Community Size-Structure of the Northwest Atlantic Groundfish Communities, Response to Direct Disturbance and a Changing Environment

Size Spectra Analysis of the Northeast US Finfish Community

Author
Affiliation

Adam A. Kemberling

Gulf of Maine Research Institute

Published

September 29, 2023

Potential Journals:

  1. ICES Journal of Marine Science

Abstract

The Northwest Atlantic Ocean is among the fastest warming areas on Earth for sea surface temperature (SST). This region is home to diverse marine ecosystems and supports some of the most valuable fisheries in the United States. The high rate of warming seen over the last decade has scientists concerned that temperatures may outpace the ability of individual species and ecosystems to adjust/acclimate and lead to a decline in ecosystem function. Ecological theory and supporting laboratory studies suggest elevated temperatures are likely to alter body-size across many species by imposing increased metabolic costs at the cellular level and at the individual level through behavior change and altered growth trajectories. However it remains unclear the extent these relationships may be mitigated by the adaptive behaviors of individuals, and how those actions will manifest at the ecological scale in an open-ocean environment. Here we’ve measured ecosystem wide changes in community size spectra, a measure of the community size structure, to track ecosystem functionality in time. We anticipated that where adaptive responses were outpaced by elevated temperatures, the size spectrum slope would steepen, reflecting a decrease in the overall energy transfer efficiency from small to large body sizes. Using fisheries independent survey data we estimated the size spectra relationships for four regions along the US NE continental shelf. Regression analyses were then used to explore how size spectra changes aligned with large-scale, bottom-up, and top-down changes in biotic and abiotic drivers. Spectra slope steepness followed a latitudinal pattern with shallower spectra found in the North and steeper spectra moving South. Changes in slope were most dynamic in the Gulf of Maine, with the other regions showing either gradual change or no change over time. Changes to the Gulf of Maine size structure are most visible among groundfish. These species showed large declines in both average length and average body weight which reflected declining abundance of larger individuals. Gulf of Maine spectra changes can be broken into three periods: a period of steepening from 1982-1998, a brief reversal from 1999-2006, and continued lows after 2006. The largest declines in spectra occurred in the 80’s and 90’s, before the rapid warming of the last decade. Recent lows happening concurrently with both elevated temperatures and declines in primary productivity. Fisheries landings were strongly correlated with these early declines, but that relationship has weakened with time. After 2006 large zooplankton abundances showed strong positive correlations with shallower slopes in the Gulf of Maine, highlighting an important bottom-up influence on the recent size structure. Other regions in this study were less well predicted by changes in fisheries landings and were more impacted by environmental factors. These results highlight the differing vulnerabilities of geographically adjacent marine communities to a common suite of large-scale drivers.

Introduction

Efforts to understand the impacts of climate change at local and regional scales are a high priority area of study among the international scientific community. Concerns about the future health of marine environments have been elevated now that temperature, acidification, sea level, and ocean circulation patterns have all shown signs of recent rapid change (Johnson and Lyman (2020); Findlay and Turley (2021); University of South Carolina et al. (2021); Frederikse et al. (2020); Neto et al. (2021)). Within the context of temperature changes, scientists anticipate ecological communities will respond to rising temperatures through actions to avoid, limit exposure, and/or adapt to unfavorable conditions. At a community-scale, adaptation will ultimately be limited by the constraints of what each constituent species’ life histories can support. Possible pathways for mitigating the impacts of rising temperatures include physical shifts in geographic distributions and depth as well as physiological changes impacting growth and maturation relationships (Atkinson (1995); Blackburn et al. (1999); Pinsky et al. (2013)). When considering ecological responses to warming, a major constraint for adaptation pathways is direct human intervention. Marine ecosystems are tightly coupled to human systems and are shaped by human interactions like fishing and other practices that alter the marine environment in unforeseen ways. These human interactions have been shown to directly alter geographic distribution and life-history features of target and non-target species. This can in turn change the available adaptive pathways and adaptive responses for climate-stressed species (Law (2000); Mills et al. (2013) ; Perälä et al. (2022) ; Rabalais and Turner (2019) ; Tu et al. (2018)). Marine communities are complex and difficult to sample accurately and consistently over large scales. Research on the early impacts of climate change on marine systems has focused on individual species or small communities that can be consistently sampled and/or have their interactions explicitly described (Pinsky et al. (2013) , Nye et al. (2009) , Pershing et al. (2015) ). One weakness in these approaches is the assumptions they rest upon. For many species distribution studies there is an implicit assumption that key ecological interactions will be preserved under projected climate change conditions, that ecosystem function and community structures will be maintained. Another lens with which to study the functionality of a community is through the study of its traits. Size is commonly referenced as the master trait and has well documented relationships to metabolism and community organization (Brown et al. (2000); White et al. (2007)). Marine communities are very-often size-structured, size correlating to trophic position, making them ideal for investigation through this lens. Size spectra relate how the abundance of individuals changes with increasing body size (Sheldon et al. (1972)). In marine systems it has been noted that the summed biomass concentration of individual aquatic organisms is roughly constant across equal logarithmic intervals of body size from bacteria to the largest predators. The rate at which abundance declines with increasing size reflects how well energy is transferred from the smallest individuals at the lowest trophic levels to larger individuals occupying higher trophic levels (Andersen and Beyer (2006) ; Hillaert et al. (2018)). Measuring size spectra is a taxon-agnostic method to detect changes in ecosystem health and has been shown to be sensitive to both large-scale environmental disturbance as well as direct human interaction (Shin et al. (2005); Kerr and Dickie (2001)). By studying changes in energy-transfer efficiency (spectrum slope) scientists can assess the robustness of communities to external stressors and environment change (Sprules and Barth (2016)). Our study focuses on changes in the size structure of the Northeast US continental shelf finfish community. This region is experiencing rapid increases to temperature tied to changes in the behaviors of the Gulf Stream and Labrador Currents (University of South Carolina et al. (2021)). This region has been sampled extensively as part of national fisheries management efforts and has long-term records on the size distribution of the finish community, providing a long-history with which to study changes in size structure. Our study measures size-structure characteristics for the finfish community across four sub-regions (Gulf of Maine, Georges Bank, Southern New England, Mid-Atlantic Bight). These subregions follow a North to South temperature gradient that supports a range of biological communities associated with their distinct temperature regimes. Based on early observational studies and ecological theory we anticipate that individual level responses to a rapidly warming environment will be detectable in the size-structure of the region’s ecological community, evidence that ecosystems are experiencing change-of-function as a result of rapid environmental change. Using the size spectra timeseries for the relative impact of major external drivers we will assess the relative impact of major regional environmental factors.

Methods

Fish Community Data Source and Processing

Abundance, biomass, and size data for Northeast U.S. Shelf fish species were collected as part of the Northeast Fisheries Science Center’s bottom trawl survey (Grosslein 1969, Azarovits 1981, Politis 2014). This survey has been conducted from Cape Hatteras, North Carolina to the Gulf of Maine in the spring and in fall of each year since 1968. The survey follows a stratified random sampling design, with stratum characterized by depth, bottom habitat, and latitude. Tows are performed for a fixed duration at each station. The aggregate abundance and biomass for each species caught is reported, as well as a length measurement for all individuals (sub-samples taken for in cases of a very large catch). Correction factors were applied to abundance and biomass values to account for changes in vessels, gear, and doors used in the survey (Sissenwine and Bowman 1978, Byrne and Forrester 1991, Miller et al. 2010). We performed an additional correction on the individual-level abundance at length information for each species to match the aggregate abundance and biomass corrections done by the science center. 68 finfish species were used in our analyses, representing 98.98% of the finfish biomass caught in the survey. These species were selected on the availability of published weight-at-length relationships (Wigley et al. 2003). Length-weight relationships (Wigley et al. 2003 ) were used to convert length data, available for all individuals, into individual weights. Each species was assigned a functional group based on its life history and geography using the definitions of Hare et al. (2016) . Functional groups included coastal fishes, diadromous fishes, elasmobranchs, groundfish, and pelagic fishes (Table 1). Six species were not included in the original work of Hare et al. 2016, these were assigned to the most relevant functional groups (see Table 1). To account for differences in sampling effort among survey stratum, all abundance information and any associated biomass values were area-stratified. All analyses were performed using these area-stratified estimates using only spring and fall surveys from 1970 to 2019. Analyses were performed for four regions composed of groups of survey-design stratum: Gulf of Maine, Georges Bank, Southern New England, Mid-Atlantic Bight (Figure 1a).


Ecological Indicators

For each region we developed the following time series of ecological indicators:

  1. Annual mean abundance and biomass by functional group
  2. Annual mean length and weight of the aggregate community and for each functional group
  3. Annual estimates of the community size spectrum slope

Total abundance and biomass for each sub-region were calculated for each year using area-stratified abundance and the estimates of weight-at-length from Wigley et al. 2003. This process was repeated for each region and for each of the four functional groups. This gave us longitudinal changes to abundance and biomass for the aggregate community and its functional components. Changes to body size were measured using the average length and weight across all 68 species. Average size estimates were weighted by area-stratified abundance. Average body length (cm) and body weight (kg) was calculated for each region and for each functional group. The data for body size estimates was not truncated using any minimum or maximum size and reflects all available catch data for the 68 species in this study. Long-term trends were determined using linear regression. Annual size spectra were calculated for each region using the extended likelihood method (MLEbin) of Edwards et al. (2020) . This method estimates the exponent of the size spectra (b) for a bounded power law relationship between length-specific abundance and length-estimated biomass. The exponent of this relationship is analogous to slope estimates from normalized biomass spectra methods that employ a linear regression on logarithmic axes Carvalho et al. (2021) . This method has been shown to be the most accurate for estimating the exponent of size spectra when tested against alternative methods ( White et al. (2008) ; Sprules and Barth (2016) ; Edwards et al. (2017) ). Using this method, the size spectra exponent (b) was estimated for each region from 1970 to 2019. A minimum body weight of 1g was used for the lower bound and a maximum biomass of 10 kg was used as an upper bound when estimating the size spectra to account for poor survey catchability at the smallest and largest sizes. This size range retained 97.83% of all the biomass for the 68 species included in this study. Exponents of size spectra (b) were calculated using code modified from the sizeSpectra package (Edwards et al. (2017); Edwards et al. (2020)) and implemented using the R statistical programming language.

Drivers of Spectra Change

Impacts of external factors on spectra slope were explored using multiple regression analyses. Annual variation in regional slope was modeled using measures of large-scale environmental conditions, anthropogenic disturbance, and bottom up resource quality. Environmental drivers were the regional SST anomalies and the Gulf Stream Index (GSI). Global SST data were obtained via NOAA’s Optimum Interpolation SST analysis (OISSTv2), which provides daily SST values at a 0.25° latitude x 0.25° longitude resolution Reynolds et al. (2007) . A daily climatology for every 0.25° pixel in the global data set was created using average daily temperatures spanning the period of 1982-2011. Daily anomalies were then computed as the difference between observed temperatures and the daily climatological average. OISSTv2 data used in these analyses were obtained from the NOAA Physical Sciences Laboratory, Boulder, Colorado, USA from their website. SST anomalies were regionally averaged to produce time series for each of the four survey sub-regions described above (Figure 1a). These were then averaged to within years to form annual time series of SST anomalies. The Gulf Stream index (GSI) quantifies the latitude of the Gulf Stream position based on ocean temperatures at 200 m depth between 55° W and 75° W longitude. The index was obtained from the ecodata package in R (Bastille & Hardison 2018), which supplies GSI data at monthly intervals following the methodology of Pérez-Hernández and Joyce (2014) and Joyce et al. (2019) , using sea level height anomaly data from the Copernicus Marine Environment Monitoring Service. Fishing pressure represents the primary top-down anthropogenic driver in the region, and was investigated using regional commercial landings data. Fishing pressure in the region was measured using state and federal commercial fishing landings. This data was obtained from the Greater Atlantic Regional Fisheries Office (GARFO) for statistical areas that are routinely used for fisheries reporting and management. Individual statistical areas were aggregated into clusters that closely aligned with the trawl survey areas (Figure 1b). Bottom-up ecological interactions were explored using two zooplankton indices. Zooplankton indices were from NOAA Fisheries’ Marine Resources Monitoring, Assessment and Prediction (MARMAP) program and Ecosystem Monitoring (EcoMon) cruises ( Morse et al. (2017) ). Abundance anomalies were computed from the expected abundance on the day of sample collection. The small copepod index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis. The “large-copepod” anomaly values are the abundance anomaly of Calanus finmarchicus, the largest copepod in the Northeast U.S. region ( Kane (2007) , Kane (2008) ). Anomalies for both large and small zooplankton groups were averaged within ecological production units by the data provider, producing annual timeseries for the Gulf of Maine, Georges Bank, and Mid-Atlantic Bight (NMFS 2023). The zooplankton timeseries for the Mid-Atlantic Bight EPU was used for both the Mid-Atlantic Bight and Southern New England regions in our analyses due to shared geographic overlap. Each of these drivers (climate, primary productivity, fishing) have independently been shown in other works to have measurable impacts on size spectra ( Duplisea and Kerr (1995) ; Blanchard et al. (2005) ; Woodworth-Jefcoats and Wren (2020) ). Our exploratory investigations here are an effort to evaluate which predictors most explain the variance seen in the regional size spectra, and whether those relationships have changed over the study period. To highlight potential cases of non-stationarity, a hallmark of regime-shift dynamics ( Blöcker et al. (2023)) , we explored the temporal structures in the regional spectra using breakpoint analyses. Breakpoint analyses were performed using the envcpt package (Killick et al. 2021). This package applies an automatic model selection process testing support for candidate model structures including constant/piecewise changes to the mean, variance, trends, & autocorrelations and any identified changepoints located using the pruned exact linear time algorithm ( Rikardsen et al. (2004) ). Results from the breakpoint analysis were used to identify whether regional spectra expressed distinct periods of change. In cases where changepoints were identified an additional “regime” term was included to the regression analyses of key drivers. This term allowed for non-linear driver effects across different periods of time. Time-lags on predictors were also evaluated to explore important non-contemporaneous relationships between drivers and size spectra. Important lagged relationships were identified through the use of cross correlation function (CCF) estimates. CCFs were performed for the dependent variable of the size spectra slope with the independent variables of SST, GSI, commercial fisheries landings, and small and large zooplankton indices for annual lags up to 10 years. Significant lags were then included as additional candidate predictors in the multiple regression analysis of spectrum slope changes. Multiple regression models were evaluated independently for each region and their performances using AICc to rank the most parsimonious models.

Results

Abundance and Biomass Changes

Abundance has been gradually increasing on the Northeast Shelf since 1970, accelerating after 2007 to peak levels in 2014. Following that peak, shelf-wide numbers have been in decline (Figure 2). In the Gulf of Maine, fish abundance was relatively low until the 1990’s when it began to steadily rise. Abundance here fell during 2002-2006, but then continued to rise after 2006. Gulf of Maine abundance peaked in 2016 and then declined through 2019. Georges Bank abundance was low from 1970 through 2010. By 2014 abundance had roughly quadrupled, propelled by notable strong recruitment classes of haddock and other groundfish species. After peaking in 2014, abundance soon fell to levels more similar to the 1970-2010. Abundance in Southern New England displayed higher inter-annual variability when compared to both Gulf of Maine and Georges Bank. Abundance began increasing rapidly in 2007, before falling back to earlier levels by the end of the 2010’s. The Mid Atlantic Bight displayed a similar inter-annual variability, but had no major trend in fish abundance. Groundfish species were the dominant functional group driving the abundance and trends in the Gulf of Maine and Georges Bank, contributing more than half the total fish abundance in these regions. The two southern regions showed a more balanced abundance distribution among the five functional groups, with a larger representation of coastal and diadromous species (Figure 2). Shelf-wide biomass has been on the rise since the late-1990’s. Biomass peaked in 2016, and fell for the next three years mirroring the pattern seen in abundance (Figure 2). Shelf biomass was concentrated among two major functional groups, groundfish and elasmobranchs. Groundfish biomass was lowest in the late 80’s and early 90’s, before rebounding in the 2000’s. Elasmobranch biomass increased throughout the study period, with the exception of southern New England (Figure 2). Biomass was higher in the two northern regions, the Gulf of Maine and Georges Bank. The Gulf of Maine followed a similar trend to the shelf as a whole. Biomass here was at its lowest during the 1980’s, but had more than doubled by 2000 and in years following, peaking in 2016 before declining each year after. The majority of the biomass here was found among groundfish and elasmobranch species. Georges Bank had a similar groundfish and elasmobranch composition. Biomass here was lowest in the early 1970’s. During the 1980’s groundfish biomass declined, but this was offset by growth in elasmobranch species. The region hit low biomass levels again in the 1990’s before climbing to peak levels in 2014. Biomass then fell here each year after. .In Southern New England the biomass changes were less steady. Increases in the 1980’s were followed by abrupt declines in the mid 1990’s. The region saw its highest biomass in 2012, with a familiar decline towards the end of the timeseries. Biomass here was represented with a majority role of elasmobranch species. This was also the case in the Mid-Atlantic Bight, a region that saw long term growth in its biomass and a peak in 2019.

Regional Abundances

Biomass Changes

Regional Biomass

Body-Size Changes

Northeast shelf length and weight of the “average” individual fish has remained relatively stable over nearly five decades (Figure 2.). Average length did not change over the study, averaging 26.06 cm (sd = 2.57). Average weight declined slowly at 8.95g year-1 of the same period.Independent trends were found within the regional communities. Fish size declines in the Gulf of Maine were the most of any region. Average length was highest in the 1970s & 1980s at around 34.7-34.8 cm, lengths fell to 28 cm in the last decade with an overall trend of -.19 cm year-1 . The average weight also fell during the 1980s, from an average of 0.8 kg in the 1970s to 0.3 kg in the 2010s, a decline of 62.5% and a long-term rate of -1.1g year-1 . Georges Bank average sizes declined in average weight (-5.48g year-1), but with no overall change in length. Declining sizes for both the Gulf of Maine and Georges Bank slowed during the late 2000s, but then continued to fall the following decade. Average body size in southern New England was relatively constant, with no long-term changes in length or weight. The Mid-Atlantic Bight region was the only area with long-term increases in both length (.26cm year-1) and weight (4.89g year-1) during the study period.

Population and Size Changes Combined

Spectra Trend Structure and Lagged Driver Exploration

Breakpoint exploratory analysis showed support for breakpoints in one region, the Gulf of Maine. Slope breakpoints were found in 1998 & 2007 where trends reversed direction, with an additional 2-year autocorrelation also supported. This structure would divide the changes to Gulf of Maine size structure into three periods: 1972-1998, 1999-2006, and 2007-2019. Changes in Georges Bank slope changes were best represented by a single trend structure, with slopes steepening over time. The reverse was the case for the Mid-Atlantic Bight, with slopes becoming more shallow with time. In Southern New England a single mean (intercept) model best reflected the lack of change. Based on these exploratory results, three regime periods were added to the Gulf of Maine multiple regression analysis. A two-year autocorrelation term was also included based on its support in the most parsimonious breakpoint model. No breakpoints or autocorrelation terms were added to the models for the remaining regions. CCF estimates of lagged drivers flagged several relationships in Georges Bank and the Mid-Atlantic Bight. The lagged predictors added to Georges Bank regression models included: A 2-year lag on the small zooplankton index, a 1-year lag on the large zooplankton index, a 1-year lag of SST, 4- & 1-year lags of the GSI, and 4- & 5-year lags of commercial landings. The lagged predictors added to Mid-Atlantic Bight regression models were: 5- & 7-year lags on the small zooplankton index and a 2-year lag on SST.

CCF Results

Size Spectra Slopes

Regional differences in size spectra slopes (b) followed a North to South gradient with shallower spectra in the North and steeper spectra in the South (Figure 4). Northeast shelf slopes were centered around a value of -1 and showed no long-term trends. The Gulf of Maine had the shallowest slope of all regions. This was particularly true in the 1970s and 1980s when spectra slopes were around -0.85 & -0.88. Slopes began declining in the 1980s and continued to steepen through the 1990s. During the early 2000s there was a reversal of this trend, but by 2010 they had begun to steepen again. Gulf of Maine spectra slope estimates remained below -0.9 for most of the last decade putting it in line with the more southern regions. Georges Bank slope values also declined over the study with the largest declines happening over the last decade. Southern New England and Mid-Atlantic Bight slope estimates were steeper than in the northern regions. Southern New England’s spectra slopes showed no long-term change over the study period. The Mid-Atlantic Bight was unique in having slopes that became less steep over the study period, ending shallower than all areas except the Gulf of Maine.

Region Most Supported Model
gom trendar2cpt
gb trend
sne meancpt
mab trend

Model rankings using AICc & delta-AICc (Table 1) for the Gulf of Maine best support a regression model containing two predictors with a constant (stationary) effect across all years, and two predictors whose effects across were allowed to vary across the regime periods identified by the changepoint analysis (non-stationary effects). The two constant-effect drivers each had a negative impact on spectra slope. They were the small zooplankton index (p = 0.011) and the two-year autocorrelation term (p = 0.033). Main effects for the regime periods themselves were not significant, highlighting important crossover effects. The inclusion of year-block interactions of the three regimes (1982-1998, 1999-2006, & 2007-2019) with both the commercial landings index and the large zooplankton index allowed these drivers to differently impact size spectrum slope between periods. During the first regime the effect of commercial landings was positive (p = 0.009), but for 1999-2006 the relationship was negative (p = 0.032). During the third regime there was no relationship between commercial landings and spectra changes (p = 0.088). For the large zooplankton index there was no relationship during either of the first two regimes (p = 0.2, p = >0.9). During the third regime large zooplankton had a positive effect on the spectrum slope (p = <0.001). For Georges Bank the top performing models (delta-AIC range of <2), four of the five retained two predictors: SST anomalies (p = 0.028) and the small zooplankton index from 2-years prior (p = 0.006). Both of these predictors shared a negative effect on size spectrum slope. A fifth model swapped out SST anomalies for the gulf stream index, again with a negative relationship to spectra slopes. Regression models here highlighted bottom-up drivers as the best predictors, suggesting that this region’s size spectra changes are most highly correlated with environmental forces and not commercial landings. Southern New-England showed no trend in size spectrum slopes. This was further confirmed by the model selection procedure. The “best” model of Southern New England retained only commercial landings, however this relationship was not significant (p = 0.13) and had very low performance (r-squared = 0.06). Three models were selected for the Mid-Atlantic Bight region. The best performing model showed that increases in the small zooplankton index had a negative effect on spectrum slope (p = 0.020). This relationship was present in the other top candidates, with an additional negative relationship with either the large zooplankton or with commercial landings. Model performance was low among the top models (adjusted r-squared 0.14-0.16).

Driver Regression Summaries

Characteristic Beta 95% CI1 p-value
blag2 -0.33 -0.64, -0.03 0.033
landings 0.06 0.01, 0.10 0.009
regime
    yrs_1982-1998 — —
    yrs_1999_2006 -0.04 -0.15, 0.06 0.4
    yrs_2007_2019 -1.9 -4.1, 0.40 0.10
zp_large -0.06 -0.17, 0.04 0.2
zp_small -0.07 -0.12, -0.02 0.011
landings * regime
    landings * yrs_1999_2006 -0.20 -0.38, -0.02 0.032
    landings * yrs_2007_2019 -2.0 -4.4, 0.33 0.088
regime * zp_large
    yrs_1999_2006 * zp_large 0.01 -0.18, 0.19 >0.9
    yrs_2007_2019 * zp_large 0.40 0.18, 0.62 <0.001
R² 0.653
Adjusted R² 0.515
Sigma 0.034
Statistic 4.71
p-value <0.001
df 10
Log-likelihood 77.3
AIC -131
BIC -112
Deviance 0.029
Residual df 25
No. Obs. 36
1 CI = Confidence Interval
Predictor GVIF Df GVIF^(1/(2*Df)) Interacts With Other Predictors
regime 1.000000e+00 17 1.000000 SST, GSI, landings, zp_small, zp_large --
SST 5.960859e+07 5 5.991435 regime GSI, landings, zp_small, zp_large
GSI 2.074176e+07 5 5.391189 regime SST, landings, zp_small, zp_large
landings 4.268892e+03 5 2.306915 regime SST, GSI, zp_small, zp_large
zp_small 1.460115e+08 5 6.552978 regime SST, GSI, landings, zp_large
zp_large 1.918367e+07 5 5.349253 regime SST, GSI, landings, zp_small

Characteristic Beta 95% CI1 p-value
SST -0.03 -0.06, 0.00 0.028
zpslag2 -0.08 -0.13, -0.02 0.006
R² 0.307
Adjusted R² 0.264
Sigma 0.052
Statistic 7.09
p-value 0.003
df 2
Log-likelihood 55.6
AIC -103
BIC -97.0
Deviance 0.085
Residual df 32
No. Obs. 35
1 CI = Confidence Interval
Predictor VIF
landings 1.929115
zpslag1 2.791261
SST 3.077146
zp_large 3.265561
zp_small 3.887965
zpslag2 4.634856
GSI 4.918679

Characteristic Beta 95% CI1 p-value
landings 0.01 0.00, 0.03 0.13
R² 0.062
Adjusted R² 0.036
Sigma 0.046
Statistic 2.37
p-value 0.13
df 1
Log-likelihood 64.4
AIC -123
BIC -118
Deviance 0.075
Residual df 36
No. Obs. 38
1 CI = Confidence Interval
Predictor VIF
zp_large 1.507136
zp_small 1.621108
GSI 1.932934
landings 2.242318
SST 2.752092

Characteristic Beta 95% CI1 p-value
zp_small -0.07 -0.13, -0.01 0.020
R² 0.142
Adjusted R² 0.118
Sigma 0.060
Statistic 5.96
p-value 0.020
df 1
Log-likelihood 53.9
AIC -102
BIC -96.9
Deviance 0.130
Residual df 36
No. Obs. 38
1 CI = Confidence Interval
Predictor VIF
landings 1.331770
zp_large 1.373941
zp_small 1.636199
SST 1.763223
GSI 1.831599

Model Selection Results

Discussion

With the rise of ecosystem-based fisheries management practices, size spectrum approaches and size-based indicators have grown in popularity for their ability to reveal system-level properties without the need for explicit parameterization of predator-prey relationships ( Greenstreet et al. (2011) ; Houle et al. (2012) ). This study notes the size structure changes for a community of 68 marine finfish species and spanning four orders of magnitude in body-weight. For the Northeast US shelf as a whole the multi-species size spectra for the has been relatively stable over the last 50 years with values around -1. This is consistent with other large-scale marine studies that measured normalized biomass spectra (Platt & Denman 1977, Quinones et al. (2003) ) and the foundational pattern first documented by Sheldon and subsequently by others ( Sheldon et al. (1972) ; Sprules and Barth (2016) ). We take this as evidence in-tact ecological functionality (as evidenced by the size spectra slope) at this broad geographic scale. While size spectra stability in the Northeast shelf give evidence that these communities have thus far proved resilient to external stress, they may also mask how differently vulnerable these shifting communities are to those same stressors. At regional scales we observed independent size structure trends that highlight differences in the regional responses to disturbance. We observed steady long-term trends in spectra slopes in three regions. These were the steepening in Georges Bank, the shallowing of spectra in the Mid-Atlantic Bight, and a flat trend within Southern New England. These three regions each exhibit spectra near the shelf-wide estimate of -1. Regression driver analyses for these steady-trend areas highlighted bottom-up drivers of small zooplankton, SST anomalies, and the Gulf-Stream index as more informative drivers than measures of fishing pressure. Fisheries are often described as a larger factor on spectra slope steepening, making this result a surprise. However; variance explained for these regional models was low (< .33), a sign that other un-observed or unmeasured factors may be more important or that changes may not be beyond expected natural fluctuation. The most dynamic changes in biomass spectra were observed in the Gulf of Maine, which was viewed across three distinct periods of behavior. In the first period slopes steepened following a decline in abundance of larger individuals primarily composed of groundfish species, a signature consistent with patterns of fishing exploitation seen in other regions ( Duplisea and Kerr (1995) , Rice & Gislaison 1996; Bianchi et al. (2000) ; Jennings and Blanchard (2004) ). Slope estimates during this period were at the time the shallowest of all regions measured, with spectra slope values notably less steep than the broader shelf-wide community >0.9. Following this period commercial landings (a proxy for fishing pressures) remained low for the remainder of the study period, setting hopes for a recovery. During the 1999-2006 period slope steepness had reversed course and became shallower, but after 2006 they then fell again to levels similar to the southern regions. The ultimate failure of the size-structure to recover, despite a release from fishing stress, highlights the challenge managers face in efforts to recover exploited populations. Our analysis suggests that the influence of prey quality in the Gulf of Maine, mainly large or small zooplankton community indices, may help explain how such shallow slopes during the early period were supported, and why slopes remain relatively steep despite a cessation in fishing. A situation likely made worse by the overall declining productivity in the region (Balch et al. 2022). Dynamic food-web models suggest that size-structure recovery can take decades depending on the severity of fishing disturbance ( Fung et al. (2013) ). This has been supported by empirical observations in other regions (Frank et al. 2011; Fogarty and Murawski (1998) ; Daan et al. (2005) ; Hutchings and Reynolds (2004) ; Choi et al. (2004) ). The changing relationships between spectra slope response to large-scale drivers suggest that the vulnerability communities to external factors may shift depending on system state, the level of disturbance, and underlying species composition. While the direct effects of fishing on spectra steepening are perhaps most obvious: removal of larger individuals steepening slope and the out-sized impact their removal has on reduced reproductive output ( Andersen and Rice (2010) ; Law (2000) ; Bianchi et al. (2000) ). The persistence of shallow spectra in periods of reduced fishing is better explained by the less obvious indirect pathways which reinforce size structure imbalances. These include the release of smaller size classes from predation pressure, preserving more biomass at one end of the size spectrum and creating opportunities for diffuse replacement by non-targeted species (Pimm & Hyman 1987; Duplisea and Kerr (1995) ). This can lead to increased competition among the smaller size classes which itself then results in a reduction in adult sizes ( Andersen and Rice (2010) ). A pattern observed in Celtic Sea haddock and here within the Gulf of Maine (Shephard et al 2012). Reduced adult size in adult size stemming from . While it is understood that fishing has large direct and indirect impacts on size spectra, climate impacts have also been shown to impact community size structure ( Blanchard et al. (2005) ). However these impacts are often through other indirect mechanisms, often operating at different time scales ( Blanchard et al. (2005) ; Pershing et al. (2015) ). As size structures become more weighted towards smaller individuals, size structure variability becomes more tightly linked to the abundance and quality of the lower trophic level prey (Shephard et al. 2012). The observation that communities have shifted their size structures toward higher abundances of smaller individuals and lower abundances of larger individuals has been seen across numerous marine systems ( Daan et al. (2005) ; Bianchi et al. (2000) ; Krumsick and Fisher (2020)) . Adding to the challenge already facing natural resource managers is the multi-faceted nature of changing climate. Changes in the distribution and composition of species in our study area have already been reported with an expectation that they will continue and worsen (Friedland et al 2023; Nye et al. (2009)). These factors also likely contribute to size structuring we have seen over the last decade. Empirical size spectra and other size based indicators have grown in popularity with the push for ecosystem based fisheries management practices. They have been shown to be sensitive to the direct human influence of fishing and indirect processes stemming from climate variability. While these taxon-agnostic methods are useful for adding broader context on ecosystem dynamics it can be difficult to isolate the forces driving observed variability. Understanding their limitations, and supporting these methods with complimentary metrics that add to their explainability or allow comparison to an unexploited benchmark should be done as a best practice when possible ( Blanchard et al. (2009) ; Greenstreet et al. (2011) ).

Our study shows that despite a history of large-scale anthropogenic disturbance and a rapidly changing regional climate, changes to the overall community size structure have been minor at broad geographic scales. The regional responses seen appear to operate at different time scales and to different features of the environment. This suggests that at large scales the impacts of a rapidly changing climate may be offset through novel species interactions and an ever-shuffling series of species replacement in a rich biological community. At more local scales we expect breakdowns in these relationships to be more apparent, highlighting the importance of determining the scale of processes involved.

Supplemental Materials

Table 1:

Common and scientific names for the species that constitute each functional group used in our analyses. X markers are used to indicate which regions each species has been caught in the data.
Functional Group Assignments and Regional Presence/Absence
Common Name Scientific Name Georges Bank Gulf of Maine Mid-Atlantic Bight Southern New England
Coastal - (18)
Atlantic Croaker micropogonias undulatus X X X
Atlantic Spadefish chaetodipterus faber X
Atlantic Thread Herring opisthonema oglinum X X
Black Sea Bass centropristis striata X X X X
Blackbelly Rosefish helicolenus dactylopterus X X X X
Blueback Herring alosa aestivalis X X X X
Bluefish pomatomus saltatrix X X X X
Butterfish peprilus triacanthus X X X X
Cunner tautogolabrus adspersus X X X X
Greater Amberjack seriola dumerili X X
Northern Kingfish menticirrhus saxatilis X X X
Scup stenotomus caprinus X X X X
Southern Kingfish menticirrhus americanus X
Spanish Mackerel scomberomorus maculatus X
Spanish Sardine sardinella aurita X
Spot leiostomus xanthurus X X
Striped Bass morone saxatilis X X X X
Weakfish cynoscion regalis X X X
Diadromous - (2)
American Shad alosa sapidissima X X X X
Atlantic Sturgeon acipenser oxyrhynchus X
Elasmobranch - (19)
Atlantic Angel Shark squatina dumeril X
Atlantic Sharpnose Shark rhizoprionodon terraenovae X
Barndoor Skate dipturus laevis X X X X
Bullnose Ray myliobatis freminvillei X X
Chain Dogfish scyliorhinus retifer X X X
Clearnose Skate raja eglanteria X X
Cownose Ray rhinoptera bonasus X
Little Skate leucoraja erinacea X X X X
Rosette Skate leucoraja garmani X X X X
Roughtail Stingray dasyatis centroura X
Sand Tiger carcharias taurus X
Sandbar Shark carcharhinus plumbeus X X
Smooth Butterfly Ray gymnura micrura X
Smooth Dogfish mustelus canis X X X X
Smooth Skate malacoraja senta X X X X
Spiny Butterfly Ray gymnura altavela X
Spiny Dogfish squalus acanthias X X X X
Thorny Skate amblyraja radiata X X X X
Winter Skate leucoraja ocellata X X X X
Groundfish - (25)
Acadian Redfish sebastes fasciatus X X X X
American Plaice hippoglossoides platessoides X X X X
Atlantic Cod gadus morhua X X X X
Atlantic Halibut hippoglossus hippoglossus X X X
Atlantic Wolffish anarhichas lupus X X X
Cusk brosme brosme X X X X
Fawn Cusk-Eel lepophidium profundorum X X X X
Fourspot Flounder paralichthys oblongus X X X X
Goosefish lophius americanus X X X X
Haddock melanogrammus aeglefinus X X X X
Longhorn Sculpin myoxocephalus octodecemspinosus X X X X
Northern Searobin prionotus carolinus X X X X
Ocean Pout macrozoarces americanus X X X X
Offshore Hake merluccius albidus X X X X
Pollock pollachius virens X X X X
Red Hake urophycis chuss X X X X
Sea Raven hemitripterus americanus X X X X
Silver Hake merluccius bilinearis X X X X
Spotted Hake urophycis regia X X X X
Summer Flounder paralichthys dentatus X X X X
White Hake urophycis tenuis X X X X
Windowpane Flounder scophthalmus aquosus X X X X
Winter Flounder pseudopleuronectes americanus X X X X
Witch Flounder glyptocephalus cynoglossus X X X X
Yellowtail Flounder limanda ferruginea X X X X
Pelagic - (4)
Atlantic Herring clupea harengus X X X X
Atlantic Mackerel scomber scombrus X X X X
Buckler Dory zenopsis conchifera X X X X
Round Herring etrumeus teres X X X X
Functional group assignments adapted from Hare et al. 2010
Top Commercial Fisheries Landings of Northeastern US (by weight)
Avg. Annual Landings (lb.) Total Landings (lb.) Total Value ($)
Gulf of Maine - 1960
Hake, Silver 16.58M 281.87M 8.71M
Herring, Atlantic 11.57M 138.83M 2.50M
Redfish, Acadian 2.12M 88.97M 3.41M
Gulf of Maine - 1970
Herring, Atlantic 22.78M 501.08M 19.70M
Menhaden, Atlantic 17.78M 373.48M 7.87M
Redfish, Acadian 3.14M 219.85M 23.87M
Gulf of Maine - 1980
Herring, Atlantic 21.78M 653.26M 34.52M
Menhaden, Atlantic 21.24M 509.75M 12.77M
Pollock 3.33M 229.57M 62.00M
Gulf of Maine - 1990
Herring, Atlantic 25.21M 958.12M 54.12M
Cod, Atlantic 2.35M 138.76M 131.76M
Shark, Dogfish, Spiny 3.34M 120.17M 15.95M
Gulf of Maine - 2000
Herring, Atlantic 2.99M 47.77M 4.31M
Monkfish/Angler/Goosefish 716.21K 31.51M 51.13M
Cod, Atlantic 692.95K 30.49M 42.30M
Gulf of Maine - 2010
Tuna, Bluefin 209.06K 3.76M 33.30M
Shark, Dogfish, Spiny 479.11K 2.87M 590.62K
Pollock 188.20K 1.69M 2.08M
Georges Bank - 1960
Haddock 15.00M 270.06M 34.41M
Hake, Silver 6.83M 95.57M 3.19M
Cod, Atlantic 4.88M 87.89M 8.12M
Georges Bank - 1970
Cod, Atlantic 7.78M 233.48M 59.16M
Flounder, Yellowtail 4.62M 138.52M 43.16M
Redfish, Acadian 2.63M 76.37M 9.09M
Georges Bank - 1980
Cod, Atlantic 10.11M 404.40M 211.60M
Flounder, Winter 2.50M 100.11M 89.84M
Haddock 2.36M 94.27M 66.68M
Georges Bank - 1990
Cod, Atlantic 4.27M 192.29M 190.26M
Hake, Silver 1.79M 76.82M 20.49M
Flounder, Winter 1.23M 56.43M 75.59M
Georges Bank - 2000
Cod, Atlantic 2.17M 62.91M 75.20M
Herring, Atlantic 3.49M 48.92M 3.73M
Haddock 1.54M 43.01M 55.37M
Georges Bank - 2010
Hake, Silver 155.88K 779.40K 499.90K
Haddock 39.65K 118.95K 143.04K
Flounder, Winter 40.40K 80.80K 216.28K
Southern New England - 1960
Other Fish, Bony 14.84M 400.77M 3.73M
Flounder, Yellowtail 6.56M 196.83M 19.12M
Flounder, Winter 2.52M 70.58M 7.01M
Southern New England - 1970
Menhaden, Atlantic 9.99M 239.84M 5.12M
Other Fish, Bony 4.05M 206.59M 2.49M
Flounder, Yellowtail 2.07M 153.55M 36.47M
Southern New England - 1980
Menhaden, Atlantic 6.60M 217.68M 10.21M
Hake, Silver 2.56M 205.02M 46.11M
Flounder, Yellowtail 1.66M 132.92M 83.38M
Southern New England - 1990
Hake, Silver 2.52M 196.81M 78.54M
Herring, Atlantic 2.12M 129.02M 7.19M
Menhaden, Atlantic 3.71M 125.98M 8.69M
Southern New England - 2000
Mackerel, Atlantic 2.55M 135.06M 15.60M
Hake, Silver 1.00M 55.25M 26.89M
Skate, Nk 950.56K 49.43M 6.53M
Southern New England - 2010
Scup 161.10K 6.44M 4.29M
Hake, Silver 145.07K 4.21M 3.12M
Flounder, Summer 80.43K 3.86M 11.54M
Mid-Atlantic Bight - 1960
Flounder, Summer 2.03K 4.05K 720.00
Flounder, Yellowtail 2.33K 2.33K 214.00
Flounder, Witch 395.00 395.00 36.00
Mid-Atlantic Bight - 1970
Menhaden, Atlantic 10.20M 50.98M 1.59M
Weakfish/Sea Trout, Squeteague 886.91K 9.76M 1.40M
Scup 876.60K 8.77M 2.09M
Mid-Atlantic Bight - 1980
Menhaden, Atlantic 30.78M 646.41M 10.94M
Flounder, Summer 1.15M 83.83M 72.00M
Scup 550.89K 37.46M 15.53M
Mid-Atlantic Bight - 1990
Menhaden, Atlantic 115.86M 4.63B 286.14M
Mackerel, Atlantic 1.67M 103.62M 13.87M
Croaker, Atlantic 1.35M 71.65M 22.53M
Mid-Atlantic Bight - 2000
Menhaden, Atlantic 69.60M 2.64B 167.17M
Croaker, Atlantic 2.16M 106.02M 42.93M
Mackerel, Atlantic 1.70M 59.41M 6.38M
Mid-Atlantic Bight - 2010
Menhaden, Atlantic 118.29M 1.89B 154.46M
Bass, Striped 1.70M 25.56M 75.05M
Croaker, Atlantic 1.08M 24.81M 21.37M
Landings data obtained from the Greater Atlantic Regional Fishing Office (GARFO)

Single Driver Correlations

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