Uncertainty in spatially explicit population models

Minor, E S, McDonald, R I, Treml, E A and Urban, D L 2008, Uncertainty in spatially explicit population models, Biological Conservation, vol. 141, no. 4, pp. 956-970, doi: 10.1016/j.biocon.2007.12.032.

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Title Uncertainty in spatially explicit population models
Author(s) Minor, E S
McDonald, R I
Treml, E AORCID iD for Treml, E A orcid.org/0000-0003-4844-4420
Urban, D L
Journal name Biological Conservation
Volume number 141
Issue number 4
Start page 956
End page 970
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2008
ISSN 0006-3207
Keyword(s) Habitat mapping
Hylocichla mustelina
Model error
Parameterization
Sensitivity
Wood thrush
Science & Technology
Life Sciences & Biomedicine
Biodiversity Conservation
Ecology
Environmental Sciences
Biodiversity & Conservation
Environmental Sciences & Ecology
parameterization sensitivity
PLOVER CHARADRIUS-MELODUS
WOOD THRUSHES
FRAGMENTED LANDSCAPES
REPRODUCTIVE SUCCESS
FOREST MANAGEMENT
DISPERSAL
CONSERVATION
DYNAMICS
VIABILITY
SURVIVAL
Summary Spatially explicit population models (SEPMs) are often used in conservation planning. However, confidence intervals around predictions of spatially explicit population models can greatly underestimate model uncertainty. This is partly because some sources of uncertainty are not amenable to the classic methods of uncertainty analysis. Here, we present a method that can be used to include multiple sources of uncertainty into more realistic confidence intervals. To illustrate our approach, we use a case study of the wood thrush (Hylocichla mustelina) in the fragmented forest of the North Carolina Piedmont. We examine 6 important sources of uncertainty in our spatially explicit population model: (1) the habitat map, (2) the dispersal algorithm, (3) clutch size, (4) edge effects, (5) dispersal distance, and (6) the intrinsic variability in our model. We found that uncertainty in the habitat map had the largest effect on model output, but each of the six factors had a significant effect and most had significant interactions with the other factors as well. We also found that our method of incorporating multiple sources of uncertainty created much larger confidence intervals than the projections that incorporated only sources of uncertainty included in most spatially explicit population model predictions. © 2008 Elsevier Ltd. All rights reserved.
Language eng
DOI 10.1016/j.biocon.2007.12.032
Indigenous content off
Field of Research 05 Environmental Sciences
06 Biological Sciences
07 Agricultural and Veterinary Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30125538

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