Browsing by Author "Butchart, Stuart H. M."
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- Connectivity between countries established by landbirds and raptors migrating along the African–Eurasian flywayPublication . Guilherme, João L.; Jones, Victoria R.; Catry, Inês; Beal, Martin; Dias, Maria P.; Oppel, Steffen; Vickery, Juliet A.; Hewson, Chris M.; Butchart, Stuart H. M.; Rodrigues, Ana S. L.The conservation of long-distance migratory birds requires coordination between the multiple countries connected by the movements of these species. The recent expansion of tracking studies is shedding new light on these movements, but much of this information is fragmented and inaccessible to conservation practitioners and policy makers. We synthesized current knowledge on the connectivity established between countries by landbirds and raptors migrating along the African–Eurasian flyway. We reviewed tracking studies to compile migration records for 1229 individual birds, from which we derived 544 migratory links, each link corresponding to a species’ connection between a breeding country in Europe and a nonbreeding country in sub-Saharan Africa. We used these migratory links to analyze trends in knowledge over time and spatial patterns of connectivity per country (across species), per species (across countries), and at the flyway scale (across all countries and all species). The number of tracking studies available increased steadily since 2010 (particularly for landbirds), but the coverage of existing tracking data was highly incomplete. An average of 7.5% of migratory landbird species and 14.6% of raptor species were tracked per country. More data existed from central and western European countries, and it was biased toward larger bodied species. We provide species- and country-level syntheses of the migratory links we identified from the reviewed studies, involving 123 populations of 43 species, migrating between 28 European and 43 African countries. Several countries (e.g., Spain, Poland, Ethiopia, Democratic Republic of Congo) are strategic priorities for future tracking studies to complement existing data, particularly on landbirds. Despite the limitations in existing tracking data, our data and results can inform discussions under 2 key policy instruments at the flyway scale: the African–Eurasian Migratory Landbirds Action Plan and the Memorandum of Understanding on the Conservation of Migratory Birds of Prey in Africa and Eurasia.
- Measuring trends in extinction risk: a review of two decades of development and application of the Red List IndexPublication . Butchart, Stuart H. M.; Akçakaya, H. Resit; Berryman, Alex J.; Brooks, Thomas M.; Burfield, Ian J.; Chanson, Janice; Dias, Maria P.; Donaldson, John S.; Hermes, Claudia; Hilton-Taylor, Craig; Hoffmann, Mike; Luedtke, Jennifer A.; Martin, Rob; McDougall, Amy; Neam, Kelsey; Polidoro, Beth; Raimondo, Domitilla; Rodrigues, Ana S. L.; Rondinini, Carlo; Rutherford, Claire; Scott, Tom; Simkins, Ashley T.; Stuart, Simon N.; Vine, JemmaThe Red List Index (RLI) is an indicator of the average extinction risk of groups of species and reflects trends in this through time. It is calculated from the number of species in each category on the IUCN Red List of Threatened Species, with trends influenced by the number moving between categories when reassessed owing to genuine improvement or deterioration in status. The global RLI is aggregated across multiple taxonomic groups and can be disaggregated to show trends for subsets of species (e.g. migratory species), or driven by particular factors (e.g. international trade). National RLIs have been generated through either repeated assessments of national extinction risk in each country or through disaggregating the global index and weighting each species by the proportion of its range in each country. The RLI has achieved wide policy uptake, including by the Convention on Biological Diversity and the United Nations Sustainable Development Goals. Future priorities include expanding its taxonomic coverage, applying the RLI to the goals and targets of the Kunming–Montreal Global Biodiversity Framework, incorporating uncertainty in the underlying Red List assessments, integrating into national RLIs the impact of a country on species’ extinction risk abroad, and improving analysis of the factors driving trends.
- Modelling the probability of meeting IUCN Red List criteria to support reassessmentsPublication . Henry, Etienne G.; Santini, Luca; Butchart, Stuart H. M.; González‐Suárez, Manuela; Lucas, Pablo M.; Benítez‐López, Ana; Mancini, Giordano; Jung, Martin; Cardoso, Pedro; Zizka, Alexander; Meyer, Carsten; Akçakaya, H. Reşit; Berryman, Alex J.; Cazalis, Victor; Di Marco, MorenoComparative extinction risk analysis—which predicts species extinction risk from correlation with traits or geographical characteristics—has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.