Imperfect Detection Research Articles

Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods

AbstractAimGroundwater ecosystems harbour a unique biodiversity, but remain poorly studied, mainly due to difficulties in accessibility and imperfect species detection. Consequently, knowledge on the state and change of groundwater biodiversity remains highly deficient. In the context of global warming and excessive groundwater extraction, understanding groundwater from an ecosystem‐perspective, including organism diversity and distribution, is essential. This study presents the largest ever systematic assessment of groundwater amphipods, which are a key component of European groundwater biodiversity.LocationSwitzerland (41,285 km2), including data from 906 sampling sites.TaxonGroundwater amphipods, genera Niphargus and Crangonyx (Crustacea, Amphipoda).MethodsWe applied a highly standardized citizen science approach to collect repeated groundwater fauna samples in collaboration with municipal drinking water providers. Using detection–nondetection data of the genetically identified groundwater amphipod species, we assessed the overall species diversity of both rare and common species. The distribution of commonly found species was predicted using multispecies occupancy modelling.ResultsWe retrieved 3882 samples from 906 sites, yielding 2350 groundwater amphipod individuals. We identified a remarkable species diversity, comprising few commonly and many rarely found species. Considering commonly found species, we identified distinct distribution ranges, low local species richness and a predominance of negative co‐occurrences. In contrast, a major portion of species were found rarely (generally at just one or two sites each), distributed uniformly throughout the study area and unrelated to common species' recognized hotspots. Many of these rarely found species are not yet formally described.Main ConclusionsOur results give robust emphasis on the rare occurrence and narrow distribution of many groundwater dwellers. Our systematic and standardized sampling data of groundwater amphipods suggest that rarity is particularly prominent and inherent to groundwater organisms. We emphasize the need of systematic data to integrate rare groundwater species in biodiversity assessments, especially in times of global change.

Accounting for imperfect detection when estimating species-area relationships and beta-diversity.

Ecologists have historically quantified fundamental biodiversity patterns, including species-area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species-area and β-diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β-diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non-parametric diversity estimator (iNEXT.3D) and Multi-Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β-diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z-values, and overestimated SAR c-values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z-values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β-diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β-diversity model coefficients resulted from a systematic, area-related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β-diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated.

How ignoring detection probability hurts biodiversity conservation

Conservation priorities and legal protections are often based on confirmed species occurrences. However, imperfect detection is likely the norm in biological surveys, resulting in negative consequences for conservation. Focusing on threatened species in the US and Canada, we show that detectability information appears to be lacking for most species that are conservation priorities. Although more research on species detection is needed, detectability estimates are important for many immediate decisions. Thus, we recommend: (1) estimating and accounting for detectability and designing rigorous surveys when confirming presence or absence is crucial. Otherwise, absence in surveys should be considered suggestive only and critical habitat should be managed even if species presences are unconfirmed. (2) When directly estimating detectability is prohibitively difficult, indirect estimates should be explored, for example through expert elicitation or trait‐based predictors. (3) Detectability should be explicitly incorporated into decisions to ensure that surveys and management actions are directed where they have the greatest potential benefit.

Stream size‐dependent effect of urban land use on occupancy of an apex freshwater carnivore recolonizing past ranges

AbstractAccurate assessment of population trends and factors that limit distributions of threatened species is key to conserving biodiversity. A reduction in impacts of human land use on species habitats may be one of the factors involved in the recolonization of former ranges by native species. The signature of this process could be detected as a temporal reduction of land use effects on species distributions, or as a spatial reduction, with effects persisting only in habitats more sensitive to anthropogenic stressors. We explored these hypotheses in a recolonizing semiaquatic carnivore. Our species of interest was the Eurasian river otter (Lutra lutra) in Italy, a nationally vulnerable species with a restricted but expanding range. Human land use is likely to strongly affect riparian and stream communities through habitat degradation. We used three independent survey datasets collected in 2003, 2009, and 2019 at the southern range periphery, and unpaired‐site occupancy models to account for imperfect detection in otter surveys. To assess how land use affects recolonization by otters, we tested three main model predictions: (1) occupancy increased over years, (2) effects of agricultural and urban land cover on occupancy decreased over years, (3) effects of agricultural and urban land cover were constant across years and changed with stream size and elevation. We found evidence that otter occupancy increased over years, with predicted mean occupancy of 0.27 in 2003 and 0.65 in 2019. An interaction effect of urban land cover with stream size was the strongest predictor of otter occupancy. Interestingly, a higher percentage of urban land cover around streams was predicted to reduce the probability of occupancy at smaller streams, but did not influence occupancy at larger streams. Despite a large increase in occupancy, otters at the southern periphery of the Italian range are still unable to colonize small rivers in urbanized areas. This pattern supports our hypothesis that human land use can affect recolonizing species at sensitive habitats, considering the intrinsic vulnerability of small stream ecosystems to anthropogenic stressors. Restoring small streams impacted by urban areas can be a valuable conservation action to increase habitat connectivity and favor recolonization of otters.

Random forest and spatial cross-validation performance in predicting species abundance distributions

Random forests (RF) have been widely used to predict spatial variables. Several studies have shown that spatial cross-validation (CV) methods consistently cause RF to yield larger prediction errors compared to standard CV methods. This study examined the impact of species characteristics and data features on the performance of the standard RF and spatial CV approaches for predicting species abundance distribution. It compared the standard 5-fold CV, design-based validation, and three different spatial CV methods, such as spatial buffering, environmental blocking, and spatial blocking. Validation samples were randomly selected for design-based validation without replacement. We evaluated their predictive performance (accuracy and discrimination metrics) using artificial species abundance data generated by a linear function of a constant term (β0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _0$$\\end{document}) and a random error term following a zero-mean Gaussian process with a covariance matrix determined by an exponential correlation function. The model was tuned over multiple simulations to consider different mean levels of species abundance, spatial autocorrelation variation, and species detection probability. Here we found that the standard RF had poor predictive performance when spatial autocorrelation was high and the species probability of detection was low. Design-based validation and standard K-fold CV were found to be the most effective strategies for evaluating RF performance compared to spatial CV methods, even in the presence of high spatial autocorrelation and imperfect detection for random samples. For weakly or moderately clustered samples, they yielded good modelling efficiency but overestimated RF’s predictive power, while they overestimated modelling efficiency, predictive power, and accuracy for strongly clustered samples with high spatial autocorrelation. Globally, the checkerboard pattern in the allocation of blocks to folds in blocked spatial CV was found to be the most effective CV approach for clustered samples, whatever the degree of clustering, spatial autocorrelation, or species abundance class. The checkerboard pattern in spatial CV was found to be the best method for random or systematic samples with spatial autocorrelation, but less effective than non-spatial CV approaches. Failing to take data features into account when validating models can lead to unrealistic predictions of species abundance and related parameters and, therefore, incorrect interpretations of patterns and conclusions. Further research should explore the benefits of using blocked spatial K-fold CV with checkerboard assignment of blocks to folds for clustered samples with high spatial autocorrelation.

Modeling abundance and nest occupation of Pseudomyrmex termitarius as a function of detection factors and habitat structure in fragments of urban vegetation

Understanding the relationship between abundance and habitat variability is an underlying theme of ecological studies. Due to imperfect detection, robust estimates of population size require large efforts, especially for megadiverse taxa such as Formicidae, where most of the surveys rely on presence-absence or community data. Here, we used mixed occupation models to estimate the population abundance and local distribution of Pseudomyrmex termitarius (Formicidae: Pseudomyrmecinae) in nests when detection was imperfect. Our goal was to create a sampling protocol to estimate unbiased population parameters, taking into account several sources of heterogeneity that can affect the estimates (e.g., researcher detection variability, survey period, habitat variables). The advantage of this protocol is that it is time- and cost-effective for accessing common and easily identifiable ant species. We distributed 44 transects (2.5 × 20 m) with five subplots each in 2.33 ha of the vegetation remnants of the Cuiabá Campus of the Federal University of Mato Grosso (UFMT). In each subsample, we recorded the presence or absence of P. termitarius workers, as well as the count of other cooccurring ant species during two consecutive days and time periods (midday and 16:00). Canopy openness and soil compaction were recorded in each transect. Among all the sampling trials, we directly counted a maximum of 364 P. termitarius workers in 92 subplots. Both nest abundance and occupation estimates were directly related to soil compaction. The estimated probability of detection was 39% and was not affected by the period. On the other hand, despite P. termitarius being a noncryptic species in the study area, it had a stronger effect on detectability. Although P. termitarius is a subordinate species, the co-occurrence of other species does not explain its abundance and distribution in our study. Perhaps P. termitarius's preference for sites with more compact soils could favor it by filtering out or reducing the abundance of potential competitors. After taking into account soil compaction and researcher detection variability, we estimated that the species occurred in 70% of the study area with a density of approximately 0.09 nests/m². We expect that this protocol can be an additional tool to improve the knowledge of ants in general and, especially, the management of alien or economically relevant ant species.

Environmental impact assessment for large carnivores: a methodological review of the wolf Canis lupus monitoring in Portugal

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities being a particular risk to large carnivores. Environmental impact assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of approximately 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions. Do wolf programs examine adequate biological parameters to meet monitoring objectives? Is the study design suitable for measuring impacts? Are data collection methods and effort sufficient for the stated inference objectives? Do statistical analyses of the data lead to robust conclusions? Overall, we found a mismatch between the stated aims of wolf monitoring and the results reported, and often neither aligns with the existing national wolf monitoring guidelines. Despite the vast effort expended and the diversity of methods used, data analysis makes almost exclusive use of relative indices or summary statistics, with little consideration of the potential biases that arise through the (imperfect) observational process. This makes comparisons of impacts across space and time difficult and is therefore unlikely to contribute to a general understanding of wolf responses to infrastructure‐related disturbance. We recommend the development of standardized monitoring protocols and advocate for the use of statistical methods that account for imperfect detection to guarantee accuracy, reproducibility, and efficacy of the programs.

Regional and local factors interact to shape colonization and extinction dynamics of invasive Hydrilla verticillata in a patchy landscape.

Understanding the response of species to global change requires disentangling the drivers of their distributions across landscapes. Colonization and extinction processes, shaped by the interplay of landscape-level and local patch-level factors, are key determinants of these distributions. However, disentangling the influence of these factors, when larger-scale processes manifest at local scales, remains a challenge. We addressed this challenge by investigating the colonization and extinction dynamics of the aquatic plant, Hydrilla verticillata, in a complex riverine rock pool system. This system, with hundreds of rock pools experiencing varying flooding frequencies, provided a natural laboratory to examine how a single landscape-level disturbance can differentially impact colonization and extinction depending on local patch characteristics to shape species distributions. Using 5 years of data across over 500 sites and more than 5000 surveys, we employed dynamic occupancy models to model colonization, extinction, and changes in Hydrilla patch occupancy while accounting for imperfect detection. Our results revealed that larger, infrequently flooded pools closer to the river were more likely to be colonized. In contrast, local extinction of Hydrilla was more likely in smaller pools closer to the river that flooded frequently. These findings underscore the importance of considering context-dependence in species distribution models. The same landscape-level disturbance (flooding) had opposing effects on colonization and extinction, with the direction and magnitude of these effects varying with local patch characteristics. Our study highlights the need for integrating local and landscape-level factors, and considering how larger-scale processes play out at the patch level, to understand the complex dynamics that shape species distributions.

Distance models reveal biases associated with passive trapping methods for measuring wild bee abundance

IntroductionThere is overwhelming evidence of declines in native bee populations and therefore a need for increased monitoring to track these declines and assist in conservation and restoration efforts. Bees can be sampled non-lethally through visual surveys (e.g., distance transects) or lethally through active (e.g., hand netting) or passive (e.g., traps that lure insects from afar) methods. These lethal methods suffer from imperfect detection that is difficult to account for and can confound inferences about habitat characteristics. Additionally, evidence suggests that lethal sampling methods can even invert habitat quality patterns such that high-quality sites yield fewer individuals and low-quality sites yield more individuals.MethodsTo study potential biases associated with imperfect detection, we used hierarchical density estimation with visual surveys to estimate density of bees within 40 young forest patches across Pennsylvania, USA. We surveyed bee communities non-lethally using visual surveys and lethally using blue-vane traps and bee bowls every two weeks between May and September 2019. We collected data on blooming flowers, vegetation structure, and weather during times of survey.ResultsWe found that bee densities estimated from distance transects had a positive relationship with floral resource availability. In contrast, abundance measured via bee bowls and blue-vane traps had no relationship, or sometimes even negative trends with habitat quality, including floral resource availability. Raw bee counts within 2-m of the transect always correlated with modeled densities, showing that some methods do not share the biases of attractive traps.DiscussionOur study demonstrates that failing to account for imperfect detection can impact the interpretation of pollinator surveys and adds to a growing body of literature that acknowledges the value of distance sampling for insects like bees to better understand species’ habitat needs and to monitor populations for conservation.

Introducing recurrent event analyses to assess species interactions based on camera‐trap data: A comparison with time‐to‐first‐event approaches

Abstract Camera‐trap surveys are increasingly used to assess species interactions, with a growing focus on proximate co‐occurrence, the conditional probability of one species occurring after another within a given time window. However, existing time‐to‐first‐event models, commonly employed for this purpose, suffer from multiple limitations. For instance, they cannot fully quantify the interaction strength, that is how strongly one species affects the occurrence probability of another, nor the duration of this effect, nor identify the directionality of the interaction. We propose reframing the question in terms of recurrent event analysis, which enables the use of a rich set of techniques to provide more information about the data‐generating process. In particular, we use the piece‐wise exponential additive mixed model (PAMM) to take into account all available information (including recurrent occurrences of species) and to estimate the non‐linear temporal dynamics of the visitation rate of a species after the occurrence of another. As the estimation is performed within the framework of generalized additive models, it offers a rich, robust and established inferential toolbox. We evaluated PAMM's performance in estimating species interactions through a simulation study involving two species under various scenarios of attraction or repulsion, with and without imperfect detection. Additionally, we compared the PAMM to six time‐to‐first‐event methods commonly used in camera‐trap studies and illustrated its application with real data. PAMMs outperformed the other methods in terms of statistical power, output interpretability and ability to disentangle interactions. The robust performance of PAMMs underscores their potential as a valuable tool for studying species interactions based on camera‐trap data. To facilitate its adoption, we present the new package ctrecurrent and provide a reproducible workflow from data preparation to model fitting.

Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data

Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data

Water temperature and microenvironmental factors predict the presence and detection of the snail host of Fasciola hepatica in Andean Patagonia

The transmission of Fasciola hepatica occurs only where there are -or recently were- aquatic or amphibious snails of the Lymnaeidae family, the intermediate host of this parasite. Direct detection of these snails is time-consuming and imprecise, hindering accurate and detailed mapping of transmission risk. To identify which microenvironmental factors could be used as proxies for the occurrence of the lymnaeid snail Galba viator, a major intermediate host in South America, a total of 183 1-m2 quadrants across diverse water bodies in an endemic area in Andean Patagonia were manually timed-searched for snails and microenvironmental variables were registered. Data was analyzed using a Bayesian hierarchical occupancy model that assessed the effects of the microenvironmental variables on the presence of snails while considering imperfect snail detection. The model estimated that G. viator predominantly inhabits shallow aquatic environments, in the presence of grasses, where snails of the genus Biomphalaria are also detected, and with scarce tree canopy cover. Physical factors affecting occupancy presumably act as proxies for the average water temperature, while the temperature at the time of sampling was found to affect snail detectability. The identified variables are easy, fast, and inexpensive to measure, and can complement management decisions and risk maps based on coarser remote-sensing data, particularly relevant in a context of growing resistance to anthelminthic drugs.

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Life on the edge: Considering ecotonal habitat for the conservation of alpine reptile metacommunities

Conservation planning focuses on core habitats, often overlooking ecotones, which can hold unrecognised potential for biodiversity conservation. This study focused on ecotonal habitat use in alpine reptile metacommunities, with emphasis on a grassland specialist, the critically endangered alpine she-oak skink, Cyclodomorphus praealtus. Survey transects in grasslands and woodlands in Kosciuszko National Park in the Australian Alps were used to investigate reptile occupancy and richness. Data was analysed using a multi-species occupancy model in relation to (1) distance from nearest grassland to assess grassland specialists' occupancy into woodland areas while accounting for imperfect detection, and (2) normalised difference vegetation index (NDVI) to assess the importance of high resource patches. Among the 11 reptile species detected, our hypothesis that grassland specialists would inhabit woodland ecotones was confirmed. The furthest occurrence of C. praealtus from the closest grassland was 41 m with a predicted occupancy of 0.06 (0.004 – 0.169 95 % Bayesian credible intervals) at 100 m. Contrary to conventional edge effect theories, our results revealed no increase in species richness nearer to the woodland ecotone from grasslands or with higher NDVI values. This study underscores the importance of including ecotonal buffers in ecological impact assessments and conservation plans for habitat specialists, that may still use ecotones of adjacent habitats. The findings emphasize the need for broader survey efforts beyond core habitats to capture the distribution of threatened species effectively. Incorporating ecotonal habitats into conservation strategies is crucial, particularly for specialist species whose dynamic use of ecotones may help safeguard them amidst predicted habitat transformation from anthropogenic influences and climate change.

Imperfection Sensitivity Detection in Pultruded Columns Using Machine Learning and Synthetic Data

Experimental and theoretical solutions have shown that imperfections in wide-flanged structural columns may reduce the failure load of the column by as much as 30% with respect to that of a perfect column. Therefore, the early detection and prevention of such imperfections, which would likely reduce the load capacity of a structure, are critical for avoiding catastrophic failure. In the present article, we show how machine learning may be used to detect imperfection sensitivity in pultruded columns using observable column deformations occurring at loads as low as 30% of the design load. Abaqus simulations were used to capture the behavior of such columns of various lengths under service load. The deformations found from the simulations were used to train the machine learning algorithm. Similar deformations could be easily collected from in-service columns using inexpensive instrumentation. With over 3000 test cases, 95% accuracy in the correct detection of imperfection sensitivity was found. We anticipate that the proposed machine learning pipeline will enhance structural health monitoring, providing timely warning for potentially compromised structures.

Using the dead to infer about the living: Amphibian roadkill spatiotemporal dynamics suggest local populations' reduction

Roads represent one of the main sources of wildlife mortality, population decline, and isolation, especially for low-vagility animal groups. It is still not clearly understood how wildlife populations respond to these negative effects over space and time. Most studies on wildlife road mortality do not consider the spatial and temporal components simultaneously, or the imperfect roadkill detection, both of which could lead to inaccurate assumptions and unreliable mitigation actions. In this study, we applied a multi-season occupancy model to a 14-year amphibian mortality dataset collected along 120 km of roads, combined with freely available landscape and remote sensing metrics, to identify the spatiotemporal patterns of amphibian roadkill in a Mediterranean landscape in Southern Portugal. Our models showed an explicit general decrease in amphibian roadkill. The Iberian painted frog (Discoglossus galganoi) experienced roadkill declines over time of ∼70 %, while the spiny common toad (Bufo spinosus) and the fire salamander (Salamandra salamandra) had a loss of nearly 50 %, and the Southern marbled newt (Triturus pygmaeus) had 40 %. Despite the decreasing trend in roadkill, spatial patterns seem to be rather stable from year to year.Multi-season occupancy models, when combined with relevant landscape and remote sensing predictors, as well as long-term monitoring data, can describe dynamic changes in roadkill over space and time. These patterns are valuable tools for understanding roadkill patterns and drivers in Mediterranean landscapes, enabling the differentiation of road sections with varying roadkill over time. Ultimately, this information may contribute to the development of effective conservation measures.

Assessment of the global population size of the Mongolian gazelle Procapra gutturosa

Abstract The Mongolian gazelle Procapra gutturosa is a wild ungulate ubiquitous across the largest remaining temperate grasslands of Mongolia, Russia and China. The species is nomadic and ranges over long distances, resulting in widely fluctuating abundance in any given location. Therefore, a comprehensive and range-wide survey is required to accurately estimate its global population size, but challenges are posed by the expansive geographical distribution and the political boundaries across the species’ vast range. To obtain an estimate of the total population, we compiled data from recent range-wide surveys. During 2019–2020, we estimated the population size in Mongolia by conducting line transect distance surveys and total counts, and by deriving numerical predictions for unsurveyed areas through data analysis. The gazelle's population in Russia was surveyed in 2020 across its summer range using simultaneous counts, transect surveys and expert knowledge. The distance sampling surveys in Mongolia revealed that slightly more than half of the gazelles along the transects were detected. Our assessment of the gazelle population, although probably an underestimate, suggests there are c. 2.14 million individuals in Mongolia and c. 30,000 in Russia. These results confirm that the Mongolian gazelle is the most abundant nomadic ungulate in the open plains across its range. However, to obtain more accurate estimates across all range states and effectively monitor the gazelle's population status, it is essential to implement standardized survey protocols that correct for imperfect detection. At present, the management of the Mongolian gazelle is inadequate, as there is a lack of regular monitoring to identify any adverse population changes that could necessitate conservation interventions.

Camera trap surveys of Atlantic Forest mammals: A data set for analyses considering imperfect detection (2004-2020).

Camera traps became the main observational method of a myriad of species over large areas. Data sets from camera traps can be used to describe the patterns and monitor the occupancy, abundance, and richness of wildlife, essential information for conservation in times of rapid climate and land-cover changes. Habitat loss and poaching are responsible for historical population losses of mammals in the Atlantic Forest biodiversity hotspot, especially for medium to large-sized species. Here we present a data set from camera trap surveys of medium to large-sized native mammals (>1 kg) across the Atlantic Forest. We compiled data from 5380 ground-level camera trap deployments in 3046 locations, from 2004 to 2020, resulting in 43,068 records of 58 species. These data add to existing data sets of mammals in the Atlantic Forest by including dates of camera operation needed for analyses dealing with imperfect detection. We also included, when available, information on important predictors of detection, namely the camera brand and model, use of bait, and obstruction of camera viewshed that can be measured from example pictures at each camera location. Besides its application in studies on the patterns and mechanisms behind occupancy, relative abundance, richness, and detection, the data set presented here can be used to study species' daily activity patterns, activity levels, and spatiotemporal interactions between species. Moreover, data can be used combined with other data sources in the multiple and expanding uses of integrated population modeling. An R script is available to view summaries of the data set. We expect that this data set will be used to advance the knowledge of mammal assemblages and to inform evidence-based solutions for the conservation of the Atlantic Forest. The data are not copyright restricted; please cite this paper when using the data.

Estimating species distribution from camera trap by‐catch data, using jaguarundi (Herpailurus yagouaroundi) as an example

AbstractAimPlanning conservation action requires accurate estimates of abundance and distribution of the target species. For many mammals, particularly those inhabiting tropical forests, there are insufficient data to assess their conservation status. We present a framework for predicting species distribution using jaguarundi (Herpailurus yagouaroundi), a poorly known felid for which basic information on abundance and distribution is lacking.LocationMesoamerica and South America.Time PeriodFrom 2003 to 2021.Taxa Herpailurus yagouaroundi. MethodsWe combined camera‐trap data from multiple sites and used an occupancy modelling framework accounting for imperfect detection to identify habitat associations and predict the range‐wide distribution of jaguarundis.ResultsOur model predicted that the probability of jaguarundi occupancy is positively associated with rugged terrain, herbaceous cover, and human night‐time light intensity. Jaguarundi occupancy was predicted to be higher where precipitation was less seasonal, and at intermediate levels of diurnal temperature range. Our camera data also revealed additional detections of jaguarundis beyond the current International Union for Conservation of Nature (IUCN) range distribution, including the Andean foothills of Colombia and Bolivia.Main ConclusionOccupancy was predicted to be low throughout much of Amazonian lowlands, a vast area at the centre of jaguarundi known range. Further work is required to investigate whether this area represents sub‐optimal conditions for the species. Overall, we estimate a crude global jaguarundi population of 35,000 to 230,000 individuals, covering 4,453,406 km2 of Meso‐ and South America at the 0.5 probability level of occupancy. Our current framework allows for an initially detailed, well‐informed species distribution that should be challenged and refined with improved habitat layers and additional records of jaguarundi detection. We encourage similar studies of lesser‐known mammals, pooling existing by‐catch data from the growing bank of camera‐trap surveys around the world.