TY - JOUR

T1 - Predictive mapping of plant species and communities using GIS and Landsat data in a southern Mongolian mountain range

AU - von Wehrden, Henrik

AU - Zimmermann, Heike

AU - Hanspach, Jan

AU - Ronnenberg, Katrin

AU - Wesche, Karsten

PY - 2009/9

Y1 - 2009/9

N2 - We assessed presence/absence prediction of plant species and communities in a southern Mongolian mountain range from geospatial data using a randomized sampling approach. One hundred randomized vegetation samples (3 × 3 m) were collected within the 2 × 2 km summit region of the Dund Saykhan range, which forms part of the core zone of the Gobi Gurvan Saykhan National Park in arid southern Mongolia. Using logistic regression, habitat preference models for all abundant species (n = 52) and communities (n = 5) were constructed; predictors were derived from Landsat 5 imagery and a digital elevation model. Nagelkerkes r 2 was used for an initial data mining, and all significant models were validated by splitting the data and using one half for accuracy assessment based on the AUC (Area Under the receiver operating characteristic Curve)-values. Significant models could be built for half of the species. Altitude proved to be the most important predictor followed by variables derived from Landsat data. The clear altitudinal distribution patterns most definitely reflect precipitation; overall biodiversity in this arid environment is widely controlled by moisture availability. The chosen approach may prove valuable for applied studies wherever spatial data on species distributions are required for conservation efforts.

AB - We assessed presence/absence prediction of plant species and communities in a southern Mongolian mountain range from geospatial data using a randomized sampling approach. One hundred randomized vegetation samples (3 × 3 m) were collected within the 2 × 2 km summit region of the Dund Saykhan range, which forms part of the core zone of the Gobi Gurvan Saykhan National Park in arid southern Mongolia. Using logistic regression, habitat preference models for all abundant species (n = 52) and communities (n = 5) were constructed; predictors were derived from Landsat 5 imagery and a digital elevation model. Nagelkerkes r 2 was used for an initial data mining, and all significant models were validated by splitting the data and using one half for accuracy assessment based on the AUC (Area Under the receiver operating characteristic Curve)-values. Significant models could be built for half of the species. Altitude proved to be the most important predictor followed by variables derived from Landsat data. The clear altitudinal distribution patterns most definitely reflect precipitation; overall biodiversity in this arid environment is widely controlled by moisture availability. The chosen approach may prove valuable for applied studies wherever spatial data on species distributions are required for conservation efforts.

KW - Ecosystems Research

KW - Area Under Curve

KW - Central Asia

KW - Gobi desert

KW - Habitat preference

KW - Logistic regression model

KW - Species distribution

KW - Validation

KW - Biology

KW - Geography

UR - http://www.scopus.com/inward/record.url?scp=79951809240&partnerID=8YFLogxK

U2 - 10.1007/s12224-009-9042-0

DO - 10.1007/s12224-009-9042-0

M3 - Journal articles

VL - 44

SP - 211

EP - 225

JO - Folia Geobotanica

JF - Folia Geobotanica

SN - 1211-9520

IS - 3

ER -