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

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. ResultsThe 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 conclusionsThe 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

Can Species Richness Patterns Be Interpolated From a Limited Number of Well-Known Areas? Mapping Diversity Using GLM and Kriging

“…Our results are similar to those of Parmentier et al (2011), who found that kriging predictions of rainforest tree diversity were as good as regression-based ones-among other techniques-, but also that the performance of all techniques was good in well-sampled areas and bad in poorly inventoried territories. Kriging and other related techniques such as co-kriging can be used for interpolating spatially autocorrelated data (Legendre & Legendre 1998), but their success in extrapolating the dependent variable to new geographical domains will depend on how consistent is its spatial structure across the studied region; in our case, on how stationary are the spatial trends in species richness.…”

“…Kriging is much less used for this purpose (but see e.g. Ter Steege et al 2003;Parmentier et al 2011).…”

Tree diversity and conservation value of Ngovayang’s lowland forests, Cameroon

“…As strategies for conserving species and communities focuses as much on species richness and endemism (Myers et al 2000, Lovett et al 2000, Kier and Barthlott 2001, more detailed information about the coastal forests of Cameroon, especially on the poorly known Ngovayang Massif, is necessary. Better data from such unexplored areas would greatly increase the reliability of modeling patterns of tree alpha diversity in African rain forests (Parmentier et al 2011).…”

Investigation of a possibility of spatial modelling of tree diversity using environmental and data mining algorithms

“…According to Franklin (1998) and Shataee et al (2012), nearest neighbours, support vector machine and random forest, which can do both classification and regression, are the most common algorithms among data mining algorithms. Many researchers used these algorithms to model the quantity of forest characteristics (Ismail, Mutango 2010;O'Sullivan et al 2010;Parmentier 2011;Yazdani 2011;Shataee et al 2012). Moreover, the advantages of non-parametric algorithms rely on the fact that they are not sensitive to a high number of independent variables as inputs for modelling.…”

“…Some of the ecological factors which can affect biodiversity are elevation, aspect, slope, climate, and human activities (Ejtehadi et al 2010). So far there are different kinds of studies that have been conducted in different territories, trying to predict or investigate diversity distribution related to topographic factors (Pourbabaee 1998;Marvie Mohajer 2005;Gracia et al 2007;Ismailzadeh, Hosseini 2007;Ghanbari 2008;Saatchi et al 2008;Kymasi 2012), edaphic (Qomioghli et al 2006;Ejtehadi et al 2010;Kymasi 2012) and climatic factors (Mehdinya et al 2006;Parmentier 2011;Gixhari et al 2012). One of the main purposes of modelling research is to clarify the most appropriate method, regarding the spatial prediction of forest characteristics, based on sampling methods (Kint et al 2003).…”