A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China

Abstract

Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-Nmass-LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model

“…The model showed different behaviors in different sites, but expectedly consistent across species behaving in a similar functional way. These results may suggest that using functional traits combinations to provide physiological parameters, instead of fixed species-specific ones, may produce still reliable and more general predictions, particularly useful in case of larger spatial/temporal simulations [76][77][78]. Using a species-level parameterization, in fact, may result in a too-fine "resolution" because: (1) it would require excessive computational resources and a finely-detailed parameterization, usually inaccessible on a broad scale [79]; and (2) the model's rationale in predicting forest structure is mainly driven by competition for resources.…”

Correlation between vegetation and environment at different levels in an arid, mountainous region of China

“…This relationship can be analyzed by models (Graumlich & Davis, 1993;Yang et al, 2016;Zuo et al, 2012), metrics (Peng, Mi, Qing, & Xue, 2016;Schindler, Von Wehrden, Poirazidis, Wrbka, & Kati, 2013;Sohoulande Djebou et al, 2015;Zhang, Van Coillie, De Clercq, Ou, & De Wulf, 2013), or statistical calculation (Carleton, 1984;Dias & Melo, 2010). Landscape metrics is often used to quantify vegetation pattern and spatial organization and is sensitive to scale, as are many other ecological approaches (Bekker, Clark, & Jackson, 2009;Gardner, Milne, Turnei, & O'neill, 1987;Meentemeyer & Box, 1987;Turner, O'neill, Gardner, & Milne, 1989).…”

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China

“…Gaussian functions and their combinations are widely applied in bio-statistics to describe complex distributions and classifications (for algorithm details, see Witte et al, 2007 and Yang et al, 2016). Once Gaussian density distributions are ascertained in the discriminant classification, we can easily obtain the classification probability associated with each class.…”

“…Once Gaussian density distributions are ascertained in the discriminant classification, we can easily obtain the classification probability associated with each class. A GMM is a combination of several Gaussian components that do not require any arbitrary and potentially restrictive assumptions in the form of probability density functions, and it is an effective vegetation classifier in trait-based modeling (Laughlin et al, 2015; Yang et al, 2016).…”

“…It is widely recognized that modeling the vegetation distributions with Gaussian Mixture Models (GMMs) based on plant trait covariations is indispensable for our understanding of climate change impacts on ecosystems and it is a key output of the new generation of DGVMs (Sitch et al, 2008; van Bodegom et al, 2014). This method has been successfully applied in modeling vegetation distributions under historical climate conditions (Yang et al, 2016), although the response of vegetation to future climate scenarios for China based on the method of co-located trait–climate relationships remains unclear.…”