Linking Puget Sound Primary Production to Stratification and Atmospheric Drivers on Seasonal to Inter-Decadal Scales

Linking Puget Sound Primary Production to Stratification and Atmospheric Drivers on Seasonal to Inter-decadal Scales

Parker MacCready1* Neil Banas2

February 1, 2016

1School of Oceanography, University of Washington, Box 355351, Seattle, WA 98195-5351, USA

2Dept of Mathematics and Statistics, University of Strathclyde, 26 Richmond St, Glasgow G1 1XQ, UK

ABSTRACT

The populations of Chinook and Coho salmon in Puget Sound have declined substantially in recent decades. Working from the hypothesis that this decline was caused by changes in food availability, data from different sources was analyzed to see if there were clear changes over this period in physical drivers (rivers, winds, sunshine), water conditions in Puget Sound (stratification), and phytoplankton abundance and bloom timing. Time series of these properties were constructed from all readily available data sources, each having different sampling frequency and duration. Short, irregularly sampled, and infrequent records, especially for phytoplankton, hinder the analysis, making it difficult to characterize anomalies in the seasonal cycle of chlorophyll and establish its relation to forcing variables. Wind mixing has potential as a predictive variable, but more analysis is needed.

Introduction

Over the past several decades Chinook and Coho salmon populations in the Salish Sea have decreased markedly. Coho smolt survival in Puget Sound and the Strait of Georgia declined by factor of three or more over the period 1977-2010 (Zimmerman et al. 2015). In contrast, nearby coastal Coho showed variable or even increasing survival. The fact that both regions are subject to similar large scale climatic forcing suggests that changes to conditions within the Salish Sea may be responsible. Within the Salish Sea there are long term records of both environmental and ecosystem properties in the Strait of Georgia, and these point to “regime shifts” around the late 1970’s, mid 1980’s, and mid 1990’s (Perry and Masson 2013). In particular they identify six variables that are statistically related to these regime shifts: “sea surface temperature, wind speed, the North Pacific Gyre Oscillation index, human population surrounding the Strait of Georgia, recreational fishing effort, and the number of hatchery releases of Chinook salmon (Oncorhynchus tshawytscha) into the Strait.” One clear climatic signal is that SST in the Strait of Georgia increased from 10.5 °C to 12 °C over the period 1971-2007. Bottom water temperature increased as well, by a similar amount (Riche et al. 2014).

It has been suggested that bottom-up pressure such as change in food availability has led to the decline in Salish Sea Chinook and Coho (Zimmerman et al. 2015). This motivates the current study, where we explore variation of phytoplankton abundance in Puget Sound, under the assumption that it will in turn control zooplankton abundance and hence smolt survival. There are challenges to this goal. Puget Sound has fewer long-term data records than the Strait of Georgia, so the strategy of this project is to attempt to correlate physical drivers such as river flow and weather (which have relatively long records) to phytoplankton (for which data records are relatively short). A similar approach has been shown to work in the Strait of Georgia (Allen and Wolfe 2013) where the timing of the spring bloom has been successfully predicted using primarily wind speed and cloudiness. The

2 mechanistic link this prediction relies on is that the spring bloom in the Strait of Georgia tends to be initiated as soon as there is a significant break from the vertical mixing of winter storms, allowing a surface density stratification that keeps phytoplankton in the euphotic zone at the same time as there is enough sunshine to support a bloom.

Here we analyze data records in Puget Sound of very different lengths and frequencies:

Atmospheric forcing: 1948-present River flow: 1980-present Marine Water Temperature and Salinity (monthly): 1991-present Marine Water Chlorophyll (monthly): 2002-present Marine Water Temperature, Salinity, and Chl (daily or better): 2005-present

Of these, the final category of “daily or better” water column measurements would be the most useful, but the records are too short to correlate with salmon declines on their own. Our approach is instead to look for ways that the longer records may be leveraged to extend our predictions of phytoplankton (taking chlorophyll concentration as a proxy for phytoplankton biomass). Specifically, we seek to test the primary hypothesis that seasonal or interannual variations in chlorophyll may be predicted using some combination of density stratification, wind mixing, and sunshine. A secondary hypothesis is that density stratification may be predicted using river flow. The primary hypothesis amounts to an extremely simplified ecosystem model, one that attempts to use correlation as a substitute for more mechanistic models such an NPZD (Nutrients, Phytoplankton, Zooplankton, Detritus) nutrient-cycling model embedded in a detailed circulation model (Banas et al. 2009; Davis et al. 2014).

We find that, while we are not able to disprove the primary hypothesis, we also are unable to use existing data to make predictions. In particular we find no meaningful correlations between monthly anomalies of sunshine or stratification with monthly chlorophyll anomalies. Here “anomaly” is defined as the deviation from the mean annual cycle, which is strong for all variables.

In the Methods section we give details of the data gathering and processing. The correlation between variables is presented in Results, and some thoughts about future directions are given in the Discussion.

Methods

Here we present the details of the data sources used, at a level that is meant to inform future researchers who want to use the data for other purposes.

Rivers

3 Flow data can be downloaded from the USGS for most rivers around Puget Sound from http://waterdata.usgs.gov/nwis/. Python code was written to automate this and flow data from 1980-2015 was downloaded for the Skagit, Snohomish, Stillaguamish, Columbia, Puyallup, Duwamish, Nisqually, Deschutes, Skokomish, Duckabush, Dosewallips, Hamma Hamma, Cedar, Nooksack and Samish rivers (Fig. 1). In most cases data on river flow is available for a much longer time span, e.g. back to 1940 for the Skagit. Data for some rivers does not extend back to 1980 in the USGS online files: the Columbia starts in 1992, the Deschutes starts in 1991, and the Samish starts in 1997. In late 2009 the USGS stopped reporting Skokomish data during high flow conditions. This can be circumvented using a combination of nearby gauged rivers, but was not done for this project. In some cases the rivers are not gauged, or have significant watershed area below the gauge. Alternate gauges and scaling factors have been developed by the Washington State Dept. of Ecology (Mohamedali et al. 2011) and these were used to create the final stream flow records used in this project.

Daily flow data for 1980-2012 from the Fraser River was downloaded by hand from Environment Canada through the website http://wateroffice.ec.gc.ca/search/search_e.html?sType=h2oArc using river code 08MF005. Data for 2013 and 2014, absent from the online archive, was procured by special request to Environment Canada, and 2015 came from an archive compiled from Fraser daily flow that is e-mailed daily to MacCready by Environment Canada as part of another project.

Figure 1. Map or rivers entering Puget Sound, from http://pubs.usgs.gov/fs/2011/3083/, showing the location of major rivers.

Figure 2. Annual cycle of flow in selected rivers used in the study. All years are plotted with thin gray lines, and averages of all data as thick black lines. The year 2015 is highlighted in red and exemplifies the large anomalies possible, in this case the much-reduced spring freshet due to low snowpack in the winter of 2014-15.

Data from all rivers was processed using python into a common format with daily frequency.

Atmosphere

Weather data was obtained from three sources.

Daily weather observations from near SeaTac Airport were downloaded by hand from http://www.ncdc.noaa.gov/cdo- web/datasets/GHCND/stations/GHCND:USW00024233/detail, which goes back to 1948. As is apparent in Fig. 3, the record is complete for precipitation and temperature, but only shorter, non-overlapping segments are available for variables related to sunshine and wind. As a result this data was not used in the later analysis, except as ground-truth for the other weather data sources.

Figure 3. Observed weather data from SeaTac airport, for the past 65 years. Blue lines are monthly averages and red lines are annual averages.

Three-hourly fields of all relevant weather are available for 2002-2009 on a 4 km resolution grid over Puget Sound as part of an archive created by MacCready for a modeling project (Giddings et al. 2014) out of the regional MM5 and WRF numerical weather forecasts run by Cliff Mass’ group at UW (Mass et al. 2003). This type of atmospheric data is excellent for our purposes because of its high spatial and temporal resolution, and because it compares well to observations. However, the time period is short for our purposes. With some work it could be extended from 2009 to the present, but a more promising approach would be to use output from a much longer reanalysis, for example the 12 km WRF reanalysis of the Pacific Northwest for 1970-2069 by Eric Salathé, UW Bothel (Salathé et al. 2014). For this project time series of 10 m wind speed and downward shortwave radiation (sunshine) at a location in the Eastern end of the Strait of Juan de Fuca (123° W, 48.3° N) were extracted. A “wind mixing” time series was created as the sum of the absolute value of the cubed wind speeds. The wind speed cubed is proportional to the work done by wind stress (which varies as wind speed squared). A similar index was used in an analysis of the Strait of Georgia spring bloom (Allen and Wolfe 2013). Both the sunshine and wind mixing series were then averaged into weekly bins.

The third source of atmospheric data comes from the NCEP (National Centers for Environmental Prediction, part of NCAR, http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) reanalysis products. Monthly means at 2.5° resolution (very coarse for our purposes, about 250 km) are available as far back as 1948. For this project we downloaded monthly means of Surface Downward Shortwave Radiation Flux (essentially a measure of

6 how sunny it is, including the effects of clouds. Data were obtained from http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.surfacefl ux.html. Examples of records from this source are presented as part of the combined analysis below. A comparison of NCEP shortwave flux and SeaTac observed sunshine are shown on Fig. 4, showing that the NCEP fields have at least moderate skill in this crude comparison.

Figure 4. Twenty-year comparison of monthly “sunshine” anomalies for NCEP (downward shortwave radiation) and SeaTac observations (% sunshine), plotted as time series (left) and a scatterplot (right). The signals are positively correlated but only weakly, so caution is advisable when using NCEP.

Finally, three-hourly reanalysis products at 32 km resolution from the NARR (NCEP North American Regional Reanalysis, http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html) model are available from 1979 onwards, but were not used for this project because of the substantial time that would have been required for downloading.

Puget Sound Water Column Data from CTD Casts

The data source most central to this analysis is the monthly CTD casts that have been taken by the Washington State Department of Ecology as part of their Ambient Monitoring Program. Quality data are available online from 1991 onwards from 37 core stations, 29 of which are in Puget Sound (Fig. 5). Older data exists but may be of lower quality and so was not used. Data from 13 of these stations was downloaded as .csv files from https://fortress.wa.gov/ecy/eap/marinewq/mwdataset.asp. At the top of the form choose the station and at the bottom choose "csv" and "all years" and then "get file." The specific stations used, and some comments on data quality are: • BLL009_0, Bellingham Bay • BUD005_0, Budd Inlet • DNA001_0, Dana Passage o 2001 deep T bad in July o 2002 shallow s low value Feb, shallow T spike Oct

7 o 2006 lots of little T, s spikes all depths, many months • HCB004_0, Hood Canal in Lynch Cove • PSB003_0, Main Basin off Seattle o 1994 salinity spike 4 m Feb/Mar o 2007 many T, s spikes, especially deep • HCB003_0, Hood Canal, middle of main channel (Hamma Hamma R.) • SAR003_0, middle of Whidbey Basin • GOR001_0, Gordon Point - South Puget Sound • PSS019_0, South Whidbey Basin - off Everett • CRR001_0, Carr Inlet • ADM002_0, Admiralty Inlet just outside Puget Sound

Figure 5. Locations and codes of Ecology long term CTD stations.

The Ecology CTD data has values every 0.5 m from near the sea surface to near the bottom. Often data spikes appeared near the bottom of casts and these were removed in processing. In addition a number of casts appeared to have repeated depths with slightly different values, perhaps because repeat casts were mistakenly

8 placed in the archive together. All processing was done in python. Examples of casts at two of the stations are shown in Fig. 6.

Figure 6. CTD cast data for all years, colored by month, at two stations. Fields plotted are salinity, temperature, sigma (density minus 1000 kg m-3), chlorophyll concentration, and dissolved oxygen. The upper row is from Main Basin off Seattle, and the lower row is from lower Hood Canal in Lynch Cove. Near-surface stratification is apparent in both places, as is hypoxia in deep Hood Canal in the late summer. The phytoplankton bloom extends somewhat deeper than the surface layer defined by density.

The CTD data was then processed to create averages of all data above and below 5 m depths. Time series of these averaged properties at all stations are shown in Fig. 7. Of these properties we will focus mostly on the chlorophyll, because of its potential as zooplankton food, and “stratification,” defined here as the difference between density averaged below 5 m and that averaged above. The 5 m depth chosen as the dividing line between surface and deep waters is somewhat arbitrary, and is based on a subjective estimate, after looking at all the data. Other authors have constructed stratification indices using slightly different techniques (Moore et al. 2008; Sutherland et al. 2011; Giddings et al. 2014). Our technique was chosen to minimize noise when applied to the data available.

Figure 7. Time series of monthly water column properties averaged above and below 5 m depth for all Ecology CTD stations analyzed (rows, station names on right panels), and for all properties (columns). The time series extend from 1991 to 2014 for temperature and salinity, but only begin in 2002 for chlorophyll.

Puget Sound Water Column Data from Profiling Buoys

The final data source considered comes from the ORCA profiling buoys (Devol et al. 2007). Data was obtained in the form of MATLAB files provided on request by Wendy Reuf at University of Washington. The buoys have an instrument package on a winch that is lowered and raised through the water column several times a day (Fig. 8). The stations and their locations are: • CI: South Sound - Carr Inlet • HC_DB: Hood Canal - Dabob Bay • HC_DK: Hood Canal - Duckabush • HC_HP: Hood Canal - Hoodsport • HC_NB: Hood Canal - North Buoy • HC_TW: Hood Canal - Twanoh • PW: Main Basin - Point Wells The resulting time series are similar to those one could create from the Ecology CTD casts, but have much better temporal resolution. In particular they are able to resolve phytoplankton blooms, which have time scales of days to weeks. In contrast the monthly CTD casts may seriously under-sample, or even miss, bloom events. The ORCA data was processed in the same way as the CTD casts: averaged into bins above and below 5 m depth. The resulting time series from all seven stations are

10 shown in Fig. 9. One limitation of the ORCA data is that there are only 7 stations, 4 of which are in Hood Canal.

Figure 8. Locations (left) of six out of seven ORCA profiling buoys, and a picture of a buoy (right). The Duckabush location is not shown.

Figure 9. Time series from all 7 ORCA profiling buoys (by row, station names in left panels), with data fields in columns. The fluorescence data is reported in arbitrary “fluor units” but we assume in the analysis that this may be compared at least qualitatively with the chlorophyll concentration from CTD casts. The water column data from each profile has been averaged into values above 5 m (red) and below 5 m (blue) and so should be directly comparable to the CTD time series.

The primary problem with using the ORCA data for this analysis is evident in Fig. 9. The records only begin in 2005, and are often short and intermittent. Only two of the records (Hood Canal, at Hoodsport and Twanoh) approach a decade in length.

12 Results

Combining the various data records we may address the original hypotheses of this project. Starting first with the “secondary” hypothesis that stratification is related to river flow, a correlation analysis is shown in Fig. 10. There is large annual variation of stratification at all locations, changing by a factor of 2 to 5, although some stratification is always present. The stratification is modestly correlated with river flow, and shows the strongest correlation near the Skagit River in Saratoga Passage. The correlation coefficients shown in the lower panels of Fig. 10, are maximum lagged correlations, but the lag is subject to uncertainty caused by the binning process and is indistinguishable from 0 months.

Figure 10. Comparison of stratification and river flow for 5 selected CTD station – river pairs. Each column represents a CTD station – river pair. The top row is the average annual cycle of all stratification data for a station, and the middle row is the average annual cycle of river flow, in monthly bins. In the bottom row are scatterplots of stratification versus river flow, where each point is an individual monthly average. Correlation coefficients are also shown in these panels.

Moving to the primary hypothesis, we seek to find a pattern linking environmental variables and chlorophyll concentration. We do this at first by lining up all the data for a location as time series. In this case a “location” means a combination of a river, an ORCA buoy, and a CTD station, as well as weather data. Examples are shown in Figs. 11 and 12.

13 Figure 11. Integrated time series for Carr Inlet (Ecology CTD station CRR001, ORCA buoy CI, the Nisqually River, and regional weather data). • Upper panel: Chlorophyll averaged above (red) and below 5 m (blue) from monthly CTD casts (circles) and daily-averaged ORCA profiles (lines). The ORCA data is in uncalibrated “fluor units.” • Second panel: Nisqually River flow. • Third panel: Stratification (average density below 5 m minus average density above) from monthly CTD casts (circles) and daily-averaged ORCA profiles (line). • Bottom panel: Monthly downward shortwave radiation anomaly from NCEP reanalysis at a point near Seattle. The anomaly is calculated by removing the average annual cycle. This field is a measure of how sunny a month was relative to the average for that month. The fields are plotted for 2011-13, even though many of the fields have much longer records. The time limits are based on the limits of the ORCA data.

Figure 12. Integrated time series for South Hood Canal (Ecology CTD station HCB003, ORCA buoy HP = Hoodsport, the Skokomish River, and weather data). • Upper panel: Chlorophyll from monthly CTD casts (circles) and daily- averaged ORCA profiles (lines). • Second panel: Wind mixing (defined in the text), calculated from MM5/WRF 4 km weather forecasts. Weekly averages. • Third panel: Skokomish River flow (floods are clipped late in the record). • Fourth panel: Stratification. • Bottom panel: Monthly downward shortwave radiation anomaly from NCEP reanalysis at a point near Seattle (filled line). The same field from weekly- averaged MM5/WRF fields is also plotted (blue line). The fields are plotted for 2006-2009, even though many of the fields have much longer records. The time limits are based on optimizing the overlap of fields.

15 The integrated time series at Carr Inlet (Fig. 11) shows a clearly defined phytoplankton bloom, starting around April or May, and ending in the fall. For the purposes of this project we would like to be able to identify differences in bloom timing or strength in different years. The time series demonstrate the difficulty of doing this with the data in hand. For example the ORCA records suggests that the 2012 and 2013 blooms were much larger than 2011, but that would be difficult to guess from the CTD data. In terms of timing, it appears from the CTD data that the 2013 bloom started a month earlier than the other two years, but there is a gap in ORCA data at the start of the 2013 bloom that makes it impossible to corroborate the timing. The stratification in Carr Inlet has a clear seasonal cycle that peaks around the middle of the plankton bloom, and the stratification from ORCA profiles and the CTD casts match well. However there are no clear inter-annual differences in stratification, and hence no reason to conclude that stratification controls productivity on this timescale in a statistical sense (this is addressed more below in a formal correlation analysis). The sunshine anomaly from NCEP shows that all three springs were less sunny than average. Overall we may conclude that three years is too few to begin to understand the system of phytoplankton blooms here. This motivates us to try looking for signals in longer records using CTD and weather data (and so not constrained by the short ORCA records). This is done below.

The integrated time series in South Hood Canal (Fig. 12) uses a longer ORCA record, and we add an additional data source from the MM5/WRF regional weather forecast, bringing in weekly sunshine and wind mixing time series. Here the phytoplankton bloom signal has spring and fall peaks, with clear inter-annual differences apparent in the ORCA data (2007 was a high productivity year, 2009 was low). There is a gap in the CTD data at this station (HCB003) covering the second half of the four-year period shown, but it is clear that it would be extremely difficult to estimate bloom strength or timing from monthly CTD casts at this location. There is some suggestion that the spring bloom starts when wind mixing becomes weak in the spring, and that the fall bloom is terminated when wind mixing first becomes, however wind mixing does not explain the low phytoplankton in mid summer. The two estimates of sunshine (bottom panel) do not obviously explain inter-annual differences in productivity – for example 2007, the year with highest phytoplankton in the period, had anomalously low sunshine by both NCEP monthly values and weekly MM5/WRF values. It is also apparent that the monthly NCEP sunshine values may miss a great deal of information, and appear to correlate poorly with the high resolution MM5/WRF estimates.

The above two examples suggest that: • Monthly CTD casts may not do a very good job of capturing phytoplankton bloom strength or timing. • Wind mixing is a promising field to look at, but currently we are limited to 2002-9. • Stratification is well captured by the monthly CTD data, but it is not clearly the controlling factor in seasonal patterns of phytoplankton production.

16 • Sunshine anomalies may or may not be useful.

These negative results on the statistical relationship between stratification and chlorophyll are striking given that we have every reason to believe a priori that the mechanistic link is real and at least transiently important in Puget Sound, as in so many other temperate marine ecosystems. A possible explanation for the discrepancy is a mismatch of timescales, as two further brief analyses explore.

First, past work (Newton and Van Voorhis 2002) suggests that stratification and incoming light might be important controls on Puget Sound primary production in winter and spring, but secondary to nutrient limitation in summer. Figure 13 shows month-by-month correlations between stratification and chl in Carr Inlet using monthly CTD data. We find no significant relationship in any season, although the statistical power of this analysis is low.

Second, both seasonal (Figs. 11-12) and monthly (Fig. 13) analyses might fail to demonstrate a real influence of stratification on chlorophyll if the relevant timescale is much shorter, i.e. the 2–10 d scale of both weather events and individual phytoplankton blooms. There is anecdotal reason to believe this might be the case. Figure 14 shows the Carr Inlet ORCA data replotted, three years superimposed by yearday (and with an alternate stratification index, surface-bottom density difference). In this view, the seasonal cycle of stratification appears quite similar across the three years, but when we focus in on daily values April–May, we find three examples of 5–10 d events in which a well-defined peak in stratification briefly precedes a well-defined peak in integrated chlorophyll (arrows: two events in 2012, one in 2013). This is mechanistically satisfying but, as the analyses above show, of little use in predicting chl statistically on longer scales.

Figure 13. Monthly chlorophyll anomaly versus monthly stratification anomaly, from monthly CTD casts in Carr Inlet, plotted by month. The anomalies are relative to the average of all values in a given month. Carr Inlet ORCA, 2011, 2012, 2013

1.6 1.6

1.2 1.2

0.8 0.8 drho drho

0.4 0.4

0 0 Jan Apr Jul Oct Jan Apr May Jun

800 800

600 600

chl int 400 chl int 400

200 200

0 0 Jan Apr Jul Oct Jan Apr May Jun

Figure 14. Alternate indices of tidally averaged stratification (“drho,” bottom–surface density difference, in sigma-t units) and chlorophyll (vertically integrated, mg m–2) at the Carr Inlet ORCA buoy, for 2011 (blue), 2012 (green), and 2013 (red). Each point represents one day. The shaded time period in the left panels is expanded in the right panels.

18 Discussion

Here we have presented a preliminary analysis of data sets available as time series for Puget Sound related to phytoplankton, motivated by the substantial, documented decline of Chinook and Coho salmon in the Salish Sea over the past 35 years. Using time series of environmental physical forcing variables (rivers, sunshine, wind), a physical response variable (water column stratification), and a biological response variable (chlorophyll concentration) we attempted to look for clear correlations that would explain inter-annual differences or trends in phytoplankton blooms. The result of this initial analysis is that no clear correlations were found.

The main data limitation in the analysis is that monthly chlorophyll records throughout Puget Sound are only available for, at best, the last 14 years, and daily values only for the last 10 (in a couple of locations). Moreover, while monthly CTD casts may be adequate for characterizing the annual cycle of chlorophyll in the different basins of Puget Sound, they are likely inadequate for characterizing inter- annual differences in bloom timing or strength. It is however likely that if chlorophyll showed the same linear decrease over the past decades as Chinook and Coho salmon this would be evident from a longer record (say 30 years) of monthly chlorophyll data.

Some strategies for future work on this problem are suggested by our analysis. Exploration of changes in wind mixing may be promising, and this could be accomplished with high-resolution reanalysis data sets that exist. A more sophisticated ecosystem model could be used, as opposed to the relatively crude correlation analysis presented here. In the Strait of Georgia the predictions of spring bloom timing (Allen and Wolfe 2013) relied on a mid-complexity, 1-D water column biogeochemical model. The Strait of Georgia case may be more amenable to a 1-D approach, because it is relatively homogeneous over a broad area, whereas Puget Sound is characterized by spatial heterogeneity. The brute-force approach, a realistic 3-D circulation-biogeochemistry model of Puget Sound, is also likely to give useful insights, provided reliable atmospheric, river, and ocean boundary conditions can be assembled for the past 40 years. Some of these are already available, and given trends in reanalysis products it is likely others will be available in the near future.

The next step planned by our group is a compromise between the 1-D and 3-D model approaches, a 2-D (depth and along-channel distance), tidally-averaged model for the thalweg from South Sound through Main Basin to the Strait of Juan de Fuca, in which a full NPZD model can be implemented and explored systematically. Our inference from the analysis above, along with model insights from Winter et al. (1975) and Banas (2009), is that stratification and surface light are important to Puget Sound primary production in close combination with the along-channel advection and vertical fluxes associated with the estuarine circulation, and that a model that resolves all these aspects of the physics (and the combined effects of

19 light limitation, nutrient limitation, and vertical export/regeneration) is more likely to correctly predict phytoplankton biomass accumulation than a statistical treatment of any of these factors alone. The data compiled here form a crucial resource for calibration and validation of such a model, and as such might yet prove to provide a basis for retrospective analysis and future prediction of trends in primary production.

Acknowledgments

This work was funded by Long Live the Kings, as part of the Salish Sea Marine Survival Project.

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