DePerno, C. S. | Search bioRxiv

bioRxiv2022

Genetic architecture and evolution of color variation in American black bears

Color variation is a frequent evolutionary substrate for camouflage in small mammals but the underlying genetics and evolutionary forces that drive color variation in natural populations of large mammals are mostly unexplained. The American black bear, Ursus americanus, exhibits a range of colors including the cinnamon morph which has a similar color to the brown bear, U. arctos, and is found at high frequency in the American southwest. Reflectance and chemical melanin measurements showed little distinction between U. arctos and cinnamon U. americanus individuals. We used a genome-wide association for hair color as a quantitative trait in 151 U. americanus individuals and identified a single major locus (P < 10-13). Additional genomic and functional studies identified a missense alteration (R153C) in Tyrosinase-related protein 1 (TYRP1) that impaired protein localization and decreased pigment production. Population genetic analyses and demographic modeling indicated that the R153C variant arose 9.36kya in a southwestern population where it likely provided a selective advantage, spreading both northwards and eastwards by gene flow. A different TYRP1 allele, R114C, contributes to the characteristic brown color of U. arctos, but is not fixed across the range. HIGHLIGHTSO_LIThe cinnamon morph of American black bears and brown bears have different missense mutations in TYRP1 that account for their similar coloration C_LIO_LITYRP1 variants in American black bears and brown bears are loss-of-function alleles associated with impaired protein localization to melanosomes C_LIO_LIIn American black bears, the variant causing the cinnamon morph arose 9,360 years ago in the western lineage where it provides an adaptive advantage, and has spread northwards and eastwards by migration C_LI

evolutionary biology↗

bioRxiv2020

A comprehensive framework for handling location error in animal tracking data

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. Current approaches to dealing with location error largely fall into two categories--either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilatcralizcd animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilatcralized acoustic and cellular location data. We apply our error-modelselection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed, and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. Because error-induced biases depend on many factors--sampling schedule, movement characteristics, tracking device, habitat, etc.--differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.

ecology↗