The Role of Assumptions in Predictions of Habitat Availability and Quality

Abstracting a complex reality into ecological models composed of maps, diagrams, and mathematical equations forces modelers to organize information, distinguish essential from superfluous components, and define relationships among variables. Within this context, an assumption is a premise, stated or unstated, which characterizes model variables and relationships as essential or irrelevant to the model’s ­setting and purpose. For example, assumptions about how species interact with their environment at a specific time and place can be used to justify the thematic, spatial, and temporal extent and grain of the input data, given a model’s intended application. These assumptions also justify the use or rejection of specific model variables, parameters, and mathematical functions describing the relationship between focal species and their environment. Furthermore, models improve over time through incremental steps of testing assumptions as hypotheses to establish empirical knowledge. Hence, the utility of any habitat model is both empowered by and limited by its assumptions. Therefore it is critical that project objectives and ecological theory inform assumptions, rather than allowing these decisions to be driven by data availability and knowledge gaps.