Search USGS⌕ Search

USGS · 70248367

Genetic diversity and IUCN Red List status

Abstract

The International Union for Conservation of Nature (IUCN) Red List is an important and widely used tool for conservation assessment. The IUCN uses information about a species’ range, population size, habitat quality and fragmentation levels, and trends in abundance to assess extinction risk. Genetic diversity is not considered, although it affects extinction risk. Declining populations are more strongly affected by genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, attempts have been made to find relationships between red-list status and genetic diversity. Yet, there is still no consensus on whether genetic diversity is captured by the current IUCN Red List categories in a way that is informative for conservation. To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red-list status is not a useful metric for informing species-specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data. Thus, there is a need to develop and assess metrics specifically designed to assess genetic diversity and inform conservation policy, including policies recently adopted by the UN's Convention on Biological Diversity Kunming-Montreal Global Biodiversity Framework.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chloe Schmidt, Sean M. Hoban, Margaret Hunter, Ivan Paz-Vinas, Colin J. Garroway. 2023-04-06. Genetic diversity and IUCN Red List status. https://doi.org/10.1111/cobi.14064

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Cognitive and institutional barriers to effective natural resource management under global change

Many of the most challenging decisions facing natural resource managers today can be understood as time dependent. The concept of time-dependent decisions is useful when considering natural resource management decisions that are characterized by nonstationarity in the underlying ecological system, uncertainty surrounding key ecological relationships, efficacy of management interventions, and difficult trade-offs between the present and the future. In a time-dependent decision, the optimal action changes depending on when the decision is made. We surveyed literature from the fields of cognitive psychology, environmental decision-making, and natural resource management to identify a key set of cognitive and institutional barriers that prevent decision makers from recognizing their decisions as time dependent. This failure obscures the complexity of nonstationarity, uncertainty, and intertemporal choice in natural resource management and facilitates assumptions of stationarity and time independence. We used land acquisition in the context of climate change as an example to explore the barriers to effective time-dependent decision-making at both individual and institutional scales. Using a theoretical model of land acquisition, we quantified the value of accounting for time dependence by comparing the performance of time-dependent, nonstationary decision models with models that assume time independence and stationarity. We found that the conservation value of land acquisition decisions was significantly improved through the use of time-dependent optimization models and that this improvement increased as nonstationarity increased. Finally, explicitly recognizing the time dependence of many natural resource management decisions will help improve the utility of existing decision tools and frameworks.

Conservation Biology↗

Improving the use of expert opinion in disease risk analysis for conservation translocations

Conservation translocations are subject to considerable uncertainty and risk, of which disease is one of the most recognized. To address disease risks, several protocols for qualitative disease risk analysis (qDRA) exist and are used for responsible conservation translocation planning. Existing qDRA protocols usually rely on expert judgment, but they lack quantitative aggregation of elicited estimates, transparent treatment of uncertainty, and explicit comparison of alternative disease risk management pathways. We added these factors to a qDRA for translocation of a charismatic species that is extinct in the wild, the sihek (Guam kingfisher) ( Todiramphus cinnamominus ). We asked a panel of seven independent experts to quantify their judgment of risks of pathogen release, exposure, and consequences for seven pathogen hazards under three translocation management scenarios. Experts suggested that implementing a full biosecurity protocol would reduce risk for several pathogen hazards but would have variable effectiveness (11–65% depending on the hazard). There was substantial uncertainty associated with individual experts and noise among different experts, showing the potential for error if decisions are based on single-point judgments (i.e., providing judgment of a central tendency without accounting for uncertainty) from one expert or on collaborative consensus of multiple experts, as is current practice.

Conservation Biology↗

The collective application of shorebird tracking data to conservation

Addressing urgent conservation issues, such as the drastic declines of North American migratory birds, requires creative, evidence-based, efficient, and collaborative approaches. The abundance of over 50% of monitored North American shorebird populations has declined by over 50% since 1980. To address these declines, we developed a partnership of scientists and practitioners called the Shorebird Science and Conservation Collective (hereafter the collective ). The collective was founded to translate the combined findings of shorebird tracking data into on-the-ground conservation action. With advice from an advisory group, the collective acts as an intermediary whereby dedicated staff collate and analyze data contributions from scientists to support knowledge requests from conservation practitioners. In its first three years, data contributions from 75 organizations include over 7.1 million shorebird observations forming movement paths of 3420 individuals representing 36 species tracked across the Americas and have informed 18 conservation projects spanning education, land and species management, land conservation, and policy requests. Others engaged in translational science from big data could consider similar knowledge-sharing models that prioritize usable data products, foster collaborative engagement between science experts and practitioners, build focused communities around topics or taxonomic groups, and employ a proof-of-concept phase to develop scalable solutions while making progress toward long-term funding to sustain impact. As the volume of scientific data continues to grow, intermediaries, such as the collective, can be vital liaisons to rapidly integrate and interpret research to support conservation action. Dedicated to the memory of Shiloh Schulte and his conservation achievements for shorebirds.

Conservation Biology↗