2.4. Climate niche modeling
Three distinct algorithms were employed within an ensemble modeling
framework to assess the current and future climate niche of the species.
These algorithms included the Generalized Linear Model (GLM) proposed by
Nelder and Wedderburn in 1972, the Generalized Boosted Model (GBM)
introduced by Friedman in 2001, and the Maximum Entropy (MaxEnt) method
developed by Phillips and others in 2006, as outlined by Breiner et al.
in 2015.
To generate pseudo-absence data, 10,000 background points were created
by randomly sampling at a specific distance from the presence points,
following the methodology suggested by Naimi et al. in 2022 and
Collart et al. in 2023.
The evaluation process involved splitting the occurrence data into two
sets: 70% for calibration and 30% for evaluation, following the
guidelines set by Ngila et al. in 2023 and Collart et al. 2023. This
entire procedure was repeated ten times to ensure the creation of
predictions independent of the training data, as recommended by Guisan
et al. in 2017.
For all three modeling algorithms, the default parameters provided by
the biomod2 R package, as detailed by Thuiller et al. in 2009, were
utilized.
The performance of the models was assessed using two distinct evaluation
metrics: firstly, the Area Under the Curve (AUC) of the Receiver
Operating Characteristic (ROC) curve, as introduced by Jiménez‐Valverde
in 2012, and secondly, the True Skill Statistics (TSS) method developed
by Allouche et al. in 2006.
Finally, an ensemble habitat suitability map was created using a
weighted average approach for two species, following the procedure
described by Ahmadi et al. in 2023. The predictions derived from the
ensemble models were classified into five categories: (1) unsuitable (≤
0.2), (2) low (0.21-0.4), (3) moderate (0.41-0.6), (4) high (0.61-8),
and (5) excellent (≥ 0.81). Notably, the high and excellent categories
were considered acceptable thresholds for the analysis.Top of Form