World Spread of Tropical Soda Apple (Solanum viarum) under Global Change: Historical Reconstruction, Niche Shift, and Potential Geographic Distribution

Abstract

Solanum viarum has become extensively invasive owing to international trade, climate change, and land–use change. As it is classified as a quarantine weed by countries such as the U.S. and Mexico, it is critical to understand the prevailing historical dispersal, ecological niche dynamics, and distribution patterns. We reconstructed the historical invasion countries and analyzed the ecological niche shift of S. viarum. Using MaxEnt based on the conservativeness of ecological niches, we studied variations in the potential geographical distributions (PGDs) of S. viarum in ecosystems and variations in suitability probabilities along latitudinal gradients. The invasion history in six continents involved three phases: lag (before 1980), spread (1980–2010), and equilibrium (2010–present). The ecological niche remains conserved. The area of S. viarum PGDs had increased by 259 km2; the PGDs will expand to reach a maximum in the 2050s, SSP5–8.5. The PGDs of S. viarum will migrate to higher latitudes under the same future climate scenarios. The latitudes subject to high threats range from 20° to 30° in forest and cropland ecosystems, 15.5° to 27.5° (northern hemisphere) and 33.1° to 42.8° (southern hemisphere) in grassland ecosystems, and 20° to 35° in urban ecosystems. Global change has led to an increased threat of S. viarum at high latitudes. These findings provide a theoretical basis to monitor and control S. viarum.

Potential Distribution and Identification of Critical Areas for the Preservation and Recovery of Three Species of Cinchona L. (Rubiaceae) in Northeastern Peru

“…Species distribution models (SDMs) are tools that link species occurrence information to a range of bioclimatic, topographic, and edaphic variables. These tools play a vital role in protecting species, studying biogeography, and obtaining information on the impacts of climate change [23][24][25][26]. Within the field of SDMs, the maximum entropy algorithm (MaxEnt) estimates the potential distribution of a species, based on the principle that the most accurate prediction is achieved by optimizing the entropy of the distribution under specific environmental conditions [26].…”