RESUMEN
Background: Xicotli data is the short name given to the dataset generated within the project framework "Integration of Biodiversity Data for the Management and Conservation of Wild Bee-Plant Interactions in Mexico (2021-2023)", as xicotli is the generic word for a bee in Nahuatl. The team comprised eco-informaticians, ecologists and taxonomists of both native bees and flora. The generated dataset contains so far 4,532 curated records of the plants, which are potential hosts of species of three focal families of bees native to Mexico: Apidae, Halictidae and Megachilidae and morphological and ecological data of the plant-bee interactions. This dataset was integrated and mobilised from citizen observations available at naturalista.mx (iNat), which were compiled through the iNaturalist project. New information: The new information obtained with the Xicotli data project was: Taxonomic information about bee species curated by taxonomists based on the information contained in iNaturalist;Taxonomic identification of the host plants by a botanist from the photos compiled by the Xicotli Data project;Data on the ecomorphological traits of bees and plants based on expert knowledge and literature. All the data were integrated into the Xicotli Data Project via the creation of new "observation fields". The visibility of the information originally contained in iNaturalist was maximized and can be consulted directly on the iNaturalist platform.
RESUMEN
The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF, iDigBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES, an open, web-based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open-access platform available online (http://species.conabio.gob.mx), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network-based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.
