2.4. Ecological predictive models’ development and evaluation
To calibrate our models, we employed the maximum entropy method
(Elith et al. 2010;
Phillips et al. 2005) implemented
in Maxent ver. 3.4.4. The algorithm has been extensively tested and
benchmarked (Phillips & Dudík 2008;
Richards et al. 2007). Many
studies have reported Maxent as one of the highest performing
presence-background algorithms (Elithet al. 2006; Merow et al.2013). As the selection of sample points can influence model
performance in Maxent (Phillips et
al. 2009), we restricted the selection of background points using a
regularization of 10,000 background points
(Elith et al. 2006). For
simplification of the modeling algorithms, we used the default settings
(feature class and regularization) in Maxent for each of the three
models. Models were trained with data from the present and projected in
the future. Three Maxent models were generated with different occurrence
datasets: first, all accessions location points grouped together without
genetic information were used in projecting the entire distribution of
KG (model 1); model 2 and model 3 were developed using separately
occurrence data of Pop 1 and Pop 2, the genetically defined populations
(Supplemental Table S1).
We used a tenfold cross-validation method, which uses 90% of the data
for model training and 10% for model testing for 10 iterations
(Elith et al. 2010).
Each model performance was evaluated using traditional Receiver Operator
Characteristics (ROC) - area under the curve (AUC) scores
(Merow et al. 2013) by specifying
500 iterations with the omission threshold set at ten percent
(Peterson et al. 2008). A model is
considered as having a good fit when its AUC is close to one (AUC≥0.75)
(Elith et al. 2006). Minimum
training presence (MTP) values were also used as thresholds for testing
the performance of each model (Phillipset al. 2005).
The outputs from Maxent were processed in ArcGIS ver. 10.7.1 to
construct maps of the distribution of Kersting’s groundnut areas
cultivability. The continuous probabilities generated by Maxent (Ten
Percentile Training Presence) were converted into binary
presence–absence maps to identify the levels of areas suitability. Two
different levels were therefore, defined: unsuitable and suitable.
Finally, we quantified the dynamic of the cultivated zones of the crop
in the scenarios RCP 4.5 and RCP 8.5 of the horizon 2055 using the
following equation:
\begin{equation}
\bigtriangleup(\%)\ =\frac{\left(\text{FA}_{\text{ij}}-\text{CA}_{j}\right)*100}{\text{TA}}\nonumber \\
\end{equation}Where, FAij corresponds to the extent value (in
number of pixels) under the scenario i of future horizon in the
environment j (area suitability); CA is the extent value of
current condition; TA corresponds to the total extend of all cultivated
zones of the present day. Negative, null and positive values represent
range lost, stable and gained, respectively.
Furthermore, to visualize the potential changes of suitable areas for
Kersting’s groundnut production, we compared current and future
distribution ranges of the crop and of genetic populations using package
“tmap” version 3.3-1 (Tennekes 2018) in
R.