Imperfect Detection Of Species Research Articles

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

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

Determining abundance predictors of the European Hare (Lepus europaeus), a global invader in southeastern Brazil

Abstract European Hare (Lepus europaeus), like many invasive species, have declined in much of their native range but flourished in non-native regions (e.g. South America). Previous studies suggested that loss of farmland heterogeneity due to agricultural intensification is the main driver of the species decline in its native range in Europe. Yet, little is known about the role of spatial and temporal heterogeneity and land cover types as predictors of European Hare local abundance in Neotropical agricultural landscapes. We hypothesized that spatial and temporal heterogeneity, rather than land cover types, would be the most influential predictors of hare local abundance in intensively managed Neotropical agricultural landscapes. We sampled 55 sites embedded within agricultural-dominated landscapes from southeastern Brazil with camera traps and transect surveys. Sites were selected along an uncorrelated gradient of native vegetation cover and compositional heterogeneity. We estimated the relative abundance of European hares using an occupancy model that accounts for imperfect detection of species induced primarily by variation in local abundance. We found that land cover diversity, sugarcane, and savanna cover were the best predictors of European Hare relative abundance. Hare relative abundance estimates were low and did not vary dramatically among sites, suggesting that this invader has not yet attained high local density in our study region. European Hare attained the highest relative abundance in agricultural landscapes that locally combine higher compositional heterogeneity, including sugarcane crops, and little to no native savanna. Areas with these combined features may represent the most important nascent foci, fostering ongoing northward spread of this invasive species in the Neotropics.

Niche filtering, competition and species turnover in a metacommunity of freshwater molluscs

Metacommunity structure reflects the interplay of various processes, including niche filtering, extinction/colonization and interspecific interactions. Spatial patterns of species distributions are often analyzed to infer these processes. However, such inferences rely on often unrealistic equilibrium assumptions, and remain ambiguous, as different processes can produce similar patterns. Temporal data may improve these inferences. For example, stochastic species turnover may occur in local communities, while, on the long run, temporal changes are kept within limits set by locally available niches. Our objective is to explore how the joint analysis of spatial and temporal patterns can clarify the contribution of different processes to metacommunity structure. We recorded the occurrences of 21 freshwater mollusc species, and environmental data, in 250 sites over 17 successive years in a network of ponds in Guadeloupe (Lesser Antilles). We analyzed variation in α and β‐diversities in space and time, and used a joint‐species distribution mode to characterize species–environment and species–species relationships. Local communities showed pronounced temporal variation reflecting both imperfect species detection and true stochastic species turnover. On the long term however, local communities were largely controlled by niche filtering along two main environmental gradients, one driven by site connectivity, the other by hydrological stability and aquatic vegetation. Two gastropod clades, caenogastropods and pulmonates, showed contrasted spatio–temporal distributions resulting from different responses to these gradients, and these distributions seemed little altered by interspecific competition. Our study illustrates the benefit of using spatiotemporal metacommunity data to discern long‐term impacts of niche filtering and species interactions behind short‐term stochasticity.

Estimating species misclassification with occupancy dynamics and encounter rates: A semi‐supervised, individual‐level approach

Abstract Large‐scale, long‐term biodiversity monitoring is essential to conservation, land management and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false‐positive/false‐negative detection and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species‐level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including ‘unknown’, morphospecies designations and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic rates, occurrence and species richness. Here we present a joint classification‐occupancy model that accounts for species non‐detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain ‘morphospecies’ designations and makes use of individual specimens with known species identities in a semi‐supervised setting. We compare the performance of our model to a classification‐only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed. The model supported imperfect classifier accuracy and favoured certain true species classifications strongly for some morphospecies. The model outperformed (e.g. precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species. Spatial and temporal dynamics from modelled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data and non‐species classifications.

A new statistical approach for identifying rare species under imperfect detection

AbstractAimSpecies rarity is often used as a measure to assess the risk of extinction of species, and thus, different methods have been developed to describe the composition of rare species in biological communities. These methods usually depend on species attributes that are not always available and very often ignore imperfect species detection. In this work, we developed a new method to characterize species rarity in a community when species are detected imperfectly. Our modelling framework is based on Bayesian occupancy models to estimate species distributions under imperfect detection using presence‐nondetection data.InnovationWe propose a finite mixture occupancy model to identify rare species based on their occupancy and class‐membership probabilities. Here, we explored a two‐class finite mixture model to distinguish between rare and common species classes and presented the general modelling framework for a problem with more than two classes. By using simulations, we were able to compare our model results under different scenarios obtaining a high‐classification performance across all of them. Additionally, we applied our model to a data set of Odonata occurrence records that were partially observed due to imperfect detection and quantified the proportion of rare species on a national scale across waterbodies in the United Kingdom.Main conclusionsNowadays, biodiversity conservation involves monitoring programmes that target multiple species within a community where individual species responses may vary widely. This high variability makes the task of identifying the ecological processes that drive distributions of rare species difficult. Thus, our method represents a new approach to characterize the composition of a community in terms of species rarity while correcting for detectability bias. Our modelling framework also suggests lines of research and future developments for the understanding of how species rarity can be measured in a wide range of scenarios.

Why Are Species’ Traits Weak Predictors of Range Shifts?

We examine the evidence linking species’ traits to contemporary range shifts and find they are poor predictors of range shifts that have occurred over decades to a century. We then discuss reasons for the poor performance of traits for describing interspecific variation in range shifts from two perspectives: ( a) factors associated with species’ traits that degrade range-shift signals stemming from the measures used for species’ traits, traits that are typically not analyzed, and the influence of phylogeny on range-shift potential and ( b) issues in quantifying range shifts and relating them to species’ traits due to imperfect detection of species, differences in the responses of altitudinal and latitudinal ranges, and emphasis on testing linear relationships between traits and range shifts instead of nonlinear responses. Improving trait-based approaches requires a recognition that traits within individuals interact in unexpected ways and that different combinations of traits may be functionally equivalent.

Multi‐Species Occupancy Modeling Provides Novel Insights into Amphibian Metacommunity Structure and Wetland Restoration

A fundamental goal of community ecology is to understand species-habitat relationships and how they shape metacommunity structure. Recent advances in occupancy modeling enable habitat relationships to be assessed for both common and rare species within metacommunities using multi-species occupancy models (MSOM). These models account for imperfect species detection and offer considerable advantages over other analytical tools commonly used for community analyses under the elements of metacommunity structure (EMS) framework. Here, we demonstrate that MSOM can be used to infer habitat relationships and test metacommunity theory, using amphibians. Repeated frog surveys were undertaken at 55 wetland sites in southeastern Australia. We detected 11 frog species from three families (Limnodynastidae, Myobatrachidae, and Pelodryadidae). The rarest species was detected at only one site whereas the most common species was detected at 42 sites (naive occupancy rate 0.02-0.76). Two models were assessed representing two competing hypotheses; the best-supported model included the covariates distance to the nearest site (connectivity), wetland area, presence of the non-native eastern mosquitofish (Gambusia holbrooki), proportion cover of emergent vegetation, an interaction term between Gambusia and emergent vegetation cover, and the proportion canopy cover over a site. Hydroperiod played no detectable role in metacommunity structure. We found species-habitat relationships that fit with current metacommunity theory: occupancy increased with wetland area and connectivity. There was a strong negative relationship between occupancy and the presence of predatory Gambusia, and a positive interaction between Gambusia and emergent vegetation. The presence of canopy cover strongly increased occupancy for several tree frog species, highlighting the importance of terrestrial habitat for amphibian community structure. We demonstrated how responses by amphibians to environmental covariates at the species level can be linked to occupancy patterns at the metacommunity scale. Our results have clear management implications: wetland restoration projects for amphibians and likely other taxa should maximize wetland area and connectivity, establish partial canopy cover, and eradicate Gambusia or provide aquatic vegetation to mitigate the impact of this non-native fish. We strongly advocate the use of MSOM to elucidate the habitat drivers behind animal occupancy patterns and to derive unbiased occupancy estimates for monitoring programs.

Small ponds support high terrestrial bird species richness in a Mediterranean semiarid region

Ponds are among the world’s most endangered freshwater ecosystems. A comprehensive knowledge of pond biodiversity is urgently required to inform effective pond management and conservation actions. Most studies about pond biodiversity focus on aquatic taxa, while the terrestrial biodiversity, especially of birds, has been little studied. Moreover, the few studies existing on pond biodiversity do not account for different detection rates of species, thus yielding biased results. Here, we apply a hierarchical Bayesian modelling technique to data obtained from visual censuses to estimate bird species richness associated with small ponds in a semiarid region, considering the imperfect detection of species. The model incorporates specific responses to site characteristics (pond typology), landscape (environmental heterogeneity) and at regional scale (mean annual precipitation). The studied ponds were used by two thirds of the terrestrial breeding bird community of the study region. Our modelling approach increased by an average of 7.5 species the observed site-specific richness. Drinking troughs supported a greater richness than other pond types. Environmental heterogeneity was positively related with species richness, whereas no clear relation was observed between richness and precipitation. In addition to ecosystem services provided by ponds to human welfare, our results suggest these small isolated habitats may act as key landscape elements for terrestrial birds in semiarid regions.

Multi-species occupancy modeling provides novel insights into amphibian metacommunity structure and wetland restoration.

A fundamental goal of community ecology is to understand species-habitat relationships and how they shape metacommunity structure. Recent advances in occupancy modeling enable habitat relationships to be assessed for both common and rare species within metacommunities using multi-species occupancy models (MSOM). These models account for imperfect species detection and offer considerable advantages over other analytical tools commonly used for community analyses under the elements of metacommunity structure (EMS) framework. Here, we demonstrate that MSOM can be used to infer habitat relationships and test metacommunity theory, using amphibians. Repeated frog surveys were undertaken at 55 wetland sites in southeastern Australia. We detected 11 frog species from three families (Limnodynastidae, Myobatrachidae, and Pelodryadidae). The rarest species was detected at only one site whereas the most common species was detected at 42 sites (naive occupancy rate 0.02-0.76). Two models were assessed representing two competing hypotheses; the best-supported model included the covariates distance to the nearest site (connectivity), wetland area, presence of the non-native eastern mosquitofish (Gambusia holbrooki), proportion cover of emergent vegetation, an interaction term between Gambusia and emergent vegetation cover, and the proportion canopy cover over a site. Hydroperiod played no detectable role in metacommunity structure. We found species-habitat relationships that fit with current metacommunity theory: occupancy increased with wetland area and connectivity. There was a strong negative relationship between occupancy and the presence of predatory Gambusia, and a positive interaction between Gambusia and emergent vegetation. The presence of canopy cover strongly increased occupancy for several tree frog species, highlighting the importance of terrestrial habitat for amphibian community structure. We demonstrated how responses by amphibians to environmental covariates at the species level can be linked to occupancy patterns at the metacommunity scale. Our results have clear management implications: wetland restoration projects for amphibians and likely other taxa should maximize wetland area and connectivity, establish partial canopy cover, and eradicate Gambusia or provide aquatic vegetation to mitigate the impact of this non-native fish. We strongly advocate the use of MSOM to elucidate the habitat drivers behind animal occupancy patterns and to derive unbiased occupancy estimates for monitoring programs.

An integrated approach to measure hunting intensity and assess its impacts on mammal populations

Abstract Unsustainable hunting of wildlife is one of the greatest threats to diverse and healthy forests, yet our understanding of hunting activity is limited by our methods of accurately identifying its intensity and distribution. Several methods have been used to quantify hunting in past studies (e.g. interviews, ranger patrols and camera traps). However, none of these alone have been able to produce precise spatiotemporal measures of hunting activity. In this study, we used a new method to detect hunters through passive acoustic monitors and developed an integrated approach to measure hunting activity while simultaneously assessing its impacts on mammal populations using camera traps. We applied a hierarchical community occupancy model that accounted for the imperfect detection of species on data from 45 trap locations, surveyed from January to June 2018, to investigate the impacts of spatial variation and intensity in hunting pressure on mammal species richness and occurrence in four protected areas in southern Belize. We developed spatiotemporally explicit indices of hunting activity separately from camera trap and acoustic monitor data and used a Bayesian model selection framework to identify predictors of site occurrence for individual species and three functional groups: carnivores, herbivores and omnivores. We found that camera traps under‐detected hunting activity in the region by 939% compared to acoustic monitors. Consistent with our predictions, hunting intensity was negatively correlated with site‐level species richness, with an average decrease in richness of 31% across its range of variation. Occurrence patterns for the three functional groups were also negatively associated with hunting intensity. Often the target of hunters, herbivores, displayed the strongest negative response to hunting, while omnivores were least affected. Synthesis and applications. Unsustainable hunting of wildlife is a global phenomenon with wide‐ranging implications for ecological communities, especially mammals. Our study highlights mammal sensitivity to increasing hunting pressure at the community and species level and emphasizes the necessity for developing robust tools to accurately monitor hunting activity, while also providing a flexible framework for simultaneously assessing hunting and its impacts on mammal communities.

Environmental correlates of richness, community composition, and functional traits of terrestrial birds and mammals in a fragmented tropical landscape

Tropical forest loss and fragmentation and the associated loss in species diversity are increasing in both magnitude and scope. Much attention has been paid to how attributes of forest fragments, such as area and forest structure, impact the diversity and functional composition of vertebrate communities, while more recent work has begun to consider the importance of landscape-level variables, such as surrounding tree cover. Yet, the relative impacts of these factors on species diversity and functional composition remain unclear, particularly among under-studied taxonomic groups. We quantified how species richness, community composition, and functional traits of terrestrial birds and mammals are associated with variation in fragment area, elevation, habitat structure and surrounding tree cover. Our goal was to determine the degree to which these diverse explanatory variables contribute to species diversity. We used motion-activated camera traps to sample terrestrial birds and mammals in 22 forest fragments in northwestern Ecuador. We used a hierarchical multi-species occupancy model accounting for imperfect species detection to estimate species richness and species composition differences among fragments, weighted multiple regression and distance matrix regression to assess covariates of richness and composition, and an RLQ ordination to assess co-variation of environmental conditions and species traits. Terrestrial mammals and birds exhibited similar relationships to key environmental variables, but also showed guild-specific differences. Elevation was significantly associated with differences in species richness and community composition for both groups. Forest cover in the surrounding matrix was associated with higher species richness and changes in community composition in mammals, but not terrestrial birds. Canopy openness showed a positive association with mammalian species richness but no relationship with bird species richness. There was no association between density of large trees and richness for either group. We found no significant associations between environmental variation and functional composition among forest fragments. This work highlights the general importance of elevation and forest cover in shaping patterns of species diversity and composition in forest fragments and suggests heightened sensitivity to matrix conditions in mammals relative to terrestrial birds.

Seasonal habitat use indicates that depth may mediate the potential for invasive round goby impacts in inland lakes

Abstract The round goby (Neogobius melanostomus) is among the fastest‐spreading introduced aquatic species in North America and is radiating inland from the Great Lakes into freshwater ecosystems across the landscape. Predicting and managing the impacts of round gobies requires information on the factors influencing their distribution in habitats along the invasion front, yet this information is not available for many recently invaded ecosystems. We evaluated the seasonal habitat use and biomass of round gobies in an inland temperate lake to define the spatiotemporal scope of biological interactions at the leading edge of the round goby invasion. Using novel statistical approaches, we combined hierarchical models that control for imperfect species detection with flexible smooth terms to describe non‐linear relationships between round goby abundance and environmental gradients. Subsequently, we generated accurate detection‐corrected estimates of the standing stock biomass of round gobies. Our results show seasonally differentiated habitat niches, where suitable round goby habitat in summer months is restricted to shallow depths (<18.4 m) with a mixture of vegetative and mussel cover. We found high round goby biomass of 122 kg/ha in occupied habitats during the summer, with a total lake‐wide biomass of 766,000 kg. In winter, round gobies migrate to deep offshore habitats and disperse, dramatically altering their scope for biological interactions with resident aquatic species across summer and winter seasons. The results of this study indicate that the scope of biological interactions in inland lakes may be seasonally variable, with potential for high round goby biomass in shallow lakes or at the periphery of deep lakes in the summer months. Such shallow‐water habitats may therefore present higher risk of ecological impacts from round gobies in invaded lentic ecosystems. As round gobies expand inland, consideration of seasonal habitat use will be an important factor in predicting the impacts of this pervasive invader.

A mechanistic–statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France

Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90’s. Using partial differential equations for modeling species diffusion and growth in a fragmented landscape, we develop a mechanistic–statistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007–2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts.

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.

Direct and indirect effects of climate on bird abundance along elevation gradients in the Northern Appalachian mountains

AbstractAimThe stratification of organisms along elevational gradients is widely reported, with montane communities characterized by species occurring in relatively small and isolated populations; these species are of considerable interest to ecologists and conservationists. This stratification is generally attributed to climatic zonation. Evidence that species are shifting upward in elevation has fuelled speculation that species are tracking their climatic niches in response to climate change. Uncertainty regarding the degree to which climate directly influences species abundance versus the degree to which climate has an indirect influence via vegetation represents a key impediment to understanding the ecology of montane species; here, we evaluate these direct and indirect effects.LocationWhite Mountains, New Hampshire, USA.MethodsWe used N‐mixture models to correct for imperfect detection of species, principal component analysis to represent gradients in vegetation structure and composition and structural equation models to assign variation to the direct and indirect effects of climate upon birds.ResultsAnalysis of 13 species revealed that climate exerts direct influences on bird abundance and indirect influences mediated by vegetation composition and structure. All species exhibited indirect effects of climate via forest habitat, with 77% exhibiting both direct and indirect effects and 53% exhibiting stronger indirect effects.Main conclusionsWe provide insight into the mechanistic pathways of how climate influences the distribution of species along elevational gradients, underscoring the complex vulnerability of species to climate change. Our results reveal that the majority of species experience both direct and indirect effects of climate, implying that forests play a key role in mediating climate effects. For species that are primarily influenced by climate directly, typical climate envelope models may continue to be informative, but for the majority of the species included in this study, we show that distribution models should also include measures of habitat.

The effects of mountain pine beetle outbreaks on avian communities in lodgepole pine forests across the greater Rocky Mountain region

Disturbance is an integral component of forest ecosystems. Therefore, contemporary changes to the frequency and severity of disturbances in forests may have lasting ecological consequences. In recent decades, widespread mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have contributed more to total tree mortality than other disturbances such as wildfire. Past studies have used a broad range of measurements to evaluate the local effects of beetle outbreaks on birds (e.g., dead trees per acre versus percent mortality) and this has led to some challenges and inconsistencies in generalizing results across larger spatial scales. We sought to address these inconsistencies by using a single data source estimating outbreak-related tree mortality across the western United States to better understand the effect of beetle outbreaks on avian communities in lodgepole pine forests. This study uses five years of avian monitoring data (2008–2012) from the Integrated Monitoring in Bird Conservation Regions program, collected across Colorado, Idaho, Montana, and Wyoming. We developed a hierarchical multispecies occupancy model to quantify changes in the forest bird community following beetle outbreaks while accounting for the imperfect detection of species. Mean species richness (N) did not differ between MPB outbreak sites (N = 19.35, 95%CRI = 17.00, 22.01) and sites with no outbreak (N = 20.23, 95%CRI = 16.83, 24.25). Furthermore, we found weak evidence for a community-level response to increasing outbreak severity and time since outbreak, due in part to the variability among species within each of the four nesting guilds analyzed in this study (canopy, cavity, shrub, and ground nesting birds). However, we did find evidence of turnover in community composition with just over 50% of species (28 of 55) having statistically different occurrence rates following MPB outbreaks. Our study underscores the notion that species richness alone does not fully express changes in the forest bird community. Our results also suggest many species, particularly cavity nesters, utilize beetle-killed forests and persist at higher occurrence rates for up to 10 years post-outbreak. This period includes the timeframe when beetle-killed trees are most readily salvageable and emphasizes the need for continued evaluation of snag-retention policies prior to the implementation of post-outbreak management activities.

Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius)

Abstract The crucian carp (Carassius carassius) is one of few fish species associated with small ponds in the UK. These populations contain genetic diversity not found in Europe and are important to conservation efforts for the species which has declined across its range in Europe. Detection and monitoring of extant crucian carp populations are crucial for conservation success. Environmental DNA (eDNA) analysis could be very useful in this respect as a rapid, cost‐efficient monitoring tool. We developed a species‐specific quantitative polymerase chain reaction (qPCR) assay for eDNA surveillance of crucian carp to enable non‐invasive, large‐scale distribution monitoring. We compared fyke netting and eDNA analysis at ponds with (n = 10) and without (n = 10) crucian carp for presence–absence detection. We examined biotic (crucian carp density represented by catch‐per‐unit‐effort [CPUE] estimate) and abiotic influences on eDNA detection probability using a hierarchical occupancy model, and eDNA quantification using a mixed‐effects model. eDNA analysis achieved 90% detection for crucian carp (n = 10), failing in only one pond where presence was known. CPUE estimate and conductivity had positive and negative influences on eDNA detection probability in qPCR replicates respectively. Similarly, conductivity had a negative effect on DNA copy number, whereas copy number increased with CPUE estimate. Our results demonstrate that eDNA analysis could enable detection of crucian carp populations in ponds and benefit ongoing conservation efforts, but imperfect species detection in relation to biotic and abiotic factors and eDNA workflow requires further investigation. Nonetheless, we have established an eDNA framework for the crucian carp as well as sources of imperfect detection which future investigations can build upon.

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA.

Site occupancy-detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false-positive and false-negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true-/false-positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true-/false-positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.

The importance of accounting for imperfect detection when estimating functional and phylogenetic community structure

Incorporating imperfect detection when estimating species richness has become commonplace in the past decade. However, the question of how imperfect detection of species affects estimates of functional and phylogenetic community structure remains untested. We used long-term counts of breeding bird species that were detected at least once on islands in a land-bridge island system, and employed multi-species occupancy models to assess the effects of imperfect detection of species on estimates of bird diversity and community structure by incorporating species traits and phylogenies. Our results showed that taxonomic, functional, and phylogenetic diversity were all underestimated significantly as a result of species' imperfect detection, with taxonomic diversity showing the greatest bias. The functional and phylogenetic structure calculated from observed communities were both more clustered than those from the detection-corrected communities due to missed distinct species. The discrepancy between observed and estimated diversity differed according to the measure of biodiversity employed. Our study demonstrates the importance of accounting for species' imperfect detection in biodiversity studies, especially for functional and phylogenetic community ecology, and when attempting to infer community assembly processes. With datasets that allow for detection-corrected community structure, we can better estimate diversity and infer the underlying mechanisms that structure community assembly, and thus make reliable management decisions for the conservation of biodiversity.

Effects of agriculture and topography on tropical amphibian species and communities.

Habitat loss is the greatest threat to the persistence of forest-dependent amphibians, but it is not the only factor influencing species occurrences. The composition of the surrounding matrix, structure of stream networks, and topography are also important landscape characteristics influencing amphibian distributions. Tropical forests have high diversity and endemism of amphibians, but little is known about the specific responses of many of these species to landscape features. In this paper, we quantify the response of amphibian species and communities to landscape-scale characteristics in streams within the fragmented Brazilian Atlantic Forest. We surveyed amphibian communities during a rainy season in 50 independent stream segments using Standardized Acoustic and Visual Transect Sampling (active) and Automated Acoustic Recorders (passive) methods. We developed a hierarchical multi-species occupancy model to quantify the influence of landscape-scale characteristics (forest cover, agriculture, catchment area, stream density, and slope) on amphibian occurrence probabilities while accounting for imperfect detection of species using the two survey methods. At the community level, we estimated an overall mean positive relationship between amphibian occurrence probabilities and forest cover, and a negative relationship with agriculture. Catchment area and slope were negatively related with amphibian community structure (95% credible interval [CI] did not overlap zero). The species-level relationships with landscape covariates were highly variable but showed similar patterns to those at the community level. Species detection probabilities varied widely and were influenced by the sampling method. For most species, the active method resulted in higher detection probabilities than the passive approach. Our findings suggest that small streams and flat topography lead to higher amphibian occurrence probabilities for many species in Brazil's Atlantic Forest. Our results combined with land use and topographic maps can be used to make predictions of amphibian occurrences and distributions beyond our study area. Such projections can be useful to determine where to conduct future research and prioritize conservation efforts in human-modified landscapes.