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Large grazers (megaherbivores) have a profound impact on ecosystem functioning. However, how ecosystem multifunctionality is affected by changes in megaherbivore populations remains poorly understood. Understanding the total impact on ecosystem multifunctionality requires an integrative ecosystem approach, which is especially challenging to obtain in marine systems. We assessed the effects of experimentally simulated grazing intensity scenarios on ecosystem functions and multifunctionality in a tropical Caribbean seagrass ecosystem. As a model, we selected a key marine megaherbivore, the green turtle, whose ecological role is rapidly unfolding in numerous foraging areas where populations are recovering through conservation after centuries of decline, with an increase in recorded overgrazing episodes. To quantify the effects, we employed a novel integrated index of seagrass ecosystem multifunctionality based upon multiple, well‐recognized measures of seagrass ecosystem functions that reflect ecosystem services. Experiments revealed that intermediate turtle grazing resulted in the highest rates of nutrient cycling and carbon storage, while sediment stabilization, decomposition rates, epifauna richness, and fish biomass are highest in the absence of turtle grazing. In contrast, intense grazing resulted in disproportionally large effects on ecosystem functions and a collapse of multifunctionality. These results imply that (i) the return of a megaherbivore can exert strong effects on coastal ecosystem functions and multifunctionality, (ii) conservation efforts that are skewed toward megaherbivores, but ignore their key drivers like predators or habitat, will likely result in overgrazing‐induced loss of multifunctionality, and (iii) the multifunctionality index shows great potential as a quantitative tool to assess ecosystem performance. Considerable and rapid alterations in megaherbivore abundance (both through extinction and conservation) cause an imbalance in ecosystem functioning and substantially alter or even compromise ecosystem services that help to negate global change effects. An integrative ecosystem approach in environmental management is urgently required to protect and enhance ecosystem multifunctionality. [ABSTRACT FROM AUTHOR]

Ecological pattern theory has highlighted spatial vegetation patterns that can be used as indicators of ecosystem resilience. Combining this spatial pattern theory with aerial imagery from drones and automated image processing with deep learning methods, we show how monitoring of natural ecosystems can be enhanced through quantifying vegetation spatial patterns. We demonstrate this approach in a tropical seagrass ecosystem with a high abundance of turtles that generate vegetation patches when grazing. Past field observations suggest that patch size and density reflect the seagrass meadow resilience, but understanding the natural variation in vegetation patchiness is crucial. Employing the deep learning methods of semantic segmentation and object detection, we quantify vegetation patchiness metrics and turtle distribution across 12 ha of seagrass meadow in the years 2012 and 2022. The resulting output facilitates spatial and temporal comparisons, revealing areas of low resilience. In 2012, turtle grazing across the entire site yielded vegetation patch sizes averaging 2 ± 0.2 m2 (95% confidence interval). Reduced patch sizes of 0.24 ± 0.05 m2 and 0.67 ± 0.6 m2 at the reef edge and beach slope respectively, in conjunction with a reduced patch density, indicated lower resilience at the seagrass meadow edges. Analysis of the 2022 dataset indicates a general decrease in patch size over time, suggesting declining resilience. A retraining experiment of the semantic segmentation model was conducted where the initial model was retrained on the 2022 dataset and demonstrated the adaptability of the deep learning methods. Despite using different equipment, the model achieved high accuracy with only 5–10 additional training images. By providing the tools to conduct these analyses, we aim to stimulate the uptake of deep learning for enhancing the data obtained from aerial imagery to improve the monitoring and conservation of natural ecosystems. • Seagrass patterns and turtle distribution efficiently detected with deep learning. • Seagrass patchiness initiated by grazing and debris and exacerbated by waves. • Lower ecosystem resilience at meadow edges, and resilience declined over a decade. • Deep learning models can be efficiently retrained with new aerial imagery. • Provide a quantitative method for monitoring ecosystems and inferring resilience. [ABSTRACT FROM AUTHOR]

Global oceanic pH is lowering, which is causing great concern for the natural functioning of marine ecosystems. Current pH predictions are based on open ocean models; however, coastal zones are dynamic systems with seawater pH fluctuating temporally and spatially. To understand how coastal ecosystems will respond in the future, we first need to quantify the extent that local processes influence the pH of coastal zones. With this study, we show that over a single diurnal cycle, the total pH can fluctuate up to 0.2 units in a shallow seagrass‐dominated bay, driven by the photosynthesis and respiration of the vegetation. However, these biologically controlled pH fluctuations vary significantly over small distances. Monitoring conducted at neighboring sites with contrasting hydrodynamic regimes highlights how water motion controls the extent that the local pH is altered by the metabolism of vegetation. The interactive effects of hydrodynamics and vegetation were further investigated with an in situ experiment, where the hydrodynamics were constrained and thus the local water residence time was increased, displaying the counteractive effect of hydrodynamics on the pH change caused by vegetation. With this research, we provide detailed in situ evidence of the spatial variation of pH within marine ecosystems, highlighting the need to include hydrodynamic conditions when assessing the pH‐effects of vegetation, and identifying potential high‐pH refuges in a future low pH ocean. [ABSTRACT FROM AUTHOR]