Addressing data gaps on the IUCN Red List

It is probably time that I introduce my PhD project, and explain my interest/obsession on Data Deficient and poorly-known species. I am mostly writing this for my family and friends, who are continually confused about my professional endeavours!

In light of rapid global change, the 12^th target of the Strategic Plan of the Convention on Biological Diversity states that by “2020 the extinction of known threatened species has been prevented and their conservation status, particularly of those most in decline, has been improved and sustained”. The IUCN Red List of Threatened Species is the foremost inventory for classifying species extinction risk at a global scale, and is key to monitoring progress towards this target. However, 1 in 6 of the 65,000+ species assessed by the IUCN are listed as Data Deficient due to lack of information on their taxonomy, geographic distribution, population status and/or threat processes. Data Deficient species are of great conservation concern as they contribute to considerable uncertainty in estimates of threat (Figure 1). Additionally, genuinely threatened Data Deficient species may be neglected by conservation programmes due to their uncertain extinction risk status.

My PhD focuses on the effect of Data Deficient species on global conservation prioritization. Do data gaps influence perceived patterns of extinction risk, and if so, how can we reduce this uncertainty cost-effectively?

Figure 1. Uncertainty in estimates of threat conferred by Data Deficient species across taxonomic groups. Numbers in brackets indicate the number of species in IUCN Red List assessments.

Invertebrates in particular show very high levels of data deficiency, with 35% of dragonflies, 49% of freshwater crabs and 21% of crayfish listed as Data Deficient. The first chapter of my thesis (Bland et al. 2012) concentrated on the effects of data uncertainty on patterns of threat using crayfish, freshwater crabs and dragonflies as model taxa. I showed that in freshwater invertebrates, data uncertainty considerably affects patterns of threat at continental scales. I also concluded that given the current levels of data uncertainty, the relative importance of biological characteristics and threatening processes in driving extinctions in freshwater invertebrates could not be determined. Finally, I recommended that Data Deficient species should be given high research priority to determine their true conservation status.

Lepilemur microdon, a Data Deficient lemur predicted to be at risk of extinction

In the second chapter of my thesis (Bland et al. in prep), I predicted the likely conservation status of Data Deficient mammals. Mammals are well-studied and exhibit a fairly low prevalence of data deficiency (15%), hence provide a good starting point for developing predictive models of extinction risk (Figure 2). I compared the ability of seven Machine Learning algorithms to determine the threat status of data-sufficient species using taxonomic, life-history, geographical and anthropogenic threat information. Machine Learning tools showed very high ability to correctly identify threatened species (up to 94%), and ability to correctly identify geographical centres of threatened species richness. Applying this model to Data Deficient species, I predicted 313 out of 493 Data Deficient species (64%) to be at risk, increasing the estimated proportion of threatened terrestrial mammals from 22% to 27% globally. I found that regions predicted to contain large numbers of threatened Data Deficient species were already conservation priorities, but showed considerably higher levels of species imperilment than previously recognized. I concluded that unless directly targeted for monitoring and conservation, species classified as Data Deficient were likely to slide towards extinction unnoticed.

Figure 2. Global distribution of Data Deficient terrestrial mammals.

Given the limited financial resources available for conservation, data should be collected in the most cost-efficient way to inform conservation decisions. My current work focuses on incorporating economic considerations into the prioritization of Data Deficient species. I estimated that monitoring and re-assessing all 10,673 Data Deficient species according to IUCN criteria would cost a minimum of USD 150 million. Predicting extinction risk from species traits and geographical information could provide a cost-effective approach for reducing uncertainty in global risk estimates. Models could also enable the systematic prioritization of species for monitoring according to predicted extinction risk, evolutionary diversity, and cost of monitoring.

Finally, my broader research interests include the interaction between human knowledge, species traits and threatening processes. I am particularly interested in patterns of species discoveries, rediscoveries and extinctions. Areas containing Data Deficient species have been shown to contain more recently described species than expected by chance, suggesting that these sites are indicative of spatial knowledge deficiency and might hold many undiscovered species. Accounting for Data Deficient species could help direct biodiversity inventories, and enable the incorporation of the world’s poorly-known and undiscovered species into conservation planning.

Bland LM, Collen B, Orme CDL, Bielby J (2012) Data uncertainty and the selectivity of extinction risk in freshwater invertebrates. Diversity and Distributions 18(12) 1211-1220