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Aim: Our aims are: (1) to highlight the power of dynamic occupancy models for analysing species range dynamics while accounting for imperfect detection; (2) to emphasize the flexibility to model effects of environmental covariates in the dynamics parameters (extinction and colonization probability); and (3) to illustrate the development of predictive maps of range dynamics by projecting estimated probabilities of occupancy, local extinction and colonization. Location: Switzerland. Methods: We used data from the Swiss breeding bird survey to model the Swiss range dynamics of the European crossbill (Loxia curvirostra) from 2000 to 2007. Within-season replicate surveys at each 1 km 2 sample unit allowed us to fit dynamic occupancy models that account for imperfect detection, and thus estimate the following processes underlying the observed range dynamics: local extinction, colonization and detection. For comparison, we also fitted a model variant where detection was assumed to be perfect. Results: All model parameters were affected by elevation, forest cover and elevation-by-forest cover interactions and exhibited substantial annual variation. Detection probability varied seasonally and among years, highlighting the need for its estimation. Projecting parameter estimates in environmental or geographical space is a powerful means of understanding what the model is telling about covariate relationships. Geographical maps were substantially different between the model where detection was estimated and that where it was not, emphasizing the importance of accounting for imperfect detection in studies of range dynamics, even for high-quality data. Main conclusions: The study of species range dynamics is among the most exciting avenues for species distribution modelling. Dynamic occupancy models offer a robust framework for doing so, by accounting for imperfect detection and directly modelling the effects of covariates on the parameters that govern distributional change. Mapping parameter estimates modelled by spatially indexed covariates is an under-used way to gain insights into dynamic species distributions.

Over the last two decades, a large number of camera trap surveys have been carried out around the world and camera traps have been proposed as an ideal tool for inventorying and monitoring medium to large‐sized terrestrial vertebrates. However, few studies have analysed camera trap data at the community level. We developed a multi‐session multi‐species occupancy model that allows us to obtain estimates for species richness and occupancy combining data from multiple camera trap surveys (sessions). By estimating species presence at the session‐level and modelling detection probability and occupancy for each species and sessions as nested random effects, we could improve parameter estimates for each session, especially for species with sparse data. We developed two variants of our model: one was a binary latent states model while the other used a Royle–Nichols formulation for the relationship between detection probability and abundance. We applied both models to data from eight camera trap surveys from south‐eastern Peru including six study sites, 263 camera stations and 17 423 camera days. Sites covered protected areas, a logging concession and Brazil nut concessions. We included habitat (terra firme vs. floodplain) as a covariate for occupancy and trail vs. off‐trail as a covariate for detection. Among‐camera heterogeneity was a serious problem for our data and the Royle–Nichols variant of our model had a much better fit than the binary‐state variant. Both models resulted in similar species richness estimates showing that most of the sites contained intact large mammal communities. Detection probabilities and occupancy values were more variable across species than across sessions within species. Three species showed a habitat preference and four species showed preference or avoidance of trails. Synthesis and applications. Our multi‐session multi‐species occupancy model provides improved estimates for species richness and occupancy for a large data set. Our model is ideally suited for integrating large numbers of camera trap data sets to investigate regional and/or temporal patterns in the distribution and composition of mammal communities in relation to natural or anthropogenic factors or to monitor mammal communities over time.

First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

Urbanization is considered the fastest growing form of global land‐use change and can dramatically modify habitat structure and ecosystem functioning. While ecological processes continue to operate within cities, urban ecosystems are profoundly different from their more natural counterparts. Thus, ecological predictions derived from more natural ecosystems are rarely generalizable to urban environments. In this study, we used data from a large‐scale and long‐term camera trap project in Chicago IL, USA, to determine whether urbanization alters predator‐avoidance behaviour of urban prey species. We studied three behavioural mechanisms often induced by the fear of predation (spatial distribution, daily activity patterns and vigilance) of white‐tailed deer (Odocoileus virginianus) and eastern cottontail (Sylvilagus floridanus) when coyote (Canis latrans)—an urban apex predator—was present. We found no evidence of spatial segregation between coyote and either prey species. Furthermore, neither white‐tailed deer nor eastern cottontail changed their daily activity or increased vigilance in urban areas when coyotes were present. Eastern cottontail, however, had their uppermost level of vigilance in highly urban sites when coyotes were absent. Our study demonstrates that predator–prey dynamics might be modified in urban ecosystems—moving from what is traditionally thought of as a two‐player system (predator and prey) to a three‐player system (predator, prey and people). The authors found that urban prey species are less “afraid” of predators in cities, indicating that interactions between predators and prey in urban ecosystems may be better understood by considering three players instead of two: predators, prey and people.

Invasive alien anurans are introduced worldwide in freshwater ecosystems where they can have a strong impact on native organisms such as amphibians. The risk for natives is dependent on the degree of niche overlap and co-occurrence in pond-breeding sites. In the present study, we focused on alien marsh frogs ( Pelophylax ridibundus ) that are invading nationwide areas in Western Europe and which prey on both caudates and anurans. We assessed aquatic habitat preferences, pond use and environmental niche overlap between invasive populations of marsh frogs and five species of native amphibian prey of the Larzac plateau (southern France). Due to their large environmental niche, marsh frogs have become the most ubiquitous amphibians in the area. Occupancy models revealed that they had aquatic habitat preferences (e.g., water depth and aquatic vegetation) similar to most species of native amphibians. This resulted in a large overlap between the environmental niche of the invader and its potential prey. The frequent coexistence in ponds therefore exposed native species to predation risk and other potential disturbances caused by marsh frogs. Altogether, these results highlight on the risks posed by such opportunist invaders for native amphibians that occur in their wide invasion range.

Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of five mammal species. Specifically, we focused on two species of conservation concern (mountain gorilla [Gorilla beringei beringei] and golden monkey [Cercopithecus mitis kandti]), and three species targeted by poachers (black-fronted duiker [Cephalophus nigrifrons], bushbuck [Tragelaphus scriptus], and African buffalo [Syncerus caffer]). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics can be combined with information on the relationship between ranger patrols and poaching activity to develop models useful for making wise decisions about ranger patrol deployment.

Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coefficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We develop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub (https://github.com/AllanClark/Rcppocc). We developed a Gibbs sampling algorithm to undertake various occupancy type models when using logit link functions.

Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.

Aim Agriculture is a primary factor underlying worldwide declines in biodiversity. Incorporating semi‐natural habitat features within agricultural landscapes is considered an effective strategy for mitigating the biodiversity loss associated with agricultural intensification. However, few studies have investigated whether and how the biodiversity‐supporting capacity of semi‐natural habitats varies across landscape‐level agricultural intensity gradients. Location Agroecosystems of Central‐Eastern Jilin, China. Methods The 84 passive acoustic monitors were deployed across agricultural intensity gradients for 30 days, collecting avian vocalisation data from 04:00 to 07:00 on alternate days. The collected avian vocalisation data were processed using BirdNET (an AI‐based sound analysis tool) and were complemented by expert verification. We employed multi‐species occupancy models to estimate bird occupancy rates, with subsequent analysis examining the relative influence of semi‐natural habitats on these rates under different agricultural intensity gradients. Results Our results indicated that bird occupancy probabilities were higher in low‐ and middle‐intensity agricultural landscapes compared to high‐intensity agricultural landscapes, particularly for habitat edge‐dependent insectivores. The supportive role of semi‐natural habitats on bird occupancy was strongest in middle‐intensity agriculture, with insectivores benefiting most significantly. Specifically, enhancing both the number of semi‐natural habitat types and woodland coverage under middle‐intensity agricultural practices would benefit various bird guilds. Increased waterbody coverage within farmland ecosystems positively impacted insectivorous birds regardless of agricultural intensity. Additionally, open‐habitat species benefit from diversified crop cultivation patterns in low‐to‐middle intensity systems. Main Conclusions Our results demonstrate that enhancing bird occupancy rates by semi‐natural habitats depends on both the agricultural intensity context and the functional group. Our findings provide critical evidence for biodiversity conservation strategies in agricultural ecosystems and contribute to reducing geographical biases in agro‐ecological research on avifauna.

Ecologists have historically quantified fundamental biodiversity patterns, including species‐area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species‐area and β‐diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β‐diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non‐parametric diversity estimator (iNEXT.3D) and Multi‐Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β‐diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z‐values, and overestimated SAR c‐values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z‐values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β‐diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β‐diversity model coefficients resulted from a systematic, area‐related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β‐diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated. Using simulations, we show that imperfect detection substantially biases estimates of the coefficients of species‐area relationships and pairwise β‐diversity models. Although the use of both iNEXT.3D and Multi‐Species Occupancy Models improved observed biases in SAR coefficient estimates, neither method fully resolved them. Importantly, however, MSOMs consistently corrected for imperfect detection in β‐diversity estimates, providing highly accurate estimates of pairwise β‐diversity trends even in sub‐optimal conditions.