The extent of windfarm infrastructures on recognised European blanket bogs.

Peatland environments are the Earth's largest terrestrial carbon store and have the potential to act as carbon sinks. However, the development of windfarms on peatlands is affecting their morphology, hydrology, ground-level climate conditions, carbon functions and vegetation, and long-term consequences still need to be assessed. Blanket bogs are a rare type of ombrotrophic peatland that are typical of oceanic areas with high precipitation and low temperatures. Their distribution has been mapped across Europe, where they are mainly located on hill summits where wind energy potential is higher, making them attractive sites for windfarm developments. The promotion of renewable energy is currently a priority given the environmental and economic drive to increase low-carbon energy production. Establishing windfarms on peatland in pursuit of greener energy, therefore, risks compromising and undermining the green-energy transition. Despite this, the extent of windfarm infrastructures on blanket bogs have not yet been reported at the European scale. This research reports the extent of windfarm infrastructures on recognised blanket bogs, with a geographical focus on Europe, where blanket bogs have been mapped systematically. Under the EU Habitats Directive (92/43/EEC), there are 36 European regions NUTS level 2 with recognised blanket bogs. Of these, 12 have windfarm developments, including 644 wind turbines, 253.4 km of vehicular access tracks and an affected area of 207.6 ha, mainly in Ireland and Scotland where the extent of blanket bogs is also higher. However, despite Spain having under 0.2% of Europe's recognised blanket bogs area, this was the most affected country. In Scotland, a comparison of the recognised blanket bogs under the Habitats Directive (92/43/EEC) with blanket bogs recorded in national inventories indicates that the extent of windfarm developments was higher, with 1,063 wind turbines and 634.5 km of vehicular access tracks. Our results highlight the extent of windfarm developments on blanket bog habitat, both in areas where peatlands are broadly distributed across the landscape, and also in areas where this recognised habitat is particularly rare. There is a pressing need to assess the long-term impacts of windfarms on peatlands to ensure that efforts to meet energy targets result only in carbon sequestration, and do not jeopardise ecosystem services. Blanket bogs represent a particularly vulnerable habitat, the study of which should be prioritised updating national and international inventories to protect and restore this habitat.

Assessing the link between farming systems and biodiversity in agricultural landscapes: Insights from Galicia (Spain).

Agriculture is a major driver of change with manifold impacts on biodiversity and ecosystem services. As social-ecological systems, agricultural landscapes result from the intertwined interaction between farmers and nature, and contribute to several ecosystem services key to human well-being. The social-ecological outcomes of farmlands ultimately reflect the management practices of the dominant farming systems (FS) at the landscape level. However, data-driven research linking agricultural management and biodiversity is still scarce, a knowledge gap limiting our understanding on the impacts of different farming systems on biodiversity at the landscape level. This research contributes to fill this knowledge gap, by being among the few explicitly exploring the relationship between FS and patterns of biodiversity at the landscape level, using as illustrative case the region of Galicia, northwest Spain. Using data from agricultural policies paying agencies, and protected species and habitats data, the following research questions were pursued: (1) Can farm-level data be used to map and characterize different FS at the landscape-level? and, (2) Is the occurrence of specific FS linked with higher levels of biodiversity? Results allowed the identification and characterization of seven different FS distributed across Galicia, which dominance allowed to identify seven landscape types. Moreover, besides depicting the dominance of cattle-based farming systems in Galicia, results showed a gradient of management from the most intensive located in coastal lowlands (west) towards less intensive mountain areas (east). Such gradient of decreasing management intensity matched a gradient of increasing nature value of farmlands, reflected as higher habitat diversity and richness for some of the targeted taxonomic groups. To our knowledge, this research is among the few explicitly addressing the relationship between FS and biodiversity at the landscape level. By highlighting potential links (positive or negative) between specific landscape types and habitats and/or species richness across targeted taxonomic groups, these results constitute a preliminary assessment of the agricultural practices promoting species and habitat richness. Further scrutinizing this assessment can support the identification of farm-level indicators that can be then translated into the design of policies (biodiversity or agriculture-related) fostering biodiversity at several scales of decision making.

New multiproxy data obtained from the sedimentary fill of the Ría de Ferrol, NW Iberia.

Several gravity cores and vibro-cores were recovered from selected sites in the inner sector of Ría de Ferrol, NW Iberia (Muñoz Sobrino et al., 2021) [1]. These sediment cores were obtained during the surveys ECOMER-2014 and ECOMER-2015, developed from 2014 to 2015 on-board the R/V Mytilus (Consejo Superior de Investigación Científica) and the Amarradores Mil (Amarradores del Puerto y Ría de Ferrol, S.L.), respectively. Sedimentary and other multiproxy data presented here belong to four selected sediment cores located in the innermost part of the study area. Two were recovered using a gravity corer and another two using a vibro-corer. The depth of the cores and samples obtained is referred to the NMMA (the mean sea level in Alicante), which is the Spanish orthometric datum. One half of each core was subjected to non-destructive analysis using an ITRAX core scanner providing X-ray fluorescence (XRF) elemental data. Particle size distribution was characterised by laser diffraction. For radiocarbon dating, well-preserved articulated valves, small remains of wood and very organic bulk sediment from one location free of biogenic gas were selected. Palynological analyses were performed on selected sections of the sediment. All samples were spiked with Lycopodium spores for absolute palynomorph estimation and analysed using 400x and 600x magnifications. The ratio of dinoflagellate cyst concentrations to pollen, fern spore and dinoflagellate cyst concentrations (D/P ratio, ranging between 0 and 1) was calculated for each sample to show the temporal variation. Combined seismic, lithological, elemental, chronological and palynological data enable reconstructing the environmental changes that occurred during the local marine transgression. Besides, the combination of evidence identified may also be applied to other areas or periods in order to perform local reconstructions of changing coastal ecosystems. This type of high-resolution spatial-temporal reconstructions of past changes in estuarine environments may be a valuable tool for modelling, predicting and managing the changes and threats linked to the global warming and sea-level rise associated.

Automatic habitat classification methods based on satellite images: a practical assessment in the NW Iberia coastal mountains.

Although remote sensing is increasingly in use for habitat mapping, traditional image classification methods tend to suffer shortcomings due to non-normality of spectral signatures, as well as overlapping and heterogeneity in radiometric responses of natural and semi natural vegetation. Methods using non-parametric classifiers and object-oriented analysis have been suggested as possible solutions for overcoming these limitations. In this paper, we aimed at evaluating the performance of some of these techniques for the European Natura 2000 network of protected areas habitats mapping. For this purpose, we tested different methods of supervised image classification in the Northern Mountains of Galicia, Spain, an area included in the Natura 2000 network, which is characterized by a highly heterogeneous landscape. Methods involved the use of maximum likelihood and nearest neighbour decision rules in per-pixel and per-object classification analyses on Landsat TM imagery. Per-object classifications were completed using the segment mean and segment means plus standard deviation feature spaces. The results showed the existence of significant differences in the accuracies for the different methodologies, their strengths and weaknesses and identified the most adequate approach for habitat mapping. Analyses pointed out that significant improvements in accuracy were achieved only under certain combinations of per-object analysis, non-parametric classifiers and high dimensionality feature space.