Using decoys and camera traps to estimate depredation rates and neonate survival.

Ungulate neonates-individuals less than four weeks old-typically experience the greatest predation rates, and variation in their survival can influence ungulate population dynamics. Typical methods to measure neonate survival involve capture and radio-tracking of adults and neonates to discover mortality events. This type of fieldwork is invasive and expensive, can bias results if it leads to neonate abandonment, and may still have high uncertainty about the predator species involved. Here we explore the potential for a non-invasive approach to estimate an index for neonate survival using camera traps paired with decoys that mimic white-tailed deer (Odocoileus virginianus) neonates in the first month of life. We monitored sites with camera traps for two weeks before and after the placement of the neonate decoy and urine scent lure. Predator response to the decoy was classified into three categories: did not approach, approached within 2.5 m but did not touch the decoy, or physically touched the decoy; when conducting survival analyses, we considered these second two categories as dead neonates. The majority (76.3%) of the predators approached the decoy, with 51.1% initiating physical contact. Decoy probability of survival was 0.31 (95% CI = 0.22, 0.35) for a 30-day period. Decoys within the geographic range of American black bear (Ursus americanus) were primarily (75%) attacked by bears. Overall, neonate survival probability decreased as predator abundance increased. The camera-decoy protocol required about ½ the effort and 1/3 the budget of traditional capture-track approaches. We conclude that the camera-decoy approach is a cost-effective method to estimate a neonate survival probability index based on depredation probability and identify which predators are most important.

Ungulate responses and habituation to unmanned aerial vehicles in Africa's savanna.

This article tests the hypothesis that "the likelihood that the species will react and level at which they do to the unmanned aerial vehicle (UAV) is related to the altitude, number of passes, sound intensity, type of UAV, takeoff distance, and species." This paper examined the behavioral responses of a group of free ranging ungulate species (Oryx, Kudu, Springbok, Giraffe, Eland, Hartebeest, and Impala) found in an animal reserve in Namibia to the presence of different in-flight UAV models. The study included 397 passes (trials) over 99 flights at altitudes ranging from 15 to 55 meters in three categories of response level: No response, Alert, and Movement. The ungulates were unhabituated to the UAVs and the study was conducted in the presence of stress-inducing events that occur naturally in the environment. Certain species were found to be more reactive than others, in addition to several displaying different response levels in single or mixed herd environments. Zebras were found to be less responsive in mixed herd environments while Oryx were present, as compared to when the Oryx were not; suggesting that some species may respond based on other species perception of threat or their relative fitness levels. The UAVs also produced inconsistent response rates between movement and alert behavior. The reference vehicle, Phantom 3 was much more likely than the Mavic to induce an alert response, while both having similar probabilities of inducing a movement response. Furthermore, the Custom X8 showed significantly more alert and movement responses than the other UAVs. This shows there may be several aspects to the UAVs that affect the responses of the ungulates. For instance, the sound intensity may alert the species more often, but close proximity may induce a movement response. More generally, the data shows that when the UAV is flying above 50 meters and has a measured sound intensity below 50 dB, the likelihood of inducing a movement response on an ungulate species is below 6% regardless of the vehicle on the first pass over the animals. Additionally, with each subsequent pass the likelihood of response dropped by approximately 20 percent. The results suggest a stronger correlation between flight altitude and response across the different ungulates, and the evidence suggests rapid habituation to the UAVs.

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.

LEPTOSPIRA, PARVOVIRUS, AND TOXOPLASMA IN THE NORTH AMERICAN RIVER OTTER (LONTRA CANADENSIS) IN NORTH CAROLINA, USA.

The North American river otter (Lontra canadensis) is the largest mustelid in North Carolina, US, and was once extirpated from the central and western portions of the state. Over time and after a successful reintroduction project, otters are now abundant and occur throughout North Carolina. However, there is a concern that diseases may have an impact on the otter population, as well as on other aquatic mammals, either through exposure to emerging diseases, contact with domestic animals such as domestic cats (Felis catus), or less robust condition of individuals through declines in water quality. We tested brain and kidney tissue from harvested otters for the pathogens that cause leptospirosis, parvovirus, and toxoplasmosis. Leptospirosis and toxoplasmosis are priority zoonoses and are maintained by domestic and wild mammals. Although parvovirus is not zoonotic, it does affect pets, causing mild to fatal symptoms. Across the 2014-15 and 2015-16 trapping seasons, we tested 220 otters (76 females, 144 males) using real-time PCR for Leptospira interrogans, parvovirus, and Toxoplasma gondii. Of the otters tested, 1% (3/220) were positive for L. interrogans, 19% (41/220) were positive for parvovirus, and 24% (53/220) were positive for T. gondii. Although the pathogens for parvovirus and toxoplasmosis are relatively common in North Carolina otters, the otter harvest has remained steady and the population appears to be abundant and self-sustaining. Therefore, parvovirus and toxoplasmosis do not currently appear to be negatively impacting the population. However, subsequent research should examine transmission parameters between domestic and wild species and the sublethal effects of infection.

Metal contamination of river otters in North Carolina.

Aquatic apex predators are vulnerable to environmental contaminants due to biomagnification. North American river otter (Lontra canadensis) populations should be closely monitored across their range due to point and nonpoint pollution sources. Nonetheless, no information exists on environmental contaminants in the North Carolina otter population. Metals and metalloids occur naturally across the landscape, are essential for cellular function, and become toxic when concentrated unnaturally. We conducted our study across the three Furbearer Management Units (FMU) and 14 river basins of North Carolina. We determined the concentrations of arsenic, cadmium, calcium, cobalt, copper, iron, lead, magnesium, manganese, mercury, molybdenum, selenium, thallium, and zinc in liver and kidney samples from 317 otters harvested from 2009 to 2016. Arsenic, lead, and thallium samples were tested at levels below the limit of detection. With the exception of cadmium, we detected all other elements at higher levels in the liver compared with the kidney. Specifically, cadmium, cobalt, copper, iron, magnesium, manganese, mercury, molybdenum, and zinc levels differed by tissue type analyzed. Most element concentrations remained stable or increased with otter age. We detected higher levels of mercury and selenium in the Lower Pee Dee and Cape Fear river basins. River basins within the Mountain FMU were higher in cadmium, copper, iron, lead, and zinc, whereas the Coastal Plain FMU was lower in cobalt and manganese. None of the elements occurred at toxic levels. Our research establishes baseline concentration levels for North Carolina, which will benefit future monitoring efforts and provide insight into future changes in the otter population.

Bird community shifts associated with saltwater exposure in coastal forests at the leading edge of rising sea level.

Rising sea levels dramatically alter the vegetation composition and structure of coastal ecosystems. However, the implications of these changes for coastal wildlife are poorly understood. We aimed to quantify responses of avian communities to forest change (i.e., ghost forests) in a low-lying coastal region highly vulnerable to rising sea level. We conducted point counts to sample avian communities at 156 forested points in eastern North Carolina, USA in 2013-2015. We modelled avian community composition using a multi-species hierarchical occupancy model and used metrics of vegetation structure derived from Light Detection and Ranging (LiDAR) data as covariates related to variation in bird responses. We used this model to predict occupancy for each bird species in 2001 (using an analogous 2001 LiDAR dataset) and 2014 and used the change in occupancy probability to estimate habitat losses and gains at 3 spatial extents: 1) the entire study area, 2) burned forests only, and 3) unburned, low-lying coastal forests only. Of the 56 bird species we investigated, we observed parameter estimates corresponding to a higher likelihood of occurring in ghost forest for 34 species, but only 9 of those had 95% posterior intervals that did not overlap 0, thus having strong support. Despite the high vulnerability of forests in the region to sea level rise, habitat losses and gains associated with rising sea level were small relative to those resulting from wildfire. Though the extent of habitat changes associated with the development of ghost forest was limited, these changes likely are more permanent and may compound over time as sea level rises at an increasing rate. As such, the proliferation of ghost forests from rising sea level has potential to become an important driver of forest bird habitat change in coastal regions.

Resolving misaligned spatial data with integrated species distribution models.

Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change-of-support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser-quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

Is the Red Wolf a Listable Unit Under the US Endangered Species Act?

Defining units that can be afforded legal protection is a crucial, albeit challenging, step in conservation planning. As we illustrate with a case study of the red wolf (Canis rufus) from the southeastern United States, this step is especially complex when the evolutionary history of the focal taxon is uncertain. The US Endangered Species Act (ESA) allows listing of species, subspecies, or Distinct Population Segments (DPSs) of vertebrates. Red wolves were listed as an endangered species in 1973, and their status remains precarious. However, some recent genetic studies suggest that red wolves are part of a small wolf species (C. lycaon) specialized for heavily forested habitats of eastern North America, whereas other authors suggest that red wolves arose, perhaps within the last ~400 years, through hybridization between gray wolves (C. lupus) and coyotes (C. latrans). Using published genetic, morphological, behavioral, and ecological data, we evaluated whether each evolutionary hypothesis would lead to a listable unit for red wolves. Although the potential hybrid origin of red wolves, combined with abundant evidence for recent hybridization with coyotes, raises questions about status as a separate species or subspecies, we conclude that under any proposed evolutionary scenario red wolves meet both criteria to be considered a DPS: they are Discrete compared with other conspecific populations, and they are Significant to the taxon to which they belong. As population-level units can qualify for legal protection under endangered-species legislation in many countries throughout the world, this general approach could potentially be applied more broadly.

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease.

A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at out-break. We derive a Bayesian on-line estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.

Integrating multiple data sources in species distribution modeling: a framework for data fusion.

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

The ecology of microscopic life in household dust.

We spend the majority of our lives indoors; yet, we currently lack a comprehensive understanding of how the microbial communities found in homes vary across broad geographical regions and what factors are most important in shaping the types of microorganisms found inside homes. Here, we investigated the fungal and bacterial communities found in settled dust collected from inside and outside approximately 1200 homes located across the continental US, homes that represent a broad range of home designs and span many climatic zones. Indoor and outdoor dust samples harboured distinct microbial communities, but these differences were larger for bacteria than for fungi with most indoor fungi originating outside the home. Indoor fungal communities and the distribution of potential allergens varied predictably across climate and geographical regions; where you live determines what fungi live with you inside your home. By contrast, bacterial communities in indoor dust were more strongly influenced by the number and types of occupants living in the homes. In particular, the female : male ratio and whether a house had pets had a significant influence on the types of bacteria found inside our homes highlighting that who you live with determines what bacteria are found inside your home.

Fungi identify the geographic origin of dust samples.

There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.

Efficient use of information in adaptive management with an application to managing recreation near golden eagle nesting sites.

It is generally the case that a significant degree of uncertainty exists concerning the behavior of ecological systems. Adaptive management has been developed to address such structural uncertainty, while recognizing that decisions must be made without full knowledge of how a system behaves. This paradigm attempts to use new information that develops during the course of management to learn how the system works. To date, however, adaptive management has used a very limited information set to characterize the learning that is possible. This paper uses an extension of the Partial Observable Markov Decision Process (POMDP) framework to expand the information set used to update belief in competing models. This feature can potentially increase the speed of learning through adaptive management, and lead to better management in the future. We apply this framework to a case study wherein interest lies in managing recreational restrictions around golden eagle (Aquila chrysaetos) nesting sites. The ultimate management objective is to maintain an abundant eagle population in Denali National Park while minimizing the regulatory burden on park visitors. In order to capture this objective, we developed a utility function that trades off expected breeding success with hiker access. Our work is relevant to the management of human activities in protected areas, but more generally demonstrates some of the benefits of POMDP in the context of adaptive management.

Guidelines for a priori grouping of species in hierarchical community models.

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process "borrows" from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Addressing structural and observational uncertainty in resource management.

Most natural resource management and conservation problems are plagued with high levels of uncertainties, which make good decision making difficult. Although some kinds of uncertainties are easily incorporated into decision making, two types of uncertainty present more formidable difficulties. The first, structural uncertainty, represents our imperfect knowledge about how a managed system behaves. The second, observational uncertainty, arises because the state of the system must be inferred from imperfect monitoring systems. The former type of uncertainty has been addressed in ecology using Adaptive Management (AM) and the latter using the Partially Observable Markov Decision Processes (POMDP) framework. Here we present a unifying framework that extends standard POMDPs and encompasses both standard POMDPs and AM. The approach allows any system variable to be observed or not observed and uses any relevant observed variable to update beliefs about unknown variables and parameters. This extends standard AM, which only uses realizations of the state variable to update beliefs and extends standard POMDP by allowing more general stochastic dependence among the observable variables and the state variables. This framework enables both structural and observational uncertainty to be simultaneously modeled. We illustrate the features of the extended POMDP framework with an example.