Given the impacts of climate change and other anthropogenic stressors on marine systems, there is a need to accurately predict how species respond to changing environments and disturbance regimes. The use of genetic tools to monitor temporal trends in populations gives ecologists the ability to estimate changes in genetic diversity and effective population size that may be undetectable by traditional census methods. Although multiple studies have used temporal genetic analysis, they usually involve commercially important species, and rarely sample before and after disturbance. In this study, we run a temporal analysis of giant kelp, Macrocystis pyrifera, genetic diversity over the scope of 10 years (2008-2018) using the same microsatellite marker panel to assess the genetic consequences of disturbance in several populations of giant kelp (Macrocystis pyrifera) in the Southern California Bight. The study is a rare pre- and post-disturbance microsatellite analysis that included declines to giant kelp caused by the 2015/16 El Nino Southern Oscillation event. We used canopy biomass estimated by remote sensing (Landsat) to quantify the extent of disturbance to kelp beds, and sea surface temperature data to understand how kelp was pushed towards its temperature limits during this period. Despite prolonged periods with decreased canopy at several sites, no changes in genetic structure and allelic richness were observed. We argue that giant kelp in the region is best described as a “patchy population” system where true extinctions are rare. We discuss how deep refugia of subsurface sporophytes and cryptic microscopic life stages could have kept genetic diversity through disturbance. Given the increasing effects of climate change and uncertainty in modeling impacts of species with cryptic life history stages, we suggest further investigation to reveal the role such stages play in species resilience. Genetic monitoring studies of sites selected by remote census demographic and climate surveys should be continued in the future given the predicted impacts of climate change.

Sampling

To conduct a temporal genetic analysis, we sampled giant kelp at five sites between 2018 and 2019 located in three regions differing in genetic coancestry in the Southern California Bight (SCB), hereafter referred to as 2018 samples. These regions had also been sampled before, in 2007 and 2008, and genotyped by Johansson et al. (2015) using microsatellite marker analysis, hereafter referred to as 2008 samples. Two of our locations are continental, Leo Carrillo, and Camp Pendleton, with one site sampled in each. Our third location was Catalina Island, where we sampled three sites .  We note that the 2018 site from this location is located 12 km south of the 2018 site (San Mateo) from Johannsson et al. (2015), due to logistic constraints that precluded sampling the same site. Genetic differentiation at this scale is weak, Johansson et al. (2015) reported that kelp forests from a large swath of the coast, south of Los Angeles to Baja California, belonged to the same genetic group, and all sites had similar allelic richness.

New sample collections occurred between January 2018 and June 2019. The sampling protocol involved haphazardly collecting (~n=30 per site) sporophyte blade tissue by snorkeling and SCUBA. We collected tissue from individual holdfasts to ensure non-repetitive sampling. Blades were either dried in silica or immediately frozen and desiccated using an Eppendorf Vacufuge Plus (Hamburg, Germany) for subsequent DNA extraction.

Genotyping

We extracted DNA using the NucleoSpin 96 Plant Kit II (Macherey-Nagel, Duren, Germany) with standard protocols and genotyped them for six microsatellite loci used in Johansson et al., (2015) (Mpy-8, Mpy-14, BC-4, BC-18, BC-19, BC-25), following modified PCR protocols in Alberto et al., (2009b)⁠. PCR product fragment sizes were separated and scored on an ABI 3730 FVNPL (Applied Biosystems) using GeneScan-500 LIZ as a size standard (at the UW-Wisconsin Biotechnology Center). We scored alleles using STRAND v. 2.4.110 and binned them using the MsatAllele v. 1.0.4 R package (Alberto, 2009b)⁠. While binning the new data set, we genotyped a few standard samples from Johansson et al. (2008) to help match the new and old MsatAllele databases, ensuring that all alleles were binned using the same system.

The data format is that of Structure program (https://web.stanford.edu/group/pritchardlab/structure.html)