Urbanization can result in the fragmentation of once contiguous natural landscapes into a patchy habitat interspersed within a growing urban matrix. Animals living in fragmented landscapes often have reduced movement among habitat patches because of avoidance of intervening human development, which potentially leads to both reduced gene flow and pathogen transmission between patches. Mammalian carnivores with large home ranges, such as bobcats (Lynx rufus), may be particularly sensitive to habitat fragmentation. We performed genetic analyses on bobcats and their directly transmitted viral pathogen, feline immunodeficiency virus (FIV), to investigate the effects of urbanization on bobcat movement. We predicted that urban development, including major freeways, would limit bobcat movement and result in genetically structured host and pathogen populations. We analysed molecular markers from 106 bobcats and 19 FIV isolates from seropositive animals in urban southern California. Our findings indicate that reduced gene flow between two primary habitat patches has resulted in genetically distinct bobcat subpopulations separated by urban development including a major highway. However, the distribution of genetic diversity among FIV isolates determined through phylogenetic analyses indicates that pathogen genotypes are less spatially structured-exhibiting a more even distribution between habitat fragments. We conclude that the types of movement and contact sufficient for disease transmission occur with enough frequency to preclude structuring among the viral population, but that the bobcat population is structured owing to low levels of effective bobcat migration resulting in gene flow. We illustrate the utility in using multiple molecular markers that differentially detect movement and gene flow between subpopulations when assessing connectivity.

Abstract. When natural marks provide sufficient resolution to identify individual animals, noninvasive sampling using cameras has a number of distinct advantages relative to ''traditional'' mark-recapture methods. However, analyses from photo-identification records often pose additional challenges. For example, it is often unclear how to link left-and rightside photos to the same individual, and previous studies have primarily used data from just one side for statistical inference. Here we describe how a recently developed statistical method can be adapted for integrated mark-recapture analyses using bilateral photo-identification records. The approach works by assuming that the true encounter history for each animal is a latent (unobserved) realization from a multinomial distribution. Based on the type of photo encounter (e.g., right, left, or both sides), the recorded (observed) encounter histories can only arise from certain combinations of these latent histories. In this manner, the approach properly accounts for uncertainty about the true number of distinct animals observed in the study. Using a Markov chain Monte Carlo sampling procedure, we conduct a small simulation study to show that this approach has reasonable properties and outperforms other methods. We further illustrate our approach by estimating population size from bobcat photoidentification records. Although motivated by bilateral photo-identification records, we note that the proposed methodology can be used to combine and jointly analyze other types of mark-recapture data (e.g., photo and DNA records).

Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses do not use all of the partial identity capture histories, reducing precision, and while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM out-performs other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and in the ocelot example, individual sex determined from photographs is used to further resolve partial identities, one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard 2 camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.

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