Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community

Lahoz-Monfort, Jose J. and Harris, Michael P. and Wanless, Sarah and Freeman, Stephen N. and Morgan, Byron J. T. (2017) Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community. Journal of Agricultural, Biological, and Environmental Statistics, 22 (2). pp. 140-160. ISSN 1085-7117. (doi:https://doi.org/10.1007/s13253-017-0279-4) (Full text available)

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http://dx.doi.org/10.1007/s13253-017-0279-4

Abstract

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data).We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats.

Item Type: Article
Uncontrolled keywords: Bayesian inference; long-term monitoring; mark-resight-recovery; state-spce model; survival; synchrony
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QL Zoology
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Byron Morgan
Date Deposited: 28 Apr 2017 15:04 UTC
Last Modified: 08 Jun 2017 08:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61585 (The current URI for this page, for reference purposes)
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