The link between ‘fire mosaics’ and persistence of animal species is part of a prominent ecological/land management paradigm. This paradigm deals largely with the effects of fire on animals on the basis of individual events. The universality of the paradigm can be questioned on a variety of grounds, a major deficiency being the inability to deal with quantitative effects of recurrent fire (the fire regime). A conceptual model of fire-related habitat elements is proposed for exploration of a continuum of species/habitat/landscape/fire regime combinations. This approach predicts that the dependence of species on fire-mediated habitat heterogeneity will be highly variable and strongly context-dependent. A spatially explicit simulation model was used to examine the persistence of malleefowl (Leipoa ocellata) in a specific landscape/habitat context where dependence on fire-mosaics should be high. Results suggest that persistence of L. ocellata populations will be dependent on intervention using small patchy fires but that there is an optimum rate of intervention. Results were sensitive to spatial pattern of prescribed fire, landscape type (topography) and probability of wildfire. Underlying effects of the fire-interval distribution (the ‘invisible’ mosaic) on plant species and habitat account for these results. A management emphasis on species/landscape context and awareness of the ‘invisible’ mosaic is advocated.

Summary1. Plant functional types (PFTs) are groups of species sharing traits that govern their mechanisms of response to environmental perturbations such as recurring fires, inundation, grazing, biological invasions and global climate change. The key components of a PFT approach are an underlying model of vegetation dynamics for a given system and a classification of functional types based on traits deduced from key processes in the model. 2. Prediction and generalization underpin the potential utility of the PFT approach for understanding ecosystem behaviour. For PFTs to be useful in ecosystem management, they (in concert with their underlying model) must reliably predict vegetation change under given environmental scenarios and they must produce robust generalizations across the species that are classified and the range of environments in which they occur. 3. The efficacy of plant functional types has been explored using various approaches in a wide range of ecosystems. However, very few studies have tested the accuracy and generality of PFT predictions against vegetation changes observed empirically over medium to long time scales. 4. We applied this approach to examine the predictive accuracy and generality of a PFT classification and an associated model of vegetation dynamics for a fire-prone, species-rich wet heathland in south-eastern Australia. We assigned each species to one of six PFTs derived using a deductive approach based on the vital attributes scheme. We measured their initial abundance at a set of sample sites distributed across local environmental gradients. We used the PFT traits and processes in the underlying model to predict qualitative changes in abundance in response to a fire regime scenario observed at the sample sites during a subsequent period of 21 years. We then re-surveyed the sample sites to compare predictions with observed changes in abundance. 5. The PFTs and their underlying model produced an accurate prediction of average vegetation responses over the 21-year period. The majority of species within each PFT exhibited the predicted response and few species had strongly opposing responses in different environments. However, not all species within a PFT underwent the predicted direction of change, and responses of individual species were not uniform across the environmental gradients. 6. Synthesis . We conclude that plant functional types based on vital attributes are very useful tools for prediction and generalization in ecosystem management, although interpretations need to be tempered by the fact that PFTs may not accurately predict responses of all species across all environments.

The influence of plant traits on forest fire behaviour has evolutionary, ecological and management implications, but is poorly understood and frequently discounted. We use a process model to quantify that influence and provide validation in a diverse range of eucalypt forests burnt under varying conditions. Measured height of consumption was compared to heights predicted using a surface fuel fire behaviour model, then key aspects of our model were sequentially added to this with and without species-specific information. Our fully specified model had a mean absolute error 3.8 times smaller than the otherwise identical surface fuel model (p < 0.01), and correctly predicted the height of larger (≥1 m) flames 12 times more often (p < 0.001). We conclude that the primary endogenous drivers of fire severity are the species of plants present rather than the surface fuel load, and demonstrate the accuracy and versatility of the model for quantifying this.

The probability of large-fire (≥1000 ha) ignition days, in the Sydney region, was examined using historical records. Relative influences of the ambient and drought components of the Forest Fire Danger Index (FFDI) on large fire ignition probability were explored using Bayesian logistic regression. The preferred models for two areas (Blue Mountains and Central Coast) were composed of the sum of FFDI (Drought Factor, DF = 1) (ambient component) and DF as predictors. Both drought and ambient weather positively affected the chance of large fire ignitions, with large fires more probable on the Central Coast than in the Blue Mountains. The preferred, additive combination of drought and ambient weather had a marked threshold effect on large-fire ignition and total area burned in both localities. This may be due to a landscape-scale increase in the connectivity of available fuel at high values of the index. Higher probability of large fires on the Central Coast may be due to more subdued terrain or higher population density and ignitions. Climate scenarios for 2050 yielded predictions of a 20–84% increase in potential large-fire ignitions days, using the preferred model.

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Aim Stochastic threats such as disease outbreak, pollution events, fire, tsunami and drought can cause rapid species extinction and ecosystem collapse. The ability of a species or ecosystem to persist after a stochastic threat is strongly related to the extent and spatial pattern of its geographical distribution. Consequently, protocols for assessing risks to biodiversity typically include geographic range size criteria for assessing risks from stochastic threats. However, owing in part to the rarity of such events in nature, the metrics for assessing risk categories have never been tested. In this study, we investigate the performance of alternative range size metrics, including the two most widely used, extent of occurrence (EOO) and area of occupancy (AOO), as predictors of ecosystem collapse in landscapes subject to stochastic threats. Methods We developed a spatially explicit stochastic simulation model to investigate the impacts of four threat types on a dataset of 1350 simulated geographic distributions of varying pattern and size. We empirically estimated collapse probability in response to each threat type and evaluated the ability of a set of spatial predictors to predict risk. ResultsThe probability of ecosystem collapse increased rapidly as range size declined. While AOO and EOO were the most important predictors of collapse risk for the three spatially explicit threats included in our model (circle, swipe and cluster), core area, patch density and mean patch size were better predictors for edge effect threats.Main conclusions Our study is the first to quantitatively assess the range size metrics employed in biodiversity risk assessment protocols. We show that the current methods for measuring range size are the best spatial metrics for estimating risks from stochastic threats. Our simulation framework delivers an objective assessment of the performance of hitherto untested but widely used measures of geographic range size for risk assessment.