| Summary: | Manuscript Abstract: Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using gray wolves ( Canis lupus ). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs, and tradeoffs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk, and seasonality. It accomplished this using simple behavioral rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behavior. Model & Data Abstract: In support of the above manuscript, all model files and data to re-create the analyses for the manuscript are included on Dryad. The model can be run in NetLogo (installation file included), using the associated input files to build the Montana landscape for wolves. Expertise in NetLogo is strongly recommended for using this model. Output files are likewise included along with code to create each plot in the manuscript and SI. Software files for the model and code to create each plot in the manuscript are located at ...
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