:pheophorbides, chlorophyll c, fucoxanthin), and biopolymeric carbon (carbohydrates, proteins, lipids, proteins:carbohydrates) [57].

• Stratified sampling along environmental gradients: Depth and chlorophyll-a concentrations have been identified as key variables influencing trophic structure [59].

• Multi-trophic level inclusion: Ensure sampling captures primary producers, intermediate consumers, and top predators to characterize complete energy pathways.

• Temporal replication: Account for time lags in community responses to organic matter input, which can vary from hours to months in different ecosystems [57].

Comparative Analysis of Ecosystem Types

Pelagic vs. Benthic Food Web Structure

Pelagic and benthic ecosystems differ fundamentally in their network structure and energy pathways. Benthic food webs differ from their pelagic counterparts in supporting higher degrees of omnivory and connectivity [57].

Table 2: Pelagic vs. Benthic Food Web Characteristics

Characteristic	Pelagic Food Webs	Benthic Food Webs
Primary energy source	Water column primary production	Sedimenting organic matter, benthic microalgae
Connectance	Generally lower	Higher degrees of omnivory and connectivity [57]
Key functional groups	Phytoplankton, copepods, planktivorous fish	Deposit feeders, filter feeders, infaunal predators
Response time to perturbations	Relatively fast	Slower, with bioturbation modifying organic matter availability [57]
Role of detritus	Less prominent	Central, with detrital compartment as heterogeneous food source [57]
Spatial structure	Three-dimensional water column	Two-dimensional with vertical stratification in sediments

Benthic-pelagic coupling is mediated by biological communities, with fish serving as key connectors through their foraging movements [59]. In the Beibu Gulf, research demonstrates that depth is a primary driver of coupling strength, with shallower systems exhibiting stronger benthic-pelagic connections [59].

Tropical vs. Polar Food Web Structure

Polar ecosystems are characterized by relatively low metazoan diversity and the apparent dominance of a small number of species in energy flow between lower and higher trophic levels [58]. This creates a skew in functional roles, with a small number of species performing most core ecological functions (low functional redundancy) [58].

Table 3: Tropical vs. Polar Food Web Characteristics

Characteristic	Tropical Systems	Polar Systems
Functional redundancy	High	Low at key trophic levels [58]
Seasonality	Low	Extreme, driven by light climate and sea ice dynamics [58]
Primary production patterns	Relatively constant year-round	Intense pulses during spring/summer blooms [58]
Dominant energy pathways	Diverse, with significant microbial loops	Short, efficient chains dominated by few species [58]
Benthic-pelagic coupling	Variable	Strong in Arctic due to shallow shelves [58]
Response to warming	Tropicalization [5]	Polar amplification, sea ice loss

A critical difference between Arctic and Antarctic food webs lies in their mid-trophic level connections: zooplankton-fish connections dominate in Arctic regions, whereas direct zooplankton-seabird and marine mammal pathways dominate in the Southern Ocean [58]. Additionally, benthic-pelagic interactions are more important in Arctic food webs because of extensive shallow shelf areas, compared to deeper continental shelves in the Southern Ocean [58].

Climate Change Impacts and Structural Responses

Marine Heatwaves and Food Web Alteration

Marine heatwaves (MHWs) have become longer, more frequent, and more intense in recent decades, causing significant alterations to marine food web structure and function [5]. These discrete events provide natural experiments for understanding how structural uncertainty manifests under extreme conditions.

EcoTroph modeling reveals that MHWs cause significant declines in biomass, with more pronounced effects in the Northern Hemisphere and Pacific Ocean [5]. Critically, MHW-induced declines in high trophic-level biomass were larger than in lower trophic levels and lasted longer post-MHW [5]. For example, in the northeastern Pacific Ocean, modeling simulated an MHW-induced decline in biomass of 8.7% ± 1.0 (standard error) from 2013 to 2016 [5].

MHWs affect trophodynamics by altering both the amount and speed of matter and energy transfer in food webs. Ocean warming increases flow kinetic (the speed of energy transfer through the food web) and decreases biomass transfer efficiency, leading to independent and cumulative declines in consumer biomass [5].

System-Specific Vulnerability to Climate Change

Polar systems are experiencing rapid changes in multiple climate and oceanic processes affecting ocean circulation, biogeochemistry, and sea-ice distribution [58]. The low functional redundancy at key trophic levels makes these ecosystems particularly sensitive to change [58]. The traditional view of polar ocean food webs as short chains with aggregated networks is being revised to account for alternative pathways that maintain energy transfer and resilience, though these more complex routes cannot provide the same rate of energy flow to highest trophic-level species [58].

Benthic systems face dual pressures from warming and changes in food quality. As pelagic community composition shifts under climate change, the quality of organic matter reaching the benthos changes, with potential consequences for benthic community structure and function [57]. Well-mixed sediments with organic matter and macrofauna penetrating into deeper layers characterize low food quality stations, while diffusive mixing dominates high food quality areas [57].

Conceptual Framework and Visualization

Integrated Framework for Cross-Ecosystem Comparison

The determinants of food web structure across ecosystems can be conceptualized as a hierarchy of filters, where physical drivers (light, temperature, stratification) constrain biological potential, which is then filtered through regional species pools and interaction networks to produce observable food web structures. This framework emphasizes how structural uncertainty propagates through each level, creating distinct vulnerabilities in different ecosystems.

Methodological Workflow for Addressing Structural Uncertainty

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Methodological Components for Food Web Research

Research Component	Function/Application	Ecosystem Considerations
Stable Isotopes (δ13C, δ15N)	Tracing carbon sources and trophic position [59]	Requires distinct basal sources; interpretation varies by ecosystem
Fatty Acid Analysis	Assessing food quality and trophic transfer [57]	Particularly important for benthic systems where detrital quality varies
Biopolymeric Carbon Analysis	Quantifying labile organic fractions [57]	Critical for understanding benthic food quality
Qualitative Network Models	Exploring structural uncertainty [1]	Flexible framework applicable across systems with limited data
EcoTroph Modeling	Simulating biomass flows along continuum [5]	Effective for comparing ecosystem function across regions
Bayesian Mixing Models	Quantifying resource use with uncertainty [59]	Essential for benthic-pelagic coupling studies
Environmental DNA	Characterizing biodiversity and diet	Emerging tool for difficult-to-sample systems

Addressing structural uncertainty in marine food web research requires a multi-faceted approach that combines empirical studies across environmental gradients with modeling frameworks that explicitly acknowledge and explore alternative network configurations. The comparative framework presented here highlights fundamental differences in how pelagic, benthic, tropical, and polar systems are structured and therefore how they respond to anthropogenic pressures.

Priority research areas should include:

• Targeted studies on key functional groups identified through sensitivity analysis as having disproportionate influence on food web outcomes [1].
• Integrated benthic-pelagic sampling along environmental gradients to better understand coupling dynamics [59].
• Experimental manipulation of food quality parameters to quantify thresholds for consumer responses [57].
• Development of model ensembles that systematically vary both structure and parameters to bound uncertainty [1] [60].
• Application of trait-based approaches to improve predictions of how novel communities may reassemble under future conditions.

By adopting this comparative framework and explicitly addressing structural uncertainty, researchers can develop more robust projections of ecosystem responses to environmental change and identify critical leverage points for conservation and management.

Leveraging Long-Term Observational Data and Robotic Float Networks for Validation

Predicting the response of marine ecosystems to environmental change remains a formidable challenge in oceanography and climate science. A primary source of this difficulty is structural uncertainty—incomplete knowledge about which species and interactions constitute the food web and how they are interconnected [4]. This uncertainty is exacerbated by the fact that marine food webs are not static; climate change is causing shifts in species distributions and abundances, effectively rewiring food webs in real-time [4]. Traditional modeling approaches often overlook these dynamic interactions or make simplifying assumptions that mask structural uncertainties, potentially leading to overconfident projections [4] [61].

This technical guide outlines a rigorous framework for leveraging modern observational technologies—specifically, long-term time-series data and autonomous robotic float networks—to identify, quantify, and reduce structural uncertainty in marine food web models. By grounding models in empirical data, researchers can move from qualitative conceptual models to quantitatively validated representations that more reliably forecast ecosystem responses to perturbation.

A Primer on Structural Uncertainty in Marine Food Webs

Defining Structural Uncertainty

In the context of marine food webs, structural uncertainty encompasses several key dimensions:

• Node Uncertainty: Uncertainty about which functional groups (e.g., trophic species) must be represented in a model [4] [20].
• Link Uncertainty: Uncertainty about the presence, sign (positive/negative), and strength of interactions between functional groups [4].
• Dynamic Uncertainty: Uncertainty about how the above factors change over time in response to environmental drivers [4].

The Qualitative Network Analysis (QNA) approach exemplifies how to explicitly confront this uncertainty by testing multiple plausible network structures rather than relying on a single model configuration [4]. Research analyzing 217 global marine food webs has demonstrated that omitting these structural considerations can yield misleading conclusions, such as apparent negative diversity-stability relationships that reverse sign when structural mediation is properly accounted for [20].

Consequences for Prediction and Policy

Neglecting structural uncertainty can profoundly impact model projections. For instance, models that fail to account for changing predator-prey interactions during marine heatwaves have proven inadequate for predicting salmon survival [4]. Similarly, ice-sheet-ocean models used for sea-level rise projections show dramatically different sensitivities depending on their structural assumptions about melt processes [62]. Reducing this uncertainty is therefore not merely an academic exercise but a prerequisite for reliable scientific advice to policymakers [4] [62] [61].

Validation Framework: An Integrated Approach

A robust validation framework connects observational data directly to model structures through a cyclical process of hypothesis testing and refinement. The core components and workflow of this framework are illustrated below.

Long-Term Observational Time Series

Long-term observational data provide the temporal context necessary to detect slow trends, identify regime shifts, and capture ecosystem responses to anomalous conditions [61]. The table below summarizes key data types and their applications for validating food web structure.

Table 1: Long-Term Observational Data for Food Web Validation

Data Type	Key Measurements	Applications to Food Web Validation	Example Programs/Sources
Fisheries-Independent Surveys	Species abundance, size distributions, diet composition	Quantify predator-prey relationships, identify key functional groups	NOAA Juvenile Salmon & Ocean Ecosystem Survey, Joint U.S.-Canada Integrated Ecosystem Survey [4]
Marine Mammal & Bird Observations	Distribution, abundance, foraging behavior	Validate top-down control assumptions, identify critical predation pathways	NOAA coastal surveys, academic-state-tribal partnerships [4]
Plankton Time Series	Phytoplankton/zooplankton abundance, community composition	Validate bottom-up energy pathways, detect changes in primary production	Continuous Plankton Recorder, NOAA Newport Line [61]
Hydrographic Measurements	Temperature, salinity, nutrients, chlorophyll	Link physical drivers to biological responses, validate environmental niches	CalCOFI, HOT, BATS [61]

Effective utilization of these data requires adherence to statistical best practices, including accounting for temporal autocorrelation, spatial heterogeneity, and sampling biases [61]. The power of long-term data is maximized when integrated into coordinated intercomparison projects, such as the Marine Ice Sheet-Ocean Model Intercomparison Project (MISOMIP2), which provides standardized "MIPkits" of observational data specifically designed for model evaluation [62].

Robotic Float Networks and Advanced Sensing

Autonomous platforms dramatically enhance the spatial coverage and temporal resolution of ocean observations. Modern robotic float networks, particularly those with advanced biogeochemical and biological sensing capabilities, provide unprecedented capacity to observe food web processes.

Table 2: Robotic Float Applications for Food Web Validation

Platform Type	Key Sensor Capabilities	Food Web Applications	Validation Insights
Biogeochemical Argo Floats	Oxygen, nitrate, chlorophyll, pH, backscatter	Quantify primary production, export flux, respiration rates	Validate carbon flow pathways, constrain production estimates [63]
Underwater Imaging Systems	High-resolution imagery of plankton/particles	Size-structured abundance, species identification, behavior	Validate trophic interaction rules, identify overlooked functional groups [63]
Acoustic Doppler Profilers	Current velocity, backscatter intensity	Water mass movement, organism distribution (deep-scattering layers)	Validate advection processes, link physical transport to biological distributions [63]
Environmental DNA (eDNA) Samplers	Collection/filtration of genetic material	Biodiversity assessment, species presence/absence	Detect cryptic species, validate assumed species distributions [63]

These platforms enable the construction of four-dimensional representations of ecosystem processes, capturing how water masses, nutrients, and organisms co-vary in space and time. For example, acoustic data can reveal the structure of deep-scattering layers—dense aggregations of mid-water organisms that form critical trophic linkages between surface waters and the deep sea [63].

Experimental Protocols for Structural Validation

Qualitative Network Analysis (QNA)

Purpose: To systematically explore structural uncertainty by testing multiple plausible food web configurations.

Methodology:

• Develop Conceptual Model: Identify key functional groups (nodes) and potential interactions (links) through literature review and expert consultation [4].
• Define Alternative Structures: Create multiple network versions differing in:
  • Presence/absence of specific nodes
  • Sign of interactions (positive, negative, neutral)
  • Which species respond directly to climate drivers [4]
• Construct Community Matrix: For each structure, build a matrix where entries represent the sign and strength of interactions between nodes [4].
• Perturbation Analysis: Apply simulated press perturbations (e.g., sustained warming) and track the direction of change in state variables [4].
• Stability Assessment: Analyze matrix eigenvalues to identify structures that produce stable, plausible dynamics [4].
• Scenario Comparison: Compare outcomes across structures to identify interactions that most strongly influence predictions [4].

Interpretation: Structures that consistently produce unstable dynamics or contradict empirical observations can be rejected. Sensitivity analysis identifies which uncertain interactions warrant targeted research [4].

Path and Structural Equation Modeling (SEM)

Purpose: To quantify direct versus indirect pathways through which diversity and structure influence ecosystem stability.

Methodology:

• Define Causal Hypotheses: Develop a priori conceptual model of hypothesized relationships between diversity, food web structure, and stability metrics [20].
• Calculate Structural Metrics: From empirical data or model outputs, compute:
  • Number of Living Groups (NLG): Measure of diversity [20]
  • Connectance Index (CI): Proportion of possible links realized [20]
  • Interaction Strength Indices (ISI): Variance of interaction strengths [20]
• Quantify Multidimensional Stability:
  • Local Stability: Asymptotic return rate to equilibrium after small perturbations [20]
  • Resistance: Maximum biomass change during disturbance [20]
  • Resilience: Percentage biomass recovery after disturbance [20]
• Model Fitting: Use piecewise SEM to test simultaneous relationships and estimate path coefficients [20].
• Mediation Analysis: Partition total effects into direct effects (diversity → stability) and indirect effects (diversity → structure → stability) [20].

Application: This approach revealed that the negative diversity-stability relationship becomes positive when accounting for structural mediation through connectance and interaction strength [20].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Analytical Tools for Food Web Structural Validation

Tool/Category	Specific Examples	Function & Application
Model Intercomparison Frameworks	MISOMIP2, ISOMIP+, MISMIP+	Standardize model evaluation against observations; identify robust behaviors across model structures [62]
Qualitative Network Analysis Software	Custom implementations in R, Python	Test multiple network configurations; identify critical uncertainties in food web structure [4]
Structural Equation Modeling Platforms	piecewiseSEM (R), lavaan (R)	Quantify direct and indirect pathways; test mediation hypotheses in observational data [20]
Data Assimilation Tools	Ensemble Kalman Filters, Variational Methods	Integrate heterogeneous observations into models; estimate uncertain parameters [62]
Food Web Metrics Packages	Network analysis in R, Python	Calculate connectance, interaction strength, trophic position, and other structural indicators [20]
Observational Data Repositories	NOAA Ocean Exploration database, Ecopath models, MIPkits	Provide standardized datasets for model validation and benchmarking [63] [62] [20]

Confronting structural uncertainty is not a barrier to progress but a necessary step toward more reliable marine ecosystem forecasts. By strategically integrating long-term observational data with cutting-edge robotic float networks within a rigorous validation framework, researchers can transform qualitative conceptual models into quantitatively grounded tools for prediction. The protocols and tools outlined here provide a pathway to identify which structural uncertainties matter most for specific prediction problems, ultimately leading to more efficient research investment and more credible scientific guidance for ecosystem management in a changing climate.

Understanding the structure and dynamics of marine food webs is fundamental to predicting how ecosystems will respond to change. However, this endeavor is fraught with structural uncertainty—limitations in our knowledge of which species and trophic interactions are present, their strengths, and how they vary in space and time. This uncertainty is particularly acute in vast, remote, and complex systems like the Southern Ocean. Research into Southern Ocean food webs must contend with the challenge that these ecosystems are not the simple, short food chains once imagined but are complex networks with regionally distinct characteristics [64]. This case study explores how simplified models of these food webs are constructed, validated, and applied to generate critical insights for policymakers. By explicitly acknowledging and addressing structural uncertainty, these models provide a robust, albeit simplified, foundation for managing human impacts and conserving ecosystem function in the face of climate change and other anthropogenic pressures [65].

Southern Ocean Food Web Context and Policy Imperative

The Southern Ocean is a critical component of the Earth's system. It supports unique biodiversity, influences global nutrient distributions and oceanic carbon sequestration, and hosts commercial fisheries [64]. These globally important services are underpinned by the structure and function of its food webs [65]. These ecosystems are experiencing rapid changes, including rising temperatures, sea-ice loss, and ocean acidification, alongside historical and ongoing pressures from harvesting activities [64].

Policymakers, particularly those involved with bodies such as the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), require scientifically sound projections to implement an ecosystem-based management approach. The core challenge is the complex adaptive nature of these ecosystems, where a myriad of direct and indirect interactions among species, each with highly variable spatial and seasonal dynamics, complicates prediction [65]. Simplified food web models are essential tools to navigate this complexity, reduce structural uncertainty, and test the potential consequences of different policy options, from establishing marine protected areas (MPAs) to setting fishery catch limits [65] [66].

A range of simplified modeling approaches has been employed to understand Southern Ocean food webs, each tackling structural uncertainty from a different angle. These models move beyond the outdated view of a simple, krill-dominated food chain to capture more complex network structures [64].

Table 1: Key Simplified Food Web Modeling Approaches and Their Characteristics

Model Type	Core Principle	Primary Use	Handling of Structural Uncertainty
Generalized Cascade Model [3]	Species consume others based on a random, hierarchically structured probability.	Analyzing global and local topological properties of food webs (e.g., network motifs).	Provides a statistical null model to identify which subgraphs are over- or under-represented in empirical webs.
Ecopath with Ecosim (EwE) [34]	Creates a static, mass-balanced snapshot of the system (Ecopath) for dynamic simulation (Ecosim).	Addressing ecological questions, evaluating ecosystem effects of fishing, exploring management policy.	Represents a best estimate of network structure; uncertainty is explored through dynamic simulations under perturbation.
Qualitative/Network Models [65]	Represents trophic interactions without requiring precise quantitative data on fluxes.	Initial characterization of food web structure, identifying key species and energy pathways.	Allows for the development of a structural base from collective, yet incomplete, knowledge.
Allometric/Trait-Based Models [65]	Uses body-size and species traits to infer trophic interactions and energy flow.	Identifying critical interactions that stabilize food webs and how structure may adapt to change.	Reduces uncertainty by relying on general traits rather than species-specific interaction data.

These approaches demonstrate a progression from describing network topology to simulating system dynamics, all while employing strategies to manage the inherent uncertainty in food web structure.

Quantitative Analysis of Food Web Structure and Motifs

A powerful method for quantifying structural uncertainty and validating model performance is the analysis of network motifs—patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks [3]. This approach allows for a rigorous, quantitative comparison of the local structure between model and empirical food webs.

For the generalized cascade model, the probabilities of different three-species subgraphs (motifs) can be derived analytically and are a function of a single variable, the directed connectance (C) [3]. This allows for a unified description of food webs of different sizes. The analysis focuses on key motifs like the simple food chain (S1), omnivory (S2), and exploitative competition (S4), providing insight into the fundamental building blocks of these ecosystems.

Table 2: Probabilities of Three-Node Subgraphs in the Generalized Cascade Model

Subgraph (Motif)	Ecological Description	Analytical Probability	Insight
S1	Simple food chain	pS1 = ⟨xAxB⟩ - ⟨xA²xB⟩ [3]	Represents the simplest energy transfer pathway.
S2	Omnivory	pS2 = ⟨xA²xB⟩ [3]	Represents a species feeding on two different trophic levels.
S4	Exploitative competition	pS4 = ⟨xAxB⟩ - ⟨xA²xB⟩ [3]	Represents two predators sharing a common prey.
S3	Three-species loop	pS3 = 0 [3]	Forbidden in the model, highlighting a structural limitation.

Agreement between the motif profiles of the generalized cascade model and empirical food webs indicates that the model's simple rules—a niche hierarchy and an exponentially decaying probability of predation—can capture not just global properties but also the local structure of real ecosystems [3]. This reduces structural uncertainty by confirming that these core mechanisms are sufficient to generate realistic network architectures.

Experimental and Modeling Protocols

Protocol for Ecological Network Analysis (ENA)

Objective: To quantify the structure, function, and resilience of a Southern Ocean food web using a static, mass-balanced model.

• System Definition: Define the spatial and temporal boundaries of the ecosystem (e.g., Scotia Sea in summer). Compile a list of all functional groups or species, from primary producers to top predators.
• Diet Matrix Construction: For each functional group, define the proportion of each prey item in its diet. This is a primary source of structural uncertainty and is based on stomach content analysis, literature review, and expert judgment [64].
• Parameter Estimation: For each group, estimate key parameters including:
  • Biomass (B): The total biomass of the group in the system.
  • Production/Biomass (P/B): The mortality rate, equivalent to total production.
  • Consumption/Biomass (Q/B): The consumption rate per unit biomass.
  • Ecotrophic Efficiency (EE): The proportion of production that is consumed within the system or exported.
• Mass-Balance Adjustment: Use an algorithm (e.g., in Ecopath [34]) to solve the system of linear equations ensuring that for each group, consumption equals production, respiration, and unassimilated food. This step often requires iterative refinement of initial parameters to achieve a balanced model.
• Network Analysis: Calculate ecological indices from the balanced model, such as:
  • Ascendancy: A measure of the system's organization and growth.
  • System Overhead: A measure of the system's resilience and reserve capacity.
  • Trophic Efficiency: The efficiency of energy transfer between trophic levels. Trends in these indices can indicate whether the ecosystem's resilience is increasing or decreasing over time [66].

Protocol for Subgraph (Motif) Analysis

Objective: To identify which small-scale interaction patterns (motifs) are statistically over- or under-represented in an empirical food web.

• Data Compilation: Obtain a high-quality, directed adjacency matrix of an empirical food web, where a link A -> B indicates that species A eats species B.
• Subgraph Enumeration: For all possible sets of three species (triplets) in the web, count the occurrences of each of the 13 possible three-node connected subgraphs [3].
• Randomized Ensemble Generation: Create a large number (e.g., 1000) of randomized versions of the empirical web. These randomizations preserve the number of prey and predators for each species (the degree sequence) but otherwise randomize the connections.
• Subgraph Enumeration in Randomized Webs: Repeat Step 2 for every randomized web in the ensemble.
• Z-score Calculation: For each subgraph type i, calculate a Z-score: Zi = (Nempirical - <Nrandom>) / σrandom, where Nempirical is the count in the empirical web, and <Nrandom> and σrandom are the mean and standard deviation of the counts in the randomized ensemble. A high absolute Z-score indicates the motif is a significant building block of the network [3].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Analytical Tools for Food Web Research

Item/Software	Function	Application in Food Web Studies
Ecopath with Ecosim (EwE) [34]	Free ecosystem modeling software suite for mass-balance analysis (Ecopath), time-dynamic simulation (Ecosim), and spatial analysis (Ecospace).	The primary tool for constructing quantitative, whole-ecosystem models to evaluate fishing impacts and test management policy options.
Stable Isotope Analysis	Measures ratios of stable isotopes (e.g., δ¹⁵N, δ¹³C) in animal tissues.	Used to empirically determine the trophic position of organisms and identify primary carbon sources, helping to verify and refine diet matrices.
Generalized Cascade Model [3]	A static food web model that generates network topology based on a niche hierarchy and random feeding.	Serves as a null model to test the significance of network properties and motifs found in empirical data.
Network Analysis Indices (e.g., Degree, Betweenness) [65]	Metrics from graph theory that quantify the topology of a network.	Used to identify keystone species (e.g., high betweenness centrality) and characterize the overall connectivity and modularity of food webs.
Diet Sampling Data	Direct observation of stomach or gut contents.	The foundational empirical data used to construct the diet matrix in models like Ecopath, though often limited by sample size and seasonality.

Visualization of Food Web Structure and Modeling Workflow

Diagram: Southern Ocean Food Web Key Pathways

Diagram: Food Web Modeling and Policy Workflow

Simplified food web models are indispensable for transforming the complex, uncertain reality of Southern Ocean ecosystems into tractable frameworks for policy development. By identifying key energy pathways and structurally important species, these models help predict how systems may respond to perturbations like climate change and fishing [65] [64]. The analysis of network motifs provides a quantitative means to validate models and uncover the fundamental building blocks of ecosystem architecture, thereby reducing a key dimension of structural uncertainty [3]. Furthermore, ecological network analyses deliver integrative indices that can signal changes in ecosystem resilience, providing a crucial gauge of ecosystem health for managers [66].

For future research, priorities include the development of a generic qualitative food web model as a common structural base, the extension of species-based models to trait-based architectures, and the deeper integration of food web models with projected biogeochemical models from Earth System models [65]. Advancing these areas will progressively reduce structural uncertainty and provide policymakers with increasingly robust tools to conserve the critical ecosystem services of the Southern Ocean in an era of unprecedented change.

Benchmarking Model Performance Against Extreme Events like the 'Blob' MHW

Marine Heatwaves (MHWs) are prolonged extreme oceanic warm water events that are becoming more frequent and intense due to climate change [67]. These extreme events significantly stress marine ecosystems, causing mass mortality, range shifts, and profound alterations to food web structure and function [68] [5]. The 2013-2016 Northeast Pacific MHW, known as "the Blob," represents a compelling case study for benchmarking ecological model performance against observed ecosystem transformations [68] [69].

A critical challenge in modeling these impacts lies in structural uncertainty—the uncertainty arising from how ecological systems are conceptually represented in models, including which components and processes are included or excluded [1] [70]. This guide provides a technical framework for using extreme events like the Blob to benchmark model performance, explicitly addressing structural uncertainty in marine food web research.

The 'Blob' Marine Heatwave: A Benchmarking Case Study

The Blob manifested in the Northeast Pacific during 2013-2016 with sea surface temperatures reaching 3°C above normal [68]. This event provides critical benchmarking data due to its documented ecosystem-wide effects, which challenged existing ecological models.

Table 1: Documented Impacts of the 'Blob' Marine Heatwave on the Northern California Current Ecosystem

Ecosystem Component	Documented Impact	Magnitude of Change	Source
Pyrosomes	Unprecedented population increase	From essentially absent to dominant	[68]
Sessile Invertebrates	Decline in cover and diversity	71% decline in cover	[69]
Phytoplankton	Reduced biomass & production	Anomalously low chlorophyll-a	[69]
Chinook Salmon	Population decline	67% decline in commercial harvest	[68]
Kelp Forest Communities	Persistent community shift	Invasion of non-indigenous species	[69]
Overall Ecosystem Biomass	Trophic-level declines	8.7% decline from 2013-2016	[5]

The Blob's ecological impacts were notable for their persistence beyond the warming event itself, with some systems failing to return to pre-heatwave states six years later [69]. The event triggered a dramatic increase in pyrosomes (Pyrosoma atlanticum), gelatinous filter-feeding tunicates previously rare in the region [68]. This shift created an energetic bottleneck, with >98% of pyrosome biomass ending up in detritus rather than transferring to higher trophic levels [68].

Structural Uncertainty in Marine Food Web Models

Structural uncertainty represents a fundamental challenge in ecosystem modeling, arising from decisions about how to conceptualize and represent ecological systems. In food web modeling, this manifests primarily through three aspects:

Model Architecture Selection

Different model architectures conceptualize trophic interactions distinctly. Ecopath with Ecosim (EwE) represents ecosystems as interconnected functional groups with diet matrices [68], while EcoTroph models biomass as a continuous flow up a trophic gradient [5]. OSMOSE employs individual-based approaches to simulate multispecies interactions [23]. Each structure embodies different assumptions about ecosystem organization.

Functional Group Resolution

Structural uncertainty emerges in deciding how to aggregate species into functional groups. Modeling the Blob's impact required determining whether gelatinous zooplankton should be represented as a single group or separated into distinct groups like "pyrosomes," "jellies," and "other gelatinous filter-feeders" [68]. In the Northern California Current case, the unprecedented pyrosome bloom revealed a structural gap in pre-Blob models that had not separately parameterized this previously minor component [68].

Interaction Representation

How models represent species interactions introduces further structural uncertainty. Qualitative Network Models can test different interaction types (positive, negative, or no interaction) between species pairs [1]. Research on salmon outcomes demonstrated that varying these interaction structures produced dramatically different predictions, with negative outcomes for salmon ranging from 30% to 84% depending on configuration [1].

Diagram 1: Framework of structural uncertainty sources in marine food web models. Different colors represent distinct categories of uncertainty that must be addressed when benchmarking models against extreme events.

Benchmarking Methodologies and Experimental Protocols

Pre-Post Event Comparison Framework

A robust approach for benchmarking model performance involves comparing models parameterized for periods before and after extreme events:

Experimental Protocol:

• Pre-Event Model Parameterization: Compile abundance data, diet compositions, and physiological parameters for all functional groups from historical data (e.g., 1999-2012) [68]
• Post-Event Model Parameterization: Repeat parameterization using data collected during/after the event (e.g., 2014-2022) [68]
• Model Validation: Compare model outputs against observed post-event ecosystem states not used in parameterization
• Structural Comparison: Analyze differences in emergent properties (energy flow, trophic efficiency, biomass distribution)

This method revealed that the Northern California Current ecosystem underwent substantial restructuring after the Blob, with energy flow pathways shifting from fish-dominated to gelatinous zooplankton-dominated systems [68].

Qualitative Network Modeling for Structural Uncertainty

Qualitative Network Models (QNMs) provide a formal methodology for testing structural uncertainty across alternative food web configurations:

Experimental Protocol:

• Develop Alternative Structures: Create multiple plausible food web configurations with varying interaction types (positive, negative, or no interaction) between species pairs [1]
• Apply Press Perturbation: Simulate an MHW-like perturbation across all model structures
• Compare Outcomes: Analyze variance in outcomes across different structures
• Identify Critical Interactions: Determine which interactions most strongly influence outcome variance

In salmon food web research, this approach demonstrated that certain configurations produced consistently negative outcomes regardless of specific parameter values, highlighting particularly influential predator-prey feedbacks [1].

Global Sensitivity Analysis

Techniques like the Morris Method provide systematic approaches for identifying influential parameters and quantifying their contribution to model uncertainty:

Experimental Protocol:

• Parameter Selection: Identify all potentially influential parameters in the ecosystem model [23]
• Elementary Effects Method: Apply the Morris Method to compute elementary effects for each parameter across parameter space [23]
• Parameter Ranking: Rank parameters by their influence on key model outputs
• Uncertainty Quantification: Use Monte Carlo simulation to explore parameter error propagation [23]

Application to an OSMOSE model of the Cooperation Sea food web revealed that community indicators were particularly sensitive to parameters related to larval mortality, growth rate, and predation pressure [23].

Diagram 2: Comprehensive workflow for benchmarking model performance against marine heatwaves. The process integrates multiple methodological approaches to address structural uncertainty.

Research Tools and Reagent Solutions

Table 2: Essential Research Tools for MHW Food Web Modeling

Tool Category	Specific Tools/Platforms	Primary Function	Application in MHW Research
Ecosystem Modeling Platforms	Ecopath with Ecosim (EwE)	Mass-balanced trophic network modeling	Pre/post-MHW ecosystem comparison [68]
	EcoTroph/EcoTroph-Dyn	Continuous biomass spectrum modeling	Global assessment of MHW biomass impacts [5]
	OSMOSE	Individual-based, multispecies modeling	Food web dynamics under environmental change [23]
Observational Data Sources	Satellite SST Products (OSTIA, NOAA OI)	Sea surface temperature monitoring	MHW detection and characterization [67] [71]
	Argo Float Network	Subsurface temperature profiles	3D structure of MHWs [67]
	Long-term Ecological Surveys	Species abundance & distribution	Model parameterization/validation [68] [69]
Uncertainty Analysis Tools	Morris Method	Global sensitivity analysis	Identifying influential parameters [23]
	Monte Carlo Simulation	Parameter uncertainty propagation	Quantifying prediction uncertainty [23]
	Qualitative Network Modeling	Structural uncertainty exploration	Testing alternative interaction networks [1]
Forecasting Tools	Convolutional Neural Networks (CNN)	Pattern recognition in SST data	MHW prediction 3-7 days in advance [72]
	NOAA Physical Sciences Laboratory Tools	MHW mapping and forecasting	Experimental MHW forecasts [71]

Advanced Benchmarking Metrics for Extreme Events

Beyond traditional model validation, benchmarking against extreme events requires specialized metrics:

Table 3: Advanced Metrics for Benchmarking Model Performance Against MHWs

Metric Category	Specific Metrics	Calculation Method	Interpretation
Trophic Structure Metrics	Trophic Level Distribution	Mean trophic level of community	Indicates fishing pressure or energy pathway changes
	Connectance	Number of realized trophic links ÷ total possible links	Measures food web complexity and potential stability [68]
	Link Density	Number of links per functional group	Indicates trophic specialization/generalization [68]
Energy Flow Metrics	Biomass Transfer Efficiency	Proportion of energy transferred between trophic levels	Measures ecosystem functioning efficiency [5]
	Flow Kinetic	Speed of energy transfer through food web	Indicates dominance of short-lived species [5]
	Detritus Production	Proportion of biomass directed to detritus pools	Identifies trophic "dead ends" [68]
Regime Shift Indicators	Biomass Trophic Spectra	Distribution of biomass across trophic levels	Detects structural ecosystem changes [5]
	Functional Group Dominance	Relative biomass of key functional groups	Tracks foundational changes in energy pathways [68]
	Novel Species Impact	Energetic contribution of range-shifting species	Quantifies impacts of new entrants (e.g., pyrosomes) [68]

Benchmarking ecological models against extreme events like the Blob marine heatwave provides unprecedented insights into structural uncertainties within marine food web modeling. The methodologies outlined here—including pre-post event comparison, qualitative network modeling, and global sensitivity analysis—offer robust approaches for evaluating model performance under non-stationary climatic conditions.

As MHWs increase in frequency and intensity [67] [73], embracing structural uncertainty becomes essential for developing reliable predictive models. The integration of multiple model structures, explicit treatment of alternative hypotheses about species interactions, and rigorous validation against observed ecosystem responses will enhance our capacity to project food web responses to future extreme events, ultimately supporting more effective ecosystem-based management under climate change.

Conclusion

Confronting structural uncertainty is not a limitation but a fundamental step toward robust marine ecosystem science. This synthesis demonstrates that a multi-pronged approach—combining exploratory qualitative models, advanced empirical tools like CSIA-AA, rigorous sensitivity testing, and comparative validation—is essential for credible forecasting. The findings underscore that food webs are not universally resilient; their compartmentalized and context-dependent nature means that disruptions can cascade in unpredictable ways, with direct consequences for fisheries yields, carbon sequestration, and biodiversity. Future efforts must prioritize the systematic integration of socioeconomic factors, expand global monitoring networks to ground-truth models, and foster interdisciplinary collaboration to move from simply describing uncertainty to actively managing the risks it poses in a changing ocean.

References

• 1. Climate Change and Marine Food Webs [https://pubmed.ncbi.nlm.nih.gov/40145641/]
• 2. Food Webs [https://www.vims.edu/research/units/programs/multispecies_fisheries_research/interaction/food-webs/]
• 3. Quantitative analysis of the local structure of food webs [https://pmc.ncbi.nlm.nih.gov/articles/PMC2128744/]
• 4. Climate Change and Marine Food Webs - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC11948457/]
• 5. Marine heatwaves deeply alter marine food web structure ... - BG [https://bg.copernicus.org/articles/22/6583/2025/]
• 6. Species loss in key habitats accelerates regional food web ... [https://www.nature.com/articles/s42003-025-08396-y]
• 7. A network simplification approach to ease topological ... [https://www.nature.com/articles/s41598-022-17508-1]
• 8. Food-web assembly and collapse: mathematical models and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2685428/]
• 9. The complex structure of aquatic food webs emerges from ... [https://www.nature.com/articles/s41559-025-02647-1]
• 10. The complex structure of aquatic food webs emerges ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11976281/]
• 11. Aquatic food webs | National Oceanic and ... [https://www.noaa.gov/education/resource-collections/marine-life/aquatic-food-webs]
• 12. Stabilizing effects of seagrass meadows on coastal water ... [https://www.sciencedirect.com/science/article/abs/pii/S0022098118302211]
• 13. Increased resilience and a regime shift reversal through ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11686943/]
• 14. Coral Bleaching 2025: What It Means for the Reef [https://www.barrierreef.org/the-reef/threats/coral-bleaching]
• 15. Marine Research Vessel “Jaywun” Research Reveals the ... [https://nyuad.nyu.edu/en/news/latest-news/science-and-technology/2025/october/research-suggests-gulf-ecosystem-adaptability.html]
• 16. 84% of the world's coral reefs impacted in the most intense ... [https://icriforum.org/4gbe-2025/]
• 17. Envisioning a future for Caribbean coral reefs - USC Dornsife [https://dornsife.usc.edu/wrigley/2025/07/11/envisioning-a-future-for-caribbean-coral-reefs/]
• 18. Marine heatwaves modulate food webs and carbon ... [https://www.nature.com/articles/s41467-025-63605-w]
• 19. A Stable ISOtope database of MEDiterranean marine food ... [https://www.nature.com/articles/s41597-025-05981-y]
• 20. Mediating role of food web structure in linking diversity to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12513428/]
• 21. Trait networks: Assessing marine community resilience and ... [https://www.sciencedirect.com/science/article/pii/S2589004224021874]
• 22. Building resilience of marine ecosystems is achievable by ... [https://10insightsclimate.science/year-2021/9-building-resilience-marine-ecosystems-achievable/]
• 23. Global sensitivity and uncertainty analyses of an ... [https://www.sciencedirect.com/science/article/abs/pii/S0165783625000827]
• 24. Network Visualization (Chapter 6) [https://www.cambridge.org/core/books/network-science-in-archaeology/network-visualization/F5E17F0B723BFCC92CAE8BBC3C802F39]
• 25. Visualizing Psychological Networks: A Tutorial in R [https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2018.01742/full]
• 26. 8 Ways for Visualizing Complex Networks on Maps [https://www.maplibrary.org/1609/visualizing-complex-networks-on-maps/]
• 27. & Bold High - ColorMagic Contrast Color Palettes Contrast Colors [https://colormagic.app/palette/explore/high-contrast]
• 28. Compound-specific isotope analysis of amino acids for ... [https://www.sciencedirect.com/science/article/pii/S0165993624005211]
• 29. A Bayesian model for assessing organic matter supply in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12640130/]
• 30. Deep-sea mining discharge can disrupt midwater food webs [https://www.nature.com/articles/s41467-025-65411-w]
• 31. Ecosystem models of Lake Victoria (East Africa) [https://www.sciencedirect.com/science/article/abs/pii/S0380133019301790]
• 32. Ecopath with Ecosim: methods, capabilities and limitations [https://www.sciencedirect.com/science/article/abs/pii/S030438000300365X]
• 33. Quantitative models, including Ecopath, take food web ... [https://www.pugetsoundinstitute.org/quantitative-models-including-ecopath-take-food-web-studies-to-a-higher-level-of-analysis/]
• 34. Ecopath with Ecosim – Ecopath with Ecosim food web ... [https://ecopath.org/]
• 35. Co-development [https://ecopath.org/co-development/]
• 36. New Approaches to Assess Food Web Stability in Aquatic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12334362/]
• 37. A new modeling approach to define marine ecosystems ... [https://www.sciencedirect.com/science/article/abs/pii/S0079661115000531]
• 38. A large-scale field experiment across six rivers illustrates how ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10229716/]
• 39. a pilot study from International Ocean Discovery Program ... [https://www.sciencedirect.com/science/article/pii/S0009254119305789]
• 40. New study reveals coral reef food webs are more siloed ... [https://www.uri.edu/news/2025/07/new-study-reveals-coral-reef-food-webs-are-more-siloed-and-vulnerable-than-previously-understood/]
• 41. Food web models: a plea for groups [https://pubmed.ncbi.nlm.nih.gov/19453619/]
• 42. Employing dynamic model to study food web stability and ... [https://www.sciencedirect.com/science/article/pii/S1470160X21003861]
• 43. Quantifying stability and resilience of eco-social keystone ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11293858/]
• 44. Uncertainties in a Baltic Sea Food-Web Model Reveal ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3428477/]
• 45. EU Ministers to Decide Baltic Sea Catch Limits Amid Stock ... [https://thefishingdaily.com/latest-news/eu-ministers-to-decide-baltic-sea-catch-limits-amid-stock-decline/]
• 46. Short-sighted decision by EU fisheries ministers pushes ... [https://seas-at-risk.org/press-releases/short-sighted-decision-by-eu-fisheries-ministers-pushes-baltic-sea-ecosystem-closer-to-collapse/]
• 47. Food web assessments in the Baltic Sea: Models bridging ... [https://link.springer.com/article/10.1007/s13280-021-01692-x]
• 48. Marine heat waves disrupt the ocean food web in ... - Newsroom [https://news.oregonstate.edu/news/marine-heat-waves-disrupt-ocean-food-web-northeast-pacific-ocean]
• 49. - Wikipedia Trophic cascade [https://en.wikipedia.org/wiki/Trophic_cascade]
• 50. Regime shifts in exploited marine : detecting mechanisms... food webs [https://pmc.ncbi.nlm.nih.gov/articles/PMC4247399/]
• 51. Experimental demonstration of a trophic cascade in the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5400256/]
• 52. Maintaining ecological stability for... | Nature Communications [https://www.nature.com/articles/s41467-025-64179-3?error=cookies_not_supported&code=5214c6c1-14de-4ba4-bc7a-a87bfb43820c]
• 53. A novel approach to tracing microplastics through the food ... [https://joint-research-centre.ec.europa.eu/jrc-news-and-updates/novel-approach-tracing-microplastics-through-food-web-seas-2025-09-26_en]
• 54. Is there a “sweet spot” of model complexity for qualitative ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12270125/]
• 55. A Framework for Simulating Ecosystem Effects in Data ... [https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2021.799068/full]
• 56. A simulation-based approach to assess the stability of ... [https://www.sciencedirect.com/science/article/pii/S1470160X23015534]
• 57. Rethinking the importance of food quality in marine benthic ... [https://www.sciencedirect.com/science/article/abs/pii/S0079661115300471]
• 58. Understanding the structure and functioning of polar pelagic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5204148/]
• 59. PMC - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC11851788/]
• 60. Robust model-based indicators of regional differences in ... [https://www.sciencedirect.com/science/article/pii/S0924796321000543]
• 61. Quantitative approaches in climate change ecology - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3597248/]
• 62. Experimental design for the Marine Ice Sheet–Ocean Model ... [https://gmd.copernicus.org/articles/17/7105/2024/]
• 63. Collecting and Visualizing Deep-Sea Data [https://oceanexplorer.noaa.gov/explainers/data/]
• 64. Developing integrated models of Southern Ocean food webs: Including ecological complexity, accounting for uncertainty and the importance of scale - ScienceDirect [https://www.sciencedirect.com/science/article/abs/pii/S0079661112000237]
• 65. Frontiers | Southern Ocean Food Web Modelling: Progress, Prognoses, and Future Priorities for Research and Policy Makers [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2021.624763/full]
• 66. Food webs Thematic Assessment - OSPAR - Assessments [https://oap.ospar.org/en/ospar-assessments/quality-status-reports/qsr-2023/thematic-assessments/food-webs/]
• 67. A global overview of marine heatwaves in a changing climate [https://www.nature.com/articles/s43247-024-01806-9]
• 68. Marine heatwaves disrupt ecosystem structure and function ... [https://www.nature.com/articles/s41467-024-46263-2]
• 69. The Blob marine heatwave transforms California kelp forest ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9614761/]
• 70. Comparing the structural uncertainty and ... [https://www.sciencedirect.com/science/article/pii/S1364815222001177]
• 71. Marine Heatwaves - Physical Sciences Laboratory - NOAA [https://psl.noaa.gov/marine-heatwaves/]
• 72. Surf's up and so are marine heatwaves! How AI is forewarning ... [https://blogs.egu.eu/geolog/2025/06/18/surfs-up-and-so-are-marine-heatwaves-how-ai-is-forewarning-the-mediterraneans-ocean-sizzle/]
• 73. future seafloor marine heatwaves will surpass surface ... [https://pml.ac.uk/news/deep-trouble-future-seafloor-marine-heatwaves-will-surpass-surface-extremes/]