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Simple Machine Learning with Aerial Imagery Reveals Severe Loss of a Salt Marsh Foundation Species

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Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea-level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (> 94.59% accuracy for S. patens across all models for 2006 and 2019). Our methods allowed us to characterize large landscapes (two geospatially proximal areas, > 7000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1 m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem type.

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The raw data, manipulated data, and R scripts used in this study’s analyses are openly available in FigShare at


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We would like to thank Modeling and Simulation Engineer Kaitlyn E. Minsavage-Davis for contributions to the synthesis of our machine learning methodology.


T.M.R., C.D.M.-D., and V.S. were supported through graduate research fellowships at Georgetown University. V.S. was also supported by the National Science Foundation Graduate Research Fellowship under grant number 1937959.

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Conceptualization, T.M.R.; methodology, C.D.M.-D., V.S.; formal analyses, C.D.M.-D., V.S.; data curation, C.D.M.-D.; writing—original draft preparation, T.M.R., C.D.M.-D., V.S.; writing—review and editing, T.M.R., C.D.M.-D., G.M.W., V.S. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Charles D. Minsavage-Davis.

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The authors declare no competing interests.

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Communicated by Brian B. Barnes

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Rippel, T.M., Minsavage-Davis, C.D., Shirey, V. et al. Simple Machine Learning with Aerial Imagery Reveals Severe Loss of a Salt Marsh Foundation Species. Estuaries and Coasts 46, 1110–1122 (2023).

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