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Macroalgal forests play a key role in shallow temperate rocky reefs worldwide, supporting communities with high productivity and providing several ecosystem services. Sea urchin grazing has been increasingly influencing spatial and temporal variation in algae distributions and it has become the main cause for the loss of these habitats in many coastal areas, causing a phase shift from macroalgae habitats to barren grounds. The low productive barrens often establish as alternative stable states and only a major reduction in sea urchin density can trigger the recovery of macroalgal forests. The present study aims to assess if the 2018 disease outbreak, responsible for a strong reduction in the sea urchin Diadema africanum densities in Madeira Island, was able to trigger a reverse shift from barren grounds into macroalgae-dominated state. By assessing the diversity and abundance of benthic sessile organisms, macroinvertebrates and fishes before, during and after that particular mass mortality event, we evaluate changes in benthic assemblages and relate them to variations in grazer and herbivore densities. Our results revealed a clear shift from barren state to a macroalgae habitat, with barrens characterized by bare substrate, sessile invertebrate and Crustose Coralline Algae (CCA) disappearing after the mortality event. Overall variations in benthic assemblages was best explained by four taxa (among grazers and herbivores species). However, it was the 2018 demise of D. africanum and its density reduction that most contributed to the reverse shift from a long stable barren state to a richer benthic assemblage with higher abundance of macroalgae. Despite this recent increase in macroalgae dominated habitats, their stability and persistence in Madeira Island is fragile, since it was triggered by an unpredictable disease outbreak and depends on how D. africanum populations will recover. With no control mechanisms, local urchin populations can easily reach the tipping point needed to promote a new shift into barren states. New conservation measures and active restoration are likely required to maintain and promote the local stability of macroalgal forests.

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Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Mapping the distribution and evaluating the impacts of marine non-indigenous species (NIS) are two fundamental tasks for management purposes, yet they are often time consuming and expensive. This case study focuses on the NIS gilthead seabream Sparus aurata escaped from offshore farms in Madeira Island in order to test an innovative, cost-efficient combined approach to risk assessment and georeferenced dispersal data collection. Species invasiveness was screened using the Aquatic Species Invasiveness Screening Kit (AS-ISK), and revealed a high invasion risk. Occurrences of S. aurata were assessed involving citizens in GIS participatory mapping and data from recreational fishing contests. A probability map showed that S. aurata is well dispersed around Madeira Island. This assessment proved to be a cost-efficient early warning method for detecting NIS dispersal, highlighting the urgent need for additional surveys that should search for sexually mature individuals and assess the direct and indirect impacts in the native ecosystem.