Social network analysis of mating patterns in American black bears (Ursus americanus)

Abstract

Nonrandom mating can structure populations and has important implications for population-level processes. Investigating how and why mating deviates from random is important for understanding evolutionary processes as well as informing conservation and management. Prior to the implementation of parentage analyses, understanding mating patterns in solitary, elusive species like bears was virtually impossible. Here, we capitalize on a long-term genetic data set collected from black bears (Ursus americanus) (N = 2422) in the Northern Lower Peninsula (NLP) of Michigan, USA. We identified mated pairs using parentage analysis and applied logistic regression (selection) models that controlled for features of the social network, to quantify the effects of individual characteristics, and spatial and population demographic factors on mating dynamics. Logistic regression models revealed that black bear mating was associated with spatial proximity of mates, male age, the time a pair had coexisted, local population density and relatedness. Mated pairs were more likely to contain older males. On average, bears tended to mate with nearby individuals to whom they were related, which does not support the existence of kin recognition in black bears. Pairwise relatedness was especially high for mated pairs containing young males. Restricted dispersal and high male turnover from intensive harvest mortality of NLP black bears are probably the underlying factors associated with younger male bears mating more often with female relatives. Our findings illustrate how harvest has the potential to disrupt the social structure of game species, which warrants further attention for conservation and management.

Spatial genetic patterns indicate mechanism and consequences of large carnivore cohabitation within development

“…The observed changes in philopatry between landscape contexts, in association with a negative relationship between housing density and relatedness, contradict this expectation. While male‐biased dispersal can effectively reduce inbreeding (Costello et al., ), our results build on recent work demonstrating context‐specific variability in black bear dispersal and mating systems (Moore, Xu, Frank, Draheim, & Scribner, ; Roy, Yannic, Côté, & Bernatchez, ), suggesting inbreeding avoidance may not be the evolutionary driver of dispersal behavior. Simulations have shown that, relative to other factors, inbreeding likely contributes very little selective pressure driving the evolution of dispersal (Guillaume & Perrin, ).…”

“…Relative weights on landscape variables were 10 (high), 5 (medium), 1 (low), and 0. observed changes in philopatry between landscape contexts, in association with a negative relationship between housing density and relatedness, contradict this expectation. While male-biased dispersal can effectively reduce inbreeding (Costello et al, 2008), our results build on recent work demonstrating context-specific variability in black bear dispersal and mating systems (Moore, Xu, Frank, Draheim, & Scribner, 2015;Roy, Yannic, Côté, & Bernatchez, 2012), suggesting inbreeding avoidance may not be the evolutionary driver of dispersal behavior.…”

Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore

“…Inferred methods (e.g. parentage/kinship analysis) have therefore been the preferred method of reconstructing kin relationships [31][32][33][34][35][36][37][38][39][40][41]. However, large carnivores are often long-lived and have overlapping generations, making it difficult to obtain the spatial and temporal genetic data needed to construct accurate pedigrees.…”

Detecting black bear source–sink dynamics using individual-based genetic graphs

“…Fourth, land ownership does not limit hunter access as approximately 48-62% of hunters used private land during harvest [55]. Finally, local harvest density has repeatedly been found to be associated with other features of the NLP population, including natal dispersal distance [32] and mate selection [39], based on pedigree analysis.…”

“…In addition, we reclassified land cover data into a coarser habitat suitability classification as either bear habitat (MF, FW; 100) or non-habitat (EF, NFU, AG, NFW, DEV; 1). We used localized harvest density, based on bear harvest locations, as a proxy for local bear population density, as in Moore et al [32,39]. To estimate local harvest density we used annual harvest locations from 2002 -2010, generated kernel density grids in ARCGIS v. 10.0 and reclassified them into a range of 1 -10 (low to high as previously defined [32]).…”