| 研究課題/領域番号 |
20F20774
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| 研究機関 | 沖縄科学技術大学院大学 |
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研究代表者 |
ECONOMO Evan 沖縄科学技術大学院大学, 生物多様性・複雑性研究ユニット, 教授 (30648978)
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| 研究分担者 |
KASS JAMIE 沖縄科学技術大学院大学, 生物多様性・複雑性研究ユニット, 外国人特別研究員
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| 研究期間 (年度) |
2020-09-25 – 2023-03-31
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| キーワード | Abundance / Ants / Community ecology / Community variability / Ecological stability / Multispecies model / Seasonality / Time-series analysis |
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| 研究実績の概要 |
Since September 2020, much progress has been made on both proposed projects. We have finished most of the analyses for the community temporal variability project, and am in the process of finalizing the methods, interpreting results, and composing an outline for a manuscript. We have developed a unique methodology that combines community ecology metrics with time-series decomposition analysis to try and explain the mechanisms behind temporal variability. Kass presented on this project in Japanese at the Ecological Society of Japan’s 68th Annual Meeting (online). We have been working on the multispecies abundance modeling project with OIST Ph.D. candidate Yazmin Hanani Zurita Gutierrez, and we have entered a collaboration with colleagues from the University of Helsinki to analyze the data. Once we are done parameterizing and fitting the models, which are highly complex, we expect to have preliminary results soon. These results will be species interaction correlation matrices and modeled relationships between abundance and environmental predictor variables.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research for the first proposed project is nearing completion and about to enter the manuscript-writing phase. The methodology for second proposed project is proceeding more quickly since the inception of our new collaboration, and we could have preliminary results in a few months. We have not yet begun working on the generalized dissimilarity model, but we did complete a proof-of-concept last year and understand how to implement it.
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| 今後の研究の推進方策 |
Our plans for future work include integrating the products of a remote-sensing analysis to use as potential predictor variables in the multispecies abundance model, as well as to use in future analyses that build off the results of the present research. We purchased very fine-scale imagery of the areas surrounding some of our study sites with the KAKENHI funds, and we plan to classify these images to estimate forest diversity. We are now planning to build a training dataset with field validation points and existing botany inventories led by OIST researcher Dr. Juanita Choo. This new dataset will elucidate how diverse existing forests are, which will help us differentiate between disturbed and non-disturbed forests.
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