2.3. Environmental variables
We used bioclimate layers combined with soil properties to project
current and future niches for the species and each genetic group. A
total of 14 bioclimate variables were downloaded from Africlim online
regional climate models (RCMs) data portal
(https://webfiles.york.ac.uk/KITE/AfriClim/)
(Platts et al. 2014). Current and
future variables averaged between the time periods 1986-2015 (2000) and
2041-2060 (2055) were downloaded at a 30 arc-second (~1
km) spatial resolution. For future climatic conditions, predictions from
the Ensemble model (Platts et al.2014) were used. This model simulates changes based on a set of
scenarios. The projections were run under Representative Concentration
Pathway (RCP), RCP 4.5 and RCP 8.5 for the 2055 time horizon
(Meinshausen et al. 2011). In all
RCPs, the climatic conditions are extreme in RCP 8.5 scenarios compared
to RCP 4.5. RCP 4.5 projects temperatures to rise above industrial
levels by at least 1.5°C in West Africa, with atmospheric
CO2 reaching 500 ppm while in RCP 8.5 projections,
temperatures are predicted to rise by 2.8°C and atmospheric
CO2 to be over 550 ppm
(IPCC 2013). These climate projections
were statistically downscaled to match the bioclimatic variables using
the delta method, (Ramirez-Villegas &
Jarvis 2010).
Data related to soil characteristics were available in the World Soil
Information (ISRIC) databases
(Soil-property-maps-of-Africa-at-250-m-resolution)at 250 m resolution (Hengl et al.2015). These spatial predictions of soil properties were generated
based on two predictive approaches such as random forests and linear
regression (Hengl et al. 2015).
Soil characteristics identified as relevant to M. geocarpumagricultural management included 11 variables related to the soil
physical, chemical and nutritional properties. Soil data were then
converted to 30 arcseconds using ArcGis software v 10.7.1 to match with
bioclimate layers. Finally, using shapefile boundaries of four West
African countries (Benin, Burkina Faso, Ghana and Togo) we cropped all
variables to encompass the broad geographic regions that define
Kersting’s groundnut global distribution.
Jackknife Procedure in Maxent 3.4.4 was used to reduce the number of
variables to be included in the prediction models
(Phillips et al. 2005). The six
variables with highest contribution proportions were selected and were
used in the final models of the species, and with genetic information.