Altered ignition catchments threaten a hyperdiverse fire-dependent ecosystem.

Human activities affect fire in many ways, often unintentionally or with considerable time-lags before they manifest themselves. Anticipating these changes is critical, so that insidious impacts on ecosystems, their biodiversity and associated goods and services can be avoided, mitigated or managed. Here we explore the impact of anthropogenic land cover change on fire and biodiversity in adjacent ecosystems on the hyperdiverse Cape Peninsula, South Africa. We develop a conceptual framework based on the notion of an ignition catchment, or the spatial extent and temporal range where an ignition is likely to result in a site burning. We apply this concept using fire models to estimate spatial changes in burn probability between historical and current land cover. This change layer was used to predict the observed record of fires and forest encroachment into fire-dependent Fynbos ecosystems in Table Mountain National Park. Urban expansion has created anthropogenic fire shadows that are modifying fire return intervals, facilitating a state shift to low-diversity, non-flammable forest at the expense of hyperdiverse, flammable Fynbos ecosystems. Despite occurring in a conservation area, these ecosystems are undergoing a hidden collapse and desperately require management intervention. Anthropogenic fire shadows can be caused by many human activities and are likely to be a universal phenomenon, not only contributing to the observed global decline in fire activity but also causing extreme fires in ecosystems where there is no shift to a less flammable state and flammable fuels accumulate. The ignition catchment framework is highly flexible and allows detection or prediction of changes in the fire regime, the threat this poses for ecosystems or fire risk and areas where management interventions and/or monitoring are required. Identifying anthropogenic impacts on ignition catchments is key for both understanding global impacts of humans on fire and guiding management of human-altered landscapes for desirable outcomes.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Defining functional biomes and monitoring their change globally.

Biomes are important constructs for organizing understanding of how the worlds' major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30-year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture-limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function.

Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa.

The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree- and grass-dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2 . Many sites where currently both tree-dominated and grass-dominated biomes are possible become deterministically tree-dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts.

Tree allometries reflect a lifetime of herbivory in an African savanna.

Theories of plant allometry provide a general description of allometric scaling that is supposedly applicable across a wide array of environmental conditions. Scaling theories, however, ignore disturbances such as herbivory in their derivation. Here we examine the influence of herbivores on the scaling of height and diameter of two common African savanna tree species. Using Bayesian piecewise regressions, we show that herbivores modify tree allometry. We also show that the pattern of allometric modification contains information regarding herbivore foraging behavior and the resultant alteration of plant architecture. Interpreting realized allometries in the light of theoretical predictions based on assumptions of zero disturbances may help reveal the degree of herbivore impacts. However, predictions of plant form and function that fail to include disturbances such as herbivory may struggle to find general applicability.

The future distribution of the savannah biome: model-based and biogeographic contingency.

The extent of the savannah biome is expected to be profoundly altered by climatic change and increasing atmospheric CO2 concentrations. Contrasting projections are given when using different modelling approaches to estimate future distributions. Furthermore, biogeographic variation within savannahs in plant function and structure is expected to lead to divergent responses to global change. Hence the use of a single model with a single savannah tree type will likely lead to biased projections. Here we compare and contrast projections of South American, African and Australian savannah distributions from the physiologically based Thornley transport resistance statistical distribution model (TTR-SDM)-and three versions of a dynamic vegetation model (DVM) designed and parametrized separately for specific continents. We show that attempting to extrapolate any continent-specific model globally biases projections. By 2070, all DVMs generally project a decrease in the extent of savannahs at their boundary with forests, whereas the TTR-SDM projects a decrease in savannahs at their boundary with aridlands and grasslands. This difference is driven by forest and woodland expansion in response to rising atmospheric CO2 concentrations in DVMs, unaccounted for by the TTR-SDM. We suggest that the most suitable models of the savannah biome for future development are individual-based dynamic vegetation models designed for specific biogeographic regions.This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'.