Assessing the changing state of global biodiversity efficiently and robustly demands an understanding of the species and locations for which we need new data. This project will explore whether approaches may allow us to broaden and accelerate our assessments of biodiversity trends in extinction risk and population abundance. Based at University College London (London, UK) with secondments to the Zoological Society of London (London, UK; 10.8 months).
This is Project 9 out of 15 PhD positions currently available as part of the Inspire4Nature training programme. Deadline for applications: 16 April 2018 (midnight, Brussels time). We are no longer accepting applications to this project. Have you submitted an application and are you wondering what happens next? Check this page!
Biodiversity is in decline. However, most of our understanding about the status of species and habitats comes from a relatively narrow set of genera or regions. While we know that vertebrate species are increasingly at risk of extinction, for most species we have much sparser information. Sampled approaches, such as the Sampled Red List Index (Baillie et al. 2008), use existing information to try to estimate how many species we need to assess to effectively determine the trends in extinction risk for larger groups (e.g. reptiles; Böhm et al. 2013). However, even this approach requires considerable effort in generating and collating species assessments and may not be sustainable.
Other indices suffer from similar problems. The data underlying the Living Planet Index are geographically and taxonomically biased (McRae et al. 2017) and may be limited for groups of interest. Understanding how many populations (or species) are required to make robust estimates of the trends for a particular group or region is critical. Applying sampled approaches to indices such as the Living Planet index will address this question and reveal the potential of sampled abundance indices.
Further, novel approaches are now attempting to predict extinction risk (Bland et al. 2016) or abundance trends for species or populations. In this project, we aim to combine these approaches, exploring the application of new sampling approaches for global biodiversity indicators and attempting to understand for which species such predictive assessments are appropriate, and for which we need further expert information. Understanding how predictability relates to species traits or geographical factors may allow us to revise our sampling regime to focus on those groups that will most usefully inform the index.
The main outcomes of this project will be the development of new approaches to assessing little known groups of species – assessing both their extinction risk and average trends in abundance. These approaches will contribute to the development of both the Living Planet Index and Sampled Red List Index, as well as other biodiversity indicators.
- Böhm, M et al. (2013) The conservation status of the world’s reptiles, Biological Conservation. 157, 372-385
- Boakes, EH, et al. (2010) Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data. PLOS Biology 8(6)
- Baillie, JEM et al. (2008) Toward monitoring global biodiversity. Conservation Letters, 1: 18–26
- Bland, L.M, et al. (2015) Predicting the conservation status of data-deficient species. Conservation Biology, 29: 250–259
- McRae L, et al. (2017) The Diversity-Weighted Living Planet Index: Controlling for Taxonomic Bias in a Global Biodiversity Indicator. PLoS ONE 12(1)
Institutional context and supervision
The PhD student will be hired by the University College London (UCL), one of the world’s top Universities. S/he will be based at the Centre for Biodiversity and Environment Research (CBER), undertaking research at the interface between biodiversity and environmental change, and actively engaged in communicating new research and relating findings to policy. The academic supervisor of this project is Piero Visconti, Research Fellow at UCL-CBER and at ZSL-IoZ.
This project is in close collaboration with the Zoological Society of London (ZSL), where the student spend 10.8 months in secondments. ZSL runs conservation programmes worldwide to conserve wild animals and their natural habitats, working with local communities to conserve their environment and promote sustainability. The student will work closely with the ZSL’s Institute of Zoology, where s/he will be supervised by Monika Bohm (post-doctoral researcher) in collaboration with Robin Freeman (Head of Indicators and Assessments Unit), and also in collaboration with ZSL’s Conservation Programmes, in particular with Mike Hoffmann (Head of Global Conservation Programmes).
Candidates must meet all the general eligibility conditions applicable to all Inspire4Nature PhD positions, as described under “check if you are eligible” in this page. In particular: candidates cannot have resided or carried out their main activity (work, studies, etc.) in the United Kingdom for more than 12 months within the previous 3 years, and must be early-stage researchers (no PhD yet, within the first 4 years of their research careers). In addition:
Required for this position:
- We seek an enthusiastic and focussed candidate with a strong interest in conservation and ecology.
- An interest in the understanding extinction risk and population abundance trends and the drivers of trend patterns.
- Strong analytical background, ideally with experience in statistics and R or a similar programming language.
- Excellent data handling skills and confident working with large datasets.
- A minimum of an upper second-class UK Bachelor’s degree in an appropriate subject or an overseas qualification of an equivalent standard, or a recognised Master’s degree. See here for a database of equivalent degrees.
- Good English knowledge. Candidates being offered a scholarship whose first language is not English will have to provide written evidence of good English knowledge prior to enrolling to UCL. Further information can be found on UCL English language requirements.
Desirable for this position:
- Experience with spatial data analysis (in ArcGIS, Python and/or R), GIS, machine learning.
Shortlisted candidates will be invited for an interview planned for the 4 - 5 June; please keep these dates open.
For any questions regarding application procedures, check this page first. If you cannot find your answer there, contact us. For any questions regarding the scientific content and institutional context of the PhD, contact Dr. Piero Visconti.
Ready to apply?
For the instructions on how to prepare and submit your application, go to this page.
Only applications that are complete, in English, that respect the instructions in this page and that have been submitted before the deadline (16 April 2018) will be considered eligible.
We are no longer accepting applications to this project. Have you submitted an application and are you wondering what happens next? Check this page!