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2011 LIS Spatial Prioritization to Support Benthic Mapping
20110729
Kevin O'Brien
CT Dept of Energy & Environmental Protection, Office of Long Island Sound Programs
Kevin.OBrien@ct.gov
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Priority mapping areas of the Long Island Sound Seafloor Habitat Mapping Initiative. Data presented show completion progress of benthic mapping and seafloor characterization for elements of different project phases within these areas.
CT Dept. of Energy & Environmental Protection Land & Water Resources Division, EPA Long Island Sound Partnership, Long Island Sound Cable Fund Steering Committee
Suitable for planning level activities only. These smoothed and classifed zones should serve as a guide to strategically focus assests and efforts of a mapping program, but should not be taken to define hard and fast boundaries of collection or processing activities.
Not intended for regulatory, jurisdicational, or other similar or comparable uses.
<div style='text-align:Left;'><p>Priority mapping areas of the Long Island Sound Seafloor Habitat Mapping Initiative (LISSHMI; https://longislandsoundstudy.net/research-monitoring/seafloor-mapping/). Data presented show completion progress of benthic mapping and seafloor characterization for elements of different project phases within these areas. LISSHMI is administered by the Long Island Sound Cable Fund Steering Committee, comprised of representatives of the EPA Region 1 and Region 2 offices, the Connecticut Department of Energy and Environmental Protection, the New York State Department of Environmental Conservation, and Connecticut and New York Sea Grant Programs, and managed by the Connecticut Department of Energy & Environmental Protection Land & Water Resources Division. Project phases for seafloor mapping and habitat characterization have been funded under the Long Island Sound Research and Restoration Fund, EPA-Long Island Sound Study supplemental budget program funds, and the NOAA Office of Coast Survey Rear Admiral Richard T. Brennan Ocean Mapping Matching Fund Program (for Phase VA).</p></div>
Kevin O'Brien
CT Dept of Energy & Environmental Protectio , Office of Long Island Sound Programs
Kevin.OBrien@ct.gov
Long Island Sound
Connecticut
New York
ESRI ArcGIS 10.0.0.2414
Long Island Sound Seafloor Habitat Mapping Initiative
LISSHMI
Long Island Sound
LIS
LIS mapping
LIS benthic mapping
LIS seafloor habitat
In Spring 2011, key representatives from the LIS community were asked to provide the spatial extent and a description of priority management issues and criteria for critical areas of Long Island Sound. (The primary survey contents are provided below.)
Survey Content: Respondents were asked to identify priority areas on an LIS map & for each area answer the following.
Priority: How soon the area should be addressed (based on data collection/ analysis, not deliverables)?
• High (1-2 yrs), Medium (2-5 yrs), Low (5-10 yrs)
Management Issue: What is the overarching management issue driving the "Priority" designation?
• Regulatory - Data needed to inform permitting or regulatory assessments
• Impact Assessment - Data needed to inform a non-regulatory impact assessment
• Resource Management - Data needed to inform resource management decisions including harvested as well as protected species (e.g., fisheries, shellfisheries, aquaculture, SAV, etc.)
• Monitoring/Research Design - Data needed to inform the design of monitoring strategies or research programs.
• Evaluate Management Success - Data need to inform or assess management decisions
• CMSP - Data needed to inform Coastal Marine Spatial Planning processes.
• Other - brief description on other management issue not included above.
Ranking Criteria (1-3): Why the area is relevant? (1 = most important; 2 & 3 = optional; successively less important.)
• Multiple Use Conflict - multiple non-authoritative competing uses (e.g., commercial fishing, recreational boating)
• Managed Areas - Special use, managed resource areas, or other designated State/Federal/Local managed areas (e.g., shellfish beds, channels/anchorages, dredge disposal sites) with well delineated existing boundaries.
• Significant Natural Areas - Areas of unique or important natural value, but not having any official or political designation or boundary (e.g., eelgrass beds, etc)
• High Use Areas - (e.g., shipping lanes, fishing areas, economic development zones, etc.)
• Existing Infrastructure – in-situ items (e.g., cable, pipeline, etc)
• Potential Infrastructure - looking forward and considering the capacity of the area, could it be targeted for future infrastructure projects (e.g., cable, pipeline, wind/wave turbines, tidal energy devices, etc)
• Knowledge Gap - Areas where there is no/limited/dated information
• Other Conflict – Other areas where conflict may occur (e.g., military exclusion zone, cultural resources, etc.)
• Other General - Brief description of another criterion that captures an activity or theme not included above.
1.1 Data Compilation:
Survey data was provided either by e-mail content or on pre-provided data sheets. Responses were extracted and compiled into a master Excel Workbook with worksheets identifying the content from each respondent. Information was received from the following groups:
• CTDEP, NYDEC, NYDOS, LDOE, UCONN, The Nature Conservancy (CT) USDA-NRCS, USACOE, USEPA Regions 1 & 2.
2.1 Chi-Squared Statistical Analysis:
Once the data was summarized, we conducted an Investigation to determine if there were statistically significant relationships between issues and priorities and/or issues and criteria that could be used to help identify or describe priority areas. To address this we used a chi-squared test; this is a statistical tool commonly used to compare observed data with data one would expect to obtain according to a specific hypothesis. (Chi-square test assumptions: http://www.okstate.edu/ag/agedcm4h/academic/aged5980a/5980/newpage28.htm) For example, were the deviations (differences between observed and expected) the result of chance, or were they due to other factors? How much deviation can occur before one must conclude that something other than chance is at work, causing the observed to differ from the expected? The chi-square test is always testing the null hypothesis, which states that there is no significant difference between the expected and observed results.
Chi Sqaured Results: Issues vs. Priority
The issue most strongly associated with High Priority was CMSP; with Medium was Resource Management; with Low was Regulatory.
3.1 Spatial Processing:
One of the primary goals of this effort is to use the survey data grid and responses to spatially locate and assess areas where a LIS mapping effort should focus on. The following steps identify the process used and the resulting priority areas.
3.1.1. Basic & Composite GIS layers:
Using the spreadsheet response compilations, we created individual spatial data layers representing location and interests provided by respondents. From these we next created a composite layer. This provides an assessment of the study area on a grid-cell by grid-cell basis, displaying data from the individual spatial data layers. Here, multiple instances of the same grid cells are preserved, thus showing all unique responses at that location.
3.1.2. Merged GIS layer & Scoring Strategy:
From the composite layer we then created a merged data layer that reduces multiple instances of grid cells to a single instance with sums for the associated high, medium and low priority fields as well as fields totaling the sums of each survey issue category. A frequency field was provided to capture the number of times each cell received a response as well as the ability to provide a score to each grid cell, described below.
• Scoring Strategy: A scenario based on calculated priority counts from the survey responses (High, Med, Low) as well as priority inferences derived from the statistical Chi-Squared analysis (Coastal/Marine Spatial Planning (CMSP) is highest, Resource Management (RM) is medium, and Regulatory (Reg) is lowest.) The following assigns equal weights (50%-50%) to the stated priority from the survey (Wp) and issue priority (Wi) components; the individual weights within each component is reflected by a 50%-30%-20% breakdown for the high, medium, and low priority elements:
Wp = 0.5
Wi = 0.5
[Wp*((0.5 * [SUM_Rank_H]) + (0.3 * [SUM_Rank_M]) + (0.2 * [SUM_Rank_L]))] + [Wi*((0.5 * [SUM_CMSP]) + (0.3 * [SUM_RM]) + (0.2 * [SUM_Reg]))]
3.1.3. Spatial Clustering Analysis:
The ArcGIS Hot Spot Analysis tool calculates the Getis-Ord Gi* statistic (pronounced G-i-star) for each feature in the merged dataset. The resultant Z-scores (standard deviations) and P-values (probability of random chance) tell you where features with either high or low values cluster spatially. This tool works by looking at each feature within the context of neighboring features. To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. For statistically significant positive Z-scores, the larger the Z-score is, the more intense the clustering of high values (hot spot). For statistically significant negative Z-scores, the smaller the Z-score is, the more intense the clustering of low values (cold spot). To assess the neighboring features, we use a fixed band Euclidean metric:
• Fixed Band-Euclidean: Uses a moving window of influence based on a fixed distance. Per the suggested methodology, we define it by first iteratively running a spatial autocorrelation process on the input data with varying thresholds to determine at what distance the Z-score values peak. Here, that distance is roughly 11000m.
3.1.4. Spatial Clustering Results & Interpretation:
Most statistical tests begin by identifying a null hypothesis. The null hypothesis for the pattern analysis tools is Complete Spatial Randomness (CSR). The Z-scores and P-values returned by the pattern analysis tools tell you whether you can reject that null hypothesis or not.
The P-value is the probability that the observed spatial pattern was created by some random process. When the P-value is very small, it is very unlikely (i.e., a small probability) that the observed spatial pattern is the result of random processes, so you can reject the null hypothesis. Z-scores are simply standard deviations. If the tool returns a Z-score of +2.5, you would say that the result is 2.5 standard deviations.
Very high or very low (negative) Z-scores associated with very small P-values are found in the tails of the normal distribution. When you run a feature pattern analysis tool and it yields small P-values and either a very high or a very low Z-score, this indicates it is unlikely that the observed spatial pattern reflects the theoretical random pattern represented by your null hypothesis (CSR).
3.1.5. Spatial Clustering Caveats:
• Hotspot approach susceptible to edge effects on the Area of Interest boundary where there are no surrounding data.
• Results are scale dependent based on size of the grid cell. Smaller or larger cells may have modified results.
• User input was unconstrained and input unequally allocated.
4.1 Conclusions and Priority Area Details:
The prioritization resulting from the statistical analysis and subsequent spatial processing can be used to define a set of locations in LIS considered as high-priority zones to focus mapping efforts on. Those zones are more completely analyzed in the following pages. As a general note the boundaries are simply taken from the grid and should not be considered absolute but somewhat fluid; the results of the analysis, however, indicate that the maximum level of interest is concentrated in these vicinities.
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