Experimental and modeling study of de novo formation of PCDD/PCDF on MSW fly ash

The effect of sulfur dioxide （SO2） on the formation of polychlorinated dibenzo-p-dioxins （PCDDs） and polychlorinated dibenzofurans （PCDFs） has been studied in an entrained-flow reactor （EFR） under simulated waste combustion conditions. A chlorination model based on conditional probability was employed to evaluate the homologue patterns of PCDDs and PCDFs. Results revealed that the presence of SO2 did not alter the formation pathway although SO2 suppressed PCDD/F formation. The prediction model of PCDF showed good agreement with the experimental data （R--0.95）, whereas the prediction for PCDDs did not correlate well with the experimental data. This may be explained because potential chlorination pathways play a significant role in PCDF formation, whereas PCDDs are mainly formed through condensation reactions. Furthermore, the result indicated that the steric hindrance during formation has more effects on PCDD than on PCDF due to the symmetric molecular structures of PCDDs.     

Three ways to deliver a net positive impact with biodiversity offsets

Biodiversity offsetting is the practice of using conservation actions, such as habitat restoration, management, or protection, to compensate for ecological losses caused by development activity, including construction projects. The typical goal of offsetting is no net loss (NNL), which means that all ecological losses are compensated for by commensurate offset gains. We focused on a conceptual and methodological exploration of net positive impact (NPI), an ambitious goal that implies commitment beyond NNL and that has recently received increasing attention from big business and environmental nongovernmental organizations. We identified 3 main ways NPI could be delivered: use of an additional NPI multiplier; use of slowly developing permanent offsets to deliver additional gains after NNL has first been reached during a shorter offset evaluation time interval; and the combination of permanent offsets with partially temporary losses. An important and novel variant of the last mechanism is the use of an alternate mitigation hierarchy so that gains from the traditional third step of the mitigation hierarchy (i.e., onsite rehabilitation) are no longer be counted toward reduced offset requirements. The outcome from these 3 factors is that for the same ecological damage, larger offsets will be required than previously, thereby improving offset success. As a corollary, we show that offsets are NNL only at 1 ephemeral point in time, before which they are net negative and after which they become either NPI or net negative impact, depending on whether permanent offsets are combined with partially temporary losses or if temporary offset gains are combined with partially permanent losses. To achieve NPI, offsets must be made permanent, and they must achieve NNL during an agreed-upon offset evaluation period. An additional NPI-multiplier and use of the modified mitigation hierarchy will deliver additional NPI gains. Achieving NPI is fully conditional on prior achievement of NNL, and NNL offsets have been frequently observed to fail due to inadequate policy requirements, poor planning, or incomplete implementation. Nevertheless, achieving NPI becomes straightforward if NNL can be credibly reached first.     

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.     

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.     

Reallocating budgets among ongoing and emerging conservation projects

Conserving biodiversity and combating ecological hazards require cost-effective allocation of limited resources among potential management projects. Project priorities, however, can change over time as underlying social-ecological systems progress, novel priorities emerge, and management capabilities evolve. Thus, reallocation of ongoing investments in response to shifting priorities could improve management outcomes and address urgent demands, especially when additional funding is not available immediately. Resource reallocation, however, could incur transaction costs, require additional monitoring and reassessment, and be constrained by ongoing project commitments. Such complexities may prevent managers from considering potentially beneficial reallocation strategies, reducing long-term effectiveness. We propose an iterative project prioritization approach, based on marginal return-on-investment estimation and portfolio optimization, that guides resource reallocation among ongoing and new projects. Using simulation experiments in 2 case studies, we explored how this approach can improve efficacy under varying reallocation constraints, frequencies, costs, and rates of project portfolio change. Periodic budget reallocation could enhance the management of stochastically emerging invasive weeds in Australia and thus reduce the overall risk by up to 50% compared with a static budget. Reallocation frequency and the rate of new weed incursion synergistically increased the conservation gains achieved by allowing unconstrained reallocation. Conversely, budget reallocation would not improve the International Union for Conservation of Nature conservation status of threatened Australian birds due to slow rates of transition among conservation states; extinction risk could increase if portfolio reassessment is costly. Although other project prioritization studies may recommend periodic reassessment and reallocation, our findings revealed conditions when reallocation is valuable and demonstrated a structured approach that can help conservation agencies schedule and implement iterative budget-allocation decisions cost-effectively.     

Multiyear monitoring of survival following mitigation-driven translocation of a long-lived threatened reptile

Translocation is used by managers to mitigate the negative impacts of development on species. Moving individuals to a new location is challenging, and many translocation attempts have failed. Robust, posttranslocation monitoring is therefore important for evaluating effects of translocation on target species. We evaluated the efficacy of a translocation designed to mitigate the effects of a utility-scale solar energy project on the U.S. federally listed Mojave desert tortoise (Gopherus agassizii). The species is a long-lived reptile threatened by a variety of factors, including habitat loss due to renewable energy development in the Mojave Desert and portions of the Colorado Desert in southern California (southwestern United States). We translocated 58 individual tortoises away from the project's construction site and intensively monitored them over 5 years (2012–2017). We monitored these individuals and tortoises located in the translocation release area (resident tortoises; n = 112) and control tortoises (n = 149) in a nearby location. We used our tortoise encounter data and known-fate survival models to estimate annual and cumulative survival. Translocated tortoises in each of 2 size classes (120–160 mm, >160 mm) did not survive at lower rates than resident and control tortoises over the study period. For models with different sets of biotic and abiotic covariates, annual and cumulative estimates of survival were always >0.87 and >0.56, respectively. Larger tortoises tended to have higher survival, but translocated tortoises were not differentially affected by the covariates used to model variation in survival. Based on these findings, our translocation design and study protocols could inform other translocation projects for desert species. Our case study highlights the benefits of combining rigorous scientific monitoring with well-designed, mitigation-driven management actions to reduce the negative effects of development on species of conservation concern.     

Use of ambiguous detections to improve estimates from species distribution models

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.     

Importance of species translocations under rapid climate change

Species that cannot adapt or keep pace with a changing climate are likely to need human intervention to shift to more suitable climates. While hundreds of articles mention using translocation as a climate-change adaptation tool, in practice, assisted migration as a conservation action remains rare, especially for animals. This is likely due to concern over introducing species to places where they may become invasive. However, there are other barriers to consider, such as time-frame mismatch, sociopolitical, knowledge and uncertainty barriers to conservationists adopting assisted migration as a go-to strategy. We recommend the following to advance assisted migration as a conservation tool: attempt assisted migrations at small scales, translocate species with little invasion risk, adopt robust monitoring protocols that trigger an active response, and promote political and public support.     

Effect of decision rules in choice experiments on hunting and bushmeat trade

Providing insight on decisions to hunt and trade bushmeat can facilitate improved management interventions that typically include enforcement, alternative employment, and donation of livestock. Conservation interventions to regulate bushmeat hunting and trade have hitherto been based on assumptions of utility- (i.e., personal benefits) maximizing behavior, which influences the types of incentives designed. However, if individuals instead strive to minimize regret, interventions may be misguided. We tested support for 3 hypotheses regarding decision rules through a choice experiment in Tanzania. We estimated models based on the assumptions of random utility maximization (RUM) and pure random regret maximization (P-RRM) and combinations thereof. One of these models had an attribute-specific decision rule and another had a class-specific decision rule. The RUM model outperformed the P-RRM model, but the attribute-specific model performed better. Allowing respondents with different decision rules and preference heterogeneity within each decision rule in a class-specific model performed best, revealing that 55% of the sample used a P-RRM decision rule. Individuals using a P-RRM decision rule responded less to enforcement, salary, and livestock donation than did individuals using the RUM decision rule. Hence, 3 common strategies, enforcement, alternative income-generating activities, and providing livestock as a substitute protein, are likely less effective in changing the behavior of more than half of respondents. Only salary elicited a large (i.e. elastic) response, and only for one RUM class. Policies to regulate the bushmeat trade based solely on the assumption of individuals maximizing utility, may fail for a significant proportion of the sample. Despite the superior performance of models that allow both RUM and P-RRM decision rules there are drawbacks that must be considered before use in the Global South, where very little is known about the social–psychology of decision making.     

Generation lengths of the world's birds and their implications for extinction risk

Birds have been comprehensively assessed on the International Union for Conservation of Nature (IUCN) Red List more times than any other taxonomic group. However, to date, generation lengths have not been systematically estimated to scale population trends when undertaking assessments, as required by the criteria of the IUCN Red List. We compiled information from major databases of published life-history and trait data for all birds and imputed missing life-history data as a function of species traits with generalized linear mixed models. Generation lengths were derived for all species, based on our modeled values of age at first breeding, maximum longevity, and annual adult survival. The resulting generation lengths varied from 1.42 to 27.87 years (median 2.99). Most species (61%) had generation lengths <3.33 years, meaning that the period of 3 generations—over which population declines are assessed under criterion A—was <10 years, which is the value used for IUCN Red List assessments of species with short generation times. For these species, our trait-informed estimates of generation length suggested that 10 years is a robust precautionary value for threat assessment. In other cases, however, for whole families, genera, or individual species, generation length had a substantial impact on their estimated extinction risk, resulting in higher extinction risk in long-lived species than in short-lived species. Although our approach effectively addressed data gaps, generation lengths for some species may have been underestimated due to a paucity of life-history data. Overall, our results will strengthen future extinction-risk assessments and augment key databases of avian life-history and trait data.