Selecting optimal models using expert approach
We visually screened and compared which of the suitable/unsuitable
binary maps produced from the optimal models selected using the four
sequential approaches were ecologically plausible. If none of the
optimal models selected through the four sequential approaches resulted
in ecologically plausible binary maps we then moved to the next best
choice and continued to screen till we arrived at the best possible
ecologically plausible map using expert knowledge (Galante et al. 2018).
We qualitatively assessed ecological plausibility by considering the
known elevation range and the predicted suitable habitat. We also
compared the predicted map with any available literature, such as IUCN
distribution maps or authors’ field experience. However, we acknowledge
here that given the relatively high number of fish and odonate species
modelled, compounded by very poor literature on the species from Bhutan,
some of the chosen optimal models could be subjective. We developed the
binary maps using the “balance
training omission, predicted area and threshold value Cloglog
threshold” (hereafter ‘balance threshold’) out of 11 Cloglog thresholds
generated by Maxent for each model iteration. Though lower thresholds
can overpredict suitable habitat, they are better for species with few
occurrence data (Pearson et al. 2007, Radosavljevic and Anderson2014)
and can also uncover potentially informative distribution areas (Pearson
et al. 2007). We also found using the 10th percentile
threshold restricted the predicted suitable habitats around occurrence
points, or predicted the whole study area as unsuitable, in some cases
(See ‘Predicted Habitat’ in Result) (Pearson et al. 2007, Radosavljevic
and Anderson, 2014; Galante et al. 2018). We produced binary maps using
ArcGIS10.2.2 (ESRI 2014).