A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) , Radosavljevic and Anderson (2014) , Peterson et al. (2018) , Cobos et al. (2019) , Alkishe et al. (2020) , Machado-Stredel et al. (2021) , Arias-Giraldo and Cobos (2024) , Cobos et al. (2024) .

Aim: Rapid technological advancements and the biodiversity crisis have motivated efforts to document species before their extinction. However, taxonomic coverage gaps, where certain species are underrepresented in biodiversity databases, can distort our understanding of ecosystems. Here, we quantified how many of the plant species found in a hotspot are invisible, i.e. they would be excluded from studies due to insufficient occurrence data. Additionally, we identified factors influencing the invisibility of species.Location: Atlantic Forest hotspot, Brazil.Methods: We downloaded and filtered occurrence data from 15,010 plant species from online biodiversity databases. We utilized multiple thresholds, each representing a minimum required number of records, to classify species as “invisible” if their record count fell below these thresholds. We fitted logistic models to estimate how factors such as life form, presence of a vernacular name, geographical distribution, endemism, and year of taxonomic publication influence the odds of species exclusion.Results: The proportion of invisible species ranged from 14% when employing simple tools requiring just three records to as high as 64% with more demanding tools requiring at least 60 records. Species with specific characteristics are more prone to invisibility, including non‐tree species, species without vernacular names, species with restricted distributions within Atlantic Forest, endemic species, and species with names published more recently. A significant portion of these invisible species are distributed along the coastline. In contrast, the continental portion of the biome exhibits fewer taxonomic coverage gaps of known species, most likely due to lower rates of new species descriptions.Main Conclusions: Coverage gaps are shaped by the interaction of biological traits, societal preferences, limited technical support, and human activities. Studies relying on distributional data must balance the rigour of filters and thresholds to achieve both geographical reliability and taxonomic coverage, adjusting them to align with each study's specific data and goals.

Premise: The Flora e Funga do Brasil project is the most comprehensive effort to reliably document Brazilian plant and fungal diversity. It involves the collaborative work of hundreds of taxonomists, integrating detailed and standardized morphological descriptions, nomenclatural status, and geographic distribution information of plants, algae, and fungi collected throughout Brazil. Despite the extensive information available, managing the information from the Flora e Funga do Brasil website poses certain challenges.Methods and Results: florabr is an R package developed to facilitate the exploration and geographical analysis of species information derived from the Flora e Funga do Brasil. Unique to florabr is its ability to interact with the latest, or any other version of the dataset, which undergoes weekly updates. I illustrate the practical application of florabr in common tasks in biogeography and conservation studies.Conclusions: florabr is anticipated to be of significant interest to biogeographers, ecologists, curators of biological collections, and taxonomists actively contributing to the Flora e Funga do Brasil.

The recent detection of populations of the mosquito Aedes vittatus in the Dominican Republic and Cuba has raised concerns over its potential for broader invasion in the Western Hemisphere, and particularly on the North American mainland. This species has been associated with the maintenance and transfer of yellow fever, Zika, chikungunya, and dengue viruses on its native distributional area. A previous study identified parts of North America with environments apparently suitable for the species, but different modeling approaches can give different results. Using models calibrated across the region historically accessible to the species, we re-examined the distributional potential of this species. Our models identified areas with year-round environmental suitability for this species along the southern coasts of the US, Mexico, Caribbean islands, and Central America. These environmentally suitable areas coincide with those for other Aedes species and overlap with urban settings, potentially placing large human populations at increased risk in case of successful establishment and invasion by this species. Targeting entomological surveillance in these and nearby areas is important for early detection to avoid establishment of populations of this species.

The Catálogo Taxonômico da Fauna do Brasil (CTFB) is a comprehensive digital biodiversity database that provides standardized taxonomic and ecological information for animal species in Brazil. Although widely used in biodiversity studies, its accessibility and reproducibility can be enhanced through integration with computational tools like R packages. Here, we introduce faunabr, an R package designed to facilitate the retrieval, exploration, and spatialization of taxonomic data from CTFB. I demonstrate its application in key biogeographic and conservation tasks, including taxonomic verification, selection of target species based on traits and distribution, and identification of erroneous occurrence records outside their known ranges. By simplifying these processes, faunabr enhances the accessibility and practical use of CTFB data for zoologists, biogeographers, ecologists, taxonomists, and curators of biological collections, ultimately supporting more rigorous and reproducible biodiversity research.