Tuesday, February 24th, 2026
Marine ecosystems are complex networks of interactions. Species feed on one another, compete for resources, respond to environmental shifts and are shaped by human activities such as fishing and coastal development. On the long run, ecological and socio-economic impacts are produced. Understanding these interdependencies has been a central task in marine science for decades. One of the most widely used tools for doing this is the Ecopath with Ecosim suite, often referred to as EwE. A recent EcoTwin report, “Preliminary report on framework of causal models”, highlighted how this modelling approach aligns naturally with modern ideas from causal inference”.
EwE as a modelling framework comprises three core components: Ecopath, Ecosim, and Ecospace. Ecopath provides a static, mass-balanced snapshot of energy and biomass flows within an ecosystem. The central principle is that production equals the sum of predation, catches, natural mortality and other losses. Ecopath’s strength is its clarity. It forces researchers to account for every major flow of biomass in a given year, making it easier to understand the trophic web structure, the energy flux through the system and the level of uncertainties.
Ecosim builds on this static mass-balanced model by adding time dynamics. Instead of simply looking at one equilibrium snapshot, Ecosim uses differential equations to model how biomasses change year by year in response to drivers such as fishing pressure, predation, environmental changes and management interventions both historically and into the future. This feature allows researchers to test management scenarios such as increasing/reducing fishing effort or simulating climate-driven changes in productivity.
Ecospace then takes these dynamics and maps them onto geographic space. It divides a region into a grid of cells and models how biomass moves between them and how environmental conditions, habitat suitability, and human activity shape spatial patterns of abundance. This spatially explicit layer is particularly important for coastal and shelf systems where species and pressures vary widely by location.
The EcoTwin report explored several case studies that demonstrate its use. Examples include applications in the Celtic Sea, the North Aegean Sea, and the Pagasitikos Gulf, each focusing on how climate change, fishing pressure, and governance influence ecological outcomes. These case studies illustrate the versatility of the modelling framework and its long history of informing fisheries management and marine spatial planning.
The report then makes a compelling observation. Although EwE has traditionally been viewed as an ecosystem modelling tool, its structure aligns closely with the principles of causal inference. The equations in Ecopath resemble structural causal equations. Each interaction term, such as a predation rate or consumption flow, can be interpreted as a causal pathway linking one species to another.
The report highlights that when researchers run Ecosim scenarios, they are effectively performing causal interventions. For example, if one changes fishing mortality on a target species within an Ecosim model, this corresponds to the causal act of fixing a variable at a particular value. In the language of causal inference, this is analogous to applying the do operator. The Ecosim engine then propagates the consequences of this intervention through the trophic network, producing predicted ecological outcomes.
The trophic networks represented in EwE can also be viewed as directed graphs. Each species or functional group is a node. Each trophic interaction is an edge with a particular strength and direction. This graphical representation mirrors the structure of modern causal graphs, which use nodes and arrows to represent cause-and-effect relationships. The report notes that tools like Mixed Trophic Impact analysis, which quantify the influence of one group on another, provide a form of causal sensitivity analysis embedded within the EwE framework.
Seen through this lens, EwE becomes more than a descriptive model. It becomes a causal scaffold. This reframing has significant implications for policy and management. It allows existing ecosystem studies to be interpreted as causal assessments. For example, if an EwE model suggests that reducing fishing effort by 50% results in a 20% increase in biomass for a particular species, this can be understood not simply as a simulated correlation but as an estimated causal effect generated by a well-specified model. The socio-economic implications of such change are the focus of Ecotwin.
The report argues that combining EwE with causal inference techniques can strengthen marine decision-making. By embedding EwE structures within a broader causal framework, researchers can formalise their assumptions, identify confounding variables, and test alternative causal hypotheses. This integration also opens the door to more advanced analyses, such as probabilistic reasoning, counterfactual simulation, and the identification of leverage points. These are locations in the system where interventions produce the greatest effect.
The EwE suite has long been an important tool for understanding marine ecosystems and their dynamics. The EcoTwin report shows that it also provides a natural foundation for causal reasoning. Its network structure, energy balance equations, and scenario testing capabilities align well with the principles of causal modelling. By viewing EwE through this lens, researchers and policymakers gain a clearer understanding of the mechanisms driving ecological change and a more rigorous basis for evaluating interventions. As digital twins of the ocean become more capable and data-driven, this blend of ecosystem modelling and causal inference will play an increasingly central role.