Integrated Environmental Assessment of the Mid-Atlantic Region with Analytical Network Process

A decision analysis method for integrating environmental indicators was developed. This was a combination of Principal Component Analysis (PCA) and the Analytic Network Process (ANP). Being able to take into account the interdependency among variables, the method was capable of ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic Region of the United States, we were able to point out areas which were in relatively poor condition and/or vulnerable to future deterioration regarding various environmental aspects. The method offered an easy and comprehensive way to combine the strengths of conventional multivariate statistics (PCA) and decision-making science tool (ANP) for integrated environmental assessment.     

Emissions of NO and N2O from soils

Nitric oxide (NO) and nitrous oxide (N₂O) fluxes were measured from agricultural, forest and moorland environments, using chamber techniques. Maximum emissions of NO and N₂O were measured from the agricultural soils shortly after fertiliser application (7 ng NO-N m^–2 s^–1 and 91 ng N₂O-N m^–2 s^–1). For the non-agricultural soils the NO flux ranged from –0.3 to 0.5 ng NO-N m^–2 s^–1 and the N₂O flux ranged from 1 to 2.7 ng N₂O-N m^–2 s^–1. Emissions, however, were increased 2 to 7 fold when N deposition (uplands) and N fixation (alder plantations) contributed to the pool of soil available N. The best predictors of the NO emission were soil NO ₃ ^– and soil temperature, accounting for 60% of the variability observed. The prediction of N₂O was less successful. Only 30% of the variability could be explained by the soil NO ₃ ^– and the soil moisture content, soil temperature did not have a significant effect on the N₂O emission.     

Precision And Accuracy Of Pest And Pathogen Damage Assessment In Young Eucalypt Plantations

Fungal pathogens, browsing mammals, birds, insects, nutrient deficiencies, drought, frost and waterlogging are all damaging agents to plantation species. The subsequent loss in leaf tissue or reduced photosynthetic potential can reduce growth and potentially lead to tree death. The Crown Damage Index (CDI) was developed in Australia to quantify damage in young eucalypt plantations. The accuracy and precision of assessing damage at a tree level were determined to ensure the reliability, objectivity and repeatability of the CDI method. Nine assessors, with varying levels of experience, estimated damage on three plots of fifty trees each, to obtain an understanding of the subjectivity of assessing damage caused by insects (e.g. Chrysophtharta spp.) and fungal pathogens (e.g. Mycosphaerella spp.) on Eucalyptus globulus. Damage levels were measured by destructive sampling to enable direct comparisons between estimates and damage levels to be made. The most experienced assessors provided the most repeatable estimates and were generally the most accurate. The incidence of foliar necrosis was the least subjective measure while defoliation was the most subjective and the least accurate of the indices measured. All assessors, regardless of experience, were able to predict the Crown Damage Index (a combined index of all damage classes) to within 12% of measured damage levels.     

An Overview of Data Integration Methods for Regional Assessment

The U.S. Environmental Protections Agency's (U.S. EPA) Regional Vulnerability Assessment(ReVA) program has focused much of its research over the last five years on developing and evaluating integration methods for spatial data. An initial strategic priority was to use existing data from monitoring programs, model results, and other spatial data. Because most of these data were not collected with an intention of integrating into a regional assessment of conditions and vulnerabilities, issues exist that may preclude the use of some methods or require some sort of data preparation. Additionally, to support multi-criteria decision-making, methods need to be able to address a series of assessment questions that provide insights into where environmental risks are a priority. This paper provides an overview of twelve spatial integration methods that can be applied towards regional assessment, along with preliminary results as to how sensitive each method is to data issues that will likely be encountered with the use of existing data.     

A Preliminary Assessment of the Montréal Process Indicators of Air Pollution for the United States

Air pollutants pose a risk to forest health and vitality in the United States. Here we present the major findings from a national scale air pollution assessment that is part of the United States' 2003 Report on Sustainable Forests. We examine trends and the percent forest subjected to specific levels of ozone and wet deposition of sulfate, nitrate, and ammonium. Results are reported by Resource Planning Act (RPA) reporting region and integrated by forest type using multivariate clustering. Estimates of sulfate deposition for forested areas had decreasing trends (1994-2000) across RPA regions that were statistically significant for North and South RPA regions. Nitrate deposition rates were relatively constant for the 1994 to 2000 period, but the South RPA region had a statistically decreasing trend. The North and South RPA regions experienced the highest ammonium deposition rates and showed slightly decreasing trends. Ozone concentrations were highest in portions of the Pacific Coast RPA region and relatively high across much of the South RPA region. Both the South and Rocky Mountain RPA regions had an increasing trend in ozone exposure. Ozone-induced foliar injury to sensitive species was recorded in all regions except for the Rocky Mountain region. The multivariate analysis showed that the oak-hickory and loblolly-shortleaf pine forest types were generally exposed to more air pollution than other forest types, and the redwood, western white pine, and larch forest types were generally exposed to less. These findings offer a new approach to national air pollution assessments and are intended to help focus research and planning initiatives related to air pollution and forest health.     

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

Agricultural water management (AWM) is an interdisciplinary concern, cutting across traditional domains such as agronomy, climatology, geology, economics, and sociology. Each of these disciplines has developed numerous process‐based and empirical models for AWM. However, models that simulate all major hydrologic, water quality, and crop growth processes in agricultural systems are still lacking. As computers become more powerful, more researchers are choosing to integrate existing models to account for these major processes rather than building new cross‐disciplinary models. Model integration carries the hope that, as in a real system, the sum of the model will be greater than the parts. However, models based upon simplified and unrealistic assumptions of physical or empirical processes can generate misleading results which are not useful for informing policy. In this article, we use literature and case studies from the High Plains Aquifer and Southeastern United States regions to elucidate the challenges and opportunities associated with integrated modeling for AWM and recommend conditions in which to use integrated models. Additionally, we examine the potential contributions of integrated modeling to AWM — the actual practice of conserving water while maximizing productivity. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Spatial Convergence in Major Dissolved Ion Concentrations and Implications of Headwater Mining for Downstream Water Quality

Spatial patterns in major dissolved solute concentrations were examined to better understand impact of surface coal mining in headwaters on downstream water chemistry. Sixty sites were sampled seasonally from 2012 to 2014 in an eastern Kentucky watershed. Watershed areas (WA) ranged from 1.6 to 400.5 km² and were mostly forested (58%–95%), but some drained as much as 31% surface mining. Measures of total dissolved solutes and most component ions were positively correlated with mining. Analytes showed strong convergent spatial patterns with high variability in headwaters (<15 km² WA) that stabilized downstream (WA > 75 km²), indicating hydrologic mixing primarily controls downstream values. Mean headwater solute concentrations were a good predictor of downstream values, with % differences ranging from 0.55% (Na⁺) to 28.78% (Mg²⁺). In a mined scenario where all headwaters had impacts, downstream solute concentrations roughly doubled. Alternatively, if mining impacts to headwaters were minimized, downstream solute concentrations better approximated the 300 μS/cm conductivity criterion deemed protective of aquatic life. Temporal variability also had convergent spatial patterns and mined streams were less variable due to unnaturally stable hydrology. The highly conserved nature of dissolved solutes from mining activities and lack of viable treatment options suggest forested, unmined watersheds would provide dilution that would be protective of downstream aquatic life.     

DNA fingerprints of orange roughy,Hoplostethus atlanticus: a population comparison

Genetic variability among Hoplostethus atlanticus collected from two spawning grounds east and west of New Zealand was examined using DNA fingerprints as revealed by hybridization with three clonal probes: 33.15, M13 and 3HVR. The 33.15 and 3HVR fingerprints revealed a complex pattern of restriction fragments, apparently refecting a multi-locus system of highly variable minisatellite alleles similar to the pattern of alleles reported in other vertebrates. The M13 fingerprints revealed a distinct pattern of restriction fragments of high molecular weight, reflecting a single-locus system that overlapped with the family of minisatellite alleles observed in 33.15 fingerprints. In a sample of 12 orange roughy collected on a single regional spawning site, the average percent similarity of 33.15 fingerprints was 21.15% (SD=17.75), the average percent similarity of 3HVR fingerprints was 14.32% (SD=14.45) and the inferred average allelic frequency of the M13 single-locus system was 0.071. A comparison of 33.15 and M13 fingerprints from two distant spawning sites ground New Zealand revealed no obvious regional differences. The variability of orange roughy fingerprints was so great, however, that regional comparisons could not be considered conclusive indicators of genetic identity. Our results provide a preliminary assessment of the power and pitfalls of using DNA-level markers for the population analysis of marine fish.     

Quantifying the relative irreplaceability of important bird and biodiversity areas

World governments have committed to increase the global protected areas coverage by 2020, but the effectiveness of this commitment for protecting biodiversity depends on where new protected areas are located. Threshold‐ and complementarity‐based approaches have been independently used to identify important sites for biodiversity. We brought together these approaches by performing a complementarity‐based analysis of irreplaceability in important bird and biodiversity areas (IBAs), which are sites identified using a threshold‐based approach. We determined whether irreplaceability values are higher inside than outside IBAs and whether any observed difference depends on known characteristics of the IBAs. We focused on 3 regions with comprehensive IBA inventories and bird distribution atlases: Australia, southern Africa, and Europe. Irreplaceability values were significantly higher inside than outside IBAs, although differences were much smaller in Europe than elsewhere. Higher irreplaceability values in IBAs were associated with the presence and number of restricted‐range species; number of criteria under which the site was identified; and mean geographic range size of the species for which the site was identified (trigger species). In addition, IBAs were characterized by higher irreplaceability values when using proportional species representation targets, rather than fixed targets. There were broadly comparable results when measuring irreplaceability for trigger species and when considering all bird species, which indicates a good surrogacy effect of the former. Recently, the International Union for Conservation of Nature has convened a consultation to consolidate global standards for the identification of key biodiversity areas (KBAs), building from existing approaches such as IBAs. Our results informed this consultation, and in particular a proposed irreplaceability criterion that will allow the new KBA standard to draw on the strengths of both threshold‐ and complementarity‐based approaches.     

Learning and local government in coastal South Australia: towards a community of practice framework for adapting to global change

Social learning can be a vital tool in assisting communities to adapt to change. Local governments can be a conduit between the communities they serve and the policy that they are trying to implement. Social learning in this context can be an iterative, often organic process. Based on a case study of coastal planning in South Australia, Australia, this paper presents the results of a qualitative mixed-method approach that documents the aspects of social learning within coastal management and evaluates the various lessons learned by local governments in South Australia. The role of social learning and adaptive governance is discussed. The paper concludes that by deliberatively incorporating the notion of communities of practice into learning frameworks, local governments can more effectively manage their coastal zones in response to global change.