1 Introduction: Cape Perpetua Marine Reserve Hook and Line Surveys

Hook and line (HnL) sampling target demersal fishes living on rocky reef habitats using catch and release methods. All fish caught are identified to species level and measured for length. Fish are caught using standardized gear for a fixed amount of time, providing data on effort.

Hook and Line surveys (HnL) began at the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area in 2013, before implementation of the marine reserve in 2014. Sampling is conducted in the marine reserve and comparison area (see methods Appendix for additional information about comparison area selection). Monitoring efforts resulted in four years of data for our analysis and inclusion in the synthesis report.

Table 1: Cape Perpetua Marine Reserve Hook and Line Monitoring Efforts. Sample sizes represent the number of cell-days per year at each site.

Data from hook and line surveys can be used to explore questions about fish abundance and size from a survey tool that is similar to local commercial nearshore hook and line fishing efforts. We can also explore these data with questions about diversity and community composition to compare across monitoring tools to understand tool bias or to validate trends seen across tools. This can further help us understand how the fish communities at these sites are similar or different. Data on abundance and size enable us to explore how fish catch, biomass, and size have changed over time; and if these trends are similar inside and outside the reserve, indicating the influence of large scale ocean processes. Our expectation is that these two sites are inherently different from each other for diversity, community composition, and abundance questions because of their different depths and reef structures. For all data our main focus is exploring trends by site and year.

1.1 Survey Maps

1.1.1 Cape Perpetua Marine Reserve

Fig. 1: Map of Hook and Line Sampling Cells at the Cape Perpetua Marine Reserve

1.1.2 Postage Stamp Comparison Area

Fig. 1: Map of Hook and Line Sampling Cells at the Postage Stamp Comparison Area

1.2 Research Questions

Diversity

Does species diversity vary by site or year?

Community Composition

Does fish community structure vary by site or year?
- If yes, what species drive this variation?
- If no, what other factors may structure the fish community?

Aggregate Abundance

Does aggregate abundance vary by site or year?
Does aggregate biomass vary by site or year?

Focal Species Abundance

Does focal species abundance vary by site or year?
Does focal species biomass vary by site or year?
Does focal species size vary by site or year?

2 Takeaways

Here we present a summary of our Hook and Line 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 Hook and Line Results Summary

Species diversity metrics differed between the Cape Perpetua Marine Reserves and its associated comparison area.

Although the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area had a similar total species richness, many of the diversity analyses highlighted differences between the two sites. The sites differed in number of unique, rare, and common species, with more common species in the marine reserve. The Cape Perpetua Marine Reserve had greater effective number of species for 2 of 3 diversity indices, and the marine reserve had a higher species richness for an average day of sampling.

Community composition has some structuring between sites.

Community composition was much more variable at the Cape Perpetua Marine Reserve compared to the Postage Stamp Comparison Area, with site driving more variation than year or cell. In addition, variation between the two fish communities was driven by Lingcod and Canary Rockfish densities and depth.

Aggregate abundance was higher at the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area. Yearly trends were opposite in the first years of sampling but both sites had similar declining trends in the final years of sampling.

The Cape Perpetua had a higher aggregate CPUE than its comparison area. The yearly trends differed by site in the first two years of sampling (2013, 2014), with an increase at the marine reserve and a decrease at the comparison area. The comparison area’s declining trend continued in the final two years of sampling (2016, 2018) and noticeable declines at the marine reserve were observed between 2016 and 2018. Takeaways for the aggregate BPUE mirrored those of aggregate CPUE.

Species abundances differed between the marine reserve and its comparison area, with mostly higher average abundance at the marine reserve.

Both Lingcod and Canary Rockfish CPUE and BPUE were greater at the Cape Perpetua Marine Reserve. While Black Rockfish CPUE and BPUE did not differ between sites, there was a larger average size at the marine reserve. Although there were very few observations of both Yelloweye Rockfish and Cabezon, there were notable differences between sites. Yelloweye Rockfish were only observed at the Cape Perpetua Marine Reserve whereas Cabezon were only observed at the Postage Stamp Comparison Area. No China Rockfish were observed at either site over four years of sampling.

Table 2: Snapshot of Mean Abundance Results

We were able to detect natural, interannual variability in CPUE, BPUE, and size for select species.

Out of six focal species, we only had enough data to statistically analyze Black Rockfish and Lingcod to explore differences by site or trends by year. Canary Rockfish, an important species from our community composition analysis, also had enough data to statistically analyze. While patterns were inconsistent across species and survey locations, a majority of significant trends in CPUE and BPUE decreased between 2016 and 2018 across both sites. Although yearly trends in mean size were identified for several species, the time series plots reveal mean sizes do not fluctuate more than a 1-2 cm per year, per site, and likely represents natural variation as opposed to biological significance through time.

Table 3: Snapshot of Abundance Trend Results

Larger top quartile sizes of two focal species detected in the Cape Perpetua Marine Reserve.

The largest individuals of the two focal species analyzed, Black Rockfish and Lingcod, were found at Cape Perpetua Marine Reserve.

2.2 Conclusions

This is the most comprehensive report evaluating the Cape Perpetua Marine Reserve and its comparison areas with Hook and Line gear

The first summary of hook and line gear from the Cape Perpetua Marine Reserve provided a summary of baseline data with no real conclusions. Some species from the baseline report were found in either only the marine reserve (Yelloweye or Brown Rockfish) or comparison area (Cabezon or Pacific Staghorn Sculpin), and were unique to each of these areas after three additional years of data collection. The most abundant species - Black Rockfish, Canary Rockfish and Lingcod- observed in the Cape Perpetua Marine Reserve were the same in our analysis as in the first summary of data (ODFW 2015). Black Rockfish and Yellowtail Rockfish were the most abundant species in the Postage Stamp Comparison Area with the baseline data summary, but Lingcod replaced Yellowtail Rockfish with additional years of data. This report provides new information about comparisons of species diversity, community composition and aggregate and species abundance between the marine reserve and Postage Stamp Comparison Area.

Differences between sites identified in this report met our expectations with known differences of depth and habitat between sites

The Cape Perpetua Marine Reserve is unique in that it has a small, deep, patchy reef area, and there is no hard bottom habitat nearby at similar depths. The Postage Stamp Comparison Area is a shallower reef area at the southern tip of the heavily fished reef off Seal Rock. We expected there to be differences in species composition and abundance because of these known depth and habitat factors, and our results detected these expected differences. We detected differences at the broader fish species diversity and community composition level, as well as across species abundance metrics. We did observe some similar trends (declines) in aggregate and Black Rockfish abundances in the last year of sampling at both sites, which suggest larger ecosystem processes may play similar roles at each site despite known habitat differences.

We had limited ability to analyze data on solitary demersal rockfish species with our statistical approach in this report.

For Cabezon, Yelloweye, Copper and Quillback Rockfish the number of observations per year per site for these species was typically less than twenty individuals, with high variability in observations between sites. This presented challenges in analyzing data for these species in this report. We chose not to do a species-specific modeling approach because it was unclear if alternative approaches would have yielded interpretable results. Hook and line data gather information on other fisheries targeted species (e.g. Copper, Quillback, Vermillion Rockfish), but these solitary demersal fish also had small sample sizes per site per year and similarly would have required an individualized approach for analysis.

Monitoring with hook and line surveys will continue at current levels and intervals.

Current monitoring efforts are able to detect interannual trends for the most abundant species but there is limited ability to detect changes of solitary demersal species with current efforts (e.g. Yelloweye, Cabezon, Quillback). It is unclear whether increasing effort across sites would increase our ability to detect change for these species, given that they are found in relatively low densities with this survey tool. Future analytical efforts may explore different techniques than the ones in this report, which may be more appropriate for data-poor species (e.g. occupancy modeling), or exploring species-habitat relationships. Without an increase to program budget or staff, hook and line survey efforts will likely continue at current levels and intervals.

3 Hook & Line Methods

Hook and Line (HnL) surveys were conducted in the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area. Surveys began in 2013 with unequal survey effort; more effort is possible in the Postage Stamp Comparison Area because it is closer to port (Newport). Survey effort targeted 2 days in the reserve, 2 days at the comparison area for both spring and fall surveys. Each day between four to five cells (500m x 500 m grids) are targeted, with 3 fifteen-minute fishing drifts occurring in each cell. All catch data collected from drifts are combined for a single cell; the unit of replication for hook and line catch data is at the cell-day level, while all size data is at the individual fish level. All fish caught during each drift are identified to species, measured and released. During each drift we also determined the amount of time not spent fishing, such as when anglers hang up on the bottom, stop to rest, or take a photo with their fish caught. This time is factored into the total time spent fishing, to truly represent fishing effort during these surveys. We then calculate both a catch and biomass per unit effort for each given cell-day and species. For additional details on data collection, please review documentation in the Methods Appendix.

3.1 Diversity

With hook and line gear, we explored several concepts related to species diversity at a given site:

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

3.1.1 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 survey years because each site (reserve or comparison area) likely has a species pool larger than can be surveyed in any one year. We excluded unidentified species from the summaries as well as species not well targeted by hook and line gear (e.g. Wolf eel).

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

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

3.1.2 Unique, Common, and Rare Species

Richness alone does not comprise all aspects of species biodiversity - 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. These tables exclude the unidentified species and species not well targeted by HnL gear. The species count tables include a total count for each species summed for all years by area, and for each year-area 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 was defined as the proportion of cells surveyed that a species was observed. 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%. The 50% threshold for common species represents that a species is caught in one out of every two cells surveyed. We also identified species that were unique to each marine reserve and comparison area.

3.1.3 Diversity Indices

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

3.1.4 Diversity Through Time

Finally we explored how diversity changed through time. First we plotted annual species rarefaction curves to determine if we had sampled appropriately (i.e. reached an asymptote) to compare species diversity from year to year. When our survey effort was not adequate to compare across years, we pooled data from all years to compare average daily diversity using an anova. Average cell diversity provides useful information about the expected number of species per hook and line sample unit, but is not directly related to total expected species richness in a given survey area.

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

3.2 Community Composition

We focused our community composition analysis around understanding if the variation in fish community structure 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 area and year, we also explored several other potential drivers of variation including species-specific differences, habitat and environmental factors.

To explore variation by site and year, we used catch per unit effort (CPUE) data with a dispersion weighting transformation to downweight species that have high variability within each site. This allows us to better deal with highly aggregated schooling species without enhancing importance of rare species (Clarke et al. 2006). CPUE data are considered a rate (catch per angler hour) so a Euclidean distance resemblance matrix was selected. A dummy variable (=1) was added prior to creating the resemblance matrix due to the high prevalence of zeros in the dataset. To visualize the data, we ran a cluster analysis and generated nMDS plots by area, and year. Because of the known differences between the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area, we also explored data visualizations by area using cell as centroids.

To test the statistical significance in our data of variation by site and year we ran a permutational ANOVA, using a nested mixed model with site and year as fixed, and cell, our sampling replicate for hook and line gear, as a random nested factor under site. To explore if any significant results of the PERMANOVA were related to true differences in location or differences in dispersion of samples (either by area or year to year), we ran a PERMDISP, a distance based test for homogeneity of multivariate dispersions (Anderson and Walsh 2013). Significant factors resulting from PERMANOVA test (Site,Cell, and/or Year) were then analyzed using a PERMDISP. If a factor was significant in the PERMANOVA but not the PERMDISP, then it can be inferred that the significance is related to a location effect, but not a dispersion effect. If the factor is also significant in the PERMDISP, then significance in the PERMANOVA is related to dispersion, but may also be a location effect.

Beyond site and year, we explored several additional factors that could be driving the variation in fish community structure. We extended our data visualization, by performing a vector analysis of fish species in the community, selecting only the species with > 0.5 Pearson correlations (Hinkle et al. 2003). We then generated density plots of the identified species to visualize their relationship to area or year. To better understand how these species contributed to variation in the data, we ran a principal coordinates (PCO) analysis, using a euclidean distance resemblance matrix, which provides information on the percent of variation explained by each axis. If more than four species were identified with Pearson correlations > 0.5, we only reported on those species with correlations > 0.7 (those considered as a high correlation)

In addition to species specific drivers of variation, we also explored the relationship between community composition and environmental variable. We employed a multivariate model incorporating month, proportion of hard bottom (rock) within a sampling cell, and average drift depth (averaged among cell/day combinations) to test if these habitat or environmental variables explained significant variation across sites or years. Due to strict requirements of these variables needing to match with each specific biological sample, only samples that contained estimates of all the above variables were used for analysis. An initial histogram of data yielded non-normal distributions so an overall data transformation (Log(x+1)) was employed. CPUE data are considered a rate (catch per angler hour) so a distance based resemblance matrix was selected, using a euclidean distance with an addition of a dummy variable (=1). With these data a distance-based linear model (DistLM) and a distance-based redundancy analysis (dbRDA) were conducted to determine which variables may explain variation across sites or years (Legendre and Anderson 1999). DistLM is akin to a multivariate multiple regressions model where the relationship between a multivariate data cloud (resemblance matrix) and one or more predictor variables are analyzed and modeled. The dbRDA routine then visualizes the model and fits it into a multi-dimensional space. In the DistLM model AIC values were used as the selection criteria and a Best selection procedure was employed to find the best combination of variables with the lowest AIC value as the best model fit.

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

3.3 Abundance

We explored changes in aggregate and focal species catch rates (catch per unit effort, CPUE), biomass rates (biomass per unit effort, BPUE), and size (focal species only) by site and year with generalized additive mixed models (GAMM). We modeled raw catch and biomass data with an offset for fishing effort (angler hour) (Maunder and Punt 2004, Zuur 2012); size data were modeled without an offset. A negative binomial distribution was used for the CPUE model and a gaussian distribution on log-transformed biomass for the BPUE model. After exploration of spatial-temporal auto correlation of residuals with focal species data, a gaussian distribution for the size model. GAMMs were chosen to account for non-linear trends in metrics by year detected in preliminary data exploration (Veneables and Dichmont 2004, Zuur et al. 2009). GAMMs were fitted using the mgcv package in R. Site was treated as a fixed categorical variable, while Year was continuous and smoothed with the thin-plate smoother ‘s()’ (Zuur et al 2009; Zuur 2012), grouped by Site, and k was restricted to 3 knots to prevent over-fitting. Cell was included as a random effect in the model to account for the nested nature of the sampling design and for random differences in depth and habitat among cells. We limited our modeling exercise to focus on Site and Year as these are two of the primary questions of interest. For species with very low CPUE across most sites and years, no statistical analyses were conducted as the data violated assumptions of the model framework.

Specifically we analyzed aggregate CPUE and BPUE, and species-specific CPUE, BPUE and size for focal species. Additionally for focal species, we complemented the GAMM modeling results for size with an analysis of variance (ANOVA) exploring changes in the mean top quartile size of fish by site. The mean top quartile size of fish is a measure of how the mean size of the largest quartile of fish changes through time, a technique borrowed from fish longevity studies (Choat and Robertson 2002), whereas the GAMM modeling results evaluate change in mean size of all individuals of a species. The mean size of fish may obscure gains in the larger size classes through time if there has been a strong recruitment of juvenile fishes. When results were significant, a Tukey test was run to determine significant differences among sites.

There are six focal fish species for the OR Marine Reserves Ecological Monitoring Program:

Black Rockfish; Sebastes melanops
Blue/Deacon Rockfish; Sebastes mystinus / S. diaconus
China Rockfish; Sebastes nebulosus
Yellow-eye Rockfish; Sebastes ruberrimus
Cabezon; Scorpaenichthys marmoratus
Lingcod; Ophiodon elongatus

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

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

CPUE Model = gam(Catch ~ Site + s(Year, by = Site, k = 3) + s(Cell_ID, bs = “re”), offset = log(Effort), family = nb)

BPUE Model = gam(log(Biomass + 1) ~ Site + s(Year, by = Site, k = 3) + s(Cell_ID, bs = “re”), offset = log(Effort), family = gaussian)

Size Model = gam(Length ~ Site + s(Year, by = Site, k = 3) + s(Cell_ID, bs = “re”), family = gaussian)

4 Cape Perpetua Marine Reserve Results

Hook and line survey efforts at Cape Perpetua Marine Reserve and its comparison area resulted in four years of data collection, where varying sample sizes were collected per year (Fig. 2). All years of sampling resulted in more survey effort in the Postage Stamp Comparison Area because it is closer to port, and therefore has shorter transit times that allow for more sampling effort.

Fig. 2: Hook and line monitoring efforts at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area resulted in varied sample sizes over the four years of data collection. Sample size is represented in cell-days.

Table 4: Cape Perpetua Marine Reserve Hook and Line Monitoring Efforts

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4.1 Diversity

4.1.1 Species richness

Species richness is similar between the Cape Perpetua Marine Reserve and its Comparison Area.

Over the four years of sampling with hook and line gear a total of 13 species (or species groups) were observed in the Cape Perpetua Marine Reserve (Table 5). The Postage Stamp Comparison Area had the same number species, 13. These observed numbers of species richness are similar to the estimated numbers of total species richness (Table 5).

library(kableExtra)
pna <- data.frame(Site = c("Cape Perpetua Marine Reserve", "Postage Stamp Comparison Area"), Observed_Richness = c("13","13"), Estimated_Richness = c("14","15"), LCL = c("13","13"), UCL = c("27", "35"))


  kbl(pna, caption = "Table 5: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits") %>% 
  kableExtra::kable_classic()

Table 5: Observed and estimated species richness by site with lower (LCL) and upper (UCL) 95% confidence limits
Site	Observed_Richness	Estimated_Richness	LCL	UCL
Cape Perpetua Marine Reserve	13	14	13	27
Postage Stamp Comparison Area	13	15	13	35

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Species rarefaction curves highlight that species richness among areas is similar between the two sites (Fig. 3). As effort across sites increases, similar numbers of rare species are observed at each site (Fig. 3, Table 2).

Fig. 3: Species rarefaction curves for the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area. Data are pooled across all years of sampling for each site.

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4.1.2 Unique, common and rare species

The Cape Perpetua Marine Reserve has a different composition of unique, common and rare species, than its comparison area, despite similar species richness.

Both the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area have four unique species. At the Cape Perpetua Marine Reserve, Yelloweye Rockfish, Brown Rockfish,S. auriculatus, Bocaccio, S. paucispinis, and Tiger Rockfish, S. nigrocinctus were unique to the reserve. Cabezon, Pacific Staghorn Sculpin,Leptocottus armatus , Brown Irish Lord, Hemilepidotus spinosus, and the Spotted Ratfish, Hydrolagus colliei, were unique to the Postage Stamp Comparison Area (Fig. 4).

The Cape Perpetua Marine Reserve had more common species (n=5) than the Postage Stamp Comparison Area (n=2). While the two most common species by count and frequency of occurrence were Black Rockfish and Lingcod at both sites, the marine reserve additionally had Canary Rockfish, Yellowtail Rockfish and Quillback Rockfish as common species (Tables 6, 8). The Cape Perpetua Marine Reserve had four rare species (Brown Rockfish, Buffalo Sculpin, Bocaccio and Tiger Rockfish), whereas the Postage Stamp Comparison Area had six rare species (Canary Rockfish, Quillback Rockfish, Copper Rockfish, Pacific Staghorn Sculpin, Brown irish Lord and the Spotted Ratfish).

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

4.1.2.1 Cape Perpetua Marine Reserve

Fig. 4: Relative frequency of occurrence of species observed at the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area.

4.1.2.2 Postage Stamp Comparison Area

Fig. 4: Relative frequency of species observed at the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area.

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4.1.3 Diversity Indices

The Cape Perpetua Marine Reserve has higher effective number of species than the Postage Stamp Comparison Area when incorporating concepts of evenness into the diversity indices.

The Cape Perpetua Marine Reserve has higher effective number of species for Hill numbers 1 and 2 than the Postage Stamp Comparison Area, despite similar estimates for the index derived from species richness (Fig. 5). These indices incorporate concepts of species evenness into the estimates of effective number of species, suggesting a greater evenness in catch composition at the marine reserve. Differences in Hill numbers between the two sites are not surprising given that they are located in different environments (e.g. depth, reef size).

Fig. 5: Comparing effective number of species (Hill diversity numbers) across the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area. 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).

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4.1.4 Diversity through time

We did not get enough samples to evaluate change in species diversity through time at the Cape Perpetua Marine Reserve or its comparison area.

Species rarefaction curves by year for each site indicated that we did not sample enough on a yearly basis to compare changes in mean species richness through time (Fig. 6-7). When plotting annual species rarefaction curves 95% confidence intervals, there is high overlap among the years suggesting that additional sampling is needed detect temporal patterns in species richness.

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For an average day of sampling, the Cape Perpetua Marine Reserve has higher species richness than the Postage Stamp Comparison Area.

When comparing mean species richness for an average day of sampling, there were statistically significant differences among sites (F = 27.555, p < 0.001). Tukey HSD tests reveal that the Cape Perpetua Marine Reserve has a statistically more species richness observed in a day of sampling than its comparison area (adj. p < 0.001, Fig. 8).

$Fig. 8: Mean species richness by area with 95% confidence intervals at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.$

Fig. 8: Mean species richness by area with 95% confidence intervals at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.2 Community Composition

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4.2.1 Variation by Area and Year

The Cape Perpetua Marine Reserve has a distinct catch composition that is more variable than its comparison area.

Results from nMDS plots at both the sample and cell centroid level, show high variability in catch composition at the Cape Perpetua Marine Reserve but low variability at the Postage Stamp Comparison Area (Fig. 9). Results at the cell centroid level further refine our understanding of catch composition, showing that the marine reserve cells are distinct from those in the Postage Stamp Comparison Area and more variable within the reserve than cells in the comparison area (Fig. 9).

There are no apparent groupings of catch composition by year at the Cape Perpetua Marine Reserve or its comparison area with hook and line data.

With four years of sampling with hook and line gear, there are no apparent groupings of catch composition by year at the Cape Perpetua Marine Reserve or the Postage Stamp Comparison Area (Fig. 9).

Multivariate statistics indicate differences by site, cell and year.

PERMANOVA results indicate that site, cell, and year were significant for catch composition, including the interaction between site and year (Table 10). Site accounted for the highest variability of all the variables/interactions (25%), but together with year (8%), cell (13%) and the interaction between site and year (14%) explained more than 60% of the variability in catch composition. The residuals explained approximately 39% of the variability in the data.

Table 10: PERMANOVA results

PERMDISP results by site indicate that dispersion was significantly different between the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area (p = 0.001). The marine reserve had higher dispersion than the comparison area (Table 11).

There was also significant dispersion between years and between cells (p < 0.05). 2018 was significantly different than all other years as mean dispersion was approximately half of dispersion of other years (Table 11). Among cells, significant differences arose largely between cells at the marine reserve vs. cells at hte comparison area, with mairne reserve cells having higher mean dispersion than comparison area cells.

These results suggests the significance identified in the PERMANOVA is likely a combination of both differences in dispersion and location for site, cell and year. The comparison area has little dispersion compared to the marine reserve, and in general samples in 2018 were much more similar to each other than samples collected in previous years. Together, these results indicate that Cape Perpetua Marine Reserve differs from its comparison area in both a location effect (different composition) and a dispersion effect (higher variability in the reserve).

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4.2.1.1 Area: Samples

Fig. 9: Results from nMDS plots by site demonstrating that catch composition is distinct and has more variability at the Cape Perpetua Marine Reserve than its comparison area. There are no apparent groupings of catch composition by year. See separate tabs for area and year.

4.2.1.2 Area: Centroids

4.2.1.3 Year

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4.2.2 Other drivers of variation

Lingcod and Canary Rockfish drive the majority of variation in catch composition between the marine reserve and comparison area and within the marine reserve itself.

We explored species-specific drivers of variation, and found that Canary Rockfish and Lingcod had high correlations with variation in catch composition (Fig. 10). The Cape Perpetua Marine Reserve had higher catch of both Canary Rockfish and Lingcod compared to the comparison area. Within the marine reserve itself, catch of these two species was variable, and there appear to be trade-offs between high catch of Lingcod and high catch of Canary Rockfish.

Catch composition differences observed between Cape Perpetua and its comparison area are mostly attributable to differences in depth.

DISTLM results indicate all four environmental variables (year, month, average drift depth, and proportion rock), were significant and the best model included all of these variables (Table 16). Depth roughly correlated with the x axis and explained 74% of model variation and 18% of the total variation. The three other variables all correlated (> 0.5) with the y-axis but combined only contributed to 16% of the model variation and < 4% of the overall variability. The small variation explained by these three variables indicates as currently incorporated they are not substantial drivers of the fish community, but that depth does contribute to the distribution of fish, especially within the marine reserve.

Table 16: Cape Perpetua DistLM results

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4.2.2.1 Species Vector Plot

Fig. 10: Results from species correlations and principal coordinate analysis demonstrating that Canary Rockfish and Lingcod drive variation in community structure at the Cape Perpetua Marine Reserve and its surrounding comparison area. See separate tabs for vector and bubble plots. Bubble color/size represents species-specific densities in each sample (species density range indicated in legend).

4.2.2.2 Species Bubble Plot

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4.3 Aggregate Abundance

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4.3.1 Aggregate CPUE

Significantly higher aggregate CPUE in the Cape Perpetua Marine Reserve than its comparison area.

Cape Perpetua Marine Reserve had higher aggregate CPUE than the Postage Stamp Comparison Area (p < 0.05; Table 17).

Significant yearly trends in aggregate CPUE at both the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

Significant yearly trends in aggregate CPUE were detected at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area (p < 0.05; Table 18). At the Cape Perpetua Marine Reserve, CPUE increased to a peak from 2014-2016, followed by a decline through the most recent survey efforts in 2018. The trend differed at the Postage Stamp Comparison Area, with a decline through time from initial survey efforts in 2013 to 2018.

The random effect of cell (unit of replication) was not a significant component of variation (Table 18).

GAMM model results can be found in the links below:

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4.3.1.1 Aggregate CPUE timeseries

Fig. 11: Aggregate catch per unit effort (CPUE) timeseries and modeled GAMM results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison areas. See separate tabs for timeseries and GAMM results.

4.3.1.2 Aggregate CPUE modeled GAMM results

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4.3.2 Aggregate BPUE

Significantly higher aggregate BPUE in the Cape Perpetua Marine Reserve than it comparison area.

Cape Perpetua Marine Reserve had higher aggregate BPUE than its comparison area (p < 0.05, Table 19).

Significant yearly trends in aggregate BPUE at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

There were significant yearly trend in aggregate BPUE at the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area (p < 0.05; Table 20). At the Cape Perpetua Marine Reserve, BPUE increased to a peak in 2016, followed by a decline through the most recent survey efforts in 2018. The trend differed at the Postage Stamp Comparison Area, with a decline through time from initial survey efforts in 2013 to 2018.

The random effect of cell (unit of replication) was a significant component of variation (Table 20).

GAMM model results can be found in the links below:

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4.3.2.1 Aggregate BPUE timeseries

Fig. 12: Aggregate biomass per unit effort (BPUE) timeseries and GAMM model results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.3.2.2 Aggregate BPUE modeled GAMM results

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4.4 Focal Species Abundance & Size

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4.4.1 Black Rockfish, S. melanops

$~$

4.4.1.1 CPUE

No difference in Black Rockfish CPUE between the Cape Perpetua Marine Reserve and its comparison area.

There was no difference in Black Rockfish CPUE between the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area (p < 0.05; Table 21).

Significant yearly trends in Black Rockfish CPUE at the Postage Stamp Comparison Area.

There was a significant trend in Black Rockfish CPUE by year at the Postage Stamp Comparison Area, with a decreasing trend through time (p < 0.05; Table 22). There was no significant yearly trend at the Cape Perpetua Marine Reserve (p > 0.05; Table 22),

The random effect of cell was identified as a significant component of variation (Table 22).

GAMM model results can be found in the links below:

$~$ $~$

4.4.1.1.1 Black Rockfish CPUE Timeseries

Fig. 13: Black Rockfish catch per unit effort (CPUE) timeseries and GAMM model results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.4.1.1.2 Black Rockfish CPUE Modeled GAMM Results

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4.4.1.2 Size

The Cape Perpetua Marine Reserve had larger mean size Black Rockfish than the Postage Stamp Comparison Area..

Larger mean size Black Rockfish were caught in the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area ( p < 0.05, Table 23).

Significant yearly trends in Black Rockfish mean size at the Cape Perpetua Marine Reserve.

There were significant yearly trends in Black Rockfish mean size at the Cape Perpetua Marine Reserve (p < 0.05, Table 24). At the marine reserve, Black ROckfish mean size increased through time over the four years of sampling. There was no significant yearly trend in Black Rockfish mean size at the Postage Stamp Comparison Area (p > 0.05). Even though the model results reveal statistically significant yearly trends, the mean size timeseries underscores that mean sizes do not fluctuate more than 1-2 cm per year, per site, and likely represents natural variation as opposed to biological significance through time (Fig. 14).

The random effect of cell (unit of replication) was identified as a significant component of variation (Table x).

GAMM model results can be found in the links below:

$~$ $~$

The Cape Perpetua Marine Reserve has significantlylarger top quartile sizes of Black Rockfish than the Postage Stamp Comparison Area.

There were differences by site in the top quartile of sizes of Black Rockfish (F. 347.401, p. <0.05). The marine reserve had significantly larger top quartile sizes of Black Rockfish than its comparison area (adj. p < 0.05).

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4.4.1.2.1 Black Rockfish Mean Size Timeseries

Fig. 14: Black Rockfish mean size timeseries and GAMM model results with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.4.1.2.2 Black Rockfish Size Modeled GAMM Results

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4.4.1.3 BPUE

No difference in Black Rockfish BPUE between the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

The Cape Perpetua Marine Reserve did not differ in its Black Rockfish BPUE than its comparison area (p > 0.05; Table 25).

Significant yearly trends in Black Rockfish BPUE at both the Cape Perpetua Marine Reserve and its comparison area.

There were significant yearly trends at the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area (all p > 0.05; Table 26) for Black Rockfish BPUE. The Cape Perpetua Marine Reserve, BPUE increased to a peak in 2016, followed by a decline through the most recent survey efforts in 2018. The trend differed at the Postage Stamp Comparison Area, with a decline through time from initial survey efforts in 2013 to 2018.

The random effect of cell (unit of replication) was identified as a significant component of variation (Table 26).

GAMM model results can be found in the links below:

$~$ $~$

4.4.1.3.1 Black Rockfish BPUE Timeseries

Fig. 15: Black Rockfish biomass per unit effort (BPUE) timeseries and GAMM model results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison areas. See separate tabs for timeseries and GAMM results.

4.4.1.3.2 Black Rockfish BPUE Modeled GAMM Results

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4.4.2 Blue/Deacon Rockfish, S.mystinus / S.diaconus

$~$

4.4.2.1 CPUE

Too few observations of Blue/Deacon Rockfish to detect differences in CPUE by site or year.

Catch rates of Blue/Deacon Rockfish were very low across all sites and years (e.g. 1 fish caught per 10 angler hours fishing at the reserve only; Fig. 16), so statistical analyses were not conducted.

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4.4.2.1.1 Blue/Deacon Rockfish CPUE Timeseries

Fig. 16: Blue/Deacon Rockfish catch per unit effort (CPUE) timeseries with 95% confidence intervals, at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.4.2.2 Size

Too few observations of Blue/Deacon Rockfish to detect differences in size by site or year.

Catch rates of Blue/Deacon Rockfish were very low across all sites and years (e.g. 1 fish caught per 10 angler hours fishing at the reserve only; Fig. 16), so statistical analyses were not conducted.

4.4.2.2.1 Blue/Deacon Rockfish Mean Size Timeseries

Fig. 17: Blue/Deacon Rockfish mean size timeseries with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area.

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4.4.2.3 BPUE

Too few observations of Blue/Deacon Rockfish to detect differences in BPUE by site or year.

Catch rates of Blue/Deacon Rockfish were very low across all sites and years (e.g. 1 fish caught per 10 angler hours fishing at the reserve only; Fig. 16), resulting in low BPUE estimates, so statistical analyses on BPUE data were not conducted.

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4.4.2.3.1 Blue/Deacon BPUE Timeseries

Fig. 18: Blue/Deacon Rockfish biomass per unit effort (BPUE) timeseries with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area.

4.4.3 China Rockfish, S. nebulosus

$~$

4.4.3.1 CPUE

No observations of China Rockfish to detect differences in CPUE by site or year.

Four years of fishing effort at the Cape Perpetua Marine Reserve and its comparison area resulted in no China Rockfish caught across all sites and years.

$~$ $~$

4.4.3.2 Size

No observations of China Rockfish to detect differences in size by site or year.

Four years of fishing effort at the Cape Perpetua Marine Reserve and its comparison area resulted in no China Rockfish caught across all sites and years.

$~$ $~$

4.4.3.3 BPUE

No observations of China Rockfish to detect differences in BPUE by site or year.

Four years of fishing effort at the Cape Perpetua Marine Reserve and its comparison area resulted in no China Rockfish caught across all sites and years.

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4.4.4 Yelloweye Rockfish, S.ruberrimus

$~$

4.4.4.1 CPUE

Too few observations of Yelloweye Rockfish to detect differences in CPUE by site or year.

Catch rates of Yelloweye Rockfish were very low across all sites and years (e.g. 1 fish caught per 10 angler hours fishing at the reserve only; Fig. 19), so statistical analyses were not conducted. No Yelloweye Rockfish were caught at the Postage Stamp Comparison Area during four years of survey efforts.

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4.4.4.1.1 Yelloweye Rockfish CPUE Timeseries

Fig. 19: Yelloweye Rockfish catch per unit effort (CPUE) timeseries with 95% confidence intervals, at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.4.4.2 Size

Too few observations of Yelloweye Rockfish to detect differences in size by site or year.

$~$ $~$

4.4.4.3 BPUE

Too few observations of Yelloweye Rockfish to detect differences in BPUE by site or year.

Catch rates of Yelloweye Rockfish were very low across all sites and years (e.g. 1 fish caught per 10 angler hours fishing at the reserve only; Fig. 19), resulting in low BPUE estimates, so statistical analyses on BPUE data were not conducted. No Yelloweye Rockfish were caught at the Postage Stamp Comparison Area during four years of survey efforts.

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4.4.4.3.1 Yelloweye Rockfish BPUE Timeseries

Fig. 20: Yelloweye Rockfish biomass per unit effort (BPUE) timeseries with 95% confidence intervals, at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.4.5 Cabezon, Scorpaenichthys marmoratus

$~$

4.4.5.1 CPUE

Too few observations of Cabezon to detect differences in catch rate by site or year.

Catch rates of Cabezon were very low across all sites and years (e.g. 1 fish caught per 20 angler hours fishing at the comparison area only; Fig. 21), so statistical analyses on CPUE data were not conducted. No Cabezon were caught in four years of survey efforts at the Cape Perpetua Marine Reserve.

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4.4.5.1.1 Cabezon CPUE Timeseries

Fig. 21: Cabezon catch per unit effort (CPUE) timeseries with 95% confidence intervals, at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.4.5.2 Size

Too few observations of Cabezon to detect differences in size by site or year.

Catch rates of Cabezon were very low across all sites and years (e.g. 1 fish caught per 20 angler hours fishing at the comparison area only; Fig. 21), so statistical analyses on size data were not conducted. No Cabezon were caught in four years of survey efforts at the Cape Perpetua Marine Reserve.

$~$ $~$

4.4.5.3 BPUE

Too few observations of Cabezon to detect differences in BPUE by site or year.

Catch rates of Cabezon were very low across all sites and years (e.g. 1 fish caught per 20 angler hours fishing at the comparison area only; Fig. 21) resulting in low BPUE estimates, so statistical analyses on size data were not conducted. No Cabezon were caught in four years of survey efforts at the Cape Perpetua Marine Reserve.

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4.4.5.3.1 Cabezon BPUE Timeseries

Fig. 22: Cabezon biomass per unit effort (BPUE) time series with 95% confidence intervals, at the Cape Perpetua Marine Reserve and Postage Stamp Comparison Area.

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4.4.6 Lingcod, Ophiodon elongatus

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4.4.6.1 CPUE

Significantly higher Lingcod CPUE in the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area.

There was significantly higher Lingcod CPUE in the marine reserve than its comparison area (p < 0.05, Table 27).

Significant yearly trend in Lingcod CPUE at the Cape Perpetua Marine Reserve.

Only the Cape Perpetua Marine Reserve had a significant yearly trend in Lingcod CPUE (p < 0.05; Table 28), with an increasing trend to peak in 2016, followed by a decline through 2018. There was no significant trend at the Postage Stamp Comparison Area (p > 0.05, Table 28).

The random effect of cell (unit of replication) was not significant (Table 28).

GAMM model results can be found in the links below:

$~$ $~$

4.4.6.1.1 Lingcod CPUE Timeseries

Fig. 23: Lingcod catch per unit effort (CPUE) timeseries and GAMM model results with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.4.6.1.2 Lingcod CPUE Modeled GAMM Results

Fig. 23: Lingcod catch per unit effort (CPUE) timeseries and GAMM model results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

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4.4.6.2 Size

No significant difference in mean size of Lingcod between the Cape Perpetua Marine Reserve and its comparison area.

The mean size of Lingcod was not significantly different between the Cape Perpetua Marine Reserve and its comparison area (p > 0.05, Table 37).

No significant yearly trends in Lingcod mean size at the Cape Perpetua Marine Reserve or its comparison area.

There was no significant yearly trend in Lingcod mean size at the either the marine reserve or comparison area (p > 0.05, Table 30).

The random effect of cell (unit of replication) was identified as a significant component of variation for mean size of Lingcod (Table 30).

GAMM model results can be found in the links below:

$~$

The Cape Perpetua Marine Reserve has larger top quartile sizes of Lingcod than the Postage Stamp Comparison Area.

There were differences by site in the top quartile of sizes of Lingcod (F.55.458, p.< 0.05). The Cape Perpetua Marine Reserve had larger top quartile sizes of Lingcod than the comparison area (all adj. p < 0.05).

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4.4.6.2.1 Lingcod Mean Size Timeseries

Fig. 24: Lingcod mean size timeseries and GAMM model results with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.4.6.2.2 Lingcod Size Modeled GAMM Results

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4.4.6.3 BPUE

Significantly higher Lingcod BPUE in the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area.

Lingcod BPUE was higher in the Cape Perpetua Marine Reserve than in its comparison area (p < 0.05; Table 31).

Significant yearly trends in Lingcod BPUE at the Cape Perpetua Marine Reserve.

Only the Cape Perpetua Marine Reserve had a significant yearly trend in Lingcod BPUE (p < 0.05; Table 32), with an increasing trend to peak in 2016, followed by a decline through 2018. There was no significant trend at the Postage Stamp Comparison Area (p > 0.05, Table 32).

The random effect of cell (unit of replication) was not a significant component of variation (Table 32).

GAMM model results can be found in the links below:

$~$ $~$

4.4.6.3.1 Lingcod BPUE Timeseries

Fig. 25: Lingcod biomass per unit effort (BPUE) timeseries and modeled GAMM results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.4.6.3.2 Lingcod BPUE Modeled Biomass Results

Fig. 25: Lingcod biomass timeseries and modeled GAMM results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

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4.5 Additional Species Abundance & Size

4.5.1 Canary Rockfish, S. pinniger

$~$

4.5.1.1 CPUE

Significantly higher Canary Rockfish CPUE in the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area.

There was significantly higher Canary Rockfish CPUE in the marine reserve than its comparison area (p < 0.05; Table 33).

Significant yearly trend in Canary Rockfish CPUE at the Cape Perpetua Marine Reserve.

Only the Cape Perpetua Marine Reserve had a significant yearly trend in Canary Rockfish CPUE (p < 0.05; Table 34), with a decreasing trend through time. There was no significant trend at the Postage Stamp Comparison Area (p > 0.05, Table 34).

The random effect of cell (unit of replication) was identified as a significant component of variation (Table 34).

GAMM model results can be found in the links below:

$~$ $~$

4.5.1.1.1 Canary Rockfish CPUE Timeseries

Fig. 26: Canary Rockfish catch per unit effort (CPUE) timeseries and GAMM model results with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.5.1.1.2 Canary Rockfish CPUE Modeled GAMM Results

Fig. 26: Canary Rockfish catch per unit effort (CPUE) timeseries and GAMM model results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

$~$ $~$

4.5.1.2 Size

No significant difference in mean size of Canary Rockfish between the Cape Perpetua Marine Reserve and its comparison area.

The mean size of Canary Rockfish was not significantly different between the Cape Perpetua Marine Reserve and its comparison area (p > 0.05, Table 35).

Significant yearly trends in Canary Rockfish mean size at both the Cape Perpetua Marine Reserve and its comparison area.

There was a significant yearly trend in Canary Rockfish mean size at both the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area. (p < 0.05, Table 36). At the marine reserve there was a slight gradual increase in Canary Rockfish mean size through time. The trend observed in Canary Rockfish at the comparison area is driven by those caught in 2014 and 2016, as only one individual was observed at this site in 2013 and 2018. Due to this lack of data in 2013 and 2018, the trend should be interpreted with caution.

The random effect of cell (unit of replication) was identified as a significant component of variation for mean size of Canary Rockfish (Table 36).

GAMM model results can be found in the links below:

$~$

No difference in top quartile sizes of Canary Rockfish between the Cape Perpetua Marine Reserve and the Postage Stamp Comparison Area.

There were no differences by site in the top quartile of sizes of Canary Rockfish (F.0.077, p.> 0.05).

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4.5.1.2.1 Canary Rockfish Mean Size Timeseries

Fig. 27: Canary Rockfish mean size timeseries and GAMM model results with 95% confidence intervals at the Cape Perpetua Marine Reserve and its associated comparison area. See separate tabs for timeseries and GAMM results.

4.5.1.2.2 Canary Rockfish Size Modeled GAMM Results

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4.5.1.3 BPUE

Significantly higher Canary Rockfish BPUE in the Cape Perpetua Marine Reserve than the Postage Stamp Comparison Area.

Canary Rockfish BPUE was higher in the Cape Perpetua Marine Reserve than in its comparison area (p < 0.05; Table 37).

Significant yearly trends in Canary Rockfish BPUE at the Cape Perpetua Marine Reserve.

There was a significant yearly trend in Canary Rockfish BPUE at the Cape Perpetua Marine Reserve (p > 0.05; Table 38), with a decline between 2016 to the final sampling in 2018. There was no significant trend at the Postage Stamp Comparison Area (p > 0.05, Table 38).

The random effect of cell (unit of replication) was not identified as a significant component of variation for Canary Rockfish BPUE (Table 38).

GAMM model results can be found in the links below:

$~$ $~$

4.5.1.3.1 Canary Rockfish BPUE Timeseries

Fig. 28: Canary Rockfish biomass per unit effort (BPUE) timeseries and modeled GAMM results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area.See separate tabs for timeseries and GAMM results.

4.5.1.3.2 Canary Rockfish BPUE Modeled Biomass Results

Fig. 28: Canary Rockfish biomass timeseries and modeled GAMM results with 95% confidence intervals, at the Cape Perpetua Marine Reserve and its associated comparison area.See separate tabs for timeseries and GAMM results.

$~$ $~$

5 References

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

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

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

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

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

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

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

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

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

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

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

Maunder, M. N., & Punt, A. E. (2004). Standardizing catch and effort data: a review of recent approaches. Fisheries research, 70(2-3), 141-159.

ODFW (2015). Oregon Marine Reserve Ecological Monitoring Report 2012-2013. Oregon Department of Fish and Wildlife. Marine Resources Program. Newport Oregon. 1-126.

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

Starr, R. M., Wendt, D. E., Barnes, C. L., Marks, C. I., Malone, D., Waltz, G., … & Yochum, N. (2015). Variation in responses of fishes across multiple reserves within a network of marine protected areas in temperate waters. PLoS One, 10(3), e0118502.

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

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

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

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

#beepr::beep()