Primary productivity and the carrying capacity for herring in NE Pacific marine ecosystems

Progress in Oceanography 77 (2008) 241–251

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Primary productivity and the carrying capacity for herring in NE Pacific marine ecosystems R. Ian Perry *, Jacob F. Schweigert Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Road, Nanaimo, BC, Canada V9T 6N7

a r t i c l e

i n f o

Available online 26 March 2008 Keywords: Alaska Annual surplus production British Columbia Carrying capacity Pacific herring Primary productivity

a b s t r a c t The carrying capacity for Pacific herring (Clupea pallasi) and its relationship to primary productivity was examined for eight populations in the NE Pacific and eastern Bering Sea. Data on biomass (ages 3 and older) and catches of herring in British Columbia (Georgia Strait, west coast Vancouver Island, Central Coast, Queen Charlotte Islands, Prince Rupert) and Alaska (Sitka, Prince William Sound, eastern Bering Sea) during the second half of the 20th Century derived from analytical stock assessments were used to calculate annual surplus production of herring. There was considerable interannual variability in herring productivity among all populations, however, only three (Georgia Strait, Prince Rupert, Prince William Sound) showed significant differences in mean productivity on decadal time scales (productivity regimes). Carrying capacity for the most recent productivity regime for each population was estimated using the Schaefer surplus production model. Mean annual primary productivity was estimated from remotely-sensed (SeaWiFS) chlorophyll data for British Columbia and Sitka ecosystems, and from in situ chlorophyll data for Prince William Sound and the eastern Bering Sea. The carrying capacity for herring populations in the NE Pacific ranged from 28,000 to 250,000 tonnes, and to 325,000 tonnes in the eastern Bering Sea. When considered on the basis of their distributional area, the west coast of Vancouver Island and Georgia Strait populations had the highest carrying capacity per unit area (9.3– 13.8 tonnes km2) and the eastern Bering Sea had the lowest (0.7 tonnes km2). There is a significant positive linear relationship between the productivity of herring populations at carrying capacity and primary productivity on a per unit area basis. Although similar direct relationships have been observed between phytoplankton standing stock (as chlorophyll biomass) and total catches of resident fish populations from these regions, such a direct relationship was unexpected between primary productivity and herring carrying capacity because of multiple trophic pathways and other factors (e.g. spawning habitat) which should limit the carrying capacity for individual populations of particular species of fish. These results may be used to forecast potential responses of herring in NE Pacific ecosystems to global changes in ocean productivity. Crown Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved.

1. Introduction Control of the abundance level of marine populations is one of the four principal components of population regulation proposed by Sinclair (1988). The other components are temporal fluctuations in abundance (the recruitment problem), the number of different populations of a species (population ‘richness’), and the geographical patterns of these populations. Control of the absolute abundance level of a population is a central concept for understanding how marine ecosystems function and respond to perturbations, and for developing ecosystem-based marine management. It is also closely related to the concept of carrying capacity.

* Corresponding author. Fax: +1 250 756 7137. E-mail address: [email protected] (R.I. Perry).

Carrying capacity is one of the two major themes of the 10-year PICES integrating program called ‘Climate Change and Carrying Capacity’. At the start of this program, Kashiwai (1995) produced an excellent review of the history of the carrying capacity concept as an index of marine ecosystem productivity. He noted that carrying capacity has commonly been identified with the k parameter of the logistic growth equation, but originally it was defined independently of this model as a characteristic abundance level for wild populations determined primarily by habitat conditions (Kashiwai, 1995). Commonly, ‘‘the concept of carrying capacity is the asymptotic population biomass supported by an ecosystem under the limitations of food, shelter, etc., and the effects of predation and exploitation” (Kashiwai, 1995, p. 81). It is synonymous with the general productivity, or ‘productive capacity’, of an ecosystem. Carrying capacity for marine populations has typically been considered in terms of sustainable catch, with catch often the

0079-6611/$ - see front matter Crown Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2008.03.005

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aggregate of many species (e.g. Mueter and Megrey, 2006). Used in this way, it defines the total ecosystem productivity for species considered of economic value to humans. Catch patterns, however, are usually complicated by management activities, which makes the success at identifying relationships between food supply, e.g. primary productivity, and fish yield (e.g. Nixon, 1988) surprising. This may have worked because, up until recently, time series of fish catches have been dominated by developing periods when regulations limiting catches were non-existent, declining catches for some species may have been offset in the aggregate by increases for other species, and because of the use of very large (often global) spatial domains. Such relationships have also been identified at regional spatial scales. For example, Ware and Thomson (2005) determined a linear relationship between chlorophyll standing stock and the yield of resident fish populations in the NE Pacific. However, it is not clear how these concepts of carrying (or productive) capacity apply to particular species and their individual populations in local marine ecosystems. Considering multiple trophic pathways and multi-species linkages, it is not clear a priori whether individual populations will have the same relationship to ecosystem productivity as the aggregate of all species. The purpose of this study is to examine the capacity of NE Pacific marine ecosystems to produce Pacific herring (Clupea pallasi), i.e. to determine the carrying capacities of these ecosystems for Pacific herring. We also examine whether these carrying capacities are related to the primary productivities of these regional ecosystems. We conclude with a consideration of whether future changes in the carrying capacities of these ecosystems for Pacific herring can be predicted from possible changes in primary productivity. 2. Methods 2.1. Primary productivity Data on eight populations of Pacific herring in six regional marine ecosystems of the NE Pacific were available for analysis. These ecosystems range from the Strait of Georgia and the west coast of Vancouver Island in British Columbia, Canada, to the eastern Bering

Eastern Bering Sea

Sea in Alaska (Fig. 1). Determining the primary productivity of these relatively small regional ecosystems is not straightforward. Remote sensing algorithms for primary productivity of such small coastal areas are poorly developed. Most of these ecosystems, however, are too large and remote for detailed and year-round in situ productivity measurements to be adequately representative of the entire region. In order to apply a consistent approach to estimating the annual primary productivity of these regional ecosystems, we used annual mean satellite-derived measures of the surface chlorophyll a concentration from Ware and Thomson (2005), supplemented by data from Hopcroft et al. (2005, their Fig. 3) for Prince William Sound (in situ chlorophyll measurements from March to December 2002) and Sugimoto and Tadokoro (1997, their Table 2) for the eastern Bering Sea (mean summer data 1954– 1994). Ware and Thomson (2005) used SeaWiFS data from 1st January 1998 to 31st December 2003 at 1.1 km spatial resolution. They noted that data for much of Alaska (above 52°N) are reduced or missing from November through February due to low sun angles. To convert chlorophyll-a standing stock (mg Chl m3) to daily primary productivity, we used the simplified depth-integrated u model from Falkowski (1981) as restated by Behrenfeld and Falkowski (1997), their Eq. (26) X Pp ¼ uE0 C sfc Z eu ð1Þ with RPp the daily carbon fixation integrated from the surface to Zeu (mgC m2 d1), u the light utilisation index for photosynthesis [mgC (mg Chl)1 (mol quanta)1 m2], E0 the light intensity at the surface (mol quanta m2 d1), Csfc the chlorophyll-a concentration at the sea surface (mg m3) and Zeu the depth at which the light intensity is 1% of that at the surface (i.e. the euphotic depth, m). The value of the parameter u was taken as 0.43 mgC (mg Chl)1 (mol quanta)1 m2 (Behrenfeld and Falkowski, 1997) and E0 was taken from Melin and Hoepffner (2004) using their annual values for PAR (photosynthetically active radiation) for the North Pacific Epicontinental Sea Province (Melin and Hoepffner, 2004), their Table B.5.1 for the eastern Bering Sea and the Alaska Downwelling Coastal Province (Melin and Hoepffner, 2004; their Table B.5.4) for all other locations. Annual mean euphotic zone (Zeu) depths (Table 1) for Georgia Strait and Hecate Strait were back-calculated

Prince William Sound

Sitka

Prince Rupert

Queen Charlotte Islands Hecate Strait

Central Coast Georgia Strait

West Coast Vancouver Island

Fig. 1. Map of the NE Pacific showing the locations of the eight Pacific herring populations examined in this study (solid arrows) and the location of the Hecate Strait ecosystem (dashed arrow).

243

R.I. Perry, J.F. Schweigert / Progress in Oceanography 77 (2008) 241–251 Table 1 Primary productivity estimates for the six NE Pacific regional ecosystems containing the eight herring populations, and comparisons with literature estimates Areaa(km2)

u

E0 (mol quanta m2 d1)

Chlsfc (mg m3)

Zeu (m)

RPp (mgC m2 d1)

Productivity (gC m2 yr-1)

Productivity (Pp; t km2 yr1)

Literature values (gC m2 yr1)

Literature sources

8803

0.43

21.2

6.92

25

1577

576

5181

120–345

Parsons et al. (1970), Stockner et al. (1979)

Lower west coast Vancouver Island Upper west coast Vancouver Island Hecate Strait

11,312

0.43

21.2

4.26

37

1437

524

4720 345–702b

10,099

0.43

21.2

3.30

37

1113

406

3656

Robinson (1994), Sackmann et al. (2004)

44,158

0.43

21.2

2.41

37

813

297

2670

223

SE Alaska Prince William Sound Eastern Bering Sea

43,342 9059

0.43 0.43

21.2 21.2

2.79 2.57

31 25

788 586

288 214

2590 1924

150–200

500,000

0.43

22.1

2.15

27

552

201

1812

50–200

Region

Georgia Strait

Ware and McQueen (2006) Coyle and Pinchuk (2003) NRC (1996)

a

Data from sources as indicated in the text (Section 2.3). b The estimate of 702 gC m2 yr1 for the west coast of Vancouver Island from Sackmann et al. (2004) is derived by doubling their estimate of 353 gC m2 for the 6-month period from April to September of each year 1998–2002.

from data in Appendix 2 of Perry (1984). The annual mean euphotic depth for the west coast of Vancouver Island was set equal to that for Hecate Strait, considering their broadly similar depth ranges and oceanographic conditions. The annual mean euphotic depth for southeast Alaska (Sitka population) was set equal to the value back-calculated from Perry (1984, his Appendix 2) using data for Dixon Entrance just north of Hecate Strait. The mean euphotic depth for Prince William Sound was from Oakey and Pauly (1999). The value for the eastern Bering Sea was calculated from data in Sugimoto and Tadokoro (1997); their Table 2 using Chl = 457  TD2.37 to determine TD (the Secchi depth, m), attenuation coefficient kd = 1.7/TD, and then Zeu = 4.6/kd as the euphotic depth. Daily carbon fixation values were converted to annual productivity (gC m2 yr1) by multiplying by 0.365. These values were converted to tonnes wet weight per square meter per year by multiplying by 9, which assumes a Carbon: wet weight conversion of 1:9 (Pauly and Christensen, 1995; Ware, 2000). Model parameters and input data are summarised in Table 1.

average relationships as between ages 4 and 5 fish. Specifically, we assumed a survival rate of 60% between ages 3 and 4, such that Nage 3 = (1/0.6)  Nage 4, and assumed that weights of individual age 3 fish were 80% of age 4 fish, wage 3 = 0.80  wage 4. 2.3. Carrying capacity For each herring population, we calculated the total biomass in year t (Bt) as the sum over ages (a) 3+ of the product of numbers-atage (Na,t) and weight-at-age (wa,t) Bt ¼

11 X

N a;t wa;t

ð2Þ

a¼3

Total catch (Ct) was calculated in the same way using the catchat-age (ca,t) and weight-at-age Ct ¼

11 X

ca;t wa;t

ð3Þ

a¼3

2.2. Herring data The individual herring populations, years of available data, and data sources are presented in Table 2 . All data are derived from recent analytical stock assessments for Pacific herring, using the time series of reconstructed annual numbers-at-age, weight-at-age, and catch-at-age. British Columbia assessments consider herring of age 2 and older; however, coastal Alaskan assessments consider ages 3 and older herring. In this study, we include herring of ages 3 and older (3+) so that results and interpretations will be consistent among all eight herring populations. For the Togiak herring population in the eastern Bering Sea, for which assessments are conducted on ages 4 and older fish, we estimated the number and weight-at-age of age 3 fish based on age 4 fish assuming the same

The productivity of each population in each year was then calculated using a Schaefer surplus production model   Bt  Ct ð4Þ Btþ1 ¼ Bt þ rBt 1  k in which the biomass next year is equal to the current biomass plus the annual surplus production (ASP; Jacobson et al., 2001) less the year’s catch. Since we are interested in the total biomass of herring produced by each ecosystem in each year, regardless of whether it is caught, we add the commercial catch into the biomass for year t + 1. Annual surplus production (ASPt) is then calculated as   Bt ASPt ¼ Btþ1 þ C t  Bt ¼ rBt 1  k

ð5Þ

Table 2 Sources of Pacific herring (Clupea pallasi) data used in this study

British Columbia

Alaska

Population

Population code

Years of available data

Source

Georgia Strait West coast Vancouver Island Central Coast Queen Charlotte Islands Prince Rupert District Sitka Prince William Sound Eastern Bering Sea (Togiak)

GS WCVI CC QCI PR Sit PWS EBS

1951–2004 1951–2004 1951–2004 1951–2004 1951–2004 1971–2004 1980–2004 1978–2004

Schweigert (2004) Schweigert (2004) Schweigert (2004) Schweigert (2004) Schweigert (2004) Sherri Dressel (pers. comm., Alaska Dept. Fish and Game, Alaska) Marty et al. (2004) Jennifer Boldt (pers. comm., NMFS, Seattle)

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R.I. Perry, J.F. Schweigert / Progress in Oceanography 77 (2008) 241–251

in which r and k represent the usual parameters of the intrinsic rate of increase and the carrying capacity, respectively. This equation can be rearranged to calculate these parameters from a quadratic regression of ASPt versus annual biomass, with the intercept forced through the origin (Jacobson et al., 2001) ASPt ¼ aBt þ bB2t with a ¼ r

and

b ¼ ðr=kÞ

ð6Þ

For such a regression, the carrying capacity k is calculated as k = r/(b), i.e. the biomass at which the quadratic regression curve returns to an ASP value of zero. The uncertainties about these estimates of carrying capacity, k, were calculated using a bootstrap randomisation procedure. This involved randomly sub-sampling with replacement the annual surplus production – annual biomass data pairs, then recalculating the quadratic regression (forced through the origin) to determine the carrying capacity k. Confidence limits (95%) were expressed as the 2.5 and 97.5 percentiles of the distribution of k from 1000 repetitions. For six of the eight populations, some values of the 1000 randomisations (max = 64, mean = 27) were negative for the carrying capacity k. Since these are unrealistic, the 95% confidence intervals were calculated only for values of k greater than zero. The area over which each population occurs was estimated based on long-standing knowledge of their general distributions. No information is available on how these areas might expand or contract with changes in environmental conditions or stock sizes, therefore the areas were considered as time-invariant. The Central Coast, Queen Charlotte Islands, and Prince Rupert populations in B.C. all use the Hecate Strait region, although they have separate spawning areas. Since our analysis is concerned with the ecosystem carrying capacity for herring, we summed the carrying capacities of these three populations and applied them to the area of Hecate Strait and Dixon Entrance (Table 3), as calculated by Ware and Thomson (2005). The Sitka population in southeast Alaska was assigned the area for the South-Eastern Alaska region of the North Pacific Anadromous Fisheries Commission (Table 3), also as calculated by Ware and Thomson (2005). The area of Prince William Sound (Table 3) was taken from Oakey and Pauly (1999) and is the smallest of the regions considered here. However, some herring from Prince William Sound do migrate outside the Sound and use an (unknown) fraction of the coastal Gulf of Alaska. The Togiak herring population in the Bering Sea is largely associated with the southeastern shelf (NRC, 1996; their Fig. 4.22); Trites et al. (1999) calculated an area of 500,000 km2 for approximately this same region. The herring population along the west coast of Vancouver Island occupies the entire region, therefore it was assigned the sum of the areas for the southern and northern regions off Vancouver Island from Ware and Thomson (2005). The herring population in the Strait of Georgia spawns and rears in Georgia Strait, but as adults they migrate to summer feeding grounds off the southwest coast of Vancouver Island (Ware and Schweigert, 2002). Accordingly, the distributional area for the Georgia Strait population was assigned the sum of the areas for these two regions from Ware and Thomson (2005). All carrying capacity estimates and their uncertainties were normalised to per unit area (t km2) by dividing by the surface area of each marine ecosystem. Carrying capacity calculated as above, however, is a standing stock and not sufficient for comparison with primary productivity in each region. What is required is the rate of herring productivity (t km2 yr1) when the population is at its carrying capacity. Total stock productivity for herring (H) in any year (PH,t) is equal to the productivity needed to maintain the current biomass plus the annual surplus productivity   P Bt PH;t ¼ Bt þ rBt 1  ð7Þ B k

However, at the carrying capacity, k is equal to Bt and the annual surplus productivity is zero, therefore PH;k ¼

P Bk B

ð8Þ

with PH,k and Bk the productivity and biomass of the herring population at its carrying capacity, respectively, and P/B the production per unit of biomass needed to just replace the population each year. For herring populations in the NE Pacific, which mostly mature at ages 3 and with typical population doubling times of about 4 years (medium productivity; Musick et al., 2000), P/B = 0.17 yr1 [calculated as (loge 2)/4]. If a doubling time of 5 years were used, the P/B ratio would be 0.14, which means that population productivity would be reduced by 18% from that used in this study. The discussion above assumes that carrying capacity is constant over time. This is unlikely to be true, at least on long time scales, and there is evidence that the capacity of the North Pacific to produce fish has varied on regime (multi-decadal) time scales (e.g. King, 2005). The significant point for our study is that the carrying capacity should remain (generally) constant relative to the replacement time scale for herring populations (i.e. about 4 years). In order to detect decadal (regime) scale variability of herring productivity for each stock, time series of total herring productivity were calculated using Eq. (7). These time series were then tested for significant ‘regime shifts’ using Rodionov’s sequential regime shift detector algorithm1 (Rodionov and Overland, 2005). The default parameters were accepted with significance level as 0.10 with a cut-off length (equivalent to minimum regime duration) of 10 years and Huber’s weight parameter equal to 1. 2.4. Primary productivity required to support herring at carrying capacity One objective of our study was to determine how much primary productivity is required to support the production of herring populations when they are at their carrying capacity for that ecosystem. We calculated this using commonly-assumed ecological relationships between primary and fish production. Following Ware (2000), the potential productivity of fish (Pf) at any trophic level (TL) is a function of the amount of primary productivity (Pp) retained within the ecosystem (the retention factor Rf), the primary productivity that is shunted to the microbial loop (Ml), and the trophic transfer efficiency (TE) raised to the power of the trophic level (subtracting 1 to account for the primary producers being at the first trophic level): Pf ¼ Rf M l P p TEðTL1Þ

ð9Þ

Ware (2000) suggested that reasonable values for these parameters are Rf = 0.95, Ml = 0.42, and that the trophic transfer efficiency from primary producers to mesozooplankton is 25%. Replacing these into Eq. (9) and assuming a 10% transfer efficiency between all other trophic levels (Pauly and Christensen, 1995; Ware, 2000) produces Pf ¼ 0:95  0:42  0:25  Pp  0:1ðTL2Þ

ð10Þ

For this study on herring, Eq. (10) can be re-arranged to calculate the amount of primary productivity required to support herring populations at their carrying capacities (PPRH,k; Pauly and Christensen, 1995), assuming that Pacific herring typically occupy a trophic level of 3.15 (based on analyses of stomach contents data by Brodeur and Livingston, 1988, cited in Fishbase: Froese and Pauly, 2000) PPRH;k ¼ ðP H;k =cÞ  10ðTL2Þ ¼ ðPH;k =cÞ  101:15

ð11Þ

1 This algorithm is available as an EXCEL plug-in at http://www.beringclimate.noaa.gov/regimes/.

245

R.I. Perry, J.F. Schweigert / Progress in Oceanography 77 (2008) 241–251 Table 3 Carrying capacity (k) estimates for eight Pacific herring populations in the NE Pacific Population

Years

Carrying capacity k (t)

95% Confidence Interval about k (t)

Surface Area (km2)

k/Area (t km-2)

Replacement productivity at k (PH,k) (t km2 yr1)

‘Available’ primary productivity (Pp: t km2 yr1)

PPRH.k/Pp

Georgia Strait

1951–2003 1951–1964 1965–2003 1951–2003 1951–2003 1951–2003 1966–2003 1951–1965 1951–2003 1971–2003 1980–2003 1980–1991 1992–2003 1978–2003

249,099 244,577 250,617 199,030 61,074 88,367 57,722 116,728 136,157 83,734 123,348 123,599 28,413 325,132

195,790–398,946 180,062–280,927 147,452–1,644,796 131,748–560,979 50,032–68,190 73,583–105,576 52,036–66,511 89,922–177,608 84,676–606,779 66,749–92,327 32,516–252,156 71,107–174,115 4896–67,698 82,912–597,578

18,212 18,212 18,212 21,411 44,158 44,158 44,158 44,158 44,158 43,342 9059 9059 9059 500,000

13.68 13.43 13.76 9.30

2.33 2.28 2.34 1.58

3595 3595 3595 2971

0.07 0.07 0.07 0.05

4.69

0.80

2670

0.04

1.93 13.62 13.64 3.14 0.65

0.33 2.31 2.32 0.53 0.11

2590 1924 1924 1924 1812

0.02 0.17 0.17 0.04 0.01

West coast Vancouver Island Central Coast Queen Charlotte Islands Prince Rupert

Sitka Prince William Sound

Eastern Bering Sea

Surface area and primary productivity (Pp) estimates are for the main distributional areas of the herring populations, e.g. Georgia Strait includes the lower west coast of Vancouver Island; west coast Vancouver Island includes lower and upper sectors; Central Coast, Queen Charlotte Islands and Prince Rupert are combined into Hecate Strait. PPRH,k/Pp refers to the proportion of primary productivity that is required to support these herring populations at their carrying capacities compared with the total amount of primary productivity that is supporting all animal production at the trophic level of herring (3.15) (see text for details). Bold entries are the recent herring productivity regimes used in the analyses.

with c = Rf  Ml  trophic transfer efficiency from phytoplankton to mesozooplankton, i.e., c = 0.95  0.42  0.25. Here, PH,k is the productivity of the herring population at its carrying capacity, k, i.e. when the annual surplus production is zero, as calculated by Eq. (8). Therefore, the proportion of primary productivity that is required to support these herring populations at their carrying capacities compared with the total amount of primary productivity in that ecosystem that is supporting all animal production at the trophic level of herring (3.15) is PPRH;k =Pp

ð12Þ

Determining the proportion of primary productivity required to support the herring populations in the Strait of Georgia and the west coast of Vancouver Island is complicated by the migration of the Georgia Strait population to the lower west coast of Vancouver Island during summer. Since the biomass at carrying capacity for these two populations are similar (250,000 tonnes for Georgia Strait; 200,000 tonnes for west coast Vancouver Island; Table 3), we made the assumption that they equally share the primary productivity of the lower west coast of Vancouver Island. Therefore, the primary productivity ‘‘available” to the Georgia Strait herring population was calculated as the weighted (by the surface area of each ecosystem, Table 1) average of the primary productivity of Georgia Strait and one-half that of the lower west coast of Vancouver Island (Table 3). The primary productivity ‘‘available” to the west coast Vancouver Island (WCVI) herring population was calculated in a similar fashion, using the upper WCVI and one-half the lower WCVI primary productivities (Table 3). 3. Results 3.1. Primary productivity Annual primary productivities calculated from Eq. (1) for the ecosystems corresponding to these eight herring populations ranged from 201 gC m2 yr1 (eastern Bering Sea) to 576 gC m2 yr1 (Georgia Strait; Table 1). When converted to tonnes wet weight km2, the values range from 1812 to 5181 tonnes km2 yr1. Calculated values for Hecate Strait, Prince William Sound, and the eastern Bering Sea are similar to the annual primary productivity estimates for these regions as published in the literature (Table 1), despite the use of broad input parameters and the assumption

that daily values can be extrapolated to annual mean values. The productivities calculated for the west coast of Vancouver Island seem high, but are within the ranges estimated for this region from an ecosystem model (Robinson, 1994) and as calculated from satellite-derived surface chlorophyll using an empirical relationship (Sackmann et al., 2004). The actual estimate for the lower west coast of Vancouver Island by Sackmann et al. (2004) was 353 gC m2 for the 6-month period from April to September of each year 1998–2002; in Table 1 we have doubled this value to estimate annual primary productivity assuming that phytoplankton were growing at the same rate (as during summer) for the entire year. Since this is not likely to be true, the annual primary productivity rate is probably close to that estimated by the u model (Table 1). Our calculated value for Georgia Strait may also be high (Table 1), however, it is also possible that annual primary productivity estimates in the literature, largely derived from sporadic surveys and isolated in situ productivity measurements, may be low (e.g. see discussion in Harrison et al., 1983). 3.2. Herring carrying capacity Time series of herring biomass for the eight populations considered in this study show the expected interannual and decadal scale variability (Fig. 2). The largest population is in the eastern Bering Sea (Togiak), followed by Georgia Strait and the west coast of Vancouver Island. Except for the period of the herring reduction fishery in B.C., prior to the late 1960s, herring catches have remained a small fraction of total (ages 3+) herring biomass. These biomass estimates for British Columbia populations are somewhat higher than those calculated by Schweigert (1995) because we have included all ages 3+ herring whether mature or immature. Time series of total annual productivity of herring populations Eq. (7) tested for ‘regime scale’ variability using Rodionov’s regime shift detector algorithm indicated that three populations had significant shifts: Georgia Strait, Prince Rupert, and Prince William Sound (Fig. 3). The Georgia Strait and Prince Rupert populations shifted at similar years (1965: Georgia Strait, significance level 0.001; 1965: Prince Rupert, significance level 0.002). The second shift in Georgia Strait in 2000 was not significant (P-level = 0.12; Fig. 3). The timing of these shifts is consistent with the collapse of herring populations in B.C. and the closures of the herring reduction fisheries that had operated through the 1950s and early 1960s.

15

20

10

15

5

10

0 25

Biomass (10,000 t)

5 0 25

B.C. Central Coast

5

10

10

15

15

20

20

Queen Charlotte Islands

0

0

5

Biomass (10,000 t)

West Coast Vancouver Island

20

Georgia Strait

25

R.I. Perry, J.F. Schweigert / Progress in Oceanography 77 (2008) 241–251

25

246

1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

25

Sitka

15

15

5

10

10

Eastern Bering Sea

50

60

0

0

0

10

5

20

10

30

15

40

20

25

Prince William Sound

0

Biomass (x10,000 t)

Year

20

20

Prince Rupert

5

Biomass (10,000 t)

25

Year

1950 1960 1970 1980 1990 2000

Year

1950 1960 1970 1980 1990 2000

Year

Fig. 2. Time series of Pacific herring biomass (ages 3+; solid line) and catch (ages 3+; dashed line) for the eight populations examined in this study. Ordinate scaling is identical for all graphs except for the Eastern Bering Sea.

The productivity of herring in Prince William Sound shifted down (Fig. 3) in 1992 (significance level 0.005) which is consistent with the timing of the impacts of the 1989 oil spill (note our analyses are for herring of ages 3 and older). No other herring population showed a significant shift in their mean level of productivity using

the defined parameters (significance level 0.10 and length greater than 10 years). Carrying capacities calculated for the herring populations from estimates of annual surplus production and biomass (Fig. 4) are consistent with the qualitative estimates of stock sizes from their

247

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biomass time series. To recognise the potential for decadal-scale variations in carrying capacity, we have restricted the following analyses to those time periods over which estimated herring total productivity was not significantly different, i.e. from 1965 to 2004 for Georgia Strait, 1966–2004 for Prince Rupert, 1992–2004 for Prince William Sound, and the full-time series for the remaining populations (Table 2). In general, the bootstrap estimated confidence intervals about these carrying capacities are large (mean range between lower and upper confidence interval is 164% of the carrying capacity k). Four populations had confidence interval ranges less than 100% of their carrying capacity: Central Coast, Queen Charlotte Islands, Prince Rupert and Sitka (Table 3). There is a rough positive relationship between increasing variance of the population biomass time series (Fig. 2) and the range between lower and upper 95% confidence limits of the carrying capacities. This is largely due to the influence of very large outliers and how they are resampled (with replacement) by the bootstrap estimation procedure (Figs. 2 and 4). It makes the carrying capacity for the recent period for the Strait of Georgia population the most uncertain, with a very high upper confidence limit. It also makes

the 95% limits for the carrying capacity of the eastern Bering Sea population (which had the highest biomass variance) encompass the range of the entire biomass time series (compare Table 3 and Fig. 2 for this population). This may result from the generally monotonic decline of the population (Fig. 2), and/or because the variability in the carrying capacity for herring in this region occurs at time scales less than 1-2 population replacement periods (e.g. Fig. 3). In both of these situations, the population never has the chance to adjust to, or reach, a ‘constant’ carrying capacity. Normalising the calculated carrying capacity of each population by the size of the area over which they are distributed provides a comparative measure of the carrying (or productive) capacity of each ecosystem for herring (Table 3). Considered within recent productivity regimes, the Georgia Strait ecosystem had the highest carrying capacity per unit area for herring (13.76 t km2) followed by the west coast of Vancouver Island. The ecosystems of northern B.C. (Hecate Strait, which includes the Central Coast, Queen Charlotte Islands, and Prince Rupert herring populations) and southeast Alaska (Sitka and Prince William Sound) have similar capacities (1.93–4.69 t km2) whereas the eastern Bering Sea, whose

200

Strait of Georgia

150 100 50 0

Total Annual Herring Productivity (1000 t)

200

West Coast Vancouver Island

150 100 50 0 200

B.C. Central Coast

150 100 50 0

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Queen Charlotte Islands

150 100 50 0 1950

1960

1970

1980

1990

2000

2010

Year Fig. 3. Annual total herring productivity time series for the eight Pacific herring populations examined in this study. Heavy solid line indicates mean productivity levels and significant (P < 0.10) decadal-scale shifts in these levels for populations in Georgia Strait, Prince Rupert District, and Prince William Sound. The apparent shift in the Strait of Georgia population in 2000 is not significant (P > 0.10).

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200

Prince Rupert

150 100 50 0

Total Annual Herring Productivity (1000 t)

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Sitka

150 100 50 0 200

Prince WIlliam Sound

150 100 50 0

400

Eastern Bering Sea

300 200 100 0 1950

1960

1970

1980

1990

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2010

Year Fig. 3 (continued)

population is distributed over the largest area, has the lowest carrying capacity per unit area. The productivities of herring needed to just replace each population and maintain them at their carrying capacities during the most recent productivity regimes range from 0.11 to 2.34 t km2 year1 (Table 3). 3.3. Primary productivity required to support herring at carrying capacity Carrying capacities for the herring populations in the Georgia Strait, the lower and upper west coast of Vancouver Island, and Central Coast, Queen Charlotte Islands, and Prince Rupert (Table 3) were combined into ecosystems which matched the distributional areas of these populations. There is a clear linear relationship between herring productivity at carrying capacity and the primary productivity available in each ecosystem (PH,k = 2.0536 + 0.0012  Pp, r2 = 0.82, P = 0.013; Fig. 5). This indicates that increased primary productivity is consistent with a greater carrying capacity for herring in these NE Pacific regional ecosystems. The proportion of the primary productivity available in each ecosystem that is required to support the productivity of these herring populations at their carrying capacities (i.e. at the maximum herring productivity

that the region can sustain, PPRH,k/Pp) ranges from 1% in the eastern Bering Sea to 7% in the Strait of Georgia (Table 3). The carrying capacities for herring in these NE Pacific marine ecosystems can be grouped into two categories (on a per unit primary productivity per unit area basis; PPRH,k/Pp): (1) the eastern Bering Sea and Sitka, whose ecosystems can support maximum sustainable herring populations of 1–2% of primary productivity; and (2) the B.C. herring populations plus Prince William Sound, whose ecosystems can support herring populations at 4–7% of the primary productivity available for the trophic level of herring (Table 3).

4. Discussion Ecosystems with higher carrying capacities for Pacific herring in the NE Pacific are associated with higher primary productivity on a per unit area basis. Such a direct relationship between primary productivity and carrying capacity for herring populations in the NE Pacific is surprising. While a direct relationship between primary production (as indexed by chlorophyll biomass) and total fish yield has been identified for several ecosystems (most notably here

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CC = 199,030 t

-40

-20

-20

0

0

20

CC = 250,617 t

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40

60

60

80

80

(1965-2003)

0

50

0

100 150 200 250 300

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B.C. Central Coast 60

60

40

40

20

CC = 61,074 t

0

50

100 150 200 250 300

-40

-20

-20

0

0

20

CC = 88,367 t

0

50

Sitka

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CC = 83,734 t

0 -20 -40

-20

0

20

CC = 57,722 t

20

40

60

60

(1966 - 2003)

100 150 200 250 300

Biomass age 3+ (t)

80

80

Prince Rupert

-40

50

100 150 200 250 300

80

Prince William Sound

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100 150 200 250 300

Eastern Bering Sea

CC = 325,132 t

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-20

0

0

20

CC = 28,413 t

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(1992-2003)

0

300

0

0

50

100 150 200 250 300

Biomass age 3+ (t)

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Annual Surplus Production (1000 t)

Biomass age 3+ (t)

Annual Surplus Production (1000 t)

100 150 200 250 300

80

80

Queen Charlotte Islands

-40

Annual Surplus Production (1000 t)

West Coast Vancouver Island

Georgia Strait

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Annual Surplus Production (1000 t)

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0

100 200 300 400 500 600

Biomass age 3+ (t)

Fig. 4. Pacific herring annual surplus production (ASP) versus Biomass (all ages 3+) and estimated carrying capacity (CC) of each population examined in this study. ASP is calculated as the biomass at which the quadratic curve crosses zero surplus production. Bootstrap 95% confidence intervals about the carrying capacity (CC) are shown in Table 3. Note Eastern Bering Sea population has different scaling.

for the NE Pacific by Ware and Thomson, 2005), it was unexpected for specific populations of a particular species. This is because there are a number of other critical life history aspects that may vary among regions and limit the carrying capacity, such as the

area of suitable spawning habitat. The relationship between primary productivity and herring carrying capacity implicitly includes a number of processes, such as adequate food, numbers of predators, etc. However, the area of suitable spawning habitat should

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2.0

GS

1.0

1.5

WCVI

HS

0.5

Herring replacement productivity at k (t km -2 yr-1)

250

PWS Sit EBS

2000

2500

3000

3500

Primary Productivity (t km-2 yr-1) Fig. 5. Productivity of herring required to replace the population at its carrying capacity as a function of the primary productivity available in each ecosystem. Symbols as defined in Table 2, with ‘HS’ being Hecate Strait (the ecosystem supporting the Central Coast, Queen Charlotte Islands, and Prince Rupert herring populations). The solid line is the least squares regression (PH,k = 2.0536 + 0.0012  Pp, r2 = 0.82, P = 0.013).

be an absolute factor which would be relatively time-invariant (except for human habitat modification of the coastal zone) and place an upper limit on the carrying capacity of any particular region for herring. While this may be an upper limit, it may not be a practical limit restricting interannual to interdecadal variability in population size because mapping of herring spawning grounds along the B.C. coast (Hay et al., 1989) suggests that the proportion of total spawning habitat that is used each year is relatively small. Our results indicate that the carrying capacities for herring among these eight populations form three groups (on a per unit area basis): (1) the most southerly populations (Georgia Strait and the west coast of Vancouver Island) have the highest carrying capacities, at 1.5–2.3 t km2 year1; (2) the remaining B.C. populations (within the Hecate Strait ecosystem) plus SE Alaska (including Prince William Sound), at 0.3–0.8 t km2 year1; and (3) the eastern Bering Sea (Togiak) population at 0.1 t km2 year1. The herring population in the eastern Bering Sea has the largest absolute carrying capacity, but it also has the largest distributional area. When normalised by area it is relatively less productive than populations in the rest of the NE Pacific. Both Williams and Quinn (2000) and Hay et al. (2008) have noted that the eastern Bering Sea herring population should be considered as belonging to the Asian rather than North American herring stocks. Williams and Quinn (2000) found a separation in time trends of recruitment and weights-at-age of the eastern Bering Sea herring population from other populations in the NE Pacific. They also found the B.C. populations were self-similar but different from populations in SE Alaska, with the Prince Rupert population intermediate between these two groups. This is similar to our results, however on the basis of the carrying capacity we found the separation occurred between the two southern-most populations (Georgia Strait and west coast Vancouver Island) and those in central and northern B.C. and SE Alaska. Although our analyses did not specifically examine interannual variability in either recruitment or weightat-age, the broad similarity with the results of Williams and Quinn (2000) and the relatively short population replacement time (4 years) suggests the variability of these herring populations is dominated by recruitment and therefore their carrying capacity is likely to be largely determined by processes acting on the larval and juvenile stages. The primary productivity required to support herring at their carrying capacity as a proportion of the primary productivity avail-

able (PPR/Pp) indicate that some regions are more productive of herring (i.e. support higher carrying capacities) per unit area and per unit primary productivity than other regions. For example, 4–7% of primary productivity supports herring at their carrying capacities in west coast Vancouver Island, Georgia Strait, Hecate Strait, and Prince William Sound ecosystems compared with 1–2% in Sitka and eastern Bering Sea ecosystems. This may reflect greater retention of plankton and herring, and therefore greater utilisation of this productivity, within the former ecosystems. Georgia Strait, Hecate Strait, and Prince William Sound are largely enclosed bodies of water with limited exchanges with the open ocean. The west coast of Vancouver Island is more exposed. However, the major herring spawning areas are mostly located within the large Sounds and sheltered coastlines of this region (Hay et al., 1989), which may help to retain larvae. In addition, the circulation along the west coast of Vancouver Island shelf after the spring transition when the herring are in their larval and juvenile phases may set up a barrier and/or a recirculation (Thomson et al., 1989) which retains herring larvae (and plankton) within this region. This relationship between herring carrying capacity and primary productivity may be useful to forecast how NE Pacific herring populations might respond to future changes in productivity. The Sackmann et al. (2004) analysis of SeaWiFS data for the lower west coast of Vancouver Island indicated an interannual range of primary productivity of about 14% of the mean productivity over the period 1998–2003. Ware and McQueen (2006), using observed winds, water temperatures, and light from 1958 to 1998 to model annual primary productivity in Hecate Strait during the upwelling season, suggest interannual variability of about 50% of the mean over their time series. Sarmiento et al. (2004), using coupled climate models to forecast responses of primary productivity (at regional scales) in 2040– 2060 to climate warming scenarios, predict that primary productivity (gC m2 d1) in the sub-polar North Pacific could increase by 10–20%. If the relationship between herring carrying capacity and primary productivity (Fig. 5) is predictive, a 10–20% increase in primary productivity would result in a 20% or greater increase in herring carrying capacity, within the range of productivities calculated in our study. This assumes, of course, that all other factors determining herring carrying capacity are not limiting. For example, if the increased primary productivity resulting from a global warming scenario were to be accompanied by increased sea temperatures, the carrying capacity of some of the more southerly regions for herring may be reduced, and herring populations lost entirely if temperatures exceeded maximum ranges for this species. Models which couple temperature, nutrients, and plankton production to the growth of herring, e.g. Rose et al. (2007), show promise in understanding how changes in ocean conditions may interact with herring productivity and carrying capacity. In regards to ecosystem-based management of marine resources, these carrying capacity estimates provide guidance as to how herring are using the food resources within their ecosystem, with herring sequestering from 1 to 7% (depending on the ecosystem and time period) of the total annual productivity of all animals at the same trophic level as herring (3.15). While it must be recognised that carrying capacity as calculated in this study may seem to be a statistical construct based on the available data, it can provide reasonable estimates of the levels of herring biomass, the herring productivity required to maintain that biomass, and the primary productivity that is needed to support that biomass. Further research is needed to better define the annual primary productivity of small shelf ecosystems, to identify changes in distribution patterns of herring populations, and to identify the roles of other factors, such as the amount of spawning habitat, that may set absolute

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