DOI: 10.1016/j.foreco.2012.10.046
Scopus记录号: 2-s2.0-84871887429
论文题名: Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables
作者: Chambers D. ; Périé C. ; Casajus N. ; de Blois S.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 291 起始页码: 20
结束页码: 29
语种: 英语
英文关键词: Climate change
; Eastern North America
; Predictive performance
; Random Forest
; Species abundance model
; Temperate and boreal forests
Scopus关键词: Boreal forests
; Eastern north america
; Predictive performance
; Random forests
; Species abundance
; Climate change
; Climate models
; Conservation
; Decision trees
; Forecasting
; Forestry
; Vegetation
; Population distribution
; biogeographical region
; climate modeling
; environmental conditions
; forest management
; relative abundance
; spatial analysis
; species occurrence
; statistical data
; topographic effect
; tree planting
; Climates
; Conservation
; Decision Making
; Forecasts
; Forestry
; Models
; Plants
; Random Processes
; Species Identification
; Trees
; North America
; Acer
; Acer barbatum
; Castanea dentata
; Picea mariana
英文摘要: Improving predictions of the location of suitable environmental conditions for species using species distribution models (SDM) is at the core of biodiversity/climate change research, but modelling species abundance, rather than distribution, is proving particularly challenging. Using data from more than 200,000 forest plots in eastern North America and Random Forest, we evaluated the performance of species abundance models (SAM) in predicting the relative abundance (measured as importance value) of each of 105 tree species in relation to climate, edaphic, and topographic variables. We calculated the coefficient of determination RSAM2 between observed and predicted abundances as a measure of model performance for each species. We also performed multiple linear regressions to explain variation of RSAM2 among species using five biogeographical or spatial attributes of species as explanatory variables. Predictive performances of SAM RSAM2 were generally low, ranging from 0.016 to 0.815 (mean = 0.258). Black spruce (Picea mariana) had the best predictive model and Florida maple (Acer barbatum) and American chestnut (Castanea dentata) the worst. Thirty-seven of the 41 best performing species RSAM20.3 had climate ranked as the best and/or second best predictor. Species with the best performance tended to be those that could reach dominance, showed aggregated distribution of abundance, and/or had high latitudinal limits in the study area. Climate change is likely to affect patterns of dominance in communities by altering patterns of co-occurrences, but for many species that constitute the bulk of tree diversity, predictions based solely on the current distribution of relative abundances may not be reliable enough to inform conservation or management decisions. Predicting tree abundance in a warming climate using SAM remains a challenge, but it is only by reporting performances across a range of climate and statistical models, regions and species, as well as by highlighting model limitations and strengths, that we will improve the reliability of predictions and in turn better inform forest conservation and management. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66750
Appears in Collections: 影响、适应和脆弱性
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作者单位: McGill School of Environment, Department of Plant Science, Macdonald Campus of McGill University, 21111 Lakeshore, Sainte-Anne-de-Bellevue, H9X 3V9, Canada; Direction de la recherche forestière, Ministère des Ressources naturelles et de la Faune, 2700 rue Einstein, Quebec City, G1P 3W8, Canada; Conservation of Northern Ecosystems and Centre of Nordic Studies, Quebec University at Rimouski, 300 allée des Ursulines, Rimouski, G5L 3A1, Canada
Recommended Citation:
Chambers D.,Périé C.,Casajus N.,et al. Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables[J]. Forest Ecology and Management,2013-01-01,291