Library Dissertation Showcase

Using species distribution models for a data-rich keystone species, Pteropus poliocephalus, to inform conservation decisions for a data-poor keystone species, Eidolon helvum

  • Year of Publication:
  • 2023

Global environmental change is altering species distributions around the world, causing serious impacts on ecosystems and their functioning, especially when keystone species are affected. A keystone species can be defined as a species that has a disproportionately large impact on ecosystem functioning, structure, and health, making it an important candidate for conservation. However, many keystone species in data-poor regions lack effective conservation policies due to insufficient monitoring and data collection. This means that many already vulnerable species are put at further risk of distribution and population decline. Furthermore, such disparity between data-rich and data-poor keystone species conservation has led to the development of a large knowledge gap within the ecological sciences.

This research aims to bridge this knowledge gap by showing how species distribution models (SDMs) for a data-rich keystone can be used to inform conservation decisions for a data-poor keystone. The chosen species for this research were the Australian (data-rich) Pteropus poliocephalus (grey-headed flying fox) and sub-Saharan (data-poor) Eidolon helvum (African straw-coloured fruit bat). SDMs for the flying fox were created using the BCCVL modelling wizard within the EcoCommons platform and combined current and future climate data, land-use and vegetation data, and RCP 4.5 and 8.5 scenarios for the present, 2045 and 2085 to illustrate the range shifts under different climatic conditions. Models ran using the Artificial Neural Network (ANN), Random Forest (RF), and Maximum Entropy (MaxEnt) algorithms.

Results indicated that the greatest reductions in habitat suitability occurred when land-use was incorporated and during RCP 8.5 scenarios, especially for 2085. However, evaluation of model performance indicated that the RF algorithm underpredicted habitat suitability, while MaxEnt was least accurate, receiving an Area Under the Curve (AUC) score of only 0.5, while the other algorithms performed at 0.9-1.0. Overall, however, the results showed important range shifts that would likely apply to the African straw-coloured fruit bat and gave the opportunity to show how proxy SDMs could be applied to conservation policy. This was achieved by drawing on the ecological similarities between the two bat species, alongside their threats, and using existing grey-headed flying fox conservation policy to make suggestions on how the African straw-coloured fruit bat could be protected.

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