食物網の性質におけるスケール不変性
G Sugihara1, K Schoenly, A Trombla
1Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093.
まとめ
データの解像度が低下しても,フードウェブの特性は一貫しています. この発見は,さまざまなデータスケールにおけるフードウェブ分析の信頼性を裏付けています.
科学分野:
- エコロジー エコロジー エコロジー
- 理論的なエコロジー
- フード・ウェブ・ダイナミクス
背景:
- 食物網理論は,詳細な生態学的データに依存しています.
- 批評家は,フードウェブの特性がデータ解像度 (トロフィック・アグレゲーション) に敏感であるかどうか疑問に思う.
- 頑丈性を理解することは,フードウェブモデルの検証の鍵です.
研究 の 目的:
- 5つの一般的な食物網の特性の強さを評価する.
- データの解像度が変化しても,これらの性質が一定であるかどうかを判断する.
- 食物網メトリックのトロフィック集積に対する感受性に関する懸念に対処するために.
主な方法:
- 60の無脊椎動物が支配するコミュニティの食物網の分析.
- データ解像度を減らすため,トロフィックグループを体系的に集約する.
- 食物連鎖の5つの主要な統計の検討: 鎖の長さ,捕食者/獲物の比率,上位/中位/下位の種の割合,そして硬い回路.
主要な成果:
- 5つの一般的な食品ネットワークの特性は,データ解像度が最大50%減少したときに驚くほど一貫性を示しました.
- 1つを除くすべての検査された性質は,集約された食物網で確認されました.
- フードウェブの統計はスケーリングされ,幅広いデータ解像度で不変のままであることが判明しました.
結論:
- 観察された食物網の性質は,2の因数でトロフィック集積に強固である.
- この発見は,データ解像度の敏感性に関するフードウェブ理論の一般的な批判に対応しています.
- 検証されたフードウェブ統計は,異なるデータ解像度を持つ生態系を比較するために信頼できる方法で使用できます.
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