Furthermore, we did not have adequate aquatic plant data or established thresholds to use this indicator in our assessment. Rather than fish or aquatic plants, we chose to use the stream macroinvertebrate indices, Ephemeroptera, Plecoptera, and Trichoptera EPT , and Hilsenhoff Biologic Index HBI to assess the biological integrity of waterways at the parks. EPT taxa richness is the number of taxa from the insect orders Ephemeroptera, Plecoptera, and Trichoptera.
These orders are generally considered pollution sensitive, and EPT index values are usually depressed in polluted ecosystems Wallace et al. The HBI uses the relative organic pollution tolerance of all macroinvertebrate taxa and their relative abundance to assign a numerical value to aquatic communities. This bioassessment study examined the relationship between stream macroinvertebrates and microhabitat characteristics as well as examining the correlation among indices used to assess biological integrity of waterways.
Their findings indicated that the EPT index was the best index of habitat condition and ecological integrity within the park watersheds Patrick Center for Environmental Research However, if the data to inform the EPT index were not available, the HBI was used as an alternative or secondary assessment. ATtiLA generates numerous potential metrics for assessing the condition of terrestrial resources.
However, previous studies have noted the high degree of redundancy in landscape configuration metrics and have used correlation analysis and factor analysis to determine metrics that provide unique information Cifaldi et al. Additionally, several studies Kearns et al. Given these caveats landscape metric redundancy and spatial sensitivity of metrics , we reviewed the literature and examined the science-based evidence for selecting landscape thresholds to denote ecological integrity Tierney et al.
Percent intact forest and percent impervious surface have accepted thresholds that are not directly related to landscape area making them ideal landscape indicators for our study. We developed and applied a decision support system DSS model that integrated our selected indicators and thresholds to provide a comprehensive quantitative and graphic geospatial watershed-based assessment of the ecological condition of selected natural resources in the parks.
Although the field of fuzzy logic began as a way to model language ambiguities, its ability to quantify ambiguity e. DSS models are useful for ecological assessments because data from different domains, formats, and sources can be integrated to assist in management decisions and understanding Rausher and Potter ; Recknagel As a stand-alone tool, NetWeaver has been used to evaluate lake water chemistry Saunders et al. As a component of larger, integrated decision support systems with GIS capabilities, it has been used to address environmental issues such as carbon sequestration Wang et al.
Our DSS incorporated fuzzy arguments that compared current condition of selected indicators against associated ecological thresholds for each indicator. Fuzzy statistical modeling integrates expert judgment with statistics of vague data and imprecise information to model ecological condition at multiple scales Li The fuzzy argument compares the data values against a fuzzy set membership function that returns a level of trueness based on the degree of membership in the fuzzy set.
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NetWeaver models consist of dependency networks. A dependency network is a graphical representation of a rule or syllogism Fig.
Data that are entered into a NetWeaver model are first evaluated by data links. A data link can be a simple data link, which will compare the data value to an argument in order to assign a trueness level. Alternatively, some data may require mathematical manipulation and these data are evaluated by a calculated data link. Calculated data links also may have arguments to interpret the trueness level of the output of the calculation contained within it. Data links are at the bottom of dependency networks and are connected to the dependency network by logical nodes.
A dependency network as displayed in NetWeaver.https://harralasdeto.tk
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In this dependency network, there are three data links represented by the squares at the bottom of the figure. Each of the data links evaluates the data value according to the extent to which it satisfies its arguments. The network can be read as a rule as follows: IF Data 1 satisfies the argument arg. The degree to which the assertion is true is a function of the degree s to which the individual data satisfy their arguments and the types and arrangements of the logical nodes used within the network.
Arguments within data links can be decision thresholds that return a discrete value of true or false.
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Alternatively, a fuzzy argument can be used in which the data are compared to a fuzzy set e. The use of fuzzy arguments to address the interpretation of environmental indicators greatly reduces the modeling effort and enhances the interpretation of indicator levels. For example, the various break points characterizing the intermediate levels used as thresholds in the original EPT Index are quantized to assist in their interpretation.
Is it a low fair value of 7 or a high fair value of 13? A dependency network calculates its membership in the fuzzy set true by evaluating the trueness level calculated by the data links and then passing that value to the logical node to which they are connected. The membership of a logical node is calculated differently for each type of node.
It is at this logical node that the overall fuzzy set membership of a dependency network is calculated. An OR node always takes on the trueness value of the most true antecedent. In contrast, an AND node calculates its trueness value using the formula:. In traditional fuzzy logic, an AND node assumes the fuzzy set membership value of the least true antecedent to which it is attached. In this modified approach, our NetWeaver model was run with partial or incomplete data.
This ability to run the model with missing data permits the software to provide an interim evaluation based upon the data at hand, and permits the software to report to the user the rank order of missing data starting with the most important or influential of those missing data. This calculation of influence is based upon the topology of the network, and the values of the data already populating the model.
This feature optimizes data collection and ensures that appropriate emphasis is placed on collecting the most important of the data that are missing. The dashboard displays a variety of features that permits the reader e. The dashboard displays two vertical bars. The right bar represents the area-weighted average of data needs that were met e. The colored oval represents the area-weighted average quality for the assessment or indicator.
In addition, the brightness of the oval can be read to determine data sufficiency e. Finally, the dashboard displays a qualitative watershed map with corresponding colors to graphically and rapidly indicate quality of individual watersheds for which we have data Fig. Sample ecological assessment model dashboard display demonstrating features that permit the reader a rapid understanding of assessment outcomes.
The right vertical bar represents the area-weighted average of data needs that were met e. The brightness of the oval can be read to determine data sufficiency e. Most indicators had multiple overall sources of data; however the data sources were not uniformly available over the area of study. The DSS model selected a data source for each metric, watershed by watershed, based on availability of data and a prioritization of data sources for each metric so that at each watershed the best data for that particular metric was used. In some cases, there were no data available at a watershed for a metric.
In these cases, missing data were given a neutral score zero for purposes of propagating values up through the sub models. The lack of desired data was also tracked and reflected in graphical representation of watershed condition. Thus, data insufficiencies are monitored and noted but do not unduly handicap analysis. High water quality was particularly evident for watersheds, where complete WQI scores were available.
For example, the average score for watersheds with complete WQI scores was Our model permits park managers to add additional data as it becomes available to complete the WQI for individual watersheds. Once more data are collected and added to the model, we expect the average WQI for all watersheds, individually and combined, to increase.
Based on available data, the overall assessment of biologic indicators was generally good ecologically unimpaired ; however, Brodhead Creek watershed in DEWA had an ecologically impaired EPT score of 6. Several pollution sources within this watershed e. When the landscape measures percent forested, percent impervious surface are converted to the fuzzy logic scale of the DSS model, the parks had an overall landscape model score of 0.
Our data compilation and analysis resulted in the ability to assess natural resource condition over two national park units by summarizing chemical, biologic, and landscape indicators within watershed units in a DSS context, even where data were incomplete. By scoring indicators using a fuzzy logic scale, we were able to avoid hard decision thresholds, which can lead to abrupt transitions in assessment scores. Our model permits resource managers to do both.
For example, each component score for the WQI may be reported for watersheds for which we have data. Our model also contains the formula for calculating the EPT index so users can report the raw species richness data for each category of macroinvertebrate.