Gaps and opportunities for estimating species extinction risk

Project Details

This CASE project is supported by ZSL

Key Questions

1. Which species have poorer data availability of conservation state information and where should we target to update this information

2. How can we model species’ key conservation state information and extinction risk


In order to prioritise what species to conserve, it is important to understand the degree to which species are threatened with extinction. However, for many species, the fundamental species data that underpin their IUCN Red List assessments (measure of species extinction risk) are out of date, leading to potentially inaccurate assessments of extinction risk, or missing, and therefore extinction risk cannot be estimated. This results in some highly threatened species receiving little or no conservation attention (due to not being recognised as threatened), or conservation attention being directed towards less threatened species (Collar 1998). The increasing availability of a range of global datasets including remote sensing layers, citizen science and improved species knowledge could provide a useful way to estimate species extinction risk and reduce costs and resources needed for Red List assessments (Collen et al. 2016). There have been some efforts to infer and model species extinction risk using land-cover change, human impact and species biology (Buchanan et al. 2008; Di Marco et al. 2014; Tracewski et al. 2016 Santini et al. 2019), but these have had limited uptake into the Red List

Aims of the Project

To identify species with out-of-date or missing information in the Red List, the underlying patterns and biases in species’ Red List datasets, and use a combination of additional datasets and modelling to complete knowledge gaps and predict species’ current risk of extinction, thereby identifying priority species for reassessment and facilitate more regular monitoring of species

Project Description

Conservation funds are limited and therefore must be prioritised in order to limit species extinctions and the loss of biodiversity (Brooks et al. 2006). When determining which species to prioritise for conservation, it is important to understand the degree to which species are threatened with extinction. For many species, the key information that underpin their estimated extinction risk (and so IUCN Red List Assessment) are out of date and for many is absent, leading to potentially over and under estimations of extinction risk, or for some species, unable to estimate extinction risk at all. The costs of updating the Red List are ever increasing with the increasing number of species assessed in the Red List. It is therefore important to determine ways to generate estimates of extinction risk for all species, and to update Red List estimates of extinction for all species more frequently. This however is challenging due to a range of reasons including the costs of collecting the data regularly for >128,000 species and to undertake and review the assessments. The increasing availability of large public datasets such as citizen science data and satellite imagery presents opportunities for both updating key species datasets and for modelling their extinction risk (Collen et al.2016). Past efforts have explored some of these areas (Buchanan et al. 2008; Di Marco et al. 2014; Tracewski et al.2016 Santini et al. 2019), but have not been consistently applied across taxa and not generally been taken up by the Red List. In this project, species with out-of-date or missing information in the Red List would be identified and patterns in knowledge gaps would be explored with the aim of identifying areas to target to update the information for key areas of neglect (in terms of taxonomy and geographically) which could yield the most efficient update to Red List data (in terms of number of species vs effort). A combination of available datasets and modelling would then be used to help complete knowledge gaps and predict species extinction risk, which could include sourcing and analysis of a range of datasets to estimate of key parameters needed to estimate extinction risk and to estimate extinction risk directly. This would be across a range of taxonomic groups, to explore how patterns of knowledge gaps and model effectiveness varies between taxa. The proposed project would be supervised by Associate Professor Rob Salguero-Gomez within the SalGo research group in Oxford’s Department of Zoology, with additional supervision from ZSL (CASE partner -TBC) and BirdLife International (the IUCN Red List Authority for Birds). The applicant would also work closely with the sRedList working group on a broader project to model species extinction risk.



Brooks, T.M., Mittermeier, R.A., da Fonseca, G.A., Gerlach, J., Hoffmann, M., Lamoreux, J.F., Mittermeier, C.G., Pilgrim, J.D. and Rodrigues, A.S., 2006. Global biodiversity conservation priorities. science, 313(5783), pp.58-61.


Buchanan, G.M., Butchart, S.H., Dutson, G., Pilgrim, J.D., Steininger, M.K., Bishop, K.D. and Mayaux, P., 2008. Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds. Biological Conservation, 141(1), pp.56-66.


Collar, N.J., 1998. Extinction by assumption; or, the Romeo Error on Cebu. Oryx, 32(4), pp.239-244.


Collen, B., Dulvy, N.K., Gaston, K.J., Gärdenfors, U., Keith, D.A., Punt, A.E., Regan, H.M., Böhm, M., Hedges, S., Seddon, M. and Butchart, S.H., 2016. Clarifying misconceptions of extinction risk assessment with the IUCN Red List. Biology letters, 12(4), p.20150843.


Di Marco, M., Buchanan, G.M., Szantoi, Z., Holmgren, M., Grottolo Marasini, G., Gross, D., Tranquilli, S., Boitani, L. and Rondinini, C., 2014. Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1643), p.20130198.


Di Marco, M., Collen, B., Rondinini, C. and Mace, G.M., 2015. Historical drivers of extinction risk: using past evidence to direct future monitoring. Proceedings of the Royal Society B: Biological Sciences, 282(1813), p.20150928.


Santini, L., Butchart, S.H., Rondinini, C., Benítez‐López, A., Hilbers, J.P., Schipper, A.M., Cengic, M., Tobias, J.A. and Huijbregts, M.A., 2019. Applying habitat and population‐density models to land‐cover time series to inform IUCN Red List assessments. Conservation Biology, 33(5), pp.1084-1093.


Tracewski, Ł., Butchart, S.H., Di Marco, M., Ficetola, G.F., Rondinini, C., Symes, A., Wheatley, H., Beresford, A.E. and Buchanan, G.M., 2016. Toward quantification of the impact of 21st‐century deforestation on the extinction risk of terrestrial vertebrates. Conservation Biology, 30(5), pp.1070-1079

Methods to be used

Data handling, statistical modelling, GIS/spatial analysis, remote sensing and modelling (of extinction risk and other variables)

Specialised skills required

Good quantitative skills, particularly in modelling and programming; knowledgeable about conservation science and the IUCN Red List

Please contact Rob Saguero-Gomez on if you are interested in this project