DOI: 10.5061/dryad.h65f4
title: Data from: Logging-induced changes in habitat network connectivity shape behavioral interactions in the wolf-caribou-moose system

description: Habitat connectivity influences the distribution dynamics of animals.
 Connectivity can therefore shape trophic interactions, but little
 empirical evidence is available, especially for large mammals. In forest
 ecosystems, logging alters functional connectivity among habitat patches,
 and such activities can affect the spatial game between large herbivores
 and their predators. We used graph theory to evaluate how
 harvesting-induced changes in habitat connectivity influence patch choice
 and residency time of GPS-collared caribou (Rangifer tarandus caribou) and
 moose (Alces alces) in winter in the boreal forest. We then investigated
 the predator–prey game by assessing how GPS-collared wolves (Canis lupus)
 adjusted their movements to changes in landscape properties and in the
 networks of their prey species. We built prey habitat networks using
 minimum planar graphs organized around species-specific, highly selected
 habitat patches (i.e., network nodes). We found that spatial dynamics of
 large herbivores were influenced not only by the intrinsic quality of
 habitat patches, but also by the connectivity of those network nodes.
 Caribou and moose selected nodes that were connected by a high number of
 links, and moose also spent relatively more time in those nodes. By
 limiting node accessibility, human disturbances influenced travel
 decisions. Caribou and moose avoided nodes that were surrounded by a high
 proportion of cuts and roads, but once within these nodes, moose stayed
 longer than in other nodes. Caribou selectively moved among nodes with low
 distance costs, and their residency time increased with distance costs
 required to reach the nodes. Wolves selected their prey's nodes,
 where vegetation consumed by caribou and moose was highly abundant.
 Furthermore, wolves discriminated among those nodes by selecting the most
 connected ones. In fact, selection by wolves was stronger for their
 prey's nodes than for the prey's utilization distribution per
 se, a difference that increased with the level of human disturbance.
 Considering the difficulty of keeping track of highly mobile prey,
 predators may benefit by targeting not only their prey's resource
 patches, but also the most highly connected patches. Matrix quality and
 connectivity are therefore key elements shaping the predator–prey spatial
 game in human-altered landscapes because of their impact on the spatial
 dynamics of the interacting species.
description: Data for compositional
 analysis of caribouData used to
 characterize the land cover type of node of network for caribou. Used
 proportions of various land cover types are estimated based on the
 observed GPS locations. Available proportions of various land cover types
 are calculated based on the 100% minimum convex
 polygon.Compositional_Analysis_Caribou.xlsxData for compositional analysis of mooseData used to characterize the land cover type of node of network for moose. Used proportions of various land cover types are estimated based on the observed GPS locations. Available proportions of various land cover types are calculated based on the 100% minimum convex polygon.Compositional_Analysis_Moose.xlsxGraph biological relevanceData used to model the exponential relationship between the percentage of links in a given network and the percentage of observed inter-patch movements of caribou and moose that occurred between connected nodes for that individual, in a given year (2005-2009) (Figure 3).Graph_biological_relevance.xlsData for conditional logistic regression for caribouData used to model the relative probability of used an inter-patch movement for caribou in winter as a function of network connectivity and landscape features (Tables 2 and 3). Covariates are provided at observed inter-patch movement and at available inter-patch movement.Interpatch_Caribou.xlsData for conditional logistic regression for mooseData used to model the relative probability of used an inter-patch movement for moose in winter as a function of network connectivity and landscape features (Tables 2 and 3). Covariates are provided at observed inter-patch movement and at available inter-patch movement.Interpatch_Moose.xlsData for mixed-effects Cox model for caribouData used to model the risk of patch departure for caribou in winter as a function of network connectivity and landscape features (Table 4).ResidenceTime_Caribou.xlsData for mixed-effects Cox model for mooseData used to model the risk of patch departure for moose in winter as a function of network connectivity and landscape features (Table 4).ResidenceTime_Moose.xlsxData for RSF analysis of caribouData used to model the relative probability of occurrence of caribou in winter as a function of landscape features (Table 1). Landscape features (various land cover types and variation of elevation within a 50-m radius buffer) are provided at observed GPS locations and at random locations.RSF_Caribou.xlsData for RSF analysis of mooseData used to model the relative probability of occurrence of moose in winter as a function of landscape features (Table 1). Landscape features (various land cover types and variation of elevation within a 50-m radius buffer) are provided at observed GPS locations and at random locations.RSF_Moose.xlsData for RSF analysis of wolfData used to model the relative probability of occurrence of wolf in winter as a function of features of the networks of its prey (Tables 5 and 6). Network features are provided at observed GPS locations and at random locations.RSF_Wolf.xlsData for SSF analysis of wolfData used to model the relative probability of making steps for wolf in winter as a function of step length and features of the networks of its prey (Table 7). Covariates are provided at observed steps and at random steps.SSF_Wolf.xls