Habitat mapping of giant kelp (Macrocystis pyrifera) and devil weed (Sargassum horneri) off the coast of Santa Catalina Island, California
The cover image illustrates peak weather regime intensities over the easter Northern Pacific.
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Keywords

Giant kelp
Geomorphometry
Habitat mapping
Invasive species
Species distribution modeling

How to Cite

Espriella, M., Schaper, T., Atchia, A., Rose, K., & Lecours, V. (2019). Habitat mapping of giant kelp (Macrocystis pyrifera) and devil weed (Sargassum horneri) off the coast of Santa Catalina Island, California. McGill Science Undergraduate Research Journal, 14(1), 34–39. https://doi.org/10.26443/msurj.v14i1.51

Abstract

Background: Macrocystis pyrifera, commonly known as giant kelp, is a fast-growing brown alga that typically inhabits temperate waters. In southern California, M. pyrifera provides many ecologically and economically significant ecosystem services. Sargassum horneri, a non-native brown macroalga commonly known as devil weed, often outcompetes M. pyrifera while providing fewer ecological or economical benefits. Examining potential areas of species overlap is key to understanding the invasion potential of S. horneri and essential to the implementation of removal efforts. This study aims to map the suitable habitat of M. pyrifera and invasive S. horneri in the coastal waters of Santa Catalina Island, California, and to quantify any overlapping habitat between the two macroalgae.

Methods: Broadly defined potential habitats were characterized around Santa Catalina Island using an unsupervised approach to habitat mapping based on a series of abiotic surrogates mapped at a 2 m spatial resolution. In situ substrate data were then overlaid onto the unsupervised classification to identify spatial associations between substrate type and potential habitats, and to interpret the classes. To predict the distribution of M. pyrifera and S. horneri around Santa Catalina Island based on their respective association with the environment, maximum entropy (MaxEnt) was used to produce species distribution models. The resulting models for M. pyrifera and S. horneri were overlaid to identify potential areas of conflict based on suitable habitat overlap.

Results: The unsupervised approach to habitat mapping resulted in a map of four potential habitats around Santa Catalina Island based on substrate cover. Sand was the most dominant type of substrate. The supervised approach using MaxEnt identified 10.27% of the study area as suitable habitat for M. pyrifera and 7.37% as suitable habitat for S. horneri. A total of 33.56% of the suitable habitat for M. pyrifera was found to also be suitable for S. horneri.

Limitations: The characterization of habitats and the species distribution modeling were limited to the study of benthic terrain characteristics due to the unavailability of other high-resolution environmental data (e.g., hydrodynamics and chemical data) around Santa Catalina Island. In addition, data were not available for the very shallow waters near the coast, where giant kelp is often found. Given the complexity of this ecosystem, the addition of other variables and data coverage closer to the coast would potentially make the maps and models more representative of the actual distribution of M. pyrifera and S. horneri and provide a more complete understanding of their environmental preferences.

Conclusion: This study provides insight into the kelp forest ecosystems found in California’s Channel Islands; it is a vital first step in order to understand the potential areas for invasion of M. pyrifera by S. horneri, thus supporting decision making and efforts to control S. horneri abundance.

https://doi.org/10.26443/msurj.v14i1.51
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