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A geometric approach to scaling individual distributions to macroecological patterns
https://oist.repo.nii.ac.jp/records/782
https://oist.repo.nii.ac.jp/records/7829d1484f8-1e94-44d7-b01f-5bfe93ce9310
名前 / ファイル | ライセンス | アクション |
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Takashina_et_al_JTB_2019 (2.7 MB)
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Creative Commons
Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2019-10-17 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | A geometric approach to scaling individual distributions to macroecological patterns | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者(英) |
Takashina, Nao
× Takashina, Nao× Kusumoto, Buntarou× Kubota, Yasuhiro× Economo, Evan P. |
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書誌情報 |
en : Journal of Theoretical Biology 巻 461, p. 170-188, 発行日 2018-10-16 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Understanding macroecological patterns across scales is a central goal of ecology and a key need for conservation biology. Much research has focused on quantifying and understanding macroecological patterns such as the species-area relationship (SAR), the endemic-area relationship (EAR) and relative species abundance curve (RSA). Understanding how these aggregate patterns emerge from underlying spatial pattern at individual level, and how they relate to each other, has both basic and applied relevance. To address this challenge, we develop a novel spatially explicit geometric framework to understand multiple macroecological patterns, including the SAR, EAR, RSA, and their relationships. First, we provide a general theory that can be used to derive the asymptotic slopes of the SAR and EAR, and demonstrates the dependency of RSAs on the shape of the sampling region. Second, assuming specific shapes of the sampling region, species geographic ranges, and individual distribution patterns therein based on theory of stochastic point processes, we demonstrate various well-documented macroecological patterns can be recovered, including the tri-phasic SAR and various RSAs (e.g., Fisher's logseries and the Poisson lognormal distribution). We also demonstrate that a single equation unifies RSAs across scales, and provide a new prediction of the EAR. Finally, to demonstrate the applicability of the proposed model to ecological questions, we provide how beta diversity changes with spatial extent and its grain over multiple scales. Emergent macroecological patterns are often attributed to ecological and evolutionary mechanisms, but our geometric approach still can recover many previously observed patterns based on simple assumptions about species geographic ranges and the spatial distribution of individuals, emphasizing the importance of geometric considerations in macroecological studies. | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0022-5193 | |||||
PubMed番号 | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/30336157 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1016/j.jtbi.2018.10.030 | |||||
権利 | ||||||
権利情報 | © 2018 Elsevier Ltd. | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://www.sciencedirect.com/science/article/pii/S002251931830506X?via%3Dihub | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa |