Quantifying relationships between bird and butterfly community shifts and environmental change.

Quantifying the manner in which ecological communities respond during a time of decreasing precipitation is a first step in understanding how they will respond to longer-term climate change. Here we coupled analysis of interannual variability in remotely sensed data with analyses of bird and butterfly community changes in montane meadow communities of the Greater Yellowstone Ecosystem. Landsat satellite imagery was used to classify these meadows into six types along a hydrological gradient. The northern portion of the ecosystem, or Gallatin region, has smaller mean patch sizes separated by ridges of mountains, whereas the southern portion of the ecosystem, or Teton region, has much larger patches within the Jackson Hole valley. Both support a similar suite of butterfly and bird species. The Gallatin region showed more overall among-year variation in the normalized difference vegetation index (NDVI) when meadow types were pooled within regions, perhaps because the patch sizes are smaller on average. Bird and butterfly communities showed significant relationships relative to meadow type and NDVI. We identified several key species that are tightly associated with specific meadow types along the hydrological gradient. Comparing taxonomic groups, fewer birds showed specific habitat affinities than butterflies, perhaps because birds are responding to differences in habitat structure among meadow types and using the landscape at a coarser scale than the butterflies. Comparing regions, the Teton region showed higher predictability of community assemblages as compared to the Gallatin region. The Gallatin region exhibited more significant temporal trends with respect to butterflies. Butterfly communities in wet meadows showed a distinctive shift along the hydrological gradient during a drought period (1997-2000). These results imply that the larger Teton meadows will show more predictable (i.e., static) species-habitat associations over the long term, but that the smaller Gallatin meadows may be an area that will exhibit the effects of global climate change faster.     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

Evaluation of airborne particulate matter and metals data in personal,indoor and outdoor environments using ED-XRF and ICP-MS and co-located duplicate samples

Factors and sources affecting measurement uncertainty associated with monitoring metals in airborne particulate matter (PM) were investigated as part of the Windsor, Ontario Exposure Assessment Study (WOEAS). The assessment was made using co-located duplicate samples and a comparison of two analytical approaches: ED-XRF and ICP-MS. Sampling variability was estimated using relative percent difference (RPD) of co-located duplicate samples. The comparison of ICP-MS and ED-XRF results yields very good correlations (R² ≥ 0.7) for elements present at concentrations that pass both ICP-MS and ED-XRF detection limits (e.g. Fe, Mn, Zn, Pb and Cu). PM concentration ranges (median, sample number) of 24-h indoor PM₁₀ and personal PM₁₀ filters, and outdoor PM_2.5 filters were determined to be 2.2–40.7 (11.0, n = 48) μg m^?3, 8.0–48.3 (11.9, n = 48) μg m^?3, and 17.1–42.3 (21.6, n = 18) μg m^?3, respectively. The gravimetric analytical results reveal that the variations in PM mass measurements for same-day sampling are insignificant compared to temporal or spatial variations: 92%, 100% and 96% of indoor, outdoor and personal duplicate samples, respectively, pass the quality criteria (RPD ≤ 20%). Uncertainties associated with ED-XRF elemental measurements of S, Ca, Mn, Fe and Zn for 24-h filter samples are low: 78%–100% of the duplicate samples passed the quality criteria. In the case of 24-h filter samples using ICP-MS, more elements passed the quality criteria due to the lower detection limits. These were: Li, Na, K, Ca, Si, Al, V, Fe, Mn, Co, Cu, Mo, Ag, Zn, Pb, As, Mg, Sb, Sn, Sr, Th, Ti, Tl, and U. Low air concentrations of metals (near or below instrumental detection limits) and/or inadvertent introduction of metal contamination are the main causes for excluding elements based on the pass/fail criteria. Uncertainty associated with elemental measurements must be assessed on an element-by-element basis.     

Comprehensive analysis of the carbon impacts of vehicle intelligent speed control

In recent years sophisticated technologies have been developed to control vehicle speed based on the type of road the vehicle is driven on using Global Positioning Systems and in-car technology that can alter the speed of the vehicle. While reducing the speed of road vehicles is primarily of interest from a safety perspective, vehicle speed is also an important determinant of vehicle emissions and thus these technologies can be expected to have impacts on a range of exhaust emissions. This work analyses the results from a very large, comprehensive field trial that used 20 instrumented vehicles with and without speed control driven almost 500,000 km measuring vehicle speed at 10 Hz. We develop individual vehicle modal emissions models for CO₂ for 30 Euro III and Euro IV cars at a 1-Hz time resolution. Generalized Additive Models were used to describe how emissions from individual vehicles vary depending on their driving conditions, taking account of variable interactions and time-lag effects. We quantify the impact that vehicle speed control has on-vehicle emissions of CO₂ by road type, fuel type and driver behaviour. Savings in CO₂ of ≈6% were found on average for motorway-type roads when mandatory speed control was used compared with base case conditions. For most other types of road, speed control has very little effect on emissions of CO₂ and in some cases can result in increased emissions for low-speed limit urban roads. We also find that there is on average a 20% difference in CO₂ emission between the lowest and highest emitting driver, which highlights the importance of driver behaviour in general as a means of reducing emissions of CO₂.     

Adsorption of arsenate on soils. Part 2: modeling the relationship between adsorption capacity and soil physiochemical properties using 16 Chinese soils

An attempt has been made to elucidate the effects of soil properties on arsenate adsorption by modeling the relationships between adsorption capacity and the properties of 16 Chinese soils. The model produced was validated against three Australian and three American soils. The results showed that nearly 93.8% of the variability in arsenate adsorption on the low-energy surface could be described by citrate-dithionite extractable Fe (Fe(CD)), clay content, organic matter content (OM) and dissolved organic carbon (DOC); nearly 87.6% of the variability in arsenate adsorption on the high-energy surface could be described by Fe(CD), DOC and total arsenic in soils. Fe(CD) exhibited the most important positive influence on arsenate adsorption. Oxalate extractable Al (Al(OX)), citrate-dithionite extractable Al (Al(CD)), extractable P and soil pH appeared relatively unimportant for adsorption of arsenate by soils.     

The ageing effect on the bioaccessibility and fractionation of arsenic in soils from China

Ingestion of contaminated soil has been recognized as an important exposure pathway of arsenic for humans, especially for children through outdoor hand-to-mouth activities. An improved sequential extraction procedure was employed in an attempt to reveal the relationship between bioaccessibility and fractionation of As in five soils from China. Arsenic bioaccessibility in acidic ( approximately pH 4.5) soils reached approximately stable levels after a sharp decline within one week of ageing. In contrast, As bioaccessibility in higher pH (>6.0) soils was found to be significantly higher and took two weeks of ageing to reach stable levels. The artificially added As was more labile than indigenous As. The main proportions of added As were found in the specifically sorbed and amorphous and poorly-crystalline hydrous Fe/Al oxide-bound fractions. Correlation analysis shows that the non-specifically and specifically sorbed As are likely to constitute the main proportion of bioaccessible soil As. The soil content of amorphous and crystalline Fe/Al oxides and soil pH appear to be the key factors controlling, not only the time needed to reach a steady state, but also the magnitude of the bioaccessibility of As added to the soils.