FecalSeq enrichment with RAD Sequencing from non-invasive environmental samples holds promise for genetic monitoring of an imperiled lagomorph

Despite advances in genomic sequencing and bioinformatics, conservation genomics is still often hindered by a reliance on non-invasive samples. The presence of exogenous DNA and the low quantity and poor quality of DNA in non-invasive samples have been a roadblock to sequencing, thereby limiting the potential for genomic monitoring of endangered species. Recent molecular advances, such as host DNA enrichment, hold promise for facilitating sequencing from non-invasive samples. We used the FecalSeq method to enrich DNA extracted from wild-collected fecal pellets of the imperiled New England cottontail and identified SNPs from 3RAD Sequencing. We obtained SNPs from rabbit pellets, including pellets that were collected in poor environmental conditions and samples that performed poorly with microsatellites. Measures of sequencing success improved with greater amounts of starting DNA and 32% of samples generated SNP genotypes that passed quality control filtering. Genotyping error rates were high, however, and the approach was unable to consistently distinguish unique individuals or matching genotypes, while it was suitable for recovering the expected population structure. Pairing FecalSeq enrichment with RADseq is a promising low-cost method for monitoring wild populations using non-invasive samples in an environmental context, but it may be better suited for informing conservation through population genomics.

Using a cognitive model to understand crowdsourced data from citizen scientists

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources.

Separating Proactive Conservation from Species Listing Decisions

Proactive Conservation is a paradigm of natural resource management in the United States that encourages voluntary, collaborative efforts to restore species before they need to be protected through government regulations. This paradigm is widely used to conserve at-risk species today, and when used in conjunction with the Policy for Evaluation of Conservation Efforts (PECE), it allows for successful conservation actions to preclude listing of species under the Endangered Species Act (ESA). Despite the popularity of this paradigm, and recent flagship examples of its use (e.g., greater sage grouse, Centrocercus urophasianus), critical assessments of the outcomes of Proactive Conservation are lacking from the standpoint of species status and recovery metrics. Here, we provide such an evaluation, using the New England cottontail (Sylvilagus transitionalis), heralded as a success of Proactive Conservation efforts in the northeastern United States, as a case study. We review the history and current status of the species, based on the state of the science, in the context of the Conservation Initiative, and the 2015 PECE decision not to the list the species under the ESA. In addition to the impacts of the PECE decision on the New England cottontail conservation specifically, our review also evaluates the benefits and limits of the Proactive Conservation paradigm more broadly, and we make recommendations for its role in relation to ESA implementation for the future of at-risk species management. We find that the status and assurances for recovery under the PECE policy, presented at the time of the New England cottontail listing decision, were overly optimistic, and the status of the species has worsened in subsequent years. We suggest that use of PECE to avoid listing may occur because of the perception of the ESA as a punitive law and a misconception that it is a failure, although very few listed species have gone extinct. Redefining recovery to decouple it from delisting and instead link it to probability of persistence under recommended conservation measures would remove some of the stigma of listing, and it would strengthen the role of Species Status Assessments in endangered species conservation.

Estimating density of ocelots in the Atlantic Forest using spatial and closed capture–recapture models

Monitoring variation in population features such as abundance and density is essential for evaluating and implementing conservation actions. Camera trapping can be important for assessing population status and trends and is increasingly used to generate density estimates through capture–recapture models. Moreover, success in using this technique can vary seasonally given shifting animal distributions and camera encounter rates. Notwithstanding these potential advantages, a gap still exists in our understanding of the performance of such models for estimating density of cryptic Neotropical terrestrial carnivores with low encounter rate probability with cameras. In addition, scanty information is available on how sampling design can affect the accuracy and precision of density estimates for Neotropical carnivores. We evaluate the performance of spatially explicit versus nonspatial capture–mark–recapture models for estimating densities and population size of ocelots (Leopardus pardalis) within an Atlantic Forest fragment in Brazil. We conducted two spatially concurrent surveys, a random camera-trap deployment covering the entire study area and a systematic camera-trap deployment in a small portion of the study area, where trails and unpaved roads were located. We obtained 244 photographs of ocelots in the Rio Doce State Park from April 2016 to November 2017, using 54-double camera stations spaced approximately 1.5 km apart (random placement) totaling 4,320 trap-nights and 15-double camera stations spaced from 0.3–10 km apart (systematic placement) totaling 1,200 trap-nights. Using the random placement design, ocelot density estimates were similar during the dry season, 14.0 individuals/km2 (± 5.6 SE, 6.6–30.0, 95% CI) and 13.78 individuals/km2 (± 4.25 SE, 5.4–22.1, 95% CI) from spatially explicit capture–recapture and nonspatial models, respectively. Using the systematic placement design spatially explicit models had smaller and less precise ocelot density estimates than nonspatial models during the dry season. Ocelot density was 12.4 individuals/100 km2 (± 5.0 SE, 5.8–26.7, 95% CI) and 19.9 individuals/km2 (± 5.2 SE, 9.7–30.1, 95% CI) from spatially explicit and nonspatial models, respectively. During the rainy season, we found the opposite pattern. Using the systematic placement design, spatial-explicit models had higher and less precise estimates than nonspatial models. Ocelot density was 24.6 individuals/100 km2 (± 13.9 SE, 8.7–69.4, 95% CI) and 11.89 individuals/km2 (± 3.93 SE, 4.19–19.59, 95% CI) from spatially explicit and nonspatial models, respectively. During the rainy season, we could not compare models using the random placement design due to limited number of recaptures to run nonspatial models. In addition, a single recapture yielded an imprecise population density estimate using spatial models (high SE and large 95% CIs), thus precluding any comparison between nonspatial and spatially explicit models. We demonstrate relative differences and similarities between the performance of spatially explicit and nonspatial capture–mark–recapture models for estimating density and population size of ocelots and highlight that both types of capture–recapture models differ in their estimation depending on the sampling design. We highlight that performance of camera surveys is contingent on placement design and that researchers need to be strategic in camera distribution according to study objectives and logistics. This point is especially relevant for cryptic or endangered species occurring at low densities and having low detection probability using traditional sampling methods.

Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data

Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Precision and bias of spatial capture-recapture estimates: A multi-site, multi-year Utah black bear case study

Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km² . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture-recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.

Agent-based models reveal limits of mark-release-recapture estimates for the rare butterfly, Bhutanitis thaidina (Lepidoptera: Papilionidae)

Insect diversity and abundance are in drastic decline worldwide, but quantifying insect populations to better conserve them is a difficult task. Mark-release-recapture (MRR) is widely used as an ecological indicator for insect populations, but the accuracy of MRR estimates can vary with factors such as spatial scale, sampling effort and models of inference. We conducted a 3-year MRR study of B. thaidina in Yanzigou valley, Mt. Gongga but failed to obtain sufficient data for a robust population estimate. This prompted us to integrate B. thaidina life history information to parameterize agent-based models and evaluate the conditions under which successful MRR studies could be conducted. We evaluated: (1) the performance of MRR models under different landscape types, and (2) the influence of experimental design on the accuracy and variance of MRR-based estimates. Our simulations revealed systematic underestimates of true population parameters by MRR models when sampling effort was insufficient. In a total of 2772 simulations, subjective decisions in sampling protocol (e.g., frequency, number of sampling locations, use of spatially explicit models, type of estimands) accounted for nearly half of the variation in estimates. We conclude that MRR-based estimates could be improved with the addition of more field-specific parameters.

OUP accepted manuscript

Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, such as density or abundance, with a level of precision that is fit for purpose. Such estimates can be difficult to attain for many populations that occur in situations that are poorly suited to common survey methods. We evaluated the utility of combining camera trap survey data, in which a small proportion of the sample is individually recognizable using natural markings, with spatial mark–resight (SMR) models to estimate deer density in a variety of situations. We surveyed 13 deer populations comprising four deer species (Cervus unicolor, C. timorensis, C. elaphus, Dama dama) at nine widely separated sites, and used Bayesian SMR models to estimate population densities and abundances. Twelve surveys provided sufficient data for analysis and seven produced density estimates with coefficients of variation (CVs) ≤ 0.25. Estimated densities ranged from 0.3 to 24.6 deer km⁻². Camera trap surveys and SMR models provided a powerful and flexible approach for estimating deer densities in populations in which many detections were not individually identifiable, and they should provide useful density estimates under a wide range of conditions that are not amenable to more widely used methods. In the absence of specific local information on deer detectability and movement patterns, we recommend that at least 30 cameras be spaced at 500–1,000 m and set for 90 days. This approach could also be applied to large mammals other than deer.

Population Density and Driving Factors of North China Leopards in Tie Qiao Shan Nature Reserve

Simple Summary

The North China leopard is a subspecies of leopard distributed in China, but little is known about its population status. This study selected the most active areas of North China leopards to determine the population density and distribution of North China leopards. We found that different prey had different effects on the density distribution of North China leopards. Environmental factors and human factors are also important factors affecting the population density distribution of North China leopards. These results provided an effective basis for the protection of North China leopard population and management evaluation of the reserve. It also provided effective methods for the protection and management of other endangered species.

Abstract

The North China leopard (Panthera pardus japonesis) is a rare leopard subspecies distributed only in China. In this study, we conducted camera-trap surveys of a North China Leopard population in Tie Qiao Shan Nature Reserve, Shanxi Province, China. We estimated population abundance and density distribution, and explored the effects of distribution of different prey populations, habitat, and anthropogenic factors on the spatial distribution of North China leopard density. Our results suggested that the North China leopard density was 4.23 individuals/100 km², and that 17.98 individuals might live within the study area. The population density of the North China leopard increased with the distribution of wild boars, and, on the contrary, decreased with the distribution of roe deer. We found that habitat environmental factors and anthropogenic interference also significantly affected the population density and spatial distribution of the North China leopard. These insights informed us that in order to protect this predator, which is only distributed in China, we should adopt a comprehensive customized adaptive landscape protection strategy.

An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou

Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from noninvasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou), which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of nonindependence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures was strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.