Testing decision rules for categorizing species' extinction risk to help develop quantitative listing criteria for the U.S. Endangered Species Act

Assessing recovery of spectacled eiders using a Bayesian decision analysis

Assessing species status and making classification decisions under the Endangered Species Act is a critical step towards effective species conservation. However, classification decisions are liable to two errors: i) failing to classify a species as threatened or endangered that should be classified (underprotection), or ii) classifying a species as threatened or endangered when it is not warranted (overprotection). Recent surveys indicate threatened spectacled eider populations are increasing in western Alaska, prompting the U.S. Fish and Wildlife Service to reconsider the federal listing status. There are multiple criteria set for assessing spectacled eider status, and here we focus on the abundance and decision analysis criteria. We estimated population metrics using state-space models for Alaskan breeding populations of spectacled eiders. We projected abundance over 50 years using posterior estimates of abundance and process variation to estimate the probability of quasi-extinction. The decision analysis maps the risk of quasi-extinction to the loss associated with making a misclassification error (i.e., underprotection) through a loss function. Our results indicate that the Yukon Kuskokwim Delta breeding population in western Alaska has met the recovery criteria but the Arctic Coastal Plain population in northern Alaska has not. The methods employed here provide an example of accounting for uncertainty and incorporating value judgements in such a way that the decision-makers may understand the risk of committing a misclassification error. Incorporating the abundance threshold and decision analysis in the reclassification criteria greatly increases the transparency and defensibility of the classification decision, a critical aspect for making effective decisions about species management and conservation.

Comparative Analysis of Plant Demographic Traits Across Species of Different Conservation Concern: Implications for Pesticide Risk Assessment

Pesticide risk assessment for "listed" (threatened and endangered) plant species is hampered by a lack of quantitative demographic information. Demographic information for nonlisted plant species could provide risk-assessment data and inform recovery plans for listed species; however, it is unclear how representative demography of the former would be for the latter. We performed a comparison of plant demographic traits and elasticity metrics to explore how similar these are between listed and nonlisted species. We used transition matrices from the COMPADRE Plant Matrix Database to calculate population growth rate (λ), net reproductive rate (R_o ), generation time (T_g ), damping ratio (ρ), and summed elasticities for survival (stasis), growth, fertility (reproduction), and evenness of elasticity (EE). We compared these across species varying in conservation status and population trend. Phylogenetic generalized least squares (PGLS) models were used to evaluate differences between listed and nonlisted plants. Overall, demographic traits were largely overlapping for listed and nonlisted species. Population trends had a significant impact on most demographic traits and elasticity patterns. The influence of T_g on elasticity metrics was consistent across all data groupings. In contrast, the influence of λ on elasticity metrics was highly variable, and correlated in opposite directions in growing and declining populations. Our results suggested that population models developed for nonlisted plant species may be useful for assessing the risks of pesticides to listed species. Environ Toxicol Chem 2019;38:2043-2052. © 2019 SETAC.

Implicit decision framing as an unrecognized source of confusion in endangered species classification

Legal classification of species requires scientific and values-based components, and how those components interact depends on how people frame the decision. Is classification a negotiation of trade-offs, a decision on how to allocate conservation efforts, or simply a comparison of the biological status of a species to a legal standard? The answers to problem-framing questions such as these influence decision making in species classifications. In our experience, however, decision makers, staff biologists, and stakeholders often have differing perspectives of the decision problem and assume different framings. In addition to differences between individuals, in some cases it appears individuals themselves are unclear about the decision process, which contributes to regulatory paralysis, litigation, and a loss of trust by agency staff and the public. We present 5 framings: putting species in the right bin, doing right by the species over time, saving the most species on a limited budget, weighing extinction risk against other objectives, and strategic classification to advance conservation. These framings are inspired by elements observed in current classification practices. Putting species in the right bin entails comparing a scientific status assessment with policy thresholds and accounting for potential misclassification costs. Doing right by the species adds a time dimension to the classification decision, and saving the most species on a limited budget classifies a suite of species simultaneously. Weighing extinction risk against other objectives would weigh ecological or socioeconomic concerns in classification decisions, and strategic classification to advance conservation would make negotiation a component of classification. We view these framings as a means to generate thought, discussion, and movement toward selection and application of explicit classification framings. Being explicit about the decision framing could lead decision makers toward more efficient and defensible decisions, reduce internal confusion and external conflict, and support better collaboration between scientists and policy makers.

Effects of uncertainty and variability on population declines and IUCN Red List classifications

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.

Evaluation of Two Approaches to Defining Extinction Risk under the U.S. Endangered Species Act

The predominant definition of extinction risk in conservation biology involves evaluating the cumulative distribution function (CDF) of extinction time at a particular point (the "time horizon"). Using the principles of decision theory, this article develops an alternative definition of extinction risk as the expected loss (EL) to society resulting from eventual extinction of a species. Distinct roles are identified for time preference and risk aversion. Ranges of tentative values for the parameters of the two approaches are proposed, and the performances of the two approaches are compared and contrasted for a small set of real-world species with published extinction time distributions and a large set of hypothetical extinction time distributions. Potential issues with each approach are evaluated, and the EL approach is recommended as the better of the two. The CDF approach suffers from the fact that extinctions that occur at any time before the specified time horizon are weighted equally, while extinctions that occur beyond the specified time horizon receive no weight at all. It also suffers from the fact that the time horizon does not correspond to any natural phenomenon, and so is impossible to specify nonarbitrarily; yet the results can depend critically on the specified value. In contrast, the EL approach has the advantage of weighting extinction time continuously, with no artificial time horizon, and the parameters of the approach (the rates of time preference and risk aversion) do correspond to natural phenomena, and so can be specified nonarbitrarily.

Conservation Risks: When Will Rhinos be Extinct?

We develop a risk intelligence system for biodiversity enterprises. Such enterprises depend on a supply of endangered species for their revenue. Many of these enterprises, however, cannot purchase a supply of this resource and are largely unable to secure the resource against theft in the form of poaching. Because replacements are not available once a species becomes extinct, insurance products are not available to reduce the risk exposure of these enterprises to an extinction event. For many species, the dynamics of anthropogenic impacts driven by economic as well as noneconomic values of associated wildlife products along with their ecological stressors can help meaningfully predict extinction risks. We develop an agent/individual-based economic-ecological model that captures these effects and apply it to the case of South African rhinos. Our model uses observed rhino dynamics and poaching statistics. It seeks to predict rhino extinction under the present scenario. This scenario has no legal horn trade, but allows live African rhino trade and legal hunting. Present rhino populations are small and threatened by a rising onslaught of poaching. This present scenario and associated dynamics predicts continued decline in rhino population size with accelerated extinction risks of rhinos by 2036. Our model supports the computation of extinction risks at any future time point. This capability can be used to evaluate the effectiveness of proposed conservation strategies at reducing a species' extinction risk. Models used to compute risk predictions, however, need to be statistically estimated. We point out that statistically fitting such models to observations will involve massive numbers of observations on consumer behavior and time-stamped location observations on thousands of animals. Finally, we propose Big Data algorithms to perform such estimates and to interpret the fitted model's output.

A simple, sufficient, and consistent method to score the status of threats and demography of imperiled species

Managers of large, complex wildlife conservation programs need information on the conservation status of each of many species to help strategically allocate limited resources. Oversimplifying status data, however, runs the risk of missing information essential to strategic allocation. Conservation status consists of two components, the status of threats a species faces and the species' demographic status. Neither component alone is sufficient to characterize conservation status. Here we present a simple key for scoring threat and demographic changes for species using detailed information provided in free-form textual descriptions of conservation status. This key is easy to use (simple), captures the two components of conservation status without the cost of more detailed measures (sufficient), and can be applied by different personnel to any taxon (consistent). To evaluate the key's utility, we performed two analyses. First, we scored the threat and demographic status of 37 species recently recommended for reclassification under the Endangered Species Act (ESA) and 15 control species, then compared our scores to two metrics used for decision-making and reports to Congress. Second, we scored the threat and demographic status of all non-plant ESA-listed species from Florida (54 spp.), and evaluated scoring repeatability for a subset of those. While the metrics reported by the U.S. Fish and Wildlife Service (FWS) are often consistent with our scores in the first analysis, the results highlight two problems with the oversimplified metrics. First, we show that both metrics can mask underlying demographic declines or threat increases; for example, ∼40% of species not recommended for reclassification had changes in threats or demography. Second, we show that neither metric is consistent with either threats or demography alone, but conflates the two. The second analysis illustrates how the scoring key can be applied to a substantial set of species to understand overall patterns of ESA implementation. The scoring repeatability analysis shows promise, but indicates thorough training will be needed to ensure consistency. We propose that large conservation programs adopt our simple scoring system for threats and demography. By doing so, program administrators will have better information to monitor program effectiveness and guide their decisions.

Combined Influences of Model Choice, Data Quality, and Data Quantity When Estimating Population Trends

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Comparisons and Uncertainty in Fat and Adipose Tissue Estimation Techniques: The Northern Elephant Seal as a Case Study

Fat mass and body condition are important metrics in bioenergetics and physiological studies. They can also link foraging success with demographic rates, making them key components of models that predict population-level outcomes of environmental change. Therefore, it is important to incorporate uncertainty in physiological indicators if results will lead to species management decisions. Maternal fat mass in elephant seals (Mirounga spp) can predict reproductive rate and pup survival, but no one has quantified or identified the sources of uncertainty for the two fat mass estimation techniques (labeled-water and truncated cones). The current cones method can provide estimates of proportion adipose tissue in adult females and proportion fat of juveniles in northern elephant seals (M. angustirostris) comparable to labeled-water methods, but it does not work for all cases or species. We reviewed components and assumptions of the technique via measurements of seven early-molt and seven late-molt adult females. We show that seals are elliptical on land, rather than the assumed circular shape, and skin may account for a high proportion of what is often defined as blubber. Also, blubber extends past the neck-to-pelvis region, and comparisons of new and old ultrasound instrumentation indicate previous measurements of sculp thickness may be biased low. Accounting for such differences, and incorporating new measurements of blubber density and proportion of fat in blubber, we propose a modified cones method that can isolate blubber from non-blubber adipose tissue and separate fat into skin, blubber, and core compartments. Lastly, we found that adipose tissue and fat estimates using tritiated water may be biased high during the early molt. Both the tritiated water and modified cones methods had high, but reducible, uncertainty. The improved cones method for estimating body condition allows for more accurate quantification of the various tissue masses and may also be transferrable to other species.