A lifetime of experiences: Modelling habitat quality through cumulative effects on individual survival

Variation in habitat quality affects individual fitness through the accumulation of benefits and costs over time. Although an individual's fitness and susceptibility to mortality are consequences of these past experiences, current analytical models do not quantify the cumulative effects of resources, risks, and environmental conditions on survival. We developed the Survival and Habitat Quality model (SHQ), which redefines survival as a cumulative process and measures habitat quality by its aggregate effect on survival through time. SHQ is an autoregressive time-series model that uses fine-scale tracking data, remotely sensed environmental data, and computational power to quantify the cumulative effects of spatial variation in habitat quality on survival without relying on subjective, user-defined lag effects. We tested SHQ on simulated data and on pronghorn data in South Dakota, USA. Compared to a traditional survival model, SHQ was more precise and accurate at estimating cumulative effects of habitat on survival. Using model output, we were also able to generate maps predicting areas of high and low pronghorn survival. SHQ is a conceptual and methodological advance that explicitly integrates individuals' day-to-day interactions with their surroundings to identify ultimate sources of mortality. The model is a novel and accurate tool for assessing habitat quality and identifying management actions that increase individual survival and population growth. More broadly, SHQ's flexible mathematical framework captures the full spatial and temporal scope of processes affecting survival, providing a powerful means for understanding the environmental basis of fitness.