News, Papers, Publications

Metabarcoding paper published in JASA

eDNAPlus: A Unifying Modeling Framework for DNA-based Biodiversity Monitoring

by Alex Diana, Eleni Matechou, Jim Griffin, Doug Yu, Richard Griffiths and others.

Abstract

DNA-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 article, we present a unifying modeling framework for DNA-based survey data that allows estimation of changes in DNA biomass within species, across sites and their links to environmental covariates, while 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-parameterization 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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

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News, Papers

New Biometrics paper

A new modelling framework for fast Bayesian inference in large occupancy data sets by Alex, Emily, Eleni and Byron just published in Biometrics.

The model accounts for spatio-temporal autocorrelation and gives robust inference on species presence, as evidence by the simulation results in the paper and the two case studies on one common and widespread and one rare species, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years.

 

 

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News, Papers, Publications

New paper in Environmetrics by Alex

A vector of point processes for modeling interactions between and within species using capture-recapture data

Alex Diana, Eleni Matechou, Jim E. Griffin, Yadvendradev Jhala, Qamar Qureshi

Abstract

Capture-recapture (CR) data and corresponding models have been used extensively to estimate the size of wildlife populations when detection probability is less than 1. When the locations of traps or cameras used to capture or detect individuals are known, spatially-explicit CR models are used to infer the spatial pattern of the individual locations and population density. Individual locations, referred to as activity centers (ACs), are defined as the locations around which the individuals move. These ACs are typically assumed to be independent, and their spatial pattern is modeled using homogeneous Poisson processes. However, this assumption is often unrealistic, since individuals can interact with each other, either within a species or between different species. In this article, we consider a vector of point processes from the general class of interaction point processes and develop a model for CR data that can account for interactions, in particular repulsions, between and within multiple species. Interaction point processes present a challenge from an inferential perspective because of the intractability of the normalizing constant of the likelihood function, and hence standard Markov chain Monte Carlo procedures to perform Bayesian inference cannot be applied. Therefore, we adopt an inference procedure based on the Monte Carlo Metropolis Hastings algorithm, which scales well when modeling more than one species. Finally, we adopt an inference method for jointly sampling the latent ACs and the population size based on birth and death processes. This approach also allows us to adaptively tune the proposal distribution of new points, which leads to better mixing especially in the case of non-uniformly distributed traps. We apply the model to a CR data-set on leopards and tigers collected at the Corbett Tiger Reserve in India. Our findings suggest that between species repulsion is stronger than within species, while tiger population density is higher than leopard population density at the park.

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News, Papers

JASA paper by Eleni and Raffaele

The paper, titled “Capture-recapture models with heterogeneous temporary emigration”, first published online on the 14th of September 2022, develops a novel modelling framework for capture-recapture data without relying on the assumption of permanent emigration. The model is built within a Bayesian non-parametric & changepoint process framework, and is demonstrated on data on salmon anglers in Norway.

 

 

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Papers

New paper: A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data

The paper, A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data, by Stephen Freeman, Nicholas Isaac, Panagiotis Besbeas, Emily Dennis and Byron Morgan has just been published in the Journal of Agricultural, Biological and Environmental Statistics

 https://doi.org/10.1007/s13253-020-00410-6

Abstract

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species’ indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

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Papers

New Paper: Size‐ and stage‐dependence in cause‐specific mortality of migratory brown trout

The paper Size‐ and stage‐dependence in cause‐specific mortality of migratory brown trout by Chloé R. Nater, Yngvild Vindenes, Per Aass, Diana Cole, Øystein Langangen, S. Jannicke Moe, Atle Rustadbakken, Daniel Turek, Leif Asbjørn Vøllestad and Torbjørn Ergon was published in Journal of Animal Ecology.

Abstract

  1. Evidence‐based management of natural populations under strong human influence frequently requires not only estimates of survival but also knowledge about how much mortality is due to anthropogenic vs. natural causes. This is the case particularly when individuals vary in their vulnerability to different causes of mortality due to traits, life history stages, or locations.
  2. Here, we estimated harvest and background (other cause) mortality of landlocked migratory salmonids over half a century. In doing so, we quantified among‐individual variation in vulnerability to cause‐specific mortality resulting from differences in body size and spawning location relative to a hydropower dam.
  3. We constructed a multistate mark–recapture model to estimate harvest and background mortality hazard rates as functions of a discrete state (spawning location) and an individual time‐varying covariate (body size). We further accounted for among‐year variation in mortality and migratory behaviour and fit the model to a unique 50‐year time series of mark–recapture–recovery data on brown trout (Salmo trutta) in Norway.
  4. Harvest mortality was highest for intermediate‐sized trout, and outweighed background mortality for most of the observed size range. Background mortality decreased with body size for trout spawning above the dam and increased for those spawning below. All vital rates varied substantially over time, but a trend was evident only in estimates of fishers’ reporting rate, which decreased from over 50% to less than 10% throughout the study period.
  5. We highlight the importance of body size for cause‐specific mortality and demonstrate how this can be estimated using a novel hazard rate parameterization for mark–recapture models. Our approach allows estimating effects of individual traits and environment on cause‐specific mortality without confounding, and provides an intuitive way to estimate temporal patterns within and correlation among different mortality sources.
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News, Papers, Publications

New paper by Eleni and colleagues published in JABES

The paper, titled  “Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data”, by Eleni Matechou, Stephen N. Freeman, and Richard Comont is available open-access. 

The work presents novel dynamic mixture models for the monitoring of bumblebee populations on an unprecedented geographical scale, motivated by the UK citizen science BeeWalk.

The models allow us for the first time to estimate bumblebee phenology and within-season productivity, defined as the number of individuals in each caste per colony in the population in that year, from citizen science data.

All of these parameters are estimated separately for each caste, giving a means of considerable ecological detail in examining temporal changes in the complex life cycle of a social insect in the wild. Due to the dynamic nature of the models, we are able to produce population trends for a number of UK bumblebee species using the available time-series. Via an additional simulation exercise, we show the extent to which useful information will increase if the survey continues, and expands in scale, as expected.

Bumblebees are extraordinarily important components of the ecosystem, providing pollination services of vast economic impact and functioning as indicator species for changes in climate or land use. Our results demonstrate the changes in both phenology and productivity between years and provide an invaluable tool for monitoring bumblebee populations, many of which are in decline, in the UK and around the world.

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Papers

Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference

The review paper, by Dormann, C.F., Calabrese, J.M., Gurutzeta, G., Matechou, E., Bahn, V., Bartoń, K., et al. to appear in Ecological Monographs, explores different model averaging techniques in terms of ways to calculate the model weights and to combine predictions from different models. The advantages and disadvantages of model averaging are discussed and code for methods falling under three categories (Bayesian, information theoretical and tactical) is provided.

Read a blog post written by the main contributors of the paper here.

 

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