Steven I. Higgins

Determinants of woody cover in African savannas

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than ∼650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered ‘stable’ systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of ∼650 mm, savannas are ‘unstable’ systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics.

Predicting Plant Migration Rates in a Changing World: The Role of Long‐Distance Dispersal

Models of plant migration based on estimates of biological parameters severely underestimate the rate of spread when compared to empirical estimates of plant migration rates. This is disturbing, since an ability to predict migration and colonization rates is needed for predicting how native species will distribute themselves in response to habitat loss and climate change and how rapidly invasive species will spread. Part of the problem is the difficulty of formally including rare long‐distance dispersal events in spread models. In this article, we explore the process of making predictions about plant migration rates. In particular, we examine the links between data, statistical models, and ecological predictions. We fit mixtures of Weibull distributions to several dispersal data sets and show that statistical and biological criteria for selecting the most appropriate statistical model conflict. Fitting a two‐component mixture model to the same data increases the spread‐rate prediction by an average factor of 4.5. Data limit our ability to fit more components. Using simulations, we show that a small proportion (0.001) of seeds moving long‐distances (1–10 km) can lead to an order of magnitude increase in predicted spread rate. The analysis also suggests that most existing data sets on dispersal will not resolve the problem; more effort needs to be devoted to collecting data on long‐distance dispersal. Although dispersal had the strongest effect on the predicted spread rate, we showed that dispersal interacts strongly with plant life history, disturbance, and habitat loss in influencing the predicted rate of spread. The importance of these interactions means that an approach that integrates local and long‐distance dispersal with plant life history, disturbance, and habitat availability is essential for predicting migration rates.

ARE LONG-DISTANCE DISPERSAL EVENTS IN PLANTS USUALLY CAUSED BY NONSTANDARD MEANS OF DISPERSAL?

It has been argued that nonstandard mechanisms of dispersal are often responsible for long-distance dispersal in plants. For example, plant seeds that appear to be adapted for wind dispersal may occasionally be dispersed long distances by birds, or vice versa. In this paper, we explore whether existing data on dispersal distances, colonization rates, and migration rates support the idea that dispersal processes suggested by the morphology of the dispersal unit are responsible for long distance dispersal. We conclude that the relationship between morphologically defined dispersal syndrome and long-distance dispersal is poor. This relationship is poor because the relationship between the morphology of dispersal units and the multiple processes that move seeds are often complex. We argue that understanding gleaned from the often anecdotal literature on nonstandard and standard means of long distance dispersal is the foundation for both statistical and mechanistic models of long-distance dispersal. Such models hold exciting promise for the development of a quantitative ecology of long-distance dispersal.

EFFECTS OF FOUR DECADES OF FIRE MANIPULATION ON WOODY VEGETATION STRUCTURE IN SAVANNA

The amount of carbon stored in savannas represents a significant uncertainty in global carbon budgets, primarily because fire causes actual biomass to differ from potential biomass. We analyzed the structural response of woody plants to long-term experimental burning in savannas. The experiment uses a randomized block design to examine fire exclusion and the season and frequency of burn in 192 7-ha experimental plots located in four different savanna ecosystems. Although previous studies would lead us to expect tree density to respond to the fire regime, our results, obtained from four different savanna ecosystems, suggest that the density of woody individuals was unresponsive to fire. The relative dominance of small trees was, however, highly responsive to fire regime. The observed shift in the structure of tree populations has potentially large impacts on the carbon balance. However, the response of tree biomass to fire of the different savannas studied were different, making it difficult to generalize about the extent to which fire can be used to manipulate carbon sequestration in savannas. This study provides evidence that savannas are demographically resilient to fire, but structurally responsive.

Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally

It is possible that anthropogenic climate change will drive the Earth system into a qualitatively different state. Although different types of uncertainty limit our capacity to assess this risk, Earth system scientists are particularly concerned about tipping elements, large-scale components of the Earth system that can be switched into qualitatively different states by small perturbations. Despite growing evidence that tipping elements exist in the climate system, whether large-scale vegetation systems can tip into alternative states is poorly understood. Here we show that tropical grassland, savanna and forest ecosystems, areas large enough to have powerful impacts on the Earth system, are likely to shift to alternative states. Specifically, we show that increasing atmospheric CO2 concentration will force transitions to vegetation states characterized by higher biomass and/or woody-plant dominance. The timing of these critical transitions varies as a result of between-site variance in the rate of temperature increase, as well as a dependence on stochastic variation in fire severity and rainfall. We further show that the locations of bistable vegetation zones (zones where alternative vegetation states can exist) will shift as climate changes. We conclude that even though large-scale directional regime shifts in terrestrial ecosystems are likely, asynchrony in the timing of these shifts may serve to dampen, but not nullify, the shock that these changes may represent to the Earth system.

Savanna vegetation-fire-climate relationships differ among continents.

Surveying Savannas Savannas are structurally similar across the three major continents where they occur, leading to the assumption that the factors controlling vegetation structure and function are broadly similar, too. Lehmann et al. (p. 548) report the results of an extensive analysis of ground-based tree abundance in savannas, sampled at more than 2000 sites in Africa, Australia, and South America. All savannas, independent of region, shared a common functional property in the way that moisture and fire regulated tree abundance. However, despite qualitative similarity in the moisture–fire–tree-biomass relationships among continents, key quantitative differences exist among the three regions, presumably as a result of unique evolutionary histories and climatic domains. Evolution cannot be overlooked when aiming to predict the potential global impacts on savanna dynamics in a warming world. Ecologists have long sought to understand the factors controlling the structure of savanna vegetation. Using data from 2154 sites in savannas across Africa, Australia, and South America, we found that increasing moisture availability drives increases in fire and tree basal area, whereas fire reduces tree basal area. However, among continents, the magnitude of these effects varied substantially, so that a single model cannot adequately represent savanna woody biomass across these regions. Historical and environmental differences drive the regional variation in the functional relationships between woody vegetation, fire, and climate. These same differences will determine the regional responses of vegetation to future climates, with implications for global carbon stocks.

A review of models of alien plant spread.

Abstract Alien plants invade many ecosystems worldwide, often having substantial negative effects on ecosystem structure and functioning. The apparent complexity of invasions has impaired the development of a predictive framework of alien plant spread. Such a framework requires both a conceptual understanding of the ecology of invasions and appropriate modelling tools. We demonstrate, using a simple conceptual model and illustrative examples from the literature, that a predictive understanding of invasions can be established. Potential modelling tools are reviewed by categorizing models of plant spread as either simple-demographic, spatial-phenomenological or spatial-mechanistic, based on the models data inputs and outputs. The assumptions, predictive potential, knowledge and data requirements of these modelling tools are discussed in the context of selecting the most appropriate alien plant spread model for a given case.

Next-generation dynamic global vegetation models: learning from community ecology.

Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition. We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a sites biotic and abiotic conditions. The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization-competition trade-offs. The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.

Modeling Invasive Plant Spread: The Role of Plant-Environment Interactions and Model Structure

Alien plants invade many ecosystems worldwide and often have substantial negative effects on ecosystem structure and functioning. Our ability to quantitatively predict these impacts is, in part, limited by the absence of suitable plant-spread models and by inadequate parameter estimates for such models. This paper explores the effects of model, plant, and environmental attributes on predicted rates and patterns of spread of alien pine trees (Pinus spp.) in South African fynbos (a mediterranean-type shrubland). A factorial experimental design was used to: (1) compare the predictions of a simple reaction-diffusion model and a spatially explicit, individual-based simulation model; (2) investigate the sensitivity of predicted rates and patterns of spread to parameter values; and (3) quantify the effects of the simulation models spatial grain on its predictions. The results show that the spatial simulation model places greater emphasis on interactions among ecological processes than does the reaction-diffusion model. This ensures that the predictions of the two models differ substantially for some factor combinations. The most important factor in the model is dispersal ability. Fire frequency, fecundity, and age of reproductive maturity are less important, while adult mortality has little effect on the models predictions. The simulation models predictions are sensitive to the models spatial grain. This suggests that simulation models that use matrices as a spatial framework should ensure that the spatial grain of the model is compatible with the spatial processes being modeled. We conclude that parameter estimation and model development must be integrated pro- cedures. This will ensure that the models structure is compatible with the biological pro- cesses being modeled. Failure to do so may result in spurious predictions.

Using a dynamic landscape model for planning the management of alien plant invasions.

Biological invasions are widespread phenomena that threaten the integrity and functioning of natural ecosystems. In this paper we develop a model that is designed to be a decision-making tool for planning and managing alien plant control operations. Most decision tools adopt a static approach; in this application we integrate a dynamic simulation model of alien plant spread with decision-making tools commonly used for reserve design. The model is a landscape-scale implementation of a fine-grained individual- based spread model. We first describe the scaling up of the fine-scaled model into a landscape extent model. Comparisons between the fine-grained local-scale and coarse-grained land- scape-scale model show that the scaling-up process did not introduce significant artifacts into the behavior of the model. The landscape model is used to explore a range of strategies and funding schedules for clearing alien plants. These strategies are evaluated in terms of the cost of the clearing operation, the time it takes to eradicate the plants, and the impact the plants have on three components of native plant diversity (all species, rare and threatened species, endemic species). Clearing strategies that prioritize low-density sites dominated by juvenile alien plants proved to be the most cost effective. Strategies that used information on the distribution of plant diversity were not much more expensive than the most cost- effective strategy, and they substantially reduced the threat to native plant diversity. De- laying the initiation of clearing operations had a strong effect on both the eventual costs of the clearing operation and the threat to native plant diversity. We conclude that the integration of dynamic modeling with decision-making tools, as illustrated here, will be useful for the management of biodiversity under global change.