Lucie Bland

Curriculum Vitae:

• 2010: PhD student, Institute of Zoology and Imperial College London.
• 2009: Intern in the Species Programme, IUCN Headquarters, Gland (Switzerland).
• 2007-2010: BA (Hons) First Class, Biological Sciences, University of Oxford.
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Research Interests:

One of the major challenges of conservation science, in light of rapid global change, is how to accurately predict which species are more likely to go extinct. The IUCN Red List is the most well-known and widely used tool for assessing the conservation status of species at a global and regional level. However, if insufficient information exists to make an assessment then a species may be classified as Data Deficient (DD). My PhD project aims to identify and address the effects of data gaps (DD species) in the understanding of global patterns of threat. My general interests include spatial biodiversity modelling, risk analysis, decision-making under uncertainty and macroecology/macroevolution.

Figure 1. Uncertainty in threat estimates across IUCN Red List assessments. The histogrammes indicate the proportion of threatened species in a taxon if DD species are assumed to be as threatened as non-DD species; lower error bars indicate the proportion if no DD species were threatened; the upper error bars indicate the proportion if all DD species were threatened.

Invertebrates in particular show very high levels of data deficiency, with 35% of dragonflies, 49% of freshwater crabs and 21% of crayfish listed as DD. The first chapter of my thesis concentrates on the effects of data uncertainty on patterns of threat using crayfish, freshwater crabs and dragonflies as model taxa. I show that in freshwater invertebrates, data uncertainty considerably affects patterns of threat at sub-global scales. I also conclude that given the current levels of data uncertainty, the relative importance of biological characteristics and threatening processes in driving extinctions in freshwater invertebrates cannot be determined. Finally, I recommend that DD species should be given high research priority to determine their true conservation status.

In the second chapter of my thesis, I aim to predict the likely conservation status of DD mammals. Mammals are well-known taxa and exhibit a fairly low proportion of data deficiency (15%), hence provide a good starting point for developing predictive models of extinction risk. I have compared the ability of 6 machine-learning algorithms to determine the threat status of non-DD species using taxonomic, life-history, geographical and anthropogenic threat information. So far (03/2012), models show high predictive accuracy and ability to correctly identify threatened species. Preliminary results suggest that DD species could be disproportionately at threat compared to non-DD species, hence threats to mammals may have been underestimated globally. I would be interested in extending the models to predictions of conservation status under future scenarios of land-use and climate change, as well as predicting the conservation status of DD species in other taxa.

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Links:

IUCN Red List of Threatened Species .
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Supervisors:

Ben Collen (Institute of Zoology)
Jon Bielby (Institute of Zoology)
David Orme (Imperial College London)