Environmental Dataset
As temperature influences intertidal organisms at all life-history
stages, we calculated minimum, maximum, and average SST and AT in winter
(December-February) and summer (June-September) months between
2008-2018. We included September in the summer calculations to encompass
the entire potential larval period of S. umbilicalis . We used
seasonal averages for the decade prior to and including the year of
sampling so that the temporal resolution of the environmental data
encompassed multiple generations of selective pressure (Flanagan et al.
2018). SST data was obtained as monthly means per year at a resolution
of ~11 x 7.5 km2 from the EU
Copernicus Marine Service Information Atlantic-European North West Shelf
Ocean Physics Reanalysis dataset (REF: CMEMS-NWS-PUM-004-009). AT data
was obtained as temperature 2 m from the sea surface, at a resolution of
9 km2 from the EU Copernicus Climate Change Service
ERA5-Land dataset (DOI: 10.24381/cds.68d2bb30).
We also incorporated descriptors of historical climate using five
bioclimatic variables obtained from the WorldClim dataset (Fick &
Hijmans 2017): maximum AT of the warmest month, minimum AT of the
coldest month, AT annual range, mean AT of the warmest quarter and mean
AT of the coldest quarter. These variables represent historic 30-year
monthly averages from 1970-2000 at a resolution of 5
km2.
Wave exposure data was taken from the UK dataset of Burrows et al.
(2008), who developed a grid-based model to calculate indices of wave
fetch, a parameter that describes the distance to the nearest land cell.
Most available records of environmental variables were either
“land-based” or “sea-based” with few encompassing the intertidal
environment. To address this, we took averages of all recorded
temperature values within a 10 km radius of each sampling site. For
site/environmental variable combinations where no data was available
within this radius, we used the nearest data cell to characterize the
site. As the exposure dataset is high-resolution coastal data, we were
able to obtain more precise estimates for each site and retained the
average wave fetch value of the nearest cell only. We used average wave
fetch (representing the average of the summed fetch values for the cell
and its immediate neighbours) to incorporate some variability in the
local degree of exposure at each site. All environmental data
manipulations were conducted in ArcGIS (v10.7.1; ESRI 2011).
We conducted a principal component analysis (PCA) in R (v4.0.3; R Core
Team 2020) to reduce the set of 18 environmental variables into fewer,
orthogonal PCs that describe environmental variation at our study sites.
We retained PCs that had eigenvalues >1 and assessed the
loadings of environmental variables onto each retained component. We
used a cut-off value of 0.32 to attribute variables to each PC, as this
threshold represents environmental variables that have at least 10% of
their variance explained by the PC (Dormann et al. 2013). We then
calculated the standardized scores of each retained PC for subsequent
analyses.