5 2 Reserve - nsgl.gso.uri.edunsgl.gso.uri.edu/cuimr/cuimrc02001/1KARPOV_part1a.pdfReserve Konstantin Karpov1 Dale Sweetnam1 ... their source or their use, ... We suggest that the

Quantitative Inventory ofHabitat and Species ofManagement Concern atPunta Gorda EcologicalReserve

Konstantin Karpov1

Dale Sweetnam1

Mike Prall2

Vicky Kirby3

Andrew Lauermann2

John DeMartini4

Pat Iampietro5

R. Villa2

D. A. Powers2

Douglas Albin6

Mary Patyten1

Rikk Kvitek7

Carolyn K. Bretz7

Frank Shaughnessy4

Paul Viesze1

John Geibel1

Phillip Buttolph4

Christopher Malzone5

1 California Department of Fish and Game19160 S. Harbor Dr.Ft. Bragg, CA 95437

2 California Department of Fish and Game619 Second StreetEureka, CA 95501

3 Hopkins Marine StationStanford University120 Oceanview Blvd.Pacific Grove, CA 93950

4 Humboldt State UniversityArcata, CA 95521

5 ABA ConsultantsP.O. Box 587Moss Landing, CA 95039

6 California Department of Fish and Game1031 S. MainFt. Bragg, CA 95437

7 California State University at Monterey Bay100 Campus CenterSeaside, CA 93955

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Marine Ecological Reserves Research Program

Project Number PG-1January 2001

Disclaimer

The mention of commercial products, their source or their use, in connection with material reported herein isnot to be construed as either an actual or implied endorsement of such products by the state of California or itsagents.

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his project enlisted the talents and energies of many different groupsof researchers from across the state of California. These groups took

on very dissimilar research tasks to provide insights into the physical andbiological parameters that frame Punta Gorda Ecological Reserve. In thispublication, we have created separate parts for the ecosystem-survey research(i.e. bathymetry and substrate mapping, SCUBA surveys and ROV transects)and the abalone DNA studies to make the report easier to read, understand,and use. Ecosystem survey research will be presented in Part One: Survey ofPunta Gorda Ecological Reserve (PGER). Abalone DNA research will bepresented in Part Two: Abalone DNA Studies. The diverse works in theseparts will be synthesized to achieve an ecosystem overview of the reserve inthe Discussion and Conclusions section of each part.

Prologue

TTTTT

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e undertook a quantitative inventory of Punta Gorda EcologicalReserve (PGER), the northernmost marine protected area (MPA) in

California, to fill a critical need for marine resource information that couldaid managers in making sound marine management decisions. The recentlyestablished reserve comprises about half of the Northern California marinereserve area. Although the area has historically supported harvest of inverte-brates and finfish, the resource value of PGER has never been quantified andis largely unknown.

Our hydrographic survey of PGER resulted in hardcopy and digital mapsof reserve bottom types, interpreted from sidescan sonar data. Fathometerreadings were used to produce a bathymetric map of the reserve, and RoxAnndata were used to estimate substrate type. Digital forms of the bathymetric,sidescan sonar, and Roxann maps were incorporated into ArcView GeographicInformation System (GIS) files. These maps promise to serve many purposes,including describing habitat types and facilitating assessments of changeover time.

This project is the first to successfully identify DNA markers in redabalone. We found unique markers that distinguished northern populations(north of Point Conception) from southern populations (south of PointConception). We suggest that the gonad is the best type of tissue for abaloneDNA studies, and outline a new, nonlethal method for taking gonad samples.We initiated a genomic library of abalone tissue from PGER (and othernorthern coastal sites), assisting in completion of a state-wide library. Ulti-mately, DNA fingerprinting technology may allow us to distinguish betweenabalone populations, monitor the success or failure of abalone restocking andconservation efforts, and provide forensic markers for law enforcement ofprotected stocks.

Summary of Resultsand Accomplishments

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Summary of Results and Accomplishments

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Divers identified at least two major habitat types within the reserve: sand-impacted bedrock and non-sand-impacted bedrock. Lists of associated algaland invertebrate species were obtained from subtidal collections and videoimages. Qualitative observations suggest that both habitat types are domi-nated primarily by filter-feeding invertebrates.

Over 9 hours of remotely operated vehicle (ROV) video recordings docu-mented and quantified the reserve’s habitats and inhabitants during 5 days ofROV surveys. These recordings helped to quantify and identify species ofmanagement concern, along with habitat types at specific depth ranges. ROVrecordings will provide additional evidence of habitat types at depth.

This report will provide fishery managers and other interested parties withdifficult-to-obtain information about PGER. The work reported here willprovide a baseline for future studies examining the response of the reserve’sbiological resources to protected status, which may lead to a determinationof whether no-take reserves such as PGER are truly useful fishery manage-ment tools.

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unta Gorda Ecological Reserve was established in 1994 in responseto the Marine Resources Protection Act (MRPA) of 1990, which

ordered the creation of four new ecological reserves in California. PuntaGorda Ecological Reserve (PGER) is located in Humboldt County, about 20 km south of Cape Mendocino (Figure 1). It is bounded by waters 3 fathoms and greater to a maximum of 30 fathoms, between a line extending 235 degrees magnetic from the Punta Gorda lighthouse, and a line extending252 degrees magnetic from a point on the mainland shore .75 mi north of Punta Gorda, said line extending through Christmas Tree Rock (Figure 2). The PGER area experiences the strongest upwelling of cold, nutrient-rich waters in the state (Calif. Dept. of Fish and Game 1993). Upwelling is strongly associated with northwesterly winds, coastal geometry, and the deep,nearshore submarine canyons to the north and south of Punta Gorda(Figure 1). South of Punta Gorda, water currents tend to flow north, andnorth of Punta Gorda currents flow southward, producing a strong offshoreflow (Figure 3). The freshwater influence from the mouth of the MattoleRiver, 4 km to the north, is probably negligible, as the coastline is fullyexposed to the mixing effects of winter waves, and no estuarine plant oranimal species have been found beyond the mouth of the river (Anonymous,1979b). The only above-water landmark within the Reserve is Gorda Rock,located 1,200 m offshore, which reaches a height of 10 m above mean lowerlow water (MLLW; Figure 3).

As outlined in the MRPA, the California Department of Fish and Game(CDFG) solicited recommendations for reserve sites from academic institu-tions, scientific groups, federal and state agencies, California coastal commu-nities, commercial and sport fishing industries, conservation groups and thegeneral public (CDFG 1993). Punta Gorda Ecological Reserve was selectedbased on three criteria: research potential for species of management concern,current use, and enforceability (Table 1). Research potential was assessedbased on research needs, habitat available for species of concern, and ease ofaccess; these were all rated as good on a scale of good, fair, or poor. Currentuse was also rated subjectively as either high, moderate, or low. With regard

Part OnePart OnePart OnePart OnePart OneSurvey of Punta GordaEcological Reserve

Introduction PPPPP

Part One: Survey of Punta Gorda Ecological Reserve

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to commercial fishing, current use was rated low, and political acceptabilitywas rated as moderate. Finally, enforceability in terms of identifiable bound-aries and ease of access were both rated fair.

No-take reserves such as PGER recently gained strong scientific backing as“highly effective but under-appreciated and under-utilized tools that can helpalleviate many… problems.” (Gaines et al. 2001) Despite this consensusreached by an international team of 161 scientists, and an increasing body ofwork suggesting that marine refuges provide multiple benefits to marinestocks and ecosystems (Hutchings and Meyers 1994, Jagielo 1999, Kalvassand Hendrix 1997, Karpov et al. 1995, Karpov et al. 2001, MacCall et al.1999, Orensanz et al. 1998), there remains a paucity of true marine refuges inNorthern California.

The 470 total miles of ocean frontage north of the Golden Gate containsroughly 825 square miles within the 3-mile state-jurisdiction zone. Onlyabout 10 miles (2%) of the frontage and about 4 square miles (0.5%) of thearea are reserves with restrictions on certain sport or commercial take. PuntaGorda Ecological Reserve represents about half of the total Northern Califor-nia marine no-take reserve area, and is the major reserve component inNorthern California (Table 2).

Fisheries managers in state and federal resource agencies have recognizedfor some time that existing assessment strategies and management approacheswere not protecting and sustaining nearshore finfish and invertebrates, theirhabitats, and the coastal California communities that rely on marine re-sources. A much more precautionary approach is needed to insure that theresource is protected (Dayton 1998; Lauck et al. 1998).

No-take refugia appear to be highly effective in increasing faunal densitieswithin their borders (Alcala 1988, Davis 1989). Tag-and-recapture studiesshow dispersal into adjacent fished areas for snow crabs (Yamasaki andKuwahara 1990) and pink shrimp (Gitschlag 1986). However, such studiesare rare. Also, it is difficult to quantify contribution to areas adjacent torefuges, although several examples suggest refuges can increase fish catch innearby harvested habitats (Alcala 1988, Booth 1979, Davis and Dodrill 1980).

Further, refuge populations must contain “source” populations thatcontribute new recruits to adjacent areas, rather than “sink” populations,which do not produce surplus individuals (Harrison et al. 1988, Pulliam1988). Emigration of recruits from areas of high productivity can sustainpopulations well beyond refuge boundaries (Yamasaki and Kuwahara 1990).Reproductively important habitats should be encompassed within harvestrefugia to replenish harvested areas with new recruits. Quantifying habitattype, density of adult spawners, and evidence of recruitment through YOYsurveys are all essential components of establishing the recruitment potentialof a refuge area (Rogers-Bennett et al. 1995).

Value ofRefugia


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AbaloneThe red abalone, Haliotis rufescens, ranges from Sunset Bay, Oregon to

Bahia Tortugas, Baja California. North of Point Conception, it is foundintertidally and subtidally to depths of 20 m (60 ft). Recent declines inabalone stocks have led to the closure of the fishery in central and SouthernCalifornia at a time when some areas were near extirpation (Edwards 1913,Karpov et al. 2001, Tegner et al. 1996). This has led to a heightened interestin discovering the genetic differences between abalone stocks on a spatialscale. Identifying source populations is essential if certain areas are consideredfor refuge status. In addition, genetic differences (if significant between areas)could be used for forensic application to identify animals taken illegally fromclosed areas.

Red abalone management in Southern and Central California, wherestock protection was based on size limit alone, failed (Haaker 1996, Karpovet al. 2001, Tegner et al. 1992). White abalone, Haliotis sorenseni, which wasonce sought commercially, has declined to the point of possible extinction(Davis et al. 1996, Tegner et al. 1996). Only three living abalone were foundafter surveying 30,600 m2 of suitable habitat at 15 locations (Haaker 1996).The closure of the red abalone fishery in Southern California increases thepossibility of poaching from the north coast, and underscores the need forpotential source populations for restocking efforts. As the red abalone fisherycontinues to decline in central and Southern California, the Northern Cali-fornia populations will become increasingly important.

Subtidal emergent surveys and invasive surveys must be conducted toassess stock size and habitat suitability for source populations of red abalone(Tegner et al. 1989). For an area to qualify as a source population of redabalone it must include “nursery” areas for juveniles and suitable habitat foradults (Ault and DeMartini 1987). Additionally, sufficient aggregations ofadults of both sexes are required for localized spawning to occur. Rockysubtidal areas, covered with crustose red algae, are considered to be juvenilered abalone “nurseries” (Morse et al. 1979). These are areas where post-larvaesettle and remain at sizes of less than 4 cm (Hines and Pearse 1982). Juvenilesat sizes of less than 15 cm move to cryptic habitat below boulders and increvices. Adult abalone habitat ideally includes both crevice (Tegner 1989)and exposed areas where drift algae can aggregate (Ault and DeMartini 1987,Tegner et al. 1992).

Currently, the CDFG cannot determine whether a fished abalone origi-nated from commercially fished areas outside of California or was poachedfrom inside the state. The work of Dr. Vicky Kirby in identifying DNAmarkers in red abalone may provide a precise forensics tool that can be usedto identify poached abalone. Abalone DNA markers (microsatellites) aresmall tandem repeats of DNA that are employed as genetic markers in foren-sic and population studies in a variety of organisms (Gertsch et al., 1995,Hughes and Queller 1993, Nielsen et al. 1994, O’Reilly and Wright 1995).

Species ofManagementConcern


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DNA fingerprints are generally accepted tools for genetic identification dueprimarily to their comparative ease of assay and accuracy.

Red sea urchinThe Northern California red sea urchin, Strongylocentrotus franciscanus,

fishery provides another example of a management regime based on size limitsand seasonal closures which is threatening to fail (Kalvass and Hendrix 1997).Following peak landings in the late 1980s, stocks (especially in NorthernCalifornia) have declined precipitously. The only known areas of high urchindensity remaining in the north are refuges such as Bodega Marine Life Ref-uge, Point Cabrillo Marine Reserve, and the Caspar Urchin Closure Zone(Karpov et al. 2001, Kalvass and Hendrix 1997). As with abalone, reproduc-tion, growth and recruitment in sea urchins is habitat-specific (Kato andSchroeter 1985, Keats et al. 1984, and Vadas 1977). Establishing habitattype, algae type, algae abundance and evidence of recruitment is essential indetermining if an area contains a source or sink population.

Sea cucumbersThe sea cucumber Parastichopus californicus, fishery is a growing industry

on the Pacific West Coast (Bradbury et al. 1998). Distribution and abun-dance of this species is poorly known in California. Sea cucumbers wereobserved at depths of 5 to 185 m (15 to 255 ft.) in submarine-based surveysoff Alaska (Zhou and Shirley 1996). SCUBA, drop camera, and remotely-operated vehicle (ROV), and submarine surveys have already been directed atdetermining habitat associations and abundance of this species off Washing-ton (Bradbury et al. 1998), Alaska (Zhou and Shirley 1996), and BritishColumbia (Da Silva et al. 1986). Annual landings of this species, which iscurrently taken mostly in Southern California, are estimated at 30.4 mt(67,000 lbs) (CDFG unpublished landing data 2000). As other resourcesdecline, fishing pressure is likely to be increasingly directed towards speciessuch as sea cucumbers, which are not yet fully utilized in California.

Nearshore reef fishesAmong the fishes most vulnerable to overutilization are territorial

nearshore species, such as brown rockfish Sebastes auriculatus, canary rockfishS. pinniger, blue rockfish S. mystinus, black rockfish S. melanops (Karpov et al.1995), gopher rockfish S. carnatus, and china rockfish S. nebulosus (Karpov etal.1995, Lea et al. 1999), which can potentially benefit from protection byrefuge (Davis 1989).

Declines in Northern California shallow-water (<73 meters) and widedepth range rockfish populations are currently of concern to the CDFG, theCalifornia legislature, and the public. Nearshore resource allocation conflictshave recently been exacerbated by a growing nearshore long-line commercialfishery, This new fishery depletes nearshore rockfish populations traditionally


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harvested by sport commercial passenger fishing vessels (CPFVs) and privaterental boats in Central and Northern California (Karpov et al. 1995). Rock-fish species are mainstays of the northern and Central California marine sportfishery, comprising about half of the total sport catch by weight (Karpov et al.1995). Rockfish species caught in the sport fishery include blue rockfish S.mystinus, black rockfish S. melanops, china rockfish S. nebulosus, gopherrockfish S. carnatus, grass rockfish S. rastrelliger, black-and-yellow rockfish S.chrysomelas, brown rockfish S. auriculatus, and olive rockfish S. serranoides(Karpov et al. 1995, Miller and Geibel 1973, Miller and Gotshall 1965).Important wide-depth-range rockfish include yellowtail rockfish S. flavidus,canary rockfish S. pinniger, copper rockfish S. caurinus, and vermilion rock-fish S. miniatus. In addition to rockfish, lingcod Ophiodon elongatus, kelpgreenling Hexagrammos decagrammus, rock greenling H. superciliosus andcabezon Scorpaenichthyes marmoratus are also of concern in nearshore areas.

An in-depth historical summary using sport and commercial data from1958 to 1986, identified signs of population stress in blue rockfish S. mystinus(decrease in catch), canary rockfish S. pinniger and yellowtail rockfish S.flavidus (decrease in mean length in recreational and trawl catch, and highincidence of sexually immature fish in recreational catch), and brown rockfishS. auriculatus (decrease in mean length and high incidence of sexually imma-ture fish in recreational catch) (Karpov et al. 1995). During 1980-86, bluerockfish, yellowtail rockfish, and canary rockfish comprised 51% by numberof the sport catch from boats in Central and Northern California.

Since 1986 the average weights of all major nearshore rockfish species,lingcod O. elongatus, kelp greenling H. decagrammus, and cabezon S.marmoratus in the sport fishery have decreased (Karpov et al. 1995). Duringthe frequent El Niño conditions of this time period, commercial hook-and-line take of rockfish approximately tripled, while sport take remained rela-tively constant. This increased commercial take of nearshore rockfish, whichis now about equal to sport take, has threatened the sustainability of thenearshore rockfish fishery. Declining sizes of nearshore species suggest thatstocks cannot sustain the current levels of sport and commercial harvestwithout degrading the fishery.

Adding to the urgency of the situation is a recent increase in nearshorereef fish commercial fishing activity in the Fort Bragg and Eureka areas,creating potential for sport-commercial user group conflicts and addedpressure on fish populations. Before 1996, the Mendocino coast had noestablished live fish dealers. However, there are now two dealers in NoyoHarbor and also regular buying operations at the ports of Albion and PointArena. In addition, the adjacent ports of Bodega Bay and Eureka also havedeveloped active live-fish fisheries. Commercial fishers are regularly takingfish from the same reefs fished by sport anglers and party boats. The demandfor the demersal species such as china rockfish S. nebulosus, gopher rockfish S.caurinus, cabezon S. marmoratus, and kelp greenling H. decagrammus is high,


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with ex-vessel prices of up to $4.00 per pound. The demand for the less-colorful midwater species such as blue rockfish S. mystinus and yellowtailrockfish S. flavidus is low; and those species are not targeted. The live-fishfishery has not yet spread to the PGER area.

As California stocks of nearshore reef fish, abalone, urchin, and other inverte-brates decline because of disease and overfishing, alternate managementstrategies such as refugia need to be examined. In addition to providingmetapopulations, studies on such refuges could provide test beds for newmanagement approaches to stock assessment for residential finfish andnonmotile invertebrates where models such as egg-per-recruit have failed(Davis 1989, Tegner et al. 1989).

Currently, both the National Oceanographic and Atmospheric Adminis-tration (NOAA) and the CDFG’s Marine Region are developing researchplans to address heavily impacted nearshore rockfish (Bailey In Prep andCDFG unpublished data). Both suggest a conceptual model that stresses theneed to develop fisheries-independent abundance estimates related to habitatfor residential nearshore species of reef fish, as opposed to conventional stockabundance modeling approaches to assessment. Density-based abundancesurveys using strip transects at index or random locations have long been inuse to assess finfish and invertebrates (Davis 1989, Karpov et al. 1998,Karpov et al. 2001). Increasingly, ROV and submersibles have been used forcomparable assessments at depths beyond the limitations of SCUBA (Fox etal. 1999, Stein et al. 1992, Yoklavich et al. 2000). More recently, GIS-basedhabitat maps using sidescan or multi-beam sonar have been used in conjunc-tion with visual sampling to estimate abundance and habitat associations(Yoklovich et al. 2000, Fox et al. 1999). While designing a new assessmentstrategy for nearshore reef fish, the CDFG’s Marine Region described regionalstudy areas that would be trans-geographic and sampled over time (CDFGunpublished data). Proposed “observatories” for the nearshore environmentwould include existing MPAs, and randomly selected near-port and far-portareas under a Before-After-Control-Impact (BACI) design (Under-wood1995), to assess the impact of management change or area closure in a newMPA (CDFG unpublished data). Central to this assessment proposal is theincorporation of new sampling methods to replace outdated stock modelingapproaches for residential species. Methods are needed that allow directenumeration of stock abundance by combining remote multi- and single-beam sonar habitat mapping with SCUBA, ROV, or submarine-based surveys.

The purpose of our study at PGER was primarily to evaluate this MPA formanagement purposes and provide a GIS-referenced basis for identifyingchanges in species abundance and essential habitat over time. We sought to

ResearchPriorities

Purpose ofOur Study


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do this by mapping the substrate, identifying the biota and their habitatassociations, quantifying species of finfish and invertebrates of managementimportance, and evaluating the genetic differences among geographicallydisplaced red abalone. Functionally, our study was divided into three majorcomponents: 1) mapping bathymetry and substrate, 2) qualitative andquantitative dive surveys, and 3) remotely-operated vehicle (ROV) surveys.In addition to evaluating the reserve and providing a basis for future compari-sons, we also sought to integrate and improve on these new methods as a firststep to future habitat-based stock assessment.

The three components of our study overlap in a combined effort toproduce a GIS, habitat–based, multi-species assessment of finfish and inverte-brates at PGER. Our project integrates these three approaches to develop newmethods for researchers to assess stocks using GIS habitat-based methodswith sonar, ROV, and diver-based survey techniques. The fourth approach,the genetic-based red abalone study, was intended to stand alone for evaluat-ing if red abalone populations at PGER were distinct from other areas of thestate and if genetic markers could be discovered for future research andforensic application. Finally, an overriding goal of our project was to evaluate,for our Fish and Game Commission, if PGER actually met the criteriaoutlined in the initial EIR as a quality MPA with protective value, researchpotential, and enforceability (Table 1).

The GIS habitat-based assessment can be divided into three integrated com-ponent studies: 1) sonar-based bathymetry and habitat mapping, 2) remotelyoperated vehicle studies, and 3) SCUBA diver-based surveys. The sonar andbathymetry mapping was intended to provide a GIS-referenced, precisehabitat map of substrate with sub-meter precision and an overlay of detailedbathymetry. The ROV and SCUBA-based survey had two major purposes;first to sample the biota throughout the depth range of PGER, and second-arily to provide visual validation and refinements for the sonar-based survey.In addition, both the ROV and SCUBA projects were undertaken as initialsteps in developing methodologies to provide quantitative bases for futurespatial and temporal comparisons of finfish and invertebrates at control sitessuch as PGER.

The ROV, GIS-based video record was also intended to allow the identifi-cation of spatial associations of finfish and invertebrates for future targetedsampling comparisons of abundance, stratified by habitat type. In addition,the spatially- referenced ROV video observations were intended to allowindependent validation of statistically determined clustering of species andhabitat.

The SCUBA survey was primarily intended to produce a species list ofinvertebrates, finfish, and algae at depths where samples could be collected.Secondarily, given acceptable diving conditions, we intended to quantify

GIS Habitat-BasedAssessment


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macroinvertebrates and finfish of management importance at shallow depths,and to calibrate diver survey methods to ROV methods at deeper depths.ROV and diver surveys were both geo-referenced to allow visual validation or“truthing” of the sonar-based substrate maps.

Survey design and executionSurvey design and preparation was performed using TNT Mips GIS

(MicroImages, Inc., Lincoln, NE) and Hypack (Coastal Oceanographics,Inc., Middlefield, CT) software. The planned survey consisted of 30 parallellines, 3800 m long and 100 m apart, oriented roughly parallel to shore anddepth contours. Line spacing was chosen to provide adequate bathymetricdata density and sidescan imagery overlap, while maximizing the chances ofcompletion of the survey in light of the variable and unpredictable conditionsat the study site.

The bathymetric and sidescan sonar survey was conducted August 23–25,1997 aboard the F/V Miss Michelle (Skipper John Holcombe). Approximately91.5 km of tracklines were surveyed, covering the entire reserve from aminimum depth of 3m to a maximum depth of 58 m (relative to MLLW).All data were acquired and recorded using ABA Consultants’ integratedseafloor mapping system. Differentially-corrected (dGPS) position data wereprovided by a Trimble 4000RL GPS with USCG differential correctionssupplied by a ProBeacon receiver (horizontal accuracy +/- 2–5 m). AnInnerspace 448 digital fathometer (208 kHz, 8° beam width) was used togenerate bathymetric soundings. A bar check was performed at the beginningof each survey day for calibration of the Innerspace fathometer. Soundingdata from the Innerspace 448 were also routed to a RoxAnn unit (MarineMicro Systems, Inc., Aberdeen, UK) for generation of seafloor substrateclassification data. RoxAnn is a parallel processor that uses the signal strengthsof the first (E1) and second (E2) returns from the depth sounder as a measureof roughness and hardness, respectively. A detailed discussion of the theory ofoperation and strengths and limitations of the RoxAnn system (as well asother seafloor habitat mapping technologies) can be found in MappingTechnology Review (Kvitek et al.1999; http://seafloor.csumb.edu/taskforce/pdf_2_web/nedp_revfinal.pdf ). The E1 and E2 values from RoxAnn can bepost-processed to provide point-soundings with tentative seafloor substrateclassifications. The RoxAnn unit was calibrated according to manufacturer’sinstructions immediately prior to the survey (i.e., over harbor mud, depth = 4m). Bathymetric, RoxAnn, and dGPS position data were logged to a PCrunning the Hypack software.

Sidescan sonar (SSS) imagery was acquired using an EG&G 272TD/260TH dual-frequency system (EdgeTech, Inc., Milford MA). Both 100 and500 kHz sidescan data were collected. Range was set to 150 m (total swath

Methods

Bathymetryand SubstrateMapping


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width = 300 m) for all tracklines to maintain optimal overlap for productionof mosaicked imagery. Hard copy printouts of imagery from each tracklinewere produced in real time for use in creation of an analog hard copy mosaic.In addition, digital sidescan data were recorded to Exabyte tape using anEG&G Model 380 tape unit (EdgeTech, Inc., Milford MA)

Data processing and GIS creationBathymetric and RoxAnn data were cleaned using Hypack software to

remove soundings with erroneous depth and/or position information. Bathy-metric data were tidally corrected to MLLW using predicted tide informationfor each survey day (WWW Tide predictor http://tbone.biol.sc.edu/tide/sitesel.html). Bathymetric soundings were then exported from Hypack as anx,y,z ASCII text file (UTM Zone 10, WGS 1984 datum, soundings inmeters). A total of approximately 8000 soundings were then combined withshoreline data derived from a USGS DEM dataset and imported into theSurfer software package (Golden Software, Inc., Golden, CO) for girding.Shoreline x,y positions were assigned a z (depth) value of 0 to force interpola-tion of the depth grid towards the surface at the inshore extent of the sound-ing data and around offshore rocks. Data were girded using a Kriging algo-rithm (20 m cell size) and 2 m interval contour lines were generated. Contourlines were then exported from Surfer in AutoCAD (.dxf ) format, importedinto TNT Mips GIS software, and trimmed to the extents of the originalsounding data. A bathymetry polygon layer was also created during trimmingby generating a border around the extraction area; the resulting polygons wereattributed with the depth value from the deeper of the two bounding contourlines.

Cleaned RoxAnn soundings were classified using a combination of “by-eye” cluster analysis and comparison to sidescan sonar imagery. Clustergrouping was performed by plotting E1 vs. E2 values in Cartesian space andvisually defining groups having similar characteristics within the resultingpoint cloud (the “RoxAnn Square” or “-Box” method). Initial E1 and E2boundaries for these groups were derived from past studies in settings similarto PGER. The resultant groups were given tentative classifications based upontheir respective relative roughness and hardness values, and then representa-tive subsets of points were compared to the corresponding preliminary SSSimagery for refinement of tentative class designations. RoxAnn soundings(approximately 41,000) were then exported from Hypack as an ASCII x,y,ztext file (UTM Zone 10, WGS 9184 datum, with z being a numerical valuerepresenting RoxAnn class I.D.). This text file was imported into TNT Mipsand polygons were hand-drawn to fit the RoxAnn point distribution. In veryheterogeneous areas the predominant substrate type was assigned to thepolygon even though it encompassed points from 2 or more classes.

Sidescan sonar imagery was processed to produce both analog hard-copyand digitally processed mosaics. Manual analog processing was completed as


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follows: 100kHz sidescan hard copy records were photo-reduced 50% andmosaicked using position information from each record, as well as plottedvessel course tracklines. Interpretation of the resulting mosaic was performedby visually relating patterns of backscatter intensity to probable seafloorsubstrates and morphologies. A mylar overlay was created showing thesetentative substrate classifications, the overlay was scanned on a large-formatdrum scanner, and the resulting digital image was georeferenced. Thegeoreferenced raster image of substrate type boundaries was then vectorized tocreate polygon themes of each substrate type.

The hardcopy mosaic was also digitized and included to aid in visualiza-tion and further interpretation of substrate and habitat types within PGER. Itwas created by digitally imaging the hardcopy sidescan mosaic using a LeafLumina camera/scanner (2400 x 3400 pixel resolution). The mosaic wasimaged in 4 overlapping pieces, which were then imported into TNT Mipssoftware for georeferencing and re-mosaicking.

Digital processing of sidescan sonar data was completed in August 2000.Digital processing and mosaicking was accomplished using the Isis Sonar andDelph Map software packages (Triton Elics International, Watsonville, CA)and TNT Mips GIS software. Raw sidescan data were extracted from Exabytetape and converted to Triton Extended Format (.xtf ) using the Tape 380utility. Individual trackline files were replayed and bottom tracking of thesonar was monitored and adjusted where necessary to aid in proper slant-range correction. Line files were snipped to remove portions with poorimagery or particularly bad position/time stamp information from the begin-ning and/or end of the trackline. Time stamp errors were corrected using theIsis Sonar FixTime utility. Tracklines were then slant-range and laybackcorrected and the position data for each line was smoothed using a speedfilter. Each line was then girded and georeferenced and exported from IsisSonar/Delph Map in GeoTIFF format (0.20 m pixel size, UTM Zone 10,WGS1984). Individual trackline TIFF images were imported into TNT MipsGIS software and areas of poor image quality were extracted and removed.Individual tracklines were then overlaid to produce a mosaic image.

All vector GIS content [bathymetry point (sounding) data, contour lines,and depth polygons, RoxAnn point and polygon data, sidescan sonar inter-pretation polygons] were then warped to Albers Equal Area Conic projection,NAD-27 datum, and exported from TNT Mips as ArcView shapefiles (.shp).Raster content (digitized SSS hardcopy mosaic and the entirely digitallyprocessed mosaic and individual tracklines) were resampled to Albers projec-tion and exported as georeferenced TIFF images (.tif, with accompanyingArcWorld .tfw files). All GIS content was provided to CDFG on CD-ROMwith accompanying “Readme” metadata files (Appendix 1). Most content wasincorporated into ArcView project (.apr) files for quick visualization. Allcontent except the digitally processed SSS mosaic and individual tracklineswas also provided in unprojected (Lat./Long.) decimal degrees format for usein the field with the real-time ArcView Tracking Analyst extension.


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Truthing bathymetry and substrate mappingTwo approaches were used to “truth” or compare substrate types identi-

fied in sidescan sonar mosaic interpretations and RoxAnn habitat maps usinggeoreferenced to the substrate types identified with the ROV. Graphicalcomparisons were made to examine percent overlap in our modified primarysubstrate classification to both the sidescan sonar mosaic interpretation andthe RoxAnn “by eye” classifications. In addition, ArcView mapping of themosaic interpretations and primary RoxAnn polygons were overlaid withROV transects color coded by our primary substrate classifications for directspatial comparison of both methods.

Qualitative SCUBA surveys were conducted by swimming 50 m–250 mtransects along compass courses from deep to shallow areas of the reserve.Divers collected video records and specimens for post-processing. Whenpossible, dives covered the entire SCUBA depth range from 18 m to 3 m.Differential-GPS-equipped skiffs, aided by the reserve’s lack of surfacecanopy, easily tracked the positions of divers towing surface floats.

SCUBA video transects were recorded using a Sony “Hi-8” camera (SonyCorporation of America, New York, N.Y.) in a Light and Motion Industries(Monterey, CA) “TopDawg” underwater housing. Methodologies using thishand-held camera at PGER were not standardized; therefore, SCUBA videofootage was only used for species presence/absence analyses and substrate mapground-truthing. Videotapes were post-processed to estimate rocky habitatalgal cover and type of rocky relief at less than 18-m depths. Relative avail-ability of surface algae to percent cover of algae was estimated. Algae andcover were classified in five categories: encrusting, coralline algae turf, foliose,understory, and canopy kelp. In Northern California, understory kelp in-cludes brown algae such as Pterygophera californica and Laminaria dentigerawith surface canopy provided by the annual bull kelp Nereocystis luetkeana.This classification allowed direct qualitative comparison to algal abundance attwo other reserves in Northern California, Point Cabrillo in MendocinoCounty and Bodega Bay Marine Reserve in Sonoma County, as reported byKarpov et al. (2001).

Quantitative ROV video transects were conducted using two methodologies.The primary method consisted of long (up to 3 km), continuous striptransects using a “live-boat” technique, where a vessel followed the ROV as itwas piloted on a fixed compass course along the sea floor. However, “live-boating” in rough weather or high-current situations can be extremely dan-gerous since the ROV or umbilical can become tangled with underwaterobstacles or the propeller, and the equipment easily lost. An alternate, at-anchor transect technique was used when current and wind conditions

Diver-BasedSurveys

RemotelyOperatedVehicleSurveys


Karpov 17

prevented safe vessel operation near the ROV. These shorter (<150 m)transects were limited by the length of the ROV umbilical.

The practical reality of limited access time on the reserve due to severeweather conditions necessitated using long, continuous strip transects as themost efficient use of our research time. This methodology reduced theamount of launch, recovery, and transit time while maximizing the amountof survey time (Barry and Baxter 1993). Placement of these strip transectswithin the reserve was not random. Guided by the sidescan sonar mapsobtained in the first year of the project, we attempted to sample certain areasof the reserve by habitat type and depth. However, since the ROV was forcedto move with strong oceanic currents, we were forced to sample the reserve ina random-haphazard manner, which may have decreased the accuracy ofdensity estimates for the reserve (Barry and Baxter 1993).

Initially ROV video surveys were conducted using a Phantom HD-2remotely operated vehicle (Deep Ocean Engineering (DOE), San Leandro,CA) (Figure 4), which was maneuvered using 2 horizontal thrusters, 1 lateralthruster, and 1 vertical thruster. In July, 1999 we upgraded this ROV to aHD-2+2 model with two additional horizontal thrusters to deal with thestrong current conditions we encountered at PGER. All thrusters werepowered by direct current electric motors.

The ROV systems were controlled from a surface control console, whichwas attached to the ROV by a 200 m umbilical containing a set of 32 copperwires. The ROV pilot used a joystick on the control console to operate thethrusters, which allowed the ROV to be guided in three-dimensions. Thepilot controlled all other ROV systems with electronic toggle switches on thecontrol console.

Our HD 2+2 ROV was equipped with an analog pressure and compasssensor, which sent variable voltage charges to the surface control consolewhere they were converted to depth and compass headings. Depth, date,compass heading, and time data were displayed on the video monitor by theDOE Pisces data overlay box. These data were sent over an RS-232 serialconnection to a personal computer where they were viewed and stored as datafiles for future reference and analysis.

Video imagery was recorded with a Sony high-resolution color videocamera adapted by DOE for remote control of focus and zoom, and mountedin a pressure housing with a flat optical port. Images were viewed on a Sonycolor video monitor with 450 lines horizontal by 350 lines vertical resolution.Camera focus, zoom (12:1), and tilt were controlled at the surface with theROV control console. The central axis of camera tilt was 48 cm above theseafloor when the ROV rested on the bottom. Camera tilt capabilities rangedfrom 90° below the horizontal axis (straight down) to 90° above (straight up).A constant camera angle of 35° below the horizon was maintained while ontransect, and camera zoom was maintained at its widest field of view.

Two 15-milliwatt, Class III A diode lasers (Power Technology Incorpo-rated, Little Rock, AK) were mounted equi-distant from, and parallel to, the

Karpov 18

optical axis of the color camera at a width of 10 cm to allow for scaling ofvideo images and measurement of observed objects (Auster et al. 1989, Davisand Tusting 1991). Two 250-watt tungsten-halogen lamps, one mounted onthe ROV crash frame and one mounted on the camera tilt unit, providedillumination. An Imagex Model 855 color-imaging, rotary-scanning sonar(Imagex Technology Corporation, Port Coquitlam, B.C., Canada) wasmounted on the forward port thruster pod. It was capable of scanning 360°with a rotating 1.7°-wide by 30° (+/-15°) vertical beam. Range could beadjusted from 5 to 90 m radii. The color-gradated sonar map provided imagesof the surrounding substrate relief, and was consulted to aid in safe navigationof the ROV in turbid conditions.

ROV surveys were performed aboard two vessels: the CDFG P/V Bluefin,a 65-ft patrol boat with a crew of three, and the CDFG R/V Mako, an 85-foot research trawler with a crew of five. The department’s Wildlife ProtectionUnit supplied additional rigid-hull inflatable skiffs and crews that weredeployed during SCUBA surveys, and served as backup vessels.

Vessel-based equipmentTopside equipment for ROV surveys consisted of three separate 19-inch

rack mount boxes with internal shock absorption mounts. Control of all sub-sea ROV components was through control consoles. These consisted of twojoysticks and various electronic switches that separately controlled the ROVsub-systems.

Essentially, input from the ROV pilot passed through the control consoleto the ROV control panel box, which coordinated inputs and relayed them tothe ROV through umbilical conductors. The control panel box also housedthe primary 14-inch ROV pilot video monitor. The second rack mount box(the sonar box) housed the Imagex sonar control console and VGA computermonitor. Both the control panel box and the sonar box were positioned fordirect viewing and easy access by the ROV pilot. The third rack-mount box(the recording box) contained the primary digital VCR (Sony DSR-20 SanJose, CA), the secondary super-VHS VCR, the Horita GPS3 time code reader(Horita Corporation, Mission Viejo, CA), the dGPS receiver, the audio mixer,and a three-way video switch (Figure 5). Video and Horita GPS3 positiondata were recorded redundantly on the primary and secondary video taperecorders. The audio mixer combined audio input from the ROV’s internalhull-mounted microphone and the topside observer microphone, sendingboth to the video recorders, where they were recorded onto one of the stereochannels on the videotape. Two sources of video were available: 1) the ROVmain camera and 2) the ROV sonar image. The three-way video switch allowedthe data recorder to select the video source to send to the VCRs.

ROV operationsThe primary operator of the ROV (“ROV pilot”), was responsible for

completing the dive mission and returning the ROV safely to the vessel. The


Karpov 19

pilot was directed by the ROV data recorder and scientific observer to posi-tion the ROV and its camera to view organisms of interest and to start andstop the transect.

The umbilical tender directed the vessel operator and deck crew to assistin maintaining correct vessel orientation during live boating. The umbilicalwas coiled on the deck, much like a garden hose, and paid out or taken in bythe tender to maintain the shortest possible distance between the vessel andROV. The umbilical tender also directed launch and retrieval of the ROVfrom the vessel and deployment of the attached GPS surface float, and tookmental notes of the amount of umbilical deployed during a dive, entering anestimated average umbilical length into the ROV dive log database for thatparticular dive.

A third crewmember, the ROV recorder, was responsible for all datacollection during a dive. This included videotape management and operationof the video-logging program on a personal computer. The ROV recorder wasdirected by verbal cues from the pilot or observer to type simple remarks inassociation with a particular video sequence, mark events, or record samplingprotocols.

The log file produced on the laptop was in plain ASCII text format. Ittabulated time, video frame number, dGPS position, and written annotations,as well as mission information such as videocassette ID number, location, dateand crew. At five-minute intervals, the ROV recorder directed the pilot toland the ROV and remain stationary while the recorder taped the sonar imageon the videotape. This was accomplished by switching the input video fromthe ROV video to sonar video with the three-way video switch. Sonar videowas recorded during one complete sweep of the sonar head, and then videoinput was switched back to the ROV main camera and the pilot was directedto resume the transect.

ROV dive protocols: Transects were performed using two methods atPGER. The primary protocol for both methods was for the ROV pilot tomaintain a pre-determined compass course. In most cases this was achieved byusing the ROV autopilot feature, which kept a constant heading using theROV onboard electronic compass. The pilot also attempted to maintainconstant speed over the bottom and a constant altitude of 0.5 m above the seafloor. While on transect, constant adjustment of ROV altitude was requiredby the pilot to follow changing sea floor depth and to compensate for current,surge, and umbilical drag. During transects, the ROV pilot also left thetransect heading and altitude to measure fish and invertebrates with the ROVlasers. Also, as these surveys were exploratory in design, the pilot was oftendirected by the data recorder and scientific observers to zoom in on newspecies as well as other observations that required magnification of the field ofview. These off-transect deviations were logged by the data recorder and re-moved from the quantitative analysis during post-processing of the videotape.

Positional data: Two methods were developed in this study to recordROV differential Global Positioning System (dGPS) positions during “live-


Karpov 20

boating” and anchored transects. During live-boating transects, acquisition ofROV positional data began with reception of dGPS data through an inte-grated dGPS antenna mounted on the vessel. This shipboard GPS integratedGPS signals and the U.S. Coast Guard beacon dGPS radio signals to producereal-time, differentially-corrected positions of the surface vessel.

The shipboard dGPS sent time and position data once every second to aHorita GPS3 time code reader. This device integrated GPS time and positiondata and generated Society of Motion Picture and Television Engineers(SMPTE) time-code and serial data output. Society of Motion Picture andTelevision Engineers time code is a binary-coded, square-wave signal that canrecord dGPS time and position coordinates in latitude and longitude onaudio tracks of primary and secondary videocassette recorders (Figure 5).Positional data were linked to events observed on video by GPS time andvideo frame number, and were viewable in a video overlay window generatedby the Horita GPS3 time code reader (Figure 6). The time code window wasrecorded with the video image and other data windows. Positional data couldbe retrieved from the videotape audio track and sent as serial data to a per-sonal computer by playing the tape back through the Horita GPS3 time codereader. This permanent record allows precise dGPS positional data to berecorded at one-second intervals on the videotape record, and provides thefoundation for spatial analysis of events recorded in the video record for bothGPS positioning methods.

When anchored, ROV position was recorded by a buoy-mounted TrimbleGeoexplorer II GPS (Trimble Navigation Limited, Sunnyvale, CA) housed ina watertight container and attached to the ROV umbilical. A weighted keelwas attached to the float to maintain correct antenna-to-horizon orientationof the GPS unit inside the sealed float. The Geo-explorer II was programmedto store a GPS position once every second. This data was downloaded to apersonal computer after each transect, and post-differentially corrected fromcorrection files obtained from a shore-based Community Base Station. Thispositional data was referenced to events on the video record of Universal TimeCode (UTC) time, and was accessed in the time-code window created by theHorita GPS3 time code recorder. Each of the two methods (live-boating andanchoring) had inherent biases that decreased the accuracy from the sourcedGPS positions. The live-boat method relied on vessel position to approximateROV position. While operating, the ROV was offset from the vessel position,with the amount of offset typically twice the operating depth. A comparableposition offset error was incurred from anchored stations. In this case theGPS, mounted in a float, trailed behind typically at a distance less than twicethe operating depth.

Post-processing of digital videotapeSeparate viewings of the ROV videotape were completed for fish and

invertebrate enumeration, and substrate classification (Figure 7). A single


Karpov 21

technician performed each viewing. Data recorded from video observationswere entered into a relational database created with Microsoft Access software,which referenced each observation to a time code and a unique identifyingnumber assigned to each dive. Within the database, a customized data-entryscreen was developed for efficient data entry. To eliminate typing of observa-tions and time code reference, an Inc. “X-keys” programmable 52 key key-board (P.I. Engineering, Williamston, WI) was programmed with speciesnames and substrate types to allow for single keystroke data entry. A Horitatime-code wedge was used to sample time codes directly from the videotapeaudio track. This device reads the SMPTE time code data coming from theaudio track, or from the Horita GPS3 audio output, translates the data intoserial characters, and then inserts these characters in the active cell of thedatabase entry screen. The combination of these elements permitted thetechnician to record an observation with one or two keystrokes while viewingthe videotape, thus reducing total post-processing time. Organisms found ingroups were counted and referenced to the same time code. Data on speciesabundance were summarized and individual observations were plotted on aGIS map for spatial analysis of species occurrence.

Substrate classification: Substrate viewed in the ROV video transects wasclassified with regards to primary, secondary, and minor components. Thesecomponents were designated using an adaptation of the habitat classificationscheme proposed in Greene et al. (1999) (Table 3), with components adaptedfrom Stein et al. (1992) and Yoklavich et al. (2000). Primary substrate wasdefined as the dominant component, which provided habitat for the organ-isms of interest (in this study, fish and sessile invertebrates). Given this defini-tion, primary substrate was not necessarily the substrate type with the greatestspatial coverage. For example, a sand field with isolated bedrock outcroppingswould be classified with a primary component of rock and a secondary com-ponent of sand even though the sand covered more area. Sand was listed asthe primary component if it was the only substrate type viewed for longerthan ten seconds. Secondary substrate was defined as the substrate type thatwas less dominant than the primary substrate. Spatial coverage of secondarysubstrate was determined by the viewer as follows: 1 = < 33% coverage, 2 =3–66% coverage, 3 = > 66% coverage. Minor components were defined assubstrate types distinct from primary and secondary components but havinglower spatial coverage.

Codes were used to describe the degree of vertical relief of the primaryand secondary substrate. The codes were entered as “1”, “2”, or “3” for allrelevant substrate types; however, the meaning of the code varied dependingon the substrate type referenced (Table 3). Substrate types were furtherdescribed by using substrate modifiers. These were qualitative descriptorsapplied to either primary or secondary substrate that helped further definedthe substrate. For example, sand (as primary or secondary substrate) wasfurther described as being fine, medium, or coarse as an indication of sand


Karpov 22

particle size. Modifiers applied to bedrock and boulders included “hum-mocky,” “vertical face,” “crevice,” and “cave.”

These substrate definitions were used to characterize ROV video transectsin 10-second increments or greater. At the beginning of each transect, theviewer recorded the time, depth (feet), and heading (degrees magnetic) fromvideo data overlays on the tape. Ten seconds of tape was then played. The sub-strate viewed during this interval was characterized according to primary,secondary and minor components along with associated relief codes and modi-fiers. The viewer then continued to play the tape until the substrate changed,and at that time the depth, heading and time were recorded (Figure 6). Thenext increment was then marked with time forwarded one second from theending time of the previous increment, and the same depth and heading asthe end of the previous increment. Again, ten seconds of tape was viewed, thesubstrate was classified, and that classification was kept until the substratechanged. Time increments varied from 10 seconds to almost 14.5 minutesdepending on the homogeneity of the substrate and the speed at which theROV made forward progress. If a segment of video was of poor quality or nota linear transect, that increment was recorded as unusable and not included inthe substrate classification. Segments of video where the ROV sonar data wasinserted were also not included in substrate classification.

Determination of the substrate segment length: Length of substratesegments was determined by calculating the distance between the estimatedROV positions at the beginning of the segment to the estimated position atthe end of the segment. Although the ROV did not travel in a straight line,we believe this calculation was more accurate than summarizing the distancebetween all positions within a substrate segment. The “all position” distancewould have been greater than the actual distance traveled by the ROV becausevessel movement caused by wave action and side-to-side maneuvering createdsubstantial “wandering” of the GPS positional records.

Determination of area surveyed: Area surveyed for each substrate seg-ment was calculated by multiplying the average width of the field of view bythe length of each substrate segment. The ROV parallel lasers, projected ontothe sea floor, were used to calculate width of the field of view. To obtain ameasurement of the width of the seafloor viewable, the width of the videomonitor screen was divided by the width of the projected laser points andmultiplied by 100 mm (the true laser width). Width of the field of view wasmeasured at 30-second intervals determined from the video time code overlayand averaged over each transect. At any given time, the lasers frequently werenot visible or were not projected on a perpendicular surface for proper mea-surement. When this situation occurred, the tape was advanced one frame(1/30 s) at a time until a usable laser projection was acquired. If no usableframe was acquired in ten subsequent frames, the tape was advanced onesecond from the starting time code. This ten-frame sampling at each secondmark was continued up to 10 seconds. If no suitable measurement was found


Karpov 23

within this ten-second and ten-frame interval, the tape was advanced to thenext 30-second interval and the process was repeated.

Fish and macroinvertebrate enumeration: Using an “X-keys” entrysystem, each identified species observation was entered into the MS Accessdatabase, not only with its corresponding timecode, but with a substrate andorientation code as well. Substrate and orientation were specific to eachindividual (or colony) identified, and were based on the invertebrate’s point ofattachment to the substrate. Substrate categories used during data entry were“sand”, “boulder”, and “bedrock”. Orientations used were “sand covered” forany surface that was completely covered by sand, “scour zone” for any surfacewhich appeared to be strongly sand scoured, “horizontal surface” for anysurface which appeared to have a slope < 45º (from horizontal), and “verticalsurface” for any surface which appeared to have a slope > 45º (from horizon-tal). Surface slope was visually estimated and was not based on actual mea-surements. Many rocky outcroppings had complex surfaces with repeatingorientations, making orientation categories a measure of slope and sandinfluence and not a measure of placement, such as bottom, side, or top. Usingthe substrate and orientation observations, invertebrates enumerated within adefined substrate segment were described more specifically.

Determination of animal lengths: In situ fish lengths, as well as seacucumber lengths, were determined with ROV lasers. When fish were en-countered on the transect, an attempt was made to project the laser fiducialmarks onto its lateral aspect. In most cases, this necessitated leaving thetransect to pursue the fish with the ROV. Sea cucumbers were measured byprojecting the fiducial marks onto the lateral or dorsal surface of the cucum-ber or onto adjacent substrate. During post-processing, video sequences wherethis projection was successful were played one frame at a time (1/30 s) untilthe best image of the animal and projected fiducial marks was found. Thetime reference of this exact frame was recorded along with the measurementsfor future reference. Only images where the animal was perpendicular to thecentral optical axis were used for length determination. After an image framewas selected, the video was paused and the distance on the surface of thevideo monitor between fiducial marks was measured to the nearest mm. Atthe same time, the total length of the animal was measured to the nearestmm. The measured animal width was divided by the measured laser lengthand multiplied by 100 mm, the true width of the laser fiducial marks. Forconsistency, all measurements were made using the same video monitor.

Species-habitat association analysis: Densities of both fishes and inverte-brates in each substrate segment were determined by dividing the counts ofeach organism by the estimated area of each substrate segment. Except forcolonial species, all macroinvertebrates and finfish were recorded as individualorganisms for density determinations. This substrate segment was our basicsampling unit for analysis. The area of each substrate segment was calculatedby multiplying the average width of the field of view by the length of each


Karpov 24

substrate segment. Species-specific densities were weighted by the length ofeach transect because segments varied by the area of each continuous patch ofsimilar substrate (Solkal and Rohlf 1980). We therefore assumed that thesesegments were independent of each other because they were determined byhabitat differences not by a standard unit of measure. Mean weighted densi-ties ( sy ) reported in the following tables were calculated as:

∑∑ ∗

=i

iis

s

syy

)(

where sy is the mean weighted density, y is the density (counts/area), and s is

the weighting factor, segment length.The similarity of species-habitat associations was investigated for both fish

and macroinvertebrates using hierarchical cluster analyses. Cluster analysescan be useful for identifying groups of co-occurring species (Legendre andLegendre 1998, Krebs 1999) and has been used to identify fish/habitatassociations (Stein et al. 1992, Yoklavich et al. 2000). Only species whoserelative abundance > 1 % of the species observed were used in the analyses.Clustering of the weighted species densities was done using the averagelinkage method with dissimilarity measured in Euclidean distance (SYSTAT2000). Substrate/habitat classifications were first stratified by the primarysubstrate type of each segment (i.e., rock, boulder, sand) and then by relief.Results are presented in both “dendrograms” and “permuted data matrices”which graphically represent cluster analyses of both rows and columns simul-taneously (SYSTAT 2000). Different colored branches in the dendrogramsrepresent distinct clusters; different colors in the matrices represent themagnitude of each number in the matrix (SYSTAT 2000). The number ofcutpoints in the data matrix was determined by an algorithm in the clusteringprogram (SYSTAT 2000).

This visual representation of clustering by both rows and columns allowsinsight as to how species densities within different habitats can affect theclustering. Although clustering is not as statistically rigorous as some othermultivariate analyses, we believe that it visually can best depict species/habitatassociations across the range of habitat types encountered. For each datamatrix, the corresponding dendrograms from the clustering by rows andcolumns are presented alongside the matrix. Euclidean distances greater than50% of the total dissimilarity are often interpreted as major divisions betweengroups (Yoklavich et al. 2000). However in our analyses, the distinct speciesor clusters representing greater than 50% of the total dissimilarity weresystematically removed to reveal underlying associations.

Species orientation analysis: Substrate and orientation specific to eachindividual (or colony) identified was extremely useful since it identified howthe organism was oriented in its environment (e.g., an invertebrate’s point ofattachment to the substrate, or a fish’s proximity to the bottom). Thesecharacteristics, presented as percentages, were only used for comparisonbetween substrate categories.


Karpov 25

Species-habitat spatial analysis: Species with similar habitat associationslocated on each ROV transect were mapped on PGER ArcView overlays ofsonar grayscale maps of substrate (with bathymetry). Color-coding of ROV-determined primary substrate and relief allowed a direct visual comparison forinterpreting validity of habitat associations imputed from cluster analysis. Inaddition, this also allowed us to visually reference species locations and ROV-based habitats to sonar-based maps.

Difficulty reaching the reserve and poor weather conditions impeded our fieldwork at PGER. Weather predictions from the Blunt’s Reef buoy (NOAABuoy Number 46030), located 25 km to the north of the reserve, often didnot accurately portray weather conditions encountered at the reserve. Surfacewinds and a strong northerly current often produced short period wind wavesat PGER, creating unacceptable conditions for ROV or SCUBA surveys. Asa result, during the three years of fieldwork, bad weather conditions andstrong currents allowed only 5 workable days out of 30 days either on site orprepared for deployment (Table 4). Generally, we were unable to work atPGER when wave height exceeded ~2 m with a period of less than 8 seconds(Figure 8). Under these conditions, traveling the 37 km north from our stag-ing site at Shelter Cove often proved futile. In addition, two sets of loggingthermisters deployed at PGER were lost during winter period rough seas.

Given these conditions, we modified the SCUBA and ROV surveyapproach at PGER. We abandoned attempts to quantify abundances offinfish and macro-invertebrates using SCUBA, relying for the most part onROV video recordings. In addition, strong currents forced a random-haphaz-ard deployment for ROV transects.

Mapping performed by ABA consultants produced bathymetry contours at2 m increments from 4 to 56 m depths (Figure 9), RoxAnn data classifiedsubstrate points and polygons of like substrate (Figure 10), grayscale imagesof analog sidescan sonar data (Figure 11), grayscale images of digital sidescansonar data (Figure 12), and a “by-eye” substrate classification from the inter-pretation of the sidescan sonar grayscale image mosaic (Figure 13). A descrip-tion of interpreted substrate types is presented (Table 5). The boundary ofPGER does not fall completely within the area surveyed because, as men-tioned previously, there are several different definitions of the reserve.

Bathymetry contours at 2 m increments from 4 to 56 m depth indicatethat rocky reef within PGER, in general, has vertical relief of less than twometers. Isolated outcrops of higher relief are scattered throughout the reserve(Figure 9). Analysis of RoxAnn classification point values produced eight

Results

General

Bathymetryand SubstrateMapping


Karpov 26

general substrate types that are shown as spatially-referenced polygons(Figure 10). Grayscale imagery of the analog sidescan mosaic at approximately1.5 m2 resolution indicates that the hard substrate at PGER consists of amixture of rock outcrops with sections of fractured rock, boulders and largeexpanses of sediment (Figure 11). Many small pockets of sediment, andchannels between hard substrate features are also visible in this image. Thedigital processing of the sidescan data produced grayscale imagery of higherresolution, approximately 0.25 m2 (Figure 12). But, because of distortionsthat were introduced by inaccurate time and position data, the digital imagerywas not usable for comparisons across the entire reserve. However, there aresome high-definition portions of the imagery that are useful for identificationof individual rocks, boulders and similar features. Using the combination ofthe RoxAnn classification, point soundings, polygons, and grayscale imagery,we interpreted the sidescan sonar mosaic and created nine substrate classifica-tions (Table 5) and then plotted the georeferenced polygons for these classifi-cations (Figure 13).

The mosaic interpretation and RoxAnn polygon classification attributeswere “ground-truthed” by comparing spatially-associated positions of theROV vessel track and their related video substrate classification. In general,for both the mosaic interpretation and the RoxAnn polygons, our videoclassification identified a mixture of similar substrate types due to the finerresolution used in our interpretations (Figures 14 and 15). Prominent high-relief rock features did not precisely match positions within our ROVtransects, however they were within the limits of precision for our naviga-tional capability. Note the 63 m offset between ROV position and themapped reef features in a general view of the reserve (Figure 16) and in theclose up of northern transect 25 (Figure 17).

The northern section of the reserve (covered in transects 30, 36, 37, 38,and part of 25) was dominated by fine-grain sand, mostly low-relief (lowenergy) sand with minor segments of low relief rocks. Bathymetry andsidescan visual images between 10 and 30 m reveals sand areas as “sand dune”types, elevated about 2 m, grading from east to west from sand into similarlyelevated sediment areas (Figures 16 and 18).

The substrate in the central section of the reserve, where transects 25 and31 intersect, was interpreted from RoxAnn data as a rock-gravel matrix. Wefound this area to be one of the most consistent rock habitat areas in thereserve, with the highest relief (>3 m) (Figures 16 and 18). Bathymetryvalidates that this section is very complex. To the east along transect 31, wefound medium relief and coarse-grained sand.

In the southern sections of the reserve along transect 26, we intersectedanother complex area that was more rock-sand matrix than sand, with lesssand than in the central section (Figure 19). Bathymetry revealed a trougharea running from northwest to southeast. The northern margin of this areawas surveyed by most of transect 27 and by the lower part of transect 31 to


Karpov 27

the base of Gorda Rock. Substrate here was mostly rock interspersed withsand or high energy sand (sand ripples > 10 cm), especially along transect 27.Estimates refined after consulting substrate maps in conjunction with moredetailed video ground-truthing suggest that over two-thirds of PGER is sandor sediment.

Eleven SCUBA surveys were completed in the reserve. Dives ranged in depthfrom 6 to 20 m, spanning 50 m to 250 m perpendicular to the depth contour(Table 6 and Figure 16). Most dives were observational, with 4 dives resultingin useable video records for post-processing.

The reserve’s shallow waters, from 9 to 15 m, were dominated by sandwith scattered sand-impacted rock habitat that lacked invertebrate species ofmanagement importance. Invertebrate species identified within PGER largelyconsisted of suspension-feeders that were restricted to rock outcroppingswhere sand scour was reduced. Algal species were few, and cover was very lowin the shallow waters of PGER, except for Gorda Rock, where most algalcover was observed.

Diver observations and videos indicate that PGER’s shallow waterscontain two primary habitat types: sand and rock. Sand habitat consisted ofwell-sorted coarse sand, which was highly rippled, indicating a large amountof sand movement. Ripple patterns had no apparent recurring height ordirection, and according to diver accounts were regularly moved by wavesurge. Rock habitat consisted of low-relief rock outcroppings (< 1.5 m),which were highly sand-impacted and scoured. Rock outcroppings varied insize, but typically had a well-rounded dome shape, with few ledges, cracks, orcrevices. Sand dominated all surveyed locations, except for Gorda Rock,where sand influence was not a factor due to the vertical relief (approximately25 m in height).

Sample collections and video recordings indicated that the dominantspecies within PGER’s shallow waters are primarily colonial suspension-feeding invertebrates, for example sponges, hydroids, anthozoans, bryozoans,and tunicates. Other commonly-observed invertebrates were motile predatorssuch as nudibranchs and sea stars. Algal species were few and cover was low.Herbivorous species were not observed to be common within PGER, excepton Gorda Rock, where sub-story algae were the most abundant.

Species/habitat associationsInvertebrate species found within PGER were primarily restricted to

rocky substrate where sand influence was reduced. A total of 95% (329/348)of the invertebrates enumerated from SCUBA video were observed on rocksubstrate, with 83% of these found on a horizontal surfaces (Table 7). Thosefound on vertical surfaces and on sand-covered surfaces accounted for 9% and8% of invertebrate species, respectively. Only 5% of the invertebrates enu-

Diver-BasedSurveys


Karpov 28

merated were found on sand habitat, which was observed as the largestavailable habitat type. Predatory invertebrate species accounted for 48% ofthe total number of invertebrates enumerated, with the snail Nucella lamellosaaccounting for 50% of the total predators. The five most abundant speciesenumerated were the aggregated nipple sponge Polymastia pachymastia, thesnail N. lamellosa, the leather star Dermasteria imbricata, the blood starHenricia leviuscula, and the stalked tunicate, Styela montereyensis, whichcombined accounted for 56% of the total enumerated invertebrates. Thehydroids Abietinaria sp. and Aglaophenia sp., the barnacle Balanus crenatus,the bryozoan Scrupocellaria californica, and the tunicate Aplidium sp. weredominant, but were not enumerated during post-processing. The barnacle B.crenatus occupied the interface between rock and sand, or rock surfaces thathad high levels of apparent sand scour or sand cover. No invertebrate speciesof management importance were observed.

Algal cover on sand-impacted rocky substrate was low (Table 8) andmostly composed of red algal species (Rhodophyta) (Table 9). Few brownalgae (Phaeophyta) were observed in sand-impacted areas. Algal cover aroundGorda Rock, a non-sand-impacted habitat, was considerably higher and wasdominated by the brown algae Laminaria setchellii and Pleurophycus gardneriwhich formed a substantial understory. Red algae found on the rock werecommon and formed significant foliose cover.

The assemblage of invertebrate species found on Gorda Rock was distinctlydifferent from other sites in PGER (Table 10). The anemone Metridiumsenile, which had a relative abundance of 96% dominated this rock. Otherspecies not common elsewhere within PGER were red sea urchins, and purplesea urchins Strongylocentrotus purpuratus, the sea cucumber Cucumariaminiata, and the California mussel Mytilus californicus , which were not quan-tified during post-processing. Divers commonly observed herbivorous speciessuch as turban snails Tegula spp. and chitons Cryptochiton spp. at Gorda Rock.The only invertebrate species of management importance found on GordaRock was the red sea urchin, which had a relative abundance of 0.05%.

A total of 12 ROV dives were completed at PGER, with seven additionaldives made at Delgada Canyon (29 km south of PGER), Shelter Cove (37 kmsouth of PGER), and Point Cabrillo (110 km south of PGER) when weatherconditions at PGER were too hazardous (Table 11). A total of 9 hours and 52minutes of useable videotape footage was compiled from 10 out of the 12dives completed within the reserve (Table 12). Over 10 km of strip transectswere completed, representing a total area of 25,640 m2 (2.564 hectares)(Figure 16).

A total of 580 usable substrate segments representing over 80% (580/720)of the total number of segments and 92% (9:52:09 out of 10:41:24



Karpov 29

hrs:min:sec) of the entire videotape record of PGER were analyzed (seeAppendix 2 for complete listing). Segment width, calculated from the lasersand the field of view, varied considerably between transects depending onvisibility, current, surge, and umbilical drag. However, the variability of thewidth of field within each transect was far less than the variability betweentransects (ANOVA, df = 11, 966; f = 26.19; p<0.001). Therefore, we used anaverage width of field for each transect to determine segment area.

Useable substrate segments were grouped by primary substrate type foranalyses, and segments identified as “gravel” and “pebble” were removed fromany further analyses since only one segment of each was observed. Because wechose to identify our primary substrate segments based on the organismshabitat preferences, “rock” was our most dominant substrate, even thoughcover of the secondary substrate “sand” was often greater (Table 13a). In fact,74% (286/387) of all the “rock” segments had “sand” as the secondary sub-strate. And, sand comprised nearly half of all the secondary substrate typesobserved (286/580 [Appendix 2]). In order to describe these sand-associatedrock substrates, we devised three new rock substrate categories termed“RockSand” which were classified by the percentage of sand cover:“RockSand1” had < 33% sand cover, “RockSand2” had 34-66% sand cover,and “RockSand3” had > 66% sand cover (Table 13b). This “modified pri-mary substrate” classification allowed us to evaluate increasing levels of sandinfluence within the reserve.

The average size (mean area per segment in Table 13a) of the substratesegments grouped by primary substrate type was significantly different (One-way ANOVA, df = 2,573 f = 3.364, p = 0.035). A post-hoc Scheffe’s multiplecomparisons test indicated that Sand segments were significantly larger thanBoulder segments. Interestingly, when we compared the mean area per segmentfor the modified primary substrates (Table 13b), the substrate segments werenot significantly different (One-way ANOVA, df = 2,575, f = 1.49, p = 0.19).

Fish/habitat associationsWe counted 1,231 fish representing at least 16 species at PGER

(Table 14). Juvenile rockfishes (Sebastes spp.), which could not be identifiedto species, comprised nearly 50% of all fishes observed. Species other thanrockfish accounted for less than 16% of all species observed. Over fiftyflatfishes that could not be identified to species from the videotape weregrouped together as Pleuonectiformes. Most were either the Pacific sanddabCitharyichthys sordidus, or the speckled sanddab C. stigmaeus, (Robert Leapers. comm.); however, the group included both Pleuronectids and Bothids,hence we used the ordinal name. The mean weighted densities of each fishspecies by modified primary substrate type are presented (Table 15), and themean weighted densities of each fish species by vertical relief and the originalprimary substrate are presented (Table 16).

Based on the weighted densities of the top 9 species groups (>1% relativeabundance), the substrate/habitat categories grouped into two distinct clusters


Karpov 30

(Figure 20). The Rock substrate was the most dissimilar from the five othersubstrate types at a Euclidian distance of 0.034. The next most dissimilarsubstrate grouping was the Boulder substrate at a distance of 0.019, althoughit was not considered distinct from the other substrate types and zero (asindicated by the red bars in Figure 20). Only two clusters of fishes wereobserved, with juvenile rockfish separating from all other species at a distanceof 0.050 (Figure 21). Peak densities of juvenile rockfish and blue rockfish S.mystinus, over Rock habitat identified this habitat as most dissimilar from theothers (Figure 22). Juvenile rockfish were most abundant over Rock (0.112/m2) (Table 15), but were also abundant over Boulder (0.065/m2) andRockSand1 (0.022/m2). They were rarely found over Sand or RockSand3substrates. Blue rockfish were found almost entirely over Rock (0.038/m2 withonly minor occurrences over RockSand1 (0.003/m2) and Sand (0.002/m2)substrates.

Removing juvenile rockfish from the analysis resulted in similar substrateclusters, with the Rock substrate distinct from the other habitats, and bluerockfish S. mystinus the most dissimilar at a distance 0.014 from all the otherspecies, which were clustered at a distance of 0.005 (not shown in a figure).Upon removing the strong influence of both juvenile rockfish and bluerockfish dissimilarities were revealed for the less-abundant species and theirassociated substrates (Figure 23). In this analysis, two habitat groupings andtwo species groupings were distinguished. The RockSand1, Rock and Sandhabitats were the most dissimilar. Three species - canary rockfish S. pinniger,kelp greenling Hexagrammos decagrammus, and black rockfish S. melanops,comprised the most dissimilar cluster. Canary rockfish were unique among allnine species as they were most frequently found over RockSand1 habitat(0.014/m2) (Table 15) and rarely found over Rock habitat (0.001/m2). Bothcanary rockfish and kelp greenling were also found on RockSand, Boulder,and Sand habitats, with kelp greenling evenly distributed within a narrowrange of densities (0.004 to 0.006/m2) on all habitats except Sand (<0.001m2). The flatfish were found almost exclusively on the Sand (0.006/m2), buttwo (0.001/m2) were also observed over sand in RockSand2 habitat. Theseflatfishes were the only species group that showed any strong association withthe Sand habitat. One species of management concern, lingcod, was widelydistributed over all substrates including Sand. However, the observed densities(ranging from 0.001 to 0.003/m2) were too low to show meaningful associa-tions in our cluster analyses.

When weighted fish densities were clustered against the primary substratetypes grouped by relief, high relief Rock habitat (Rock > 3 meters high)was distinct from the other habitat groups at a Euclidian distance of 0.045(Figure 24). Blue rockfish S. mystinus and juvenile rockfish were the mostdissimilar at distances of 0.05 and 0.042, respectively. They were distinctfrom all other species at a Euclidean distance of 0.008.

The pattern of fish densities across habitats and relief strongly influencedthe clustering. Blue rockfish S. mystinus were observed almost exclusively in


Karpov 31

high relief Rock habitat (0.131/m2) (Table 16). This density was by far thehighest density observed for any fish group in any of the habitat/relief catego-ries and suggests that blue rockfish are only associated with high relief struc-tures, e.g. Gorda Rock, where observations supporting this association weremade (De Martini Pers. Comm.). Juvenile rockfish, while more numerousthan blue rockfish, were distributed across a greater variety of relief andhabitats. They were most abundant on low relief (<1m) Boulder (0.073/m2),and medium relief (1-3m) Rock (0.062/m2), followed by high relief Rock(0.043/m2). Removing both blue rockfish and juvenile rockfish from thecluster analyses revealed underlying associations for the seven less-abundantspecies (Figure 25). High and medium relief Rock clustered from the otherhabitat/relief categories at a Euclidean distance of 0.006 and separated fromeach other at a distance of 0.004. Sand and medium-relief Boulder groupedtogether as well as low-relief Boulder and Rock. Black rockfish S. melanopswere the most dissimilar fish species at a Euclidean distance of 0.008 and wasgrouped with canary rockfish S. pinniger at a distance of 0.005. Kelp green-ling H. decagrammus was the next most dissimilar at a distance of 0.004 andwas grouped with the remaining species. Black rockfish were discrete from theother species due to similar densities observed over high and medium reliefRock substrate (0.017/m2 and 0.015/m2, respectively). Canary rockfish wereobserved predominantly around high relief Rock substrate (0.011/m2) butwere also found around low relief Rock and Boulder substrate (both 0.007/m2).Kelp greenling notably decreased in abundance with increasing relief. Theirhighest abundances were on low relief Rock (0.007/m2) and Boulder sub-strates (0.006/m2), decreasing to (0.002/m2) on high relief Rock substrates.

Fish orientation and lengths: Relative occurrence by orientation of all reeffish was also examined within three basic substrate types (Sand, Boulder, andRock) without applying cluster analyses or including the RockSand substratedivisions (Table 17). Blue rockfish S. mystinus and black rockfish S. melanopswere the most pelagic species, as both were observed at different distancesabove the bottom. Most blue rockfish (86%) were observed in schools andwere removed from the substrate (>1 m) i.e., never in contact with it, whilefully 37% of black rockfish were near the bottom (<1 m) with 11% actuallyin contact with vertical or horizontal rock surfaces. Essentially, all the speciesobserved above bottom (>1 m) were over Rock, Boulder and Sand substrate.Only three other groups of fish were observed above bottom on Rock sub-strate: Canary rockfish S. pinniger (35%), juvenile rockfish (25%), andlingcod O. elongatus (4%). Canary rockfish were observed on Sand as near,but not in contact with, the bottom (10%). Flatfish were always in contactand only over sand on both Sand (90%) and Rock (10%) substrate. Kelpgreenling H. decagrammus were almost always in contact with the substratewhen first approached, either over rock or sand. These fish were also the mostfrequently measured, because of their orientation in contact with the bottom.The 26 kelp greenlings measured ranged in size from 102 to 411 mm, averag-


Karpov 32

ing 293 mm (Table 18). Lingcod were mostly observed near the bottom onRock (32%) and on horizontal surfaces over Rock (25%) and Boulder (4%).They were also observed in contact with the bottom on Sand (21%) orhorizontal substrate on Rock bottoms.

Spatial relationships of fishes: Mapped spatial displays of actual locationsof numbers of fish encountered in relation to observed substrates were pro-duced for selected species. Spatial distribution maps of blue rockfish indicatethat this species was among the most limited in distribution. Blue rockfishwere only found in 16 segments centered on the rock-dominated (brown)central portion of PGER where transect 25 and 31 intersected. Eight of the16 segments were high relief (>3 m). Sightings of blue rockfish also corre-sponded to areas showing clear vertical relief in bathymetry and on sidescanimages, such as Gorda Rock and the high-relief features on transects 30 and36 (Figures 26a–c). Black rockfish S. melanops were also found on high reliefsegments, generally in close proximity to blue rockfish. Black rockfish, whichwere found in 32 discrete segments throughout the reserve, were more widelydistributed than blue rockfish (Figures 26a–c and 27a–c). Canary rockfish S.pinniger were observed peripheral to rocky areas that could be described as aRock-Sand interfaces (Figures 26a-c). This is most apparent in the centralarea, where their distribution surrounds the central rocky area in associationwith Sand, RockSand3 or RockSand2 substrate that has low or medium relief.Kelp greenling H. decagrammus and lingcod O. elongatus were both the mostspatially-distributed species of the finfish throughout the reserve (Figures26a–c and Figures 27a–c, respectively). Black rockfish and quillback rockfishS. maliger appeared centrally in most Rock or RockSand areas with copperrockfish S. caurinus apparently peripheral to the central rocky area at PGER(Figures 27a–c). However, low numbers of both species preclude generalizingfrom these distributions. Flatfish were generally restricted to low relief, fine-texture Sand areas, mostly in the northern part of PGER (Figures 27a–c).

Fish behavioral observation: Finfish reactions to the ROV, lights, laserbeams, and interactions were occasionally worth noting. Blue rockfish S.mystinus and black rockfish S. melanops were often observed in schools withindividuals moving actively towards the camera, which made measuring thefish with the lasers difficult. Canary rockfish S. pinniger were often observedin small schools on the rock-sand interface, although individuals often movedbeyond this interface to inspect laser spots in the sand. Lingcod would oftenmove towards the ROV, occasionally aggressively, with one individual actuallybumping the frame of the ROV. Predator-prey interactions were occasionallyobserved. A Kelp greenling H. decagrammus was first observed followed by anactively pursuing lingcod O. elongatus. Quillback rockfish S. maliger wereobserved as solitary individuals. These would often swim directly towards theROV, turn broadside to the camera, then extend their dorsal fins in a display.Copper rockfish S. caurinus would also turn broadside and remain stationarywithout displaying their dorsal fins. The only intra-species interactions


Karpov 33

observed, other than schooling, was an approach of an adult vermillionrockfish S. miniatus to another vermillion rockfish tilted on its side.

Macroinvertebrate/habitat associationsA total of 8,722 macroinvertebrates representing 42 species were observed

in the 10 ROV dives at PGER (Table 19). The white-plumed anemone M.giganteum was by far the most numerous, making up nearly 40% of theobservations. The aggregated nipple sponge Polymastia pachymastia and anunidentified gorgonian were the next most numerous. Observed invertebratespecies of management concern included 268 sea cucumbers, Parastichopuscalifornicus, and 44 red sea urchins Strongylocentrotus franciscanus. No redabalone Haliotis rufescens were found using the ROV. A complete listing of themacroinvertebrates observed in both the SCUBA and ROV surveys is pre-sented in Appendix 3.

When the weighted densities of the most abundant 17 macroinvertebratespecies (Table 20) were clustered by modified primary habitat types, twomain clusters resulted (Figure 28). The RockSand1 and Rock cluster wasseparated from the other cluster containing all the other habitat designationsat a Euclidian distance of 0.068. The most similar habitat types of RockSand2and RockSand3 (E. distance = 0.015) grouped with Boulder at a distance of0.024 and grouped with the Sand substrate at a distance of 0.032.

Clustering by macroinvertebrates resulted in the white-plumed anemoneM. giganteum being the most dissimilar from the cluster of all other species ata distance of 0.192 (Figure 29). The next most dissimilar species was theclub-tipped anemone, Corynactis californica, at a distance of 0.037. Highdensities of the anemone M. giganteum in both the RockSand1 (0.390/m2)and Rock (0.282/m2) substrates as well as moderate densities across theBoulder (0.128/m2), RockSand2 (0.100/m2), and RockSand3 (0.074/m2)substrates resulted in this distinct clustering (Table 20 and Figure 30). Mostof the other macroinvertebrates generally showed a similar pattern of reducedabundance in sandy habitats, with none of the species included in this analy-sis present in the Sand habitat.

Once the anemone M. giganteum was removed from analyses, threedistinct clusters were produced (Figure 31). These groupings appear torepresent both abundance and distributional patterns across the differentsubstrate types (Figure 32). The sponge P. pachymastia was the most dissimilarspecies at a distance of 0.037. It was widely distributed across all substratetypes with the highest densities in RockSand1 (0.079/m2) and RockSand2(0.076/m2) (Table 20). It also was observed in all other habitat types includ-ing one observation in the Sand habitat. The anemone C. californica was thenext most dissimilar species at a distance of 0.036. It separated from the otherspecies due to its unique pattern of high densities in Boulder (0.081/m2)substrate as compared to Rock (0.066/m2), RockSand2 (0.023/m2), andRockSand1 (0.013/m2). The California hydrocoral, Stylaster californicus, was


Karpov 34

the third species to separate out in the most dissimilar cluster at a distance of0.021, although the linkage between the most similar cluster was at a distanceof 0.023 (Figure 31). The hydrocoral S. californicus was found almost exclu-sively in the Rock (0.063/m2) and RockSand1 (0.047/m2) substrates, and notfound in habitats with more sand influence.

The next most dissimilar cluster contained three species, the unidentifiedgorgonian, the elephant ear tunicate, Polyclinum planum, and the orangepuffball sponge, Tethya aurantia. These species clustered together because theywere found at similar densities in all the hard substrates including theRockSand2 and RockSand3 categories, which have more sand influence(Table 20). The one species of management importance included in ouranalysis, the sea cucumber P. californicus, had its highest densities in RockSand1(0.035/m2) and Rock (0.031/m2) categories, with much lower densities(0.011-0.001/m2) in the Boulder and more sand-dominated substrates.

When relief was added to the primary substrate/habitat descriptions, thedistributional patterns changed. The anemone M. giganteum densities werehighest in high relief Rock (0.761/m2) substrates, followed by decliningdensities in medium relief Rock (0.401/m2), Boulder (0.140/m2) and lowrelief Rock (0.110/m2) (Table 21). When the strong influence of the anemoneM. giganteum was removed, two very different patterns emerged for theanemone C. californica and the hydrocoral S. californicus (Figure 33). Weobserved the anemone C. californica at higher densities in boulder habitatsthan in rock habitats. In fact, the anemone’s highest densities were observedin low-relief Boulder (0.081/m2) and medium-relief Boulder (0.077/m2)substrates. In contrast, the hydrocoral S. californicus was rarely found inBoulder (0.005/m2) habitat, and densities were highest on high-relief Rock(0.083/m2) declining to low values on low relief Rock (0.015/m2). The threeother species in the second cluster, the sponge P. pachymastia, the unknowngorgonian, and the sponge T. aurantia all showed a more generalized patternof similar densities across most of the relief categories in the Rock and Boul-der substrate classifications. Of the remaining species, the tunicate P. planumand the sea cucumber P. californicus had higher densities in low to mediumrelief substrates than in the high relief substrates, whereas most of the otherswere observed mainly in the high to medium relief Rock substrates.

Macroinvertebrate orientation: The anemone M.giganteum, as men-tioned above, was observed in large numbers in all substrate classificationsexcept Sand, with 79.6% observed in Rock habitat and 20.4% observed inBoulder habitat (Table 22). Orientation of this anemone throughout theRock substrates was nearly 50-50 (1759/1517), on horizontal surfaces relativeto vertical surfaces, whereas in Boulder substrate, three quarters were observedon horizontal surfaces.

The sponge P. pachymastia, in contrast, was observed almost exclusively(96.2%) on horizontal surfaces with 83.1% found in Rock, 14.2% found inBoulder, and 2.7% found in Sand substrates (Table 22). In fact, within allhabitats, 15% were described as being attached to sand-covered substrate (i.e.,


Karpov 35

the On Sand category). Clearly this sponge, as with most of the sessile inver-tebrates observed at PGER, would need to attach to hard substrate in order tomaintain its position in its environment. However, there were numerousobservations of sand covering many of the most numerous sessilemacroinvertebrates including the fish-eating urticina, Urticina piscivora, thesponge T. aurantia, the tunicate P. planum and the unknown gorgonian.Nearly 20% of the unknown gorgonians were attached to sand-coveredsubstrate and only 1.3% were found on vertical surfaces. The urticina U.piscivora was nearly equally distributed among horizontal (38.6%) and verti-cal surfaces (36.7%), and to a lesser extent on sand surfaces. This anemonewas also frequently (3.0%) in the sand-scour zone, suggesting that it might bean early colonizer of recently cleared hard substrata.

The majority of both anemone C. californica and hydrocoral S. californicuswere on the Rock substrate (89% and 84% respectively) with neither beingobserved in Sand substrate or in the On Sand category. However, they ori-ented quite differently in their habitat. Eighty percent of all anemone C.californica were on vertical surfaces, while 90% of hydrocoral S. californicuswere on horizontal surfaces. Other species showing little affinity for sandhabitats included the orange hydroid, Garveia annulata, the gray puffballsponge, Craniella arb, the urchin, S. franciscanus, the northern staghornbryozoan Heteropora pacifica, the bread crumb sponge Halichondria panicea,and lobed tunicates Cystodes lobatus.

Spatial relationships of macroinvertebrates: The anemone M. giganteumwas widely distributed on most hard substrates throughout the reserve over adepth range of 23 m to 40 m (Figures 34a-c). It appeared to be most concen-trated in the center of transects 25 and 31, which included the higher reliefbedrock areas of the central reef (Figure 34b) and in the southern portionalong transect 26 (Figure 34c). The only species of invertebrates more broadlydistributed were the blood star, H. leviuscula which, while less abundant thanthe anemone M. giganteum, was found on all of the ROV transects from 16 mto 46 m, covering the range of reef habitat surveyed by ROV at the reserve(not shown). The leather star, Dermasterias imbricata, was also found on alltransects, especially in the center of transects 25 and 31, but at lower concen-trations than the star H. leviuscula (Figures 34a-c). The anemone C.californica, a known prey of the leather star, was most abundant in the south-ern half of the reserve near the area of highest current flow (Figure 34c). Mostof these anemones were found on transect 26 at a depth of about 30 meters.They were also abundant inshore on the southern segment of transect 25, thenorthern part of transect 29, and the eastern end of transect 31 at depths to17 meters. Although the anemone C. californica was observed at high densi-ties on Boulder substrate, the small number of Boulder segments (29) makesit difficult to observe (Figures 34b and 34c).

The sponge P. pachymastia was observed in higher numbers in the north-ern and central portions of the reserve (Figures 35a-c). Its highest concentra-


Karpov 36

tions were in the RockSand1 and RockSand2 categories of transects 25, 27and 31, although it was also found farther south in the RockSand3 areas oftransects 25 and 30 (Figures 35b and 35c). In contrast, the hydrocoral S.californicus was concentrated in the high relief sections of transects 25 and 31(Figure 35b). It was also observed along the low relief RockSand3 section oftransect 27, which may suggest that the sand cover in that area was recentlydeposited. This hydrocoral was observed only rarely on the higher relief areasof transects 25 and 26 near Gorda Rock (Figure 35c).

The unidentified gorgonian distribution pattern was very similar to thatof sponge P. pachymastia (Figures 36a–c). It was most abundant on transects25, 27 and 37 at depths from 29 to 44 meters (Figures 36b and 36c). It wasabsent in depths of less than 20 m, as seen in transect 29 (Figure 36c). Close-up images showed this species as most abundant on substrates with compo-nents of sand and occasionally on sand (i.e. RockSand1, Rocksand2, andRockSand3).

The sponge T. aurantia, while found on most ROV transects (except fortransects 32, 36, and 37), was actually quite narrowly distributed on severaltransects (Figure 36a) This species was centrally located in the reserve’s reefarea where transects 31 and 25 intersected, and on transect 26 (Figures 36band 36c).

Red Abalone, Haliotis rufesens: none were observed in PGER with the ROV.

Red Sea Urchin, Strongylocentrotus franciscanus: only observed by divers at thebase of Gorda Rock from a depth of 20 m to near the surface. Strongylocentrotusfranciscanus was also rare in the ROV surveys, with only 41 sea urchinscounted within the entire reserve (Figures 37a–c).

Sea Cucumber, Parastichopus californicus: most abundant on RockSand1 andRock substrate-habitats (0.031 and 0.035 m2 respectively, Tables 20 and 23).Densities decreased with the increasing sand component of the substrate. Atotal of 268 sea cucumbers were found distributed on all substrates exceptsand, and were associated with medium- and high-relief rock followed by low-relief rock and boulders. They were observed mainly at moderate depthsalthough we did not survey hard substrates at depths greater than 40 m(Figures 37a–c). They were most common in the central crossing of transects25 and 31 at depths from 29 to 35 m. They were also abundant along thenorthern half of transect 30 (Figure 37b) and 26 (Figure 37c). These are areascomposed mostly of Rock or RockSand1 substrate with mostly moderate andoccasionally high relief. Bathymetry reveals that the central area of the reserveis elevated and more topographically complex than the more uniform transect27 and the westward part of transect 31, both of which were more sanded,deeper, and devoid of sea cucumbers. The inshore transect, at shallowerdepths of 12 to 18 m, had substrate comparable to the deeper central transects

Species ofManagementConcern


Karpov 37

25 and 31, but was essentially devoid of sea cucumbers. This was, however,the only area where red sea urchins were observed in limited abundances.

Sea cucumber P. californicus was the only invertebrate species commonenough to warrant measuring lengths. Fifty-one sea cucumbers from fivetransects were measured for total length at PGER and compared to 30 cu-cumbers, that could be measured at Point Cabrillo Marine Reserve (PCMR[Figure 38]).

It was unfortunate that difficulties in accessing the reserve and poor weatherconditions impeded our fieldwork at PGER. Weather observations from theclosest weather buoy, 25 km to the north of the reserve, often did not accu-rately portray weather conditions at the reserve. Surface winds and a strongnortherly current often produced short period wind waves at PGER, creatingunacceptable conditions for ROV and SCUBA surveys. These somewhatdemanding conditions mandated modifications in our SCUBA and ROVsurvey protocol at PGER. We abandoned attempts to quantify abundances offinfish and macroinvertebrates using SCUBA, relying for the most part onROV video recordings.

In light of the poor study site conditions, it is not surprising that weunder-sampled significant portions of the reserve. Most notably under-sampled were the north and southwest corners of the reserve using the ROV(Figure 16). Poor conditions limited SCUBA surveys to only 11 stations, andmost dives were reduced in purpose to qualitative observations and thecollection of invertebrates and algae for reference. Post-processing of the six ofSCUBA videos did not include density estimates since these lacked the spatialreference (time code and depth) and laser reference points used to determineROV transect width and the location of the track surveyed.

The combined findings of our habitat mapping, dive, and ROV surveyreveal that PGER is a high-energy, sand-dominated habitat, poor in herbi-vores and standing algae. As such, the site is poorly suited as a control or apotential source population for species of management importance (i.e.,species common on temperate reef communities with high algal production).

Our field experience demonstrates that PGER is a poorly-sited MarineProtected Area in terms of access for practical research and enforcement(Table 1). While we made every effort to quantify fish, invertebrate, and algalcommunity and habitat within the reserve, it is important to understand thatthe remote access and poor, unpredictable weather conditions seriouslyimpeded our fieldwork.

We believe that in spite of these limitations our study provides a fairassessment of the biota and habitats available on the reserve. This studyshould be adequate for evaluating the value of this reserve as either a controlstudy site or potential source population for species of management concern.

DiscussionandConclusions

Overview


Karpov 38

BathymetryThe original bathymetry grid and contour lines produced revealed a

discrepancy regarding the position of Gorda Rock in the USGS shoreline datathat had been combined with the sounding data prior to gridding. Some ofthe soundings collected during the survey traversed a portion of the supposedlocation of Gorda Rock, and that the resulting contour lines around GordaRock seemed more confused and complex than might be expected. CaliforniaDepartment of Fish and Game personnel provided dGPS position data loggedwhile circling Gorda Rock during a July 1998 cruise, thus confirming that thepoints being used to represent this feature were indeed approximately 125 msouth and 25 m west of the true location. To remedy the situation, bathymet-ric data were re-gridded using the corrected Gorda Rock position data, andnew contour line and polygon themes were created.

While the bathymetric data collected are of high quality, they suffer fromthe limitations of single-beam depth soundings. Namely, they are of a muchhigher along-track than across-track density. Because the survey tracklines hadto be spaced 100 m apart to survey PGER in the time allowed, the griddeddataset is heavily interpolated and the resulting resolution is fairly coarse.Thus, bathymetric features occurring between the survey lines may have beeninaccurately modeled or even missed entirely. Derived attributes such as slopeand aspect that are generated from this dataset likewise must be used withsome caution, keeping in mind the original data density and degree of inter-polation used in gridding. A full-coverage bathymetric survey, using a multi-beam system, would provide a much higher resolution bathymetric map withmuch greater utility.

RoxAnnThe RoxAnn system provides an independent, complimentary means of

seafloor substrate classification with relatively little added equipment andprocessing effort. Because the system uses the signals from the depth sounderbeing used to acquire bathymetry data, RoxAnn requires no additional in-water sensors and no separate offsets need be applied to RoxAnn relative tobathymetry data. RoxAnn data are especially useful in areas where sidescansonar use is curtailed by kelp canopy, sea state, or other conditions.

However, as with all acoustic seafloor substrate classification systems,RoxAnn gives only an estimate of what the seafloor composition at the loca-tion of each sounding. Groundtruthing by grab or core sampling or directobservation by diver, submersible, or ROV is crucial if RoxAnn classificationsare to be meaningful and accurate. In addition, as with the bathymetry dataconsiderations discussed above, extrapolation of the point classificationsacross the relatively widely spaced tracklines to produce classification poly-gons is almost surely an oversimplification. While extrapolation of the classifi-cations gives the illusion of full bottom coverage, such information should beused with caution.

Substrateand HabitatMapping


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Another consideration regarding the use of RoxAnn data concerns themethod used to convert the raw E1 and E2 values into substrate classes. The“RoxAnn square” method used in this study, while it is the one suggested bythe manufacturer, may not be the most effective means of classifying RoxAnndata. By design, the RoxAnn square method is flawed in that it requires theareas of Cartesian E1, E2 space designated to each substrate class be rectilin-ear, while in fact the point cloud is often not amenable to binning in thismatter. While more complex bounding shapes for each class can be createdusing a combination of different sized boxes with different aspect ratios, thisis cumbersome and requires that the resulting multiple classes (one for eachbox) be lumped together after classification. This remedy also fails to addressthe second major shortcoming of the RoxAnn square method, the fact that itdoes not account for the spatial component of the data in any way. In recentstudies Fox et al. (1998), and Kvitek et al. (1999) found that methods ofunsupervised classification using GIS can be more effective than the RoxAnnsquare method and yield more accurate and meaningful substrate classes. Themethod used by Kvitek et al. (1999) involved exporting RoxAnn soundingdata as separate x,y,e1 and x,y,e2 files. Each file was then imported into a GIS(TNT Mips) and converted to raster format. The two RoxAnn soundingraster objects were then treated as separate channels in a multi-spectral imageand classified using unsupervised classification with the “Isodata” method.This algorithm allows a number of user-specified variables to be set; the valuesused were based on previous studies wherein all parameters were iterativelyvaried until the values resulting in the “best” classification scheme weredetermined. Quality of classification scheme was assessed subjectively byobserving the number of classes generated as well as patterns among classes,and comparing these to known substrate distributions in the study area.Because this method was developed after processing of the PGER RoxAnndata was complete, it was not used in the present study. The data could,however, be reprocessed using this method.

Hardcopy sidescanThe digitized hardcopy sidescan sonar mosaic was originally provided in

lieu of the digitally processed mosaic to aid in visualization and interpreta-tions of PGER seafloor habitat using ArcView GIS. Because of the manner inwhich it was created, a great deal of the original sidescan data and detail hasbeen lost and are not visible in this product. Much more information isavailable in the digitally processed sidescan imagery. Reasons for the lowresolution of the digitized hardcopy mosaic include: 1) the original sidescanhardcopy printouts contain only 16 shades of gray, rather than 255 as recordedto digital tape; 2) the hardcopy printouts were photoreduced 50% to createthe mosaic (see Methods section); 3) the scanning of the large-format (122cmx 107 cm) hardcopy mosaic further decreased the quality of the imagery; and4) georeferencing, re-mosaicking and export may have degraded the imagery


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as well. Because of the process involved in creating this mosaic, its spatialaccuracy varies across the image. Using bathymetric features and comparisonto the digitally processed sidescan data, we determined that the accuracy ofthe original hardcopy sidescan image varies between 2–75 m.

Digital sidescanDigital sonar processing requires that frequent, consistent, and accurate

position and time stamps be associated and recorded with imagery data dur-ing the survey. Because every pixel of every sonar ping will be geo-referenced,this position and time information is much more important in digitalmosaicking than when producing a hard-copy analog mosaic by hand, whereonly select points (“event marks”) along the sonar trackline are geo-referenced,and the mosaic is fit together partially “by eye.” Thus, while digital processingcan yield a much higher resolution mosaic with highly improved spatialaccuracy relative to hand-mosaics, its effectiveness is more severely compro-mised by data quality problems that may occur in the position and timestamp information recorded during the survey. Digital processing of PuntaGorda sidescan data collected in August 1997 was considerably hampered bythe above concerns. Because of equipment problems (i.e., malfunction andbasic inadequacy) and GPS signal quality issues during the survey, position,and time stamp data recorded to Exabyte tape was of poorer than desirablequality for use in digital mosaicking. Position fixes were recorded at less thanoptimal frequency, and both position and time stamp data strings were oftengarbled and unusable. However, with extensive processing, many of thesedata quality problems were corrected or minimized, and a digital mosaicwas produced.

Overall, the resolution and spatial accuracy of the digital mosaic is farsuperior to that of the hardcopy mosaic produced previously, but in someareas the imagery is distorted due to the position and time errors describedabove. Overall horizontal positional accuracy of the imagery is a factor of theaccuracy of both the GPS data and the calculated approximate position of thetowfish. Approximate DGPS positional accuracy of the Trimble 4000RL GPSreceiver with ProBeacon is +/- 5 m. The error due to inaccurate towfishlayback calculation and positioning is unknown, but probably comparable toDGPS accuracy. The resulting overall accuracy is thus approximately +/- 10 m.

Available time and funding did not allow the creation of a new sidescaninterpretation theme for the digital mosaic, despite the fact that in somecases the edges of previously defined features moved by tens of meters in thedigital mosaic relative to the hardcopy mosaic. However, with the improvedresolution of the digital mosaic, CDFG staff should be able to quite effectivelyre-interpret the sidescan imagery using terms and a classification scheme thatis optimal for their uses. This effort will be greatly aided by their growinglibrary of ROV and diver video ground-truthing data and their increasinglevel of experience with SSS data.


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Sidescan Sonar InterpretationAs with RoxAnn data, substrate types defined from sidescan sonar inter-

pretation need to be groundtruthed if high confidence is to be placed in theiraccuracy. Unlike RoxAnn’s point sounding data, however, SSS yields backscat-ter imagery across a swath, which yields total bottom coverage if survey designand execution allow for appropriate overlap of adjacent swaths.

Because interpretation of sidescan sonar imagery is a subjective process,best performed by a geologist or other trained individual, several consider-ations must be kept in mind when using such data. These considerationscenter primarily on the classification system used, and the spatial scale onwhich interpretation is performed. It is of great importance that the classesused by the individual interpreting the SSS imagery to define areas of theseafloor be as applicable as possible to the intended uses of the interpretationdata. If certain types of features (e.g. fissures, high relief areas, boulders, etc.)are of particular interest from a habitat or other standpoint, the interpretermust be aware of these and interpret the image accordingly. It may be thatother data (bathymetry, core or sub-bottom profiles (see below), etc.) need tobe examined along with the SSS imagery to optimize the classification schemeused. If interpretation is to extend beyond substrate classification into truehabitat classification, then a consistent, universally accepted scheme should beused if at all possible. See Kvitek et al. 1999, and Greene et al. 1999 for adiscussion of habitat classification schemes.

Secondly, it is important to consider the spatial scale on which the inter-pretation is performed. The potential resolution of sidescan sonar imagery(tens of centimeters) is such that very small features, such as sediment ripplesand individual cobbles, may be visible in the final mosaic. This presents adilemma to the interpreter, who, on the one hand, would like to delineate allfeatures of interest visible in the imagery but, on the other hand, must per-form this task across several square kilometers of seafloor. Put simply, it is toocostly in terms of time and effort for an interpreter to circle every rock he cansee in the SSS mosaic of the entire study area (although this is an extremeexample). This is particularly true in areas of high substrate heterogeneity.Accordingly, there is a compromise that must be made between the minimumsize and maximum number of polygons that will be drawn, and the timeallowed for interpretation. Classification of some areas may require generaliza-tion or simplification to some degree.

In addition, the potential for temporal variation must be considered whenusing sidescan sonar imagery and interpretive data. The sidescan imagery is asnapshot in time, and must be viewed as such. In some areas, currents orother phenomena may cause sediment to move seasonally or other changesmay alter seafloor morphology. Thus, substrate classification performed in agiven survey may be more or less accurate at a later date depending on thedegree of temporal variability at the study site. This may be particularly


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relevant in the present study. Several sites within the large portion of theinshore area of PGER classified as “rock outcrop” from the August 1997 SSSimagery were found to have a higher than expected percentage cover of sand ayear later during the July 1998 dive surveys. This discrepancy is quite likelydue to a combination of temporal variability (seasonal sand transport) andpotential over-generalization during interpretation (see above). In any event,the discrepancy underscores the need for proper ground-truthing which, bydefinition, should take place as concurrently with the survey or as soon afteras possible. Especially in areas of high variability, timely groundtruthing isvital if high confidence in the substrate classification is desired. Finally, ifmeaningful habitat information and effective resource management is thegoal, evidence of seasonal or other temporal variation in substrate (and thushabitat) in the Punta Gorda study site argues for repeat sampling as a meansof assessing the level of variability and the relative transience or permanenceof the habitats observed. Regarding the particular case of sediment transportand burial of rocky substrate, a time series of sidescan sonar imagery will showchanges in the coverage of sediment and rock, while another acoustic method,sub-bottom or seismic profiling, can give information on sediment depths aswell. This information can be used to reveal rocky substrate underlyingsediment patches and assess whether it is likely that sediment will deposit orerode from an area, uncovering or burying rocky substrate in the process. Aswith SSS imagery, time-series sub-bottom data can be particularly useful inmonitoring highly variable settings.

A goal of this study was to develop and apply SCUBA methodologies forquantifying distribution and abundance of subtidal marine organisms. Usingquadrats for estimating counts and percent cover of marine organisms provedtoo difficult, in part due to adverse weather conditions that allowed access tothe reserve for less than 40% of the scheduled study time, and the low abun-dance of hard substrate, where our targeted epifauna would have been found.

We gave divers a preliminary course of instruction concerning quadrat usebefore beginning our SCUBA surveys. Divers were quickly taught how toidentify invertebrates, and received instructions for deploying transect linesand properly using quadrats for estimating percent cover. To insure properidentification and to assess soundness of the resulting data, video footage wastaken along transects and of each quadrat for post-processing.

While this training showed much promise on land, the approach provedto be impractical when applied during our study. Extreme ocean conditionsprohibited the use of quadrats. For example, strong surge often did not allowdivers to keep quadrats in place for videotaping. Moving, arborescent formslike algae, hydroids, and bryozoans also masked many of the aspects of thequadrat, making the identification of organisms difficult for divers. In addi-tion, the scattered rocky substrate made the random placement of quadrats

DiverSurveys


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impractical, as the oft-encountered sand habitat provided little of interest toobserve. As a result, the deployment and use of quadrats was abandoned infavor of covering more of the reserve within the available study time.

Post-processing of SCUBA video footage was also difficult due to inter-mingled organisms of similar color and form. While many species were none-theless identified via SCUBA video footage, estimates of percent cover orrelative abundance were not possible because area covered could not be calcu-lated. This was in part due to the strong surge, which moved both quadrat andcamera operator, and the masking of individual species by arborescent forms.Also, difficulties in post-processing were caused by the low resolution of thevideo camera, which produced fuzzy and difficult-to-identify images. Whilelarger invertebrate forms were easily identified from SCUBA video footage,most small organisms were not identified owing to poor image quality.

The identification of a species from video footage was directly related tohow well it could be distinguished from the background turf that coveredmuch of the rocky substrate. Species that stood out from the brownish back-ground, such as the anemone C. californicus, were easily identified for counts.Other species, such as the sponge P. pachymastia and the anemone M. senile,were easily identified by their shape and color. Species such as the tunicate P.planum, which tended to have an easily-identified, surge-generated, back-and-forth movement, were very noticeable. Those species that formed brownishturfs were not enumerated due to our inability to consistently count thesetypes of organisms. The species that were enumerated usually represented asmaller proportion of the total area covering a rock surface. Invertebrates,such as the yellow zoanthid E. scotinus, the bryozoan S. californica, and thetunicates Aplidium californicum, and Distaplia smithii, were difficult to countaccurately due to their dull appearance and tendency to form intermixedturfs. While the turf species represented a large proportion of the inverte-brates encountered, their densities could not be estimated using the method-ologies of this study.

Turf organisms could have been regularly identified with slight changes inthe methodologies used. For example, spending more time closely examiningrock surfaces would have facilitated enumeration of many of the difficult-to-identify species. This would certainly have been undertaken had not theextreme weather conditions encountered at the reserve eliminated more than80 % of our scheduled study opportunities. To make up for lost study time,both diver and ROV teams were forced to spend very time at each rockoutcropping. The trade-off between increasing the distance covered anddecreasing the detail of area covered was necessary to maximize the limitedamount of time available to study PGER.

The vertical relief of rocky outcrop-pings appeared to be a considerablefactor influencing the biotic assemblages found in PGER. Areas of lower-reliefrock had considerably lower densities and fewer identified species than areasof higher-relief rock. The relationship between relief and biota is most likely


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related to sand influence and water movement, two factors that are affected bythe vertical relief of rocky outcroppings.

Within shallow subtidal depths (9 to 15 m), rocky outcroppings wereworn smooth and varied biologically, in part depending on the degree of sandscouring, which was proportional to the height of the substrate. The relief ofthe rock had a great impact on the biota found within shallow waters. Rockoutcroppings < 0.5 m were almost entirely covered by sand. This relief typeexperienced regular sand abrasion and typically had a smooth, roundedsurface, which mostly lacked macrobiota. Barnacles, possibly B. crenatus, werethe common sessile invertebrates found in this area. Motile invertebratesincluded predatory species that feed on barnacles, such as the snail N.lamellosa, the short-spined sea star Pisaster brevispinus, the common ochre seastar, Pisaster ochraceus, and the sunflower star, Pycnopodia helianthoides. Rockoutcroppings between > 0.5 m and < 1 m high were less influenced by sandscouring, but was still low enough to be regularly inundated by sand. Com-mon invertebrates found on this relief were those capable of tolerating sand,such as the sponge P. pachymastia, the zoanthid E. scotinus, and the compoundascidian Archidistoma psammion. Rocks > 1m high were least influenced bysand abrasion and represented the smallest proportion of rocky habitat foundwithin diver depths. Here, many of the invertebrates were colonial suspensionfeeders, which covered large areas of the rocky outcroppings. Species com-monly found included the following: The sponges A. erithacus, Cliona celata,C. arb, H. panicea, Leucilla nuttingi, and P. pachymastia; the hydroidsAbietinaria sp., Aglaophenia sp., and Plumularia sp.; the anthozoansAnthopleura elegantissima, Anthopleura xanthogrammica, Balanophyllia elegans,C. californica, Epiactis prolifera, and Gersemia rubiformis; the bryozoans H.pacifica, Flustrellidra corniculata, and S. californica; and the tunicates Aplidiumspp., Cystodites lobatus, D. smithii, Ritterella aequalisiphonis, and S.montereyensis. Other dominant suspension-feeders included: the polychaeteworm Dodecaceria fewkesi; the rock scallop Crassedoma giganteum; the bar-nacle Balanus nubilus; and the sea star H. leviuscula.

While colonial suspension feeders dominated the high-relief Rock sub-strate, rock-dwelling predators were also found on this relief type. Invertebratepredators commonly found included the following: The nudibranchsAnisodoris nobilis, Flabellinopsis iodinea, and Phidiana crassicornis; the snail N.lamellose; the asteroids D. imbricata, P. brevispinus, P. giganteus, P. ochraceus,and Pycnopodia helianthoides. Herbivorous species were not seen in significantnumbers within the shallow subtidal area.

Zonation within PGER’s shallow waters appeared to be based on verticalrelief and orientation. Species richness was observed to be highest on rockoutcroppings that had considerable vertical relief or were sheltered from wavesurge. Dome-shaped rocks were the most common form of rocky substrate,with the vertical surface being highly exposed to surge-generated sand abra-sion and the horizontal surface being less exposed, based on visual accounts of


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sand abrasion. Of the 141 sessile invertebrates enumerated in Table 7, 91%occupied the horizontal surface and 8% occupied the vertical surface. Only 1sessile invertebrate was observed on sand-covered rock surface.

The limited availability of space on hard substrates likely resulted in sandburial or abrasion. High numbers of barnacle scars were found at the bases ofscoured rocks, indicating their recent presence. Species of the barnacleBalanus are reported to be the first to colonize newly available substrata(Raffaelli 1988). The paucity of invertebrate species where barnacles, B.crenatus, occurred suggests that recurring and intense abrasion, followed bysettlement of a pioneer species, like B. crenatus, occurs regularly on lowerrelief rocks in areas of high water movement. The regular occurrence ofinvertebrates associated with strong surge- or wave-impacted environments,such as hydroids (Harvell and Labarbera 1985), the tunicate S. montereyensis(Young 1978), and the ascidian P. planum (Holyoak 1997), indicates strongwater movement within all depths of the reserve.

Inter-species and habitat associations both amongnearshore reef fish and macroinvertebrates at PGER

Cluster analyses of both finfish and invertebrates that exhibited lowoverall abundance were of little value for comparing species and habitatassociations. Species with low densities were clustered in a grouping that weconsidered “background” without imputing associations. Other multivariateanalysis such canonical correlation analysis (CCA) or the use of similarityindices may be incorporated in future analysis to define species/habitatassociations.

Defining inter-species and between-habitat associations in our study wasaccomplished by integrating results from our cluster analysis of primarysubstrate and relief in combination with other spatial parameters such asorientation to substrate (Tables 17 and 22) and actual mapped locations atPGER (Figures 26a–c, 27a–c, 34, 35, 36, and 37a–c). Ultimately, combiningsuch parameters at multiple locations over time with the inclusion of macro-algae will allow wider definition of essential habitat to be developed forfinfish, invertebrates, and macroalgae. Macroalgae were not included in thisanalysis since they were a minor component at PGER.

Nearshore reef fishA paucity of macroalgae, generally low relief reef (<2 m), low rugosity,

and the predominance of sand all conspired to minimize available habitat forthe reef-based fishes found at PGER. Larson and DeMartini (1984) deter-mined that vertical stratification provided by canopies of the giant kelpMacrocystis pyrifera increases the standing stock of fishes in temperate reefcommunities. In a Central California study, Bodkin (1986) found that theperennial giant kelps supported 2.4 times the biomass of fish, such as the blue



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rockfish, relative to annual bull kelp N. luetkeana. Bodkin (1986) mainlyobserved differences in midwater abundance and not in densities of fish nearthe bottom. Matthews (1989) examined seasonal differences in adult andyoung-of-the-year (YOY) density for quillback rockfish S. maliger, copperrockfish S. caurinus and brown rockfish S. auriculatus on high-relief rock,low-relief rock reefs, artificial reef, and sand habitats. The highest constantdensities were found on high-relief rock habitats. These populations showedless fluctuation than those on both artificial reef and low-relief reef, especiallyduring winter months when algal cover disappeared. Matthews also foundthat sand/eel grass habitat was the least utilized by adults of YOY of thesespecies. Likewise, Richards (1987) found in British Columbia that for bothcopper rockfish S. caurinus and quillback rockfish S. maliger, densities weregreatest in high-relief areas, with copper rockfish more widely spatially distrib-uted than quillback rockfish. Richards also found that quillback rockfishincreased in abundance with flat-blade algal cover while this was not the casewith copper rockfish. Density comparisons between our study and others areinstructive only when examining gross similarities in fishing mortality, meth-ods, and depths that are masked by differences between locations. Our studyused weighted densities applied to habitat that were identified by post-processing. Most other studies have applied methods using fixed-lengthtransects, either randomly deployed or over limited habitat such as areas withsurface canopy (Miller and Geibel 1973, Bodkin 1986, Yoklovich et al. 2000,Davis 1989, Karpov et al. 1998). Previous studies of these nearshore speciesare mostly SCUBA-based and thus depth limited (<20 m) (Miller and Geibel1973, Bodkin 1986, Larson and DeMartini 1984, Matthews 1989, Richards1987). Our ROV transect depths were deeper than this at PGER, rangingfrom 15 to 51 m with an average of 29.8 m (Table 12 and Figure 16). Anadditional potential problem is that fish may react differently to ROV andSCUBA. We observed that some species such as quillback rockfish S. maligerand lingcod O. elongatus were attracted to the ROV, while others were initiallyattracted and than moved away (e.g., blue rockfish S. mystinus, black rockfishS. melanops, and canary rockfish S. pinniger—see Results section). Anotherfactor to consider is that effectively camouflaged fishes, such as Pluero-nectiformes in sand and cabezon S. marmoratus on certain reefs, furtherconfound efforts to census populations in areas with abundant understorykelps. Studies are needed to calibrate SCUBA, ROV, and submarine surveymethods in vari-ous habitats to examine differences between methods andother potential biases.

In spite of these differences, our observed densities for many of the specieson rocky reefs within PGER were similar to those at comparable depths andsubstrate in the nine segments sampled off PCER and at SCUBA stationsin kelp forests off Monterey (Miller and Geibel 1973 and Bodkin 1986)(Table 24). Lingcod O. elongatus and kelp greenling H. decagrammus hadcomparable densities in all three study areas. Notably, the midwater blue


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rockfish S. mystinus and the striped perch Embiotica lateralis, were moreabundant in areas with kelp (Miller and Geibel 1973; Bodkin 1986), espe-cially in giant kelp forests, where canopy provides more habitat and year-round protection from predation than the thin-striped annual bull kelp or theRock substrate at PGER, the latter being devoid of kelp. Two species, blackrockfish S. melanops and quillback rockfish S. maliger, were more abundant atPCER than the other two study areas, although it should be noted that blackrockfish is more northern in distribution, with Monterey at the southern endof its range (Karpov et al. 1995).

Characterizing habitat associations is important for pre-stratifying sam-pling for species of importance, and ultimately estimating area-wide abun-dance when habitats are remotely quantified. Ultimately, sites such as PGERcan be assessed for their value in terms of the extent and quality of suchhabitats for species of concern. In our study, several combined approacheswere useful in characterizing species-habitat associations. Cluster analysis washelpful in revealing species associations and defining their habitat by substrateand relief. The orientation analysis and visual observations allowed under-standing habitat use in relation to other species. The spatial location plotsallowed visualizing and verifying the associations imputed through the clusteranalyses in terms of their physical locations within the reserve. Finally, on agross scale, the mapped distribution of the biota throughout the reservefurther allowed characterization how these habitats were related to theirphysical environment.

Distributions of blue rockfish S. mystinus were found to be uniquelyassociated high relief Rock habitats (Figure 22). Together, the distribution ofblue and juvenile rockfish served to isolate Rock from the other primarysubstrates (Figure 24). Blue rockfish were also unique as the most pelagicspecies found above, but not in contact with the few rock-associated habitatsavailable at PGER (Table 17). Miller and Geibel (1973) described this speciesas often feeding on planktonic organisms in mid-water. Juvenile rockfish werealso mostly found over Rock habitat, but were more broadly distributed byrelief. The limited distribution of blue rockfish and relatively low densities ofareas with kelp (Miller and Geibel 1973) suggests PGER is a “sink” as op-posed to “source” area for blue rockfish S. mystinus.

Another species group whose distribution helps to characterize availablehabitat types at PGER were the Pleuronectiformes (Figures 27a–c). Thisgroup was uniquely distributed in northern sand areas of PGER, with thehighest concentration on the “dune” areas of transect 25. These areas werecharacterized by medium- and fine-grained, medium-relief sand at intermedi-ate depths from 30 to 40 m (Figure 18). These fish were notably absentinshore on comparable habitat at 18 to 20 m (transects 36, 37, and 38), inthe high-energy trough area of medium-relief sand (southern transect 25),and in coarse, medium-relief sand (transects 27 and eastern 31).

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5 2 Reserve - nsgl.gso.uri.edunsgl.gso.uri.edu/cuimr/cuimrc02001/1KARPOV_part1a.pdfReserve Konstantin Karpov1 Dale Sweetnam1 ... their source or their use, ... We suggest that the