Chapter 3: Spatial Analyses - Marine Spatial Planning

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Chapter 3: Spatial AnalysesAs part of the marine spatial planning process, the interagency team 1 commissionedseveral analyses to provide additional information relevant to present and potential futureconditions in the Study Area. Analyses were selected to fill known data gaps and to fulfillseveral of the requirements outlined in RCW 43.372.040(6)(c). Results of these analyses includedata products previously not available through empirical datasets alone. These products informand support many of the spatial and management recommendations outlined in the plan.This chapter will not provide specific recommendations, which are described in detail inthe Marine Spatial Plan (MSP) Management Framework presented in Chapter 4. Rather, itbriefly describes the data, tools, and methods used to perform analyses that have contributed tothe development of those recommendations and the planning process as a whole. In addition,each section also provides a brief overview of important results, and highlights some of theproducts from three projects completed to support the marine spatial planning process inWashington:1. Ecological modeling of seabird and marine mammal distributions by NOAA2. Ecologically Important Areas (EIA) analysis by the Washington Department of Fishand Wildlife (WDFW)3. A use analysis comparing the location and intensity of existing uses with technicalsuitability for offshore renewable energyDue to a lack of comparable spatial data, the estuaries in the Study Area were excludedfrom these analyses. While the maps and other information provided here do not include data forthese areas, estuaries are known and considered in the plan to be highly ecologically importantand heavily used by many existing use sectors. For additional information on how estuaries areaddressed by the Marine Spatial Plan (MSP), please see the discussions of this topic in Sections3.2 and 4.3.3, as well as Appendix C: Data Sources, Methods, and Gaps.3.1 Seabird and Marine Mammal ModelingThe National Centers for Coastal and Ocean Science (NCCOS) at NOAA conductedseveral analyses for the state planning process, including developing ecological models ofpredicted seabird and marine mammal distributions within the Study Area. It is important to notethat this process did not predict abundance, but rather relative density. The resulting maps showwhere in the Study Area one would expect to find the highest density of each species, rather thanpredicting the number of animals actually present in the planning area or comparing abundancenumbers across species.NCCOS produced ecological models for eight species of birds and six species of marinemammals. Species were selected for analysis by WDFW because they are either of managementconcern (such as threatened or endangered species), or are representative of groups of specieswith important life history strategies or ecological functions. Seabird species included theMarbled Murrelet (Brachyramphus marmoratus), Tufted Puffin (Fratercula cirrhata), CommonMurre (Uria aalge), Black-footed Albatross (Phoebastria nigripes), Northern Fulmar (Fulmarus1Interagency team refers to the State Ocean Caucus, as described in RCW 43.372.020.Chapter 3: Spatial Analyses3-1

glacialis), Pink-footed Shearwater (Puffinus creatopus), Sooty Shearwater (Puffinus griseus),and Rhinoceros Auklet (Cerorhinca monocerata). An analysis for Short-tailed Albatross, also alisted species, could not be completed because of insufficient data.Maps of marine mammals included two species of pinniped, the Steller sea lion(Eumetopias jubatus) and harbor seal (Phoca vitulina), and four species of cetaceans: thehumpback whale (Megaptera novaeangliae), gray whale (Eschrichtius robustus), harborporpoise (Phocoena phocoena) and Dall’s porpoise (Phocoenoides dalli). Insufficientobservations were available to produce models for the Sei whale (Balaenoptera borealis), bluewhale (Balaenoptera musculus), fin whale (Balaenoptera physalus), Southern Resident killerwhale (Orcinus orca) or sperm whale (Physeter macrocephalus).This section summarizes the data and general methods used to create the relative densitymaps, and highlights some key results. Reports from NCCOS covering additional technicaldetails and maps for all species are available on the state’s MSP website 2 (Menza et al., 2016).Data SourcesNCCOS staff and other contributors synthesized information from eleven existingmonitoring programs that have collected data on sightings of species within the Study Area(Table 3.1). While all of these programs overlap with the Study Area, they vary in geographicextent and years of operation. For the NCCOS study, data collected between 2000 and 2013within or just beyond the Study Area boundary was used. All observations were made at seafrom ships or aircraft, typically along transects ranging in length from 25 kilometers to severalhundred kilometers.2 3: Spatial Analyses3-2

Table 3.1: Summary of seabird and mammal survey data compiled by NCCOS for the distribution analysis.Survey nameData collectorsData categoryHarbor porpoise surveysCascadia Research Collective,NOAA Alaska Fisheries Science CenterMammalsLeatherback turtle aerialsurveyNOAA Southwest Fisheries ScienceCenterMammals(incidentallycollected duringturtle surveys)Pacific Continental ShelfEnvironmental Assessment(PaCSEA)USGS Western Ecological ResearchCenter, Bureau of Ocean EnergyManagement (BOEM)Birds and MammalsCalifornia Current EcosystemSurveys (includesORCAWALE and CSCAPEsurveys)NOAA Southwest Fisheries ScienceCenterBirds and MammalsNorthwest Fisheries ScienceCenter Northern CaliforniaCurrent Seabird SurveysNOAA Northwest Fisheries ScienceCenterBirdsOlympic Coast NationalMarine Sanctuary Seabird andMarine Mammal SurveysCascadia Research Collective,Olympic Coast National MarineSanctuary (OCNMS),NOAA Southwest Fisheries ScienceCenterBirds and MammalsPacific Coast Winter Sea DuckSurveySea Duck Joint Venture,Washington Dept. of Fish and WildlifeBirdsPacific Orcinus DistributionSurvey (PODS)NOAA Northwest Fisheries ScienceCenterMammalsLarge whale surveys offWashington and OregonCascadia Research Collective,Washington Dept. of Fish and Wildlife,Oregon Dept of Fish and WildlifeMammalsNorthwest Forest Plan MarbledMurrelet EffectivenessMonitoring Program(Raphael, M.G. et al., 2007)US Forest Service,US Fish and Wildlife Service,Washington Dept. of Fish and WildlifeBirds and MammalsSeasonal Olympic CoastNational Marine Sanctuaryseabird surveysNOAA Olympic Coast National MarineSanctuaryBirdsChapter 3: Spatial Analyses3-3

Figure 3.1 provides an overview of the spatial coverage of bird, cetacean, and pinnipedsurveys used in modeling. Through additional analysis of the location and timing of transects,NCCOS also identified seasonal patterns in survey effort for the study period. Effort per squarekilometer was more concentrated in the northern section of the Study Area for birds andmammals during the summer, whereas winter bird survey effort was more evenly distributedfrom north to south.Figure 3.1: Spatial distribution of the 11 surveys used in bird and mammal models. The gray line in each frameindicates the boundaries of the Olympic Coast National Marine Sanctuary. Additional details on this figureand its source data provided in the final NCCOS report (Menza et al., 2016).Chapter 3: Spatial Analyses3-4

Numerous datasets describing environmental and temporal parameters were used aspredictor variables in the modeling process. Environmental predictors included geographic,topographic, oceanographic, and biological information, either collected as part of survey data oracquired by NCCOS from other sources. Table 3.2 summarizes the full list of predictor variablesassessed. For the analysis, all spatial datasets were averaged or extrapolated to a resolution ofthree km. Additional details about data sources, data selection, and processing steps forindividual predictor variables are provided in the final report (Menza et al., 2016).Table 3.2: Summary of predictor variables incorporated into distribution Coordinates (X,Y)DepthJulian DaySurvey platformDistance to keyhabitats likecolonies or haulouts for: Tufted Puffin CommonMurre MarbledMurrelet Steller sea lion Harbor sealBathymetricposition indicesYearProbability ofcyclonic andanticyclonic eddyringsUpwelling indexSea surface salinityProfile curvaturePlanform curvatureSlopeBeaufort Sea State(marine mammalsonly)Indices for: El Niño-SouthernOscillation North PacificGyre Oscillation Pacific DecadalOscillationSurvey IDSea surfacetemperatureTransect IDProbability of seasurface temperaturefrontSurface chlorophyllaFrequency ofchlorophyll peaksindexMethodsIn order to standardize and synthesize information from programs with diverseprocedures for recording and collecting observations, datasets were first processed using a seriesof steps outlined in detail in the final project report (Menza et al., 2016). Data was grouped intosummer (April to October) or winter (November to March) seasons based on the assumption thatdistribution patterns are affected by seasonal differences in environmental conditions or animalbehavior. Statistical modeling was used to identify the ecological variables from Table 3.2 thatbest predict density for each species and season combination. To account for variations inobservations due to survey methods and timing, the models also incorporated variables related tosurvey methods and conditions. These included weather and whether a survey was done from aboat or from an aircraft.For most species, sufficient data was not available to conduct analysis for the winterperiod. Models and maps were produced for the Common Murre, Rhinoceros Auklet, and Blackfooted Albatross for both seasons, and for summer only for all other bird and mammal species.NCCOS produced multiple models for each species and season, and then used variousdiagnostic tools to assess and compare model performance before making a final selection. AfterChapter 3: Spatial Analyses3-5

identifying the best available model, further diagnostic steps included identifying limitations andcaveats applicable to the results for each species.After selecting a final model for each season and species combination, the outputs ofeach model were mapped to illustrate the areas of highest and lowest long-term relative predicteddensity as well as where the coefficient of variation is highest These latter set of maps provide asense of uncertainty. Areas with higher coefficients had a greater amount of variability in results,and therefore have a higher amount of uncertainty associated with how well model predictionsalign with the actual distribution of species. For detailed performance results and uncertaintyinformation for each model, please see Appendix C of the NCCOS report (Menza et al., 2016).ResultsThe output maps from NCCOS provide general predictions for areas of highest andlowest density of the selected species at a broad scale. However, all models have inherentuncertainties and limitations. While each model was assessed to ensure that it provides the bestpossible representation of relative density at the planning scale based on available data, all mapsshould be considered in the context of uncertainty and other available data and expertise.Figures 3.2 and 3.3 provide examples of maps produced for each species and seasoncombination. The species discussed in this section are of particular interest because of theirconservation status, or because they represent datasets that are particularly robust or can berepresentative of patterns seen in other species. Figure 3.2 illustrates the best long-term densityprediction for Tufted Puffin, a nearshore species, in summer. This best prediction modelrepresents the median of predicted values. 3 It is shown with an overlay depicting uncertainty andan inset showing survey coverage and species density for actual field observations. Uncertaintyvaried greatly by species. Any interpretation of distribution information from a specific modelshould include careful consideration of areas with high coefficients of variation (a measure ofuncertainty used in this analysis), particularly if assessing a specific site.In addition to the best prediction median map (a), Figure 3.3 presents a spatialrepresentation of uncertainty based on the coefficient of variation (b), and two quantile maps (cand d). The quantile maps show two additional potential distributions based on different levels ofstatistical confidence. These results could be of interest in cases where a more or lessconservative approach to predictions is desired.Nearshore species such as the Tufted Puffin and Marbled Murrelet (Figures 3.2 - 3.4)were generally predicted to be concentrated within 10 to 15 km from shore during summer, butwith greater variation in north to south distribution than pelagic species. The predicted relativedensity of pelagic species was generally highest in the northern part of the Study Area. Patternsfor pelagic species tended to be associated with the continental shelf or other geological features,such as submarine canyons. Models for some species, such as the gray whale (Figure 3.5), mayhave been affected by the relationship between survey timing and migration patterns. Possibleanomalies of note for several specific species are discussed in the full NCCOS report (Menza etal., 2016).3The median provides the midpoint, or central tendency. In this case, the middle value of a frequency distribution ofthe predicted values. It means half of the numbers predicted by the model are lower, and half are higher. Median isgenerally a preferred measure (over average, or mean) for these analyses to address datasets with skeweddistribution (datasets that have a few very high or low values).Chapter 3: Spatial Analyses3-6

Figure 3.2: Long-term predicted relative density for Tufted