The evolutionary mechanisms generating the tremendous biodiversity of islands have long fascinated evolutionary biologists. Genetic drift and divergent selection are predicted to be strong on islands and both could drive population divergence and speciation. Alternatively, strong genetic drift may preclude adaptation. We conducted a genomic analysis to test the roles of genetic drift and divergent selection in causing genetic differentiation among populations of the island fox (Urocyon littoralis). This species consists of 6 subspecies, each of which occupies a different California Channel Island. Analysis of 5293 SNP loci generated using Restriction-site Associated DNA (RAD) sequencing found support for genetic drift as the dominant evolutionary mechanism driving population divergence among island fox populations. In particular, populations had exceptionally low genetic variation, small Ne (range = 2.1–89.7; median = 19.4), and significant genetic signatures of bottlenecks. Moreover, islands with the lowest genetic variation (and, by inference, the strongest historical genetic drift) were most genetically differentiated from mainland gray foxes, and vice versa, indicating genetic drift drives genome-wide divergence. Nonetheless, outlier tests identified 3.6–6.6% of loci as high FST outliers, suggesting that despite strong genetic drift, divergent selection contributes to population divergence. Patterns of similarity among populations based on high FST outliers mirrored patterns based on morphology, providing additional evidence that outliers reflect adaptive divergence. Extremely low genetic variation and small Ne in some island fox populations, particularly on San Nicolas Island, suggest that they may be vulnerable to fixation of deleterious alleles, decreased fitness, and reduced adaptive potential.
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 location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. 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. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals 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. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.
Summary Listeria monocytogenes is the causative agent of the foodborne illness listeriosis, which can result in severe symptoms and death in susceptible humans and other animals. L. monocytogenes is ubiquitous in the environment and isolates from food and food processing, and clinical sources have been extensively characterized. However, limited information is available on L. monocytogenes from wildlife, especially from urban or suburban settings. As urban and suburban areas are expanding worldwide, humans are increasingly encroaching into wildlife habitats, enhancing the frequency of human–wildlife contacts and associated pathogen transfer events. We investigated the prevalence and characteristics of L. monocytogenes in 231 wild black bear capture events between 2014 and 2017 in urban and suburban sites in North Carolina, Georgia, Virginia and United States, with samples derived from 183 different bears. Of the 231 captures, 105 (45%) yielded L. monocytogenes either alone or together with other Listeria. Analysis of 501 samples, primarily faeces, rectal and nasal swabs for Listeria spp., yielded 777 isolates, of which 537 (70%) were L. monocytogenes. Most L. monocytogenes isolates exhibited serotypes commonly associated with human disease: serotype 1/2a or 3a (57%), followed by the serotype 4b complex (33%). Interestingly, approximately 50% of the serotype 4b isolates had the IVb‐v1 profile, associated with emerging clones of L. monocytogenes. Thus, black bears may serve as novel vehicles for L. monocytogenes, including potentially emerging clones. Our results have significant public health implications as they suggest that the ursine host may preferentially select for L. monocytogenes of clinically relevant lineages over the diverse listerial populations in the environment. These findings also help to elucidate the ecology of L. monocytogenes and highlight the public health significance of the human–wildlife interface.
Mortalities from collisions with vehicles have created concern for the welfare of the San Clemente Island fox (Urocyon littoralis clementae); 1 of only 6 genetically distinct subspecies of island fox. To find possible solutions for minimizing these mortalities, we compared 9 characteristics of roads and roadsides at kill-sites and control-sites to ascertain whether certain features were associated with risk of collisions. We found that kill-sites were positively associated with the volume of traffic, and negatively associated with the distance of motorists' visibility, which had not been previously identified for island foxes. Additionally, visual obstructions along roadsides (i.e., steep ditches and tall vegetation) showed some evidence of increasing mortalities. We also found that gravel mounds, a possible pseudo-barrier along roadsides, were associated with reduced mortalities. Speeds of vehicles, presence of drainages, cacti, and culverts, and seasonality showed minimal effects on road-kills. Our findings suggest that efforts to reduce mortalities should focus on roads with high volumes of traffic and high amounts of visual obstruction for motorists. Possible methods for reducing road-kills include installing signs and speed bumps on curves of roads, regular mowing of roadsides, constructing gravel-mound barriers along edges of roads, and educating motorists. ß 2011 The Wildlife Society.
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