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– Please note the applicable attribution statement is as follows: ‘includes 
Crown copyright, Cefas [2023]'**


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            <gco:CharacterString>The purpose of this study was to predict how climate change could affect the 
species richness of consumers lower in the food web (planktivores), compared 
to those intermediate (benthivores) and higher up (piscivores). In brief, a 
collation of trophic interactions spanning the northeast Atlantic shelf seas 
has been applied to define feeding guilds by grouping fish species size 
classes (we use the taxonomic level of Gobiidae and Ammodytes for taxa that 
are not consistently identified to species) that have prey taxa in common, and 
whose prey differentiate them from other predator guilds based on cluster 
analysis. Taxon-specific size categories were defined as: &lt;3 cm as larvae; 
small juvenile fish between 3 cm and half of length at maturity; 
juvenile-medium fish from half of length at maturity to length at maturity; 
medium fish from length at maturity to half-maximum length; and all remaining 
larger fish as large. Taxon-specific length at maturity and maximum length 
(i.e., asymptotic length at infinity) were estimated using the R package 
*Fishlife* (Thorson et al., 2017).

Environmental projections were derived from the coupled marine ecosystem 
models POLCOMS/NEMO-ERSEM. We focus on two emission scenarios (representative 
concentration pathways, RCPs) developed for the Intergovernmental Panel on 
Climate Change (IPCC)’s fifth phase of the Coupled Model Intercomparison 
Project (CMIP5): RCP4.5, the “stabilization scenario”, characterised by medium 
emissions and high mitigations, and the “no mitigation policy” scenario 
RCP8.5, derived from high fossil fuel emission and low mitigations. We use 
projections of temperature, salinity, pH, nitrate, phosphate, dissolved 
oxygen, current velocity, chlorophyll, gross primary production, non-living 
organic carbon, zooplankton carbon concentration and secondary carbon 
production by zooplankton in our predictive habitat models. For temperature, 
salinity, current velocity, dissolved oxygen, and pH both surface and bottom 
mean annual averages were considered, and in the case of temperature, also the 
difference between bottom and surface values, to account for stratification. 
For chlorophyll, gross primary production, non-living organic carbon, 
phosphate, nitrate, zooplankton carbon concentration and secondary carbon 
production by zooplankton the total across the water column was used, rather 
than surface or bottom values. We also include depth (from the General 
Bathymetric Chart of the Oceans GEBCO; `www.gebco.net`_, at 15 second 
resolution), distance to coast and substrate composition (median grain size 
and percentages of mud, sand and gravel from Wilson et al., 2018) to capture 
key spatial gradients that affect habitat suitability for fish. 

All environmental data was processed onto a 10 km by 10 km grid, and because 
an annual mean of, e.g., temperature, does not capture the environmental 
variability that ultimately determines the thresholds within which biota must 
survive; for temperature, salinity, pH, oxygen and current speed we also 
include the standard deviation of the 12 monthly means in each year, for all 
locations within a radius of 75 km of each grid cell, in order to provide a 
measure of spatio-temporal heterogeneity. For the environmental variables 
where surface and bottom values were extracted, we use sea surface values to 
model habitat suitability for planktivores, which are largely pelagic species, 
and seabed values for the benthivores and piscivores which are largely 
demersal species. Pairwise Pearson correlation coefficients were computed for 
the set of environmental variables used to model the habitat of planktivores 
and of non-planktivores separately, to assess multicollinearity. Variables 
were removed if they correlated with another &gt;0.7.

Additionally we provide data on model performance and presence/absence 
thresholds, in the file "Model performance and threshold.xlsx". Model 
performance is measured via the area under the curve (AUC) of the Receiver 
Operating Characteristic (ROC) plot, the AUC of the Precision-Recall (PR) plot 
(He &amp; Garcia, 2009), and the Miller slope (Miller et al., 1991). We assess 
model performance in both space and time. Spatial performance was analysed via 
8-fold block cross validation using the R library “blockCV” (Valavi et al., 
2019). Temporal performance of the models was assessed by training a new model 
that excluded the last 5 years of survey data (2015 to 2019), using the 
resulting model to predict to those 5 years, a “novel time period” for that 
model. 

These data were produced for the study in the article "Climate change affects 
the distribution of diversity across marine food webs", by Murray S. A. 
Thompson, Elena Couce, Michaela Schratzberger and Christopher P. Lynam, in 
Global Change Biology (in press). 


.. _`www.gebco.net`:
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