Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions

Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human‐mediated changes to land use and water use. We created a transferable conceptual framework to enable multiscale assessm...

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Veröffentlicht in:	Conservation biology 2024-06, Vol.38 (3), p.e14203-n/a
Hauptverfasser:	Delaney, John T., Larson, Danelle M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Alto Río Mississippi Aquatic environment Aquatic plants Conservation of Natural Resources - methods ecological thresholds Ecosystem ecosystem state Ecosystems estado del ecosistema habitat suitability Habitats idoneidad de hábitat Land use Learning algorithms Machine Learning macrophytes macrófitas Mississippi River occurrence probability plantas acuáticas prediction Predictions probabilidad de presencia Restoration Rivers Solid suspensions submerged aquatic vegetation Substrates Suspended particulate matter Suspended solids umbral ecológico Upper Mississippi River vegetación acuática sumergida Vegetation Water depth Water use wildlife management 出现率大型植物密西西比河上游水生植物沉水植物生境适宜性生态系统状态生态阈值
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