2020 Fiscal Year Annual Research Report
Patterns and drivers of temporal variability for native and invasive Okinawa ant communities
| Project/Area Number |
20F20774
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
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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| Co-Investigator(Kenkyū-buntansha) |
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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| Project Period (FY) |
2020-09-25 – 2023-03-31
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| Keywords | Abundance / Ants / Community ecology / Community variability / Ecological stability / Multispecies model / Seasonality / Time-series analysis |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
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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[Journal Article] Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish2021
Author(s)
Zhang, Z., Kass, J. M., Mammola, S., Koizumi, I., Xuecao, L., Tanaka, K., Ikeda, K., Suzuki, T., Yokota, M., & Usio, N.
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Journal Title
Diversity and Distributions
Volume: 27
Pages: 684-695
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models2020
Author(s)
Ryo, M., Angelov, B., Mammola, S., Kass, J. M., Benito, B. M., & Hartig, F.
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Journal Title
Ecography
Volume: 44
Pages: 199-205
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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