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Exploitation of wild species globally: exploiting the internet for data

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  • Full or part time
    Prof J Scharlemann
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Biological diversity continues to decline, with direct exploitation identified as the second most important cause of observed changes in terrestrial biodiversity. Exploitation of living biomass has increased over 3-fold between 1970s and today and looks set to increase.

Despite the pressure posed by exploitation, quantitative studies of the exploitation of terrestrial wild species have until now been conducted at site level, e.g. individual settlements or protected areas. Without quantitative information on the distribution and magnitude of exploitation of terrestrial wild species, assessment of the importance of exploitation and guiding conservation actions, such as regulation of hunting, is impossible. Furthermore, exploitation likely impacts human wellbeing by disrupting ecosystem functions, e.g. the removal of seed dispersers affecting forest regeneration.

Combining datasets collated over the last few years by Scharlemann for Africa with text analysis tools developed by Weeds, we can offer a PhD project that will produce the first comprehensive global maps of the distribution and magnitude of exploitation of terrestrial and freshwater wild species. A challenge is finding the data in peer-reviewed and grey literatures, which will be overcome by using text analysis tools. These tools combine statistical methods to identify key words and phrases of interest in papers known to be relevant; machine translation to translate key words and phrases into other languages of interest; web-scraping technology to pull back from the internet more documents containing various combinations of key words and phrases and/or their translations; semi-automatic text classification algorithms to filter and categorise the identified documents; and multi-lingual named entity recognition techniques to identify portions of documents with relevant information.

The collected data will be analysed using spatial modelling techniques, ancillary datasets and GIS to identify drivers of global exploitation, and map the human exploitation ‘footprint’. Analyses will be performed at local, continental to global scales. Further, predictions of future exploitation pressure and which species may be affected will be modelled.

The student will be based in Jörn Scharlemann’s group in the School of Life Sciences, University of Sussex (http://www.sussex.ac.uk/lifesci/scharlemannlab/) and co-supervised by Dr Julie Weeds (http://www.sussex.ac.uk/profiles/116624). The School holds considerable expertise in evolutionary and conservation ecology (www.sussex.ac.uk/lifesci/ebe/), with emphasis on applying rigorous quantitative analyses to inform conservation policy. The student will receive excellent scientific training in statistical analysis, computer modelling, and conservation policy, will work within a dynamic group of students and early-career researchers, be part of the multi-disciplinary Sussex Sustainability Research Programme (www.sussex.ac.uk/ssrp), all embedded within the wonderful natural environment of the Brighton and Lewes Downs UNESCO biosphere, surrounded by the South Downs National Park, and minutes away from Brighton.

For enquiries about the project contact Jörn Scharlemann [Email Address Removed] and Julie Weeds [Email Address Removed]

How to apply:
Please submit a formal application using our online application system at http://www.sussex.ac.uk/study/phd/apply, including a CV, degree transcripts and certificates, statement of interest and names of two academic referees. On the application system use Programme of Study – PhD Biology

Funding Notes

This funded position covers Home / EU tuition fees and a stipend at standard UKRI rates.

Ideal candidates will have a strong background in ecology with additional experience in data science including statistical analysis and programming (python preferred); or a strong background in computer science with additional experience in ecology. Eligible candidates will have recently received an MSc and/or a First or high 2:1 BSc in a relevant subject. Candidates for whom English is not their first language will require an IELTS score of 6.5 overall, with not less than 6.0 in any section.

References

Brotherton S, Joyce CB & Scharlemann JPW (in press) Global offtake of wild animals from wetlands: critical issues for fish and birds. Hydrobiologia
Ingram DJ, Coad L, Abernethy KA, Maisels F, Stokes EJ, Bobo KS, Breuer T, Gandiwa E, Ghiurghi A, Greengrass E, Holmern T, Kamgaing TOW, Ndong Obiang A-M, Poulsen JR, Schleicher J, Nielsen MR, Solly H, Vath CL, Waltert M, Whitham CEL, Wilkie DS, Scharlemann JPW (2018) Assessing Africa-wide pangolin exploitation by scaling local data. Conservation Letters 11: e12389.
Ingram DJ, Coad LM, Collen B, Kümpel NF, Breuer T, Fa J, Gill D, Maisels F, Schleicher J, Stokes EJ, Taylor G & Scharlemann JPW (2015) Indicators for wild animal offtake: methods and case study for African mammals and birds. Ecology & Society 20(3): 40. DOI: 10.5751/ES-07823-200340.
Taylor GL, Scharlemann JPW, Rowcliffe M, Kümpel N, Harfoot M, Fa J, Melisch R, Milner-Gulland EJ, Bhagwat S, Abernethy K, Albrechtsen L, Allebone-Webb S, Brown E, Brugiere D, Colell M, Cowlishaw G, Crookes D, De Merode E, Dupain J, East T, Edderai D, Gill D, Greengrass E, Hodgkinson C, Ilambu O, Jeanmart P, Juste J, Linder J, MacDonald D, Noss A, Okorie PU, Okouyi V, Pailler S, Poulsen J, Riddell M, Schulte-Herbruggen B, Starkey M, Schleicher J, van Vliet N, Whitham C, Willcox A, Wright J & Coad LM (2015) Synthesising bushmeat research effort in West and Central Africa: a new regional database. Biological Conservation 181: 199-205.
Wang D, Weeds J & Comley I (2020) Improving Mental Health using Machine Learning to Assist Humans in the Moderation of Forum Posts. In Proceedings of the 13th International Conference on Health Informatics (HealthInf) Valletta, February 2020
Schmidt L, Weeds J & Higgins J (2020) Data Mining in Clinical Text: Transformers for Classification and Question Answering Tasks. In Proceedings of the 13th International Conference on Health Informatics (HealthInf) Valletta, February 2020

Related Subjects

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