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

Sensitivity index for conservation priority ranking in the oil spill response: A case study for the coastal and marine species and habitat types in the Baltic Sea

Numerous anthropogenic stressors, such as habitat alteration and nutrient enrichment, affect coastal and marine ecosystems around the globe. An additional threat to these ecosystems is accidental oil pollution. The proactive planning of efficient oil spill response actions requires a firm understanding of the spatiotemporal distribution of ecological coastal values at stake, and how these values can be protected in case of an oil spill. In this paper, literature and expert knowledge regarding the life history attributes of coastal and marine species were used to build a sensitivity index to assess the differences in the potential of species and habitat types to be safeguarded from oil. The developed index prioritizes sensitive species and habitat types based on 1) their conservation value, 2) the oil-induced loss and recovery potential, and 3) the effectiveness of oil retention booms and protection sheets to safeguard these entities. The final sensitivity index compares the predicted difference in the state of populations and habitat types five years after an oil spill with and without protective actions. The higher the difference, the more worthwhile the management actions are. Hence, compared to other oil spill sensitivity and vulnerability indexes presented in the literature, the developed index considers the usefulness of protective measures explicitly. We apply the developed index to a case study area in the Northern Baltic Sea to demonstrate the approach. It is noteworthy that the developed index is applicable to other areas as well, as the approach is based on the biological attributes of species and habitat types instead of individual occurrences.

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.

Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social-ecological systems

When evaluating the impact of a biodiversity conservation intervention, a counterfactual is typically needed. Counterfactuals are possible alternative system trajectories in the absence of an intervention. Comparing observed outcomes against the chosen counterfactual allows the impact (change attributable to the intervention) to be determined. Because counterfactuals by definition never occur, they must be estimated. Sometimes, there may be many plausible counterfactuals, including various drivers of biodiversity change and defined on a range of spatial or temporal scales. Here, we posit that, by definition, conservation interventions always take place in social-ecological systems (SES) (i.e., ecological systems integrated with human actors). Evaluating the impact of an intervention in an SES, therefore, means taking into account the counterfactuals assumed by different human actors. Use of different counterfactuals by different actors will give rise to perceived differences in the impacts of interventions, which may lead to disagreement about its success or the effectiveness of the underlying approach. Despite that there are biophysical biodiversity trends, it is often true that no single counterfactual is definitively the right one for conservation assessment, so multiple evaluations of intervention efficacy could be considered justifiable. Therefore, we propose calculating the sum of perceived differences, which captures the range of impact estimates associated with different actors in a given SES. The sum of perceived differences gives some indication of how closely actors in an SES agree on the impacts of an intervention. We applied the concept of perceived differences to a set of global, national, and regional case studies (e.g., global realization of Aichi Target 11 for marine protected areas, effect of biodiversity offsetting on vegetation condition in Australia, and influence of conservation measures on an endangered ungulate in Central Asia). We explored approaches for minimizing the sum, including a combination of negotiation and structured decision making, careful alignment of expectations on scope and measurement, and explicit recognition of any intractable differences between stakeholders.

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.

Incorporating stakeholders' values into environmental decision support: A Bayesian Belief Network approach

Participatory modelling increases the transparency of environmental planning and management processes and enhances the mutual understanding among different parties. We present a sequential probabilistic approach to involve stakeholders' views in the formal decision support process. A continuous Bayesian Belief Network (BBN) model is used to estimate population parameters for stakeholder groups, based on samples of individual value judgements. The approach allows quantification and visualization of the variability in views among and within stakeholder groups. Discrete BBN is populated with these parameters, to summarize and visualize the information and to link it to a larger decision analytic influence diagram (ID). As part of ID, the resulting discrete BBN element serves as a distribution-form decision criteria in probabilistic evaluation of alternative management strategies, to help find a solution that represents the optimal compromise in the presence of potentially conflicting objectives. We demonstrate our idea using example data from the field of marine spatial planning. However, this approach is applicable to many types of management cases. We suggest that by advancing the mutual understanding and concrete participation this approach can further facilitate the stakeholder involvement also during the various stages of the environmental management process.