Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and...

Area of Science:

• Environmental Science
• Ecological Modeling
• Ecosystem Dynamics

Background:

• Cumulative impacts of multiple stressors are a key research priority in environmental science.
• Ecological models, such as Ecopath with Ecosim (EwE), are vital tools for simulating ecosystem component interactions.
• Understanding human impacts requires modeling diverse stressors like climate change, land/sea use, pollution, and invasive species.

Purpose of the Study:

• To review how the Ecopath with Ecosim (EwE) modeling platform has been utilized to study human impacts beyond fisheries.
• To identify research gaps in applying EwE for multiple stressor analysis in aquatic ecosystems.
• To propose avenues for enhancing EwE's utility in multi-stressor research.

Main Methods:

• Conducted a literature review of 166 studies using EwE for non-fisheries stressors, primarily in aquatic environments.
• Analyzed the types of stressors modeled, the number of stressors per study, and the pathways investigated.
• Examined the methods for defining environmental response functions and the statistical research designs employed.

Main Results:

• Physical climate change was the most modeled stressor (60 studies), followed by species introductions (22), habitat loss (21), and eutrophication (20).
• A significant gap exists in studies addressing multiple stressors (only 12% investigated >=3) and their various impact pathways.
• Few studies considered human dimensions (excluding fisheries) or employed advanced statistical designs for stressor attribution (only 3%).

Conclusions:

• While EwE is widely used, its potential for multi-stressor research is limited by a focus on single stressors and simplified impact pathways.
• Gaps in understanding environmental response functions, incorporating human dimensions, and utilizing robust statistical designs hinder comprehensive multi-stressor analysis.
• Integrating EwE with advances in environmental science and computational statistics can address these limitations and improve ecosystem stressor research.