Portrait of Dr Eleni Matechou

Dr Eleni Matechou

Lecturer in Statistics


Eleni obtained her BSc in Statistics from Athens University of Economics and Business in Greece and an MSc in Statistics with Applications in Medicine from the University of Southampton. Her PhD, supervised by Professor Byron Morgan and Dr David Thomson, was a joint project between the University of Kent and the Max Planck Institute for Demographic Research in Rostock, Germany.

Eleni spent a year at Victoria University of Wellington as a Research and Teaching fellow, working with Prof Shirley Pledger and Dr Ivy Liu.

Eleni was a Departmental Lecturer at the University of Oxford for three years and a Stipendiary Lecturer at St Peter’s College for two of those years.

She has worked here at the University of Kent since 2014 and Eleni is a member of the Statistical Ecology @ Kent (SE@K) group and of the National Centre for Statistical Ecology (NCSE).

Research interests

Eleni's research mainly focuses on statistical ecology and she has developed models for studying populations of birds, mammals, amphibians and invertebrates. With a particular interest in new modelling approaches for the study of migration patterns, she has investigated the use of mixture models from a classical and Bayesian, parametric and non-parametric, approach. 


Eleni has supervised students working on a wide range of topics, such as removal modelling, integrated population modelling and Bayesian non-parametric models for ecological data.

All her students have successfully completed their projects within the required timeframe while publishing their work in high-quality peer-reviewed ecology and statistics journals. 


Committee member of the Environmental Statistics Section of the Royal Statistical Society.

BIR local champion. 



  • Zhou, M. et al. (2019). Removal models accounting for temporary emigration. Biometrics [Online] 75:24-35. Available at: https://doi.org/10.1111/biom.12961.
    Removal of protected species from sites scheduled for development is often a legal requirement in order to minimize the loss of biodiversity. The assumption of closure in the classic removal model will be violated if individuals become temporarily undetectable, a phenomenon commonly exhibited by reptiles and amphibians. Temporary emigration can be modeled using a multievent framework with a partial hidden process, where the underlying state process describes the movement pattern of animals between the survey area and an area outside of the study. We present a multievent removal model within a robust design framework which allows for individuals becoming temporarily unavailable for detection. We demonstrate how to investigate parameter redundancy in the model. Results suggest the use of the robust design and certain forms of constraints overcome issues of parameter redundancy. We show which combinations of parameters are estimable when the robust design reduces to a single secondary capture occasion within each primary sampling period. Additionally, we explore the benefit of the robust design on the precision of parameters using simulation. We demonstrate that the use of the robust design is highly recommended when sampling removal data. We apply our model to removal data of common lizards, Zootoca vivipara, and for this application precision of parameter estimates is further improved using an integrated model.
  • Jimenez-Munoz, M. et al. (2019). Estimating age-dependent survival from age-aggregated ringing data - extending the use of historical records. Ecology and Evolution [Online] 9:769-779. Available at: https://doi.org/10.1002/ece3.4820.
    Bird ring-recovery data have been widely used to estimate demographic parameters
    such as survival probabilities since the mid-twentieth century. However,
    while the total number of birds ringed each year is usually known, historical
    information on age at ringing is often not available. A standard ring-recovery
    model, for which information on age at ringing is required, cannot be used
    when historical data are incomplete. We develop a new model to estimate agedependent
    survival probabilities from such historical data when age at ringing
    is not recorded; we call this the historical data model. This new model provides
    an extension to the model of Robinson (2010) by estimating the proportion of
    the ringed birds marked as juveniles as an additional parameter. We conduct
    a simulation study to examine the performance of the historical data model
    and compare it with other models including the standard and conditional ringrecovery
    models. Simulation studies show that the approach of Robinson (2010)
    can cause bias in parameter estimates. In contrast, the historical data model
    yields similar parameter estimates to the standard model. Parameter redundancy
    results show that the newly developed historical data model is comparable
    to the standard ring-recovery model, in terms of which parameters can be
    estimated, and has fewer identifiability issues than the conditional model. We
    illustrate the new proposed model using Blackbird and Sandwich Tern data.
    The new historical data model allows us to make full use of historical data and
    estimate the same parameters as the standard model with incomplete data and
    in doing so, detect potential changes in demographic parameters further back
    in time.
  • Matechou, E., Freeman, S. and Comont, R. (2018). Caste-specific demography and phenology in bumblebees; modelling BeeWalk data. Journal of Agricultural, Biological, and Environmental Statistics [Online] 23:427-445. Available at: https://doi.org/10.1007/s13253-018-0332-y.
    We present novel dynamic mixture models for the monitoring of bumblebee populations on an
    unprecedented geographical scale, motivated by the UK citizen science scheme 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.
  • Dormann, C. et al. (2018). Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference. Ecological Monographs [Online]. Available at: https://doi.org/10.1002/ecm.1309.
    In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ regarding in their behaviour and performance.
    Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model?averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models and uncertainty about model weights.
    We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met.
    Many different methods to derive averaging weights exist, from from Bayesian over information?theoretical to cross?validation optimised and resampling approaches. A general recommendation is difficult, because the performance of methods is often context?dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights.
    We also investigate the quality of the confidence intervals calculated for model?averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non?parametric methods such as cross?validation for a reliable uncertainty quantification of model?averaged predictions.
  • Matechou, E. and Caron, F. (2017). Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data. Annals of Applied Statistics [Online] 11:21-40. Available at: http://dx.doi.org/10.1214/16-AOAS989.
    We present a Bayesian nonparametric approach for modelling wildlife migration patterns using capture–recapture (CR) data. Arrival times of individuals are modelled in continuous time and assumed to be drawn from a Poisson process with unknown intensity function, which is modelled via a flexible nonparametric mixture model. The proposed CR framework allows us to estimate the following: (i) the total number of individuals that arrived at the site, (ii) their times of arrival and departure, and hence their stopover duration, and (iii) the density of arrival times, providing a smooth representation of the arrival pattern of the individuals at the site. We apply the model to data on breeding great crested newts (Triturus cristatus) and on migrating reed warblers (Acrocephalus scirpaceus). For the former, the results demonstrate the staggered arrival of individuals at the breeding ponds and suggest that males tend to arrive earlier than females. For the latter, they demonstrate the arrival of migrating flocks at the stopover site and highlight the considerable difference in stopover duration between caught and not-caught individuals.
  • Matechou, E. et al. (2016). Biclustering models for two-mode ordinal data. Psychometrika [Online]. Available at: http://link.springer.com/article/10.1007/s11336-016-9503-3?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst.
    The work in this paper introduces finite mixture models that can be used to simul-
    taneously cluster the rows and columns of two-mode ordinal categorical response data,
    such as those resulting from Likert scale responses. We use the popular proportional
    odds parameterisation and propose models which provide insights into major patterns
    in the data. Model-fitting is performed using the EM algorithm and a fuzzy allocation
    of rows and columns to corresponding clusters is obtained. The clustering ability of the
    models is evaluated in a simulation study and demonstrated using two real data sets.
  • Matechou, E. et al. (2016). Open models for removal data. Annals of Applied Statistics [Online]. Available at: http://dx.doi.org/10.1214/16-AOAS949.
    Individuals of protected species, such as amphibians and reptiles, often need to be removed from sites before development commences. Usually, the population is considered to be closed. All individuals are assumed to i) be present and available for detection at the start of the study period and ii) remain at the site until the end of the study, unless they are detected. However, the assumption of population closure is not always valid. We present new removal models which allow for population renewal through birth and/or immigration, and population depletion through sampling as well as through death/emigration. When appropriate, productivity may be estimated and a Bayesian approach allows the estimation of the probability of total population depletion. We demonstrate the performance of the models using data on common lizards, Zootoca vivipara, and great crested newts, Triturus cristatus.
  • Matechou, E. et al. (2016). Bayesian analysis of Jolly-Seber type models; Incorporating heterogeneity in arrival and departure. Environmental and Ecological Statistics [Online]:1-17. Available at: http://doi.org/10.1007/s10651-016-0352-0.
    We propose the use of finite mixtures of continuous distributions in modelling
    the process by which new individuals, that arrive in groups, become part of a
    wildlife population. We demonstrate this approach using a data set of migrating semipalmated
    sandpipers (Calidris pussila) for which we extend existing stopover models
    to allow for individuals to have different behaviour in terms of their stopover duration
    at the site. We demonstrate the use of reversible jump MCMC methods to derive
    posterior distributions for the model parameters and the models, simultaneously. The
    algorithm moves between models with different numbers of arrival groups as well as
    between models with different numbers of behavioural groups. The approach is shown
    to provide new ecological insights about the stopover behaviour of semipalmated sandpipers
    but is generally applicable to any population in which animals arrive in groups
    and potentially exhibit heterogeneity in terms of one or more other processes.
  • Matechou, E. et al. (2015). Reproductive consequences of the timing of seasonal movements in a non-migratory wild bird population. Ecology [Online] 96:1641-1649. Available at: http://dx.doi.org/10.1890/14-0886.1.
  • Matechou, E. et al. (2014). Monitoring abundance and phenology in (multivoltine) butterfly species: a novel mixture model. Journal of Applied Ecology [Online] 51:766-775. Available at: http://dx.doi.org/10.1111/1365-2664.12208.
  • Matechou, E. et al. (2013). Estimating age-specific survival when age is unknown: open population capture–recapture models with age structure and heterogeneity. Methods in Ecology and Evolution [Online] 4:654-664. Available at: http://dx.doi.org/10.1111/2041-210X.12061.

    When studying senescence in wildlife populations, we are often limited by the sparseness of the available information on the ages of the individuals under study. Additionally, heterogeneity between individuals can be substantial. Ignoring this heterogeneity can lead to biased estimates of the population parameters of interest and can mask senescence.
    This article demonstrates the use of a recently developed capture–recapture model for extracting age-dependent estimates of survival probabilities for individuals of unknown age and extends the model by allowing for heterogeneity in survival and capture probabilities using finite mixtures.
    Using simulation, we show that the estimates of age-dependent survival probabilities when age is unknown can be biased when heterogeneity in capture probabilities is not modelled, in contrast to the case of time-dependent survival probabilities when the estimates are robust to similar violations of model assumptions.
    The methods are demonstrated using a long-term data set of female brushtail possums (Trichosurus vulpecula Kerr) for which age-specific models for survival probabilities indicating senescence are strongly favoured. We found no evidence of heterogeneity in survival but strong evidence of heterogeneity in capture probabilities.
    These models have a wide range of applications for estimating age dependence in survival when the age is unknown as they can be applied to any capture–recapture data set, as long as it is collected over a period which is longer, and preferably considerably so, than the life span of the species studied.
  • Matechou, E. et al. (2013). Integrated Analysis of Capture–Recapture–Resighting Data and Counts of Unmarked Birds at Stop-Over Sites. Journal of Agricultural, Biological, and Environmental Statistics [Online] 18:120-135. Available at: http://dx.doi.org/10.1007/s13253-013-0127-0.
    The models presented in this paper are motivated by a stop-over study of semipalmated sandpipers, Calidris pusilla. Two sets of data were collected at the stop-over site: a capture–recapture–resighting data set and a vector of counts of unmarked birds. The two data sets are analyzed simultaneously by combining a new model for the capture–recapture–resighting data set with a binomial likelihood for the counts. The aim of the analysis is to estimate the total number of birds that used the site and the average duration of stop-over. The combined analysis is shown to be highly efficient, even when just 1 % of birds are recaptured, and is recommended for similar investigations. This article has supplementary material online.


  • Diana, A., Griffin, J. and Matechou, E. (2019). A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population. in: Bayesian Statistics: New Challenges and New Generations - BAYSM 2018.. Available at: https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/wade/2018baysmconference/.
    Many ecological sampling schemes do not allow for unique marking of
    individuals. Instead, only counts of individuals detected on each sampling occasion
    are available. In this paper, we propose a novel approach for modelling count data
    in an open population where individuals can arrive and depart from the site during
    the sampling period. A Bayesian nonparametric prior, known as Polya Tree, is used
    for modelling the bivariate density of arrival and departure times. Thanks to this
    choice, we can easily incorporate prior information on arrival and departure density
    while still allowing the model to flexibly adjust the posterior inference according to
    the observed data. Moreover, the model provides great scalability as the complexity
    does not depend on the population size but just on the number of sampling occasions, making it particularly suitable for data-sets with high numbers of detections.
    We apply the new model to count data of newts collected by the Durrell Institute of
    Conservation and Ecology, University of Kent.
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