Data uncertainty and the selectivity of extinction risk in freshwater invertebrates

Seabird meta-Population Viability Model (mPVA) methods

The seabird meta-population viability model (mPVA) uses a generalized approach to project abundance and quasi-extinction risk for 102 seabird species under various conservation scenarios. The mPVA is a stage-structured projection matrix that tracks abundance of multiple populations linked by dispersal, accounting for breeding island characteristics and spatial distribution. Data are derived from published studies, grey literature, and expert review (with over 500 contributions). Invasive species impacts were generalized to stage-specific vital rates by fitting a Bayesian state-space model to trend data from Islands where invasive removals had occurred, while accounting for characteristics of seabird biology, breeding islands and invasive species. Survival rates were estimated using a competing hazards formulation to account for impacts of multiple threats, while also allowing for environmental and demographic stochasticity, density dependence and parameter uncertainty.•The mPVA provides resource managers with a tool to quantitatively assess potential benefits of alternative management actions, for multiple species•The mPVA compares projected abundance and quasi-extinction risk under current conditions (no intervention) and various conservation scenarios, including removal of invasive species from specified breeding islands, translocation or reintroduction of individuals to an island of specified location and size, and at-sea mortality amelioration via reduction in annual at-sea deaths.

Conservation Status of Brachycephalus Toadlets (Anura: Brachycephalidae) from the Brazilian Atlantic Rainforest

The number of described anurans has increased continuously, with many newly described species determined to be at risk. Most of these new species inhabit hotspots and are under threat of habitat loss, such as Brachycephalus, a genus of small toadlets that inhabits the litter of the Brazilian Atlantic Rainforest. Of 36 known species, 22 were described in the last decade, but only 11 have been assessed according to the IUCN Red List categories, with just one currently listed as Critically Endangered. All available data on occurrence, distribution, density, and threats to Brachycephalus were reviewed. The species extent of occurrence was estimated using the Minimum Convex Polygon method for species with three or more records and by delimiting continuous areas within the altitudinal range of species with up to two records. These data were integrated to assess the conservation status according to the IUCN criteria. Six species have been evaluated as Critically Endangered, five as Endangered, 10 as Vulnerable, five as Least Concern, and 10 as Data Deficient. Deforestation was the most common threat to imperiled Brachycephalus species. The official recognition of these categories might be more readily adopted if the microendemic nature of their geographical distribution is taken into account.

Distribution pattern of anurans from three mountain complexes in southeastern Brazil and their conservation implications

Biogeographic tools support spatial distribution pattern hypotheses and help to determine priority areas for conservation. Our aim was to verify biogeographic patterns for anurans in three mountain complexes in southeastern Brazil, as well as to discuss the status of species conservation recorded and the biogeographical units evaluated. We selected 16 areas distributed in the Serra da Mantiqueira complex, south of Serra do Espinhaço and Serra da Canastra. We used the occurrence (geographic coordinates) of each species in the localities to determine areas of endemism applying the Endemicity Analysis method. We also tested whether similarity between areas was explained by geographic distance (Multiple Regression on distance Matrices-MRM). The Serra do Itatiaia, Serra da Canastra, Plateau of Poços de Caldas and Serra do Cipó were the areas that presented the highest number of species restricted to them. Through the Endemicity Analysis, we identified four areas of endemism with higher scores. The MRM revealed that the geographic distance explained 41% of species dissimilarity between areas. Most of the endemic species from these areas have inaccurate conservation statuses (data deficient or unevaluated). These results highlight the need for greater research efforts towards understanding species restricted by distribution, as well as the priority in conserving these endemic areas.

Toward reassessing data-deficient species

One in 6 species (13,465 species) on the International Union for Conservation of Nature (IUCN) Red List is classified as data deficient due to lack of information on their taxonomy, population status, or impact of threats. Despite the chance that many are at high risk of extinction, data-deficient species are typically excluded from global and local conservation priorities, as well as funding schemes. The number of data-deficient species will greatly increase as the IUCN Red List becomes more inclusive of poorly known and speciose groups. A strategic approach is urgently needed to enhance the conservation value of data-deficient assessments. To develop this, we reviewed 2879 data-deficient assessments in 6 animal groups and identified 8 main justifications for assigning data-deficient status (type series, few records, old records, uncertain provenance, uncertain population status or distribution, uncertain threats, taxonomic uncertainty, and new species). Assigning a consistent set of justification tags (i.e., consistent assignment to assessment justifications) to species classified as data deficient is a simple way to achieve more strategic assessments. Such tags would clarify the causes of data deficiency; facilitate the prediction of extinction risk; facilitate comparisons of data deficiency among taxonomic groups; and help prioritize species for reassessment. With renewed efforts, it could be straightforward to prevent thousands of data-deficient species slipping unnoticed toward extinction.

Integrating data-deficient species in analyses of evolutionary history loss

There is an increasing interest in measuring loss of phylogenetic diversity and evolutionary distinctiveness which together depict the evolutionary history of conservation interest. Those losses are assessed through the evolutionary relationships between species and species threat status or extinction probabilities. Yet, available information is not always sufficient to quantify the threat status of species that are then classified as data deficient. Data‐deficient species are a crucial issue as they cause incomplete assessments of the loss of phylogenetic diversity and evolutionary distinctiveness. We aimed to explore the potential bias caused by data‐deficient species in estimating four widely used indices: HEDGE, EDGE, PDloss, and Expected PDloss. Second, we tested four different widely applicable and multitaxa imputation methods and their potential to minimize the bias for those four indices. Two methods are based on a best‐ vs. worst‐case extinction scenarios, one is based on the frequency distribution of threat status within a taxonomic group and one is based on correlates of extinction risks. We showed that data‐deficient species led to important bias in predictions of evolutionary history loss (especially high underestimation when they were removed). This issue was particularly important when data‐deficient species tended to be clustered in the tree of life. The imputation method based on correlates of extinction risks, especially geographic range size, had the best performance and enabled us to improve risk assessments. Solving threat status of DD species can fundamentally change our understanding of loss of phylogenetic diversity. We found that this loss could be substantially higher than previously found in amphibians, squamate reptiles, and carnivores. We also identified species that are of high priority for the conservation of evolutionary distinctiveness.

Regional Red List assessment of tree species in upper montane forests of the Tropical Andes

The Tropical Andes are characterized by a high level of endemism and plant species richness but are under pressure from human activities. We present the first regional conservation assessment of upper montane tree species in this region. We identified 3,750 tree species as occurring in this region, of which 917 were excluded because of a lack of data on their distribution. We identified a subset of 129 taxa that were restricted to higher elevations (> 1,500 m) but occurred in more than one country, thus excluding local endemics evaluated in previous national assessments. Distribution maps were created for each of these selected species, and extinction risk was assessed according to the IUCN Red List categories and criteria (version 3.1), drawing on expert knowledge elicited from a regional network of specialists. We assessed one species, Polylepis microphylla, as Critically Endangered, 47 species as Endangered and 28 as Vulnerable. Overall, 60% of the species evaluated were categorized as threatened, or 73% if national endemics are included. It is recommended that extinction risk assessments for tree species be used to inform the development of conservation strategies in the region, to avoid further loss of this important element of biodiversity.

Multiple drivers of decline in the global status of freshwater crayfish (Decapoda: Astacidea)

Rates of biodiversity loss are higher in freshwater ecosystems than in most terrestrial or marine ecosystems, making freshwater conservation a priority. However, prioritization methods are impeded by insufficient knowledge on the distribution and conservation status of freshwater taxa, particularly invertebrates. We evaluated the extinction risk of the world's 590 freshwater crayfish species using the IUCN Categories and Criteria and found 32% of all species are threatened with extinction. The level of extinction risk differed between families, with proportionally more threatened species in the Parastacidae and Astacidae than in the Cambaridae. Four described species were Extinct and 21% were assessed as Data Deficient. There was geographical variation in the dominant threats affecting the main centres of crayfish diversity. The majority of threatened US and Mexican species face threats associated with urban development, pollution, damming and water management. Conversely, the majority of Australian threatened species are affected by climate change, harvesting, agriculture and invasive species. Only a small proportion of crayfish are found within the boundaries of protected areas, suggesting that alternative means of long-term protection will be required. Our study highlights many of the significant challenges yet to come for freshwater biodiversity unless conservation planning shifts from a reactive to proactive approach.

Predicting the conservation status of data-deficient species

There is little appreciation of the level of extinction risk faced by one-sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data-deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species' life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity.