We are facing a global loss of biodiversity due to climate change. This will lead to unpredictable changes in ecosystems, affecting the goods and services they provide and facilitating the introduction of non-indigenous marine species. This represents one of the major threats to marine biodiversity and therefore, there is a strong need to assess, map and monitor these alien species. The appearance of non-indigenous species is especially dangerous in fragile ecosystems, and it is of great importance to better understand the invasion mechanisms of these invasive species. This is the case for invasive alga Asparagopsis armata, present in the Azores Archipelago. In this study, we propose a methodology to define the realized ecological niche of this invasive alga, alongside the native Asparagopsis taxiformis, to understand better its distribution and potential impact on native communities and ecosystem services. These objectives comply with the EU Biodiversity strategy for 2020 goals and the need to map and assess ecosystems and their services. The lack of reliable high-resolution data makes this a challenging task. Within this scope, we propose a combination of Remote Sensing, Unmanned Aerial Vehicle based imagery together with in-situ field data to build ecological niche modelling approaches as a cost-effective methodology to identify and characterize vulnerable marine ecosystems. Our results show that this combination can help achieve monitoring, leading to a better understanding of ecological niches and the consequences of non-indigenous species invasion in fragile ecosystems, like small islands, when faced with limited data.

Despite their ecological and socio-economic importance, seagrasses are often overlooked in comparison with terrestrial ecosystems. In the Canarian archipelago (Spain), Cymodocea nodosa is the best-established species, sustaining the most important marine ecosystem and providing ecosystem services (ES) of great relevance. Nevertheless, we lack accurate and standardized information regarding the distribution of this species and its ES supply. As a first step, the use of species distribution models is proposed. Various machine learning algorithms and ensemble model techniques were considered along with freely available remote sensing data to assess Cymodocea nodosa’s potential distribution. In a second step, we used InVEST software to estimate the ES provision by this phanerogam on a regional scale, providing spatially explicit monetary assessments and a habitat degradation characterization due to human impacts. The distribution models presented great predictive capabilities and statistical significance, while the ES estimations were in concordance with previous studies. The proposed methodology is presented as a useful tool for environmental management of important communities sensitive to human activities, such as C. nodosa meadows.

Cymodocea nodosa seagrass meadows provide several socio-economically ecosystem services, including nurseries for numerous species of commercial interest. These seagrasses are experiencing a worldwide decline, with global loss rates approaching 5% per year, mainly related to coastal human activities. Cymodocea nodosa, the predominant seagrass in the Canary Archipelago (Spain), is also exposed to these threats, which could lead to habitat loss or even local disappearance. In this case study, we estimated the potential economic value of Cymodocea nodosa seagrass meadows for local fisheries at an archipelago scale. Habitat suitability maps were constructed using MAXENT 3.4.1, a software for modelling species distributions by applying a maximum entropy machine-learning method, from a set of environmental variables and presence and background records extracted from historical cartographies. This model allows characterising and assessing the C. nodosa habitat suitability, overcoming the implicit complexity derived from seasonal changes in this species highly dynamic meadows and using it as a first step for the mapping and assessment of ecosystem services. In a second step, value transfer methodologies were used, along with published economic valuations of commercially-interesting fish species related to C. nodosa meadows. We estimate that the potential monetary value of these species can add up to more than 3 million euros per year for the entire Archipelago. The simplicity of the proposed methodology facilitates its repeatability in other similar regions, using freely available data and hence, being suitable for data-scarce scenarios.

Mapping and Assessment of Ecosystems and their Services (MAES) has been widely applied on the European Union (EU) mainland, whereas the EU Overseas entities still bear potential for implementation. This paper presents novel applications of the MAES procedure in the EU Outermost Regions and Overseas Countries and Territories ("EU Overseas"). Eight case studies from different geographical areas were analysed through a comparative assessment by applying an established framework following key steps in the MAES process, in order to stipulate lessons learned and recommendations for MAES in the EU Overseas. These key steps include the identification of policy questions, stakeholder networks and involvement, application of MAES methods, dissemination and communication and implementation. The case studies were conducted and analysed under the umbrella of the EU MOVE pilot project, including the Azores, the Canary Islands, Saint Martin, French Guiana, Martinique, Reunion Island and the Falkland Islands. Each case study represented different governance, policy and decision-making frameworks towards biodiversity and environmental protection. Case studies predominantly addressed the policy domains of Nature and Biodiversity Conservation and Marine and Maritime Policy. Ecosystem Services (ES) were assessed across a wide range of themes, biomes and scales, focusing on terrestrial, coastal and marine ecosystems. Results show that the implementation of the case studies was accompanied by extensive communication and dissemination activities. First success stories were visible, where the MAES exercise led to meaningful uptake of the ES concept to policies and decision-making. Yet, there is still work to be done - major bottlenecks were identified related to the MAES implementation centring around financial resources, training and technical expertise. Addressing these aspects can contribute to an enhanced implementation of MAES in the EU Overseas in the future.

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