Keith M. Reynolds

Knowledge-based assessment of watershed condition

The USDA Forest Service and Environmental Protection Agency have cooperatively developed a knowledge base for assessment and monitoring of ecological states and processes in sixth-code watersheds. The knowledge base provides a formal logical specification for evaluating watershed processes, patterns, general effects of human influence, and specific effects on salmon habitat. The knowledge base was designed in the NetWeaver knowledge base development system and evaluated in the Ecosystem Management Decision Support (EMDS) system. EMDS is an application framework for knowledge-based decision support of ecological landscape analysis at any geographic scale. The system integrates geographic information system and knowledge base system technologies to provide an analytical tool for environmental assessment and monitoring. The basic objective of EMDS is to improve the quality and completeness of environmental assessments and the efficiency with which they are performed. This paper presents an overview of the NetWeaver and EMDS systems, describes the general structure of the knowledge base for watershed assessment, and presents a small example of its use for evaluating erosion processes.

The science/policy interface in logic-based evaluation of forest ecosystem sustainability

Abstract Numerous efforts around the world are underway to apply the Montreal criteria and indicators to assess the sustainability of temperate and boreal forests. In this paper, we describe a logic-based system for evaluating the sustainability of forests at regional and national levels. We believe that such a system can make evaluation of sustainability more consistent and transparent. This effort also makes two points abundantly clear: (1) a systematic way to organize expert judgment about ecological, economic, social and institutional relationships (here, using ‘fuzzy logic’) is crucial to building such a system and (2) that the structure of this logic-based system reflects a policy framework and a series of decisions about values and what is meant by ‘sustainability’.

Decision Support Systems in Forest Management

Numerous decision support systems have been developed for forest management over the past 20 years or more. In this chapter, the authors briefly review some of the more important and recent developments, including examples from North America, Europe, and Asia. In addition to specific systems, we also review some of the more-significant methodological approaches such as artificial neural networks, knowledge-based systems, and multicriteria decision models. A basic conclusion that emerges from this review is that the availability of DSSs in forest management has enabled more-effective analysis of the options for and implications of alternative management approaches for all components of forest ecosystems. The variety of tools described herein, and the approaches taken by the different systems, provide a sample of the possible methods that can be used to help stakeholders and decision makers arrive at reasoned and reasonable decisions.

Integrating the DPSIR approach and the analytic network process for the assessment of forest management strategies.

Introduction Within the scope of forest research there is variety of approaches of handling with SFM. The more “traditionalistic” view which comes from the original understanding of sustained (timber) yield is implemented in modelling sustainable timber production systems and sustainable forest economy systems respectively. On the other hand, an eco-physiological understanding of SFM is keeping up resulting in modelling of systemic states and dynamics as functions of ecosystem processes and substance flows. In this context, methods both serving communicational demands and dealing with multiple criteria and objectives gained in importance to go beyond the spheres where modelling approaches as described above convincingly operate.

Suitability for conservation as a criterion in regional conservation network selection

The process of selecting candidate areas for inclusion in a regional conservation network should include not only delineating appropriate land units for selection and defining targets for representing features of interest, but also determining the suitability of land units for conservation purposes. We developed an explicit rating of conservation suitability by applying fuzzy-logic functions in a knowledge base to ecological condition and socio-economic attributes of land units in the interior Columbia River basin, USA. Suitability was converted to unsuitability to comprise a cost criterion in selecting regional conservation networks. When unsuitability was the sole cost criterion or was combined with land area as cost, only about one-third of the area selected was rated suitable, due to inclusion of unsuitable land to achieve representation of conservation targets (vegetation cover-type area). Selecting only from land units rated suitable produced networks that were 100% suitable, reasonably efficient, and most likely to be viable and defensible, as represented in our knowledge-based system. However, several conservation targets were not represented in these networks. The tradeoff between suitability and effectiveness in representing targets suggests that a multi-stage process should be implemented to address both attributes of candidate conservation networks. The suitability of existing conservation areas was greater than that of most alternative candidate networks, but 59% of land units containing conservation areas received a rating of unsuitable, due in part to the presence of units only partially occupied by conservation areas, in which unsuitability derived from conditions in non-conserved areas.

Relations between activity of Dendroctonus rufipennis Kirby on Lutz spruce and blue stain associated with Leptographium abietinum (Peck) Wingfield

Pruning and girdling treatments to frontalin-baited Lutz spruce (Picea × lutzii Little) trees were evaluated in 1987 and 1988 for their effect on five responses: number of spruce beetle (Dendroctonus rufipennis Kirby) attacks, length of egg galleries, percent of stemwood perimeter and area visibly affected by blue stain, and percent of cambial discoloration. All responses decreased significantly with sample height both in 1987 and 1988. Treatment generally had little or no effect on either spruce beetle activity or blue stain development; the only exception was the effect of treatment on percent cambial discoloration in 1987. For both years, spruce beetle activity and blue stain development were highly interralated. Number of successful attacks was more highly correlated with the three blue-stain measures than was total number of attacks. For both years, gallery length was more highly correlated with the blue-stain measures than was the number of attacks, but successful gallery length was more closely related to the blue-stain measures than was total gallery length. Results of canonical correlation analyses for both years indicated that spruce beetle activity and blue-stain development were equally effective in accounting for variation in the opposite set of measures. Spruce beetle measures related to successful attacks were more highly correlated with blue-stain development than were those for total attacks. Blue stain occurred in 120 of 144 discs and in 30 of 36 discs cut from trees in 1987 and 1988, respectively. Of the symptomatic discs, 74.2 and 83.3% yielded isolates of blue-stain fungi in 1987 and 1988 respectively. Representative isolates were all identified as Leptographium abietinum (Peck) Wingfield.

Prioritizing Salmon Habitat Restoration with the AHP, SMART, and Uncertain Data

Ecological assessments provide essential background information about ecosystem states and processes and are thus a useful starting point for applying adaptive ecosystem management. As a logical follow-up to ecological assessment, managers may wish to identify, and set priorities for, ecosystem maintenance and restoration activities. The Simple Multi-Attribute Rating Technique (SMART) is a useful extension to the standard AHP model that allows characterisation of uncertainty in attribute values of alternatives, and thus is one way of incorporating risk analysis into the standard AHP model. Criterium DecisionPlus is used to demonstrate application of the AHP and SMART methods to the problem of evaluating priorities for salmon habitat restoration projects.

Computer-aided decision making

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Risk and hazard of spruce beetle attack in unmanaged stands on the Kenai Peninsula, Alaska, under epidemic conditions

Abstract Forest community type was the most important variable determining risk of a spruce beetle ( Dendroctonus rufipennis Kby) outbreak. Black spruce ( Picea mariana B.S.P.) communities exhibited low to moderate risk overall, but stands in these communities with deep accumulations of fibrous organic matter on the soil surface (more than 7.5 cm) were at significantly greater risk than stands with shallower accumulation of fibrous organic matter. Differences in risk between black spruce communities with shallow and deep fibrous organic layers were not paralleled by differences in hazard as measured by either affected stems or basal area. Low-elevation mixed spruce-paper bitch ( Picea-Betula papyrifera Marsh.) communities, in which the principal spruce species were white ( Picea glauca (Moench) Voss) and/or Lutz spruce ( Picea X lutzii Little), exhibited the greatest overall risk of spruce beetle outbreak, and the greatest hazard was associated with these commuinities. Within this group, stands with open canopies had a significantly greater incidence of outbreaks than stands with closed canopies, but open-and closed-canopy stands did not differ with respect to hazard. The group of forest communities with the lowest overall risk of spruce beetle outbreak consisted of a diverse mixture of communities. Communities in which white and/or Lutz spruce were the principal overstory species also occurred in this group. Within the group, increasing elevation was associated with increasing risk and hazard. In low-elevation stands (less than 150 m), a further division between low- and high-risk stands could be made on the basis of forest community. In stands of intermediate elevation (150–300 m), depth of the fibrous organic layer exceeding 5 cm was associated with increased risk.

Machine learning and linear regression models to predict catchment‐level base cation weathering rates across the southern Appalachian Mountain region, USA

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous map of BCw within the southern Appalachian Mountain region. More than 50 initial candidate predictor variables were submitted to a variety of conventional and machine learning regression models. Predictors included aspects of the underlying geology, soils, geomorphology, climate, topographic context, and acidic deposition rates. Low BCw rates were predicted in catchments with low precipitation, siliceous lithology, low soil clay, nitrogen and organic matter contents, and relatively high levels of canopy cover in mixed deciduous and coniferous forest types. Machine learning approaches, particularly random forest modeling, significantly improved model prediction of catchment-level BCw rates over traditional linear regression, with higher model accuracy and lower error rates. Our results confirmed findings from other studies, but also identified several influential climatic predictor variables, interactions, and nonlinearities among the predictors. Results reported here will be used to support regional sulfur critical loads modeling to identify areas impacted by industrially derived atmospheric S inputs. These methods are readily adapted to other regions where accurate CL estimates are required over broad spatial extents to inform policy and management decisions.