| Project/Area Number |
19H03298
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 45040:Ecology and environment-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2021: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2020: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2019: ¥7,670,000 (Direct Cost: ¥5,900,000、Indirect Cost: ¥1,770,000)
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| Keywords | 個体群動態 / 生態学 / 個体群 / 進化学 / 進化 / 生態生物学 / デモグラフィー / 個体群生態学 |
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| Outline of Research at the Start |
Population ecologists analyze the dynamics of biological populations assuming that the condition of an individual in some year is affected only by its condition in the previous year. However, we now know that this assumption leads to incorrect conclusions about population dynamics. This project will answer the question: Does individual history matter to population dynamics? It will do so through empirical analysis of case studies, meta-analysis, and a theoretical framework for analysis.
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| Outline of Final Research Achievements |
We hypothesized that the histories of individuals would influence population dynamics strongly, and that this influence is lost in currently used models. We found that individual history does, indeed, have such strong influences, and our results cast doubt on the utility of currently used methods to project population dynamics and predict population size. We developed a software package that allows researchers to project populations forward in time with or without individual history incorporated, and have also developed a large amount of technical material allowing researchers and students to learn these methods for their own research. We have also run workshops on these methods.
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| Academic Significance and Societal Importance of the Research Achievements |
今回の研究は、現在利用されている個体群予測のモデルや手法が、個体履歴の影響を正確に考慮していないことを明確に示している。この結果は、個体群生態学者に、標準的なマトリックスモデル手法から、個体履歴を考慮することで生じる長期的なトレードオフや関連現象を組み込んだ大規模マトリックス手法への移行を促すものである。このことがもたらす潜在的な影響の1つは、人間の人口規模を予測するために用いられる手法の見直しである。現在の人口予測はばらつきが大きすぎて使い物にならないが、我々のアプローチは、予測を意味のあるものに絞り込むのに役立つかもしれない。
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