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Coastal ecosystems of the Mediterranean Sea are among the richest in non-indigenous species, mostly due to the establishment of species coming from the Red Sea through the Suez Canal. Two herbivorous rabbitfishes, Siganus rivulatus and Siganus luridus, are already invasive in the south-eastern part of the Mediterranean Sea where they cause ecological damage by overgrazing algae. The early detection and the counting of these non-indigenous species in the rest of the Mediterranean Sea is thus a major challenge for scientists and ecosystem managers. However, analysing images from divers or remote cameras is a demanding task. Here, a dataset of 31,285 images of Siganus spp. and of six common native fishes to the Mediterranean Sea was built from 40 underwater videos recorded at three reef habitats. A deep learning algorithm was then trained to identify Siganus spp. on images containing the eight Mediterranean species. Finally, the algorithm and a post-processing filtering were tested with an independent dataset of 2024 images. The model had a recall of 0.92 for the Siganus genus (i.e., two Siganus species combined). After a confidence-based post-processing, the recall increased to 0.98 with only 4 out of 272 images of Siganus spp. being misclassified. Accuracy reached a score of 0.61 meaning that experts would have to discard false positives. Images of five native species not present in the training dataset yielded similar false positive rates than species present in the training dataset. Overall, the automatic processing of images by the model and then the checking of putative Siganus images by experts required up to five times less effort than a full processing by experts. The algorithm can help to efficiently detect these two invasive fishes in underwater images to evaluate progress towards conservation objectives and accelerate citizen-based monitoring of coastal ecosystems.

In the marine environment, fish contribute to key ecological processes such as controlling food-webs through top-down impacts, especially on algae. To date, the assessment of fish grazing activity has mostly been performed using short-term (<1 h) censuses by divers or remote cameras which do not allow estimating the variability of grazing rate within and between days. However, understanding the temporal variation of fish activity and hence contribution of species to ecosystem functioning is of particular interest in the context of biological invasion. Here, using long-duration remote underwater cameras, we recorded fish abundance and grazing events over three consecutive days in October 2019 in a shallow Mediterranean ecosystem from northern Crete. This novel approach allowed us to assess temporal variation of abundance and grazing activity of the two native (Sarpa salpa and Sparisoma cretense) and the two non-indigenous fish species (Siganus rivulatus and Siganus luridus). Non-indigenous Siganus rivulatus was the most common species in the studied coastal habitat, followed by the two native species while the non-indigenous Siganus luridus was scarce. Overall, the non-indigenous S. rivulatus and the native S. salpa are responsible for more than 90% of the recorded grazing activity with similar bite rates between the two species. More than 70% of the grazing activity arose in grazing pulses in the afternoon, supporting the diel feeding hypothesis according to which feeding is greater in the afternoon when nutritive quality of macrophytes is the highest. In addition, some of the highest peaks in grazing activity were driven by a few individuals. Hence, surveys of only abundance could not provide accurate estimates of herbivory. Last, Siganus rivulatus presence did not significantly affect grazing activity of the native Sarpa salpa. Our results demonstrate that long-duration remote underwater videos are a useful tool to accurately assess the contribution of fishes to e

In the marine environment, fish contribute to key ecological processes such as controlling food-webs through top-down impacts, especially on algae. To date, the assessment of fish grazing activity has mostly been performed using short-term (<1 h) censuses by divers or remote cameras which do not allow estimating the variability of grazing rate within and between days. However, understanding the temporal variation of fish activity and hence contribution of species to ecosystem functioning is of particular interest in the context of biological invasion. Here, using long-duration remote underwater cameras, we recorded fish abundance and grazing events over three consecutive days in October 2019 in a shallow Mediterranean ecosystem from northern Crete. This novel approach allowed us to assess temporal variation of abundance and grazing activity of the two native (Sarpa salpa and Sparisoma cretense) and the two non-indigenous fish species (Siganus rivulatus and Siganus luridus). Non-indigenous Siganus rivulatus was the most common species in the studied coastal habitat, followed by the two native species while the non-indigenous Siganus luridus was scarce. Overall, the non-indigenous S. rivulatus and the native S. salpa are responsible for more than 90% of the recorded grazing activity with similar bite rates between the two species. More than 70% of the grazing activity arose in grazing pulses in the afternoon, supporting the diel feeding hypothesis according to which feeding is greater in the afternoon when nutritive quality of macrophytes is the highest. In addition, some of the highest peaks in grazing activity were driven by a few individuals. Hence, surveys of only abundance could not provide accurate estimates of herbivory. Last, Siganus rivulatus presence did not significantly affect grazing activity of the native Sarpa salpa. Our results demonstrate that long-duration remote underwater videos are a useful tool to accurately assess the contribution of fishes to e

unctional diversity (FD), the diversity of organism attributes that relates to their interactions with the abiotic and biotic environment, has been increasingly used for the last two decades in ecology, biogeography and conservation. Yet, FD has many facets and their estimations are not standardized nor embedded in a single tool. mFD (multifaceted functional diversity) is an R package that uses matrices of species assemblages and species trait values as building blocks to compute most FD indices. mFD is firstly based on two functions allowing the user to summarize trait and assemblage data. Then it calculates trait-based distances between species pairs, informs the user whether species have to be clustered into functional entities and finally computes multidimensional functional space. To let the user choose the most appropriate functional space for computing multidimensional functional diversity indices, two mFD functions allow assessing and illustrating the quality of each functional space. Next, mFD provides 6 core functions to calculate 16 existing FD indices based on trait-based distances, functional entities or species position in a functional space. The mFD package also provides graphical functions based on the ggplot library to illustrate FD values through customizable and high-resolution plots of species distribution among functional entities or in a multidimensional space. All functions include internal validation processes to check for errors in data formatting which return detailed error messages. To facilitate the use of mFD framework, we built an associated website hosting five tutorials illustrating the use of all the functions step by step.