Kelp canopy: ‘area’ (m2 surface canopy
within a 900m2 pixel) and ‘biomass’ (kg
within a 900m2 pixel)
Kelp canopy variables were derived from Landsat imagery at a 30m
resolution following methods in Cavanaugh et al. 2011 and Bell et
al. 2020.
Surface temperature: ‘temperature’ (degrees C)
Sea surface temperature (SST) is the mean daily surface
temperature from the NOAA Coral Reef Watch SST data (5km
resolution; see more
here: https://coralreefwatch.noaa.gov/product/5km/index_5km_ssta.php).
The time period of SST covers April 1985 to December 2021.
Surface nitrate: ‘nitrate’ (umol L-1)
Surface nitrate is derived from the daily temperature data
following Snyder et al. 2020, including a new temperature to
nitrate relationship derived from CalCOFI data in Northern CA
(north of San Francisco), which is similar to the relationship
derived in García-Reyes et al. 2014. The time period of surface
nitrate covers April 1985 to 2021.
Depth: ‘depth’ (meters)
Depth of each 30m pixel was derived from the Seafloor Mapping
project, except for areas around Port San Luis, CA from NOAA NCEI,
and to the far south near Imperial Beach, CA from GEBCO.
Maximum significant wave height: ‘hsmax’ (meters)
Maximum significant wave height was modeled using the CDIP MOPv1.1
wave model on an hourly time scale at 1km coastline segments.
These data are available for the mainland, but we extended this
model to make hindcasts for the offshore Channel Islands. All wave
data prior to 2004 was hindcasted by developing a non-linear
statistical model (generalized additive model using the mgcv
package in R) between CDIP data and data from one of 18 offshore
US Army Corp Wave Information Study (WIS) model sites (1984 –
2019). The site that produced the best model estimating CDIP wave
height from WIS wave height, period, and direction was used to
model the daily maximum significant wave height back to 1984.
Net primary production: ‘npp’ (mgC m-2
d-1)
Phytoplankton net primary production is the mean monthly net
primary production from the Vertically Generalized Production
Model (VGPM) decribed by Behrenfeld and Falkowski (1997) and
available here:
http://sites.science.oregonstate.edu/ocean.productivity/index.php.
The VGPM model is based on the dynamics of chlorophyll pigment
standing stock combined with a rate term (chlorophyll-specific
assimilation efficiency for carbon fixation), day length, and a
volume function. This volume function is the product of the
euphotic zone depth and a light-dependent term that accounts for
the vertical variability in photosynthesis across this depth. The
time period of net primary production covers 1998 to 2021.
PAR at bottom: ‘parbttm_mean’, ‘parbttm_max’ (Einstein m-2 d-1)
Photosynthetically available radiation (PAR) at bottom (both mean
and max) was modeled using 8-day mean PAR and Kd490 data from a
combined 9km dataset from SeaWiFS, MODIS-Aqua, and VIIRS. Kd490
data was converted to KdPAR using equations from Saulquin et al.
2013. PAR at surface was then modeled to the bottom depth using
the KdPAR value and the depth of each pixel. Preliminary
validation with actual light sensors in the Pt. Loma kelp forest
showed that the dynamics of light through time matched well
between the model and the data, but that the model overestimated
the magnitude of PAR. This model assumes that there is no canopy
that is shading the benthos. Kelp canopy can reduce light at
bottom by up to 99%. The time period of PAR at bottom variables
covers July 1997 to 2021.
References:
Behrenfeld, M. J., and Falkowski, P. G. (1997). Photosynthetic
rates derived from satellite-based chlorophyll concentration.
Limnology and Oceanography 42, 1–20. doi:
10.4319/lo.1997.42.1.0001.
Bell, T. W., Allen, J. G., Cavanaugh, K. C., and Siegel, D. A.
(2020). Three decades of variability in California’s giant kelp
forests from the Landsat satellites. Remote Sensing of Environment
238, 110811. doi: 10.1016/j.rse.2018.06.039.
Cavanaugh, K. C., Siegel, D. A., Reed, D. C., and Dennison, P. E.
(2011). Environmental controls of giant-kelp biomass in the Santa
Barbara Channel, California. Marine Ecology Progress Series 429,
1–17. doi: 10.3354/meps09141.
García-Reyes, M., Largier, J. L., and Sydeman, W. J. (2014).
Synoptic-scale upwelling indices and predictions of phyto- and
zooplankton populations. Progress in Oceanography 120, 177–188.
doi: 10.1016/j.pocean.2013.08.004.
O’Reilly, W. C., Olfe, C. B., Thomas, J., Seymour, R. J., and
Guza, R. T. (2016). The California coastal wave monitoring and
prediction system. Coastal Engineering 116, 118–132. doi:
10.1016/j.coastaleng.2016.06.005.
Saulquin, B., Hamdi, A., Gohin, F., Populus, J., Mangin, A., and
d’Andon, O. F. (2013). Estimation of the diffuse attenuation
coefficient KdPAR using MERIS and application to seabed habitat
mapping. Remote Sensing of Environment 128, 224–233. doi:
10.1016/j.rse.2012.10.002.
Snyder, J. N., Bell, T. W., Siegel, D. A., Nidzieko, N. J., and
Cavanaugh, K. C. (2020). Sea Surface Temperature Imagery
Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp
Aquaculture Siting in the Southern California Bight. Frontiers in
Marine Science 7. Available at:
https://www.frontiersin.org/articles/10.3389/fmars.2020.00022.
