Tavis Forrester

Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus)

Findings suggest that North American white-tailed deer commonly harbor cryptic infection with the only known New World mammalian Plasmodium. Malaria parasites of the genus Plasmodium are diverse in mammal hosts, infecting five mammalian orders in the Old World, but were long considered absent from the diverse deer family (Cervidae) and from New World mammals. There was a description of a Plasmodium parasite infecting a single splenectomized white-tailed deer (WTD; Odocoileus virginianus) in 1967 but none have been reported since, which has proven a challenge to our understanding of malaria parasite biogeography. Using both microscopy and polymerase chain reaction, we screened a large sample of native and captive ungulate species from across the United States for malaria parasites. We found a surprisingly high prevalence (up to 25%) and extremely low parasitemia of Plasmodium parasites in WTD throughout the eastern United States. We did not detect infections in the other ungulate species nor in western WTD. We also isolated the parasites from the mosquito Anopheles punctipennis. Morphologically, the parasites resemble the parasite described in 1967, Plasmodium odocoilei. Our analysis of the cytochrome b gene revealed two divergent Plasmodium clades in WTD representative of species that likely diverged 2.3 to 6 million years ago, concurrent with the arrival of the WTD ancestor into North America across Beringia. Multigene phylogenetic analysis placed these clades within the larger malaria parasite clade. We document Plasmodium parasites to be common in WTD, endemic to the New World, and as the only known malaria parasites from deer (Cervidae). These findings reshape our knowledge of the phylogeography of the malaria parasites and suggest that other mammal taxa may harbor infection by endemic and occult malaria parasites.

Cats are Rare Where Coyotes Roam

Domestic cats (Felis catus) have caused the extinction of many island species and are thought to kill many billions of birds and mammals in the continental United States each year. However, the spatial distribution and abundance of cats and their risk to our protected areas remains unknown. We worked with citizen scientists to survey the mammals at 2,117 sites in 32 protected areas and one urban area across 6 states in the eastern United States using camera traps. We found that most protected areas had high levels of coyote (Canis latrans) activity, but few or no domestic cats. The relative abundance of domestic cats in residential yards, where coyotes were rare, was 300 times higher than in the protected areas. Our spatial models of cat distribution show the amount of coyote activity and housing density are the best predictors of cat activity, and that coyotes and cats overlap the most in small urban forests. Coyotes were nocturnal at all sites, while cats were nocturnal in protected areas, but significantly more diurnal in urban sites. We suggest that the ecological impact of free-ranging cats in the region is concentrated in urban areas or other sites, such as islands, with few coyotes. Our study also shows the value of citizen science for conducting broadscale mammal surveys using photo-vouchered locations that ensure high data quality.

Visual Informatics Tools for Supporting Large-Scale Collaborative Wildlife Monitoring with Citizen Scientists

Collaborative wildlife monitoring and tracking at large scales will help us understand the complex dynamics of wildlife systems, evaluate the impact of human actions and environmental changes on wildlife species, and answer many important ecological and evolutionary research questions. To support collaborative wildlife monitoring and research, we need to develop integrated camera-sensor networking systems, deploy them at large scales, and develop advanced computational and informatics tools to analyze and manage the massive wildlife monitoring data. In this paper, we will cover various aspects of the design of such systems, including (1) long-lived integrated camera-sensor system design, (2) image processing and computer vision algorithms for animal detection, segmentation, tracking, species classification, and biometric feature extraction, (3) cloud-based data management, (4) crowd-sourcing based image annotation with citizen scientists, and (5) applications to wildlife and ecological research.

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 multispecies occupancy model for two or more interacting species

Summary Species occurrence is influenced by environmental conditions and the presence of other species. Current approaches for multispecies occupancy modelling are practically limited to two interacting species and often require the assumption of asymmetric interactions. We propose a multispecies occupancy model that can accommodate two or more interacting species. We generalize the single-species occupancy model to two or more interacting species by assuming the latent occupancy state is a multivariate Bernoulli random variable. We propose modelling the probability of each potential latent occupancy state with both a multinomial logit and a multinomial probit model and present details of a Gibbs sampler for the latter. As an example, we model co-occurrence probabilities of bobcat (Lynx rufus), coyote (Canis latrans), grey fox (Urocyon cinereoargenteus) and red fox (Vulpes vulpes) as a function of human disturbance variables throughout 6 Mid-Atlantic states in the eastern United States. We found evidence for pairwise interactions among most species, and the probability of some pairs of species occupying the same site varied along environmental gradients; for example, occupancy probabilities of coyote and grey fox were independent at sites with little human disturbance, but these two species were more likely to occur together at sites with high human disturbance. Ecological communities are composed of multiple interacting species. Our proposed method improves our ability to draw inference from such communities by permitting modelling of detection/non-detection data from an arbitrary number of species, without assuming asymmetric interactions. Additionally, our proposed method permits modelling the probability two or more species occur together as a function of environmental variables. These advancements represent an important improvement in our ability to draw community-level inference from multiple interacting species that are subject to imperfect detection.

Do occupancy or detection rates from camera traps reflect deer density

Camera trapping is a powerful tool for studying mammal populations over large spatial scales. Density estimation using camera-trap data is a commonly desired outcome, but most approaches only work for species that can be individually recognized, and researchers studying most mammals are typically constrained to measures of site occupancy or detection rate. These 2 metrics are often used as measures of relative abundance and presumed to be related directly to animal density. To test this relationship, we estimated density, occupancy, and detection rate of male white-tailed deer (Odocoileus virginianus) using camera-trap data collected from 1,199 cameras across 20 study sites. Detection rate and density exhibited stronger positive linear correlation (r2 = 0.80) than occupancy and density (r2 = 0.27). When hunted and unhunted paired areas were compared, detection rate and density showed the same trend between paired sites 62.5% of the time compared to 87.5% for occupancy and density. In particular, agreement between estimates was lowest for pairs of sites that had the largest differences in surrounding housing density. Although it is clear occupancy and detection rate contain some information about density, models suggested different ecological relationships associated with the metrics. Using occupancy or detection rate as proxies for density may be particularly problematic when comparing between areas where animals might to move or behave differently, such as urban–wild interfaces. In such cases, alternate methods of density approximation are recommended.

An Open Standard for Camera Trap Data

Camera traps that capture photos of animals are a valuable tool for monitoring biodiversity. The use of camera traps is rapidly increasing and there is an urgent need for standardization to facilitate data management, reporting and data sharing. Here we offer the Camera Trap Metadata Standard as an open data standard for storing and sharing camera trap data, developed by experts from a variety of organizations. The standard captures information necessary to share data between projects and offers a foundation for collecting the more detailed data needed for advanced analysis. The data standard captures information about study design, the type of camera used, and the location and species names for all detections in a standardized way. This information is critical for accurately assessing results from individual camera trapping projects and for combining data from multiple studies for meta-analysis. This data standard is an important step in aligning camera trapping surveys with best practices in data-intensive science. Ecology is moving rapidly into the realm of big data, and central data repositories are becoming a critical tool and are emerging for camera trap data. This data standard will help researchers standardize data terms, align past data to new repositories, and provide a framework for utilizing data across repositories and research projects to advance animal ecology and conservation.

Mammal communities are larger and more diverse in moderately developed areas

Developed areas are thought to have low species diversity, low animal abundance, few native predators, and thus low resilience and ecological function. Working with citizen scientist volunteers to survey mammals at 1427 sites across two development gradients (wild-rural-exurban-suburban-urban) and four plot types (large forests, small forest fragments, open areas and residential yards) in the eastern US, we show that developed areas actually had significantly higher or statistically similar mammalian occupancy, relative abundance, richness and diversity compared to wild areas. However, although some animals can thrive in suburbia, conservation of wild areas and preservation of green space within cities are needed to protect sensitive species and to give all species the chance to adapt and persist in the Anthropocene.