Improving species status assessments under the U.S. Endangered Species Act and implications for multispecies conservation challenges worldwide

Is the Endangered Species Act living to its full potential? The reassessment of the conservation status and recovery of Macbridea alba Chapm. as a case study

Since 1988, the Cooperative Endangered Species Conservation Fund or “Section 6” fund facilitates partnerships between the U.S. Fish and Wildlife Service and state agencies that aim to provide data pertinent to the recovery of Endangered Species Act (ESA) protected species. Despite the success of these efforts, research for rare plants is chronically underfunded and many species experience long periods of research inactivity that hinders their conservation. One example is Macbridea alba Chapm. (white birds-in-a-nest, Lamiaceae, M. alba from hereon), a federally threatened and state endangered mint endemic to four counties within the Florida panhandle. The species is a candidate for delisting after 30 years of protection under the ESA, however a lack of up-to-date data associated with the species has continually challenged the implementation of effective conservation programs and prolonged the recovery process. The focus of this paper is to review the timeline of recovery goals for M. alba, present a summary of recent research findings (i.e., species distribution models, habitat associations, reproductive ecology), and identify achievements as well as persistent obstacles to recovery and delisting. Our research focused on 5 of 10 recovery actions listed in the recovery plan for M. alba. Our findings provide updated data and make novel contributions to the protection of M. alba that will prioritize and improve management efforts. Overall, our work highlights frequent barriers to the recovery and delisting of rare species, using an endemic plant species as a case-study. Importantly, we outline effective methods for the rapid assessment of at-risk plant species that due to enduring data gaps, face an uncertain future in listing and recovery. We hope our work provides a convincing case demonstrating the critical need for current and expanded ESA funding and encourages a diversity of individuals and institutions to participate in critical rare plant research to swiftly fill research gaps and expedite recovery of some of the rarest plant species across the United States.

Automated assessment reveals that the extinction risk of reptiles is widely underestimated across space and phylogeny

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.