CR2025_46 Assessing extinction risk for the African Baobab (Adansonia digitata L.) under climate change and habitat degradation.

Lead Supervisor: Aisling Devine, Department of Biosciences, Swansea University

Email: A.p.devine@swansea.ac.uk

Co-supervisors: Steve Bachman, Royal Botanic Gardens, Kew; Alice Haughan, Department of Sustainable Land Management, University of Reading; Miguel Lurgi Rivera, Department of Biosciences, Swansea University

The African baobab (Adansonia digitata L.) is an iconic tree that is ecologically, economically and culturally important. It has been identified as one of the most important trees to be conserved and domesticated in Africa. It is a keystone species and provides important ecosystem services, yet it is facing complex threats regarding climate change and habitat degradation and regeneration.

Baobabs grows in cultivated areas, where individuals are often preserved for economic importance. However, land use changes and agricultural expansion due to rising human populations have reduced baobab habitat. Additionally, recent concerns include over-harvesting of fruit for the “superfood” trade and excessive bark harvesting, both threatening tree health. Increasing droughts can lead to die-offs, as high internal water pressure is needed to keep trees standing; lack of water can cause trunk collapse and death, with recent reports of mature adult mortality during droughts. Concerns about seedling establishment and regeneration have also risen, as a lack of rejuvenation is linked to expanding human populations, land use change, infrequent rainfall, and high livestock numbers (Venter and Witkowski, 2013).

The IUCN Red list is used to examine the extinction risk for species, using a quantifiable framework to assess multiple aspects of a species to reliably estimate if a species is threatened or not.  However, despite the threats facing the baobab, it is a complex species to estimate extinction risk for through the IUCN Red List criteria and using current data alone on the geographic distribution the species would most likely be classed as “Least Concern”.

Some of the taxa most vulnerable to habitat loss and climate change are those with delayed maturation and low reproductive rates and low dispersal rates. As these traits limits a species capacity to recover from population declines and hinder their ability to colonize new suitable areas, that are already impacted by land use changes. (Sanchez et al., 2011).

Assessing extinction risk in long-lived, widely distributed species like the African baobab is challenging due to limited data on population change. To fully evaluate population trends, an alternative approach is to quantify habitat degradation and quality. Currently, this type of assessment has not been attempted for the African baobab. Additionally, climate change could significantly impact this species by decreasing available habitats and increasing mortality rates among mature individuals. Limited research exists on mapping population and habitat changes for the baobab under climate change and land use scenarios. Though recent work (Huang et al., 2024) has mapped large areas of adult baobabs in the Sahel, providing insights into baobab demographics, limited work exists on spatially examining habitat and land use change for baobabs continentally.

This project aims to use existing baobab occurrence data  alongside high resolution climate data, drought metrics, and a combination of existing high-resolution land cover data (e.g., https://catalogue.ceda.ac.uk/uuid/f107a4ce186844bb8adf8cd1f2f6d552/) and remotely sensed data (e.g., Sentinel 2) to map and quantify habitat and climate change over time within the baobab’s current distribution.  Species distribution modelling will then be employed to examine the existing range of the baobab and to identify future changes or decline in habitat suitability (Mancini et al., 2023), which will be used to estimate extinction risk and inform conservation. Additionally occupancy / metapopulation models will be applied to predict the spatial distribution of regeneration for the Baobab, to better understand current problems the species is facing with recruitment.

This work, in collaboration with the Royal Botanic Gardens, Kew and Botanic Gardens Conservation International (BGCI), will contribute to the IUCN Global Tree assessment, ensuring that the baobab’s conservation status reflects current environmental challenges and supports effective conservation efforts. This PhD project will address the following questions:

• Can the quantification of habitat and land use change across the range of the baobab tree be used to apply Red List criteria related to population reductions? If so, does this lead to a higher/different Red List classification?
• Using differing climate change scenarios and applying land use change data, can habitat suitability and population modelling be used to predict regeneration in the future?
• How can combining assessments of the baobab’s current extinction risk with its future regeneration potential and establishment help prioritize conservation efforts for areas suitable for baobab re-establishment?

Training opportunities: 

The student will receive comprehensive training aligned with IUCN Red List standards for assessing extinction risk of species. They will be trained in remote sensing techniques to quantify habitat change and assess species-specific spatial distributions. The student will have the opportunity to work with BGCI on the Red List for trees, gaining firsthand experience with an evidence-based conservation NGO. The student will be trained in population and species distribution modelling, enabling them to analyse species trends and apply models to real-world conservation challenges. This training will provide essential skills for quantifying extinction risk, understanding habitat dynamics, and addressing species conservation. 

Student profile:

The student should have a background in environmental science (such as ecology, biology, or physical geography) with a strong interest in plant ecology and conservation. They should have a solid understanding of plant biology and ecosystem function in relation to population establishment, environmental stressors, and mortality. The student should possess numerical and statistical analysis skills applicable to population modelling, along with a willingness to learn or expand their R coding skills. Ideally, the student will have experience with remote sensing data and its application to ecology and conservation or be familiar with these techniques with a readiness to learn.