Connectivity at home: A data-driven connectivity modeling framework for home range movements in heterogeneous landscapes

Abstract

AbstractLandscape connectivity analyses can serve landscape planning with designing functional conservation networks. However, existing frameworks face challenges, such as achieving consistency between integrated data and modeled processes, implementing data-driven parameterization, and addressing connectivity within the home range. We propose restricting connectivity analyses to the home range scale to better relate landscape resistance, barrier effects and resource accessibility to daily movements and home range establishment.We introduce a home range connectivity modeling framework that allows deriving important connectivity parameters empirically. We identify areas that can support home ranges based on available resources and calculate resource patch accessibility using graph and circuit theory. Resistance values, patch isolation distances, and connectivity metrics are selected from statistical models using movement data. We demonstrate the framework’s utility through a case study on urban blackbirds and increase its applicability by testing whether the use of simple presence/absence data without additional movement data, and the use of a coarser resolution affect the estimation of parameter values.In statistical analyses, the local connectivity showed a strong significant positive association with the probability of blackbird movement (β = 0.23, p < 0.005). We uncover that the connectivity parameters are better assessed with graph theory-derived metrics when parametrized with movement data. We find a high barrier effect of high buildings and a moderate barrier effect of lower buildings and streets on blackbird movement. However, when assessed with presence/absence data, model parametrization can result in similar values only when using circuit theory metrics. Changing the resolution from 10 to 30 m has minimal impact on parametrization results with movement data.Our study showcases a data-driven parameterization using statistical model selection, which addresses several of the main limitations of recent connectivity modeling approaches. Presence/absence data and coarser resolution can be used judiciously, but independently. Restricting analyses to home ranges yields valuable insights into home range ecology, landscape impacts on movement in highly heterogeneous landscapes, and species distribution.