Expert judgment and uncertainty regarding the protection of imperiled species

Decisions concerning the appropriate listing status of species under the U.S. Endangered Species Act (ESA) can be controversial even among conservationists. These decisions may determine whether a species persists in the near term and have long‐lasting social and political ramifications. Given the ESA's mandate that such decisions be based on the best available science, it is important to examine what factors contribute to experts’ judgments concerning the listing of species. We examined how a variety of factors (such as risk perception, value orientations, and norms) influenced experts’ judgments concerning the appropriate listing status of the grizzly bear (Ursus arctos horribilis) population in the Greater Yellowstone Ecosystem. Experts were invited to complete an online survey examining their perceptions of the threats grizzly bears face and their listing recommendation. Although experts’ assessments of the threats to this species were strongly correlated with their recommendations for listing status, this relationship did not exist when other cognitive factors were included in the model. Specifically, values related to human use of wildlife and norms (i.e., a respondent's expectation of peers’ assessments) were most influential in listing status recommendations. These results suggest that experts’ decisions about listing, like all human decisions, are subject to the use of heuristics (i.e., decision shortcuts). An understanding of how heuristics and related biases affect decisions under uncertainty can help inform decision making about threatened and endangered species and may be useful in designing effective processes for protection of imperiled species.     

Projecting biodiversity benefits of conservation behavior-change programs

Biodiversity loss is driven by human behavior, but there is uncertainty about the effectiveness of behavior-change programs in delivering benefits to biodiversity. To demonstrate their value, the biodiversity benefits and cost-effectiveness of behavior changes that directly or indirectly affect biodiversity need to be quantified. We adapted a structured decision-making prioritization tool to determine the potential biodiversity benefits of behavior changes. As a case study, we examined two hypothetical behavior-change programs––wildlife gardening and cat containment––by asking experts to consider the behaviors associated with these programs that directly and indirectly affect biodiversity. We assessed benefits to southern brown bandicoot (Isoodon obesulus) and superb fairy-wren (Malurus cyaneus) by eliciting from experts estimates of the probability of each species persisting in the landscape given a range of behavior-change scenarios in which uptake of the behaviors varied. We then compared these estimates to a business-as-usual scenario to determine the relative biodiversity benefit and cost-effectiveness of each scenario. Experts projected that the behavior-change programs would benefit biodiversity and that benefits would rise with increasing uptake of the target behaviors. Biodiversity benefits were also predicted to accrue through indirect behaviors, although experts disagreed about the magnitude of additional benefit provided. Scenarios that combined the two behavior-change programs were estimated to provide the greatest benefits to species and be most cost-effective. Our method could be used in other contexts and potentially at different scales and advances the use of prioritization tools to guide conservation behavior-change programs.     

Software for prioritizing conservation actions based on probabilistic information

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.     

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC < 0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β = 0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.     

Eliciting expert knowledge in conservation science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.     

Factors associated with preemptive conservation under the U.S. Endangered Species Act

In recent decades, there has been an increasing emphasis on proactive efforts to conserve species being considered for listing under the U.S. Endangered Species Act (ESA) before they are listed (i.e., preemptive conservation). These efforts, which depend on voluntary actions by public and private land managers across the species’ range, aim to conserve species while avoiding regulatory costs associated with ESA listing. We collected data for a set of social, economic, environmental, and institutional factors that we hypothesized would influence voluntary decisions to promote or inhibit preemptive conservation of species under consideration for ESA listing. We used logistic regression to estimate the association of these factors with preemptive conservation outcomes based on data for a set of species that entered the ESA listing process and were either officially listed (n = 314) or preemptively conserved (n = 73) from 1996 to 2018. Factors significantly associated with precluded listing due to preemptive conservation included high baseline conservation status, low proportion of private land across the species’ range, small total range size, exposure to specific types of threats, and species’ range extending over several states. These results highlight strategies that can help improve conservation outcomes, such as allocating resources for imperiled species earlier in the listing process, addressing specific threats, and expanding incentives and coordination mechanisms for conservation on private lands.     

Evidence-based guidelines for automated conservation assessments of plant species

Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.     

Nonadditive effects among threats on rare plant species

The current loss of biodiversity has put 50,000 plant species at an elevated risk of extinction worldwide. Conserving at-risk species is often complicated by covariance or nonadditivity among threats, which makes it difficult to determine optimal management strategies. We sought to demographically quantify covariance and nonadditive effects of more threats on more rare plant species than ever attempted in a single analysis. We used 1082 population reports from 186 populations across 3 U.S. states of 27 rare, herbaceous plant species collected over 15 years by citizen scientists. We used a linear mixed-effects model with 4 threats and their interactions as fixed predictors, species as a random predictor, and annual growth rates as the response. We found a significant 3-way interaction on annual growth rates; rare plant population sizes were reduced by 46% during the time immediately after disturbance when populations were also browsed by deer (Odocoileus virginianus) and had high levels of encroachment by woody species. This nonadditive effect should be considered a major threat to the persistence of rare plant species. Our results highlight the need for comprehensive, multithreat assessments to determine optimal conservation actions.     

Assigning trend-based conservation status despite high uncertainty

Estimates of temporal trends in species’ occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision-making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.     

When all life counts in conservation

Conservation science involves the collection and analysis of data. These scientific practices emerge from values that shape who and what is counted. Currently, conservation data are filtered through a value system that considers native life the only appropriate subject of conservation concern. We examined how trends in species richness, distribution, and threats change when all wildlife count by adding so-called non-native and feral populations to the International Union for Conservation of Nature Red List and local species richness assessments. We focused on vertebrate populations with founding members taken into and out of Australia by humans (i.e., migrants). We identified 87 immigrant and 47 emigrant vertebrate species. Formal conservation accounts underestimated global ranges by an average of 30% for immigrants and 7% for emigrants; immigrations surpassed extinctions in Australia by 52 species; migrants were disproportionately threatened (33% of immigrants and 29% of emigrants were threatened or decreasing in their native ranges); and incorporating migrant populations into risk assessments reduced global threat statuses for 15 of 18 species. Australian policies defined most immigrants as pests (76%), and conservation was the most commonly stated motivation for targeting these species in killing programs (37% of immigrants). Inclusive biodiversity data open space for dialogue on the ethical and empirical assumptions underlying conservation science.     

Incorporating Climate and Ocean Change into Extinction Risk Assessments for 82 Coral Species

Many marine invertebrate species facing potential extinction have uncertain taxonomies and poorly known demographic and ecological traits. Uncertainties are compounded when potential extinction drivers are climate and ocean changes whose effects on even widespread and abundant species are only partially understood. The U.S. Endangered Species Act mandates conservation management decisions founded on the extinction risk to species based on the best available science at the time of consideration—requiring prompt action rather than awaiting better information. We developed an expert‐opinion threat‐based approach that entails a structured voting system to assess extinction risk from climate and ocean changes and other threats to 82 coral species for which population status and threat response information was limited. Such methods are urgently needed because constrained budgets and manpower will continue to hinder the availability of desired data for many potentially vulnerable marine species. Significant species‐specific information gaps and uncertainties precluded quantitative assessments of habitat loss or population declines and necessitated increased reliance on demographic characteristics and threat vulnerabilities at genus or family levels. Adapting some methods (e.g., a structured voting system) used during other assessments and developing some new approaches (e.g., integrated assessment of threats and demographic characteristics), we rated the importance of threats contributing to coral extinction risk and assessed those threats against population status and trend information to evaluate each species’ extinction risk over the 21st century. This qualitative assessment resulted in a ranking with an uncertainty range for each species according to their estimated likelihood of extinction. We offer guidance on approaches for future biological extinction risk assessments, especially in cases of data‐limited species likely to be affected by global‐scale threats. Incorporación del Cambio Climático y Oceánico en Estudios de Riesgo de Extinción para 82 Especies de Coral     

Identifying and Managing Threatened Invertebrates through Assessment of Coextinction Risk

Abstract: Invertebrates with specific host species may have a high probability of extinction when their hosts have a high probability of extinction. Some of these invertebrates are more likely to go extinct than their hosts, and under some circumstances, specific actions to conserve the host may be detrimental to the invertebrate. A critical constraint to identifying such invertebrates is uncertainty about their level of host specificity. We used two host‐breadth models that explicitly incorporated uncertainty in the host specificity of an invertebrate species. We devised a decision protocol to identify actions that may increase the probability of persistence of a given dependent species. The protocol included estimates from the host‐breadth models and decision nodes to identify cothreatened species. We applied the models and protocol to data on 1055 insects (186 species) associated with 2 threatened (as designated by the Australian Government) plant species and 19 plant species that are not threatened to determine whether any insect herbivores have the potential to become extinct if the plant becomes extinct. According to the host‐breadth models, 18 species of insect had high host specificity to the threatened plant species. From these 18 insects, the decision protocol highlighted 6 species that had a high probability of extinction if their hosts were to become extinct (3% of all insects examined). The models and decision protocol have added objectivity and rigor to the process of deciding which dependent invertebrates require conservation action, particularly when dealing with largely unknown and speciose faunas.     

Prioritizing species conservation programs based on IUCN Green Status and estimates of cost-sharing potential

Over 1 million species around the world are at risk of extinction, and conservation organizations have to decide where to invest their limited resources. Cost-effectiveness can be increased by leveraging funding opportunities and increasing collaborative partnerships to achieve shared conservation goals. We devised a structured decision-making framework to prioritize species’ conservation programs based on a cost–benefit analysis that takes collaborative opportunities into account in an examination of national and global conservation return on investment. Conservation benefit is determined by modifying the novel International Union for the Conservation of Nature Green Status for Species to provide an efficient, high-level measure that is comparable among species, even with limited information and time constraints. We applied this prioritization approach to the Wilder Institute/Calgary Zoo, Canada, a nonprofit organization seeking to increase the number of species it assists with conservation translocations. We sought to identify and prioritize additional species’ programs for which conservation translocation expertise and actions could make the most impact. Estimating the likelihood of cost-sharing potential enabled total program cost to be distinguished from costs specific to the organization. Comparing a benefit-to-cost ratio on different geographic scales allowed decision makers to weigh alternative options for investing in new species’ programs in a transparent and effective manner. Our innovative analysis aligns with general conservation planning frameworks and can be adapted for any organization.     

Effect of risk aversion on prioritizing conservation projects

Conservation outcomes are uncertain. Agencies making decisions about what threat mitigation actions to take to save which species frequently face the dilemma of whether to invest in actions with high probability of success and guaranteed benefits or to choose projects with a greater risk of failure that might provide higher benefits if they succeed. The answer to this dilemma lies in the decision maker's aversion to risk—their unwillingness to accept uncertain outcomes. Little guidance exists on how risk preferences affect conservation investment priorities. Using a prioritization approach based on cost effectiveness, we compared 2 approaches: a conservative probability threshold approach that excludes investment in projects with a risk of management failure greater than a fixed level, and a variance‐discounting heuristic used in economics that explicitly accounts for risk tolerance and the probabilities of management success and failure. We applied both approaches to prioritizing projects for 700 of New Zealand's threatened species across 8303 management actions. Both decision makers’ risk tolerance and our choice of approach to dealing with risk preferences drove the prioritization solution (i.e., the species selected for management). Use of a probability threshold minimized uncertainty, but more expensive projects were selected than with variance discounting, which maximized expected benefits by selecting the management of species with higher extinction risk and higher conservation value. Explicitly incorporating risk preferences within the decision making process reduced the number of species expected to be safe from extinction because lower risk tolerance resulted in more species being excluded from management, but the approach allowed decision makers to choose a level of acceptable risk that fit with their ability to accommodate failure. We argue for transparency in risk tolerance and recommend that decision makers accept risk in an adaptive management framework to maximize benefits and avoid potential extinctions due to inefficient allocation of limited resources. El Efecto de la Aversión de Riesgo sobre la Priorización de Proyectos de Conservación     

Use of surrogate species to cost-effectively prioritize conservation actions

Conservation efforts often focus on umbrella species whose distributions overlap with many other flora and fauna. However, because biodiversity is affected by different threats that are spatially variable, focusing only on the geographic range overlap of species may not be sufficient in allocating the necessary actions needed to efficiently abate threats. We developed a problem-based method for prioritizing conservation actions for umbrella species that maximizes the total number of flora and fauna benefiting from management while considering threats, actions, and costs. We tested our new method by assessing the performance of the Australian federal government's umbrella prioritization list, which identifies 73 umbrella species as priorities for conservation attention. Our results show that the federal government priority list benefits only 6% of all Australia's threatened terrestrial species. This could be increased to benefit nearly half (or 46%) of all threatened terrestrial species for the same budget of AU$550 million/year if more suitable umbrella species were chosen. This results in a 7-fold increase in management efficiency. We believe nations around the world can markedly improve the selection of prioritized umbrella species for conservation action with this transparent, quantitative, and objective prioritization approach.     

A global ecological signal of extinction risk in terrestrial vertebrates

To determine the distribution and causes of extinction threat across functional groups of terrestrial vertebrates, we assembled an ecological trait data set for 18,016 species of terrestrial vertebrates and utilized phylogenetic comparative methods to test which categories of habitat association, mode of locomotion, and feeding mode best predicted extinction risk. We also examined the individual categories of the International Union for Conservation of Nature Red List extinction drivers (e.g., agriculture and logging) threatening each species and determined the greatest threats for each of the four terrestrial vertebrate groups. We then quantified the sum of extinction drivers threatening each species to provide a multistressor perspective on threat. Cave dwelling amphibians (p < 0.01), arboreal quadrupedal mammals (all of which are primates) (p < 0.01), aerial and scavenging birds (p < 0.01), and pedal (i.e., walking) squamates (p < 0.01) were all disproportionately threatened with extinction in comparison with the other assessed ecological traits. Across all threatened vertebrate species in the study, the most common risk factors were agriculture, threatening 4491 species, followed by logging, threatening 3187 species, and then invasive species and disease, threatening 2053 species. Species at higher risk of extinction were simultaneously at risk from a greater number of threat types. If left unabated, the disproportionate loss of species with certain functional traits and increasing anthropogenic pressures are likely to disrupt ecosystem functions globally. A shift in focus from species- to trait-centric conservation practices will allow for protection of at-risk functional diversity from regional to global scales.     

Use of standardized methods to improve extinction-risk classification

Standardized classification methods based on quantifiable risk metrics are critical for evaluating extinction threats because they increase objectivity, consistency, and transparency of listing decisions. Yet, in the United States, neither federal nor state agencies use standardized methods for listing species for legal protection, which could put listing decisions at odds with the magnitude of the risk. We used a recently developed set of quantitative risk metrics for California herpetofauna as a case study to highlight discrepancies in listing decisions made without standardized methods. We also combined such quantitative metrics with classification tree analysis to attempt to increase the transparency of previous listing decisions by identifying the criteria that had inherently been given the most weight. Federally listed herpetofauna in California scored significantly higher on the risk-metric spectrum than those not federally listed, whereas state-listed species did not score any higher than species that were not state listed. Based on classification trees, state endemism was the most important predictor of listing status at the state level and distribution trend (decline in a species’ range size) and population trend (decline in a species’ abundance at localized sites) were the most important predictors at the federal level. Our results emphasize the need for governing bodies to adopt standardized methods for assessing conservation risk that are based on quantitative criteria. Such methods allow decision makers to identify criteria inherently given the most weight in determining listing status, thus increasing the transparency of previous listing decisions, and produce an unbiased comparison of conservation threat across all species to promote consistency, efficiency, and effectiveness of the listing process.     

A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species’ current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.     

Phylogeny-based conservation priorities for Australian freshwater fishes

Conservation scientists are increasingly interested in the question of how extinction prunes the tree of life. This question is particularly important for Australian freshwater fishes because there is a broad mix of ∼300 old and young species, many of which are severely threatened. We used a complete species-level phylogeny of Australian freshwater fishes to examine phylogenetic nonrandomness of extinction risk. We computed the potential loss of phylogenetic diversity by simulating extinction across the tree under a pattern weighted based on International Union for Conservation of Nature extinction risk category and compared this loss to projected diversity loss under a random null model of extinction. Finally, we calculated EDGE (evolutionary distinctiveness, global endangerment) scores for 251 freshwater and 60 brackish species and compiled a list of high-priority species for conservation actions based on their extinction risk and evolutionary uniqueness. Extinction risk was not random and was clustered in both diversity cradles (recently diversifying, species-rich clades, such as Galaxiidae and Percichthyidae) and museums (older, species-poor groups, such as freshwater chondrichthyans). Clustered extinction made little difference to the average expected loss of phylogenetic diversity. However, the upper bound of loss was higher under a selective model of extinction, particularly when the counts of species lost were low. Thus, the loss of highly threatened species would diminish the tree of life more than a null model of randomly distributed extinction.  High priority species included both widely recognized and charismatic ones, such as the Queensland lungfish (Neoceratodus forsteri), river sharks, and freshwater sawfishes, and lesser-known species that receive less public attention, including the salamanderfish (Lepidogalaxias salamandroides), cave gudgeons, and many galaxiids, rainbowfishes, and pygmy perches.