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Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Standard

Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas. / Ohse, Bettina; Huettmann, Falk; Ickert-Bond, Stefanie M. et al.
in: Polar Biology, Jahrgang 32, Nr. 12, 12.2009, S. 1717-1729.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Ohse, B, Huettmann, F, Ickert-Bond, SM & Juday, GP 2009, 'Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas', Polar Biology, Jg. 32, Nr. 12, S. 1717-1729. https://doi.org/10.1007/s00300-009-0671-9

APA

Ohse, B., Huettmann, F., Ickert-Bond, S. M., & Juday, G. P. (2009). Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas. Polar Biology, 32(12), 1717-1729. https://doi.org/10.1007/s00300-009-0671-9

Vancouver

Ohse B, Huettmann F, Ickert-Bond SM, Juday GP. Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas. Polar Biology. 2009 Dez;32(12):1717-1729. doi: 10.1007/s00300-009-0671-9

Bibtex

@article{da2ed2bc6cd04c3e92155a6c58ec6a33,
title = "Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas",
abstract = "Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca) in Alaska (4 km resolution, accuracy over 90%). Our presented concept represents a role-model for predicting tree species distribution for remote areas world-wide. Although this model intends to be accurate in making predictions rather than to give detailed biological mechanistic explanations, it can also be used as a baseline for further research and testable hypothesis on the importance of the environmental variables used to build a generalizable model. Further, we emphasize that work like presented here is a pre-condition for assessing human impacts and impacts of climate change on species distribution in a quantitative and transparent fashion, allowing for improved sustainable decision-making world-wide.",
keywords = "Ecosystems Research, Species distribution models (SDM), White spruce Picea glauca, Alaska, Tree species, Predictive modeling, Open access (oa)",
author = "Bettina Ohse and Falk Huettmann and Ickert-Bond, {Stefanie M.} and Juday, {Glenn P.}",
year = "2009",
month = dec,
doi = "10.1007/s00300-009-0671-9",
language = "English",
volume = "32",
pages = "1717--1729",
journal = "Polar Biology",
issn = "0722-4060",
publisher = "Springer Science and Business Media Deutschland",
number = "12",

}

RIS

TY - JOUR

T1 - Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

AU - Ohse, Bettina

AU - Huettmann, Falk

AU - Ickert-Bond, Stefanie M.

AU - Juday, Glenn P.

PY - 2009/12

Y1 - 2009/12

N2 - Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca) in Alaska (4 km resolution, accuracy over 90%). Our presented concept represents a role-model for predicting tree species distribution for remote areas world-wide. Although this model intends to be accurate in making predictions rather than to give detailed biological mechanistic explanations, it can also be used as a baseline for further research and testable hypothesis on the importance of the environmental variables used to build a generalizable model. Further, we emphasize that work like presented here is a pre-condition for assessing human impacts and impacts of climate change on species distribution in a quantitative and transparent fashion, allowing for improved sustainable decision-making world-wide.

AB - Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca) in Alaska (4 km resolution, accuracy over 90%). Our presented concept represents a role-model for predicting tree species distribution for remote areas world-wide. Although this model intends to be accurate in making predictions rather than to give detailed biological mechanistic explanations, it can also be used as a baseline for further research and testable hypothesis on the importance of the environmental variables used to build a generalizable model. Further, we emphasize that work like presented here is a pre-condition for assessing human impacts and impacts of climate change on species distribution in a quantitative and transparent fashion, allowing for improved sustainable decision-making world-wide.

KW - Ecosystems Research

KW - Species distribution models (SDM)

KW - White spruce Picea glauca

KW - Alaska

KW - Tree species

KW - Predictive modeling

KW - Open access (oa)

U2 - 10.1007/s00300-009-0671-9

DO - 10.1007/s00300-009-0671-9

M3 - Journal articles

VL - 32

SP - 1717

EP - 1729

JO - Polar Biology

JF - Polar Biology

SN - 0722-4060

IS - 12

ER -

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