DOI: 10.1016/j.foreco.2013.07.023
Scopus记录号: 2-s2.0-84881448320
论文题名: Remotely sensed forest structural complexity predicts multi species occurrence at the landscape scale
作者: Zellweger F. ; Braunisch V. ; Baltensweiler A. ; Bollmann K.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 307 起始页码: 303
结束页码: 312
语种: 英语
英文关键词: Biodiversity
; Birds
; Habitat
; LiDAR
; NDVI
; Structure
Scopus关键词: Boosted regression trees
; Habitat
; NDVI
; Plant species composition
; Species distribution models
; Structural characteristics
; Structural heterogeneity
; Topographic positions
; Biodiversity
; Birds
; Ecosystems
; Forecasting
; Information management
; Optical radar
; Plants (botany)
; Population distribution
; Remote sensing
; Space optics
; Structure (composition)
; Forestry
; avifauna
; conservation planning
; forest ecosystem
; forest edge
; habitat quality
; lidar
; mountain region
; NDVI
; prediction
; remote sensing
; snag
; species diversity
; species occurrence
; stand structure
; sympatry
; Biodiversity
; Birds
; Ecosystems
; Forecasts
; Forestry
; Radar
; Remote Sensing
; Switzerland
英文摘要: Along with plant species composition forest structural complexity is an important determinant of forest biodiversity, but difficult to predict in space from field data. We analyzed forest structural complexity based on a comprehensive set of variables derived from nationally available, area-wide remote sensing, particularly LiDAR data. We generated variables related to vertical and horizontal structural heterogeneity, as well as site factors potentially indicating the abundance of weakened trees or snags. We used them to predict the occurrence of four bird species with narrow and complementary structural habitat requirements, together being indicative of structurally diverse forests. Presence/absence data of Capercaillie (Tetrao urogallus), Hazel Grouse (Bonasa bonasia), Three-toed Woodpecker (Picoides tridactylus) and Pygmy Owl (Glaucidium passerinum) from three biogeographic mountain regions in Switzerland were used to calibrate species distribution models (boosted regression trees BRT) for each species individually, as well as for the sympatric occurrence of at least three of the four target species. The predictive deviances explained and the AUC values obtained from cross-validation ranged from 15.5% to 63.1% and 0.77% to 0.97% respectively. Sympatric species occurrence reflecting overall forest structural complexity was predicted best, with an outstanding accuracy. To support management and monitoring schemes we identified variable threshold effects based on partial dependence plots. Variables related to vertical foliage distributions were most important, followed by horizontal structural attributes such as canopy height variations, forest edges and gaps. Site factors such as topographic position improved all models and were most important for the species depending on weakened trees and dead wood. We conclude that recent advances in remote sensing allow for large-scale determination of forest structural characteristics suitable for developing species and habitat distribution models of considerable generality, while keeping an unprecedented level of detail. Our approach allows forest managers to amend regional and countrywide management plans with reliable maps depicting areas of high forest structural complexity and habitat quality, which will facilitate the integration of conservation-relevant information into multifunctional forestry. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66382
Appears in Collections: 影响、适应和脆弱性
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作者单位: Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland; Conservation Biology, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland; Forest Research Institute of Baden-Württemberg, Wonnhaldestrasse 4, 79100 Freiburg, Germany
Recommended Citation:
Zellweger F.,Braunisch V.,Baltensweiler A.,et al. Remotely sensed forest structural complexity predicts multi species occurrence at the landscape scale[J]. Forest Ecology and Management,2013-01-01,307