Methods to determine response factors for infrared gas imagers used as quantitative measurement devices

Response factors (RF) can be used to characterize relative sensitivity of one compound vs. another compound for the same measurement instrument. Use of RF allows the analysts/operators to calibrate the instrument with one compound and make measurement for a large number of compounds. This method is adopted for Flame Ionization Detector (FID) based survey instruments used in the Leak Detection and Repair (LDAR) practice for control of fugitive emissions of volatile organic compounds. Gas detecting Infrared (IR) cameras have been used for leak detection. However, the RF for IR cameras has not been well established despite some attempt to develop a method for IR camera RF. In addition to a method proposed earlier (Method 1), two new methods for IR camera RF are proposed in this paper: Method 2 based on theoretical approach and Method 3 based on experimental approach. All three methods are examined and compared. Both Methods 2 and 3 have shown the ability to characterize the behavior of RF for various compounds and substantially higher accuracy than Method 1. Method 2 provides a mechanism to generate RF for a large number of compounds without conducting experiments, and is recommended for implementation. The RF derived from this method can be used both in the emerging field of Quantitative Optical Gas Imaging (QOGI) and to answer the most common question that IR camera users ask—whether a particular compound can be imaged by a particular IR camera.Implications: Infrared imager is an efficient tool for detecting gas leaks from process equipment and has been used in leak detection and repair (LDAR) programs for control of fugitive emissions. However, the information regarding which chemical compounds can be imaged and how sensitive a given infrared imager is for various compounds is limited. A theoretical method is presented in this paper that can answer these questions without conducting resource-intensive experiment. The results of this theoretical method has good agreement with experimental data. The method has been used to predict relative sensitivity for 398 compounds.     

Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits

Wetlands provide multiple ecosystem services, the sustainable use of which requires knowledge of the underlying ecological mechanisms. Functional traits, particularly the community-weighted mean trait (CWMT), provide a strong link between species communities and ecosystem functioning. We here combine species distribution modeling and plant functional traits to estimate the direction of change of ecosystem processes under climate change. We model changes in CWMT values for traits relevant to three key services, focusing on the regional species pool in the Norrström area (central Sweden) and three main wetland types. Our method predicts proportional shifts toward faster growing, more productive and taller species, which tend to increase CWMT values of specific leaf area and canopy height, whereas changes in root depth vary. The predicted changes in CWMT values suggest a potential increase in flood attenuation services, a potential increase in short (but not long)-term nutrient retention, and ambiguous outcomes for carbon sequestration.     

Creative and disruptive elements in Norway´s climate policy mix: the small-state perspective

ABSTRACT

Recent scholarship has argued that effective and credible national climate policy mixes should encompass measures that promote new low-carbon technologies alongside those instruments aimed at constraining and phasing out support for existing polluting industries. The creative and disruptive policy measures in Norway´s climate policy mix are analysed by focusing on both national and international climate mitigation efforts. Norway´s climate policy mix at home has been more ambitious in the transport sector with a growing electric vehicle market than in the energy sector where niche support and disruptive policies have remained weak. Abroad, Norway has been increasingly active in supporting new low-carbon technologies and disrupting the fossil-fuel industry, especially coal. This is explained by the consensus-seeking and oil and gas dominated small-state social-investment political economy in Norway, combined with a forward-looking foreign policy based on norm-setting and multilateralism.     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Identifying impediments to long‐distance mammal migrations

In much of the world, the persistence of long‐distance migrations by mammals is threatened by development. Even where human population density is relatively low, there are roads, fencing, and energy development that present barriers to animal movement. If we are to conserve species that rely on long‐distance migration, then it is critical that we identify existing migration impediments. To delineate stopover sites associated with anthropogenic development, we applied Brownian bridge movement models to high‐frequency locations of pronghorn (Antilocapra americana) in the Greater Yellowstone Ecosystem. We then used resource utilization functions to assess the threats to long‐distance migration of pronghorn that were due to fences and highways. Migrating pronghorn avoided dense developments of natural gas fields. Highways with relatively high volumes of traffic and woven‐wire sheep fence acted as complete barriers. At crossings with known migration bottlenecks, use of high?quality forage and shrub habitat by pronghorn as they approached the highway was lower than expected based on availability of those resources. In contrast, pronghorn consistently utilized high?quality forage close to the highway at crossings with no known migration bottlenecks. Our findings demonstrate the importance of minimizing development in migration corridors in the future and of mitigating existing pressure on migratory animals by removing barriers, reducing the development footprint, or installing crossing structures. Identificación de los Impedimentos para las Migraciones de Larga Distancia de Mamíferos     

A framework for evaluating the impact of the IUCN Red List of threatened species

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a species extinction risk assessment tool, has been guiding conservation efforts for over 5 decades. It is widely assumed to have been instrumental in preventing species from moving closer to extinction and driving recoveries. However, the impact of the IUCN Red List in guiding conservation has not been evaluated. We conducted, transcribed, and coded interviews with experts who use the IUCN Red List across a range of sectors to understand how the list is used in conservation. We developed a theory of change to illustrate how and why change is expected to occur along causal pathways contributing to the long-term goal of the IUCN Red List and an evaluation framework with indicators for measuring the impact of the IUCN Red List in generating scientific knowledge, raising awareness among stakeholders, designating priority conservation sites, allocating funding and resources, influencing development of legislation and policy, and guiding targeted conservation action (key themes). Red-list assessments were the primary input leading to outputs (scientific knowledge, raised awareness), outcomes (better informed priority setting, access to funding and resource availability, improved legislation and policy), and impact (implemented conservation action leading to positive change) that have resulted in achievement of IUCN Red List goals. To explore feasibility of attributing the difference made by the IUCN Red List across themes, we studied increased scientific knowledge, raised awareness, access to funding and resource allocation, and increased conservation activity. The feasibility exploration showed increased scientific knowledge over time identified through positive trends in publications referring to the IUCN Red List in the literature; raised awareness of the list following high IUCN activity identified by peaks in online search activity; an increased proportion of conservation funding bodies requesting IUCN Red List status in the application process; and, based on interviews with Amphibian Specialist Group members, red-list assessments were essential in connecting relevant stakeholders and ensuring conservation action. Although we identified the IUCN Red List as a vital tool in global conservation efforts, it was challenging to measure specific impacts because of its ubiquitous nature. We are the first to identify the influence of the IUCN Red List on conservation.     

Application of multiple-population viability analysis to evaluate species recovery alternatives

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5–10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.