School of Agriculture, Policy and Development, University of Reading, Reading, UK.
Department of Geography and Environmental Science, University of Reading, Reading, UK.
Sci Rep. 2018 May 8;8(1):7168. doi: 10.1038/s41598-018-25437-1.
Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.
物种分布模型已被用于预测入侵物种的分布，以进行保护规划。了解生态位预测的空间可转移性对于促进物种-栖息地保护和预测易受入侵的区域至关重要。预测变量的粒度是影响物种分布模型准确性和可转移性的重要因素。粒度的选择通常取决于所使用的预测变量的类型，而预测因子的选择有时依赖于数据的可用性。本研究采用 MAXENT 物种分布模型，研究了粒度对入侵植物物种模型可转移性的影响。我们在英国威尔士模拟了 Rhododendron ponticum 的分布，并通过改变粒度（50m、300m 和 1km）来测试模型性能和可转移性。基于 MAXENT 的模型对粒度敏感，对变量的选择也敏感。我们发现，过度依赖常用的生物气候变量可能会导致模型不够准确，因为这通常会牺牲生物物理变量的更精细粒度，而这些变量在小空间尺度上可能是物种分布的更重要决定因素。随着粒度的减小，模型的准确性可能会增加。然而，成功的模型可转移性可能需要优化模型粒度。
