Prioritising conservation areas in Southeast Asia: highlighting zoological biodiversity hotspots and assessing spatiotemporal patterns of the felids guild
Keywords: Sunda
Sumatra
Borneo
biodiversity
Southeast Asia
landscape ecology
hotspot

Biodiversity is declining globally, at rates comparable with the five previous mass extinctions of Earth’s history. The causes of the current sixth mass extinction are to be found in the effects of human population and economic growth on natural resources. Growth of human population determines increases of the resources required, which are provided by anthropic enterprises. These, in turn, lead to direct exploitation of habitats and species, land use degradation and climate change. Southeast Asia is one of most biodiverse region, but it has also experienced extraordinary human population and economic growths in the last decades, leading to pristine habitat loss and deforestation rates among the highest globally. Conservation actions are therefore urgent but, for them to have effective outcomes, and considering the general paucity of resources for conservation, it is important to prioritise the most suitable areas in order to provide precise and reliable indications about where conservation actions can be most effective. Southeast Asia encompasses four of the biodiversity hotspots identified by Myers on the basis of endemism and natural habitat loss. Specifically, biodiversity hotspots have originally been identified as those global areas containing at least 0.5% or 1,500 of the World’s vascular plant species as endemic and that have lost at least 70% of their primary vegetation. However, these hotspots are at a scale too coarse for prompting direct and focused conservation actions. Instead, it is necessary to highlight, within the hotspots, the regions that effectively harbour the highest numbers of species richness, which have been defined as “hotspots within hotspots” or “micro-hotspots”. In Chapter 2 of my thesis, by assessing the multivariate relationships between a vast suite of Southeast Asian terrestrial vertebrates and ecologically meaningful habitat factors, I mapped the most biodiverse areas in mainland Southeast Asia. Similarly, in Chapter 3 I evaluated the multivariate relationships with habitat factors for species sampled in the islands of Borneo and Sumatra. I highlighted that, within this vast area that encompasses four biodiversity hotspots and that extends for almost 5,000,000 km2, only a few smaller regions are characterised by high levels of biodiversity, and even smaller biodiverse areas are scattered throughout disturbed landscapes. Large, biodiverse regions are mostly associated with pristine landscapes, while anthropic factors were the main predictors of low levels of species richness. In the continental extents of Southeast Asia, the most biodiverse areas occurred in the mountainous region of Southwest China and in the Thai-Malay Peninsula, both characterised by an extent ranging between 250 and 300 km2, providing a more precise distribution of the terrestrial vertebrate biodiversity within the original biodiversity hotspots that, otherwise, have a total extent of almost 3,500,000 km2 in the mainland. In Borneo the areas of highest terrestrial vertebrate richness have been mapped mainly in the Malaysian state of Sabah, and in Sumatra along the Barisan Mountains, with an extent, respectively, of approximately 180 km2 and 150 km2, within the Sundaland biodiversity hotspot characterised by an area of 1,500,000 km2. Then, focusing solely on the medium- and small-sized felid species, of which Southeast Asia represents a centre of endemism, in Chapter 4 I tested different algorithms to model habitat suitability for this often understudied group of species. Then, I compared the differences and the predictive performances of the varying modelling algorithms, finding that there were large and systematic differences in the modelled probability layers, irrespective of the underlying ecological relationships, but driven by the choice of the algorithm. In Chapter 5, by using the most supported model for each species modelled in the previous chapter, I produced multi-species predictive surfaces for Southeast Asian small felids. I highlighted that the most suitable habitats for this group of species showed often pronounced differences with the hotspots for terrestrial vertebrate biodiversity modelled in the first two chapters of this thesis, likely due to the fact that small felids are more specialised than the pool of species used to model biodiversity hotspots, and they require larger and more intact habitats. In mainland Southeast Asia, I predicted the hotspots for smaller felids to occur in the Northern Forest Complex of Myanmar, and in the extensive forested area between Myanmar and Laos. In Borneo, the most suitable habitats for small felids occurred in the central highlands of the island, while in Sumatra it occurred along the Barisan Mountains. Together, my findings provide a useful tool to help preserving the unique and endangered Southeast Asia biodiversity. By highlighting the main habitat factors driving the simultaneous presence of terrestrial vertebrate biodiversity, as well as the habitat suitability for individual species of small felids, I provided useful insights for habitat restoration and protection. Additionally, by mapping the areas harbouring the highest numbers of terrestrial vertebrate, as well as the most suitable habitats for small felids, I provided a means for guiding prioritisation of conservation areas. Considering the current policy discussion on protection of habitats and species (e.g., the 30 by 30 target), my thesis could represent an invaluable tool to guide conservation actions towards more efficient and effective strategies aimed at maximising the protected biodiversity. The differences between habitat suitability models for small felids dependent on the algorithm applied that I highlighted, however, suggest conservationists to apply and test different modelling frameworks, finding the one that is more appropriate to the specific context.