Figure - available from: Journal of Applied Ecology
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The experimental design of the project. (a) The 24 study landscapes (numbered black circles on the map) located in the Upper Murray region, shown on the map of Australia in red. Landscapes outside of the fire footprint are control landscapes. (b) Single landscapes from within the fire grounds highlighting areas with greater extents of unburnt, high severity and fire severity diversity, from top to bottom. Black dots within the landscapes show the location of camera trap sites. (c) Figures showing the variation in the proportional extent of unburnt areas, high severity fire and the amount diversity of fire severity classes (pyrodiversity) across all landscapes.
Source publication
Climate change is altering fire regimes globally, leading to an increased incidence of large and severe wildfires, including gigafires (>100,000 ha), that homogenise landscapes. Despite this, our understanding of how large, severe wildfires affect biodiversity at the landscape scale remains limited.
We investigated the impact of a gigafire that occ...
Citations
... PC4 is the most important predictor among the recovery level predictors. Ref. [54] showed and revealed that species richness is highly needed to predict resilience after wildfires occur in that ecosystem. Moreover, our observation of better recovery rates in mid-altitude areas is supported by [55], who argue that species assemblages in mid-altitude areas tend to recover faster because microclimatic conditions are conducive. ...
This study investigated post-fire vegetation recovery in Algeria’s Tenira forest using statistical traits (PCA), RFM, and LANDIS-II spatial analysis. The dataset included satellite imagery and environmental variables such as precipitation, temperature, slope, and elevation, spanning over a decade (2010–2020). Tenira forest is composed of Mediterranean species (36.5%); the biological types encountered are dominated by therophytes (39.19%). Ninety fire outbreaks were recorded, resulting in a loss of 1400.56 ha of surface area. Following the PCA results, precipitation, temperature, slope, and elevation were the main drivers of recovery (PC1 explained 43% alone, with the first five principal components accounting for 90% of observed variance, reflecting significant environmental gradients). Based on these components, an RFM predicted the post-fire recovery with an overall accuracy of 70.5% (Cost-Sensitive Accuracy), Quantity Disagreement of 3.1%, and Allocation Disagreement of 76%, highlighting spatial misallocation as the primary source of errors. The evaluation also identified PC4 (species richness) and PC3 (elevation) as significant predictors, collectively accounting for >50% of the variation in post-fire recovery. In the spatial analysis using LANDIS-II, the growth of vegetation, mainly in mid-altitude areas, was shown to be stronger, with the species consisting of those areas being more diverse. As a result, it demonstrated the connection between species richness and recovery capability. These findings can be useful in developing a management and development strategy, as well as proposing actions for species recovery after fire, such as the construction of firebreaks or the introduction of fireproof species, to make the forest more resistant to weather changes in Mediterranean ecosystems.
