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.




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


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.


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.


  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.
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.


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.



Paper: A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

Anita and Rachel have had a paper accepted in Journal of Agricultural Biological and Environmental Statistics.

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

Anita Jeyam, Rachel S. McCrea, Thomas Bregnballe, Morten Frederiksen and Roger Pradel

The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman–Kruskal’s gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie’s test of equal catchability and Carothers’ extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions.

The full article is available as open access and can be found here:


Biometrics Paper: Hidden Markov Models for Extended Batch Data

The paper Hidden Markov Models for Extended Batch Data by Laura L. E. Cowen, Panagiotis Besbeas, Byron J. T. Morgan and Carl J. Schwarz has been published online early in Biometrics

Summary. Batch marking provides an important and efficient way to estimate the survival probabilities and population sizes of wild animals. It is particularly useful when dealing with animals that are difficult to mark individually. For the first time, we provide the likelihood for extended batch-marking experiments. It is often the case that samples contain individuals that remain unmarked, due to time and other constraints, and this information has not previously been analysed. We provide ways of modelling such information, including an open N-mixture approach. We demonstrate that models for both marked and unmarked individuals are hidden Markov models; this provides a unified approach, and is the key to developing methods for fast likelihood computation and maximisation. Likelihoods for marked and unmarked individuals can easily be combined using integrated population modelling. This allows the simultaneous estimation of population size and immigration, in addition to survival, as well as efficient estimation of standard errors and methods of model selection and evaluation, using standard likelihood techniques. Alternative methods for estimating population size are presented and compared. An illustration is provided by a weather-loach data set, previously analysed by means of a complex procedure of constructing a pseudo likelihood, the formation of estimating equations, the use of sandwich estimates of variance, and piecemeal estimation of population size. Simulation provides general validation of the hidden Markov model methods developed and demonstratestheir excellent performance and efficiency. This is especially notable due to the large numbers of hidden states that may be typically required​



Paper: Efficient occupancy model-fitting for extensive citizen-science data

The following paper has been recently published in PLOS ONE and is available online at:

Efficient occupancy model-fitting for extensive citizen-science data

Emily B. Dennis, Byron J.T. Morgan, Stephen N. Freeman, Martin S. Ridout, Tom M. Brereton, Richard Fox, Gary D. Powney & David B. Roy


Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.