10:50 AM - 11:10 AM
[ACG41-02] Current status and future of ecosystem monitoring of seagrass beds in Japan and Asia: from basic field observation to the policy making and international planning.
★Invited Papers
Keywords:Asia-Pacific Marine Biodiversity Observation Network (AP-MBON), Ecosystem Service, Nature's Contributions to people, Remote sensing, in-situ observation, Zostera marina
With the increasing interest in the global environment in recent years, essential observation variables of ecosystems and environments have been examined in international frameworks such as GEO, GOOS, and MBON. There is growing momentum for monitoring and data integration. In Japan monitoring of coastal ecosystems has been conducted a couple of times around the 1990s as the nationwide Green Census conducted by the Ministry of the Environment. However nationwide surveys have not been conducted since then until recently. On the other hand, annual surveys to know temporal changes of ecosystems have been conducted as Monitoring sites 1000 program since 2008 and continuing. In the Asia-Pacific region, there have been a couple of surveys and monitoring programs from Japan, Europe, and the United States. In some countries, surveys also have been conducted by domestic organizations. These data are aggregated in recent years (Fortes et al. 2018; Sudo and Nakaoka 2020). In addition, new analysis and research methods such as deep learning, Drone, and environmental DNA are expected to be used. At the same time, the IPBES is considering the assessment of Nature's Contributions to People (NCP) and the assessment of future projection of such effects under socio-economic scenarios (such as SSP).
In this report, we will review the above situation and describe the current status under the monitoring site 1000 program and future plans of international monitoring (Takeuchi et al. 2021) by focusing on the eelgrass. Secondly, as an example of a new monitoring method applied in the Asia-Pacific region, we introduce an application of deep learning for the extraction of seagrass bed change in Thailand. Finally, estimation of carbon fixation and its changes due to scenarios are introduced as examples of the application of obtained monitoring data which may be more important than simply indication of the crisis of the ecosystem itself.
As a result of the first subject, 10 years monitoring of seagrass beds showed significant impact caused by Tsunami and ground subsidence by the Great East Japan Earthquake in 2011 on the Tohoku region. In addition, trends of increase or decrease of the seagrass area were recorded in several survey points. However, there was no nationwide trend of change and the trends differed from point to point and differed by the spatial resolution. We also identified signs of rapid changes in some regions. For example, a disappearance of seagrasses after a typhoon in Ibusuki, Kagoshima, and a sudden decrease after a heatwave in the intertidal zone of Futtsu, Tokyo Bay.
The classical monitoring by field survey can be conducted only at a limited number of sites. Remote sensing is an approach to detect changes through more extensive and long-term monitoring. As a result of this second subject, we analyzed past aerial photo images using deep learning and showed that even black and white (grayscale) images can be used to extract seagrass beds with higher accuracy than that previously possible. For example, in the Hat Chao Mai National Park, Trang, Thailand, our analysis detected that changes in the flow path of the small channels from the mouth of the river were a major factor in the changes of seagrass (Yamakita et al. 2019).
Lastly, as an example of how to utilize and evaluate the results of such monitoring, an estimate of carbon fixation based on information on the nationwide distribution of seagrass/algal beds in Japan is presented. As a result, high values were found in narrow areas with a large area of the beds, and they were sparsely distributed throughout the country, mainly in bays.In addition, we examined future changes of the bed area by scenario. The results indicate that future changes are not only along climate gradients but may also be regionally different (Yamakita et al. 2020; Hori and Yamakita 2021). It is important to consider the regional features of these ecosystem services when planning for development or conservation.
References:
Fortes MD, Ooi JLS, Tan YM, et al (2018) Bot. Mar. 61:269–288
Sudo K, Nakaoka M (2020) Ecol Res 35:994–1000. https://doi.org/10.1111/1440-1703.12137
Takeuchi Y, Muraoka H, Yamakita T, et al (2021)The Asia-Pacific Biodiversity Observation Network: 10-year achievements and new strategies to 2030. Ecol.Res. in press
Hori M, Yamakita T (2021) History and Future of the Sea. Asakura Publishing co. ltd. https://www.google.com/search?q=ISBN978-4-254-18544-7
Yamakita T, Sodeyama F, Whanpetch N, et al (2019) Bot Mar 62:291–307. https://doi.org/10.1515/bot-2018-0017
Yamakita T, Yamano H, Nakaoka M, etal (2020) PANCES Policy Brief, No. 3 http://pances.net/eng/policybrief.html
In this report, we will review the above situation and describe the current status under the monitoring site 1000 program and future plans of international monitoring (Takeuchi et al. 2021) by focusing on the eelgrass. Secondly, as an example of a new monitoring method applied in the Asia-Pacific region, we introduce an application of deep learning for the extraction of seagrass bed change in Thailand. Finally, estimation of carbon fixation and its changes due to scenarios are introduced as examples of the application of obtained monitoring data which may be more important than simply indication of the crisis of the ecosystem itself.
As a result of the first subject, 10 years monitoring of seagrass beds showed significant impact caused by Tsunami and ground subsidence by the Great East Japan Earthquake in 2011 on the Tohoku region. In addition, trends of increase or decrease of the seagrass area were recorded in several survey points. However, there was no nationwide trend of change and the trends differed from point to point and differed by the spatial resolution. We also identified signs of rapid changes in some regions. For example, a disappearance of seagrasses after a typhoon in Ibusuki, Kagoshima, and a sudden decrease after a heatwave in the intertidal zone of Futtsu, Tokyo Bay.
The classical monitoring by field survey can be conducted only at a limited number of sites. Remote sensing is an approach to detect changes through more extensive and long-term monitoring. As a result of this second subject, we analyzed past aerial photo images using deep learning and showed that even black and white (grayscale) images can be used to extract seagrass beds with higher accuracy than that previously possible. For example, in the Hat Chao Mai National Park, Trang, Thailand, our analysis detected that changes in the flow path of the small channels from the mouth of the river were a major factor in the changes of seagrass (Yamakita et al. 2019).
Lastly, as an example of how to utilize and evaluate the results of such monitoring, an estimate of carbon fixation based on information on the nationwide distribution of seagrass/algal beds in Japan is presented. As a result, high values were found in narrow areas with a large area of the beds, and they were sparsely distributed throughout the country, mainly in bays.In addition, we examined future changes of the bed area by scenario. The results indicate that future changes are not only along climate gradients but may also be regionally different (Yamakita et al. 2020; Hori and Yamakita 2021). It is important to consider the regional features of these ecosystem services when planning for development or conservation.
References:
Fortes MD, Ooi JLS, Tan YM, et al (2018) Bot. Mar. 61:269–288
Sudo K, Nakaoka M (2020) Ecol Res 35:994–1000. https://doi.org/10.1111/1440-1703.12137
Takeuchi Y, Muraoka H, Yamakita T, et al (2021)The Asia-Pacific Biodiversity Observation Network: 10-year achievements and new strategies to 2030. Ecol.Res. in press
Hori M, Yamakita T (2021) History and Future of the Sea. Asakura Publishing co. ltd. https://www.google.com/search?q=ISBN978-4-254-18544-7
Yamakita T, Sodeyama F, Whanpetch N, et al (2019) Bot Mar 62:291–307. https://doi.org/10.1515/bot-2018-0017
Yamakita T, Yamano H, Nakaoka M, etal (2020) PANCES Policy Brief, No. 3 http://pances.net/eng/policybrief.html