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 fish 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 fish 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.
Diversity
Community Composition
Aggregate Abundance
Focal Species Abundance
Here we present a summary of our ROV fish 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.
Fish species richness was greatest at Orford Reef Comparison Area per unit of sampling compared with Redfish Rocks Marine Reserve.
A greater number of observed and estimated total fish species were found at the Orford Reef Comparison Area per unit sampling compared to the Redfish Rocks Marine Reserve. The Orford Reef Comparison Area also had a higher effective number of species across all three Hill diversity indices than the Redfish Rocks Marine Reserve indicating greater diversity at this site.These results may reflect the broader depth range available at the Orford Reef Comparison Area and the inclusion of some deeper sampling at that site. There was greater similarity between the Redfish Rocks Marine Reserve and Humbug Comparison Area across the three Hill diversity indices. While there were more unique species at the Orford Reef Comparison Area, there were similarities in common species between the marine reserve and its two comparison areas.
Fish community composition is generally similar between the marine reserve and its comparison areas across sites and years with ROV monitoring data; community structure driven by three species.
Densities of Black Rockfish, Blue/Deacon Rockfish and China Rockfish were most influential in structuring the community composition with ROV monitoring data, rather than variation by site or year. There was strong co-occurrence between China and Blue/Deacon Rockfish, which were found both together and at higher densities at Orford Reef than either Redfish Rocks Marine Reserve or Humbug Comparison Area. Season, depth and hard bottom habitat variables did not explain much variation in fish community composition.
Aggregate fish density was greater at the marine reserve than at one of the comparison areas, and decreased over the samping period.
Aggregate fish density was greater at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison area. Between 2010 and 2019, overall aggregate fish density decreased despite a significant increase in 2018. Aggregate density was driven largely by the schooling, semi-pelagic Blue/Deacon Rockfish and Black Rockfish, along with the very common solitary demersal species Kelp Greenling.
Some species density differences between the marine reserve and its comparison areas were detected, mainly with higher average density at the marine reserve than Orford Reef Comparison Area.
Four species had higher densities in the Redfish Rocks Marine Reserve than Orford Reef Comparison Area - Black Rockfish, Cabezon, Kelp Greenling and Quillback Rockfish; densities were similar between the marine reserve and Humbug Comparison Area for these species. For three focal species there were non-significant trends toward higher densities at the Orford Reef Comparison Area than the Redfish Rocks Marine Reserve: Blue/Deacon Rockfish, China Rockfish and Yelloweye Rockfish. Among these, Blue/Deacon Rockfish was similarly abundant at the marine reserve and Humbug Comparison Area. Lingcod densities were similar between the marine reserve and Orford Reef Comparison Area, but greater densities of Lingcod were observed at the Humbug Comparison Area than at the marine reserve.
Most species densities were not significantly different in 2019 than the initial densities in 2010 at the Redfish Rocks Marine Reserve, though two common species declined and one increased
Five of six focal species had 2019 densities that did not differ from initial 2010 densities at the Redfish Rocks Marine Reserve, including Black, Blue/Deacon, China, and Yelloweye Rockfish, and Cabezon. Lingcod and Kelp Greenling exhibited 2019 densities at all sites that were significantly lower than initial densities in 2010. Quillback Rockfish was the only species with increased 2019 densities at all sites compared to initial densities in 2010.
Species density responses to depth varied by site except for Blue/Deacon, Yelloweye and Quillback Rockfish.
Species densities were influenced by relationships with depth to varying degrees and were mostly site specific. For three species - Blue/Deacon, Yelloweye and Quillback Rockfish - density relationships with depth were generic - with the same relationship described regardless of site. For Blue/Deacon and Yelloweye Rockfish densities both increased with increasing depths. Quillback Rockfish exhibited a unimodal peaked response to depth, peaking at around 35 m across all sites.
Density generally increased with increasing percentage of hard substrate but responses varied among species and sites.
Species responses to the percentage of hard substrate were variable and mostly site specific, with varying degrees of nonlinearity. Generally densities either increased with increasing hard substrate or were neutral. This result supports the importance of assessing the effect of non-target habitats (i.e. sandy substrates) on density estimates for the targeted rocky reef-associated fish.
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 and season (spring v. fall) 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. For assessing seasonal effects, additional within-year sampling is needed to distinguish cyclical seasonal changes from inter-annual trends. Since the high cost of ROV surveys typically prohibits surveying more than once per year, future monitoring efforts should focus on surveying in the same season (either spring or fall) each year. More investigation of the effect of oceanographic conditions on density observations on any given day are needed.
This is the first ecological monitoring report to summarize ROV 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 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 and exposure to a more similar set of oceanographic conditions. From a diversity and community composition perspective the differences among sites are minimal, and variations in the most common species are likely responsible for such differences. Given that two of the most abundant species, Black and Blue/Deacon Rockfish, are schooling semi-pelagic species for which the ROV provides density estimates of only a portion of the schools that are near the seafloor, additional community structure analyses are warranted that consider only those species (solitary demersals) that are fully represented in the ROV dataset. From an abundance perspective, while some species exhibited density differences between the Redfish Rocks Marine Reserve and one or both comparison areas, the tendency was for species to exhibit inter-annual changes in concert across sites, suggesting that assessments of change over time are viable despite sometimes differing baseline abundance levels.
We are able to detect changes in density over time for select species with ROV sampling.
Even though ROV sampling occurs at infrequent intervals, there were species-specific, inter-annual patterns detected. For example, Lingcod density increased at all sites in 2016 relative to densities in 2010, but by 2019 densities at all sites had decreased and were no longer different than initial densities in 2010. For a number of species, there was a change in density detected during at least one sampling interval. Quillback Rockfish was the only species that increased in density from 2010 and remained higher at the end of the sampling period in 2019. It was unclear what the driving factor was behind changes in density from year to year, but the contrasting changes in density among species (for example several species increased substantially in 2018 while others declined) made it clear that there were no systematic methodological artifacts, such as changes in camera lenses, video quality or video review practices driving the apparent density changes.
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.
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 fish for this report.
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. All fish observed were identified to species where possible, and otherwise were recorded in higher level taxonomic groupings. Primary and secondary substrates were assessed continuously along the transect.
All video data were continuously categorized along transects as either “gap” or “nongap” (see ROV Methods Appendix), indicating the suitability for use in fish density estimates. For all fish data analyses in this report, only nongap data were used, incorporating only portions of transects with suitable view characteristics for consistent detection and calculation of viewed area. Fish data from each transect, generally 500 m long, were subdivided into smaller units (“segments”) for analysis based on the cumulative nongap view area along the transect. Equal-area segments of 200 m^2 each were assigned, leaving a variably sized segment at the end of each transect. This last segment was excluded from analyses if its area was less than 25 m^2, a procedure meant to minimize the effect of spuriously high density estimates in very small segments.
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 fish analyses, we included the percentage of hard substrate for each segment as a potential covariate. Any segment with less than 25% hard substrate was excluded, a procedure meant to reduce the influence of non-target habitats (e.g. bare sand) which systematically decreased overall density estimates for the rocky-reef associated fish that were the objective of our ROV sampling. Eliminating sand-dominated segments helped alleviate analytical problems associated with overdispersion in the data (in some instances caused by a greater than expected prevalence of zero counts in the datasets).
For additional details on data collection, video review and data filtering, please review documentation in the ROV Methods Appendix.
With ROV fish surveys, we explored several concepts related to species diversity at a given site:
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 as well as species not well targeted by the ROV. Eelpout and Northern Ronquil were lumped into one species group to account for changing methodology with these species. Species richness metrics are highly sensitive to survey effort. While the ROV survey targets transect lengths of 500m, habitat and ocean conditions result in variable transect lengths - some much shorter than the target length. In order to overcome the confounding variable of transect size on species richness, transects were divided into equal 200 m^2 segments. These segments were used in the species rarefaction process to estimate species richness as a function of sample size (number of segments).
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 richnessd to estimate the asymptote of the species accumulation curve, or the estimated total number of species observable by ROV 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.
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.
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.
To gain additional insight into species diversity, we explored several diversity indices by comparing Hill diversity numbers with segment data 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.
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.
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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 hard substrate habitat as potential habitat-related drivers of variation.
To explore variation by site and year, we used untransformed fish 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).
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Fish density data were generated per transect segment (see segmentation approach above) by summing individual counts within each transect segment and dividing by the view area summed within each segment. 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. In addition, densities of all fish species combined (“aggregate abundance”) were generated following the same procedures.
Statistical analyses of fish density were conducted on the selected focal species and a few additional species that were highlighted as important in community composition analyses, as well as the aggegrate abundance. Data explorations suggested the potential for influential nonlinear relationships of fish densities with continuous covariates Depth and Percent Hard Substrate. We employed generalized additive models (GAMs) incorporating smooth functions of the covariates along with fixed-effects factors Site and Year to compare individual fish species densities across sites and years. We first developed a fixed set of possible GAMs, and selected the model with the lowest AIC. When two models AIC scores were effectively tied, we chose the simpler model. All models included Site and Year. Four models additionally contained linear effects of either Depth or Percent Hard Substrate, both covariates, or neither covariate. The remainder of the set included models with and without all combinations of the following:
For all potential model covariates, the smoothness parameter ‘k’ was fixed at 3 in order to avoid overfitting. 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 coefficients provided in tables are in log space (i.e. exponentiating the coefficient 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, absence 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 and the analysis was run with the remaining sites.
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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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.
Variation among years in the depth distribution of sampling at the Orford Reef Comparison Area may be important in interpreting species’ density patterns.
Figure 3 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.
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Slight differences in species richness across the Redfish Rocks Marine Reserve and its comparison areas
Over the four years of sampling with the ROV a total of 18 species (or species groups) were observed in the Redfish Rocks Marine Reserve (Table 4). The Humbug Comparison Area had slightly more species, 20, whereas Orford Reef Comparison Area had the most total species observed with 24 (Table 4). These observed numbers of species richness are similar to the estimated numbers of total species richness for the Redfish Rocks Marine Reserve, but estimated richness is higher for the comparison areas (Table 4).
library(kableExtra)
<- data.frame(Area = c("Redfish Rocks Marine Reserve", "Humbug Comparison Area", "Orford Reef Comparison Area"),
pna Observed_Richness = c("18","20","24"),
Estimated_Richness = c("18","24","26"),
LCL = c("18","20","24"),
UCL = c("23", "49","43"))
kbl(pna, caption = "Table 4: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits") %>%
::kable_classic() kableExtra
Area | Observed_Richness | Estimated_Richness | LCL | UCL |
---|---|---|---|---|
Redfish Rocks Marine Reserve | 18 | 18 | 18 | 23 |
Humbug Comparison Area | 20 | 24 | 20 | 49 |
Orford Reef Comparison Area | 24 | 26 | 24 | 43 |
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Species rarefaction curves highlight that at small samples sizes (sample unit = a 200m^2 transect segment), such as those for any given year, the species richness among sites is slightly different (Fig. 4). More rare species are observed at the comparison areas than the marine reserve, resulting in higher estimated species richness for these sites (Fig. 4, Table 4). Both the Redfish Rocks Marine Reserve and Orford Reef Comparison Area rarefaction curves levels off, suggesting saturation in species richness with this tool at this site.
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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 or Humbug Comparison Area. Four unique species were observed at the Orford Reef Comparison Area, including the Puget Sound Rockfish, S.emphaeus, Red Irish Lord, Hemilepidotus hemilepidotus, Starry skate, Raja stellulata, and Widow Rockfish S.entomelas.
The Redfish Rocks Marine Reserve (n = 5) had similar numbers of common species to both the Humbug Comparison Area (n = 6) and the Orford Reef Comparison Area (n = 6). All the common species of the marine reserve were also considered common species in the comparison areas (Tables 3-5). Additionally, China Rockfish were considered common at the Orford Reef Comparison Area and Cabezon were considered common at the Humbug Comparison Area.The Redfish Rocks Marine Reserve had fewer rare species (n = 6) than the Humbug (n = 10) and Orford Reef (n = 8) Comparison Areas.
Many of the other species of fisheries interest - Quillback, Yelloweye, Copper and Vermillion Rockfish - were not observed frequently resulting in low pooled counts. 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:
Table 5: Redfish Rocks Marine Reserve Pooled Species Counts and Frequency of Occurrence
Table 6: Redfish Rocks Marine Reserve Species Counts by Year
Table 7: Humbug Comparison Area Pooled Species Counts and Frequency of Occurrence
Table 9: Orford Reef Comparison Area Pooled Species Counts and Frequency of Occurrence
Table 10: Orford Reef Comparison Area Species Counts by Year
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Orford Reef has a higher effective number of species than the Redfish Rocks Marine Reserve across all three diversity indices.
Slight differences in the fish community can be seen when comparing Hill diversity numbers (effective number of species) across sites (Fig. 6). For all three Hill diversity numbers, the Orford Reef Comparison Areas has a higher number of effective species than the Redfish Rocks Marine Reserve. When q = 0, the Humbug Comparison Area also has a higher number of effective species than the marine reserve, but for q = 1 and q = 2, the effective number of species is almost identical between these two sites (Fig. 6).
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Annual differences in species diversity at the Redfish Rocks Marine Reserve and its comparison areas likely driven by relative differences in habitat type surveyed each year
Species rarefaction curves by year for each site appear to level off in many years, suggesting saturation in richness (or close to it) in many years (Fig. 7-9). The confidence intervals 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 year to year.
Fig. 7: Redfish Rocks Marine Reserves species rarefaction curves by year from ROV data.
Fig. 8: Humbug Comparison Area species rarefaction curves by year from ROV data.
Fig 9: Orford Reef Comparison Area species rarefaction curves by year from ROV sR.
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Fish community composition was similar across sites and years at the Redfish Rocks Marine Reserve and its surrounding comparison areas with ROV fish data.
There was no distinct structuring of fish community composition data across sites and years with ROV fish data at the Redfish Rocks Marine Reserve and its comparison areas. (Fig. 10).
While multivariate statistics indicate some differences by year, they account for little of the total variation in community composition.
PERMANOVA results indicate that the year and site were significant factors (p < 0.05) but the interaction between year and site was not significant (p > 0.05) (Table 11). Estimated variation described by each of the variables and variable interactions was relatively small.Year accounted for the highest variability (16%), but site only explained 4% of the total variation. The residuals explained approximately 78% of the variability in the data.
PERMDISP results for site were not significant (p > 0.05) while there were significant differences between years (p < 0.05, Tables 12-15). Many of the pairwise differences in years were significant but there were no discernable trends over time (Tables 12-15).
Table 12: PERMDISP mean dispersions and standard errors by Site
Table 14: PERMDISP mean dispersions and standard errors by site
These results suggest the significance identified in the PERMANOVA is likely a combination of both differences in dispersion and location for year and a difference in location for site. The low variance described by site in the model supports a relative similarity between sites, but there is a cluster of samples at Orford Reef that appear distinct from the rest of the samples, and are likely driving site significance. Given that there were significant differences in dispersions between multiple pairs of years, the significance of year i the model is likely the result of intrinsic variability between years and not necessarily reflective of a general change in community structure across time.
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Three species drive the majority of variation in fish community composition data at the Redfish Rocks Marine Reserve and its comparison areas.
We explored species-specific drivers of variation, and found that Black Rockfish, Blue/Deacon Rockfish and China Rockfish were driving the majority of variation in fish community structure (Fig. 11). Principal coordinate analysis revealed that ~33% of the variation is explained by the x axis and is represented by densities of all three species. Trade-offs in densities between Blue-Deacon / China Rockfish and Black Rockfish are associated with the y-axis and explain an additional ~ 16% of variation. Together the abundance of these three species accounts for over 49% of model variability.
Of note is the strong co-occurrence between China Rockfish and Blue/Deacon Rockfish, which are found both together and at higher densities at Orford Reef than either Redfish Rocks Marine Reserve or Humbug Comparison Area.
No clear differences in fish community composition data by depth, season, or % hard bottom habitat at the Redfish Rocks Marine Reserve and its comparison areas.
We explored depth, season, and hard bottom habitat as correlates of fish community composition with ROV density data and found no clear relationships from data visualizations of principal component analysis (Fig. 11). With the data visualization of depth, some separation of the deepest depth bin, 40-45 m, seems apparent, and correlates with high densities of China and Blue/Deacon Rockfish. This trend is likely related to transects from Orford Reef, which in general is a deeper, hard-bottom/high relief dominated region.
Trends in fish community structure by season or hard bottom habitat are not apparent, as both season and % cover of hard bottom habitat were highly overlapping and dispersed throughout the multivariate data cloud.
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Higher aggregate fish density at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area.
The Redfish Rocks Marine Reserve had overall higher aggregate fish densities than the Orford Reef Comparison Area (p < 0.05, Fig. 12, Table 16, Table 17). The selected GAM model excluded the interaction between Year and Site and included a single smooth effect of depth across sites and individual smooth effects of percent hard substrate at each Site. The selected model was:
Count = Year + Site + s(Depth, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
Aggregate density decreased across the sampling period, ending lower in 2019 than in 2010 despite a transient increase in 2018.
Aggregate density increased between 2010 and 2018, and then decreased in 2019 (p < 0.05, Fig. 12, Table 16, Table 17). Trends in aggregate density largely reflect the abundance of a few of the most abundant species. Here, the overall reduction of aggregate density in 2019 reflects the fact that 2019 was a low density year for both Blue/Deacon Rockfish and Kelp Greenling, two of the three most frequently observed fish species. The high aggregate density in 2018 at the Orford Reef Comparison Area is largely due to the very high densities of schooling Blue/Deacon Rockfish observed in the deeper portion of that site, areas that were proportionately less represented in other years’ sampling.
Across all sites, aggregate fish density showed a unimodal response to depth, peaking at around 35 m depth (p < 0.05, Fig. 12, Table 18). At the two comparison areas, aggregate fish density generally increased with an increasing proportion of hard substrate, with varying degrees of nonlinearity in the response (p < 0.05, Fig. 12, Table 12). At the Redfish Rocks Marine Reserve, the response to hard substrate was nonsignificant.
GAM model results can be found in the links below:
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Significantly higher Black Rockfish density across years at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area
The Redfish Rocks Marine Reserve had overall higher densities of Black Rockfish than the Orford Reef Comparison Area (p < 0.05, Fig. 13, Table 19), with the selected GAM model excluding the interaction between Year and Site and including separate smooth effects of depth and percent hard substrate within each Site. The selected model was:
Count = Year + Site + s(Depth, by = Site, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
There was a significant overall increase in Black Rockfish density from 2010 to 2016 and 2018 (p < 0.05, Fig. 13, Table 20), but no overall difference by 2019.
At the Orford Reef Comparison Area, Black Rockfish density showed a unimodal response to depth, decreasing at deeper depths (p < 0.05, Fig. 13, Table 21), but depth was not a significant predictor of density at the other sites. At all sites, Black Rockfish density generally increased with an increasing proportion of hard substrate (p < 0.05, Fig. 13, Table 21), with varying degrees of nonlinearity in the response.
GAM model results can be found in the links below:
Black Rockfish are a schooling species that occupy midwater areas above rocky reefs as well as the seafloor where they are accessible to the ROV. Consequently, the ROV estimates of density are known to represent only a portion of the Black Rockfish occupying the study sites.
No consistent pattern in Blue/Deacon Rockfish density among sites and years
Abundance patterns for Blue/Deacon Rockfish were dominated by very large schools observed at the Orford Reef Comparison Area in 2018. No consistent effect of Year or Site was observed (Fig. 14, Table 22), with the selected GAM model incorporating a significant interaction between Year and Site, a single smooth effect of depth for all sites, and separate smooths for percent hard substrate for each Site. The selected model was:
Count = Year + Site + Year * Site + s(Depth, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
Blue/Deacon Rockfish density increased relatively linearly with increasing depth across all sites (p < 0.05, Fig. 14, Table 24), and responded significantly but in varying ways to the proportion of hard substrate at the three sites (p < 0.05, Fig. 14, Table 24). Density was generally higher at high proportions of hard substrate, except at the Redfish Rocks Marine Reserve.
GAM model results can be found in the links below:
Blue/Deacon Rockfish are a schooling species that occupy midwater areas above rocky reefs as well as the seafloor where they are accessible to the ROV. Consequently, the ROV estimates of density are known to represent only a portion of the Blue/Deacon Rockfish occupying the study sites.
High variability in China Rockfish density among years, but too few observations at the Humbug Comparison area to quantitatively compare
The relative rarity of China Rockfish at the Humbug Comparison Area (only a total of 9 individuals observed across years) led to the exclusion of this site from all analyses (Fig. 15). At the other two sites, densities were significantly higher in 2018 than in 2010, but there was no difference by 2019 (p < 0.05, Table 25, Table 26). The trend toward higher overall density at the Orford Reef Comparison Area was not significant (Table 26). The selected GAM model excluded any interaction between Year and Site and included separate smooth effects of depth by site and a single linear (not smoothed) effect of percent hard substrate. The selected model was:
Count = Year + Site + s(Depth, by = Site, k = 3) + Hard.pct, offset = log(area), family = nb
At the Orford Reef Comparison Area, China Rockfish density increased at greater depths (p < 0.05, Fig. 15, Table 27) before starting to decrease slightly deeper than 40 m. Depth did not significantly predict density at the Redfish Rocks Marine Reserve. At both sites, China Rockfish density increased linearly with an increasing proportion of hard substrate (p < 0.05, Table 27).
GAM model results can be found in the links below:
High variability in Yelloweye Rockfish density among years, but too few observations at the Humbug Comparison area to quantitatively compare
Results for Yelloweye Rockfish were strikingly similar to those for China Rockfish, consistent with our understanding of these two species as deep-dwelling fish that prefer highly complex, bouldery habitats. The relative rarity of Yelloweye Rockfish at the Humbug Comparison Area (only a total of 5 individuals observed across years) led to the exclusion of this site from all analyses (Fig. 16). At the other two sites, there was an overall significant effect of Year (p < 0.05, Table 28), with density highest in 2018. The trend toward higher overall density at the Orford Reef Comparison Area was not significant (Table 29). The selected GAM model excluded any interaction between Year and Site and included a single smooth effect of depth and separate smooth effects of percent hard substrate at each site. The selected model was:
Count = Year + Site + s(Depth, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
Yelloweye Rockfish density increased at greater depths at both sites assessed (p < 0.05, Fig. 16, Table 30). At the Orford Reef Comparison Area, Yelloweye Rockfish density increased rapidly with higher proportions of hard substrate (p < 0.05, Fig. 16, Table 30), but the relationship was not significant at the Redfish Rocks Marine Reserve.
GAM model results can be found in the links below:
Cabezon, a camouflaged species poorly suited to consistent detection by the ROV, were observed more frequently at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area.
Cabezon density increased in 2016 and 2018 relative to 2010 across sites (Fig. 17, Table 31, Table 32), but there was no overall difference by 2019. Cabezon density was higher at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area (p < 0.05, Fig. 17, Table 32). At the Humbug Comparison Area, some relatively high densities of Cabezon were observed resulting in large variability at this site, but the pattern was not statistically significant. The selected GAM model excluded any interaction between Year and Site and included separate smooth effects of depth at each site and a single smooth effect of percent hard substrate. The selected model was:
Count = Year + Site + s(Depth, by = Site, k = 3) + s(Hard.pct, k = 3), offset = log(area), family = nb
Despite their inclusion in the best model, none of the smooth effects of depth or hard substrate were significant (Fig. 17, Table 33), and the overall deviance explained by the model was only 15.4%.
GAM model results can be found in the links below:
Lower Lingcod density at the Redfish Rocks Marine Reserve than at the Humbug Comparison Area
The Redfish Rocks Marine Reserve had overall lower densities of Lingcod than the Humbug Comparison Area (p < 0.05, Fig. r6, Table 34, Table 35), with the selected GAM model excluding any interaction between Year and Site and including separate smooth effects of depth and percent hard substrate within each Site. The selected model was:
Count = Year + Site + s(Depth, by = Site, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
Variable density among years, with an overall decrease in density across the study period.
There was a significant overall decrease in Lingcod density from 2010 to 2019, despite a transient increase between 2010 and 2016 (p < 0.05, Fig. 18, Table 35). High variability in Lingcod density among transects was reflected in high standard errors and a low proportion of total deviance explained by the model (8.2%).
At the Orford Reef Comparison Area, Lingcod density increased with depth to around 40 m before decreasing slightly (p < 0.05, Fig. 18, Table 36), but a depth effect was not significant at the other sites. Lingcod density generally increased with an increasing proportion of hard substrate (p < 0.05, Fig. 18, Table 36), except at the Humbug Comparison Area where there was no effect.
GAM model results can be found in the links below:
While not identified as particularly influential in the multivariate community composition analyses, two additional species are presented here because they represent better targets for ROV sampling than some of the focal species, by virtue of being solidarity, demersal, relatively abundant fish with no issues of detection. Additionally, they exhibited contrasting and relatively pronounced changes in abundance over the study period.
Substantially decreasing Kelp Greenling densities at all sites across the study period.
Kelp greenling densities dropped substantially across all sites between 2010 and 2019 (p < 0.05, Fig. 19, Table 37, Table 38).
The selected GAM model included a significant interaction between Year and Site, separate smooth effects of depth at each site, and a single smooth effect of percent hard substrate for all sites. The selected model was:
Count = Year * Site + s(Depth, by = Site, k = 3) + s(Hard.pct, k = 3), offset = log(area), family = nb
Kelp Greenling density was higher in 2010 at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area (p < 0.05, Table 39), but by 2019 the reduced densities at both sites were more similar.
At the Humbug Comparison Area, Kelp Greenling density decreased with depth (p < 0.05, Fig. 19, Table 39), but a depth effect was marginally non-significant at the other sites. At all sites, Kelp Greenling density increased nonlinearly with an increasing proportion of hard substrate (p < 0.05, Fig. 19, Table 39).
GAM model results can be found in the links below:
Higher Quillback Rockfish density at the Redfish Rocks Marine Reserve than at the Orford Reef Comparison Area
The Redfish Rocks Marine Reserve had overall higher densities of Quillback Rockfish than the Orford Reef Comparison Area (p < 0.05, Fig. 20, Table 40, Table 41), with the selected GAM model excluding the interaction between Year and Site and including a single smooth effect of depth across sites and separate smooth effects of percent hard substrate for each Site. The selected model was:
Count = Year + Site + s(Depth, k = 3) + s(Hard.pct, by = Site, k = 3), offset = log(area), family = nb
Variable Quillback Rockfish density among years, with an overall increase since 2010 across sites.
There was a significant overall increase in Quillback Rockfish density from 2010 to 2019 (p < 0.05, Fig. 20, Table 41).
Quillback Rockfish exhibited a unimodal peaked response to depth across all sites (p < 0.05, Fig. 20, Table 42), with the density peaking at around 35 m depth. Quillback Rockfish density generally increased with an increasing proportion of hard substrate (p < 0.05, Fig. 20, Table 42), except at the Humbug Comparison Area where the response was non-significant.
GAM model results can be found in the links below:
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