Waite Ian R, Kennen Jonathan G, May Jason T, Brown Larry R, Cuffney Thomas F, Jones Kimberly A, Orlando James L
U.S. Geological Survey, Portland, Oregon, United States of America.
U.S. Geological Survey, West Trenton, New Jersey, United States of America.
PLoS One. 2014 Mar 27;9(3):e90944. doi: 10.1371/journal.pone.0090944. eCollection 2014.
We developed independent predictive disturbance models for a full regional data set and four individual ecoregions (Full Region vs. Individual Ecoregion models) to evaluate effects of spatial scale on the assessment of human landscape modification, on predicted response of stream biota, and the effect of other possible confounding factors, such as watershed size and elevation, on model performance. We selected macroinvertebrate sampling sites for model development (n = 591) and validation (n = 467) that met strict screening criteria from four proximal ecoregions in the northeastern U.S.: North Central Appalachians, Ridge and Valley, Northeastern Highlands, and Northern Piedmont. Models were developed using boosted regression tree (BRT) techniques for four macroinvertebrate metrics; results were compared among ecoregions and metrics. Comparing within a region but across the four macroinvertebrate metrics, the average richness of tolerant taxa (RichTOL) had the highest R(2) for BRT models. Across the four metrics, final BRT models had between four and seven explanatory variables and always included a variable related to urbanization (e.g., population density, percent urban, or percent manmade channels), and either a measure of hydrologic runoff (e.g., minimum April, average December, or maximum monthly runoff) and(or) a natural landscape factor (e.g., riparian slope, precipitation, and elevation), or a measure of riparian disturbance. Contrary to our expectations, Full Region models explained nearly as much variance in the macroinvertebrate data as Individual Ecoregion models, and taking into account watershed size or elevation did not appear to improve model performance. As a result, it may be advantageous for bioassessment programs to develop large regional models as a preliminary assessment of overall disturbance conditions as long as the range in natural landscape variability is not excessive.
我们针对完整区域数据集和四个独立生态区开发了独立的预测干扰模型（完整区域模型与独立生态区模型），以评估空间尺度对人类景观改造评估、溪流生物群预测响应的影响，以及其他可能的混杂因素（如流域面积和海拔）对模型性能的影响。我们从美国东北部四个相邻生态区（中北部阿巴拉契亚山脉、岭谷区、东北高地和北部皮埃蒙特）中选择了符合严格筛选标准的大型无脊椎动物采样点用于模型开发（n = 591）和验证（n = 467）。使用增强回归树（BRT）技术针对四个大型无脊椎动物指标开发模型；对各生态区和指标的结果进行了比较。在一个区域内比较但跨越四个大型无脊椎动物指标时，耐受性分类单元的平均丰富度（RichTOL）在BRT模型中具有最高的R²。在这四个指标中，最终的BRT模型有四到七个解释变量，并且总是包括一个与城市化相关的变量（例如，人口密度、城市百分比或人工渠道百分比），以及水文径流的一种度量（例如，4月最小值、12月平均值或月径流最大值）和（或）一个自然景观因子（例如，河岸坡度、降水量和海拔），或河岸干扰的一种度量。与我们的预期相反，完整区域模型对大型无脊椎动物数据的方差解释几乎与独立生态区模型一样多，并且考虑流域面积或海拔似乎并未提高模型性能。因此，对于生物评估计划而言，只要自然景观变异性的范围不过大，开发大型区域模型作为对整体干扰状况的初步评估可能是有利的。
