Biomod2 modeling for predicting the potential ecological distribution of three Fritillaria species under climate change

Abstract

The Fritillaria species ranked as a well-known traditional medicine in China and has become rare due to excessive harvesting. To find reasonable strategy for conservation and cultivation, identification of new ecological distribution of Fritillaria species together with prediction of those responses to climate change are necessary. In terms of current occurrence records and bioclimatic variables, the suitable habitats for Fritillaria delavayi, Fritillaria taipaiensis, and Fritillaria wabuensis were predicted. In comparison with Maxent and GARP, Biomod2 obtained the best AUC, KAPPA and TSS values of larger than 0.926 and was chosen to construct model. Temperature seasonality was indicated to put the greatest influence on Fritillaria taipaiensis and Fritillaria wabuensis, while isothermality was of most importance for Fritillaria delavayi. The current suitable areas for three Fritillaria species were distributed in south-west China, accounting for approximately 17.72%, 23.06% and 20.60% of China's total area, respectively. During 2021–2100 period, the suitable habitats of F. delavayi and F. wabuensis reached the maximum under SSP585 scenario, while that of F. taipaiensis reached the maximum under SSP126 scenario. The high niche overlap among three Fritillaria species showed correlation with the chemical composition (P ≤ 0.05), while no correlation was observed between niche overlap and DNA barcodes, indicating that spatial distribution had a major influence on chemical composition in the Fritillaria species. Finally, the acquisition of species-specific habitats would contribute to decrease in habitat competition, and future conservation and cultivation of Fritillaria species.

Predicting the Potential Geographic Distribution of Invasive Freshwater Apple Snail Pomacea canaliculate (Lamarck, 1819) under Climate Change Based on Biomod2

“…BIOMOD2 (BIOdiversity MODeling) has the capability to select superior SDMs for constructing EM, thereby enabling a more precise simulation of PGDs and permitting the deduction of the primary environmental factors influencing species distribution [43]. Due to BIOMOD2's ability to compensate for the limitations of individual models, it has rapidly emerged as the predominant model for predicting species distribution in recent years [44][45][46].…”