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Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival

Lahoz-Monfort, Jose J., Harris, Michael P., Morgan, Byron J. T., Freeman, Stephen N., Wanless, Sarah (2014) Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival. Journal of Applied Ecology, 51 (2). pp. 534-543. ISSN 0021-8901. (doi:10.1111/1365-2664.12214) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:41240)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1111/1365-2664.12214

Abstract

1. Long-term monitoring programmes often involve substantial input of skilled staff time. In

ing/resighting individuals. Given increasing budgetary constraints, it is essential to streamline

trends or ecological effects.

als by resampling existing mark–recapture–recovery data to construct plausible scenarios of

guillemot Uria aalge monitoring programme at a major North Sea colony. We also assess the

integrated population models (IPM) fitted to data including information on breeding adults.

data.

halved while still maintaining the capacity to monitor first-year survival and detect the effect

4. The IPM appears robust for estimating survival, productivity or abundance of the breed-

data are omitted. If productivity were not monitored, the inclusion of chick data would be

5. Synthesis and applications. Post-study evaluation can help streamline existing long-term

thinning of existing mark–recapture–recovery data to identify potential field effort reductions.

ulation models when data are collected on different aspects of demography and abundance.

adjusting field protocols to collect demographic data. The framework has broad applicability

and we discuss its use in different contexts.

Item Type: Article
DOI/Identification number: 10.1111/1365-2664.12214
Uncontrolled keywords: data thinning, hidden parameters, individual covariates, integrated population model, juvenile survival, long-term monitoring, mark–recapture–recovery, productivity, survey design, Uria aalge
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Q Science > QL Zoology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Byron Morgan
Date Deposited: 30 May 2014 12:41 UTC
Last Modified: 16 Feb 2021 12:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41240 (The current URI for this page, for reference purposes)
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