Condors in space: an individual-based population model for California condor reintroduction planning

Abstract

Context Existing demographic models of California condors have not simultaneously considered individual condor movement paths, the distribution and juxtaposition of release sites, habitat components, or the spatial distribution of threats. Objectives Our objectives were to develop a dynamic spatially explicit and individual-based model (IBM) of California condor demography and to evaluate its ability to replicate empirical data on demography and distribution from California (1995–2019). Methods We built a female-only spatially explicit California condor IBM in HexSim, using a daily timestep that allowed us to simulate the foraging behavior of condors, changes in food distribution and availability, and the ephemeral threat of lead in decaying food resources. Results Simulated population size was highly correlated with annual population census data once the population became established with > 50 females (r2 = 0.99). Mean simulated fecundity and mortality estimates were not significantly different from empirical trends (p > 0.05), although empirical data had higher interannual variability. The geographic distribution of modeled condors was similar to the empirical distribution with an overall accuracy of 79%, a commission error of 27%, and an omission error of 9%. Simulated movement density corresponded moderately well to the density of observed GPS locations (weighted kappa = 0.44). Conclusions We developed a spatially explicit California condor IBM that is well-calibrated to empirical data from California. Given its mechanistic underpinnings and flexibility to incorporate a variety of spatial and demographic inputs, we expect our model to be useful for assessing the relative risks and benefits of future condor reintroduction and management scenarios.

HexFire: A Flexible and Accessible Wildfire Simulator

“…HexSim [24] is a well-established software application that has been used in >60 peer-reviewed publications. Although HexSim models have traditionally been designed to conduct population viability analysis for species of conservation concern [25][26][27][28][29][30][31][32][33][34], use of the platform has expanded to a wider range of topics (e.g., disease models [35]). HexSim and HexFire are spatially explicit and can easily incorporate multiple static or dynamic maps quantifying the amounts and patterns of fuels, moisture levels, wind, ignition sites, management interventions, and other drivers relevant to wildfire.…”