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

Horswill, C, Manica, A, Daunt, F, Newell, Marie-Louise, Wanless, S, Wood, Matthew J ORCID: 0000-0003-0920-8396 and Matthiopoulos, J (2021) Improving assessments of data‐limited populations using life‐history theory. Journal of Applied Ecology. doi:10.1111/1365-2664.13863 (In Press)

[img]
Preview
Text (Peer-reviewed version)
9481-Wood-(2021)-Improving-assessments-of-data-limited-populations.pdf - Accepted Version
Available under License Creative Commons Attribution 4.0.

Download (10MB) | Preview

Abstract

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: Article
Article Type: Article
Uncontrolled Keywords: Black-legged kittiwake; Data-limited- Environmental impact assessment; Fecundity; Marine renewables; Missing data; Population assessment; Seabird; Survival
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QL Zoology > QL605 Chordates. Vertebrates > QL671-699 Birds
Divisions: Schools and Research Institutes > School of Natural & Social Sciences > Environmental Sciences
Research Priority Areas: Environmental Dynamics & Governance
Depositing User: Rhiannon Goodland
Date Deposited: 17 Mar 2021 16:03
Last Modified: 17 Mar 2021 16:15
URI: http://eprints.glos.ac.uk/id/eprint/9481

University Staff: Request a correction | Repository Editors: Update this record

University Of Gloucestershire

Bookmark and Share

Find Us On Social Media:

Social Media Icons Facebook Twitter Google+ YouTube Pinterest Linkedin

Other University Web Sites

University of Gloucestershire, The Park, Cheltenham, Gloucestershire, GL50 2RH. Telephone +44 (0)844 8010001.