Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The role of digital data entry in participatory environmental monitoring

Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.     

Molten carbonate fuel cells fed with biogas: Combating H2S

The use of biomass and waste to produce alternative fuels, due to environmental and energy security reasons, is a high-quality solution especially when integrated with high efficiency fuel cell applications. In this article we look into the coupling of an anaerobic digestion process of organic residues to electrochemical conversion to electricity and heat through a molten carbonate fuel cell (MCFC). In particular the pathway of the exceedingly harmful compound hydrogen sulphide (H₂S) in these phases is analysed. Hydrogen sulphide production in the biogas is strongly interrelated with methane and/or hydrogen yield, as well as with operating conditions like temperature and pH. When present in the produced biogas, this compound has multiple negative effects on the performance and durability of an MCFC. Therefore, there are important issues of integration to be solved.Three general approaches to solve the sulphur problem in the MCFC are possible. The first is to prevent the formation of hydrogen sulphide at the source: favouring conditions that inhibit its production during fermentation. Secondly, to identify the sulphur tolerance levels of the fuel cell components currently in use and develop sulphur-tolerant components that show long-term electrochemical performance and corrosion stability. The third approach is to remove the generated sulphur species to very low levels before the gas enters the fuel cell.     

Anhydrous ethanol production by extractive distillation: A solvent case study

Production of anhydrous ethanol in large scale has been made by extractive distillation using conventional solvents like ethylene-glycol. In the present paper, extractive distillation process is studied to obtain pure ethanol using ethylene-glycol and tetraethylene-glycol as solvents. Residue curve maps are used to analyse the proposed distillation processes in interpreting mixture behaviours and feasibility of distillation columns. The industrial process is simulated at steady state from residue curve map analysis. Simulation results for the ethanol/water mixture using ethylene-glycol, the conventional solvent, and tetraethylene-glycol, an alternative solvent, are presented. These results showed that the process using tetraethylene-glycol is reliable, although it requires more energy than the process with ethylene-glycol. However, ethylene-glycol has a considerable toxicity level while tetraethylene-glycol is non-toxic.     

Evaluating the Quality of Citizen‐Scientist Data on Pollinator Communities

Abstract: Concerns about pollinator declines have grown in recent years, yet the ability to detect changes in abundance, taxonomic richness, and composition of pollinator communities is hampered severely by the lack of data over space and time. Citizen scientists may be able to extend the spatial and temporal extent of pollinator monitoring programs. We developed a citizen‐science monitoring protocol in which we trained 13 citizen scientists to observe and classify floral visitors at the resolution of orders or super families (e.g., bee, wasp, fly) and at finer resolution within bees (superfamily Apoidea) only. We evaluated the protocol by comparing data collected simultaneously at 17 sites by citizen scientists (observational data set) and by professionals (specimen‐based data set). The sites differed with respect to the presence and age of hedgerows planted to improve habitat quality for pollinators. We found significant, positive correlations among the two data sets for higher level taxonomic composition, honey bee (Apis mellifera) abundance, non‐Apis bee abundance, bee richness, and bee community similarity. Results for both data sets also showed similar trends (or lack thereof) in these metrics among sites differing in the presence and age of hedgerows. Nevertheless, citizen scientists did not observe approximately half of the bee groups collected by professional scientists at the same sites. Thus, the utility of citizen‐science observational data may be restricted to detection of community‐level changes in abundance, richness, or similarity over space and time, and citizen‐science observations may not reliably reflect the abundance or frequency of occurrence of specific pollinator species or groups.     

Leading and lagging: Process safety climate–incident relationships at one year

In order to evaluate the leading and lagging effects of process safety climate on incidents, we correlated safety climate survey data with organizational safety records from before and after the survey time period. We obtained data from a large, multinational organization with manufacturing operations involving a number of complex processes, chemicals, and hazardous substances. A total of 7728 employees from 62 sites responded to a safety climate survey in 2007. Individual responses were aggregated to the site-level and matched to site-level organizational records of process safety incidents 1 year before and 1 year after survey administration. Employees’ perceptions of good routine housekeeping were significantly related to environmental impact incidents as both a leading and a lagging indicator, as well as fires/explosions and property damage outcomes. Employees’ perceptions of systems to prevent backlogs and the extent to which health and safety problems are promptly corrected were also related to environmental releases and fires/explosions. Implications for process safety climate research, organizational survey strategies, and organizational climate change are discussed.     

Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods

We here examine species distribution models for a Neotropical anuran restricted to ombrophilous areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two calibration areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted area around 10⁶ km² larger than predicted by other SDMs. With increased calibration area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased calibration area, while MAXENT models were more consistent across calibration areas. MAXENT was the only method that retrieved consistent predictions across calibration areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms.