LAGOS-NE Shallow Lakes: a dataset of lake variables and multi-scaled ecological context variables used to predict and compare trophic status and TP:CHLa relationships between shallow and non-shallow lakes in the Upper Midwest and Northeastern United States.

We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.

本研究针对美国中西部北部与东北部地区的2210个浅水湖泊开展大尺度调研，浅水湖泊定义为平均水深≤3米或最大水深≤5米的水体。本研究旨在厘清三大科学问题：大尺度下浅水湖泊总磷（Total Phosphorus, TP）、叶绿素a（Chlorophyll a, CHLa）浓度及二者关联关系的分布模式与驱动因子；上述特征与4360个非浅水湖泊存在何种差异；研究结果是否会因水文连通性类别的不同而产生变化。为解答上述问题，本研究整合现有LAGOS-NE、LAGOS-DEPTH及LAGOS-CLIMATE数据集，构建了本文所述的LAGOS-NE浅水湖泊数据集。本数据集的响应变量为夏季（即6月15日至9月15日）可获取的总磷与叶绿素a浓度的中位数。预测变量选取两个空间尺度的数据，用于分层模型构建：在局地或湖泊专属尺度（涵盖单个湖泊、湖间流域[iws]或对应HU12水文单元流域），变量包含土地利用/覆被、水文、气候、湖貌特征及酸沉降相关指标；在区域尺度（如HU4水文单元流域），仅选取少量水文与土地利用/覆被相关的预测变量。本数据集同时包含LAGOS-NE分配的唯一标识符（lagoslakeid）、研究湖泊的经纬度、最大与平均水深及水深分类（浅水或非浅水）、水文连通性类别（即分为具有入水口与出水口的连通型湖泊，以及仅缺乏入水口的非连通型湖泊），以及各湖泊所属HU4水文单元的区域编码。本数据集附带用于构建预测TP或CHLa浓度的分层模型的R脚本（TPorCHL_predictive_model.R），以及针对研究湖泊按水深与连通性分组的TP-CHLa关联关系模型的R脚本（TP_CHL_CSI_Model.R）。