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

Cite this dataset

Moore, Jennifer A. et al. (2015). Data from: Social network analysis of mating patterns in American black bears (Ursus americanus) [Dataset]. Dryad. https://doi.org/10.5061/dryad.c61q0

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.

Usage notes

Location

USA
Michigan