1 Introduction: Redfish Rocks ROV Invertebrate Survey Report

The remotely operated vehicle (ROV) is our most complex monitoring tool. The ODFW Marine Reserves program partners with the ODFW Marine Habitat Project, a partner research group within ODFW to conduct this type of monitoring in the marine reserves. The ROV is driven by an operator from a boat, controlled via an umbilical cable. The ROV can swim up, down, and around obstacles and follow along a transect line, like a SCUBA diver. It collects high-definition video that is later used to analyze fish, invertebrates and benthic habitat structure within the marine reserve and its associated comparison areas. The ROV is perfect for surveying rocky habitats all the way out to the deepest parts of the reserves.

ROV surveys were initiated prior to reserve closure at the Redfish Rocks in 2010, two years before harvest restrictions began. Sampling is conducted in the marine reserve and its associated comparison areas, Humbug and Orford Reef (see methods Appendix for additional information about comparison area selection). We sampled at these sites over several years, with varied levels of success in achieving usable data - data that met requirements for view, visibility, and benthic habitat type (rocky substrates). These efforts results in four years of usable data for our analysis and inclusion in the synthesis report.

Data from ROV monitoring efforts can be used to explore questions about invertebrate relative abundance from a non-extractive, fisheries-independent tool used to survey other deep reefs off the Oregon and the US West Coast. We can use metrics for diversity and community composition derived from these data to compare across monitoring tools, to understand tool bias, or to validate trends in relative abundance observed across tools. Data on relative abundance also enables us to explore how invertebrate communities change 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 ROV transects at the Redfish Rocks Marine Reserve

Fig. 1: Map of ROV transects at the Redfish Rocks Marine Reserve

1.1.2 Humbug Comparison Area

Fig. 1: Map of ROV transects at the Humbug Comparison Area

Fig. 1: Map of ROV transects at the Humbug Comparison Area

1.1.3 Orford Reef Comparison Area

Fig. 1: Map of ROV transects at the Orford Reef Comparison Area

Fig. 1: Map of ROV transects at the Orford Reef Comparison Area

1.2 Research Questions

Diversity

  • Does species diversity vary by site or year?

Community Composition

  • Does community composition vary by site or year?
    • If yes, what species drive this variation?

Aggregate Abundance

  • Does aggregate density vary by site or year?

Focal Species Abundance

  • Does focal species density vary by site or year?
  • Does focal species size vary by site or year?

2 Takeaways

Here we present a summary of our ROV invertebrate 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 ROV Invertebrate Results Summary

Invertebrate species diversity was similar between Redfish Rocks Marine Reserve and its associated comparison areas.

Species invertebrate diversity is similar between the Redfish Rocks Marine Reserve and Humbug and Orford Reef Comparison Areas as evidenced by the results of multiple analyses in this report. They have similar numbers of estimated and observed species, as well as similar numbers of unique and common species. There were some differences in the Hill Diversity indices among sites, indicating slight differences in the concepts of species rarity and evenness. We did not obtain enough sampling at all sites to assess changes through time.

Invertebrate community composition differed minimally by site but varied more substantially by year, largely linked to changes in sea star and urchin densities.

Variability in invertebrate community composition among transects was highest at the Orford Reef Comparison Area and the lowest at the Redfish Rocks Marine Reserves.The most substantial among-year differences in invertebrate community composition were seen between 2010 and 2016. Of the species with the strongest correlations to community composition, 2010 was characterized by high densities of Pink Stars that declined to almost zero in all other survey years. 2016 was characterized by high densities of Blood Stars and Burrowing Cucumbers, whereas 2018 and 2019 were characterized by increasing Red Urchin densities.

Notable trends at the aggregate level for invertebrates include substantial reductions in density of nudibranchs, sponges and sea stars, and overall rise in urchins.

Our aggregate summary of 12 broad taxonomic groups detected notable trends in four categories at all sites. Two of these categories represent the impact of sea star wasting with a decline in sea stars and an increase in sea urchins. The causes for the declines in nudibranchs and sponges are unclear; noting that data for nudibranchs are known to be incomplete because of their small size and sensitivity to viewing conditions.

Some species density differences between the marine reserve and its comparison areas were detected, but no clear patterns emerge.

Among the sea stars assessed, two (the Sunflower Star and the Pink Star) were less abundant at the marine reserve than at one or both comparison areas before they were lost to Sea Star Wasting Syndrome. A third, the Blood Star, survived the wasting pandemic and tended toward greater abundance at the marine reserved than at the Humbug Comparison Area, though a Site * Year interaction reflected inter-site patterns that were inconsistent among years, especially relative to the Orford Reef Comparison Area. Four other species also had Site * Year interactions; of these, Rock Scallops and White Plumose Anemones tended to have the highest densities at the marine reserve, CA Sea Cucumbers tended to have the lowest densities at the marine reserve, and Short Red Gorgonians tended to have intermediate densities at the marine reserve. Finally, Red Sea Urchins had a more consistent site difference, with the Orford Reef Comparison Area having the highest densities, the Humbug Comparison Area the lowest, and the marine reserve intermediate.

By 2019, clear declines in sea star species, coupled with an increase in Red Sea Urchins from baseline levels in 2010 detected at all sites

Despite irregular sampling, by 2019 there were clear declines in three sea star species - Sunflower Star, Blood Star, and Pink Star - from initial baseline densities in 2010. These declines were apparent at the Redfish Rocks Marine Reserve and its two comparison areas. A rise in Red Sea Urchins from initial baseline levels in 2010 were also detected in 2019 at all three sites. These results highlight the impact of sea star wasting disease at these sites.

Invertebrate density relationships with depth varied by site.

Species densities were influenced by relationships with depth to varying degrees and were mostly site specific. Invertebrate responses to depth tended to be more variable among sites than did fish responses, perhaps reflecting greater sensitivity of invertebrates to the depth distribution of specific habitats and substrates.

Differences among years in sampling depth and season may have affected the observed density trends and should be considered in future sampling efforts.

Differences in sampling depth among years may have influenced the observed density patterns. Further analysis of the existing data relative to depth may help identify individual species and depth ranges for which sampling stratification by depth should be pursued. Even though most invertebrates are sessile, some (e.g. Burrowing Sea Cucumbers) were observed at greater densities in years sampled during spring. Futher assessment of potential seasonal effects is warranted.

Invertebrate density relationships with percentage of boulder substrate varied by site.

Species densities were influenced by relationships with percentage of boulder substrate to varying degrees and were site specific. No species had the same relationship with boulder substrate at all sites.

2.2 Conclusions

This is the first ecological monitoring report to summarize ROV invertebrate data from the Redfish Rocks Marine Reserve and its associated comparison areas.

Despite completion of the first ROV surveys in 2010, this report provides the first summary of ROV invertebrate monitoring data at the Redfish Rocks Marine Reserve. This report documents the general similarity of the marine reserve to its two comparison areas - Humbug and Orford Reef. Overall, the Humbug Comparison Area tended to show greater similarity to the marine reserve, a pattern consistent with its closer proximity, more similar depth profile, and exposure to a more similar set of oceanographic conditions. From a diversity and community composition perspective the differences among sites are minimal relative to changes over time. From an abundance perspective, some species exhibited density differences between the Redfish Rocks Marine Reserve and one or both comparison areas. Among these species some exhibited inter-annual changes in concert across sites suggesting that assessments of change over time are viable despite sometimes differing baseline abundance levels. However, others with more dynamic populations and site-specific responses (e.g. species that exhibited Site * Year interactions) may need more careful evaluation and follow-up with analysis with respect to depth, season, and substrate before drawing conclusions about comparisons between sites.

We are able to detect natural, interannual variability in density for select species with ROV sampling.

Even though ROV sampling occurs at infrequent intervals, there were species-specific, inter-annual patterns detected. Changes in sea star densities provide a more comprehensive picture of subtidal Sea Star Wasting Syndrome at deeper reef locations at each site (especially for Sunflower Stars and Pink Stars, which were essentially eradicated), and a large recruitment event for Red Sea Urchins was detected; Red Sea Urchins were the only analyzed species that maintained density differences from 2010 across all subsequent sampling intervals. For several other focal species, increasing densities were detected in 2016, that by 2019 had reduced to levels similar to 2010 surveys (e.g. Rock Scallop, CA Sea Cucumber). For a number of species, there was a change in density detected during at least one sampling interval.

Despite a wealth of information from ROV monitoring surveys, the continuity of future sampling is uncertain without any increase in support

From an ROV monitoring perspective, sampling occurs at irregular intervals because of the high cost of chartering vessels for ROV sampling and the small budget of the Marine Reserves Program. Much of the sampling that was conducted between 2010 and 2019 was enabled by successfully pursuing external funding for various research topics and capitalizing on the funding to conduct the research at Marine Reserve and comparison area sites. A federal grant provided funding for charter costs in surveys prior to 2017, but that grant source became unavailable from that point onward. Adding data that was funded and planned externally to marine reserves monitoring (e.g. 2018) provided a useful snapshot of species densities for the marine reserve and Orford Reef Comparison Area that would otherwise not be available, allowing us to feel more confident in interpreting trends over time at irregular sampling intervals. The ODFW Marine Reserves and Habitat programs have struggled with reporting results of monitoring data at regular intervals because of the small budget and staff of both programs. Despite these challenges, a wealth of information lies in the data gathered from ROV monitoring, including the ability to understand species-habitat relationships in both the marine reserve and its two comparison areas. Importantly, the program has accumulated significant methodological and analytical infrastructure (e.g. well-developed protocols, databases, video review skill, statistical and interpretive skill, and computer code) that can facilitate much more efficient cycles of data collection and reporting in the future. The Marine Reserves program will attempt to continue data collection at the Redfish Rocks Marine Reserve and surrounding comparison areas with the ROV, ideally if a new grant source could be identified or its base budget increased. Without new funds, continued sampling, even at irregular intervals, as well as analysis and reporting will continue to be a challenge for the program.

3 ROV Invertebrate Methods

Detailed methods documenting the survey design, field sampling methods, and video review methods for the Remotely Operated Vehicle (ROV) video sampling are presented in the ROV Methods Appendix. The following sections briefly summarize the ROV methods and describe the data treatment and analytical approach for invertebrates for this synthesis.

3.1 ROV video sampling

Remotely Operated Vehicle (ROV) video sampling is conducted in the Redfish Rocks Marine Reserve, Humbug Comparison Area and Orford Reef Comparison Area. Monitoring began in 2010, and occurred at irregular intervals because of the high cost of chartering vessels for ROV sampling and the small budget of the Marine Reserves Program. Sampling occurred once in each sampled year - either in spring or fall, with variable effort across years depending on the availability of external funds to support vessel charters. Each day, approximately 8-14 500 m long transects were surveyed from a list of randomized transects that intersect a minimum proportion of mapped rocky substrate at the appropriate depths (see ROV Methods Appendix for more detail on transect selection and sampling protocols).

All video data collected from ROV sampling were reviewed and filtered to meet data quality criteria before inclusion in analysis. Segments of transects with poor visibility, terrain obstructions, or piloting actions that invalidate the assumptions of belt transect sampling were excluded. Transect widths were derived through measurement of the on-screen width of a pair of parallel lasers. Along-transect distance was derived from an acoustic ROV tracking system. Transect length and width were multiplied to calculate the total area viewed, forming the denominator for organism density calculations.

3.2 Data filtering

To account for varying suitability of portions of video for assessing the density of small invertebrates, data were filtered according to a categorization called ViewScale (see ROV Methods Appendix). This limited the use of video data to discrete segments along transects that provided consistent ability to detect macroinvertebrates such as seastars and sea urchins, categorized as ViewScale = 3.

Substrate types were classified during video review according to primary and secondary habitats continuously along each transect (see ROV Methods appendix). The various observed combinations of primary and secondary substrate types were reduced for this analysis to two overall categories: “soft substrates”, composed of substrate groups sand and gravel, and “hard substrates”, composed of substrate groups cobble, boulder, and bedrock.

For the invertebrate analyses, we excluded all soft substrate data in order to compare similar hard-substrate habitats across sites and years. Preliminary assessment of the soft substrate associated invertebrate data showed that differences among transects in the proportion of soft substrate habitat sampled had the potential to skew the perception of differences in invertebrate densities and community composition among sites and years. Invertebrate densities in soft substrates were plotted in comparison to hard-substrate densities, and the plots (not included in this report) show very few observations of hard-substrate-associated species (e.g. rock scallops) in transect portions tagged as soft substrate, confirming the utility of the substrate data for filtering out soft-substrate portions of transects.

To explore invertebrate relationships with specific substrate types within the overall hard substrate category, we included the percent of each transect’s data that was categorized as boulder as a potential covariate in the analyses. Boulders are of special interest because of their ability to provide distinct habitats such as large protected interstitial spaces.

3.3 Data aggregation and sample units

Invertebrate densities were calculated and analyzed at the transect scale. Invertebrate counts and total viewed area were summed across all ViewScale 3, hard-substrate portions of transects, so that transects are the sample unit for invertebrate analysis. Aggregating the discrete segments of ViewScale 3 data across each transect helps reduce the effect of any inaccuracies in the transect view area estimate, because summing the total view area across the transect helps average out over- and under-estimated view areas for specific points along the transect. Densities are presented in timeseries plots as weighted mean densities with confidence intervals calculated from weighted standard errors. The weighting variable was the view area used for the density calculation. This procedure effectively reduces the influence of smaller transects (e.g. those that encountered less rocky habitat) on the overall mean density.

For additional details on data collection, video review and data filtering, please review documentation in the ROV Methods Appendix.

3.4 Data Analysis

3.4.1 Diversity

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

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

3.4.1.1 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 each site (reserve or comparison area) likely has a species pool larger than can be sampled in any one year. We excluded unidentified species from the summaries. Species richness metrics are highly sensitive to survey effort. While the ROV survey targets transect lengths of 500m, habitat and ocean conditions result in highly variable transect lengths. In order to overcome the confounding factor of transect size on species richness, diversity rarefaction curves were standardized by the number of individuals observed (Gotelli and Colwell 2001; Chao et al 2014).

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 individual-based abundance data and the iNext package in R to estimate the asymptote of the species accumulation curve, or the estimated total number of species observable by ROV at a given site. These curves are expressed as the mean expected number of species per number of individuals observed. 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.

3.4.1.2 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 at the transect level. These tables exclude the unidentified individuals and species not well targeted by the ROV. 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.

Frequency of occurrence is defined here as the proportion of surveys that contained a given species. 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 one out of every two transects). We also identified species that were unique to each marine reserve and comparison area.

3.4.1.3 Diversity Indices

To gain additional insight into species diversity, we explored several diversity indices by comparing Hill diversity numbers 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 individual based abundance 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.

3.4.1.4 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.

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

3.4.2 Community Composition

We focused our community composition analysis on the question of whether variation in density 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 depth and proportion of boulder habitat as potential habitat-related drivers of variation.

To explore variation by site and year, we used untransformed invertebrate density data calculated from ROV count data (# individuals / area) so a similarity-based resemblance matrix was selected. To visualize the clustering or spread of the multivariate dataset with respect to the key variables we plotted 2D nMDS biplots symbolized by site and year.

To test the statistical significance of variation by site and year we ran a permutational analysis of variance (PERMANOVA), using Site and Year as fixed factors and Depth and Percent Boulder as continuous covariates. To explore if any significant results of the PERMANOVA were related to differences in location or differences in dispersion of samples (among sites or among years), we ran a permutational dispersion test, a distance based test for homogeneity of multivariate dispersions (Anderson and Walsh 2013). Significant heterogeneity of dispersions can lead to erroneously significant PERMANOVA results, so this test is used to distinguish where differences in dispersion may be influential in interpreting PERMANOVA results.

To better understand the quantitative contribution of various factors in explaining variation in the data, we ran a principal coordinates (PCO) analysis using a Bray-Curtis resemblance matrix, providing information on the percent of variation explained by each axis. To identify the species most strongly correlated with the PCO ordination, we used a vector analysis and displayed species vectors on the PCO plot for those species with significant correlations and r^2 > 0.2. We also plotted individual species bubble plots showing the density of the highly correlated species on the PCO ordination to visualize their abundance relative to the two PCO axes.

To explore the relationship of environmental variables with the observed patterns in community structure, we also used the PCO ordination to display trends in depth, season and percent cover of boulder habitat.

The community composition analyses (NMDS, PCO, PERMANOVA, and dispersion tests) were implemented in R using package “vegan” v. 2.5-7 (Oksanen et al 2020).

3.4.3 Abundance

Invertebrate density data were generated per transect by summing individual counts across the ViewScale 3, hard-substrate portions of each transect and dividing by the view area summed across those same portions. Densities are expressed in plots as individuals per 100 square meters, a convention used simply for better readability of typically low-density figures. 95% confidence intervals of density were generated as +/- 1.96 times the standard error. Individual species density data are presented for the selected focal species and additionally for species identified as influential in community composition analyses.

3.4.3.1 Aggregate abundance

Densities of invertebrate species within several major taxonomic groupings were generated following the same procedures. Groupings were as follows: Anemone, Barnacle (mostly Giant Barnacle), Bivalve (mostly Rock Scallop), Cephalopod (Red Octopus and Giant Pacific Octopus), Crustacean (including Dungeness Crab), Cucumber (including Burrowing Sea Cucumber and California Sea Cucumber), Nudibranch, Ophiuroid (e.g. brittle stars, basket stars), Sea Star, Sponge (colonies counted as individuals), Tunicate, Urchin (mostly Red Urchin, also Purple Urchin). Densities of aggregate groups are presented graphically but were not statistically analyzed. Several other taxa that were observed are not summarized because they were not consistently reviewed by the same methodology across all years, including polychaetes, anthozoans, and gastropods.

3.4.3.2 Individual species abundance

Statistical analyses of invertebrate density were conducted on the selected focal species and a few additional species that were highlighted as important in community composition analyses. Data explorations suggested the potential for influential nonlinear relationships of invertebrate densities with continuous covariates Depth and Percent Boulder. We employed generalized additive models (GAMs) incorporating smooth functions of the covariates along with fixed-effects factors Site and Year to compare individual invertebrate species densities across sites and years. We first developed a full set of possible GAMs with models including (in addition to Site and Year):

  • a Site * Year interaction
  • a linear Depth covariate across all sites, or a single Depth smooth across all sites, or a separate Depth smooth for each site
  • a linear Percent Boulder covariate across all sites, or a single Percent Boulder smooth across all sites, or a separate Percent Boulder smooth for each site

For all potential model covariates, the smoothness parameter ‘k’ was fixed at 3 in order to avoid overfitting. We selected the model with the lowest AIC (except in the case of virtual ties in which case we chose the simpler model). The selected model varied among species, and is reported in the results section for each species. The GAM modeled count of individuals using a negative binomial distribution with a log link, with viewed area included as an offset, thereby accounting for density across transects with varying total survey areas. Therefore the model covariates provided in tables are in log space (i.e. exponentiating the covariate estimates puts them on the density scale of individuals per square meter). Smooth plots of covariates are provided showing the predicted invertebrate density across the range of each continuous covariate while the other factors and covariates in the model are held at fixed (mean) values. Where the selected model included separate smooth functions for each site, smooth plots at all three sites are displayed in the results section. Where the selected model included a single smooth across sites, just a single plot is displayed. In some smooth plots the extent of the shaded 95% confidence interval is truncated by limiting the display of the y axis, allowing better visualization of patterns in the predicted mean density. This generally happened when model fits were poor and does not affect interpretation of significant covariate smooths.

In certain cases, loss of species precluded analysis across all years. In this situation, models were run on individual years’ data. In other cases, certain species were not sufficiently abundant at a given site to warrant inclusion in the statistical analyses. In these cases, the site in question was dropped from the analysis if the data violated the required assumptions.

Where significant Site * Year interactions were identified, we did not pursue further lower-level analyses comparing individual years or sites. Rather we restricted our inferences to the significance of each factor level identified in the GAM’s output table, which treated the Redfish Rocks Marine Reserve as the reference level of Site against which the other sites were compared, and 2010 as the reference year against which other years were compared. All analyses were conducted in R (R Core Team (2021)). GAM models of individual species densities were implemented in package “mgcv” v. 1.8-35 (Wood 2011).

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4 Redfish Rocks Results

4.1 Sampling effort summary

4.1.1 Number of transects

ROV sampling efforts at Redfish Rocks and its comparison areas resulted in four years of data collection, where varying sample sizes were collected per year (Fig. 2). The first year of sampling (2010) resulted in the largest sampling effort across all sites.

Fig. 2: ROV monitoring efforts represented as number of transects at the Redfish Rocks Marine Reserve and surrounding comparison areas.

Fig. 2: ROV monitoring efforts represented as number of transects at the Redfish Rocks Marine Reserve and surrounding comparison areas.

4.1.2 Sampled depth distribution

Variation among years in the depth distribution of sampling at the Orford Reef Comparison Area may be important in interpreting species’ density patterns.

Figure DPTH presents the total survey area included in fish density analyses in each Year and Site within 5 m depth intervals. Numerous environmental and logistical factors affected the ability of the ROV to acquire video data that would ultimately pass all data quality and habitat-based filtering steps and be included in analyses. Daily ROV operations were sometimes limited by water clarity or currents, which both tended to vary across depths within sites. In these cases, the field crew generally substituted other randomly selected transects in areas (depths or sites) that were productive for sampling. Therefore ROV sampling resulted in varying degrees of effort across depths. In addition, in 2018 at the Orford Reef Comparison Area the ROV sampled more area at the deeper end of the depth range than in other years because that year’s sampling was funded and planned as part of an external study with a targeted maximum depth of 50 m. Finally, the distinct geomorphology of each Site (i.e. the amount of rocky and sandy habitats at different depths) influenced the amount of data that was excluded by the filtering step that excluded transect segments with less than 25% hard substrate.

Fig. 3: Depth distribution of total survey area included in fish density analyses at the Redfish Rocks Marine Reserve and its associated comparison areas. The area includes only portions of transects that passed all data quality and habitat-based filtering steps for inclusion in fish density analysis. X-axis labels indicate the shallower end of each 5 m depth interval.

Fig. 3: Depth distribution of total survey area included in fish density analyses at the Redfish Rocks Marine Reserve and its associated comparison areas. The area includes only portions of transects that passed all data quality and habitat-based filtering steps for inclusion in fish density analysis. X-axis labels indicate the shallower end of each 5 m depth interval.

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4.2 Diversity

4.2.1 Species richness

Species richness at Redfish Rocks Marine Reserve is very similar to the comparison areas

Over the four years of sampling with the ROV a total of 42 species (or species groups) were observed in the Redfish Rocks Marine Reserve (Table 3). The Humbug Comparison Area (n = 43) and Orford Reef Comparison Area (n = 43) had similar numbers of invert species observed (Table 3). These observed numbers of species richness are similar to the estimated numbers of total species richness (Table 3).

library(kableExtra)
pna <- data.frame(Area = c("Redfish Rocks Marine Reserve", 
                           "Humbug Comparison Area",
                           "Orford Reef Comparison Area"),
                  Observed_Richness = c("42","43","43"),
                  Estimated_Richness = c("42", "51", "44"),
                  LCL = c("42", "44", "43"),
                  UCL = c("43", "108","51"))


  kbl(pna, caption = "Table 3: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits") %>% 
  kableExtra::kable_classic()
Table 3: 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 42 42 42 43
Humbug Comparison Area 43 51 44 108
Orford Reef Comparison Area 43 44 43 51

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Species rarefaction curves highlight that at small samples sizes , such as those for any given year, the species richness among sites is very similar (Fig. 4). Rarefaction curves levels off, suggesting saturation in species richness with this tool at this site.

Fig. 4: Species rarefaction curves for the Redfish Rocks Marine Reserve and its two comparison areas. Data are pooled across all years of sampling for each site.

Fig. 4: Species rarefaction curves for the Redfish Rocks Marine Reserve and its two comparison areas. Data are pooled across all years of sampling for each site.

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4.2.2 Unique, common and rare species

Although the number of rare species differ between the Redfish Rocks Marine Reserve and its comparison areas, the number of unique and common species among all sites is similar.

No unique species were observed at the Redfish Rocks Marine Reserve. At Humbug Comparison Area the Opalescent Nudibranch was unique and at Orford Reef Comparison Area the Trumpet Sponge was unique.

The Redfish Rocks Marine Reserve (n = 9) had the fewest number of common species compared with Humbug Comparison Area (n = 11) and the Orford Reef Comparison Area (n = 13). All the common species of the marine reserve were also considered common species in the comparison areas (Tables 4-9). The top 6 species at Redfish Rocks Marine Reserve : Blood Star, Giant Plumose Anemone, Fish Eating Anemone, Giant Sea Cucumber, Burrowing Cucumber, Red Urchin were also the top species at Orford Reef Comparison Area and all but Red Urchin were the top species at Humbug Comparison Area. The greatest number of rare species were observed at Redfish Rocks Marine Reserve (n = 16), followed by Orford Reef Comparison Area (N = 14) and Humbug Comparison Area (n = 11)

Not all species were observed each year, for a summary of species counts 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:

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4.2.2.1 Redfish Rocks Marine Reserve

Fig. 5: Relative frequency of occurrence of species observed at the Redfish Rocks Marine Reserve and its associated Comparison Areas in ROV transects. See separate tabs for each site.

Fig. 5: Relative frequency of occurrence of species observed at the Redfish Rocks Marine Reserve and its associated Comparison Areas in ROV transects. See separate tabs for each site.

4.2.2.2 Humbug Comparison Area

Fig. 5: Relative frequency of species observed at the Redfish Rocks Marine Reserve and the comparison areas in ROV transects. See separate tabs for each site.

Fig. 5: Relative frequency of species observed at the Redfish Rocks Marine Reserve and the comparison areas in ROV transects. See separate tabs for each site.

4.2.2.3 Orford Reef Comparison Area

Fig. 5: Relative frequency of species observed at the Redfish Rocks Marine Reserve and the comparison areas in ROV transects. See separate tabs for each site.

Fig. 5: Relative frequency of species observed at the Redfish Rocks Marine Reserve and the comparison areas in ROV transects. See separate tabs for each site.

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4.2.3 Diversity Indices

Slight differences in the fish community among the marine reserve, Humbug Comparison Area and Orford Reef can be seen when comparing rarefaction and interpolation of the hill diversity numbers across the three sites (Fig. 6). When q = 0, this is the equivalent of comparing species richness across sites. The 95% confidence intervals on this graph suggest that at low sample sizes (<1000 individuals) the species richness is slightly greater at Orford Reef Comparison Area. As sample sizes increased, species richness is equivalent.There is greater uncertainty in the Humbug Comparison Area estimates due to lower sample sizes; and it may not be appropriate to directly compare species richness with the other two sites.

When q = 1, this is the equivalent of comparing a Shannon-Weiner diversity index across sites - where abundance is also factored into the accumulation of species. Here we see non-overlapping 95% confidence intervals across all survey areas(Fig. 4). If we directly compare total invert counts among Redfish Rocks Marine Reserve (n = 47026), Humbug Comparison Area (n = 30092) and Orford Reef Comparison Area (n = 63396), it is apparent that Orford Reef had the greatest number of individual invert organisms.

When q = 2, this is the equivalent of comparing the simpson evenness index across sites, which focuses on the evenness of abundance of species at each given location. Here we see that Orford Reef Comparison Area also has a greater evenness of abundance of species than either the marine reserve or Humbug Comparison Area (Fig. 6).

Fig. 6: Comparing Hill diversity numbers across the Redfish Rocks Marine Reserve and its associated Comparison Areas from ROV samples. 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). Note sample unit is an individual organismFig. 6: Comparing Hill diversity numbers across the Redfish Rocks Marine Reserve and its associated Comparison Areas from ROV samples. 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). Note sample unit is an individual organism

Fig. 6: Comparing Hill diversity numbers across the Redfish Rocks Marine Reserve and its associated Comparison Areas from ROV samples. 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). Note sample unit is an individual organism

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4.2.4 Diversity through time

We did not get enough samples to evaluate annual changes in species diversity 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 for all Areas (Fig. 7). When plotting mean species richness by year with 95% confidence intervals, the confidence often overlap indicating that total species richness appears relatively stable year to year. Slight differences in total species obtained are likely driven by the presence or absence of rare species during the ROV survey or by differences in relative habitat type surveyed. Orford Reef Comparison Area was the only survey area where rarefaction curves consistently reached an asymptote each year, indicating that a saturation in the estimate of species richness has been achieved.

Fig. 7: Species rarefaction curves by year and Area from ROV data. Note that x-axis is scaled by 1000s of individuals.

Fig. 7: Species rarefaction curves by year and Area from ROV data. Note that x-axis is scaled by 1000s of individuals.

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4.3 Community Composition

4.3.1 Variation by Site and Year

Invertebrate community structure differed slightly among the Redfish Rocks Marine Reserve and its associated comparison areas

There was little apparent structure in the 2D nMDS plot relative to Site (Fig. 8). The relatively poor ability of the multi-dimensional ordination to be represented in two dimensions was indicated by the high stress level of 0.2, a value that suggests interpretation should be restricted to major, obvious features of the plot. The PERMANOVA results indicated significant effects of Site and Year, in addition to a Site * Year interaction and significant effects of the covariates Depth and Percent Boulder (Table 10). Despite statistical significance, the proportion of variation explained by these factors was relatively low; Site explained only 9% of the variation, and the significant interaction means that Site was not an important predictor of community structure in all years. The comparatively large sample size (n = 259 transects) likely contributed to the detection of significant differences despite the large degree of overlap in the sites’ ordination plots.

Differences in community composition by year were apparent, accounting for 21% of the total variation in community composition.

Among the four years of sampling, transects from 2010 and 2016 formed the most distinct groupings in the nMDS plot (Fig. 8), with the other years 2018 and 2019 intermediate. The PERMANOVA analysis showed that Year explained 21% of the variation in community composition (p < 0.05, Table 10). Assessment of the group dispersions by Year showed significant heterogeneity of dispersions among years and among sites (p < 0.05, Table 11, Table 12). Pairwise comparison of individual years and sites by the Tukey HSD test showed that all sites differed in dispersion (Table 13), with the Redfish Rocks Marine Reserve the lowest and the Orford Reef Comparison Area the highest, and that dispersion was lower in 2016 than in 2010 and 2019 (Table 14). These results indicate that the significant PERMANOVA result described above is likely influenced by differences among sites and years in the inherent variability in community structure among transects (a “dispersion effect”) in addition to any contribution of systematic differences in the structure of communities (a “location effect”).

4.3.1.1 NMDS plot by Site

Fig. 8: Results from nMDS plots with ROV invertebrate data, demonstrating similarity in invertebrate community composition at the Redfish Rocks Marine Reserve and its surrounding comparison areas. The raw density data were not transformed and a Bray-Curtis similarity matrix was used. See separate tabs for site and year.

Fig. 8: Results from nMDS plots with ROV invertebrate data, demonstrating similarity in invertebrate community composition at the Redfish Rocks Marine Reserve and its surrounding comparison areas. The raw density data were not transformed and a Bray-Curtis similarity matrix was used. See separate tabs for site and year.

4.3.1.2 NMDS plot by Year

Fig. 8: Results from nMDS plots with ROV invertebrate data, demonstrating similarity in invertebrate community composition at the Redfish Rocks Marine Reserve and its surrounding comparison areas. The raw density data were not transformed and a Bray-Curtis similarity matrix was used. See separate tabs for site and year.

Fig. 8: Results from nMDS plots with ROV invertebrate data, demonstrating similarity in invertebrate community composition at the Redfish Rocks Marine Reserve and its surrounding comparison areas. The raw density data were not transformed and a Bray-Curtis similarity matrix was used. See separate tabs for site and year.

4.4 Aggregate Density

Trends in aggregate density of various taxonomic categories are presented in Figure 11. Notable trends across the sampling period include the overall rise in urchins (especially at the Orford Reef Comparison Area), the dramatic reduction of sea stars at all sites, substantial reductions in density for nudibranchs and sponges, and the divergent trends exhibited among sites for anemones and for ophiuroids (dominated by basket stars).

Fig. 11: Aggregate invertebrate density by taxonomic category with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

Fig. 11: Aggregate invertebrate density by taxonomic category with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

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4.5 Focal Species Density

4.5.1 Ochre Seastar (Pisaster ochraceus)

Ochre Seastars are a shallow-dwelling species not suited for sampling with the ROV

Ochre Seastars were observed by the ROV only occasionally at the shallowest depths, generally shallower than the depth range targeted for ROV sampling. Accordingly no statistical analysis of their densities was conducted. Overall densities are shown in Fig. 12.

Fig. 12: Ochre Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

Fig. 12: Ochre Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

4.5.2 Sunflower Star (Pycnopodia helianthoides)

Complete disappearance of the Sunflower Star across all sites

The Sunflower Star was eradicated by the Seastar Wasting Syndrome that devastated seastar communities across the West Coast starting in 2013 and 2014. Total counts were reduced to zero in 2019 (Table 15).

Because the loss of most Sunflower Stars by 2016 complicates any statistical modeling of the full dataset, we restricted this assessment to 2010 data. In 2010, the Redfish Rocks Marine Reserve had a much lower density of Sunflower Stars than the Humbug Comparison Area, but similar to the Orford Reef Comparison Area (p < 0.05, Fig. 13, Table 17). The selected GAM model included individual smooth effects of depth at each site and a single smooth effects of percent boulder for each Site. The selected model was:

Count = Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Sunflower Star density in 2010 had a unimodal relationship with depth at the Humbug Comparison Area, peaking at around 25 m and effectively disappearing by around 35 m depths (p < 0.05, Fig. 13, Table 18). At the Orford Reef Comparison Area, Sunflower Star density rapidly decreased nonlinearly with increasing depth (p < 0.05, Fig. 13, Table 18). At the Redfish Rocks Marine Reserve, the highest densities were observed at around 30 m depths but the depth smooth was not significant. The smooth effect for percent boulder was not significant at any site. The GAM model explained a substantial 57% of the deviance in the dataset.

4.5.2.1 Sunflower Star Density Timeseries

Fig. 13: Sunflower Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 13: Sunflower Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.2.2 GAM smooth for Depth

Fig. 13: Sunflower Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 13: Sunflower Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.2.3 GAM smooth for Boulder

Fig. 13: Sunflower Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 13: Sunflower Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.5.3 Purple Sea Urchin (Strongylocentrotus purpuratus)

Large recruitment of Purple Sea Urchins at the Orford Reef Comparison Area after 2010.

While Purple Sea Urchins are generally shallower than the depth range targeted for ROV sampling and are not generally suited for ROV sampling, we did observe the consequences of a massive recruitment event at the Orford Reef Comparison Area between 2010 and 2016 that resulted in substantial densities of Purple Sea Urchins (Fig. 14). These urchins extended deeper than seen at other sites, to depths where they could be observed by the ROV. Data were not statistically analyzed because the complete absence of urchins in 2010 and in most years at the other two sites violated assumptions of the modeling framework. Overall densities are shown in Fig. 14. The period of high urchin abundance at Orford Reef corresponded with a dramatic decline in the surface extent of bull kelp at the site, as documented by other researchers.

Fig. 14: Purple Sea Urchin density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

Fig. 14: Purple Sea Urchin density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas.

4.5.4 Red Sea Urchin (Mesocentrotus franciscanus)

Dramatic recruitment of Red Sea Urchins at the Redfish Rocks Marine Reserve and the Orford Reef Comparison Area

Red Sea Urchin density increased substantially at the Redfish Rocks Marine Reserve and the Orford Reef Comparison Area across the study period (p < 0.05, Fig. 15, Table 19, Table 20). Overall Red Sea Urchin density at the Redfish Rocks Marine Reserve was lower than at the Orford Reef Comparison Area and higher than at the Humbug Comparison Area (p < 0.05, Table 20). The selected GAM model excluded any Site * Year interaction and included individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Red Sea Urchin density had different relationships with depth at the three sites; generally, density was highest at the shallowest depths (p < 0.05, Fig. 15, Table 21). Red Sea Urchin density increased rapidly at high percentages of boulder at the Humbug Comparison area but the smooth effect for boulder was not significant at the other two sites.

4.5.4.1 Red Sea Urchin Density Timeseries

Fig. 15: Red Sea Urchin density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 15: Red Sea Urchin density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.4.2 GAM smooth for Depth

Fig. 15: Red Sea Urchin GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 15: Red Sea Urchin GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.4.3 GAM smooth for boulder

Fig. 15: Red Sea Urchin GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 15: Red Sea Urchin GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.5.5 Rock Scallop (Crassadoma gigantea)

Rock Scallop density peaked in 2016, with variability among sites in the magnitude of change across years

Rock Scallop densities were characterized by a significant Site * Year interaction that reflected differential changes in abundance at each site (Fig. 16, Table 22, Table 23). The selected GAM model included a Site * Year interaction and individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + Year * Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Rock Scallop density had distinct relationships with depth at the Redfish Rocks Marine Reserve (increasing fairly linearly) and the Orford Reef Comparison Area (peaking very slightly at mid-depths) (p < 0.05, Fig. 16, Table 24). Rock Scallop density decreased with increasing percent boulder at the Orford Reef Comparison Area but the smooth effect for boulder was not significant at the other two sites.

4.5.5.1 Rock Scallop Density Timeseries

Fig. 16: Rock Scallop density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 16: Rock Scallop density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.5.2 GAM smooth for Depth

Fig. 16: Rock Scallop GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 16: Rock Scallop GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.5.3 GAM smooth for boulder

Fig. 16: Rock Scallop GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 16: Rock Scallop GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.5.6 California Sea Cucumber (Parastichopus californicus)

Lower densities of California Sea Cucumbers at the Redfish Rocks Marine Reserve compared to the Orford Reef Comparison Area

California Sea Cucumber densities were characterized by a significant Site * Year interaction that reflected relatively stable densities at the Redfish Rocks Marine Reserve and the Orford Reef Comparison Area but increasing density at the Humbug Comparison Area (Fig. 17, Table 25, Table 26). The selected GAM model included a Site * Year interaction and individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + Year * Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

California Sea Cucumber density had distinct relationships with depth at each site (p < 0.05, Fig. 17, Table 27), rising quickly in the deeper portions of the Redfish Rocks Marine Reserve and the Orford Reef Comparison Area, and exhibiting a peak density at 35 m depth at the Humbug Comparison Area. California Sea Cucumber density decreased with increasing percent boulder at the Orford Reef Comparison Area but the smooth effect for boulder was not significant at the other two sites.

4.5.6.1 California Sea Cucumber Density Timeseries

Fig. 17: California Sea Cucumber density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 17: California Sea Cucumber density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.6.2 GAM smooth for Depth

Fig. 17: California Sea Cucumber GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 17: California Sea Cucumber GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.6.3 GAM smooth for boulder

Fig. 17: California Sea Cucumber GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 17: California Sea Cucumber GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.5.7 White Plumose Anemone (Metridium farcimen)

Differing initial densities of White Plumose Anemones in 2010 became more similar across sites by 2019

White Plumose Anemone densities were characterized by a significant Site * Year interaction that reflected stable to increasing densities at the Humbug Comparison Area and the Orford Reef Comparison Area but stable to decreasing density at the Redfish Rocks Marine Reserve (Fig. 18, Table 28, Table 29). The selected GAM model included a Site * Year interaction and individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + Year * Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

White Plumose Anemone density had distinct relationships with depth at each site (p < 0.05, Fig. 18, Table 30), rising quickly in the deeper portions of the Redfish Rocks Marine Reserve, rising steadily at the Orford Reef Comparison Area, and exhibiting a peak density around 30 m depth at the Humbug Comparison Area. White Plumose Anemone density had distinct relationships with increasing percent boulder at the Humbug Comparison Area and the Orford Reef Comparison Area (p < 0.05, Fig. 18, Table 30), but the smooth effect for boulder was not significant at the Redfish Rocks Marine Reserve.

4.5.7.1 White Plumose Anemone Density Timeseries

Fig. 18: White Plumose Anemone density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 18: White Plumose Anemone density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.7.2 GAM smooth for Depth

Fig. 18: White Plumose Anemone GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 18: White Plumose Anemone GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.7.3 GAM smooth for boulder

Fig. 18: White Plumose Anemone GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 18: White Plumose Anemone GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.5.8 Short Red Gorgonian (Swiftia spauldingi)

The Short Red Gorgonian is a focal species for ROV surveys only because its distribution corresponds better with the ROV’s deeper sampling depths.

Generally higher densities of Short Red Gorgonians at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area, though not in all years.

Due to the high density of Short Red Gorgonians at some sites, a specialized video review technique was used to subsample the available videos and count gorgonians intercepted by a restricted portion of the full video review frame. Densities were then extrapolated to full-frame equivalents using ratios derived from pilot studies that counted gorgonians with both the full-frame and reduced-frame protocols. Density data for Short Red Gorgonians were not available for 2016.

Short Red Gorgonian densities were characterized by a significant Site * Year interaction that reflected a trend toward higher mean densities at all sites in 2019 than in 2010, though high variability among transects contributed to the non-significant Year effect (Fig. 19, Table 31, Table 32). The selected GAM model included a Site * Year interaction and individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + Year * Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Short Red Gorgonian density had distinct relationships with depth at the Humbg Comparison Area (peaking in density at around 30 m depth) and the Orford Reef Comparison Area (increasing slightly at the deepest depths (p < 0.05, Fig. 19, Table 33), but no significant relationship at the Redfish Rocks Marine Reserve. Short Red Gorgonian density had decreasing relationships with increasing percent boulder at the Redfish Rocks Marine Reserve and the Orford Reef Comparison Area but no significant relationship at the Humbug Comparison Area.

4.5.8.1 Short Red Gorgonian Density Timeseries

Fig. 19: Short Red Gorgonian density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 19: Short Red Gorgonian density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.5.8.2 GAM smooth for Depth

Fig. 19: Short Red Gorgonian GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 19: Short Red Gorgonian GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.5.8.3 GAM smooth for boulder

Fig. 19: Short Red Gorgonian GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 19: Short Red Gorgonian GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

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4.6 Additional Species Density

4.6.1 Blood Star (Henricia spp.)

Blood Stars showed smaller reductions in density by 2019 than many other seastar species affected by Seastar Wasting Syndrome.

Blood Stars escaped the most severe consequences of Seastar Wasting Syndrome that affected many other seastars, but were still reduced in density by 2019 (p < 0.05, Fig. 20, Table 34, Table 35). Blood Star densities were characterized by a significant Site * Year interaction that reflected a greater increase in density at the Orford Reef Comparison Area in 2016 than at the other two sites (Fig. 20, Table 35). The selected GAM model included a Site * Year interaction and individual smooth effects of depth and percent boulder for each Site. The selected model was:

Count = Year + Site + Year * Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Blood Star density decreased nonlinearly with depth at the two comparison areas (p < 0.05, Fig. 20, Table 36), and had a similar though nonsignificant trend at the Redfish Rocks Marine Reserve. Blood Star density decreased relatively linearly with increasing percent boulder at the Redfish Rocks Marine Reserve, but had no significant response at the other two sites (p < 0.05, Fig. 20, Table 36).

4.6.1.1 Blood Star Density Timeseries

Fig. 20: Blood Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 20: Blood Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.6.1.2 GAM smooth for Depth

Fig. 20: Blood Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 20: Blood Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.6.1.3 GAM smooth for boulder

Fig. 20: Blood Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 20: Blood Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

4.6.2 Pink Star (Pisaster brevispinus)

Near-complete disappearance of the Pink Star across all sites

The Pink Star was almost eradicated by the Seastar Wasting Syndrome that devastated seastar communities across the West Coast starting in 2013 and 2014 (though likely later in Oregon). Total counts were reduced to near zero in 2019 (Table 37).

Because the loss of most Pink Stars by 2016 complicates any statistical modeling of the full dataset, we restricted this assessment to 2010 data. In 2010, the Redfish Rocks Marine Reserve had a much lower density of Sunflower Stars than the Humbug Comparison Area (p < 0.05, Fig. 21, Table 38, Table 39), but was not significantly different from the Orford Reef Comparison Area. The selected GAM model included individual smooth effects of depth and percent boulder at each site. The selected model was:

Count = Site + s(Depth, by = Site, k = 3) + s(Boulder.pct, by = Site, k = 3), offset = log(area), family = nb

GAM model results can be found in the links below:

Pink Star density decreased nonlinearly with depth at the two comparison areas (p < 0.05, Fig. 21, Table 40), but no significant depth trend at the Redfish Rocks Marine Reserve. Pink Star density decreased nonlinearly with increasing percent boulder at the Orford Reef Comparison Area, but had no significant response at the other two sites (p < 0.05, Fig. 21, Table 40).

4.6.2.1 Pink Star Density Timeseries

Fig. 21: Pink Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

Fig. 21: Pink Star density with 95% confidence intervals at the Redfish Rocks Marine Reserve and its associated comparison areas. Data are See separate tabs for density timeseries and plots of GAM covariate smooths.

4.6.2.2 GAM smooth for Depth

Fig. 21: Pink Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

Fig. 21: Pink Star GAM smooth for depth at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of depths, holding other factors in the model constant.

4.6.2.3 GAM smooth for boulder

Fig. 21: Pink Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

Fig. 21: Pink Star GAM smooth for percent boulder at the Redfish Rocks Marine Reserve and its associated comparison areas. The value is the predicted density (+/- 95% confidence interval) across the range of percent boulder, holding other factors in the model constant.

5 References

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