1 Introduction: Cape Perpetua Hook and Line Biology & Oceanography Report

Data from Hook and Line (HnL) surveys can be used to explore questions about fish abundance and size from a fishery-independent monitoring method. Oceanographic data that are collected near HnL sampling sites provide the opportunity to understand local oceanographic processes that may influence the performance of hook and line surveys to represent the fish community. We analyzed HnL data together with co-located and nearby oceanographic data to assess the influence of time-varying environmental conditions on the responses of fish communities as represented by hook and line sampling. Specifically, we assess the effect of bottom dissolved oxygen, wind stress (as a proxy for upwelling), and bottom temperature on the aggregate catch and biomass rates of all species, as well as the catch and biomass rates and size for select focal species (for more information about focal species, see: Methods).

1.1 Research Questions

  • Do oxygen levels influence fish responses as gathered with hook and line data ?

  • Does wind-stress (as a proxy for upwelling) influence fish responses as gathered with hook and line data?

  • Does mean bottom temperature influence fish responses as gathered with hook and line data?

For each of the above research questions, we assess fish responses to oceanographic conditions in aggregate and individually for each focal species. Aggregate fish responses with hook and line data include catch and biomass rates (CPUE and BPUE). Focal species responses with hook and line data include catch and biomass rates (CPUE and BPUE), and mean size.

We have the following hypotheses:

  1. Low oxygen (hypoxic) conditions will lead to lower catch and biomass rates (CPUE and BPUE) with hook and line gear.

  2. Stronger upwelling conditions (negative N-S wind stress) will lead to higher catch and biomass rates (CPUE and BPUE) with hook and line gear.

  3. There are two possible hypotheses related to changes in bottom temperatures. The first is that warmer bottom temperature will lead to higher catch and biomass rates, or larger sizes, with hook and line gear, as documented for when warmer bottom temperatures influence metabolic rates (Bakun et al 2015). Alternatively, colder bottom temperatures can be tightly coupled with coastal upwelling, and therefore colder temperature could lead to higher catch and biomass rates, with warmer temperatures associated with lower catch and biomass rates (Bakun et al 2015).

2 Takeaways

We now review the results of our analyses in light of our research questions and summarize what they tell us in a broader context about hook and line sampling results in the Cape Perpetua Marine Reserve.

Combining oceanographic data with hook and line monitoring data provided a first look at how fish responses correlate to oceanographic conditions at the Cape Perpetua Marine Reserve

In the Cape Perpetua Hook and Line Report, we identified significant yearly trends in several fish responses including aggregate CPUE and BPUE, Black Rockfish CPUE, BPUE and mean size, and Lingcod CPUE and BPUE. Cape Perpetua is known to be affected annually by low oxygen and low pH water. Combining available oceanographic and biological data at this site helped us understand the link between fish responses in monitoring data and oceanographic processes along the Oregon coast.

Low oxygen conditions were correlated with some fish responses.

We observed low oxygen (below 1.4 ml/L) conditions during fall HnL sampling in 2018 only. We hypothesized that fish responses would correlate with bottom dissolved oxygen values, and found that larger Black Rockfish were caught during periods of low oxygen conditions. We did not find evidence of Lingcod response to bottom dissolved oxygen. The fall 2018 sampling days had our two lowest aggregate catch and biomass values out of all years of data, although correlation values were non-significant (p>0.05).

We did not find significant correlations with fish responses and upwelling conditions (N-S wind stress)

We hypothesized that catch or biomass rates (CPUE, BPUE) would increase with stronger upwelling conditions (negative N-S wind stress) but did not find any evidence supporting this hypothesis. The lack of evidence was apparent at both the aggregate and focal species level. At the focal species level both Black Rockfish, a schooling species, and Lingcod, a benthic species, did not show responses to upwelling at the ranges or scales explored in this study.

We found significant correlations with Black Rockfish mean size and colder bottom temperatures

We found that with Black Rockfish, larger mean size was correlated with colder bottom temperatures. We did not find evidence linking responses to bottom temperature with Lingcod or at the aggregate level.

This analysis provides a foundation for future analyses exploring other oceanographic variables or spatiotemporal scales that may better correlate with measured fish responses. With the expected increase climate-change induced effects, such as large-scale marine heatwaves, it is imperative to understanding how changing ocean conditions influence the biological monitoring data and fish communities along Oregon’s coast.

3 Methods

3.1 Hook and Line Surveys

Hook and Line (HnL) surveys were conducted in the Cape Perpetua Marine Reserve starting in 2013. Survey effort targeted 2 days in the reserve for both spring and fall surveys. HnL data were collected for four years: 2013, 2014, 2016, and 2018.

Each day between four to five cells (500m x 500 m grids) are targeted, with 3 fifteen- each cell. All data collected from drifts are averaged for a single cell; the unit of replication for hook and line surveys is at the cell-day 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, and the recorded total fishing effort is adjusted accordingly. We then calculate both a catch and biomass per unit effort (fish/angler-hour and weight(kg)/angler-hour) for each given cell-day and species. For additional details on data collection, please review documentation in the Methods Appendix.

Focal Species

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.

3.2 Oceanographic data

The Partnership for Interdisciplinary Study of Coastal Oceans (PISCO) has been monitoring oceanographic conditions on the central Oregon Coast since 2003. Their moorings are located in and near the area that that eventually became the Cape Perpetua Marine Reserve (implementation in 2014). This area is known to be affected annually by low oxygen and low pH water (Adams et al 2013, Chan et al 2017). PISCO maintains a variety of sensors at various depths on the moorings for the purposes of monitoring physiologically-relevant oceanographic parameters such as pH, dissolved oxygen, wind-stress and temperature.

We selected dissolved oxygen and bottom temperature from the 70 meter mooring and surface wind-stress data to pair with biological responses gathered from hook and line data. North-South wind stress is proxy for upwelling, where more negative values correlate with stronger upwelling. Daily wind stress estimates were made using winds measured at NOAA’s National Data Buoy Center Station NWPO3, Newport Oregon (available at http://damp.coast.oregonstate.edu/windstress). PISCO provided averaged daily values for these oceanographic and atmospheric forcing variables on the day-of and day prior to hook and line surveys. We selected dissolved oxygen, bottom tempterature, and wind stress as target variables because hypoxia (low oxygen) issues are a new nearshore management priority for Oregon, and becasue oxygen, temperature and coastal currents can affect fish metabolism and behavior. Cape Perpetua is situated in a region of hte Oregon continental shelf that is particularly prone to hypoxia events, and may offer us a window to explore the responses of organisms to climate-mediated ocean changes.

3.3 Analysis methods

Levels of data aggregation

Oceanographic variables were aggregated to a mean value per day, as were biological data. For the dissolved oxygen analysis, we also visualized data at the day x year scale to explore if there was a particular year driving any data trends. Oceanographic and biological response data were joined based on the common value of date.

Statistical analyses

We visualized the oceanographic data by year to understand the range of variation in the data since no oceanographic summary of Cape Perpetua data was available at the time of writing this report.

We used correlation analyses to assess the relationship between oceanographic variables and biological response variables at both the aggregate and focal species level. We used the Spearmann’s rho correlation coefficient rather than the Pearson correlation coefficient because our sample size was limited in some cases. Spearmann’s rho is a nonparametric correlation coefficient based on ranked variables. Spearmann’s rho is robust to small sample sizes, outliers, and violations of the assumption of normally distributed variables.

We first visualized the trends for all focal species where data were available. China Rockfish and Cabezon were not observed at Cape Perpetua Marine Reserve and were excluded from data visualizations and analysis. When assessing single-species biological responses, due to sample size concerns, we conducted correlation analyses for only the two most abundant species, Black Rockfish and Lingcod. Hook and line modeling efforts were also restricted to Black Rockfish and Lingcod due to sample size.

4 Cape Perpetua Marine Reserve Hook and Line and Oceanography Results

Hook and line survey efforts at Cape Perpetua Marine Reserve with paired oceanography data resulted in 16 days of data collection over four years of monitoring efforts (Fig. 1). Note: Some hook and line days do not have paired dissolved oxygen or bottom temperature data.

Fig. 1: Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Sample size is represented in sampling days.

Fig. 1: Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Sample size is represented in sampling days.

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4.1 Dissolved Oxygen

Dissolved oxygen levels were variable during the days of hook and line sampling over the four years of data collection (n = 14 days). Only one year, 2018, resulted in sampling on days when dissolved oxygen conditions were considered hypoxic (below 1.4 ml/L).

Fig. 2: Dissolved oxygen values (ml/L) over the four years of Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Values below 1.4 ml/L are considered hypoxic.

Fig. 2: Dissolved oxygen values (ml/L) over the four years of Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Values below 1.4 ml/L are considered hypoxic.

4.1.1 Aggregate Data

No significant correlations between mean aggregate CPUE or BPUE and dissolved oxygen.

No significant correlations in mean aggregate CPUE or BPUE with dissolved oxygen levels (p > 0.05, Fig.3). While non-significant, the mean CPUE values on hypoxic days appear to be lower than any mean CPUE values on non-hypoxic days.

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4.1.1.1 Day

Fig. 3: Average aggregate CPUE and BPUE correlations with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. Dark blue dots represent dissolved oxygen values that are considered hypoxic (at or below 1.4 ml/L). See separate tabs for day and day by year.

Fig. 3: Average aggregate CPUE and BPUE correlations with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. Dark blue dots represent dissolved oxygen values that are considered hypoxic (at or below 1.4 ml/L). See separate tabs for day and day by year.

4.1.1.2 Day x Year

Fig. 3: Average aggregate CPUE and BPUE correlations with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. See separate tabs for day and day by year.

Fig. 3: Average aggregate CPUE and BPUE correlations with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. See separate tabs for day and day by year.

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4.1.2 Focal Species Data

No significant correlations between Black Rockfish and Lingcod mean CPUE or BPUE and dissolved oxygen.

No significant correlations in Black Rockfish and Lingcod mean CPUE or BPUE with dissolved oxygen levels (p > 0.05, Fig. 5).

Significant negative correlation between Black Rockfish mean size and dissolved oxygen.

There was a significant correlation between Black Rockfish mean size and dissolved oxygen (p < 0.05, Fig. 5), with larger mean size correlated with lower dissolved oxygen levels. There was no significant correlations between Lingcod mean size and dissolved oxygen (p > 0.05, Fig. 5)

For data visualizations of all focal species observed at Cape Perpetua please see link below:

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Fig. 5: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. Dark blue dots represent dissolved oxygen values that are considered hypoxic (at or below 1.4 ml/L).

Fig. 5: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with dissolved oxygen values taken from the 70 m mooring at the Cape Perpetua Marine Reserve. Dark blue dots represent dissolved oxygen values that are considered hypoxic (at or below 1.4 ml/L).

4.2 N-S Wind Stress (upwelling)

N-S wind stress levels on the day of and day prior to hook and line sampling were variable over the four years of data collection (n = 16 days). All years resulted in some sampling on days when upwelling (negative N-S wind stress) occurred.

Fig. 6: N-S wind stress over the four years of Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Negative values represent upwelling conditions.

Fig. 6: N-S wind stress over the four years of Hook and line monitoring efforts at the Cape Perpetua Marine Reserve. Negative values represent upwelling conditions.

4.2.1 Aggregate Data

No significant correlation between aggregate CPUE or BPUE and upwelling conditions.

No significant trends in aggregate CPUE or BPUE were observed on the day of or day prior to sampling with N-S wind stress (upwelling) (p > 0.05; Fig 7).

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4.2.1.1 Day of and Day Prior (lag) to HnL Sampling

Fig. 7: Average aggregate CPUE and BPUE correlations with N-S wind stress values (proxy for upwelling) taken at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress value).

Fig. 7: Average aggregate CPUE and BPUE correlations with N-S wind stress values (proxy for upwelling) taken at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress value).

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4.2.2 Focal Species Data

No significant correlations in CPUE or BPUE for Black Rockfish and Lingcod during upwelling conditions.

There were no significant correlations in CPUE or BPUE for Black Rockfish and Lingcod (p>0.05) during upwelling conditions on the day of or day prior to HnL Sampling (p > 0.05; Fig 9).

No significant correlations in Black Rockfish and Lingcod mean size with upwelling conditions.

No significant difference in correlations of Black Rockfish and Lingcod mean size with upwelling conditions (p > 0.05, Fig.9)

For data visualizations of all focal species observed at Cape Perpetua please see link below:

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4.2.2.1 Day of Sampling

Fig. 9: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with N-S wind stress values at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress). See separate tabs for day of and day prior to HnL sampling.

Fig. 9: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with N-S wind stress values at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress). See separate tabs for day of and day prior to HnL sampling.

4.2.2.2 Day Prior to Sampling

Fig. 9: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with N-S wind stress values at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress). See separate tabs for day of and day prior to HnL sampling.

Fig. 9: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with N-S wind stress values at the Cape Perpetua Marine Reserve. Green circles represent upwelling conditions (negative N-S wind stress). See separate tabs for day of and day prior to HnL sampling.

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4.3 Bottom Temperature

Bottom Temperature levels on the day of and day prior to hook and line sampling were variable over the four years of data collection (n= 14 days) (Fig 10).

Fig. 10: Bottom Temperature (Celsius) over the four years of hook and line monitoring efforts at the Cape Perpetua Marine Reserve.

Fig. 10: Bottom Temperature (Celsius) over the four years of hook and line monitoring efforts at the Cape Perpetua Marine Reserve.

4.3.1 Aggregate Data

No significant correlation between aggregate CPUE or BPUE and bottom temperature.

No significant trends in aggregate CPUE or BPUE were observed on the day of or day prior to sampling with bottom temperature (Fig 11).

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4.3.1.1 Day of and Day Prior (lag) to HnL Sampling

Fig. 11: Average aggregate CPUE and BPUE correlations with bottom temperature (Celsius) taken at the 70 m mooring at the Cape Perpetua Marine Reserve.

Fig. 11: Average aggregate CPUE and BPUE correlations with bottom temperature (Celsius) taken at the 70 m mooring at the Cape Perpetua Marine Reserve.

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4.3.2 Focal Species Data

No significant correlations in CPUE or BPUE for Black Rockfish and Lingcod with bottom temperature.

There were no significant correlations in CPUE or BPUE for Black Rockfish and Lingcod (p > 0.05) with bottom temperature on the day of or day prior to HnL Sampling (Fig 13).

Significant negative correlation between Black Rockfish mean size and both day of and day prior bottom temperature.

There was a significant correlation between Black Rockfish mean size and both day of and day prior bottom temperature (p < 0.05, Fig.13), with longer lengths correlated with colder bottom temperatures. There was no significant correlations between Lingcod mean size with bottom temperature (p > 0.05, Fig.13)

For data visualizations of all focal species observed at Cape Perpetua please see link below:

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4.3.2.1 Day of Sampling

Fig. 13: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with bottom temperature (Celsius) at the Cape Perpetua Marine Reserve. See separate tabs for day of and day prior to HnL sampling.

Fig. 13: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with bottom temperature (Celsius) at the Cape Perpetua Marine Reserve. See separate tabs for day of and day prior to HnL sampling.

4.3.2.2 Day Prior to Sampling

Fig. 13: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with bottom temperature (Celsius) at the Cape Perpetua Marine Reserve. See separate tabs for day of and day prior to HnL sampling.

Fig. 13: Average CPUE, BPUE, and mean size correlations of Black Rockfish and Lingcod with bottom temperature (Celsius) at the Cape Perpetua Marine Reserve. See separate tabs for day of and day prior to HnL sampling.

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5 Discussion

Cape Perpetua is known to be affected annually oceanographic conditions including low oxygen and low pH water that are potentially stressful to fish. This provides a valuable case study to explore the potential impact of ocean stressors on the performance of HnL surveys. In the Cape Perpetua Hook and Line Report, we identified significant yearly trends in several biological responses at the Cape Perpetua Marine Reserve including aggregate CPUE and BPUE, Black Rockfish BPUE and mean size, and Lingcod CPUE and BPUE. While we did not find any significant correlations with fish responses at the aggregate level, we did observe our lowest CPUE and BPUE in the fall of 2018 while surveying during hypoxic (oxygen below 1.4 ml/L) conditions. While these two days of sampling were not strong enough to fully drive a significant correlation, it does provide some evidence that catch and biomass rates can be influenced by hypoxic conditions, and flags that this should be a relationship we continue to explore as monitoring continues. It also enables us to reflect on the results from our Appendix Report on Hook and Line Sampling at the Cape Perpetua Marine Reserve that the yearly trends in both aggregate and Lingcod CPUE and BPUE identified in this report are lowest in 2018. Future analysis may include addition of oceanographic parameters into our biological models to better understand these relationships.

We did find significant correlations between larger Black Rockfish mean size with both low oxygen and colder bottom temperature conditions. These results may be linked as seasonal upwelling is known to bring cooler nutrient rich water to coastal areas that is also associated with lower dissolved oxygen levels. Although we did not find any evidence of biological responses of Black Rockfish (or Lingcod) to N-S windstress, whether this proxy for upwelling at the day scale captures how fishes integrate the time and space scales of environmental change should be explored further.

Combining available oceanographic and biological monitoring data at this site helped us understand the link between the performance of Hook and Line surveys and oceanographic processes at a marine reserve site with highly variable oceanographic conditions. While we detected some evidence of fish responses to changing ocean conditions, this analysis merely scratched the surface of possibilities to understand the patterns and processes associated with species level responses to ecosystem changes. Environmental stressors may co-occur and affect organisms in a non-additive way, and may result in accumulating impacts over months to years. We lack sufficient understanding of the effect of multiple and/or long-term stressors on our focal species’ biology to understand if the biological responses observed with hook and line gear truly reflect the effect of environmental stressors. Maintaining coupled oceanographic and biological monitoring is critical to increase our understanding about how fish communities respond to climate-mediated changes in our oceans.

#Acknowledgements

Thank you to Dr. Jack Barth and Dr. Francis Chan who contributed PISCO oceanographic data and provided feedback on a draft report.

6 References

Adams, K. A., J. A. Barth and F. Chan, 2013. Temporal variability of near-bottom dissolved oxygen during upwelling off central Oregon. Journal of Geophysical Research 118, doi:10.1002/jgrc.20361.

Allen, J. S. (1980). Models of wind-driven currents on the continental shelf. Annual Review of Fluid Mechanics 12, 389–433.

Amaya, D. J., Miller, A. J., Xie, S.-P., and Kosaka, Y. (2020). Physical drivers of the summer 2019 North Pacific marine heatwave. Nature communications 11, 1–9.

Bakun, A., Black, B. A., Bograd, S. J., Garcia-Reyes, M., Miller, A. J., Rykaczewski, R. R., & Sydeman, W. J. (2015). Anticipated effects of climate change on coastal upwelling ecosystems. Current Climate Change Reports, 1(2), 85-93.

Chan, F., Barth, J. A., Blanchette, C. A., Byrne, R. H., Chavez, F., Cheriton, O., Feely, R. A., Friederich, G., Gaylord, B., Gouhier, T., Hacker, S., Hill, T., Hofmann, G., McManus, M. A., Menge, B. A., Nielsen, K. J., Russell, A., Sanford, E., Sevadjian, J., and Washburn, L., 2017. Persistent spatial structuring of coastal ocean acidification in the California Current System. Nature Scientific Reports 7, 2526, doi:10.1038/s41598-017-02777-y.

Rabalais, N. N., Diaz, R. J., Levin, L. A., Turner, R. E., Gilbert, D., and Zhang, J. (2010). Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 7, 585–619.

Samelson, R., Barbour, P., Barth, J., Bielli, S., Boyd, T., Chelton, D., et al. (2002). Wind stress forcing of the Oregon coastal ocean during the 1999 upwelling season. Journal of Geophysical Research: Oceans 107, 2–1.

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