Tree Spacing and Coexistence in Semiarid Savannas

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. British Ecological Society is collaborating with JSTOR to digitize, preserve and extend access to Journal of Ecology.Summary 1 In the debate on the stability of savanna vegetation, spatial processes are often neglected. A spatial simulation model based on a cellular automata approach was constructed to identify the factors and processes crucial to the coexistence of trees and grass, and their effects on the spatial arrangement of trees in arid and semiarid savannas. 2 The simulation shows that the traditional key determinants of savannasrain, fire and grazinggenerate and sustain a coexistence of trees and grasses only under specific conditions. 3 An increase in the rainfall (improved tree establishment) or in grazing (reduced competition from grass), led to an increase in the woody component in the model. Where this trend was reversed by occasional fires, the simulation indicated that trees would be patchily distributed in thickets that excluded fire. 4 For an intermediate range of fire, grazing and rainfall variables this strongly clumped distribution pattern of trees represented a stable tree-grass mixture for more than 20 000 simulated years. 5 The hypothesis is formulated that factors or processes other than competition for moisture, herbivory and fire are needed in addition to induce a long-term persistence of scattered trees. 6 By exploring the long-term and spatial consequences of altering the variables that were thought to be key determinants of savanna vegetation, this spatiotemporal model provides a novel insight into the understanding of savanna dynamics.

The Use and Misuse of Climatic Gradients for Evaluating Climate Impact on Dryland Ecosystems - an Example for the Solution of Conceptual Problems

“…Spatially explicit, stochastic simulation models have been found successful in using short-term and small-scale information to gain understanding of and to predict longer-term and larger-scale processes, as described by Jeltsch and Moloney (2003). Grid-based, modular simulation models of vegetation dynamics are especially able to link information on differing scales (Jeltsch et al, 1996(Jeltsch et al, , 1997Jeltsch et al, 1999). However, in the type of climate change studies proposed in the present paper, phenomena on at least three different spatial scales have to be distinguished: (1) responses of individual plants, e.g., growth, seed production, mortality and, possibly, physiological adaptation mechanisms (e.g., Petru et al, 2006); (2) small-scale intra-and interspecific interactions between individuals of contrasting growth forms, among which interactions between herbaceous and woody vegetation, including competition and facilitation mechanisms, are of especial importance (Holzapfel et al, 2006); and (3) the effects of these interactions on vegetation pattern formation on the landscape level, with feedbacks to spatial processes such as runoff, soil moisture distribution and availability, fire, grazing, and other types of land use.…”

Modelling patch dynamics on rocky shores using deterministic cellular automata

“…At each node, population dynamics of 1 or more species often follow classical formulations such as the Nicholson-Bailey host-parasitoid model (Comins et al 1992), while dispersal processes exchange populations between neighbouring nodes according to simple diffusion (Comins et al 1992) or density-dependent dispersal (Rohani & Miramontes 1995), Cellular automata (CAs; see Ermentrout & Edelstein-Keshet 1993 for review) similarly consist of connected nodes or cells, but express the state of each node as one of a number of discrete categories representing recognisable ecological states, Changes in cell states depend on the states of the surrounding cells in either a probabilistic or deterministic manner. Cellular automata thus have the advantage over other techniques in that expert knowledge on underlying biological processes can be incorporated as 'transition rules' without the need for extensive parameterization (Jeltsch et al 1996). It is therefore the most appropriate methodology for formalising the current conceptual model of the dynamics of algal patches on rocky shores.…”

A mechanistic description of the formation and evolution of vegetation patterns

“…Cellular automata models, for example, have been successfully adopted to investigate the spatial distribution of Acacia trees in desertic areas, (Wiegand et al, 1999(Wiegand et al, , 2000 or to analyze gap dynamics and cohesistence of trees and grass in savannas (Jeltsch et al, 1996). However, most of the models that have been used specifically to reproduce the geometric vegetation structures that are the objects of this study belong to three categories: (1) kernel based models (Lefever and Lejeune, 1997;Thiéry et al, 1995;D'Odorico et al, 2006); (2) advectiondiffusion models (HilleRisLambers et al, 2001;Rietkerk et al, 2002); and (3) differential flow instability models (Klausmeier, 1999;Sherrat, 2005;Saco et al, 2007).…”