Anyone who has studied biology has come across this sentence: Ecology is the study of how living organisms interact with each other and their environment. This simple definition, actually hides a tremendous complexity that’s at the root of our ecological modeling work. To begin with, the broad “environment” encompasses physical surroundings which can be hard to understand on their own. All one has to do is consider the hydrodynamic laws that explain river flow or the climate and its unpredictable changes at various spatial scales to understand that “environment” is an overly simple descriptor. Then there are all the interactions among organisms themselves: competition, mating, predation, parasitism, and mutualism, just to name a few. Each of these interactions can potentially influence the others in a never-ending feedback loop. Considered together, the simple “environment” is revealed to be a complex world where scientists, particularly ecological modelers, thrive. 

A crucial tool for chemical risk assessment and environmental management, ecological modeling provides a means for investigating complex interactions of environmental stressors and their impact on natural resources. To get to the heart of those complex interactions, Waterborne’s scientists develop and apply mathematical models to describe an ecological system. Through ecological modeling, we can examine a network of interactions and run various simulations without expending costly resources required for laboratory or field studies.

Similar to how an artist would draw a human body by first studying how each part works together, our ecological modelers begin by identifying the main features that define a system before representing them through mathematical formalism. For example, when representing the life cycle of an organism, processes such as growth, reproduction, and death are usually considered and accounted for in mathematical equations. The more information we have, the greater the number of processes that can be depicted in the model. A mathematical model grounded on scientific knowledge (and good-quality data) provides a means for investigating the complex ecological interactions and the effects of environmental stressors. This, in turn, expands our understanding of the studied system. By developing and applying mathematical models to describe an ecological system, we can examine different scenarios without expending the costly resources required for laboratory or field studies.

Ecological models also have the capacity to focus on various levels of organism complexity, from the individual to the population or entire ecosystem. For these reasons, ecological modeling can be an applied solution to inform regulatory risk assessment questions or to investigate environmental concerns including impacts of climate change, habitat restoration, or multi-stressor concerns. For example, these models can answer:

• How can we use data at the individual level to infer stressor effects at the population or community level? 
• How do exposure effects on a species translate across the web chain (e.g., on the non-exposed predators)? 
• Which biological and ecological factors play a major role in population recovery? 
• Which remediation effort could be the most efficient? 

Our modelers also address the challenge of assessing exposure risk for listed species for which only a few data are available. Using trait-based approaches, they infer exposure effects on exposed, data-poor species by studying data-reach species that are closely related. While similar modeling approaches can be applied to potentially any species, Waterborne’s modelers have found this a particularly useful tool when considering fish and pollinators.

By combining expertise in multiple modeling approaches, applications of available models and development of novel tools, our ecological modelers are able to answer the complex questions associated with chemical risk assessment and environmental management.