Distribution Modeling, evaluation and niche metrics
I have used BIOMOD2 package 3.4.11 (Thuiller et al. , 2009) in
RStudio Version 1.2.5033 (R Core Team, 2020) to perform ensemble
modeling using a combination of five algorithms. These are Generalized
Additive Models (GAMs), Surface Range Envelope (SRE), Random Forest
(RF), Generalized Boosting Model and Artificial Neural Network (ANN).
GAM is a regression-based algorithm that uses smoothers which are a set
of polynomials for generating curves by local fitting to the subsection
of data. SRE is an envelope-style method that creates arrays of values
for each environmental variable at the given presence points. RF is a
bagging approach classifies data into different classes based on
homogeneity and generates result from classification tree. Under the
Generalized Boosting Model which is a boosting method, each of the
individual models possesses regression trees. ANN is a mathematical
technique which makes an attempt to simulate biological neural networks
and are trained by back propagation algorithm. Unlike modeling through
single algorithm, ensemble modeling are considered to produce better
accuracy (Araujo and New, 2007).
The models were selected with True Kill Statistic (TSS) evaluation
metric with a threshold of 0.7. Data partitioning of 80% training
dataset and 20% testing dataset was used. The entire procedure was
repeated three times. Each model was evaluated by ROC (Relative
Operating Characteristic) (=AUC) and TSS metrics. The AUC has a range
from 0 to 1 and TSS range from -1 to 1. As per the refined AUC scale,
AUC > 0.9 are considered excellent, 0.80 < AUC
< 0.90 are considered good and so on (Araújo et al. ,
2005) whereas the TSS score +1 indicates perfect agreement and values of
zero or less indicate a performance no better than random.
Niche overlap values for the taxa were calculated using Schoener’s D
(Schoener, 1968) and Hellinger’s I niche similarity metrics (Warrenet al. , 2008b) using the overlap function in ’dismo’ package
(Hijmans et al. , 2017). D and I qualitatively similar results
although I metric tends to give higher values than D for any given
comparison (Culumber and Tobler, 2016). The niche equivalency test was
performed using ’phyloclim’ package (Heibl et al. , 2018) with 99
replicates. This test asks whether the niches of the taxa under study
are effectively indistinguishable (Warren et al. , 2008a). The
equivalency of two niches are rejected if the niche overlap falls
outside the 95% of the null hypotheses. The niche equivalency test
using this package was chosen over some of the other popular methods
such as use of ’ecospat’ package as it uses occurrence density grids
(Cola et al. , 2017). This is problematic in this case because the
South Asian Dolphins do not share same geographical space and the models
built would need to be projected into the species’ geographical
distribution elsewhere.
Results