Using decision science to evaluate global biodiversity indices.

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.

Uso de las Ciencias de la Decisión para Evaluar los Índices Globales de Biodiversidad Resumen Los índices globales de biodiversidad se usan para medir el cambio ambiental y el avance hacia los objetivos de conservación, aunque pocos han sido evaluados completamente en cuanto a su capacidad para detectar las tendencias de interés como las declinaciones de especies amenazadas o la función del ecosistema. Evaluamos cualitativamente nueve índices de uso común para dar seguimiento a la biodiversidad a escala global y regional contra cinco criterios relacionados con los objetivos, diseño, comportamiento, incorporación de la incertidumbre y restricciones (p. ej.: costos y disponibilidad de datos) mediante una estrategia estructurada basada en las ciencias de la decisión. La evaluación se basó en la literatura de referencia para los índices disponibles al momento del análisis. Identificamos cuatro vacíos importantes en los índices estudiados: las vías para lograr los objetivos (objetivos medios) no fueron siempre claras o relevantes para los resultados deseados (objetivos fundamentales); el análisis del índice y el entendimiento del comportamiento esperado casi siempre fueron escasos; pocas veces se consideró o explicó la incertidumbre; y casi nunca se consideraron los costos de la implementación. Estos vacíos pueden hacer que los índices sean inadecuados en ciertos contextos de toma de decisiones y son problemáticos para los índices vinculados a los objetivos de biodiversidad y las metas de sustentabilidad. Es de suma importancia asegurarse que los objetivos del índice sean claros y que su diseño esté respaldado por un modelo de procesos relevantes para tratar con los vacíos identificados en nuestro estudio. La aceptación y el uso productivo de los índices mejorarán si el desempeño del índice es evaluado rigurosamente y las suposiciones e incertidumbres se les comunican claramente a los usuarios finales. Lo anterior aumentará la precisión y valor del índice en el seguimiento de los cambios de la biodiversidad y en el apoyo a las decisiones políticas nacionales y mundiales, como el marco de trabajo para la biodiversidad post-2020 establecido por la Convención sobre la Diversidad Biológica.

Forecasting species range dynamics with process-explicit models: matching methods to applications.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

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.

Using multiple lines of evidence to assess the risk of ecosystem collapse.

Effective ecosystem risk assessment relies on a conceptual understanding of ecosystem dynamics and the synthesis of multiple lines of evidence. Risk assessment protocols and ecosystem models integrate limited observational data with threat scenarios, making them valuable tools for monitoring ecosystem status and diagnosing key mechanisms of decline to be addressed by management. We applied the IUCN Red List of Ecosystems criteria to quantify the risk of collapse of the Meso-American Reef, a unique ecosystem containing the second longest barrier reef in the world. We collated a wide array of empirical data (field and remotely sensed), and used a stochastic ecosystem model to backcast past ecosystem dynamics, as well as forecast future ecosystem dynamics under 11 scenarios of threat. The ecosystem is at high risk from mass bleaching in the coming decades, with compounding effects of ocean acidification, hurricanes, pollution and fishing. The overall status of the ecosystem is Critically Endangered (plausibly Vulnerable to Critically Endangered), with notable differences among Red List criteria and data types in detecting the most severe symptoms of risk. Our case study provides a template for assessing risks to coral reefs and for further application of ecosystem models in risk assessment.

Implications of different population model structures for management of threatened plants.

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species-specific threat. Long-term demographic data sets were used to construct an individual-based model (IBM), a complex stage-based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species' life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species' responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.

Detecting extinction risk from climate change by IUCN Red List criteria.

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.

Predicting species distributions for conservation decisions.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

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

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency.

Fire management to combat disease: turning interactions between threats into conservation management.

As the number and intensity of threats to biodiversity increase, there is a critical need to investigate interactions between threats and manage populations accordingly. We ask whether it is possible to reduce the effects of one threat by mitigating another. We used long-term data for the long-lived resprouter, Xanthorrhoea resinosa Pers., to parameterise an individual-based population model. This plant is currently threatened by adverse fire regimes and the pathogen Phytophthora cinnamomi. We tested a range of fire and disease scenarios over various time horizons relevant to the population dynamics of the species and the practicalities of management. While fire does not kill the disease, it does trigger plant demographic responses that may promote population persistence when disease is present. Population decline is reduced with frequent fires because they promote the greatest number of germination events, but frequent fires reduce adult stages, which is detrimental in the long term. Fire suppression is the best action for the non-seedling stages but does not promote recruitment. With disease, frequent fire produced the highest total population sizes for shorter durations, but for longer durations fire suppression gave the highest population sizes. When seedlings were excluded, fire suppression was the best action. We conclude that fire management can play an important role in mitigating threats posed by this disease. The best approach to reducing declines may be to manage populations across a spatial mosaic in which the sequence of frequent fires and suppression are staggered across patches depending on the level of disease at the site.

Optimal eradication: when to stop looking for an invasive plant.

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter.

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.

A practical guide to the application of the IUCN Red List of Ecosystems criteria.

The newly developed IUCN Red List of Ecosystems is part of a growing toolbox for assessing risks to biodiversity, which addresses ecosystems and their functioning. The Red List of Ecosystems standard allows systematic assessment of all freshwater, marine, terrestrial and subterranean ecosystem types in terms of their global risk of collapse. In addition, the Red List of Ecosystems categories and criteria provide a technical base for assessments of ecosystem status at the regional, national, or subnational level. While the Red List of Ecosystems criteria were designed to be widely applicable by scientists and practitioners, guidelines are needed to ensure they are implemented in a standardized manner to reduce epistemic uncertainties and allow robust comparisons among ecosystems and over time. We review the intended application of the Red List of Ecosystems assessment process, summarize 'best-practice' methods for ecosystem assessments and outline approaches to ensure operational rigour of assessments. The Red List of Ecosystems will inform priority setting for ecosystem types worldwide, and strengthen capacity to report on progress towards the Aichi Targets of the Convention on Biological Diversity. When integrated with other IUCN knowledge products, such as the World Database of Protected Areas/Protected Planet, Key Biodiversity Areas and the IUCN Red List of Threatened Species, the Red List of Ecosystems will contribute to providing the most complete global measure of the status of biodiversity yet achieved.

Scientific foundations for an IUCN Red List of ecosystems.

An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world's ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity.