In species distribution modelling and ecological niche modelling (SDM & ENM), the region from where background or pseudoabsence points are picked is key to how well a model turns out. This region should include sufficient localities for the model to … Continue reading →

Another frequent question of my students is how to obtain a polygon map of the seas and oceans, rather than the land polygons (countries, etc.) that are commonly imported with R spatial data packages. You can mostly just use the … Continue reading →

A student recently asked me how exactly the R terra::extract() function worked when using method="bilinear" to get raster values for points. The help file rightly says that ‘With “bilinear” the returned values are interpolated from the values of the four … Continue reading →

It is well known, though often dismissed, that the areas of spatial units (cells, pixels) based on unprojected coordinates (longitude-latitude degrees, arc-minutes or arc-seconds) are wildly inconsistent across the globe. Towards the poles, as the longitude meridians approach each other, … Continue reading →

Presence probability, typically obtained with presence-(pseudo)absence modelling methods like GLM, GAM, GBM or Random Forest, is conditional not only on the suitability of the environmental conditions, but also on the general prevalence (proportion of presences) of the species in the … Continue reading →

I often come across GBIF users who are unaware that the records available for a given taxon are not necessarily all presences: there’s a column named “occurrenceStatus” whose value can be “PRESENT” or “ABSENT”! The absence records can, of course, … Continue reading →

Maps of continents, mainlands and islands can be useful, for example, for selecting areas — and then cropping or masking variables — for modelling a species’ distribution. Here’s a way to obtain such maps using the ‘geodata’ and ‘terra’ R … Continue reading →

In my species distribution modelling courses, for a quick and safe removal of the most common biodiversity database errors, I’ve so far used functions from the scrubr R package, namely ‘coord_incomplete’, ‘coord_impossible’, ‘coord_unlikely’, ‘coord_imprecise’ and ‘coord_uncertain’. There are other R … Continue reading →

According to modern recommendations in data viz, lollipop charts are generally a better alternative to bar charts, as they reduce the visual distortion caused by the length of the bars, making it easier to compare the values. So, in the … Continue reading →

The Boyce index (Boyce et al. 2002) is often described as a presence-only metric for evaluating the predictions of species distribution (or ecological niche, or habitat suitability) models (e.g. Hirzel et al. 2006, Cianfrani et al. 2010, Bellard et al. … Continue reading →

This version of R package “model Evaluation and Analysis” includes some bug fixes (thanks to Huijie Qiao, Ying-Ju Tessa Chen, Oswald van Ginkel and Alba Estrada), some new functions (predPlot, confusionLabel, and mod2obspred, which is now used internally by several … Continue reading →

The ‘confusionLabel‘ function below labels the predictions of a binary response model according to their confusion matrix categories, i.e., it classifies each prediction into a false positive, false negative, true positive or true negative, given a user-defined threshold value: Usage … Continue reading →

Diagnostic plots are always a nice, visually explicit way to assess our data and predictions, and I’ve just added a new one to the modEvA R package. The ‘predPlot‘ function plots predicted values separated into observed presences and absences, and … Continue reading →

Especially since I had to start working as a freelancer, it became essential to keep an accurate record of how much time I spend on each task and project. I checked out some software tools available online (namely TimeCamp, which … Continue reading →

The ‘plot_outliers‘ function below draws a boxplot and a scatterplot of a numeric variable x and plots the values of the outliers (currently not offset, even if they overlap). For relatively small datasets, it can be a quick way to … Continue reading →

The newest version of R package fuzzySim (3.0) is now on CRAN! It includes new functions such as ‘favClass‘, ‘bioThreat‘ and ‘gridRecords‘; improvements to some functions, help files and examples; updated e-mail and citation information [ see citation(“fuzzySim”) ]; clarifications … Continue reading →

The ‘gridRecords‘ function, which has just been added to the ‘fuzzySim‘ package (from version 2.6 on), takes a raster stack and a set of spatial coordinates of a species’ presence (and optionally absence) records, and returns a data frame with … Continue reading →