Spatially explicit integrated population models

Summary Studies of demographic processes are typically restricted to small geographic areas and short time periods due to the costs of marking and monitoring individuals. However, environmental changes are occurring at much broader spatial and temporal scales, and thus, inferences about the mechanisms governing population dynamics need to be scaled accordingly. Recently developed integrated population models (IPMs) represent an approach for doing so, by jointly analysing survey data and capture–recapture data. Although promising, several shortcomings of conventional IPMs exist, including difficulties accounting for spatial variation in demographic, movement and detection parameters; limited ability to make spatially explicit predictions of abundance or vital rates; and a requirement that the survey data and the capture–recapture data are independent. We demonstrate how each of these limitations can be resolved by adopting a spatial population dynamics model upon which both the survey data and the capture–recapture data are conditioned. We applied the model to 6 years of hair data collected on the threatened Louisiana black bear Ursus americanus luteolus. For years in which the hair samples were genotyped, the resulting data are information-rich (but expensive) spatial capture–recapture (SCR) data. For the remaining years, the data are binary detection data, of the type often analysed using occupancy models. We compared estimates of demographic parameters and annual abundance using various combinations of the SCR and detection data, and found that combining the SCR data and the detection data resulted in more precise estimates of abundance relative to estimates that did not use the detection data. A simulation study provided additional evidence of increased precision, as well as evidence that the estimators of annual abundance are approximately unbiased. The ability to combine survey data and capture–recapture data using a spatially explicit model opens many possibilities for designing cost effective studies and scaling up inferences about the demographic processes influencing spatial and temporal population dynamics.

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