Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore

Abstract

A major unresolved question for omnivorous carnivores, like most species of bears, is to what degree are populations influenced by bottom–up (food supply) or top–down (human‐caused mortality) processes. Most previous work on bear populations has focused on factors that limit survival (top–down) assuming little effect of food resource supply. When food resources are considered, most often they consider only the availability/supply of a single resource, particularly marine‐subsidized or terrestrial sources of protein (carnivory) or alternately hard or soft mast (frugivory). Little has been done to compare the importance of each of these factors for omnivorous bears or test whether complementary resources better explain individual animal and population measures such as density, vital rates, and body size. We compared landscape patterns of digestible energy (kcal) for buffaloberry (a key source of carbohydrate) and ungulate matter (a key source of protein and lipid) to local measures in grizzly bear Ursus arctos abundance at DNA hair snag sites in west‐central Alberta, Canada. We tested support for bottom–up hypotheses in either single (carnivory [meat] versus frugivory [fruit]) or complementary (additive or multiplicative) food resources, while accounting for a well‐known top–down limiting factor affecting bear survival (road density). We found support for both top–down and bottom–up factors with complementary resources (co‐limitation) supported over single resource supplies of either meat or fruit. Our study suggests that the availability of food resources that provide complementary nutrients is more important in predicting local bear abundance than single foods or nutrients (e.g. protein) or simply energy per se. This suggests a nutritionally multidimensional bottom–up limitation for a low density interior population of grizzly bears.

Effects of habitat quality and access management on the density of a recovering grizzly bear population

“…Many species are inventoried for immediate management needs such as assessing population status, investigating causes of decline or managing harvest. Analyses similar to ours could be a value‐added component to many inventories around the world because the link between top‐down and bottom‐up influences on animal density is a key ecological question (Nielsen, Larsen, Stenhouse, & Coogan, ), with many immediate applications.…”

“…Analyses similar to ours could be a value-added component to many inventories around the world because the link between top-down and bottom-up influences on animal density is a key ecological question (Nielsen, Larsen, Stenhouse, & Coogan, 2017), with many immediate applications.…”

Factors associated with black bear density and implications for management

“…We treated standardized NPP values as a linear covariate of D such that we assumed a positive β NPP would support the hypothesis that bear density is directly related to abundance of natural foods. Because general productivity indices may not always be the best way to represent important bear foods (Nielsen et al ), we used forest type (U.S. Environmental Protection Agency; https://www.epa.gov/eco-research/; accessed 12 Apr 2016) as a broad‐scale category such that type and abundance of food items remain consistent within each category: subalpine fir and lodgepole pine forests, Douglas fir and western redcedar forests, and Douglas fir and ponderosa pine forests. Because of forest management practices that have altered the underlying landscape, we created a management type raster in the WNC using ArcMap 10.4 that grouped the area by land ownership.…”

Spatial Heterogeneity of the Forest Canopy Scales with the Heterogeneity of an Understory Shrub Based on Fractal Analysis

“…Aspen canopies have been found to transmit 14-40% of incident light, compared to 5-11% for those dominated by white spruce [24], with light transmission decreasing with spruce abundance in mixedwoods [24,25]. Light variability can structure understory shrub patterns at both fine spatial scales [82] and the stand-level [83], with model-based sunlight being an important predictor of buffaloberry shrub presence [84]. Evergreen trees may additionally decrease local soil moisture content, pH, and temperature [16,[85][86][87][88] which could reduce buffaloberry growth, therefore suggesting that the overall negative effect of the evergreen canopy on shrub presence may not be fully explained by light availability.…”

“…Other environmental factors affecting both canopy and buffaloberry spatial patterns may therefore have contributed to our findings, and it is not likely that these are exclusively due to differential regulatory effects of canopy types on understory resources such as light. Mean annual temperature, growing season precipitation, soil texture, and soil pH have been identified as important predictors of buffaloberry presence [84], and presumably also influence the distribution of tree species in the study area. These considerations point to complex linkages between forest overstory and understory heterogeneity.…”