Variability in Species Composition Between Two Monterey Bay Kelp Forests

Catalina Macdonald BIOE 161 Kelp Forest Ecology October 24, 2011 Total= 3+22+38+36 Lit Cited= 4

Variability in Species Composition Between Two Monterey Bay Kelp Forests [3 but good for title to address the hypothesis you are testing… exposure differences]

Abstract [22] Understanding spatial and temporal variability is key to understanding the ecology of a community. We investigated this variability [in what??] in the context of two Monterey central California Bay kelp beds, Hopkins Marine Sanctuary and Point Lobos Reserve. We assessed whether site and day had an effect on species composition, whether there was an interaction affect between site and day, and whether these effects varied by taxa. We conducted surveys of a selection of algae, invertebrates and fish at both locations on October 11 and 13, 2011two days and compared the resulting species assemblages with PERMANOVA statistical analysis. We found that overall species composition varied by site but not by day or interaction effect between site and day. However, taxa groups varied in their responses – algae and invertebrates were affected by site but not by day or interaction effect, while fish were affected by day and interaction effect but not by site. This is likely because fish changed their behavior and visibility due to larger surge on the second day of sampling. Further study of the effect of weather on fish behavior could be helpful. The information gathered here can be of use in developing future studies at these sites. Introduction [38]

Understanding spatial and temporal variability is key to understanding the ecology of a community. Studies of spatial and temporal variability can describe something as small as how local species interactions like competition structures the distribution of species inside a kelp bed, or as large as themajor environmental drivers like effect of El Nino on infleunce kelp forests from Northern to Baja California (Watanabe 1984, Edwards 2003). An understanding of spatial variability can help assess causes of kelp bed mortality, describe how deforestation changes the structure of food webs, and can even inform ecological environmental policy, as in the assessment of Marine Protected Reserves (Cole and Syms 1999, Graham 2004, Paddack and Estes 2000). There are three types of variability in data; variability relating directly to the hypothesis, variability from a known source unrelated to the research question, which negatively affects the ability to test a hypothesis, and variability from an unknown source, which also negatively affects hypothesis testing. In some studies spatial and temporal variability directly address the research hypothesis (Cole and Syms 1999). In other studies, they are a known source of variability which must be understood and taken into account when addressing the true research question. We looked at variability between two sites – Hopkins Marine Sanctuary and Point Lobos ---- on October 11, 2011, and October 13, 2011. We hypothesized that species composition would differ between the sites, and that this difference would vary by taxa (algae, invertebrates, or fish). [why?] We also hypothesized that species composition would differ between days, also varying by taxa. [why?]Finally we hypothesized that there would be an interaction effect between day and site, which would also vary by taxa. [why?] In addressing our hypotheses we also looked at the adequacy of our sampling design, and the significance of this for future studies at these sites. We asked if the number of days a site was sampled mattered, and how this varied by taxa. [no, not really… we asked if any site effects we detected were consistent across sampling days and if not, if that possibly affected our ability to detect differences between sites] We examined how many transects should be sampled per site in order to maximize statistical power, and how this varied by taxa. We also looked at which species were best able to be adequately sampled, and what species traits characterized such specieswere related to sampling adequacy. Kelp beds are particularly well suited to studying the effects of site and sampling day on species composition. Kelp forests support a diverse community structure whose composition is influenced by many biotic and abiotic factors, such as species interactions, wave action, substrate composition, access to light and exposure to upwelling. These factors may vary by site and by day. Studying spatial and temporal variation between and within kelp forests is key to understanding these important ecosystems. In order to asses these questions we sent groups of divers toconducted underwater surveys at two sites, Hopkins and Lobos, to measure the relative densities of to collect quantitative data on an identical selection of algae, invertebrates and fishes. Two days later the divers switched sites and repeated their surveys. The resulting species composition data was analyzed with PERMANOVA for significant variability byWe tested for differences in the overall structure of the dcommunity, and separately for the algae, invertebrates and fishes by site, day, and any interaction effect between site and day., overall and separately for algae, invertebrates and fish. Overall we found significant variability by site, but not by day, with no interaction effect between site and day (p .001, p .328, p .375). Individually, algae and invertebrates also showed difference by site but not by day and no site by day interaction effect. Fish, however, showed no difference by site, but did show an effect by day and an interaction effect between site and day (p .319, p .043, p.015). Conclusion? Methods [36]

General Approach [2] In order to assess the difference in species composition between Hopkins and Lobos, we sent groups of divers to conducted underwater surveys at each site to collect quantitative data onto estimate the relative densities of a selection of algae, invertebrates, and fishes. [highlighted is detail that should be described in methods below.]Each buddy pair ran out two 30m transects, conducting a fish survey on the way out and a survey of invertebrates and algae on the way back. Species were recorded every 10m. Macrocystis stipes were counted at 1m off the bottom. Each transect constituted a replicate. To assess whether any differences we saw between sites were consistent in time, we sampled each site on two days.]Two days after the first survey the groups switched sites and repeated their surveys. We analyzed this data with PERMANOVA to account for variability by site, day, and interaction effect between site and day.

Study System [8] Hopkins Marine Sanctuary’s patches of granitic reef and sandy bottom support a kelp bed with a diverse assemblage of algae, fish and invertebrates. It faces north east and enjoys [????] some protection form from the area’s typical north west swell (Watanabe 1984). Point Lobos is located about ten miles down the coast, with similar granitic bottom composition, but is more exposed to swell (Paddack and Estes 2000). [lat, lon???] Both are no-take reserves… why was that useful for yur comparison?? We conducted our surveys on October 11 and 13, 2011. Normally we wouldn’t expect a difference in species composition over a period of two days, however, swell was substantially higher on the 13th (1.5m) than on the 11th (0.8m) (National Data Buoy Center 2011). While this didn’t affect algal or invertebrate assemblages, it changed the behavior of fish, which hid in cracks and crevices rather than remaining out in the open. [this is a result or conclusion… does not belong in Methods] We chose the study species based on their abundance, ease of identification and ecological significance. We chose some species that we expected to find at both sites (Macrocystis pyrifera, Balanophyllia elegans, Sebastes mystinus, Oxylebius pictus) and others that we expected to find at only one site (for example, Pterygophora californica and Eisenia arborea are present at Lobos but not at Hopkins).

Difference in Species Composition by Site To test for a difference in species composition by site we analyzed our data from Hopkins and Lobos with PERMANOVA. We used this analysis to compare the relative abundance of all species combined and separately for the algae, invertebrate and fish assemblages to determine if any differences were greater than that expected by random chance. We expected a p value less than .05, indicating a significant difference in species composition between sites. We also plotted examined Multidimensional scaling plots (MDS) for evidence of differences [in the above… spell out]. These graphical representations of similarity in assemblages compare each transect as a separate point on a relative difference graph, where points closest to each other have the most similar species composition and points farthest away from each other have the least similar species composition. Since we expected predicted [!!! You never expect a result!] to see a difference in species composition by site, we expected predicted the transects from Lobos to clump roughly together and the transects from Hopkins to clump roughly together. An even or random distribution of transects from Hopkins and Lobos would indicate that there is not a marked difference in species composition by site. Difference in Species Composition by Site Varies by Taxa Each taxa taxonomic group, (algae, invertebrates and fishes), was also analyzed independently. We still expected to see a p value of less than .o5 indicating a significant difference in species composition by site for each taxa, but we didn’t expect that these values would necessarily be the same. On the relative difference charts for each taxa, we still expected to see rough groupings of points corresponding to Lobos and groups corresponding to Hopkins, indicating the difference in species composition between the two sites. We didn’t expect these graphs to be the same shape as each other. I would expect the algae graph to show the tightest grouping of points, since algae is the easiest taxa to survey reliably, and there are distinct algal species differences between Hopkins and Lobos. I would expect the fish graph to have the loosest grouping of points, since fish are most difficult to survey and change their behavior and visibility form day to day.

Difference in Species Composition by Day We also ran PERMANOVA to determine whether species composition varies by day. A significant difference in species composition between our two sampling days would result in a p value less than .05, while a p value greater than .05 would indicate that the difference between days was not significant. On the graphs of relative difference, we expected days one and days two to be roughly grouped separately. A random distribution of days one an two would indicate that there was no difference in species composition by day.

Difference in Species Composition by Day Varies by Taxa We expected the difference in species composition by day to vary by taxa. We didn’t expect to see a difference in the composition of algae or invertebrates in only two days, so we expected that the p value for difference by day for those taxa would be greater than .05, indicating a nonsignificant difference. We expected the relative difference graphs for algae and invertebrates to show no groupings of day one and day two. However, since fish changed their behavior in response to the larger swell on October 13th, we did expect to see a day effect for fish, with a p value less than .05 indicating a significant result. We would expect the graph of relative difference for fish to show a rough grouping of day one and day two points.

Interaction Effect Between Site and Day A significant interaction effect between site and day would indicate that day one at Hopkins, day two at Hopkins, day one at Lobos, and day two at Lobos all had distinct species compositions. [no… it means the extent to which the relative abundance of species differed between days was different for the two sites.] A p value of less than .05 in the PERMANOVA results will indicate a significant interaction effect between day and site, while a p value of greater than .05 will indicate that the interaction effect between day and site is not significant. If there is an interaction effect between day and site, the relative difference graph will show distinct clumpings of points for day one at Hopkins, day two at Hopkins, day one at Lobos, and day two at Lobos. [no… how the two sites clumped by day would be different… but good that you are trying to describe how you would interpret the graph to test your hypothesis!]

Interaction Effect Varies by Taxa We expected the size of the interaction effect between site and day to vary by taxa. Since algae and invertebrates assemblages should not change substantially over two days, we didn’t expect to see an interaction effect for these taxa. A p value of greater than .05, and a relative difference graph that does not show four clumpings of points by day and site would indicate that the interaction effect is not significant for algae and invertebrates. For fish we did expect to see an interaction effect between site and day – that is, we expect the fish counts on day one at Hopkins, day two at Hopkins, day one at Lobos, and day two at Lobos to be distinct from each other. A p value of less that . 05, and a difference graph showing some sort of clumping by day and site, would indicate that there is an interaction effect between site and day for fish species.

Adequacy of Sampling Design We calculated the power index for all the taxa we sampled, and for each individual species, in order to assess the adequacy of our sampling design and assess which species made better candidates for this type of analysis. To assess the number of transects needed for adequate sampling, we graphed number of transects by power index for each taxa. The optimal number of transects occurs when this line approaches an asymptote – more transects no longer add substantially greater statistical power. In assessing which individual species are more appropriate for statistical analysis, a higher power index is considered better, but any power index over two is considered acceptable.

Results

Difference in Species Composition by Site We found a significant difference in species composition between Hopkins and Point Lobos (Table 1, PERMANOVA: site effect, p = .001). Transects from Hopkins generally showed a more similar species composition to each other than to transects from Lobos (Fig 1). The species that made the greatest contribution to this difference were Balanophyllia elegans, Pterygophora californica, and Cystoseira osmundacea, which contributed 31.5%, 12.3%, and 8.8% to the total difference in community composition (Table 2).

Table 1. PERMANOVA results for difference in total species composition by site, sampling day and site sampling day interaction effect. Difference is significant if p < .05

Figure 1. Relative difference graph for all species by transect. Each point represents one transect, points closer together have more similar species composition. Transects surveyed on day one are marked 1, transects surveyed on day two are marked 2.

ALL SPECIES - COMPARISON OF SITES Groups Hopkins & Lobos Average dissimilarity = 72.64

Group Hopkins Group Lobos Species Av.Abund Av.Abund Contrib% Balanophyllia elegans 28.67 9.88 31.54 Pterygophora californica 0.15 8.25 12.34 Cystoseira osmundacea 7.88 1.77 8.8 Chondracanthus corymbif 6.38 0.31 8.51 Patiria miniata 9.69 6.96 8.47 Dictyonueropsis reticula 3.39 0.03 4.8 Pachycerianthus fimbriat 3.01 0.07 4.46 Calliostoma ligatum 4.27 0.77 3.91 Dictyoneurum californicu 2.01 0.05 3.18 Eisenia arborea 0.15 2.31 2.95 Pisaster giganteus 2.58 0.41 2.73 Table 2. Percent contribution of individual species to total difference between sites. Average abundances are the average number of individuals counted per transect at Hopkins and Lobos. Difference in Species Composition by Site Varies by Taxa The difference in species composition between sites varies by taxa. Algal species composition between Hopkins and Lobos was significantly different (Table 3, PERMANOVA: algae site effect p=.001). Algal species assemblages on transects within Hopkins were generally more similar to each other than to algal species assemblages on transects within Lobos (Fig 2). Invertebrate species composition was also different by site (Table 3, PERMANOVA: invertebrate site effect p=.001). Invertebrate transects from Hopkins generally showed a more similar species composition to each other than to transects from Lobos (Fig 2). However, fish species composition did not vary significantly by site (Table 3, PERMANOVA: fish site effect p=.319). Fish assemblages on transects within Hopkins were not generally more similar to each other than to transects from Lobos (Fig 2). Table 3. PERMANOVA results for difference in algal, invertebrate and fish species composition by site, sampling day and site sampling day interaction effect. Difference is significant if p < .05

Figure 2. Relative difference graph for algal, invertebrate and fish species by transect. Each point represents one transect, points closer together have more similar species composition.

Transects surveyed on day one are marked 1, transects surveyed on day two are marked 2. Difference in Species Composition by Day We did not find an overall difference in species composition by sampling day (Table 1, PERMANOVA: day effect, p=.382). Transects sampled on October 11 had an overall species composition that was not markedly more similar to each other than to transects sampled on October 13 (Fig. 1).

Difference in Species Composition by Day Varies by Taxa The difference in species composition by day varied by taxa. Algal and invertebrate species assemblages did not differ significantly between sampling day one and sampling day two (Table 3, PERMANOVA: algae day effect, p=.724, invertebrate day effect, p=.505). Algal and invertebrate transects taken on day one were not more similar to each other than transects taken on day two (Fig. 2). In contrast, fish species composition was significantly different between day one and day two (Table 3, PERMANOVA: fish day effect, p=.043). Fish assemblages on transects from day one were generally more similar to each other than to assemblages on transects from day two (Fig. 2).

Interaction Effect Between Site and Day Site and day did not produce a significant interaction effect (Table 1, PERMANOVA: site x day, p=.375). In terms of overall species composition, transects surveyed on day one at Hopkins, day two at Hopkins, day one at Lobos, and day two at Lobos were not more similar within these groupings than across them (Fig. 1).

Interaction Effect Varies by Taxa The interaction effect produced by site and day varied across taxa. Algae and invertebrates species assemblages did not experience an interaction effect (Table 3, PERMANOVA: algae site x day, p=.926, invertebrate site x day, p=.569). Transects for day one Hopkins, day two Hopkins, day one Lobos and day two Lobos were not substantially more similar within groups than across groups (Fig. 2). Fish assemblages, however, did experience a substantial interaction effect between site and day (Table 3, PERMANOVA: fish site x day, p=.015). Fish assemblages on transects at Hopkins day one, Hopkins day two, Lobos day one and Lobos day two were closer to transects within the same group than to other transects (Fig 2).

Adequacy of Sampling Design Species with the highest statistical power indices included the algae Macrocystis pyrifera (9.5) and Cystoseira osmundacea (5), invertebrates Patiria miniata (7) and Tethya aurantia (5) and fish Sebastes atrovirens (3.5) and Embiotoca lateralis (3.5). Species with low power indices (below two) included Dictyoneuropsis reticulata, Strongylocentrotus franciscanus, and Sebastes chrysomelas (Fig. 3). Of the taxa groups, fish had the lowest average power indices, but even so all fish species except S. chrysomelas had adequate statistical power for analysis (Fig. 3). The optimal number of algal transects for statistical power is 24. For fish and invertebrates, however, the 32 transects surveyed were not enough to show how many transects should ideally be used in a study (Fig. 4). Since the entire variance in species composition for algae and invertebrates could be explained by site and not by day, we can conclude that two days of sampling were adequate for these taxa groups. Since nearly 80% of the variance in fish species is explained by day rather than site, it appears that more days of fish surveys are needed in order to adequately sample this environment (Fig. 5).

Figure 3. Power index of species, from right to left algae, invertebrates and fish. Higher power indices are preferable, any power index above 2 is adequate. Figure 4. Power index by number of transects for algae, fish and invertebrates. When a line approaches an asymptote the optimal number of transects has been reached.

Figure 5. Percent of variance explained by day vs. by source for algae, fish and invertebrates.

Discussion We found an overall difference in species composition between Hopkins and Point Lobos, but not an overall difference between the two sampling days or an interaction effect between day and site. However these results varied across taxa. Algae and invertebrate species assemblages differed substantially by site but not by day or interaction effect. Fish species composition, on the other hand, differed between sampling days and showed an interaction effect between site and day, but didn’t show a substantial difference between sites. The species contributing the greatest to the difference between the sites were Balanophyllia elegans, which was more abundant at Hopkins than at Lobos, Pterygophora californica, which was nearly absent at Hopkins but present at Lobos, and Cystoseira osmundacea, which was found more frequently at Hopkins than at Lobos. Proper sampling design can help eliminate unknown (and unwanted) variability from results. We found that, using 30m transects as replicates, 24 transects was sufficient in designing a survey of algal species. However, we were unable to determine the optimal number of replicates to use in survey of fish and invertebrates, which appear to be more than the 32 transects that our class surveyed. Relatively rare and cryptic species such as Sebastes chrysomelas and Strongylocentrotus franciscanus were the most difficult to sample sufficiently. Moving slowly and looking carefully into crevices while sampling might improve the validity of sampling species like these, as well as the validity of fish counts on high surge days when many species hide in crevices. I was not surprised by the results for algae and invertebrates, since it makes sense that species assemblages would vary by site but not change in a time period of only two days. The variability in fish assemblages in the space of only two days was more unexpected. Fish are highly mobile and may change their behavior and visibility based on weather conditions like surge. This had a large impact on our data gathering. These results suggest practical applications for the design of future experiments at these sites. Algae and invertebrates can be sampled adequately from day to day regardless of changes in surge. Studies of kelp forest fish assemblages will be more tricky. Anyone studying fish behavior or abundance will need to take into account the variability introduced by changes in their study species behavior depending on day and weather conditions. This becomes a known, but unhelpful, source of variability in addressing a hypothesis. A study specifically addressing how fish behavior and location changes day by day with different weather and surge conditions would be valuable. Works Cited [don’t capitalize words in a jopurnal article title]

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National Data Buoy Center – Station 46240 Real time data. 2011. Scripps Institution of Oceanography. www.ndbc.noaa.gov/data/realtime2/46240.spec.