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AbstractDNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read ‘DNA barcode’ sequences, generating count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation, and cannot alone estimate the species-specific amount of DNA present at a surveyed site (DNA biomass). In this paper, we present a unifying modelling framework for DNA-based survey data that allows estimation of changes in DNA biomass within species, across sites and their links to environmental covariates, whilst for the first time simultaneously accounting for key sources of variation, error and noise in the data-generating process, and for between-species and between-sites correlation. Bayesian inference is performed using MCMC with Laplace approximations. We describe a re-parameterisation scheme for crossed-effects models designed to improve mixing, and an adaptive approach for updating latent variables, which reduces computation time. Theoretical and simulation results are used to guide study design, including the level of replication at different survey stages and the use of quality control methods. Finally, we demonstrate our new framework on a dataset of Malaise-trap samples, quantifying the effects of elevation and distance-to-road on each species, and produce maps identifying areas of high biodiversity and species DNA biomass. [ABSTRACT FROM AUTHOR]

Wildlife monitoring for open populations can be performed using a number of different survey methods. Each survey method gives rise to a type of data and, in the last five decades, a large number of associated statistical models have been developed for analyzing these data. Although these models have been parameterized and fitted using different approaches, they have all been designed to either model the pattern with which individuals enter and/or exit the population, or to estimate the population size by accounting for the corresponding observation process, or both. However, existing approaches rely on a predefined model structure and complexity, either by assuming that parameters linked to the entry and exit pattern (EEP) are specific to sampling occasions, or by employing parametric curves to describe the EEP. Instead, we propose a novel Bayesian nonparametric framework for modeling EEPs based on the Polya tree (PT) prior for densities. Our Bayesian nonparametric approach avoids overfitting when inferring EEPs, while simultaneously allowing more flexibility than is possible using parametric curves. Finally, we introduce the replicate PT prior for defining classes of models for these data allowing us to impose constraints on the EEPs, when required. We demonstrate our new approach using capture–recapture, count, and ring‐recovery data for two different case studies. [ABSTRACT FROM AUTHOR]

In recent years, the study of species' occurrence has benefited from the increased availability of large‐scale citizen‐science data. While abundance data from standardized monitoring schemes are biased toward well‐studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species' changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya‐Gamma scheme, which allows for fast inference, and incorporate spatio‐temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: subset of regressors and nearest neighbor GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species' occurrence, which are used to assess biodiversity change. [ABSTRACT FROM AUTHOR]

We propose a novel approach for modeling capture-recapture (CR) data on open populations that exhibit temporary emigration, while also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modeling approach combines changepoint processes—fitted using an adaptive approach—for inferring individual visits, with Bayesian mixture modeling—fitted using a nonparametric approach—for identifying clusters of individuals with similar visit patterns or capture probabilities. The proposed method is extremely flexible as it can be applied to any CR dataset and is not reliant upon specialized sampling schemes, such as Pollock's robust design. We fit the new model to motivating data on salmon anglers collected annually at the Gaula river in Norway. Our results when analyzing data from the 2017, 2018, and 2019 seasons reveal two clusters of anglers—consistent across years—with substantially different visit patterns. Most anglers are allocated to the "occasional visitors" cluster, making infrequent and shorter visits with mean total length of stay at the river of around seven days, whereas there also exists a small cluster of "super visitors," with regular and longer visits, with mean total length of stay of around 30 days in a season. Our estimate of the probability of catching salmon whilst at the river is more than three times higher than that obtained when using a model that does not account for temporary emigration, giving us a better understanding of the impact of fishing at the river. Finally, we discuss the effect of the COVID-19 pandemic on the angling population by modeling data from the 2020 season. for this article are available online. [ABSTRACT FROM AUTHOR]

There is increasing availability and use of unstructured and semi‐structured citizen science data in biodiversity research and conservation. This expansion of a rich source of 'big data' has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets.We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects.We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models.While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity. [ABSTRACT FROM AUTHOR]

Summary: Environmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the probabilities of error. We present a novel Bayesian model for analysing environmental DNA data by proposing informative prior distributions for logistic regression coefficients that enable us to overcome parameter identifiability, while performing efficient Bayesian variable selection. Our methodology does not require the use of transdimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models. [ABSTRACT FROM AUTHOR]

Illegal wildlife trade can threaten biodiversity and economic development. Criminal enterprises may add wildlife products to their list of illicit goods by using established trade routes, networks, and individuals. On the Caribbean coast of Costa Rica, killing of sea turtles and removal of their eggs is commonplace. However, beyond conservation NGOs reporting evidence of illegal take, little is known about this activity. Through semi-structured interviews with law enforcement, community members, NGOs, and illegal harvesters, alongside anecdotal information and observations, we aimed to understand the motivations for illegal take. To cross-reference these findings, we assessed sensitivities surrounding illegal harvesting by asking the general public sensitive questions using the randomized response technique; a method used to elicit sensitive information whilst insuring the anonymity of respondents. We included a questionnaire to establish if differences in demographics affected the probability respondents would admit to a turtle-related crime. Our findings identified a rare example of illegal extraction of a wildlife product driven by motivations that were not exclusively livelihood based. We found the majority of illegal take was undertaken by relatively few individuals, dependent on narcotics. The most cited reason for illegal take was that turtle eggs could be used to procure drugs. Law enforcement was under resourced, and informants reported that prosecutions were rare. Local people preferred to purchase rather than harvest eggs suggesting the trade is supply-driven. Those interviewed did not generally regard the subject of illegal harvest as sensitive. Low education levels, high unemployment rates, and marginalization of certain groups may increase susceptibility to narcotics. Although substance misuse and addiction appear to drive illegal trade, associated poverty and marginalization may explain why drug dependency is so prevalent in Caribbean Costa Rica. Increased work opportunities and drug rehabilitation programs may assist in reducing illegal take of turtle eggs on nesting beaches. [ABSTRACT FROM AUTHOR]