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Digital biodiversity datasets reveal breeding phenology and its drivers in a widespread North American mammal

Citation

McLean, Bryan; Guralnick, Robert (2021), Digital biodiversity datasets reveal breeding phenology and its drivers in a widespread North American mammal, Dryad, Dataset, https://doi.org/10.5061/dryad.zgmsbcc8w

Abstract

Shifts in reproductive timing are among the most commonly documented responses of organisms to global climate change. However, our knowledge of these responses is biased towards taxa that are easily observable and abundant in existing biodiversity data sets. Mammals are common subjects in reproductive biology, but mammalian phenology and its drivers in the wild remain poorly understood because many species are small, secretive, or labor-intensive to monitor. We took an informatics-based approach to reconstructing breeding phenology in the widespread North American deer mouse (Peromyscus maniculatus) using individual-level reproductive observations from digitized museum specimens and field censuses spanning >100 years and >45 degrees of latitude. We reconstructed female phenology in different regions and tested the importance of three environmental variables (photoperiod, temperature, precipitation) as breeding cues. Photoperiod and temperature were strong positive and negative breeding cues, respectively, while precipitation was not a significant predictor of breeding phenology. However, phenologies and the use of environmental cues varied substantially among regions, and we found evidence that these cueing repertoires are tuned to ecosystem-specific limiting conditions. Our results reiterate the importance of ecological context in optimizing reproduction and demonstrate how harmonization across biodiversity data resources allows new insight into phenology and its drivers in wild mammals.

Funding

National Science Foundation, Award: 144,162,817,598,981,000,000