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Cyclone Gabrielle hit New Zealand in February 2023 and triggered a large number of shallow landslides. This resulted in extensive land damage and substantial sediment deposition in downstream environments. In response to this event, we assessed the effectiveness of existing trees in reducing the number of shallow landslides and the landslide-derived sediment load delivered to streams on 50 farms in the Hawke’s Bay region (North Island, NZ). Farm selection focused on areas that experienced high rainfall during Cyclone Gabrielle but showed varied levels of landsliding. Our analysis combined a high-resolution shallow landslide susceptibility model that incorporated the influence of individual trees with a morphometric landslide-to-stream connectivity model. By coupling these models, we estimated sediment delivery to streams for two scenarios: 1) a treeless scenario, where individual trees within pastoral areas on the selected farms were removed, and 2) an existing trees scenario, comprising the contemporary tree cover derived from the 2020/21 regional LiDAR survey. Comparing model predictions for these scenarios provided a basis for assessing the influence of existing trees on farm-scale landslide erosion and sediment delivery to streams. Cyclone Gabrielle triggered 20,392 shallow landslides on the selected farms based on mapped data. Our analysis indicated that tree cover in pastoral areas prevented an estimated 1,865 additional landslides. This resulted in a median 7% reduction in landslide count and a 10% decrease in sediment yield to streams across the 50 farms. Where trees were mostly present on susceptible slopes near streams, reductions in farm-scale sediment delivery to streams of up to 24% were estimated. The main driver in reducing sediment delivery was tree density in landslide-prone pastoral areas where runout was likely to connect to streams. Further reductions in sediment delivery could be achieved by planting more spaced trees on susceptible pasture areas that are likely to produce connecting landslides.