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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.

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.

Dolphins play a key role in marine food webs as predators of mid-trophic level consumers. Because of their mobility and relatively long life span, they can be used as indicators of large scale changes in the ecosystem. In this study, we calculated the trophic position (TP) of five dolphin species from the Canary, Madeira and Azores Islands using bulk and compound-specific stable isotope ratios from muscle tissue to assess trophic adaptations to recent changes in the availability of feeding resources. Dolphin TP values were then compared with those of seven other species of cetaceans from this region. Analysis of stable nitrogen isotopes in amino acids of the common dolphin indicated non-significant effects of changes in the basal resources of the food web and thus supported the use of bulk samples for TP estimations. Dolphins occupied an intermediate TP (mean 3.91 to 4.20) between fin (3.25) and sperm whales (4.95). Species-specific TP were equivalent among islands. However, TP increased for the common dolphin and decreased for the striped dolphin (the latter also becoming more oceanic) between 2000 and 2018 in the Canary Islands. These results suggest different impacts of recent changes in the oceanography and in the pelagic food web of the Macaronesian region on the trophic ecology of dolphin species.

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.

Background: In the last 20 years, Cetacean Morbillivirus (CeMV) has been responsible for many die-offs in marine mammals worldwide, as clearly exemplified by the three dolphin morbillivirus (DMV) epizootics of 1990–1992, 2006–2008 and 2011 that affected Mediterranean striped dolphins (Stenella coeruleoalba). Systemic infection caused by DMV in the Mediterranean has been reported only during these outbreaks. Results: We report the infection of five striped dolphins (Stenella coeruleoalba) stranded on the Spanish Mediterranean coast of Valencia after the last DMV outbreak that ended in 2011. Animal 1 stranded in late 2011 and Animal 2 in 2012. Systemic infection affecting all tissues was found based on histopathology and positive immunohistochemical and polymerase chain reaction positive results. Animal 3 stranded in 2014; molecular and immunohistochemical detection was positive only in the central nervous system. Animals 4 and 5 stranded in 2015, and DMV antigen was found in several tissues. Partial sequences of the DMV phosphoprotein (P), nucleoprotein (N), and hemagglutinin (H) genes were identical for Animals 2, 3, 4, and 5, and were remarkably different from those in Animal 1. The P sequence from Animal 1 was identical to that of the DMV strain that caused the epizootic of 2011 in the Spanish Mediterranean. The corresponding sequence from Animals 2–5 was identical to that from a striped dolphin stranded in 2011 on the Canary Islands and to six dolphins stranded in northeastern Atlantic of the Iberian Peninsula. Conclusions: These results suggest the existence of an endemic infection cycle among striped dolphins in the Mediterranean that may lead to occasional systemic disease presentations outside epizootic periods. This cycle involves multiple pathogenic viral strains, one of which may have originated in the Atlantic Ocean.

Revista oficial de la Asociación Española de Teledetección, [EN] We have developed a methodology for detection of observable phenomena at pixel level over time series of daily satellite images, based on using a Bayesian classifier. This methodology has been applied successfully to detect burned areas in the North American boreal forests using the LTDR dataset. The LTDR dataset represents the longest time series of global daily satellite images with 0.05° (~5 km) of spatial resolution. The proposed methodology has several stages: 1) pre-processing daily images to obtain composite images of n days; 2) building of space of statistical variables or attributes to consider; 3) designing an algorithm, by selecting and filtering the training cases; 4) obtaining probability maps related to the considered thematic classes; 5) post-processing to improve the results obtained by applying multiple techniques (filters, ranges, spatial coherence, etc.). The generated results are analyzed using accuracy metrics derived from the error matrix (commission and omission errors, percentage of estimation) and using scattering plots against reference data (correlation coefficient and slope of the regression line). The quality of the results obtained improves, in terms of spatial and timing accuracy, to other burned area products that use images of higher spatial resolution (500 m and 1 km), but they are only available after year 2000 as MCD45A1 and BA GEOLAND-2: the total burned area estimation for the study region for the years 2001-2011 was 28.56 millions of ha according to reference data and 12.41, 138.43 and 19.41 millions of ha for the MCD45A1, BA GEOLAND-2 and BA-LTDR burned area products, respectively., [ES] Se ha desarrollado una metodología para la detección de cambios de la cubierta vegetal, a nivel de píxel, en se-ries temporales de imágenes de satélites diarias mediante la utilización de un clasificador bayesiano. Dicha metodología ha sido aplicada satisfactoriamente a la detección de áreas quemadas en los bosques boreales de Norte América en el período 1981 a 2011, utilizando el conjunto de datos Long Term Data Record (LTDR) que constituye la serie temporal más larga de imágenes diarias de satélite a escala global, con una resolución espacial de 0,05° (~5 km). La metodología pro-puesta consta de varias etapas: 1) pre-procesamiento de las imágenes diarias y obtención de imágenes compuestas de ndías; 2) construcción del espacio de las variables o atributos a considerar; 3) diseño del algoritmo, mediante la selección y refinamiento de los casos de entrenamiento; 4) obtención de los mapas de probabilidad relacionados con las clases temáticas a considerar; 5) post-procesamiento para mejorar los resultados obtenidos mediante la aplicación de múltiples técnicas (filtros, rangos, coherencia espacial, etc.). Los resultados finales obtenidos son comparados con los datos de referencia mediante métricas de exactitud derivadas de la matriz de error (errores de comisión y omisión, porcentaje de estimación) y de gráficos de dispersión (coeficiente de correlación y pendientes de la recta de regresión, etc.). La calidad de los resultados obtenidos al aplicar esta metodología a las imágenes LTDR para la detección de área quemada en la región boreal de Norte América mejora en términos de exactitud espacio-temporal a la de los otros dos productos de área quemada globales comparados (MCD45A1, BA GEOLAND-2) a pesar de que utilizan imágenes de mayor resolución espa-cial (y sólo disponibles a partir del año 2000): la estimación de área quemada total sobre la región de estudio en el periodo 2001-2011 fue de 28,56 millones de hectáreas según los datos de referencia y de 12,41, 138,43