A simple survey protocol for assessing terrestrial biodiversity in a broad range of ecosystems

Finding standard cost-effective methods for monitoring biodiversity is challenging due to trade-offs between survey costs (including expertise), specificity, and range of applicability. These trade-offs cause a lack of comparability among datasets collected by ecologists and conservationists, which is most regrettable in taxonomically demanding work on megadiverse inconspicuous taxon groups. We have developed a site-scale survey method for diverse sessile land organisms, which can be analyzed over multiple scales and linked with ecological insights and management. The core idea is that field experts can effectively allocate observation effort when the time, area, and priority sequence of tasks are fixed. We present the protocol, explain its specifications (taxon group; expert qualification; plot size; effort) and applications based on >800 original surveys of four taxon groups; and we analyze its effectiveness using data on polypores in hemiboreal and tropical forests. We demonstrate consistent effort-species richness curves and among-survey variation in contrasting ecosystems, and high effectiveness compared with casual observations both at local and regional scales. Bias related to observer experience appeared negligible compared with typical assemblage variation. Being flexible in terms of sampling design, the method has enabled us to compile data from various projects to assess conservation status and habitat requirements of most species (specifically rarities and including discovery of new species); also, when linked with site descriptions, to complete environmental assessments and select indicator species for management. We conclude that simple rules can significantly improve expert-based biodiversity surveys. Ideally, define (i) a common plot size that addresses multiple taxon groups and management goals; (ii) taxon groups based on field expertise and feasible number of species; (iii) sufficient and practical search time; (iv) a procedure for recording within-plot heterogeneity. Such a framework, combined with freedom to allocate effort on-site, helps utilizing full expertise of observers without losing technical rigor.

Differentiating Footprints of Sympatric Rodents in Coastal Dune Communities: Implications for Imperiled Beach Mice

Identifying techniques for conducting frequent, effective, and inexpensive monitoring of small mammals can be challenging. Traditional approaches such as livetrapping can be laborious, expensive, detrimental to animal health, and ineffective. Passive approaches such as tracking (e.g., from tracks on the ground or footprints collected at a tracking station) have been shown to lessen those burdens, but a problem with tracking, particularly for rodents, is the uncertainty in identifying species from footprints. To address the need for a more accurate method of identifying small mammal tracks, we measured footprints from live-captured rodents and developed a classification tree for distinguishing between subspecies and species using footprint widths treated as having known or unknown identification. We captured rodents within or near the coastal dunes of Florida and Alabama with a focus on areas occupied by threatened and endangered beach mice Peromyscus polionotus subspp., whose populations warrant regular monitoring but whose tracks are not easily distinguished from those of some sympatric species. We measured 6,996 front and hind footprints from 540 individuals across eight species. The overall accuracy of our classification tree was 82.6% and we achieved this using only the front footprint width. Footprint width cutoffs for species identification were < 5.5 mm for house mice Mus musculus, 5.5–6.7 mm for beach mice, and 6.7–8.3 mm for cotton mice Peromyscus gossypinus. We were most successful in confirming the identity of beach mice: we correctly classified approximately 94% of beach mice, while we misclassified fewer than 6% as house mice and fewer than 1% as cotton mice. When we input a beach mouse individual into the classification tree as of an unknown species, we correctly identified 78.1% of individuals as beach mice from their tracks, and most incorrect identifications were of house mouse tracks. Our study demonstrates that researchers can identify sympatric rodent species in coastal dune communities from tracks using quantitative classification based on footprint width. Accurate identification of beach mice or other imperiled species from tracks has important management implications. Not only can wildlife managers determine the presence of a species accurately, but they can monitor populations with considerably less effort than livetrapping requires. Although our study was specific to coastal dune communities, our methods could be adapted for the creation of a classification tree for identifying tracks from suites of species in other areas.

Measuring agreement among experts in classifying camera images of similar species

Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging-but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (Lynx canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss' kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of "unknown," and many images (39%) had some experts classify the image as "bobcat" while others classified it as "lynx." Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar-looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.

Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA

Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs- Sus scrofa) from DNA in water bodies. We used a multi-scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability >90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least two extractions per sample, and conduct five qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.

Fish Misidentification and Potential Implications to Monitoring Within the San Francisco Estuary, California

Fish monitoring programs often rely on the collection, species identification, and counting of individual fish over time to inform natural resource management decisions. Thus, the utility of the data used to inform these decisions can be negatively affected by species misidentification. Fish species misidentification bias can be minimized by confirming identification using genetic techniques, training observers, or adjusting monitoring data using estimates of incomplete detection and false-positive misidentification. Despite the existence of well-established fish identification training and quality control programs, there is considerable uncertainty about fish species false-positive misidentification rates and the effectiveness of fish identification training programs within the San Francisco Estuary. We evaluated the misidentification of fish species among Delta Juvenile Fish Monitoring Program observers by conducting five fish identification exams under controlled conditions at the Lodi Fish and Wildlife Office in Lodi, California, between 2012 and 2014. To assess the variability in false-positive misidentification, we fitted data to species and observer characteristics using hierarchical logistic regression. We found that fish species misidentification was fairly common, averaging 17% among 155 test specimens and 32 observers. False-positive misidentification varied considerably among species and was negatively related to fish size, the abundance of the species within monitoring samples, and observer experience. In addition, observers who were not formally trained or used as full-time observers were, on average, 6.0 times more likely to falsely identify a species. However, false-positive misidentification rates among observers and specimens still varied considerably after controlling for observer experience and training, and species and size, respectively. Our results could be used to improve fish identification training and testing, increase the accuracy of fish occupancy or abundance estimation, and justify the allocation of resources to continually use and formally train full-time observers within long-term monitoring programs operating in the system.

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non-detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false-positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species-specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false-positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false-positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.

The Certainty of Uncertainty: Potential Sources of Bias and Imprecision in Disease Ecology Studies

Wildlife diseases have important implications for wildlife and human health, the preservation of biodiversity and the resilience of ecosystems. However, understanding disease dynamics and the impacts of pathogens in wild populations is challenging because these complex systems can rarely, if ever, be observed without error. Uncertainty in disease ecology studies is commonly defined in terms of either heterogeneity in detectability (due to variation in the probability of encountering, capturing, or detecting individuals in their natural habitat) or uncertainty in disease state assignment (due to misclassification errors or incomplete information). In reality, however, uncertainty in disease ecology studies extends beyond these components of observation error and can arise from multiple varied processes, each of which can lead to bias and a lack of precision in parameter estimates. Here, we present an inventory of the sources of potential uncertainty in studies that attempt to quantify disease-relevant parameters from wild populations (e.g., prevalence, incidence, transmission rates, force of infection, risk of infection, persistence times, and disease-induced impacts). We show that uncertainty can arise via processes pertaining to aspects of the disease system, the study design, the methods used to study the system, and the state of knowledge of the system, and that uncertainties generated via one process can propagate through to others because of interactions between the numerous biological, methodological and environmental factors at play. We show that many of these sources of uncertainty may not be immediately apparent to researchers (for example, unidentified crypticity among vectors, hosts or pathogens, a mismatch between the temporal scale of sampling and disease dynamics, demographic or social misclassification), and thus have received comparatively little consideration in the literature to date. Finally, we discuss the type of bias or imprecision introduced by these varied sources of uncertainty and briefly present appropriate sampling and analytical methods to account for, or minimise, their influence on estimates of disease-relevant parameters. This review should assist researchers and practitioners to navigate the pitfalls of uncertainty in wildlife disease ecology studies.

Surveillance of vector-borne pathogens under imperfect detection: lessons from Chagas disease risk (mis)measurement

Vector-borne pathogens threaten human health worldwide. Despite their critical role in disease prevention, routine surveillance systems often rely on low-complexity pathogen detection tests of uncertain accuracy. In Chagas disease surveillance, optical microscopy (OM) is routinely used for detecting Trypanosoma cruzi in its vectors. Here, we use replicate T. cruzi detection data and hierarchical site-occupancy models to assess the reliability of OM-based T. cruzi surveillance while explicitly accounting for false-negative and false-positive results. We investigated 841 triatomines with OM slides (1194 fresh, 1192 Giemsa-stained) plus conventional (cPCR, 841 assays) and quantitative PCR (qPCR, 1682 assays). Detections were considered unambiguous only when parasitologists unmistakably identified T. cruzi in Giemsa-stained slides. qPCR was >99% sensitive and specific, whereas cPCR was ~100% specific but only ~55% sensitive. In routine surveillance, examination of a single OM slide per vector missed ~50–75% of infections and wrongly scored as infected ~7% of the bugs. qPCR-based and model-based infection frequency estimates were nearly three times higher, on average, than OM-based indices. We conclude that the risk of vector-borne Chagas disease may be substantially higher than routine surveillance data suggest. The hierarchical modelling approach we illustrate can help enhance vector-borne disease surveillance systems when pathogen detection is imperfect.

ednaoccupancy: An r package for multiscale occupancy modelling of environmental DNA data

In this article, we describe ednaoccupancy, an r package for fitting Bayesian, multiscale occupancy models. These models are appropriate for occupancy surveys that include three nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). ednaoccupancy allows users to specify and fit multiscale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analysing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population

Biodiversity conservation requires reliable species assessments and rigorously designed surveys. However, determining the survey effort required to reliably detect population change can be challenging for rare, cryptic and elusive species. We used a tropical bromeliad-dwelling frog as a model system to explore a cost-effective sampling design that optimizes the chances of detecting a population decline. Relatively few sampling visits were needed to estimate occupancy and detectability with good precision, and to detect a 30% change in occupancy with 80% power. Detectability was influenced by observer expertise, which therefore also had an effect on the sampling design - less experienced observers require more sampling visits to detect the species. Even when the sampling design provides precise parameter estimates, only moderate to large changes in occupancy will be detected with reliable power. Detecting a population change of 15% or less requires a large number of sites to be surveyed, which might be unachievable for range-restricted species occurring at relatively few sites. Unless there is high initial occupancy, rare and cryptic species will be particularly challenging when it comes to detecting small population changes. This may be a particular issue for long-term monitoring of amphibians which often display low detectability and wide natural fluctuations.

The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation

Digitized species occurrence data provide an unprecedented source of information for ecologists and conservationists. Species distribution model (SDM) has become a popular method to utilise these data for understanding the spatial and temporal distribution of species, and for modelling biodiversity patterns. Our objective is to study the impact of noise in species occurrence data (namely sample size and positional accuracy) on the performance and reliability of SDM, considering the multiplicative impact of SDM algorithms, species specialisation, and grid resolution. We created a set of four 'virtual' species characterized by different specialisation levels. For each of these species, we built the suitable habitat models using five algorithms at two grid resolutions, with varying sample sizes and different levels of positional accuracy. We assessed the performance and reliability of the SDM according to classic model evaluation metrics (Area Under the Curve and True Skill Statistic) and model agreement metrics (Overall Concordance Correlation Coefficient and geographic niche overlap) respectively. Our study revealed that species specialisation had by far the most dominant impact on the SDM. In contrast to previous studies, we found that for widespread species, low sample size and low positional accuracy were acceptable, and useful distribution ranges could be predicted with as few as 10 species occurrences. Range predictions for narrow-ranged species, however, were sensitive to sample size and positional accuracy, such that useful distribution ranges required at least 20 species occurrences. Against expectations, the MAXENT algorithm poorly predicted the distribution of specialist species at low sample size.

Design- and model-based recommendations for detecting and quantifying an amphibian pathogen in environmental samples

Accurate pathogen detection is essential for developing management strategies to address emerging infectious diseases, an increasingly prominent threat to wildlife. Sampling for free‐living pathogens outside of their hosts has benefits for inference and study efficiency, but is still uncommon. We used a laboratory experiment to evaluate the influences of pathogen concentration, water type, and qPCR inhibitors on the detection and quantification of Batrachochytrium dendrobatidis (Bd) using water filtration. We compared results pre‐ and post‐inhibitor removal, and assessed inferential differences when single versus multiple samples were collected across space or time. We found that qPCR inhibition influenced both Bd detection and quantification in natural water samples, resulting in biased inferences about Bd occurrence and abundance. Biases in occurrence could be mitigated by collecting multiple samples in space or time, but biases in Bd quantification were persistent. Differences in Bd concentration resulted in variation in detection probability, indicating that occupancy modeling could be used to explore factors influencing heterogeneity in Bd abundance among samples, sites, or over time. Our work will influence the design of studies involving amphibian disease dynamics and studies utilizing environmental DNA (eDNA) to understand species distributions.

How to limit false positives in environmental DNA and metabarcoding?

Environmental DNA (eDNA) and metabarcoding are boosting our ability to acquire data on species distribution in a variety of ecosystems. Nevertheless, as most of sampling approaches, eDNA is not perfect. It can fail to detect species that are actually present, and even false positives are possible: a species may be apparently detected in areas where it is actually absent. Controlling false positives remains a main challenge for eDNA analyses: in this issue of Molecular Ecology Resources, Lahoz-Monfort et al. () test the performance of multiple statistical modelling approaches to estimate the rate of detection and false positives from eDNA data. Here, we discuss the importance of controlling for false detection from early steps of eDNA analyses (laboratory, bioinformatics), to improve the quality of results and allow an efficient use of the site occupancy-detection modelling (SODM) framework for limiting false presences in eDNA analysis.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Amphibian breeding phenology and reproductive outcome: an examination using terrestrial and aquatic sampling

Worldwide amphibian declines highlight the need for programs that monitor species presence and track population trends. We sampled larval amphibians with a box trap at 3-week intervals for 23 months in eight wetlands, and concurrently trapped adults and juveniles with drift fences, to examine spatiotemporal patterns of tadpole occurrence; explore relationships between breeding effort, tadpole abundance, and recruitment; and compare the efficacy of both methods in detecting species presence and reproductive outcome. Intermittent detection of species within and among wetlands suggested high mortality, followed by deposition of new eggs and tadpole cohorts. Breeding effort, tadpole abundance, and juvenile recruitment were generally not correlated. The reasons for this may include differential bias in detecting species or life stages between methods and high incidence of egg or tadpole mortality. Drift fences detected more species than box traps, but each provided insights regarding amphibian presence and recruitment. Our results illustrate shortfalls in the ability of infrequent aquatic sampling to detect local species richness of larval amphibians, as occurrence of many species is spatially and temporally variable. We also show the importance of using different sampling methods to detect species’ presence, as well as difficulties associated with both methods in tracking breeding effort, tadpole occurrence, or reproductive outcome.

Categorizing click trains to increase taxonomic precision in echolocation click loggers

Passive acoustic monitoring is an efficient way to study acoustically active animals but species identification remains a major challenge. C-PODs are popular logging devices that automatically detect odontocete echolocation clicks. However, the accompanying analysis software does not distinguish between delphinid species. Click train features logged by C-PODs were compared to frequency spectra from adjacently deployed continuous recorders. A generalized additive model was then used to categorize C-POD click trains into three groups: broadband click trains, produced by bottlenose dolphin (Tursiops truncatus) or common dolphin (Delphinus delphis), frequency-banded click trains, produced by Risso's (Grampus griseus) or white beaked dolphins (Lagenorhynchus albirostris), and unknown click trains. Incorrect categorization rates for broadband and frequency banded clicks were 0.02 (SD 0.01), but only 30% of the click trains met the categorization threshold. To increase the proportion of categorized click trains, model predictions were pooled within acoustic encounters and a likelihood ratio threshold was used to categorize encounters. This increased the proportion of the click trains meeting either the broadband or frequency banded categorization threshold to 98%. Predicted species distribution at the 30 study sites matched well to visual sighting records from the region.

Characterisation of false-positive observations in botanical surveys

Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person’s ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species; however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.

Designing better frog call recognition models

Advances in bioacoustic technology, such as the use of automatic recording devices, allow wildlife monitoring at large spatial scales. However, such technology can produce enormous amounts of audio data that must be processed and analyzed. One potential solution to this problem is the use of automated sound recognition tools, but we lack a general framework for developing and validating these tools. Recognizers are computer models of an animal sound assembled from "training data" (i.e., actual samples of vocalizations). The settings of variables used to create recognizers can impact performance, and the use of different settings can result in large differences in error rates that can be exploited for different monitoring objectives. We used Song Scope (Wildlife Acoustics Inc.) to build recognizers and vocalizations of the wood frog (Lithobates sylvaticus) to test how different settings and amounts of training data influence recognizer performance. Performance was evaluated using precision (the probability of a recognizer match being a true match) and sensitivity (the proportion of vocalizations detected) based on a receiver operating characteristic (ROC) curve-determined score threshold. Evaluations were conducted using recordings not used to build the recognizer. Wood frog recognizer performance was sensitive to setting changes in four out of nine variables, and small improvements were achieved by using additional training data from different sites and from the same recording, but not from different recordings from the same site. Overall, the effect of changes to variable settings was much greater than the effect of increasing training data. Additionally, by testing the performance of the recognizer on vocalizations not used to build the recognizer, we discovered that Type I error rates appear idiosyncratic and do not recommend extrapolation from training to new data, whereas Type II errors showed more consistency and extrapolation can be justified. Optimizing variable settings on independent recordings led to a better match between recognizer performance and monitoring objectives. We provide general recommendations for application of this methodology with other species and make some suggestions for improvements.

A multispecies dependent double-observer model: A new method for estimating multispecies abundance

Conservation of biological communities requires accurate estimates of abundance for multiple species. Recent advances in estimating abundance of multiple species, such as Bayesian multispecies N‐mixture models, account for multiple sources of variation, including detection error. However, false‐positive errors (misidentification or double counts), which are prevalent in multispecies data sets, remain largely unaddressed. The dependent‐double observer (DDO) method is an emerging method that both accounts for detection error and is suggested to reduce the occurrence of false positives because it relies on two observers working collaboratively to identify individuals. To date, the DDO method has not been combined with advantages of multispecies N‐mixture models. Here, we derive an extension of a multispecies N‐mixture model using the DDO survey method to create a multispecies dependent double‐observer abundance model (MDAM). The MDAM uses a hierarchical framework to account for biological and observational processes in a statistically consistent framework while using the accurate observation data from the DDO survey method. We demonstrate that the MDAM accurately estimates abundance of multiple species with simulated and real multispecies data sets. Simulations showed that the model provides both precise and accurate abundance estimates, with average credible interval coverage across 100 repeated simulations of 94.5% for abundance estimates and 92.5% for detection estimates. In addition, 92.2% of abundance estimates had a mean absolute percent error between 0% and 20%, with a mean of 7.7%. We present the MDAM as an important step forward in expanding the applicability of the DDO method to a multispecies setting. Previous implementation of the DDO method suggests the MDAM can be applied to a broad array of biological communities. We suggest that researchers interested in assessing biological communities consider the MDAM as a tool for deriving accurate, multispecies abundance estimates.

Monitoring for the Management of Disease Risk in Animal Translocation Programmes

Monitoring is best viewed as a component of some larger programme focused on science or conservation. The value of monitoring is determined by the extent to which it informs the parent process. Animal translocation programmes are typically designed to augment or establish viable animal populations without changing the local community in any detrimental way. Such programmes seek to minimize disease risk to local wild animals, to translocated animals, and in some cases to humans. Disease monitoring can inform translocation decisions by (1) providing information for state-dependent decisions, (2) assessing progress towards programme objectives, and (3) permitting learning in order to make better decisions in the future. Here we discuss specific decisions that can be informed by both pre-release and post-release disease monitoring programmes. We specify state variables and vital rates needed to inform these decisions. We then discuss monitoring data and analytic methods that can be used to estimate these state variables and vital rates. Our discussion is necessarily general, but hopefully provides a basis for tailoring disease monitoring approaches to specific translocation programmes.

Environmental DNA (eDNA) Detection Probability Is Influenced by Seasonal Activity of Organisms

Environmental DNA (eDNA) holds great promise for conservation applications like the monitoring of invasive or imperiled species, yet this emerging technique requires ongoing testing in order to determine the contexts over which it is effective. For example, little research to date has evaluated how seasonality of organism behavior or activity may influence detection probability of eDNA. We applied eDNA to survey for two highly imperiled species endemic to the upper Black Warrior River basin in Alabama, US: the Black Warrior Waterdog (Necturus alabamensis) and the Flattened Musk Turtle (Sternotherus depressus). Importantly, these species have contrasting patterns of seasonal activity, with N. alabamensis more active in the cool season (October-April) and S. depressus more active in the warm season (May-September). We surveyed sites historically occupied by these species across cool and warm seasons over two years with replicated eDNA water samples, which were analyzed in the laboratory using species-specific quantitative PCR (qPCR) assays. We then used occupancy estimation with detection probability modeling to evaluate both the effects of landscape attributes on organism presence and season of sampling on detection probability of eDNA. Importantly, we found that season strongly affected eDNA detection probability for both species, with N. alabamensis having higher eDNA detection probabilities during the cool season and S. depressus have higher eDNA detection probabilities during the warm season. These results illustrate the influence of organismal behavior or activity on eDNA detection in the environment and identify an important role for basic natural history in designing eDNA monitoring programs.

Integrating occurrence and detectability patterns based on interview data: a case study for threatened mammals in Equatorial Guinea

Occurrence models that account for imperfect detection of species are increasingly used for estimating geographical range, for determining species-landscape relations and to prioritize conservation actions worldwide. In 2010, we conducted a large-scale survey in Río Muni, the mainland territory of Equatorial Guinea, which aimed to estimate the probabilities of occurrence and detection of threatened mammals based on environmental covariates, and to identify priority areas for conservation. Interviews with hunters were designed to record presence/absence data of seven species (golden cat, leopard, forest elephant, forest buffalo, western gorilla, chimpanzee and mandrill) in 225 sites throughout the region. We fitted single season occupancy models and recently developed models which also include false positive errors (i.e. species detected in places where it actually does not occur), which should provide more accurate estimates for most species, which are susceptible to mis-identification. Golden cat and leopard had the lowest occurrence rates in the region, whereas primates had the highest rates. All species, except gorilla, were affected negatively by human settlements. The southern half of Río Muni showed the highest occurrence of the species studied, and conservation strategies for ensuring the persistence of threatened mammals should be focused on this area.

Patterns of Snow Leopard Site Use in an Increasingly Human-Dominated Landscape

Human population growth and concomitant increases in demand for natural resources pose threats to many wildlife populations. The landscapes used by the endangered snow leopard (Panthera uncia) and their prey is increasingly subject to major changes in land use. We aimed to assess the influence of 1) key human activities, as indicated by the presence of mining and livestock herding, and 2) the presence of a key prey species, the blue sheep (Pseudois nayaur), on probability of snow leopard site use across the landscape. In Gansu Province, China, we conducted sign surveys in 49 grid cells, each of 16 km² in size, within a larger area of 3392 km². We analysed the data using likelihood-based habitat occupancy models that explicitly account for imperfect detection and spatial auto-correlation between survey transect segments. The model-averaged estimate of snow leopard occupancy was high [0.75 (SE 0.10)], but only marginally higher than the naïve estimate (0.67). Snow leopard segment-level probability of detection, given occupancy on a 500 m spatial replicate, was also high [0.68 (SE 0.08)]. Prey presence was the main determinant of snow leopard site use, while human disturbances, in the form of mining and herding, had low predictive power. These findings suggest that snow leopards continue to use areas very close to such disturbances, as long as there is sufficient prey. Improved knowledge about the effect of human activity on large carnivores, which require large areas and intact prey populations, is urgently needed for conservation planning at the local and global levels. We highlight a number of methodological considerations that should guide the design of such research.