A global distribution of biodiversity inferred from climatic constraints: Results from a process-based modelling study

Authors:

Axel Kleidon and Harold A. Mooney

Abstract:

We investigated the connection between plant species diversity and climate by using a process-based, generic plant model. Different “species” were simulated by different values for certain growth-related model parameters. Subsequently, a wide range of values were tested in the framework of a “Monte Carlo” simulation for success, that is, the capability of each plant with these parameter combinations to reproduce itself during its lifetime. Species diversity was approximated by the range of successful parameter combinations. This method was applied to a global grid, using daily atmospheric forcing from a climate model simulation. The computed distribution of plant “species” diversity compares very well with the observed, global-scale distribution of species diversity, reproducing the majority of “hot spot” areas of biodiversity. A sensitivity analysis revealed that the predicted pattern is very robust against changes of fixed model parameters. Analysis of the climatic forcing and of two additional sensitivity simulations demonstrated that the crucial factor leading to this distribution of diversity is the early stage of a plant’s life when water availability is highly coupled to the variability in precipitation because in this stage root-zone storage of water is small. We used cluster analysis in order to extract common sets of species parameters, mean plant properties and biogeographic regions (biomes) from the model output. The successful “species” cannot be grouped into typical parameter combinations, which define the plant’s functioning. However, the mean simulated plant properties, such as lifetime and growth, can be grouped into a few characteristic plant “prototypes”, ranging from short-lived, fast growing plants, similar to grasses, to long-lived, slow growing plants, similar to trees. The classification of regions with respect to similar combinations of successful “species” yields a distribution of biomes similar to the observed distribution. Each biome has typical levels of climatic constraints, expressed for instance by the number of “rainy days” and “temperature days”. The less the number of days favorable for growth, the greater the level of constraints and the less the “species” diversity. These results suggest that climate as a fundamental constraint can explain much of the global scale, observed distribution of plant species diversity.

Reference:

Global Change Biology, 6 (5), 507-523.
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Postprint of this manuscript (accepted version of the paper formatted by author).
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Figure 1: Schematic diagram of the model setup. The model consists of a generic plant component which simulates the development of an isolated plant and a land surface component which primarily simulates land surface hydrology. The two components interact through land surface parameters, which are derived from the plant’s state and through the land surface conditions (mainly soil moisture availability), which is simulated by the land surface component.

Figure 2: Global distribution of species diversity. The top map (a) shows the simulated distribution of species diversity. The values are grouped into 9 groups: (1) < 2%, (2) 2 - 4%, (3) 4 - 10%, (4) 10 - 20%, (5) 20 - 30%, (6) 30 - 40%, (7) 40 - 60%, (8) 60 - 80%, and (9) ≥ 80% of the maximum diversity value simulated. The bottom map (b) shows a map based observations (Barthlott et al. 1996, 1999) for comparison. Note that the scaling is similar in both maps. Figure 2b is available on the internet at http://www.botanik.uni-bonn.de/system/biomaps.htm.

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Figure 3: Biogeographic classification of regions. The map shows a regional classification with respect to similar sets of successful “species”. This classification was obtained by applying cluster analysis to the set of successful “species” at all grid points. The groups are sorted in an increasing order of climatic constraints (see Figure 4). Each of the groups can be associated mainly with one major biome (see Table 7 for associations). Within each group, a distinct set of model parameters is most constrained (Table 7).

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Figure 4: Mean climatic conditions within each “biome”, expressed in terms of days with favourable growing conditions (precipitation and temperature). The “biomes” are sorted in an increasing order of climatic constraints, thus decreasing in terms of diversity. The mean diversity of the “biomes” are (top to bottom): 75%, 48%, 30%, 29%, 12%, 12%, 5% and 1% respectively of the maximum achieved value. Error bars denote one standard deviation.