Total = 4+ 22+38+36+Lit cited= 6

ANALYSIS OF SOURCE VARIANCE ASSOCIATED WITH TEMPORAL AND SPATIAL VARIATION ON QUANTIFIEDIn the ABUNDANCE of ALGAE, INVERTEBRATE AND FISHES

ABUNDANCE IN KELP FORESTS.[4]

Roxanne S. Beltran

ABSTRACT [22]

Spatial comparisons of species abundances in ecosystems provide insights into the biotic and abiotic processes that structure communities. Kelp forests provide structure for a diverse group of organisms that interact to form important trophic linkages. Different habitats and environmental conditions can facilitate great changes in species assemblages. One potentially important source of variation in the structure of kelp forest communities is their relative exposure to ocean swell. Because of Hopkins’ protection from swell and Lobos’ protection greater exposure to from swell, we hypothesized that species with more mobility with be more present at Hopkins, and species with less mobility would be more present at Lobos. Kelp forests provide structure for a diverse group of organisms that interact to form important trophic linkages. Different habitats and environmental conditions can facilitate great changes in species assemblages. In order to determine the differences in biotic compositions at these two sites, we used observationalconducted underwater visual surveys using SCUBA surveys to quantify the density of 29 species of algae, invertebrates and fishes. The results from this study showWe found that for species with limited or no mobility (i.e. algae and sessile invertebrates), species abundance varies by surveyd more by site rather than by sampling date, and that for species with more mobility (i.e. fishes), species abundance can varyvaired by site or and by day. Variance in immobile species composition between sites was most likely due to different swell exposure levels between sites.

INTRODUCTION [38]

Spatial comparisons of species abundances in ecosystems provide insights into the biotic and abiotic processes that structure communities. Information regarding species abundance and distribution is crucial in the understanding of species interactions, assemblages, predation, habitat partitioning, resource competition and biological indicators. [[instead of much of the following… it would have been better to strength this point by giving examples from the literature that used spatial comparisons to generate inferences about ecological processes responsible for those difference in

community structure (e.g., Ling et al, Duggins et al. Estes et al, Edwards, and others)… See your fourth paragraph… would have been good to have that material here. ]] Information about the composition of ecosystems can be obtained through quantitative surveys, which are more accurate and reliable, or qualitative surveys, which are quicker and more applicable to broader spatial scales. The object of quantitative sampling as related to ecology is to provide an accurate numeric estimate of some individual, population, or community parameter, while qualitative sampling methods provide a rough estimate of relative abundances. Organizations such as the Tagging of Pacific Predators (TOPP) or the Partnership for Interdisciplinary Studies of our Coastal Oceans (PISCO), which assess natural and atypical fluctuations in community assemblages, aid in maintaining a baseline understanding of the distribution patterns of biota.

By regularly sampling the composition of biological communities, scientists can attribute variations to specific causes. In general, there are three types of variability associated with scientific data collection and analysis: (1) Explained variability used to directly test the hypothesis, which allows scientists to measure the effect of certain sources on the results, (2) Explained variability associated with a known source that negatively affects testing of the hypothesis, and (3) Unexplained variability that negatively affects testing of the hypothesis. The third explanation for variability is typically controllable through effective survey design and sufficient replication.

Using quantitative sampling methods as a form of estimation can produce variance in the true value of a parameter, driven by some or all of the following: (1) The degree of intrinsic spatial or temporal variability in the parameter, (2) The distribution of individuals, (3) The size of the sampling unit, (4) The number of replicates that are used in estimation and (5) Bias in the distribution of replicates in space or time (Carr and Raimondi 2011). In the initial experimental design, we took the possible effects of the aforementioned sources into account.

Previous studies indicated that study sites within similar locations, similar substrate composition and with same species pools available would allow the examination of the causes of between-site differences (Harrold 2008). Wave exposure effects on growth in several algae, invertebrate and fish species has been studied at various sites (Harrold 1988) in the absence of episodic disturbance events. Additionally, much work has been done examining the effect of disturbance events on species assemblages (Edwards 2004, Sala 2002).

In order to assess the effects of spatial and temporal variance on mean species composition, we used an observational, field study approach to address the following three questions: (1) What is the difference in species composition between Hopkins and Point Lobos, and how do any differences between these sites differ between is variance observed between the algae, inverts and fishestaxas? (2) What is the difference in species composition between sampling days, and how do any differences between sampling days differ between the algae, inverts and fishesis variance observed between taxas? (3) Is there an interaction effect seen between the site locations and sampling days in affecting species composition, and do these interactions differ between the algae, inverts and fishes is variance observed between taxas?

We chose to sample the study sites over the course of two days, to account for any possible effects of temporal variation on mean abundance values. We didn’t expect a large difference in values due to the short time interval between sample dates. We hypothesized that if the conditions remained relatively similar, species assemblages would not vary with temporal variance.

[[this paragraph would have been best in the Study Systems section of the Methods] We chose to sample two distinct sampling sites, Lobos and Hopkins, because of both their similarities and dissimilarities. Both sites were Marine Protected Areas, and were protected from inconsistencies in species distribution and abundance from human fishing, which has the potential to impede population restoration in functionally important ecosystems (Steneck 2002). The sites were located in relatively close proximity to one another, and were easily accessible to the SCUBA divers. While Hopkins was very sheltered from swell, however, Lobos was very exposed to swell. For these reasons, Hopkins and Lobos were good candidates for analyzing the effect of spatial variance on species assemblages.

All species studied [by who???] are important components of the kelp forest system, and are crucial to the transfer of energy between trophic levels (Graham 2004). Variation in assemblages of the three taxonomic groups [what three groups???] is important to quantify because of the different mobilities mobility and abundances of the various taxa. We hypothesized that species with more mobility with be more present at Hopkins, and species with less mobility would be more present at Lobos.

By providing an accurate numeric estimate of some individual, population, or community parameter, we will be able to understand the effect of temporal and spatial variance on species assemblages.

METHODS

General approach [36]

To assess the effects of temporal and spatial sources of variability on the distribution and abundance of kelp forest species, we conucted underwater visual surveys employed along swath transects using SCUBA and quantified the abundance density of targeted algae, invertebrate and fish species in two subtidal kelp forest sampling sites over two separate days. Data was were collected between approximately 0900 and 1100 on two sampling days, 11 October 2011 and 13 October 2011.

Transects were sampled by pairs of diversEach dive team collected data at a designated survey marker at 5m intervals. At each specific marker, dive teams reeled out 30m transect tapes. When devising a sampling scheme, it is important to consider which scale will be appropriate to the species being sampled. [yes!! But not necessary to make that point here.] We used a swath transect method, which was appropriate for the large, countable, conspicuous target species. Each individual diver was responsible for surveying a (1m wide x wm length x 1m high) volume of water on one side of the transect tape, while the other diver in the team surveyed the same volume on the other side of the transect tape.[excellent description.] Fishes were sampled while as each

transect was setting out the transect tapedeployed, to avoid artifactsvague… disturbing the fish, altering their distribution and underestimating their densities, and b. Both invertebrates and algae were sampled while the transect tape was reeled in. Fish were sampled in number per unit volume (give the volume here), while invertebrates and algae were sampled in a planar area (give the area here) . Each diver recorded the number of individual algae, fish or invertebrate species in 10m increments along the 30m transect tape. Observations were recorded in smaller increments in order to allow finer resolution of depth-related changes. To conduct observational surveys, wWe quantified the abundances density of seven species of algae, nine species of fishes and thirteen species of invertebrates using 26 dive teams on SCUBA (Table 1). For each transect, dive teams recorded the following data: (1) Time start, (2) Time finish, (3) Depth start, (4) Depth finish. The data collected during the two days were used for all analyses.

Study system

Systems Sites that were surveyed in this study consisted ofincluded Hopkins Reef at Hopkins Marine Station in Monterey, California, United StatesSA (36°37'13"N Latitude, 121°54'16"W Longitude) and Whalers Cove at Point Lobos State Natural Reserve in MontereyCarmel Bay, California, United States (36°31'1.56"N Latitude, 121°56'33.36"W Longitude).

Both Hopkins and Point Lobos exemplified typical kelp forests, with high densities of perennial Macrocystis pyrifera (giant kelp) as the structural base of the tremendously diverse ecosystem. Kelp forests are common among central California coastlines, and provide a fast-growing source of food, nutrients and habitat for a multitude of species in different taxas [taxa is already plural](Watanabe 1984). You are referring to the specific species you counted, so indicate that and direct reader to Table 1.] Fishes included both benthos and mid-water species, invertebrates ranged from sessile to sedentary to mobile individuals and colonies, and algaes consisted of canopy, subcanopy and benthic assemblages (Table 1). While the ecosystem at Hopkins was protected from swell, the ecosystem at Point Lobos was exposed to swell. [others found papers that described this… would be good to cite more studies for this description.]

[[Because the study was more about site difference than temporal differences, would have been good to reverse the order and descritons of these hypotheses.]]Effect of sampling day variance on species composition

To examine the effect of sampling day variance on species composition, we tested the hypothesis that sampling day does not have a significant effect on species composition. We used multi-dimension scaling graphs (MDS) as visual representations of multi-dimensional PERMANOVA analyses [don’t link like this… the MDS is a separate analysis in itself] , because high proximity between individual points suggests less variance [need to exaplain this interpreatation better.] . In our study, each point on the MDS graphs represents a single transect, la beled with respectidentified by to site and

day. The stress value, found in the upper right-hand corner of the graphs, defines how well the data fitsvariation in assemblage structure is explained by site and day, measured as (1-r)2. A lower stress value suggests better results, and an ideal stress value would be less than 0.2.

Additionally, the table ofWe also used a PERMANOVA results is shown to supplement the MDS visual graphs. The table considers the following to test for three types of variance: (1) SI = sample site, (2) SA = sample day, (3) SIXSA = interaction strength.[but this ection is ONLY about day effects! Indicate that youused the day effect in the PERMANOVA to test for differences between days] A P-value of less than 0.05 would show a significant effect of one each of these sources type of variance on species compositions.

[what about the variance components analysis? How was that used?]

Effect of sampling day variance on species composition by taxa

To examine separately determine how the algae, inverts and fishes differed… the effect of sampling day variance on species composition by taxa, we tested the hypothesis that different species would have different relative variances per sampling day. We used a multi-plot graph to graphcompared the mean number of individual organismsdensity per transect as a function of each species, to determine which species contributed to differences within each taxonomic grouping (algae, fishes, inverts)taking spatial and temporal variances into account.

Effect of survey site variance on species composition

To examine the effect of survey site variance on species composition, we tested the hypothesis that survey site does have a significant effect on species composition. How? What aspect of the MDS, PERMANOVA and variance components analysis did you use for this specific hypothesis?? [each of these sections should be more in-depth]

Effect of survey site variance on species composition by taxa

To examine the effect of survey site variance on species composition by taxa, we tested the hypothesis that different species will have different relative variances per survey site.

Interaction [of what and what?] effects on species composition

To examine interaction effects between spatial and temporal variance on species composition, we tested the hypothesis that it was important to consider both spatial and temporal effects, because an alteration in species composition could be attributed to one or the other, or both, and the effect of one could overwhelm our ability to see the effect of the other.

Interaction effects on species abundances by taxa

To examine interaction effects between spatial and temporal variance on composition of different taxonomic groups, we tested the hypothesis that different species would have different relative variances per sample day.

Validation of experimental design

[combine all of this into one paragraph] To examine the adequacy of the our experimental design, we performed a power index analysis on the number of transects sampled, number of sites, number of sampling days and number of species sampled. Power index analyses were performed by calculating (mean*n)/stdev. A power value greater than 2 is ideal, and greater power index suggests more validity in results. [how?]

When plotted this power index as a function of number of transects we sampled. in this study, aAn asymptote would suggest that the number of transects sampled provided an adequate representation of natural species compositions and provide maximum statistical power. An inadequate number of transects would not be sufficient for acceptance or rejection of our hypothesis, but an excess number of transects would be a waste of precious time underwater.

When plotted as a function of species composition, a higher power index value would suggest that a certain species is a good candidate for quantitative sampling, while a lower power index value would suggest that a certain species is a poor candidate for quantitative sampling.

Additionally, we performed a supplementary box plot on the sites sampled to examine the depth of transect as a function of site and therefore understand possible differences between the two sites.

RESULTS

Effect of survey site variance on species composition

When plotted in a Multi-Dimensional Scaling graph, species varied as a function of site rather than as a function of day. Additionally, the transects at Hopkins were all closer together than the transects at Point Lobos, suggesting higher site similarity at Hopkins than Point Lobos (Figure 1). The stress value was satisfactory. Additionally, there was a significant (<0.05) P-Value for site, suggesting that site did have an effect on mean species abundance (Figure 5).

Effect of survey site variance on species composition by taxa

Algae species varied as a function of site rather than as a function of day (Figure 6, Figure 9). Additionally, the transects at Hopkins were all closer together than the transects at Point Lobos, suggesting higher site similarity at Hopkins than Point Lobos

(Figure 2). Algae had the lowest stress value of all taxa. For algae, suvey site did not have a significant effect on mean species abundance (Figure 5). Algae had the least amount of site variance and interaction effect of the three taxa (Figure 5). The highest effect of site was seen in species that were clearly more abundant at one site, including Condracanthus corymbifera at Hopkins and Eisenia arborea at Point Lobos (Figure 6).

Invertebrate species varied as a function of site rather than as a function of day (Figure 8, Figure 9). Additionally, the transects at Hopkins were all closer together than the transects at Point Lobos, suggesting higher site similarity at Hopkins than Point Lobos (Figure 3). Invertebrates had a satisfactory stress value. There was more variation seen between Point Lobos than between Hopkins.

When plotted in a Multi-Dimensional Scaling graph, fish species did not appear to correlate with survey site or sampling date. Fish species varied more by sampling date than by survey site (Figure 9). However, at Point Lobos, less variation was observed at different sample days – notice that the day one transects are clustered on the left side of the graph while the day two transects are clustered on the right side of the graph (Figure 3). Fish species had a satisfactory stress value. Fish did not have a significant site variation value (Figure 5).

Effect of sampling day variance on species composition

When plotted in a Multi-Dimensional Scaling graph, species varied as a function of site rather than as a function of day. Additionally, the transects at Hopkins were all closer together than the transects at Point Lobos (Figure 1). The stress value was satisfactory. The P-value for sampling day was not significant, so sampling day did not have a significant effect on species abundance (Figure 5).

Effect of sampling day variance on species composition by taxa

Sampling day did not have a significant effect on algae species abundances (Figure 5).

Sampling day did have a significant effect on fish species abundances. In fishes, variability between sampling days overwhelms our ability to fully understand the variability between survey sites (Figure 7). While there are no apparent correlations between species abundance and variance, species with a higher average number per transect appear to have less variance.

Interaction effects on species composition

There was no significant interaction effect on the species sampled (Figure 5).

Interaction effects on species composition by taxa

There was no significant interaction effect for algae. Algae also had the least amount of site interaction effect of the three taxa (Figure 5). There was a significant interaction effect for fish species.

Validation of experimental design

While algae asymptotes, suggesting that 32 is an adequate number of transects for ideal statistical power, fish and invertebrates do not (Figure 10). Fish and invertebrates did not asymptote, suggesting that more transects may have produced more accurate results.

All algae species had power index values of >2, suggesting satisfactory statistical power. The most powerful species were Cystoseira osmundacea, Macrocystis pyrifera, Pterygophora californica and Eisenia arboria. Of these, P. californica, C. osmudacea and E. arborea were the most common species, suggesting that species with higher abundances have higher statistical power (Figure 11).

All fish species had power index values of >2, suggesting satisfactory statistical power, except for Sebastes chrysomelas, which tended to be less abundant (Figure 7). The most powerful fish species were Sebastes atrovirens, Sebastes mystinus and Embiotoca lateralis, which were all the most common fish species.

In invertebrates, power index values clearly correlated with species abundances (Figure 13). While Patiria miniata, Tethya californica and Urticina piscivora were the most powerful and most abundant species, Strongylocentrotus purpuratus was the least powerful and least abundant of the invertebrates.

Overall, depth varied more at Hopkins than it did at Point Lobos (Figure 14).When Macrocystis pyrifera stipes per transect and per individual were plotted

using variance components analysis, they varied more by site than by day. There were more stipes per plant and stipes per transect at Hopkins than at Point Lobos (Figure 15). For number of stipes per individual plant, there was a significant effect of site on species abundances, but no significant sample day or interaction effect P-Value (Figure 16). For number of stipes per transect, there was a significant effect of site on species abundances, but no significant sample day or interaction effect P-Value (Figure 17).

DISCUSSION

Overall, species assemblages varied as a function of site rather than as a function of day, which was the expected result, because of the dissimilarities in wave exposure of the two survey sites and because of the close temporal proximity of the sampling days. The similarity in transects at Hopkins was most likely due to the fixed cable that was used as a basis for all perpendicular transects. The transects at Point Lobos showed more dissimilarity than at Hopkins, especially between different sampling days, because the perpendicular swath transects were not permanent nor standardized between days.

For algae and invertebrate species, survey site had a significant effect on assemblage, while sampling day did not. The invertebrate and algae species sampled in this study tended to be much less mobile than the fish species, which accounts for the

nonsignificant effect of sampling day – the species most likely wouldn’t be completely removed from an area over the course of 48 hours. The fish species, however, ranged from somewhat stagnant to very mobile, and had the potential to relocate. For fish species, sampling day had a significant effect on assemblage, while survey site did not.

The number of transects sampled was adequate for algae but not ideal for fish or invertebrates. A future study could use similar methods with an increased number of transects sampled, in order to produce a satisfactory asymptote in power values.

Species composition or taxa is a multivariate term, so once variation has been observed, we need to look at different species to see how they contributed to the differences. In this study, species that were more prevalent and recognizable to divers (ex: M.pyrifera, P. miniata) had less variance in abundance.

Wave action has been shown to factor into the distribution of various kelps, as the morphology of the plants can be more or less prone to damage from increased wave action (Program et al. 2006). Kelp-associated invertebrates and fishes can then vary in site distribution with differential wave exposure. In our study, there were more stipes per plant and stipes per transect at protected Hopkins than at exposed Point Lobos, suggesting perhaps that M. pyrifera is more vulnerable to wave action than other kelp species and is found less often at sites that are more exposed to wave action.

The results from this study show that for species with limited or no mobility, species abundance varies by survey site rather than by sampling date, and that for species with more mobility, species abundance can vary by site or by day. Variance in immobile species composition between sites was most likely due to different swell exposure levels between Point Lobos and Hopkins.

REFERENCES CITED

Carr, Mark M. and P. Raimondi, P. Class Lecture. Kelp Forest Ecology. University of

California, Santa Cruz, CA. 22 October 2011.

Ebeling, A.W., Laur, D.R., Rowley, R.J. 1985. Severe storm disturbances and reversal of

community structure in a southern California kelp forest. Marine Biology 84:

287-294.

Edwards, M.S. 2004. Estimating scale dependency in disturbance impacts: El Niños and

giant kelp forests in the Northeast Pacific. Oecologia 138: 436-447.

Graham MH, BS Halpern, MH Carr. 2008. Diversity and dynamics of Californian subtidal

kelp forests. Pp. 103-134 in McClanahan, TR and GR Branch (eds), Food Webs

and the Dynamics of Marine Benthic Ecosystems, Oxford University Press.

Graham, M.H. Effects of local deforestation on the diversity and structure of southern

California giant kelp forest food webs. Ecosystems. 7:341–357. 2004.

Harrold, C, J Watanabe, and S Lisin. “Spatial Variation in the Structure of Kelp Forest

Communities Along a Wave Exposure Gradient.” Marine Ecology 9.2 (1988) :

131-156.

Program, L. O., Pew, T., Trusts, C., Springer, Y., Hays, C., Carr, M., Mackey, M., et al.

(2006). Ecology and Management of the Bull Kelp , Nereocystis luetkeana : A

Synthesis with Recommendations for Future Research. Management.

Reed, D.C., and M.S. Foster. 1984. The effects of canopy shading on algal recruitment

and growth in a giant kelp (Macrocystis pyrifera) forest. Ecology 65: 937-948.

Reed, Daniel C., David R. Laur, and Alfred W. Ebeling. 1988. Variation in Algal Dispersal

and Recruitment: The Importance of Episodic Events. Ecological Monographs

58:321–335.

Sala, E. & M.H. Graham. 2002. Community-wide distribution of predator-prey

interaction strength in kelp forests. Proceedings of the National Academy of

Sciences of the USA 99: 3678-3683.

Steneck, R. S., Graham, M. H. , Bourque, B. J., Corbett, D., Erlandson, J. M, Estes, J. A.,

and Tegner, M. J. Kelp forest ecosystem: biodiversity, stability, resilience and

their future. Environmental Conservation. 29(4): 436-459.

Watanabe, James M. 1984. The Influence of Recruitment, Competition, and Benthic

Predation on Spatial Distributions of Three Species of Kelp Forest Gastropods

(Trochidae: Tegula) Ecology 65:920–936.

FIGURES AND TABLES

IMAGE 1. Permanent cable running north-south approximately 300 meters offshore. Dive teams descended at the buoy marking the 100m cable mark, and subsequently swam to their meter mark on the cable. Each transect was reeled out shallow-deep (east-west) at 90degrees.

IMAGE 2. Point Lobos State Reserve. Swath transects were sampled in Whaler’s Cove. The high exposure of the cove provides very minimal protection from surge.

ALGAECystoseira osmundaceaChondracanthus corymbiferaDictyoneurum californicumMacrocystis pyriferaDictyonueropsis reticulataPterygophora californicaEisenia arboreaFISHESOxylebius pictusHexagrammos decagrammusSebastes mystinusSebastes carnatusSebastes chrsomelasSebastes atrovirensEmbiotoca jacksoniEmbiotoca lateralisDamalichthys vacca

INVERTEBRATESPatiria miniataPycnopodia helianthoidesPisaster brevispinusPisaster giganteusUrticina piscivoraUrticina lofotensisPachycerianthus fimbriatusBalanophyllia elegansTethya aurantiaCalliostoma ligatumLoxorhynchus grandisHaliotis rufescensStrongylocentrotus fransiscanusTABLE 1. Target species list. The target species were selected prior to the study, and were thought to provide a general representation of the community as a whole. Species are all easily quantified and include a variety of levels of mobility, abundances and sizes.

FIGURE 1. Multi-dimensional scaling graph depicting species as a function of site and day. Each point is one transect on one given day. Abundance of species varies by function of survey site (distinct colored symbols), not sampling day (distinct numbers over symbols). Hopkins transects show more proximity than Lobos transects. Satisfactory stress value.

All SpeciesTransform: Fourth rootResemblance: S17 Bray Curtis similarity

SiteHopkinsLobos

2

1

1

2

1

21

2

1

1

2

1

2

2

2

1

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21

2

11

1

2

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2

1

2

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2

2 2D Stress: 0.18

FIGURE 2. Multi-dimensional scaling graph depicting algae species as a function of site and day. Abundance of species varies by function of survey site (distinct colored symbols), not sampling day (distinct numbers over symbols). Hopkins transects show more proximity than Lobos transects. Satisfactory stress value.

AlgaeTransform: Fourth rootResemblance: S17 Bray Curtis similarity

SiteHopkinsLobos

2

1

1

212

12

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1

2122

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11 2

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111

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2D Stress: 0.11

FishTransform: Fourth rootResemblance: S17 Bray Curtis similarity

SiteHopkinsLobos

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11

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21

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1 2

1

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2D Stress: 0.14

FIGURE 3. Multi-dimensional scaling graph depicting fish species as a function of site and day. Abundance of species does not vary by function of survey site (distinct colored symbols) or sampling day (distinct numbers over symbols). However, an interaction effect is seen between the Lobos site and sampling day 1. Satisfactory stress value.

FIGURE 4. Multi-dimensional scaling graph depicting invertebrate species as a function of site and day. Abundance of species varies by function of survey site (distinct colored symbols), not sampling day (distinct numbers over symbols) with the exception of one outlier on sample day 2 at Hopkins site. Satisfactory stress value.

Inverts Transform: Fourth rootResemblance: S17 Bray Curtis similarity

SiteHopkinsLobos

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1

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212

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2

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2

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2 2D Stress: 0.18

FIGURE 5. PERMANOVA table of results for all species and each taxa, depicting the value of mean abundances as a function of site, day and interaction effect. P-values can be seen in the right-hand column. Sources with P-values of <0.05 (shown in bold font) had a significant effect on species composition.

Stats (Permanovas)

Site

Sam

ple

Day

Hopkins

1

0

2

4

6

8

10

12

Num

ber

Lobos

Chond

racan

thus

corym

bif

Cystos

eira o

smun

dace

a

Dictyo

neuru

m califo

rnicu

Dictyo

nuero

psis

reticu

la

Eisenia

arbo

rea

Macroc

ystis

pyrife

ra

Pteryg

opho

ra ca

liforni

ca

Species

2

Chond

racan

thus

corym

bif

Cystos

eira o

smun

dace

a

Dictyo

neuru

m califo

rnicu

Dictyo

nuero

psis

reticu

la

Eisenia

arbo

rea

Macroc

ystis

pyrife

ra

Pteryg

opho

ra ca

liforni

ca

Species

0

2

4

6

8

10

12

Num

ber

FIGURE 6. Multiplot graph depicting algae species composition as a function of site and day. Algae affected by site, and minimally by day.

Site

Sam

ple

Day

Hopkins

1

0.0

0.1

0.2

0.3

0.4

0.5

Num

ber

Lobos

Damali

chthy

s vac

ca

Embiotoc

a jac

kson

i

Embiotoc

a late

ralis

Hexag

rammos

deca

grammus

Oxyleb

ius pi

ctus

Sebas

tes at

rovire

ns

Sebas

tes ca

rnatus

Sebas

tes ch

rysom

elas

Sebas

tes m

ystin

us

Species

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Damali

chthy

s vac

ca

Embiotoc

a jac

kson

i

Embiotoc

a late

ralis

Hexag

rammos

deca

grammus

Oxyleb

ius pi

ctus

Sebas

tes at

rovire

ns

Sebas

tes ca

rnatus

Sebas

tes ch

rysom

elas

Sebas

tes m

ystin

us

Species

0.0

0.1

0.2

0.3

0.4

0.5

Num

ber

FIGURE 7. Multiplot graph depicting fish species composition as a function of site and day. No apparent site or sampling day effect.

Site

Sam

ple

Day

Hopkins

1

0

10

20

30

40

Num

ber

Lobos

Balano

phyll

ia ele

gans

Callios

toma l

igatum

Halioti

s rufe

scen

s

Loxo

rhync

hus g

randis

Pachy

ceria

nthus

fimbri

at

Patiria

mini

ata

Pisaste

r brev

ispinu

s

Pisaste

r giga

nteus

Pycno

podia

helia

nthoid

es

Strong

yloce

ntrotu

s fran

c

Tethya

auran

tia

Urticina

lofot

ensis

Urticina

pisc

ivora

Species

2

Balano

phyll

ia ele

gans

Callios

toma l

igatum

Halioti

s rufe

scen

s

Loxo

rhync

hus g

randis

Pachy

ceria

nthus

fimbri

at

Patiria

mini

ata

Pisaste

r brev

ispinu

s

Pisaste

r giga

nteus

Pycno

podia

helia

nthoid

es

Strong

yloce

ntrotu

s fran

c

Tethya

auran

tia

Urticina

lofot

ensis

Urticina

pisc

ivora

Species

0

10

20

30

40

Num

ber

FIGURE 8. Multiplot graph depicting invertebrate species composition as a function of site and day. Invertebrate effected by site, and minimally by day.

FIGURE 9. Variance components analysis depicting the percentage variance as a function of source, contrasting survey site and sampling date. Clear sources of variance are seen in algae and invertebrate species, but not in fish species.

FIGURE 10. Power index as a function of number of transects (n=32 transects) divided by taxa.

Day SiteSource of Variance

0

20

40

60

80

100

Per

cent

of V

aria

nce

Exp

lain

ed

Algae

Day SiteSource of Variance

0

20

40

60

80

100

Per

cent

of V

aria

nce

Exp

lain

ed

Fish

Day SiteSource of Variance

0

20

40

60

80

100

Per

cent

of V

aria

nce

Exp

lain

ed

Inverts

AlgaeFishInverts

0 5 10 15 20 25 30 35Number of Transects

0

1

2

3

4

5

Pow

er In

dex

0 5 10 15 20 25 30 35Number of Transects

0

1

2

3

4

5

Pow

er In

dex

0 5 10 15 20 25 30 35Number of Transects

0

1

2

3

4

5

Pow

er In

dex

Chondr

acanthus

corym

bif

Cystos

eira osm

undacea

Dictyo

neuru

m califo

rnicu

Dictyo

nuero

psis re

ticula

Eisenia ar

borea

Macro

cysti

s pyri

fera

Pteryg

opho

ra ca

liforn

ica

Species

1

2

3

4

5

6

7

8

9

10

Pow

er In

dex

A

FIGURE 11. Power index values as a function of algae species.

Damalichthy

s vac

ca

Embiotoca

jack

soni

Embiotoca

latera

lis

Hexagra

mmos deca

grammus

Oxylebius

pictus

Sebastes a

trovir

ens

Sebastes c

arnatus

Sebastes c

hrys

omelas

Sebastes m

ystin

us

Species

1

2

3

4P

ower

Inde

x

F

FIGURE 12. Power index values as a function of fish species.

Balanoph

yllia elegans

Calliosto

ma liga

tum

Haliotis

rufes

cens

Loxo

rhyn

chus

gran

dis

Pachyc

erianth

us fim

briat

Patiria m

iniata

Pisaste

r bre

vispinu

s

Pisaste

r giga

nteus

Pycnopo

dia helia

nthoides

Strongylo

centro

tus fra

nc

Tethya aur

antia

Urticina lo

fotensis

Urticina pisc

ivora

Species

1

2

3

4

5

6

7P

ower

Inde

x

I

FIGURE 13. Power index values as a function of invertebrate species.

FIGURE 14. Box plot depicting depth of swath transects as a function of site.

FIGURE 15. Variance components analysis depicting Macrocystis pyrifera stipes per transect (left) and stipes per individual (right) as a function of site and day.

Hopkins LobosSite

33

34

35

36

37

38

39

40

41

Dep

th o

f Tra

nsec

t (fe

et)

21

Sample Day

Hopkins LobosSite

0

10

20

30

40

50

60

Stip

es p

er In

divi

dual

(med

ian)

Hopkins LobosSite

0

10

20

30

40

50

60

70

Stip

es p

er tr

anse

ct (m

edia

n) Macrocystis Stipe counts

FIGURE 16. Analysis of variance table of results. In right-hand column, P-value of stipes per transect as a function of site, day and interaction effect for transect stipes.

FIGURE 17. Analysis of variance table of results. In right-hand column, P-value of stipes per transect as a function of site, day and interaction effect for individual plant stipes.

Analysis of Variance

Source Type III SS df Mean Squares

F-Ratio p-Value

Sample Day 75.281219 1 75.281219 0.421419 0.520035Site 4,733.02990

21 4,733.02990

226.495156 0.000008

Site*Sample Day

19.308347 1 19.308347 0.108087 0.744092

Error 6,966.864710

39 178.637557

Stipes per Transect

Analysis of Variance

Source Type III SS df Mean Squares

F-Ratio p-Value

Sample Day 96.605548 1 96.605548 2.342362 0.134888

Site 988.906583 1 988.906583 23.977680 0.000022

Site*Sample Day

110.903272 1 110.903272 2.689034 0.110000

Error 1,443.497861

35 41.242796

Stipes per Individual