Modelling and validation of the spatial distribution of suitable habitats for the recruitment of invasive plants on climate change scenarios: An approach from the regeneration niche
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The regeneration niche concept states that plant species only occur in habitats where the environmental conditions allow their recruitment. This study focuses on this concept and proposes a novel approach for modelling and experimentally validating the distribution of suitable habitats for the recruitment of invasive plants under the current and future climate. The biological invasion of the Peruvian peppertree (Schinus molle) in Mexico is used as practical example. The values of eight bioclimatic variables associated to sites in which young, naturally established seedlings and saplings were detected were used to model the current distribution of recruitment habitats. A machine-learning algorithm of maximum entropy (MaxEnt) was used to calibrate the model and its output indicated the distribution of occurrence probabilities of young peppertrees in Mexico under the current climate. This model was projected on climate change scenarios predicted for the middle of this century, which indicated that the cover of suitable recruitment habitats for this invasive species will shrink. To validate these predictions, field experiments were performed at three sites where the model predicted reduced occurrence probabilities of young peppertrees. In these experiments, emergence and survival rates of peppertree seedlings were assessed under the current climate and under simulated climate change conditions. As seedling emergence and survival rates were lower under simulated climate change conditions, the experiments validated the model predictions. These results supported our proposal, which combines modelling and experimental approaches to make accurate and valid predictions about the distribution of suitable recruitment habitats for invasive plants in a warmer and drier world. © 2021 Elsevier B.V.
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Biological invasions; Habitat suitability models; MaxEnt; Open-top chambers; Peruvian peppertree; Rainout shelters Climate change; Climate models; Forecasting; Learning algorithms; Machine learning; Maximum entropy methods; Population distribution; Probability distributions; 'current; Biological invasion; Climate change scenarios; Habitat-suitability models; Invasive plants; Maximum-entropy; Open top chambers; Peruvian peppertree; Rainout shelter; Suitable habitat; Ecosystems; biological invasion; climate change; habitat quality; invasive species; niche; plant; recruitment (population dynamics); regeneration; shelter; spatial distribution; tree; article; climate change; controlled study; field experiment; habitat; invasive species; machine learning; maximum entropy model; Mexico; nonhuman; prediction; regeneration; sapling; Schinus; seedling emergence; simulation; species invasion; survival rate; ecosystem; entropy; introduced species; Mexico [North America]; Schinus molle; Climate Change; Ecosystem; Entropy; Introduced Species; Mexico
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