Bayesian inference of environmental effects on seaweed production in Japan via a production-environmental suitability model
Journal
Botanical Studies
Journal Volume
60
Journal Issue
1
Date Issued
2019-12-01
Author(s)
CHEN, Hungyen
Abstract
© 2019, The Author(s). Background: Both natural and human-induced disturbances are commonly responsible for an overall decrease of the world’s seaweed. Along Japan’s coastal areas, edible seaweed production has been decreasing for decades. In this study, a production-environmental suitability model to estimate the impacts of environmental factors on seaweed production was developed. The developed model not only estimates human-induced disturbances but also quantifies the impacts of environmental factors responsible for the decline of annual seaweed production. The model estimated the temporal variation in human-induced disturbances and the effects of environmental factors (i.e., rainfall, CO 2 concentrations, temperature, typhoons, solar radiation, water nutrient levels, and water quality) on edible seaweeds in Japan from 1985 to 2012. Results: The environmental suitability for seaweed production in Japan was about 4.6 times greater in 1992 than in 2011, meanwhile as a result of human activities, human-induced disturbances of seaweed increased at a rate of 4.9 times faster during the period of 1998–2012 than the period of 1985–1997. The ratio of decreased production to decreased environmental suitability for seaweed production in Japan increased by 15.2% during the study years, which means that seaweed production has become more sensitive to environmental disturbances, including climatic factors and human activities in 1998–2012. Conclusions: The results are novel in demonstrating temporal variations in the level of environmental suitability to seaweed production by using a simple mathematical model. The production-environmental suitability model successfully predicted seaweed production by reflecting the 28-year temporal variation of the observed seaweed production in Japan.
Subjects
Bayesian estimation | Climatic change | Seaweed growth | Yield prediction
Publisher
SPRINGEROPEN
Type
journal article