We performed a systematic literature review to identify the main remote sensing data, methodological approaches, and performance metrics used to map restinga, a coastal ecosystem in Brazil. Restinga is distributed along the entire Brazilian coast, classified as part of the Atlantic Forest, and is a highly heterogeneous environment, with herbaceous, shrubby, and forest vegetation. We performed quantitative, co-word, and performance analyses to identify the main themes in this research field, classifying them as motor, niche, emerging or declining, and basic themes. A total of 31 studies from 2004 to 2026 met the search criteria, with most studies published from 2013 onward. Interestingly, most studies relied on relatively traditional approaches, with a large proportion of studies using manual classification or algorithms such as maximum-likelihood supervised for restinga mapping, and images from the Landsat satellite series were most used. Co-word analyses identified four main clusters, with remote sensing and restinga as main key terms and in a central position in the network. The restinga cluster was connected to all other clusters, confirming that we achieved the objective proposed in this study. However, most key terms were classified as niche or basic themes, reflecting the specialized nature of this research field. Protected areas and environmental vulnerability were also key terms in the studies, evidencing the researchers’ interest in assessing restinga conservation and compliance with Brazilian environmental law regarding its protection. In addition, topics of global relevance such as conservation and ecosystem services were connected to the mangrove cluster. Our findings highlight that despite high map accuracy reported in the studies, most of the data used tended to overlook specific restinga characteristics, which can contribute to the uncertainties in data obtained from remote sensing images. Furthermore, there was a lack of studies using high spatial resolution data, advanced techniques, and machine and deep learning algorithms for restinga mapping, which can limit support for efficient environmental policies related to ecosystem management, environmental law compliance, restoration, and ecosystem services provision in the Brazilian coastal zone.
Seagrass meadows provide key ecosystem services, including shelter, nursery grounds and food resources for numerous fishes. Meadow boundaries create edge-interior gradients that can reorganize community structure, yet most assessments emphasize species richness or density rather than complementary biodiversity dimensions. Here, we quantified functional diversity and taxonomic-distance proxies of phylogenetic structure across edge and interior positions in three Cymodocea nodosa meadows in Gran Canaria Island (NE Atlantic), while also testing for seasonal variation. Meadow edges supported greater functional richness, functional dispersion and Rao's quadratic entropy, whereas functional evenness was higher in meadow interiors. Taxonomic-distance metrics showed a contrasting response, with lower nearest-neighbour taxonomic distances and stronger clustering at edges. Several diversity indices also varied seasonally, generally reaching higher functional values in summer, but season did not consistently modify the edge-interior contrast. These results show that functional expansion can coexist with taxonomic clustering and demonstrate why edge effects cannot be characterised by species richness alone. Integrating complementary biodiversity dimensions therefore provides a more complete description of how seagrass-associated fish communities vary between meadow boundaries and interiors.
