Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants

Abstract

The datasets and code presented in this article are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets”1. The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.

Modelled distributions and conservation status of the wild relatives of chile peppers (CapsicumL.)

“…Variables for slope and aspect were also incorporated after having been calculated from the altitude dataset using the terrain function in R package “raster” (Hijmans, ). All ecogeographic predictors were processed at a spatial resolution of 2.5 arc‐min (~5 km 2 at the equator) (values available in Appendix , sheet 2 in the Supporting Information; raw data available from Khoury et al, ).…”

Distributions, conservation status, and abiotic stress tolerance potential of wild cucurbits (Cucurbita L.)

“…Variables for slope and aspect were also incorporated, calculated from the altitude dataset using the terrain function in R package raster (Hijmans, ). All ecogeographic predictors were processed at a spatial resolution of 2.5 arc‐minutes (approximately 5 km 2 at the equator) (results available in Dataset , sheet 2; raw data available from Khoury et al, ).…”

“…The number of comparative background points (“pseudo‐absences”) were determined per taxon in proportion to the total number of pixels within the spatial background, which was calculated based on pertinent ecoregion boundaries, i.e., the ecoregions defined in Olson et al () (available from Khoury et al, ) wherein occurrence data fell, bounded by the borders of the countries wherein occurrence data fell, with a maximum of 5,000 pseudo‐absences per taxon. Pseudo‐absence points that fell within the same cell as a presence point were removed.…”

“…The ERSex compares the ecoregional diversity encompassed in ex situ conservation repositories to the diversity throughout the distribution models, considering comprehensive conservation to have been accomplished only when every ecoregion potentially inhabited by a taxon was included at least once within the set of CA50 buffered areas. The layer used for estimating the ERSex contained 867 distinct terrestrial ecoregions worldwide (Olson et al, ) (available from Khoury et al, ). A Final Conservation Score for ex situ (FCSex) was derived by calculating the average of the three individual ex situ conservation metrics.…”

“…Occurrence data, processed ecogeographic data, and interactive taxon‐level modeling and conservation status results and metrics are provided in the Supporting Information. Associated ecogeographic and spatial input data are available through open access repositories (Khoury et al, ). All code implemented in the analysis is available at: https://github.com/dcarver1/cwrSDM.…”