Department of Earth Sciences, University of Memphis, Memphis, Tennessee, United States of America.
Department of Geography, University at Buffalo, Buffalo, New York, United States of America.
PLoS One. 2018 Sep 18;13(9):e0203881. doi: 10.1371/journal.pone.0203881. eCollection 2018.
Assessing geographic patterns of species richness is essential to develop biological conservation as well as to understand the processes that shape these patterns. We aim to improve geographic prediction of tree species richness (TSR) across eastern USA by using: 1) gridded point-sample data rather than spatially generalized range maps for the TSR outcome variable, 2) new predictor variables (forest area FA; mean frost day frequency MFDF) and 3) regression models that account for spatial autocorrelation. TSR was estimated in 50 km by 50 km grids using Forest Inventory and Analysis (FIA) point-sample data. Eighteen environmental predictor variables were employed, with the most effective set selected by a LASSO that reduced multicollinearity. Those predictors were then employed in Generalized linear models (GLMs), and in Eigenvector spatial filtering (ESF) models that accounted for spatial autocorrelation. Models were evaluated by model fit statistics, spatial patterns of TSR predictions, and spatial autocorrelation. Our results showed gridded TSR was best-predicted by the ESF model that used, in descending order of influence: precipitation seasonality, mean precipitation in the driest quarter, FA, and MFDF. ESF models, by accounting for spatial autocorrelation, outperformed GLMs regardless of the predictors employed, as indicated by percent deviance explained and spatial autocorrelation of residuals. Small regions with low TSR, such as the Midwest prairie peninsula, were successfully predicted by ESF models, but not by GLMs or other studies. Gridded TSR in Florida was only correctly predicted by the ESF model with FA and MFDF, and was over-predicted by all other models.
评估物种丰富度的地理格局对于制定生物保护措施以及理解塑造这些格局的过程至关重要。我们旨在通过以下方法提高美国东部树木物种丰富度（TSR）的地理预测能力：1）使用网格化点样本数据而非 TSR 结果变量的空间概括范围图，2）使用新的预测变量（森林面积 FA；平均霜日频率 MFDF），以及 3）考虑空间自相关的回归模型。TSR 是使用森林清查和分析（FIA）点样本数据在 50 公里×50 公里的网格中估算的。使用了 18 个环境预测变量，通过减少多重共线性的 LASSO 选择了最有效的变量集。然后，将这些预测变量用于广义线性模型（GLMs）和考虑空间自相关的特征向量空间滤波（ESF）模型中。通过模型拟合统计量、TSR 预测的空间格局和空间自相关来评估模型。我们的结果表明，网格化 TSR 最佳预测模型是使用特征向量空间滤波的 ESF 模型，按影响降序排列，依次为降水季节性、最干旱季度的平均降水、FA 和 MFDF。ESF 模型通过考虑空间自相关，无论使用何种预测变量，都优于 GLMs，这表现在解释的百分比偏差和残差的空间自相关上。低 TSR 的小区域，如中西部草原半岛，成功地被 ESF 模型预测，但 GLMs 或其他研究则无法预测。佛罗里达州的网格化 TSR 仅通过包含 FA 和 MFDF 的 ESF 模型正确预测，而其他所有模型都高估了该地区的 TSR。
