Quantifying Causality of Upstream Water Quality, Meteorology, and Ocean Conditions on Coastal Algal Bloom in Southern California using Convergent Cross Mapping
Abstract
The increasing occurrence of harmful algal blooms poses significant risks to marine ecosystem structure and human health through the creation of hypoxic zones and the bioaccumulation of toxins when produced. Driven by excess nutrients, favorable environmental conditions, and microbiological community interactions, microalgae growth and blooms follow highly nonlinear patterns. This complexity makes blooms increasingly difficult to understand and predict as anthropogenic sources of nutrient pollution increases under a shifting climate. Being able to define significant drivers and quantify their impacts on blooms allow for more effective analysis on specific bloom conditions. With this knowledge, approaches such as defining conditional system thresholds can improve prevention efforts for future blooms. Convergent cross mapping (CCM) is a data driven causal inference technique that uses information embedded in a time series to predict another that is expected to be forcing. By measuring the prediction skill of the algorithm, CCM can quantify causality. To improve our understanding of the responses of coastal Californian algal biomass to environmental measures from the confluence of ocean and terrestrial fluxes, we analyzed a unique dataset consisting of remote sensing measurements of chlorophyll-a (an algal biomass proxy), coastal environmental conditions from the National Data Buoy Center (NDBC), and upstream nutrient measurements from the California Environmental Data Exchange Network (CEDEN). We apply the CCM algorithm on chlorophyll-a to help characterize causal variables and quantify their degree of impact on chlorophyll-a. Through this we show relative contributions from the oceanic and inland fluxes on algal biomass growth.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMOS21C1754B
- Keywords:
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- 0414 Biogeochemical cycles;
- processes;
- and modeling;
- BIOGEOSCIENCES;
- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCES;
- 4817 Food webs;
- structure;
- and dynamics;
- OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL;
- 4855 Phytoplankton;
- OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL