Zooplankton time series from the Strait of Georgia: Results from year-round sampling at deep water locations, 1990–2010 David Mackas a,d,⇑ , Moira Galbraith a , Deborah Faust a,i , Diane Masson a , Kelly Young a , William Shaw b,c , Stephen Romaine a , Marc Trudel b,e , John Dower d , Rob Campbell d,g , Akash Sastri d,h , Elizabeth A. Bornhold Pechter f,j , Evgeny Pakhomov f , Rana El-Sabaawi d a Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, BC V8L 4B2, Canada b Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, BC V9T 6L4, Canada c Fisheries and Oceans Canada, 3225 Stephenson Point Rd., Nanaimo, BC V9T 1K3, Canada d University of Victoria, School of Earth and Ocean Sciences, Victoria, BC V8W 2Y2, Canada e University of Victoria, Department of Biology, Victoria, BC V8W 2Y2, Canada f Univeristy of British Columbia, Department of Earth, Ocean and Atmospheric Sciences, Vancouver, BC V6T 1Z4, Canada g Prince William Sound Science Center, Cordova, AK 99574, USA h Université du Québec à Montréal, Department of Biological Sciences, Montreal H3C 3P8, Canada i 963 Chancery St., Kingston, ON K7P 1R4, Canada j Fisheries and Oceans Canada, Field Office, Campbell River, BC V9W 2P8, Canada article info Article history: Available online 1 June 2013 abstract We have compiled and archived a large fraction of the zooplankton data collected from the Strait of Geor- gia during the past 50 years. Although the full dataset is very heterogeneous and gappy, sampling since 1990 has been consistent and frequent enough to examine interannual variability of the full zooplankton community. In this paper we focus on deep tows at mid-Strait deep-water locations, where vertical- migratory zooplankton can be captured at all times of day and all seasons. Average zooplankton dry- weight biomass is high (9gm 2 ) and varies seasonally between a winter minimum (4gm 2 ) and a broad late-spring to autumn maximum (10–11 g m 2 ). Much of the biomass in all seasons consists of large crustaceans (copepods, euphausiids and amphipods with oceanic and subarctic zoogeographic affinities) that undergo strong diurnal or seasonal vertical migrations. Their interannual variability is very strong: about an order of magnitude within most zooplankton categories, and nearly two orders of mag- nitude for euphausiids, large copepods, and chaetognaths. Most (73%) of the interannual variability is accounted for by three principal components. The dominant mode (36%) is a low-frequency decadal fluc- tuation shared by most zooplankton taxa: declining from 1990 to 1995, increasing to a maximum 1999–2002, declining to a second minimum in 2005–2007, and then recovering to near-average levels by 2010. This zooplankton signal correlates positively with the North Pacific Gyre Oscillation (NPGO) cli- mate index, negatively with temperature anomalies throughout the water column, and positively (but less consistently) with survival anomalies of Strait of Georgia salmon and herring. Proximal causal mech- anisms are less certain, but probably include estuarine advective exchange with outer coast populations, and timing match–mismatch within the Strait. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. 1. Introduction The ‘Salish Sea’ is the collective name for the network of straits and inlets lying between Vancouver Island, the Olympic Peninsula, and the mainland coasts of British Columbia and Washington State (Fig. 1). The largest component in both area and volume is the Strait of Georgia (6515 km 2 , mean and max depths 161 and 420 m), followed by Juan de Fuca Strait (4068 km 2 , mean/max depths 103 and 300 m), Puget Sound (2132 km 2 , mean/max depths 65 and 284 m), and several smaller but deeper tributary fjords (depth and area data from Thomson and Foreman, 1998). There has been a long history of scientific study of these waters and of their resident biota, but the majority has consisted of inten- sive but relatively brief (1–3 year duration) individual research projects. Collectively, these have provided a good knowledge of the identities and seasonal cycles of the dominant taxa within each trophic level, and of how their distributions and seasonal produc- tivity are affected by circulation and mixing processes (see reviews by Harrison et al., 1983; LeBlond, 1983; and papers in Wilson et al., 1994). However, except for a few commercially-harvested species, there have been few continuous and internally-consistent 0079-6611/$ - see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pocean.2013.05.019 ⇑ Corresponding author. Tel.: +1 250 363 6442; fax: +1 250 363 6690. E-mail address: [email protected](D. Mackas). Progress in Oceanography 115 (2013) 129–159 Contents lists available at SciVerse ScienceDirect Progress in Oceanography journal homepage: www.elsevier.com/locate/pocean
Transcript
Progress in Oceanography 115 (2013) 129–159
Contents lists available at SciVerse ScienceDirect
David Mackas a,d,⇑, Moira Galbraith a, Deborah Faust a,i, Diane Masson a, Kelly Young a, William Shaw b,c,Stephen Romaine a, Marc Trudel b,e, John Dower d, Rob Campbell d,g, Akash Sastri d,h,Elizabeth A. Bornhold Pechter f,j, Evgeny Pakhomov f, Rana El-Sabaawi d
a Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, BC V8L 4B2, Canadab Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, BC V9T 6L4, Canadac Fisheries and Oceans Canada, 3225 Stephenson Point Rd., Nanaimo, BC V9T 1K3, Canadad University of Victoria, School of Earth and Ocean Sciences, Victoria, BC V8W 2Y2, Canadae University of Victoria, Department of Biology, Victoria, BC V8W 2Y2, Canadaf Univeristy of British Columbia, Department of Earth, Ocean and Atmospheric Sciences, Vancouver, BC V6T 1Z4, Canadag Prince William Sound Science Center, Cordova, AK 99574, USAh Université du Québec à Montréal, Department of Biological Sciences, Montreal H3C 3P8, Canadai 963 Chancery St., Kingston, ON K7P 1R4, Canadaj Fisheries and Oceans Canada, Field Office, Campbell River, BC V9W 2P8, Canada
a r t i c l e i n f o a b s t r a c t
Article history:Available online 1 June 2013
We have compiled and archived a large fraction of the zooplankton data collected from the Strait of Geor-gia during the past 50 years. Although the full dataset is very heterogeneous and gappy, sampling since1990 has been consistent and frequent enough to examine interannual variability of the full zooplanktoncommunity. In this paper we focus on deep tows at mid-Strait deep-water locations, where vertical-migratory zooplankton can be captured at all times of day and all seasons. Average zooplankton dry-weight biomass is high (�9 g m�2) and varies seasonally between a winter minimum (�4 g m�2) and abroad late-spring to autumn maximum (10–11 g m�2). Much of the biomass in all seasons consists oflarge crustaceans (copepods, euphausiids and amphipods with oceanic and subarctic zoogeographicaffinities) that undergo strong diurnal or seasonal vertical migrations. Their interannual variability is verystrong: about an order of magnitude within most zooplankton categories, and nearly two orders of mag-nitude for euphausiids, large copepods, and chaetognaths. Most (73%) of the interannual variability isaccounted for by three principal components. The dominant mode (36%) is a low-frequency decadal fluc-tuation shared by most zooplankton taxa: declining from 1990 to 1995, increasing to a maximum�1999–2002, declining to a second minimum in 2005–2007, and then recovering to near-average levelsby 2010. This zooplankton signal correlates positively with the North Pacific Gyre Oscillation (NPGO) cli-mate index, negatively with temperature anomalies throughout the water column, and positively (butless consistently) with survival anomalies of Strait of Georgia salmon and herring. Proximal causal mech-anisms are less certain, but probably include estuarine advective exchange with outer coast populations,and timing match–mismatch within the Strait.
Crown Copyright � 2013 Published by Elsevier Ltd. All rights reserved.
1. Introduction
The ‘Salish Sea’ is the collective name for the network of straitsand inlets lying between Vancouver Island, the Olympic Peninsula,and the mainland coasts of British Columbia and Washington State(Fig. 1). The largest component in both area and volume is theStrait of Georgia (�6515 km2, mean and max depths 161 and420 m), followed by Juan de Fuca Strait (�4068 km2, mean/maxdepths 103 and �300 m), Puget Sound (�2132 km2, mean/max
depths 65 and 284 m), and several smaller but deeper tributaryfjords (depth and area data from Thomson and Foreman, 1998).
There has been a long history of scientific study of these watersand of their resident biota, but the majority has consisted of inten-sive but relatively brief (1–3 year duration) individual researchprojects. Collectively, these have provided a good knowledge ofthe identities and seasonal cycles of the dominant taxa within eachtrophic level, and of how their distributions and seasonal produc-tivity are affected by circulation and mixing processes (see reviewsby Harrison et al., 1983; LeBlond, 1983; and papers in Wilson et al.,1994). However, except for a few commercially-harvested species,there have been few continuous and internally-consistent
Fig. 1. Map of the Strait of Georgia study area, and adjoining straits, inlets, and landfeatures. Circles show the spatial distribution of zooplankton sampling effort (circlearea is proportional to the number of samples). Black circles are the three primaryzooplankton monitoring locations, gray circles indicate sums within other Straitsub-regions. Only deep locations and samples were chosen because they span theseasonal and diel vertical migratory ranges of the dominant zooplankton taxa.White triangle shows the location of the Nanoose Bay temperature time series.Inverted black triangle shows the location of the Sand Heads wind time series.Bathymetry is indicated by shading, with 100 m and 200 m isobaths highlighted bycontour lines. High resolution bathymetry courtesy Isaac Fine, Institute of OceanSciences.
130 D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159
multi-year biological time series. Fisheries and environmentalagencies in Canada, the USA, and many other nations are now com-mitted to development of ‘‘ecosystem-based marine management’’(e.g. Jamieson et al., 2010). This new approach will require a broad-er range of ecological time series to identify and respond to long-term fluctuations and trends in productivity and species composi-tion of all trophic levels.
As part of a recent Canadian Department of Fisheries andOceans ‘‘Ecosystem Research Initiative’’ on the Strait of Georgia(DFO, ERI; see Masson and Perry, 2013, for overview of other ERIcomponents), we compiled and archived a large fraction of the zoo-plankton data collected from the Strait of Georgia during the past50 years. The full zooplankton dataset is very heterogeneous interms of both methodology and spatial and temporal coverage.This is not surprising, because samples were collected by a numberof different projects and investigators, for different purposes, usinga variety of sampling gears, and targeting different locations anddepth ranges (see Section 2 for details). This means that the dataand methods for examining long term zooplankton time seriesfrom the Strait of Georgia cannot be ‘‘one size fits all’’. Instead, zoo-plankton time series analyses need to be customized for differingsubsets of taxa and time spans, and need to examine different sub-sets of samples at differing levels of taxonomic detail. In this paper,our primary goals are to describe interannual variability of the fullzooplankton community, and to have year-round seasonal cover-age. Because many of the dominant taxa are vertical migrators thatleave the upper �100 m for part of each day (diel migrators) or forseveral months out of the year (seasonal migrators), we have se-lected for our analysis the subset of samples that were collectedat deep (mostly �mid-Strait) locations between 1990 and 2010using tows that covered most of the water column, and were pro-cessed completely (all taxa in the sample were counted and classi-fied, not just one or two target species). Working from the samecommunal database but using a different and non-overlappingset of samples, Li et al. (2013) examine a second (1992–2007) time
series of upper-water-column (0–50 m) net tows collected in latesummer and early autumn at locations around the perimeter ofthe Strait.
2. Zooplankton collection, sample processing, and data archivalmethods
2.1. Criteria for selection of samples for statistical analysis
As noted above, our choice of samples for this analysis was con-strained by the fact that most of the dominant large-bodied zoo-plankton taxa in the Strait of Georgia undergo extensive verticalmigrations at either daily or annual time scales (Fig. 2, adaptedfrom Harrison et al., 1983). Limited-depth-range zooplankton sam-pling has often been preferred by researchers whose primary goalwas to estimate prey availability to predators that themselves onlyaccess a portion of the water column while foraging. For thesesamples, the zooplankton vertical migrations impose a downwardbias on estimates of total population abundance and biomass be-cause net tow depth ranges do not cover the full depth range occu-pied by the target taxa or life stages. In this paper, we selected foranalysis a smaller, but internally intercomparable, subset of Straitof Georgia zooplankton samples collected between 1990 and 2010that do cover the full seasonal and diurnal depth ranges of thedominant taxa, and use them to describe their seasonal and inter-annual changes in biomass. We filtered our total Strait of Georgiadata archive (held in a larger MSAccess database at the Instituteof Ocean Sciences; see Supplementary information for details ofdatabase structure) for samples that fit the following criteria:
� For all zooplankton taxa, select locations in the Strait wherewater column depth is >250 m.� For analyses of the seasonal migratory copepods, net tow depth
>200 m during the deep dormant season (�May–February), and>150 m during the spring growing season (March–April).Because we had few samples in 2007 (a year of particular inter-est due to poor early marine survival for several fish species, seee.g. Irvine et al., 2013; Araujo et al., 2013; Perry and Masson,2013), we also included a group of 0–50 m oblique net tows col-lected at night in April 2007, a month when populations ofthese copepod species consisted of actively feeding and growingjuveniles, all or most inhabiting the upper 50 m. This selectionyielded a total of 223 samples.� For analyses of other taxa (many of which undergo diel migra-
tion to �100–150 m, see Fig. 2), we required minimum daytimenet tow depth range >100 m (mostly >150 m). We also includednight 0–50 m tows from May 2006 and April 2007, after check-ing that their abundance and biomass data were similar to dee-per tows in the same years. This selection yielded a total of 364samples (seasonal and interannual distributions summarized inTable 1). Many of these additional samples were 0–150 m towscollected in years 2008–2010.
2.2. Sampling locations, timing, and methods
The majority of samples that fit our selection criteria come froma small number of mid-Strait locations (Fig. 1) that since 1990 havebeen sampled repeatedly and throughout the annual cycle by var-ious DFO, University of British Columbia and University of Victoriaresearchers (the major contributors are included as co-authors ofthis paper). The available ‘‘deep tow’’ Strait of Georgia zooplanktondata provide year-round coverage, but the seasonal distribution ofsampling effort has differed among years (Table 1). This lack ofhomogeneity has consequences for our choice of averaging proto-cols and ‘‘amount’’ metrics (Section 2.4). From 1990 to 1995, most
Fig. 2. Schematic diagram showing the depth and seasonal distributions of the zooplankton taxa that have historically been dominant in the Strait of Georgia (adapted fromHarrison et al., 1983 and updated to show recent shifts in the timing and biomass of large copepods). An important characteristic of the zooplankton community in the Straitis that many of the dominant taxa migrate below 100 m on either diel or annual cycles.
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 131
of the samples were collected by the Pacific Biological Station CO-PRA (Cooperative Plankton Research and Monitoring) program.These were full-water-column oblique bongo net tows (blackframe and netting, 0.25 m2 mouth diameter, 0.25 mm black mesh,TSK or General Oceanics flowmeter, additional details available inShaw, 1994) at two standard locations (CPF1 = 49.36�N,124.05�W; CPF2 = 49.47�N, 124.5�W). These two locations are indi-cated by the two northwest-most large circles in Fig. 1. In subse-quent years, CPF1 and CPF2 continued as targeted sampling sites,but additional locations were added by university and DFO pro-grams. Of these, the most often sampled was a station near thedeepest point in the Strait (GEO1 = 49.25�, 123.75�W, largest circlein Fig. 1). After 1996, most samples are vertical bongo net tows(similar net but 0.23 mm black mesh, TSK flowmeter except in1996–1997), a few are vertical ring net tows (SCOR net, 0.25 m2
mouth, 0.23 mm white mesh, usually fitted with a TSK flowmeterexcept between 1996 and early 1998, when most volume-filteredestimates were based on tow depth). To improve coverage in2006 and 2007, we also included data from a small number ofupper-water column oblique tows with a flow-metered TuckerTrawl (1 m2 mouth area, fitted with 0.35 mm mesh; because this
mesh size has reduced retention efficiency for our smallest zoo-plankton size categories, we excluded these samples when com-puting their annual averages and anomalies).
For all of the above samples, net tow catches were preserved ina 10% seawater solution of borax-buffered formalin, and samplesplus meta-data were returned to shore for further processing.Additional samples were collected and partially analyzed by otherDFO researchers but their data have not yet been released into thecommunal zooplankton data archive.
2.3. Taxonomic identification and enumeration
COPRA samples (1990–1995) were transferred to, and identifiedand enumerated by the Polish Plankton Identification and SortingCenter (Szczecin, Poland). Data from these samples were providedin spreadsheet format, and were later transferred/translated intoour composite relational database (see Supplementary informationfor details of database structure). All other samples were identifiedand enumerated locally (either by coauthor M. Galbraith, or by stu-dents and staff trained and supervised by her) and data were en-tered directly into the local database.
Table 1Seasonal and interannual distribution of sample numbers for the Strait of Georgia zooplankton time series. Where present, numbers in parentheses are for the larger copepod timeseries, for which excluded shallower net tows during the dormancy season (see text). Overall seasonal coverage is relatively even, but data from some months are concentrated ina subset of years and vice-versa.
Year January–February March–April May–June July–August September–October November–December Annual total
Table 2aCopepod size classes used in this analysis, plus the dominant species within each group within the Strait of Georgia, their primary food source (H = herbivore, M = mixed,P = predator, D = detritivore), and their respective zoogeographic distributions.
Major group Body size Dominant species, feeding type, and life-stages included in sizeclass
Primary zoogeographic range
Calanoid copepods Very large (>5 mm prosomelength)
P. mimus Mid-latitude to Subarctic (NE margin)P. minutus Mid-latitude to Subarctic (trans-Pacific)P. moultonii Mid-latitude to Subarctic (inlets)Acartia longiremis [M] (adult) (plus earlier stages of largercopepods)
Mid-latitude to Subarctic (mostlymargins)
Small (<1 mm prosome length) Paracalanus indicus[H] Mid-latitude (trans-Pacific)Microcalanus spp. [H] Subpolar–polar (both hemispheres)Scolecithricella minor [D] Oceanic (cosmopolitan)Pseudocalanus newmani [H] (plus early life stage ofmedium-sized and very early stages of large and verylarge copepods)
Coastal (cosmopolitan)
Non-calanoid copepods Small (<1 mm prosome length).Summed into ‘‘total copepods’’,but not retained for separatestatistical analysis due to lowbiomass and variable captureefficiency
Oithona similis [M] and O. atlantica [M] Mid-latitude to Polar (bothhemispheres)
Corycaeus spp. [P] Cosmopolitan (mostly oceanic)
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Taxonomic resolution differed significantly between the twodata streams, especially for non-crustacean taxa and for early-life-stage copepods (see Supplementary information for additionaldetails). These differences (and perhaps also errors in translationbetween archival formats) introduced a number of uncertaintiesin species-level comparability of pre- vs. post-1995 data. To deal
with these, we sorted the raw data into broader taxonomic catego-ries (Table 2) that could be unambiguously compared. The same‘‘major taxa’’ rollup categories were used by Li et al. (2013) foranalysis of an independent set of Strait of Georgia zooplanktontime series samples (0–50 m oblique tows collected at nightaround the margins of the Strait, mostly in early autumn).
Table 2bRemaining zooplankton taxa and size classes used in this analysis, plus the dominant species within each group, and their respective zoogeographic distributions and feeding type(codes as in 2a). See Fig. 3 for overall average and seasonal contributions to total dryweight biomass.
Major group Body size Dominant species and feeding type Primary zoogeographic range
Euphausiids(Euphausia,Thysanoessa)
Adult and late juveniles (>1 cm body length) Euphausia pacifica (usually >70%) [H] Mid-latitude to Subarctic(trans-Pacific)
Thysanoessa (T. spinifera [H] + T.longipes [P] + T. raschii [H]
Mid-latitude to Subarctic (NEmargin)
Juveniles (0.5–1 cm) and Larvae (<5 mm). Abundance � size used incalculation of total euphausiid biomass, summed for statistical analysis
(as above, but juveniles incompletelydifferentiated, larvae notdifferentiated)
As above Themisto (= Parathemisto) pacifica (60–90%) [P]
Subarctic (trans-Pacific)
Primno abyssalis [P]
Ostracods As above (but mostly <3 mm) Discoconchoecia [D] + Alacia [D] Mid-latitude to polar (trans-Pacific)
Chaetognaths As above Parasagitta elegans (=Sagitta) [P]Eukrohnia hamaca [P]
Mid-latitude to polar(margins to oceanic)Subpolar
Medusae As above Clytia gregaria (formerly Phialidium) [P] Mid-latitude to polar (mostlyalong margins)Aglantha digitale [P]
Aequorea victoria [P]Aegina citria [P]
Polychaetes As above Tomopteris septentrionalis [P] Subpolar to polarSiphonophores As above Dimophyes [P], Muggiaea [P] Mid-latitude to polarCtenophores As above Pleurobrachia [P], Beröe [P] Mid-latitude to polarPteropods Mostly <2 mm Limacina helicina [H] Mid-latitude to polarLarvaceans Mostly <2 mm Oikopleura [H], Fritillaria [H] Mid-latitude to
subarcticSubarctic to polarShrimps Mostly >1 cm Pasiphaea pacifica [P] plus planktonic
larvae of Pandalid shrimpsSubarctic (mostly margins)
Crab larvae Mostly <1 cm Cancer spp. [M] and misc. anomurans Mid-latitude to Subarctic (NEmargin)
Cladocera Mostly <1 mm Evadne, Podon Neritic, mid-latitude to polar
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 133
Similarities and differences in results from the two time series willbe described in Section 3.4.2. For our own analysis, we furthersub-classified the major taxa into body size categories (e.g.<1 mm, 1–3 mm, 3–5 mm, 5–10 mm, and >1 cm) and furtherseparated adult euphausiids based on genus. Table 2 identifieswhich species and developmental stages were dominant from1998 to 2010 within each major taxon and size category, and alsosummarizes their large-scale zoogeographic distributions, depthranges, and feeding types.
2.4. Data analysis
2.4.1. Numeric pre-processing of zooplankton dataWe quantified the amount of zooplankton as a sample � taxon
matrix of log-transformed vertically-integrated dryweight bio-mass. The steps in this calculation are as follows:
� For each taxon identified in the sample (typically a size ordevelopmental stage within a species) we multiplied estimatedlocal abundance (# m�3) by nominal body size (mg individ-ual�1). For both this paper and Li et al. (2013), a 3x multiplica-tive correction for day vs. night differences in capture efficiency(caused by visual net avoidance) was applied to adult euphausi-ids, following Shaw and Robinson (1998).� We sorted and summed individual species into the ‘‘major taxa’’
categories listed in Table 2.� We then multiplied by tow depth range and switched mass
units to get g m�2.� Finally, we log-transformed the biomass total within each
taxonomomic category as (log10(g m�2 + X)), where X is a ‘small’offset needed to allow log transformation of ‘‘absent’’
abundance- and biomass-per-sample. The values chosen for Xare taxon specific, and approximate their presence–absencedetection threshold in our samples: 0.001 g m�2 for the smallertaxa, 0.01 g m�2 for individually larger but numerically lessabundant categories (e.g. adult euphausiids and amphipods,decapods, gelatinous predators, polychaetes, and larval fish).
Log-transformation is very commonly used in zooplankton timeseries analyzes (e.g. Mackas et al., 2012a,b; O’Brien et al., 2011;Keister et al., 2011; Mackas and Beaugrand, 2010; Lavaniegosand Ohman, 2007) because it normalizes the usually stronglyskewed amount-frequency distributions of the raw abundanceand biomass data (this normalization is necessary because approx-imately multivariate-normal distributions are assumed by severalof the ordination and correlation methods that we applied subse-quently). However, two consequences of log-transformation arethat back-transformed multi-sample averages (geometric means)are biased low compared to their corresponding arithmetic means,and that, because log-transformed data include negative values,some resemblance metrics (e.g. Bray-Curtis) cannot be applied.
2.4.2. Zooplankton time series for individual taxonomic categoriesVariability of zooplankton biomass and community composi-
tion includes components operating at a wide range of time andspace scales. Three of the most important (each often accountingfor 3–10 fold variability) are:
� Small scale transient patchiness. This can be very intense in theStrait of Georgia due to interactions of zooplankton behaviorwith localized flow-field convergences (e.g. Mackas and Louttit,1988; Romaine et al., 2002). However, small-scale spatial vari-
134 D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159
ability is typically poorly resolved by the sampling designs usedfor long term zooplankton monitoring. For time series purposes,the usual approach (also adopted here) is to filter-out this com-ponent of variability by spatial and temporal averaging of ‘rep-licate’ samples.� The annual seasonal cycles (which we quantify by averaging
within-taxon, within-month, across-years to produce estimatedclimatologies for each of the taxa), and� Interannual variability, which we quantified in two ways: as
within-year averages of log biomass, and as log-scale anomalies(see Mackas et al. (2001) and Mackas and Beaugrand (2010) foranomaly equations and additional discussion) relative to thelocal average seasonal cycles described above.
This partitioning of time and space scales works very well if atime series extends over many years, and contains large numbersof individual samples spread relatively homogeneously acrossmonths and years. Unfortunately, although our total set of Straitof Georgia samples gives good coverage of all seasons, and fair-to-good coverage of most years, the within-year seasonal distribu-tion is often inhomogeneous (Table 1). For example, 83% of thesamples from 2002 were collected in February, and 23% of all Feb-ruary samples were collected in 2002. Similarly, 62% of the 2008and 2009 samples were collected in July and October, and those2 years accounted for 52% of the total for those months. In 2004,56% of the samples were collected in March–April, and the years2004 and 2008 accounted for 32% of the March–April totals.2004 also included the only January sample. This unevenness canconfound seasonal and interannual variability. The problem isobvious for simple annual averages of biomass-within-taxon: theannual average for a given year is biased low if most samples comefrom the low-biomass winter season; high if most samples comefrom near the annual peak in late spring–summer. Fortunately,the aliasing of seasonal variability can largely be eliminated byexpressing results as anomalies relative to the estimated averageseasonal cycle (see Mackas et al., 2001, 2012b; Mackas and Beau-grand, 2010 for further discussion).
However, there is also an effect (considerably weaker) on theaccuracy of the seasonal climatology estimates, and through theseon the annual anomaly time series. A year with a large number ofsamples in a given month has a strong influence on the estimatedmulti-year average for that month (the overall monthly climatol-ogy will therefore tend to be biased toward the monthly averagein that year and the monthly anomaly in that year will tend tobe biased toward zero). The same month also has a large influenceon the annual average anomaly for that year (the annual anomalywill therefore also tend to be small).
The within-year biases of annual average biomass and of annualbiomass anomaly described in the previous paragraph are presentin our data, but more-or-less independent. To assess their occur-rence and magnitude, we calculate and display our zooplanktontime series in three different but overlaid formats. The first is asunaveraged time series of individual samples plotted against sam-pling date (decimal year). At exploratory stages in our data analy-sis, these single sample data proved very useful for detection ofoutlier samples, meta-data errors, and shifts in taxonomic resolu-tion and naming convention (see Supplementary information).Averages of the individual samples across-months within-yearsproduced time series of annual average log biomass. Within-yearaverages of monthly anomalies produced time series of annualanomalies. Because 1996 had only four samples in only 2 months(half of them outside the primary zooplankton growing season)we did not report or analyze annual averages from 1996.
The individual-sample time series were very noisy, even aftersuspected outlier samples were identified and either verified, cor-rected, or removed. But the time series of annual average log
biomass and of annual biomass anomalies proved to be very simi-lar, suggesting that both have successfully captured much of thereal interannual differences. In our judgment, the anomaly timeseries are more reliable because they better minimize aliasing ofthe seasonal cycle. We have therefore chosen the anomaly timeseries as inputs to our subsequent multivariate analyses (methodsdescribed in the next sub-sections).
2.4.3. ‘‘Effective degrees of freedom’’ compensation for effects oftemporal autocorrelation
Many standard methods for statistical comparison assume thatthe individual observations of each variable (in our case, yearlyaverages for zooplankton biomass, zooplankton anomalies, andenvironmental indices) are statistically independent. The presenceof temporal autocorrelation (adjoining years tending to be similarin both the zooplankton and environmental time series) reducesthe validity of this assumption, and can lead to overestimation ofthe significance-of-association between variables. One way to cor-rect for this is to base significance estimates on a smaller numberof ‘‘effective degrees of freedom’’ N� (Pyper and Peterman, 1998)calculated from
1N�¼ 1
Nþ 2
N
XN=2
j¼1
ðN � jÞN
rXXðjÞrYYðjÞ
where N is the number of data points (years) in the time series, andrXX(j) and rYY(j) are the autocorrelations of time series variables Xand Y at time lag j. Because our time series are relatively short, indi-vidual pairwise estimates of N� proved to be noisy but most werecentered near N� = 12 d.f. for the 21 year time span (mean ± stan-dard error 12.4 ± 1.3). We have used N� = 12 for significance testsof both pairwise zooplankton–zooplankton and zooplankton–envi-ronment correlations, and in the F-tests used to assess significanceof multivariate regressions. For pairwise correlations, the revisedp = 0.1 significance threshold is about |r| = 0.45, vs. |r| = 0.36 if yearsare assumed independent. Note that this correction is intended fortesting the probability that an observed positive or negative corre-lation will persist into the future. The same threshold is very con-servative for assessing whether variables were associated withinthe observation time period (we will show in Sections 3.4 and 3.6that although the majority of pairwise zooplankton–zooplanktonand zooplankton–environment correlations are weaker than 0.45,they nevertheless are persistent across alternative comparisonmethods, and have frequency distributions with central tendenciesthat clearly differ from zero).
2.4.4. Interannual covariance among zooplankton taxa (zooplanktoncommunity composition time series)
To detect and quantify the similarities and differences amongthe anomaly time series of individual zooplankton taxa, we usedbivariate product-moment correlations among pairs of anomalytime series, and ordinations of the annual anomaly vs. year andtaxon matrix. The input anomaly time series all have near-zeromean due to how they are defined and calculated, but rangesand standard deviations differ among taxa (ranges are larger fortaxa with more intense percent interannual variability). Withoutstandardization of range, the more variable taxa therefore exertlarger influence on the ordination, an outcome which we believeis desirable because we want to examine both sign and amplitudeof variability. Ordinations were done using routines from the PRI-MER 6 statistical package (Clark and Warwick, 2001). Our primarychoice was Principal Components Analysis (PRIMER routine PCA),which projects shared variance onto a reduced number of orthog-onal axes (the principal components), quantifies the fraction of to-tal variance accounted for by each component (the Eigen value forthat component), and provides a matrix of weighting coefficients
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 135
(Eigen vectors) that describe how strongly each taxon contributesto the annual score of each component. As a check for sensitivity tochanges in taxonomic resolution and naming convention, we ap-plied PCA to two separate subsets of the zooplankton anomalyvs. year and taxon data matrix: 1990–2010 for taxa that were re-solved by both the COPRA and subsequent data processing streams,and 1997–2010 for the full suite of taxa listed in Table 2. As a checkon the assumptions and stability of the PCA results, we also appliedNon-Metric Multidimensional Scaling ordinations (PRIMER routineMDS) to year vs. year resemblance matrices (Euclidean Distance)calculated from zooplankton anomaly matrices by the PRIMER rou-tine Resemblance. Note that the commonly-used Bray-Curtisresemblance index cannot be used if input data matrices containnegative values, which are present by definition in anomaly timeseries. Because NMDS attempts to preserve rank resemblanceamong taxa and years, rather than full metric resemblance, thismethod is less sensitive than PCA to extreme outliers. However,the 2D and 3D groupings of years produced by PCA and MDS ordi-nations proved to be very similar, except for the sign conventions(arbitrary) of the ordination axes. We therefore selected the1990–2010 PCA outputs (plus the individual taxon anomaly timeseries) as additional inputs to subsequent comparisons of zoo-plankton with environmental and predator time series.
2.4.5. Environmental time series: selection of indices, source data, andmethods for comparison with the zooplankton time series
Physical and chemical oceanographic conditions within theStrait of Georgia are controlled by exchanges of water, dissolvedmaterials, and energy with the surrounding land, the atmosphere,and the adjoining Northeast Pacific continental margin and deepocean. The physical mechanisms and pathways linking them tozooplankton interannual variability will be discussed in Sections3.1 and 3.6. In this section, we explain our choice of environmentalindices, identify sources for their time series, and describe thesummarization and statistical steps we followed to compare themwith the univariate and multivariate zooplankton time series. Twoimportant statistical constraints are that the number of availableand plausible indices of environmental forcing of zooplankton ex-ceeds the degrees of freedom in our zooplankton time series (espe-cially if we recognize between-year temporal autocorrelation), andthat some of the available environmental indices share both statis-tical and causal associations. We therefore had to pick a relativelyshort list of environmental indices. Based on strength of associa-tion in earlier studies, and in the present data set, we selectedthe following:
� Changes in ocean temperature. Temperature anomalies havelarge effects on zooplankton distributions, community compo-sition, and phenology (e.g. Richardson, 2008; Beaugrand et al.,2009; Mackas et al., 2012a). Temperature appears to be the pri-mary axis of the ecological niche for many species (Helaouëtand Beaugrand, 2007, 2009). To index local environmental tem-perature within the Strait of Georgia, we use time and depthaverages of temperature anomalies at the Nanoose Bay navaltest site (location shown in Fig. 1). To produce these, weupdated through 2010 the 1970–2005 time series analyzed byMasson and Cummins (2007), which consists of biweekly depthprofiles. Although we will show fully-depth-resolved tempera-ture climatologies and anomalies in Section 3.1, our compari-sons with the zooplankton time series are based on univariate(depth- and time-averaged) and time-lagged (September toAugust) annual temperature anomalies at the Nanoose site.� Riverine inputs of freshwater drive the positive estuarine circu-
lation of the Salish Sea system, which in turn drives much of theexchanges of water, salt, and dissolved nutrients with the off-shore North Pacific (Mackas and Harrison, 1997). Over much
of the Strait (including our major sampling sites), river dis-charge is also a primary control of near-surface density stratifi-cation, and of turbidity and light penetration. The Fraser River isby far the biggest source of freshwater (about 73% of the total).We indexed interannual variability in the amount and timing ofFraser River discharge using Environment Canada monthly flowmeasured at Hope BC (station 08MF005; data downloaded fromwww.wsc.ec.gc.ca/applications/H2O/index-eng.cfm). We pro-duced three annual indices from annual cumulative flowcurves: ‘‘Fraser amount’’ = the sum of the monthly discharges,‘‘Fraser Start’’ = start of freshet = the date (decimal month) thatcumulative flow reached 25% of the annual total, and mid-fre-shet (‘‘Fraser Peak’’) = date (decimal month) that cumulativeflow reached 50% of the annual total. The latter index is alsousually quite close to the peak flow date used by Li et al. (2013).� The combination of local tides and local winds drives much of
the vertical mixing in the Strait of Georgia. Although tidal mix-ing is strongly dominant overall (LeBlond, 1983), its interannualvariability is relatively small compared to other importantphysical processes (although the 18.6 year modulation of tidalmixing associated with the lunar nodal tide has significant con-sequences in many parts of the Pacific; McKinnell and Crawford,2007; Tadokoro et al., 2009). Tidal mixing is also spatially local-ized, with highest intensity within the tidal passes at the SE andNW entrances to the Strait.� The relative importance of wind mixing increases with distance
from these passes, and is greater in the central and northernStrait regions that include most of our zooplankton samples.There are many land locations surrounding the Strait wherewind speed is monitored, but the site with the best combinationof long time series and weak influence of nearby terrestrialtopography is Sand Heads off the Fraser River mouth (locationshown in Fig. 1). Transfer of mechanical energy to the ocean isproportional to the cube of the wind speed. We obtained timeseries of monthly-averaged Sand Heads wind-speed-cubedfrom colleagues (Li et al., 2013; Allen and Wolfe, 2013). Fromthese, we calculated seasonally-partitioned indices of annualwind intensity. The motive for the seasonal partitioning is thelarge seasonal contrast (see Fig. 3 and Section 3.1.3) in windspeed, combined with the fact that although interannual vari-ability of wind speed has some positive autocorrelation amongadjoining months, it is essentially uncorrelated between winter(�November–February) and spring-autumn (�March–October;our averaging truncated to March–July for relevance to annualzooplankton productivity).� Various indices of variations in atmosphere and ocean climate
at ocean-basin-scale have been shown to be correlated withinterannual variability of zooplankton and fish communitiesoff Vancouver Island (e.g. Mackas et al., 2001, 2007; McFarlaneet al., 2000; King et al., 2000) and, more broadly, throughout theCalifornia Current (e.g. Peterson and Keister, 2003; Ohman andVenrick, 2003; Mackas et al., 2006; Lavaniegos and Ohman,2007; DiLorenzo et al., 2008; Keister et al., 2011) and through-out the oceanic Subarctic Pacific and Bering Sea (e.g. Hare andMantua, 2000; Chiba et al., 2006, 2008; McKinnell and Dagg,2010). We selected three climate indices that have both strongsignals and strong ecological associations in the Northeast Paci-fic (Overland et al., 2010). The Pacific Decadal Oscillation(‘‘PDO’’, Mantua et al., 1997; monthly data from http://jisao.washington.edu/pdo/PDO.latest) is the leading EmpiricalOrthogonal Function (EOF) of detrended SST variability in theNorth Pacific. Positive PDO sign corresponds to warmer-than-average temperature along the eastern margin of the NorthPacific. The North Pacific Gyre Oscillation (‘‘NPGO’’, DiLorenzoet al., 2008, monthly data from http://www.o3d.org/npgo/npgo.php) is the leading EOF of North Pacific sea-surface eleva-
Fig. 3. Seasonal (a) and interannual (b) variability of wind mixing in the Strait ofGeorgia, based on measurements of wind-speed-cubed at the Sands Heads lightstation (location shown in Fig. 1). Winds are strongest in during late autumn andlate winter storms, and weakest May–September. Both winter (NDJF) and spring(MAMJ) averages have a weak upward trend, overlaid in winter by very largeinterannual variability.
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tion anomalies, but has very similar spatial signature and timeseries to the second EOF of North Pacific SST variability (a.k.a.the ‘‘Victoria pattern’’, e.g. Overland et al., 2008). Positive NPGOsign corresponds to intensified circulation of the North Pacificsubarctic and central gyres, and also to cooler-than-averagetemperatures off the BC coast. The Southern Oscillation Index(‘‘SOI’’; monthly data from http://www.bom.gov.au/climate/current/soi2.shtml) measures anomalies of the atmosphericpressure difference between the tropical eastern and westernPacific. Negative SOI sign indicates El Niño conditions, includingabove average temperatures, and a deeper-than-average pycno-cline in the eastern tropical Pacific (and often at higher latitudesalong the entire eastern boundary of the Pacific). Both previousstudies and our own exploratory analyses show strongest corre-lations with annual zooplankton anomalies if these indices aretime averaged over several preceding months. For annual cli-mate indices, we therefore used averages of the monthly cli-mate indices within the following windows: PDO January–August, NPGO January–June, SOI previous September–August.
We standardized all environmental time series to zero-meanand unit standard deviation within the 1990–2010 time span priorto input into two methods for quantifying strength of environ-ment–zooplankton associations. One comparison method usedthe BIOENV routine from PRIMER 6. This is a non-parametric per-mutation method which identifies combinations of environmentaltime series that most closely match the overall among-year resem-blance from the set of zooplankton time series. BIOENV takes asinput the matrix of year-to-year ‘‘biology’’ resemblance (‘‘dis-
tances’’ between years in the zooplankton NMDS ordination ou-puts. Methods are described in Section 2.4.4, and results reportedin Section 3.4), and compares this to among-year resemblancematrices for all possible subsets of the standardized environmentaltime series. Similarity between the zooplankton and environmen-tal resemblance matrices is quantified by the Spearman rank-cor-relation rS between corresponding elements in the two matrices,and BIOENV reports as ‘‘best’’ the combinations of 1, 2, 3, . . . envi-ronmental time series with the largest rS. We will show later that,at least for our data sets, rS was always low (<0.5). One reason forlow overall rS is that, unlike PCA and multiple regression methods,the BIOENV weighting of environmental variables is binary. Re-tained environmental time series all have equal influence on theamong-year environmental resemblance, while all excluded envi-ronmental variables are assumed to have zero influence onamong-year resemblance of the zooplankton. A second factor con-tributing to low rS is that agreement on among-year resemblancewill be greatest if the environmental influences are uniform acrosstaxa. We will show in Section 3.6 that, despite significant sharedtemporal variability, the zooplankton variables had many differ-ences in the sign and strength of their environmental correlations.
Our second zooplankton–environment comparison used a bidi-rectional stepwise linear regression routine from the R statisticalpackage (command ‘‘step(lm(Z � E1, E2, . . . ,En’’)), where Z is thedependent variable time series, and Ei are the time series of theindependent variables). This method presumes little or no univer-sal coherence among the individual zooplankton time series (eachtaxon may be ‘‘going its own way’’ to an important degree), and in-stead asks which subset of the environmental time series contrib-utes significantly to prediction of each individual zooplankton timeseries. Candidate independent variables were the standardizedenvironmental time series described above. Dependent variables(one per regression) were the anomaly time series of individualzooplankton taxa, total zooplankton biomass, and of the zooplank-ton annual PC scores. Note that the PC scores are weighted sums ofthe individual zooplankton time series. By including the PC timeseries as dependent variables, our regression results can also di-rectly represent modes of variability that are shared among indi-vidual zooplankton taxa. The stepwise linear model adds anddeletes independent variables in an order that (at each step) max-imizes reduction of the Akaike Information Criterion (AIC). Themethod assumes that observations (=years for our application)are statistically-independent. The presence of temporal autocorre-lation reduces the validity of this assumption, and stepwise regres-sion therefore tends both to overfit (retain dependent variables forwhich the true reduction in AIC is less than the estimate) and to as-sign a stronger-than-justified significance level to the F test of thefinal fit. We did not formally correct for the first problem, except bylimiting regressions to a maximum of four independent variables(if n > 4 on the initial stepwise regression, we excluded the n > 4variables with the smallest regression sum of squares, and re-ranthe stepwise regression with fewer input variables). For the secondproblem, we compensated by using our estimate of ‘‘effective de-grees of freedom’’ N� in the denominator of the F-test (substitutingd.f.true � N� � variables fitted for d.f.model = years � variables fit-ted). Multiple regression coefficients (whether or not the regres-sion is stepwise) can also become unstable if there is strongcovariance among two or more dependent variables (‘‘multicollin-earity’’). Multicollinearity was extreme among the temperatureanomaly time series (pairwise correlations among depth strataaveraged +0.89 and ranged as high as +0.97), and moderatelystrong among three other pairs of independent variables (in orderof decreasing severity: timing of the start and peak of the Fraserfreshet, r = +0.85; SOI with PDO, r = �0.69; and NPGO with Frasertiming, r = +0.65). We dealt with multicollinearity by:
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 137
� Merging the highly inter-correlated depth-stratified tempera-ture anomaly time series into a single composite covering the fullwater column (additional discussion follows in Section 3.6.7.3).� For Fraser discharge timing, we retained only ‘‘FraserStart’’ (of
the two timing estimates, this consistently had stronger associ-ation with the zooplankton time series).� If both SOI and PDO, or both NPGO and FraserStart, were
retained by the initial stepwise regression, we excluded which-ever of the intercorrelated pair had smaller regression sum-of-squares, and re-ran the stepwise regression starting with onefewer independent variables.
3. Results and discussion
3.1. The Strait of Georgia as an environment for zooplankton:oceanographic conditions and connections
Several features of the Strait of Georgia make it a special envi-ronment that is linked to, but in various ways qualitatively differ-ent from the adjoining coastal and oceanic NE Pacific. Thefollowing summary of the major environmental drivers is basedon several detailed reviews and analyses of the Strait’s physicaland chemical oceanography (e.g. LeBlond, 1983; Thomson, 1994;Mackas and Harrison, 1997; Masson and Cummins, 2007; Johann-essen and Macdonald, 2009), but also includes annual time seriesdata for the environmental variables that we chose to comparewith and interpret our Strait of Georgia zooplankton time series.
3.1.1. EnclosureThe Strait of Georgia is almost entirely surrounded by mainland
and islands (Fig. 1). The two connections to the Pacific are locatedat the extreme southeast and northwest ends of the Strait, with themajority of exchange occurring via the SE entrance. Although oftendeep, both connecting passages are long and narrow, and are usu-ally strongly vertically mixed by fast tidal currents. Further sea-ward, a broad sill located south of Victoria (�95 m) in the innerpart of Juan de Fuca Strait, and a sill at the mouth of Queen Char-lotte Strait (�51�N, 127.8�, �135 m) further restrict the depth ofwater exchange between the tidal passes and the open Pacific. To-gether with the vertical mixing that occurs within the tidal passes,this limits the source depth, temperature, salinity, and density ofentering deep water. The adjoining continental land mass producesa (slightly) more continental atmospheric climate than that of theouter coast. Lower air temperature in winter intensifies wintercooling and convection, while higher air temperature in summerintensifies near-surface density stratification.
3.1.2. Positive estuarine circulationLarge annual river discharges (�1.5 � 1011 m3 year�1, �73% by
the Fraser River) mix with saltier underlying water and drive astrong estuarine circulation. On average and for most of the year,the direction of flow is seaward in the upper �20–50 m and intothe Strait at greater depths, although strong downwelling windsoff the mouth of Juan de Fuca Strait can cause transient reversalsin winter (Thomson et al., 2007). The incoming estuarine flow con-sists (Masson and Cummins, 2004, 2007; Pawlowicz et al., 2007) ofa year-round input of mid-density water formed by vertical mixingin the SE and NW tidal passes (this water intrudes into the Strait atdepths between 50 and 200 m), plus briefer late-summer-autumndeep water renewal events (intrusions during neap tides of waterdense enough to sink to 250–400 m in the Strait). The annual estu-arine exchange averages �1.6 � 1012 m3 year�1 (Pawlowicz et al.,2007) and is therefore larger than the volume of the Strait(1.0 � 1012 m3). Mean residence times of water (and of zooplank-ton if they lack behavioral retention mechanisms that take thembelow the surface seaward flow) are therefore short, ranging from
about a year at depths >250 m, to about 150 d for the 50–250 mdepth range, to as short as a few weeks for the surface water duringthe peak discharge season (Pawlowicz et al., 2007; Johannessenand Macdonald, 2009).
3.1.3. Strong seasonality of physical forcingWinds within the Strait can be strong during intense winter and
equinoctial storms but are usually very gentle from late springthrough early autumn (Fig. 3a). Runoff and near-surface densitystratification have annual maxima in early summer and mid-sum-mer respectively. The water properties of offshore deep waterentering the bottom layers of Juan de Fuca Strait (this is the sourcewater type for most of the incoming estuarine circulation) alsohave strong seasonal variation due to the seasonal alternation ofupwelling/downwelling along the outer coast. Salinity and nutrientcontent of the entering deep water are maximal in late summer,while temperature is lowest and oxygen content highest in latewinter (Masson, 2002). Intensity of vertical mixing within the en-trance tidal passes fluctuates on a fortnightly spring-neap cycle,and, as noted above, replacement/renewal of deep water occursas relatively brief events (primarily in late summer and late win-ter) when neap tides and reduced vertical mixing in the tidalpasses coincide with upward displacement of isopycnals in Juande Fuca Strait, and with seasonal minima of freshwater discharge(Masson, 2002).
3.1.4. Depth profiles and variability of sub-surface water propertiesMuch of the Strait is deeper than the adjoining outer coast conti-
nental shelf, although much shallower than the oceanic subarcticPacific. Average depth is 155 m, 67% of the area is deeper than100 m, 33% deeper than 200 m, and 13% deeper than 300 m (Thom-son and Foreman, 1998). Although most of the same zooplanktonspecies are present within the Strait, along the outer coast continen-tal margin, and in the adjoining oceanic subarctic Pacific, the domi-nance hierarchy of the Strait of Georgia zooplankton community ismore similar to the oceanic subarctic Pacific than to the BC continen-tal shelf (Table 2; more detailed discussion follows in Section 3.2).However, because of the sills at the outer entrances and vertical mix-ing within the entrance passages, deep water within the Strait isboth fresher and somewhat warmer than at corresponding depthsoffshore (Fig. 4), and vertical gradients of water properties belowthe upper 100 m are much weaker. This lack of deep stratificationcombined with the strong tidal mixing at some locations allows sea-sonal and interannual variability of the deep water column to bequite strong within the Strait (Fig. 4). Relatively large seasonal andinterannual temperature fluctuations occur in the Strait at all depths(Fig. 5a and b), with a lag of about 4–5 months between the surfaceand 200–400 m (Masson and Cummins, 2007 and Fig. 5b). However,at annual and longer time scales, the temperature anomalies arestrongly correlated among all depth strata. For example, pairwise ramong temperature anomalies within different depth strata rangefrom 0.74 to 0.97. Highest correlations are between the deeper lay-ers, where temperature variability is less affected by high frequencynoise caused by recent weather.
3.1.5. Large scale climate forcingLow-frequency variability and trends of the larger scale North
Pacific climate (indexed in this paper by NPGO, PDO, and SOI, timeseries shown in Fig. 6a–c) affect both offshore areas and Strait ofGeorgia system, although probably in different ways and by differ-ent pathways and mechanisms. Offshore variations in temperature,wind, and surface currents contribute to interannual variability ofoffshore upwelling, which determine the water properties of thedeep estuarine input flow. This source water variability, combinedwith the sign and intensity of surface air–sea heat exchange, drivesseasonal and interannual temperature variability within the Strait
Fig. 4. Summary comparisons of vertical, seasonal, and interannual variability of temperature profiles between (a) the Nanoose test range site in the Strait of Georgia(location shown in Fig. 1, data updated from Masson and Cummins, 2007), (b) Station P4 on the continental slope off Vancouver Island (48�44.60N, 127�400W; data fromTabata and Weichselbaumer, 1992b), and (c) Stn P in the Alaska Gyre (50�N, 145�W, data from Tabata and Weichselbaumer, 1992a.). Heavy black profiles are year-roundaverages, medium gray profiles the climatological monthly minima (late winter–early spring), light gray profiles the climatological monthly maxima (late summer–earlyautumn). Error bars show interannual variability (root-mean-square deviations of monthly averages from the local climatologies). Deeper layers in the Strait of Georgia (100–400 m) are warmer than at the offshore sites, but also experience stronger seasonal and much stronger interannual variations of temperature.
Fig. 5. 1970–2010 time series of vertical profiles of temperature (top) and temperature anomalies (bottom) at the Nanoose site in the Strait of Georgia (updated from Massonand Cummins, 2007). Both seasonal and interannual fluctuations propagate relatively rapidly downward through the water column (surface-deep time lag �4 months,Masson and Cummins, 2007). The Strait of Georgia water column was very cold in the early 1970s, moderately to very warm for most of the years between 1980 and 2007(warmest 2004–2007), and cool 2008 and 2009. The zooplankton time series analyzed in this paper (years 1990–2010) corresponds to the right-hand half of this figure.
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(Figs. 5 and 6d). Large scale climate variability also affects the localintensity of winter and early spring storms (Fig. 3b), and theamount and timing of precipitation and snow melt, which in turndrives interannual variations in total freshwater discharge and fre-shet timing (Fig. 7). Conversely, due to the degree of enclosure byland, zooplankton in the Strait are only weakly exposed to and af-fected by the climate-linked seasonal and interannual fluctuationsin poleward vs. equatorward alongshore surface currents that areevident in outer coast time series.
3.2. Average zooplankton biomass and community composition(1990–2010)
The primary productivity and phytoplankton biomass of theStrait of Georgia is large (Harrison et al., 1983; Masson and Peña,2009). Total zooplankton biomass was also on average high inour samples (arithmetic mean dry weight 12.9 g m�2; geometricmean 10.1 g m�2). Although the species-richness component ofdiversity is relatively high (we have recorded more than 240
Fig. 6. 1990–2010 time series of dominant large-scale climate indices in the NorthPacific, plus vertically-averaged temperature anomalies in the Strait of Georgia. (a)The North Pacific Gyre Oscillation index (NPGO, averaged here from January–June ofthe index year) is the leading EOF of sea-surface height variability in the NorthPacific. Positive NPGO is correlated with intensification of the N Pacific subarcticand central gyres, below average temperatures, and above average salinity andnitrate concentrations in the Alaska Gyre and coastal NE Pacific (DiLorenzo et al.,2008). (b) The Pacific Decadal Oscillation (PDO, averaged here from January–Augustof the index year) is the leading EOF of detrended sea-surface temperaturevariability in the N Pacific. Positive PDO is correlated with positive temperatureanomalies and with several modes of ecological variability in the NE Pacificincluding poleward displacement of zooplankton distributions (e.g. Hare andMantua, 2000; Keister et al., 2011). (c) The Southern Oscillation Index (SOI, bottompanel, averaged here from previous September through August of the index year) iscalculated from the east–west atmospheric pressure gradient in the equatorialPacific. Negative values are associated with El Niño conditions (positive temper-ature anomalies and deepened pycnocline) along the eastern margin of the Pacific.(d) Strait of Georgia temperature anomalies, vertically-averaged from the Nanoosetime series shown in Fig. 5. Gray line shows monthly averages, squares and blackline show time-lagged annual average (previous September through August of theindex year). All three climate indices contribute significantly to local temperatureanomalies (see text).
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 139
holozooplankton species), the species-evenness component ofdiversity is low. Much of the biomass in each season (Fig. 8b)and also in the overall annual average (Fig. 8a) is accounted forby only about a dozen crustacean taxa and their gelatinous preda-tors (see also Table 2). Species-evenness in individual samples isusually even smaller. Crustacean taxa are the largest contributorsto total vertically-integrated biomass (Fig. 8a): calanoid copepods
(39.5% arithmetic mean, 42% geometric mean), euphausiids(28.5% arithmetic mean, 16% geometric mean – lower becausemore variable sample-to-sample), gammarid and hyperiid amphi-pods (8.2% arithmetic mean, 8.7% geometric mean), and ostracods(4% arithmetic mean, 3.4% geometric mean). Additional quantita-tively-important non-crustacean taxa included chaetognaths (3%,mostly Parasagitta elegans), several hydrozoan medusae (3.8%),pteropods (3%, mostly Limacina helicina), and planktonic polychae-tes (3%, mostly Tomopteris).
This list of dominant species overlaps, but differs considerablyin ranking, from the list for the outer coast of Vancouver Island(as reported in Mackas, 1992; Mackas et al., 2001). One major dif-ference is that, despite its coastal, mid-latitude, and enclosed loca-tion, most of the dominant crustacean zooplankton taxa in theStrait of Georgia (Table 2) have zoogeographic home ranges thatare both high latitude/cold water (Subarctic or Transition Zone)and oceanic. Examples include the large copepods Eucalanus bungii,Neocalanus plumchrus, and Pareuchaeta elongata; medium-sizecopepods Calanus pacificus and Metridia pacifica; euphausiid Eup-hausia pacifica; amphipods Themisto pacifica, Primno abysallis andCyphocaris challengeri; pteropods Limacina helicina and Clione lima-cina; chaetognaths Parasagitta elegans and Eukrohnia hamata; andthe ostracod Discoconchoecia. High dominance by subarctic oceanictaxa is also pronounced in the deep fjords that cut into the BCmainland coast (Romaine and Galbraith, unpublished; Stone,1980; Gardner, 1980 and pers. comm. (G. Gardner, Memorial Uni-versity, St. Johns, NFLD.).
A second difference is near-absence in the Strait of Georgia ofmost of the southern-origin (California Current and Central Gyre)copepods, chaetognaths and doliolids that become abundant alongPacific Northwest continental margins in warmer-than-averageyears (for examples and further discussion of these outer coast lat-itudinal displacements, see Mackas et al., 2006; Hooff and Peter-son, 2006; Keister et al., 2011).
Both of these differences are probably caused by the strong po-sitive estuarine circulation of the Salish Sea system (Section 3.1.2).Nearly all of the offshore water and biota that enter the Strait do sobelow �50 m depth, and because of summer upwelling of isopyc-nals along the continental margin, the ultimate offshore sourcedepth is often >200 m. Conversely, transport of water and organ-isms in the upper �20–50 m is seaward from the Strait. It is there-fore difficult for organisms that spend all of their time in thesurface layer to enter or remain within the Strait, but relativelyeasy for organisms that spend much of each day (or much of eachyear) between 50 and 400 m to both enter and be retained. Thisdepth-dependent selectivity is not limited to holozooplankton,but also affects the community and genetic structure of Strait ofGeorgia meroplanktonic benthos and fish. For example, Jamiesonand Phillips (1993) showed that night-time depth distributions ofthe planktonic larvae of Dungeness crab (Cancer magister) differ be-tween populations resident in the Strait of Georgia (deep) and pop-ulations along the BC and Washington outer coast (near-surface).
A third difference is near-absence in the Strait of several off-shore zooplankton taxa that are permanently bathypelagic, or un-dergo especially deep diel or seasonal migrations, or have very lowoptimal temperatures. The two most conspicuous examples are thevery large copepod Neocalanus cristatus (abundant in the AlaskaGyre and along the BC continental slope, but with seasonal dor-mancy depth >1000 m) and several salp species that are intermit-tently very abundant in both the subarctic Pacific and the northernCalifornia Current.
A final difference (compared to the outer coast continental mar-gin) is somewhat lower biomass and occurrence rates of largescyphomedusae such as Cyanea and Chrysaora, and near-absenceof hyperiid amphipod species that are specialized commensals ofsalps and scyphomedusae.
Fig. 7. Interannual variability of Fraser River discharge and seasonal timing. Total discharge (top) is driven by the amount of precipitation since the previous autumn.Discharge timing (bottom) is controlled mostly by atmospheric temperature (earlier in warmer years) which affects both the balance between rain vs. snow, and the rate ofspring snow melt.
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The seasonal cycle of vertically-integrated zooplankton biomass(Fig. 8b) in the Strait of Georgia has a late-spring to late-summermaximum (geomean �8–12 g m�2) and a winter–early-springminimum (�4–5 g m�2). Seasonal variability of biomass withinthe surface layer is more pronounced (and has an earlier peak) be-cause the dominant large herbivorous copepods (N. plumchrus andE. bungii) and many of the dominant medium-sized copepods(Calanus marshallae, C. pacificus, Pseudocalanus spp.) have seasonalontogenetic migrations that take them from the surface layer(where they reside during their spring and/or summer growingseason) to depths of 100–400 m for the remainder of the year. Tim-ing and depth range of this migration differ among species. Severalprevious studies (e.g. Bornhold, 2000; Mackas et al., 2007; Battenand Mackas, 2009; Johannessen and Macdonald, 2009; Mackaset al., 2012a) have documented large within-species variability ofNeocalanus phenology that is strongly correlated with upper-oceantemperature during the spring growing season. Warming of theStrait since the 1970s (Fig. 5) has led to a trend toward very earlyonset of Neocalanus dormancy (perhaps now in most years tooearly for successful growth and survival of this species within theStrait).
Fig. 8. Zooplankton community composition (vertically integrated dry weightbiomass) in our 1990–2010 samples. (a) Pie graph shows overall arithmetic mean ofall samples included in this analysis. Medium to large crustaceans (medium-to-large copepods, euphausiids, amphipods, and ostracods) strongly dominate averagedryweight biomass, (b) column graph shows average seasonal cycles (geometricmeans resolved to bimonthly intervals) of the dominant major taxa. Copepodbiomass is further partitioned among size classes. Seasonal migration anddormancy of the large herbivorous copepods causes a progressive vertical shift oftheir population from the surface layer to below 200 m (compare Fig. 2). Line graphshows the number of samples averaged within each seasonal period.
3
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3.3. Interannual variability of biomass within individual major taxa
Over the 20 years of the time series, most of the zooplanktontaxa listed in Table 2 showed large interannual deviations fromthe long term average biomass shown in Fig. 8a and b. These fluc-tuations and trends are illustrated in Fig. 9 (total dryweight bio-mass, and annual biomass anomalies for the dominant majortaxa), Fig. 10 (further size and taxonomic resolution within the cal-anoid copepod and euphausiid categories), and Fig. 11 (four addi-tional taxa for which annual biomass estimates are notcomparable between the early (1990–1995) and later (1997–2010) segments of the time series due to changes in taxonomicand size resolution; however these taxa also contributed smallerpercentages of total biomass than the categories shown in Figs. 9and 10). In each of these time series, we provide three indices oftemporal variability: biomass in individual samples (small trian-gles), annual average biomass (gray circles connected by blacklines), and annual average biomass anomaly (white squares con-nected by gray lines). Due to a very small number of samples from
Fig. 9. Interannual variability of total biomass, and of biomass within the dominant mindividual samples (small triangles, point plotted on the X-axis indicate absence in thatscale on left hand axis), and annual average biomass anomaly (white squares with gray cvery low sample numbers in that year. All plots are log-scale on the Y axes. Error bars forthe annual mean (i.e. years whose error bars do not overlap differ at p < 0.05).
1996, and resulting unreliability of annual averages, we have notreported nor analyzed annual means from 1996. Numeric datafor the annual time series are provided in the online Supplemen-tary information as Tables ST2 (annual geomeans) and ST3 (annualanomalies). Section 2.4.2 described calculation methods, informa-tion content, and relative strengths/weaknesses of these indices.In our opinion, the annual average anomalies provide the by farthe most reliable index of interannual differences, but in mostcases all three indices show very similar trends and fluctuations.The total range of interannual variability over a 20 year time spanhas been about factor of 10 (max–min �1 on log scale) within mostzooplankton categories, and approaching factor of 100 (max–min�2 on log scale) for euphausiids, chaetognaths, ostracods, ptero-pods, and perhaps medusae (some of the step-like increase of me-dusa between 1995 and 1998 may be an artifact of lowertaxonomic resolution 1990–1995, and resulting uncertainty ofthe individual body size coefficient used to convert abundance tobiomass). For all variables except ctenophores and ostracods(whose time series include large single year peaks or troughs,
ajor zooplankton taxa. Three indices are shown in each time series: biomass insample), annual average biomass (gray-shaded circles with black connecting lines,onnecting lines, scale on right hand axis). Averages are not reported for 1996 due toannual average biomass and for annual average anomaly are ± one standard-error of
Fig. 10. As Fig. 9 except a sub-classification of the two major taxa (calanoid copepods and late juvenile–adult euphausiids) that are the largest contributors to total biomass.Partitioning of the copepod biomass is by size class (but can be cross-referenced to species for the larger sizes). Partitioning of the euphausiids is by genus.
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Fig. 9), the dominant time scales of variability are long: fluctua-tions with periods of about 8–10 years, in some cases overlaid byan overall 20 year trend.
Many of the zooplankton time series include a strongly-sharedpattern of low frequency variation (the PCA analysis in the nextsection will pick this up as the time series of the first PrincipalComponent). All of the categories in Fig. 9 except medusae andsiphonophores had their overall maximum average biomass andmost positive annual anomalies during the 1998–2002 interval,and a local minimum during the 2003–2008 interval. Many alsohad an earlier decline from moderately high levels in 1990 to aminimum in 1993–1995, and an upward trend in the final yearsof the time series (starting in either 2008 or 2009). This patternis clearest and most intense in the euphausiid, chaetognath andcopepod time series, but is also evident in the time series of totalbiomass, amphipods, pteropods and planktonic polychaetes.Medusae and siphonophores shared the initial 1990–1995 decline,and the 2008–2010 increase, but had zero-to-positive biomassanomalies between 2004 and 2007 when other taxa were low to
very low. The modes and intensity of covariance among the zoo-plankton taxa will be examined in more detail and by additionalmethods in Section 3.4.
Interannual variability within the copepods and euphausiids(the two dominant major taxonomic categories, see Fig. 8) isshown with more size and taxonomic resolution in Fig. 10. Small(<1 mm prosome length), medium-sized (1–3 mm) and large (3–5 mm) calanoid copepods, and the dominant euphausiids (E. pacif-ica, Thysanoessa spinifera and T. longipes) all follow the decadal fluc-tuation described in the previous paragraph. However, very largecopepods (>5 mm prosome length, mostly adult E. bungii in oursamples) do not. Although their total biomass was relatively smallin all years (always <1 g m�2), they had a large and steep increasebetween 1995 and 2000, and remained above their long term aver-age in 9 out of 10 subsequent years.
Interannual differences within the large calanoid copepod cate-gory (3–5 mm prosome length) were caused mostly by largechanges in abundance of juvenile and adult stages (C4–C6) of N.plumchrus. This species dominated spring-season zooplankton bio-
Fig. 11. Post-1998 interannual variability of four additional zooplankton taxa for which taxonomic and sizing resolution was not sufficient to compare early (1990–1995) vs.later (1998–2010) data records. Crab larvae had high biomass in 2006–2007, and larvaceans had moderately high biomass in 2003–2005 – years when most otherzooplankton taxa were anomalously low.
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mass for many of the past 50 years (Harrison et al., 1983; Black,1984). The STRATOGEM program (University of British Columbia/University of Victoria) subsequently documented a very large(�factor of 100) decrease between 2001 and 2006 in the deep over-wintering population of this species, accompanied by an ongoingphenologic shift to earlier onset of dormancy (Sastri and Dower,2009; El-Sabaawi et al., 2009; Johannessen and Macdonald, 2009;all STRATOGEM samples fitting our bottom- and tow-depth criteriaare included in the present data set). Our additional non-STRATO-GEM samples also show an earlier substantial decline (between1990 and 1994–1995, note however that there were also relativelyfew samples in 1994–1995) followed by a recovery 1996–1998.Both the 1990–1995 and the 2001–2006 declines occurred duringsequences of years in which the Strait of Georgia water columnwas anomalously warm (Fig. 5), forcing very early onset of N. plum-chrus dormancy. However, data from other years do not support ahypothesis that Neocalanus biomass changes are solely a mono-tonic response to annual temperature anomalies. Their populationtrend was positive but small during the recent 2008–2009 returnto very cool conditions (although preliminary data not includedin this analysis do suggest a larger recovery in 2011–2012). Also,an earlier 1971–1975 study by Gardner (1977) detected a �10Xdecline of N. plumchrus (accompanied by a �10� increase of C.marshallae) during a sequence of years in which the water columnof the Strait was anomalously cold (Fig. 5). We will revisit these re-sults in our discussion of the environmental forcing of zooplanktoninterannual variability (Section 3.6).
Interannual variability of the medium-sized calanoid copepods(1–3 mm prosome length) was somewhat less extreme than thatof the larger copepods, and also smoother from year to year. The sizeclass as a whole followed the slow down-up–down-up pattern ofvariability described at the start of this section, and also showed asignificant upward trend in the final years of our time series. The1–3 mm size category is more taxonomically diverse than the3–5 mm and >5 mm copepods, and includes nine species that
sometimes make significant contributions to the size class biomasstotal (Table 2a). After 1998 (when species-level resolution of juve-nile stages improved, see Supplementary information), juvenileand adult M. pacifica and C. pacificus contributed most (60–90%) ofthe biomass in this size category, but early juveniles of N. plumchrusaccounted for 10–20% prior to the 2002–2006 decline of this species.
Within the euphausiids (bottom row of Fig. 10), the time seriesof adult–late juvenile E. pacifica and total adult–late-juvenile Thy-sanoessa spp. were similar both to each other (r = +0.52) and tothe medium, large and total copepod categories (averager � +036) but less correlated with the >5 mm copepods (r = +0.17and �0.25 respectively). Between 2003 and 2007, adult euphausi-ids of both genera were frequently absent in our mid-Strait sam-ples. Li et al. (2013) report a similar, but earlier (1999–2000),drop in euphausiid mean biomass and occurrence rate in samplescollected around the margin of the Strait. Euphausiids are mobileand have very patchy spatial distributions, so it is possible thatsome of the changes reflect shifts in spatial distribution withinthe Strait, in addition to interannual changes in size of the residentpopulation.
As noted in Section 2.3 and in Supplementary information,identification, enumeration, and archival protocols differed beforevs. after 1995. Our preliminary analyzes showed that resulting bio-mass estimates for crab larvae, shrimps, larvaceans, and cladocer-ans (and perhaps also for the medusae) were biased low in theearlier years. We have therefore limited our display (Fig. 11) andanalysis of their time series to 1997–2010. The larval crab time ser-ies is noteworthy because high values occurred in years (2006 and2007) when most other taxa where at or near their 20-year min-ima. Preikshot et al. (2010) examined decadal changes in the dietof juvenile sockeye salmon in the Strait of Georgia, and foundgreatly increased percentage of crab larvae between 2004 and2008. In contrast, the planktonic decapod shrimps share the pat-tern of temporal variability seen in Figs. 9 and 10 (correlations withthose time series were usually weak but all were positive).
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Larvaceans had above average biomass 1999–2001 and after 2008,in common with many of the other taxa. But larvaceans also had aweak tertiary peak in 2003–2005 when many other taxa were in asteep decline. The cladoceran time series has very low average bio-mass and low occurrence rates in most years, and is dominated bysingle-year spikes due to high abundance in a relatively smallnumber of samples from those years. Their 1999 and 2001 peaksdid coincide with maxima of the medusae, but we are not confi-dent how much of the interannual signal of the cladocerans is realvs. how much is aliased spatial and advective variability (marinecladocerans prefer neritic habitat, and are more common in shal-low water, low salinity environments around the margin of theStrait than at the deep mid-Strait locations examined in thisstudy).
In summary, individual Strait of Georgia zooplankton time ser-ies show strong interannual variability. The range can be quanti-fied by multiplying the root-mean-square log-scale anomalies byfour (essentially ± two standard deviations, so this range wouldbe expected to include�95% of years). The smallest ranges of inter-annual variability (but still relatively large) were in the categoriestotal biomass, hyperiid amphipods, ctenophores, shrimps andcladocera (4�RMS � 0.5–0.75 on log scale, multiplicative factor of3.5–5.5x). Conversely, interannual variability was especially strongin two of the dominant crustacean groups (euphausiids and largercalanoid copepods), and in chaetognaths, pteropods and larvaceans(4�RMS between 1.3 and 1.9 on log scale, equivalent to multiplica-tive factors of 20–80). Several taxa had qualitatively similar timeseries that were dominated by slow fluctuations with time scalesof about 8–10 years. In the next section, we will describe and quan-tify this shared variability.
3.4. Multivariate analysis of similarities among years and taxonomiccategories in the Strait of Georgia zooplankton time series
Considerable past experience (e.g. Peterson and Keister, 2003;Beaugrand et al., 2003; Beaugrand, 2005; Mackas et al., 2001,2007; Pershing et al., 2005, 2010; Li et al., 2013) shows that multi-variate methods such as ordination and cluster analysis are effec-tive tools for examining zooplankton time series and their
Fig. 12. Color-coded matrix and frequency histogram showing sign and strength of tcorrelations among the anomaly time series, lower diagonal shows correlations amongcorrelations (numerous and often strong), cool colors negative correlations (less commo
association with variability of the physical environment, other zoo-plankton species, and other trophic levels. At least in part, this isbecause the multivariate methods emphasize strong and sharedpatterns of zooplankton interannual variability, while filteringout some of the statistical noise present in univariate time series.In this section, we examine covariance among the zooplanktontime series, with goals of:
� quantifying similarities among years and taxa,� identifying years with high zooplankton biomass likely to have
provided good conditions for higher trophic level predators, and� evaluating the nature of trophic-controls (bottom-up vs. top-
down vs. wasp-waist).
We will subsequently include the annual principle componentscores as additional inputs to our examinations of covariability ofzooplankton with their predators (Section 3.5) and with physicaland climatic drivers (Section 3.6).
3.4.1. Covariability among individual Strait of Georgia zooplanktontime series
Pairwise correlations between zooplankton taxonomic and sizecategories (summarized graphically in Fig. 12, numeric values inSupplementary Table ST4) are mostly weak-to-moderate instrength but are also mostly positive (r averages +0.32, ranges from�0.32 to +0.94; 84% of pairwise correlations are >0). Half of thenegative pairwise correlations are between gelatinous predators(medusae, siphonophores, ctenophores) and various crustaceans,but even for these taxon pairs the negative correlations are weak(r between �0.02 and �0.32) and positive coefficients are morefrequent than negative. About half of the strong positive correla-tions are between categories that overlap in taxonomic composi-tion (e.g. total biomass with most other categories, totalcopepods with all copepod size classes except >5 mm, total eup-hausiids with both E. pacifica and Thysanoessa spp.). However, theremaining half include pairs without taxonomic overlap. Many ofthem also differ in trophic level and feeding type (for example her-bivorous copepods covary strongly and positively with predatorychaetognaths, amphipods, and polychaetes, with detritivorous
he correlations among individual zooplankton time series. Upper diagonal showsthe time series of annual average biomass. Warm colors indicate positive pairwisen and always weak).
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 145
ostracods, and with mucus-web-feeding pteropods). Positive cor-relation among adjoining trophic levels (prey–predator pairs) is of-ten interpreted as evidence of bottom-up trophic control.Conversely, correlations that switch sign at successive trophicsteps are interpreted as evidence of trophic cascades driven bytop-down control (Moloney et al., 2010). In the Strait of Georgia,the positive correlations (Fig. 12 and Supplementary Table ST4)are strong and frequent enough to suggest that the primary driversof population changes among the holoplanktonic crustaceans,polychaetes, chaetognaths and pteropods involve interannual var-iability of bottom-up forcing (i.e. variation in total productivityand/or suitability of the physical environment). However, a prom-inent bump on the negative side of the otherwise-Gaussian corre-lation coefficient frequency histogram (Fig. 12) is evidence ofbimodality of the correlation structure. We suspect that high abun-dance of gelatinous predators in the Strait of Georgia may some-times depress the biomass of their crustacean prey, and also ofcompeting predators such as amphipods and chaetognaths (this
Fig. 13. 2D plots of eigen vectors (taxon loadings) for the first three principal componenPC1 (X-axis in both a and b) accounts for 35.7% of the total variance, and can be interpretcategories had positive coefficients on this axis). PC2 (Y-axis of a) accounts for 22.5% of vby calanoid copepods in the <5 mm size classes. PC3 (Y-axis of b) accounts for 1copepods < 5 mm, but negative anomalies of euphausiids and copepods >5 mm.
certainly happens in other coastal and estuarine environments,see examples in Moloney et al., 2010). However, we stress thatany top-down control within the Strait of Georgia zooplanktoncommunity appears to be weak and is probably intermittent.
3.4.2. Ordination analysis of resemblance among zooplankton taxa andyears
Principal components ordination (PCA) of the 1990–2010 zoo-plankton anomaly time series provides another view of the similar-ities between taxonomic categories and between years, and also ofthe temporal patterns for the dominant modes of variation. Thethree leading principal components accounted for a very large frac-tion of the total variance in these eighteen time series (72.9% in to-tal; 35.7%, 22.5% and 15.0% for components one through threerespectively). Plots of the Eigen vector coefficients (taxon loadings)for the three leading components are shown in Fig. 13a and b. Thecoefficients quantify how much each taxonomic and size categorycontributes to the annual score for that component (larger absolute
ts of the 1990–2010 zooplankton anomaly time series (18 zooplankton categories).ed as a gradient from negative to positive biomass anomalies of all or most taxa (allariance, negative scores can be interpreted as strong dominance by euphausiids and5%, positive scores can be interpreted as positive anomalies of pteropods and
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values indicate stronger positive or negative correlation with thePC time series). Proximity of taxa in the 3D ordination space alsoapproximates similarity of their time series, because these threeleading components capture so much of the total variability. How-ever, overall resemblance also includes proximity on the remaininglower-ranking components that are not shown.
A striking result from the Strait of Georgia PCA is that the load-ing coefficients for PC1 (35.7% of total variance, coefficients plottedon the X-axis in Fig. 13a and b) are positive for all zooplankton tax-onomic and size categories except medusa, which have a smallnegative coefficient. PC1 (the dominant mode of zooplanktoninterannual variability in the Strait of Georgia) can therefore beinterpreted as an index of overall zooplankton productivity: a po-sitive annual PC1 score implies above-average biomass for nearlyall of the zooplankton community, while a negative annual PC1score implies negative anomalies of most categories. This resultconfirms the impression of a shared pattern of interannual vari-ability that we noted earlier, and variation in its sign and intensityare shown by the PC1 time series (Fig. 14a). The PC1 loading coef-ficients also suggest that responses to whatever is driving thismode have the same sign for nearly all taxa (a conclusion thatwas suggested but not guaranteed by the preponderance of posi-tive between-category correlation coefficients in Fig. 12). Becausethe zooplankton taxa span at least two adjoining trophic levels,this in turn supports our interpretation that bottom-up rather thantop-down interactions dominate within the Strait of Georgia zoo-plankton community. The taxa with the strongest PC1 loadingsare the euphausiids, the larger copepods, chaetognaths, pteropods,and gammarid amphipods. Polychaetes, the smaller copepods,hyperiid amphipods, and total biomass have intermediate load-ings, while the other gelatinous predators (ctenophores andsiphonophores) all have small positive coefficients.
Fig. 14. Time series of annual scores for the three leading principal components.PC1 is dominated by a low frequency fluctuation, and summarizes the strong andcommunity-wide pattern of high vs. low biomass. PC2 is dominated by a step-likechange �1997–1999. PC3 is dominated by shorter-term variability.
PC2 accounts for an additional 22.5% of the overall variance. PC2loading coefficients are plotted on the Y-axis of Fig. 13a, and thetime series of PC2 scores in Fig. 14b. The strongest positive PC2loadings are on the largest calanoid copepod size class (>5 mm,mostly E. bungii), chaetognaths, and medusae. The strongest nega-tive loadings are on the euphausiids. The 1–3 mm and 3–5 mmcopepod size classes plus siphonophores and pteropods also havenegative but weaker loadings. Coefficients for the remaining cate-gories (including total biomass) are all near zero. We see no obvi-ous ecological interpretation of the groupings into positively vs.negatively correlated taxa, but the time series pattern (Fig. 14b)is clear: greater biomass of euphausiids and small-to-large cope-pods 1990–1995, and greater biomass of Eucalanus, chaetognaths,and medusae 1998–2010. The trajectory of the PC2 time seriesstrongly resembles the before-vs.-after 1998 step change of the au-tumn zooplankton community described by Li et al. (2013).
The eigen vector coefficients for PC3 (15% of total variance, plot-ted on Y-axis of Fig. 13b) are dominated by strong positive loadingsfor the pteropods (large positive anomalies 1998–2000), moderatepositive loadings for the 1–3 mm calanoid copepods (time seriesmaximum in 1999), and strong negative loadings for all euphausiidcategories. All other taxa have weak loadings on this component.The PC3 time series (Fig. 14c) varies less smoothly (8 sign changesover 21 years, many of them clear-cut) than either PC1 (4 signchanges) or PC2 (only one pronounced sign change). The remainingvariance (unaccounted for by these three leading PCs) is only 27%of the total, and is spread in small amounts among all higher ordercomponents.
Similarities among years are shown in Fig. 15a and b (as withFig. 13, these are 2D projections onto the three leading PCs). Yearswith positive scores on PC1 (the X-axis in both 15a and 15b) havepositive annual biomass anomalies of all or most taxa. Positivescores on PC2 (Y-axis of 15a) indicate negative anomalies of eup-hausiids and most copepods, but positive anomalies of chaetog-naths, medusae and calanoid copepods >5 mm. Positive scores onPC3 (Y-axis of 15b) indicate negative anomalies of both euphausi-ids and of the >5 mm copepods, but positive anomalies of ptero-pods and of the remaining copepod size classes. Years with‘‘more zooplankton biomass’’ and ‘‘biomass concentrated in largeenergy-rich crustaceans’’ are generally thought to provide superiorfeeding environments for planktivorous fish (Peterson and Sch-wing, 2003; Mackas et al., 2007; El Saabawi et al., 2013). Years lo-cated on the right hand side of Fig. 15 therefore probably provided‘‘good’’ feeding environments (especially if in the lower right handquadrants), while years located in the upper left hand corners were‘‘poor’’. The trajectory of the zooplankton community through timecan be seen from the year labels and the lines connecting adjoiningyears. Overall, the years are distributed relatively uniformly in theordination space, without obvious gaps, and without distinct tightclusters. However, there has been a clear migration around theordination space. Adjoining years are almost always close together,meaning that this progression has mostly been gradual and thatthe zooplankton community changes usually develop over timescales of several years, consistent with the low frequency contentof the two leading PC modes. From 1990 to 1994–1995, PC1 (com-munity biomass) declined from above average to below average,but the relative contribution of euphausiids and copepods to thetotal remained large (negative PC2). Results from other studies(Bornhold, 2000; Haro-Garay and Huato Soberanis, 2008; Li et al.,2013) indicate that both N. plumchrus and E. pacifica had low abun-dance in 1997 compared to 1996 or 1998. From 1998 to 2002, totalcommunity biomass was high in our samples, but there were shiftsof dominance between the copepods and pteropods (highest 1999and 2000) and euphausiids (highest 1998 and 2002). Most taxa hadbelow average biomass 2003–2008 (especially in 2005 and 2007,which along with 1994 had the lowest PC1 scores of the entire time
Fig. 15. 2D ordination plots of annual scores on the three leading principalcomponents, showing the year-to-year progression of zooplankton productivity andcommunity composition. Years close together on the plots have similar zooplank-ton anomaly vectors. Years located toward the lower right of the plots had positiveanomalies of biomass for most taxa and especially of large, energy-rich crustaceantaxa. They probably provided good feeding environments for higher trophic levelpredators. Years located toward the upper left had negative anomalies of thesegroups and probably provided poor feeding environments. See text for additionalinterpretation and discussion.
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series). The mid-to-late 2000s biomass minima were especiallypronounced for the euphausiids, medium (1–3 mm) and large (3–5 mm) copepods, chaetognaths, and gammarid amphipods. Thesedifferences contributed to positive scores on PC2 in all years be-tween 2000 and 2010, and on PC3 in 2005 and 2007. Sometime be-tween 2008 and 2010, most taxa returned to near-average biomasslevels (all PC scores close to zero in 2010).
As a check on the stability of the 1990–2010 PCA results, wecompared two alternative ordinations of the zooplankton anomalytime series: an NMDS ordination (non-metric multidimensionalscaling) of the same set of 1990–2010 anomaly time series, and aPCA analysis that included additional taxa (those shown inFig. 11), but was restricted to the 1997–2010 years in which alltaxa were analyzed with consistent size and taxonomic resolution.Details of results and of the differences in methodology and inputdata are provided in the online Supplementary information (graph-ical outputs from the comparison analyses as SupplementaryFigs. SF1 and SF2). The key finding is that the results were stable:both of the comparison runs produced PC1 and PC3 ordination out-puts nearly identical to each other and to those from the primary1990–2010 PCA. After correcting for (arbitrary) switches in signconvention of the weighting coefficients and annual scores, thevectors of annual scores on the three leading ordination axes werevery highly correlated between analyzes (average r = 0.91, stan-dard deviation = 0.17, n = 6), as were the vectors of taxon loadingsbetween the 1990–2010 and 1998–2010 PCAs (average r = 0.58,range 0.52–0.93, n = 3). The main differences were that the modedescribed by PC2 in the full 1990–2010 time series (Fig. 14b) con-
tained less variance after 1997; the PC2 for 1997–2010 instead fo-cused more on changes during the 2000s that were not fullycaptured by PCs 1 or 3. Changes in empirical ordination rankingsand weights are not unexpected when comparing relatively briefsequences of years with time series that are dominated by low-fre-quency fluctuations. For example, it is now well documented thatduring most of the 1990s and 2000s, the long-term second modeof North Pacific SST variability (‘‘Victoria pattern’’) accounted formore SST variance than did the longer term leading mode PDO(Overland et al., 2008, 2010).
3.5. Similarities and differences between Strait of Georgia zooplanktonand fish time series
An important motive for assembling and analyzing the Strait ofGeorgia zooplankton time series is to use this information to helpunderstand why growth and survival of fish has varied over time.Specifically, we seek to identify which time periods provided high-er- vs. lower-than-average food supply for higher trophic levels,and to learn which zooplankton taxa and size classes are key ingre-dients of a high quality food supply. Two fishery recruitment pat-terns of particular contemporary interest are:
� A strong overall decline since the late 1980s of marine survivalrates of Strait of Georgia coho, Chinook and sockeye salmon. Theapproximately linear downward trend was briefly interrupted(or overlaid) by higher survival of the 1998–2002 ocean entryyears (OEYs). The peak of this local maximum was in 2000.� Exceptionally poor survival of the 2007 and 2005 OEYs, fol-
lowed by above average survival of the 2008 OEY.
Environmental correlates/predictors of the salmon survival andrecruitment time series are analyzed in detail elsewhere in this is-sue see papers by Araujo et al., Irvine et al., and Perry and Masson),in part using the zooplankton time series developed in this paper.However, we note briefly here some shared patterns in the Strait ofGeorgia fish and zooplankton time series, and also some importantdifferences. In the previous section, we interpreted positive annualscores of the zooplankton PC1 time series (Figs. 14a and 15) asindicative of high productivity/biomass of the entire zooplanktoncommunity, and especially of high biomass of the larger crustaceantaxa. The PC1 time series lacks any significant overall trend (unlikesalmon marine survival), but does include two shorter declines(1990–1994, and 2000–2007). The above average PC1 scores1998–2002 match years in which residuals from the salmon mar-ine survival trend were also positive. Our zooplankton time seriesalso suggest that 2005 and 2007 probably provided ‘‘poor’’ foodavailability to zooplankton predators (below average biomass ofmost of the dominant crustacean taxa). However, an important dif-ference between the salmon survival and zooplankton biomasstime series is that for the zooplankton, 2005 and 2007 fall on themargin of a continuous distribution, rather than standing apartas extreme outliers. In addition, 2008 in the zooplankton time ser-ies is near the 20 year average, not a positive anomaly. Based onthis, our present interpretation is that variability of zooplanktonbiomass within the Strait has been an important contributing fac-tor, but is probably not the sole cause of poor salmon recruitmentin the last decade.
3.6. Covariability of zooplankton and physical environmentalconditions
In this section we explore connections between the zooplank-ton and ‘‘ocean climate’’ by three methods: examination of thepairwise correlations between zooplankton anomalies andindividual environmental time series, comparison of zooplankton-
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vs. environment-based estimates of similarity among years, andregression of individual zooplankton time series on multiple envi-ronmental time series.
3.6.1. Zooplankton–environment correlation matrixSigns and magnitudes of the pairwise correlations between zoo-
plankton annual anomalies and the time series of Strait of Georgiaphysical environmental variables and North Pacific climate indicesare summarized in Fig. 16 (underlying numeric values are in Sup-plementary Table ST5). Analysis and interpretation of zooplank-ton–environment covariability is complicated by the fact thatseveral of the environmental time series either measure or aremechanistically linked to ocean temperature variability, and aretherefore moderately-to-strongly inter-correlated at annual andlonger time scales (often more strongly than they are correlatedwith the zooplankton anomalies). In Fig. 16, the temperature-linked environmental variables are grouped in the left hand col-umns, and color coded to indicate whether positive anomalies ofthe various zooplankton indices are associated with the projection
Fig. 16. Color-coded matrix and frequency histogram showing sign and intensity of p(columns). Environmental variables that measure or are correlated with temperature arezooplankton to cool (blue) or warm (red) anomalies (the sign reversal for some enviconvention). Environmental variables with weak or no temperature covariance are grnegative-to-positive correlation. White indicates pairwise r near zero. After correction f
of ‘‘cool’’ (blue) or ‘‘warm’’ (pink) conditions onto each of theseenvironmental time series. Several interesting features can benoted in the resulting heat diagram:
� Individual zooplankton–environment correlation coefficientsare quite weak (on average only about half as strong as theinter-correlations among zooplankton categories shown inFig. 12, and much weaker than the inter-correlation amongthe environmental indices linked to water temperature shownin Supplementary Table ST5). Only 9% of the zooplankton–envi-ronment pairwise correlations exceed the p = 0.1 significancethreshold if degrees of freedom are adjusted for temporal auto-correlation, only 21% exceed the threshold even if years areassumed independent. Plots of individual zooplankton–envi-ronment associations (not shown) indicate that the low correla-tions are primarily due to large scatter, rather than anydetectable non-linearity. Some of this scatter is attributable tonoise in the zooplankton annual anomaly estimates, butbecause stepwise regressions (presented later in this section)
airwise correlations between zooplankton (rows) and environmental time seriesgrouped in the left hand columns; color gradient indicates positive response of the
ronmental indices is to maintain a ‘‘blue = negative correlation = cool preference’’ouped in the right hand columns, with orange-to-green color gradient indicatingor autocorrelation, the p < 0.1 significance threshold is approximately |r| > 0.46.
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 149
often identify two or more independent variables, we think themain cause is that in most cases the zooplankton variability isdriven by multiple environmental forcing variables andmechanisms� Most of the zooplankton taxa do better when temperature-
related environmental variables (those in the left hand columnsof Fig. 16) indicate a ‘‘cool’’ ocean climate in the Strait of Georgiaand NE Pacific. This can be seen in the table of pairwise correla-tion estimates for the individual zooplankton taxa (only thegelatinous taxa, ostracods, cladocerans, crab larvae, and hyperi-ids include pink cells, and pink cells predominate only forsiphonophores, ostracods and crab larvae), but is perhaps moreobvious in their frequency distribution histogram. For the zoo-plankton time series for which positive sign means ‘‘more bio-mass’’ (zooplankton PC1, plus all of the individual anomalytime series), 87% of the correlation estimates, and all thatexceed the significance threshold, are negative.� Despite widespread overall indications that cool is favorable,
individual zooplankton categories often differ in the sign andintensity of their responses to local and direct (water columntemperature anomalies within the Strait) vs. larger scale andindirect (PDO, NPGO, SOI, Fraser freshet timing) indicators ofwarm vs. cool climate. Euphausiids and zooplankton PC3(which includes a strong negative loading on the euphausiidstime series) respond most strongly to the temperature anoma-lies. Zooplankton PC1, PC2, and the remaining biomass anoma-lies all appear to have stronger association with large scaleclimate and/or freshet timing.� For the environmental indices that are �uncorrelated with
water temperature (right-hand columns of Fig. 16), most zoo-plankton–environment correlations are even weaker. However,most taxa do show a small positive response to greater totalFraser discharge (which should lead to stronger annual estua-rine exchange, and also stronger summer salinity stratificationwithin the Strait). Correlation with stronger-than-average win-ter wind mixing (associated with a delayed spring phytoplank-ton bloom, Allen and Wolfe, 2013) is strongly positive forctenophores and cladocera, weakly positive for the two smallercopepod size classes, weakly negative for the larger copepods,larvaceans and crab larvae, and strongly negative for polychae-tes and siphonophores.� The environmental time series with strongest average correla-
tion with the zooplankton anomalies are NPGO, SOI and timingof the Fraser freshet. All have mean |r| � 0.2–0.3). These envi-ronmental time series are also strongly correlated with eachother (r = 0.4–0.7). Their correlations with water column tem-perature anomalies are also moderately strong (mean r = 0.3–0.4), but are weaker than the corresponding associations oftemperature anomalies with PDO (r = 0.54).
We interpret the scatter of the individual pairwise zooplank-ton–environment associations as evidence that forcing of the zoo-plankton variability often involves multiple environmentalvariables and multiple mechanisms and pathways. If this is thecase, we would like to know how many and which environmentaltime series are needed to give adequate description of the set ofzooplankton time series. This question can be asked in two differ-ent ways (results described in the next two sub-sections).
3.6.2. Matching zooplankton- with environment-based estimates ofamong year resemblance
In this section we ask what combination of environmental vari-ables best reproduces the pattern of among-year resemblance ofthe entire zooplankton community variability, using PRIMER rou-tine BIOENV. A starting suite of nine environmental time serieswas provided to the BIOENV analysis (and to the stepwise multiple
regressions described in the next Section). This list of environmen-tal variables is the same as the column headers in the zooplank-ton–environment correlation matrix (Fig. 16) except that depth-stratified temperature anomalies are combined into a single verti-cal average. The surviving list includes three large-scale climateindices (NPGO, PDO and SOI), plus six ‘‘local’’ time series (verti-cally-integrated temperature anomaly, winter and spring windmixing, annual Fraser discharge, and seasonal timing of the startand middle of the Fraser freshet). All of these environmental timeseries are normalized to zero mean and unit standard deviation.We ran the BIOENV analyzes for two subsets of the zooplanktontime series: 1990–2010 using 18 zooplankton categories (the zoo-plankton ordinations in Fig. 15 and SF1) and 1997–2010 using 22zooplankton categories (the zooplankton ordination shown inFig. SF2). For both of these sets of years, descriptions of year-to-year resemblance based on the full suite of environmental timeseries (NMDS ordinations shown in Fig. 17a and c; PCAs of environ-ment time series showing loading vectors in SupplementaryFig. SF3a and c) have relatively little in common with the zooplank-ton-based assessments (Figs. 15, SF1 and SF2). In particular, theenvironmental ordinations show 1999 as an extreme outlier year(strongly separated from 2000 to 2002, although grouped withthem by the zooplankton PCA). The environmental ordinations alsoplot 2007 (the largest outlier in the zooplankton ordinations) assimilar to 1991 and 2002. Similarity of year-to-year resemblanceremains low if the environment-based ordinations are restrictedto subsets of variables identified by BIOENV as ‘‘best’’ (Fig. 17band d and Fig. SF3b and d). Overall rank correlation (Table 3) peaksat <0.47 for the 1998–2010 time span, and for the complete timeseries is always <0.33, suggesting that only about 10–25% of theyear-to-year variability is shared. Despite these caveats, the BIO-ENV selection of environmental variables as ‘‘important’’ (Table 3)is relatively consistent, and the list of variables not selected is evenmore consistent:
� Wind mixing during the previous winter (November throughFebruary). BIOENV identifies this as the best single predictorin both time periods. However, except for cladocera polychae-tes, siphonophores, and ctenophores (all minor contributors tototal biomass), the bivariate correlations of wind with zoo-plankton (Fig. 16) are weak and inconsistent. For example,1999 (the year with by far the strongest winter wind) had posi-tive anomalies of many taxa, but the years with the next stron-gest winter wind were 2007 (very low biomass of mostzooplankton taxa) and 2002 (positive biomass anomalies foreuphausiids and decapod shrimps; intermediate biomass butdeclining from the two previous years for most other taxa).� NPGO, SOI and Fraser freshet timing all show persistent ‘‘cool is
favorable’’ covariance with many of the biomass anomaly timeseries (compare Fig. 6a and c with Figs. 14, 9, 10 and 12, and thecorresponding column and rows of Fig. 16).� Annual total Fraser discharge (‘‘Fraser amount’’). This selection
by BIOENV is also puzzling due to inconsistent zooplanktonresponse. Like the winter wind time series and several zoo-plankton taxa, Fraser discharge had a large positive anomalyin 1999. However, other years with above average dischargehad both high (1990, 1991, 2002) and very low (2005, 2007)zooplankton biomass.� Water-column temperature anomaly was not selected as a
‘‘best’’ predictor by BIOENV, but was often included in combina-tions of 4–5 environmental variables that had only slightlylower rank correlations than those shown in Table 3.� PDO was never selected by BIOENV. This was a bit surprising,
given the well-documented association of PDO with outer-coastanomalies of zooplankton biomass and community composition
Fig. 17. Resemblance among years based on standardized large scale and local environmental time series. (a) Years 1990–2010, eight environmental time series (SOI, NPGO,PDO, full water column temperature anomaly, winter and spring wind mixing, annual Fraser river discharge, and seasonal timing of the start of the annual freshet). (b) Years1990–2010, ‘‘best’’ environmental variables for this time span as selected by BIOENV (winter wind, and NPGO). (c) Years 1998–2010, all eight environmental time series. (d)Years 1998–2010, five ‘‘best’’ environmental variables (winter wind, SOI, NPGO, Fraser amount, and Fraser Start).
Table 3Performance of various combinations of environmental time series at matching the 1990–2010 among-year Euclidean distance resemblance pattern summarized by thezooplankton ordinations. The set of standardized environmental time series provided to the PRIMER BIOENV routine is the same set of variables used in the stepwise multipleregression (depth-stratified water temperature anomalies are combined into a full water column average).
# of environmental time series 1990–2010, 18 zooplankton time series 1998–2010, 22 zooplankton time series
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(e.g. Mackas et al., 2001, 2007; Peterson and Keister, 2003; Keis-ter et al., 2011). However, it is consistent with the multipleregression results that we will discuss next.
3.6.3. Stepwise regression of zooplankton anomalies on multipleenvironmental time series
Although the zooplankton PCA results showed that the stron-gest mode of zooplankton variability involved same-sign responsesby all or most taxa, the PCA identified two additional modes inwhich responses by different taxa were negatively correlated.The matrix of zooplankton–environment correlations (Fig. 16) alsoindicates important among-taxa differences. This suggests thatenvironmental influences involve both taxon-specific (individualtime series) and shared (PC time series) zooplankton responses.We examine both using two-way stepwise regressions (R Projectroutine ‘‘step’’) in which either an individual principal componenttime series or an individual biomass anomaly time series is thedependent variable, and a suite of environmental time series areoffered as independent variables. We originally hoped that this ap-proach might identify which depth stratum has the most influence
on the interannual variability of each zooplankton category (3–90 m surface layer vs. 90–165 m day-depth of diel migrants vs.255–405 m dormancy depth of the large copepods vs. �165–255 m ‘‘deep water’’ formed by subsidence and mixing of the highdensity estuarine intrusions). However, strong multicollinearity ofthe four depth-stratified temperature anomaly time series, and ofthe two Fraser seasonal timing indices (details in Section 2.4.4), re-sulted in unstable regression coefficients for these variables. Wetherefore reverted to a single temperature anomaly time series(averaged over the full water column), and retained only the ‘‘startof freshet’’ timing index FraserStart (which had stronger correla-tions with the zooplankton time series than mid-freshet timing).We also compensated for weaker multcollinearity among twoadditional pairs of environmental variables (NPGO with Fraser-Start, and PDO with SOI) by excluding the weaker correlate fromeach pair if an initial stepwise fit retained both variables.
Regression results are summarized in Table 4. Regressions dif-fered among taxa both in overall goodness-of-fit, and in the subsetof independent variables which were retained. However, theenvironmental variable with the strongest and broadest overall
Table 4Results of stepwise regression of nineteen zooplankton time series (rows) on nine environmental time series. Regressions differ among taxa, but the environmental variables withthe strongest and most frequent association with the zooplankton time series are NPGO, as noted in Fig. 16 and the text, several environmental indices are correlated with watertemperature anomalies in the Strait. First row of the table indicates which sign of the regression coefficient corresponds to ‘‘cool is favorable’’. Approximate significance levels ofindividual regression coefficients (T-test, based on assumed N�–k degrees of freedom) are indicated by symbols below each coefficient: Overall goodness-of-fit (R2
adj) andsignificance level of the complete multiple regression (F-test (N��k,k) d.f.) are shown in the four right-hand columns.
SOI NPGO PDO WindWint WindSpr FraserAmt FraserStart Tanom R2adj
Blank (>0.1).* p < 0.10.** p < 0.05.*** p < 0.01.**** p < 0.001.
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 151
influence on the various zooplankton time series is NPGO (positivecoefficients in 10 regression fits, 7 coefficients significant atp < 0.1). NPGO was also identified as an important environmentaldriver/predictor of Strait of Georgia fishery time series (Perry andMasson, 2013). For the zooplankton, NPGO is followed in frequencyand strength of association by:
� Winter wind (7 coefficients of mixed sign, 4 significant).� FraserStart (4 positive coefficients, all significant).� SOI (6 coefficients of mixed sign, 3 significant).� Water-column temperature anomaly (‘‘Tanom’’, 5 fits of mixed
sign, 5 significant).
The remaining environmental time series were selected as pre-dictors less frequently, and often at lower significance: Spring windmixing (5,2), total annual Fraser discharge (‘‘FraserAmount’’, 5,1),and PDO (2,2). As noted above, low strength of association be-tween PDO and Strait of Georgia zooplankton is consistent withthe correlation matrix (Fig. 16) and the BIOENV results (Table 3),but contrasts strongly with the strong association of PDO with out-er coast zooplankton time series (Peterson and Keister, 2003; Keis-ter et al., 2011; Mackas et al., 2001, 2007).
Among the various zooplankton categories, multiple regressiongoodness-of-fit and significance (R2 and F test, adjusted for reduceddegrees of freedom) was highest for chaetognaths (63% of varianceaccounted for), E. pacifica (50%), and copepods >5 mm (43%). Good-ness-of-fit was intermediate (R2
adj20—40%) for zooplankton princi-pal components 1 and 2, and for total biomass, total euphausiids,gammarid amphipods, copepods < 1 mm, pteropods, polychaetes,medusae, and ctenophores. Fit was low (R2
adj10—20%) but signifi-cant at p < 0.1 for total and 1–3 mm copepods, and ostracods. Zoo-PC3, 3–5 mm copepods, Thysanoessa spp., hyperiid amphipods, andsiphonophores all had non-significant overall regressions, althoughwith the exception of the siphonophores, all had strong and plau-sible regression coefficients for at least one environmentalpredictor.
Signs of the regression coefficients for NPGO, PDO, and Fraser-Start are consistent with our ‘‘cool is favorable’’ interpretation fromthe zooplankton–environment correlation matrix (Fig. 16). For SOIand Tanom, signs of the coefficients are mixed. Taxa for which po-sitive anomalies were consistently associated with warm condi-tions include ostracods and the Zooplankton PC3 scores (forwhich positive sign indicates lower-than-average biomass of eup-hausiids). Zooplankton categories with mixed-sign regressions ontemperature-correlated environmental indices include:
� Euphausia pacifica (‘‘cool’’ fit to Tanom and NPGO, but ‘‘warm’’fit to SOI),� Thysanoessa spp. (‘‘cool’’ fit to Tanom, ‘‘warm’’ fit to SOI),� polychaetes (‘‘cool’’ fit to FraserStart and PDO, ‘‘warm’’ fit to
Tanom,� medusae (‘‘cool’’ fit to NPGO and PDO, ‘‘warm’’ fit to Tanom, and� ostracods (‘‘cool’’ fit to FraserStart, ‘‘warm’’ fit to SOI.
However, for both the euphausiids and polychaetes, mixed-signregression does not match their consistently ‘‘cool is favorable’’pairwise environmental correlations (Fig. 16), suggesting that their‘‘warm’’ sign regression coefficients are artifacts ofmulticollinearity.
The signs of the winter wind regression coefficients suggest thatweak wind mixing and an earlier-than-average spring phytoplank-ton bloom (Allen and Wolfe, 2013) are favorable for the copepods,and for some predatory zooplankton (chaetognaths, planktonicpolychaetes, and sipnonophores), but unfavorable for euphausiidsand ctenophores.
The frequency and order-of-selection of environmental variableselection by the stepwise regressions (Table 4) resembles, but isnot identical to, the results of the BIOENV analysis (Section 3.6.2and Table 3). For both methods, NPGO and winter wind are theenvironmental variables most frequently selected, while springwind and PDO have the weakest associations. However, the regres-sion analysis indicates stronger associations with NPGO, freshet
152 D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159
start timing, and water column temperature anomalies, and some-what weaker associations with winter wind and total annual Fraserdischarge. Although the goodness-of-fit performance measures dif-fer between the two methods, coefficients of determination for theindividual stepwise regressions are larger (mean R2
adj ¼ 0:24) thanthe composite r2
s for ‘‘best’’ BIOENV combinations (mean for the1990–2008 comparisons 0.08, range 0.05–0.10).
3.6.4. Obstacles to deducing process and causation from zooplankton–environmental statistical associations
Interpreting local causal mechanisms from the Strait of Georgiazooplankton–environment correlations and regressions is notstraightforward. Of the nine environmental time series, onlyTanom quantifies a variable that interacts directly and locally withthe zooplankton. The remainder are well-measured indices ofeither regional-scale atmospheric drivers (wind and precipita-tion/runoff) or basin-scale climate conditions. These indices areknown to covary with water properties and water movementswithin the Strait, which in turn potentially affect zooplankton pop-ulation sizes by altering the annual amount of phytoplankton pro-ductivity, the timing match of this productivity with zooplankton,water temperature, and rate and depth distribution of advectiveexchange with the open Pacific. Unfortunately, time series datafor the intermediate and final steps are incomplete, and the corre-spondence between environmental index and local ecological pro-cess is not one-to-one (Table 5). In particular, most environmentalindices are correlated with more than one local ecological process,and vice versa. This adds a lot of uncertainty and ambiguity to thediscrimination process, making this discussion more speculativethan we would prefer. Nevertheless, we think we can narrow thefield of possibilities by comparing scales and strengths of associa-tion, both within our own data set and with other nearby zoo-plankton time series.
3.6.5. Constraints on the range of candidate ecological mechanismsThere are several key observations with which proximal ecolog-
ical mechanism(s) should be consistent. These include:
Table 5Cross-classification of environmental time series (columns) with underlying local procesbiomass. Row two describes the spatial scale at which each index is observed (L = Local, Ninterannual variability (I = year-to-year, E = 1–2 year events separated by 5–10 year gaps, Dwith zooplankton time series (summarized from Tables 3 and 4). Lower rows describeassociation are indicated by symbols.
Environmental time series ?
Spatial scale(‘‘Local’’ to global)Dominant time scaleStrength/frequency of correlation with zooplankton time series ?Ecological process or relationship ;Subarctic zooplankton temperature niche (‘‘cool is favorable’’)Oxygen requirement/toleranceVariability of annual primary productivity due to:
Horizontal nutrient supply from Pacific (volume of estuarine exchange)(nutrient concentration in deep offshore source water)Vertical nutrient flux in summer(entrainment and wind mixing)Seasonal light limitation (deep mixed layer, cloudiness, turbidity)
Advective loss of near-surface zooplanktonAdvective supply of deep zooplanktonSeasonal timing mismatch due to:
Spring bloom timingTiming of zooplankton dormancy or reproduction
Aliasing due to within-Strait shifts in spatial distribution and/or degree of aggregation(no change in total population size)
? = suspected but weak. C indicates statistical correlation but no known direct mechani* Detectable.** Moderate.*** Strong.**** Very strong.
� The strongest mode of zooplankton variability in the Strait ofGeorgia (ZooPC1) is a slow fluctuation (time scale �10 years)that is positively correlated across most zooplankton taxa, andwith indicators of ‘‘cool’’ environmental conditions. Neverthe-less, the strongest statistical associations of ZooPC1 are withNPGO and a late Fraser freshet, not directly with water columntemperature. NPGO is (mostly) slowly varying (good match ofdominant time scale). It directly measures the strength of theatmospheric and oceanic gyres in the North Pacific but is alsostrongly correlated with surface layer salinity and nutrient con-centrations in the Alaska Gyre, and with nutrient and chloro-phyll concentrations in the California Current (DiLorenzoet al., 2008). The next strongest mode of zooplankton variabilityin the Strait (ZooPC2) is also ‘‘slow’’ (either trend-like or step-like), and also identifies NPGO as an important environmentalcorrelate, although it more strongly associated with winterwind mixing.� Despite spatial proximity, zooplankton anomaly time series
from the Strait of Georgia differ considerably from time seriesfrom nearby outer-coast regions, especially when comparedyear by year (Fig. 18). Several of the dominant Strait of Georgiazooplankton categories (the copepods larger than 3 mm bodylength, plus euphausiids, hyperiid and gammarid amphipods,and chaetognaths) are all strongly dominated by a single spe-cies (Tables 2 and 3), and therefore can be compared directlywith species-level zooplankton time series from the outer coastof Vancouver Island (the data analyzed in Mackas et al., 2001,2007; subsequent summary updates are available in annualDFO State of the Ocean reports e.g. Crawford and Irvine,2011). The Strait and the outer coast have tended to share someof the low frequency variability (positive anomalies of north-ern-origin taxa in 1999–2002, and negative anomalies 1994–1997 and 2004–2006), but several years were very different(our data indicate that 1998 was a very poor year on the outercoast but an above average year in the Strait; 2007 was anabove average year on the outer coast but a very poor year inthe Strait). Correlations over the past 20 years (1990–2010)
ses and mechanisms (rows) that could cause interannual variability of zooplanktonP = North Pacific, P = Pacific). Row three describes the dominant time scales of their= 5–10+ years). Fourth row describes statistical associations of environmental indicesassociation of environmental indices with local ecological processes. Strengths of
T�anoms
NPGO SOI PDO Freshetstart
Fraseramount
Wind(winter)
Wind(spring)
L NP P NP L L L LE to D D E D E to D I I to D I to D** **** * ? ** * ** ?
*** * * ** C C* * *
* * C* * **
? * *
* * **
* **
? ? ? *
* * ***
** * * *
U U U U ? ? U ?
sm, U indicates linkage unknown.
Fig. 18. Comparisons of annual zooplankton biomass anomalies between corresponding taxonomic categories in the Strait of Georgia and off the outer coast off SouthernVancouver Island (continental shelf break and slope). Despite spatial proximity, overall correlation of years is absent or weakly positive for most taxa, although both regionsoften share positive anomalies 1999–2002 and negative anomalies 1994–1995 and 2004–2006. Only the ‘‘Eucalanus/ Calanoid copepods >5 mm’’ comparison is statisticallysignificant (p < 0.1).
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 153
are negligible to weakly positive for same-year comparisons.Only the positive correlations (Fig. 18b and c) of ‘‘Eucalanus bun-gii’’(offshore) with ‘‘Calanoid copepods > 5 mm’’ (Strait of Geor-gia), and of Strait and outer coast E. pacifica approach statisticalsignificance. Time-lagged correlations (not shown) are in gen-eral even weaker (the one exception is a positive correlation(r = 0.36) between Themisto/hyperiids for Strait of Georgia lead-ing the outer coast by 1 year). This very weak interannualcoherence among adjoining ocean regions is a sharp contrastto the stronger alongshore coherence of zooplankton time seriesthroughout the California Current system, and especiallybetween �45–50�N (central Oregon through southern Vancou-ver Island, Mackas et al., 2006). The zooplankton communitiesfrom the outer coast continental margins off Vancouver Islandand Oregon include the dominant species in the Strait of Geor-gia, share with the Strait a pattern of reduced biomass of large,northern-origin zooplankton during warm years, and (seawardof the shelf break) have similar species dominance hierarchies.But the leading mode of zooplankton interannual variability inboth outer coast regions consists of dominance switches (i.e.negative temporal correlations) between ‘‘northern’’ and‘‘southern’’ origin taxa. This mode is nearly absent in the Straitof Georgia – southern origin taxa are much less abundant thanon the outer coast. In addition, PDO is a major environmentalcorrelate of zooplankton anomalies off the outer coast of BC(Mackas et al., 2001), and is even more important off Oregon(Peterson and Keister, 2003; Keister et al., 2011). In contrast,our data (and also Li et al., 2013) indicate that PDO has only asecondary influence on zooplankton variability in the Strait.PDO directly measures surface temperature anomalies in theNorth Pacific, but also has strong correlations with interannualvariability of nutrients at the northern end of the California Cur-rent (DiLorenzo et al., 2008), and with variability of the speedand direction of alongshore and cross-shore transport (Keisteret al., 2011).
� Most of the Strait of Georgia zooplankton–zooplanktoncorrelations suggest that the forcing mechanisms operate in abottom-up fashion. Interannual variability of non-gelatinouszooplankton is positively correlated across trophic levels (yearswith more predators are years with more prey). Although thescatter is greater, this pattern also extends to juvenile fish. Yearssuch as 2005 and 2007 with low biomass of preferred zooplank-ton prey were years with poor growth and survival of youngsalmon and also herring (Schweigert et al., 2013). However,large scatter arises because positive anomalies of zooplanktonbiomass are not always accompanied by good growth andsurvival of the young fish.
3.6.6. Ecological processes behind the zooplankton and environmentalassociations
The ecological processes listed in Table 5 can affect zooplanktonpopulation size by altering the balances between three pairs ofrates.
The first is internal energetic balance – are the zooplankton ableto obtain enough food to metabolize, grow, and reproduce? Gov-erning variables are primarily food availability and temperature.Because the Strait has relatively high and seasonally-prolongedprimary productivity and phytoplankton biomass (Masson andPeña, 2009; Pawlowicz et al., 2007), and high annual average zoo-plankton biomass, the metabolic balances of individual zooplank-ton (both grazers and predators) are almost certainly positivefrom early in the spring bloom (usually sometime in March)through mid-late autumn (if they continue to feed), but may be-come negative from about November through February. For manyof the copepods, the season of net metabolic loss is extended byseasonal dormancy during which the zooplankton voluntarily stopfeeding in late spring or summer after accumulating large lipid re-serves. In compensation, the dormant zooplankton reduce subse-quent costs of both basal and active metabolism (foodacquisition and predator avoidance) and of predation mortality
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by moving to deeper, darker and colder locations in the water col-umn. The prevalence of this strategy suggests that it has usuallybeen successful in the past, as does the fact that much of the lipidreserve accumulated during the previous year growing season canremain available at the end of the winter and the start of reproduc-tion (Campbell et al., 2004). But dormancy probably works lesswell in years when warm temperature anomalies throughout thewater column shorten the spring growing season, extend the dura-tion of dormancy, and increase metabolic rates during dormancy.This calculation has not yet been done but is planned by a subsetof our authors.
The second rate balance is between increase of prey populationbiomass by somatic growth and reproduction, and loss of biomassto predation mortality. For zooplankton, both of these rates aretypically large (several percent per day). The sign of the rate differ-ence is less clear than it was for the balance between zooplanktoningestion vs. zooplankton metabolism. But the predominately posi-tive interannual correlations across trophic levels (both zooplank-ton–zooplankton and zooplankton–fish) are consistent with highprey biomass enabling predator success, rather than high predatorbiomass depressing prey biomass. Even with field sampling de-signs that are intensive and targeted toward this purpose, precisezooplankton mortality rate estimates are very difficult to obtain(see Ohman (2012) for detailed discussion of issues and remedies).Unfortunately, our Strait of Georgia zooplankton time series datalack by a wide margin the temporal, spatial, and age-structure res-olution sufficient to make these estimates by life-table methods.An alternative approach for estimating predation mortality is tosum daily food intake across the suite of potential predators. Thisis less species-specific (because predator selection is based moreon size than on species) and requires good knowledge of predatorbiomass pool sizes. The latter are often assessed annually withgreater precision than the zooplankton pool sizes. But good dataabout predator diet composition are also required. Reconcilingthe matrices of pool sizes and of diet compositions is the approachused in mass-balance ecosystem models such as Ecopath (e.g. Pre-ikshot, 2007; Preikshot et al., 2013). However, an underlyingassumption is that growth and mortality are in balance or nearlyso. While mass-balance models can roughly estimate how rateswould change due to natural or anthropogenic changes in predatorpool sizes, they are less able to predict what prey pool sizes (orprey preference spectra) would be able to ‘‘restore’’ balance.
The third important rate balance is between advective gains andlosses of zooplankton biomass (and also of phytoplankton biomassand of dissolved nutrients that enable phytoplankton growth). Asnoted in Section 3.1.2, the estuarine outflow imposes a large advec-tive loss rate (especially in summer) on plankton occupying thesurface layer. But the deep estuarine inflow is the main supplierof dissolved nutrients for Strait of Georgia primary productivity,and could also provide an advective input of zooplankton biomassfrom the outer coast and Juan de Fuca Strait for zooplankton spe-cies and stages distributed deeper than �50 m. Most of the domi-nant zooplankton in the Strait do carry out diurnal and/or seasonalvertical migrations, but usually only the later life stages migratedeep. Larvae and early juveniles of most taxa (but especially ofthe copepods and euphausiids) are typically concentrated nearthe surface, so our a priori expectation is that juvenile life stagesexperience net export from the Strait in most seasons and years.
3.6.7. Magnitudes of oceanographic processes and their statisticalconsistency with environmental indices and observations
In this section, we examine each of the ecological processes andrelationships listed in Table 5 in terms of their interannual variance(how much change in zooplankton biomass they could plausiblyintroduce) and their consistency of time scale and phasing withour zooplankton data.
3.6.7.1. Volume of estuarine exchange. Large interannual changes intotal estuarine transport could affect annual zooplankton biomassin three ways: (1) by altering the annual primary productivity ofthe Strait (through increased deep nutrient supply when transportis large), (2) by altering the export/retention of phytoplankton bio-mass (increased export when transport is large), or (3) by alteringthe export/retention of zooplankton biomass.
The average volume transport of the estuarine circulation isabout 10-fold larger than the average annual freshwater input thatdrives it (Pawlowicz et al., 2007). Monthly freshwater dischargevaries by about a factor of seven (�2–14 � 103 m3 s�1), and totalannual discharge by about a factor of two (Fig. 7). However, recentanalyses (Riche, 2011; Pawlowicz et al., 2007) indicate that sea-sonal and interannual variability of the total estuarine transportis much smaller (about ±20%) due to compensating changes in ver-tical mixing and entrainment. Any resulting variations in annualprimary productivity and retained phytoplankton biomass areprobably of similarly small magnitude.
Because the larger zooplankton have behavioral control overtheir depth distribution and can maintain preferred depths againstall but the strongest vertical mixing and upwelling rates, the buf-fering of their transport might be weaker, and changes in annualimport/export of zooplankton biomass might be somewhat largerthan ±20%. But they are unlikely to be as large as the 5–10� inter-annual changes we have observed in average biomass within tax-on. In addition, the time series of zooplankton and annual riverdischarge have only weak statistical association (Fig. 16, Table 4),and are dominated by different time scales (mostly decadal forthe zooplankton, mostly interannual for river discharge). However,one plausible candidate for advective control of Strait of Georgiabiomass is the large copepod E. bungii (the main contributor tothe >5 mm size category). Correlation between outer coast andStrait of Georgia anomalies (Fig. 18b) is both positive and mostlydecadal (negative anomalies in the 1990s and mostly positiveanomalies in the 2000s). However, for reasons we will discuss la-ter, we think that interannual differences in import of Eucalanusare important but are less related to volume of water exchangedthan to large interannual changes in offshore population densityand offshore water properties (discussed below).
The above observations do not mean that estuarine import andexport are unimportant to the zooplankton ecology of the Strait.But because the strong estuarine circulation has relatively weakinterannual variability, its largest influence is probably on themean zooplankton community composition. In particular, the sea-sonal modulation of daylength causes strong diurnal migrants suchas adult euphausiids, amphipods and chaetognaths to spend �75%of each day at depth in the summer, when the estuarine exchangeis strongest. The seasonal migrant Neocalanus completes its upper-ocean growing season in spring (March–May depending on year)and enters dormancy well before the peak freshet in June. In con-trast, many of the zooplankton that occupy the upper 20–50 mthroughout the day and throughout their life cycle (e.g. small cope-pods, larvaceans) have their annual peak during the freshet. Prob-ably for this reason, they are less abundant in the Strait than on theouter-coast continental shelf. Year-to-year differences in annualtiming (rather than annual amount) of the peak estuarine transportmight also interact with zooplankton phenology to produce amatch–mismatch of growth and advection timing – we will discussthis possibility in Section 3.6.7.4.
3.6.7.2. Variability of offshore source water and Strait of Georgia deepwater. The input of nutrients and deep zooplankton also varies dueto seasonal and interannual differences in the characteristics of theoffshore deep water that enters Juan de Fuca Strait (and also up-wells onto the outer coast continental shelf, Crawford and Pena,2013). The causes for source water changes are of two sorts –
Fig. 19. Interannual variability of the deep offshore source water for the annualdeep water renewal in the Strait of Georgia. Temperature, salinity, nutrient anddissolved oxygen data (from Crawford and Peña, 2013) were collected at 200 mdepth at station P04 (48�400N, 126�400W, bottom depth 1350 m) during annual latesummer Line P surveys. TS diagram (top panel) shows the axis rotation associatedwith translating temperature and salinity variability into changes in potentialdensity (rt) and changes in ‘‘spiciness’’ p (warmer and saltier vs. cooler and fresheron a given density surface). Symbol size shows associated variability of nitrateconcentration (proportional to ([NO3] + [NO2] �30 lM)). Symbol color showsvariability of dissolved oxygen concentration (white is <2 ml l�1, light gray is 2–2.5, medium gray is 2.5–2.7, and black is >2.7).Lower panel shows time series of pand rt. Spice and rt calculations are courtesy H.J. Freeland, IOS.
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 155
vertical displacement of density surfaces due to variations inupwelling, and variations in deep meridional and zonal horizontalflow. Stronger summer upwelling along the outer coast causesgreater elevation of isopycnal surfaces along the continental slope,allowing deeper, denser (typically both colder and saltier), andmore nutrient rich water to flow into the bottom layers of Juande Fuca Strait. On any given constant density surface, the waterproperties can be either warmer and saltier or cooler and fresher.This difference is quantified by a variable p (called ‘‘spice’’, Fla-ment, 2002). In our region, higher spice indicates larger contribu-tion of tropical-origin water carried poleward by the CaliforniaUndercurrent. Lower spice indicates larger contribution of offshoreSubarctic North Pacific water. Both potential density (indicative ofthe source isopycnal surface) and spice (indicative of the sourcewater mass) of the source water for the annual deep water renewalin the Strait (found in late summer �200 m depth along the Van-couver Island continental slope) have varied considerably since1990 (Fig. 19).
Nutrient and dissolved oxygen content of the offshore sourcewater have also varied interannually (Crawford and Pena, 2013).Fig. 19 also shows the association of oxygen and nutrient variabil-ity with interannual differences in potential-density-at-depth andwater mass origin. Since 1990, nitrate + nitrite in the offshoresource water has fluctuated around its multi-year mean by about±7% (range 31.9–36.1 lM). The changes in nitrate are mostly corre-lated with changes in isopycnal depth (rt), which are in turn aboutequally associated with changes in PDO, SOI, and NPGO. There is nosignificant correlation of source water nitrate with spice, and onlyweak correlation with our Strait of Georgia zooplankton time ser-ies. The lack of correlation may be because the changes in input ni-trate concentration are not large enough to cause significantinterannual differences in upper layer nutrient supply and primaryproductivity within the Strait.
Dissolved oxygen in the offshore source water has varied byabout ±30% (range 1.57–2.81 ml l�1 = 68.4–122.2 lmol kg�1). Thisrange crosses the hypoxic threshold (2 ml l�1), and is large enoughto be ecologically significant, at least until the entering deep wateris vertically mixed during its passage through Haro Strait. Oxygencontent of the offshore source water is significantly correlated withboth rt and p; and also with PDO, SOI, and NPGO. Our zooplanktontime series suggest that low oxygen levels (at P200 m depth overthe continental shelf break, and between 100 and 200 m depth inJuan de Fuca Strait) are tolerable-to-favorable for the outer coastpopulations of E. bungii (r = �0.62) and for Strait of Georgia > 5 mmcalanoid copepods (mostly Eucalanus, r = �0.39). We are unawareof data on hypoxic limits for E. bungii, but tropical members of thisgenus tolerate extreme hypoxia during both diurnal and seasonalmigrations. Within the subarctic NE Pacific, E. bungii is seasonallydormant between 250 and 500 m depth from �September–March(Miller et al., 1984). Oxygen concentrations in much of this depthrange are <2 ml l�1 (Tabata and Weichselbaumer 1992a). Even dur-ing their growing season, Eucalanus often remain below the mixedlayer, probably to avoid turbulence (Mackas et al., 1993). The com-bination of mid depth dormancy plus sub-surface growing-seasondepth preference could produce a large net transport into andretention within the Strait, especially in years when the offshorepopulation is large (Fig. 18b). At present, this is our best explana-tion for the within-strait interannual variability of the very large(>5 mm) calanoid copepods, and also for the time series associa-tions of ZooPC2 (which loads heavily on their anomalies). Quanti-fication via direct measurement of the importance of advectivesupply would be highly desirable (advective flux of biomass ofthe larger-bodied zooplankton could potentially be monitored,either within Juan de Fuca Strait or within Haro Strait, by depthand time integration of cross-products of acoustic backscatterand ADCP velocity profiles).
3.6.7.3. Zooplankton tolerance of temperature within the Strait ofGeorgia. The decadal negative correlation of most of the zooplank-ton taxa with Strait of Georgia temperature anomalies is consistentwith our earlier observation that most of the dominant zooplank-ton taxa in the Strait are near the southern limit of their zoogeo-graphic ranges, and may therefore be near the upper limit oftheir temperature tolerance. Compared to offshore habitats, Straitof Georgia temperatures at depths greater than 100 m are espe-cially warm, and also more strongly variable from year to year(Fig. 4). For many individual zooplankton categories and for Zoo-PC1, the years with lowest Strait of Georgia biomass (2004–2007,Figs. 9, 10 and 14) were also the warmest years within the Strait(Figs. 5 and 6c). The coolest years (2008–2009 and 1990–1991)had high and/or increasing zooplankton biomass.
However, several details of the zooplankton–environment cor-relations (see Fig. 16 and Supplementary Table ST5) suggest thatphysiological tolerance is not the primary mechanism linkingzooplankton variability to temperature anomalies. Among our
156 D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159
zooplankton categories, E. pacifica had the strongest negative asso-ciation with warm temperature anomalies, yet its zoogeographicrange extends much further south than most of the other dominanttaxa (in the California Current to �30�N, where it encounters sur-face temperatures that are higher throughout the year than sum-mer temperatures in the Strait (between 15 and 20 �C,Lavaniegos and Ambriz-Arreola, 2012). Among the different depthlayers, many taxa that live mostly above 150 m (euphausiids, cope-pods < 1 mm, chaetognaths) nevertheless had stronger correlationwith temperature anomalies below 165 m depth. The propertiesof Strait of Georgia deep water are mostly established during therelatively brief deep water renewal events (in late summer andsometimes in late winter), and we suspect that these renewals playan important role in the Strait ecosystem, partly by providing up-welled nutrients for late summer primary productivity, and partlyby depth-selective import of zooplankton biomass. Unfortunately,our time series sampling has not been frequent or regular enoughto resolve zooplankton changes associated with these events, norto compare renewal events across years. As noted in the previoussection, quantification of zooplankton advective flux during thesummer and early autumn would greatly increase ourunderstanding.
3.6.7.4. Zooplankton tolerance of oxygen levels within the Strait ofGeorgia. At present, oxygen is unlikely to limit zooplankton popu-lations within the Strait. Despite the low oxygen content of the off-shore source water, oxygen concentrations within the Strait ofGeorgia remain above the hypoxic threshold even in the deep ba-sins (recent minima �2.2 ml l�1, Masson, 2006; Pawlowicz et al.,2007) due to strong vertical mixing and atmospheric exchangeduring transit through the boundary tidal passes. In addition, thetaxa that occupy the deep layers in the Strait (principally the dor-mant stages of the large copepods, and adult gammarid amphi-pods) regularly encounter lower oxygen levels in offshoreregions. However, a potential concern for the future is that thesource water oxygen time series includes a significant downwardtemporal trend (Crawford and Pena, 2013). This appears to belinked in part to reduced deep winter mixing along the westernmargin of the Subarctic Pacific (Whitney et al., 2007), but is also af-fected by local within-summer-season depletion along the conti-nental shelf and slope (Crawford and Pena, 2013).
3.6.7.5. Timing match–mismatch. The phenology (within-year tim-ing of seasonal events) of the Strait of Georgia is quite variablefrom year-to-year, especially for events that occur in the springand early summer. Since 1990, the dates of freshet timing (bothstart and peak) have varied by �25 d (std dev. 14 d), spring phyto-plankton bloom date (Allen and Wolfe, 2013) by �50 days (std.dev. 13 d), timing of peak upper layer biomass of N. plumchrus(Johannessen and Macdonald, 2009) by at least 40 d (std. dev.16 d), herring mean spawning date (Schweigert et al., 2013) by�15 d (std. dev. 4.5 d), and Strait of Georgia entry date of FraserRiver sockeye (Preikshot et al., 2012) by �14 d since 1998 (std.dev. 5.5 d). The major drivers of interannual changes in phenologyalso differ among variables. Fraser freshet timing is controlled byrate of inland snowmelt. Phytoplankton bloom timing is controlledmostly by wind and cloud cover but also covaries with NPGO (Allenand Wolfe, 2013). Timing of copepod life history often covarieswith upper water column temperature anomalies (Mackas et al.,2012a); this association is especially strong for N. plumchrus. Con-trol of the timing of euphausiid spawning differs among ocean re-gions, but in both the Strait of Georgia and in Puget Sound appearsto be closely tied to the timing of the spring phytoplankton bloom(Ross and Quetin, 2000). Sockeye outmigration timing is set mostlyby timing of ice breakup in their nursery lakes (Burgner, 1991).
The combination of 0.5–2 month phenologic variability withdiffering timing controls and cues creates a lot of opportunity fortiming mismatch. Schweigert et al. (2013) present evidence thatit is one of the dominant drivers of juvenile herring survival inthe Strait. Unfortunately, our interpretation of Strait of Georgiazooplankton phenology quickly becomes data-limited. Our knowl-edge of changes in zooplankton timing within the Strait is quitegood for N. plumchrus, fair for the other copepods and euphausiids,but scant-to-absent for other important taxa such as the amphi-pods and chaetognaths. Exploratory analyses using linear correla-tion or regression methods are insensitive and potentiallymisleading because the range of timing variability is often widerthan the window of optimal timing – ‘‘too late’’ and ‘‘too early’’in a seasonal sequence both lead to a mismatch. In addition, theoptimal timing window for a given zooplankton taxon may haveits beginning and end dates set by two (or more) different environ-mental variables. For example, populations of grazing copepods(whose phenology is often cued by water temperature, Mackaset al., 2012a) should ideally delay their peak food demand untilthe start of the spring phytoplankton bloom (timing controlledby local wind mixing and cloud cover) to guarantee sufficient foodfor reproduction and somatic growth. The window of food avail-ability is strongly asymmetric in the Strait of Georgia – pre-bloomphytoplankton biomass is much lower than post-bloom biomass(Masson and Peña, 2009). This suggests that a very late springbloom is likely to cause a more severe food supply mismatch thana very early bloom. Allen and Wolfe (2013) show that late bloomtiming is associated with stronger-than-average December–Marchwind mixing, but also with positive NPGO. However, our stepwiseregression results (Table 4: frequent negative coefficients for win-ter wind, but more frequent and often stronger positive coefficientsfor NPGO) provide a mixed message about optimal phytoplanktonbloom timing. Our tentative interpretation of this discrepancy isthat late blooms cause larger mismatch problems in warm years(also associated with negative NPGO) because the zooplanktonphenology is also shifted early in warm years.
Independent of food supply match–mismatch, a late start of thezooplankton reproductive season could expose the juvenile zoo-plankton to increased advective export and predation mortality ifthe juveniles do not reach a vertical migrant life stage prior to thesummer maxima of river discharge and estuarine transport (timingcontrolled by the start and peak of the Fraser freshet, in turn con-trolled by inland air temperatures, and by amount of snowpackaccumulated during the previous winter) and the summer in-creases of biomass of predatory zooplankton and juvenile fish.
Despite these issues, available evidence lets us conclude withreasonable certainty that annual cohorts of the large copepod Neo-calanus are now often developing earlier than the Strait of Georgiaphytoplankton bloom (bad for the copepods), and that their nowsmall populations are entering dormancy prior to the ocean entryof most Strait of Georgia stocks of juvenile coho, sockeye and Chi-nook salmon (probably bad for the fish). We need to develop a sim-ilar level of knowledge for other dominant zooplankton taxa, sothat we can we can describe and model the phenology of the entirecommunity.
3.6.7.6. NPGO as a driver of ecological variability in the Strait ofGeorgia. The four strongest environmental correlates of zooplank-ton interannual variability in the Strait of Georgia are NPGO, winterwind mixing, water column temperature anomalies, and timing ofthe start of the Fraser freshet. The last three have fairly clear andsimple associations with plausible ecological mechanisms:
� Temperature strongly affects the seasonal timing of zooplank-ton reproduction and dormancy, and may limit the physiologi-cal niche of subarctic zooplankton.
D. Mackas et al. / Progress in Oceanography 115 (2013) 129–159 157
� Winter wind mixing strongly affects the timing of the springphytoplankton bloom, and may cause mismatch of food supplyand demand.� The Fraser freshet affects near-surface density stratification in
the Strait, and alters the amount and depth distribution of estu-arine exchange with the North Pacific, and thereby affectsadvective gains and losses.
Although NPGO has the most frequent and strongest statisticalassociations with the zooplankton time series (Table 4), it is lesseasy to identify a dominant coupling mechanism. Several are plau-sible, most of which cross-link to the other dominant local environ-mental variables (positive NPGO is associated with cooltemperature anomalies, with stronger winter winds, and with laterfreshet timing). Positive NPGO is also associated (DiLorenzo et al.,2008) with stronger Ekman pumping in the Alaska Gyre and stron-ger summer upwelling in the California Current, contributing toabove-average surface nutrient and salinity in both regions. NPGOalso affects the water properties, and perhaps the zooplankton con-tent, of the deep estuarine source water. Future research effortscombining observations with numerical modeling would likelyprovide useful quantification of the relative importance of theselinkages (as was done by Keister et al., 2011 for PDO influenceson the outer coast ecosystem).
4. Summary and conclusions
4.1. Major findings
Zooplankton in the Strait of Georgia are on average abundantand highly productive (vertically-integrated dryweight biomassranging seasonally between about 4 and about 12 g m�2). Commu-nity composition is dominated by relatively large-bodied crusta-ceans (medium to large copepods, euphausiids and amphipods)and secondarily by non-crustacean gelatinous predators (chaetog-naths, hydromedusae, and siphonophores). Despite the enclosedand nearshore environment, many of the dominant taxa have sub-arctic and oceanic zoogeography, and many undergo deep verticalmigrations at daily and/or annual periods. The large amount oftime spent below the surface layer is likely to help them minimizeseaward advective loss imposed by the strong positive estuarinecirculation of the Strait.
We have examined recent interranual-decadal variability of theStrait of Georgia zooplankton by analyzing available historic zoo-plankton data collected between 1990 and 2010, selecting onlysamples from deep-water locations that were likely to capturethe vertical-migratory species. Between-year differences withinthe Strait are large, but only weakly-correlated with interannualvariability of zooplankton along the outer coast. Over the past20 years, the dominant mode of zooplankton variability in theStrait (accounting for about 36% of total variance/covariance) hasbeen a fluctuation at �10 year time scale that covaries positivelyamong most taxa. Our data show zooplankton biomass maximain the very early 1990s, and between �1999–2002, and biomassminima in 1994–1995 and �2004–2007, followed by a ongoingrecovery to average or above average in 2008–2010. The low zoo-plankton biomass in 2005 and 2007 was accompanied by very poorgrowth and survival of juvenile salmon and herring.
Correlations of the zooplankton interannual variability withindividual local or large scale environmental time series are rela-tively weak (|r| mostly 0.0–0.4) but are consistent with higher bio-mass of most of the dominant zooplankton taxa in years when theenvironment of the Strait is ‘‘cool’’. Only a few taxa (siphono-phores, medusa, ostracods, and crab larvae) show evidence of posi-tive response to warm conditions. However, the strongest
environmental predictors are the North Pacific Gyre Oscillation(NPGO), the strength of late winter winds (which affect the timingof the spring phytoplankton bloom), and the El Niño SouthernOscillation index (SOI), not local water temperature anomalies.We have (somewhat tentatively) identified seasonal timingmatch–mismatch and deep estuarine exchange, rather than inter-annual differences in nutrient supply and primary productivity,as the dominant drivers of the observed zooplankton variability.
4.2. Caveats and recommendations
The zooplankton data used in this analysis were compiled froma variety of sources, many of which were not designed as compre-hensive zooplankton monitoring programs. Taxonomic resolutionof juvenile life stages was lower in samples collected prior to1996, and the seasonal and spatial distribution of samples variedamong years. It is therefore unfortunate but likely that our annu-ally-averaged zooplankton time series include some biases and alot of noise (due to low sample numbers in many years). For thehistoric time series, we have tried to minimize this by our choiceof analysis methods and taxonomic categories. But future monitor-ing effort in the Strait can and should be improved.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.pocean.2013.05.019.
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