Machine learning reveals hierarchical spatial patterns in salt marsh mosquito ditching along U.S. Atlantic Coast

Abstract

Abstract The loss of salt marshes and their ecosystem services following anthropogenic disturbances necessitates restoration built on a scale-dependent understanding of how the prevalence and intensity of these disturbances are linked to ecosystem functioning. A conspicuous legacy modification of marshes, which lacks a standardized and scale-able assessment, is mosquito ditching. Consequently, U.S. Atlantic coast resource managers must devote limited resources to quantifying local-scale ditching or make restoration decisions based on a literature of subjective ditching assessments (low vs. high) from a subset of locations with contradictory impacts to ecosystem functions. Here, we combined freely available satellite imagery with machine learning to generate a multi-scale database of ditching prevalence and intensity in 634 marshes from Maine through Florida. The algorithm consistently detected ditches despite the heterogeneous appearance of this disturbance and marshes across regions, seasons, and tidal stages. In contrast to the oft-quoted historical ditching prevalence of 90%, the algorithm quantified a much lower current average of 38%, with the size of this discrepancy varying regionally from an average prevalence of 87% in the Gulf of Maine to 20% in the South Atlantic Bight. Ditching intensity showed further hierarchical spatial variation, but at the state and within-state levels, as opposed to regional level. Within regions, intensely ditched states (5% area removed) were opposed by mildly ditched states (1.9% area removed). With this standardized database of ditching prevalence and intensity, researchers and resource managers may now conduct scale-dependent assessments of ecosystem responses to ditching to inform restoration and management of this valuable habitat.