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

José J. Lahoz-Monfort (), Michael P. Harris, Sarah Wanless, Stephen N. Freeman and Byron J. T. Morgan
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José J. Lahoz-Monfort: The University of Melbourne
Michael P. Harris: Centre for Ecology and Hydrology
Sarah Wanless: Centre for Ecology and Hydrology
Stephen N. Freeman: Centre for Ecology and Hydrology
Byron J. T. Morgan: University of Kent

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 2, No 2, 140-160

Abstract: 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. Supplementary materials accompanying this paper appear on-line.

Keywords: Bayesian inference; Long-term monitoring; Mark-resight-recovery; State-space model; Survival; Synchrony (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-017-0279-4

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