互花米草(Spartina alterniflora)潜在分布格局的空间尺度效应

了解不同空间尺度下外来入侵植物互花米草(Spartina alterniflora)的潜在分布格局，有助于制定科学的防治管理策略，维护滨海湿地的生物多样性。研究基于有效的地理分布点位和环境变量数据集，设置了3个研究区幅度(区域、国家、全球)和5种环境变量粒度(30″、1.0′、2.5′、5.0′、10′),应用最大熵(MaxEnt)模型预测互花米草在不同幅度和粒度下的潜在分布，探究互花米草分布格局及其环境影响因子对空间尺度响应。结果表明：(1)MaxEnt模型在不同空间尺度下的预测效果较好，各尺度下测试集的受试者曲线下面积(AUC)值均大于0.8,真实技巧统计值(TSS)值则超过0.56,但模型的预测精度对空间尺度变化较为敏感；(2)不同空间尺度下互花米草的潜在分布格局存在着显著的差异性，表现为适生区面积会随着空间范围扩大或环境变量分辨率降低而提高，且质心位置也在不断发生地带性转移；(3)空间尺度的变化会削弱主要环境变量的解释力。在大尺度范围和低分辨率环境变量图层中，气候因子的重要性较大，而在相反尺度下地形因子的影响度得到提升；(4)研究区范围与环境变量分辨率不匹配时，模型预测精度和物...

Spatial scale effect of potential distribution pattern of Spartina alterniflora

To formulate effective management strategy and protect the biodiversity of coastal wetlands, there is an urgent need to understand the potential distribution pattern of Spartina alterniflora across different spatial scales. Based on the datasets available of known presences and environmental variables, we set up three study extents from region to global and five grain sizes of environmental variables from 30 arc-second to 10 arc-minute, and used maximum entropy modelling (MaxEnt) to predict the potential distribution of Spartina alterniflora under different spatial scales. On this basis, the habitat areas and its centroid position were measured at three study extents and five grain sizes, respectively. The effects of spatial scale on the relationships between environmental variables and species distributions were analyzed. Results showed that:(1) the performance of MaxEnt model was satisfactory, and the area under ROC curve (AUC) and true skill statistic (TSS) were greater than 0.8 and 0.56, respectively. However, model accuracy was sensitive to changes in spatial scale. AUC and TSS were negative correlated with study extent, but positive correlated with spatial resolution of the environmental variables. (2) There were significant changes in the distribution pattern of Spartina alterniflora across different spatial scales. When increasing study extent from region to global, the suitable area of Spartina alterniflora displayed an enlarging trend, with a significant zonal transfer for geometric centroids, while an increase in spatial resolution of environmental variables reduced the suitable habitats at the regional extent. (3) The importance of main environmental factors would be weakened with the change of spatial scale. At large-scale regions and low-resolution environmental layers, climatic variable type were the main drivers and contributed more than 60% of the variation, whereas at finer scales, this contribution decreased, but that of topography variable increased. (4) Notably, the significant shifts of the prediction accuracy and the distribution pattern occurred when a scale mismatch between study extents and spatial resolution of environmental variables. Therefore, it is recommended to use less than 1.0 arc-minute resolution of environmental variables as input to MaxEnt model for predicting the locally spatial pattern of Spartina alterniflora. When it exceeds 1.0 arc-minutes, the nation or global extent may be more appropriate.