Generation lengths of the world's birds and their implications for extinction risk

Citations Over TimeTop 1% of 2020 papers

Abstract

Birds have been comprehensively assessed on the International Union for Conservation of Nature (IUCN) Red List more times than any other taxonomic group. However, to date, generation lengths have not been systematically estimated to scale population trends when undertaking assessments, as required by the criteria of the IUCN Red List. We compiled information from major databases of published life-history and trait data for all birds and imputed missing life-history data as a function of species traits with generalized linear mixed models. Generation lengths were derived for all species, based on our modeled values of age at first breeding, maximum longevity, and annual adult survival. The resulting generation lengths varied from 1.42 to 27.87 years (median 2.99). Most species (61%) had generation lengths <3.33 years, meaning that the period of 3 generations-over which population declines are assessed under criterion A-was <10 years, which is the value used for IUCN Red List assessments of species with short generation times. For these species, our trait-informed estimates of generation length suggested that 10 years is a robust precautionary value for threat assessment. In other cases, however, for whole families, genera, or individual species, generation length had a substantial impact on their estimated extinction risk, resulting in higher extinction risk in long-lived species than in short-lived species. Although our approach effectively addressed data gaps, generation lengths for some species may have been underestimated due to a paucity of life-history data. Overall, our results will strengthen future extinction-risk assessments and augment key databases of avian life-history and trait data.

Related Papers

• → Prioritizing the reassessment of data‐deficient species on the IUCN Red List(2023)35 cited
• → Data Deficient birds on the IUCN Red List: What don’t we know and why does it matter?(2009)118 cited
• → Local ecological knowledge to assist conservation status assessments in data poor contexts: a case study with the threatened sharks of the Brazilian Northeast(2021)40 cited
• → Assessing extinction risk from geographic distribution data in Neotropical freshwater fishes(2021)27 cited
• → Automated conservation assessment of the orchid family using deep learning(2020)8 cited