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Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model
Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model
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.
african rain forests, biodiversity, climate, kriging, map, modelling, ordinary least squares, random forest, spatial autocorrelation, tree alpha diversity
0305-0270
1164-1176
Parmentier, Ingrid
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Harrigan, Ryan J.
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Buermann, Wolfgang
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Mitchard, Edward T.A.
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Saatchi, Sassan
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Malhi, Yadvinder
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Bongers, Frans
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Hawthorne, William D.
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Leal, Miguel E.
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Lewis, Simon L.
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Nusbaumer, Louis
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Sheil, Douglas
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Sosef, Marc S.M.
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Affum-Baffoe, Kofi
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Bakayoko, Adama
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Chuyong, George B.
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Chatelain, Cyrille
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Comiskey, James A.
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Dauby, Gilles
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Doucet, Jean-Louis
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Fauset, Sophie
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Gautier, Laurent
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Gillet, Jean-François
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Kenfack, David
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Kouamé, François N.
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Kouassi, Edouard K.
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Kouka, Lazare A.
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Parren, Marc P.E.
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Peh, Kelvin S.-H.
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Senterre, Bruno
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Sonké, Bonaventure
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Sunderland, Terry C.H.
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Swaine, Mike D.
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Tchouto, Mbatchou G.P.
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Thomas, Duncan
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Van Valkenburg, Johan L.C.H.
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Hardy, Olivier J.
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Parmentier, Ingrid
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Harrigan, Ryan J.
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Buermann, Wolfgang
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Mitchard, Edward T.A.
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Saatchi, Sassan
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Malhi, Yadvinder
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Bongers, Frans
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Hawthorne, William D.
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Leal, Miguel E.
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Lewis, Simon L.
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Nusbaumer, Louis
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Sheil, Douglas
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Sosef, Marc S.M.
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Affum-Baffoe, Kofi
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Bakayoko, Adama
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Chuyong, George B.
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Chatelain, Cyrille
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Gautier, Laurent
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Parren, Marc P.E.
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Peh, Kelvin S.-H.
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Reitsma, Jan M.
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Senterre, Bruno
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Sonké, Bonaventure
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Sunderland, Terry C.H.
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Swaine, Mike D.
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Tchouto, Mbatchou G.P.
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Thomas, Duncan
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Van Valkenburg, Johan L.C.H.
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Hardy, Olivier J.
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Parmentier, Ingrid, Harrigan, Ryan J., Buermann, Wolfgang, Mitchard, Edward T.A., Saatchi, Sassan, Malhi, Yadvinder, Bongers, Frans, Hawthorne, William D., Leal, Miguel E., Lewis, Simon L., Nusbaumer, Louis, Sheil, Douglas, Sosef, Marc S.M., Affum-Baffoe, Kofi, Bakayoko, Adama, Chuyong, George B., Chatelain, Cyrille, Comiskey, James A., Dauby, Gilles, Doucet, Jean-Louis, Fauset, Sophie, Gautier, Laurent, Gillet, Jean-François, Kenfack, David, Kouamé, François N., Kouassi, Edouard K., Kouka, Lazare A., Parren, Marc P.E., Peh, Kelvin S.-H., Reitsma, Jan M., Senterre, Bruno, Sonké, Bonaventure, Sunderland, Terry C.H., Swaine, Mike D., Tchouto, Mbatchou G.P., Thomas, Duncan, Van Valkenburg, Johan L.C.H. and Hardy, Olivier J. (2011) Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model. Journal of Biogeography, 38 (6), 1164-1176. (doi:10.1111/j.1365-2699.2010.02467.x).

Record type: Article

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.

Full text not available from this repository.

More information

e-pub ahead of print date: 7 February 2011
Published date: June 2011
Keywords: african rain forests, biodiversity, climate, kriging, map, modelling, ordinary least squares, random forest, spatial autocorrelation, tree alpha diversity
Organisations: Centre for Biological Sciences

Identifiers

Local EPrints ID: 352980
URI: http://eprints.soton.ac.uk/id/eprint/352980
ISSN: 0305-0270
PURE UUID: ee80b9f1-ac18-4291-89c4-6f03122bb15e
ORCID for Kelvin S.-H. Peh: ORCID iD orcid.org/0000-0002-2921-1341

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Date deposited: 22 May 2013 13:05
Last modified: 20 Jul 2019 00:41

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Contributors

Author: Ingrid Parmentier
Author: Ryan J. Harrigan
Author: Wolfgang Buermann
Author: Edward T.A. Mitchard
Author: Sassan Saatchi
Author: Yadvinder Malhi
Author: Frans Bongers
Author: William D. Hawthorne
Author: Miguel E. Leal
Author: Simon L. Lewis
Author: Louis Nusbaumer
Author: Douglas Sheil
Author: Marc S.M. Sosef
Author: Kofi Affum-Baffoe
Author: Adama Bakayoko
Author: George B. Chuyong
Author: Cyrille Chatelain
Author: James A. Comiskey
Author: Gilles Dauby
Author: Jean-Louis Doucet
Author: Sophie Fauset
Author: Laurent Gautier
Author: Jean-François Gillet
Author: David Kenfack
Author: François N. Kouamé
Author: Edouard K. Kouassi
Author: Lazare A. Kouka
Author: Marc P.E. Parren
Author: Jan M. Reitsma
Author: Bruno Senterre
Author: Bonaventure Sonké
Author: Terry C.H. Sunderland
Author: Mike D. Swaine
Author: Mbatchou G.P. Tchouto
Author: Duncan Thomas
Author: Johan L.C.H. Van Valkenburg
Author: Olivier J. Hardy

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