Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Ecological network analyses and their use for establishing reference domain in functional assessment of an estuary

We developed a framework to use ecological network analysis for functional assessment of large aquatic ecosystems in the context of ecosystem-based management. We established a reference domain for the Neuse River Estuary, North Carolina, USA, from changes over time. Four reference network models of the trophic structure of the estuary during early and late summers of 1997 and 1998 were constructed and analyzed. The estuary has experienced various symptoms of eutrophication during the past 20 years, including summer-time hypoxia and fish kills. The networks were used to quantify indices of nominal trophic dynamics and their variation. The ratio of biomass of nekton to that of macrobenthos, derived from network construction, was used to index severity of eutrophication and to promote accessibility of ecological network analysis to environmental management. The ratio increased from early to late summer, and network metrics demonstrated a variety of responses in association with that change. Some variables from network analysis, especially related to consumers, reflected some but not all of this change. Others reflected the most severe increase in the ratio in late summer 1997 when hypoxia was most extensive. We evaluated uncertainty and the modulating effects of hierarchy by comparing variation of input biomasses with integrative response variables. Relative variation in input variables was generally greater than that of the integrative response variables as predicted by hierarchy theory. Ecological network analysis has previously served as support for ecosystem-based management of large aquatic systems with some success. However, its use can be enhanced by making it more accessible to environmental managers and policy makers. Ways to do this include promoting simple metrics from network construction and explicitly associating network analysis to concepts familiar to the management community, such as functional assessment and reference.     

A simple model for simulation of insect pheromone dispersion within forest canopies

Synthetic pheromones and other behavioral chemicals are used by land managers to prevent insect-caused tree mortality or crop failure in forest and agricultural systems. Currently, no method exists to continuously measure pheromone concentration or movement in real-time. To improve our understanding of pheromone fate and transport under different forest canopies, results from a set of surrogate pheromone (sulfur hexafluoride tracer) experimental trials were used to evaluate a simple, instantaneous, three-dimensional Lagrangian dispersion model. The model was designed to predict both instantaneous and time-averaged pheromone concentrations. Overall, the results from the model show simulated time-averaged arc maximum concentrations within a factor of two of the observed data. The model correctly matched the sharp peaks and narrow widths of the meandering plumes observed in the instantaneous data, however the magnitude of the instantaneous peaks was often under-estimated. This model and evaluation provide the basis for a tool that can be used to guide deployment of synthetic pheromones or other semiochemicals for monitoring, mass trapping, or disruption of mating or aggregation.     

Australian agriculture: coping with dangerous climate change

Australian agriculture has operated successfully in one of the world’s most hostile environments for two centuries. However, climate change is posing serious challenges to its ongoing success. Determining what might constitute dangerous climate change for Australian agriculture is not an easy task, as most climate-related risks are associated with changes in the highly uncertain hydrological cycle rather than directly to more predictable changes in temperature. In addition, the adaptive capacity of Australian producers is generally high, as they have had to cope with a highly variable climate in which periodic, severe droughts are the norm. As the underlying global trends in climate interact with the continent’s patterns of natural variability, producers can generally deal with gradual changes in climate but are most concerned about high rates of change in regional and local climates and with abrupt, unexpected shifts in climate patterns. Perhaps the best indicator of dangerous climate change for Australian agriculture is the persistence, or not, of the drying trends in many of the Country’s most productive regions and the strength of the linkage between these trends and global climate change.     

Gasification of refuse derived fuel in a fixed bed reactor for syngas production

Steam gasification of two different refuse derived fuels (RDFs), differing slightly in composition as well as thermal stability, was carried out in a fixed-bed reactor at atmospheric pressure. The proximate and ultimate analyses reveal that carbon and hydrogen are the major components in RDFs. The thermal analysis indicates the presence of cellulose and plastic based materials in RDFs. H2 and CO are found to be the major products, along with CO2 and hydrocarbons resulting from gasification of RDFs. The effect of gasification temperature on H2 and CO selectivities was studied, and the optimum temperature for better H2 and CO selectivity was determined to be 725 degrees C. The calorific value of product gas produced at lower gasification temperature is significantly higher than that of gas produced at higher process temperature. Also, the composition of RDF plays an important role in distribution of products gas. The RDF with more C and H content is found to produce more amounts of CO and H2 under similar experimental conditions. The steam/waste ratio showed a notable effect on the selectivity of syngas as well as calorific value of the resulting product gas. The flow rate of carrier gas did not show any significant effect on products yield or their distribution.     

How Perceived Exposure to Environmental Harm Influences Environmental Behavior in Urban China

Rapid environmental degradation in China makes understanding how perceived exposure to environmental harm influences environmental attitudes and participation in pro-environmental behaviors among the Chinese people crucial. We used a nation-wide survey dataset in urban China to test two hypotheses: experiencing environmental harm directly affects environmental behavior; environmental attitudes mediate the relationship between experiencing environmental harm and environmental behavior. We found respondents who experienced environmental harm had more pro-environmental attitudes. Experiencing environmental harm positively influenced pro-environmental behavior both directly and indirectly through the mediation of pro-environmental attitudes. Among the pro-environmental behaviors, environmental litigation was most strongly related with exposure to environmental harm. Our results suggest that more participation in pro-environmental behaviors may be expected as rapid economic development increases public exposure to environmental harm in urban China.     

Investigating differential binding of polychlorinated dibenzo-p-dioxins/dibenzofurans in soil and soil components using selective supercritical fluid extraction

Supercritical fluid extraction (SFE) with pure carbon dioxide was performed at increasingly strong conditions to investigate differential binding of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) in two impacted soils, in their sieved size fractions, and in small (a few mg) samples of industry-related waste products separated from impacted soil. The binding strengths of PCDD/Fs were shown to be different in the two soils, and in their different soil particle size fractions. As might be expected based on surface area considerations, one soil showed the strongest binding in the smallest (<5 μm) sieved fraction. However, the other soil showed the strongest binding in the larger sized fractions, possibly indicating that process-related particles could be controlling PCDD/F binding. Selective SFE of various types of particles including black carbon and charcoal (separated from soil), and from a suspected process anode residue did show different PCDD/F binding behavior ranging from quite weak binding (charcoal) to very strong binding (anode particles). Shifts to the stronger SFE fractions in the soils after activated carbon treatment agreed well with the decreases previously found in the uptake of PCDD/Fs by earthworms, as well as decreases in their freely-dissolved aqueous concentrations in soil/water slurries. These results show that, as previously demonstrated for PAHs and PCBs, selective SFE can be a useful tool to investigate differences in PCDD/F binding behaviors in impacted soils and sediments and their component parts, as well as a rapid tool for estimating the effectiveness of activated carbon treatments on decreasing the bioavailability of PCDD/Fs in soils and sediments.     

Mapping bundles of ecosystem services reveals distinct types of multifunctionality within a Swedish landscape

Ecosystem services (ES) is a valuable concept to be used in the planning and management of social–ecological landscapes. However, the understanding of the determinant factors affecting the interaction between services in the form of synergies or trade-offs is still limited. We assessed the production of 16 ES across 62 municipalities in the Norrström drainage basin in Sweden. We combined GIS data with publically available information for quantifying and mapping the distribution of services. Additionally, we calculated the diversity of ES for each municipality and used correlations and k-means clustering analyses to assess the existence of ES bundles. We found five distinct types of bundles of ES spatially agglomerated in the landscape that could be explained by regional social and ecological gradients. Human-dominated landscapes were highly multifunctional in our study area and urban densely populated areas were hotspots of cultural services.     

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of ?1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the ‘most’ appropriate system when a review or upgrade of the network infrastructure is required.