WEKO3
アイテム
Effectiveness and implications of spatial background restrictions on model performance and predictions: a special reference for Rattus species
http://hdl.handle.net/10126/0002000550
http://hdl.handle.net/10126/00020005508126baef-d637-42b0-8c75-001c779de252
| 名前 / ファイル | ライセンス | アクション |
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LEE_21-495-509.pdf (1.3 MB)
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LEE_21-495-509_Figure.pdf (10.1 MB)
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LEE_21-495-509_Tables.pdf (169 KB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2026-03-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Effectiveness and implications of spatial background restrictions on model performance and predictions: a special reference for Rattus species | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | background selection | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | sampling bias | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | road | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | buffer size | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | rodents | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | random | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | biased | |||||||||
| 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 | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 著者 |
Diane, Shiela C. Castillo
× Diane, Shiela C. Castillo
× Motoki, Higa
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Controlling background data selection in presence-only models is crucial for addressing sampling biases and enhancing model performance. While numerous studies have evaluated the impact of various background data selection techniques across different taxa, research remains limited on how spatially restricted background areas and employing random and biased distribution methods, influence model performance for Rattus species predictions. These species often present challenging collection conditions and low trap success rates, potentially leading to spatial biases in the occurrence records that may affect the accuracy of model predictions. Thus, this study examined methods to assess model accuracy variability for Rattus species by applying spatial background restrictions within the study area. These restrictions were defined by four main criteria: (1) areas within islands with documented species occurrences, (2) areas within the species' extent of occurrence according to IUCN range maps, (3) defined road distance, and (4) varying buffer areas around recorded species occurrences. To further assess the effects of spatial background restrictions on model performance, we used two methods to distribute the background sampling points: random and biased (bias file) method. Among the spatial background restrictions employed, specifying a defined road distance significantly improved model performance, while overly narrow or restricted buffer sizes decreased performance. Additionally, the choice of distribution method for background sampling points, whether random or biased, significantly influences model performance. This study found that random distribution achieved higher model performance for the Rattus species examined compared to the biased method. However, the selection of the most appropriate method depends on the specific modeling objectives and the representation of environmental data across the study area. While random distribution provided better results in this context, both methods have distinct strengths and their suitability may vary depending on different scenarios and species characteristics. This highlights the need for tailored approach in choosing the distribution method to ensure accurate and effective species distribution modeling. This effort demonstrated that despite challenges in collecting Rattus species occurrence data in terms of quality and quantity, reliable model predictions are still achievable with careful adjustments to the background data selection. | |||||||||
| 言語 | en | |||||||||
| 書誌情報 |
en : Landscape and Ecological Engineering 巻 21, 号 3, p. 495-509, 発行日 2025-03-25 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1860-188X | |||||||||
| DOI | ||||||||||
| 関連タイプ | isVersionOf | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1007/s11355-025-00653-w | |||||||||
| 権利 | ||||||||||
| 権利情報 | The version of record of this article, first published in Landscape and Ecological Engineering, is available online at Publisher’s website: http://dx.doi.org/10.1007/s11355-025-00653-w | |||||||||
| 言語 | en | |||||||||
| 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 | |||||||||
| 出版者 | ||||||||||
| 出版者 | Springer Nature | |||||||||
| 言語 | en | |||||||||
