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ICES Journal of Marine Science ICES Journal of Marine Science (2012), 69(2), 151 –162. doi:10.1093/icesjms/fsr195 Common large-scale responses to climate and fishing across Northwest Atlantic ecosystems Nancy L. Shackell1*, Alida Bundy 1, Janet A. Nye2, and Jason S. Link 2 1 Fisheries and Oceans, Bedford Institute of Oceanography, PO Box 1006, Dartmouth, NS, Canada B2Y 4A2 National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole Laboratory, 166 Water Street, Woods Hole, MA 02543, USA 2 *Corresponding Author: tel: +1 902 4077538; fax: +1 902 4269710; e-mail: [email protected]. Shackell, N. L., Bundy, A., Nye, J. A., and Link, J. S. 2012. Common large-scale responses to climate and fishing across Northwest Atlantic ecosystems. – ICES Journal of Marine Science, 69: 151 – 162. Received 31 March 2011; accepted 21 November 2011; advance access publication 13 January 2012. Investigating whether there were common biological responses to climate and fishing across seven Northwest Atlantic ecosystems, a minimum/maximum autocorrelation factor analysis of biological indicators for each region revealed a common primary multivariate trend of a rapid change during the 1980s and early 1990s. There was a strong common pattern in the biological indicators responsible for the primary multivariate temporal trend in the five more northerly regions: an increase in the abundance of phytoplankton, an increase in biomass at mid-trophic levels, and a decline in predatory groundfish size. The common associations between patterns and drivers were fishing indices and the Atlantic Multidecadal Oscillation, but all associations weakened when co-varying drivers were held constant. The results are consistent with known long-term effects of intense fishing, such as a decline in average fish size and changes in species composition. Less fishing pressure has allowed some regions to recover to former predatory biomass levels since the late 1990s, but the bulk of the biomass consists of fewer species. However, fishing was not the only driver, and a more mechanistic understanding of how the climate affects lower trophic levels is needed to contextualize climate effects in heavily fished ecosystems. Keywords: Atlantic Multidecadal Oscillation, body size, climate, fishing, fishing-induced evolution, marine ecosystem, Northwest Atlantic. Introduction The physical oceanography of a region sets the stage for trophic dynamics by determining base productivity, which in turn influences the abundance of higher trophic levels. Production of upper trophic levels in aquatic ecosystems is ultimately determined by productivity at the base of the foodweb (Cushing, 1975; Aebischer et al., 1990; Ware and Thomson, 2005; Chassot et al., 2010). The variability of oceanographic parameters can modify the composition and abundance of species (Alheit et al., 2005; Stenseth et al., 2006), and as such can intensify, or buffer, the effects of intense harvesting (Daskalov et al., 2007; Casini et al., 2009). Fishing also influences ecosystems. Direct fishing effects on ecosystems can be significant through reductions in the biomass and size of targeted fish. Indirect fishing effects have also been observed. In several marine systems, overfishing of large-bodied, dominant predatory fish resulted in increases in their prey and indirect changes in the trophic structure (Worm and Myers, 2003; Bundy, 2005; Frank et al., 2005; Daskalov et al., 2007; Oguz and Gilbert, 2007; Casini et al., 2009). Shackell et al. (2010) hypothesized that even a reduction in predator size can influence # 2012 trophic structure. However, other studies have not fully concurred with the proposed magnitude of fishing effects and have concluded that the combined effects of fishing and oceanographic variability influenced how ecosystem dynamics (such as trophic interactions) have responded (Link et al., 2002, 2009, 2010; Gaichas et al., 2009). Frank et al. (2007) hypothesized that colder, species-poor ecoregions are less resilient to overfishing because of the slower growth rates and a smaller pool of potential predator compensators that could regulate the prey. What is clear from all these studies, though, is the need to explore regions simultaneously to identify the processes influencing an ecosystem, particularly because climate change and fishing influences are anticipated to continue or escalate (sensu Link et al., 2002). Comparative ecosystem responses among contiguous ecosystems can reveal whether common, large-scale forcing drives common ecosystem responses (Alheit et al., 2005). In Northwest Atlantic marine ecosystems, trophic-level dynamics in some regions have been examined independently, but not as an ensemble. Here, therefore, we offer a comparative analysis of seven contiguous regions of the NW Atlantic with different International Council for the Exploration of the Sea. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] 152 N. L. Shackell et al. Figure 1. Map of the study domain showing seven biogeographical regions: ESS, eastern Scotian Shelf; WSS, western Scotian Shelf; BoF, Bay of Fundy; GoM, Gulf of Maine; GB, Georges Bank; SNE, southern New England; MAB, Mid-Atlantic Bight. The total area is 409 064 km2, estimated using an appropriate planar projection. Thick black lines demarcate ecosystem boundaries. temperature regimes, ranging from the colder eastern Scotian Shelf (ESS) to the warmer Mid-Atlantic Bight (MAB). The goal was to explore whether there were common ecological patterns among the seven regions, then to determine whether climate or fishing, or both, were associated with those patterns. Material and methods Of the seven biogeographic regions examined, three are under Canadian jurisdiction (ESS; western Scotian Shelf, WSS; and Bay of Fundy, BoF) and four under US jurisdiction (Gulf of Maine, GoM; Georges Bank, GB; southern New England, SNE; and MAB; see Figure 1). Time-series of data on climate, fishing, and biological indicators were gathered for each of the seven regions from a range of Canadian and US sources. As the data came from sources that had used different sampling gear, mesh sizes, etc., comparison of absolute differences among all regions was obviously going to be difficult. Therefore, all data were log-transformed and standardized before analysis (annual standardized anomalies from the long-term mean). In this manner, focus was on the trends in the time-series among regions and not absolute differences. A list of indicators is provided in Table 1, with further descriptions provided below. Climate oceanographic indicators Four indicators of large temporal- and spatial-scale climate conditions that affect the study domain were examined. Two represented water mass properties as the percentage of warm slope water (i) in the Emerald Basin, and (ii) at a standard oceanographic sample site known as the Halifax line (Therriault et al., 1998; see Figure 1), which was sampled regularly as part of the Atlantic zonal monitoring program (AZMP). Water mass indicators were included because advection has been recognized as an important determinant of zooplankton composition (Kane, 2007), influencing the study regions included here. Two hemispheric-scale indicators were included, the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO; see Table 1). The NAO index represents the difference between the surface pressure of the subtropical (Azores) high and the subpolar (Iceland) low; variation influences local sea surface temperature (SST), temperature at depth, and precipitation in the NW Atlantic (Hurrell et al., 2003). NAO effects are not the same throughout the region, and in part of the study domain, are weak relative to effects north of 458N (Petrie, 2007). Following the rationale of Hurrell et al. (2003), we used the mean winter NAO index. The AMO index is a measure of warm and cold phases in Atlantic SST at multidecadal time-scales 153 Common large-scale responses to climate and fishing across NW Atlantic ecosystems Table 1. Summary description of indicators by region, with the years sampled abbreviated, e.g. 60-08 refers to 1960– 2008 (see text for detail, and the Supplementary material for a list of fish species within functional groups). Indicator Description DRIVER: Climate Large-scale climate (common to all regions) AMO Atlantic Multidecadal Oscillation NAO Winter North Atlantic Oscillation WarmSlope3 % warm slope water on Halifax sampling line WarmSlopeE % warm slope water in Emerald Basin Regional-scale climate (unique to each) SST Sea surface temperature Temp50 Temperature at 50 m Temp100 Temperature at 100 m SSTCV Intra-annual variation in SST (among monthly values) Temp50CV Intra-annual variation in temperature at 50 m (among monthly values) Temp100CV Intra-annual variation temperature at 100 m (among monthly values) Stratif Stratification DRIVER: Fishing LandingsMT Total landings GrFishLands Main predatory groundfish landings PlankLands Planktivore (small pelagic) landings RelativeMortalityGR Relative exploitation (landings/biomass) of main predators (groundfish) RelativeMortalityPL Relative exploitation (landings/biomass) of main planktivores (small pelagic) RESPONSE: Biological Lower trophic levels Colour Phytoplankton Centropage Centropages typicus SmallCopes Small copepods: Paracalanus spp., Pseudocalanus spp. Metridia Metridia lucens Oithona Oithona spp. Cal14 Calanus copepodite stages I–IV LargeCope Calanus finmarchicus, C. hyperboreus, C. glacialis Macroinvertebrates biomass (BM) BM_Decapod Decapod BM_Squid Squid BM_Bivalve Bivalve Fish biomass (BM) BM_Plankti Planktivore BM_NonComm Non-commercial fish (longhorn sculpin) BM_MdBenth Medium-sized benthivore BM_Zoopisc Zoopiscivore BM_LgBenth Large benthivore BM_Pisc Piscivore Fish size/community structure SZ_Plankti Planktivore SZ_MdBenth Medium-sized benthivore SZ_Zoopisc Zoopiscivore SZ_LgBenth Large benthivore SZ_Pisc Piscivore Evenness Pielou evenness index of all fish species ESS WSS BoF GoM GB SNE MAB 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 60-08 77-07 77-07 77-07 77-07 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 70-08 70-08 70-08 67-08 67-08 67-08 67-08 62-73 91-06 62-73 91-06 62-73 91-06 62-73 91-06 62-73 91-06 62-73 91-06 62-73 91-06 61-73 91-06 61-73 91-06 61-73 91-06 61-73 91-06 61-73 91-06 61-73 91-06 61-73 91-06 na 61-08 na na na na 61-08 77-07 77-07 77-07 na 61-08 77-07 77-07 77-07 na 61-08 77-07 77-07 77-07 na 61-08 77-07 77-07 77-07 na 61-08 77-07 77-07 77-07 na 61-08 77-07 77-07 77-07 70-08 70-08 na 70-08 70-08 na 70-08 70-08 na 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 70-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 67-08 60-08 60-08 70-08 70-08 “na” represents the indicators that were unavailable (e.g. there were no comparable lower trophic level indicators in the BoF). BM denotes the biomass (kg tow21). SZ denotes the mean individual weight (kg). 154 (Sutton and Hodson, 2005), although how the AMO influences local physical conditions (and hence biota) is not completely known. Following Nye et al. (2010), the detrended Kaplan SST dataset (58 latitude × 58 longitude grids) from 0 to 708N was used (Knight et al., 2005; Sutton and Hodson, 2005). The detrending removed the linear effects of anthropogenic climate change. The resultant index represents natural variation in SST (Sutton and Hodson, 2005), so a positive AMO reflects a North-Atlantic-scale warm phase regardless of (or in addition to) anthropogenic warming. Seven regional-scale climate indicators (Table 1) were examined. For each, temperature data were obtained from databases available at http://www.mar.dfo-mpo.gc.ca/science/ocean/ database/data_query.html, processed to correct for differences in monthly sampling, and expressed as annual standardized anomalies. Data that fell within the boundaries of each region were used. Mean SST and temperature at 50 and 100 m were selected because they influence the growth and availability of food at all trophic levels. The coefficients of variation (CVs) of monthly SST and temperature at depth were also included in the analyses. Any increase in the variability of temperature (associated with climate change) may interfere with biological production schedules, e.g. the spring bloom, or recruitment events. The range of SSTs off the northeastern United States has increased since the 2000s (Friedland and Hare, 2007). Stratification indices were provided by Roger Pettipas (BIO, DFO, Dartmouth, NS, Canada) and Jon Hare (NMFS, NEFSC, Narragansett, RI, USA) and were derived from the difference in density between 50 m and the surface; positive values reflect stronger stratification. Stratification was included in the analysis because it has been hypothesized to influence the population dynamics of lower trophic levels (Greene and Pershing, 2007). Fishing indicators In all, five indices of fishing pressure were used in the analysis: (i) total landings of groundfish, small pelagic, and invertebrates, summed for each region from Canadian and US commercial fisheries landings databases, then further refined into (ii) main predatory groundfish landings, (iii) planktivore (small pelagic) landings, (iv) relative exploitation of main predatory groundfish (landings/ biomass), and (v) relative exploitation of planktivores. Biological indicators The biological data included information for a range of lower to upper trophic level biota and two community-level indicators: evenness and the average size of functional groups. Lower trophic levels Two major sources of data were used to derive lower trophic level indices for phytoplankton and zooplankton: broad-scale bongo surveys throughout the United States, and Continuous Plankton Recorder (CPR) information for both regions. Zooplankton indicators for US regions were provided fully processed by Jon Hare, with survey and processing detailed by Kane (2007). The CPR is a high-speed plankton net used to sample the surface abundance of phytoplankton and zooplankton (http://www.sahfos.org). The survey has used ships of opportunity at a sampling interval of one month since 1961. We used zooplankton data as provided to DFO, Canada, for two regions (WSS and ESS) and phytoplankton data for three (WSS, ESS, and GoM). Phytoplankton data were not available for BoF, GB, N. L. Shackell et al. SNE, and MAB, and no zooplankton data were available for the BoF. In the Canadian regions, there is a gap in the series from 1974 to 1990. All CPR data were processed to account for seasonality and expressed as counts per sample. Years when fewer than 5 months had been sampled were omitted, and for those years with an enough months but with missing samples, the average of the preceding and following months was taken, following the approach of Head and Sameoto (2007). An annual index was calculated by taking the average of all months within a year, grouping zooplankton species into six functional groups (Table 1). Mid- and upper trophic levels Two major sources of data were used to derive upper trophic level indices: the US and Canadian fishery-independent research vessel (RV) surveys are designed to monitor the distribution and abundance of commercial and non-commercial species. The US conducts annual multispecies surveys in spring and autumn, but we used just the autumn time-series for this study. The US surveys use a #36 Yankee (or similar) bottom trawl towed at 6.5 km h21 for 30 min at each station. Further details of the sampling methodology are provided by Azarovitz (1981) and NEFSC (1988). Since 1970, DFO, Canada, has conducted annual, scientific RV surveys in July over the Scotian Shelf. The surveys used a #36 Yankee bottom trawl from 1970 to 1981, and a Western IIA trawl from 1982. The trawl is towed at 5.5 km h21 for 30 min per set. Further details of the sampling methodology are provided by Simon and Comeau (1994). Biomass (stratified weight per tow) and abundance (stratified number per tow) time-series (1967–2008 in the United States and 1970–2008 in Canada) were derived for 46 species from the RV survey databases (see Supplementary material). Macroinvertebrates and finfish were divided into functional groups, macroinvertebrates into decapods, squid, and bivalves. Although the US survey has consistently enumerated macroinvertebrates and “Cancer” crabs since 1973, the Canadian survey has done so only since 1999. Therefore, Canadian abundance information was only available for two invertebrates, Homarus americanus (American lobster), representing decapods, and Ilex spp. (squid) from 1970 to present. Note that lobster may not represent all decapods on the Scotian Shelf. Finfish species were divided into functional groups based on the maximum body size ever observed (Froese and Pauly, 2009) and diet (Shackell and Frank, 2007). Six finfish functional groups were denoted: medium (,80 cm maximum body size) and large (≥80 cm maximum body size) benthivores (primarily consumers of bottom dwellers), piscivores (primarily consumers of fish, i.e. consumers of planktivores and other smaller fish), zoopiscivores (consumers of amphipods, euphausiids, and small fish), planktivores (consumers of zooplankton), and a category with a single species, longhorn sculpin, as a noncommercial functional group. Longhorn sculpin are found in all regions, but in relatively low abundance in the most southerly region, MAB. Hereafter, piscivores, medium and large benthivores, and zoopiscivores are referred to collectively as main predators. Fish community Two community-level indicators were included: evenness and the average size of a functional group. Evenness was measured as Pielou evenness index (Pielou, 1966) of all fish species in a given year–region combination (see the Supplementary material for a Common large-scale responses to climate and fishing across NW Atlantic ecosystems species list). If total biomass is shared more or less equally by all member species, the value of evenness is larger. As body size is recognized as a determinant of trophic structure and energy flow in size-structured ecosystems (Woodward et al., 2005), average body size of each functional group was used as a community-level indicator. We first calculated the total weight/ total abundance per species, then the biomass-weighted (by species) geometric average body size for each functional group was estimated. Statistical analysis The aim was to explore whether there were common ecological patterns among the seven regions then to determine whether climate or fishing, or both, were associated with those patterns. Biological variables are referred to as response indicators, and climate and fishing indicators as drivers. The first step was to extract the primary multivariate temporal trend of response 155 indicators from each region. To do this, we used a data-reduction technique, comparable with principal component analysis but designed for ordered time-series, named minimum/maximum autocorrelation factor analysis (MAFA). MAFA was originally developed by Solow (1994) and further developed by Zuur et al. (2007). It uses the autocorrelation structure of each response indicator to extract temporal trends, then tests whether they are significant. In this broad-scale scoping exercise, focus was on the primary trend (MAF axis 1 or MAF1), because it represents the strongest temporal signal. As it is for a PC axis, the direction of a MAF axis is arbitrary and is equivalent to its opposite by reversing the sign of each coefficient in the linear combination. To ease interpretation among regions, regional primary multivariate trends were adjusted to have a common direction. Comparable with the variable loadings on a principal components axis, the sign and the magnitude of canonical correlations of each response indicator with the primary multivariate temporal trend reflect Figure 2. Canonical relationship of response indicators with the primary multivariate temporal trend (MAF1) superimposed (dots are annual values and lines smoothed loess using a span of 0.3) in each region. Horizontal dashed lines indicate significance levels for correlations (+0.315). The r critical value for the Scotian Shelf was 0.32 and for the United States was 0.31. Their average was used for depiction. Axis values on the top and the right refer to MAF1 and on the bottom and the left to canonical correlations. The sign and the magnitude of canonical correlations of each response indicator with the primary temporal trend reflect their influence on that trend. If the correlation is positive and strong, then the original response variable follows the primary trend closely, and vice versa. Response indicator abbreviations are as in Table 1. 156 N. L. Shackell et al. their influence on that trend; if the correlation is positive and strong, then the original response variable follows the primary trend closely, and vice versa. We then quantified the extent to which the external drivers (climate and fishing) might be influencing the primary trends in biological characteristics, by conducting a correlation analysis between each external driver and the primary trend. Note that unlike the response indicators, the external drivers were not included in an MAFA. To account for multicollinearity among drivers, partial correlation analyses (correlation between MAF1 and a driver holding all other drivers constant) were also used. Ecological responses may lag behind their drivers owing to the biological processes of survival, recruitment, and reproduction (Stenseth et al., 2006). In these systems, we would not expect any correlations .5 –6 years, given the lifetime of the lower trophic levels as well as the average lifespan for exploited species (although they may have a much higher natural lifespan). Therefore, some correlation at time-lag 0, though perhaps not the highest correlation, would be expected. Recognizing that further exploration into lagged drivers would be insightful, for this initial study, we present canonical correlations between extracted trends and drivers without lags, because different trophic levels would require different lags. MAFAs were conducted using Brodgar software Version 2.5.6 (Highland Statistics, Inc., http://www.Brodgar.com). Data compilation, scaling, and processing were performed using the open-source R statistical software Version 2.8.1 (R Development Core Team, 2008). Results Primary temporal trend The primary multivariate temporal trends (MAF1) were all statistically significant (Figure 2; probabilities for all regions p , 0.001 except for GB, where p , 0.007), with a common monotonic trend starting roughly in late 1970s/early 1980s to early 1990s in all ecosystems. This monotonic trend continued in four of the seven regions: ESS, WSS, BoF, and SNE, abated slightly in the ESS and reversing in the GoM, GB, and MAB in the early 1990s (Figure 2). By superimposing the canonical correlations on MAF1 (Figure 2), patterns within regions can be interpreted: the response indicators significantly correlated with MAF1 (positive or negative) are those most responsible for the collective ecological trend (Table 2). On the ESS, MAF1 increased steadily starting in 1980 and abating slightly by 1990. The abundance of phytoplankton and the biomass of decapods and planktivores were positively associated with that trend, whereas abundance of large copepods and copepodite stages I –IV, and the biomass of three predator groups declined. The body size of all fish functional groups declined (Figure 2, Table 2). On the WSS, MAF1 was stable until the mid 1980s and increased steadily thereafter. The response indicators that followed that trend (strongly and positively correlated) included phytoplankton, the biomass of decapods and planktivores, the biomass of a non-commercial fish and medium benthivores, and, to a lesser extent, the biomass of zoopiscivores. The body size of all fish functional groups declined as did evenness within the fish community (Figure 2, Table 2). Table 2. The relationship between response variables and the primary multivariate temporal trend (MAF1) in each region, with significant correlations of 0.31 (+) for the US regions and 0.32 for the Canadian regions shown emboldened (abbreviations defined in Table 1). Indicator Lower trophic Colour Centropage Metridia Oithona SmCope Cal14 LrgCope Mid-trophic BM_Bivalve BM_Decapod BM_Squid BM_Plankti Upper trophic BM_NonComm BM_Zoopisc BM_MdBenth BM_LgBenth BM_Pisc Fish community traits SZ_Plankti SZ_Zoopisc SZ_MdBenth SZ_LgBenth SZ_Pisc Evenness ESS WSS BoF 0.45 0.23 0.27 0.20 20.05 20.28 20.25 0.43 20.04 0.30 20.08 20.23 0.04 20.11 na na na na na na na na 0.57 20.25 0.77 na 0.71 0.02 0.69 0.06 20.39 20.76 20.16 20.76 20.40 20.74 20.93 20.55 20.81 0.17 GoM GB SNE MAB 0.31 0.55 0.56 0.63 0.81 0.46 0.65 na 0.25 20.21 0.29 20.08 20.31 20.38 na 0.00 0.24 0.59 0.18 0.12 0.06 na 0.31 0.00 0.23 0.03 20.11 20.06 na 0.63 20.07 0.68 0.07 0.43 20.11 0.81 0.22 0.37 0.08 0.69 0.42 0.02 0.34 20.59 0.37 20.04 20.15 0.06 0.69 0.27 0.52 0.04 20.19 0.39 0.37 0.45 20.02 0.55 0.66 0.08 20.70 20.36 20.28 0.18 20.26 20.34 20.17 0.02 20.47 20.74 20.53 0.29 20.10 20.69 20.67 20.68 0.26 20.72 20.54 20.55 20.80 20.63 20.62 20.49 20.76 20.68 20.98 20.45 20.59 20.69 20.35 20.71 20.91 20.74 20.87 20.39 20.04 20.50 20.31 20.03 20.11 20.25 20.52 20.67 20.13 20.06 20.04 20.21 20.66 20.38 20.60 20.39 20.09 20.05 The sign and the magnitude of canonical correlations of each response indicator with the primary temporal trend reflect their influence on that trend; if the correlation is positive and strong, then the original response variable follows the primary trend closely, and vice versa. 157 Common large-scale responses to climate and fishing across NW Atlantic ecosystems In the BoF, MAF1 increased linearly over time. There were no data available for lower trophic levels. The trend reflected an increase in the biomass of decapods, planktivores, non-commercial fish, and three main predatory groundfish groups. The body size of all fish functional groups declined, as did evenness within the fish community (Figure 2, Table 2). In the GoM, MAF1 revealed a monotonic increase until 1990, which then reversed. Here, the lower trophic level data were complete, and there were clear increases until 1990 in the abundance of phytoplankton, six functional groups of zooplankton, and the biomasses of decapods, planktivores, and a non-commercial fish. The biomass of medium and large benthivores was negatively correlated with the primary trend. The body size of all fish functional groups declined, as did evenness within the fish community (Figure 2, Table 2). On GB, MAF1 declined from the late 1960s to 1980s, after which it increased before starting to decline in 1990. The response indicators that followed this trend strongly were the biomasses of decapods and planktivores. The indicators that responded in the opposite direction were the abundance of Calanus stages I –IV, large copepods, the biomass of medium benthivores, and the body size of medium benthivores and zoopiscivores (Figure 2, Table 2). In SNE, MAF1 increased monotonically from the early 1980s on. The response indicators that followed that trend were the abundance of Oithona and the biomass of squid and bivalves. Since the mid 1980s, planktivore biomass has declined, as did that of a non-commercial fish, medium benthivores, and zoopiscivores. The size of planktivores and zoopiscivores also declined (Figure 2, Table 2). In the MAB, MAF1 increased monotonically until 1990. Only the biomass of Centropages and bivalves was strongly positively correlated with the trend, whereas the biomass of a noncommercial fish and the biomass of three main predatory groundfish declined. Body size declined in four of the five fish functional groups (Figure 2, Table 2). Common response indicators Common responses are evident among the individual patterns across the seven regions, particularly for the mid- and upper trophic levels (Figure 2; Table 2). To reiterate, the response indicators significantly correlated with MAF1 (positive or negative) are those most responsible for the collective ecological trend. On the ESS, WSS, BoF, GoM, and GB, there was an increase in decapods and planktivores, which lessened by the 1990s and even reversed in the GoM and GB. On the WSS, BoF, and GoM, the noncommercial fish biomass increased at least until the 1990s. In all regions, body size declined through the 1980s, significantly for all functional groups in ESS, WSS, BOF, and GoM, significantly for four groups in MAB and for two groups in GB and SNE. The decline in body size, until at least the 1990s, was stronger in the more northerly regions. Pielou evenness index declined in regions with increasing or no clear trend in predatory groundfish Table 3. Correlation of MAF1 to drivers and the partial correlation of MAF1 to the suite of significant drivers in each region (abbreviations defined in Table 1). Indicator Driver AMO NAO SST SSTCV Stratif Temp100 Temp100CV Temp50 Temp50CV WarmSlope3 WarmSlopeE LandingsMT GrFishLands PlankLands RelativeMortalityGR RelativeMortalityPL Partial AMO NAO SST SSTCV Stratif Temp100 Temp100CV Temp50 LandingsMT RelativeMortalityGR RelativeMortalityPL ESS WSS BoF GoM GB SNE MAB 0.73 0.17 0.11 0.12 0.55 20.59 0.20 -0.42 0.15 0.20 20.05 20.89 20.86 na 20.72 na 0.77 0.05 20.02 0.27 0.38 20.35 0.26 20.24 0.16 0.08 0.02 20.76 20.93 20.57 20.75 20.72 0.77 0.14 20.11 0.17 0.07 0.09 20.44 20.18 0.00 0.18 20.08 20.20 20.51 20.71 20.71 20.81 0.45 0.40 0.31 20.55 0.03 0.20 20.17 0.25 20.26 0.27 0.01 20.60 20.60 0.35 20.32 20.75 0.47 0.20 20.08 20.04 0.17 20.10 20.04 20.01 20.07 0.27 20.05 20.76 20.72 20.24 20.58 20.82 0.66 0.10 0.38 20.51 0.28 0.01 0.13 0.29 20.22 20.03 0.00 0.32 20.59 20.38 20.41 20.55 0.39 0.25 0.00 0.02 20.06 20.02 20.07 0.00 20.09 0.22 0.00 0.84 20.55 20.23 20.83 20.54 20.02 na na na 0.00 20.26 na 0.01 20.71 0.27 na 0.32 na na na 20.16 20.16 na na 20.23 20.37 20.39 0.59 na na na na na 20.33 na 0.16 20.38 20.50 0.21 0.24 0.45 na na na na na 20.14 0.24 20.54 20.07 na na na na na na na 20.42 0.25 20.59 0.57 na 0.04 20.26 na na na na 0.33 0.11 20.19 0.06 na na na na na na na 0.56 20.19 20.33 The correlation of driver variables with MAF1 is an independent measure of the association between the two: the driver variables were not included in the construction of MAF1. Drivers that were significant in the full correlations were used as input to the partial correlation matrices. “na” indicates that a given driver was not included. Levels for significance are 0.31 (+) for US regions and 0.32 for Canadian regions and are shown emboldened. 158 biomass (WSS, BoF, and GoM), indicating that fewer species now make up most of the fish biomass. The emerging picture for the lower trophic levels is less clear, because some indicators are missing or have data gaps in some regions, potentially understating the influence of lower trophic levels on the primary trends. However, where phytoplankton was measured (ESS, WSS, and GoM), it increased from the beginning of the time-series to at least the 1990s. The pattern of correlation between each of the five zooplankton groups and the relevant primary temporal trend was not consistent across the seven regions, but the data gaps may have obscured the results. The zooplankton has been measured since 1961 in the GoM, and all functional groups were significantly and positively associated with an increasing trend up to the early 1990s. Drivers The correlation between the drivers and MAF1 varied among regions. The consistent drivers were the AMO, which was positively correlated in all regions, and fishing indices, which were generally negatively correlated (Table 3). Available AMO data date back to the 1850s (Figure 3). It has been increasing since the start of the RV surveys in 1970, yet was in a negative phase until around 1995. In most regions, relative exploitation of both main predatory groundfish and planktivores declined along with the landings, an indication that there was less fishing pressure over time. The exception was in the GoM, where the relative exploitation of planktivores increased as landings fell (Figure 3). Total landings (which N. L. Shackell et al. include invertebrates) in five regions declined over time and show no discernible pattern off SNE since the late 1970s, but a modest increase in MAB that had abated by 1990. These patterns are reflected in their correlations with MAF1 (Figure 4, Table 3). In general, total landings were strongly negatively correlated in four of the seven regions, and positively correlated in the two most southerly regions (Table 3), whereas predator groundfish landings, planktivore landings, and relative exploitation indices were negatively correlated in all regions, except planktivore landings in the GoM (there were no planktivore landings or exploitation on the ESS). Within regions, cooler temperature at depth and increasing stratification were associated with the primary multivariate trend on the ESS and the WSS. In the BoF, the decline in the intra-annual variability of temperature was negatively associated with the primary trend. A decline in SST intra-annual variability and increasing mean SST, for part of the time-series, were associated with the primary trends in GoM and SNE. On GB and MAB, only the increasing AMO (and landings) was associated with the primary multivariate trend (Figure 4, Table 3). Some of the drivers themselves are correlated with each other, obscuring the direct link to MAF1. For each region, the drivers selected were those that were significantly correlated with MAF1, and a partial correlation matrix was calculated. The partial correlations between MAF1 and selected drivers (Table 3) reflect the correlation between MAF1 and a given driver, holding all other drivers constant. This allowed us to gain some insight into partial associations. Always, the strength of the correlations Figure 3. AMO (the solid line is the 5-year running mean) and fishing index anomalies. Total landings refer to all fish and invertebrate catches. GroundfishLands refer to aggregate main predatory groundfish landings. PlankLands refer to aggregate planktivore landings. RelExpGroundfish refers to relative exploitation of main predatory groundfish (landings/biomass). RelExpPlank refers to relative exploitation of planktivores (landings/biomass). Lines are smoothed using loess smoothers. Dashed vertical lines in the upper panel indicate the start of the US and Canadian RV surveys used in this study, 1967 and 1970, respectively. Common large-scale responses to climate and fishing across NW Atlantic ecosystems 159 Figure 4. Correlation of drivers with primary multivariate temporal trend (MAF1) superimposed (dots are annual values and lines smoothed loess using a span of 0.3) in each region. Horizontal dashed lines indicate the significance level for correlations (+0.315). The r critical value for the Scotian Shelf was 0.32 and for the United States was 0.31. Their average was used for depiction. Axis values on the top and the right refer to MAF1 and on the bottom and the left to correlations. Driver indicator abbreviations are as in Table 1. decreased, demonstrating that although fishing indices prevail, there is no single driver responsible for trends. Discussion The emergence of a common primary multivariate temporal trend (MAF1) across all seven ecosystems, shaped by similar changes in biological indicators over time, suggests that over the whole study domain, these ecosystems have experienced similar forcing factors and have responded similarly. The strongest common patterns (declines in body size of fish functional groups, increase in biomass of planktivores and macroinvertebrates) were in the more northerly ecosystems, ESS, WSS, BoF, GoM, and, to a lesser extent, GB. Common patterns were associated with observed less fishing pressure on main predators and planktivores in five of the seven regions and were also positively correlated with large-scale environmental forcing (AMO) in all ecoregions. The two warmest, most southerly regions, SNE and MAB, behaved slightly differently. In those regions, landings were stable or showed no clear trend, and there were declines in the biomass of all upper trophic levels, except that of large benthivores. Note also that the increase in planktivores observed in the five more northerly regions was not found in those regions. Species composition differs in SNE and MAB (Supplementary material), and those regions are biogeographically less similar to the others (Link et al., 2010). Although those two regions differ in their patterns of fishing pressure, they have both experienced declines in fish predator biomass and body size. In all regions, the strength of correlations between MAF1 and the drivers decreased in the partial correlation analysis. Fishing indices dominated, but no singular driver was found to be responsible for the common trends. In all, 11 climate indices were included in the analysis, yet only the AMO was correlated with the primary multivariate temporal trends across all ecosystems. Local environmental indices reflective of climatic conditions included in this analysis, such as stratification, SST, and warm slope water, were not common drivers of the primary multivariate temporal trends among regions, but some were locally important. The AMO was included in this analysis because it includes SST variability and is related to thermohaline circulation (Knight 160 et al., 2005). It is not yet clear how the AMO is mechanistically related to the ecological changes observed, or even if there is a cause and effect between community structure and broad-scale climate. The AMO may have different local effects depending on geography, as has been found with the NAO (Petrie, 2007). Perhaps persistent “warm” and “cool” phases indicated by the AMO can drive trends in population size and community dynamics to a greater extent than local high-frequency climate variability. However, the multidecadal-scale, low-frequency signal of the AMO is difficult to interpret relative to the shortness of the biological time-series. Climate, including the AMO, might be expected to influence recruitment success, vital rates, and spatial distribution. In this study, the primary common trend in each ecoregion was best correlated with changes in the size and biomass of macroinvertebrates and fish, traits more likely to be directly related to fishing than to climate. The results are consistent with a suite of studies demonstrating that fishing, through selective temporary or permanent reductions in biomass or size, and as modulated by environmental factors, is a dominant force in shaping ecosystems, particularly at a large scale in the area of study. All seven ecosystems have supported significant commercial fisheries for centuries (Rosenberg et al., 2005; Rose, 2007). They have been subjected to considerable fishing pressure, often resulting in sequential stock depletion (Gough, 1993; Fogarty and Murawski, 1998); on the ESS in fact, there has been a groundfishing moratorium since 1993 (Bundy, 2005). As reflected in this study, fishing pressure on main predators and planktivores declined over time in most regions. There have been temporary reductions in aggregate biomass coinciding with higher levels of relative exploitation in several of the study regions, and compensation within functional groups by other species south of ESS (Shackell and Frank, 2007; Auster and Link, 2009). The lower fishing pressure during the later portion of the time-series, combined with compensation, has allowed for recovery or maintenance of aggregate predatory groundfish biomass south of ESS. The results support the notion that colder regions (e.g. ESS) are less resilient to overfishing because of the lower demographic rates and a smaller pool of species that could compensate (Frank et al., 2007). Compensation of aggregate predatory groundfish biomass at the community level occurred in response to either individual species declines or to less fishing pressure in most regions (Shackell and Frank, 2007; Auster and Link, 2009). In contrast, body-size declines were a much stronger common signal. Worldwide and locally, declines in average size and size- and age-at-maturity have been associated with the preferential harvest of older and larger fish in both commercial fisheries and their bycatch (Olsen et al., 2004; Swain et al., 2007; Darimont et al., 2009; Fisher et al., 2010; Shackell et al., 2010). Here, fish size declined most in the more northerly regions as a consequence of the relatively “slower” life-history traits for a given species compared with their southern, smaller-bodied, faster maturing counterparts (Fisher et al., 2010). Climate and fishing have been examined, but trophic interactions only incompletely. Trophic interactions are important on the ESS, where a decline in predator groundfish biomass has been related to a planktivore increase (Bundy, 2005; Frank et al., 2005) as well as on the WSS, where trophic interactions through changes in body size have seemingly contributed to planktivore N. L. Shackell et al. increase (Shackell et al., 2010). However, trophic interactions are not invoked solely as explanations of planktivore increases through the GoM and GB (Link et al., 2002, 2009; Gaichas et al., 2009). The influence of trophic interactions on ecosystem response cannot be addressed adequately here by comparing regional, multitrophic ordination axes developed from MAFA, largely because subtler signals are lost. Here, we have determined common dynamics at multiple trophic levels among the seven regions and have suggested that common fishing and/or climate indices were the drivers. A different approach to addressing the influence of trophic interactions on system changes within regions will be pursued in other work. Summary Using the joint patterns of relative exploitation, planktivore landings, and total landings as indicators of fishing pressure, fishing would seem to be the dominant driver of ecosystem response, although local climate regimes either exacerbate (in northerly regions) or mitigate (in southerly regions) the response. Remarkably, common trends in upper trophic levels were observed across all seven regions spanning 128 of latitude, with some latitudinal differences in species assemblages over an area of almost half a million square kilometres. This comparative analysis of the seven ecosystems suggests that the ecosystem response to perturbation is similar regardless of species composition. The effects of climate and fishing interact, and differences in regional lower trophic dynamics ultimately determine upper trophic productivity (Chassot et al., 2010). At a broad scale, the AMO increased and was well correlated with all primary multivariate trends. 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