Data from: Patch-burn grazing increased structural heterogeneity in southwestern North Dakota rangelands

Who: USDA ARS and NDSU range and wildlife researchers, graduate students, and undergraduate technicians
What: Structural characteristics and community composition collected from southwestern North Dakota rangelands from 2017 through 2020
Where: Hettinger Research Extension Center in Hettinger, North Dakota USA
6, 65 ha patch-burn grazing pastures were the primary data collection locationsWhy: These two files come from a patch-burn grazing study in southwestern North Dakota that were comparing an iteration of patch-burn grazing with cattle to a version of patch-burn grazing with sheep for the grazing component. Feel free to contact me at [e-mail] or [e-mail].
How: We used 0.5m x 0.5m quadrats to measure vegetation structure characteristics and community composition along 100m transects in patches (subsections) of larger pastures or management units. We measured 1 quadrat spaced every 10 m starting at 0 on both sides of the transect for 22 total quadrats per transect in patch-burn grazing pastures. Transects were distributed amongst patches of each pasture and management unit.
Data were analyzed using a combination of mixed-effect models and ordinations to compare time since fire (TSF) and grazer type (cattle or sheep).17_18_19_20vegFG.csv is the primary dataset for this paper and repository here. We collected vegetation structure and community composition data in 2017, 2018, 2019, and 2020.
Columns Year through PastPatch are various grouping variables used throughout the analysis.Pasture is the primary ID for a given unitBlock is the assigned set of pastures the pasture matchesTSF is the time since fire for a given locationUse is whether the pasture or management unit was managed for heterogeneity or homogeneityManagement is the grazer type for pbg pastures and hay or idle for management unitsPatch is a subsection of the pasture or management unitPastPatch is a combination of the pasture name with the patch numberVOR: Visual Obstruction Reading was measured using a Robel pole marked and recorded in 0.25 dm increments. We took four readings per quadrat and calculated an average score from these.MaxLive and MaxDead: these were the tallest living and tallest standing dead plant material within the quadrat measured in 0.25 dm increments using the Robel pole.LitMean: We measured litter depth using a ruler to the nearest cm in the four corners of each quadrat. After 2017, we started recording all four measurements instead of just recording the average of the four measurements.BGCover: bare ground cover is any exposed soil surface than can be seen when looking down on the quadrat. We expected this to be higher in recently burned patches.GCover: ground litter cover is any visible horizontal ground litter than can be seen when looking down on the quadrat. We expected this to be higher in recently burned patches.LitCover: vertical litter cover is any visible standing or vertical litter than can be seen when looking down on the quadrat. We expected this to be lower in recently burned patches.Columns ACMI through VIAM are the 4 letter species codes used during data collection on a tablet to record cover by cover class. The tablet was programmed to autorecord a '0' for species that were not present in the quadrat.Columns NatForb through NatShrub are the calculated cover values for finer scale groupings based on native and introduced status.Columns Forb through Litter are additional calculated cover values.RadGraph.csv was used to expedite making a community composition figure that is now in the supplemental materials for the paper.

本数据集的研究团队为美国农业部农业研究服务局（USDA ARS）与北达科他州立大学的牧场与野生动物研究者、研究生及本科技术员。
数据集采集内容为2017至2020年间北达科他州西南部牧场的植被结构特征与群落组成。
数据采集核心地点为美国北达科他州赫廷格市的赫廷格研究推广中心（Hettinger Research Extension Center），6块面积为65公顷的斑块火烧放牧（patch-burn grazing）牧场为主要数据采集点位。
本数据集源自北达科他州西南部的一项斑块火烧放牧研究，旨在对比肉牛放牧与绵羊放牧两种模式下的斑块火烧放牧方案。可通过[e-mail]或[e-mail]联系作者获取更多信息。
我们在大型牧场或管理单元的斑块（亚区块）内，沿100米样带设置0.5m×0.5m的样方，以采集植被结构特征与群落组成数据。样带两侧从0米处开始，每间隔10米设置1个样方，每条样带共设置22个样方。样带分布于各牧场及管理单元的所有斑块中。
本研究采用混合效应模型与排序分析相结合的方法，对比了火烧后时间（time since fire, TSF）与放牧者类型（肉牛或绵羊）对植被的影响。17_18_19_20vegFG.csv为本论文及本仓库的核心数据集，我们于2017、2018、2019及2020年完成了所有植被结构与群落组成数据的采集。
字段说明：
1. Year至PastPatch为全分析流程中使用的各类分组变量：
- Pasture：对应单个管理单元的唯一标识
- Block：牧场所属的指定分组集合
- TSF：目标点位的火烧后时间
- Use：牧场或管理单元的管理模式（异质性管理或同质性管理）
- Management：斑块火烧放牧牧场的放牧类型，以及管理单元的刈割或休耕模式
- Patch：牧场或管理单元的亚区块
- PastPatch：牧场名称与斑块编号的组合标识
2. VOR（可视阻碍读数，Visual Obstruction Reading）：采用标记精度为0.25 dm的Robel测杆进行测量，每个样方采集4次读数并计算平均值。
3. MaxLive与MaxDead：分别为样方内最高存活植株与最高枯立植株的高度，采用Robel测杆以0.25 dm为增量进行测量。
4. LitMean：采用直尺测量每个样方四个角落的枯落物厚度，精度至1厘米。2017年之后，我们开始记录全部4次测量值，而非仅记录平均值。
5. BGCover：裸土覆盖度，即样方俯视视角下可见的裸露土壤占比，我们推测近期火烧斑块的裸土覆盖度更高。
6. GCover：地面枯落物覆盖度，即样方俯视视角下可见的水平地面枯落物占比，我们推测近期火烧斑块的该指标值更高。
7. LitCover：垂直枯落物覆盖度，即样方俯视视角下可见的直立或垂直枯落物占比，我们推测近期火烧斑块的该指标值更低。
8. 列ACMI至VIAM为数据采集时使用的4字母物种代码，通过平板设备按覆盖等级记录物种盖度。平板程序会自动为样方中未出现的物种标记为"0"。
9. 列NatForb至NatShrub为基于原生/外来物种属性划分的精细类群盖度计算值。
10. 列Forb至Litter为额外计算的覆盖度分类值。
RadGraph.csv用于快速生成群落组成图，该图现已收录于论文的补充材料中。