SPATIODIVERSITY: Towards a Unified Spatial Theory of Biodiversity
A central element of the department’s research, fostered by the ERC advanced grant
SPATIODIVERSITY, is to advance in a persistent challenge in contemporary ecology; to understand the relative importance of processes and factors that govern the composition and dynamics of species-rich communities such as tropical forests. Advances in this issue have important implications for efforts to protect terrestrial biodiversity from climate and land use change. Our novel contribution to this question is use the large amount of information on spatial patterns which is contained in the fully mapped (20-50ha) mega-plots of forests by using techniques of spatial statistics, last generation dynamic vegetation models and huge computational power. This information extraction involves three tasks:
The goal is to quantify the manifold spatial patterns at fully mapped vegetation plots with special attention to an assessment of the degree of non-random spatial structures in the uni-, bi-, and multivariate species interactions (Wiegand et al. 2009, 2012). This involved developments of novel statistical methods of spatial point pattern analysis (Wiegand & Moloney 2014), especially for multivariate analysis to enable quantification of the complex spatial structures in species, functional and phylogenetic diversity (Wang et al. 2016, 2015; Punchi-Manage 2015; Shen et al. 2013).
Spatial Point Pattern Analysis
The goal is to develop a spectrum of individual-based vegetation dynamic simulation models, ranging from simple neutral models
(were all species are functionally equivalent; May et al. 2015) to complex process based models of the FORMIND family
(Kazmierczak et al. 2014; Hartig et al. 2014). This involved development of spatially-explicit extensions of neutral
models and of the FORMIND model.
Forests and Grassland Dynamics
Model selection based on spatial patterns
Finally, the spatial patterns generated by the vegetation simulation models developed in task 2 were systematically compared with the patterns quantified in task 1 to identify the most parsimonious model(s) that account simultaneously for all observed patterns (May et al. 2015, 2016). This involved development of novel techniques of inverse parameterization and model selection (statistical inference) of stochastic simulation models (Hartig et al. 2011, 2014; Lehmann and Huth 2015).
A key result emerging from the combined analyses of forest simulation models and spatial pattern analysis in the ERC project is that much simpler models than anticipated do already provide a correct characterization of the complex spatial structure and several additional structural properties of hyperdiverse forests (May et al. 2015, 2016). This result has important consequences for the theoretical foundation of ecology.
ERC workshop on Dynamics and assembly of species rich communities and their spatial structure
The main goal of this workshop was the discuss ways of quantifying multivariate spatial patterns in fully stem-mapped vegetation plots and its importance of these patterns for community assembly and dynamics.
The high species richness of communities such as tropical forests has challenged coexistence theory for decades. We synthesize recent coexistence theories by focusing on the role of stochasticity in promoting biodiversity. Because of variability in the biotic neighborhood of individuals, the interactions between species may in a sense become “diluted” and less predictable and therefore prevent particular species from outcompeting others. We test the new stochastic coexistence theory using spatial analysis of forest plots and by conducting fully controlled biodiversity simulation experiments.
sDiv workshop sNiche