Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis.

Knowledge of leaf chemistry, physiology, and life span is essential for global vegetation modeling, but such data are scarce or lacking for some regions, especially in developing countries. Here we use data from 2021 species at 175 sites around the world from the GLOPNET compilation to show that key physiological traits that are difficult to measure (such as photosynthetic capacity) can be predicted from simple qualitative plant characteristics, climate information, easily measured ("soft") leaf traits, or all of these in combination. The qualitative plant functional type (PFT) attributes examined are phylogeny (angiosperm or gymnosperm), growth form (grass, herb, shrub, or tree), and leaf phenology (deciduous vs. evergreen). These three PFT attributes explain between one-third and two-thirds of the variation in each of five quantitative leaf ecophysiological traits: specific leaf area (SLA), leaf life span, mass-based net photosynthetic capacity (Amass), nitrogen content (N(mass)), and phosphorus content (P(mass)). Alternatively, the combination of four simple, widely available climate metrics (mean annual temperature, mean annual precipitation, mean vapor pressure deficit, and solar irradiance) explain only 5-20% of the variation in those same five leaf traits. Adding the climate metrics to the qualitative PFTs as independent factors in the model increases explanatory power by 3-11% for the five traits. If a single easily measured leaf trait (SLA) is also included in the model along with qualitative plant traits and climate metrics, an additional 5-25% of the variation in the other four other leaf traits is explained, with the models accounting for 62%, 65%, 66%, and 73% of global variation in N(mass), P(mass), A(mass), and leaf life span, respectively. Given the wide availability of the summary climate data and qualitative PFT data used in these analyses, they could be used to explain roughly half of global variation in the less accessible leaf traits (A(mass), leaf life span, N(mass), P(mass)); this can be augmented to two-thirds of all variation if climatic and PFT data are used in combination with the readily measured trait SLA. This shows encouraging possibilities of progress in developing general predictive equations for macro-ecology, global scaling, and global modeling.     

Trends in Concentrations and Effects of Persistent Toxic Contaminants in the Great Lakes: Their Significance for Inferring Cause-Effect Relationships and Validating Management Actions

This paper summarizes a workshop on temporal trends in levels and effects of persistent toxic contaminants in the North American Great Lakes. Information on trends in contaminant levels is reasonably good for sediments, fish, and birds, but is scanty or absent for other ecosystem components. Information on trends in effects has been reported for birds, but is scanty or absent for other groups of organisms. In principle, information on differential trends in effects of contaminants could be used to validate or improve hypotheses about cause-effect relationships and to verify the effectiveness of management actions. However, little or no useful information on differential trends appears to be available. Use of trend data for these purposes will require collection of more detailed information and greater attention to conceptual formulation of hypotheses.     

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

• PyLUR comprises four modules for developing and applying a LUR model. • It considers both conventional and novel potential predictor variables. • GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. • Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.     

Modeling lifetime greenhouse gas emissions associated with materials for various end-of-life treatments

This research has developed mathematical models for computing lifetime greenhouse gas (GHG) emissions associated with materials. The models include embodied carbon (EC) emissions from the manufacture of materials, and GHG emissions from incineration, or landfill gas (LFG) production from landfill disposal of the material beyond their service lives. The models are applicable to all materials; however, their applications here are demonstrated for the lumber from a residential building with 50- and 100-year service lives, and with incineration, landfill, and deconstruction as end-of-life treatments. This paper introduces a new metric for lifetime GHG emissions associated with materials termed “Global Warming Impact of Materials (GWIM).” The GWIM is subdivided into two portions: (i) productive portion (GWIM_p) that includes the materials’ emissions until the service life of the facility and (ii) non-productive portion (GWIM_np) which includes the materials’ GHG emissions beyond the service life until they are eliminated from the atmosphere. In place of the current, static, EC measurements (kgCO₂e or MTCO₂e), this model reports the GWIMs in units of kgCO₂e-years or MTCO₂e-years, which includes the effects of “time of use” of a facility. Using the models, this paper has computed GHG reductions by deconstruction, with material recoveries of 30%, 50%, and 70% at demolition for reuse, recycle, or repurpose. A 70% material recovery, after a 50-year service life of the building, affected a savings of 47% and 52% if the remaining 30% debris was incinerated or landfilled respectively. All of the values computed using models checked out with manual calculations.     

Metal contamination in water, sediment and biota from a semi-enclosed coastal area

This study identifies and quantifies the spatial variations of metal contamination in water, sediment and biota: the common cockle (Cerastoderma edule) and the Mermaid’s glove sponge (Haliclona oculata), within a heavily anthropogenically impacted semi-enclosed estuarine–coastal area with a low ability to disperse and flush contaminants (Poole Harbour, UK). The results showed that metal contamination was detected in all environmental compartments. Water was polluted with As, and Hg sediment metals were mostly within “the possible effect range” in which adverse effects occasionally occurs. Cockles had considerable concentrations of Ni, Ag and Hg in areas close to pollution sources, and sponges accumulate Cu and Zn with very high magnitude. A systematic monitoring approach that includes biological monitoring techniques, which covers all embayments, is needed, and an integrated management of the semi-enclosed coastal zones should be based on the overall hydrological characteristics of these sensitive areas and their ability to self‐restore which is different than open coastal zones.     

Determination of the compositional change (1999–2006) in the pine forests of British Columbia due to mountain pine beetle infestation

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia and Alberta is the largest recorded forest pest infestation in Canadian history. We integrate a spatial hierarchy of mountain pine beetle and forest health monitoring data, collected between 1999 and 2006, with provincial forest inventory data, and generate three information products representing 2006 forest conditions in British Columbia: cumulative percentage of pine infested by mountain pine beetle, percentage of pine uninfested, and the change in the percentage of pine on the landscape. All input data were formatted to a standardized spatial representation (1 ha minimum mapping unit), with preference given to the most detailed monitoring data available at a given location for characterizing mountain pine beetle infestation conditions. The presence or absence of mountain pine beetle attack was validated using field data (n?=?2054). The true positive rate for locations of red attack damage over all years was 92%. Classification of attack severity was validated using the Kruskal gamma statistic (γ?=?0.49). Error between the survey data and field data was explored using spatial autoregressive (SAR) models, which indicated that percentage pine and year of infestation were significant predictors of survey error at α?=?0.05. Through the integration of forest inventory and infestation survey data, the total area of pine infested is estimated to be between 2.89 and 4.14 million hectares. The generated outputs add value to existing monitoring data and provide information to support management and modeling applications.     

Semi-automated camera trap image processing for the detection of ungulate fence crossing events

Remote cameras are an increasingly important tool for ecological research. While remote camera traps collect field data with minimal human attention, the images they collect require post-processing and characterization before it can be ecologically and statistically analyzed, requiring the input of substantial time and money from researchers. The need for post-processing is due, in part, to a high incidence of non-target images. We developed a stand-alone semi-automated computer program to aid in image processing, categorization, and data reduction by employing background subtraction and histogram rules. Unlike previous work that uses video as input, our program uses still camera trap images. The program was developed for an ungulate fence crossing project and tested against an image dataset which had been previously processed by a human operator. Our program placed images into categories representing the confidence of a particular sequence of images containing a fence crossing event. This resulted in a reduction of 54.8% of images that required further human operator characterization while retaining 72.6% of the known fence crossing events. This program can provide researchers using remote camera data the ability to reduce the time and cost required for image post-processing and characterization. Further, we discuss how this procedure might be generalized to situations not specifically related to animal use of linear features.     

Competition,Sustainability and Packaging Policy in the UK

This paper examines the use of economic instruments in the implementation of environmental policy, based upon a recent study into the European Union Packaging and Packaging Waste Directive (94/62/EC). It explores the introduction of competitionbased models in the UK implementation of the Directive, and assesses their ability to achieve positive environmentaloutcomes in a cost-efficient manner. Several problems for the competition model are discussed, particularly the fear of profiteering within marketbased systems by some economic operators; the behaviour of economic operators towards non-profitable market segments; the ability of free-market models to engender public participation in recycling programmes; and the failure of private sector solutions to consider the full environmental costs of packaging. Neo-liberalist competitive models do not address such problems convincingly, and therefore continued government intervention is required if the UK is to meet its targets under the EU Directive. Whilst economic instruments and competition provide avenues for a more effective environmental policy, there is a continuing need for government regulation rather than unquestioning faith in the benefits of the free market.     

Soil erosion and risk-assessment for on- and off-farm impacts: A test case using the Midhurst area,West Sussex,UK

Soil erosion on agricultural land is a growing problem in Western Europe and constitutes a threat to soil quality and to the ability of soils to provide environmental services. The off-site impacts of runoff and eroded soil, principally eutrophication of water bodies, sedimentation of gravel-bedded rivers, loss of reservoir capacity, muddy flooding of roads and communities, are increasingly recognised and costed. The shift of funding in the European Union (EU) from production-related to avoidance of pollution and landscape protection, raises issues of cross-compliance: public support for agriculture has to be seen to give value-for-money. In this context risk-assessment procedures have been introduced to help farmers recognise sites where either certain crops should not be grown or anti-erosion measures are required. In England, Defra [Defra, 2005a. Controlling Soil Erosion: a Manual for the Assessment and Management of Agricultural Land at Risk of Water Erosion in Lowland England. Revised September 2005. Department for Environment, Food and Rural Affairs, London] sets out a system of risk-assessment, including ranking of crops susceptible to erosion and anti-erosion measures, that may be selected. We assess this system using field data for an area of erodible soils in the Rother valley, Sussex. The Defra approach correctly identifies most at-risk fields and, taken together with land-use maps, allows non-compliance with advice to be highlighted. We suggest a simple extension to the system which would further identify at-risk fields in terms of possible damage to roads and rivers from muddy runoff. The increased risk of erosion in the study area is associated with certain crops: potatoes, winter cereals, maize and grazed turnips and seems unlikely to be the result of changes in rainfall which over the last 130 years are minimal. We have not evaluated proposed anti-erosion measures in the area because few have been put into practice. The European Water Framework Directive will increasingly focus attention on agricultural fields as a source of river pollution. Assessing the risk of erosion and the need for field testing of suggested approaches, are not simply issues for the EU, but for the management of global agricultural systems.     

Large-eddy simulation for flow and dispersion in urban streets

Large-eddy simulations (LES) with our recently developed inflow approach (Xie, Z.-T., Castro, I.P., 2008a. Efficient generation of inflow conditions for large-eddy simulation of street-scale flows. Flow Turbul. Combust., vol. 81(3), pp. 449–470.) have been used for flow and dispersion within a genuine city area – the DAPPLE site, located at the intersection of Marylebone Rd and Gloucester Pl in Central London. Numerical results up to second-order statistics are reported for a computational domain of 1.2 km (streamwise) × 0.8 km (lateral) × 0.2 km (in full scale), with a resolution down to approximately one meter in space and one second in time. They are in reasonable agreement with the experimental data. Such a comprehensive urban geometry is often, as here, composed of staggered, aligned, square arrays of blocks with non-uniform height and non-uniform base, street canyons and intersections. Both the integrative and local effect of flow and dispersion to these geometrical patterns were investigated. For example, it was found that the peaks of spatially averaged u_rms, v_rms, w_rms and <u′w′> occurred neither at the mean height nor at the maximum height, but at the height of large and tall buildings. It was also found that the mean and fluctuating concentrations in the near-source field is highly dependent on the source location and the local geometry pattern, whereas in the far field (e.g. >0.1 km) they are not. In summary, it is demonstrated that full-scale resolution of around one meter is sufficient to yield accurate prediction of the flow and mean dispersion characteristics and to provide reasonable estimation of concentration fluctuations.