Improving assessments of data-limited populations using life-history theory

Horswill, C. , Manica, A., Daunt, F., Newell, M., Wanless, S., Wood, M. and Matthiopoulos, J. (2021) Improving assessments of data-limited populations using life-history theory. Journal of Applied Ecology, 58(6), pp. 1225-1236. (doi: 10.1111/1365-2664.13863)

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Predicting how populations may respond to climate change and anthropogenic pressures requires detailed knowledge of demographic traits, such as survival and reproduction. However, the availability of these data varies greatly across space and taxa. Therefore, it is common practice to conduct population assessments by filling in missing values from surrogate species or other populations of the same species. However, using these independent surrogate values concurrently with observed data neglects the life‐history trade‐offs that connect the different aspects of a population's demography, thus introducing biases that could ultimately lead to erroneous management decisions. We use a Bayesian hierarchical approach to combine fragmented multi‐population data with established life‐history theory and reconstruct population‐specific demographic data across a substantial part of a species breeding range. We apply our analysis to a long‐lived colonial species, the black‐legged kittiwake Rissa tridactyla, that is classified as globally Vulnerable and is highly threatened by increasing anthropogenic pressures, such as offshore renewable energy development. We then use a projection analysis to examine how the reconstructed demographic parameters may improve population assessments, compared to models that combine observed data with independent surrogate values. Reconstructed demographic parameters can be utilised in a range of population modelling approaches. They can also be used as reference estimates to assess whether independent surrogate values are likely to over or underestimate missing demographic parameters. We show that surrogate values from independent sources are often used to fill in missing parameters that have large potential demographic impact, and that resulting biases can be driven in unpredictable directions thus precluding assessments from being consistently precautionary. Synthesis and applications: Our study dramatically increases the spatial coverage of population‐specific demographic data for black‐legged kittiwakes. The reconstructed demographic parameters presented can also be used immediately to reduce uncertainty in the consenting process for offshore wind development in the UK and Ireland. More broadly, we show that the reconstruction approach used here provides a new avenue for improving evidence‐based management and policy action for animal and plant populations with fragmented and error prone demographic data.

Item Type:Articles
Additional Information:This work was partially funded by Research England to CH, Marine Alliance for Science and Technology Scotland grant SG411 to CH; UK Natural Environmental Research Council grant NE/P004180/1 to JM and CH, UK Natural Environmental Research Council grant NE/R016429/1 as part of the UK-SCaPE programme delivering National Capability to FD, funding from the UK Joint Nature Conservation Committee (DEFRA) to MW and FD.
Glasgow Author(s) Enlighten ID:Matthiopoulos, Professor Jason and Horswill, Dr Catharine
Authors: Horswill, C., Manica, A., Daunt, F., Newell, M., Wanless, S., Wood, M., and Matthiopoulos, J.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
Journal Name:Journal of Applied Ecology
ISSN (Online):1365-2664
Published Online:07 March 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Journal of Applied Ecology 58(6): 1225-1236
Publisher Policy:Reproduced under a Creative Commons License
Related URLs:
Data DOI:10.5061/dryad.qnk98sfg0

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
173666The interplay between life history evolution and population dynamics can help us conserve data-poor species and reveal how they evolvedJason MatthiopoulosNatural Environment Research Council (NERC)NE/P004180/1Institute of Biodiversity, Animal Health and Comparative Medicine