Analysis of spatial patterns in biomass across geographic scales
We analyzed the spatial patterns in demersal fish biomass for
relationships with environmental and anthropogenic drivers at three
spatial scales (ecoregion n = 21, subdivision n = 45 and grid cell n =
1083, Appendix S2, Figure S2.1), and using average demersal fish biomass
data from three time periods (1990-1995, 2000-2005 and 2010-2015; note
that all data is used in the time series analysis). For the ecoregion
and subdivision scale, we used a structural equation model (SEM), which
is a multivariate analysis to describe a network of causal relationships
(Grace 2006). The network links were inspired by recent modelling
predictions of demersal fish biomass based on a trait-based food-web
model (Petrik et al. 2019; van Denderen et al. 2021). As
such, we hypothesized that relationships between net primary production
and demersal fish community biomass are mediated by pelagic and benthic
secondary production. Following the rationale laid out in the
Introduction and Figure S2.2, we further hypothesized that demersal fish
biomass declines with increasing temperature, fishing exploitation and
the mean trophic level of the community. Lastly, we expected seafloor
depth to have an indirect effect on demersal fish biomass by changing
the flux of detritus to the benthos (Figure S2.2). SEM analyses were
performed using the package ‘Lavaan’ in R (Rosseel 2012). Since
ecoregions/subdivisions varied in their characteristics, multiple
sensitivity analyses were performed to test for potential effects of
differences in e.g., the number of grid cells and sampled depth range
(Appendix S3, Figure S3.1-2).
We further analyzed spatial changes in demersal community biomass at the
grid cell level. This analysis was not done with SEM, as we expect our
hypothesized causal structure to differ at more local spatial scales,
i.e. effects of fisheries may vary with depth and prey productivity
within each region. We used wavelet-revised model regression (Carl &
Kühn 2010) to explain finer-scale “within region” variability in
demersal fish biomass from the same set of predictor variables included
in the SEM. Wavelet-revised model regression is designed for regular
grid-based data while accounting for spatial autocorrelation and
non-stationarity (i.e. spatial autocorrelation may vary across regions).
This may be important for fish distributions due to biotic and abiotic
differences across marine regions that could affect fish movement
patterns, e.g. Windle et al. (2010). The spatial analysis at the
individual grid cell level was done for the same three time periods as
the SEM. Both biomass and the exploitation rate (catch/biomass) were
log10 transformed. Since a few grid cells had zero
catch, we added a small quantity (1 kg per km2 per
year) to avoid taking the log of zero. Model fits were assessed using
the Akaike Information Criterion (AIC) and the model with the lowest AIC
was selected as best candidate. When other candidate models had a
difference of 0–2 AIC units, we concluded that models were essentially
equivalent and the model with the fewest parameters was selected. The
analyses were performed using the package ‘spind’ in R (Carl et
al. 2018).
The set of environmental variables used as predictors in both the SEM
and wavelet-revised model regression was compiled from several sources.
Seafloor depths were measured in 96% of the survey hauls and extracted
for the remaining hauls, using the haul coordinates, from bathymetric
data per 1/12° grid from the ETOPO1 Global Relief Model with sea ice
cover (Amante & Eakins 2009). Temperatures were estimated using the
COBE sea surface temperature data per 1° grid and year
(www.esrl.noaa.gov/psd/data/gridded/data.cobe.html).
Data on bottom temperatures was
not available for the entire time series but was used to verify some of
our results (Appendix S3, Figure S3.3-4). Fish mean trophic level
(MTL ), describing the biomass-weighted mean trophic level of the
community, was calculated from the survey data using species-specific
trophic level information (Froese & Pauly 2018; Beukhof et al.2019). Fishing exploitation rates were estimated by dividing annual
fisheries catch of demersal fish with demersal fish survey biomass.
Fisheries catch data, available on a 30-minute spatial grid, were
obtained from Watson (2017) and estimated as the sum of fisheries
landings, illegal, unregulated and unreported catch and discards at sea.
Net primary production was obtained from the cafe algorithm using MODIS
data per 1/6° grid and averaged between 2005 and 2010
(http://www.science.oregonstate.edu/ocean.productivity) (Silsbeet al. 2016). Estimates of pelagic and benthic secondary
production were based on output of GFDL’s Carbon, Ocean Biogeochemistry
and Lower Trophics (COBALT) ecosystem model from a climatology of the
global earth system model (ESM2.6) representative of the contemporary
ocean under 1990 greenhouse gas concentrations (Stock et al.2014, 2017). Simulated mesozooplankton biomass and productivity in
ESM2.6 broadly captures observed and estimated contrasts across Large
Marine Ecosystems (Stock et al. 2017), and the energy available
to fish through this pelagic pathway can be estimated as mesozooplankton
production not consumed by other mesozooplankton
(Zflux ). ESM2.6-COBALT also simulates the
detrital flux that reaches the seafloor, which is used as a proxy for
benthic secondary production (Dflux ). For all the
predictor variables described above, we obtained an estimate per area
and year, averaged for each time period, with the exception of net
primary production and pelagic and benthic secondary production where a
fixed mean value was used due to data limitations and uncertainties in
the estimated values over time.
In order to compare and explore the robustness of our empirical SEM
results, we used a trophodynamic model to predict demersal fish biomass
for each subdivision and ecoregion. We compared these with the observed
estimates using linear regression and obtained the explained variance
(R2) and Root Mean Square Error (RMSE). In the
trophodynamic model, modified from Stock et al . (2017), we
assumed that energy flux into the fish community is in equilibrium with
the fisheries harvest out of the community after accounting for food
chain length variations and trophic transfer efficiency. Demersal fish
biomass B in each region i can then be estimated by
dividing the flux with the observed fisheries exploitation rate
(ER ):
\({B_{i}=(D}_{flux,i}{\times\text{TE}_{i}}^{\text{MTL}_{i}-1}+p_{i}\times Z_{flux,i}{\times\text{TE}_{i}}^{\text{MTL}_{i}-2.1})/\text{ER}_{i}\)eq. 1
Following the approach of Pauly & Christensen (1995) and Stock et
al. (2017), zooplankton were assigned to trophic level 2.1 and detritus
to 1, such that the number of trophic steps separating the
zooplankton/detritus flux (Zflux andDflux ) from the fisheries catch was estimated byMTL minus 2.1 or 1. We further assumed that only part of the
zooplankton production is available to demersal fish and this fraction
is proportional to p , which is estimated as the fraction of
demersal fish catch relative to total fish catch in each region. The
value of p was obtained from Watson (2017). The final parameter
is the trophic transfer efficiency (TE ). This parameter controls
the decay of energy between trophic levels and was varied from 0.05 to
0.15 (Eddy et al. 2021). Additionally, we varied the thermal
sensitivity (Q 10) of TE from 0.2 to 2.5:\(\text{TE}_{i}=TE\bullet{Q_{10}}^{\frac{T_{i}-10}{10}}\) ,with T being the average temperature in each region i .
Analysis of temporal
biomass variation in ecoregions
We examined whether the changes in demersal fish biomass with
temperature, as observed in the SEM and WMR spatial analyses, also drive
biomass changes in ecoregions over time. We estimated the influence of
temperature on demersal community biomass and production by fitting
different recursive biomass and surplus production models to the data
(see Table 1 for model details). We used different models to vary how
temperature may affect the demersal fish community. In each model,
ecoregion was included as a random effect and fishing catch was treated
as an offset. The temperature
term was centered on the mean temperature per ecoregion (obtained from
the COBE sea surface temperature data but see Figure S3.4) to limit our
analysis to temperature changes within each region. We scaled biomass
and production to the maximum biomass per ecoregion. We evaluated each
model with/without a temperature term using AIC, where a model with
temperature was considered the most parsimonious if it is at least 2 AIC
units lower.