Supplementary material from "Economical defense of resources structures territorial space use in a cooperative carnivore"

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...

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Bibliographic Details
Main Authors: Sells, Sarah N., Mitchell, Michael S., Ausband, David E., Luis, Angela D., Emlen, Douglas J., Podruzny, Kevin M., Gude, Justin A.
Format: Article in Journal/Newspaper
Language:unknown
Published: The Royal Society 2021
Subjects:
Online Access:https://dx.doi.org/10.6084/m9.figshare.c.5762422
https://rs.figshare.com/collections/Supplementary_material_from_Economical_defense_of_resources_structures_territorial_space_use_in_a_cooperative_carnivore_/5762422
Description
Summary: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 grey 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 behavioural 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 behaviour.