Monthly Archives: October 2018

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NERC Advanced Training Course

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.

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Paper: Removal models accounting for temporary emigration

The paper Removal models accounting for temporary emigration by Ming Shou, Rachel McCrea, Eleni Matechou , Diana Cole and Richard Griffiths has just been published online early in Biometrics.

Open access link to paper.

Summary:

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.

 

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