Appendix S1 – Seafloor geomorphology derivatives
Depth is likely to be an important predictor of fish distributions due to the known
associations of species within specific depth ranges (Eschmeyer et al., 1983, Allen & Pondella,
2006). We derived depth information directly from the digital elevation models (DEMs)
produced by the CSMP. DEMs are raster datasets that consist of depth values at regularly spaced
intervals and all DEMs used in this study were at 2m resolution.
We used the Spatial Analyst extension in ArcGIS 10 (ESRI, 2011) to calculate both slope
and slope of the slope from the DEMs. Previous studies have shown that species distributions
vary with changes in slope and areas of high slope are often correlated with greater densities of
fish than areas with little to no slope (McClatchie et al., 1997). The slope of the slope or the
maximum rate of maximum slope change between a cell and its eight neighbors was calculated
by first taking the slope of the DEM using the Spatial Analyst Slope tool and then calculating the
slope of the slope raster. Slope of the slope gives a measure of topographic complexity (units =
degrees of degrees) and has been shown to be an important predictor of fish species distributions
in coral reefs (Pittman et al., 2009; Pittman & Brown, 2011).
Topographic position index (TPI) is a measure of relative elevation and is calculated by
comparing the elevation of each cell in a DEM to the cells in the surrounding landscape. TPI is
useful for delineating features that could be of importance to many species of fish such as peaks,
ridges, flat plains, valleys, and crevices. We calculated TPI using the algorithm of Weiss (2001),
which uses an annulus ('donut') shaped neighborhood and we created these using the bathymetric
position index (BPI) tool within the Benthic Terrain Modeler (BTM) toolbar. We calculated TPI
at 20 m and 50 m scales with a five cell (10 m) annulus thickness. Previous studies have used
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TPI to explain variation in the distribution of multiple species (Weiss, 2001; Iampietro et al.,
2005; Lundbland et al., 2006; Young et al., 2010).
To create rasters that distinguish between rock and sediment, we derived vector
ruggedness measure (VRM) grids from the DEMs using the Terrain Tools toolbox in ArcGIS 9.x
(Sappington et al., 2007). VRM is a measure of terrain ruggedness using vector analysis where
the 3-dimensional orientation of the grid cells is taken into account, allowing for variation in
slope and aspect (Hobson, 1974). The values associated with VRM vary from 0 (flat, smooth
areas) to 1 (areas of higher complexity). Because rock is often more complex than the
surrounding sediment, VRM can be used to help distinguish between rock and sediment areas
(i.e. "rough" and "smooth" areas, respectively). We, therefore, used VRM as a proxy for "rock"
and "sediment." From the VRM analysis, we created a binary raster with 0 signifying soft
sediment and 1 signifying rocky substrate. The breakpoints for these two classes were based on a
threshold of VRM that captured the majority of the "rock" without erroneously classifying
artifacts or sediment features. If necessary, a substrate mask was used to mask out any
problematic areas. We used the resulting classified raster as a factor input into the model.
In addition to variations in reef structure, fish assemblages have been shown to change
with variation in latitude (McClatchie et al., 1997; Stephens et al., 2006; Carr & Reed, 2015) and
kelp density (Dean et al., 2000; Stephens et al., 2006; Carr & Reed, 2015). In order to account
for the latitudinal variation, we created a latitude raster using ArcGIS raster calculator '$$YMAP'
function. Kelp biomass rasters were created from kelp biomass values derived from highresolution satellite imagery and aerial photography (Cavanaugh et al., 2010, 2011).
Literature Cited
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Allen, L.G. & Pondella, D., II. (2006) Ecological Classification. Ecology of marine fishes:
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University of California Press, Berkeley.
Carr, M. H. & Reed, D. C. (2015) Shallow rocky reefs and kelp forests. Ecosystems of
California. (ed. by H. Mooney and E. Zavaleta), pp 311-336. University of California Press,
Berkeley.
Dean, T.A., Haldorson, L., Laur, D.R., Jewett, S.C. & Blanchard, A. (2000) The distribution of
nearshore fishes in kelp and eelgrass communities in Prince William Sound, Alaska: associations
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Eschmeyer, W.N., Herald, E.S. & Hammann, H. (1983) A field guide to Pacific coast fishes of
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Iampietro, P.J., Kvitek, R.G. & Morris, E. (2005) Recent advances in automated genus-specific
marine habitat mapping enabled by high-resolution multibeam bathymetry. Marine Technology
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trees to predict the diversity and abundance of fish and corals. Journal of Coastal Research, 53,
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Stephens, J.S., Larson, R.J. & Pondella, D, II. (2006) Rocky reefs and kelp beds. Ecology of
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Appendix S2: Spatial Autocorrelation Analysis. Spline correlograms showing the
spatial autocorrelation of the residuals from the GAMs used to associate each species or
community attribute with its environment. The spline correlograms have a 95% pointwise
bootstrap confidence interval around the correlation of the residuals. Distances along the x-axis
are measured in meters and correlation values between paired residuals along the y-axis.
a) Embiotoca lateralis:
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b) Embiotoca jacksoni
7
c) Sebastes serranoides
8
d) Sebastes atrovirens
9
e) Sebastes carnatus
10
f) Sebastes chrysomelas
11
g) Sebastes melanops
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h) Fish Species Diversity
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i) Fish Species Richness
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Appendix S3: Relationships between Response and Explanatory Variables.
Relationships of densities with environmental variables for each species of fish modelled using
GAMs. The x-axes depict the range of values of the explanatory variables and the y-axes are the
smooth functions for fish density along with the degrees of freedom. Those variables that did not
require a smooth function in the GAMs have a smoother of 1 applied in these figures.
a) Embiotoca lateralis:
15
b) Embiotoca jacksoni
16
c) Sebastes serranoides
17
d) Sebastes atrovirens
18
e) Sebastes carnatus
19
f) Sebastes chrysomelas
20
g) Sebastes melanops
21