National assessment of threatened species using sparse data: IUCN Red List classification of Anatidae in Iran

Classifying the status of threatened species using tools such as the IUCN Red List is a critical step for identifying at-risk species, and for conservation planning at global and sub-global levels. The requirement for data on population trends, geographic ranges and population sizes has proved challenging to carry out at the national level, especially in countries with unstructured and spatially limited monitoring schemes and limited conservation resources. In this study, we investigated the repeatability of risk assessments made under the IUCN Red List guidelines for assessment at the national level. Specifically, we assessed the national threat status of breeding and non-breeding populations of Anatidae in Iran using population and distribution data. The variable quality of these data led to uncertainties in decision-making. To evaluate the impact of these uncertainties on population trend estimates, we generated a range of alternative possible threat categories under three scenarios of population trend estimation. For the non-breeding populations, for which long-term population data were available, we were able to classify 93% of species, 72% of which were placed in threatened categories. For the breeding populations, 78% of the species were categorized as Data Deficient. Of those species in data-sufficient categories, 67% were classified as threatened. We conclude that effective use of the IUCN categories and criteria at the national level is hampered in situations where monitoring schemes have a short history. Therefore, available data need to be complemented with some level of standardized data collection. We further make suggestions about efficient means of data collection in such cases and the importance of the use of modeling techniques prior to Red Listing and discuss the most useful IUCN criteria for threat categorization in such circumstances