Institutional networks in cooperative population management: Exploring patterns in transfer fulfillment.

Long-term sustainability of ex-situ animal populations is important for zoos and aquariums, but challenging due to inconsistent compliance with Breeding and Transfer Plans. Transfer recommendations are key to promoting the sustainability of ex-situ animal populations by ensuring cohesive populations, genetic diversity, and demographic stability, but factors affecting their fulfillment are poorly understood. We used a network analysis framework to analyze data compiled from PMCTrack from 2011 to 2019 for three taxonomic classes (mammals, birds, and reptiles/amphibians) within the Association of Zoos and Aquariums to assess factors affecting transfer recommendation fulfillment. Of 2505 compiled transfer recommendations spanning 330 Species Survival Plan® (SSP) Programs and 156 institutions, 1628 (65%) of them were fulfilled. Transfers were most likely to be fulfilled between institutions in close proximity with an established relationship. Annual operating budget, SSP Coordinator experience, number of staff, and diversity of Taxonomic Advisory Groups in which an institution participates also influenced transfer recommendations and/or fulfillment, but effects varied with taxonomic class. Our results suggest that current practices of focusing on transfers between institutions in close proximity are working to maximize transfer success and that institutions with larger budgets and some degree of taxonomic specialization play important roles in promoting success. Success could be further enhanced by building reciprocal transfer relationships and encouraging further development of relationships between smaller institutions and larger ones. These results emphasize the utility of examining animal transfers using a network approach, which accounts for attributes of both sending and receiving institutions, describing novel patterns otherwise left undetected.

Soil Properties and Moisture Synergistically Influence Nontuberculous Mycobacterial Prevalence in Natural Environments of Hawai'i.

Nontuberculous mycobacteria (NTM) are opportunistic pathogens that cause chronic pulmonary disease (PD). NTM infections are thought to be acquired from the environment; however, the basal environmental factors that drive and sustain NTM prevalence are not well understood. The highest prevalence of NTM PD cases in the United States is reported from Hawai'i, which is unique in its climate and soil composition, providing an opportunity to investigate the environmental drivers of NTM prevalence. We used microbiological sampling and spatial logistic regression complemented with fine-scale soil mineralogy to model the probability of NTM presence across the natural landscape of Hawai'i. Over 7 years, we collected and microbiologically cultured 771 samples from 422 geographic sites in natural areas across the Hawaiian Islands for the presence of NTM. NTM were detected in 210 of these samples (27%), with Mycobacterium abscessus being the most frequently isolated species. The probability of NTM presence was highest in expansive soils (those that swell with water) with a high water balance (>1-m difference between rainfall and evapotranspiration) and rich in Fe-oxides/hydroxides. We observed a positive association between NTM presence and iron in wet soils, supporting past studies, but no such association in dry soils. High soil-water balance may facilitate underground movement of NTM into the aquifer system, potentially compounded by expansive capabilities allowing crack formation under drought conditions, representing further possible avenues for aquifer infiltration. These results suggest both precipitation and soil properties are mechanisms by which surface NTM may reach the human water supply. IMPORTANCE Nontuberculous mycobacteria (NTM) are ubiquitous in the environment, being found commonly in soils and natural bodies of freshwater. However, little is known about the environmental niches of NTM and how they relate to NTM prevalence in homes and other human-dominated areas. To characterize NTM environmental associations, we collected and cultured 771 samples from 422 geographic sites in natural areas across Hawai'i, the U.S. state with the highest prevalence of NTM pulmonary disease. We show that the environmental niches of NTM are most associated with highly expansive, moist soils containing high levels of iron oxides/hydroxides. Understanding the factors associated with NTM presence in the natural environment will be crucial for identifying potential mechanisms and risk factors associated with NTM infiltration into water supplies, which are ultimately piped into homes where most exposure risk is thought to occur.

Empirical evaluation of the spatial scale and detection process of camera trap surveys.

BACKGROUND: Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship. METHODS: We deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios. RESULTS: We document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14-71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p. CONCLUSIONS: Occupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies.

Carolina critters: a collection of camera-trap data from wildlife surveys across North Carolina.

Camera trap surveys are useful to understand animal species population trends, distribution, habitat preference, behavior, community dynamics, periods of activity, and species associations with environmental conditions. This information is ecologically important, because many species play important roles in local ecosystems as predators, herbivores, seed dispersers, and disease vectors. Additionally, many of the larger wildlife species detected by camera traps are economically important through hunting, trapping, or ecotourism. Here we present a data set of camera-trap surveys from 6,043 locations across all 100 counties of North Carolina, USA from 2009 to 2019. These data come from 26 survey initiatives and contain 215,108 records of 36 mammal species and three species of terrestrial birds. This large data set increases the geographical distribution data for these 39 mammal and bird species by >500% over what is available for North Carolina in the Global Biodiversity Information Facility (GBIF). These data can be used to conduct inquiries about species, populations, communities, or ecosystems, and to produce useful information on wildlife behavior, distribution, and interactions. There are no copyright restrictions. Please cite this paper when using the data for publication.

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August-24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

Listeria monocytogenes at the human-wildlife interface: black bears (Ursus americanus) as potential vehicles for Listeria.

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.

A two-species occupancy model accommodating simultaneous spatial and interspecific dependence.

Occupancy models are popular for estimating the probability a site is occupied by a species of interest when detection is imperfect. Occupancy models have been extended to account for interacting species and spatial dependence but cannot presently allow both factors to act simultaneously. We propose a two-species occupancy model that accommodates both interspecific and spatial dependence. We use a point-referenced multivariate hierarchical spatial model to account for both spatial and interspecific dependence. We model spatial random effects with predictive process models and use probit regression to improve efficiency of posterior sampling. We model occupancy probabilities of red fox (Vulpes vulpes) and coyote (Canis latrans) with camera trap data collected from six mid-Atlantic states in the eastern United States. We fit four models comprising a fully factorial combination of spatial and interspecific dependence to two-thirds of camera trapping sites and validated models with the remaining data. Red fox and coyotes each exhibited spatial dependence at distances > 0.8 and 0.4 km, respectively, and exhibited geographic variation in interspecific dependence. Consequently, predictions from the model assuming simultaneous spatial and interspecific dependence best matched test data observations. This application highlights the utility of simultaneously accounting for spatial and interspecific dependence.

A spatial mark--resight model augmented with telemetry data.

Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark--resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. We developed a spatial mark--resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark--resight study of raccoons (Procyon lotor) on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina, USA. We estimated a population of 186.71 +/- 14.81 individuals, which translated to a density of 8.29 +/- 0.66 individuals/km2 (mean +/- SD). The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.