| Project/Area Number |
22K21332
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1101:Environmental analyses and evaluation, environmental conservation measure and related fields
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
ROSS Samuel 沖縄科学技術大学院大学, 統合群集生態学ユニット, ポストドクトラルスカラー (60961795)
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| Project Period (FY) |
2022-08-31 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | ecology / stability / response diversity / resilience / duckweed |
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| Outline of Research at the Start |
I will conduct 3 experiments on floating plants. 1: a simple test asking does response diversity produce less variable biomass. 2: an extension to different dimensions of stability (resistance, resilience etc.). 3: an extension to spatially-linked communities where dispersal/competition are possible
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| Outline of Annual Research Achievements |
We conducted an experiment in Okinawa, using 75 100L buckets of water with floating aquatic plants. We used 4 species of plants in different combinations, and exposed them to Nitrate (to simulate agricultural runoff). Each week we measured water chemistry and took photographs of the plant communities, which reproduced clonally. We are developing a machine learning algorithm to automatically classify our plant species from photos. When finished, we will have data on plant growth and community composition changes, which we can use to understand how different species respond to Nitrogen, measure response diversity, and finally to relate response diversity to resilience of the communities. I also published a literature review about the topic of response diversity, part-supported by this grant
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
Turning photographs of the plants into useable ecological data has proven more difficult than anticipated. Instead of using an existing machine learning classifier to automatically identify our different plant species from photographs, we must develop our own tool to do this. We are currently working with a deep learning algorithm which is proving promising, but still requires several validation steps before we can use the data.
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| Strategy for Future Research Activity |
The next step is to continue validation of our deep learning algorithm to identify plants and measure changes in abundance and composition in each mesocosm (100L bucket). To do this, I am currently manually labelling thousands of image segments with species identity to aid the automated identification pipeline. I anticipate several stages of retraining based on additional manual labelling.
Then, when I have data on species abundances, I will measure growth rates of different species under different nitrogen conditions, and when species are in mixture, I can measure the diversity of their growth responses. I will finally use the diversity of these responses to predict resilience (measured here as the inverse of the temporal variability of total biomass output from the combined community).
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