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We updated a previous database that compiled all the information available in 2010 for the species distribution of spiders (Araneae) in the Iberian Peninsula, Balearic Islands (Illes Balears) included. By the end of 2018 a total of 30834 records were compiled. These belong to 1493 species, 282 of those endemic to the peninsula, across 56 families and 402 genera. This represents an increase of approximately 14% in the number of species in the last nine years. From all families found in the Iberian Peninsula, Araneidae represent the highest number of records (3315), Linyphiidae the highest species richness (302) and Dysderidae the highest endemic richness (58). When considering only the 2010 decade, Linyphiidae lead in both number of records (1417) and species (49), but Gnaphosidae have the highest newly described endemic richness (18). When looking at the full data per province, the largest number of records are located in Illes Balears (1864), followed by Barcelona (1287). When it comes to species, Huesca (474) and Barcelona (470) are the richest provinces. However, it is Illes Balears that possesses the largest known endemic richness (43), followed by Beja and Faro (39). Regarding the last decade, Illes Balears received the largest sampling effort with 901 records, followed by Girona (806). Ciudad Real had the highest increase in known richness with 191 new species to the province, followed by León and Lleida (188). The most new endemic species were found in Faro (16), followed by Almería and Cádiz (13). This checklist is accompanied by an online catalogue where all its information is fully listed.

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Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.

A main goal of ecological and evolutionary biology is understanding and predicting interactions between populations and both abiotic and biotic environments, the spatial and temporal variation of these interactions, and the effects on population dynamics and performance. Trait-based approaches can help to model these interactions and generate a comprehensive understanding of ecosystem functioning. A central tool is the collation of databases that include species trait information. Such centralized databases have been set up for a number of organismal groups but is lacking for one of the most important groups of predators in terrestrial ecosystems -spiders. Here we promote the collation of an open spider traits database, integrated into the global Open Traits Network. We explore the current collation of spider data and cover the logistics of setting up a global database, including which traits to include, the source of data, how to input data, database governance, geographic cover, accessibility, quality control and how to make the database sustainable long-term. Finally, we explore the scope of research questions that could be investigated using a global spider traits database.

Ecological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most used statistical modeling techniques can hardly accommodate the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible using massive computational power, particularly by means of artificial intelligence and machine learning methods. Here we explored the potential of symbolic regression (SR), commonly used in other areas, in the field of ecology. Symbolic regression searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems. Although the method here presented is automated, it is part of a collaborative human–machine effort and we demonstrate ways to do it. First, we test the robustness of SR to extreme levels of noise when searching for the species-area relationship. Second, we demonstrate how SR can model species richness and spatial distributions. Third, we illustrate how SR can be used to find general models in ecology, namely new formulas for species richness estimators and the general dynamic model of oceanic island biogeography. We propose that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.

While several recent studies have focused on global insect population trends, all are limited in either space or taxonomic scope. As global monitoring programs for insects are currently not implemented, inherent biases exist within most data. Expert opinion, which is often widely available, proves to be a valuable tool where hard data are limited. Our aim is to use global expert opinion to provide insights on the root causes of potential insect declines worldwide, as well as on effective conservation strategies that could mitigate insect biodiversity loss. We obtained 753 responses from 413 respondents with a wide variety of spatial and taxonomic expertise. The most relevant threats identified through the survey were agriculture and climate change, followed by pollution, while land management and land protection were recognized as the most significant conservation measures. Nevertheless, there were differences across regions and insect groups, reflecting the variability within the most diverse class of eukaryotic organisms on our planet. Lack of answers for certain biogeographic regions or taxa also reflects the need for research in less investigated settings. Our results provide a novel step toward understanding global threats and conservation measures for insects.

While many recent studies have focused on global insect population trends, all are limited either in space or taxonomic scope. Since global monitoring programs for insects are not implemented, biased data are therefore the norm. However, expert opinion is both valuable and widely available, and should be fully exploited when hard data are not available. Our aim is to use global expert opinion to provide insights on the root causes of potential insect declines worldwide, as well as on effective conservation strategies that could mitigate insect biodiversity loss. We obtained 753 responses from 413 respondents with a wide variety of expertise. The most relevant threats identified through the survey were agriculture and climate change, followed by pollution, while land management and land protection were recognized as the most significant conservation measures. Nevertheless, there were differences across regions and insect groups, reflecting the variability within the most diverse class of living organisms on our planet. Lack of answers for certain biogeographic regions or taxa also reflects the need for research, particularly in less investigated settings. Our results provide a first step towards understanding global threats and conservation measures for insects.