Potential present and future distributions of the genus Atta of Mexico

Abstract

Temperature and precipitation influence insect distribution locally and drive large-scale biogeographical patterns. We used current and future climate data from the CHELSA database to create ensemble species distribution models for three Atta leaf-cutting ant species (Atta cephalotes, A. mexicana, and A. texana) found in Mexico. These models were used to estimate the potential impact of climate change on the distribution of these species in the future. Our results show that bioclimatic variables influence the distribution of each Atta species occupying a unique climatic niche: A. cephalotes is affected by temperature seasonality, A. mexicana by isothermality, and A. texana by the minimum temperature of the coldest month. Atta texana and A. mexicana are expected to decline their range by 80% and 60%, respectively, due to rising temperatures, decreased rainfall, and increased drought. Due to rising temperatures and increased humidity, Atta cephalotes is expected to expand its range by 30%. Since Atta species are important pests, our coexistence with them requires knowledge of their ecological functions and potential future distribution changes. In addition, these insects serve as bioindicators of habitat quality, and they can contribute to the local economy in rural areas since they are eaten as food for the nutritional value of the queens. In this sense, presenting a future perspective of these species’ distribution is important for forest and crop management. Education programs also are necessary to raise awareness of the importance of these ants and the challenges they face because of climate change. Our results offer a perspective of climate change studies to define conservation and adaptation strategies for protecting vulnerable areas such as high-elevation remnant forests.

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].…”