Cástor Guisande’s research while affiliated with University of Vigo and other places

The Global Biodiversity Information Facility (GBIF) is the largest databank on primary biodiversity data. We examined the completeness and geographical biases for all insect data on GBIF to determine its representativeness. Our results demonstrate that GBIF is far from providing a reliable representation about the global distribution of insects. Despite the growing number of records during the last years, few spatial units are well‐surveyed. At coarse resolutions, 34% of the world terrestrial cells lack data and barely 0.5% have completeness values above 90%. Insects are crucial in many ecological functions, and their alarming decline makes it more pressing to have a representative sample to improve our predictive capacity. However, the dynamic nature of species distributions and the strength of anthropogenic forces call for immediate conservation decisions that cannot wait for the empirical data on the identity and distribution of insects.

• Evaluating data quality and inventory completeness must be a preliminary step in any biodiversity research, particularly in the case of insects and high biodiversity areas. Yet, this step is often neglected or, at best, assessed only for one insect group, and the degree of congruence of sampling effort ffor different insect groups remains unexplored. • We assess the congruence in the spatial distribution of sampling effort for five insect groups (butterflies, caddisflies, dung beetles, moths, and aquatic beetles) in the Iberian Peninsula. We identify well-surveyed areas for each taxonomic group and examine the degree to which the patterns of sampling effort can be explained by a set of variables related to environmental conditions and accessibility. • Irrespective of the general lack of reliable inventories, we found a general but low congruence in the completeness patterns of the different taxa. This suggests that there is not a common geographical pattern in survey effort and that idiosyncratic and contingent factors (mainly the proximity to the workplaces of entomologists) are differentially affecting each group. • After many decades of taxonomic and faunistic work, distributional databases of Iberian insects are still in a very preliminary stage, thus limiting our capacity to obtain reliable answers to basic and applied questions. • We recommend carrying out long-term, standardised and well-designed entomological surveys able to generate a reliable image of the distribution of different insect groups. This will allow us to estimate accurately insect trends and better understand the full extent of global biodiversity loss.

The aim of the present study was to predict future changes in biodiversity attributes (richness, rarity, heterogeneity, evenness, functional diversity and taxonomic diversity) of freshwater fish species in river basins around the world, under different climate scenarios. To do this, we use a new methodological approach implemented within the ModestR software (NOO3D) which allows estimating simple species distribution predictions for future climatic scenarios. Data from 16,825 freshwater fish species were used, representing a total of 1,464,232 occurrence records. WorldClim 1.4 variables representing average climate variables for the 1960–1990 period, together with elevation measurements, were used as predictors in these distribution models, as well as in the selection of the most important variables that account for species distribution changes in two scenarios (Representative Concentration Pathways 4.5 and 6.0). The predictions produced suggest the extinction of almost half of current freshwater fish species in the coming decades, with a pronounced decline in tropical regions and a greater extinction likelihood for species with smaller body size and/or limited geographical ranges.

There is consensus surrounding the need to include a third dimension when estimating Species Distribution Models (SDMs), which is of special interest for marine species. Application of the third dimension is, however, rarely available, thus users are obliged to manually combine 2D SDM outputs (i.e., suitability or presence/absence maps) for 3D distribution generation. Herein, the Niche of Occurrence 3D (NOO3D) is presented, which is a new, simple modelling procedure that provides 3D distributions using both 3D occurrence samples and environmental datasets that consist of one layer per depth value. NOO3D performance was evaluated using five virtual marine species to avoid errors associated with real data sets (three pelagic species, with wide, medium, and narrow distributions, respectively, a mesopelagic species and an abyssal species). These virtual species are distributed across the North Atlantic Ocean and were built to a 0.5° x 0.5° resolution and considering 49 depth levels (from 0.43 m to an undersea depth of 5274.7 m). NOO3D results were also compared to those provided by 3D Alpha Shapes and Maximum Entropy (MaxEnt). The True Positive Rate (TPR), or sensitivity, True Negative Rate (TNR), or specificity, False Positive Rate (FPR), or commission error, and False Negative Rate (FNR), or omission error, were employed in order to facilitate comparison between methods. MaxEnt performed best for TPR, TSS and FNR, and Alpha Shape 3D performed best for FPR and TNR. NOO3D was always the second-ranked method for all metrics considered, which indicates that it was the most suitable method. The provided results indicate that NOO3D can be considered a viable alternative in achieving three-dimensional species distribution models.

Species distribution models (SDMs) are broadly used to predict species distributions from available presence data. However, SDMs results have been criticized for several reasons mainly related to two basic characteristics of most SDMs: 1) general lack of reliable species absence information, 2) the frequent use of an arbitrary geographical extent (GE) or accessible area of the species. These impediments have motivated us to generate a procedure called Niche of Occurrence (NOO). NOO provides the probable distribution of species (realized niche) relying solely on partial information about presence of species. It operates within a natural geographical extent delimited by available observations and avoids using misleading thresholds to obtain binary presence‐absence estimations when the species prevalence is unknown. In this study the main characteristics of NOO are presented, comparing its performance with other recognized and more complex SDMs by using virtual species to avoid the omnipresent error sources of real data sets. This article is protected by copyright. All rights reserved.

The aim of the SINENVAP algorithm is to facilitate the estimation of spatial interpolations within polygons, by using simple, ordinary, and universal kriging. This algorithm is available as a function of the EcoIndR package, which is available as an RWizard application and an R package on CRAN. The main strengths of this algorithm include: the possibility of using different file formats for polygon variable and coordinate inputs (CSV, EXCEL, RData, shape or ASC), compatibility with UTM or decimal coordinates, estimation of optimal grid cell size, the possibility of selecting only points inside polygons from the entire dataset, the application of a jitter function or to estimate the mean value of the variable for duplicated coordinates, reservation a percentage of data for validation, selection of those grid coordinates nearest the data coordinates reserved for validation, the possibility of fitting 13 different models into the semivariogram, automatic selection of the model that best predicts the data reserved for validation through the use of seven accuracy measures, the possibility of using countries, regions, departments, river basins, or even alpha shape distribution as polygons, and finally, depiction of contour plots with the spatial interpolation of the variable and the error within polygons. The spatial interpolation of the temperature in North America and the distribution of a virtual species are used as examples of this algorithm's potential to perform spatial interpolations on both large and small scales.

The aim was to discriminate the countries with relatively comprehensive inventories of freshwater fishes from those with insufficiently prospected inventories. We used a data set of 16,734 freshwater fish species with a total of 1,373,449 occurrence records. Accumulation curves relating the increase in the number of species to the number of records, and completeness values obtained after extrapolating these curves to estimate the total number of predicted species were calculated for each country using the RWizard application KnowBR. Using the final slope values of the accumulation curves, the obtained completeness values, and the ratio between the number of records and the observed species, maps and plots representing the location of good, fair and poor quality inventories at country level were obtained. Inventory completeness ranged from 5.3% (Guinea-Bissau) to 108.4% (United Kingdom), with a pooled mean of 65.9%. We observed that a completeness higher than 90%, a slope lower than 0.02 and a ratio of records/species observed greater than 15 were good thresholds for identifying countries with good quality inventories; only 26 countries met these requirements, mainly located in Europe and North America. However, more than 71% of countries worldwide have inventories that can be categorised as of poor quality. Furthermore, even those countries with relatively accurate national inventories possess a high variability in the completeness of their provincial or regional inventories.

Factorial analyses are frequently used in ecological studies to identify different groups, which is a valid approach if those variables which show the highest variability also best differentiate among groups. Here we present VIDTAXA, an algorithm designed to identify statistically different groups in Principal Components and Correspondence analyses, without previous knowledge of the various potential groups present in the dataset. VIDTAXA is freely available as a function of the VARSEDIG package, which is available as an RWizard application and as an R package on CRAN. As a demonstration of VIDTAXA's potential, we used the algorithm in an example of phenetic taxonomy, for the identification of statistically different taxa in a number of marine Scorpaeniform species. This algorithm. however, it may be used with any kind of data, whether qualitative or quantitative, for the identification of statistically different groups in ecological studies.