Gut microbiomes (GMBs), complex communities of microorganisms inhabiting the gastrointestinal tracts of their hosts, perform countless micro-ecosystem services such as facilitating energy uptake and modulating immune responses. While scientists increasingly recognize the role GMBs play in host health, the role of GMBs in wildlife ecology and conservation has yet to be realized fully. Here, we use brown bears (Ursus arctos) as an ecological model to (1) characterize GMB community composition associated with location, season, and reproductive condition of a large omnivore; (2) investigate how both extrinsic and intrinsic factors influence GMB community membership and structure; and (3) quantify differences in GMB communities among different locations, seasons, sex, and reproductive conditions. To achieve these aims, we subsampled brown bear fecal samples collected during United States National Park Service research activities at three National Parks and Preserves (Katmai, Lake Clark, and Gates of the Arctic) and extracted microbial DNA for 16S rRNA amplicon sequencing and microbial taxonomic classification. We analyzed GMB communities using alpha and beta diversity indices, subsequently using linear mixed models to examine relationships between alpha diversity and extrinsic and intrinsic factors. Katmai brown bears hosted the greatest alpha diversity, whereas Gates brown bears hosted the least alpha diversity. Our results indicate that location and diet drive GMB variation, with bears hosting less phylogenetic diversity as park distance inland increases. Monitoring brown bear GMBs could enable managers to quickly detect and assess the impact of environmental perturbations on brown bear health. By integrating macro and micro-ecological perspectives we aim to inform local and landscape-level management decisions to promote long-term brown bear conservation and management.
The internal mechanisms responsible for modulating physiological condition, particularly those performed by the gut microbiome (GMB), remain under-explored in wildlife. However, as latitudinal and seasonal shifts in resource availability occur, the myriad micro-ecosystem services facilitated by the GMB may be especially important to wildlife health and resilience. Here, we use brown bears (Ursus arctos) as an ecological model to quantify the relationship between wildlife body condition metrics that are commonly used to assess individual and population-level health and GMB community composition and structure. To achieve these aims, we subsampled brown bear fecal samples collected during United States National Park Service research activities at three National Parks and Preserves (Katmai, Lake Clark, and Gates of the Arctic) and extracted microbial DNA for 16S rRNA amplicon sequencing and microbial taxonomic classification. We analyzed GMB communities using alpha diversity indices, subsequently using Spearman’s correlation analysis to examine relationships between alpha diversity and brown bear health metrics. We found no differences in GMB composition among bears with differing body conditions, nor any correlations between alpha diversity and body condition. Our results indicate that GMB composition reflects diverse foraging strategies while allowing brown bears to achieve similar body condition outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.