Advances and applications of occupancy models

The forensic potential of environmental DNA (eDNA) in freshwater wildlife crime investigations: From research to application

Environmental DNA (eDNA) is widely used in biodiversity, conservation, and ecological studies but despite its successes, similar approaches have not yet been regularly applied to assist in wildlife crime investigations. The purpose of this paper is to review current eDNA methods and assess their potential forensic application in freshwater environments considering collection, transport and persistence, analysis, and interpretation, while identifying additional research required to present eDNA evidence in court. An extensive review of the literature suggests that commonly used collection methods can be easily adapted for forensic frameworks providing they address the appropriate investigative questions and take into consideration the uniqueness of the target species, its habitat, and the requirements of the end user. The use of eDNA methods to inform conservationists, monitor biodiversity and impacts of climate change, and detect invasive species and pathogens shows confidence within the scientific community, making the acceptance of these methods by the criminal justice system highly possible. To contextualise the potential application of eDNA on forensic investigations, two test cases are explored involving i) species detection and ii) species localisation. Recommendations for future work within the forensic eDNA discipline include development of suitable standardised collection methods, considered collection strategies, forensically validated assays and publication of procedures and empirical research studies to support implementation within the legal system.

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'.

Detecting management gaps for biodiversity conservation: An integrated assessment

The latest report on the state of nature in Europe (2013-2018) shows that biodiversity is declining at an alarming rate, with most protected species and habitats in poor condition. Despite an increasing volume of collected biodiversity information, urgent action is needed to integrate biodiversity data and knowledge to improve conservation efforts. We conducted a study in Catalonia (NE Spain), where we collected management measures implemented between 2013 and 2018, including allocation, budget, pressures aimed, and habitats/species potentially benefiting. We integrated information on pressures and habitats/species with the measures to identify non-spatial management gaps. Then, we integrated the spatially explicit information to determine the spatial management gap, identifying geographical areas where species/habitats are under pressure without registered measures. We demonstrated the importance of integrating existing information. Our findings revealed that resources were often not distributed adequately across species/habitats, with biases towards certain taxa being a common issue. The non-spatial management gap analysis identified taxonomic groups, especially plants and mollusks with the wider management gaps. We also identified threatened areas, especially in the northeast of the region with the larger spatial management gaps. These results could guide priority objectives to optimize conservation efforts. Integrating different information sources provided a broader view of the challenges that conservation science is facing nowadays. Our study offers a path toward bending the curve of biodiversity loss by providing an integrative framework that could optimize the use of the available information and help narrow the knowing-doing gap. In the context of the EU, this example demonstrates how information can be used to promote some environmental policy instruments, such as the Prioritized Action Frameworks (PAFs). Additionally, our findings highlight the importance of supporting decision-making with systematic assessments to identify deficiencies in the conservation process, reduce the loss of critical ecosystems and species, and avoid biases among taxa.

Longitudinal comparison of bacterial pathogen seropositivity among wet market vendors in the Lao People's Democratic Republic

Wild animal trade for human consumption is a global issue, involving complex interactions between economics, culture, food security and conservation. Whilst being a biodiversity issue, it is also a major public health concern, with recent epidemics and pandemics of zoonotic pathogens linked to interactions with wildlife. At three time points, between March 2017 and June 2018, a longitudinal sero-survey of 150 market vendors from three wet markets in Laos (selling vegetables, domestic animal meat and/or wildlife meat) was conducted to determine if vendors had been differentially exposed to three endemic bacterial pathogens - Orientia tsutsugamushi, Rickettsia typhi, and Leptospira spp. A total of 367 serum samples were tested by IgG enzyme-linked immunosorbent assay (ELISA) and immunofluorescence assay (IFA, for scrub typhus group (STG) and typhus group (TG) only). Among vendors, 32.7% were IgG-positive for at least one pathogen, 13.3% sero-converted during the study. Multi-season occupancy modelling for STG indicated a significantly higher prevalence of STG IgG in vegetable vendors (27.3%) and wildlife vendors (28.4%) than in domestic animal meat vendors (6.9 %, p=0.05), and higher in Phonsavanh market (OR=9.6, p=0.03) compared to Lak Sao and Salavan markets. Estimated mean incidence was 57 cases per 10,000 per 7.5-month period. For TG, vendor age had a significant effect on prevalence (OR=1.04, p=0.006), estimated mean incidence was 64 cases per 10,000 per season (7.5-month period). Despite individuals selling domestic meat having a higher prevalence of Leptospira infections than those that did not (11.6% versus 4.5%), the difference was not significant. Whilst this study has a number of limitations, including vendors changing what food types they sold and no investigation of exposure outside of markets, the finding that the risk of exposure of vendors to zoonotic pathogens may be associated with types of food sold for human consumption warrants further investigation.

Occupancy of the Ethiopian endemic Moorland Francolin in pristine and degraded Afroalpine biome using a camera trap approach

Occupancy modeling is an essential tool for understanding species-habitat associations, thereby helping to plan the conservation of rare and threatened wildlife species. The conservation status and ecology of several avian species, particularly ground-dwelling birds, are poorly known in Ethiopia. We used camera trap-based occupancy modeling to investigate habitat covariate influence on occupancy (Ψ) and detection probability (ρ) estimates of Moorland Francolins Scleroptila psilolaema from spatially replicated surveys across both relatively pristine and disturbed landscapes in the Afroalpine biome of Ethiopia. Model-averaged estimate of ψ ^ across all sites was 0.76 (SD = 0.28) and ρ ^ was 0.77 (SD = 0.13) in the pristine landscape. The ψ ^ of the species in the disturbed landscape was 0.56 (SD = 0.19) and ρ ^ was 0.48 (SD = 0.06). As hypothesized, based on our model-averaged beta coefficient estimates (βmean ± SE), predators significantly negatively influenced the occupancy of Moorland Francolins in pristine habitat. We also found a significant positive association of occupancy with herb species richness. Contrary to our prediction, distance to road significantly negatively influence the occupancy of the species, suggesting that occupancy probability was highest in proximity to roadsides and trails in the pristine habitat. There was no significant influence of habitat covariates on the occupancy of the species in the disturbed habitat. The most important covariates that significantly influence the detectability of the species in pristine habitat included sampling occasion and precipitation. The greater occupancy and detectability of this endemic species in the pristine habitat could be linked with the particular conservation status and management of this biodiversity hotspot in the central highlands of Ethiopia. Our results suggest that strict legal enforcement is required to sustainably preserve Moorland Francolins and the ecological integrity of the entire Afroalpine biome. We recommend using camera traps in order to develop realistic and effective conservation and management strategies for rare, sensitive, cryptic, and ground-dwelling animals in the region.

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons (Python molurus bivittatus) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Detecting Leishmania in dogs: A hierarchical-modeling approach to investigate the performance of parasitological and qPCR-based diagnostic procedures

BACKGROUND

Domestic dogs are primary reservoir hosts of Leishmania infantum, the agent of visceral leishmaniasis. Detecting dog infections is central to epidemiological inference, disease prevention, and veterinary practice. Error-free diagnostic procedures, however, are lacking, and the performance of those available is difficult to measure in the absence of fail-safe "reference standards". Here, we illustrate how a hierarchical-modeling approach can be used to formally account for false-negative and false-positive results when investigating the process of Leishmania detection in dogs.

METHODS/FINDINGS

We studied 294 field-sampled dogs of unknown infection status from a Leishmania-endemic region. We ran 350 parasitological tests (bone-marrow microscopy and culture) and 1,016 qPCR assays (blood, bone-marrow, and eye-swab samples with amplifiable DNA). Using replicate test results and site-occupancy models, we estimated (a) clinical sensitivity for each diagnostic procedure and (b) clinical specificity for qPCRs; parasitological tests were assumed 100% specific. Initial modeling revealed qPCR specificity < 94%; we tracked the source of this unexpected result to some qPCR plates having subtle signs of possible contamination. Using multi-model inference, we formally accounted for suspected plate contamination and estimated qPCR sensitivity at 49-53% across sample types and dog clinical conditions; qPCR specificity was high (95-96%), but fell to 81-82% for assays run in plates with suspected contamination. The sensitivity of parasitological procedures was low (~12-13%), but increased to ~33% (with substantial uncertainty) for bone-marrow culture in seriously-diseased dogs. Leishmania-infection frequency estimates (~49-50% across clinical conditions) were lower than observed (~60%).

CONCLUSIONS

We provide statistical estimates of key performance parameters for five diagnostic procedures used to detect Leishmania in dogs. Low clinical sensitivies likely reflect the absence of Leishmania parasites/DNA in perhaps ~50-70% of samples drawn from infected dogs. Although qPCR performance was similar across sample types, non-invasive eye-swabs were overall less likely to contain amplifiable DNA. Finally, modeling was instrumental to discovering (and formally accounting for) possible qPCR-plate contamination; even with stringent negative/blank-control scoring, ~4-5% of positive qPCRs were most likely false-positives. This work shows, in sum, how hierarchical site-occupancy models can sharpen our understanding of the problem of diagnosing host infections with hard-to-detect pathogens including Leishmania.

Disentangling direct and indirect effects of extreme events on coastal wetland communities

One of the primary ways in which climate change will impact coastal freshwater wetlands is through changes in the frequency, intensity, timing and distribution of extreme weather events. Disentangling the direct and indirect mechanisms of population- and community-level responses to extreme events is vital to predicting how species composition of coastal wetlands will change under future conditions. We extended static structural equation modelling approaches to incorporate system dynamics in a multi-year multispecies occupancy model to quantify the effects of extreme weather events on a coastal freshwater wetland system. We used data from an 8-year study (2009-2016) on St. Marks National Wildlife Refuge in Florida, USA, to quantify species-specific and community-level changes in amphibian and fish occupancy associated with two flooding events in 2012 and 2013. We examine how physical changes to the landscape, including potential changes in salinity and increased wetland connectivity, may have contributed to or exacerbated the effects of these extreme weather events on the biota of isolated coastal wetlands. We provide evidence that the primary effects of flooding on the amphibian community were through indirect mechanisms via changes in the composition of the sympatric fish community that may have had lethal (i.e. through direct predation) or non-lethal (i.e. through direct or indirect competitive interactions) effects. In addition, we have shown that amphibian species differed in their sensitivity to direct flooding effects and indirect changes in the fish community and wetland-specific conductance, which led to variable responses across the community. These effects led to the overall decline in amphibian species richness from 2009 to 2016, suggesting that wetland-breeding amphibian communities on St. Marks National Wildlife Refuge may not be resilient to predicted changes in coastal disturbance regimes because of climate change. Understanding both direct and indirect effects, as well as species interactions, is important for predicting the effects of a changing climate on individual species, communities and ecosystems.

Drastic decline of extensive grassland species in Central Europe since 1950: Forester moths of the genus Jordanita (Lepidoptera, Zygaenidae) as a type example

The decline of biodiversity in general and of insect diversity in particular has been recognized as a major environmental problem in recent years. In this study, we analyze the distribution and the decline of populations of forester moths of the genus Jordanita in Central Europe since 1950 as a type example of the loss of grassland biodiversity, and discuss potential drivers causing this decline. Based on the extensive work in museums and private collections, a literature review and own observations, and including data as far back as 1834, this genus helps to understand the deeper reasons of insect population and biodiversity decline, as the well investigated six Central European species cover a broad range of extensive grassland habitats (fens to low-production grassland and xerothermic steppes) from low altitudes to high alpine meadows. Therefore, they monitor processes relevant also to other, less investigated grassland species. Although there are differences in research intensity over time and in different natural areas, we show that in the whole of Central Europe, the populations of all six investigated Jordanita species broke down massively in the past decades, both in terms of number of populated habitats (about 400 recorded localities after the year 2000 compared with a total number of about 1600 at all times, cumulated for all six species) and in terms of number of individuals. On the other hand, some natural areas on a regional scale have more or less maintained their Jordanita populations, due to conservative land use and due to the early implementation of conservation and protection management plans. The reasons of decline are manifold and monitored in detail by the different species with their different habitat requirements. They comprise (1) loss of habitats due to land use changes (both intensification and abandonment), (2) loss of habitats due to urbanization and construction work, (3) loss of habitat networks to cope with small-scale extinction events, (4) more intensive growth of grass at the expense of other plants in otherwise undisturbed habitats due to fertilization through the air (increased nitrogen levels due to human activities) and (5) use of pesticides.

Drivers of avian habitat use and detection of backyard birds in the Pacific Northwest during COVID-19 pandemic lockdowns

Birds living in developed areas contend with numerous stressors, including human disturbance and light, noise, and air pollution. COVID-19 pandemic lockdowns presented a unique opportunity to disentangle these effects during a period of reduced human activity. We launched a community science project in spring 2020 to explore drivers of site use by and detection of common birds in cities under lockdown in the U.S. Pacific Northwest. Our goals were twofold: (1) consider how intensity of urbanization, canopy cover, and availability of bird feeders and bird baths influenced avian habitat use; and (2) quantify how daily changes in weather, air pollution, and human mobility influenced detection of birds. We analyzed 6,640 surveys from 367 volunteers at 429 monitoring sites using occupancy models for 46 study species. Neither land cover nor canopy cover influenced site use by 50% of study species, suggesting that backyard birds may have used a wider range of habitats during lockdowns. Human mobility affected detection of 76% of study species, suggesting that birds exhibited species-specific behavioral responses to day-to-day changes in human activity beginning shortly after initial lockdown restrictions were implemented. Our study also showcases how existing community science platforms can be leveraged to support local monitoring efforts.

Detection criteria and post-field sample processing influence results and cost efficiency of occupancy-based monitoring

Optimization of occupancy-based monitoring has focused on balancing the number of sites and surveys to minimize field efforts and costs. When survey techniques require post-field processing of samples to confirm species detections, there may be opportunities to further improve efficiency. We used scat-based noninvasive genetic sampling for kit foxes (Vulpes macrotis) in Utah, USA, as a model system to assess post-field data processing strategies, evaluate the impacts of these strategies on estimates of occupancy and associations between parameters and predictors, and identify the most cost-effective approach. We identified scats with three criteria that varied in costs and reliability: (1) field-based identification (expert opinion), (2) statistical-based morphological identification, and (3) genetic-based identification (mitochondrial DNA). We also considered four novel post-field sample processing strategies that integrated statistical and genetic identifications to reduce costly genetic procedures, including (4) a combined statistical-genetic identification, (5) a genetic removal design, (6) a within-survey conditional-replicate design, and (7) a single-genetic-replicate with false-positive modeling design. We considered results based on genetic identification as the best approximation of truth and used this to evaluate the performance of alternatives. Field-based and statistical-based criteria prone to misidentification produced estimates of occupancy that were biased high (˜1.8 and 2.1 times higher than estimates without misidentifications, respectively). These criteria failed to recover associations between parameters and predictors consistent with genetic identification. The genetic removal design performed poorly, with limited detections leading to estimates that were biased high with poor precision and patterns inconsistent with genetic identification. Both statistical-genetic identification and the conditional-replicate design produced occupancy estimates comparable to genetic identification, while recovering the same model structure and associations at cost reductions of 67% and 74%, respectively. The false-positive design had the lowest cost (88% reduction) and recovered patterns consistent with genetic identification but had occupancy estimates that were ˜32% lower than estimated occupancy based on genetic identification. Our results demonstrate that careful consideration of detection criteria and post-field data processing can reduce costs without significantly altering resulting inferences. Combined with earlier guidance on sampling designs for occupancy modeling, these findings can aid managers in optimizing occupancy-based monitoring.

A penalized likelihood for multispecies occupancy models improves predictions of species interactions

Multispecies occupancy models estimate dependence among multiple species of interest from patterns of co-occurrence, but problems associated with separation and boundary estimates can lead to unreasonably large estimates of parameters and associated standard errors when species are rarely observed at the same site or when data are sparse. In this paper, we overcome these issues by implementing a penalized likelihood, which introduces a small bias in parameter estimates in exchange for a potentially large reduction in variance. We compare parameter estimates obtained from both penalized and unpenalized multispecies occupancy models fit to simulated data that exhibit various degrees of separation and to a real-word data set of bird surveys with little apparent overlap between potentially interacting species. Our simulation results demonstrate that penalized multispecies occupancy models did not exhibit boundary estimates and produced lower bias, lower mean squared error, and improved inference relative to unpenalized models. When applied to real-world data, our penalized multispecies occupancy model constrained boundary estimates and allowed for meaningful inference related to the interactions of two species of conservation concern. To facilitate the use of our penalized multispecies occupancy model, the techniques demonstrated in this paper have been integrated into the unmarked package in R programing language.

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

Modelos de ocupación para datos de cámaras trampa

El uso de las cámaras trampa (CT) en la investigación de fauna silvestre puede generar conclusiones sesgadas cuando la detectabilidad imperfecta de especies no es considerada. Herramientas analíticas como los modelos de ocupación permiten estimar simultáneamente parámetros ecológicos corregidos por la probabilidad de detección. Sin embargo, es necesario implementar e interpretar de manera correcta los parámetros estimados por estos modelos para obtener inferencias con sentido biológico. Este trabajo presenta un marco conceptual base para diseñar de manera apropiada un análisis de ocupación por medio de datos de CT. Se discuten y se señalan recomendaciones generales para la definición de los elementos del modelo, el diseño del muestreo, así como estrategias de modelamiento estadísticos apropiadas dependiendo de los objetivos del estudio, las características de la especie y el tipo de datos obtenidos. Las decisiones tomadas por el investigador para definir cada uno de los componentes del modelo deben considerar la escala adecuada para que el fenómeno de estudio tenga sentido biológico. De esta manera, es posible generar inferencias y conclusiones robustas a partir de información de CT, lo que permite avanzar en el entendimiento de los mecanismos que subyacen a la ecología espacial de fauna silvestre y por lo tanto en su conservación.

Viewing Emerging Human Infectious Epidemics through the Lens of Invasion Biology

Invasion biology examines species originated elsewhere and moved with the help of humans, and those species’ impacts on biodiversity, ecosystem services, and human well-being. In a globalized world, the emergence and spread of many human infectious pathogens are quintessential biological invasion events. Some macroscopic invasive species themselves contribute to the emergence and transmission of human infectious agents. We review conceptual parallels and differences between human epidemics and biological invasions by animals and plants. Fundamental concepts in invasion biology regarding the interplay of propagule pressure, species traits, biotic interactions, eco-evolutionary experience, and ecosystem disturbances can help to explain transitions between stages of epidemic spread. As a result, many forecasting and management tools used to address epidemics could be applied to biological invasions and vice versa. Therefore, we advocate for increasing cross-fertilization between the two disciplines to improve prediction, prevention, treatment, and mitigation of invasive species and infectious disease outbreaks, including pandemics.

Integrating eDNA metabarcoding and simultaneous underwater visual surveys to describe complex fish communities in a marine biodiversity hotspot

Marine biodiversity can be surveyed using underwater visual censuses and recently with eDNA metabarcoding. Although a promising tool, eDNA studies have shown contrasting results related to its detection scale and the number of species identified compared to other survey methods. Also, its accuracy relies on complete reference databases used for taxonomic assignment and, as other survey methods, species detection may show false-negative and false-positive errors. Here, we compared results from underwater visual censuses and simultaneous eDNA metabarcoding fish surveys in terms of observed species and community composition. We also assess the effect of a custom reference database in the taxonomic assignment, and evaluate occupancy, capture and detection probabilities, as well as error rates of eDNA survey data. We amplified a 12S rRNA fish barcode from 24 sampling sites in the gulf of California. More species were detected with eDNA metabarcoding than with UVC. Because each survey method largely detected different sets of species, the combined approach doubled the number of species registered. Both survey methods recovered a known biodiversity gradient and a biogeographic break, but eDNA captured diversity over a broader geographic and bathymetric scale. Furthermore, the use of a modest-sized custom reference database significantly increased taxonomic assignment. In a subset of species, occupancy models revealed eDNA surveys provided similar or higher detection probabilities compared to UVC. The occupancy value of each species had a large influence on eDNA detectability, and in the false positive and negative error. Overall, these results highlight the potential of eDNA metabarcoding in complementing other established ecological methods for studies of marine fishes.

Improving the reliability of eDNA data interpretation

Global declines in biodiversity highlight the need to effectively monitor the density and distribution of threatened species. In recent years, molecular survey methods detecting DNA released by target-species into their environment (eDNA) have been rapidly on the rise. Despite providing new, cost-effective tools for conservation, eDNA-based methods are prone to errors. Best field and laboratory practices can mitigate some, but the risks of errors cannot be eliminated and need to be accounted for. Here, we synthesize recent advances in data processing tools that increase the reliability of interpretations drawn from eDNA data. We review advances in occupancy models to consider spatial data-structures and simultaneously assess rates of false positive and negative results. Further, we introduce process-based models and the integration of metabarcoding data as complementing approaches to increase the reliability of target-species assessments. These tools will be most effective when capitalizing on multi-source data sets collating eDNA with classical survey and citizen-science approaches, paving the way for more robust decision-making processes in conservation planning.

Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance

The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.

A risk-based detection survey for the predatory mirid Macrolophus pygmaeus in New Zealand

Macrolophus pygmaeus, a predatory mirid used to manage greenhouse whitefly, was illegally imported into New Zealand, and for a time was reared and sold to commercial tomato growers. We designed and implemented a risk-based detection survey to determine whether M. pygmaeus was still present in New Zealand a decade later. The survey was designed to have an 80% chance of detecting a single low density (0.05 per lineal metre of host plants) population within 1 km of known points of introduction. The survey was implemented between 8 and 15 March 2018. Local habitat constraints meant that the planned sampling had to be modified but this was accounted for in the subsequent analysis. No M. pygmaeus were found in the samples, but 93 specimens from seven other mirid taxa were detected, validating the sample methods. The survey gives 60% confidence that M. pygmaeus was not present at a mean density of 0.05 per lineal metre of habitat. It gives 80% confidence that a population at 0.1 m-1 was not present and 90% confidence that no population exists at >0.18 m-1. Though there are no published data on typical field population densities of M. pygmaeus, for related species the survey would have had high confidence in detecting any medium to high density population present. Therefore, it is likely that M. pygmaeus is no longer present in New Zealand, but if extant within the sampled areas then we have high certainty that it was at low densities compared to other predaceous mirids.

Shrub and vegetation cover predict resource selection use by an endangered species of desert lizard

Globally, no species is exempt from the constraints associated with limited available habitat or resources, and endangered species in particular warrant critical examination. In most cases, these species are restricted to limited locations, and the relative likelihood of resource use within the space they can access is important. Using Gambelia sila, one of the first vertebrate species listed as endangered, we used resource selection function analysis of telemetry and remotely sensed data to identity key drivers of selected versus available locations for this species in Carrizo Plain National Monument, USA. We examined the probability of selection given different resource types. Increasing shrub cover, lower and relatively more flat sites, increasing normalized difference vegetation index, and solar radiation all significantly predicted likelihood of observed selection within the area sampled. Imagery data were also validated with fine-scale field data showing that large-scale contrasts of selection relative to available location patterns for animal species are a useful lens for potential habitat. Key environmental infrastructure such as foundation plant species including shrubs or local differences in the physical attributes were relevant to this endangered species.

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open-source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N-mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block-sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one-size-fits-all best strategy, but rather problem-specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10-12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model-generic MCMC procedures in open-source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.

Fine scale characteristics of catfish aquaculture ponds influencing piscivorous avian species foraging use in the Mississippi Delta

Piscivorous avian species are the main source of catfish depredation at aquaculture facilities in Mississippi, resulting in the economic loss of millions of dollars every year. Most notable of these avian species are the double-crested cormorant (Phalacrocorax auritus), great blue heron (Ardea herodias), and great egret (A. alba). Understanding why these species select specific ponds can increase management efficiency directed at avian dispersal and provide insight into their decision making with respect to foraging behavior. We collected species presence data on catfish ponds by flying 35 surveys from October through April of 2015-2017, during which an average of 973 catfish ponds were observed each year. We collected data associated with each pond's physical surroundings and contents and used occupancy modeling to determine their influence on avian occupancy probability. We also collected data associated with stocking practices and catfish health on a subset of ponds, and constructed resource selection functions to model their influence on avian presence. Pond area was positively related to occupancy probability of each species. Cormorant occupancy increased as pond distance from forest cover and activity centers, such as workshops and offices, increased. Distance to nearest activity center was positively related to egret occupancy, while distance to nearest forested area was negative. Ponds containing diseased catfish had an increased probability of use by both herons and egrets. In general, cormorants and egrets showed greater probability of use on the periphery of pond clusters. The abundance of catfish was positively related to cormorant and heron presence. Specific pond contents and characteristics influenced presence of each avian species in different ways, including fish species cultured, production methods, pond systems, and fish types. Many pond selection relationships were species-specific, illustrating inherent differences in their foraging ecology. Consequently, specific management actions aimed to reduce avian presence will depend on the targeted species.

Use of stochastic patch occupancy models in the California red-legged frog for Bayesian inference regarding past events and future persistence

Assessing causes of population decline is critically important to management of threatened species. Stochastic patch occupancy models (SPOMs) are popular tools for examining spatial and temporal dynamics of populations when presence-absence data in multiple habitat patches are available. We developed a Bayesian Markov chain method that extends existing SPOMs by focusing on past environmental changes that may have altered occupancy patterns prior to the beginning of data collection. Using occupancy data from 3 creeks, we applied the method to assess 2 hypothesized causes of population decline-in situ die-off and residual impact of past source population loss-in the California red-legged frog. Despite having no data for the 20-30 years between the hypothetical event leading to population decline and the first data collected, we were able to discriminate among hypotheses, finding evidence that in situ die-off increased in 2 of the creeks. Although the creeks had comparable numbers of occupied segments, owing to different extinction-colonization dynamics, our model predicted an 8-fold difference in persistence probabilities of their populations to 2030. Adding a source population led to a greater predicted persistence probability than did decreasing the in situ die-off, emphasizing that reversing the deleterious impacts of a disturbance may not be the most efficient management strategy. We expect our method will be useful for studying dynamics and evaluating management strategies of many species.

Dynamic wildlife occupancy models using automated acoustic monitoring data

Automated acoustic monitoring of wildlife has been used to characterize populations of sound-producing species across large spatial scales. However, false negatives and false positives produced by automated detection systems can compromise the utility of these data for researchers and land managers, particularly for research programs endeavoring to describe colonization and extinction dynamics that inform land use decision-making. To investigate the suitability of automated acoustic monitoring for dynamic occurrence models, we simulated underlying occurrence dynamics, calling patterns, and the automated acoustic detection process for a hypothetical species under a range of scenarios. We investigated an automated species detection aggregation method that considered a suite of options for creating encounter histories. From these encounter histories, we generated parameter estimates and computed bias for occurrence, colonization, and extinction rates using a dynamic occupancy modeling framework that accounts for false positives via small amounts of manual confirmation. We were able to achieve relatively unbiased estimates for all three state parameters under all scenarios, even when the automated detection system was simulated to be poor, given particular encounter history aggregation choices. However, some encounter history aggregation choices resulted in unreliable estimates; we provide caveats for avoiding these scenarios. Given specific choices during the detection aggregation process, automated acoustic monitoring data may provide an effective means for tracking species occurrence, colonization, and extinction patterns through time, with the potential to inform adaptive management at multiple spatial scales.

Beyond Biodiversity: Can Environmental DNA (eDNA) Cut It as a Population Genetics Tool?

Population genetic data underpin many studies of behavioral, ecological, and evolutionary processes in wild populations and contribute to effective conservation management. However, collecting genetic samples can be challenging when working with endangered, invasive, or cryptic species. Environmental DNA (eDNA) offers a way to sample genetic material non-invasively without requiring visual observation. While eDNA has been trialed extensively as a biodiversity and biosecurity monitoring tool with a strong taxonomic focus, it has yet to be fully explored as a means for obtaining population genetic information. Here, we review current research that employs eDNA approaches for the study of populations. We outline challenges facing eDNA-based population genetic methodologies, and suggest avenues of research for future developments. We advocate that with further optimizations, this emergent field holds great potential as part of the population genetics toolkit.

Dynamic multi-species occupancy models reveal individualistic habitat preferences in a high-altitude grassland bird community

Moist, high-altitude grasslands of eastern South African harbour rich avian diversity and endemism. This area is also threatened by increasingly intensive agriculture and land conversion for energy production. This conflict is particularly evident at Ingula, an Important Bird and Biodiversity Area located within the least conserved high-altitude grasslands and which is also the site of a new Pumped Storage Scheme. The new management seeks to maximise biodiversity through manipulation of the key habitat variables: grass height and grass cover through burning and grazing to make habitat suitable for birds. However, different species have individual habitat preferences, which further vary through the season. We used a dynamic multi-species occupancy model to examine the seasonal occupancy dynamics of 12 common grassland bird species and their habitat preferences. We estimated monthly occupancy, colonisation and persistence in relation to grass height and grass cover throughout the summer breeding season of 2011/12. For majority of these species, at the beginning of the season occupancy increased with increasing grass height and decreased with increasing grass cover. Persistence and colonisation decreased with increasing grass height and cover. However, the 12 species varied considerably in their responses to grass height and cover. Our results suggest that management should aim to provide plots which vary in grass height and cover to maximise bird diversity. We also conclude that the decreasing occupancy with increasing grass cover and low colonisation with increasing grass height and cover is a results of little grazing on our study site. We further conclude that some of the 12 selected species are good indicators of habitat suitability more generally because they represent a range of habitat needs and are relatively easy to monitor.

Use of ambiguous detections to improve estimates from species distribution models

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.