Description year comparison among four conservation status categories (TE: Threatened, NT: Non-Threatened, DD: Data Deficient and NE: Not-Evaluated) of New World Colubroidea snakes.

<p>Means and 95% Confidence Intervals are presented. Dashed line represents total mean. Note that the Y axis was drawn in log scale.</p

Body Size, Extinction Risk and Knowledge Bias in New World Snakes

<div><p>Extinction risk and body size have been found to be related in various vertebrate groups, with larger species being more at risk than smaller ones. We checked whether this was also the case for snakes by investigating extinction risk–body size relationships in the New World's Colubroidea species. We used the IUCN Red List risk categories to assign each species to one of two broad levels of threat (Threatened and Non-Threatened) or to identify it as either Data Deficient or Not-Evaluated by the IUCN. We also included the year of description of each species in our analysis as this could affect the level of threat assigned to it (earlier described species had more time to gather information about them, which might have facilitated their evaluation). Also, species detectability could be a function of body size, with larger species tending to be described earlier, which could have an impact in extinction risk–body size relationships. We found a negative relationship between body size and description year, with large-bodied species being described earlier. Description year also varied among risk categories, with Non-Threatened species being described earlier than Threatened species and both species groups earlier than Data Deficient species. On average, Data Deficient species also presented smaller body sizes, while no size differences were detected between Threatened and Non-Threatened species. So it seems that smaller body sizes are related with species detectability, thus potentially affecting both when a species is described (smaller species tend to be described more recently) as well as the amount of information gathered about it (Data Deficient species tend to be smaller). Our data also indicated that if Data Deficient species were to be categorized as Threatened in the future, snake body size and extinction risk would be negatively related, contrasting with the opposite pattern commonly observed in other vertebrate groups.</p></div

Geographic patterns of anuran biodiversity in South America.

<p>Maps depicting (A) species richness and (B) per-cell range size.</p

Histograms of (A) species richness and (B) range size frequency distributions.

<p>Histograms of (A) species richness and (B) range size frequency distributions.</p

Relationship between description year and body size (measured as log₁₀ maximum total body length) in New World Colubroidea snakes.

<p>Note that the Y axis was drawn in log scale.</p

Mean body size (log₁₀ maximum total body length) values of Colubroidea snake species in four conservation status categories (TE: Threatened, NT: Non-Threatened, DD: Data Deficient and NE: Not-Evaluated) across the New World.

<p>Error bars are for 95% Confidence Intervals; dashed lines represent total means.</p

Rich-rare and poor-rare sets of cells within South America, depicting the location of protected cells.

<p>Rich-rare and poor-rare sets of cells within South America, depicting the location of protected cells.</p

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

<div><p>Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist.</p></div

Forest canopy height for Neotropical region obtained through the 3D Global Vegetation Map database.

<p>For each 1° × 1° grid cell, two measures of vegetation complexity were extracted: (i) standard deviation of forest canopy height and (ii) forest canopy height range.</p

Examples of change in suitability predicted by SVM models for three species derived from the use of three predictors set.

<p>C: models with climate predictors, CE: models with climate and edaphic predictors, E: models with edaphic predictors.</p