Near-surface monitoring for the ZERT shallow CO2 injection project

As part of a collaborative effort operated by the Zero Emission Research and Technology Center (ZERT), a series of two shallow releases of CO₂ was performed at a test site in Bozeman, MT. The purpose of the experiment was to simulate possible leakage scenarios from a carbon capture and storage operation in order to further develop and verify monitoring technologies used to characterize and quantify the release of CO₂. The project included collaboration with several research groups and organizations. Presented here are the results of soil–gas monitoring conducted by researchers from the National Energy Technology Laboratory, including CO₂ flux measurement, soil–gas analysis, perfluorocarbon tracer monitoring, and soil resistivity measurements. Together, these methods proved to be effective in detecting and characterizing leakage in the near-surface.     

Threats to biodiversity from cumulative human impacts in one of North America's last wildlife frontiers

Land‐use change is the largest proximate threat to biodiversity yet remains one of the most complex to manage. In British Columbia (BC), where large mammals roam extensive tracts of intact habitat, continued land‐use development is of global concern. Extant mammal diversity in BC is unrivalled in North America owing, in part, to its unique position at the intersection of alpine, boreal, and temperate biomes. Despite high conservation values, understanding of cumulative ecological impacts from human development is limited. Using cumulative‐effects‐assessment (CEA) methods, we assessed the current human footprint over 16 regional ecosystems and 7 large mammal species. Using historical and current range estimates of the mammals, we investigated impacts of human land use on species’ persistence. For ecosystems, we found that bunchgrass, coastal Douglas fir, and ponderosa pine have been subjected to over 50% land‐use conversion, and over 85% of their spatial extent has undergone either direct or estimated indirect impacts. Of the mammals we considered, wolves were the least affected by land conversion, yet all species had reduced ranges compared with historical estimates. We found evidence of a hard trade‐off between development and conservation, most clearly for mammals with large distributions and ecosystems with high levels of conversion. Rather than serve as a platform to monitor species decline, we strongly advocate these data be used to inform land‐use planning and to assess current conservation efforts. More generally, CEAs offer a robust tool to inform wildlife and habitat conservation at scale.     

Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species’ sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human‐dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.     

Assessing the likely effectiveness of multispecies management for imperiled desert fishes with niche overlap analysis

A critical decision in species conservation is whether to target individual species or a complex of ecologically similar species. Management of multispecies complexes is likely to be most effective when species share similar distributions, threats, and response to threats. We used niche overlap analysis to assess ecological similarity of 3 sensitive desert fish species currently managed as an ecological complex. We measured the amount of shared distribution of multiple habitat and life history parameters between each pair of species. Habitat use and multiple life history parameters, including maximum body length, spawning temperature, and longevity, differed significantly among the 3 species. The differences in habitat use and life history parameters among the species suggest they are likely to respond differently to similar threats and that most management actions will not benefit all 3 species equally. Habitat restoration, frequency of stream dewatering, non‐native species control, and management efforts in tributaries versus main stem rivers are all likely to impact each of the species differently. Our results demonstrate that niche overlap analysis provides a powerful tool for assessing the likely effectiveness of multispecies versus single‐species conservation plans. Evaluación de la Posible Efectividad del Manejo Multi‐Especie paraPeces de Desierto en Peligro Mediante el Análisis de Traslape de Nichos     

Improving credibility and transparency of conservation impact evaluations through the partial identification approach

The fundamental challenge of evaluating the impact of conservation interventions is that researchers must estimate the difference between the outcome after an intervention occurred and what the outcome would have been without it (counterfactual). Because the counterfactual is unobservable, researchers must make an untestable assumption that some units (e.g., organisms or sites) that were not exposed to the intervention can be used as a surrogate for the counterfactual (control). The conventional approach is to make a point estimate (i.e., single number along with a confidence interval) of impact, using, for example, regression. Point estimates provide powerful conclusions, but in nonexperimental contexts they depend on strong assumptions about the counterfactual that often lack transparency and credibility. An alternative approach, called partial identification (PI), is to first estimate what the counterfactual bounds would be if the weakest possible assumptions were made. Then, one narrows the bounds by using stronger but credible assumptions based on an understanding of why units were selected for the intervention and how they might respond to it. We applied this approach and compared it with conventional approaches by estimating the impact of a conservation program that removed invasive trees in part of the Cape Floristic Region. Even when we used our largest PI impact estimate, the program's control costs were 1.4 times higher than previously estimated. PI holds promise for applications in conservation science because it encourages researchers to better understand and account for treatment selection biases; can offer insights into the plausibility of conventional point‐estimate approaches; could reduce the problem of advocacy in science; might be easier for stakeholders to agree on a bounded estimate than a point estimate where impacts are contentious; and requires only basic arithmetic skills.     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Future habitat loss and extinctions driven by land‐use change in biodiversity hotspots under four scenarios of climate‐change mitigation

Numerous species have been pushed into extinction as an increasing portion of Earth's land surface has been appropriated for human enterprise. In the future, global biodiversity will be affected by both climate change and land‐use change, the latter of which is currently the primary driver of species extinctions. How societies address climate change will critically affect biodiversity because climate‐change mitigation policies will reduce direct climate‐change impacts; however, these policies will influence land‐use decisions, which could have negative impacts on habitat for a substantial number of species. We assessed the potential impact future climate policy could have on the loss of habitable area in biodiversity hotspots due to associated land‐use changes. We estimated past extinctions from historical land‐use changes (1500–2005) based on the global gridded land‐use data used for the Intergovernmental Panel on Climate Change Fifth Assessment Report and habitat extent and species data for each hotspot. We then estimated potential extinctions due to future land‐use changes under alternative climate‐change scenarios (2005–2100). Future land‐use changes are projected to reduce natural vegetative cover by 26‐58% in the hotspots. As a consequence, the number of additional species extinctions, relative to those already incurred between 1500 and 2005, due to land‐use change by 2100 across all hotspots ranged from about 220 to 21000 (0.2% to 16%), depending on the climate‐change mitigation scenario and biological factors such as the slope of the species–area relationship and the contribution of wood harvest to extinctions. These estimates of potential future extinctions were driven by land‐use change only and likely would have been higher if the direct effects of climate change had been considered. Future extinctions could potentially be reduced by incorporating habitat preservation into scenario development to reduce projected future land‐use changes in hotspots or by lessening the impact of future land‐use activities on biodiversity within hotspots.