4.5 Article

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

期刊

ATMOSPHERE
卷 12, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/atmos12050543

关键词

Chelsa; ecological niche modeling; input variable selection; model comparison; model evaluation; prediction bias; Worldclim

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This study compares the global climate datasets Worldclim and Chelsa and evaluates their suitability for ecological modeling studies, especially in regions with limited data availability. Results show that Chelsa-based models outperform Worldclim-based models under current climatic conditions, but caution against the potential drawbacks in using climate data for high-altitude treeline species modeling and future projections. Collaboration between climate scientists and ecologists is emphasized to enhance the quality of climate-based ecological models at meso- to local-scales.
Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

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