Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 411514
Title Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model
Author(s) Parmentier, I.; Harrigan, R.; Buermann, W.; Mitchard, E.T.A.; Saatchi, S.; Malhi, Y.; Bongers, F.; Hawthorne, W.D.; Leal, M.E.; Lewis, S.; Nusbaumer, L.; Sheil, D.; Sosef, M.S.M.; Bakayoko, A.; Chuyong, G.; Chatelain, C.; Comiskey, J.; Dauby, G.; Doucet, J.L.; Hardy, O.
Source Journal of Biogeography 38 (2011)6. - ISSN 0305-0270 - p. 1164 - 1176.
DOI http://dx.doi.org/10.1111/j.1365-2699.2010.02467.x
Department(s) Forest Ecology and Forest Management
Resource Ecology
Biosystematics
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) tropical tree diversity - species richness - plant diversity - geographical ecology - red herrings - autocorrelation - patterns - amazon - scale - dynamics
Abstract Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height = 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances > 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.