Examining patch and landscape-level white-tailed deer connectivity using a novel, buffer and resistance-based metric

Abstract

Context: Landscape connectivity drives many ecological processes and can be quantified using numerous metrics. Few metrics can be interpreted at both patch and landscape-levels, incorporate structural and functional connectivity, and are computationally efficient. Objectives: We sought a generalizable connectivity metric for individual patches and landscapes. An ideal metric could accurately estimate the functional connectivity of white-tailed deer (Odocoileus virginianus). Methods: The Sinuous Connection Reduction (SCR) index is a functional connectivity metric modified from the Probability of Connectivity (PC) index and Equivalent Connected Area (ECA). SCR is calculated by adding patch area between adjacent patches, where the area added is reduced by a factor of the inverse of least-cost-path sinuosity between the patches. A case-study calculates SCR, PC, and ECA for white-tailed deer in NYS, and metrics are compared to historical counts of deer take. Results: SCR can be calculated for individual patches and landscapes, providing a hierarchical understanding of connectivity. Spatial panel regression models indicate ECA is the best fitting metric for white-tailed deer connectivity, followed by SCR and PC. Both PC and ECA are susceptible to boundary effects, and ECA values are partially attributed to landscape size. Geographically weighted regression models indicate opposing relationships between metrics and deer take in different regions of NYS, indicating deer populations are modulated by other locationally-specific factors apart from connectivity. Conclusions: SCR can be considered useful over PC when home-ranges apply, though it requires data-intensive least-cost path modeling. SCR is computationally efficient when modeling landscapes with many disjointed patches and incorporates both functional and structural connectivity.