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.

Abstract:

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.

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

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

Abstract:

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.

Statistical models for wildlife population assessment and conservation

Dates and Location: 7-11 January 2019, University of Kent, Canterbury Campus

Deadline for Applications: (Extended) 5pm on Wednesday 31st October 2018, by emailing advancedtraining@kent.ac.uk with a completed application form

Specifications: We have 30 fully-funded places (inc. travel within the UK and accommodation); priority will be given to NERC-funded PhD students but if spaces remain we are able to offer the funded places to other UK-based PhD students and early-career researchers.

Description:

Within the environmental sector there is currently a shortage of practitioners equipped with the statistical modelling skills to carry out reliable population assessments. Consequently, environmental impact assessments (EIAs) and development mitigation projects often use population assessment protocols that are not fit-for-purpose. The skills shortage arises because (1) recent advances in statistical models for population assessment are largely confined to the academic sector with little penetration to the end-users; and (2) although many postgraduate programmes have a statistical modelling training component, this often fails to expose PhD students to new models in the area and the potential applications these have for conservation practice. This training programme will provide a cohort of PhD students and early career researchers/practitioners with the relevant modelling skills required for a career that involves wildlife population assessment for conservation.

The workshop will focus on ecological questions that arise in conservation practice and use real case study data. Training will include individual-based models, such as capture-recapture, but will also embrace scenarios more frequently used in EIA, such as batch-marked, presence/absence, site occupancy and counts. Applications will include newts, butterflies, birds, bees, beetles, ibex and bats. Each module will be accompanied by a practical computer session using R and each module builds on the last so that delegates build a portfolio of statistical skills.

More detailed information about the workshop timetable and training outcomes is available here.

Trainers: Dr Rachel McCrea, Dr Eleni Matechou, Dr Diana Cole, Richard Griffiths.