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One of the most valuable initiatives on massive availability of biodiversity data is the Global Biodiversity Information Facility, which is creating new opportunities to develop and test macroecological knowledge. However, the potential uses of these data are limited by the gaps and biases associated to large-scale distributional databases (the so-called Wallacean shortfall). Describing and quantifying these limitations are essential to improve knowledge on biodiversity, especially in poorly-studied groups, such as mosses. Here we assess the coverage of the publicly-available distributional information on Iberian mosses, defining its eventual biases and gaps. For this purpose, we compiled IberBryo v1.0, a database that comprises 82,582 records after processing and checking the geospatial and taxonomical information. Our results show the limitations of data and metadata of the publicly-available information. Particularly, ca. 42% of the records lacked collecting date information, which limits data usefulness for time coverage analyses and enlarges the existing knowledge gaps. Then we evaluated the overall coverage of several aspects of the spatial, temporal and environmental variability of the Iberian Peninsula. Through this assessment, we demonstrate that the publicly-available information on Iberian mosses presents significant biases. Inventory completeness is strongly conditioned by the recorders' survey bias, particularly in northern Portugal and eastern Spain and the spatial pattern of surveys is also biased towards mountains. Besides, the temporal pattern of survey effort intensifies from 1970 onwards, encompassing a progressive increase in the geographic coverage of the Iberian Peninsula. Although we just found 5% of well-surveyed cells of 30’ of resolution over the 1970-2018 period, they cover about a fifth of the main climatic gradients of the Iberian Peninsula, which provides a fair – though limited – coverage. Yet, the well-surveyed cells are biased towards anthropised areas and some of them are located in areas under intense land-use changes, mainly due to the wood-fires of the last decade. Despite the overall increase, we found a noticeable gap of information in the south-west of Iberia, the Ebro river basin and the inner plateaus. All these gaps and biases call for a careful use of the available distributional data of Iberian mosses for biogeographical and ecological modelling analysis. Further, our results highlight the necessity of incorporating several good practices to increase the coverage of high-quality information. These good practices include digitalisation of specimens and metadata information, improvement on the protocols to get accurate data and metadata or revisions of the vouchers and recorders' field notebooks. These procedures are essential to improve the quality and coverage of the data. Finally, we also encourage Iberian bryologists to establish a series of re-surveys of classical localities that would allow updating the information on the group, as well as to design their future surveys considering the most important information gaps on IberBryo.

We compiled a database of firefly species records from the Atlantic Forest hotspot in Brazil and made it available at GBIF. Data were gathered from literature and from several key entomological collections, including: Coleção entomológica Prof. José Alfredo Pinheiro Dutra (DZRJ/UFRJ) and Coleção do Laboratório de Ecologia de Insetos from Universidade Federal do Rio de Janeiro (CLEI/UFRJ); Coleção Entomológica do Instituto Oswaldo Cruz (CEIOC); Museu de Zoologia da Universidade de São Paulo (MZSP); Coleção Entomológica Pe. Jesus Santiago Moure from Universidade Federal do Paraná (DZUP/UFPR); and Coleção Entomológica from Universidade Federal Rural de Pernambuco (UFRPE). This database represents the largest contribution to a public repository of recorded occurrences from Neotropical fireflies. This dataset shows the occurrence and abundance of firefly species in the Atlantic Forest hotspot. Firefly species endemic to this biome are also present and considered in the study. These data can assist scientific and societal needs, by supporting future research projects and conservation decision-making.

Beta diversity patterns are essential for understanding how biological communities are structured. Geographical and environmental factors, as well as species dispersal ability, are important drivers of beta diversity, but their relative importance may vary across spatial scales. In this study, we evaluate whether beta diversity changes across geographical scales and analyse how different drivers affect turnover patterns of native seed plants in an oceanic archipelago, the Azores (Portugal). Using a 500 x 500 m resolution grid, we selected cells that are covered by one of the following habitats: native forest, naturalized vegetation and seminatural pastures. We calculated species turnover at three spatial scales: i) between islands, ii) between cells within each island, and finally iii) between cells of each of the habitats of interest in each island. We then calculated the contribution of dispersal syndromes (endozoochory, epizoochory, hydrochory and anemochory) to turnover at each of the scales. Lastly, we assessed the relationship between geographical and climatic distances and habitat composition with turnover. Turnover was higher at the smallest scale, particularly in seminatural pastures, and decreased with increasing spatial scales, a pattern potentially associated with the historical fragmentation and current patchy distribution of native forest and seminatural habitats in the Azores. Dispersal syndromes and habitat composition had a negligible effect on turnover at all scales. Geographical distance had a positive effect on turnover at all scales, increasing with scale. The relationship between turnover and climatic distance was only significant at the intermediate and small scales in specific islands and habitats. Scale plays an important role at determining the effect of the drivers of turnover, in particular geographical and climatic distance. These results highlight the need to carefully select the scale of analysis when studying turnover patterns, as well as identifying the potential drivers associated with each scale.

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