factors determining the upper limit of giant kelp, macrocystis pyrifera agardh, along the monterey...

LJournal of Experimental Marine Biology and Ecology,218 (1997) 127–149

Factors determining the upper limit of giant kelp,Macrocystis pyrifera Agardh, along the Monterey Peninsula,

central California, USA1Michael H. Graham

Moss Landing Marine Laboratories, P.O. Box 450, Moss Landing, CA 95039, USA

Received 21 October 1996; received in revised form 17 February 1997; accepted 7 March 1997

Abstract

Abiotic and biotic factors determining the upper (shallow or nearshore) limit of giant kelp,Macrocystis pyrifera Agardh, were examined along a wave exposure gradient on the MontereyPeninsula, central California, USA. Wave modeling, analysis of aerial photographs from 1986 to1989 and SCUBA surveys from 1993 to 1995 indicated a significant positive relationship betweenwave intensity and depth of the upper limit of giant kelp; increased wave intensity resulted in theupper limit moving offshore into deeper water presumably due to direct removal of adult giantkelp plants by waves. Further, during periods of high wave intensity, plants with canopies wererestricted to deeper water than those without canopies, suggesting that wave-induced giant kelpmortality was related to plant biomass (i.e. drag). Removal of giant kelp from shallow water( # 2.5 m depth) during periods of high wave intensity may have facilitated the development ofdense algal turf assemblages by reducing light limitation; clearing experiments indicated that algalturf inhibited giant kelp recruitment at depths # 2.5 m. Under extended periods of low waveintensity, however, giant kelp can establish populations in shallow water as indicated by theshallower depth of continuous giant kelp canopies with decreasing wave exposure. Thus, algalcommunity structure in these shallow subtidal regions along the Monterey Peninsula appears to bedetermined by disturbance-mediated competition; with a lack of disturbance favoring giant kelp,disturbance favoring algal turf. These data support the hypothesis that the upper limit of giant kelpis controlled by an interaction between abiotic and biotic factors. 1997 Elsevier Science B.V.

Keywords: Macrocystis pyrifera; Giant kelp; Upper limit; Waves; Interspecific competition; Algalturf; Disturbance; Recruitment

1Present address: University of California, San Diego, Scripps Institution of Oceanography 9500 GilmanDrive, mail code 0208, La Jolla, California 92093-0208, USA. e-mail: [email protected]

0022-0981/97/$17.00 1997 Elsevier Science B.V. All rights reserved.PII S0022-0981( 97 )00072-5

128 M.H. Graham / J. Exp. Mar. Biol. Ecol. 218 (1997) 127 –149

1. Introduction

Algal zonation patterns and the factors responsible for algal distributional limits have¨been of great interest to marine ecologists (e.g. Luning, 1990; Lobban and Harrison,

1994). Connell (1972) hypothesized that abiotic factors determine the upper limit ofintertidal species, whereas biotic factors determine the lower limit. This hypothesis hasbeen supported for marine algae by many researchers (e.g. Doty, 1946; Edwards, 1977;Schonbeck and Norton, 1978; Hay, 1979; Lubchenco, 1980; Underwood, 1980).Additional work, however, has indicated that the upper limit of marine algae may alsobe determined by biotic factors alone or by an interaction between abiotic and bioticfactors (e.g. Chapman, 1973; Sousa et al., 1981; Lubchenco, 1982; Cubit, 1984;Santelices and Ojeda, 1984; Robles and Robb, 1993).

In the Northern Hemisphere, the giant kelp Macrocystis pyrifera (L.) C. Agardh isfound in coastal regions from central California, USA to Baja California, Mexico(North, 1971; Foster and Schiel, 1985). Both breaking waves and interspecificcompetition with other algae have been postulated to determine the upper limit of giantkelp (Santelices and Ojeda, 1984; Dayton, 1985; Seymour et al., 1989; also review byFoster and Schiel, 1985). In regions where wave action and interspecific competition arelow, the upper limit of giant kelp is ultimately determined by photo-damage to itsmicroscopic stages (e.g. gametophytes) due to high irradiance (Graham, 1996); thenegative effect of high irradiance generally extends to 1–2 m depth. Along the opencoast of California, however, giant kelp is rarely found shallower than 4 m depth (North,1971; Foster and Schiel, 1985; Graham, 1996) suggesting that other factors may beimportant in determining the upper limit of giant kelp prior to the effect of highirradiance (i.e. at depths . 2 m).

Increased loss of giant kelp canopy during winter (North et al., 1993) suggestsrelationships between seasonal fluctuations in wave intensity (distinguished from waveexposure as temporal variation in water motion due to waves rather than spatialvariation), giant kelp mortality and giant kelp upper limits. This hypothesis that wavesdetermine the extent of giant kelp distributions is not new, as numerous studies havestressed the importance of waves to giant kelp mortality (ZoBell, 1971; Rosenthal et al.,1974; Barrales and Lobban, 1975; Gerard, 1976; Foster, 1982; Dayton et al., 1984;Kimura and Foster, 1984; Reed and Foster, 1984; Harrold et al., 1988; Dayton et al.,1992; North et al., 1993). All of the above studies, however, based their inferences onqualitative estimates of wave exposure and intensity and none specifically addressedhypotheses concerning giant kelp upper limits. Seymour et al. (1989) and Graham et al.(1997) provided quantitative estimates of wave exposure and intensity in support of thehypothesis that waves negatively affect giant kelp survival. Further, Seymour et al.(1989) predicted that breaking waves were the primary factor determining the upperlimit of giant kelp in southern California.

Two aspects of wave intensity are likely to be important to giant kelp mortality:horizontal orbital displacement (D ) and breaking depth (Denny, 1988; Seymour et al.,H

1989). Horizontal orbital displacement estimates the horizontal distance traveled bywater parcels due to passing waves (Denny, 1988); larger values indicate increasedstretching of giant kelp plants, exertion of stress on the stipes or holdfasts and increased

M.H. Graham / J. Exp. Mar. Biol. Ecol. 218 (1997) 127 –149 129

chances of plant removal due to drag. Breaking depth (BD) is defined to be the depth atwhich the majority of waves break (Thornton and Guza, 1983; Graham, 1995); largervalues indicate waves break in deeper water (farther from shore) closer to offshore giantkelp populations. In addition to wave intensity, onshore winds may bias giant kelpcanopies in the onshore direction potentially contributing to wave-induced mortalities.Finally, extreme low tides also cause waves to break in deeper water closer to offshoregiant kelp populations. Thus, the upper limit of giant kelp may be limited by acombination of abiotic factors.

Upper limits may also be affected by processes that influence other giant kelp lifehistory stages. Recruitment may be inhibited by sand scour (Devinny and Volse, 1978),pollution (Tegner et al., 1995), poor temperature and nutrient conditions (Deysher andDean, 1986a,b), grazing (Dayton et al., 1984, 1992; Harris et al., 1984; Dayton, 1985;Moreno and Sutherland, 1987; Dean et al., 1989; Watanabe and Harrold, 1991; Leonard,1994), or interspecific competition with other algae (Wells, 1983; Reed and Foster,1984; Santelices and Ojeda, 1984; Dayton, 1985). Sand scour, pollution and poortemperature and nutrient conditions, however, are rarely observed in central Californiaover large enough spatial / temporal scales or at lethal levels to be of general importanceto giant kelp upper limits (Foster and Schiel, 1985; Harrold et al., 1988; Graham et al.,1997). Further, since the re-establishment of sea otters to the central California coast inthe 1960s, dense shallow water grazer populations (e.g. sea urchins) have beenuncommon (Watanabe and Harrold, 1991). In central California, dense algal turfdominate shallow subtidal regions (1–4 m depth) nearshore of giant kelp forests(Devinny and Kirkwood, 1974; Harrold et al., 1988; pers. obs.) and thus may also helpdetermine the upper limit of giant kelp.

The purpose of this study was to investigate processes determining the upper limit ofgiant kelp along the Monterey Peninsula, central California, USA. This was accom-plished by (1) measuring swell characteristics and modeling wave intensity along a waveexposure gradient, (2) documenting patterns of temporal and spatial variability in theupper limit of giant kelp along this gradient of wave exposure, and (3) experimentallytesting the hypothesis that interspecific competition with algal turf inhibits giant kelprecruitment to shallow water.

2. Methods

2.1. Study sites

Three sites were chosen to examine the effect of wave exposure and intensity andinterspecific competition with algal turf on the upper limit of giant kelp. Sites wereestablished on a wave exposure gradient (Harrold et al., 1988; Graham et al., 1997)extending along the north coast of the Monterey Peninsula from the Monterey BayAquarium (MBA) (most protected) to Lovers Pt. (intermediate) to Otter Pt. (mostexposed) (Fig. 1). The sites were chosen as similar as possible in all aspects exceptexposure to breaking waves in order to isolate wave exposure from confounding factors(e.g. bottom topography, sand cover, grazer density).


Fig. 1. Location of study sites along wave exposure gradient on the Monterey Peninsula. Inset shows regions atOtter Pt. study site surveyed by aerial photographs (A) and SCUBA (B). Notice that the aerial surveys werealong the nearshore edge of the continuous giant kelp canopy, whereas the SCUBA surveys were within theregion relatively devoid of giant kelp between the canopy and shore.

2.2. Wave exposure and intensity

Significant wave height (average height of 1 /3 largest waves) and wave period wereobtained from a permanent sensor (pressure transducer: InterOcean Systems, San Diego,CA, USA) anchored at MBA (10 m depth) and portable sensors (pressure transducers:Ocean Sensors, Encinitas, CA, USA) anchored at MBA (10 m depth), Lovers Pt. (15 mdepth) and Otter Pt. (15 m depth). Significant wave height was calculated as 4 3

standard deviation of the pressure record after correcting for the effect of high frequencyattenuation (W. Broenkow, pers. comm.) and a non-static water column (Denny, 1988).Significant wave heights were measured four times daily by the MBA permanent sensorand three times daily by the MBA, Lovers Pt. and Otter Pt. portable sensors andaveraged to estimate mean daily significant wave height; sampling periods are given inTable 1. Significant wave height estimates at Lovers Pt. and Otter Pt. were shoaled alonga linear depth gradient from 15–10 m depth using linear wave theory (Denny, 1988) toallow comparisons with MBA data.

Due to limited data from the Lovers Pt. portable sensor (n 5 52 days), significantwave heights and wave periods at this site were used solely to document its exposurelevel relative to MBA and Otter Pt. Significant wave height and wave period estimatesfrom the MBA permanent sensor and Otter Pt. portable sensor, however, were used todocument the relative exposure levels of these sites and also to examine the relationshipbetween wave intensity and the upper limit of giant kelp (Table 1).

A computer program based on the bore dissipation model of Thornton and Guza(1983) was used to model wave intensity (D and BD) at the MBA and Otter Pt. studyH

M.H

.G

raham/

J.E

xp.M

ar.B

iol.E

col.218

(1997)127

–149131

Table 1Source and analysis of abiotic factor and giant kelp upper limit data

Variable Source Analysis

Abiotic factorsSignificant wave height and wave period Permanent sensor: MBA (daily: 2 /21/86–6/18/94) Among-site differences in wave exposure

Portable sensor: MBA (daily: 12 /15/93–1/26/94) (ANCOVA; paired t-test)Lovers Pt. (daily: 5 /1 /95–6/21/95)Otter Pt. (daily: 4 /5 /88–10/19/91)

Horizontal orbital Produced from wave model using significant wave heights and Abiotic factors vs. upper limit depthadisplacement (D ) wave periods from MBA permanent sensor and Otter Pt. (multiple regression)H

bportable sensor (daily: 2 /21/86–9/21/89 and 11/15/93–4/15/95)Breaking depth (BD) Same as for D 2H

Wind velocity MBA anemometer (daily: 2 /21/86–9/21/89) 2

Minimum tidal height MBA tide gauge (daily: 2 /21/86–9/21/89 and 11/15/93– 2

4/15/95)BD residual, wind residual, Regression residuals from D vs. BD, wind velocity, Abiotic factors vs. upper limit depthH

tidal residual and minimum tidal height; BD residual and tidal (multiple regression)residual data available 2 /21/86–9/21/89 and 11/15/93–4/15/95,wind residual data available only 2/21/86–9/21/89

Upper limitsCanopy upper limit Aerial surveys at MBA, Lovers Pt. and Otter Pt. Among-site differences in canopy upper limit

(monthly: 3 /21/86–9/21/89) depth (ANOVA); abiotic factors vs. canopyupper limit depth (multiple regression)

Overall upper limit SCUBA surveys at MBA, Lovers Pt. and Otter Pt. Among-site differences in overall upper limit(twice monthly: 12/15/93–4/15/95) depth (ANOVA); abiotic factors vs. overall

upper limit depth (multiple regression)

Data adjustments and corrections:a Significant wave heights (6 /18/94–4/15/95) interpolated from relationship with offshore buoys at Farallon Is. and Marina, California (Coastal Data Information

2Program, 1985–1995) (linear regression: MBA 5 0.09 1 0.43 ? Farallon 1 1.81 ? Marina; n 5 562; r 5 0.95).b Significant wave heights (2 /21/86–4/4 /88 and 11/15/93–4/15/95) interpolated from relationship with MBA permanent sensor (see Fig. 2 for regression).


Table 2Pearson product–moment correlations (r) among abiotic factors measured at MBA between March 1986 andSeptember 1989

Orbital displacement (D ) Breaking depth (BD) Wind velocityH

Breaking depth (BD) 0.746 (0.000) 2 2

Wind velocity 2 0.279 (0.099) 2 0.387 (0.019) 2

Minimum tidal height 0.671 (0.000) 0.658 (0.000) 2 0.348 (0.038)

All data were maximum values for 13 days prior to date when giant kelp upper limits were sampled (seeSections 2 and 3).P values are in parentheses; n 5 36 for each correlation.

sites (Graham, 1995). This program estimated BD from mean daily significant waveheight and wave period estimates by shoaling significant wave heights (deep to shallowwater) along actual bathymetries corrected for tidal height and accounting for energydissipation due to wave breaking and friction with the substrate. In doing so, it predictedthe location (depth) of the breaker zone and height of breaking waves for any given day.Further, D was estimated from the height of breaking waves and wave period (Denny,H

1988; Graham, 1995). Profiles of depth vs. distance offshore for each site were used asbathymetries thereby correcting for among-site differences in bottom topography (see

21Section 3.1 for profile data). Average daily wind velocity (m s ) and direction (6;onshore /offshore) were measured at MBA by an anemometer on the roof of theMonterey Bay Aquarium; minimum daily tidal height was measured by a tide gauge atthe MBA study site. Procedures for abiotic factor data collection and analysis aresummarized in Table 1.

Abiotic factors measured during the study (D , BD, wind velocity, and minimum tidalH

height) were linearly correlated (Table 2). In order to reduce the collinearity amongabiotic factors for subsequent upper limit analyses, residuals from linear regressionswere obtained for each pairwise combination of abiotic factors. New variables (e.g. BDresidual) were created from the regression residuals (e.g. BD [dependent variable] vs. DH

[independent variable]), thereby removing any collinearity among the original variables.A new dataset was thus created from the four original abiotic factor variables; DH

(independent variable) was regressed with BD, wind velocity and minimum tidal height(dependent variables) resulting in D and three new variables: BD residual, windH

residual and tidal residual (Table 1). Maximum values of each abiotic factor variable inthis new dataset were then calculated every 0, 1, 2 . . . 21 days prior to the date whengiant kelp upper limits were sampled (see below). [Preliminary analyses indicated thatmaximum values explained greater variability in giant kelp upper limits than average orcumulative values].

2.3. Giant kelp upper limits

2.3.1. Aerial surveysDepth of the upper limit of the continuous giant kelp canopy at MBA, Lovers Pt. and

Otter Pt. was estimated from infrared aerial canopy photographs taken at an altitude of2500 m during periods of similar tidal, sea and weather conditions (1:28 000 scale;


EcoScan, Watsonville, CA, USA). Individual giant kelp plants were difficult to identifyfrom the aerial photographs. The nearshore edge of the continuous giant kelp canopy,however, was easily distinguished (Fig. 1, inset A) and allowed for estimation of itsdistance offshore and depth at each site. The photographs (transparencies) wereprojected and scaled using distances between known landmarks. One permanent 60-mtransect was positioned on each image parallel to shore nearshore of the historicaluppermost limit of continuous giant kelp canopies at each site (i.e. the nearest to shoresince March 1986). Ten sampling transects were run offshore (perpendicular to theshoreline) at random locations along each permanent transect. The distance to thenearshore edge of the continuous giant kelp canopy was recorded for each samplingtransect and its depth was predicted from profiles of depth vs. distance offshoredetermined at each site using SCUBA (see Section 3.1 for profile data). Average depthof the 10 sampling transects estimated the depth of the upper limit of the continuousgiant kelp canopy at each site (hereafter, the canopy upper limit); sampling periods aregiven in Table 1. Repetitive sampling (n 5 3) of the same photograph taken for MBAduring March 1986 suggested that measurement error of the depth of the canopy upperlimit was ¯ 0.25 m.

2.3.2. SCUBA surveysDepth of the upper limit of the entire giant kelp population was estimated at MBA,

Lovers Pt. and Otter Pt. in situ using SCUBA. Locations of the permanent 60-mtransects (described above) were determined at each site based on landmarks identifiedfrom the aerial photographs and marked with stainless steel eyebolts. Using compassheadings, 10 SCUBA transects were run offshore (perpendicular to the shoreline) fromthe permanent transects; the permanent transects were divided into 10 strata (each 6 mlong) and the beginning of one SCUBA transect was randomly positioned within eachstrata. Depth, number of fronds ( . 1 m long) and canopy condition (presence orabsence of fronds that reached the surface) were noted for the first giant kelp plantreached within 1 m of either side of the SCUBA transect. Depths were estimated usingcalibrated dive computers and standardized to MLLW (mean lower low water) using tidetables. After the first plant was located the diver continued along the same SCUBAtransect until a plant with the opposite canopy condition was reached (i.e. one plant witha canopy and one plant without a canopy were located per SCUBA transect). Giant kelpplants were rarely observed nearshore of the permanent transects, but when observedthey were sampled in lieu of plants on the offshore side (3 of 1000 plants sampled). Thismethod resulted in estimates of depth for the 10 plants with canopies and 10 plantswithout canopies closest to shore. From these 20 depth estimates, the average depth ofthe 10 shallowest plants overall estimated the depth of the upper limit of the entire giantkelp population at each site (hereafter, the overall upper limit); sampling periods aregiven in Table 1. Although simultaneous aerial and SCUBA surveys were not done,observations from December 1993 to April 1995 indicated that continuous giant kelpcanopies (i.e. those that would have been sampled as the canopy upper limit) were indeeper water (Fig. 1 inset A) than the region of overall upper limit sampling (Fig. 1 insetB). These observations suggest that the canopy and overall upper limits were distinctaspects of giant kelp distribution at MBA, Lovers Pt. and Otter Pt.


2.4. Interspecific competition with algal turf

Experiments were done at MBA, Lovers Pt. and Otter Pt. to test the hypothesis thatalgal turf can influence the upper limit of giant kelp by inhibiting its recruitment. Three

20.5-m clearings and replicate control plots were alternated along the permanent 60-mtransects at each site. Plots were placed on the first suitable substratum (granodioritic

2outcrops larger than 1 m ) nearshore of random locations on each permanent transect( ¯ 2.5 m depth). Clearings were made in late May 1994 by scraping the substratumleaving only crustose algae (non-geniculate coralline algae and holdfasts of geniculatecoralline and non-coralline algae) intact and thus mimicked natural disturbance to algalturf caused by moving boulders (pers. obs.).

Percent cover of macroalgae, bare substrate and sand were estimated by a point2contact method using a 0.25 m PVC quadrat with 16 systematically placed points;

although each algal layer was sampled, distinction between primary and secondary algalturf cover was not made. One quadrat was randomly placed within each plot on eachsampling date and all macroalgae were identified to species (Abbott and Hollenberg,1976; Hommersand et al., 1993, 1994). Both bare rock and crustose algae were recordedas bare substratum since bare rock was rare at the three sites (Harrold et al., 1988; pers.obs.) and giant kelp recruitment does not appear to be inhibited by crustose algae (Reedand Foster, 1984; pers. obs.). Number of giant kelp recruits and macroinvertebrate

2herbivores were counted within each 0.5-m clearing. The substratum near all recruitswas marked and the recruits were followed until their disappearance. Macroinvertebrateherbivores were identified to species, except gastropods which were identified to genus(Morris et al., 1980). Clearings and control plots were sampled every 1–3 monthsbetween May 1994 and April 1995.

2.5. Statistical analyses

All statistical analyses were done using SYSTAT (Macintosh version 5.2.1). Analysis ofcovariance (ANCOVA) was used to test for significant among-site differences in waveexposure; ANCOVA compared the slopes of regressions between significant waveheights measured by the MBA permanent sensor (independent variable) and each of theportable sensors at MBA, Lovers Pt. and Otter Pt. (dependent variables) (Table 1).Among-site differences in ANCOVA residuals were not significant using analysis ofvariance (ANOVA) indicating that the assumption of homogeneity of variances was met.

Relationships among giant kelp canopy (aerial surveys) and overall (SCUBA surveys)upper limits (dependent variables) and abiotic factors (independent variables) were testedusing multiple regression (Table 1). Individual multiple regression models consisted ofthe new abiotic factor dataset (D , BD residual, wind residual and tidal residual) at aH

specific time scale (i.e. maximum values every 0, 1, 2 . . . 21 days). Thus, 22 preliminarymultiple regression models were created: D , BD residual , wind residual , tidalH(0) (0) (0)

residual . . . D , BD residual , wind residual , tidal residual . Multiple(0) H(21) (21) (21) (21)

regressions were done between each dependent variable and each of the 22 preliminarymodels using backwards step-wise regression (abiotic factor variables with P values$ 0.15 were removed from the model). The final (best fit) model of the 22 multiple


regressions for each dependent variable was that with the highest coefficient of2determination (r ). Residuals of each final model were analyzed and indicated that the

assumptions of normality and homogeneity and independence of error terms were met.Note that since multiple regression models were tested on the entire dataset for eachdependent variable, the results of these regression analyses should be viewed asexploratory rather than hypothesis testing.

ANOVA was used to compare depth of giant kelp upper limits among sites and dates(Table 1) and density of giant kelp recruits among recruitment events. Factors werefixed in all cases and multiple comparisons were tested using Bonferroni-corrected a

values. The assumption of homoscedasticity was tested using Cochran’s test; varianceswere equal for all tests.

Statistical analysis of algal turf regrowth following experimental clearing wasproblematic. Without multiple sampling within each clearing and control plot on eachsampling date, significance of within-subject–between-subject interactions cannot betested using repeated measures ANOVA (the appropriate ANOVA model for sequentialsampling dates; von Ende, 1993; A. Underwood, pers. comm.). Therefore, recolonizationof algal turf (or bare substratum) was determined qualitatively as the time when averagepercentage cover of an algal group (or bare substratum) in cleared plots was within onestandard error of the average percentage cover of that algal group (or bare substratum) inthe control plots.

3. Results

3.1. Environmental gradients

Profiles of depth vs. distance offshore (i.e. bottom topography or bathometry) weresimilar among MBA, Lovers Pt. and Otter Pt. Depth increased linearly with increasingdistance offshore at each of the sites (linear regressions: MBA, depth 5 0.14 1 0.03 3

2 2distance, r 5 0.99, n 5 74; Lovers Pt., depth 5 0.08 1 0.05 3 distance, r 5 0.99, n 5243; Otter Pt., depth 5 0.19 1 0.03 3 distance, r 5 0.99, n 5 74). The rate of increase

was steeper at Lovers Pt. (slope: 0.0560.0006) than at MBA (slope: 0.0360.0003) andOtter Pt. (slope: 0.0360.0003), with a depth of 9 m attained approximately 175 moffshore at Lovers Pt. and approximately 310 m offshore at MBA and Otter Pt. Thesedifferences in bottom topography were accounted for by the site-specific profiles ofdepth vs. distance offshore used in the wave modeling program and to estimate the depthof the canopy upper limit (aerial surveys) (described in Section 2).

Relationships between significant wave heights estimated by the MBA permanentsensor (independent variable) and the MBA, Lovers Pt. and Otter Pt. portable sensors(dependent variables) were linear (Fig. 2). The y-intercepts were near zero suggestingthat among-site differences in the regressions between the dependent and independentvariables were due primarily to the slopes. Regression slopes increased from MBA toOtter Pt. and were significantly different among sites (ANCOVA: Site–Covariateinteraction; F 5 60.66; df 5 2, 434; P , 0.0001; multiple comparisons, Otter Pt. .

Lovers Pt. . MBA). The 1:1 relationship between the MBA permanent sensor and the


Fig. 2. Linear relationships between significant wave heights measured by the MBA permanent sensor and theportable sensors at MBA, Lovers Pt. and Otter Pt. (see Table 1 for data sources). Regression equations:

2MBA* 5 0.02(0.02) 1 0.97(0.04) ? MBA, n 5 33, r 5 0.96; Lovers Pt.* 5 0.04(0.03) 1 1.26(0.04) ? MBA, n 52 252, r 5 0.95; Otter Pt. * 5 2 0.03(0.01) 1 1.76(0.03) ? MBA, n 5 355, r 5 0.92 (asterisks indicate data from

the portable sensors). Values in parentheses are standard errors for the fitted parameters.

MBA portable sensor indicates that the permanent and portable sensors were estimatingsignificant wave height similarly (Fig. 2).

Differences in wave exposure between MBA and Otter Pt. were also determined fordays when significant wave heights were measured at both sites simultaneously (Table1). Significant wave height was always greatest at Otter Pt. and the average between-sitedifference was significantly greater than zero (paired t-test: t 5 35.51; df 5 354; P ,

0.001). Of the 355 days when simultaneous data were available, significant wave heightsgreater than 1 m were rare at MBA (n 5 7) but common at Otter Pt. (n 5 87). Significantwave heights from Lovers Pt. were not used in this analysis due to the absence ofsimultaneous significant wave height measurements between Lovers Pt. and Otter Pt.These results and those from the ANCOVA indicate that for any given day, waveexposure (i.e. significant wave height) increased significantly from MBA to Lovers Pt.to Otter Pt.

Sand cover was low throughout the study and did not account for greater than 16%cover at any site. Although sparse, sand cover did fluctuate according to variability inwave intensity and wave exposure. Sand cover was lowest in winter and highest in fall;on each sampling date MBA had the greatest cover of sand followed by Lovers Pt. andOtter Pt., however, among-site differences in sand cover were never significant (Graham,1995).


Fig. 3. Variability in (A) canopy upper limit depth (aerial surveys) at each site (mean6S.E., n 5 10), (B)horizontal orbital displacement (D ) at MBA and Otter Pt. and (C) breaking depth residual (BD residual) andH

tidal residual at Otter Pt. (see Table 1 for data sources). Data for abiotic factors are maximum values for 13days prior to canopy upper limit estimates.


Table 3ANOVAs of depth of canopy (aerial surveys) and overall (SCUBA surveys) giant kelp upper limits

Source SS df Mean square F ratio P value

Canopy upper limitSite 602.42 2 301.21 673.63 , 0.0001Date 480.23 45 10.67 23.87 , 0.0001Site–date 215.23 90 2.39 5.35 , 0.0001Error 555.35 1242 0.48

Overall upper limitSite 4.19 2 2.09 9.47 0.0001Date 80.49 15 5.37 24.26 , 0.0001Site–date 23.22 30 0.77 3.49 , 0.0001Error 95.54 432 0.22

3.2. Depth of giant kelp upper limits

3.2.1. Aerial surveysCanopy upper limit depth varied significantly among sites and dates (Fig. 3a and

Table 3). The significant site–date interaction was likely due to greater variability

Fig. 4. Variability in (A) overall upper limit depth (SCUBA surveys) at each site (mean6S.E., n 5 10) and (B)horizontal orbital displacement (D ) at MBA and Otter Pt. (see Table 1 for data sources). Data for D areH H

maximum values for 3 days prior to overall upper limit estimates. Asterisks indicate significant among-sitedifferences (Bonferroni-corrected contrasts) in overall upper limit depth.


among sites during periods of high wave intensity (November to May) than duringperiods of low wave intensity (May to November) (Fig. 3a and b). At each site, thecanopy upper limit was deepest during winter (maximum values occurred betweenDecember and February) and shallowest during late summer /early fall (minimum valuesoccurred between July and November). Despite the high variability among dates, the sitefactor explained most of the variability in canopy upper limit depth (Table 3); thecanopy upper limit was always deepest at Otter Pt. and shallowest at MBA (Fig. 3a).Temporal variability (variance among dates) in canopy upper limit depth differedsignificantly among the sites (variance among dates: MBA 5 0.17, Lovers Pt. 5 0.45,Otter Pt. 5 0.65 [Cochran’s test for equal variances: C 5 0.51; df 5 43; P , 0.05]).

3.2.2. SCUBA surveysOverall upper limit depth varied significantly among sites and dates (Fig. 4a Table 3).

The significant site–date interaction was likely due to the reversal of among-sitedifferences in depth between periods of maximum and minimum depth (Fig. 4a), ratherthan the change of among-site variability in depth described above for the canopy upperlimit (aerial surveys). Overall upper limit depth increased from MBA to Otter Pt. duringperiods of maximum depth (February 1994 and January 1995) but decreased from MBAto Otter Pt. during the period of minimum depth (September 1994; due to a greaterdecrease in depth at Otter Pt. than at MBA and Lovers Pt.), whereas canopy upper limitdepth increased from MBA to Otter Pt. throughout the study. Multiple comparisons

Fig. 5. Differences between average depth of the shallowest plants with canopies and without canopies(sampled during SCUBA surveys) as a function of wave intensity (D ). For each sampling date there wereH

three depth differences values (one from each site) and a corresponding wave intensity value; D values fromH

Otter Pt. (presented in Fig. 4b) were used to characterize wave intensity for each sampling date. Positive depthdifferences for a given sampling date indicate that plants with canopies were in deeper water than plantswithout canopies. Depth differences were positively correlated with wave intensity (Pearson product–momentcorrelation: r 5 0.589; df 5 47; P , 0.0001). A preliminary ANCOVA analysis indicated that regression slopesand y-intercepts of depth difference vs. wave intensity did not vary among sites and therefore data from allsites were pooled into one correlation analysis.


indicated significant among-site differences in overall upper limit depth during periodsof maximum and minimum depth (Fig. 4a). As with the canopy upper limit, the overallupper limit was deepest at each site during winter (maximum values occurred betweenJanuary and March) and shallowest during late summer /early fall (minimum valuesoccurred between August and October) (Fig. 4a). Temporal variability in overall upperlimit depth increased significantly from MBA to Otter Pt. (variance among dates:MBA 5 0.12, Lovers Pt. 5 0.14, Otter Pt. 5 0.40 [Cochran’s test for equal variances:C 5 0.60; df 5 16; P , 0.05]).

Differences between the average depth of the shallowest plants with canopies andthose without canopies were positively correlated with wave intensity (Pearson product–moment correlation: r 5 0.598; df 5 47; P , 0.0001; Fig. 5). This indicates that duringperiods of high wave intensity (i.e. winter), the shallowest plants with canopy biomasswere in deeper water than those without canopy biomass. During these periods, mostplants without canopies had large holdfasts and many broken fronds , 1 m longindicating they were adult plants that had lost canopy biomass rather than recruits thathad not yet grown sufficiently to form a canopy. Giant kelp recruits and juveniles,however, accounted for many of the plants without canopies during periods of low waveintensity (i.e. summer).

3.3. Giant kelp upper limits vs. abiotic factors

3.3.1. Aerial surveysOf the four abiotic factors measured during the aerial surveys (Table 1), D , BDH

residual and tidal residual were all significant predictors of canopy upper limit depth atOtter Pt., whereas only D was significant at MBA (Fig. 3, Table 4); wind residual wasH

Table 4Final multiple linear regression models for canopy (aerial surveys) and overall (SCUBA surveys) upper limitdepth vs. abiotic factors at MBA and Otter Pt.

2Upper limit Abiotic factor Slope y-Intercept F P r(dependent variable) (independent variable)

CanopyMBA DH 0.19 (0.03) 2.75 (0.08) 39.73 , 0.0001 0.539Otter Pt. Total model 2 3.96 (0.25) 8.06 0.0004 0.430

D 0.31 (0.08) 2 2 2 0.262H

BD residual 0.12 (0.06) 2 2 2 0.107tidal residual 2 0.85 (0.46) 2 2 2 0.061

OverallMBA D 0.51 (0.16) 2.00 (0.31) 7.12 0.0193 0.523H

Otter Pt. D 0.90 (0.29) 1.2 (0.64) 9.56 0.0093 0.443H

Abiotic factors measured during aerial surveys were horizontal orbital displacement (D ), breaking depthH

residual (BD residual), wind residual, and tidal residual (Table 1); abiotic factors measured during SCUBAsurveys were horizontal orbital displacement (D ), breaking depth residual (BD residual) and tidal residualH

(Table 1). Values in parentheses are standard errors of fitted parameters. Significance of linear models wastested with ANOVA. Denominator degrees of freedom: canopy upper limit, 34 for MBA and 32 for Otter Pt.;overall shallow limit, 14 for MBA and 12 for Otter Pt.


not a significant predictor of canopy upper limit depth at MBA or Otter Pt. [Lovers Pt.was omitted from abiotic factor vs. upper limit analyses due to the limited abiotic factordata for this site.] At both MBA and Otter Pt., maximum values of the abiotic factorsover 13 days prior to canopy upper limit estimates explained the greatest amount ofvariability. The final models explained greater variability in canopy upper limit depth at

2 2MBA (r 5 0.539) than at Otter Pt. (r 5 0.430). Thus, the canopy upper limit wasdeeper at MBA during periods of large D and at Otter Pt. during periods of large D ,H H

deep BD and low tides.

3.3.2. SCUBA surveysLinear relationships among overall upper limit depth and abiotic factors (D , BDH

residual, tidal residual; wind velocity was not measured during these surveys; Table 1)were different than those observed for the canopy upper limit (aerial surveys); D wasH

the only significant predictor of overall upper limit depth at both MBA and Otter Pt.(Fig. 4 Table 4). [Again, Lovers Pt. was omitted from upper limit vs. abiotic factoranalyses due to the limited abiotic factor data for this site.] At both MBA and Otter Pt.,maximum values of the abiotic factors over 3 days prior to overall upper limit estimatesexplained the greatest amount of variability (shorter than the 13 day time scale estimatedfor the canopy upper limit). The final models also explained greater variability in overall

2 2upper limit depth at MBA (r 5 0.523) than at Otter Pt. (r 5 0.443). Thus, the overallupper limit was deeper during periods of large D at both sites and therefore duringH

periods of high wave intensity.

3.4. Giant kelp recruitment to algal turf clearings

Giant kelp recruitment was never observed in control plots at any site during thestudy. Giant kelp recruitment, however, was observed in the clearings at each site after92 days (Fig. 6). Recruitment was observed at MBA only on day 92, but was observedon days 92, 141 and 192 at Lovers Pt. and Otter Pt. The density of giant kelp recruits didnot differ significantly among the observed recruitment events (one-factor ANOVA:F 5 0.78; df 5 6, 14; P . 0.60; Fig. 6). Two recruits grew to the surface and became

Fig. 6. Giant kelp recruitment (mean6S.E., n 5 3) to experimental algal turf clearings at each site 31, 48, 75,92, 141, 192, 256 and 325 days after clearings were created (May 1994). Note: recruitment was not observedin control plots at any site during the study.


Fig. 7. Density of herbivores in clearings and control plots as a function of time after clearing at each site(mean6S.E., n 5 3).

reproductive (i.e. adults), one each from Lovers Pt. and Otter Pt.; both plants were firstobserved on day 141. Recruits observed on day 92 were removed by herbivores at allsites by day 141 (as indicated by qualitative observations of blade damage and directobservations of grazing) and corresponded with peak herbivore densities in clearings(Fig. 7); bat stars (Asterina miniata), top snails (Calliostoma sp.) and turban snails(Tegula sp.) were the most common herbivores at each site. Recruits observed at LoversPt. and Otter Pt. on days 141 and 192 disappeared following an increase in waveintensity during winter (Figs. 4b and 6).

Recolonization of bare substratum by algal turf following experimental clearing wasfaster at MBA than at Lovers Pt. and Otter Pt. (Fig. 8). On day 141 (after the putative

Fig. 8. Recolonization (% cover) of bare substratum and regrowth of algal turf in clearings and control plots asa function of time after clearing at each site (mean6S.E., n 5 3). Asterisks indicate the period when all recruitsat each site were removed by grazers. Notice the change in scale of the y-axis for foliose algae.


grazing event had removed all giant kelp recruits), little bare substratum was availablefor giant kelp recruitment to the clearings at MBA, whereas ample bare substratum wasavailable on days 141 and 192 in the clearings at Lovers Pt. and Otter Pt. (Fig. 8);recolonization of bare substratum occurred at Lovers Pt. and Otter Pt. by day 256.Foliose algal cover in clearings (primarily species of Chondracanthus [formerlyGigartina; Hommersand et al., 1993, 1994], Cryptopleura, Prionitis, Rhodymenia andSarcodiotheca) equaled that in control plots by day 92 at MBA and by day 192 atLovers Pt. and Otter Pt. Geniculate coralline algae (primarily Calliarthron cheilos-porioides and C. tuberculosum) regrew slowly in the clearings at all sites and by the endof the study geniculate coralline algal cover in clearings at MBA and Otter Pt. had notreached that observed in control plots; geniculate coralline algal cover in clearings atLovers Pt. appeared to equal that in control plots by day 256, however this similaritybetween clearings and control plots was due to decreased cover in control plots ratherthan regrowth in clearings (Fig. 8).

4. Discussion

Wave exposure explained much of the spatial variability in depth of the upper limit ofgiant kelp along the Monterey Peninsula. Canopy (aerial surveys) and overall (SCUBAsurveys) upper limit depths increased significantly from MBA to Otter Pt. during periodsof high wave intensity. Similarly, wave intensity explained a significant amount oftemporal variability in depth of the upper limit of giant kelp. Seasonal trends in canopyand overall upper limit depths were significant at MBA, Lovers Pt. and Otter Pt. andwere correlated with various aspects of wave intensity at MBA and Otter Pt.,presumably due to direct removal of plants by waves. This link between temporalvariability in giant kelp distribution and wave intensity is supported by data from deepergiant kelp assemblages (Kimura and Foster, 1984; Seymour et al., 1989; North et al.,1993; Graham et al., 1997) and supports the prediction of Seymour et al. (1989) thatbreaking waves directly determine giant kelp upper limits in California, at least duringperiods of high wave intensity. That horizontal orbital displacement (D ) was aH

significant predictor of giant kelp upper limit depth at both MBA and Otter Pt., whereasbreaking depth (BD residual) was only significant at Otter Pt., suggests giant kelpdistribution may be limited by different aspects of wave intensity as wave exposurelevels vary.

In addition to direct removal of giant kelp by waves, Seymour et al. (1989) suggestedthat depth of the upper limit was determined by a combination of breaking waves andentanglement with drifting plants. Drifting plants were rarely seen during this studyshallower than 8 m at MBA, Lovers Pt. and Otter Pt. and entanglement of attachedplants with drifting plants was even rarer. Entanglement may have occurred but was notobserved directly, although the frequent sampling makes this unlikely. In fact, mostsampling periods occurred immediately after storms had subsided when abundance ofdrifting plants should have been high. Direct removal of giant kelp plants by waves,rather than entanglement with drifting plants, appeared to be a primary factordetermining the upper limit along the Monterey Peninsula.


Although breaking waves explained a significant amount of variation in giant kelpupper limit depth, greater than 40% of the temporal variability remained unexplained(Table 4). It is likely that some of this unexplained variability was due to size-specificgiant kelp mortality (Gerard, 1976). The greatest change in depth of giant kelp upperlimits usually occurred just after the first winter storms and the magnitude of waveintensity during these storms was often less than that during subsequent storms (Figs. 3and 4). These observations are similar to the results of Gerard (1976) and Graham et al.(1997) and suggest that giant kelp mortality is highest following initial increases in waveintensity.

This vulnerability may be explained by increased biomass of giant kelp plants due torapid growth and frond initiation during periods of low wave intensity (Gerard, 1976).Decreased holdfast growth may result when production is used for canopy growth(McCleneghan and Houk, 1985). Consequently, drag due to canopy biomass may exceedthe breaking strength of stipes (Utter and Denny, 1996) or attachment strength ofholdfasts resulting in high losses of fronds and entire plants with increased waveintensity. If drag of giant kelp plants that survive frond loss is reduced below the level ofholdfast attachment strength, these plants may be better able to survive periods ofincreased wave intensity. This hypothesis is supported by the shallower depth of adultplants without canopy biomass relative to those with canopy biomass (Fig. 5).Transplant and tagging experiments, however, are necessary to test rigorously thishypothesis.

Among-site differences in canopy upper limit depth (aerial surveys) were similarthroughout the year, whereas among-site differences in overall upper limit depth(SCUBA surveys) varied between periods of high and low wave intensity. Specifically,canopy upper limit depth fluctuated seasonally but always increased from MBA to OtterPt. (Fig. 3a); overall upper limit depth increased from MBA to Otter Pt. only duringperiods of high wave intensity and decreased from MBA to Otter Pt. during periods oflow wave intensity (Fig. 4a). This pattern appeared to be due to among-site variation ingiant kelp recruitment to algal turf-dominated substratum.

The algal turf clearing experiment demonstrated that dense algal turf can inhibit giantkelp recruitment in shallow subtidal regions ( # 2.5 m depth). Giant kelp did not recruitto primary substrate in the control plots and giant kelp that recruited as epiphytes onalgal turf did not survive (personal observation). Chapman (1984); Dayton et al. (1984);Reed and Foster (1984) and Kennelly and Underwood (1993) observed similar kelp–algal turf interactions in various subtidal kelp communities in deeper water than thisstudy and Wells (1983) provided correlative support for Macrocystis pyrifera–genicu-late coralline algae interactions in southern California. Similarly, Santelices and Ojeda(1984) and Dayton (1985) showed that recruitment of giant kelp could also be inhibitedby dense low intertidal bands of other kelp species (Lessonia vadosa and Durvillaeaantarctica, respectively). Although replication of the clearing experiment at MBA,Lovers Pt. and Otter Pt. was low (n 5 3 at each site), growth of recruits to adult size atLovers Pt. and Otter Pt. indicated that decreased cover of algal turf could allow forincreased abundance of adult giant kelp in the shallow subtidal during periods of lowwave intensity.

Dense algal turf assemblages at MBA, Lovers Pt. and Otter Pt. are characteristic ofshallow subtidal regions ( # 2.5 m depth) along the Monterey Peninsula (Devinny and


Kirkwood, 1974; Harrold et al., 1988; pers. obs.), likely due to their ability to survivehigh levels of physical stress (Hay, 1981) and the apparent lack of competition for lightwith continuous giant kelp canopies (Devinny and Kirkwood, 1974; Reed and Foster,1984) (continuous giant kelp canopies did not extend to depths , 3 m at these sites; Fig.3a). Further, algal turf abundance decreases as giant kelp density increases withincreasing depth (Devinny and Kirkwood, 1974; Harrold et al., 1988; pers. obs.).Recruitment of most giant kelp plants (i.e. those plants that make up the continuousgiant kelp canopy) may therefore be limited to deeper subtidal regions where baresubstrate for recruitment is more available and wave-induced giant kelp mortality islower. Increased depth of the canopy upper limit (aerial surveys) from MBA to Otter Pt.during periods of low wave intensity is consistent with this hypothesis (Figs. 2 and 3Table 3).

In contrast, depth of the overall upper limit (SCUBA surveys) was determined duringperiods of low wave intensity by the extent of giant kelp recruitment to shallow watersubstrate at each site. Algal turf composition varied as wave exposure increased fromMBA to Otter Pt. Abundance of foliose algae in control plots decreased from MBA toOtter Pt., whereas abundance of geniculate coralline algae increased from MBA to OtterPt. (Fig. 8), as previously reported by Harrold et al. (1988). Extensive random samplingof algal turf at MBA, Lovers Pt. and Otter Pt. indicated that these among-site patterns inalgal turf composition were statistically significant throughout the study (Graham, 1995).Rates of algal turf regrowth subsequently varied with these changes in speciescomposition. The assemblage at MBA (dominated by foliose algae) regrew quicklyfollowing experimental clearing probably due to vegetative propagation (vegetativepropagation has been described for many foliose algal species observed at MBA and to alesser extent, Lovers Pt.; Abbott and Hollenberg, 1976); rapid vegetative recolonizationof substrate following disturbance is commonly reported for foliose algal turf inCalifornia (e.g. Murray and Littler, 1978; Sousa et al., 1981; Robles and Robb, 1993).Regrowth of algal turf at Otter Pt. (dominated by geniculate coralline algae), however,was slow with approximately 20% cover of bare substrate still available for giant kelprecruitment in late April 1995. Increased delay in substrate recolonization by algal turf inareas dominated by geniculate coralline algae (e.g. Otter Pt.) would have allowed forlonger giant kelp recruitment windows than in areas dominated by foliose algae (e.g.MBA).

That increased cover of geniculate coralline algae results in delayed substratumrecolonization by algal turf and longer giant kelp recruitment windows is also supportedby the algal turf clearing experiment. After giant kelp recruits at MBA were completelyremoved by herbivores, quick regrowth of the foliose algae-dominated algal turf reducedthe availability of bare substratum and subsequent giant kelp recruitment was notobserved (Figs. 6 and 8). Giant kelp recruitment, however, continued at Lovers Pt. andOtter Pt. where bare substrate was available. These additional recruitment eventsallowed individuals at both Lovers Pt. and Otter Pt. to reach the surface and becomereproductive. Although giant kelp recruit density was not significantly different amongrecruitment events, longer recruitment windows may have resulted in the significantlyshallower depth of the overall upper limit (SCUBA surveys) from MBA to Otter Pt.during September 1994.

Interspecific competition with algal turf, however, was not the only factor limiting


giant kelp recruitment at MBA, Lovers Pt. and Otter Pt. during this study. Absence ofadult giant kelp plants at depths # 2.5 m likely decreased the supply of giant kelp sporeswithin the region of the experimental clearings. In addition, although photo-damage dueto high irradiance generally does not extend below 1–2 m depth (Graham, 1996), somehigh irradiance-induced mortality of settled spores, gametophytes and germlings mayhave occurred. Further, high densities of asteroids and gastropods were observed at eachof the study sites 92 days after creation of the experimental clearings (Fig. 7); extensiverandom sampling of the algal turf during this period indicated that this peak in herbivoredensity was widespread (herbivore densities were not significantly different among sites;Graham, 1995). Still, the duration of peak herbivore density was short (Fig. 7).Therefore, although other factors appeared to decrease the density of giant kelp recruitsobserved at MBA, Lovers Pt. and Otter Pt., high variability of these factors and thelimitation of giant kelp recruitment to algal turf clearings suggests interspecificcompetition with algal turf plays a more general role in inhibition of giant kelprecruitment in this region.

5. Conclusions

The results of this study support the hypothesis that wave-induced giant kelp mortalityis the primary factor directly determining giant kelp upper limits along the MontereyPeninsula during periods of high wave intensity. Wave-induced giant kelp mortality alsoappeared to determine giant kelp upper limits indirectly during periods of low waveintensity. Giant kelp is the dominant competitor for light within its distributional range(Reed and Foster, 1984; Dayton et al., 1992). In the absence of shading by continuousgiant kelp canopies, however, algal turf assemblages are the dominant competitors forsubstratum and inhibit giant kelp recruitment. Therefore, by limiting the establishment ofcontinuous giant kelp canopies to deeper subtidal regions, waves appear to allow for thepersistence and dominance of algal turf in the shallow subtidal ( # 2.5 m depth). Ifwave-induced giant kelp mortality is low, giant kelp plants that recruit to available baresubstratum or as epiphytes on algal turf should decrease the underlying algae throughshading (Reed and Foster, 1984). Such conditions would likely promote a persistentgiant kelp population in the shallow subtidal, as suggested by decreased depth ofcontinuous giant kelp canopies from Otter Pt. to MBA during this study.

This combination of direct and indirect control of giant kelp upper limits by wavesmay be described best as disturbance-meditated competition (Wells, 1983). Algal turfassemblages become dominant competitors for substrate only if wave-induced mortalityof giant kelp is high. In absence of such disturbance, giant kelp dominates the system asin subtidal regions. Thus, shallow water environments in central California may havedifferent algal compositions depending on the level of disturbance: giant kelp-dominated(low disturbance) or algal turf-dominated (high disturbance).

Acknowledgements

I acknowledge M. Foster, W. Broenkow and M. Denny for their guidance throughout


this study. I also thank my field assistants: J. Heine, J. Downing, M. Pranger, D. James,L. Marrack, J. Engle, R. Clark, R. Lagerholm, M. Edwards, S. Lamerdin, P. von Langen,L. Ferry-Graham and P. Hague. I am grateful to C. Harrold and S. Lisin for theopportunity to work with them and the staff of the Monterey Bay Aquarium and forproviding access to unpublished aerial canopy photographs. I am also grateful to M.Foster, M. Denny, J. Barry, J. Watanabe, L. Ferry-Graham, E. Vetter, A. Hobday and P.Dayton for reviewing various drafts of the manuscript. Partial funding was provided bythe David and Lucille Packard Foundation, Dr. Earl H. Myers and Ethel M. MyersOceanographic Marine Trust and Phycological Society of America (Grant-In-Aid ofResearch).

References

Abbott, I.A., G.J. Hollenberg, 1976. Marine Algae of California. Stanford University Press, Stanford, pp. 827.Anonymous, 1985–1995. Coastal Data Information Program, Vol. 109–232.Barrales, H.L., Lobban, C.S., 1975. The comparative ecology of Macrocystis pyrifera, with emphasis on the

forests of Chubut, Argentina. J. Ecol. 63, 657–677.Chapman, A.R.O., 1973. A critique of prevailing attitudes towards the control of seaweed zonation on the sea

shore. Bot. Mar. 16, 80–82.Chapman, A.R.O., 1984. Reproduction, recruitment and mortality in two species of Laminaria in southwest

Nova Scotia. J. Exp. Mar. Biol. Ecol. 78, 99–109.Connell, J.H., 1972. Community interactions on marine rocky intertidal shores. Ann. Rev. Ecol. Syst. 3,

169–192.Cubit, J.D., 1984. Herbivory and the seasonal abundance of algae on a high intertidal rocky shore. Ecology 65,

1904–1917.Dayton, P.K., 1985. The structure and regulation of some South American kelp communities. Ecol. Monogr.

55, 447–468.Dayton, P.K., Currie, V., Gerrodette, T., Keller, B.D., Rosenthal, R., Ven Tresca, D., 1984. Patch dynamics and

stability of some California kelp communities. Ecol. Monogr. 54, 253–289.Dayton, P.K., Tegner, M.J., Parnell, P.E., Edwards, P.B., 1992. Temporal and spatial patterns of disturbance

and recovery in a kelp forest community. Ecol. Monogr. 62, 421–445.Dean, T.A., Thies, K., Lagos, S.L., 1989. Survival of juvenile giant kelp: The effects of demographic factors,

competitors and grazers. Ecology 70, 483–495.Denny, M.W., 1988. Biology and the Mechanics of the Wave-Swept Environment. Princeton University Press,

Princeton, pp. 329.Devinny, J.S., Kirkwood, P.D., 1974. Algae associated with kelp beds of the Monterey Peninsula, California.

Bot. Mar. 17, 100–106.Devinny, J.S., Volse, L.A., 1978. Effects of sediments on the development of Macrocystis pyrifera

gametophytes. Mar. Biol. 48, 343–348.Deysher, L.E., Dean, T.A., 1986. In situ recruitment of sporophytes of the giant kelp, Macrocystis pyrifera (L.)

C. Agardh: effects of physical factors. J. Exp. Mar. Biol. Ecol. 103, 41–63.Deysher, L.E., Dean, T.A., 1986. Interactive effects of light and temperature on sporophyte production in the

giant kelp Macrocystis pyrifera. Mar. Biol. 93, 17–20.Doty, M.S., 1946. Critical tide factors that are correlated with the vertical distribution of marine algae and

other organisms along the Pacific coast. Ecology 27, 315–328.Edwards, P., 1977. An investigation of the vertical distribution of selected benthic marine algae with a

tide-simulating apparatus. J. Phycol. 13, 62–68.Foster, M.S., 1982. The regulation of macroalgal associations in kelp forests. In: Srivastava, L. (Eds.)

Synthetic and degradative processes in marine macrophytes, Walter de Gruyter, Berlin, pp. 185–205.Foster, M.S., Schiel, D.R., 1985. The ecology of giant kelp forests in California: a community profile. US Fish

Wildl. Serv. Biol. Rep. 85, 1–152.


Gerard, V.A., 1976. Some aspects of material dynamics and energy flow in a kelp forest in Monterey Bay,California. Ph.D. dissertation, University of California, Santa Cruz, pp. 173.

Graham, M.H., 1995. Regulation of the shallow limit of giant kelp, Macrocystis pyrifera, at three sites alongthe Monterey Peninsula. M.S. thesis, San Jose State University, pp. 75

Graham, M.H., 1996. Effect of high irradiance on recruitment of the giant kelp Macrocystis (Phaeophyta), inshallow water. J. Phycol. 32, 903–906.

Graham, M.H., Harrold, C., Lisin, S., Light, K., Watanabe, J.M., Foster, M.S., 1997. Population dynamics ofgiant kelp, Macrocystis pyrifera, along a wave exposure gradient. Mar. Ecol. Prog. Ser. 148, 269–279.

Harris, L.G., Ebeling, A.W., Laur, D.R., Rowley, R.J., 1984. Community recovery after storm damage: a caseof facilitation in primary succession. Science 224, 1337–1338.

Harrold, C., Watanabe, J., Lisin, S., 1988. Spatial variation in the structure of kelp forest communities along awave exposure gradient. P.N.Z.I. Mar. Ecol. 9, 131–156.

Hay, C.H., 1979. Some factors affecting the upper limit of the southern bull kelp Durvillaea antarctica(Chamisso) Hariot on two New Zealand shores. J.R. Soc. N.Z. 9, 279–289.

Hay, M.E., 1981. The functional morphology of turf-forming seaweeds: persistence in stressful marinehabitats. Ecology 62, 739–750.

Hommersand, M.H., Fredericq, S., Freshwater, D.W., 1994. Phylogenetic systematics and biogeography of theGigartinaceae (Gigartinales, Rhodophyta) based on sequence analysis of rbcL. Bot. Mar. 37, 193–203.

Hommersand, M.H., Guiry, M.D., Fredericq, S., Leister, G.L., 1993. New perspectives in the taxonomy of theGigartinaceae (Gigartinales, Rhodophyta). Hydrobiologia 260–261, 105–120.

Kennelly, S.J., Underwood, A.J., 1993. Geographic consistencies of effects of experimental physicaldisturbance on understorey species in sublittoral kelp forests in central New South Wales. J. Exp. Mar. Biol.Ecol. 168, 35–58.

Kimura, R.S., Foster, M.S., 1984. The effects of harvesting Macrocystis pyrifera on the algal assemblage in agiant kelp forest. Hydrobiologia 166, 425–428.

Leonard, G.H., 1994. Effect of the bat star Asterina miniata (Brandt) on recruitment of the giant kelpMacrocystis pyrifera C. Agardh . J. Exp. Mar. Biol. Ecol. 179, 81–98.

Lobban, C.S., P.J. Harrison, 1994. Seaweed Ecology and Physiology. Cambridge University Press, Cambridge,pp. 366.

Lubchenco, J., 1980. Algal zonation in the New England rocky intertidal community: an experimental analysis.Ecology 61, 333–344.

Lubchenco, J., 1982. Effects of grazers and algal competitors on fucoid colonization in pools. J. Phycol. 18,544–550.

¨Luning, K., 1990. Seaweeds: Their Environment, Biogeography and Ecophysiology, Wiley, New York, pp.527.

McCleneghan, K., Houk, J.L., 1985. The effects of canopy removal on holdfast growth in Macrocystis pyrifera(Phaeophyta; Laminariales). Cal. Fish Game 71, 21–27.

Moreno, C.A., Sutherland, J.P., 1982. Physical and biological processes in a Macrocystis pyrifera communitynear Valdivia, Chile. Oecologia 55, 1–6.

Morris, R.H., D.P. Abbott, E.C. Haderlie, 1980. Intertidal Invertebrates of California. Stanford UniversityPress, Stanford, pp. 690.

Murray, S.N., Littler, M.M., 1978. Patterns of algal succession in a perturbated marine intertidal community. J.Phycol. 14, 506–512.

North, W.J., 1971. Introduction and background. In: North, W.J., Cramer, Lehre (Eds.) The Biology of GiantKelp Beds (Macrocystis) in California. Nova Hedwigia 32, 1–97.

North, W.J., James, D.E., Jones, L.G., 1993. History of kelp beds (Macrocystis) in Orange and San DiegoCounties, California. Hydrobiologia 260–261, 277–283.

Reed, D.C., Foster, M.S., 1984. The effects of canopy shading on algal recruitment and growth in a giant kelpforest. Ecology 65, 937–948.

Robles, C., Robb, J., 1993. Varied carnivore effects and the prevalence of intertidal algal turfs. J. Exp. Mar.Biol. Ecol. 166, 65–91.

Rosenthal, R.J., Clarke, W.D., Dayton, P.K., 1974. Ecology and natural history of a stand of giant kelp,Macrocystis pyrifera, off Del Mar, California. Fish. Bull. 72, 670–684.


Santelices, B., Ojeda, F.P., 1984. Population dynamics of coastal forests of Macrocystis pyrifera in PuertoToro, Isla Navarino, Southern Chile. Mar. Ecol. Prog. Ser. 14, 175–183.

Schonbeck, M., Norton, T.A., 1978. Factors controlling the upper limits of fucoid algae on the shore. J. Exp.Mar. Biol. Ecol. 31, 303–313.

Seymour, R.J., Tegner, M.J., Dayton, P.K., Parnell, P.E., 1989. Storm wave induced mortality of giant kelp,Macrocystis pyrifera, in southern California. Estuarine Coastal Shelf Sci. 28, 277–292.

Sousa, W.P., Schroeter, S.C., Gaines, S.D., 1981. Latitudinal variation in intertidal algal community structureand influence of grazing and vegetative propagation. Oecologia 48, 297–307.

Tegner, M.J., Dayton, P.K., Edwards, P.B., Riser, K.L., Chadwick, D.B., Dean, T.A., Deysher, L., 1995.Effects of a large sewage spill on a kelp forest community: catastrophe or disturbance?. Mar. Environ. Res.40, 181–224.

Thornton, E.B., Guza, R.T., 1983. Transformation of wave height distribution. J. Geophys. Res. 88,5925–5938.

Underwood, A.J., 1980. The effects of grazing by gastropods and physical factors on the upper limits ofdistribution of intertidal algae. Oecologia 46, 201–213.

Utter, B.D., Denny, M.W., 1996. Wave-induced forces on the giant kelp Macrocystis pyrifera (Agardh): fieldtest of a computational model. J. Exp. Biol. 199, 2645–2654.

von Ende, C.N., 1993. Repeated-measures analysis: growth and other time-dependent measures. In: Scheiner,S.M., Gurevitch, J. (Eds.) Design and Analysis of Ecological Experiments, Chapman and Hall, New York,pp. 113–137.

Watanabe, J.M., Harrold, C., 1991. Destructive grazing by sea urchins Strongylocentrotus sin a centralCalifornia kelp forest: potential roles of recruitment, depth and predation. Mar. Ecol. Prog. Ser. 71,125–141.

Wells, R.A., 1983. Disturbance mediated competition between Macrocystis pyrifera and articulated corallinealgae. In: Bascom, W. (Ed.) The Effects of Waste Disposal on Kelp Communities, Southern CaliforniaWater Research Project, Long Beach, California, 181–198.

ZoBell, C.E., 1971. Drift seaweeds on San Diego County beaches. Nova Hedwigia 32, 269–314.

factors determining the upper limit of giant kelp, macrocystis pyrifera agardh, along the monterey...

Documents