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    Behavioral traits and territoriality in the symbiotic scaleworm Ophthalmonoe pettiboneae

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      Southward decrease in the protection of persistent giant kelp forests in the northeast Pacific

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      Mapping kelp persistenceThe study area for this analysis encompasses the region where Macrocystis pyrifera is the dominant canopy kelp species in the Northeast Pacific Ocean. The region extends from Año Nuevo Island in the north (latitude ~37.1°), California, USA, to Punta Prieta in the south (latitude ~27°), Baja California Sur, Mexico. We mapped the distribution of giant kelp canopy and characterized persistence using a 30-m resolution satellite-based time series covering our entire study area27. These data provide quarterly estimates of kelp canopy area across the study region from 1984 to 2018. We estimated giant kelp canopy from three Landsat sensors: Landsat 5 Thematic Mapper (1984–2011), Landsat 7 Enhanced Thematic Mapper+ (1999–present), and Landsat 8 Operational Land Imager (2013–present). We downloaded all imagery as atmospherically corrected Landsat Collection 1 Level-2 products. Each Landsat sensor has a pixel resolution of 30 × 30 m and a repeat time of 16 days (8 days when two Landsat sensors were operational). Since Landsat imagery can be obscured by cloud cover, we obtained a clear estimate of kelp areas ~16 times per year from 1984 to 2018 (mean = 16.2, std = 4.1). The repeated observations across the time series avoid missing kelp canopy due to physical processes such as tides and currents. Multiple Landsat passes over seasonal timescales are successful at mitigating the effect of tide and tidal currents on Landsat kelp canopy detection27.While the pixel resolution of Landsat sensors is 30 × 30 m, we were able to observe the presence and density of kelp canopy on subpixel scales using a fully automation procedure. We first masked all land areas using a global 30 m resolution digital elevation model (asterweb.jpl.nasa. gov/gdem.asp) and classified the remaining pixels as seawater, cloud, or kelp canopy using a binary decision tree classifier trained on a diverse array of pixels within the study region27. We then used Multiple Endmember Spectral Mixture Analysis39 to model each pixel as the linear combination of seawater and kelp canopy. This method can accurately obtain kelp canopy presence as long as kelp canopy covers ~13% of a 30 m pixel. These methods were validated using 15 years of monthly kelp canopy surveys by the Santa Barbara Coastal Long Term Ecological Research project at two sites in Southern California. We filtered errors of commission (such as free-floating kelp paddies) by removing any pixels classified as kelp canopy in More