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At least 883 records · Page 49Linked to original sources
USGS2019

Survival cost to relocation does not reduce population self‐sustainability in an amphibian

Relocations are increasingly popular among wildlife managers despite often low rates of relocation success in vertebrates. In this context, understanding the influence of extrinsic (e.g., relocation design, habitat characteristics) and intrinsic factors (e.g., age and sex) on demographic parameters, such as survival, that regulate the dynamics of relocated populations is critical to improve relocation protocols and better predict relocation success. We investigated survival in naturally established and relocated populations of yellow‐bellied toads ( Bombina variegata ), an amphibian that was nearly extinct in Belgium by the late 1980s. We quantified survival at three ontogenetic stages (juvenile, subadult, and adult) in the relocated population, the source population, and a control population. In the relocated population, we quantified survival in captive bred individuals and their locally born descendants. Then, using simulations, we examined how survival cost to relocation affects the self‐sustainability of the relocated population. We showed that survival at juvenile and subadult stages was relatively similar in all populations. In contrast, relocated adult survival was lower than adult survival in the source and control populations. Despite this, offspring of relocated animals (the next generation, regardless of life stage) survived at similar rates to individuals in the source and control populations. Simulations revealed that the relocated population was self‐sustaining under different scenarios and that the fate (e.g., stability or increase) of the simulated populations was highly dependent on the fecundity of relocated adults and their offspring. To summarize, our results indicate that survival in relocated individuals is lower than in non‐relocated individuals but that this cost (i.e., reduced survival) disappears in the second generation. A finer understanding of how relocation affects demographic processes is an important step in improving relocation success of amphibians and other animals.

Northern France and Belgium↗
USGS2021

Evaluation of camera trap-based abundance estimators for unmarked populations

Estimates of species abundance are critical to understand population processes and to assess and select management actions. However, capturing and marking individuals for abundance estimation, while providing robust information, can be economically and logistically prohibitive, particularly for species with cryptic behavior. Camera traps can be used to collect data at temporal and spatial scales necessary for estimating abundance, but the use of camera traps comes with limitations when target species are not uniquely identifiable (i.e., “unmarked”). Abundance estimation is particularly useful in the management of invasive species, with herpetofauna being recognized as some of the most pervasive and detrimental invasive vertebrate species. However, the use of camera traps for these taxa presents additional challenges with relevancy across multiple taxa. It is often necessary to use lures to attract animals in order to obtain sufficient observations, yet lure attraction can influence species’ landscape use and potentially induce bias in abundance estimators. We investigated these challenges and assessed the feasibility of obtaining reliable abundance estimates using camera-trapping data on a population of invasive brown treesnakes ( Boiga irregularis ) in Guam. Data were collected using camera traps in an enclosed area where snakes were subject to high-intensity capture–recapture effort, resulting in presumed abundance of 116 snakes (density = 23/ha). We then applied spatial count, random encounter and staying time, space to event, and instantaneous sampling estimators to photo-capture data to estimate abundance and compared estimates to our presumed abundance. We found that all estimators for unmarked populations performed poorly, with inaccurate or imprecise abundance estimates that limit their usefulness for management in this system. We further investigated the sensitivity of these estimators to the use of lures (i.e., violating the assumption that animal behavior is unchanged by sampling) and camera density in a simulation study. Increasing the effective distances of a lure (i.e., lure attraction) and camera density both resulted in biased abundance estimates. Each estimator rarely recovered truth or suffered from convergence issues. Our results indicate that, when limited to unmarked estimators and the use of lures, camera traps alone are unlikely to produce abundance estimates with utility for brown treesnake management.

Ecological Applications↗
USGS2022

Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.

Wyoming↗
USGS2023

Supervised versus unsupervised approaches to classification of accelerometry data

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised ( n = 6), semi-supervised ( n = 1), and unsupervised ( n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors ( Gymnogyps californianus ). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

Ecology and Evolution↗
USGS2024

Lesser prairie-chicken dispersal after translocation: Implications for restoration and population connectivity

Conservation translocations are frequently inhibited by extensive dispersal after release, which can expose animals to dispersal-related mortality or Allee effects due to a lack of nearby conspecifics. However, translocation-induced dispersals also provide opportunities to study how animals move across a novel landscape, and how their movements are influenced by landscape configuration and anthropogenic features. Translocation among populations is considered a potential conservation strategy for lesser prairie-chickens ( Tympanuchus pallidicinctus ). We determined the influence of release area on dispersal frequency by translocated lesser prairie-chickens and measured how lesser prairie-chickens move through grassland landscapes through avoidance of anthropogenic features during their dispersal movements. We translocated 411 lesser prairie-chickens from northwest Kansas to southeastern Colorado and southwestern Kansas in 2016–2019. We used satellite GPS transmitters to track 115 lesser prairie-chickens throughout their post-release dispersal movements. We found that almost all lesser prairie-chickens that survived from their spring release date until June undergo post-translocation dispersal, and there was little variation in dispersal frequency by release area (96% of all tracked birds, 100% in Baca County, Colorado, 94% in Morton County, Kansas, n = 55). Dispersal movements (male: 103 ± 73 km, female: 175 ± 108 km, n = 62) led to diffusion across landscapes, with 69% of birds settling >5 km from their release site. During dispersal movements, translocated lesser prairie-chickens usually travel by a single 3.75 ± 4.95 km dispersal flight per day, selecting for steps that end far from roads and in Conservation Reserve Program (CRP) grasslands. Due to this “stepping stone” method of transit, landscape connectivity is optimized when <5 km separates grassland patches on the landscape. Future persistence of lesser prairie-chicken populations can be aided through conservation of habitat and strategic placement of CRP to maximize habitat connectivity. Dispersal rates suggest that translocation is better suited to objectives for regional, rather than site-specific, population augmentation for this species.

Colorado, Kansas↗
USGS2024

Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection

Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer ( Odocoileus hemionus ) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer ( n = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; n = 8) and others in which CWD was not detected ( n = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32/91 deaths attributed to CWD and 27/91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than starved deer. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals who are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around 4 months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.

Wyoming↗
USGS2015

Integrated survival analysis using an event-time approach in a Bayesian framework

Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (&le; 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at <5 days old and were lower for chicks with larger birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

Ecology and Evolution↗
USGS2018

An analytical framework for estimating aquatic species density from environmental DNA

Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross-validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.

Ecology and Evolution↗
USGS2019

Complex immune responses and molecular reactions to pathogens and disease in a desert reptile (Gopherus agassizii)

Immune function plays an important role in an animal's defense against infectious disease. In reptiles, immune responses may be complex and counterintuitive, and diagnostic tools used to identify infection, such as induced antibody responses are limited. Recent studies using gene transcription profiling in tortoises have proven useful in identifying immune responses to various intrinsic and extrinsic stressors. As part of a larger experiment with Mojave desert tortoises ( Gopherus agassizii ), we facilitated the transmission of the pathogenic bacteria, Mycoplasma agassizii (Myag), to naïve adults and measured innate and induced immune reactions over time. Specifically, we evaluated clinical condition, presence of Myag in the nasal/oral cavity, induced antibody responses specific to Myag, and measured molecular reactions (gene transcript profiles) in 15 captive tortoises classified as naïve, exposed, or infected and 14 wild tortoises for comparison. Myag was confirmed inside the nasal/oral cavity in exposed tortoises within 30–60 days of introduction to infected animals, yet we did not detect Myag specific induced antibody responses in these individuals until 420–595 days post exposure. Surprisingly, we found no overall differences in the gene transcript profiles between our experimental treatment groups throughout this study. This work highlights the complexities in assessing immune function and diagnosing pathogen related infections in tortoises and other reptiles.

Ecology and Evolution↗
USGS2020

Paleo-metagenomics of North American fossil packrat middens: Past biodiversity revealed by ancient DNA

Fossil rodent middens are powerful tools in paleoecology. In arid parts of western North America, packrat ( Neotoma spp.) middens preserve plant and animal remains for tens of thousands of years. Midden contents are so well preserved that fragments of endogenous ancient DNA (aDNA) can be extracted and analyzed across millennia. Here, we explore the use of shotgun metagenomics to study the aDNA obtained from packrat middens up to 32,000 C 14 years old. Eleven Illumina HiSeq 2500 libraries were successfully sequenced, and between 0.11% and 6.7% of reads were classified using Centrifuge against the NCBI “nt” database. Eukaryotic taxa identified belonged primarily to vascular plants with smaller proportions mapping to ascomycete fungi, arthropods, chordates, and nematodes. Plant taxonomic diversity in the middens is shown to change through time and tracks changes in assemblages determined by morphological examination of the plant remains. Amplicon sequencing of ITS2 and rbcL provided minimal data for some middens, but failed at amplifying the highly fragmented DNA present in others. With repeated sampling and deep sequencing, analysis of packrat midden aDNA from well-preserved midden material can provide highly detailed characterizations of past communities of plants, animals, bacteria, and fungi present as trace DNA fossils. The prospects for gaining more paleoecological insights from aDNA for rodent middens will continue to improve with optimization of laboratory methods, decreasing sequencing costs, and increasing computational power.

Ecology and Evolution↗
USGS2021

Gut microbial ecology of the Critically Endangered Fijian crested iguana (Brachylophus vitiensis): Effects of captivity status and host reintroduction on endogenous microbiomes

Animals often exhibit distinct microbial communities when maintained in captivity as compared to when in the wild. Such differentiation may be significant in headstart and reintroduction programs where individuals spend some time in captivity before release into native habitats. Using 16S rRNA gene sequencing, we (i) assessed differences in gut microbial communities between captive and wild Fijian crested iguanas ( Brachylophus vitiensis ) and (ii) resampled gut microbiota in captive iguanas released onto a native island to monitor microbiome restructuring in the wild. We used both cloacal swabs and fecal samples to further increase our understanding of gut microbial ecology in this IUCN Critically Endangered species. We found significant differentiation in gut microbial community composition and structure between captive and wild iguanas in both sampling schemes. Approximately two months postrelease, microbial communities in cloacal samples from formerly captive iguanas closely resembled wild counterparts. Interestingly, microbial communities in fecal samples from these individuals remained significantly distinct from wild conspecifics. Our results indicate that captive upbringings can lead to differences in microbial assemblages in headstart iguanas as compared to wild individuals even after host reintroduction into native conditions. This investigation highlights the necessity of continuous monitoring of reintroduced animals in the wild to ensure successful acclimatization and release.

Ecology and Evolution↗
USGS2022

Face-off: Novel depredation and nest defense behaviors between an invasive and a native predator in the Greater Everglades Ecosystem, Florida, USA

We describe several photo-documented novel interactions between intraguild predators in southern Florida—the native bobcat ( Lynx rufus ) and the invasive Burmese python ( Python bivittatus ). Over several days we documented a bobcat's depredation of an unguarded python nest and subsequent python nest defense behavior following the return of both animals to the nest. This is the first documentation of any animal in Florida preying on python eggs, and the first evidence or description of such antagonistic interactions at a python nest.

Florida↗
USGS2022

Scat as a source of DNA for population monitoring

Sampling fecal droppings (scat) to genetically identify individual animals is an established method for monitoring mammal populations and could be highly useful for monitoring reptile populations. Whereas existing protocols for obtaining DNA from reptile scat focus on analyses of whole, fresh scat deposited during animal handling, the collection of scat naturally deposited by reptiles in situ, as required for non-invasive population monitoring, requires protocols to extract highly degraded DNA. Using surface swabs from such scats can reduce PCR inhibition, ecological impacts of removing scat, and zoonotic risks. We report on three related but independently designed studies of DNA analyses from scat swabs of herbivorous reptiles under natural desert conditions: two free-ranging desert tortoise species (Agassiz's desert tortoise, Gopherus agassizii , California, US, and Morafka's desert tortoise, G. morafkai , Arizona, US) and the common chuckwalla (Sauromalus atar ) (Arizona, US, and Sonora, MX). We analyzed samples from both tortoise species with the same set of 16 microsatellites and chuckwalla samples with four mtDNA markers; studies also varied in swab preservation medium and DNA extraction method. Microsatellite amplification success per sample, defined as ≥9 loci with amplification, varied between studies, with 15% for Agassiz's desert tortoise and 42% Morafka's desert tortoise. For chuckwallas, we successfully amplified and sequenced 50% of samples. We recovered fragments up to 400 bp for tortoises and 980 bp for chuckwallas from scat swab samples. This study demonstrates that genotypes can successfully be obtained from swabs of herbivorous reptile scat collected in the field under natural environmental conditions and emphasizes that repeat amplifications are necessary for genetic identification of individuals from non-invasive samples.

Arizona, California↗
USGS2019

An analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function AKDE, Silvermans rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area ) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animals movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Ecological Monographs↗
USGS2016

To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors

Roads are significant barriers to landscape-scale movements of individuals or populations of many wildlife taxa. The decision by an animal near a road to either cross or not cross may be influenced by characteristics of the road, environmental conditions, traits of the individual animal, and other aspects of the context within which the decision is made. We considered such factors in a mixed-effects logistic regression model describing the nightly road crossing probabilities of invasive nocturnal Brown Treesnakes ( Boiga irregularis ) through short-term radiotracking of 691 snakes within close proximity to 50 road segments across the island of Guam. All measures of road magnitude (traffic volume, gap width, surface type, etc.) were significantly negatively correlated with crossing probabilities. Snake body size was the only intrinsic factor associated with crossing rates, with larger snakes crossing roads more frequently. Humidity was the only environmental variable affecting crossing rate. The distance of the snake from the road at the start of nightly movement trials was the most significant predictor of crossings. The presence of snake traps with live mouse lures during a portion of the trials indicated that localized prey cues reduced the probability of a snake crossing the road away from the traps, suggesting that a snake's decision to cross roads is influenced by local foraging opportunities. Per capita road crossing rates of Brown Treesnakes were very low, and comparisons to historical records suggest that crossing rates have declined in the 60+ yr since introduction to Guam. We report a simplified model that will allow managers to predict road crossing rates based on snake, road, and contextual characteristics. Road crossing simulations based on actual snake size distributions demonstrate that populations with size distributions skewed toward larger snakes will result in a higher number of road crossings. Our method of modeling per capita road crossing probabilities as a binary response variable, influenced by contextual factors, may be useful for describing or predicting road crossings by individuals of other taxa provided that appropriate spatial and temporal resolution can be achieved and that potentially influential covariate data can be obtained.

Ecosphere↗
USGS2018

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads ( Rhinella marina ) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Ecosphere↗
USGS2020

Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife

Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.

Ecosphere↗
USGS2020

Model-based clustering reveals patterns in central place use of a marine top predator

Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul-out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population-level inference concerning central place use. Harbor seal haul-out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul-out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul-out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul-out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model-based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.

Alaska↗