Without effective management, today’s endangered species face a very real risk of extinction. These species have reached their precarious status due to a range of stressors—nearly all driven by human activity—that have led to depressed populations and diminished their capacity for recovery. Preserving these species requires a deep understanding of their ecology and minimizing additional environmental contamination. Our scientists’ knowledge of ecology and ecotoxicology provides the foundation for developing sound conservation strategies. Yet ironically, the very fact that these species are endangered restricts our ability to study them directly. Their status prevents us from conducting lab or in situ experiments, making it more challenging to assess their responses to emerging threats.

This is where population modeling and Waterborne’s experience with multiple modeling frameworks come in. When direct manipulation and measurement are not viable, computational, mathematical, and statistical modeling techniques, well-informed by empirical observation, provide our scientists with practical utility in both improving fundamental understanding of the systems under study and developing scenario-based forecasting abilities. 

Developing these models requires accurately accounting for all pertinent aspects of the species' ecology and physiology within the model structure. Therefore, when developing population models, Waterborne incorporates all viable scientific information to present an empirically vetted representation of the study species. As examples, bee models explicitly account for the details of reproductive cycles and agricultural practices, while fish population models incorporate demographic and age structure. These models utilize the best available ecotoxicological data and can be parameterized to represent existing stressors’ effects on population trajectories. In other words, the very act of building the model produces a self-contained exercise in understanding species traits and their potential stressors. 

Once developed, functional models can be mobilized towards interrogating potential outcomes and assessing risk across different scenarios. This flexibility in simulating scenarios is a critical component of forecasting current and future risk. While forecasting is a difficult exercise because no model is ever perfect and future scenarios are never entirely certain, utilizing modern computational and analysis techniques, our scientists implement both broad consideration of multiple scenario types and detailed simulations of specific scenarios to best fit the question at hand. Furthermore, years of experience conveying results across a variety of species, different modeling frameworks, and different mediums (conference presentations, written reports, etc.) means Waterborne can ensure model form and function is digestible to multiple audiences.