TY - JOUR
T1 - An evaluation of species distribution models to estimate tree diversity at genus level in a heterogeneous urban-rural landscape
AU - Stas, Michiel
AU - Aerts, Raf
AU - Hendrickx, Marijke
AU - Dendoncker, Nicolas
AU - Dujardin, Sebastien
AU - Linard, Catherine
AU - Nawrot, Tim
AU - Van Nieuwenhuyse, An
AU - Aerts, Jean Marie
AU - Van Orshoven, Jos
AU - Somers, Ben
N1 - Funding Information:
This study is a contribution to the RespirIT project, which has been supported by a grant from the Belgian Science Policy Office BELSPO (grant nr. BR/154/A1/RespirIT). The authors wish to thank Sander Heylen and Thomas Van Hecke for their contribution to the research and the editor and anonymous reviewers for their comments and constructive feedback.
Funding Information:
This study is a contribution to the RespirIT project, which has been supported by a grant from the Belgian Science Policy Office BELSPO (grant nr. BR/154/A1/RespirIT ).
Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6
Y1 - 2020/6
N2 - Trees provide ecosystem services that improve the environment and human health. The magnitude of these improvements may be related to tree diversity within green spaces, yet spatially explicit diversity data necessary to investigate such associations are often missing. Here, we evaluate two methods to model tree diversity at genus level based on environmental covariates and presence point data. We aimed to identify the drivers and suitable methods for urban and rural tree diversity models in the heterogeneous region of Flanders, Belgium. We stratified our research area into dominantly rural and dominantly urban areas and developed distribution models for 13 tree genera for both strata as well as for the area as a whole. Occurrence data were obtained from an open-access presence-only database of validated observations of vascular plants. These occurrence data were combined with environmental covariates in MaxEnt models. Tree diversity was modelled by adding up the individual species distribution models. Models in the dominantly rural areas were driven by soil characteristics (soil texture and drainage class). Models in the dominantly urban areas were driven by environmental covariates explaining urban heterogeneity. Nevertheless, the stratification into urban and rural did not contribute to a higher model quality. Generic tree diversity estimates were better when presences derived from distribution models were simply added up (binary stacking, True Positive Rate of 0.903). The application of macro-ecological constraints resulted in an underestimation of generic tree diversity (probability stacking, True Positive Rate of 0.533). We conclude that summing presences derived from species distribution models (binary stacking) is a suitable approach to increase knowledge on regional diversity.
AB - Trees provide ecosystem services that improve the environment and human health. The magnitude of these improvements may be related to tree diversity within green spaces, yet spatially explicit diversity data necessary to investigate such associations are often missing. Here, we evaluate two methods to model tree diversity at genus level based on environmental covariates and presence point data. We aimed to identify the drivers and suitable methods for urban and rural tree diversity models in the heterogeneous region of Flanders, Belgium. We stratified our research area into dominantly rural and dominantly urban areas and developed distribution models for 13 tree genera for both strata as well as for the area as a whole. Occurrence data were obtained from an open-access presence-only database of validated observations of vascular plants. These occurrence data were combined with environmental covariates in MaxEnt models. Tree diversity was modelled by adding up the individual species distribution models. Models in the dominantly rural areas were driven by soil characteristics (soil texture and drainage class). Models in the dominantly urban areas were driven by environmental covariates explaining urban heterogeneity. Nevertheless, the stratification into urban and rural did not contribute to a higher model quality. Generic tree diversity estimates were better when presences derived from distribution models were simply added up (binary stacking, True Positive Rate of 0.903). The application of macro-ecological constraints resulted in an underestimation of generic tree diversity (probability stacking, True Positive Rate of 0.533). We conclude that summing presences derived from species distribution models (binary stacking) is a suitable approach to increase knowledge on regional diversity.
UR - http://www.scopus.com/inward/record.url?scp=85080069084&partnerID=8YFLogxK
U2 - 10.1016/j.landurbplan.2020.103770
DO - 10.1016/j.landurbplan.2020.103770
M3 - Article
AN - SCOPUS:85080069084
SN - 0169-2046
VL - 198
JO - Landscape and Urban Planning
JF - Landscape and Urban Planning
M1 - 103770
ER -