Use of multi‐state models to explore relationships between changes in body condition, habitat and survival of grizzly bears Ursus arctos horribilis

Abstract

One of the principal goals of wildlife research and management is to understand and predict relationships between habitat quality, health of individuals and their ability to survive. Infrequent sampling, non‐random loss of individuals due to mortality and variation in capture susceptibility create potential biases with conventional analysis methods. To account for such sampling biases, we used a multi‐state analytical approach to assess relationships between habitat, health and survival of grizzly bears Ursus arctos horribilis over a 10‐year period along the east slopes of the Canadian Rockies in Alberta, Canada. We defined bear health states by body condition estimated from the relationship between weight and body length. We used a sequential model building process to first account for potential sampling biases, and then explored changes in body condition relative to habitat use and survival. Bears that used regenerating forest habitats (mostly due to forest harvesting) containing a diversity of age classes were more likely to see gains in their body condition, whereas bears that used older forests were more likely to see reductions in body condition. Survival rate was reduced most by road densities which in turn were positively correlated with regenerating forest habitat. Human activities which promote young regenerating forests, such as forest harvesting, therefore promotes improved health (increased body condition) in bears, but are offset by reductions in survival rates. Multi‐state analyses represents a robust analytical tool when dealing with complex relationships and sampling biases that arise from dynamic environments.

Factors associated with black bear density and implications for management

“…Similar scenarios to Washington have been observed for interior grizzly bear populations occupying areas with timber harvest activities whereby regenerating vegetation may provide bears with better nutrition, yet an increased susceptibility to human-caused mortality through road access (Ciarniello et al 2007, Boulanger et al 2013). In several of these areas, density was determined to be a function of habitat, based on food availability, and road density (Lamb et al 2018) or mortality risk (Boulanger et al 2018).…”

The Impact of Roads on the Demography of Grizzly Bears in Alberta

“…This larger study initially focused on remote sensing based habitat mapping [22] and collecting grizzly bear location data for the creation of regionally appropriate resource selection function mapping [21] for the period 1999–2005. After this time the research focus altered to monitoring individual bears in relation to anthropogenic landscape conditions and change in Alberta [12] , [13] and bear health characteristics [8] , [23] – [25] . Grizzly bears were captured and collared each spring (May-June) between 1999–2012 using either helicopter aerial darting, leg-hold snares, and culvert traps [26] , [27] .…”

“…One of the primary factors that has reduced grizzly bear populations in some portions of North America, has been the effects of unsustainable human caused mortality which has been linked to the creation of human access into prime bear habitat [1] – [3] . Roads have also affected movements and distribution of bears [4] – [6] , changes in behavior relative to roads [7] , and changes in body condition and survival rates relative to roads [8] , and have caused fragmentation of populations [9] .…”

Linking intraspecific trait variation to community abundance dynamics improves ecological predictability by revealing a growth–defence trade‐off

“…For trait change to substantially influence community dynamics, it must be sufficiently rapid to influence demographic processes on an ecological timescale (Berg & Ellers, 2010;Ellner, Geber, & Hairston, 2011;Hairston, Ellner, Geber, Yoshida, & Fox, 2005;Thompson, 1998). Observations of rapid trait change, in response to densityinduced phenotypic plasticity or evolutionary selection processes, are increasingly being reported (Becks, Ellner, Jones, & Hairston, 2010;Bolnick et al, 2011;Boulanger, Cattet, Nielsen, Stenhouse, & Cranston, 2013;Kusch, 1993;Losos, Schoener, & Spiller, 2004;Pettorelli, Hilborn, Duncan, & Durant, 2015;Relyea & Auld, 2004;Travis et al, 2014). When rapid trait change substantially impacts ecological rates, abundance-only based ecological theories will poorly explain community dynamics and may give unreliable predictions of future abundances (Ellner & Becks, 2011;Schreiber, Bürger, & Bolnick, 2011;Shertzer, Ellner, Fussmann, & Hairston, 2002;Strauss, 2014).…”