1 Introduction: Redfish Rocks Marine Reserve SCUBA Habitat and Cover Surveys

SCUBA habitat and cover sampling characterizes benthic habitat cover and associated reef attributes (substrate type, relief) following PISCO protocols for uniform point count (UPC) surveys. These surveys provide insight into the structure and function of nearshore rocky reef communities in Oregon’s state waters. Divers record three types of information every meter along a 30m transect: substrate type, physical relief and identity of the organism attached to the reef. The percent-cover of space-occupying organisms is estimated for species that are directly attached to the primary substrate and includes non-motile benthic invertebrates and algae. Two depths are targeted for these surveys 12.5 and 20 meters. Write-ins are allowed, for species not included on PISCO data-sheets.

Our SCUBA habitat and cover sampling at Redfish Rocks began in 2010, two years before harvest restrictions began. Sampling is conducted in the marine reserve and one comparison area, Humbug (see methods Appendix for additional information about comparison area selection). We conducted five years of sampling at both sites, providing five years of data for our analysis and inclusion in the synthesis report.

Table 1: Redfish Rocks SCUBA Habitat and Cover Monitoring Efforts. Sample sizes represent the number of transects per year at each site

Data from SCUBA habitat and cover monitoring efforts can be used to explore questions about benthic habitat diversity, community composition and percent cover of various species groups. Questions about diversity and community composition can be used to help us understand how the benthic communities at these sites are similar or different. Data on percent cover can enable us to explore changes over time; and whether these changes are similar both inside the reserve and outside in comparison areas. For all data our main focus is exploring trends by site and year.

1.1 Survey Maps

1.1.1 Redfish Rocks Marine Reserve

Fig. 1: Map of SCUBA transect locations at Redfish Rocks Marine Reserve

1.1.2 Humbug Comparison Area

$Fig. 1: Map of SCUBA transect locations at Humbug Comparison Area$

Fig. 1: Map of SCUBA transect locations at Humbug Comparison Area

1.2 Research Questions

Substrate

Does substrate surveyed vary by site or year?

Relief

Does relief surveyed vary by site or year?

Benthic Cover

Does the diversity of benthic cover vary by site or year?
Does community composition of benthic cover vary by site or year?
- If yes, what species drive this variation?
Does aggregate benthic cover vary by site or year?

Focal Species Benthic Cover

Does focal species benthic cover vary by site or year?

2 Takeaways

Here we present a summary of our SCUBA habitat and cover monitoring results and our conclusions. Our conclusions are written with an evaluation of our sampling design, knowledge from prior marine reserves monitoring reports, and future directions of marine reserves monitoring in mind.

2.1 SCUBA Habitat and Cover Results Summary

The sampled substrate and relief habitat categories were very similar between the Redfish Rocks Marine Reserve and Humbug Comparison Area.

The SCUBA transects conducted at Humbug Comparison Area contained slightly more small boulder substrate and a greater proportion of relief in the “1<2m” category compared with Redfish Rocks Marine Reserve. Across all other substrate and relief categories, percent cover was statistically indistinguishable between sites.

Benthic cover diversity is similar between Redfish Rocks Marine Reserve and Humbug Comparison Area.

There were similar number of observed and estimated benthic cover categories between the marine reserve and its comparison area. Both sites also had similar diversity indices (effective number of cover categories), and there were minimal differences in unique, common, and rare cover categories.

Benthic cover community composition is similar between sites, but has some slight structuring by year.

Benthic cover transects were similar between the Redfish Rocks Marine Reserve and Humbug Comparison Area. However, the early years of sampling (2010/2011) were slightly different from the later years (2015, 2019). There were three groups driving the majority of the variation in benthic cover community composition - crustose coralline algae, encrusting red algae, and bare sand/cobble - with higher percent cover of CCA in later survey years (2015-2019), a peak in percent cover of encrusting red algae and bare sand/cobble in later survey years (2014/2015).

Three taxonomic groups dominate aggregate percent cover analyses: barnacles, bryozoans, and coralline algae.

Aggregate percent cover was dominated by three taxonomic groups: barnacles, bryozoans, and coralline algae. Temporal trends in percent cover were variable across taxonomic groups at the reserve and comparison area. There were minimal apparent differences in mean percent cover of taxonomic groups by site. Similarly, there was no difference in percent cover between sites for the single focal species sampled: crustose coralline algae.

We detected trends by year in crustose coralline algae at both the Redfish Rocks Marine Reserve and Humbug Comparison Area.

Increases in percent cover of crustose coralline algae were detected through time at both the marine reserve and Humbug Comparison Area.

2.2 Conclusions

Results of this report consistent with the initial Ecological Monitoring Report of 2010/2011.

In the first Ecological Monitoring Report of 2010-2011 (ODFW 2014), the SCUBA summary for Redfish Rocks concluded that the Redfish Rocks Marine Reserve is characterized by a high diversity of benthic cover taxa that occur at low abundances. Those results are the same as this report which includes an additional three years of monitoring data. In the 2010/2011 report, it was determined that Humbug was a suitable comparison area because it had similar percent cover of those dominant species and similar species diversity as the marine reserve, and the results of this report support that conclusion. Differences in taxa-specific percent cover by site are likely driven by subtle differences in sampled habitat.

The Humbug Comparison Area is an appropriate reference site to understand changes at the Redfish Rocks Marine Reserve

The conclusions of this report support previous monitoring report results from Redfish Rocks that the Humbug Comparison Area is an appropriate reference site to understand change at the marine reserve when using SCUBA habitat and cover surveys. We detected similar trends through time at both sites with focal species and there were similar measures of diversity and community composition at both sites.

SCUBA habitat and cover data provides valuable information about red algae not gathered in other monitoring tools.

We documented higher cover of red algae relative to brown and green algae at both sites. This is not surprising given the depths targeted by SCUBA surveys at the Redfish Rocks Marine Reserve and Humbug Comparison Area. Red algae are the most diverse group of seaweeds in the Northeast Pacific, and many are used by humans for a variety of purposes including food, medical research or in cosmetics. The SCUBA habitat and cover data provides useful information about the relative cover and change over time of red algae, not gathered in other monitoring tools. Encrusting red algae was found to be an important driver of variation in benthic cover community composition. Algal-dominated communities, when examined at the functional group level, can be more temporally stable and predictable than when examined at the species level (Steneck and Dethier 1994), suggesting the algal functional group data of the SCUBA habitat and cover surveys will be useful in evaluating community stability through time.

A move towards permanent sites or transects is needed to confidently detect future trends in habitat cover with SCUBA habitat and cover surveys

The initial Ecological Monitoring Report of 2010/2011 suggested 10 transects per site are needed to characterize the benthic habitat community, in most years we achieved that sample size at the Redfish Rocks Marine Reserve, but did not achieve that sample size at the Humbug Comparison Area. Limited sample sizes were a result of challenging logistics related to a small boat based survey method in Oregon’s nearshore environment; reducing required sample sizes needed to detect change such as moving to permanent sites or transects, would be beneficial because of these challenges. The Redfish Rocks Marine Reserve and its comparison area were dominated by relatively few benthic cover categories. The relatively low abundance of most benthic cover categories will preclude formal statistics for many of these cover groups; however, these data can still contribute to future community-level analyses exploring changes through time or species-habitat relationships.

While we were able to detect several species’ yearly trends at either one site or both despite limited sample sizes, the magnitude of these changes was quite large (2-6 fold increases). In order for our program to confidently detect future changes in benthic habitat cover at smaller magnitudes of change than those detected in this report, increased sampling effort or a move towards permanent sites or transects is needed. Increased sampling effort would likely require an increase to the research budget. With a better understanding of the sea states, visibility and communities of nearshore reefs, we can now select the appropriate permanent locations to focus monitoring efforts, maximizing efficiency in data collection and power to detect change over time.

SCUBA habitat and cover surveys provide valuable context to ecological patterns detected in other SCUBA surveys

The SCUBA habitat and cover surveys at Redfish Rocks Marine Reserve and Humbug Comparison Area allowed us to collect valuable information on benthic cover species and cover groups. This tool collects reliable, fine-scale resolution data on habitat cover that provides important context to ecological patterns detected, such as with changes to crustose coralline algae and sea urchin populations. SUCBA habitat and cover surveys are conducted simultaneously with SCUBA invertebrate surveys, and when water clarity allows, SCUBA fish transects are subsequently conducted along the same transect. While beyond the capacity for formal inclusion in this report, conducting community analyses that explore change through time and by site with multiple components of the ecosystem is feasible with the suite of SCUBA surveys conducted at this site

3 SCUBA Habitat and Cover Methods

SCUBA habitat and cover sampling is conducted in the Redfish Rocks Marine Reserve and Humbug Comparison Area following PISCO uniform point count (UPC) protocols, modified for diving safety in Oregon. Monitoring began in Redfish and Humbug in 2010 with unequal sampling effort; in the initial years there was a strong focus to place more sampling effort in the reserve to ensure adequate characterization of baseline conditions prior to closure. Since then, sampling effort targeted 6 days for both spring and fall monitoring, splitting effort between the marine reserve and Humbug Comparison Area based on ocean conditions.

The purpose of UPC sampling is to characterize benthic cover and associated reef attributes (substrate type, relief). Divers record three types of information beneath 30 points (one per meter mark), along a 30 meter transect: substrate type, physical relief and identity of the organism attached to the reef. The percent-cover of space-occupying organisms is estimated for species that are directly attached to the primary substrate and includes non-motile benthic invertebrates and algae. Two depths are targeted for these surveys 12.5 and 20 m.

In 2010/2011, scientific divers from the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) selected survey locations with the intent that these sites would be permanent. Selected locations were representative of available rocky reef habitat with kelp, within targeted depth ranges. As monitoring continued, the challenges and safety concerns of diving in Oregon’s nearshore (see Methods Appendix for more details) led to the inevitable need to select alternate locations. Due to unpredictable weather and visibility conditions, sites were selected from randomly generated points based on available rocky reef habitat within targeted depth ranges. The reality of these changes resulted in greater spatial coverage of the reef and is more reflective of a stratified random sampling design, rather than one with permanent sites.

The unit of replication is at the transect level. Two replicate transects were completed at each dive location. Only fully completed, independent transects were included in analysis. Targeted 5 meter transects from early years of sampling (2010-2011) were not included because evolving OR dive safety protocols prevented continued access to these sites. For additional details on data collection, please review documentation in the Methods Appendix.

3.1 Substrate

Substrate type is recorded as one of four categories: sand/gravel (<2cm), cobble (2-10cm diameter), small boulder (10cm-1m diameter), large boulder (1-4m), or bedrock (> 4m diameter).

3.1.1 Variation by Site and Year

We focused our analysis on the question of whether variation in substrate surveyed was driven by spatial (site) or temporal (year) factors. We did this through data visualizations with non-multidimensional scaling (nMDS) and an anova of mean percent cover of substrate class by site.

To explore variation by site and year, we used substrate data collected on SCUBA UPC transects; data were not exceedingly skewed so no transformation was used (Clarke et al. 2006).Percent cover of substrate types were calculated from SCUBA UPC count data (# points/ transect) so a similarity-based resemblance matrix was selected. A dummy variable (=1) was added prior to creating the resemblance matrix due to the high prevalence of zeros in the dataset. To visualize the data, we generated nMDS plots by site and year.

All analyses and graphs were made in PRIMERe version 7 with PERMANOVA extension.

3.1.2 Variation in Substrate Category By Site

To explore variation in substrate type category by site we ran a Kruskal-Wallis comparison, and plotted mean substrate category by site with 95% confidence intervals.

3.2 Relief

Relief is recorded as one of four categories: 0 < 10 cm, 10 cm < 1m, 1 <2 m, and > 2m.

3.2.1 Variation by Site and Year

We focused our analysis on the question of whether variation in relief surveyed was driven by spatial (site) or temporal (year) factors. We did this through data visualizations with non-multidimensional scaling (nMDS) and an anova of mean percent cover of relief category by site.

To explore variation by site and year, we used substrate data collected on SCUBA UPC transects; data were not exceedingly skewed so no transformation was used (Clarke et al. 2006). Percent cover of substrate types were calculated from SCUBA UPC count data (# points/ transect) so a similarity-based resemblance matrix was selected. A dummy variable (=1) was added prior to creating the resemblance matrix due to the high prevalence of zeros in the dataset. To visualize the data, we generated nMDS plots by site and year.

All analyses and graphs were made in PRIMERe version 7 with PERMANOVA extension.

3.2.2 Variation in Relief category By Site

To explore variation in Relief category by site we ran a Kruskal-Wallis comparison, and plotted mean substrate category by site with 95% confidence intervals.

3.3 Benthic Cover

We explored three concepts related to benthic cover - diversity, community composition and changes in abundance (percent cover).

Note: SCUBA UPC transects survey both biotic and abiotic habitat cover categories. We’ve used the term ‘species’ below to mean the combination of these cover categories.

3.3.1 Diversity

With SCUBA benthic surveys, we explored several concepts related to species diversity at a given site:

species richness
unique, common & rare species
diversity indices
diversity through time

3.3.2 Species Richness

To explore species richness at a given site, we reported total observed species richness and also calculated total estimated species richness.

To report total observed species richness at a given site we used incidence data across all sampling years because species turnover at each site (reserve or comparison area) likely occurs on timescales greater than one year.

To calculate estimated species richness, we used a rarefaction and extrapolation technique as described in Hsieh et al 2016, to calculate the effective number of species at each given site. This is the equivalent of calculating Hill diversity = 0. Hill numbers represent a unified standardization method for quantifying and comparing species diversity across multiple sites (Hill 1973), and they represent an intuitive and statistically rigorous alternative to other diversity indices (Chao et al 2014).

We used the same sampling based incidence data as used to document total observed species richness, using the iNext package in R to estimate the asymptote of the species accumulation curve, or the estimated total number of species observable by SCUBA UPC surveys at a given site. We also calculated confidence intervals associated with these rarefaction and extrapolation curves and can therefore compare across sites to explore similarity of total estimated species richness for a given sampling effort.

3.3.3 Unique, Common, and Rare Species

Richness alone does not sufficiently describe species biodiversity; additionally uniqueness, rarity and common species also shape and define concepts of biodiversity.

As a first step to exploring unique, rare and common species we generated species count tables. The species count tables include a total count for each species summed for all years by site, and for each year-site combination, as well as mean frequency of occurrence across all samples. This information can tell us both about how frequently the species is observed, as well as its relative abundance.

From the species count tables we identified rare species, as those with a frequency of occurrence of 10% or less (Green and Young 1993), and common species as those with a frequency of occurrence greater than 50% (in other words, the species is observed on one of every two transects). We also identified species that were unique to each marine reserve and comparison area.

3.3.4 Diversity Indices

To gain additional insight into species diversity, we explored several diversity indices by comparing Hill diversity numbers, also known as effective number of species, across sites using the iNEXT diversity package in R (Hsieh et al 2016). Hill numbers are parameterized by a diversity order q, which determines the measures’ sensitivity to species relative abundances (Hsieh et al 2016). Hill numbers include the three most widely used species diversity measures; species richness (q = 0), Shannon diversity (q=1) and Simpson diversity (q=2) (Hsieh et al 2016). We used sampling based incidence data with the iNext package in R, to plot rarefaction and extrapolation curves for each Hill number, and compare results across sites. We also calculated 95% confidence intervals associated with these rarefaction & extrapolation curves.

3.3.5 Diversity Through Time

Finally we explored how diversity changed through time. First we plotted each species yearly rarefaction curve against the total cumulative rarefaction curve for all years combined to determine if we had sampled appropriately to compare species diversity from year to year. When our sampling effort was not adequate to compare across years, we pooled data from all years to compare average transect diversity using an analysis of variance (ANOVA). This would provide useful information about site diversity for an average sampling transect..

All analyses and graphs were created in R v4.0.2, using the iNEXT and Vegan packages.

3.3.6 Community Composition

We focused our community composition analysis on the question of whether variation in benthic cover community composition was driven by spatial (site) or temporal (year) factors. We did this through both data visualizations with non-multidimensional scaling (nMDS) plots and with statistical tests such as principal coordinates analyses (PCO),multivariate ANOVA tests (PERMANOVA), and dispersion tests (PERMDISP). In addition to site and year, we also explored several cover category-specific drivers of variation.

To explore variation by site and year, we used percent cover data collected on SCUBA UPC transects with no transformation (Clarke et al. 2006). Percent cover was calculated from SCUBA UPC count data (# points/ transect) so a similarity-based resemblance matrix was selected. A dummy variable (=1) was added prior to creating the resemblance matrix due to the high prevalence of zeros in the dataset. To visualize the data, we ran a cluster analysis and generated nMDS plots by site and year.

To test the statistical significance in our data of variation by site and year we ran a permutational analysis of variance (PERMANOVA), using a mixed model with site and year as fixed effects factors. Initial explorations of the first two years of data resulted in no apparent trends by depth, even though surveys target two depths, therefore depth was considered a random effect. To explore if any significant results of the PERMANOVA were related to true differences in location or differences in dispersion of samples (either by site or year to year), we ran a PERMDISP, a distance based test for homogeneity of multivariate dispersions for any factors that were significant in the PERMANOVA (Anderson and Walsh 2013). If a factor was significant in the PERMANOVA but not the PERMDISP, then it can be inferred that the significance is related to a location effect, but not a dispersion effect. If the factor is also significant in the PERMDISP, then significance in the PERMANOVA is related to dispersion, but there may also be a location effect.

Beyond site and year, we explored cover category-specific drivers in the variation of benthic community structure. We extended our data visualization, by performing a vector analysis of benthic cover categories in the community, selecting only the categories with > 0.5 Pearson correlations (Hinkle et al. 2003). If more than four categories were identified, we only reported on categories with high ( > 0.7) Pearson correlations. We then generated percent cover plots of the identified categories to visualize their relationship to site or year. To better understand how these categories contributed to variation in the data, we ran a principal coordinates (PCO) analysis, using a Bray-Curtis resemblance matrix, which provides information on the percent of variation explained by each axis.

In addition to cover category-specific drivers of variation, we also explored the relationship between benthic cover community composition and environmental variables. We employed a multivariate model incorporating month, targeted survey depth (depth bin), substrate class, and relief category to test if these habitat or environmental variables explained significant variation across sites or years. Due to strict requirements of these variables needing to match with each specific biological sample, only samples that contained estimates of all the above variables were used for analysis.

An initial histogram of data revealed normal distributions so no transformation was employed. Benthic cover data are considered count-based data so a Bray-Curtis resemblance matrix was selected, with an addition of a dummy variable (=1). With these data a distance-based linear model (DistLM) and a distance-based redundancy analysis (dbRDA) were conducted to determine which variables may explain variation across sites or years (Legendre and Anderson 1999). DistLM is akin to a multivariate multiple regressions model where the relationship between a multivariate data cloud (resemblance matrix) and one or more predictor variables are analyzed and modeled. The dbRDA routine then visualizes the model and fits it into a multi-dimensional space. In the DistLM model AIC values were used as the selection criteria and a Best selection procedure was employed to find the best combination of variables with the lowest AIC value as the best model fit.

All analyses and graphs were made in PRIMERe version 7 with PERMANOVA extension.

3.3.7 Abundance

We explored changes in aggregate and focal species percent cover by site and year. For aggregate percent cover we summarized data across benthic cove taxonomic groups (similar to Lester et al. 2009) to identify broad scale differences in benthic cover by site and year. Based on the cover categories observed, we had 16 broad taxonomic groupings (Table X). A list of which categories are included in each taxonomic groupings is provided in Table X.

To determine which taxonomic groups (aggregate) were the most dominant, we summarized means and 95% confidence intervals grouped by site and as a timeseries. For focal species, we analyzed changes in percent cover by site and time with generalized additive mixed models (GAMMs). We modeled percent cover by using percent cover data with a quasi-binomial distribution to account for the metric (counts with an upper limit) and overdispersion (Zuur et al 2009). GAMMs were chosen to account for non-linear trends in percent cover by year detected in preliminary data exploration (Veneables and Dichmont 2004, Zuur et al. 2009). GAMMs were fitted using the mgcv package in R. Site was treated as a fixed categorical variable, while Year was continuous and smoothed with the thin-plate smoother ‘s()’ (Zuur et al 2009; Zuur 2012), grouped by Site, and k was restricted to 3 knots to prevent over-fitting. Depth-Bin was included as a random effect in the model to account for the sampling design targeting three fixed depths. We limited our modeling exercise to focus on Site and Year as these are two of the primary questions of interest. For species groups with very low percent cover across most sites and years, no statistical analyses were conducted as the data violated assumptions of the model framework.

There are two focal species groups for the OR Marine Reserves Ecological Monitoring Program recorded on benthic habitat surveys:

Crustose Coralline Algae; a species group
Articulated Coralline Algae; a species group

SCUBA UPC surveys record data on two types of coralline algae - articulated coralline algae and crustose coralline algae. We report on coralline algae as an aggregate group, but also report on these two functional groupings of coralline algae as focal species.

Focal species were chosen based on their ecological, economic or management importance. For more information please refer to the methods Appendix detailing focal species selection. Additional species beyond focal species were included for analysis when they were identified in community analysis as being an important driver of variation.

All analyses and data plots were created in R v4.0.2, using the mgcv (version 1.8-36), mgcViz and gratia packages. Models were structured in R as follows:

Percent Cover = mgcv::gam(Percent_Cover ~ Site + s(Year, by = Site, k = 3) + s(Depth_bin, bs = “re”), family = quasibinomial)

4 Redfish Rocks Results

SCUBA habitat and cover sampling efforts at Redfish Rocks and its comparison area resulted in five years of data collection, where varying sample sizes were collected per year (Fig. 2). With the exception of 2014, sampling efforts resulted in more transects completed in the marine reserve than in the Humbug Comparison Area.

Fig. 2: SCUBA habitat and cover monitoring efforts at the Redfish Rocks Marine Reserve and the Humbug Comparison Area resulted in varied sample sizes over the five years of data collection. Sample size is represented in number of transects.

Table 4: Redfish Rocks SCUBA Habitat and Cover Monitoring Efforts

$~$ $~$

4.1 Substrate

4.1.0.1 Variation by Site and Year

No apparent differences in substrate by site or year at the Redfish Rocks Marine Reserve and Humbug Comparison Area.

There was no structuring of substrate by site or year at the Redfish Rocks Marine Reserve and Humbug Comparison Area. (Fig. 4).

More small boulder habitat surveyed in the Humbug Comparison Area than the Redfish Rocks Marine Reserve

The only significant difference in substrate type by site was in the small boulder category, where Humbug had more small boulder than the marine reserve; for all other subtrate types there was no difference in mean percent cover by site (Fig. 3, table 5).

4.1.0.1.1 Site

Fig. 4: Results from nMDS plots with SCUBA data, demonstrating similarity in substrate at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year.

4.1.0.1.2 Year

Fig. 4: Results from nMDS plots for SCUBA data, demonstrating similairity in substrate at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year

$~$ $~$

4.2 Relief

4.2.0.1 Variation by Site and Year

No apparent differences in relief by site or year at the Redfish Rocks Marine Reserve and Humbug Comparison Area.

There was no structuring of relief by site or year at the Redfish Rocks Marine Reserve and Humbug Comparison Area. (Fig. 6).

More 1<2m relief in the Humbug Comparison Area than in the marine reserve

The only significant difference in relief category by site was in the 1 <2 m category, where Humbug had more of that relief category than the marine reserve; for all other relief categories there was no difference in mean percent cover by site (Fig XX, table X).

4.2.0.1.1 Site

Fig. 6: Results from nMDS plots with SCUBA data, demonstrating similarity in relief at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year.

4.2.0.1.2 Year

Fig. 6: Results from nMDS plots for SCUBA data, demonstrating similairity in relief at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year

$~$ $~$

4.3 Benthic Cover

4.3.1 Diversity

4.3.1.1 Species richness

Benthic habitat species richness is similar across the Redfish Rocks Marine Reserve and Humbug Comparison Area

Over the five years of sampling with SCUBA habitat and cover surveys, a total of 30 species (or species groups) were observed in the Redfish Rocks Marine Reserve (Table 7). The Humbug Comparison Area had similar total number of observed species (n = 27). These observed numbers of species richness are similar to the estimated numbers of total species richness.

library(kableExtra)
pna <- data.frame(Area = c("Redfish Rocks Marine Reserve",
                           "Humbug Comparison Area"), 
                  Observed_Richness = c("30","27"),
                  Estimated_Richness = c("31","31"),
                  LCL = c("30","27"), 
                  UCL = c("43", "55"))


  kbl(pna, caption = "Table 7: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits") %>% 
  kableExtra::kable_classic()

Table 7: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits
Area	Observed_Richness	Estimated_Richness	LCL	UCL
Redfish Rocks Marine Reserve	30	31	30	43
Humbug Comparison Area	27	31	27	55

$~$ $~$

Species rarefaction curves highlight that for any samples size, including those for any given year, the species richness among sites is very similar (Fig. 7). Both rarefaction curves appear to level off, suggesting saturation in species richness with this tool at these sites.

Fig. 7: Species rarefaction curves for the Redfish Rocks Marine Reserve and Humbug Comparison Area. Data are pooled across all years of sampling for each site.

$~$ $~$ $~$ $~$

4.3.1.2 Unique, common and rare species

Similar numbers of unique, common and rare benthic habitat species at the Redfish Rocks Marine Reserve and Humbug Comparison Area.

The Redfish Rocks Marine Reserve had similar numbers of unique species to its comparison area (Table 8) The marine reserve (n = 3) had similar numbers of common species to the Humbug Comparison Area (n = 4). All three common species at the marine reserve were also common species at the Humbug Comparison Area. The Redfish Rocks Marine Reserve and Humbug Comparison Area had similar numbers of rare species (n = 12, n = 9.)

Many of the other benthic species were not observed frequently resulting in low frequency of occurrence. Not all species were observed each year, for a summary of species frequency of occurrence over the years by site please see tables below.

Pooled species counts across all years and species counts by individual sampling year are included in the following tables:

$~$ $~$

4.3.1.2.1 Redfish Rocks Marine Reserve

Fig. 8: Relative frequency of occurrence of benthic habitat species observed at the Redfish Rocks Marine Reserve and Humbug Comparison Area from SCUBA transects. See separate tabs for each site.

4.3.1.2.2 Humbug Comparison Area

Fig.8 : Relative frequency of benthic habitat species observed at the Redfish Rocks Marine Reserve and Humbug Comparison Area from SCUBA transects. See separate tabs for each site.

$~$ $~$ $~$ $~$

4.3.1.3 Diversity Indices

The Redfish Rocks Marine Reserve and Humbug Comparison Area have similar diversity indices for benthic cover species

The effective number of species for the benthic cover community are very similar for the marine reserve and Humbug Comparison Area across all three diversity indices (Fig. 9).

$~$ $~$ $~$ $~$

Fig. 9: Comparing effective number of species (Hill diversity numbers) between the Redfish Rocks Marine Reserve and Humbug Comparison Area from SCUBA transects. Hill numbers include the three most widely used species diversity measures; species richness (q = 0), Shannon diversity (q=1) and Simpson diversity (q=2) (Hsieh et al 2016).

$~$ $~$ $~$ $~$

4.3.2 Diversity through time

We did not get enough samples to evaluate change in species diversity through time at the Redfish Rocks Marine Reserve and its comparison areas.

Species rarefaction curves by year for each site indicated that we did not sample enough on a yearly basis to compare changes in mean species richness through time (Fig. 10-11). When plotting mean species richness by year with 95% confidence intervals, the confidence intervals overlap suggesting more sampling is needed to detect any meaningful changes on an annual basis.

$~$ $~$

For an average transect, benthic habitat species diversity does not differ between the Redfish Rocks Marine Reserve and the Humbug Comparison Area.

When comparing mean species richness for an average transect of sampling, there was no difference between the marine reserve and Humbug Comparison Area (F: 0.719, p>0.05) (Fig. 12).

Fig. 12: Mean species richness by area with 95% confidence intervals at the Redfish Rocks Marine Reserve and Humbug Comparison Area from SCUBA transects.

$~$ $~$

4.3.3 Community Composition

$~$ $~$

4.3.3.1 Variation by Site and Year

Benthic communities were similar at the Redfish Rocks Marine Reserve and Humbug Comparison Area.

There was no structuring of benthic cover community composition data at the Redfish Rocks Marine Reserve and Humbug Comparison Area. (Fig. 13).

Slight structuring by year with benthic community composition data at the Redfish Rocks Marine Reserve and its comparison area.

There was slight structuring by year with benthic community composition data at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. Early survey years (2010-2011) appear slightly distinct from the last two survey years (2015, 2019) (Fig. 13).

While multivariate statistics indicate some differences by year and depth, they account for little of the total variation in the data

PERMANOVA results indicate that year, (p < 0.05) and depth were significant factors for benthic cover community composition with SCUBA habitat and cover data (Table 14). Year described the largest variation in the data out of all factors (~26%), while depth accounted for 10% of model variability. In comparison the residuals describe over 56% of the variation in the model. Therefore, while depth was significant, it is likely not biologically relevant because it describes such a small portion of the variation in the data, whereas year accounts for a larger proportion of the variability and is more likely to play a role in structuring benthic cover.

Table 14: PERMANOVA results

PERMDISP results indicates differences in dispersion by year (p = 0.002). The dispersion of 2019 was much smaller than all other years,and many of the significant pairwise comparisons are between 2019 and all other years (Table 15-16).This suggests the significance identified in the PERMANOVA is likely a combination of differences in dispersion and location between years.

$~$ $~$

4.3.3.1.1 Site

Fig. 13: Results from nMDS plots with SCUBA data, demonstrating similarity in benthic cover community composition at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year.

4.3.3.1.2 Year

Fig. 13: Results from nMDS plots for SCUBA data, demonstrating similarity in benthic cover community composition at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for site and year

$~$ $~$

4.3.4 Other drivers of variation

Three benthic cover categories drive the majority of variation in community composition data regardless of site.

We explored species-specific drivers of variation, and found that Crustose Coralline Algae (CCA), Encrusting Red Algae, and Bare Sand / Cobble were driving the majority of variation in the benthic cover data (Fig. 14). Principal coordinate analysis revealed that ~27% of the variation is explained by the benthic cover of Crustose Coralline Algae (CCA), along the x axis. The y-axis accounts for ~14% of the variability and is associated with Encrusting Red Algae and Bare Cobble / Sand (Fig. 14). Together the abundance of these three species accounts for over 41% of model variability.

4.3.4.1 PCO Vector Plot

Fig. 14: Results from species correlations and principal coordinate analysis demonstrating that Crustose Coralline Algae (CCA), Encrusting Red Algae, and Bare Sand / Cobble drive variation in community structure regardless of site at the Redfish Rocks Marine Reserve and its surrounding comparison area. See separate tabs for vector and bubble plots. Bubble color / size represents species-specific percent cover in each sample (species percent cover range indicated in legend).

4.3.4.2 PCO Bubble Plot

$~$ $~$

Benthic cover differences observed at Redfish Rocks and the Humbug Comparison Area are mostly attributable to differences in year

DISTLM results indicated all environmental variables were significant and the best model selected 8 of the 11 environmental variables(sand/gravel, low relief(0-10cm, 10cm-1m), high relief (>2m), year, month and depth bin) (Table 17). Year roughly correlated with the x axis and explained 48% of model variation and 20% of the total variation. This indicates that year does contribute to differences in benthic cover. The substrate types sand/gravel, bedrock, along with depth, roughly correlated with the y axis and explained 21% of the model variation but only explained 9% of the overall variation. This indicates that although significant the combination of hard bottom habitat, sand/gravel, and depth do not explain much of the variation in benthic cover.

Table 17: Redfish Rocks DistLM results

$~$ $~$

4.4 Abundance

$~$ $~$

4.4.1 Aggregate Percent Cover

Three main taxonomic groups dominate the relative abundance at both the Redfish Rocks Marine Reserve and Humbug Comparison Area.

Three main taxonomic groups dominate the relative abundance among taxonomic groups - barnacles, bryozoans and coralline algae - at the Redfish Rocks Marine Reserve and its comparison area (Fig. 15).

No apparent differences by site in mean percent cover of benthic habitat species.

No taxonomic groups had clear differences in 95% confidence intervals between the marine reserve and Humbug Comparison Area (Fig. 15)

Variable trends through time across broad taxonomic groups.

There were variable trends through time across broad taxonomic groups (Fig. 15). The majority of taxonomic groups show no clear trends over time (e.g. bivalves, hydrozoa). For a few groups, such as coralline algae, we see an increase through time at one or both sites. With other groups (e.g. tunicates) there are declines through time at one or both sites.

4.4.1.1 Mean Aggregate Percent Cover by Site

Fig 15: Aggregate percent cover of SCUBA benthic cover taxanomic groups at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for percent cover by site and timeseries plots.

4.4.1.2 Mean Aggregate Percent Cover Timeseries

Fig 15: Aggregate percent cover timeseries of SCUBA benthic cover taxanomic groups at the Redfish Rocks Marine Reserve and the Humbug Comparison Area. See separate tabs for percent cover by site and timeseries plots.

$~$ $~$

4.5 Focal Species

$~$ $~$

4.5.1 Articulated Coralline Algae

$~$

4.5.1.1 Percent cover

Too few observations of articulated coralline algae to detect differences in percent cover by site or year.

Percent cover of articulated coralline algae was very low across sites and years (Fig 16), so statistical analyses were not conducted.

$~$ $~$

4.5.1.1.1 Articulated Coralline Algae Percent Cover Timeseries

Fig 16: Articulated coralline algae percent cover timeseries with 95% confidence intervals, at the Redfish Rocks Marine Reserve and Humbug Comparison Area.

$~$ $~$

4.5.2 Crustose Coralline Algae

$~$

4.5.2.1 Percent cover

No significant difference in crustose coralline algae percent cover between the Redfish Rocks Marine Reserve and Humbug Comparison Area.

There was no difference in crustose coralline algae percent cover between the marine reserve and Humbug Comparison Area (p > 0.05; Table 18).

Significant yearly trends in crustose coralline algae percent cover at the Redfish Rocks Marine Reserve and Humbug Comparison Area

There were significant trends by year in crustose coralline algae percent cover at the Redfish Rocks Marine Reserve and the Humbug Comparison Area (p < 0.05; Table 19). At both the marine reserve and Humbug Comparison Area, percent cover increased through time (Fig. 17).

The random effect of depth was not identified as a significant component of variation (Table 19).

GAMM model results can be found in the links below:

$~$ $~$

4.5.2.1.1 Crustose Coralline Algae Percent Cover Timeseries

Fig. 17: Crustose coralline algae percent cover timeseries and GAMM model results with 95% confidence intervals, at the Redfish Rocks Marine Reserve and Humbug Comparison Area. See separate tabs for timseries and GAMM results.

4.5.2.1.2 Crustose Coralline Algae Percent Cover Modeled GAMM Results

$~$ $~$

4.6 Additional Species Percent Cover

4.6.1 Encrusting Red Algae

$~$

4.6.1.1 Percent Cover

No significant difference in encrusting red algae percent cover between the the Redfish Rocks Marine Reserve and the Humbug Comparison Area

There was no difference in percent cover of encrusting red algae between the marine reserve and Humbug Comparison Area (p > 0.05; Table 20).

Significant yearly trends in encrusting red algae percent cover at the Redfish Rocks Marine Reserve and the Humbug Comparison Area

There were significant trends by year in encrusting red algae at the Redfish Rocks Marine Reserve and Humbug Comparison Area (p < 0.05; Table 21). At both sites percent cover of encrusting red algae non-linearly increased through 2015 and then declined in the remaining years of sampling (Fig. 18).

The random effect of depth was identified as a significant component of variation (Table 21).

GAMM model results can be found in the links below:

$~$ $~$

4.6.1.1.1 Encrusting Red Algae Percent Cover Timeseries

Fig. 18: Encrusting red algae percent cover timeseries and GAMM model results with 95% confidence intervals, at the Redfish Rocks Marine Reserve and Humbug Comparison Area. See separate tabs for timseries and GAMM results.

4.6.1.1.2 Encrusting Red Algae Percent Cover Modeled GAMM Results

Fig. 18: Encrusting Red Algae percent cover timeseries and GAMM model results with 95% confidence intervals, at the Redfish Rocks Marine Reserve and Humbug Comparison Area. See separate tabs for timseries and GAMM results.

$~$ $~$

4.6.2 Bare Sand / Cobble

$~$

4.6.2.1 Percent Cover

Too few observations of bare sand / cobble to detect differences in percent cover by site or year.

Despite identification in the community analysis as a significant driver of variation in the benthic community, percent cover of bare sand / cobble was very low across sites and years (Fig. 19), so statistical analyses were not conducted.

$~$ $~$

4.6.2.1.1 Bare Sand / Cobble Percent Cover Timeseries

Fig. 19: Bare sand / cobble percent cover timeseries with 95% confidence intervals, at the Redfish Rocks Marine Reserve and its associated comparison areas.

$~$ $~$

5 References

Anderson M.J., Walsh D.C.I. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs 83(4): 557-574.

Chao A., Gotelli N.J., Hsieh T.C., Sander E.L., Ma K.H., Colwell R.K., Ellison A.M. (2014) Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol Monogr 84:45–67

Choat, J. H., & Robertson, D. R. (2002). Age-based studies. Coral reef fishes: Dynamics and diversity in a complex ecosystem, 57-80.

Clarke K.R., Chapman M.G., Somerfield P.J., Needham H.R. (2006). Dispersion-based weighting of species counts in assemblage analyses. Mar Ecol Prog Ser 320: 11-27.

Green, R. H., & Young, R. C. (1993). Sampling to detect rare species. Ecological Applications, 3(2), 351-356.

Hill M.O. (1973) Diversity and Evenness : A Unifying Notation and Its Consequences. Ecology 54:427–432.

Hinkle D.E., Wiersma W., Jurs S.G. Applied Statistics for the Behavioral Sciences. 5th ed. Boston: Houghton Mifflin; 2003

Hsieh, T. C., Ma, K. H., & Chao, A. (2016). iNEXT: an R package for rarefaction and extrapolation of species diversity (H ill numbers). Methods in Ecology and Evolution, 7(12), 1451-1456.

Legendre P. and Anderson M.J. (1999). Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecological Monographs 69(1): 1-24.

Lester, S. E., Halpern, B. S., Grorud-Colvert, K., Lubchenco, J., Ruttenberg, B. I., Gaines, S. D., … & Warner, R. R. (2009). Biological effects within no-take marine reserves: a global synthesis. Marine Ecology Progress Series, 384, 33-46.

Love, M. S., & Yoklavich, M. M. (2006). Deep rock habitats. In The ecology of marine fishes (pp. 253-266). University of California Press.

ODFW (2014). Oregon Marine Reserve Ecological Monitoring Report 2010-2011. Oregon Department of Fish and Wildlife. Marine Resources Program. Newport Oregon. 1-131.

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/.

Steneck R.S., & Dethier M.N. (1994). A Functional Group Approach to the Structure of Algal-Dominated Communities. OIKOS 69(3), 476-498.

Venables, W. N., & Dichmont, C. M. (2004). GLMs, GAMMs and GLMMs: an overview of theory for applications in fisheries research. Fisheries research, 70(2-3), 319-337.

Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. Springer Science & Business Media.

Zuur, A. F. (2012). A beginner’s guide to generalized additive models with R (pp. 1-206). Newburgh, NY, USA: Highland Statistics Limited.

### This can be a useful function to play a sound at the end of a long script

#beepr::beep()