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Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.

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Monitoring of intertidal reefs is traditionally undertaken by on-ground survey methods which have assisted in understanding these complex habitats; however, often only a small spatial footprint of the reef is observed. Recent developments in unmanned aerial vehicles (UAVs) provide new opportunities for monitoring broad scale coastal ecosystems through the ability to capture centimetre resolution imagery and topographic data not possible with conventional approaches. This study compares UAV remote sensing of intertidal reefs to traditional on-ground monitoring surveys, and investigates the role of UAV derived geomorphological variables in explaining observed intertidal algal and invertebrate assemblages. A multirotor UAV was used to capture <1 cm resolution data from intertidal reefs, with on-ground quadrat surveys of intertidal biotic data for comparison. UAV surveys provided reliable estimates of dominant canopy-forming algae, however, understorey species were obscured and often underestimated. UAV derived geomorphic variables showed elevation and distance to seaward reef edge explained 19.7% and 15.9% of the variation in algal and invertebrate assemblage structure respectively. The findings of this study demonstrate benefits of low-cost UAVs for intertidal monitoring through rapid data collection, full coverage census, identification of dominant canopy habitat and generation of geomorphic derivatives for explaining biological variation.

At small spatial and temporal scales, genetic differentiation is largely controlled by constraints on gene flow, while genetic diversity across a species' distribution is shaped on longer temporal and spatial scales. We assess the hypothesis that oceanographic transport and other seascape features explain different scales of genetic structure of giant kelp, Macrocystis pyrifera. We followed a hierarchical approach to perform a microsatellite-based analysis of genetic differentiation in Macrocystis across its distribution in the northeast Pacific. We used seascape genetic approaches to identify large-scale biogeographic population clusters and investigate whether they could be explained by oceanographic transport and other environmental drivers. We then modelled population genetic differentiation within clusters as a function of oceanographic transport and other environmental factors. Five geographic clusters were identified: Alaska/Canada, central California, continental Santa Barbara, California Channel Islands and mainland southern California/Baja California peninsula. The strongest break occurred between central and southern California, with mainland Santa Barbara sites forming a transition zone between the two. Breaks between clusters corresponded approximately to previously identified biogeographic breaks, but were not solely explained by oceanographic transport. An isolation-by-environment (IBE) pattern was observed where the northern and southern Channel Islands clustered together, but not with closer mainland sites, despite the greater distance between them. The strongest environmental association with this IBE pattern was observed with light extinction coefficient, which extends suitable habitat to deeper areas. Within clusters, we found support for previous results showing that oceanographic connectivity plays an important role in the population genetic structure of Macrocystis in the Northern hemisphere.