Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$69.00 + tax (Refund Policy)

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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: African rain forests; biodiversity; climate; kriging; map; modelling; ordinary least squares; random forest; spatial autocorrelation; tree alpha diversity

Document Type: Research Article

Affiliations: 1: Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, CA, USA 2: School of Geosciences, University of Edinburgh, Edinburgh, UK 3: Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK 4: Centre for Ecosystem Studies, Wageningen University, Wageningen, The Netherlands 5: Department of Plant Sciences, University of Oxford, Oxford, UK 6: Missouri Botanical Garden, St. Louis, MO, USA 7: Earth & Biosphere Institute, School of Geography, University of Leeds, Leeds, UK 8: Conservatoire et Jardin botaniques de la Ville de Genève, Chambésy, Switzerland 9: Netherlands Centre for Biodiversity Naturalis (section NHN), Biosystematics Group, Wageningen University, Wageningen, The Netherlands 10: Resource Management Support Centre, Forestry Commission of Ghana, Kumasi, Ghana 11: Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Côte d’Ivoire 12: Department of Plant and Animal Sciences, University of Buea, Buea, Cameroon 13: Mid-Atlantic Network, Inventory and Monitoring Program, National Park Service, Fredericksburg, VA, USA 14: Evolutionary Biology & Ecology, Faculté des Sciences, Université Libre de Bruxelles, Brussels, Belgium 15: Laboratory of Tropical and Subtropical Forestry, Unit of Forest and Nature Management, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium 16: Nature +, Laboratory of Tropical and Subtropical Forestry, Unit of Forest and Nature Management, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium 17: CTFS-SIGEO Africa Program Coordinator, Arnold Arboretum, Harvard University, Cambridge, MA, USA 18: Laboratoire de Botanique, Université de Cocody, Abidjan, Côte d’Ivoire 19: Institut Notre Dame, Brussels, Belgium 20: Tropenbos International Congo-Basin Programme, Yaoundé, Cameroon 21: Bureau Waardenburg bv, Consultants for Environment & Ecology, Culemborg, The Netherlands 22: Ecole Normale Supérieure de Yaoundé, Université de Yaoundé I, Yaoundé, Cameroon 23: Centre for International Forestry Research (CIFOR), Bogor, Indonesia 24: Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen, UK 25: Ecole Nationale des Eaux et Forêts, Mbalmayo, Cameroon 26: Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA 27: Plant Protection Service, Wageningen, The Netherlands

Publication date: June 1, 2011

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more