Using a dynamic forest model to predict tree species distributions
Author
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Gutierrez Ilabaca, Alvaro
Author
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Snell, Rebecca
Author
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Bugmann, Harald
Admission date
dc.date.accessioned
2016-06-28T22:35:44Z
Available date
dc.date.available
2016-06-28T22:35:44Z
Publication date
dc.date.issued
2016
Cita de ítem
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GLOBAL ECOLOGY AND BIOGEOGRAPHY Volumen: 25 Número: 3 Páginas: 347-358 (2016)
en_US
Identifier
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DOI: 10.1111/geb.12421
Identifier
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https://repositorio.uchile.cl/handle/2250/139246
General note
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Artículo de publicación ISI
en_US
Abstract
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Aim It has been suggested that predicting species distributions requires a processbased
and preferably dynamic approach. If dynamic models are to contribute
towards understanding species distributions, uncertainties related to their spatial
extrapolation and bioclimatic parameters need to be addressed. Here, we analyse
the potential of a forest gap model for predicting species distributions.
Location Pacific Northwest of North America (PNW).
Methods We used the dynamic forest gap model ForClim, which includes
climate, competition and demographic processes, to simulate the distribution of 18
tree species outside the domain of the data used for fitting. We explored model
accuracy for species distributions at the regional scale by: (1) estimating species
climatic tolerances so as to maximize agreement with regional distribution maps
versus (2) employing a bioclimatic parameter set that produces high accuracy at the
local scale. We then performed the opposite tests and simulated local forest composition
in a small area in the PNW, using (3) the local bioclimatic parameters and
(4) the bioclimatic parameters that produced the highest accuracy at the regional
scale. We also compared the ForClim results with predictions from a standard
correlative species distribution model (SDM).
Results ForClim produced regional species distributions with fair to very good
agreement for 12 tree species. The optimized bioclimatic parameters consistently
improved the accuracy of regional predictions compared with simulations run with
the local parameters, and were consistent with SDM results. At the local scale,
predictions using the local parameters conformed to descriptions of forest composition,
but accuracy decreased strongly when using the regionally calibrated
parameters.
Main conclusions Forest gap models can predict regional species distributions,
but at the cost of reduced accuracy at the local scale. Future applications of gap
models to understand regional species distributions should include robust
parameterization schemes and additional ecological processes that are important at
large spatial scales (e.g. dispersal, disturbances).
en_US
Patrocinador
dc.description.sponsorship
Marie Curie Intra European Fellowship within 7th European Community Framework Programme (FORECOFUN-SSA;
PIEF-GA-2010-274798
CONICYT-PAI
82130046
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