Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records
- 2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality. 3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations. 4. We show that by using hierarchical observation models that integrate different types of2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality. 3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations. 4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.…
Author details: | Jörn PagelORCiD, Barbara J. Anderson, Wolfgang CramerORCiDGND, Richard Fox, Florian JeltschORCiDGND, David B. Roy, Chris D. Thomas, Frank Martin SchurrGND |
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DOI: | https://doi.org/10.1111/2041-210X.12221 |
ISSN: | 2041-210X |
ISSN: | 2041-2096 |
Title of parent work (English): | Methods in ecology and evolution : an official journal of the British Ecological Society |
Publisher: | Wiley-Blackwell |
Place of publishing: | Hoboken |
Publication type: | Article |
Language: | English |
Year of first publication: | 2014 |
Publication year: | 2014 |
Release date: | 2017/03/27 |
Tag: | Bayesian statistics; atlas data; biogeography; butterflies; citizen science programme; conservation biology; count data; macroecology; state-space model |
Volume: | 5 |
Issue: | 8 |
Number of pages: | 10 |
First page: | 751 |
Last Page: | 760 |
Funding institution: | Countryside Council for Wales, Defra; Joint Nature Conservation Committee; Forestry Commission; Natural England; Natural Environment Research Council; Scottish Natural Heritage; University of Potsdam Graduate Initiative on Ecological Modelling (UPGradE); German Federal Agency for Nature Conservation [FKZ 806 82 270 - K1]; German Research Foundation [SCHU 2259/3-1, SCHU 2259/5-1]; Biodiversity and Climate Ministry of Higher Education, Research and the Arts, Germany |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
Peer review: | Referiert |