Predictive Modeling of Amphibian Distribution in Central Mexico

Central Mexico harbours a remarkable diversity of amphibian fauna, many of which are micro-endemic and confined to montane cloud forests, volcanic slopes and riparian corridors. Predictive modelling of amphibian distribution integrates occurrence records, environmental layers and statistical or machine-learning algorithms to estimate habitat suitability, identify potential refugia and forecast range shifts under climate change. In this region, topography interacts with precipitation regimes and temperature gradients to create a mosaic of microclimates that shape species’ realised niches. Models commonly draw upon bioclimatic variables, land-cover maps derived from remote sensing and geophysical data to delineate areas of high conservation priority. Such approaches inform protected-area design, restoration efforts and translocation planning by pinpointing corridors for gene flow and anticipating vulnerability to anthropogenic pressure, including deforestation, invasive species and changing hydrological patterns. Recent advances have emphasised ensemble forecasting, model evaluation using true-skill statistics and area-under-curve metrics, and the incorporation of fine-scale vegetation indices to capture seasonal habitat dynamics. By linking predictive outputs with population-density surveys and genetic structure analyses, researchers are moving towards an integrated habitat-viability framework that supports evidence-based management of imperilled salamanders and frogs in the Trans-Mexican Volcanic Belt and adjacent highlands.

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Predictive Modeling of Amphibian Distribution in Central Mexico publication trend

The graph below shows the total number of articles in predictive modeling of amphibian distribution in central mexico across all publications each year (not limited to Nature Index journals).

Technical terms

Ecological niche modelling (ENM): A computational method that relates species occurrence data to environmental variables to predict spatial habitat suitability.

Maximum entropy (MaxEnt): A machine-learning algorithm widely used in ENM to estimate probability distributions of species presence from presence-only records.

Bioclimatic variables: Climate-derived parameters (e.g. annual precipitation, temperature seasonality) that influence species distributions.

Habitat connectivity: The degree to which landscape elements facilitate movement and gene flow among wildlife populations.

True-skill statistic (TSS): A model evaluation metric combining sensitivity and specificity to assess predictive performance.

References

1. Present and future ecological niche modeling of garter snake species from the Trans-Mexican Volcanic Belt. PeerJ (2018).
2. Potential distribution and habitat connectivity of Crotalus triseriatus in Central Mexico. Herpetozoa (2019).
3. Microhabitat Types Promote the Genetic Structure of a Micro-Endemic and Critically Endangered Mole Salamander (Ambystoma leorae) of Central Mexico. PLOS ONE (2014).