New paper: Duration of female parental care and their survival in the little auk Alle alle – are these two traits linked?

The paper: Duration of female parental care and their survival in the little auk Alle alle – are these two traits linked? by: Katarzyna Wojczulanis-Jakubas, Marina Jiménez-Muñoz, Dariusz Jakubas, Dorota Kidawa, Nina Karnovsky, Diana Cole and Eleni Matechou, has just been published in Behavioral Ecology and Sociobiology.


Desertion of offspring before its independence by one of the parents is observed in a number of avian species with bi-parental care but reasons for this strategy are not fully understood. This behaviour is particularly intriguing in species where bi-parental care is crucial to raise the brood successfully. Here, we focus on the little auk, Alle alle, a small seabird with intensive bi-parental care, where the female deserts the brood at the end of the chick rearing period. The little auk example is interesting as most hypotheses to explain desertion of the brood by females (e.g. “re-mating hypothesis”, “body condition hypothesis”) have been rejected for this species. Here, we analysed a possible relationship between the duration of female parental care over the chick and her chances to survive to the next breeding season. We performed the study in two breeding colonies on Spitsbergen with different foraging conditions – more favourable in Hornsund and less favourable in Magdalenefjorden. We predicted that in Hornsund females would stay for shorter periods of time with the brood and would have higher survival rates in comparison with birds from Magdalenefjorden. We found that indeed in less favourable conditions of Magdalenefjorden, females stay longer with the brood than in the more favourable conditions of Hornsund. Moreover, female survival was negatively affected by the length of stay in the brood. Nevertheless, duration of female parental care over the chick was not related to their parental efforts, earlier in the chick rearing period, and survival of males and females was similar. Thus, although females brood desertion and winter survival are linked, the relationship is not straightforward.



Parameter Redundancy and Identifiability Book Published

Diana Cole’s book Parameter Redundancy and Identifiability has been publish by Chapman and Hall/CRC

Book Synopsis

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book:

  • Detailed discussion of the problems caused by parameter redundancy and non-identifiability
  • Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods
  • Chapter on Bayesian identifiability
  • Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples
  • More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas

This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.

Book website: https://www.routledge.com/Parameter-Redundancy-and-Identifiability/Cole/p/book/9781498720878

Code for book available at: https://www.kent.ac.uk/smsas/personal/djc24/parameterredundancy.html


Guy Bronze Medal awarded to Rachel McCrea

Rachel has been awarded the 2020 Guy Bronze Medal by the Royal Statistical Society (RSS).

The Guy Medal in Bronze has been awarded for her innovative and novel work in statistical ecology, with particular reference to the development of goodness-of-fit tests and model selection strategies for complex ecological data. Important areas include (multi-state) capture-recapture-type models and integrated models. Notable publications include: the 2017 JRSSC paper ‘A new strategy for diagnostic model assessment in capture-recapture’, which identified a direct relationship between particular diagnostic tests and score tests; and the 2020 JRSSC paper ‘Diagnosing heterogeneity in transition probabilities in multistate capture-recapture data’, which developed new tests to identify unmodelled transition heterogeneity.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Dr McCrea has made a profound contribution to statistical ecology. The Society’s journals have published a number of noteworthy papers authored by Rachel, and her development of goodness-of-fit tests and model selection strategies has been particularly innovative.”

The medal will be presented to Rachel at the RSS Annual Conference in Bournemouth in September.


Royal Statistical Society Barnett Award for Byron Morgan

Emeritus Professor of Statistics, Byron Morgan, has been awarded the Barnett Award by the Royal Statistical Society.

Being the leading authority on age-structured modelling of capture-recapture and ring-recovery data, his joint paper was the first to model how survival probabilities were influenced by weather covariates. Another influential paper on integrating mark-recapture-recovery and census data was foundational to the internationally-embraced sub-field of Integrated Population Modelling within statistical ecology. Most recently, he has been at the forefront of developing computationally-efficient methods for co-analysis of the UK Butterfly Monitoring Scheme with citizen science data sources, to give insights to biodiversity in urban versus rural settings. Byron Morgan was also one of the co-founders and first director of the National Centre for Statistical Ecology, a virtual Centre that links up statistical ecologists in the UK, and internationally.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Professor Morgan has had a great influence on the world of statistics and statistical ecology. His innovative work on computationally efficient methods for co-analysis of the UK Butterfly Monitoring Scheme has led to great insights into biodiversity and he had been a significant figure in creating better networks of statistical ecologists.”

Byron will be presented with the award at the Royal Statistical Society Annual Conference in September 2020 where he will also give a keynote presentation.


New publication: Statistical Development of Animal Density Estimation Using Random Encounter Modelling

New publication in JABES: Statistical Development of Animal Density Estimation Using Random Encounter Modelling by Natoya Jourdain, Diana Cole, Martin Ridout and Marcus Rowcliffe.


Camera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from camera-trap data without the need to identify animals. Although the REM can provide reliable density estimates, it lacks the potential to account for the multiple sources of variance in the modelling process. The density estimator in REM is a ratio, and since the variance of a ratio estimator is intractable, we examine and compare the finite sample performance of many approaches for obtaining confidence intervals via simulation studies. We also propose an integrated random encounter model as a parametric alternative, which is flexible and can incorporate covariates and random effects. A data example from Whipsnade Wild Animal Park, Bedfordshire, south England, is used to demonstrate the application of these methods.




New paper: Assessing heterogeneity in transition propensity in multistate capture–recapture data

Rachel has published a new paper in the Journal of the Royal Statistical Society with former SE@K PhD student Anita Jeyam and co-supervisor Roger Pradel from CEFE, CNRS Montpellier.

The paper is available open access so you are able to download the pdf of the full paper.  The work proposes a new test which allows you to test for heterogeneity in transition probabilities from capture-recapture data before you fit any models to the data.

Assessing heterogeneity in transition propensity in multistate capture–recapture data

Multistate capture–recapture models are a useful tool to help to understand the dynamics of movement within discrete capture–recapture data. The standard multistate capture–recapture model, however, relies on assumptions of homogeneity within the population with respect to survival, capture and transition probabilities. There are many ways in which this model can be generalized so some guidance on what is really needed is highly desirable. Within the paper we derive a new test that can detect heterogeneity in transition propensity and show its good power by using simulation and application to a Canada goose data set. We also demonstrate that existing tests which have traditionally been used to diagnose memory are in fact sensitive to other forms of transition heterogeneity and we propose modified tests which can distinguish between memory and other forms of transition heterogeneity.




New paper: Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model

Rachel McCrea, Richard Griffiths (DICE) and collaborators at the Universities of St Andrews and Edinburgh have had a paper published in Annals of Applied Statistics.


The article presents an exciting piece of work using Hidden Markov model structure for the fitting of multi-season, multi-state stopover capture-recapture models.  The framework is general, with many existing capture-recapture models being a special case.

The paper can be accessed here.


Capture-recapture studies often involve collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example, annually, resulting in capture information spanning multiple sampling periods. To account for the different temporal scales, the robust design class of models have traditionally been applied providing a framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically require strong constraints, either the assumption of closure within a sampling period (the closed robust design) or conditioning on the number of individuals captured within a sampling period (the open robust design). For real datasets these assumptions may not be appropriate. We develop a general modelling structure that requires neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance within a single consistent framework. The flexibility of the novel model structure is further demonstrated by including the computationally challenging case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the significant improvement in parameter estimation using our new modelling approach in terms of both the multi-period and multi-state components through both a simulation study and a real dataset relating to the protected species of great crested newts, Triturus cristatus.