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Background: Vector infection prevalence is a key component of vectorial capacity and transmission risk. Optical microscopy observation (OM) of fecal drops has been the classic method for detecting Trypanosoma cruzi infection in triatomine bugs until the advent of polymerase chain reaction (PCR)-based techniques. However, agreement among OM- and PCR-based techniques has been highly heterogeneous. Methods: We used hierarchical site-occupancy models accounting for imperfect detection to estimate method-specific detection probabilities of T. cruzi infection in field-collected Triatoma infestans and to assess whether T. cruzi infection varied with triatomine developmental stage and collection ecotope. We also performed a scoping review of the literature on comparisons between OM and PCR for T. cruzi infection diagnosis in triatomines. Triatomines were collected before vector control interventions in Pampa del Indio houses (Argentine Chaco) and examined by OM. We amplified the variable regions of the kinetoplastid minicircle genome (vkDNA-PCR) in DNA extracted from the rectal ampoules of 64 OM-positive and 65 OM-negative T. infestans. Results: vkDNA-PCR detected T. cruzi infection in 59 (92.2%) OM-positive bugs and in 19 (29.2%) OM-negative triatomines in blind tests. The overall prevalence of infection, as determined by a positive test result by either vkDNA-PCR or OM, was 64.3% [95% confidence interval (95% CI) 55.8–72.1%]. Detection probability of T. cruzi infection by vkDNA-PCR (92%, 95% CI 83–97%) was substantially higher than for OM (76%, 95% CI 65–84%). Infection was minimal (26.2%) in peridomestic nymphs and maximal in domestic adult triatomines (81.7%). In the literature review encompassing 26 triatomine species from 11 countries, inter-method agreement ranged from 28.6% to 100%. The lowest agreement was observed in Rhodnius sp. and Panstrongylus lutzi and the highest among Triatoma sp., with wide variability in the protocols and outcomes of molecular diagnosis in comparison with OM. Conclusions: Our study provides a synthesis on the different sources (both biological and technical) of variation of the outcomes of OM- and PCR-based diagnosis of T. cruzi infection in triatomines and identifies new research needs for diagnostic improvement. [ABSTRACT FROM AUTHOR]

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52

Although previously close to extirpation in the southeastern United States, American black bear (Ursus americanus) abundance and distribution have increased with habitat management and harvest regulation. The north Georgia black bear population, the most abundant and widely distributed in the state, is currently being exposed to pressures from increasing anthropogenic disturbances, such as recreation and vehicle traffic. We evaluated the effects of environmental factors and anthropogenic activity on habitat use of black bears within a wildlife management area open to public recreation. We used detection/non‐detection data from camera surveys conducted at 448 locations from April to December 2023 to fit hierarchical single‐species occupancy models. Black bear detection was best explained by Julian date, its quadratic effect, and weekly human activity index. Detection increased through the spring, peaked in June, and declined throughout late summer and fall. In contrast to detection, occupancy was not affected by seasonal differences in black bear activity. Occupancy increased with increasing elevation, distance to water, and distance to high‐use recreation areas. The spatial distribution of food resources likely contributed to the observed black bear occupancy pattern related to elevation and distance to water, while human disturbance in the study area also influenced black bear occupancy. Thus, our results suggest that in a landscape open to public recreation, black bear occupancy was driven by a combination of environmental and anthropogenic factors. Identifying patterns of occupancy at a scale typical of management provides important information for managing black bear populations throughout the large, connected network of national forests in the Appalachian region. [ABSTRACT FROM AUTHOR]

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends. [ABSTRACT FROM AUTHOR]

Extreme wildfire events, such as the Pantanal 2020 megafires, are expected to become more common. Assessing the impacts of such extreme events on wildlife is imperative for conservation planning. Direct observation of carcasses can provide valuable information on how these impacts relate to spatial heterogeneity.Here, we use double‐observer carcass surveys to assess the numbers and spatial patterns of direct mortalities of medium‐ to large‐sized mammals resulting from the Pantanal 2020 megafires in a large reserve (1080 km2) in the northern Pantanal wetland. Accounting for imperfect detection, we model the spatial variation in mortality occurrence and abundance, testing the effects of habitat‐related variables and wildfire severity using multi‐species N‐mixture models.We found that 26 out of 27 species of medium and large‐sized mammals died from the fires with a mean estimate of around 49 thousand individuals. The most affected species included capuchin monkeys, agoutis, peccaries, tapirs, brocket deer, tamanduas, coatis and capybaras.Direct mortality of mammals by wildfires was affected by landscape factors related to species habitats, species traits (probably related to escape or refuge strategies), and the intensity of the wildfires. Mortalities presented a general positive relationship with non‐flooded forests (a fire‐sensitive habitat in Pantanal) and with the severity of wildfires. Artificial water bodies, a common landscape structure in Pantanal, had 7.5 times more deaths than other areas.Synthesis and applications: With the approach used, we were able to: (i) identify mortality hotspots associated with landscape features; (ii) simultaneously identify the most affected species and assess the average relationships for all the assemblage of medium‐ and large‐sized mammals. We discuss conservation and management actions in two contexts: species prioritization for rescuing and monitoring; and territory prioritization for fire prevention and fighting. [ABSTRACT FROM AUTHOR]

Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false‐positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white‐nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population‐wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance‐based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false‐negative and false‐positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Species richness is an important metric used for undertaking conservation management decisions. However, species richness estimates are influenced by species detection probabilities, with potential to entirely overlook species during surveys. Occupancy models which account for imperfect detection provide unbiased estimates, ensuring accurate estimates of richness. We carried out a camera trap survey in the mountains of north‐central Nepal during 2017 and documented a total of 21 mammal species. Here, we used multi‐species occupancy models within a Bayesian hierarchical framework to reassess our initial species richness estimate and to understand the influence of environmental covariates on occupancy and species richness of mammals in the area. Our model estimated the mean species richness was ~26 species (95% CRI: 21–36 species), suggesting we might have missed ~5 species during the survey. The mean probability of occupancy and detection of mammal species were estimated to be 0.2895%CRI:0.08–0.46$$ 0.28\ \left(95\%\mathrm{CRI}:0.08\hbox{--} 0.46\right) $$ and 0.02 (95% CRI:0.01–0.03) respectively. Mammalian species richness of the area had an anticipated positive relationship with tree canopy cover β=1.908,95%CI=0.989–2.827,p=1.95e−04$$ \left(\beta =1.908,95\%\mathrm{CI}=0.989\hbox{--} 2.827,p=1.95\mathrm{e}-04\right) $$ though its positive relationship with anthropogenic disturbance was surprising β=1.339,95%CI=0.334–2.344,p=0.012$$ \left(\beta =1.339,95\%\mathrm{CI}=0.334\hbox{--} 2.344,p=0.012\right) $$. Mammalian species richness had a quadratic relationship with elevation as is expected. This research contributes to baseline information of the mammal community ecology in north‐central Nepal and supports the need for future multi‐season surveys to understand the influence of temporal factors on mammalian community and species richness in the area. [ABSTRACT FROM AUTHOR]

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status.We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance.Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness.Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes. [ABSTRACT FROM AUTHOR]

Abstract Understanding mechanisms underlying coexistence among potential competitors, and between predators and prey, is a persistent challenge in community ecology. Using 6 years (2013–2018) of camera‐trapping data and species interaction models, we investigated the spatiotemporal patterns of inter‐ and intra‐guild interspecific interactions in a diverse terrestrial mammalian community in Pakke Wildlife Sanctuary and Tiger Reserve (PTR), Northeast India. We found no evidence of spatial interaction among apex predators (tiger Panthera tigris, leopard Panthera pardus, and dhole Cuon alpinus). However, dholes temporally separated themselves from tigers and leopards. Among small carnivores, marbled cat (Pardofelis marmorata) and leopard cat (Prionailurus bengalensis) exhibited temporal separation, whereas leopard cat overlapped spatially and temporally with other small carnivores. Herbivores exhibited neither spatial nor temporal separation with each other. All apex predators exhibited diel activity and space‐use patterns to overlap with their preferred prey. Our results suggest that the assembly of the diverse mammalian community of PTR is a complex process, and coexistence among potential competitors, and predators and prey is likely facilitated by several mechanisms including spatial and temporal segregation, and potentially dietary separation.

Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]

Insectivorous bats in temperate zones have evolved strategies such as migration or hibernation to overcome challenges of reduced resource availability and increased energy demand during winter. In the southeastern United States Coastal Plain, bats are either year‐round residents and remain active during winter or are migrants from colder areas seeking milder temperatures. Southeastern Coastal Plain forests also may represent important areas for remnant populations of species impacted by white‐nose syndrome. Working pine (Pinus spp.) forests comprise a large proportion of southeastern Coastal Plain forests, yet winter bat habitat associations and how forest management affects bat use remain understudied. Hence, we used hierarchical multispecies spatial occupancy models to evaluate factors influencing winter bat occupancy and foraging habitat associations in working forests of the southeastern Coastal Plain. From January to March 2020–2022, we deployed Anabat Swift acoustic detectors and measured site‐ and landscape‐level covariates on six working landscapes. We detected five species of bats and three species groups at 93% (224/240) of sites. We observed higher species richness at sites with high proportions of contiguous forest and low levels of basal area. At the species level, occupancy patterns were influenced by site and landscape covariates, which had varying effects on species with distinct foraging strategies. Temperature was an important predictor of detectability. Our findings offer new insights into the ecology of bats in working forest landscapes during winter, where we highlight positive responses in occupancy with contiguous forests and lower levels of basal area, as in previous summer work. By providing valuable information on winter community composition and foraging habitat associations, we hope to guide management decisions for forest attributes important to these species, thus increasing conservation opportunities within working forests. [ABSTRACT FROM AUTHOR]

An important symbolic and subsistence animal for many Native American Tribes, the moose (Alces alces; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (Dermacentor albipictus) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot‐based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western‐central Massachusetts, USA. We analyzed the data using occupancy and N‐mixture models. Detection probability peaked during mid‐October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub‐optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected. [ABSTRACT FROM AUTHOR]

Long‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets. [ABSTRACT FROM AUTHOR]

Camera traps have transformed the way we monitor wildlife and are now routinely used to address questions from a wide range of ecological and conservation aspects. Sampling design optimization and a better understanding of drivers determining the precision of detection rates (i.e. the number of detections per unit of effort) are important methodological issues. Little attention has been focused on the effect of placing more than one camera on each sampling point (hereafter, clustered design), and/or rotating (i.e. redeploying) the cameras to new placements during the sampling period.We explored the differences in the precision of detection rates between clustered vs. single camera designs when cameras remained in the same location during the study. Furthermore, the effect of keeping the placement of cameras fixed or rotating them (i.e. moving them to new locations during the sampling period), when a limited number of camera devices are available, was also evaluated. We used simulations and field data to test differences in detection rate precision for the different sampling designs. We simulated three different population distributions (random, trail‐based and aggregated) and three abundance scenarios. The simulations were validated with a field experiment focused on eight species with different behavioural traits, including artiodactyls, carnivores, lagomorphs, and birds.When a fixed number of sampling points were monitored simultaneously, clustered designs generally resulted in an increase in the precision of detection rates compared to single designs. The absolute reduction in the coefficient of variation by clustered designs was on average 0.07 units (min: 0.01, max: 0.15), which represents an average relative reduction in CV of 31% (min:6%, max:44%). An improvement in precision was also observed as a higher number of sampling points was used for all population distributions and sampling designs tested. When a fixed number of cameras were available, rotating the cameras to independent locations improved precision (an absolute reduction of 0.19 CV units) when monitoring aggregated populations, but not for random and trail‐based population distributions.Synthesis and applications: Our research provides a guideline for wildlife managers and researchers to improve the precision of camera trap detection rates and optimize resource allocation. In general, the study design should accommodate the behaviour of the target species (e.g. spatial aggregation and abundance), monitoring program logistic resources (both human and economic) and study area characteristics (e.g. accessibility and vandalism). [ABSTRACT FROM AUTHOR]

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms.

Abstract 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 ( $$\beta _0$$ β 0 ) 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.

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 ( β 0 ) 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. [ABSTRACT FROM AUTHOR]

Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. There are many reasons why absolute abundance can benefit biodiversity research, including monitoring, conservation, and ecology. Protocols to measure absolute and relative abundance can differ in data collection and/or analysis, with the key difference being if, and to what extent, the probability of detection is accounted for. As we attempt to 'bend the curve' of biodiversity loss in the Anthropocene, it is important to continuously (re)consider how biodiversity is measured. Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring. [ABSTRACT FROM AUTHOR]

Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets.We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species.We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA.spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities. [ABSTRACT FROM AUTHOR]

We consider a variant of the hide-and-seek game in which a seeker inspects multiple hiding locations to find multiple items hidden by a hider. Each hiding location has a maximum hiding capacity and a probability of detecting its hidden items when an inspection by the seeker takes place. The objective of the seeker (respectively, hider) is to minimize (respectively, maximize) the expected number of undetected items. This model is motivated by strategic inspection problems, where a security agency is tasked with coordinating multiple inspection resources to detect and seize illegal commodities hidden by a criminal organization. To solve this large-scale zero-sum game, we leverage its structure and show that its mixed-strategy Nash equilibria can be characterized using their unidimensional marginal distributions, which are pure equilibria of a lower dimensional continuous zero-sum game. This leads to a two-step approach for efficiently solving our hide-and-seek game: First, we analytically solve the continuous game and derive closed-form expressions of the equilibrium marginal distributions. Second, we design a combinatorial algorithm to coordinate the players' resources and compute equilibrium mixed strategies that satisfy the marginal distributions. We show that this solution approach computes a Nash equilibrium of the hide-and-seek game in quadratic time with linear support. Our analysis reveals novel equilibrium behaviors driven by a complex interplay between the game parameters, captured by our closed-form solutions. Funding: This work was supported by the Georgia Tech Stewart Fellowship and the Georgia Tech New Faculty Start Up Grant. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0012. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Local biodiversity monitoring is important to assess the effects of global change, but also to evaluate the performance of landscape and wildlife protection, since large-scale assessments may buffer local fluctuations, rare species tend to be underrepresented, and management actions are usually implemented on local scales. We estimated population trends of 58 bird species using open-population N-mixture models based on count data in two localities in southeastern Spain, which have been collected according to a citizen science monitoring program (SACRE, Monitoring Common Breeding Birds in Spain) over 21 and 15 years, respectively. We performed different abundance models for each species and study area, accounting for imperfect detection of individuals in replicated counts. After selecting the best models for each species and study area, empirical Bayes methods were used for estimating abundances, which allowed us to calculate population growth rates (λ) and finally population trends. We also compared the two local population trends and related them with national and European trends, and species functional traits (phenological status, dietary, and habitat specialization characteristics). Our results showed increasing trends for most species, but a weak correlation between populations of the same species from both study areas. In general, local population trends were consistent with the trends observed at national and continental scales, although contrasting patterns exist for several species, mainly with increasing local trends and decreasing Spanish and European trends. Moreover, we found no evidence of a relationship between population trends and species traits. We conclude that using open-population N-mixture models is an appropriate method to estimate population trends, and that citizen science-based monitoring schemes can be a source of data for such analyses. This modeling approach can help managers to assess the effectiveness of their actions at the local level in the context of global change. [ABSTRACT FROM AUTHOR]

Aim: Species distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks. Innovation: Here we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States. Main conclusions: We found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation. [ABSTRACT FROM AUTHOR]

Abstract Long‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

Question: What are the extent and the possible causes of imperfect detection in lichens? Because lichens are sessile and lack seasonality, they should be easier to survey than animals that can move or plants and fungi with seasonal morphology, and one could therefore expect relatively high detection probabilities. Location: 826 standardised sampling plots across Switzerland. Methods: Using repeated detection/non‐detection data from a national lichen survey conducted by professional lichenologists, we estimated the mean and variation in detectability for 373 tree‐living species with a multi‐species occupancy model. We also quantified the effect of species conspicuousness, identifiability and observer experience on detection probability. Results: The average detection probability for a single survey was unexpectedly low with an average of 0.49 (range across species: 0.25–0.74). Conspicuous species showed higher average detectability (0.56) than inconspicuous species (0.41), and identifiability as well as previous experience with a species substantially increased the probability of a person detecting it. Accounting for experience, the mean detection probabilities of observers ranged from 0.32 to 0.69. Conclusions: Our study confirms that detection probability per survey is often far below 1 also in sessile organisms, even when a standardised survey is conducted by experts. When species are seasonal (plants, fungi, etc.), survey areas are larger, or field personnel are less experienced, as is the case for many surveys and monitoring programs, detectabilities are likely to be substantially lower. We therefore argue that imperfect detection should systematically be considered in the survey design and data analysis also for sessile organisms. [ABSTRACT FROM AUTHOR]

Diseases occurring at low prevalence in free‐ranging wildlife can be difficult to study and predict at a landscape scale. This problem is compounded by imperfect pathogen detection.We used data from multiple surveillance methods to determine factors influencing spread of a low‐prevalence disease, bovine tuberculosis (bTB), among white‐tailed deer (Odocoileus virginianus) in a bTB endemic area over a 10‐year period. We fit dynamic occupancy models to predict spatial spread of bTB through a county in Michigan, USA, within the bTB endemic area, while accounting for imperfect detection and a suite of ecological processes potentially driving bTB spread.Detection by all surveillance methods was low overall (posterior mean detection probabilities 0.01–0.03). Probability of bTB occurrence was 1.46 times as likely in the historical centre of the outbreak compared to outside. Deer density had a weakly positive effect on bTB spread.The probability of a site being newly infected with bTB annually was low (posterior mean colonization probabilities 0.08–0.27), while the probability of persistence after initial infection was high (posterior mean persistence probability 0.67).Synthesis and applications: We used three surveillance methods to estimate probability of presence and spread of a low‐prevalence pathogen at a landscape scale under incomplete sampling coverage. Our methods are relevant for determining spatial risk of any wildlife disease that is monitored using multiple methods of surveillance, which is important for prioritization of management actions. [ABSTRACT FROM AUTHOR]

Areas used for livestock production and dominated by native grasses represent a unique opportunity to reconcile biodiversity conservation and livestock production. However, limited knowledge of individual species' responses to rangeland management restricts our capacity to design grazing practices that favor endangered species and other priority birds. In this work, we applied Hierarchical Modelling of Species Communities (HMSC) to study individual species responses, as well as the influence of traits on such responses, to variables related to rangeland management using birds of the Rio de la Plata Grasslands as a case study. Based on presence-absence data collected in 454 paddocks across 46 ranches we inferred the response of 69 species considering imperfect detection. This degree of detail fills a major gap in rangeland management, as species-level responses can be used to achieve targeted conservation goals other than maximizing richness or abundance. We found that artificial pastures had an overall negative impact on many bird species, whereas the presence of tussocks had a positive effect, including all threatened species. Grassland specialists were in general sensitive to grass height and tended to respond positively to tussocks but negatively to tree cover. Controlling grass height via adjustments in stocking rate can be a useful tool to favor grassland specialists. To favor a wide range of bird species in ranches, a mosaic of short and tall native grasslands with patches of tussocks and trees is desirable. We also found that species-specific responses were modulated by their traits: small-sized birds responded positively to tussocks and tree cover while large species responded negatively to increasing grass height. Ground foragers preferred short grass while birds that scarcely use this stratum were not affected by grass height. Results on the influence of traits on bird responses are an important novelty in relation to previous work in rangelands and potentially increase our predicting capacity and model transferability across grassland regions. [ABSTRACT FROM AUTHOR]

Large mammals in temperate climates typically display seasonal patterns of habitat use. However, these patterns are often overlooked because large mammals are usually surveyed at annual intervals. In addition, most studies focus on a single species and ignore other species with which the focal species could interact. Knowing seasonal patterns of habitat use in multiple species and understanding factors that cause these patterns can provide further detail on population dynamics and guide effective conservation planning. Here, using dynamic occupancy modeling, we analyze 11 years of camera-trap data collected in northwestern Anatolia, Turkey, to investigate seasonal habitat use of 8 large-mammal species: Brown Bear (Ursus arctos), Eurasian Lynx (Lynx lynx), Gray Wolf (Canis lupus), Red Fox (Vulpes vulpes), Wild Boar (Sus scrofa), Roe Deer (Capreolus capreolus), European Hare (Lepus europaeus), and Red Deer (Cervus elaphus). For each species, we study the strength of seasonality in habitat use and its dependence on human population density and elevation, which have been shown to affect distributions of species in the region. Although all species exhibited seasonality in habitat use, the strength of this seasonality varied among species; it was strongest in Wild Boar, Roe Deer, and Brown Bear. Moreover, except for Brown Bear, all species tended to avoid sites close to humans. The species responded differently to changing elevation; increasing elevation had both positive and negative effects on species-specific colonization and desertion probabilities, and these effects were likely related to either feeding habits or tendency to avoid humans. These results indicate that seasonality should be taken into consideration in population studies. However, because species differ, seasonality patterns should be identified separately for each species of interest, as differences in these patterns can explain the underlying dynamics of habitat-use patterns more accurately. [ABSTRACT FROM AUTHOR]

Introduction: Diadromous fish populations have strongly declined over decades, and many species are protected through national and international regulations. They account for less than 1% of fish biodiversity worldwide, but they are among the most perceptible linkages between freshwater and marine ecosystems. During their migration back and forth, diadromous fish species are subjected to many anthropogenic threats, among which river damming can severely limit access to vital freshwater habitats and jeopardize population sustainability. Here, we developed a method based on a double-observer modeling approach for estimating the abundance of diadromous fish during their migration in rivers. Methods: The method relies on two independent and synchronous records of fish counts that were analyzed jointly thanks to a hierarchical Bayesian model to estimate detection efficiencies and daily fish passage. We used simulated data to test model robustness and identify conditions under which the developed approach can be used. The approach was then applied to empirical data to estimate the annual silver eel run in the Touques River, France. Results: The analysis of simulated datasets and the study case gives evidence that the model can provide robust,accurate, and precise estimates of detection probabilities and total fish abundance in a set of conditions dependent on the information provided in the data (annual distribution of fish passage, annual number of observation, pairing period, etc.). Discussion: Then, the method can be applied to various species and counting systems, including nomad acoustic camera devices. We discuss its relevance for programs on river continuity restoration, notably to quantify population restoration associated with dam removals. [ABSTRACT FROM AUTHOR]

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi‐ or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within‐time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site‐specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long‐term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states. [ABSTRACT FROM AUTHOR]

Context. Accurately estimating wildlife roadkill is necessary to compare differentroads, periods, and species, and to plan and assess mitigation effectiveness. We must account for the two main sources of errors associated with carcass sampling – carcass detection and persistence. Open-population models are used to estimate abundance, survival probabilities, and recruitment in living animal populations, accounting for imperfect detection, and they can be used in the context of animal fatalities. Aims. The aim of this study was to explore an open-population approach to estimate comparable roadkill rates from carcass capture–recapture data, accounting for carcass detection and persistence. Methods. We surveyed carcasses of the white-eared opossum (Didelphis albiventris) and blackand-white tegu lizard (Salvator merianae) on four road stretches using two sampling designs with different number of visits and sampling sessions. Carcasses were marked to be recaptured over visits within the same sampling session,resulting in a capture history for each carcass. Encounter history data were modelled using the superpopulation formulation of the open-population capture–recapture model under Bayesian inference for different datasets. A daily roadkill rate per kilometre was derived from the model entry probability estimate. Key results. We estimated a daily roadkillrate with 1501 captures from 447 opossum carcasses and 511 captures from 218 tegu carcasses. For full data, mean carcass detection overthe sessionsranged from 0.49 to 0.85 forthe opossum and from 0.27 to 0.80 for the tegu, and mean carcass persistence ranged from 0.60 to 0.94 forthe opossum and from 0.64 to 0.91 for the tegu. Scenarios with more occasions and captures increased precision of roadkill rates. Conclusions. We were able to explicitly estimate roadkill rates using an open-population capture– recapture model under a Bayesian framework. It provides accurate roadkill numbers for a known time frame and road extension, accounting for imperfect detection and its associated uncertainty. Under scenarios of few carcasses, users should consider a higher number of occasions. Implications. Not addressing carcass sampling errors or simplistically addressing them (e.g. only once during the study period) could mislead mitigation efforts. The approach used here can be used to estimate fatalities in other locations, such as windfarms and powerlines, for which repeated observations of marked carcasses are an option. [ABSTRACT FROM AUTHOR]

Context. With the ongoing biodiversity crisis and the continued loss of species, it becomes crucial to find practical solutions to monitor threatened animal populations for wildlife conservation and management. However, in practice, monitoring is especially challenging for elusive, rare, and wideranging species, where estimating abundance is often expensive and time-consuming. Alternatively, estimating occupancy (i.e. detection/non-detection data) may be less resource-intensive, while still providing useful information for monitoring population trends. Aims. We aimed to describe a new field method, the random walk grid survey, to conduct a habitat selection study on elusive diurnal forest-dwelling primates. We explored how to improve occupancy estimates when detection probability is low and determined the minimal effort needed for reasonable estimates on the species habitat selection by using site-occupancy models. Methods. We collected data to assess the northern pigtailed macaques’ (Macaca leonina) occupancy and detection probability using a random walk survey of degraded forest fragments in Sakaerat Biosphere Reserve in Northeast Thailand. We ran simulations to identify what is required for minimum survey efforts to obtain reasonable estimates of occupancy and detection probability on small or relatively large spatial scales, covering a small primate community in Southeast Asia. Key results. Simulations showed that the probability of detecting macaques increased dramatically with an increased survey effort. However, compared with similar line-transect survey methods, the random walk grid survey was less time-consuming. Additionally, the occupancy and habitat selection estimates were similar to our knowledge of macaque distribution within the study area. Conclusions. Our findings suggest that the new random walk grid survey method is effective to assess the elusive northern pigtailed macaques’ occupancy, and to provide reliable data on habitat selection where there is low macaque abundance and detection probability in a degraded forest fragment. Implications. Our survey protocol could be used as a starting point to target high location occupancy to start habituation processes, but also for further intensive studies on primate behaviour and habitat use of primate communities. Finally, combining the random walk grid survey with automated recording devices (e.g. camera traps or passive acoustic surveys) could help improve occupancy and detection probability estimates for long-term monitoring programs and over large spatial scales. [ABSTRACT FROM AUTHOR]

Abstract Haphazard and opportunistic species occurrence (PO) data are widely used in species distribution models (SDMs) instead of high-quality species data gathered using appropriate and structured sampling methods, which is expensive and often spatially limited. Despite their widespread use in ecology, PO data are prone to errors and uncertainties, such as imperfect detectability, positional imprecision, and spatial niche truncation, which make their use analytically challenging for effective and adaptive biodiversity management and conservation. Using simulated data, this study investigates the effects of these uncertainties on the performance of spatial point process based presence-only and integrated SDMs. We investigated three SDMs in this study, one that ignores imperfect detectability: the presence-only model (PO model), and two that account for it: the thinned presence-only model (THINPO model) and the integrated model (PBPC model). The ability of these SDMs to produce accurate maximum likelihood estimates of intensity model coefficients and reliable predictions of species distributions under different data quality scenarios was investigated. The results show that SDMs that account for imperfect detectability (THINPO or PBPC models) are not applicable in situations of high detectability. In this situation, the PO model produces the most accurate maximum likelihood estimates of the models’ coefficients ( $${\hat{\beta }}_k$$ β ^ k ), and consequently the most accurate predictions of species distributions ( $${\hat{\lambda }}(s)$$ λ ^ ( s ) ). The effects of positional uncertainty and spatial niche truncation on this SDM output are minimal. However, in situations of low detectability, it is preferable to use the PBPC model. Positional uncertainty and spatial niche truncation have negligible effects on the output of this SDM, except when positionally uncertain PO data are analyzed along with truncated PC data. These minimal effects of spatial niche truncation on SDM outputs demonstrate the transferability of SDMs. However, the effects of all these uncertainties may depend on the characteristics of the species. Prior to modeling species distributions, a multivariate environmental similarity surface analysis should be performed to test the similarity between data from the restricted region to be used for model calibration and data from the entire range. If this analysis reveals dissimilarities, larger spatial and ecological scales should be considered to address the issue of spatial niche truncation. Further efforts could address the effects of species characteristics on SDMs performance and assess the effects of species-specific uncertainties.

IntroductionDiadromous fish populations have strongly declined over decades, and many species are protected through national and international regulations. They account for less than 1% of fish biodiversity worldwide, but they are among the most perceptible linkages between freshwater and marine ecosystems. During their migration back and forth, diadromous fish species are subjected to many anthropogenic threats, among which river damming can severely limit access to vital freshwater habitats and jeopardize population sustainability. Here, we developed a method based on a double-observer modeling approach for estimating the abundance of diadromous fish during their migration in rivers.MethodsThe method relies on two independent and synchronous records of fish counts that were analyzed jointly thanks to a hierarchical Bayesian model to estimate detection efficiencies and daily fish passage. We used simulated data to test model robustness and identify conditions under which the developed approach can be used. The approach was then applied to empirical data to estimate the annual silver eel run in the Touques River, France.ResultsThe analysis of simulated datasets and the study case gives evidence that the model can provide robust,accurate, and precise estimates of detection probabilities and total fish abundance in a set of conditions dependent on the information provided in the data (annual distribution of fish passage, annual number of observation, pairing period, etc.).DiscussionThen, the method can be applied to various species and counting systems, including nomad acoustic camera devices. We discuss its relevance for programs on river continuity restoration, notably to quantify population restoration associated with dam removals.

The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices. [ABSTRACT FROM AUTHOR]

The richness and composition of a small mammal community inhabiting semiarid California oak woodland may be changing in response to climate change, but we know little about the causes or consequence of these changes. We applied a capture‐mark‐recapture model to 17 years (1997–2013) of live trapping data to estimate species‐specific abundances. The big‐eared woodrat was the most frequently captured species in the area, contributing 58% of total captures. All small mammal populations exhibited seasonal fluctuations, whereas those of the California mouse, brush mouse, and pinyon mouse declined during the study period. We also applied a multispecies dynamic occupancy model to our small mammal detection history data to estimate species richness, occupancy (ψ), detection (p), local extinction (ϵ), and colonization (γ) probabilities, and to discern factors affecting these parameters. We found that ψ decreased from 0.369 ± 0.088 in 1997 to 0.248 ± 0.054 in 2013; γ was lower during the dry season (May–September) than the wet season (October–April) and was positively influenced by total seasonal rainfall (slope parameter, β = 0.859 ± 0.371; 95% CI = 0.132–1.587). Mean mammalian species richness decreased from 11.943 ± 0.461 in 1997 to 7.185 ± 0.425 in 2013. With highly variable climatic patterns expected in the future, especially increased frequency and intensity of droughts, it is important to monitor small mammal communities inhabiting threatened California oak woodlands. [ABSTRACT FROM AUTHOR]

The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the worlds largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

The joint optimization problem of transmit power and dwell time of radar for asynchronous multi-target tracking in heterogeneous multiple radar networks with imperfect detection is investigated. Firstly, all the asynchronous measurements from different radar node in each fusion sampling interval are fused into composite measurement, thus the Bayesian Cramér-Rao Lower Bound (BCRLB) analytical expression of the asynchronous target tracking error with parameters such as radar node selection, transmit power and dwell time with imperfect detection is derived and used as the asynchronous target tracking accuracy measure. Based on this, a joint optimization model of transmit power and dwell time for asynchronous multi-target tracking in heterogeneous multiple radar networks with imperfect detection is established, with the optimization objective of minimizing the asynchronous multi-target tracking error and the constraints of given system transmit resource limitations, the parameters such as radar node selection, transmit power and dwell time in different radar networks are designed adaptively and optimally so as to improve the asynchronous multi-target tracking accuracy of the heterogeneous multiple radar networks system. Finally, a four-step decomposition algorithm combined with the Sequential Quadratic Programming (SQP) algorithm and cyclic minimization method is used to solve the optimization problem. Simulation results demonstrate that the asynchronous multi-target tracking accuracy of the heterogeneous multiple radar networks outperforms existing algorithms.

Estimating occupancy while accounting for imperfect detection typically requires repeated surveys at sampling units. However, mixed sampling designs are very common, where only a subset of sites is visited repeatedly, while the remainder are visited only once, providing single-visit (SV) data. It is unclear whether SV data contribute to parameter estimates. Consequently, they have often been discarded in occupancy analyses. We conducted two simulation studies to understand the degree to which SV data contribute information to the estimation of occupancy and detection probability. In Simulation 1, we simulated detection/non-detection data under different scenarios of repeated sampling and varying magnitudes of occupancy and detection probabilities. In Simulation 2, we included continuous covariates, to see whether these could enhance the information content of SV data. To each simulated data set, we fitted models containing between 0 and 5000 SV sites and compared the standard errors of the occupancy and detection estimates. We found that SV data always contributed some information to the estimation of both occupancy and detection in a mixed design. Their relative contribution was greatest when > 2 visits were conducted at the repeated-visit sites, and for species with higher detection probabilities. These results suggest that SV data are valuable when combined with repeated-visit data and lead to more precise estimates than when repeated-visit data are used alone. Including suitable continuous covariates into the analysis of the simulated data increased the contribution of SV data even more. This suggests that, in a mixed design, occupancy estimation could be optimized by measuring and modelling continuous covariates that explain at least some heterogeneity in occupancy and detection amongst sites. Thus, we recommend that for mixed-design data all the available information be used in a joint model to obtain the most precise detection-corrected occupancy estimates.

Mammalian carnivores are key to our understanding of ecosystem dynamics, but most of them are threatened with extinction all over the world. Conservating large carnivores is often an arduous task considering the complex relationship between humans and carnivores, and the diverse range and reasons of threats they face. Climate change is exacerbating the situation further by interacting with most existing threats and amplifying their impacts. The Mountains of Central and South Asia are warming twice as rapidly as the rest of the northern hemisphere. There has been limited research on the effect of climate change and other variables on large carnivores. We studied the patterns in spatio-temporal distribution of three sympatric carnivores, common leopard, snow leopard, and Asiatic black bear in Kishtwar high altitude National Park, a protected area in the Great Himalayan region of Jammu and Kashmir. We investigated the effects of key habitat characteristics as well as human disturbance and climatic factors to understand the spatio-temporal change in their distributions between the early 1990s and around the year 2016–2017. We found a marked contraction in the distribution of the three carnivores between the two time periods. While snow leopard shifted upwards and further away from human settlements, common leopard and Asiatic black bear suffered higher rates of local extinctions at higher altitudes and shifted to lower areas with more vegetation, even if that brought them closer to settlements. We also found some evidence that snow leopards were less likely to have faced range contraction in areas with permanent glaciers. Our study underscores the importance of climate adaptive conservation practices for long-term management in the Greater Himalaya, including the monitoring of changes in habitat, and space-use patterns by human communities and wildlife. [ABSTRACT FROM AUTHOR]

The optimization of preventive maintenance and spare part ordering strategies for modern production equipment is of utmost importance, given its substantial influence on the reliability of equipment systems. Furthermore, the optimization problem discussed here has a direct impact on the reduction of maintenance expenses, thus making it a significant area of research. The optimization of preventive maintenance and spare parts ordering techniques for contemporary industrial equipment, which is a massive and complex system, faces substantial obstacles notwithstanding prior research efforts in the subject. Prior studies have typically assumed a fixed lead time for spare parts ordering, often leading to discrepancies with actual practice. When faced with a critical component failure, such as rolling bearings, it is not advisable for the decision maker to strictly adhere to the ordering strategy. Therefore, this paper presents a novel approach to the maintenance management method, which optimizes preventive maintenance and spare parts ordering strategies using a dynamic early warning period model based on different equipment states. The model incorporates two maintenance approaches, namely normal ordering and emergency ordering, and the equipment will adopt the corresponding maintenance method according to its state. Furthermore, the model takes imperfect detection of equipment states into account since equipment monitoring is not always accurate. Numerical experiments were conducted using rolling bearings, which are a crucial component in typical mechanical equipment, as a case study. The findings indicate that the improved model exhibits a unit time cost of 1.3021, whereas the original model has a unit time cost of 1.3611. Consequently, the new model effectively reduces the maintenance cost. This new method can better resolve the coordination challenge between preventive maintenance and spare parts ordering for equipment, the enhancing of equipment system reliability, and reduce maintenance expenses. In summary, the text presents a significant contribution in the form of a proposed preventive maintenance model that offers increased flexibility, aiming to effectively reduce maintenance costs. [ABSTRACT FROM AUTHOR]

Haphazard and opportunistic species occurrence (PO) data are widely used in species distribution models (SDMs) instead of high-quality species data gathered using appropriate and structured sampling methods, which is expensive and often spatially limited. Despite their widespread use in ecology, PO data are prone to errors and uncertainties, such as imperfect detectability, positional imprecision, and spatial niche truncation, which make their use analytically challenging for effective and adaptive biodiversity management and conservation. Using simulated data, this study investigates the effects of these uncertainties on the performance of spatial point process based presence-only and integrated SDMs. We investigated three SDMs in this study, one that ignores imperfect detectability: the presence-only model (PO model), and two that account for it: the thinned presence-only model (THINPO model) and the integrated model (PBPC model). The ability of these SDMs to produce accurate maximum likelihood estimates of intensity model coefficients and reliable predictions of species distributions under different data quality scenarios was investigated. The results show that SDMs that account for imperfect detectability (THINPO or PBPC models) are not applicable in situations of high detectability. In this situation, the PO model produces the most accurate maximum likelihood estimates of the models' coefficients ( β ^ k ), and consequently the most accurate predictions of species distributions ( λ ^ (s) ). The effects of positional uncertainty and spatial niche truncation on this SDM output are minimal. However, in situations of low detectability, it is preferable to use the PBPC model. Positional uncertainty and spatial niche truncation have negligible effects on the output of this SDM, except when positionally uncertain PO data are analyzed along with truncated PC data. These minimal effects of spatial niche truncation on SDM outputs demonstrate the transferability of SDMs. However, the effects of all these uncertainties may depend on the characteristics of the species. Prior to modeling species distributions, a multivariate environmental similarity surface analysis should be performed to test the similarity between data from the restricted region to be used for model calibration and data from the entire range. If this analysis reveals dissimilarities, larger spatial and ecological scales should be considered to address the issue of spatial niche truncation. Further efforts could address the effects of species characteristics on SDMs performance and assess the effects of species-specific uncertainties. [ABSTRACT FROM AUTHOR]

Simple Summary: Wildlife researchers and managers can choose from several techniques to estimate the number of animals in a population. Camera traps and drones are increasingly common and cost-effective options that can estimate the number and density of wildlife. We compared three simple methods that are used to estimate the number and density of deer in Pilot Mountain State Park: (1) mark–resighting and (2) N-mixture modeling, both using camera trap data, and (3) extrapolating transect counts from thermal videos collected from an aerial drone. We found that all three methods provided similar estimates of the population's density, with complementary strengths and weaknesses: drone surveys collected data quickly and precisely, mark–resight ratios provided estimates of different demographic groups, and N-mixture modeling revealed changes in density across different habitat qualities and seasons. Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark–resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season. [ABSTRACT FROM AUTHOR]

Multi-species occupancy (MSO) models use detection-nondetection data from species observed at different locations to estimate the probability that a particular species occupies a particular geographical region. The models are particularly useful for estimating the occupancy probabilities associated with rare species since they are seldom observed when undertaking field surveys. In this paper, we develop Gibbs sampling algorithms that can be used to fit various Bayesian MSO models to detection-nondetection data. Bayesian analysis of these models can be undertaken using statistical packages such as JAGS, Stan, and NIMBLE. However, since these packages were not developed specifically to fit occupancy models, one often experiences long run-times when undertaking analysis. However, we find that these packages that were not developed specifically to fit MSO models are less efficient than our special-purpose Gibbs sampling algorithms. [ABSTRACT FROM AUTHOR]

Occupancy models are commonly used with motion‐sensitive camera data to estimate patterns of species occurrence while accounting for false negative detection error (i.e., the species is present but not detected). False positive detection error (i.e., the species is not present but is detected) is present in camera data sets, especially when morphologically similar species co‐occur. Researchers use different approaches to address this problem: ignore the potential for false positive detections, remove all ambiguous detections and treat them as non‐detections, or model false positive detection error by dividing detections into ambiguous detections (could be true or false positives) and unambiguous detections (true positives). We performed a simulation study to compare these 3 strategies. To implement these modeling strategies, detections must be classified as ambiguous or unambiguous, or all ambiguous detections must be re‐classified as non‐detections. We also performed a simulation study to assess the impact of researcher confidence in the designation of ambiguous and unambiguous detections. Ignoring false positive detection error resulted in biased parameter estimates, whereas removing ambiguous detections and modeling false positive detections resulted in similar estimates of occupancy probability (ψ) in most situations. Researcher over‐confidence (i.e., the tendency for observers to overestimate their own ability) positively biased estimates of ψ. Moderate under‐confidence did not increase bias or decrease precision in estimates of ψ. Consistent with the patterns observed in simulations, analysis of example data from a chipmunk (Neotamias minimus atristriatus) population in the Sacramento Mountains of south‐central New Mexico during 2019 indicated that removing ambiguous detections and modeling false positives resulted in similar estimates of ψ and that over‐confidence biased estimates of ψ. Our results expand on previous literature, suggesting that removing ambiguous detections provides similar estimates of occupancy compared to modeling false positives in many scenarios, and emphasizing the importance of the designation of ambiguous and unambiguous detections. We provide guidance on simple methods to define ambiguous and unambiguous detections, thus mitigating the chances for erroneous inferences. [ABSTRACT FROM AUTHOR]

Large herbivores—such as the South American marsh deer—are one of the groups more threatened with extinction, with their disappearance having severe consequences for entire ecosystems. Methods to estimate abundance of large herbivores robustly and efficiently are fundamental to evaluate the status and monitor fluctuations of their populations in time, as well as identify key areas for conservation planning. Here, we (i) explore the use of spatiotemporally replicated drone-based counts analysed with N-mixture models to estimate the abundance of large herbivores; and (ii) investigate how the local abundance of marsh deer varies in the landscape, for the dry season in the Pantanal wetland (Brazil). We fitted marsh deer drone-based counts using binomial and multinomial N-mixture models (for single and double observer protocols respectively) and modelled local abundance in relation to vegetation greenness, distance to water bodies, and jaguar density. Marsh deer local abundance presented a strong relation with what is expected to be the high-quality areas for the species: high levels of vegetation greenness (that are expected to provide high-quality forage) and close to water bodies. We identified key areas for the population in the remaining humid environments and, in the drier region, close to artificial water bodies. Spatiotemporally replicated drone-based counts may serve as an accessible and cost-effective protocol to estimate abundance and monitor large herbivores while accounting for imperfect detection. [ABSTRACT FROM AUTHOR]