Abstract
Abstract. A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD0 (accumulated degree days above 0 °C), Cramer–Prentice α (an estimate of the ratio of actual to equilibrium evapotranspiration) and mGDD5 (mean temperature during the period above 5 °C) showed the greatest predictive power. These three variables were used to develop generalized linear models for the probability of occurrence of 16 vegetation classes, aggregated from the original 55 types by k-means clustering according to bioclimatic similarity. Each class was hypothesized to possess a unimodal relationship to each bioclimate variable, independently of the other variables. A simple calibration was used to generate vegetation maps from the predicted probabilities of the classes. Modelled and observed vegetation maps showed good to excellent agreement (κ = 0.745). A sensitivity study examined modelled responses of vegetation distribution to spatially uniform changes in temperature, precipitation and [CO2], the latter included via an offset to α (based on an independent, data-based light use efficiency model for forest net primary production). Warming shifted the boundaries of most vegetation classes northward and westward while temperate steppe and desert replaced alpine tundra and steppe in the southeast of the Tibetan Plateau. Increased precipitation expanded mesic vegetation at the expense of xeric vegetation. The effect of [CO2] doubling was roughly equivalent to increasing precipitation by ~ 30%, favouring woody vegetation types, particularly in northern China. Agricultural zones in northern China responded most strongly to warming, but also benefited from increases in precipitation and [CO2]. These results broadly conform to previously published findings made with the process-based model BIOME4, but they add regional detail and realism and extend the earlier results to include cropping systems. They provide a potential basis for a broad-scale assessment of global change impacts on natural and managed ecosystems.
Highlights
Based light use efficiency model for forest net primary pro- The same approach can be applied at the level of biomes, duction)
Wang et al.: Data-based modelling and environmental sensitivity of vegetation achieved in equilibrium with climate – is that it allows directions of change in response to environmental changes to be characterized irrespective of lags in the establishment of new vegetation types, or in the responses of agricultural systems to changed conditions
With huge advances in the availability of relevant observations to constrain models, and in the size of problems that can be tackled using statistical methods, there is considerable scope to develop relatively simple models, which are informed by process understanding and firmly based on observations
Summary
Based light use efficiency model for forest net primary pro- The same approach can be applied at the level of biomes, duction). The concentration of carbon dioxide ([CO2]) itself has been recognized as a potentially important non-climatic factor that is already shifting vegetation patterns through its effect on the competition between woody and herbaceous plants through an increase in the water use efficiency of C3 plants, in addition to its effects on climate through the greenhouse effect. This physiological effect of CO2 is a prominent candidate to explain “woody thickening”, the tendency for trees and shrubs to increase in abundance at the expense of grasses, as has been observed in savannas worldwide (Prentice et al, 2011). A further limitation of niche models as usually applied is that they do not include the modifying effects of changes in CO2 concentration, even though these are potentially very important (Keenan et al, 2011) and are absolutely required in order to account for the nature of observed, major vegetation changes over glacial–interglacial cycles (Harrison and Prentice, 2003; Prentice and Harrison, 2009; Prentice et al, 2011; Bragg et al, 2013)
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