I’m a Ph.D. fellow at the Global Mammal Assessment, Sapienza Università di Roma. I’m working in Key Biodiversity Areas.
My research interests include strategic conservation planning, spatial analysis, time series analysis and remote sensing.
PhD project: Where will further Key Biodiversity Areas be identified? A modelling approach to focus efforts
Life on Earth is facing a sixth mass extinction, and the main driver of biodiversity loss is habitat destruction. Area-based conservation has been proven vital in preventing species extinction and protecting habitats. However, the current network is not ecologically representative, diminishing the potential role of area-based conservation in reducing biodiversity loss. In this context, identifying new Key Biodiversity Areas (KBA) is essential. KBA are defined as 'sites contributing significantly to the global persistence of biodiversity'. Although the KBA approach is built on previous site-based conservation methodologies, the KBA identification process still has some challenges to overcome. With this PhD, I aimed to standardize, improve and facilitate the KBA identification process providing accurate and available biodiversity data for the identification of KBA and contributing to the KBA Guidelines for criterion E.
Area of Habitat (AOH) stands out as a crucial assessment parameter to identify KBA because it generally reduces the risk of commission error of range maps and is more available than other assessment parameters such as Area of Occupancy (AOO) or the number of mature individuals. AOH is 'the habitat available to a species, that is, habitat within its range'. The production of AOH maps requires an understanding of which habitats a species occurs in. Habitat associations are documented using the IUCN Habitats Classification Scheme. Unvalidated expert opinion is typically used to match habitat to land-cover classes, generating a source of uncertainty in AOH maps. In the first research chapter, Translating habitat classes to land cover to map Area of Habitat for terrestrial vertebrates, I developed a standardised, data-driven methodology to translate IUCN habitat classes into two land-cover maps using point locality data for mammals, birds, amphibians, and reptiles. I generated two translation tables, quantifying the strength of association between habitat and land-cover classes using the odd ratio values of logistic regression models. I calculated the association between habitat and land-cover classes as a continuous variable. However, to map AOH as binary presence or absence, it was necessary to apply an association threshold that can be chosen by the user according to the required balance between omission and commission errors. The data-driven translation provided greater standardisation, objectivity, and repeatability, and the model can be modified for regional examinations and different taxonomic groups.
In the second research chapter, Mapping Area of Habitat for the world's terrestrial birds and mammals, I produced an updated version of global AOH maps 5,481 terrestrial mammals and 10,651 terrestrial bird species. For 1,816 bird species defined by BirdLife International as migratory, I developed three AOH maps, one for the resident range, one for the breeding range and one for the non-breeding range. The maps have a resolution of 100 m. On average, AOH covered 66±28% of the range maps for mammals and 64±27% for birds. I used AOH maps to produce global maps of the species richness of mammals, birds, globally threatened mammals and globally threatened birds. These maps represent an increase in resolution compared with species richness maps produced using the IUCN range maps, helping to identify biodiversity hotspots accurately.
Having clear and standardised guidelines to identify KBA is as crucial for identifying KBA as having the most accurate high-quality data. The KBA identification process requires all users to apply the KBA Standard consistently. However, for KBA Criterion E, the guidelines are still incomplete as methods are still in development. Sites qualify as Key Biodiversity Areas (KBA) under KBA Criterion E if they have a very high irreplaceability value (>0.9 on a 0-1 scale) derived from a quantitative spatial prioritisation analysis. The irreplaceability of a site is determined by both the biodiversity found within it and the biodiversity contained in the other sites considered in the analysis. In the third research chapter, Evaluating Irreplaceability in KBA and the effects of the geographical scale, I explored the identification of KBA based on Criterion E in two geographical regions, South America and East Africa, for terrestrial mammals. In the Soth American analysis, I found that, on average, 47% of regionally irreplaceable planning units were not represented in country-level analyses, while country-level analyses mainly represented a subset of the regional ones. These results indicated that at the regional level, irreplaceability was driven by endemic species and species richness, while at the country level, exclusively by endemic species. For some African counties, the analysis produced very few or no highly-irreplaceable planning units. This could indicate that the current targets were too low for these countries. I concluded that the results obtained from the current formulation of Criterion E are affected by the geographical scale and region in which it is applied. The KBA Standards and Appeals Committee could take some actions to make the analysis more robust, such as setting the scale of application at the regional level and revising the targets. I expected that the research carried out in this PhD constitutes an advance of the current knowledge on KBA and served as a starting point for future developments.