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.

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.

Canid collision—expanding populations of coyotes (Canis latrans) and crab-eating foxes (Cerdocyon thous) meet up in Panama

The rise of the Panamanian Isthmus 3–4 million years ago enabled the first dispersal of mammals between North and South America in what is known as the Great American Biotic Interchange. Modern deforestation threatens the historic forest connectivity and creates new habitat for open-country species, as documented by recent expansions of North American coyotes (Canis latrans) and South American crab-eating foxes (Cerdocyon thous) into Central America. We used camera traps to map the expansions of these species into eastern Panama and found that, by 2015, coyote populations had colonized most agricultural area west of Lago Bayano. Most of our camera arrays east of this point documented crab-eating foxes, and evidence from roadkills showed some foxes had advanced farther west, but we never documented both species at the same camera-trap array, suggesting the possibility of fine-scale spatial avoidance. We used a data fusion approach to build species distribution models combining our camera surveys with records from the literature and roadkill. While the auxiliary data improved the predictive accuracy for both species, few clear habitat patterns emerged, which might reflect the generalist tendencies of these canids, or the fact that both are in the early stages of colonizing the region. Camera-trap photos showed that both species were nocturnal and revealed some dog-like morphology in coyotes, which could indicate their recent hybridization with dogs (Canis familiaris). Our continued monitoring of the Darién documented single coyotes moving through the western edge of the area in 2016 and 2018. This leaves only the great Darién forests between coyotes and South America. If deforestation continues in the region, these two invasive canids could represent the first of a new, Not-So-Great American Biotic Interchange, where generalist species adapted to human disturbance cross continents and threaten native biota.

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.

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.

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.

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.

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.