Biodiversity offers diverse ecosystem services responsible for increasing human well-being and moving financial capital around the world. Therefore, given the natural and human causes of biodiversity reduction, we need to use computational methods that not only show where to find high species richness, or phylogenetic and functional diversity, but also low economic cost or even high originality or distinctiveness. In this paper, we introduce a new package (‘bivariatemaps’) with methods to prioritize areas, communities or species on a large scale, and we show an example for finding the best areas for the conservation of terrestrial and flying mammals (endothermics) and the Serpentes (ectothermics). We do that by finding high potential for providing ecosystem services (measured here by Species Richness) and correlating it with Economic Cost (measured as Land Acquisition Cost). Additionally, we correlated NDVI (Normalized Difference Vegetation Index) with Economic Cost, to observe how we could maximize productivity while reducing resource usage as much as possible. To achieve this goal, we first developed the ‘bivariatemaps’ package, using it to plot bivariate maps that integrate the Species Richness with the Land Cost for each of the three studied groups. We observe that more attention should be paid to tropical countries, which have high species richness, but low land acquisition cost. We note that more attention should be paid to the Indomalayan region, which has a high richness of species low-cost sites for the conservation of species. Bivariate maps have been published in studies since the 70s, but only in the 2010s they became more used by the general public, including scientists from low-profit universities. We hope that this paper (and the ‘bivariatemaps’ package) helps to generate works planned globally and regionally in the face of natural and anthropogenic processes responsible for the loss of biodiversity that can bring us socio-economic benefits.

Biodiversity offers diverse ecosystem services responsible for increasing human well-being and moving financial capital around the world. Therefore, given the natural and human causes of biodiversity reduction, we need to use conservation prioritization methods that not only show where to find high species richness, but also low economic cost. In this paper, we suggest priority areas, on a global scale, for the conservation of terrestrial and flying mammals and the Serpentes, an endothermic group. We do that by finding high potential for providing ecosystem services (measured here by Species Richness) and correlating it with Economic Cost (measured as Land Acquisition Cost). To achieve this goal, we first developed the ‘bivariatemaps’ package, using it to plot bivariate maps that integrate the Species Richness with the Land Cost for each of the three studied groups. We observe that more attention should be paid to tropical countries, which have high species richness, but low land acquisition cost. We note that more attention should be paid to the Indomalayan region, which has a high richness of species low-cost sites for the conservation of species. Bivariate maps have been published in studies since the 70s, but only in the 2010s they became more used by the general public, including scientists from low-profit universities. We hope that this paper (and the ‘bivariatemaps’ package) helps to generate works planned globally and regionally in the face of natural and anthropogenic processes responsible for the loss of biodiversity that can bring us socio-economic benefits.

Bivariate maps have been published in studies since the 70s, but only in the 2010s the method became more used by the general public, including scientists from low-profit universities. Here I present my new R package, bivariatemaps, capable of three things: making color matrices with different quantile numbers, making a bivariate map from two rasters and a color matrix, and making gridded shapefiles while excluding cells based on area coverage. The package basically has three functions: colmat, bivariate.map and GridFilter. I show the steps in order to generate a bivariate map and show how this method can be used in the most distinct scientific areas. The package is available in both Github and CRAN (R repository) and is freely available and customizable by anyone who wants to use it.