Aristides Moustakas

Tree effects on grass growth in savannas: competition, facilitation and the stress-gradient hypothesis

Summary nThe stress-gradient hypothesis (SGH) predicts an increasing importance of facilitative mechanisms relative to competition along gradients of increasing environmental stress. Although developed across a variety of ecosystems, the SGHs relevance to the dynamic tree–grass systems of global savannas remains unclear. Here, we present a meta-analysis of empirical studies to explore emergent patterns of tree–grass relationships in global savannas in the context of the SGH. nWe quantified the net effect of trees on understorey grass production relative to production away from tree canopies along a rainfall gradient in tropical and temperate savannas and compared these findings to the predictions of the SGH. We also analysed soil and plant nutrient concentrations in subcanopy and open-grassland areas to investigate the potential role of nutrients in determining grass production in the presence and absence of trees. nOur meta-analysis revealed a shift from net competitive to net facilitative effects of trees on subcanopy grass production with decreasing annual precipitation, consistent with the SGH. We also found a significant difference between sites from Africa and North America, suggesting differences in tree–grass interactions in the savannas of tropical and temperate regions. nNutrient analyses indicate no change in nutrient ratios along the rainfall gradient, but consistent nutrient enrichment under tree canopies. nSynthesis. Our results help to resolve questions about the SGH in semi-arid systems, demonstrating that in mixed tree–grass systems, trees facilitate grass growth in drier regions and suppress grass growth in wetter regions. Relationships differ, however, between African and North American sites representing tropical and temperate bioclimates, respectively. The results of this meta-analysis advance our understanding of tree–grass interactions in savannas and contribute a valuable data set to the developing theory behind the SGH.

Facilitation or competition? Tree effects on grass biomass across a precipitation gradient.

Savanna ecosystems are dominated by two distinct plant life forms, grasses and trees, but the interactions between them are poorly understood. Here, we quantified the effects of isolated savanna trees on grass biomass as a function of distance from the base of the tree and tree height, across a precipitation gradient in the Kruger National Park, South Africa. Our results suggest that mean annual precipitation (MAP) mediates the nature of tree-grass interactions in these ecosystems, with the impact of trees on grass biomass shifting qualitatively between 550 and 737 mm MAP. Tree effects on grass biomass were facilitative in drier sites (MAP≤550 mm), with higher grass biomass observed beneath tree canopies than outside. In contrast, at the wettest site (MAPu200a=u200a737 mm), grass biomass did not differ significantly beneath and outside tree canopies. Within this overall precipitation-driven pattern, tree height had positive effect on sub-canopy grass biomass at some sites, but these effects were weak and not consistent across the rainfall gradient. For a more synthetic understanding of tree-grass interactions in savannas, future studies should focus on isolating the different mechanisms by which trees influence grass biomass, both positively and negatively, and elucidate how their relative strengths change over broad environmental gradients.

Long‐term mortality patterns of the deep‐rooted Acacia erioloba: The middle class shall die!

Abstract Question: Is there a relationship between size and death in the long-lived, deep-rooted tree, Acacia erioloba, in a semi-arid savanna? What is the size-class distribution of A. erioloba mortality? Does the mortality distribution differ from total tree size distribution? Does A. erioloba mortality distribution match the mortality distributions recorded thus far in other environments? Location: Dronfield Ranch, near Kimberley, Kalahari, South Africa. Methods: A combination of aerial photographs and a satellite image covering 61 year was used to provide long-term spatial data on mortality. We used aerial photographs of the study area from 1940, 1964, 1984, 1993 and a satellite image from 2001 to follow three plots covering 510 ha. We were able to identify and individually follow ca. 3000 individual trees from 1940 till 2001. Results: The total number of trees increased over time. No relationship between total number of trees and mean tree size was detected. There were no trends over time in total number of deaths per plot or in size distributions of dead trees. Kolmogorov-Smirnov tests showed no differences in size class distributions for living trees through time. The size distribution of dead trees was significantly different from the size distribution of all trees present on the plots. Overall, the number of dead trees was low in small size classes, reached a peak value when canopy area was 20 - 30 m2, and declined in larger size-classes. Mortality as a ratio of dead vs. total trees peaked at intermediate canopy sizes too. Conclusion: A. erioloba mortality was size-dependent, peaking at intermediate sizes. The mortality distribution differs from all other tree mortality distributions recorded thus far. We suggest that a possible mechanism for this unusual mortality distribution is intraspecific competition for water in this semi-arid environment. Nomenclature: Barnes et al. (1997).

Assessing the predictive causality of individual based models using Bayesian inference intervention analysis: an application in epidemiology

Understanding dynamics in time and the predominant underlying factors that shape them is a central question in biological and medical sciences. Data are more ubiquitous and richer than ever before and population biology in the big data era need to integrate novel methods.xa0Calibrated Individual Based Models (IBMs) are powerful tools for process based predictive modelling. Intervention analysis is the analysis in time series of the potential impact of an event such as an extreme event or an experimentally designed intervention on the time series, for example vaccinating a population. A method for big data analytics (causal impact) that implements a Bayesian intervention approach to estimating the causal effect of a designed intervention on a time series is used to quantify the deviance between data and IBM outputs. Having quantified the deviance between IBM outputs and data, IBM scenarios are used to predict the counterfactual. The counterfactual is how the IBM response metric would have evolved after the intervention if the intervention had never occurred. The method is exemplified to quantify the deviance between a calibrated IBM outputs and epidemiological data of Bovine Tuberculosis with changing the cattle TB testing frequency as the intervention covariate. The advantage of IBM data validation and uncertainty assessment as time series is also discussed.

Mammal remains at Kruger National Park, South Africa

cover: Mammal remains at Kruger National Park, South Africa. Picture courtesy of Aristides Moustakas.