Leveraging Ecospace for Spatial Management: A Comprehensive Guide for Ecosystem Researchers

Abstract

This article provides a systematic exploration of the Ecospace module, the spatial analysis component of the Ecopath with Ecosim (EwE) framework, tailored for researchers and scientists in ecosystem-based management. It covers foundational principles, from defining habitat affinity using binary maps to the advanced Habitat Foraging Capacity (HFC) model that incorporates continuous environmental drivers like temperature and salinity. The content details methodological applications for conducting area-based management scenarios, such as evaluating marine protected areas and fishing effort redistribution, supported by case studies from deep-sea and coastal systems. It further offers guidance on troubleshooting model configuration, avoiding driver correlation, and systematically validating model outputs to inform robust spatial policy and conservation strategies.

Understanding Ecospace: Core Principles of Spatially Explicit Ecosystem Modeling

Ecospace is the spatial and temporal dynamic module within the Ecopath with Ecosim (EwE) ecological modeling software suite. Its primary design purpose is for exploring impact and placement of protected areas and conducting spatial policy exploration [1] [2]. As the spatial component of the widely-used EwE framework, Ecospace allows researchers to simulate how ecosystem components and fishing activities are distributed across a seascape and how these distributions change over time in response to management interventions and environmental drivers [3].

This module extends the capabilities of the core Ecopath (static mass-balanced snapshot) and Ecosim (time-dynamic simulation) models by replicating dynamic analyses over a grid of spatial cells [3]. Ecospace dynamically allocates biomass across a raster grid map, simulating the ecosystem by accounting for trophic interaction rates based on species habitat affinities, habitat locations, and fishing method utilization patterns [3]. This functionality makes Ecospace an invaluable tool for implementing ecosystem-based spatial management approaches that are increasingly critical in marine conservation and fisheries management.

Core Principles and Functionality

Theoretical Foundation

Ecospace builds upon the mass-balance principles of Ecopath and the temporal dynamics of Ecosim, adding a spatial explicit framework that allows for simulating biomass distribution across a heterogeneous seascape. The model operates on the fundamental concept that species distributions are influenced by habitat suitability, trophic interactions, and human pressures across space [4].

The spatial dynamics in Ecospace are driven by dispersal rules and habitat foraging capacity, where organisms move toward cells that offer better foraging opportunities or more suitable environmental conditions [4]. This approach allows the model to simulate realistic species distributions and spatial trophic interactions without requiring excessively complex parameterization.

Key Modeling Components

Table 1: Core Components of the Ecospace Model

Component	Description	Function in Model
Spatial Grid	Two-dimensional raster map divided into cells	Provides the spatial framework for biomass distribution and movement
Habitat Layers	Environmental data layers defining habitat suitability	Determines foraging capacity and species distribution patterns
Species Groups	Functional groups defined in the base Ecopath model	Maintains trophic connections and physiological parameters
Fishing Fleets	Representation of fishing activities with spatial effort	Applies anthropogenic pressure across the spatial domain
Marine Protected Areas	Defined areas with restricted fishing	Allows testing of spatial management scenarios

Habitat Foraging Capacity Model

A significant advancement in Ecospace functionality is the habitat foraging capacity model, which enables implementation of foraging responses to environmental drivers and habitat preference maps derived directly from species distribution models [4]. This capability allows researchers to:

• Implement habitat preferences based on abundance hot-spots or presence/absence data [4]
• Account for shifts in species distribution over time due to environmental changes [4]
• Drive spatiotemporal changes in species distributions within Ecospace simulations [4]

The frequency of habitat updating can be customized based on research objectives, allowing for both static and dynamically changing habitat conditions throughout simulations [4].

Ecospace Application Protocols

Model Parameterization and Setup

Protocol 3.1.1: Spatial Grid Configuration

• Define the spatial extent and resolution of the model domain
• Import or create bathymetric layers and habitat maps
• Assign habitat suitability indices for each functional group
• Configure seasonal or permanent habitat changes if needed

Protocol 3.1.2: Fishing Fleet Distribution

• Map historical fishing effort patterns for each fleet
• Define fleet-specific habitat preferences and cost structures
• Establish responses to potential area closures and regulations
• Calbrate fleet dynamics using available effort and catch data

Habitat Preference Implementation

The implementation of habitat preferences requires careful consideration to avoid overly restrictive habitat definitions that can hinder the distinction between trophic and external drivers [4]. Two primary approaches have been validated:

• Abundance Hot-Spots Approach: Defines habitat preferences based on areas of high species abundance, providing more refined but potentially restrictive habitat definitions [4]

• Presence/Absence Approach: Uses broader distribution data to define suitable habitats, allowing greater flexibility for species range shifts [4]

Comparative testing through retrospective analysis is recommended to determine which approach better forecasts observed biomass trends and distributions for the specific ecosystem under study [4].

Model Validation and Skill Assessment

Protocol 3.3.1: Ecospace Output Validation A specialized skill assessment routine should be developed for Ecospace outputs that enables:

• Temporal assessment: Comparing predicted biomass trends with observed time series data
• Spatial assessment: Evaluating the accuracy of predicted species distributions
• Spatio-temporal assessment: Validating both distribution patterns and their changes over time [4]

This multi-faceted validation approach ensures that Ecospace models accurately represent both the temporal dynamics and spatial patterns observed in real-world ecosystems.

Spatial Management Applications

Marine Protected Area (MPA) Evaluation

Ecospace provides powerful capabilities for evaluating potential MPA configurations and their ecosystem-wide impacts. The model can assess trade-offs between conservation objectives and fisheries yield by simulating different MPA scenarios [4]. Key application steps include:

Protocol 4.1.1: MPA Scenario Testing

• Define candidate MPA locations and restrictions based on conservation goals
• Simulate fishing effort redistribution around closed areas
• Quantify changes in ecosystem health, food web structure, and fisheries yield
• Evaluate spillover effects and potential conflicts between sectors

Climate Change Impact Assessment

The integration of environmental drivers allows Ecospace to project how climate-induced distribution shifts might affect spatial management effectiveness. The habitat foraging capacity model can incorporate temperature preferences and other climate-related parameters to simulate future species distributions [3].

Multi-Objective Spatial Planning

Ecospace facilitates trade-off analyses between competing objectives such as:

• Energy sector spatial demands (e.g., offshore wind farms)
• Conservation targets (e.g., EU 2030 Biodiversity Strategy)
• Fisheries sustainability and profitability [4]
• Protection of sensitive benthic habitats [5]

Table 2: Spatial Management Scenarios Accessible Through Ecospace Modeling

Scenario Type	Primary Objectives	Key Output Metrics
Marine Protected Areas	Biodiversity conservation, Fishery recovery	Biomass changes, Spillover effects, Fishery yields
Fisheries Restrictions	Sustainable harvest, Bycatch reduction	Stock status, Economic performance, Discard rates
Habitat Protection	Benthic ecosystem integrity, Carbon sequestration	Habitat status, Biodiversity indicators, Ecosystem services
Climate Adaptation	Resilience to change, Adaptive management	Distribution shifts, Trophic mismatches, Management efficacy

Advanced Implementation: The Scientist's Toolkit

Essential Research Reagents and Computational Tools

Table 3: Essential Research Reagents and Tools for Ecospace Modeling

Tool/Reagent	Function/Purpose	Implementation Example
EwE Software Suite	Core modeling platform hosting Ecospace	Free download from ecopath.org; requires base Ecopath model [1]
Habitat Foraging Capacity Model	Implements species-environment relationships	Define habitat preferences from distribution models [4]
Spatiotemporal Framework	Enables dynamic distribution shifts	Configure habitat update frequency and dispersal parameters [4]
ECOIND Plug-in	Calculates ecological indicators	Assess ecosystem health and food web structure changes [4]
Species Distribution Models	Derives habitat preference maps	Input for habitat foraging capacity; presence/absence or abundance-based [4]
Environmental Data Layers	Defines habitat characteristics	Temperature, salinity, substrate type, primary production [3]
Fishing Effort Maps	Spatial allocation of anthropogenic pressure	Historical effort distribution by fleet type [5]
Skill Assessment Routine	Validates model performance	Temporal, spatial, and spatio-temporal output validation [4]

Ecospace can incorporate diverse external data sources to enhance model realism:

• Physical data: Temperature, currents, and other oceanographic parameters [3]
• Satellite data: Primary production estimates and sea surface characteristics [3]
• Benthic habitat maps: High-resolution seabed substrate and biological data [5]
• Fishery-dependent data: Vessel monitoring systems and logbook information [5]

Addressing Uncertainty in Spatial Management

Ecospace modeling incorporates approaches to address uncertainty in spatial management predictions:

Protocol 5.3.1: Robust Decision Making (RDM) Approach The RDM framework focuses on identifying management strategies that perform well across a wide range of plausible future conditions rather than predicting a single future state [3]. Implementation involves:

• Testing management strategies under multiple future scenarios without requiring agreement on exact future conditions
• Identifying weaknesses in proposed strategies and developing mitigation approaches
• Optimizing decisions across uncertain future realities rather than for specific conditions [3]

This approach is particularly valuable for long-term spatial planning in the context of climate change and other deeply uncertain future developments.

Case Study Implementation: North Sea Application

A comprehensive example of Ecospace implementation comes from the southern North Sea, where researchers developed a model utilizing the spatiotemporal framework combined with the habitat foraging capacity model [4]. This application demonstrated:

• Successful forecasting of observed biomass trends and species distributions
• Comparison of different habitat preference definitions (abundance hot-spots vs. presence/absence)
• Evaluation of habitat updating frequency on model performance
• Assessment of area closures for fishing impacts on ecosystem structure [4]

The North Sea case study highlights how Ecospace, combined with the ECOIND plug-in, can assess trade-offs resulting from spatial conflicts between energy sector development, conservation efforts, and fisheries management [4].

The Ecospace module of the Ecopath with Ecosim (EwE) framework represents a powerful tool for implementing spatial-temporal simulations to inform ecosystem-based management. A critical advancement within this module has been the evolution of its approach to habitat suitability modeling. Historically, species distributions in Ecospace were governed by binary habitat maps, where a cell was deemed either entirely suitable (1) or entirely unsuitable (0) for a given functional group based on a limited set of physical parameters [6]. This approach oversimplified the complex, multi-factorial environmental preferences that determine why "species are where they are" [6].

The introduction of the habitat capacity model marked a significant shift from this binary perspective to a continuous, dynamic representation of habitat suitability. This model replaces the binary habitat variable with a continuous habitat suitability factor, denoted as C_rcj, which varies from 0.0 to 1.0 for each functional group j in each cell (r,c) [6]. This capacity is calculated as a function of a vector of habitat attributes H_rc = (H1, H2,…Hv)_rc, such as depth, bottom type, temperature, and salinity, such that C_rcj = f_j(H_rc) [6]. This modification allows the model to account for the cumulative impacts of multiple physical, oceanographic, and environmental factors on a species' ability to forage and persist in a given location, making the model fully temporal and spatially dynamic [6].

The Theoretical Foundation of the Habitat Capacity Model

Linking Habitat Capacity to Trophic Dynamics

The core innovation of the habitat capacity model lies in its integration with the foraging arena theory that underpins Ecosim's predation dynamics. The consumption rate Q_ij of a predator j on a prey i is based on the vulnerable portion of the prey's biomass V_ij [6]. The habitat capacity C_rcj is incorporated as a modifier that influences the predation rate by effectively concentrating predation activity into smaller relative areas when foraging arena size is small [6].

The fundamental equation for vulnerable prey biomass in a spatial cell is transformed from: V_ij = (v_ij * B_i) / (2 * v_ij + a_ij * B_j) [6]

to: V_ij = (v_ij * B_i) / (2 * v_ij + (a_ij * B_j) / C_rcj) [6]

This division of the predator search term by the capacity value C_rcj means that in cells with low habitat capacity, the local predation impact on vulnerable prey increases more rapidly as predator biomass increases. This formulation results in the equilibrium predator biomass B_j being proportional to C_rcj, thereby creating a direct mechanistic link between local environmental conditions and population dynamics [6].

Initialization of Spatial Biomass Distributions

When initializing an Ecospace simulation from a balanced Ecopath model, the initial biomass densities B_rcj(0) are assigned to be proportional to the habitat capacity values C_rcj [6]. The calculation is:

B_rcj(0) = (C_rcj / TC_j) * nw * B_j^* [6]

where:

• TC_j = ΣC_rcj is the total capacity index for group j across all water cells
• nw is the number of water cells
• B_j^* is the Ecopath base biomass for group j

This assignment ensures that the Ecopath biomasses represent spatial averages, while allowing for much higher local densities in highly suitable cells.

Protocol: Implementation of Continuous Habitat Suitability in Ecospace

Prerequisite Model Development

A structured workflow is essential for successful Ecospace model development, beginning with a robust Ecopath and Ecosim foundation [7].

Figure 1: Ecospace model development workflow, highlighting the foundational steps required before implementing habitat capacity.

Protocol 3.1.1: Ecopath and Ecosim Prerequisites

• Develop and Balance an Ecopath Model: Construct a mass-balanced ecosystem model representing the study area. Follow best practices for functional group definition, diet matrix formulation, and parameter estimation [7].
• Calibrate with Ecosim: Use the Ecosim temporal dynamic module to calibrate the model against available time series data (e.g., biomass, catch, effort). This step refines key parameters like vulnerabilities and evaluates the model's ability to reproduce historical patterns, which is crucial before spatial expansion [7].
• Define Spatial Domain: In Ecospace, define the spatial grid resolution and extent. Assign base layer maps, including bathymetry, habitat types, and boundary definitions.

Configuring the Habitat Capacity Model

Protocol 3.2.1: Environmental Data Layer Preparation

• Identify Relevant Drivers: For each functional group, select environmental variables that critically influence habitat suitability (e.g., depth, temperature, salinity, substrate type, oxygen concentration) [6].
• Acquire Spatial Data: Obtain spatial data layers for each variable, ideally as time-series data for dynamic simulations. These can come from remote sensing, oceanographic models, or GIS databases [6].
• Format and Import: Ensure all layers match the Ecospace grid resolution and extent. Import layers using the Ecospace spatial-temporal data framework [6].

Protocol 3.2.2: Defining Environmental Preference Functions

• For each functional group j and environmental variable v, define a preference function f_j,v(H_v) that outputs a suitability score between 0 and 1 for any given value of the environmental variable H_v [6].
• Function shapes can be defined based on literature review, expert opinion, or statistical analysis of species-environment relationships (e.g., from Species Distribution Models).
• Calculate Composite Habitat Capacity: The overall habitat capacity C_rcj for a group j in cell (r,c) is calculated as the product of the individual environmental preference values [6]: C_rcj = f_j,1(H1) * f_j,2(H2) * ... * f_j,v(Hv) This multiplicative approach assumes that habitat constraints are cumulative and non-compensatory.

Table 1: Key Parameters in the Habitat Capacity Model Implementation

Parameter	Symbol	Description	Data Sources
Habitat Capacity	C_rcj	Continuous suitability (0-1) for group j in cell (r,c)	Calculated from environmental layers and preference functions
Environmental Variable	H_v	Value of environmental factor v (e.g., temperature, depth)	Remote sensing, oceanographic models, GIS databases
Preference Function	f_j,v()	Function translating H_v to a suitability score (0-1) for group j	Literature, expert opinion, statistical models
Total Capacity Index	TC_j	Sum of C_rcj over all cells for group j	Calculated by Ecospace during initialization
Vulnerability Exchange Rate	v_ij	Rate of flow between vulnerable and invulnerable prey biomass [6]	Calibrated in Ecosim

Workflow for Habitat-Based Simulations

Figure 2: Logical flow of the habitat capacity model, showing how environmental data and species preferences combine to influence ecosystem dynamics.

Protocol 3.3.1: Running and Validating Simulations

• Configure Base Scenario: Run Ecospace with habitat capacity activated, using static or dynamic environmental drivers.
• Spatial Model Skill Assessment: While formal calibration in Ecospace is not yet automated, perform informal "visual calibration" by comparing model outputs to independent spatial distribution data (e.g., from surveys, tracking studies, or satellite tagging) [7].
• Scenario Analysis: Implement management scenarios (e.g., spatial closures, changes in fishing effort redistribution, habitat restoration) and compare outcomes against the base scenario [8].

Application Note: Informing Spatial Management Scenarios

The continuous habitat suitability framework significantly enhances the utility of Ecospace for evaluating spatial management strategies by providing a more ecologically realistic representation of species distributions and their responses to environmental change.

Application 4.1: Evaluating Fishery Effort Redistribution

• Context: As demonstrated in a study of Nigerian coastal waters, depletion of a target species (Penaeus notialis) in deeper waters led fishers to redirect effort toward shallower-water species [8].
• Implementation: Using Ecospace with habitat capacity, two scenarios were developed: one with trawlers fishing everywhere and another with a 5-nautical-mile coastal exclusion zone [8].
• Outcome: Model results projected increases in catch for some fisheries and biomass increases for several functional groups (e.g., Small Pelagic fishes, Rays) under the effort redistribution scenario, providing quantitative forecasts to guide management decisions [8].

Application 4.2: Optimizing Restoration for Ecosystem Services

• Context: A GIS-based Habitat Suitability Index (HSI) for oyster restoration in Pamlico Sound, USA, was enhanced by integrating ecosystem service considerations, specifically water filtration [9].
• Method Integration: While this study used a standalone HSI, its framework is directly relevant to Ecospace application. It incorporated satellite-derived chlorophyll a (representing phytoplankton food availability), water flow velocities (influencing food delivery), and dissolved oxygen, in addition to persistence factors like salinity and larval connectivity [9].
• Management Insight: The model identified optimal restoration locations that were "win-win" for both metapopulation persistence and water filtration service, as well as "tradeoff" locations that optimized for a single objective, thereby providing nuanced spatial guidance for restoration practitioners [9].

Table 2: Comparison of Binary and Continuous Habitat Modeling Approaches in Ecospace

Feature	Binary Habitat Model	Continuous Habitat Capacity Model
Suitability Representation	0 (absent) or 1 (present)	Continuous value from 0.0 to 1.0
Environmental Drivers	Limited, typically static habitat maps	Multiple, dynamic factors (e.g., temperature, salinity, oxygen)
Theoretical Basis	Simple habitat association	Foraging arena theory, with capacity modifying predation rates
Spatial Biomass Prediction	Biomass confined to "suitable" cells	Biomass proportional to capacity C_rcj across the seascape
Response to Environmental Change	Step-function response	Smooth, graded response
Management Application	Basic spatial zoning	Dynamic management responsive to environmental conditions

Table 3: Key Research Reagent Solutions for Ecospace Habitat Modeling

Tool / Resource	Category	Function in Habitat Suitability Modeling
Ecopath with Ecosim (EwE)	Core Software	The foundational modeling platform containing the Ecospace module for implementing habitat capacity simulations [6] [7].
Spatial-Temporal Data Framework	Software Feature	The built-in Ecospace framework for loading, managing, and processing time-varying environmental data layers during model runs [6].
Environmental Data Layers (e.g., Depth, SST, Salinity)	Input Data	Geospatial datasets that define the habitat attributes H_rc used to calculate habitat capacity C_rcj for each functional group [6] [9].
Species Distribution Models (SDMs)	Analytical Method	Statistical or machine-learning models (e.g., Ensemble models, MaxEnt) used to derive environmental preference functions f_j,v() from species occurrence and environmental data [10] [11].
GIS Software	Supporting Tool	For processing, analyzing, and formatting spatial environmental data layers for import into Ecospace [9].
Global Biodiversity Information Facility (GBIF)	Data Source	A public repository of species occurrence data that can be used to parameterize and validate habitat preference functions [10].
Remote Sensing Data (e.g., Chlorophyll a)	Input Data	Satellite-derived environmental parameters that serve as dynamic inputs for calculating habitat capacity, especially relevant for phytoplankton-based food webs or filter feeders [9].

The Ecospace model is the spatial-temporal dynamic module of the Ecopath with Ecosim (EwE) approach, designed to evaluate spatial management interventions such as Marine Protected Areas (MPAs) and climate change impacts [12] [3]. It represents the ecosystem over a two-dimensional grid of cells, where each cell operates with the basic Ecosim trophic linkage dynamics, and cells are interconnected through biomass flows driven by spatial mixing processes and habitat preferences [13] [12]. This framework allows researchers to simulate how functional groups (species or groups of species) and fishing fleets distribute themselves across the seascape based on environmental conditions and habitat suitability, making it an indispensable tool for Ecosystem-Based Fisheries Management (EBFM) and Marine Spatial Planning (MSP) [12] [3].

Table 1: Core Components of the Ecospace Model

Component	Description	Function in Model
Spatial Grid	A rectangular grid of equally-sized cells over the model area [13] [12].	Provides the spatial framework for applying Ecosim dynamics locally and tracking biomass distribution.
Habitat Types	Discrete categories of substrate (e.g., sand, rock, seagrass) with coverage maps [13].	Determines the basic habitat suitability for functional groups based on predefined preferences.
Environmental Drivers	Dynamic variables (e.g., temperature, salinity, depth) with spatial maps and response curves [13].	Modifies habitat suitability over space and time, allowing for climate change simulations.
Functional Group Preferences	Species- or group-specific affinities for habitats and environmental conditions [13] [14].	Drives the dispersal and distribution of biomass across the spatial grid.
Fishing Fleets	Representation of fishery operations with spatial effort allocation [12].	Applies fishing mortality across the grid, influenced by closures and cost factors.

Spatial Grids: The Foundation of the Model

The Ecospace model domain is constructed from a grid of equally sized cells, with each cell representing a distinct spatial unit where ecosystem dynamics are calculated [13]. Biomass is considered homogeneous within a cell, and movement occurs across adjacent cell borders [12].

Key Characteristics and Configurations

The spatial grid can be configured in two primary ways, a critical choice that affects how spatial data is interpreted and how dispersal is calculated [15]:

• Traditional Geographic Projection (WGS84): Assumes cells are defined in decimal degrees, with cell areas tapering at higher latitudes. This is the default mode for large-scale, global models [15].
• Square Cell Projection (e.g., UTM): Assumes cells are square and of equal area, with coordinates and dimensions specified in meters. This is more appropriate for smaller regional models where positional accuracy of environmental driver data is crucial [15].

The dispersal of functional groups is governed by a set of immigration (I~i~) and emigration (e~i~) rates for each cell [12]. The fundamental equation for biomass flow out of a cell is: [ B{out,rci} = \sum\limits{d=1}^{4}m{id} \cdot B{rci} ] where (B{out,rci}) is the outbound biomass from a cell in row *r* and column *c* for group *i*, and (m{id}) is the instantaneous movement rate in one of the four cardinal directions (d) [12]. These movement rates are dynamically influenced by the habitat suitability of the neighboring cells, creating a gradient that directs biomass toward more favorable conditions [13] [12].

Figure 1: Ecospace spatial grid logic showing biomass dispersal from a central cell to its four neighbors. Movement rates (m~id~) are influenced by external factors like habitat maps and group preferences.

Defining Habitat and Environmental Suitability

The original Ecospace model used a binary habitat affinity system, where each cell was assigned a single habitat type (e.g., sand, rock, seagrass) and functional groups had fixed preferences for these habitats [13]. A significant advancement came with the introduction of the Habitat Foraging Capacity (HFC) model, which allows for a continuous representation of habitat suitability based on multiple, dynamic environmental factors [13] [12].

Habitat Affinity (Legacy Method)

This method relies on user-defined habitat types, typically 5-10, that represent relevant substrates [13].

• Habitat Maps: For each habitat type, a map expresses its fractional coverage in each cell on a scale from 0 (absent) to 1 (complete coverage). The sum of all habitat fractions in a cell cannot exceed 1 [13].
• Habitat Preferences: For each functional group, the user quantifies a preference for each habitat type, also on a scale from 0 (no preference) to 1 (optimal preference) [13].
• Cell Suitability: The overall habitat suitability for a group in a cell is a composite of the habitat presences and the group's specific preferences [13].

The HFC model provides a more nuanced and powerful approach by incorporating environmental drivers [13].

• Environmental Driver Maps: Users can define any number of environmental drivers (e.g., sea temperature, dissolved oxygen, salinity) and provide spatial maps for each. These maps can be static or change over time within the Spatial Temporal Data Framework (STDF) [13] [12].
• Functional Response Curves: For each functional group and driver, a unique response curve defines how the environment affects the group. This curve acts like a habitat suitability index, specifying the group's tolerance or preference for specific ranges of the environmental variable [13].

In current EwE versions (6.5 and above), the choice between using habitat affinity, environmental preferences, or both is a per-group setting, offering maximum flexibility [13]. It is often effective to use environmental responses for ecosystem groups and habitat affinity to restrict where fishing fleets can operate [16].

Table 2: Comparison of Habitat Affinity and Environmental Preferences Models

Feature	Habitat Affinity Model	Environmental Preferences Model (HFC)
Core Concept	Binary or fractional coverage of discrete habitat types [13].	Continuous response to environmental gradients [13].
Suitability Input	Habitat maps (substrate type) and group-specific preference values (0-1) [13].	Environmental driver maps (e.g., temperature) and group-specific response curves [13].
Temporal Dynamics	Typically static, though can be linked to time-force via the STDF.	Designed for dynamic change, with driver maps that can update over time [13].
Primary Application	Defining static habitat associations; often used for fleet operations [13] [16].	Modeling impacts of climate change and other time-varying environmental stressors [13] [14].
Advantages	Intuitive, simpler to parameterize with limited data.	More ecologically realistic; can integrate numerous environmental factors [13].

Functional groups in Ecospace disperse throughout the grid based on a trade-off between the ability to feed and the risk of predation, a process mediated by cell suitability [13]. The HFC model computes a continuous relative habitat capacity for each functional group in each cell, which defines the proportion of the cell that the group can effectively use for foraging [13]. This value, ranging from 0 to 1, directly influences the movement rates ((m_{id})), creating a gradient that directs biomass flow from less suitable to more suitable cells [13] [12].

Protocol for Parameterizing Spatial Distributions

A recommended, step-wise protocol for setting up realistic functional group distributions is as follows [13]:

• Identify Key Drivers: Begin by identifying the few most important environmental drivers or habitat types that define the distribution of the major functional groups in the ecosystem. Common starting points include bottom depth and water temperature [13] [14].
• Develop Base Maps and Responses: For the selected drivers, acquire or create spatial base maps. Then, define functional response curves for each impacted group based on scientific literature or habitat suitability models (e.g., from AquaMaps) [13] [16].
• Run Initial Simulations and Validate: Perform initial model runs and compare the predicted spatial distributions with surveyed or known distributions. Metrics for comparison include the centre of gravity, inertia, and spatial overlap [14].
• Add Complexity Iteratively: Gradually add less critical environmental features or refine response curves until a satisfactory match with observed distributions is achieved. This iterative process helps avoid the problem of over-emphasis from correlated drivers (e.g., temperature and depth) [13].
• Address Uncertainty: For policy-informing applications, use approaches like Robust Decision Making (RDM) to test management strategies across a wide range of plausible future conditions, thus accounting for uncertainty in model projections [3].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Data Solutions for Ecospace Modeling

Item/Data Type	Function in Ecospace Model	Notes and Sources
Bathymetry Data	Defines water depth per grid cell, a fundamental habitat driver for most functional groups [14] [16].	Often a base layer from which other habitats (e.g., sediment type) are derived.
Satellite-derived Primary Production	Provides a spatial map of relative primary production, a key driver for lower trophic levels [16] [12].	Can be used to create a relative primary production map in Ecospace.
Temperature & Salinity Fields	Core environmental drivers that affect metabolism, growth, and distribution of most functional groups [13] [14].	Can be static climatologies or dynamic time series from hydrodynamic models.
Sea Ice Concentration	Critical driver for polar ecosystems, affecting habitat accessibility and primary production [14].	Used in models like the Barents Sea to differentiate between cold and warm years [14].
Sediment/Habitat Maps	Define the spatial extent of substrate types (e.g., mud, gravel) for the habitat affinity model [13].	Can be derived from geological surveys or predictive modeling.
Species Habitat Suitability Models	Informs the parameterization of functional response curves for environmental preferences [13] [16].	Sources like AquaMaps can provide preliminary response parameters.
Fisheries Catch and Effort Data	Used to define initial fishing effort distribution and validate fleet dynamics [12].	Critical for realistic representation of anthropogenic pressure.

Advanced Application: Modeling Climate-Driven Distribution Shifts

Ecospace, equipped with the HFC model, is highly effective for projecting the impacts of climate change on marine ecosystems. A prominent application is demonstrated in a Barents Sea study, which modeled the spatial shifts of 74 functional groups between a cold (2004) and a warm (2013) year [14].

Experimental Protocol for Climate Scenarios

The methodology can be summarized as follows [14]:

• Scenario Definition: Two plausible annual scenarios were created: a cold year (2004) and a warm year (2013), differing by approximately 0.3°C in bottom temperature, 0.6°C in surface temperature, and 7% in ice coverage.
• Model Parameterization: The Ecospace model was developed with habitat foraging capacities for key environmental parameters, namely water temperature and bottom depth.
• Model Validation: The spatial distributions of biomass predicted by the model for the two scenarios were compared against historical survey data using quantitative metrics: centre of gravity (mean location), inertia (spatial spread), and spatial overlap.
• Analysis of Shifts: The magnitude and direction of distribution shifts between the cold and warm scenarios were calculated for each functional group and for the community as a whole.

Key Findings and Workflow

The study found that the model satisfactorily represented observed past distributions [14]. In warming conditions, most functional groups shifted their distributions poleward (northeast in the Barents Sea) and increased their distribution area. The model predicted the entire community shifted at an average rate of 4.4 km per year, or 133 km per °C of bottom temperature increase [14].

Figure 2: Workflow for modeling climate-driven spatial shifts using Ecospace, based on the Barents Sea case study [14].

This protocol provides a robust methodology for using Ecospace to forecast ecosystem responses to environmental change, offering critical insights for proactive spatial management.

Core Concepts and Model Architecture

The Habitat Foraging Capacity (HFC) model is a spatial-temporal dynamic niche model within the Ecospace module of the Ecopath with Ecosim (EwE) framework. Its primary function is to drive the foraging capacity that distributes biomass of functional groups across model grid cells by calculating continuous relative habitat suitability [13] [17]. The HFC model represents a significant advancement over the original Ecospace habitat model, which used a binary habitat use pattern where each spatial cell was either entirely suitable or entirely unsuitable for species/functional groups [13].

The HFC model computes the foraging arena size in each cell through two main methods that can be used individually or in combination [13]:

• Habitat Affinity: The legacy Ecospace approach where users define habitat types (e.g., sand, rock, seagrass) and functional group preferences for these habitats.
• Environmental Preferences: A more recent addition that functions like a habitat suitability index model, incorporating environmental drivers (e.g., temperature, salinity, oxygen concentrations) with functional response curves.

This model framework allows the cumulative effects of physical, oceanographic, and environmental conditions onto species distributions, with the proportion of a cell that functional groups can use represented as a continuous value from 0 to 1 [13].

HFC Model Architecture

Quantitative Parameters and Data Structures

Habitat Affinity Parameters

The habitat affinity method requires quantitative definitions of habitat coverage and species preferences [13]:

Table 1: Habitat Affinity Parameter Structure

Parameter	Description	Data Range	Application
Habitat Coverage	Fraction of cell coverage by habitat type	0 (no presence) to 1 (complete coverage)	Per cell, per habitat type; sums to 1 across all habitats in cell
Habitat Preference	Functional group preference for specific habitat	0 (no preference) to 1 (optimal preference)	Per functional group, per habitat type
Habitat Suitability	Combined suitability factor	0 to 1	Calculated per cell, per functional group

Environmental Preference Parameters

The environmental preferences method uses environmental drivers with spatial distribution maps and functional response curves [13]:

Table 2: Environmental Driver Implementation

Environmental Driver	Data Type	Response Curve	Implementation
Physical Factors (depth, substrate)	Spatial maps (static or dynamic)	Tolerance ranges (preferred min/max)	Defines fundamental niche space
Water Column Properties (temperature, salinity, oxygen)	Time-series spatial maps	Optimal ranges with upper/lower thresholds	Affects physiological performance
Biological Factors (primary production)	Dynamic spatial maps	Positive/negative relationships	Influences trophic interactions

Implementation Protocols

Model Setup Workflow

HFC Implementation Workflow

Ecospace Model Calibration Protocol

Prerequisite: A balanced Ecopath model calibrated in Ecosim [7].

Objective: To achieve realistic spatial biomass distribution patterns through HFC parameterization.

Step-by-Step Procedure:

• Define Spatial Domain

  • Create a grid of equally sized cells representing the modeled area [13]
  • Determine spatial resolution based on research questions and computational capacity

• Select and Prioritize Environmental Drivers

  • Initialization: Start with 2-3 most important environmental features that define species distributions [13]
  • Progressive Complexity: Add less important environmental features gradually
  • Correlation Assessment: Evaluate driver temporal and spatial correlation to avoid overemphasized effects [13]
  • Recommended Statistical Approach: Use multivariate or spatial statistical approaches (e.g., GAM modeling) for coefficient weightings, especially with strongly correlated drivers [13]

• Configure HFC Methods Per Functional Group

  • Determine for each group whether habitat affinity, environmental preferences, or both will drive spatial distribution [13]
  • Define habitat preference values (0-1) for each functional group and habitat type
  • Parameterize environmental response curves for each functional group and environmental driver

• Implement Spatial-Temporal Dynamics

  • Utilize the spatial-temporal data framework (STDF) for dynamic data exchange [18]
  • Configure time-series of environmental driver maps for temporal variation
  • Define update frequency for dynamic habitat representations (seasonal, yearly, multi-year) [17]

• Spatial Model Skill Assessment

  • Conduct visual calibration against empirical distribution data [7]
  • Compare model outputs to observational data across multiple temporal scales
  • Evaluate biomass distribution patterns and spatial fishing opportunities

Experimental Validation and Case Study Applications

Southern North Sea Case Study Protocol

A recent study applied the HFC model to a southern North Sea EwE model to identify optimal habitat preference definitions and temporal scales [17].

Experimental Design:

Table 3: Habitat Preference Modeling Approaches

Approach	SDM Method	Temporal Implementation	Performance Outcome
Presence/Absence	Generalized Additive Models (GAM) fit to survey presence/absence data	Seasonal, yearly, multi-year, static	Superior performance compared to empirical data [17]
Abundance-Based	Hurdle models (GAM with abundance data)	Seasonal, yearly, multi-year, static	Lower performance than presence/absence approach

Key Findings:

• Habitat Preference Definition: Presence/absence-based habitat maps outperformed abundance-based maps [17]
• Temporal Scaling: Variable habitat updating (vs. static) was generally superior, though optimal scale differed for biomass and catch metrics [17]
• Recommended Implementation: Sigmoidal habitat representation (presence/absence) with multi-year variable habitat preferences [17]

Model Skill Assessment Metrics

Procedure for Spatial Validation:

• Run Ecospace simulations with different HFC configurations
• Export spatial output in GIS formats through the STDF reversal pathway [18]
• Compare predicted distributions with empirical survey data
• Calculate goodness-of-fit measures for biomass and catch patterns
• Iteratively refine HFC parameters to improve spatial accuracy

Research Reagent Solutions: Essential Modeling Components

Table 4: Essential Research Tools for HFC Implementation

Component	Function	Implementation Example
Ecopath with Ecosim (EwE) Software	Core modeling platform with Ecospace spatial module	Free ecological/ecosystem modeling software; Current version: EwE 6.7 [1]
Spatial-Temporal Data Framework (STDF)	Enables dynamic data exchange into running model	Three-layer architecture: data access, data conversion, data integration [18]
Environmental Driver Data	Provides spatial-temporal environmental variables	Sea surface temperature, depth, bottom dissolved oxygen, salinity, primary production [13]
Species Distribution Models (SDMs)	Generates habitat preference maps for functional groups	Generalized Additive Models (GAMs) fit to scientific survey data [17]
GIS Integration Tools	Facilitates input/output of spatial data	Export Ecospace output in GIS formats for external analysis and visualization [18]
Statistical Calibration Packages	Supports model fitting and uncertainty analysis	Monte Carlo routines for sensitivity analysis; Bayesian methods for parameter estimation [7]

Conceptual Definitions and Framework

Within spatial ecosystem modeling, particularly in the Ecospace module of the Ecopath with Ecosim (EwE) framework, accurately representing species distributions is paramount for developing credible management scenarios. Two core concepts—habitat affinity and environmental preferences—serve as fundamental mechanisms for this purpose, each with distinct theoretical and operational bases [13].

The Ecospace module functions by dividing a modeled area into a grid of cells, each simulating basic trophic linkage dynamics. Biomass moves between cells based on spatial mixing processes and the suitability of each cell for different functional groups [13]. The conceptual relationship between these drivers and the resulting species distribution can be summarized as follows:

Habitat Affinity is a concept rooted in the legacy Ecospace model. It defines species distribution based on static, physical habitat types, typically substrate characteristics like sand, rock, seagrass, or mud [13]. Users create habitat maps where each spatial cell is assigned a fractional coverage (0 to 1) for each habitat type. Subsequently, each functional group (e.g., a species or species group) is assigned a preference value between 0 (no preference) and 1 (optimal preference) for each habitat type. The model then calculates a composite suitability score for each cell. This approach is inherently spatially explicit but temporally static, assuming that habitat associations are fixed [13].

Environmental Preferences, introduced with the more advanced Habitat Foraging Capacity (HFC) model, link species distribution to dynamic, abiotic environmental drivers [13]. This method requires spatial-temporal maps of environmental variables—such as sea surface temperature, salinity, dissolved oxygen, or primary production—that change over time. For each driver and functional group, a response curve is defined, quantifying the group's tolerance or preference for specific ranges of that environmental condition. This creates a dynamic, non-stationary suitability index that allows species distributions to shift in response to changing environmental conditions, such as those projected under climate change scenarios [13].

Quantitative Comparison and Data Structure

The operationalization of these concepts requires distinct data types and structures. The table below summarizes the key quantitative and qualitative differences.

Table 1: Comparative Framework for Habitat Affinity and Environmental Preferences in Ecospace

Feature	Habitat Affinity	Environmental Preferences
Primary Driver Type	Static, physical habitats (e.g., substrate) [13]	Dynamic, abiotic factors (e.g., temperature, salinity) [13]
Core Data Input	Habitat maps (cell coverage, 0-1); Preference matrix (0-1 per group/habitat) [13]	Environmental driver maps (spatial-temporal); Functional response curves [13]
Suitability Calculation	Composite score from habitat coverage and preference values [13]	Suitability index based on response to environmental gradients [13]
Temporal Dynamics	Low; assumes static associations [13]	High; responds to temporal environmental change [13]
Implementation in Ecospace	Legacy and continued use; "binary" use pattern originally, now continuous [13]	HFC model (post-2013); enables spatial-temporal simulations [13]
Ideal Application Context	Systems where physical structure is the dominant limiting factor [13]	Systems subject to strong seasonal, annual, or climate-driven variability [13]

The biological traits that underpin these model parameters can be categorized as "hard" traits (direct physiological responses) and "soft" traits (morpho-anatomical proxies) [19]. The following table organizes key measurable traits from empirical studies that inform the assignment of preference values and response curves in Ecospace.

Table 2: Quantitative Physiological and Morphological Traits for Parameterization

Trait Category	Specific Measurable Trait	Measurement Protocol Summary	Relevance to Model Concept
Physiological ('Hard' Traits)	Maximal stomatal conductance (gsmax)	Measure using a porometer on leaves of plants grown at soil field capacity [19].	Informs Environmental Preferences for water availability/humidity.
	Soil water potential at wilting (Ψsoilwilt)	Measure soil water content when leaves of potted plants first show wilting [19].	Directly defines physiological tolerance limits for Environmental Response Curves.
	Water use efficiency at wilting (WUEwilt)	Calculate as the ratio of net photosynthesis to transpiration at the first wilting stage [19].	Indicates efficiency under stress; key for Environmental Response Curves.
Morpho-Anatomical ('Soft' Traits)	Specific Leaf Area (SLA)	Calculate as the ratio of leaf area to leaf dry mass [19].	A proxy for habitat strategy; informs Habitat Affinity assignments.
	Leaf Dry Matter Content (LDMC)	Measure as the ratio of leaf dry mass to its saturated fresh mass [19].	Correlates with plant toughness and nutrient retention; informs Habitat Affinity.
	Specific Root Length (SRL)	Calculate as the ratio of root length to root dry mass [19].	A proxy for soil exploration strategy; informs Habitat Affinity for substrate.

Experimental and Field Protocols for Parameterization

Accurate model parameterization requires rigorous experimental and field data collection. The following protocols provide a framework for gathering data to define habitat affinities and environmental preferences.

Protocol 1: Quantifying Physiological Response Curves for Environmental Drivers

This protocol details the measurement of "hard" physiological traits to define functional response curves for environmental preferences in Ecospace [19].

Objective: To empirically determine the relationship between a key environmental driver (e.g., temperature) and a physiological rate (e.g., metabolic scope) for a marine species.

Workflow Overview:

Materials:

• Environmental Chambers: Precisely controlled systems for temperature, salinity, or pH.
• Respirometry System: Equipment to measure oxygen consumption rates.
• Water Quality Probes: Calibrated probes for pH, salinity, and dissolved oxygen.

Procedure:

• Acclimation: Hold test organisms in a controlled environment that represents a baseline condition for a sufficient period to acclimate.
• Experimental Gradient: Expose groups of organisms to a series of fixed levels of the target environmental driver (e.g., temperatures of 5°, 10°, 15°, 20°, 25°C). Maintain other conditions constant.
• Physiological Measurement: At each exposure level, measure the relevant physiological rate. For metabolism, this involves placing individuals in sealed respirometry chambers and measuring the rate of oxygen decline over time.
• Replication: Perform measurements on a minimum of n=5 individuals per treatment level to account for biological variation.
• Data Analysis: Plot the physiological rate against the environmental driver level. Fit a statistical model (e.g., Gaussian, quadratic, or sigmoidal function) to the data to characterize the optimal value and tolerance limits.
• Model Parameterization: Translate the fitted model into a functional response curve within the Ecospace "environmental preferences" interface, defining the suitability from 0 to 1 across the driver's range [13].

Protocol 2: Field-Based Habitat Use and Affinity Studies

This protocol describes how to use observational field data to infer habitat affinity values for Ecospace.

Objective: To determine the relative preference of a species for different physical habitat types in its natural environment.

Materials:

• Georeferenced Habitat Map: A GIS layer classifying substrate types or benthic habitats, typically derived from sonar, satellite data, or direct sampling [20].
• Species Distribution Data: Data from trawl surveys, benthic grabs, underwater video transects, or telemetry.
• Spatial Analysis Software: GIS platform (e.g., QGIS, ArcGIS) for overlay analysis.

Procedure:

• Habitat Mapping: Develop a high-resolution map of benthic broad habitat types (BBHTs) for the study area using a combination of remote sensing and ground-truthing samples [20].
• Species Surveys: Conduct systematic, georeferenced surveys to record the presence, absence, and density/abundance of the target species across the study area.
• Data Integration: Overlay the species observation points with the habitat map in a GIS.
• Habitat Use Analysis: For each habitat type, calculate the Utilisation (U): U_i = (Number of individuals found in Habitat i) / (Total number of individuals observed)
• Habitat Availability Analysis: Calculate the Availability (A) of each habitat type: A_i = (Total area of Habitat i) / (Total study area)
• Affinity Calculation: Compute a Habitat Affinity Index (HAI) for each habitat type: HAI_i = U_i / A_i An HAI > 1 indicates preference, HAI < 1 indicates avoidance, and HAI ≈ 1 indicates neutral use relative to availability.
• Model Parameterization: Normalize the HAI values to a 0-1 scale for input into Ecospace's habitat affinity matrix, where 1 is assigned to the habitat with the highest HAI [13].

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagent Solutions and Essential Materials

Item	Function/Application
Portable Gas Exchange System	Measures "hard" physiological traits in situ or in the lab (e.g., stomatal conductance, photosynthesis) [19].
CTD/Rosette System	Profiles key aquatic environmental drivers (Conductivity/salinity, Temperature, Depth) and collects water samples [13].
Benthic Grab Sampler & Sieves	Quantifies species presence and abundance in soft-bottom habitats and characterizes sediment type [20].
GPS/GPSEquipped Drones or AUVs	High-resolution spatial mapping of habitat types and environmental variables [21] [20].
Multibeam Echosounder	Creates detailed seabed maps (bathymetry and backscatter) used to classify physical habitats [20].
Satellite-Derived Environmental Data	Provides spatial-temporal data layers for sea surface temperature, chlorophyll concentration, etc., for Ecospace [13] [22].
Telemetry Tags (Acoustic/GPS)	Tracks animal movements to link individual positions to habitat and environmental data [23].

Integrated Application in Ecospace for Spatial Management

The power of the Ecospace framework lies in its ability to combine both habitat affinity and environmental preferences. Since EwE 6.5, this combination is a per-group setting, allowing modelers to represent the distribution of a species based on physical habitat while simultaneously constraining it by dynamic environmental tolerances [13]. This integrated approach is critical for evaluating realistic spatial management scenarios, such as:

• Marine Protected Area (MPA) Network Design: Testing how proposed MPA locations, often based on static habitat maps, will perform under future climate change by incorporating shifting temperature preferences [24] [20].
• Fisheries Management Scenarios: Evaluating the socio-economic and ecosystem consequences of spatial fishery closures, considering how fishing effort redistribution interacts with species' shifting distributions [20].
• Climate Impact Projections: Using projected future environmental driver maps (e.g., SST, pH) to simulate potential range shifts and changes in ecosystem structure [13].

Best Practice Recommendation: Start with the few environmental and habitat features most critically defining distributions. Complexity should be built gradually, adding less important features subsequently to avoid over-parameterization and the problem of correlated drivers, which can result in an overemphasized effect on species distribution [13].

Implementing Ecospace: A Step-by-Step Guide to Spatial Scenario Analysis

Spatially explicit ecosystem modeling is a cornerstone of modern ecosystem-based management (EBM), enabling researchers and policymakers to simulate the complex interactions between marine species, their habitats, and anthropogenic pressures. The Ecopath with Ecosim (EwE) framework, and specifically its Ecospace module, provides a powerful toolset for creating dynamic, spatial simulations of marine ecosystem dynamics. These models are instrumental in evaluating spatial management scenarios, such as the design of Marine Protected Areas (MPAs) and the assessment of cumulative impacts from human activities like fishing and offshore wind farm development [25] [26].

The accuracy and utility of any Ecospace model are fundamentally dependent on the quality of its underlying spatial data. Habitat and environmental driver maps form the essential base layers that determine the foraging capacity and distribution of every functional group within the model. This protocol details the methodology for constructing these critical spatial bases, framed within the context of supporting robust spatial management scenario research.

Theoretical Foundation

In Ecospace, the distribution and biomass of functional groups (FGs)—which can represent individual species, groups of species, or specific life stages—are driven by a Habitat Foraging Capacity model. This model calculates the suitability of each map grid cell for a given FG based on the cumulative effects of multiple environmental conditions and habitat types [25] [4].

The core principle is that each FG has specific affinities for particular habitats and environmental drivers. These affinities, which can be derived from species distribution models or expert opinion, are implemented as functional responses that modulate the base foraging capacity of the FG in each cell [4]. The model operates in conjunction with food web dynamics and fisheries activities, allowing for the simulation of complex spatial-temporal ecosystem changes [25] [26].

Data Requirements and Acquisition

Creating a realistic spatial base requires the compilation of multiple, high-resolution geospatial datasets. The following table summarizes the essential data layers and their roles in the Ecospace model.

Table 1: Essential Data Layers for Building the Spatial Base

Data Category	Specific Data Layers	Role in Ecospace Model	Example Sources
Bathymetry & Topography	Depth, slope, seabed terrain.	Primary physical driver of species distribution; defines habitat boundaries.	EMODnet
Habitat Types	Seagrass beds, maerl grounds, coral communities, sediment type (e.g., mud, sand, rock).	Determines habitat-specific foraging capacity for benthic and demersal species.	Regional data providers (e.g., HELCOM)
Biophysical Parameters	Sea surface temperature, salinity, chlorophyll-a concentration, primary production.	Key environmental drivers for plankton, pelagic fish, and temperature-sensitive species.	Copernicus Marine Service
Human Pressures	Fishing effort, shipping lanes, offshore wind farm locations, underwater noise.	Sources of anthropogenic pressure that modify ecosystem structure and function.	IMO, regional databases

The precision differential—the variation between the actual spatial distribution of an ecosystem service or species and what the model can capture—is a key consideration. Simply increasing spatial resolution is insufficient; the model must be carefully adapted to local contexts using the best available data to ensure legitimacy and utility [27].

Protocol: Developing Habitat Affinity Maps

This protocol outlines a method for creating habitat affinity maps, leveraging the integrated habitat foraging capacity model within Ecospace [4].

Materials and Reagents

Table 2: Research Reagent Solutions for Spatial Modeling

Item	Function/Description	Application Note
Ecopath with Ecosim (EwE)	Free, open-source ecosystem modeling software suite.	Core modeling platform. Ecospace is the spatial module.
Geographic Information System (GIS)	Software for spatial data manipulation, analysis, and map creation (e.g., QGIS, ArcGIS).	Used to prepare, visualize, and export all spatial base layers.
Species Distribution Model (SDM) Outputs	Raster maps predicting species probability of presence based on environmental correlates.	Provides a statistical basis for defining habitat preferences for functional groups.
Harmonized Regional Geodatabase	A compiled database of bathymetry, habitat, and environmental layers for the study region.	Ensures all data layers are aligned in projection, resolution, and extent.

Experimental Procedure

Step 1: Define Model Domain and Base Grid

• Determine the spatial extent of your Ecospace model (e.g., Western Baltic Sea, Mediterranean Sea) [25] [26].
• Define the grid cell resolution. Higher resolution (e.g., 8x8 km) captures finer details but increases computational load [26].
• Establish a common coordinate reference system (CRS) for all data layers.

Step 2: Map Habitat Types

• Compile or create a classified habitat map for the entire model domain. This is a categorical raster where each cell is assigned a habitat type (e.g., seagrass, sandy bottom, rocky reef).
• The classification should be ecologically relevant to the functional groups in your ecosystem model.

Step 3: Derive Habitat Affinities for Functional Groups

For each functional group, define its affinity for each habitat type. An affinity value ranges from 0 (completely unsuitable) to 1 (optimal habitat). Two primary methods exist:

• Method A: Presence/Absence (Generalist Approach): Assign a value of 1 to all habitats known to be occupied by the FG, and 0 to those not occupied. This is less restrictive [4].
• Method B: Abundance Hot-Spots (Specialist Approach): Use SDM outputs or abundance data to assign continuous values (e.g., 0.2, 0.8) reflecting relative preference. This is more specific but can be overly restrictive if not properly validated [4].

Step 4: Implement in Ecospace

• Input the habitat map and the affinity values for each FG into the Ecospace interface using the Habitat Foraging Capacity model.
• This creates a unique foraging capacity map for each FG, which acts as a base layer upon which predation, fishing, and other dynamics occur.

Validation and Skill Assessment

• Conduct a retrospective analysis by running the Ecospace model for a past period and comparing the predicted biomass trends and distributions to independent survey data [4].
• Develop a skill assessment routine to quantitatively evaluate the model's performance in temporal, spatial, and spatio-temporal dimensions [4].

Protocol: Integrating Environmental Drivers

Environmental drivers are dynamic spatial layers that influence growth and mortality rates, modifying the base habitat foraging capacity.

Key Drivers and Forcing Functions

• Sea Surface Temperature (SST): A critical driver for metabolic and growth rates. Monthly SST layers can be used to force the model.
• Primary Production: Can be used to drive the production of lower trophic levels.
• Anthropogenic Pressures: Layers such as underwater noise (split into impulsive and continuous), bottom disturbance, and surface disturbance can be linked to functional groups via response functions [25].

Procedure for Applying Driver Layers

• Acquire Time-Series Data: Obtain or create raster time-series for each environmental driver (e.g., monthly SST composites for 1995-2016) [26].
• Standardize Layers: Ensure all driver layers match the model's grid resolution and CRS.
• Define Functional Responses: For each FG, define a functional response curve (e.g., dome-shaped, linear) that describes how its productivity or mortality changes in response to the environmental driver.
• Link and Simulate: In Ecospace, link the driver layer to the FG and select the appropriate response function. The model will then dynamically apply the driver's effect during the simulation.

A structured workflow is essential for integrating these diverse data sources into a cohesive model:

Diagram 1: Spatial Base Development Workflow

Application to Spatial Management Scenarios

The primary goal of building this detailed spatial base is to inform area-based management. A validated Ecospace model can simulate various scenarios:

• Marine Protected Area (MPA) Network Design: Models can forecast changes in biomass, biodiversity, and fisheries yield under different MPA configurations [4] [26].
• Impact of Offshore Wind Farms: The model can assess the cumulative effects of construction (e.g., noise, vibrations), operation, and decommissioning on specific trophic groups [25].
• Climate Change Projections: By forcing the model with future climate scenarios (e.g., increased SST), researchers can explore potential ecosystem shifts and identify vulnerable areas and species [26].

For instance, a high-resolution Mediterranean Sea model identified coastal and shelf areas as biodiversity hotspots and predicted how biomass and fishing catches shifted over decades, providing critical insights for regional policy [26].

Table 3: Key Analytical Tools and Frameworks

Tool/Framework	Purpose	Access/Reference
Ecopath with Ecosim (EwE)	The core ecosystem modeling software used for building and running Ecospace models.	https://ecopath.org/
MSP Challenge Platform	A serious game platform that integrates with EwE to simulate maritime spatial planning scenarios with stakeholders.	[25]
Spatial Methodology Appraisal of Research Tool (SMART)	A 16-item tool for appraising the methodological quality of spatial research, covering data quality and spatial analysis methods.	[28]
WebAIM Contrast Checker	An online tool to verify color contrast ratios for maps and visualizations, ensuring accessibility for all users.	[29]
Group Concept Mapping (GCM)	A structured method for gathering expert input to define model parameters and validate conceptual frameworks.	[28]

Constructing a robust spatial base of habitat and environmental driver maps is a critical, multi-stage process that demands high-quality data and careful methodological choices. By following the protocols outlined here—from defining habitat affinities to integrating dynamic environmental drivers—researchers can develop Ecospace models that are capable of providing realistic and impactful predictions. These models are indispensable tools for supporting evidence-based, ecosystem-based spatial management, helping to navigate the trade-offs between conservation objectives and sustainable human use of marine ecosystems.

Within the Ecopath with Ecosim (EwE) framework, the Ecospace module enables dynamic, spatial-temporal ecosystem modeling for evaluating spatial management scenarios [16] [7]. The core of Ecospace is a grid of cells, each running an ecosystem model, where the distribution of functional groups is driven by their environmental preferences and habitat affinities [16] [13]. Accurately defining how functional groups respond to environmental drivers is therefore paramount for creating reliable models that can predict ecosystem shifts due to climate change or inform marine spatial planning [14]. This application note provides detailed protocols for configuring these critical parameters, focusing on the Habitat Foraging Capacity (HFC) model.

Core Concepts and Terminology

The spatial distribution of functional groups in Ecospace is primarily governed by the Habitat Foraging Capacity (HFC) model. The HFC calculates a continuous relative habitat capacity (from 0 to 1) for each functional group in every cell of the spatial grid, determining the foraging arena size and suitability [13]. The HFC model incorporates two main methods, which can be used individually or in combination for each functional group.

• Environmental Response Functions: This method uses continuous environmental drivers (e.g., temperature, salinity, oxygen) and functional response curves to define habitat suitability [16] [13]. For each driver, a spatial map defines its distribution, and a response curve defines a group's tolerance or preference for specific conditions.
• Habitat Affinity Model: This legacy method uses discrete habitat types (e.g., sand, rock, seagrass). Each cell contains fractions of different habitats, and functional groups are assigned preference values (0-1) for each habitat type [13].

It is recommended to use the environmental response model for ecosystem groups, while habitats can be effective for limiting where fishing fleets operate [16].

Experimental Protocol: Configuring Environmental Responses

The following workflow outlines the steps for defining functional group responses to environmental drivers, from initial setup to validation.

Prerequisites and Foundational Model Development

A stable Ecospace model requires a solid foundation. Adhere to this three-step process before spatial configuration:

• Develop and Balance an Ecopath Model: Construct a mass-balanced trophic model of the ecosystem. This serves as the non-spatial baseline for all dynamic simulations [7].
• Calibrate in Ecosim: Fit the Ecosim temporal dynamic model to historical time series data. This step refines key parameters like vulnerabilities, which express density-dependence and how far a group is from its carrying capacity [7]. A well-calibrated Ecosim model is more likely to produce reliable temporal dynamics in Ecospace.

Protocol: Implementing Environmental Drivers in Ecospace

This protocol details the process of integrating environmental drivers and defining functional group responses.

• Step 1: Identify and Select Key Environmental Drivers
  • Action: Based on the ecosystem and research question, select the most important environmental drivers (e.g., bottom temperature, depth, salinity, primary production, ice cover) [14] [13].
  • Rationale: Starting with a few critical drivers avoids over-parameterization and the problem of driver correlation, which can overemphasize effects on species distribution [13].
• Step 2: Prepare Spatial Driver Maps
  • Action: For each selected driver, acquire or create a spatial map (in a compatible format like ASCII grid) that represents the driver's distribution across the model domain. Maps can be static or change over time [13].
  • Data Sources: These can include output from hydrodynamic models, remote sensing data (e.g., for primary production), or interpolated survey data (e.g., for temperature) [14].
• Step 3: Define Functional Group Response Curves
  • Action: For each functional group and environmental driver, define a response curve. This curve quantifies the group's relative foraging capacity across the gradient of the environmental variable.
  • Method: The response is typically a functional form (e.g., Gaussian, skewed, trapezoidal) that defines optimal ranges, tolerance limits, and avoidance zones. This can be informed by literature, species distribution models (e.g., from AquaMaps), or expert opinion [16] [13].
• Step 4: Configure Ecospace Habitat Foraging Capacity (HFC)
  • Action: In the Ecospace interface, activate the HFC model. For each functional group, select the "environmental response" mode and link the prepared driver maps and response curves. Habitat affinity can be configured in parallel if needed [13].
• Step 5: Model Validation and Iterative Refinement
  • Action: Run the Ecospace model and compare the predicted spatial distributions of functional groups to observed survey data from different time periods.
  • Validation Metrics: Calculate spatial statistics like Centre of Gravity (mean location of biomass), inertia (dispersion around the centre), and spatial overlap between modeled and observed distributions [14].
  • Iteration: If the model fit is poor, iteratively refine the response curves and/or add additional drivers. The goal is to find the most parsimonious model that captures the main spatial patterns [7] [13].

Data Presentation and Analysis

Example Environmental Drivers and Functional Group Responses

The table below summarizes hypothetical data for configuring environmental responses in a temperate shelf sea model, illustrating the quantitative parameters involved.

Table 1: Example Environmental Driver Configuration for a Temperate Shelf Sea Model

Environmental Driver	Spatial Range in Model Domain	Functional Group Example	Response Curve Parameters (Optimum [Min, Max])	Response Curve Type
Bottom Temperature	2°C to 18°C	Atlantic Cod (Gadus morhua)	8°C [0°C, 12°C]	Skewed (Tolerant to colder)
		European Plaice (Pleuronectes platessa)	10°C [5°C, 18°C]	Gaussian
Bottom Depth	10m to 1000m	Benthic Invertebrates	150m [50m, 400m]	Gaussian
		Deep-water Sharks	600m [400m, 900m]	Gaussian
Salinity	28 PSU to 35 PSU	Coastal Phytoplankton	30 PSU [28 PSU, 32 PSU]	Trapezoidal

Case Study: Spatial Shifts in the Barents Sea

A study on the Barents Sea demonstrated the application of this protocol. An Ecospace model was developed with habitat foraging capacities for 74 functional groups based on parameters like water temperature and bottom depth [14].

• Experimental Setup: The model was run for two scenarios: a cold year (2004) and a warm year (2013), differing by ~0.3°C in bottom temperature and with 7% less ice coverage in the warm year [14].
• Results and Validation: The model satisfactorily replicated observed spatial distributions. It predicted a poleward shift of the entire community by 41 km (modeled) versus 68 km (observed), corresponding to a rate of 67 km per °C of bottom temperature increase [14]. This validates the use of Ecospace with environmental responses for predicting climate-driven distribution changes.

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions for Ecospace Modeling

Tool / Resource	Type	Function in Environmental Response Configuration
Ecopath with Ecosim (EwE)	Software Platform	The core modeling environment within which the Ecospace module operates. It provides the foundational framework and user interface for implementing these protocols.
Environmental Driver Maps	Spatial Data	Gridded data (e.g., in ASCII format) that defines the spatial-temporal distribution of an environmental variable (e.g., temperature, depth) across the model domain.
Functional Response Curves	Model Parameter	The mathematical functions that define a functional group's habitat suitability across the range of an environmental driver, determining its realized niche.
Time Series Data	Observational Data	Historical data on species biomass and/or distribution used for calibrating the underlying Ecosim model and validating the spatial outputs of Ecospace.
Species Distribution Data (e.g., from AquaMaps)	External Model Output	Provides a priori information to inform the initial shape of functional group response curves for various environmental drivers [16].
GIS Software	Data Processing Tool	Used to create, edit, and format the spatial maps of environmental drivers for import into the Ecospace model.

Application Note: Ecospace for Spatial Fisheries Management

The Ecospace module within the Ecopath with Ecosim (EwE) framework serves as a spatially explicit ecosystem model, designed primarily to evaluate policy scenarios involving marine protected areas (MPAs) and their ecosystem-wide effects [30]. As a dynamic, spatial-temporal model operating on a grid of cells across a seascape, Ecospace simulates key processes including species dispersal, habitat preferences, and fishing fleet behavior, enabling researchers to project the long-term impacts of spatial management measures on both ecosystem structure and fishery profitability [30] [31].

The core strength of Ecospace lies in its ability to move beyond the assumption that fish and fishers are homogenously distributed—an limitation that can undermine the effectiveness of traditional management [30]. By integrating food web interactions with spatial dynamics, it provides a virtual platform for assessing the complex outcomes of MPA establishment, such as effort redistribution, biomass changes, and trophic cascades [32]. Its application is particularly vital for advancing Ecosystem-Based Fisheries Management (EBFM), ensuring that management decisions account for the interconnectedness of species and the wider ecosystem context [32].

Key Findings from Recent Applications

Recent Ecospace applications demonstrate several consistent findings relevant to MPA design:

• Effort Redistribution is Central: The ecological impacts of an MPA are profoundly influenced by the subsequent redistribution of fishing effort, sometimes more so than the placement of the MPA itself [32]. Simply closing an area without managing the resulting effort displacement can lead to localized overfishing in open areas and potentially negate the MPA's benefits.
• Cascading Ecosystem Effects: MPAs trigger changes that cascade through the food web. For example, a study of the North Sea food web model revealed that reducing bottom trawl fishing effort led to increased biomass and greater typical length of organisms, not only for target species but also for some pelagic species through trophic interactions [32].
• Economic-Ecological Trade-offs: Models frequently reveal a trade-off between ecological gains and short-term fishery catches. In the Aegean Sea, scenarios with larger MPAs and stricter protections yielded more pronounced ecological benefits, particularly for demersal and benthic species, but simultaneously posed economic challenges for fisheries [33].

Experimental Protocols

Protocol 1: Designing and Running MPA Scenarios in Ecospace

This protocol outlines the steps for constructing and simulating alternative MPA management scenarios, enabling a comparative analysis of their ecological and fisheries impacts.

2.1.1 Pre-modeling Preparation and Data Curation

• Define the Management Objective: Clearly state the primary goal of the simulation (e.g., rebuilding a specific fish stock, conserving biodiversity, maximizing fishery profit).
• Acquire Spatial Data: Gather and pre-process all necessary spatial data layers for the model domain. The table below summarizes the essential and recommended data.
• Develop a Baseline (Reference) Scenario: Configure Ecospace to represent current conditions ("business-as-usual") without new management interventions. This scenario serves as the counterfactual against which all MPA scenarios will be compared [33] [34].

Table 1: Essential Spatial Data for Ecospace Model Configuration

Data Category	Specific Datasets	Function in Model	Example Sources
Bathymetry	Depth (meters)	Defines habitat types and influences species distributions.	Global Fishing Watch [34]
Habitat Types	Seabed substrate (e.g., mud, sand, rock), vegetation.	Determines habitat suitability for different functional groups.	Regional marine spatial datasets
Existing MPAs	Location, size, and protection status of current MPAs.	Establishes initial fishing restrictions.	MPA Atlas [34]
Fishing Grounds	Historical spatial distribution of fishing effort.	Used for model calibration and validation.	Global Fishing Watch (AIS/VMS data) [34] [31]
Environmental	Sea surface temperature, chlorophyll-a.	Can drive productivity and species preferences.	NOAA ERDDAP [34]
Economic	Distance from port, fuel prices.	Informs the fishing fleet cost structure.	Bunker Index, spatial calculations [34] [31]

2.1.2 Scenario Formulation and Parameterization Design distinct MPA scenarios based on the management objective. A recent Aegean Sea study provides a template for scenario design [33]:

• Scenario A (Baseline): No new MPAs; projects the future state under current fishing pressure.
• Scenario B (Small MPAs): Designate small, isolated MPAs in areas of high biodiversity value.
• Scenario C (Network): Establish a network of MPAs connected through larval and adult dispersal.
• Scenario D (Fishery Closure): Extend restrictions on specific gear types (e.g., bottom trawling) over a broad area [33].
• Scenario E (Multi-use): Integrate other spatial uses, such as offshore wind farms, which can provide modest conservation benefits [33].

For each scenario, define the MPA boundaries in the Ecospace map and specify which fleets are restricted from operating within them, either fully or during specific months [31].

2.1.3 Model Execution and Output Extraction

• Run Simulations: Execute each scenario for a sufficient simulation period (e.g., 20-50 years) to observe long-term dynamics [8].
• Replicate Runs: Perform multiple stochastic runs for each scenario to account for model uncertainty.
• Extract Key Outputs: For each scenario and time step, record outputs such as:
  • Biomass: Total biomass and per-functional group biomass, both inside and outside MPAs.
  • Catches: Total catch and catch per fleet.
  • Spatial Effort: The redistributed pattern of fishing effort for each fleet.
  • Indicators: Size-spectra slope, mean trophic level, and other ecological indicators [32].

Protocol 2: Spatial Fishing Effort Dynamics and Model Validation

A critical, yet often overlooked, component of Ecospace is the accurate representation of how fishers respond to spatial closures. This protocol details the methods for parameterizing and validating the spatial dynamics of fishing fleets.

2.2.1 Parameterizing Fleet Behavior Fishing effort is distributed in Ecospace using a gravity model, where effort in a cell is proportional to the expected net profit (revenue minus costs) from fishing in that cell [31].

• Define Revenue: Expected income can be estimated using ex-vessel fish price databases, either global or regional [31].
• Define Costs: A key cost is distance from port. Ecospace can calculate a rudimentary cost layer based on Euclidean distance from designated ports for each fleet. For more realism, import custom cost layers that account for actual sailing pathways and other operational expenses [31].
• Restrict Fleet Access: Use the habitat layer or MPA designations to explicitly prohibit fleets from operating in certain jurisdictions or habitat types [31].

2.2.2 Validating the Spatial Effort Distribution To ensure model realism, the predicted spatial effort patterns should be compared against observed data.

• Acquire Observed Effort Data: Use data from Vessel Monitoring Systems (VMS) or Automatic Identification System (AIS), such as those available from Global Fishing Watch, which provide high-resolution, spatial-temporal data on fishing activity [34] [31].
• Quantitative Comparison: Statistically compare the model's predicted effort distribution against the observed data for the baseline scenario. The fit can be measured using metrics like the Spearman rank correlation, as was done in the North Sea Ecospace model evaluation [30].
• Model Calibration: Adjust parameters related to fleet movement and cost to improve the agreement between predicted and observed effort patterns. This step is crucial for generating reliable forecasts of fisher response to new MPAs [31].

2.2.3 Analyzing Effort Redistribution

• Calculate Displacement: For each MPA scenario, calculate the difference in effort maps between the scenario and the baseline to visualize and quantify effort displacement.
• Identify Hotspots: Identify areas where effort is predicted to concentrate, as these are potential zones for increased ecosystem impacts [32].

The diagram below illustrates the integrated workflow for configuring, running, and validating an Ecospace model for MPA planning.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Data Inputs and Analytical Tools for Ecospace Modeling

Tool / Data Source	Type	Primary Function in Research
Ecopath with Ecosim (EwE)	Software Framework	Core modeling environment for constructing mass-balanced food web models and running spatial-temporal simulations [30].
Global Fishing Watch (AIS/VMS)	Fishing Effort Data	Provides high-resolution, satellite-derived data on fishing vessel positions and effort, crucial for model validation [34] [31].
Marine Protection Atlas	MPA Spatial Data	Offers spatially explicit data on existing MPAs, their boundaries, and protection levels, used for initial model conditioning [34].
NOAA ERDDAP	Environmental Data	Server for accessing and downloading essential oceanographic variables like sea surface temperature and chlorophyll-a [34].
Global Ex-vessel Fish Price Database	Economic Data	Provides average fish prices for calculating fleet revenues in the gravity model of effort distribution [31].
R Statistical Software	Analysis Environment	Used for post-processing model outputs, statistical analysis, generating figures, and reproducibility checks [34].

The following tables consolidate key quantitative findings from recent studies, providing a reference for expected outcomes under various MPA scenarios.

Table 3: Summary of MPA Scenario Outcomes from the Aegean Sea Model [33]

Scenario	Key Intervention	Impact on Total Biomass	Impact on Catches	Noteworthy Outcomes
Reference	No new MPAs	6% decline by 2050	Substantial decreases for commercial species	Baseline for comparison; prey species biomass increased inconsistently.
Scenario 1 & 2	Prohibit fisheries in Natura 2000 areas	Localized biomass increases	Reduced total catches in some cases	Broader conservation benefits; suitable for biodiversity-driven strategies.
Scenario 3	Extend bottom trawling and purse seining restriction area	Highest biomass gains for key commercial species	Moderate trade-offs in catch	Most suitable for fisheries-focused management.
OWF Integration	Use Offshore Wind Farm areas as de facto MPAs	Modest conservation benefits	Not specified	Supports potential for multi-use marine spatial planning.

Table 4: Ecosystem-Level Indicators from North Sea Food Web Model [32]

Fishing Scenario	Community Mean Trophic Level	Size-Spectra Slope	Impact on PET Species	Catch Maximization Strategy
Reduced Bottom Trawl Effort	Increase	Flattening (more large organisms)	Limited but positive response	Total catch maximized when effort was proportionally redistributed, not concentrated at MPA boundaries.
Effort Redistribution	Changes driven by food-web interactions	Changes driven by food-web interactions	Not specified	Concentrating effort at MPA boundaries is sub-optimal for total yield.

The diagram below synthesizes the cause-effect relationships of MPA establishment as predicted by Ecospace models, highlighting the role of effort redistribution.

The deep-sea ecosystems of the Azores archipelago in the North Atlantic represent a region of exceptional biological diversity and ecological value. This area supports unique and fragile island, open sea, and deep-sea environments, including vulnerable marine ecosystems (VMEs) such as hydrothermal vents, cold-water coral gardens, and deep-sea sponge aggregations [35] [36]. The Azorean sea covers approximately 1,000,000 km², representing 55% of Portuguese waters and about 15% of European waters [35] [36]. This case study examines the integrated scientific approaches, spatial planning tools, and management strategies that have positioned the Azores as a global leader in evidence-based deep-sea conservation, culminating in the establishment of the largest marine protected area network in the North Atlantic.

Quantitative Data and Research Findings

Deep-Sea Stock Assessment

Recent quantitative assessments of data-limited deep-water fish stocks in the Azores reveal significant conservation challenges, with multiple species showing signs of overexploitation [37].

Table 1: Status of Deep-Water Fish Stocks in the Azores (2025 Assessment)

Species	Stock Status	Assessment Method	Management Implication
Blackspot seabream (Pagellus bogaraveo)	Overfished	Stochastic Surplus Production Model in Continuous Time (SPiCT)	Requires reduction in current catches
Blackbelly rosefish (Helicolenus dactylopterus)	Overfished	Stochastic Surplus Production Model in Continuous Time (SPiCT)	Requires reduction in current catches
Red porgy (Pagrus pagrus)	Overfished	Stochastic Surplus Production Model in Continuous Time (SPiCT)	Requires reduction in current catches
Forkbeard (Phycis phycis)	Recovering	Stochastic Surplus Production Model in Continuous Time (SPiCT)	Requires continued monitoring and harvest control

This research demonstrates the vulnerability of deep-water stocks to overfishing and advocates for the application of the SPiCT model as a valuable tool for stock assessment in data-limited deep-water fisheries, effectively enhancing their category to data-moderated stocks [37].

Ecosystem Discoveries and Habitat Mapping

Research projects including ATLAS and MapGES have substantially advanced understanding of Azores deep-sea ecosystems through extensive exploration and mapping initiatives [38] [39].

Table 2: Key Discoveries in Azores Deep-Sea Ecosystems (2016-2025)

Discovery Type	Specific Findings	Significance	Projects
Biodiversity	Azores identified as hotspot of cold-water coral diversity; highest Octocorallia species richness in Europe and North Atlantic archipelagos	Provides baseline for monitoring and conservation	ATLAS, MapGES [38] [39]
New Species & Communities	Identification of several new species to science; 7 distinct coral garden communities dominated by different octocoral species	Enhances understanding of deep-sea biodiversity patterns	ATLAS, MapGES [38] [39]
Hydrothermal Vents	Discovery of new hydrothermal vent field on Gigante Seamount slopes with unique fluid chemistry (hydrogen and iron dominance, low temperature)	Reveals novel chemosynthetic ecosystems	ATLAS [38]
VME Identification	8 areas identified as VMEs composed of coral gardens, sponge aggregations, and hydrothermal vents	Informs spatial management and protection measures	ATLAS, MapGES [38] [39]
Climate Change Impact	>50% of cold-water coral habitats at risk; suitable habitats for commercial deep-sea fish may shift up to 100km northwards	Predicts future distribution changes for adaptive management	ATLAS, MapGES [38] [39]

The research employed advanced habitat suitability models for 13 VME indicator taxa in the Azores Exclusive Economic Zone (EEZ), revealing strong associations between predicted cold-water coral distributions and areas of local relief (island shelves, slopes, ridges, and seamounts) [38]. These models successfully discriminated between suitable and unsuitable zones even among areas of similar depths, demonstrating that outputs were not exclusively driven by depth-correlated environmental predictors [38].

Experimental Protocols and Methodologies

Deep-Sea Imaging and Exploration Protocols

Protocol Title: Low-Cost Imaging System Deployment for Deep-Sea Habitat Characterization

Background: Traditional deep-sea exploration methodologies involving Remotely Operated Vehicles (ROVs) are resource-intensive, limiting spatial coverage and observation frequency. The Azores Deep-sea Research team developed affordable imaging solutions to democratize deep-sea exploration [39].

Materials:

• Azor drift-cam system (custom-made)
• Stereo-baited remote video system
• Research vessels (of opportunity)
• GPS navigation system
• Data storage and backup systems

Procedure:

• Pre-deployment Calibration: Calibrate camera systems on land, ensuring proper alignment, focus, and lighting parameters
• Site Selection: Identify target areas based on bathymetric data and previous research findings
• System Deployment: Deploy camera systems from vessels using standardized protocols
• Data Collection: Conduct transects across target habitats, maintaining consistent altitude and speed
• Data Logging: Record associated metadata including location, depth, time, and environmental parameters
• Image Annotation: Systematically annotate species observations and habitat characteristics using standardized classification schemes
• Quality Control: Verify data integrity and completeness before analysis

Applications: During the MapGES project, the Azor drift-cam was successfully deployed over 160 times, covering nearly 100 linear km of seabed and generating more than 120 hours of seafloor images [39]. This methodology enabled greater data collection and spatial coverage at reduced cost compared to traditional ROV operations.

Habitat Suitability Modeling Protocol

Protocol Title: Predictive Distribution Modeling for Deep-Sea Species

Background: Understanding current and future species distributions is essential for effective spatial management, particularly under changing climate conditions [38].

Materials:

• Species occurrence data (from research cruises, historical records)
• Environmental variables (depth, temperature, salinity, currents, substrate)
• Modeling software (R, Python, or specialized habitat modeling tools)
• GIS platform for spatial analysis

Procedure:

• Data Compilation: Gather and standardize species occurrence records from multiple sources
• Environmental Layer Preparation: Process static (terrain) and dynamic (oceanographic) environmental variables
• Model Selection: Apply Generalized Additive Models (GAMs) or other appropriate statistical techniques
• Model Training: Use current conditions data to train models
• Model Validation: Assess model performance using cross-validation techniques
• Projection: Apply models to future climate scenarios (e.g., high-emission scenarios)
• Uncertainty Assessment: Quantify and map uncertainties in predictions

Applications: This protocol has been applied to model distributions of cold-water corals, deep-sea fishes, and elasmobranchs in the Azores EEZ, forecasting climate-induced changes in suitable habitat [38] [39]. Results indicated that over 50% of cold-water coral habitats could be at risk, with commercially important deep-sea fish species potentially shifting distribution northwards by up to 100 km [38].

VME Identification and Assessment Protocol

Protocol Title: Multi-Criteria Assessment for Vulnerable Marine Ecosystem Identification

Background: The identification of VMEs is critical for implementing area-based management tools but has historically been subjective. Researchers developed a novel multi-criteria assessment method for more objective VME identification [38].

Materials:

• Bathymetric data (multibeam sonar)
• Species distribution data
• Environmental data
• Fishing pressure information
• GIS analysis tools

Procedure:

• Criteria Selection: Identify relevant criteria for VME identification (e.g., structural complexity, species diversity, presence of indicator species)
• Data Standardization: Normalize diverse datasets to common spatial scales and measurement units
• Weighting: Apply expert-derived weights to different criteria based on ecological importance
• Spatial Analysis: Combine weighted criteria using GIS overlay techniques
• Threshold Application: Establish thresholds for VME classification based on statistical analysis
• Validation: Ground-truth identified areas using imaging systems or physical sampling
• Mapping: Produce final VME distribution maps for management applications

Applications: This protocol has been implemented to identify eight VME areas in the Azores, including diverse coral gardens, deep-sea sponge aggregations, and hydrothermal vents across multiple seamounts [38] [39]. The method provides a more systematic and standardized approach for assessing and identifying VME regions in the North-East Atlantic Ocean.

Visualization of Methodological Frameworks

Research to management workflow in Azores

Impact chain framework for deep-sea management

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Tools and Technologies for Deep-Sea Studies

Tool/Technology	Function	Application in Azores Research
Azor Drift-cam System	Custom-made, low-cost imaging system for rapid appraisal of deep-sea habitats	Enables extensive seafloor imaging without large oceanographic vessels; deployed 160+ times in MapGES [39]
Stereo-baited Remote Video	Custom video system for observing and quantifying commercially important fish populations	Provides cost-effective monitoring of fish populations and behavior [38]
Environmental DNA (eDNA) Assays	Molecular tools for detecting species presence from water samples	Six species-specific eDNA assays developed for detecting target species in pelagic and deep-water environments [38]
Habitat Suitability Models	Statistical models (GAMs) predicting species distribution based on environmental variables	Developed for 13 VME indicator taxa and commercial fish species; forecasts climate change impacts [38] [39]
Multi-Criteria Assessment Framework	Systematic method for identifying Vulnerable Marine Ecosystems	Objectively identifies VMEs using multiple ecological criteria; applied to identify 8 VME areas in Azores [38]
Stochastic Surplus Production Model in Continuous Time (SPiCT)	Fisheries stock assessment model for data-limited situations	Provided first quantitative assessment of four deep-water fish stocks in Azores [37]

Application to Area-Based Management

The scientific research conducted in the Azores has directly informed the development of one of the most comprehensive marine conservation networks globally. In 2024, the Autonomous Region of the Azores passed groundbreaking legislation designating the largest marine protected area network in the North Atlantic, safeguarding 30% (287,000 km²) of the sea surrounding the archipelago [35] [36]. Half of this network is fully protected (no extraction of natural resources), while the other half is highly protected (only light extractive activities with low total impact) [35].

This management outcome directly resulted from the research findings, with specific scientific contributions including:

• VME Identification and Mapping: Research identified eight specific areas as VMEs composed of diverse coral gardens, deep-sea sponge aggregations, and hydrothermal vents, providing the scientific basis for protection [38] [39].

• Hydrothermal Vent Protection: The 'Luso' hydrothermal vent field discovered during ATLAS research expeditions was declared a Marine Protected Area in September 2019 (Portaria no. 68/2019) based directly on project findings [38].

• Climate-Informed Planning: Predictive distribution models under future climate scenarios informed the design of a resilient MPA network that accounts for anticipated species distribution shifts [38].

• Stakeholder Engagement: The government led more than 40 meetings with representatives from fishing, maritime transport, tourism, and environmental sectors to collaboratively design the MPA network based on the best available science [35] [36].

The systematic evaluation of spatially explicit ecosystem models has been crucial for informing area-based management in the Azores deep-sea environment [24]. This approach has supported the implementation of the European Union's Marine Strategy Framework Directive and contributed to achieving Good Environmental Status for deep-sea ecosystems [39].

The Azores case study demonstrates a successful model for translating deep-sea research into effective area-based management. Through systematic data collection, technological innovation, and collaborative science-policy integration, the region has established a comprehensive network of marine protected areas that balances ecological protection with sustainable use. The methodologies and approaches developed in the Azores provide a transferable framework for evidence-based deep-sea management that can be adapted to other regions facing similar challenges of limited data and multiple competing ocean uses. Continued monitoring, research, and adaptive management will be essential to ensure the long-term effectiveness of these conservation measures in the face of ongoing climate change and human pressures.

The Nigerian Coastal Waters (NCW) represent a critical ecosystem under significant pressure from intensive fishing activities. The shift from a finfish fishery to a targeted shrimp fishery, particularly from the mid-1980s onwards, has led to notable ecosystem changes, including the depletion of key species like the Pink Shrimp (Penaeus notialis) and the expansion of fishing effort into shallower waters for Brown Shrimp (P. monodon) [40]. This case study utilizes the Ecospace module, the spatial dynamics component of the Ecopath with Ecosim (EwE) framework, to simulate and analyze the redistribution of fishing effort. The objective is to provide a scientific basis for area-based management strategies that can mitigate ecological impacts while considering socio-economic needs [24] [41]. This work is situated within a broader thesis on using the Ecospace module for spatial management scenarios research, offering a reproducible protocol for researchers and scientists.

Background and Initial Conditions

The NCW model is built upon a history of increased anthropogenic impact. By the year 2000, the number of shrimp trawlers had increased to 173, from just 40 in 1985 [40]. This intensified fishing pressure led to the Pink Shrimp biomass being depleted to less than half of its virgin biomass [40]. Two mass-balanced Ecopath models, developed for the years 1985 (medium exploitation) and 2000 (high exploitation), provide the initial steady-state conditions and baseline parameters for initiating spatial simulations in Ecospace [40].

Table 1: Key Ecosystem Characteristics for 1985 and 2000 NCW Models

Ecosystem Attribute	1985 Model	2000 Model	Implied Change
Total System Throughput (t/km²/year)	Data from source required	Data from source required	Decrease suggests reduced ecosystem productivity [40]
Sum of All Consumption (t/km²/year)	Data from source required	Data from source required	Decrease suggests lower overall predation [40]
Total Biomass (t/km²)	Data from source required	Data from source required	Decrease indicates ecosystem degradation [40]
Primary Production/Respiration	Data from source required	Data from source required	Decrease suggests reduced ecosystem maturity [40]
Fishing Pressure	Medium exploitation	High exploitation	Increase led to targeted species depletion [40]

Application Notes: Ecospace Model Configuration for NCW

Configuring an Ecospace model requires careful consideration of spatial scale, driving functions, and biological parameters to ensure realistic outputs [41].

Spatial Domain and Base Layers

• Spatial Domain and Grid Resolution: The model domain should encompass the entire NCW, including the continental shelf from the western Lagos Grounds to the eastern Calabar region [40]. A pragmatic approach to grid resolution is recommended:
  • Coarse resolution (e.g., ~50 sq km cells): For rapid model building and initial testing.
  • Intermediate resolution (e.g., ~25 sq km cells): For final scenario analysis.
  • Fine resolution (e.g., ~12.5 sq km cells): To test if increased detail significantly improves predictive power [41].
• Geospatial Projection: For the NCW, which is situated at low latitudes, the cell width tapering effect of the default World Geodetic System (WGS 84) is negligible. The "Assume square cells" option should be selected, with cell length explicitly defined in kilometers [41].
• Base Habitat Map: Develop a habitat layer influencing species distributions. Key habitats for NCW include:
  • Niger Delta mangroves: Critical nursery grounds for shrimp and finfish.
  • Inshore waters (<30m depth): Primary grounds for Brown Shrimp [40].
  • Mid-shelf waters (30-100m depth): Historical grounds for Pink Shrimp [40].

Driving Functions and Forcing Factors

• Fisheries Data: Define spatial fishing activities for different fleets (e.g., industrial shrimp trawlers, artisanal fishers). The model should incorporate historical effort distribution, which shifted from offshore Pink Shrimp grounds to inshore Brown Shrimp grounds by 2000 [40].
• Spatial Drivers of Productivity: Model primary production based on key environmental data, which can be derived from satellite data for the region.
• Advection and Dispersal: If data is available, incorporate major current patterns (e.g., the Guinea Current) to simulate the passive dispersal of larvae and plankton. Species-specific dispersal rates based on life history should also be applied [41].

Experimental Protocol: Simulating Effort Redistribution

This protocol details the steps to simulate and analyze the redistribution of fishing effort using the Ecospace model for NCW.

Model Initialization and Baseline Run

• Load the Balanced Ecosim Model: Use the calibrated 2000 NCW model as the temporal dynamic baseline [40].
• Construct Spatial Layers: Develop the habitat, depth, and initial biomass distribution maps in Ecospace, as outlined in Section 3.1.
• Parameterize Fishing Fleets: Define the spatial operating areas and relative cost/profitability for each fleet (e.g., industrial shrimp trawlers restricted to deeper waters in the baseline scenario).
• Run Baseline Simulation: Run the Ecospace model for a 15-year period (2000-2015) without management intervention. This serves as the "business-as-usual" scenario against which management interventions are compared.

Spatial Management Scenarios

Develop and run the following alternative scenarios for the same time period, comparing outcomes to the baseline.

• Scenario 1: Marine Protected Area (MPA): Close a significant portion (e.g., 30%) of the inshore Niger Delta region to all fishing, protecting critical nursery habitats.
• Scenario 2: Effort Displacement: Simulate a policy that reduces effort in inshore waters by 50%, redistributing it to historical offshore Pink Shrimp grounds, assuming some stock recovery is possible.
• Scenario 3: Spatial Effort Cap: Implement a spatial cap on fishing effort density, preventing extreme concentration in any single cell.

Table 2: Key Performance Indicators for Scenario Comparison

Performance Indicator	Baseline (BAU)	Scenario 1 (MPA)	Scenario 2 (Effort Displacement)	Scenario 3 (Spatial Cap)
Pink Shrimp Biomass	Baseline	Projected Change	Projected Change	Projected Change
Brown Shrimp Biomass	Baseline	Projected Change	Projected Change	Projected Change
Bycatch Species Biomass	Baseline	Projected Change	Projected Change	Projected Change
Total Fishery Catch	Baseline	Projected Change	Projected Change	Projected Change
Spatial Overlap Index	Baseline	Projected Change	Projected Change	Projected Change

Output Analysis and Validation

• Spatial Maps: Generate maps of biomass, catch, and effort distribution for each scenario.
• Time Series Analysis: Plot the biomass of key functional groups (Pink Shrimp, Brown Shrimp, key predators) over the simulation period.
• Ecosystem Indicators: Calculate and compare system-level indicators like total biomass and fishing pressure across scenarios [40].
• Policy Assessment: Evaluate trade-offs between conservation goals (biomass increase) and fishery objectives (catch stability).

Visualization of the Research Workflow

The following diagram illustrates the logical flow and key components of the Ecospace modeling process for this case study.

This section details the key software, data, and conceptual tools required to implement the Ecospace model for the NCW.

Table 3: Essential Research Reagents and Resources

Tool/Resource	Type	Function in the Research Process
Ecopath with Ecosim (EwE)	Software Platform	The core modeling environment used to construct the mass-balanced ecosystem model (Ecopath), simulate temporal dynamics (Ecosim), and run spatial simulations (Ecospace) [40] [41].
Spatial Data (GIS)	Data	Habitat maps, bathymetry, and primary production data used to create the base layers that drive species distributions and ecological processes in Ecospace [41].
Fisheries Landings & Effort Data	Data	Time series of catch and fishing effort by fleet and species/group, essential for calibrating the Ecosim temporal dynamics and defining fleet behavior in Ecospace [40].
Functional Groups	Modeling Concept	Aggregations of species with similar ecological roles (e.g., "Pink Shrimp", "Demersal Piscivores") that form the core components of the ecosystem model [40].
Forcing Functions	Modeling Concept	Time-series data (e.g., primary production, fishing effort) used to drive changes in the ecosystem model during simulations [41].
Gravity Model of Effort	Algorithm	A core algorithm within Ecospace that predicts the spatial distribution of fishing effort based on the profitability and accessibility of different areas [41].

Running Simulations and Interpreting Spatial-Temporal Outputs

The Ecopath with Ecosim (EwE) framework is a widely adopted tool for modeling marine ecosystems, with its Ecospace module enabling spatially explicit simulations to inform ecosystem-based management [1]. Ecospace facilitates the exploration of spatial management scenarios, such as the establishment of Marine Protected Areas (MPAs) and the assessment of cumulative human impacts, by dynamically allocating biomass across a raster grid map over time [3]. This protocol details the application of Ecospace for developing, executing, and analyzing spatial-temporal simulations, providing a critical methodology for evaluating marine spatial planning strategies.

Experimental Protocols and Quantitative Data Synthesis

Core Ecospace Modeling Workflow

The foundational protocol for implementing an Ecospace model involves a sequential process to translate ecosystem data into spatial-temporal projections [12].

Table 1: Key Protocol Steps for Ecospace Model Implementation

Step	Description	Key Inputs/Parameters
1. Base Map Definition	Define a grid of cells representing the spatial extent, with each cell designated as land/water and assigned habitat attributes (e.g., depth, sediment type) [12].	Bathymetric data, sediment maps, habitat classifications.
2. Habitat Affinity Assignment	Specify species/functional group preferences for the defined habitat types based on their ecology (e.g., depth distribution) [12].	Species distribution data, expert knowledge.
3. Foraging Capacity Calculation	Compute cell-specific habitat suitability using the Habitat Foraging Capacity Model, which integrates functional responses to multiple environmental factors [12] [25].	Environmental driver layers (e.g., temperature, primary production).
4. Dispersal Parameterization	Set dispersal rates for each functional group, which can vary based on habitat suitability and trophic conditions [12].	Movement data, larval dispersal models.
5. Fleet Operation Definition	Define spatial operation for fishing fleets, including effort distribution and cost, and designate protected areas where fishing is restricted [12].	Vessel monitoring data, fishery logbooks.
6. Scenario Execution	Run the spatial-temporal dynamic model, which solves the system of biomass dynamics equations numerically on monthly time steps across the entire grid [12].	Management scenarios (e.g., MPA layouts, fishing effort changes).

Quantifying Management Scenario Outcomes

Ecospace simulations evaluate scenarios by tracking changes in key ecological and fisheries indicators. The following table synthesizes quantitative findings from a study in the Aegean Sea, which assessed various spatio-temporal management scenarios using the ECOSPACE framework [33].

Table 2: Synthesis of Management Scenario Impacts in the Aegean Sea [33]

Scenario Description	Impact on Total Biomass	Impact on Key Commercial Species	Impact on Total Catches	Primary Management Implication
Reference (Business-as-Usual)	Projected 6% decline by 2050 [33].	Substantial decreases by 2050 [33].	Not specified in results.	Highlights urgent need for management intervention.
Scenario 3: Extended Trawling/Purse Seining Restriction	Highest biomass gains among scenarios [33].	Highest biomass gains for key commercial species [33].	Moderate trade-offs (some reduction) [33].	Suitable for fisheries-focused management.
Scenarios 1 & 2: Fisheries Prohibition in Natura 2000 Areas	Localized biomass increases within restrictions [33].	Benefits for demersal and benthic species [33].	Reduced total catches in some cases [33].	Supports biodiversity-driven conservation strategies.
Integration of Offshore Wind Farms (OWFs)	Modest conservation benefits once operational [33].	Provides refuge for some species [33].	Not specified in results.	Potential for multi-use marine spatial planning.

A broader Mediterranean Sea application demonstrated Ecospace's utility for basin-wide assessment, revealing that coastal and shelf areas hold the highest biomass and biodiversity, and identifying the Adriatic Sea and Sicilian Channel as critical areas requiring management attention [26].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Components for Ecospace Modeling

Item	Function/Purpose
Ecopath with Ecosim (EwE) Software	Free, core modeling software suite used to construct the ecosystem model and run temporal (Ecosim) and spatial (Ecospace) simulations [1] [3].
Spatial Data Layers	Base maps defining habitat types, depth, and environmental conditions (e.g., temperature) for each cell in the spatial grid. These drive the distribution of functional groups [12] [26].
Functional Groups (FGs)	Model components representing individual species, groups of species, or specific life stages lumped together by shared functional and ecological traits [25].
Human Pressure Layers	Spatial data representing stressors from human activities (e.g., fishing effort, underwater noise, seabed disturbance) used to simulate impacts on the ecosystem [25].
MSP Challenge Platform with EwE Link (MEL)	An adaptive module enabling data exchange between maritime spatial planning simulations and tailored EwE ecosystem models, facilitating stakeholder-inclusive scenario testing [25].

Visualizing Workflows and Relationships

Ecospace Simulation and Analysis Workflow

Workflow for Ecospace Simulation and Analysis

Ecospace Habitat and Biomass Dynamics

Ecospace Habitat and Biomass Dynamics

Refining Ecospace Models: Best Practices for Avoiding Pitfalls and Enhancing Performance

Within marine spatial management, the Ecospace module for spatial management scenarios provides a dynamic framework to simulate the impact of various policies and environmental changes on marine ecosystems [24]. A common pitfall in this modeling is introducing excessive complexity too early, which can obfuscate core relationships and hinder interpretability. This document outlines a structured, gradual protocol for adding complexity to Ecospace models, ensuring that each step is robust, justified, and informative for decision-making. This approach aligns with the broader objectives of the EcoScope project, which aims to facilitate ecosystem-based management (EBM) by providing integrated tools and methodologies [42].

Conceptual Framework: The Stepped Complexity Approach

The stepped complexity approach is grounded in the principle that models should start as simple representations of the system and evolve only when necessary to address specific management questions. This iterative process enhances model transparency, facilitates calibration and validation at each stage, and provides clearer insights into the drivers of system dynamics.

The following workflow visualizes the core iterative cycle for developing a robust Ecospace model:

Experimental Protocols for Incremental Model Development

Protocol 1: Establishing the Spatial Base Model

Objective: To construct a simplified, yet spatially explicit, Ecospace model that serves as a foundational baseline for all subsequent complexity.

• 3.1.1 Define the Core Habitat Map

  • Methodology: Utilize existing spatial data layers (e.g., bathymetry, seabed sediment type) to create a base map classifying the model domain into broad, ecologically relevant habitat types (e.g., soft-bottom, rocky reef, seagrass). Initially, limit the number of habitat types to 3-5.
  • Data Sources: EMODnet, GEBCO, regional marine spatial data infrastructures.

• 3.1.2 Initialize Trophic Structure

  • Methodology: Populate the model with a simplified food web. Start with 5-10 functional groups that represent key trophic levels and commercially important species. Use diet matrix data from a pre-balanced non-spatial model (e.g., Ecopath).
  • Data Sources: Existing Ecopath models for the region, local stock assessment reports, published diet studies.

• 3.1.3 Configure Basic Forcing Functions

  • Methodology: Apply a single, spatially uniform primary production profile. This serves as the primary bottom-up driver for the initial model.
  • Data Sources: Remote sensing data (e.g., MODIS, VIIRS) for chlorophyll-a, averaged over multiple years.

Key Outputs: A running spatial model demonstrating basic biomass distribution patterns across habitats and functional groups.

Protocol 2: Incorporating Environmental Drivers

Objective: To enhance the base model by integrating key environmental variables that influence species distribution and productivity.

• 3.2.1 Introduce Spatially-Explicit Primary Production

  • Methodology: Replace the uniform production with a raster layer of temporally averaged sea surface temperature (SST) and chlorophyll-a concentration. Use these to drive spatial variation in primary production.
  • Data Sources: Satellite-derived SST and chlorophyll-a climatologies.

• 3.2.2 Add a Temperature-Dependent Forcing Function

  • Methodology: For key functional groups (e.g., a commercially important fish), implement a physiological response function based on temperature. This affects consumption and metabolism rates.
  • Data Sources: Published laboratory studies on species-specific thermal tolerance, bioenergetics models.

Validation Step: Compare the model's predicted biomass hotspots with known fishing grounds or independent survey data to assess the improvement over the base model.

Protocol 3: Integrating Human Activities and Management Scenarios

Objective: To simulate the effects of fisheries and area-based management tools, enabling policy evaluation.

• 3.3.1 Map Initial Fishing Effort

  • Methodology: Introduce a single, representative fishing fleet. Distribute its effort spatially based on historical Vessel Monitoring System (VMS) data or logbook records, creating a fishing effort layer.
  • Data Sources: VMS data, fisheries-dependent data from the Regional Fisheries Management Organizations (RFMOs) or national agencies [42].

• 3.3.2 Simulate a Marine Protected Area (MPA)

  • Methodology: Designate a specific zone within the model domain as a no-take MPA. Set the fishing mortality for this cell to zero and run the simulation for a defined period (e.g., 10 years).
  • Data Sources: Coordinates of existing or proposed MPAs.

• 3.3.3 Evaluate Ecosystem Effects

  • Methodology: Compare model outputs (e.g., biomass trends inside/outside the MPA, catch per unit effort, indices of community structure) between the scenario with the MPA and the baseline model from Protocol 2.

Validation Step: Conduct a sensitivity analysis on the MPA's size and location to test the robustness of the model's predictions.

Quantitative Data and Model Parameters

The following tables summarize the core quantitative aspects and data requirements for each stage of model development.

Table 1: Summary of Stepped Model Complexity Framework

Complexity Stage	Key Added Components	Primary Data Needs	Calibration & Validation Metrics
1. Spatial Base Model	Base habitat map, 5-10 functional groups, uniform production	Bathymetry, sediment maps, diet matrix, basic production rate	Spatial biomass distribution, model stability (no crashes)
2. Environmental Drivers	Spatially explicit primary production, temperature forcing	Satellite chlorophyll/SST climatologies, species thermal response curves	Comparison with known species hotspots, improved fit to survey data
3. Human Activities	Fishing fleet effort map, Marine Protected Area (MPA)	VMS/logbook data, MPA coordinates and regulations	Biomass change inside vs. outside MPA, spillover effects, fishery catch

Table 2: Example Parameters for a Key Functional Group (Demersal Fish)

Parameter	Base Model Value	Value with Environmental Forcing	Source / Justification
Biomass (t/km²)	2.5	Emergent from model	Regional stock assessment report [42]
Consumption Rate	3.2	3.2 * TemperatureQ₁₀	Ecopath base model; Q₁₀ from bioenergetics study
Habitat Preference	Uniform across soft bottom	Prefers 100-200m depth range	Expert judgment from trawl survey data
Foraging Arena	Base setting	Expanded with added prey fields	Model tuning to match observed distribution

This table details the key "research reagents"—data, software, and tools—essential for implementing the protocols described.

Item Name	Function / Application in Ecospace Modeling	Key Considerations
Ecopath with Ecospace (EwE)	The primary software platform for constructing and simulating the trophic-spatial model.	Open-source; requires a pre-balanced Ecopath model as a starting point.
Spatial Habitat Layers	Defines the physical template of the model, influencing species distributions and rates.	Resolution and accuracy are critical; sources like EMODnet provide standardized data.
Fisheries Effort Data (VMS)	Informs the spatial distribution of fishing mortality, a key anthropogenic driver.	Data accessibility can be restricted; may require aggregation to protect confidentiality.
Remote Sensing Data	Provides spatially explicit forcing functions for primary production (Chl-a) and temperature (SST).	Requires processing to create model-ready climatologies; gaps due to cloud cover.
Ecopath Diet Matrix	Quantifies the trophic interactions between functional groups in the model.	Often a major source of uncertainty; should be based on local stomach content data where possible.

Visualization of a Spatially Explicit Management Scenario

The following diagram illustrates the logical flow and interactions of components within an advanced Ecospace scenario that incorporates environmental drivers and human management interventions, as built through the protocols.

In spatial management scenarios using the Ecospace module, environmental drivers rarely operate in isolation. The core challenge researchers face is managing driver correlation—the complex interdependencies between multiple environmental variables that collectively influence ecosystem dynamics. Effectively selecting and weighting these correlated drivers is paramount for building robust spatial models that accurately inform management decisions. In the Ecospace framework, which functions as the spatial-temporal module of the Ecopath with Ecosim (EwE) food web modeling approach, failing to account for these relationships can lead to flawed predictions and ineffective management interventions [43].

The state-space approach (SSEA) provides a valuable conceptual framework for quantifying and comparing the magnitude of responses to combinations of environmental drivers across experimental units. This approach recognizes that biological systems respond to multivariate alterations in natural habitats, where organisms are simultaneously exposed to increased temperature, habitat loss, food limitation, pollutants, ocean acidification, and deoxygenation [44]. By representing these responses in a multidimensional space, researchers can better appreciate variation in how driver combinations affect ecological systems, moving beyond simplistic classifications of interactive effects.

Theoretical Framework for Driver Selection

Conceptualizing Driver Interactions

Environmental drivers in Ecospace research exhibit complex interaction patterns that must be conceptualized before selection and weighting. These interactions generally fall into three categories: additive effects, where the combined impact of multiple drivers equals the sum of their individual effects; synergistic effects, where the combined impact exceeds the sum of individual effects; and antagonistic effects, where the combined impact is less than the sum of individual effects [44]. Understanding these potential interactions guides the initial driver selection process and helps researchers avoid oversimplifying complex ecological relationships.

The state-space approach maps these different categorical types of responses into a continuum, emphasizing the magnitude of responses among the units being compared irrespective of the qualitative type of response [44]. This conceptual framework is particularly valuable when driver intensities, species responses, genotypes, ontogeny, life-phases, or spatial scales vary across the system being studied. For instance, research on marine bryozoans has demonstrated that genotypic variation can be so substantial that the action of two drivers may vary from antagonistic to synergistic across different populations [44].

Hierarchy of Driver Influence

When selecting drivers for Ecospace models, establishing a hierarchy of influence based on their potential impact on spatial ecosystem dynamics is essential. Primary drivers typically include landscape fragmentation, land use patterns, and climatic factors, which directly shape ecosystem structure and function. Secondary drivers might include soil characteristics, topography, and hydrological patterns, while tertiary drivers often encompass more localized factors such as microclimate variations and nutrient availability.

In the Minjiang River Basin, for example, analyses revealed that the degree of landscape fragmentation and drastic land use change were the main factors causing spatial variation in eco-environmental quality, effectively overshadowing other potential drivers [45]. Similarly, research on vegetation ecological quality in the middle reaches of the Yangtze River identified slope, DEM (Digital Elevation Model), and vegetation type as significant drivers, while precipitation, temperature, and nighttime light were considered secondary factors [46]. This hierarchical understanding helps researchers prioritize driver selection when faced with numerous potential variables.

Statistical Approaches for Managing Correlation

Machine Learning Applications

Machine learning methods offer powerful approaches for managing driver correlation in Ecospace research, particularly when dealing with high-dimensional datasets. The Spatial Prediction using Random forest to Uncover Connectivity among Environments (SPRUCE) approach employs random forest algorithms to identify which spatial variables best predict migration rates or other ecological responses [47]. This method is particularly valuable because it can handle many inputs, including redundant or irrelevant variables, and is less likely to overfit data since it builds each decision tree independently [47].

Random forest applications in eastern Africa human population studies demonstrated the ability to explore more than 20 spatial variables relating to landscape, climate, and presence of tsetse flies, with the full model explaining approximately 40% of the variance in migration rate [47]. The algorithm successfully identified precipitation, minimum temperature of the coldest month, and elevation as the variables with the highest impact, effectively managing the correlations between these and other potential drivers.

Bayesian Networks for Nonlinear Relationships

Bayesian networks provide a powerful tool for representing and analyzing non-linear relationships between correlated drivers in ecological systems. Unlike traditional statistical methods limited to investigating linear relationships between individual variables and the dependent variable, Bayesian networks excel at capturing the intricate non-linear relationships linking anthropogenic activities, natural variables, and ecological responses [46]. This capability is particularly valuable in Ecospace research, where the influence of environmental variables on ecological outcomes frequently exhibits non-linear characteristics.

In vegetation ecological quality assessment, Bayesian networks have enabled quantitative assessments of the relative contributions of both natural factors and variations in human activities to shifts in vegetation ecological quality, facilitating the identification of potential driving variables [46]. The integration of spatial data into Bayesian network models can highlight areas susceptible to improvement or degradation, guiding strategic interventions and enhancing our understanding of ecological quality under complex driver interactions.

Multivariate Statistical Techniques

Multivariate statistical tools provide a complementary approach to managing driver correlation in spatial ecological research. Techniques including K-means cluster analysis, redundancy analysis (RDA), and multiscale ordination can be applied to extensive monitoring datasets to identify temporal and spatial structure and trends in ecological assemblages [48]. These approaches are particularly valuable for distinguishing between drivers operating at different spatial and temporal scales.

In the Peconic Estuary, the application of these multivariate techniques revealed distinctly different drivers for temporal and spatial patterns, with temporal factors operating on a regional scale and spatial factors tied to local sampling conditions [48]. This separation helped researchers understand that abrupt community shifts on a decadal scale were driven by changes in regional climate factors, while spatially distinct habitats and assemblages were differentiated by local conditions in bottom salinity, water depth, depth gradient, DO percent saturation, and water transparency.

Table 1: Comparison of Statistical Methods for Managing Driver Correlation

Method	Key Features	Best Use Cases	Limitations
Random Forest	Handles many inputs, robust to irrelevant variables, non-parametric	High-dimensional data, variable importance ranking	Limited interpretability of complex interactions
Bayesian Networks	Captures non-linear relationships, incorporates prior knowledge	Systems with known causal pathways, uncertainty quantification	Computationally intensive with many variables
Redundancy Analysis	Direct gradient analysis, constrained ordination	Testing hypotheses about driver effects, community data	Assumes linear relationships between drivers and response
MaxEnt Models	Effective with presence-only data, handles complex responses	Species distribution modeling, limited data availability	Sensitive to spatial autocorrelation in drivers

Experimental Protocols for Driver Evaluation

Driver Selection Workflow

A systematic approach to driver selection ensures that Ecospace models incorporate the most relevant variables while managing correlations. The following protocol provides a step-by-step methodology:

• Compile Comprehensive Driver Inventory: Begin by assembling a complete list of potential environmental drivers relevant to your research question and study system. This should include abiotic factors (temperature, precipitation, topography, soil characteristics), biotic factors (vegetation type, species distributions), and anthropogenic factors (land use, population density, infrastructure) [46] [49]. Draw from existing literature, preliminary field observations, and theoretical frameworks to ensure comprehensiveness.

• Conduct Correlation Screening: Calculate correlation coefficients between all potential driver variables. Establish a threshold for exclusion (e.g., |r| > 0.7) to identify highly correlated variable pairs. For each correlated pair, retain the variable with stronger theoretical justification, greater measurement precision, or lower collection cost [47].

• Perform Preliminary Importance Assessment: Use univariate analyses or simple multiple regression to assess the individual relationship between each driver and the response variable. This helps identify drivers with minimal explanatory power that can be considered for exclusion in early stages [46].

• Apply Machine Learning Feature Selection: Implement random forest or similar algorithms to rank driver importance. Use cross-validation to assess model performance with different driver subsets, selecting the most parsimonious set that maintains predictive power [47].

• Validate Driver Selection: Conduct spatial cross-validation by dividing the study area into regions and testing whether selected drivers maintain consistent relationships across spatial subsets. This helps identify drivers with spatially variable relationships that may require special treatment in Ecospace models [48].

Weighting Protocol Based on Explained Variance

Once drivers are selected, establishing appropriate weights is crucial for representing their relative influence accurately. The following protocol employs a variance-based weighting approach:

• Partition Variance Components: Use variance partitioning analysis (e.g., through RDA or partial regression) to quantify the unique, shared, and total variance explained by each driver. This is particularly important for correlated drivers where shared variance may be substantial [48].

• Calculate Initial Weights: Base initial weights on the unique variance explained by each driver, rescaled to sum to 1.0. This approach discounts the importance of drivers that share substantial variance with others in the model.

• Incorporate Theoretical Adjustments: Adjust initial weights based on theoretical considerations, giving additional weight to drivers known to have direct causal relationships with response variables, even if their statistical explanatory power is moderate due to measurement limitations.

• Validate Weight Stability: Use bootstrap resampling to assess the stability of driver weights. Calculate confidence intervals for each weight and consider constraining highly unstable weights to their mean or median values to improve model robustness [46].

• Sensitivity Analysis: Perform comprehensive sensitivity analysis by systematically varying driver weights and observing the effects on model predictions. Identify threshold values where small changes in weights produce substantial changes in outcomes [44].

Visualization Toolkit

Driver Correlation Network Diagram

The following diagram illustrates the relationships and correlations between environmental drivers in a typical Ecospace analysis, highlighting the complex interdependencies that must be managed during variable selection and weighting.

Diagram 1: Environmental Driver Correlation Network

Driver Selection Methodology Workflow

This flowchart illustrates the comprehensive protocol for selecting and weighting environmental drivers while managing correlations in Ecospace research.

Diagram 2: Driver Selection and Weighting Workflow

Research Reagent Solutions

Table 2: Essential Analytical Tools for Managing Driver Correlation

Tool/Platform	Primary Function	Application in Driver Management
R with randomForest package	Machine learning implementation	Handles high-dimensional data, ranks driver importance, manages correlated predictors [47]
Netica or similar Bayesian software	Bayesian network analysis	Captures non-linear relationships, incorporates expert knowledge, handles uncertainty [46]
CANOCO or vegan package	Multivariate statistical analysis	Performs redundancy analysis, variance partitioning, constrained ordination [48]
ArcGIS or QGIS	Spatial analysis and visualization	Processes spatial data, calculates spatial autocorrelation, creates visualization [49]
Google Earth Engine	Large-scale spatial data processing	Accesses and processes environmental datasets, handles multi-temporal data [21]
MaxEnt software	Species distribution modeling	Effective with presence-only data, handles complex response shapes [45]

Application to Ecospace Spatial Management

Implementing robust driver correlation management strategies enables more effective spatial management scenarios within the Ecospace framework. The integrated approach allows researchers to translate complex multivariate environmental data into meaningful information for overseeing and managing risks to critical ecosystems and infrastructure [21]. In practical applications, this means Ecospace models can more accurately identify and prioritize hotspots based on risk profiles and thresholds, including early warning systems for ecosystem changes [21].

The state-space approach (SSEA) proves particularly valuable in spatial management contexts as it facilitates the comparison of driver responses across different management units, spatial scales, or temporal periods [44]. This enables managers to identify areas where driver interactions produce particularly vulnerable or resilient ecosystem responses, allowing for targeted management interventions. Furthermore, the explicit handling of driver correlation helps prevent overestimation of management impacts by ensuring that correlated environmental factors are not double-counted in impact assessments.

When applying these methods in actual spatial management contexts, researchers should prioritize drivers that are both influential and amenable to management intervention. For instance, while climate factors may be highly influential, local land use decisions often provide more immediate management opportunities. The hierarchical approach to driver selection ensures that management-focused Ecospace scenarios balance comprehensive driver inclusion with practical actionable outcomes.

Addressing Spatial-Temporal Scale Challenges in Model Configuration

Configuring models to accurately represent processes across spatial and temporal scales is a fundamental challenge in spatial management research. Within the Ecospace module framework, these challenges are amplified by the need to integrate heterogeneous data sources and model complex ecological processes that operate at divergent scales. This document provides structured application notes and detailed experimental protocols to guide researchers in configuring robust, scale-aware models. The guidance emphasizes systematic approaches for selecting appropriate scales, integrating multi-scale data, and validating model performance to ensure reliable outcomes for environmental management and decision-making.

Quantitative Scale Configuration Guidelines

The appropriate configuration of spatial and temporal scales is critical for model accuracy and utility. The following tables summarize common scale configurations and data requirements based on a systematic review of socio-hydrological studies, which present analogous integration challenges [50].

Table 1: Common Spatial and Temporal Scales in Integrated Environmental Models

Spatial Extent / Application Context	Common Spatial Resolution	Common Temporal Extent	Common Temporal Resolution
Watershed Management	10m - 1km grid	Decades	Daily to Yearly
Urban Analysis	Block-level / Census tract	Years to Decades	Hourly to Yearly
Regional Conservation Planning	1km - 10km grid	Decades	Monthly to Yearly
Coastal & Marine Management	100m - 5km grid	Years to Decades	Daily to Seasonal

Table 2: Data Integration Challenges Across Scales

Data Type	Typical Availability & Resolution	Key Scale-Related Challenges
Hydrological (e.g., streamflow)	Often daily or sub-daily (e.g., from USGS) [50]	Mismatch with socio-economic data collected at much coarser temporal resolutions [50]
Census / Socio-economic	Often decadal (e.g., U.S. Census) [50]	Temporal misalignment with rapidly changing environmental conditions and fine-scale processes
Remote Sensing (Land Cover)	Annual to multi-annual, varying spatial resolutions	Modifiable Areal Unit Problem (MAUP); ecological processes may not align with pixel boundaries
Ecological (Species Surveys)	Irregular timing, often point-referenced	Spatial sparsity and inconsistent temporal frequency complicate continuous model integration

Experimental Protocol for Multi-Scale Ecospace Model Configuration

Protocol: Systematic Model Assessment for Spatial Management

Objective: To establish a reproducible methodology for configuring, calibrating, and validating an Ecospace model, with explicit consideration of spatial-temporal scale dependencies.

Pre-Modeling Setup and Scoping

• Define Management Objectives: Clearly articulate the spatial management scenario (e.g., Marine Protected Area design, fisheries impact assessment). The spatial extent and temporal horizon of the model must align directly with these objectives.
• Define and Justify Scale Hierarchy:
  • Spatial Domains: Determine the full model extent and the resolution of the base grid. Justify the resolution based on the management question, data availability, and computational constraints. Document the reasoning.
  • Temporal Domains: Establish the model's start/end dates, spin-up period, and time-step. The time-step must be short enough to capture critical processes (e.g., daily for movement, seasonal for reproduction) yet computationally feasible for long-term simulations.

Data Curation and Preprocessing

• Data Inventory and Gap Analysis: Compile all available datasets (bathymetry, species distributions, habitat types, anthropogenic stressors). For each dataset, document its native spatial and temporal scale and identify mismatches with the chosen model scale.
• Data Harmonization:
  • Spatial: Re-project all spatial data to a consistent coordinate reference system. Re-sample or aggregate raster data to the model's base grid using methods appropriate to the data (e.g., bilinear interpolation for continuous data, mode for categorical data).
  • Temporal: Aggregate or interpolate time-series data to match the model's time-step. Document all interpolation and aggregation methods used.

Model Configuration and Calibration

• Parameterization: Populate the Ecospace model with the harmonized data. Define species movement parameters, habitat preferences, and interaction rates based on literature and expert judgment.
• Sensitivity Analysis Across Scales: Conduct a local sensitivity analysis to identify parameters to which the model outputs are most sensitive. Crucially, repeat this analysis while varying key scale-dependent factors (e.g., grid resolution, time-step) to assess the stability of the model's behavior. This directly addresses scale challenges [24].
• Model Calibration: If historical data are available, calibrate the model by adjusting sensitive parameters within biologically plausible ranges to match observed patterns. Use appropriate goodness-of-fit metrics.

Model Validation and Diagnostics

• Spatial Validation: Compare model outputs against independent spatial data not used in calibration (e.g., from new survey data or satellite telemetry).
• Temporal Validation: Use the latter portion of a time-series dataset for validation, ensuring the model can predict future states.
• Scale-Specific Performance Metrics: Report performance metrics (e.g., RMSE, AUC) separately for different regions or time periods to diagnose where and when the model performs poorly, which may indicate scale-mismatch issues.

Workflow Visualization

The following diagram illustrates the core iterative workflow for addressing scale challenges in model configuration.

Figure 1: Iterative workflow for addressing spatial-temporal scale challenges in model configuration.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Analytical Tools and Data Solutions for Scale Challenges

Tool / Solution Category	Specific Examples & Standards	Function in Addressing Scale Challenges
Spatio-Temporal Data Mining Algorithms	CNN-LSTMs [51], ConvLSTMs [51], Spatio-Temporal Graph Neural Networks (ST-GNNs) [51]	Captures non-linear spatial and temporal dependencies simultaneously; ST-GNNs handle irregular, non-gridded data.
Spatio-Temporal Foundation Models (STFMs)	Emerging model architectures [51]	Aims for generalization across domains, space, time, and scale, potentially reducing the need for task-specific models.
Data & Model Standardization Protocols	Systematic Model Assessment [24], Reporting Guidelines [52]	Ensures reproducibility and provides a framework for documenting how scale challenges were addressed.
Color Contrast & Accessibility Tools	WebAIM Color Contrast Checker, axe DevTools [53] [54]	Ensures that all data visualizations, diagrams, and model interfaces are accessible to a diverse research audience.

Advanced Configuration: Generalization Across Scale Dimensions

For models intended for broad application, achieving generalization across four key dimensions is critical [51]. The configuration protocol should be tested against these generalization criteria:

• Domain Generalization: Test the model's performance on data from different physical systems or application categories.
• Spatial Generalization: Validate the model's accuracy when applied to new, unseen geographic regions or locations.
• Temporal Generalization: Assess the model's ability to maintain performance across different time periods, including future projections.
• Scale Generalization: Evaluate whether the model can handle data at different resolutions, frequencies, and levels of granularity without significant performance degradation.

Systematically documenting model performance across these dimensions is a hallmark of a robust, scale-aware modeling framework and is essential for building trust in model outputs for spatial management scenarios.

Spatially explicit ecosystem modelling is fundamental for advancing area-based management strategies in marine environments. The Ecospace module within the Ecopath with Ecosim (EwE) framework serves as a critical tool for this purpose, enabling researchers to simulate spatial-temporal dynamics of marine food webs and evaluate the potential outcomes of management scenarios such as marine protected areas (MPAs) [30]. However, the utility of these simulations is entirely contingent upon the realism of their biomass distributions, making model calibration not merely a technical step but a scientific necessity. Proper calibration ensures that model outputs provide a reliable match to observed ecosystem data, thereby creating a trustworthy basis for management decisions [55]. Without a rigorous calibration process, even a structurally sound model may produce spurious predictions, potentially leading to flawed policy advice [30]. This document outlines a systematic methodology for calibrating Ecospace models to achieve spatially realistic biomass distributions, framed within the broader context of spatial management research.

The Calibration Life Cycle for Ecospace Models

Model calibration is the procedure of adjusting model parameters so that simulated outputs best match observed data. For spatially explicit models like Ecospace, this process is particularly crucial as model parameters often conceptually represent upscaled physical and biological processes that cannot be directly measured [55]. The following ten-step cycle provides a structured, iterative framework for the calibration of environmental models, adapted specifically for the Ecospace context [55].

Table 1: The Ten-Step Calibration Life Cycle for Ecospace Models

Step	Calibration Activity	Key Considerations for Ecospace
1	Use sensitivity information	Identify parameters with the largest influence on spatial biomass and fishing effort fit [30].
2	Handle parameter constraints	Account for realistic biological and physical limits of dispersal and habitat parameters.
3	Manage data ranging orders of magnitude	Apply transformations to biomass and catch data to prevent bias towards high-value species.
4	Choose calibration data	Select appropriate time-series of biomass and fishing effort maps, and species distribution data [43] [4].
5	Sample model parameters	Use Latin Hypercube or Monte Carlo sampling to explore the multi-dimensional parameter space.
6	Find parameter ranges	Define plausible minima and maxima based on literature, expert opinion, and sensitivity analysis [30].
7	Choose objective functions	Select fitness criteria (e.g., Sum of Squares, AIC) to quantitatively measure match to data.
8	Select a calibration algorithm	Choose a method (e.g., non-linear optimization, Pareto fitting) to search for optimal parameters.
9	Determine multi-objective success	Evaluate trade-offs when simultaneously fitting to multiple data types (e.g., biomass and spatial effort).
10	Diagnose calibration performance	Use skill assessment routines for temporal, spatial, and spatio-temporal validation [4].

Foundational Concepts in Ecospace Calibration

Before embarking on the calibration cycle, it is essential to understand what Ecospace calibrates. Unlike Ecopath or Ecosim, the Ecospace module itself does not directly estimate core parameters [30]. Instead, its calibration involves fine-tuning spatial inputs and comparing its predictions against real-world spatial data. The primary goals are to improve the model's realism and predictive capability for spatial distributions.

A critical practice is the systematic evaluation of model performance through comparison with independent data. This involves two complementary approaches [30]:

• Sensitivity Analysis: Exploring how variation in model parameters affects the fit between model outputs and observed data.
• Quantitative Performance Evaluation: Comparing model predictions of species biomass distribution and fishing effort allocation against known spatial data to quantify the model's accuracy.

The measure of success is a improved goodness-of-fit between model predictions and observational data. Studies have shown that a carefully calibrated Ecospace model can improve the fit to data by up to 15% compared to a model using default parameter values [30].

Experimental Protocols for Key Calibration Analyses

Protocol 1: Sensitivity Analysis of Ecospace Parameters

Objective: To identify which Ecospace parameters have the largest effect on model performance, guiding focused calibration efforts.

Table 2: Key Parameter Ranges for Sensitivity Analysis

Parameter Class	Specific Parameters	Typical Range	Impact on Model Fit
Species Dispersal	Dispersal rate, larval dispersal kernel	0-1 (relative)	Determines connectivity and local biomass build-up [30].
Habitat Preference	Habitat affinity, foraging capacity	0-1 (suitability)	Directly shapes species distribution hot-spots [4].
Fishing Fleet	Effort dispersal, cost gradients	User-defined	Affects spatial effort patterns and fishing mortality [30].
Environmental Drivers	Temperature, depth preferences	Species-specific	Influences long-term distribution shifts.

Methodology:

• Parameter Selection: Choose parameters for testing based on their hypothesized ecological importance (see Table 2).
• Define Ranges: Establish plausible minimum and maximum values for each parameter from literature, expert opinion, or preliminary runs.
• Experimental Design: Use a sampling design such as Latin Hypercube Sampling to efficiently explore the multi-dimensional parameter space. This avoids the limitations of one-at-a-time parameter testing, which can miss interactions between parameters [30].
• Model Runs: Execute Ecospace for each parameter set defined by the sampling design.
• Fit Calculation: For each run, calculate a goodness-of-fit statistic (e.g., a weighted sum of squares) comparing model predictions to time-series of spatial biomass data and fishing effort data [30].
• Analysis: Analyze the results to determine which parameters cause the largest variation in the fit statistic. This identifies the most sensitive parameters requiring careful calibration.

Objective: To implement habitat preference maps in Ecospace and evaluate their impact on forecasting biomass trends and distributions.

Methodology:

• Habitat Data Preparation: Develop habitat foraging capacity maps. These can be based on:
  • Generalist Definition: Presence/absence data derived from broad-scale habitat suitability models.
  • Specialist Definition: Abundance hot-spots from high-resolution species distribution models [4].
• Model Configuration: Implement these maps within the Ecospace habitat foraging capacity model. This model allows for the implementation of foraging responses to environmental drivers and habitat preferences derived from external species distribution models [4].
• Experimental Testing: Run a retrospective analysis with different Ecospace configurations:
  • Configuration A: Generalist habitat definition.
  • Configuration B: Specialist habitat definition.
  • Test different frequencies of habitat map updating (e.g., static vs. dynamic) [4].
• Skill Assessment: Use a developed skill assessment routine to compare model outputs against observed data. This assessment should be performed across temporal, spatial, and spatio-temporal dimensions to determine which configuration most realistically forecasts trends and distributions [4].
• Risk Assessment: Be aware that overly restrictive habitat preferences can make it difficult to distinguish between trophic interactions (internal drivers) and external environmental drivers as causes for distribution changes [4].

Protocol 3: Validating Spatial Fishing Effort Predictions

Objective: To quantitatively evaluate Ecospace's capability to reproduce known spatial patterns of fleet distribution, a critical factor for assessing MPA economic impacts.

Methodology:

• Data Collection: Gather spatial data on actual fishing effort distribution, for example, from Vessel Monitoring Systems (VMS) or logbooks.
• Fleet Behavior Parameterization: Configure the Ecospace model's representation of fleet behavior, including factors such as:
  • Cost Gradients: Simulating travel costs from home ports.
  • Profit Seeking: The tendency of fleets to move towards areas of higher predicted profitability [30].
• Model Simulation: Run the Ecospace model with the calibrated parameters.
• Comparison and Validation: Quantitatively compare the model's predicted spatial effort distribution against the observed effort data. This is a key validation step often overlooked, yet it is fundamental for assessing the credibility of model predictions related to fishery profitability under different spatial management scenarios [30].

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Research Reagents and Computational Tools for Ecospace Calibration

Tool Name/Type	Function in Calibration	Application Notes
Ecopath with Ecosim (EwE)	Core modeling framework providing the foundational (Ecopath) and dynamic (Ecosim) components for Ecospace.	The model must be balanced in Ecopath and fitted to time series in Ecosim before spatial calibration in Ecospace begins.
Spatial-Temporal Framework	Ecospace component allowing the implementation of external time series of maps (e.g., habitat, climate drivers) [43].	Crucial for simulating shifting habitats and environmental conditions over time.
Habitat Foraging Capacity Model	Ecospace add-on that allows implementation of habitat preferences derived from species distribution models [4].	Enables distinction between trophic and external habitat drivers of species distribution.
ECOIND Plug-in	Calculates ecological indicators from model outputs, aiding in the assessment of ecosystem health and trade-offs [4].	Used to evaluate ecosystem-level impacts of management scenarios post-calibration.
Sensitivity & Uncertainty Analysis Tools	Software routines (e.g., in R or Python) for designing parameter sampling and analyzing the sensitivity of model fit.	Essential for performing the systematic sensitivity analysis outlined in Protocol 1.
Skill Assessment Routine	A custom-developed metric or routine for temporal, spatial, and spatio-temporal validation of model outputs [4].	Key for objectively comparing different model configurations and validating predictions.

Workflow Visualization

The following diagram illustrates the logical sequence and iterative nature of the core calibration workflow for achieving realistic biomass distributions in Ecospace.

Achieving realistic biomass distributions in Ecospace is a systematic process grounded in iterative calibration and validation against empirical data. By adhering to the described ten-step calibration life cycle and implementing the specific protocols for sensitivity analysis, habitat integration, and effort validation, researchers can significantly enhance the reliability of their spatial models. This rigor is a prerequisite for using Ecospace as a trusted tool to inform complex spatial management decisions, such as the design of marine protected area networks and the assessment of trade-offs between conservation objectives and fisheries yield. A well-calibrated model provides not just predictions, but credible insights essential for the future of ecosystem-based marine management.

Assessing Ecospace Performance: Validation Frameworks and Comparative Analysis with Other Models

Within ecological research, particularly for studies employing the Ecospace module of the Ecopath with Ecosim (EwE) framework, systematic model evaluation is a critical yet often underrepresented practice. Ecospace enables the creation of spatially explicit ecosystem models to evaluate area-based management scenarios, such as the impact of Marine Protected Areas (MPAs) or spatial fishing restrictions [1] [24] [56]. However, the predictive value and reliability of these models for informing policy depend entirely on rigorous, structured validation of their spatial outputs. Systematic evaluation moves beyond ad-hoc assessments, transforming model validation from a qualitative check into a quantitative, repeatable engineering decision backed by empirical data [57].

The fundamental challenge in spatial ecosystem modeling lies in the multidimensional nature of model performance. A model might accurately predict biomass trends in one geographic cell while failing in others, or it might capture broad spatial patterns while missing critical local dynamics. Systematic evaluation frameworks are designed to dissect this complexity, providing clarity on a model's strengths, limitations, and appropriate application contexts. This document provides application notes and detailed protocols for researchers aiming to implement such rigorous evaluation practices for Ecospace and similar spatial models, ensuring their findings are robust, transparent, and actionable for environmental management.

Core Evaluation Metrics for Spatial Model Outputs

Evaluating spatial models requires a multi-faceted approach, employing metrics that assess different aspects of model performance. The following tables summarize essential quantitative and qualitative metrics tailored for spatial ecological outputs.

Table 1: Quantitative Metrics for Spatial Model Validation

Metric Category	Specific Metric	Application in Spatial Model Evaluation
Accuracy & Precision	Accuracy, Recall, F1 Score [58]	Measures the proportion of correct spatial predictions (e.g., species presence/absence, high-biomass areas) against reference data.
Spatial Pattern	Spatial Autocorrelation Index [59]	Evaluates whether the model correctly reproduces observed spatial clustering or dispersion of ecological variables.
Error & Bias	Mean Absolute Error (MAE), Bias [60]	Quantifies the average magnitude of error in predicted values across the spatial grid and identifies systematic over- or under-prediction.
Predictive Performance	Area Under the Curve (AUC) [60]	Assesses the model's ability to discriminate between events and non-events (e.g., suitable vs. unsuitable habitat) across space.

Table 2: Qualitative and Diagnostic Checks for Spatial Models

Check Category	Specific Check	Purpose in Spatial Model Evaluation
Spatial Artifacts	Edge Effects, Checkerboarding	Identifies unrealistic spatial patterns arising from model structure or grid boundaries rather than ecological dynamics [24].
Data Quality	Geocoding Methods, Modifiable Areal Unit Problem (MAUP) [59]	Assesses the integrity of input and validation data, including how spatial units and scales might introduce bias [61] [62].
Pattern Plausibility	Visual Inspection of Distribution Maps [60]	Expert assessment of whether predicted spatial distributions (e.g., species hotspots, MPA effects) are ecologically plausible.
Sensitivity to Inputs	Forcing & Parameter Variation [24]	Tests how sensitive spatial outputs are to changes in key driving parameters or initial conditions.

A Structured Workflow for Systematic Evaluation

A systematic evaluation is a multi-stage process that integrates planning, execution, and iterative refinement. The workflow below outlines the key stages from defining objectives to implementing improvements based on evaluation findings.

Structured Evaluation Workflow for Spatial Models

Phase 1: Preparation and Baseline Establishment

Step 1: Define Evaluation Objectives and Success Criteria Before running a single test, researchers must define what "success" means for their specific model and management question. Is the primary goal to accurately predict biomass redistribution after MPA establishment? Or to capture the spatial overlap between predator and prey? Success criteria must be defined using clearly measurable metrics that align with the model's intended use [57]. For example, "The model shall achieve an AUC score >0.8 when predicting known species presence locations against absence areas."

Step 2: Curate Representative Spatial Evaluation Datasets The foundation of any evaluation is a high-quality dataset that represents the real-world scenarios the model is meant to simulate. For Ecospace, this could include:

• Historical spatial biomass data from trawl surveys or遥感.
• Georeferenced presence-absence data for key species from sources like GBIF or museum collections [60].
• Spatial management outcomes, such as fishery catch data before and after MPA implementation. Quality and coverage are more critical than sheer volume; a well-curated dataset of 500 representative spatial cells is more valuable than thousands of random samples [57]. This dataset should be split into training data (for model calibration) and a hold-out testing set (for final validation).

Step 3: Establish Baseline Performance Run the baseline Ecospace model and calculate the predefined metrics against the evaluation dataset. This establishes a performance benchmark against which all future model improvements or alternative scenarios will be measured [57]. Documenting this baseline is crucial for demonstrating progress and justifying model changes.

Phase 2: Comparative Analysis and Diagnostic Interrogation

Step 4: Conduct Structured Model Comparisons Systematic evaluation often involves comparing different model configurations or competing hypotheses. This is operationalized through side-by-side comparisons under identical evaluation conditions [57]. Examples include:

• Comparing the same model with and without a specific process (e.g., larval dispersal).
• Comparing different spatial management scenarios (e.g., MPA network A vs. B). Effective comparison requires controlling all variables except the one being tested, using identical prompts (in AI-assisted modeling), spatial resolution, and environmental drivers.

Step 5: Diagnose Spatial Errors and Biases When models underperform, systematic diagnosis is key. This involves mapping errors geographically to identify spatial patterns.

• Are errors concentrated at habitat boundaries? This may indicate issues with how edges are handled.
• Is there a systematic bias in certain depth ranges or current systems? This could point to missing environmental drivers. Techniques like spatial residual analysis can help pinpoint these areas, guiding targeted model refinement instead of ad-hoc parameter tweaking [24]. Furthermore, the evaluation should appraise how the model handles known spatial methodological challenges, such as the Modifiable Areal Unit Problem (MAUP) or ecological fallacy [61] [59].

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 3: Key Research Tools for Spatial Model Evaluation

Tool / Resource	Type	Function in Evaluation
Ecopath with Ecosim (EwE) [1]	Modeling Software	The core spatial modeling environment containing the Ecospace module for building and running simulations.
Spatial Methodology Appraisal Tool (SMART) [61]	Validation Framework	A 16-item tool for appraising methodological quality in spatial research, covering data quality and spatial analysis methods.
Group Concept Mapping (GCM) [61]	Consensus Method	A structured process for building expert consensus on evaluation criteria and quality indicators for complex spatial models.
LEval Framework [58]	Benchmarking Suite	Evaluates performance on long-context understanding, useful for testing models against complex, spatially-extended scenarios.
Azure AI Studio / Prompt Flow [58]	Commercial Platform	Provides built-in evaluation tools for comparing different models and prompts, with automated metric tracking.
Energy Efficiency Benchmark [58]	Sustainability Metric	Measures computational cost and energy consumption of model runs, important for large, iterative spatial simulations.

Detailed Experimental Protocol: Validating an Ecospace MPA Scenario

This protocol provides a step-by-step guide for systematically evaluating the output of an Ecospace model configured to test a Marine Protected Area (MPA) policy.

5.1 Protocol Title: Systematic Validation of an Ecospace MPA Simulation Using Spatial Reference Data.

5.2 Background and Purpose The Ecospace module allows for the definition of fisheries restricted areas (MPAs) to block fishing from specific cells [56]. This protocol is designed to evaluate the accuracy of a model predicting the ecosystem effects of a newly established MPA, using historical data from a pre-existing MPA as a validation benchmark. The goal is to provide a rigorous, quantitative assessment of the model's predictive capability for informing future area-based management decisions.

5.3 Experimental Workflow

Ecospace MPA Validation Protocol

5.4 Materials and Reagents

• Software: Ecopath with Ecosim (EwE) software suite, version 6.6 or later [1].
• Hardware: Computer with sufficient RAM and processing power for spatially explicit, dynamic simulations (requirements scale with grid resolution and complexity).
• Reference Data: Georeferenced biomass or catch-per-unit-effort (CPUE) data from inside and adjacent to an existing MPA, covering a time series from before and after its establishment [24].

5.5 Step-by-Step Procedure

• Prepare the Reference MPA Dataset.

  • Identify a real-world MPA with well-documented establishment date and boundaries.
  • Gather spatial biomass/abundance data for key functional groups or species from scientific surveys or fishery-independent monitoring. Data should cover periods before and after MPA implementation.
  • Process this data to create a spatial time-series for validation. For example, calculate the ratio of biomass inside vs. outside the MPA for the post-implementation period.

• Configure the Ecospace Baseline Model.

  • Build and balance an Ecopath model for the study area.
  • Calibrate the temporal dynamics in Ecosim to historical catch and biomass time series data prior to the MPA's establishment.
  • In Ecospace, configure the habitat capacity and dispersal parameters for the modeled groups. The model's initial state (i.e., the year the MPA was established) should reflect the best estimate of the actual ecosystem conditions at that time.

• Run the MPA Simulation.

  • In the Ecospace simulation, implement the MPA in the same spatial configuration and starting year as the real-world reference.
  • Use the MPA Dynamics plug-in if evaluating scenarios with changing enforcement over time [56].
  • Run the simulation for the same duration as the post-MPA monitoring period for which you have validation data.

• Extract Spatial Outputs.

  • From the completed Ecospace run, export spatial grids of biomass for the same functional groups and for the same years as your reference data.
  • Calculate the same metrics from the model outputs as you did from the real-world data (e.g., the inside:outside biomass ratio).

• Calculate Validation Metrics.

  • Quantitatively compare the model outputs to the reference data using the metrics defined in Table 1.
  • Primary Metrics: Calculate AUC to test the model's ability to distinguish high-biomass from low-biomass cells within the MPA [60]. Calculate MAE and Bias to understand the magnitude and direction of error in predicted biomass values [60].
  • Spatial Pattern Analysis: Use spatial statistics to test for significant clustering in the model residuals (errors).

• Perform Residual Spatial Analysis.

  • Create a map of the differences (residuals) between the model-predicted biomass and the observed reference biomass.
  • Visually and statistically analyze this residual map. Are errors random, or are they clustered in specific habitats or geographic regions? Systematic patterns indicate a structural issue or missing driver in the model that requires correction before the model can be reliably used for forecasting [24].

5.6 Expected Outcomes and Interpretation

• A model with high AUC and low MAE demonstrates strong predictive skill for the MPA scenario tested.
• A high Bias score indicates the model systematically over- or under-predicts biomass, suggesting a need for recalibration of productivity or consumption rates.
• Spatially clustered residuals necessitate a re-examination of the habitat capacity rules or dispersal parameters in the Ecospace model. This structured, data-driven approach moves model evaluation beyond simple visual pattern matching and provides a solid foundation for model improvement and confidence in management applications.

Spatially explicit modeling represents a critical methodology for simulating dynamic processes across geographic landscapes, serving diverse research communities. Environmental scientists employ these tools for ecosystem-based management, while drug development professionals increasingly leverage them to optimize biomedical processes. This framework provides a comparative analysis of the Ecospace module against other modeling paradigms, contextualized within spatial management scenarios. The core function of these systems is to project the distribution and interactions of modeled entities—from marine species to pharmaceutical compounds—across a defined spatial grid, enabling researchers to test hypotheses and evaluate management interventions in a virtual environment. The selection of an appropriate modeling system is paramount, as it dictates the feasibility, granularity, and ultimate applicability of the research findings.

In-Depth Analysis of the Ecospace Module

Core Architecture and Operational Principles

Ecospace operates as the spatial-temporal dynamic module within the Ecopath with Ecosim (EwE) food web modeling approach, designed specifically for modeling complex ecosystem interactions across a uniform raster grid [41] [43]. Its architecture is engineered to project the biomass densities of functional groups and the activities of fishing fleets over a user-defined spatial domain, typically corresponding to the geographic scope of an underlying Ecopath base model. A fundamental operational principle is its explicit handling of cell tapering due to latitude, where cell width (W~i~) varies as a function of latitude (Lat~i~) following the formula W~i~ = cos(Lat~i~) [41]. This necessitates critical corrections for organism dispersal, fishing effort allocation, and catch calculations, ensuring model accuracy, particularly for grids spanning wide latitudinal ranges.

The model requires users to make several foundational decisions during configuration. The choice of grid cell size represents a key trade-off, balancing computational demand against predictive detail [41]. Ecospace models commonly utilize grids ranging from 25x25 to 100x100 cells. A pragmatic, multi-resolution approach is recommended: beginning with a coarse grid for rapid scenario building and testing, progressing to an intermediate resolution for fine-tuning, and finally validating with a high-resolution grid to assess improvements in predictive power.

Key Processes and Driving Mechanisms

Ecospace simulates several core ecological processes that govern spatial dynamics:

• Species Dispersal: Movement of functional groups is driven by advection (currents), diffusion (random movement), and habitat suitability, with dispersal rates across north-south cell faces scaled by the relative width (W~i~) to account for latitudinal effects [41].
• Habitat Selection: The distribution of biomass is primarily influenced by habitat capacity, which defines the suitability of each cell for a given functional group based on environmental variables.
• Fishing Fleet Dynamics: Fishing mortality is implemented spatially using gravity models, where the attractiveness of a cell to a fleet is proportional to its profitability [41]. The total effort (E~T~) allocated to a cell is calculated as E~T~ = (E~t~ * p~i,j~ * W~i~) / Σ(p~i,j~ * W~i~), where E~t~ is the total effort and p~i,j~ is the profitability. The resulting effort density (E~i,j~) that determines fishing mortality rate is then E~i,j~ = E~T~ / W~i~.

Table 1: Critical Configuration Parameters for Ecospace Modeling

Parameter Category	Specific Parameter	Considerations & Impact on Model Output
Spatial Domain	Grid Extent	Must align with the domain of the core Ecopath model [41].
Spatial Resolution	Grid Cell Size & Count	Determines computational load and output detail; a 25x25 to 100x100 cell range is common [41].
Geospatial Projection	Cell Tapering / Square Cells	WGS84 projection accounts for latitudinal cell tapering; "square cells" option assumes a uniform grid [41].
Biological Drivers	Habitat Capacity Maps	Define environmental preferences for functional groups, directly influencing biomass distribution.
Physical Drivers	Advection Fields (Currents)	Influence directional movement of organisms and larvae [41].
Anthropogenic Drivers	Fishing Effort Allocation	Governed by gravity models based on profitability and adjusted for cell area [41].

Comparative Framework with Other Modeling Systems

A direct, feature-for-feature comparison with other spatially explicit models is constrained by the available search results, which focus exclusively on Ecospace. However, its design principles highlight its position within the broader ecosystem of spatial modeling tools. Ecospace is inherently process-based and mechanistic, simulating the underlying causes of distribution changes, such as predation, competition, and environmental drivers. This contrasts with purely correlative or statistical models that establish relationships between observed distributions and environmental parameters without simulating the underlying ecology.

Furthermore, Ecospace is explicitly ecosystem-centric, integrating multiple trophic levels and their interactions, rather than focusing on a single species or a single physical process. Its tight integration with the Ecopath and Ecosim modules facilitates a seamless workflow from a static mass-balanced snapshot (Ecopath) through temporal dynamics (Ecosim) to spatial-temporal simulations (Ecospace) [41] [43]. This holistic approach differentiates it from models designed for more specific applications, such as hydrologic transport or individual species population viability analysis.

Application Notes and Protocols for Spatial Management Scenarios

Protocol 1: Configuring an Ecospace Model for a Marine Spatial Planning Scenario

Objective: To establish a baseline Ecospace model for evaluating spatial management interventions, such as Marine Protected Areas (MPAs) or zoning plans. Workflow Description: This protocol begins with data aggregation and culminates in a running spatial model suitable for scenario testing. The process is iterative, requiring calibration and validation against independent data, such as observed species distribution maps or fisheries catch data. Materials:

• Hardware: Standard desktop computer (for small grids) to high-performance computing cluster (for large, high-resolution grids).
• Software: Ecopath with Ecosim and Ecospace software suite (Version 6.6+ recommended).
• Data: Ecopath baseline model, spatial layers (habitat types, depth, sea surface temperature), and fishing fleet operational data.

Step-by-Step Procedure:

• Define Spatial Domain and Resolution: Georeference the Ecopath model's domain. Determine an appropriate cell size based on the research question, data availability, and computational constraints [41]. Initiate with a coarse grid (e.g., 50x50 cells) for faster model tuning.
• Configure Basemap and Projection: Set the model's basemap to the defined domain. For areas at high latitudes or spanning wide latitudinal gradients, enable the default WGS84 projection to automatically handle cell tapering. For smaller, localized areas (e.g., a single estuary) where tapering is negligible, the "Assume square cells" option can be used [41].
• Load Habitat Capacity Layers: For each functional group, assign relevant spatial layers that define habitat preference (e.g., substrate type for benthic invertebrates, temperature ranges for pelagic fish). These maps drive the initial and potential distribution of biomass.
• Parameterize Movement and Dynamics: Set dispersal rates for each functional group. If relevant, load advection fields to simulate the effect of ocean currents on movement [41].
• Implement Fisheries: Define the spatial distribution of fishing effort for each fleet. Use the gravity model approach, linking effort allocation to predicted profitability in each cell, which is automatically adjusted for cell area [41].
• Calibrate and Validate: Run the model and compare outputs to independent spatial data. Adjust key parameters (e.g., dispersal rates, habitat preferences) within ecologically plausible ranges to improve the model's fit.
• Run Management Scenarios: Duplicate the calibrated baseline model to test scenarios. For MPA planning, this involves setting the fishing mortality rate to zero within the proposed reserve boundaries and comparing ecosystem outputs to the baseline.

Protocol 2: Applying Spatial Modeling Principles to Microgravity Drug Crystallization

Objective: To illustrate how spatial explicit modeling principles can inform the design and analysis of pharmaceutical experiments in microgravity environments. Workflow Description: While Ecospace itself models ecosystems, the logical principles of controlling for spatial-environmental variables are directly analogous to optimizing crystal growth in space. This protocol outlines a terrestrial and orbital workflow for identifying and producing improved drug crystals. Materials:

• Traditional Solid-State Characterization Tools: XRPD, DSC, TGA [63].
• Particle Characterization Tools: Laser diffraction, dynamic image analysis [63].
• Crystallization Hardware: Small-volume screening platforms (e.g., Crystal16) and larger automated reactors (e.g., EasyMax) with in-situ sensors (Raman, infrared) [63].
• Hypergravity Centrifuge: To simulate variable gravity conditions on Earth [63].
• In-Orbit Laboratory Platform: Such as those offered by Varda Space Industries or the International Space Station (ISS) [63] [64].

Step-by-Step Procedure:

• Terrestrial Feasibility and Process Optimization: Conduct extensive crystallization process development on Earth. Use in-situ sensors to map process windows and understand crystallization kinetics at 1g [63].
• Hypergravity Screening: Use a hypergravity centrifuge to subject crystallization experiments to a range of gravity levels (e.g., 2g, 5g). This identifies drug candidates whose crystallization kinetics and resulting particle properties (size, polymorph form) are highly sensitive to gravity, making them prime candidates for microgravity processing [63].
• In-Orbit Process Execution: For selected candidates, prepare and load samples into an autonomous orbital lab platform. The microgravity environment suppresses convective currents and sedimentation, allowing for slower, more uniform crystal growth and the formation of larger, more ordered structures [63] [64] [65].
• Sample Return and Analysis: Recover the space-produced materials upon their return to Earth. Perform comprehensive analysis using a suite of techniques (SCXRD, XRPD, DSC, SEM, dissolution testing) and compare the properties directly to terrestrial and hypergravity-produced crystals [63].

Table 2: Research Reagent Solutions for Microgravity Crystallization Experiments

Reagent / Material Category	Specific Example / Function	Application Context in Protocol
Small-Molecule Therapeutics	L-histidine, proprietary New Chemical Entities (NCEs)	Model compound for hypergravity screening; target molecules for life cycle management [63].
Biologics	Monoclonal antibodies, protein-based therapeutics	Targets for crystallization to improve formulation shelf-life and efficacy [63] [66].
Crystallization Hardware	Crystal16, EasyMax reactors	Small- and larger-scale automated reactors for terrestrial process development [63].
Process Analytical Technology (PAT)	In-situ Raman spectroscopy, video microscopy	Probes crystallization kinetics in real-time to map process windows [63].
Hypergravity Platform	Custom centrifuge-compatible reactors	Enables terrestrial testing of gravity-dependencies [63].

Mandatory Visualizations

Ecospace Model Configuration and Workflow

Microgravity Drug Crystallization Logic

Integrated Drug Development Workflow

Benchmarking Earth Observation (EO) data against independent, high-quality in-situ measurements is a critical process for validating the accuracy and reliability of satellite-derived information. This validation is fundamental for ensuring that data used in environmental monitoring, climate research, and spatial ecosystem modeling are scientifically robust. Within the context of Ecospace module research for spatial management scenarios, such benchmarking enables researchers to refine habitat suitability models, calibrate environmental driver responses, and produce reliable forecasts for ecosystem-based management. The integration of Fiducial Reference Measurements (FRMs)—metrologically traceable ground-based observations—establishes the essential trust framework for applying EO data to complex ecological questions.

Conceptual Framework for Validation

The validation of Earth Observation data follows a hierarchical framework where satellite-derived products are systematically compared against independent ground-truth data. This process quantifies biases, reduces uncertainties, and establishes the fitness-for-purpose of EO data for specific applications, including spatial ecosystem modeling.

• Fiducial Reference Measurements (FRMs): FRMs are in-situ measurements characterized by documented uncertainty assessment and metrological traceability to international standards. They provide an independent, high-accuracy benchmark for validating satellite data products across different environmental domains, including inland waters, sea ice, and land ice [67]. Projects like St3TART Follow-On (St3TART-FO) specialize in generating these FRMs for missions such as Copernicus Sentinel-3, ensuring data integrity for climate monitoring [67].

• The Ecospace Integration: In the Ecopath with Ecosim (EwE) framework, the Ecospace module utilizes spatially explicit data on environmental drivers (e.g., sea surface temperature, salinity, primary production) to define habitat foraging capacity for functional groups [13]. The accuracy of these underlying environmental maps, often derived from EO, directly influences the model's ability to realistically simulate biomass distribution and trophic interactions. Benchmarking ensures the initial conditions and forcings for Ecospace scenarios are valid.

Application Notes: Validation Scenarios and Protocols

This section outlines specific protocols for benchmarking EO data against independent measurements across three common validation scenarios.

Protocol 1: Validation of Satellite Altimetry Data over Complex Surfaces

This protocol details the process for validating satellite altimetry measurements (e.g., from Sentinel-3) over inland waters and cryospheric surfaces using FRMs.

• Objective: To validate the accuracy of satellite-derived surface height measurements for inland water bodies and ice sheets, supporting hydrology and cryospheric science.
• Independent Benchmark Data Source: Fiducial Reference Measurements from the St3TART-FO project network, which includes fixed micro-stations and airborne campaigns [67].
• Experimental Workflow:

The workflow for validating satellite altimetry data involves a continuous cycle of data collection from in-situ networks, comparison with satellite data, and refinement of products, as visually summarized in the following diagram.

• Key Technical Specifications:
  • Spatial Co-location: FRM stations are deployed as a network of "super-sites" (intensive monitoring) and "opportunity sites" (extensive monitoring) within the satellite's footprint [67].
  • Temporal Synchronization: In-situ data acquisition is synchronized with the satellite's overpass schedule.
  • Metrological Traceability: All FRMs are accompanied by a rigorous uncertainty budget, traceable to international standards (e.g., SI units) [67].
  • Data Hub Integration: Validated FRM datasets are disseminated via centralized platforms like the FRM Data Hub for user access [67].

Protocol 2: Validation of Land Cover Classification from Multispectral Imagery

This protocol describes the validation of machine learning-based land cover maps generated from satellite imagery (e.g., Landsat, Sentinel-2) using authoritative ground-truthed data.

• Objective: To assess the accuracy of automated land cover classification algorithms against a trusted reference dataset to ensure reliable change detection and ecosystem monitoring.
• Independent Benchmark Data Source: National Forest Inventory (NFI) data, which provides comprehensive, field-validated information on forest cover and conditions [68].
• Experimental Workflow:

Validating land cover classifications involves using a trusted inventory to create reference data, which is then used to assess the accuracy of satellite-based classifications in a structured process.

• Methodological Details:
  • Model Training & Prediction: As demonstrated in a Czech Republic study, multiple ML models (Random Forest, Support Vector Machine, etc.) are trained on spectral signatures from satellite imagery to predict land cover classes [68].
  • Accuracy Assessment: Model predictions are compared against the NFI reference data using a stratified random sampling approach to calculate performance metrics like overall accuracy, precision, recall, and Kappa coefficient [68]. The Random Forest model, for instance, achieved 98.3% overall accuracy in one benchmark study [68].

Protocol 3: Benchmarking Earth System Model Simulations

This protocol outlines the use of observational datasets to evaluate the performance of Earth System Models (ESMs), a practice directly relevant to providing climate forcings for Ecospace scenarios.

• Objective: To systematically evaluate ESM simulations against a diverse suite of observational data to identify model biases, assess performance, and inform model development.
• Independent Benchmark Data Source: Multi-source observational and reanalysis data for variables such as clouds, radiation, and precipitation [69].
• Experimental Workflow:
  • Define Evaluation Metrics: Select relevant performance metrics (e.g., root-mean-square error, bias, pattern correlation) for target variables.
  • Configure Diagnostic Tool: Use community tools like the Earth System Model Evaluation Tool (ESMValTool) to automate the extraction of model data and observational data [69].
  • Run Analysis: Execute predefined diagnostics to generate comprehensive model-observation comparisons across spatial and temporal scales.
  • Performance Scoring: Synthesize results to produce quantitative performance scores for different model components, aiding in model selection and weighting for ensemble forecasts [69].

The following tables consolidate key quantitative findings and reagents from the validation protocols discussed.

Table 1: Performance Summary of Machine Learning Models for Land Cover Classification

Machine Learning Model	Overall Accuracy (%)	Key Application Note
Random Forest (RF)	98.3	Demonstrated high accuracy for Sentinel-2 data validation against NFI data; robust for operational use [68].
Support Vector Machine (SVM)	Information Missing	Commonly applied, but specific accuracy depends on feature engineering and kernel selection [70].
Naive Bayes (NB)	Information Missing	A probabilistic classifier, useful as a baseline model [68].
Classification and Regression Tree (CART)	Information Missing	Provides interpretable models but may have lower accuracy than ensemble methods [68].

Table 2: Technical Specifications for Key Validation System Components

System Component	Technical Specification	Relevance to Validation Protocol
FRM Micro-station (St3TART-FO)	Sensors: GNSS, LiDAR, SnowVue; Data acquisition rate: ~100% [67].	Provides continuous, high-quality traceable measurements for altimetry validation (Protocol 1) [67].
FRM Data Hub (St3TART-FO)	Centralized platform; supports multiple data providers and users [67].	Simplifies access to benchmark datasets, promoting transparency and collaboration [67].
ESMValTool v2.12.0	Community diagnostics and performance metrics tool for ESMs [69].	Automates the evaluation of model simulations against observations (Protocol 3) [69].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Tools for EO Validation

Item	Function in Validation	Example Use Case
Fiducial Reference Measurements (FRMs)	Provides the high-accuracy, traceable benchmark against which EO products are validated [67].	Validating Sentinel-3 altimetry data over inland waters and sea ice [67].
National Forest Inventory (NFI)	Serves as an authoritative, ground-truthed reference dataset for land cover and forest attributes [68].	Validating the accuracy of Sentinel-2 and Landsat-based land cover classification maps [68].
Earth System Model Evaluation Tool (ESMValTool)	A community tool that facilitates routine evaluation of ESMs against observational data [69].	Benchmarking and monitoring the performance of ESM simulations for climate projections [69].
Habitat Foraging Capacity (HFC) Model	In Ecospace, defines continuous relative habitat capacity for functional groups based on environmental drivers [13].	Using validated EO-based environmental maps (e.g., SST) to drive realistic species distribution in spatial simulations [13].

Interpreting Model Uncertainty and Communicating Confidence in Results

Within spatial management research, particularly when utilizing tools like the Ecospace module, model uncertainty is an inherent and pervasive aspect of the scientific process. Epistemic uncertainty—the uncertainty arising from limited knowledge about facts, numbers, and scientific models—is an integral part of all stages, from the initial assumptions and observations to the final extrapolations and generalizations [71]. For researchers using Ecospace for deep-sea and marine spatial management, acknowledging and effectively communicating this uncertainty is not a sign of weakness but a fundamental component of robust scientific practice [24] [72]. Failure to do so can seriously compromise environmental decisions and policies, as evidenced by historical case studies in other fields [71]. Interpreting and conveying the confidence in model results is therefore essential for informing credible, reliable, and actionable area-based management strategies.

A Framework for Classifying Uncertainty

A structured approach to uncertainty communication is vital. The following framework identifies the core components, adapting a general model for the specific context of ecological spatial modeling [71].

• Objects of Uncertainty: The fundamental "what" that is uncertain.
  • Facts: Established observations or data points (e.g., the recorded biomass of a species at a specific location and time).
  • Numbers: Quantitative estimates derived from data (e.g., the projected carrying capacity of a marine protected area, or the growth rate parameter in a model).
  • Science: The models, hypotheses, and generalizations themselves (e.g., the structural assumptions of the Ecospace module regarding species dispersal) [71].
• Levels of Uncertainty Communication:
  • Direct Uncertainty: The explicit communication of uncertainty about a specific fact or number, such as a confidence interval around a population estimate.
  • Indirect Uncertainty: The communication of a broader lack of confidence in the underlying science or evidence base, often expressed through qualitative ratings of evidence quality [71].

Classification of Numerical Ecosystem Models

Ecospace operates within a broader ecosystem of numerical models used for monitoring and forecasting. Understanding these model types is crucial for identifying potential sources of uncertainty. The table below summarizes the primary classes of models relevant to marine spatial management [73].

Table 1: Classification of Numerical Models for Marine Ecosystems

Model Class	Typical Units	Elemental Structure	Trophic Interactions	Primary Focus	Example Model(s)
Bioenergetic Models	Individual	Somatic/Gonadic/Storage Tissues	No	Individual growth in response to environment	Dynamic Energy Budget (DEB) [73]
Population & Fishery Models	Population/Biomass	Age or Size Classes	No	Stock assessment; fishery management	Stock Synthesis (SS3); SPiCT [73]
Connectivity Models	Individual/Propagule	Particles/Life History Traits	No	Larval dispersal; population connectivity	LTRANS [73]
Species Distribution Models (SDMs)	Population/Community	Environmental Variables	No	Predicting species occurrence	Various statistical models [73]
Minimally Realistic Models	Population/Biomass	Age-structured; Few Species	Yes (Simplified)	Trophic dynamics of key species	Not specified in results
Whole Ecosystem Models	Ecosystem/Biomass	Trophic Levels/Functional Groups	Yes (Complex)	System-wide responses to pressures	Ecopath with Ecosim (EwE) / Ecospace [73]

Quantitative Measures of Uncertainty

Interpreting model uncertainty requires quantifying it. The following statistical techniques are foundational for analyzing quantitative data and expressing confidence in results.

Fundamental Statistical Measures

Descriptive Statistics help summarize sample data and provide initial insights into data variability, which is a source of uncertainty [74].

• Measures of Central Tendency: Mean, median, and mode describe what is typical for a sample.
• Measures of Spread: Standard deviation quantifies the variability or dispersion of data points around the mean.
• Parameter Estimation: Confidence intervals provide a range of values that is likely to contain the true population parameter with a certain level of confidence (e.g., 95%) [74].

Inferential Statistics are used to make predictions or test hypotheses about a population based on sample data. They directly address uncertainty about the generalizability of findings.

• P-value: Informs whether an observed effect, relationship, or difference might exist in reality, with a common threshold of 0.05 indicating statistical significance.
• Effect Size: A crucial complement to the p-value, the effect size quantifies the magnitude of the observed effect, which is key for interpreting its practical or biological significance [74].

Protocol for Systematic Model Assessment

A rigorous, systematic evaluation is critical for quantifying uncertainty in spatially explicit ecosystem models like Ecospace [24]. The following protocol outlines a detailed methodology.

• Objective: To systematically evaluate the performance and uncertainty of a spatially explicit ecosystem model (Ecospace) for informing area-based management decisions.
• Materials and Reagents:
  • Software: R statistical environment (with packages such as spict for stock assessment or similar for model fitting), Python (for potential scripting and data manipulation), and Ecopath with Ecosim (EwE) core software with the Ecospace module.
  • Data: Spatially explicit time-series data on species distributions, biomass, and catch; environmental data layers (e.g., seafloor topography, temperature); and boundary data for proposed management areas.
  • Computational Resources: High-performance computing (HPC) resources are often necessary for running multiple model scenarios and complex spatial simulations.
• Procedure:
  • Model Parameterization: Define the spatial grid, habitat layers, and dispersal parameters for the Ecospace model based on the best available data.
  • Base Model Calibration: Run the model under historical conditions and calibrate it against observed time-series data to ensure it can replicate past dynamics.
  • Sensitivity Analysis: a. Identify key input parameters (e.g., mortality rates, dispersal rates, habitat preferences). b. Systematically vary each parameter within a plausible range (e.g., ± 25%). c. Run the model for each parameter variation and record the impact on key output variables (e.g., total system biomass, species distribution shifts). d. Calculate sensitivity indices to rank parameters by their influence on model outcomes.
  • Scenario Uncertainty Analysis: a. Define a set of distinct spatial management scenarios (e.g., varying marine protected area boundaries, different fishing effort distributions). b. Run the calibrated Ecospace model for each scenario. c. For each scenario, run multiple stochastic simulations (e.g., 100+) to capture the range of outcomes due to inherent model stochasticity.
  • Output and Metric Calculation: For each scenario and simulation, calculate a suite of response metrics, such as:
    • Change in species biomass within management zones.
    • Indicators of fishery yield.
    • Ecosystem structure indices.
  • Uncertainty Quantification: Calculate confidence intervals (e.g., 95%) for each response metric across the stochastic simulations for each scenario. This provides a direct quantitative measure of outcome uncertainty.

Diagram 1: Systematic model assessment workflow for quantifying uncertainty in Ecospace scenarios.

Visualizing and Communicating Uncertainty

Effectively communicating uncertainty to diverse audiences, including scientists, managers, and policymakers, is a critical final step. The chosen format should match the audience's expertise.

A Framework for Communication

A comprehensive approach to communication must consider multiple factors [71]:

• Who: The communicator (e.g., scientist, policy expert) and their perceived credibility.
• What: The object (fact, number, science), source, and magnitude of uncertainty.
• Form: The expression (verbal, numerical, visual), format, and medium of the message.
• To Whom: The audience's characteristics (numeracy, expertise, trust).
• Effect: The impact on the audience's cognition, trust, and decision-making.

Protocol for Developing a Confidence Communication Plan

This protocol provides a step-by-step methodology for translating technical uncertainty assessments into actionable communication for decision-makers.

• Objective: To create a structured plan for communicating model uncertainty and confidence in Ecospace results to support transparent spatial management decisions.
• Procedure:
  • Audience Analysis: Identify the primary audience (e.g., fellow researchers, resource managers, policymakers). Assess their likely level of technical expertise, numeracy, and existing relationship with the scientific team.
  • Define Uncertainty Objects: Clearly articulate what is uncertain—whether specific numbers (e.g., the projected 10-year biomass increase) or the broader scientific model (e.g., the assumption of density-dependent dispersal).
  • Select Communication Formats: Choose appropriate methods for the audience.
    • For Technical Audiences: Use confidence intervals, p-values, and posterior distributions in tables and graphs.
    • For General Audiences: Use visual ranges, qualitative likelihood statements (e.g., "likely," "very likely"), and clear verbal explanations of evidence quality.
  • Design Visualizations with Accessibility: a. Create graphs that display uncertainty, such as time-series plots with confidence bands or bar charts with error bars. b. Crucially, ensure all visualizations adhere to accessibility standards. Use a color contrast checker to verify sufficient contrast between data series, text, and backgrounds. WCAG guidelines require a minimum contrast ratio of 3:1 for graphical objects [29] [75] [76].
  • Craft the Narrative: Develop a clear narrative that integrates the findings and their uncertainty. Explain what is known, with what level of confidence, and what remains uncertain. Highlight the practical implications of the uncertainty for the decision at hand.

Diagram 2: Framework for developing a communication plan for model confidence.

The Researcher's Toolkit for Uncertainty Analysis

This toolkit details essential reagents, software, and data sources required for conducting robust uncertainty analyses in spatial ecosystem modeling.

Table 2: Essential Research Reagent Solutions for Uncertainty Analysis

Tool/Resource Name	Type	Primary Function in Uncertainty Analysis	Example Use Case
R Statistical Environment	Software	Data analysis, statistical modeling, and calculation of confidence intervals and sensitivity indices.	Running the spict package for stock assessment or custom scripts for sensitivity analysis.
Ecopath with Ecosim (EwE)	Software	The core modeling platform containing the Ecospace module for building and running spatially explicit ecosystem simulations.	Implementing management scenarios and generating stochastic model runs.
Dynamic Energy Budget (DEB)	Model	A bioenergetic model theory used to represent individual-level responses to environmental conditions, reducing parameter uncertainty.	Providing informed growth and reproduction parameters for key species in Ecospace.
High-Performance Computing (HPC) Cluster	Hardware	Enables the execution of a large number of model runs (e.g., for sensitivity and scenario analysis) in a feasible timeframe.	Running 1000+ stochastic simulations of a 50-year Ecospace scenario.
Spatially Explicit Time-Series Data	Data	Used for model calibration and validation; its quality and length directly influence parameter uncertainty.	Using trawl survey data to calibrate model predictions of species distribution shifts.
WebAIM Contrast Checker	Online Tool	Ensures visualizations are accessible to all users by verifying color contrast ratios meet WCAG guidelines (AA level).	Checking that confidence intervals on a graph are distinguishable for color-blind users [29].

The Role of Ecospace in an Integrated Ecosystem-Assessment Toolkit

Ecospace is the spatial ecosystem modeling component of the Ecopath with Ecosim (EwE) framework, designed to simulate the spatial-temporal dynamics of marine ecosystems. As a spatially explicit time dynamic model, Ecospace operates on a two-dimensional grid of cells, each representing a specific spatial area with defined characteristics such as habitat type, depth, and environmental parameters [12]. This capability makes it an indispensable tool within Integrated Ecosystem Assessments (IEAs), which aim to incorporate all components of an ecosystem—including human activities—into the decision-making process [77]. The National Oceanic and Atmospheric Administration (NOAA) recognizes IEAs as a formal approach to Ecosystem-Based Management (EBM), providing a framework for balancing trade-offs and evaluating management strategies against desired ecological and socio-economic goals [77].

The integration of Ecospace into the IEA toolkit enables researchers and managers to quantitatively evaluate the potential consequences of spatial management interventions. This is particularly critical for addressing complex issues such as Marine Protected Area (MPA) placement, fishing effort allocation, climate change impacts, and cumulative human stressors on marine ecosystems [12]. By simulating how these factors interact across space and time, Ecospace provides a powerful platform for exploring trade-offs between conservation objectives and socio-economic interests, thereby supporting more informed and effective ecosystem-based management decisions [20] [4].

Ecospace Modeling Framework and Core Components

Theoretical Foundation

Ecospace builds directly upon the mass-balance principles of Ecopath and the time-dynamic simulations of Ecosim. It applies the same set of differential equations used in Ecosim to each functional group within every cell of the spatial grid [12]. The core biomass dynamics for a functional group i in a specific cell at time t are represented by the equation:

[ \frac{dBi}{dt}=gi\cdot\sum\limits{j=1}^{n}Q{ji}-\sum\limits{j=1}^{n}Q{ij}+Ii-(F{it}+ei+M0{it})\cdot B_{it} ]

Where:

• (B_{it}) = biomass of group i at time t
• (g_i) = growth efficiency
• (Q_{ji}) = consumption by predator j on prey i
• (Q_{ij}) = consumption by group i on prey j
• (I_i) = immigration rate
• (F_{it}) = fishing mortality rate
• (e_i) = emigration rate
• (M0_{it}) = other mortality rate [12]

This equation is solved numerically for each cell using an implicit integration method (BDF2, second order backward differentiation) on monthly time steps, effectively capturing the complex spatial-temporal dynamics of marine ecosystems [12].

Spatial Structure and Movement Dynamics

Ecospace represents the spatial extent of an ecosystem using a uniform raster grid where each cell is classified as land or water, with specific habitat attributes [12]. Biomass movement between cells is modeled using an Eulerian approach, treating organism movement as flows between adjacent fixed spatial reference cells. The emigration flow from a given cell is calculated as:

[ B{out,rci}=\sum\limits{d=1}^{4}m{id}\cdot B{rci} ]

Where:

• (B_{out,rci}) = total biomass outflow from a cell at row r and column c for group i
• (m_{id}) = instantaneous movement rate in direction d (up, down, left, or right)
• (B_{rci}) = biomass density in the cell [12]

Table 1: Core Structural Components of an Ecospace Model

Component	Description	Function in Model
Spatial Grid	Uniform raster of cells covering the model domain	Defines spatial resolution and extent of simulations
Habitat Types	Classification of cell types (e.g., depth strata, sediment type)	Determines species habitat preferences and distributions
Functional Groups	Ecological groups representing species or species assemblages	Basis for trophic interactions and biomass dynamics
Fishing Fleets	Representation of fishing sectors with specific characteristics	Applies fishing mortality across spatial cells
Environmental Drivers	Spatially explicit environmental variables (e.g., temperature, primary production)	Influences habitat suitability and species distributions

The movement rates ((m_{id})) vary based on functional group characteristics, habitat preferences, and can be adjusted according to trophic conditions within each cell to model organism responses to predation risk and feeding opportunities [12]. This approach allows Ecospace to realistically simulate how species distribute themselves across seascapes in response to both environmental gradients and ecological interactions.

Application Notes: Implementing Ecospace for Spatial Management

Configuration Protocols

Spatial Domain and Grid Resolution

The initial configuration of an Ecospace model requires careful consideration of the spatial domain and grid resolution. The spatial domain should correspond to the area used for parameterizing the underlying Ecopath and Ecosim models to maintain consistency across the EwE framework [41]. Selecting an appropriate grid cell size involves balancing computational efficiency against model detail. As a general guideline, most Ecospace applications utilize grids ranging from 25×25 to 100×100 cells, though non-square configurations are possible [41].

A pragmatic approach to resolution selection involves implementing three sets of resolutions:

• Coarse resolution (approximately twice the optimal cell size) for rapid model building and testing
• Intermediate resolution for final model implementation
• Fine resolution (approximately half the optimal cell size) to assess whether increased spatial detail significantly improves predictive power [41]

This multi-resolution strategy supports efficient model development while providing insights into scale-dependent processes and uncertainties.

Geospatial Projection Considerations

Ecospace assumes a World Geodetic System (WGS) projection by default, with cell sizes expressed in decimal degrees. The model automatically accounts for cell width tapering with latitude using the relative width calculation:

[ Wi = \cos(Lati) ]

Where:

• (W_i) = relative cell width at row i
• (Lat_i) = latitude in degrees at the cell top [41]

This tapering effect requires corrections in Ecospace calculations for:

• Dispersal rates across north-south cell faces (multiplied by (W_i))
• Fishing effort allocation in gravity models (effort density varies as (E{i,j}=ET{i,j}/W_i))
• Catch and discards from each cell (multiplied by (W_i)) [41]

For models where cell tapering is negligible (e.g., low-latitude applications) or when using coordinate systems expressed in meters, users can activate the "Assume square cells" option to treat the grid as uniform without width tapering [41].

Advanced Modeling Capabilities

Habitat Foraging Capacity Model

The Habitat Foraging Capacity (HFC) model represents a significant advancement in Ecospace, enabling the implementation of species distribution models and environmental drivers to predict spatial-temporal changes in habitat suitability [12] [4]. Unlike the original habitat preference approach, which assigned static preferences to predefined habitat types, the HFC model calculates cell-specific continuous habitat suitability factors based on functional responses to multiple environmental variables.

This capability allows Ecospace to simulate shifting species distributions in response to environmental change, such as climate-driven temperature shifts or alterations in primary production. Implementation involves:

• Defining environmental drivers (e.g., sea surface temperature, chlorophyll concentration)
• Establishing response functions for each functional group to these drivers
• Calibrating habitat suitability using empirical distribution data or expert knowledge [4]

Multi-Stanza and Individual-Based Modeling Options

For modeling age-structured populations, Ecospace offers two solution options:

• Multi-stanza option: Tracks overall age-structured numbers across the map while predicting local abundance variations from spatial movement patterns
• Individual-Based Model (IBM) option: Divides recruitment into discrete packets of individuals and simulates their movements and growth across the spatial grid [12]

The IBM approach provides higher resolution for simulating size- and age-dependent distribution patterns, particularly valuable for modeling fish populations with complex life histories and ontogenetic habitat shifts.

Experimental Protocols for Ecospace Applications

Protocol 1: Marine Protected Area Evaluation

Purpose: To assess the potential ecological and fisheries impacts of proposed Marine Protected Area (MPA) configurations.

Workflow:

• Baseline Configuration:
  • Implement the Ecospace model with current fishing effort distributions
  • Run the model for a 10-year spin-up period to establish stable spatial patterns
  • Validate model outputs against observed species distribution and fishery data [4]

• MPA Scenario Implementation:

  • Define MPA boundaries within the spatial grid
  • Set fishing mortality to zero within MPA boundaries for specified fleets
  • Adjust fishing effort allocation rules to redistribute effort to areas outside MPAs

• Simulation and Analysis:

  • Run projections for 20-30 years under MPA scenarios
  • Compare outcomes with business-as-usual (no MPA) scenarios
  • Quantify changes in biomass trajectories, spatial distribution patterns, and fishery indicators [4]

• Trade-off Assessment:

  • Evaluate conservation benefits (e.g., biomass increases, biodiversity indicators)
  • Assess fisheries impacts (e.g., catch changes, effort redistribution, economic indicators)
  • Identify potential conflicts and synergies between objectives [20]

Table 2: Key Performance Indicators for MPA Evaluation

Indicator Category	Specific Metrics	Calculation Method
Ecological Indicators	Total biomass, Species richness, Trophic structure	ECOIND plugin or custom indicators
Fishery Indicators	Total catch, Catch per unit effort, Revenue	Fleet-specific output metrics
Spatial Indicators	Biomass distribution, Fishing effort redistribution, Connectivity	Spatial analysis of grid cell values
Socio-economic Indicators	Fishery profits, Employment, Community impacts	Integration with economic models

Protocol 2: Climate Change Impact Assessment

Purpose: To evaluate the combined effects of climate change and fisheries management on marine ecosystems.

Workflow:

• Environmental Driver Implementation:
  • Acquire spatially explicit climate projections (e.g., temperature, pH, primary production)
  • Format environmental data to match Ecospace grid resolution and temporal scale
  • Implement drivers through the Spatial Temporal Data Framework (STDF) [12]

• Species Response Parameterization:

  • Define habitat suitability responses for each functional group to environmental variables
  • Calibrate responses using historical data or literature values
  • Validate projected distribution shifts against independent data [4]

• Scenario Development:

  • Develop climate scenarios (e.g., RCP 4.5, RCP 8.5)
  • Combine with management scenarios (e.g., current management, improved management)
  • Establish baseline (historical climate) reference simulations

• Analysis of Results:

  • Quantify climate-driven changes in species distributions and productivity
  • Assess interaction effects between climate change and fisheries management
  • Identify potential climate adaptation strategies [12]

Case Study Applications with Quantitative Outcomes

Southern North Sea Spatial Management

A recent Ecospace application to the southern North Sea demonstrated the model's capability to inform spatial planning conflicts between energy sector development, conservation goals, and fisheries [4]. Researchers implemented the Habitat Foraging Capacity model with habitat preferences derived from species distribution models, comparing two approaches:

• Abundance hot-spots: Based on areas of high species density
• Presence/absence: Based on broader distribution patterns [4]

The study evaluated area closures for fishing in the context of the EU 2030 Biodiversity Strategy, which requires significant marine space reallocation. Using Ecospace with the ECOIND plugin, researchers quantified trade-offs between ecosystem health, food web structure, and fisheries yield under different spatial management scenarios [4]. Key findings included:

• Contrasting interests between energy sector spatial demands and fishing grounds
• Redistribution effects of fishing effort when areas are closed
• Ecosystem health trade-offs under different closure configurations

Deep-Sea Area-Based Management in the Azores

A systematic evaluation of Ecospace for informing area-based management in the deep-sea demonstrated the model's utility for data-poor environments [24]. The application focused on the Azores deep-sea ecosystem, addressing challenges specific to deep-sea management:

• Limited data availability for deep-sea species and processes
• Unique vulnerability of deep-sea ecosystems to human impacts
• Connectivity considerations between shallow and deep systems

The study employed a systematic model assessment framework to evaluate Ecospace performance and identify robust management recommendations despite uncertainties [24]. This approach highlights how Ecospace can be adapted for challenging environments where data limitations might otherwise preclude spatial management.

Table 3: Essential Research Reagent Solutions for Ecospace Modeling

Resource Category	Specific Tools/Functions	Purpose and Application
Spatial Data Framework	Spatial Temporal Data Framework (STDF)	Integrates environmental data (e.g., temperature, primary production) to drive spatiotemporal simulations [12]
Habitat Capacity Model	Habitat Foraging Capacity (HFC)	Predicts cell-specific habitat suitability based on environmental drivers and species responses [12] [4]
Ecological Indicators	ECOIND Plugin	Quantifies ecosystem status through indicators (e.g., trophic structure, biodiversity metrics) for trade-off analysis [4]
Fishing Fleet Module	Effort Allocation Algorithms	Simulates spatial distribution of fishing effort using gravity models based on profitability and costs [41] [12]
Model Validation Tools	Skill Assessment Routine	Provides temporal, spatial, and spatio-temporal evaluation of model performance against observed data [4]

Visualization of Ecospace Workflows

Ecospace Model Implementation Workflow

Spatial-Temporal Dynamics in Ecospace

Ecospace represents a powerful and flexible tool within the Integrated Ecosystem Assessment toolkit, enabling researchers and managers to simulate the complex spatial-temporal dynamics of marine ecosystems under various management scenarios. Its integration of trophic interactions, human activities, and environmental drivers provides a comprehensive platform for evaluating trade-offs and exploring potential futures for marine ecosystems.

The continued development of Ecospace, particularly through advancements like the Habitat Foraging Capacity model and Spatial Temporal Data Framework, has expanded its applicability to address pressing management challenges, including climate change adaptation, marine spatial planning, and ecosystem-based fisheries management. By following standardized protocols and leveraging the essential tools outlined in this document, researchers can effectively apply Ecospace to inform spatial management decisions across diverse marine ecosystems and governance contexts.

Conclusion

Ecospace has evolved into a powerful, flexible tool for spatially explicit ecosystem modeling, moving from simple binary habitat maps to sophisticated frameworks like the Habitat Foraging Capacity model that integrate multiple environmental drivers. Its proven application in diverse scenarios—from deep-sea conservation in the Azores to fisheries management in Nigeria—demonstrates its significant value in informing area-based management. Successful implementation relies on a methodical approach that starts with core components, systematically builds complexity while avoiding correlated drivers, and rigorously validates outputs. The future of Ecospace and similar models lies in greater integration with Earth observation data, enhanced computational efficiency for high-resolution global applications, and continued development to better represent human dimensions and climate change impacts, ultimately strengthening the scientific basis for ecosystem-based management decisions.

References

• 1. Ecopath with Ecosim – Ecopath with Ecosim food web modeling approach [https://ecopath.org/]
• 2. About – Ecopath with Ecosim [https://ecopath.org/about/]
• 3. Overview of the EcoScope ecosystem models using the Ecopath with Ecosim (EwE) suite [https://ecoscopium.eu/news/article/overview-of-the-ecosope-ecosystem-models-using-the-ecopath-with-ecosim-ewe-suite]
• 4. Integrating habitat preferences into Ecospace for spatial ... [https://ecopath.org/portfolio-item/april-2023-ecospace-hab-prefs/]
• 5. Ecosystem-based spatial high-resolution tools for marine ... [https://orbit.dtu.dk/en/projects/ecosystem-based-spatial-high-resolution-tools-for-marine-fisherie/]
• 6. capacity – Ecosystem Habitat with EwE Modelling [https://pressbooks.bccampus.ca/ewemodel/chapter/habitat-capacity/]
• 7. Ecospace Workflow – User Guide for Ecopath with Ecosim ... [https://pressbooks.bccampus.ca/eweguide/chapter/ecospace-workflow/]
• 8. Simulating fishing effort in mixed-fisheries [https://ecopath.org/portfolio-item/spatial-simulation-of-redistribution-of-fishing-effort-in-a-coastal-mixed-fisheries-using-ecospace/]
• 9. Integrating ecosystem services considerations within... | PLOS One [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210936]
• 10. Habitat suitability modelling and range change dynamics of ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2025.1561640/full]
• 11. and Distribution Habitat : with... - Ecospat UNIL Suitability Models [https://www.unil.ch/ecospat/en/home/menuinst/publications/books/hsdm.html]
• 12. Introduction to Ecospace – Ecosystem Modelling with EwE [https://pressbooks.bccampus.ca/ewemodel/chapter/introduction-to-ecospace/]
• 13. Spatial Distribution – User Guide for Ecopath with Ecosim ... [https://pressbooks.bccampus.ca/eweguide/chapter/spatial-distribution/]
• 14. Modelling the spatial shifts of functional groups in ... [https://www.sciencedirect.com/science/article/pii/S0304380023000868]
• 15. Geospatial Projections – User Guide for Ecopath with ... [https://pressbooks.bccampus.ca/eweguide/chapter/geospatial-projections/]
• 16. Fish 501 - 2021 - Ecospace [https://sites.google.com/view/fish501-2021/data/ecospace]
• 17. a case study of the southern North Sea [https://www.sciencedirect.com/science/article/pii/S030438002030260X]
• 18. Spatial-Temporal Data – User Guide for Ecopath with ... [https://pressbooks.bccampus.ca/eweguide/chapter/spatial-temporal-data/]
• 19. Linking hard and soft traits: Physiology, morphology and anatomy... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5873933/]
• 20. Ecosystem-based spatial high-resolution tools for marine ... [https://orbit.dtu.dk/en/projects/ecosystem-based-spatial-high-resolution-tools-for-marine-fisherie]
• 21. MoSCI-ECOSPACE [https://business.esa.int/projects/mosci-ecospace]
• 22. ECOSPACE : a pre-operational satellite system and ... [https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10569/2307898/ECOSPACE---a-pre-operational-satellite-system-and-services/10.1117/12.2307898.full]
• 23. | Animal Behavior Class Notes | Fiveable | Fiveable Habitat preferences [https://fiveable.me/animal-behavior/unit-8/habitat-preferences/study-guide/mnPiWhBaa1PopXm8]
• 24. Systematic evaluation of a spatially explicit ecosystem ... [https://www.sciencedirect.com/science/article/pii/S0964569123003320]
• 25. Extending the human pressures-species response system ... [https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1561347/full]
• 26. Modelling the Mediterranean Sea ecosystem at high ... [https://www.nature.com/articles/s41598-022-18017-x]
• 27. Practical application of spatial ecosystem service models to ... [https://www.sciencedirect.com/science/article/pii/S2212041617302358]
• 28. The development and validation of Spatial Methodology ... [https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-025-00401-1]
• 29. Contrast Checker [https://webaim.org/resources/contrastchecker/]
• 30. The Ecospace model applied to the North Sea: Evaluating ... [https://www.sciencedirect.com/science/article/abs/pii/S0304380014006292]
• 31. Spatial fishery dynamics – Ecosystem Modelling with EwE [https://pressbooks.bccampus.ca/ewemodel/chapter/spatial-fishery-dynamics/]
• 32. Fishing effort displacement drives ecosystem impacts ... [https://www.sciencedirect.com/science/article/pii/S0304380025003229]
• 33. Integrating marine protected areas and spatial fisheries ... [https://www.sciencedirect.com/science/article/pii/S0304380025002649]
• 34. Reproducibility package for Global Expansion Of Marine ... [https://reproducibility.worldbank.org/catalog/276]
• 35. AZORES MPA NETWORK - Blue Azores [https://www.blueazores.org/azores-amp-rede]
• 36. Azores Establishes Largest Marine Protected Area Network ... [https://sevenseasmedia.org/azores-establishes-largest-marine-protected-area-network-in-north-atlantic/]
• 37. Dwindling deep-water fish stocks in the Azores [https://orbit.dtu.dk/en/publications/dwindling-deep-water-fish-stocks-in-the-azores-the-first-quantita/]
• 38. Azores Deep-sea Research — Project ATLAS [https://deepsea.uac.pt/project-1]
• 39. Azores Deep-sea Research — Project MapGES [https://deepsea.uac.pt/project-7]
• 40. Compartive network analyses for Nigerian coastal waters using two ecopath models developed for the years 1985 and 2000 - ScienceDirect [https://www.sciencedirect.com/science/article/abs/pii/S0165783618303849]
• 41. Geospatial Considerations – User Guide for Ecopath with ... [https://pressbooks.bccampus.ca/eweguide/chapter/geospatial-considerations/]
• 42. Managing fisheries and ecosystems: current good ... [https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1640487/full]
• 43. Ecospace spatial-temporal data framework a brief users ... [https://figshare.com/articles/online_resource/Ecospace_spatial-temporal_data_framework_a_brief_users_guide/13551551]
• 44. A state-space approach to understand responses of ... [https://www.nature.com/articles/s42003-021-02585-1]
• 45. Analyses of driving factors on the spatial variations in ... [https://www.sciencedirect.com/science/article/pii/S1470160X22004514]
• 46. Analysis of Driving Factors for Vegetation Ecological ... [https://www.mdpi.com/1999-4907/15/7/1263]
• 47. Predicting Environmental and Ecological Drivers of Human ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10172848/]
• 48. Coupled Multivariate Analyses Reveal Separate Climate ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12202972/]
• 49. Analysis of the Environmental Benefits and Driving Forces ... [https://www.mdpi.com/2073-445X/13/7/1059]
• 50. A Systematic Review of Spatial-Temporal Scale Issues in ... [https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2021.730169/full]
• 51. Spatio-Temporal Foundation Models: Vision, Challenges, ... [https://arxiv.org/html/2501.09045v1]
• 52. A guideline for reporting experimental protocols in life sciences [https://pmc.ncbi.nlm.nih.gov/articles/PMC5978404/]
• 53. Color contrast - Accessibility - MDN Web Docs - Mozilla [https://developer.mozilla.org/en-US/docs/Web/Accessibility/Guides/Understanding_WCAG/Perceivable/Color_contrast]
• 54. Elements must meet minimum color contrast ratio thresholds [https://dequeuniversity.com/rules/axe/4.8/color-contrast]
• 55. Ten strategies towards successful calibration of ... [https://www.sciencedirect.com/science/article/pii/S0022169423003566]
• 56. Ecopath with Ecosim MPA Dynamics plug-in - a brief users' ... [https://figshare.com/articles/code/Ecopath_with_Ecosim_MPA_Dynamics_plug-in_-_a_brief_users_guide/25298758]
• 57. AI model testing: A systematic approach to evaluation loops [https://www.braintrust.dev/articles/systematic-approach-ai-development]
• 58. Large Language Model Evaluation: 10+ Metrics & Methods [https://research.aimultiple.com/large-language-model-evaluation/]
• 59. Quality appraisal of spatial epidemiology and health ... [https://www.sciencedirect.com/science/article/abs/pii/S1353829223001454]
• 60. Evaluation metrics and validation of presence-only species ... [https://www.nature.com/articles/s41598-020-80062-1]
• 61. The development and validation of Spatial Methodology ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12125879/]
• 62. Spatial Validation of Spectral Unmixing Results [https://www.mdpi.com/2072-4292/15/11/2822]
• 63. Microgravity enables differentiated drug products [https://www.varda.com/biopharma/]
• 64. In Space Production: Applications Within Reach [https://www.nasa.gov/missions/station/applications-within-reach/]
• 65. Exobiosphere raises €2 million to develop drugs in space [https://www.labiotech.eu/trends-news/exobiosphere-biotech-space/]
• 66. Emergence of a New Era for Pharmaceutical Industry [https://pubmed.ncbi.nlm.nih.gov/40826996/]
• 67. St3TART-FO: advancing Earth Observation validation - Sentinel [https://sentinels.copernicus.eu/web/success-stories/-/st3tart-fo-advancing-earth-observation-validation]
• 68. Validation of sentinel 2 based machine learning models for ... [https://www.sciencedirect.com/science/article/pii/S1574954125001426]
• 69. Monitoring and benchmarking Earth system model ... [https://gmd.copernicus.org/articles/18/1169/2025/gmd-18-1169-2025-relations.html]
• 70. Machine Learning Algorithms of Remote Sensing Data ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12111970/]
• 71. Communicating uncertainty about facts, numbers and science [https://pmc.ncbi.nlm.nih.gov/articles/PMC6549952/]
• 72. Introduction: The importance of communicating uncertainty [https://eco.confex.com/eco/2009/techprogram/P20268.HTM]
• 73. Numerical models for monitoring and forecasting ocean ... [https://sp.copernicus.org/articles/5-opsr/13/2025/]
• 74. An Overview of the Fundamentals of Data Management ... [https://www.sciencedirect.com/science/article/pii/S0749208123000293]
• 75. Designing for Accessibility: Contrast Ratio [https://www.heroku.com/blog/designing-for-accessibility-contrast-ratio/]
• 76. Color Contrast Guidelines for Text & UI Accessibility [https://daily.dev/blog/color-contrast-guidelines-for-text-and-ui-accessibility]
• 77. Integrated Ecosystem Assessment: Front Page [https://www.integratedecosystemassessment.noaa.gov/]