Removing invasive giant reed reshapes desert riparian butterfly and bird communities

Sampling design We used a space-for-time sampling design, sampling within the available floodplain area to capture conditions in unburned locations with and without giant reed and burned locations at different times since the last burn. We identified 167 sites distributed throughout the Rio Grande floodplain in BIBE, ranging from Santa Elena Canyon in the western portion of the park to Boquillas Canyon in the east. We used the following criteria to assign sites into 4 management groups, which we used for the basis of our analyses. We used the group recent (n = 21) for sites burned ≤3 years before sampling. These were characterized by bare ground, sparse vegetation, and some resprouting giant reed. The group older (n = 19) described sites burned ≥4 years before sampling. These were characterized by the regrowth of riparian vegetation and some resprouting giant reed. We used the group unburned floodplain (n = 16) for unburned, non-forest sites without significant giant reed cover (<3% cover). We used this group to characterize typical non-forest floodplain conditions. Of the original 167 sites, our analysis included 73 that met the above criteria. The additional 94 sites were primarily in more upland forested floodplain sites (principally honey mesquite gallery forest), which, as previously indicated, was not a target of the management activities Butterfly and bird surveys We surveyed birds and butterflies at each site from May to July 2016 and 2017. We conducted 3 counts at each site each year, with a fourth butterfly count in 2017 to capture mid-summer monsoonal activity. We used 5-minute point counts to record all birds detected by sight or sound within a 100-m radius. To survey butterflies, we established 10 × 100-m belt transects, centered at a bird point count location and oriented approximately parallel to the river. An observer slowly walked the centerline, recording butterflies within a 5-m grid of the observer. Habitat characterization: remote sensing and field surveys We quantified habitat characteristics in the floodplain using a cloud-free NAIP color-infrared air photo mosaic with 1-m2 spatial resolution collected in 2016 (Figure 2). From this image, we computed a vegetation greenness index (the normalized difference vegetation index [NDVI]). We calculated NDVI for each image pixel, then computed the mean value within each 100-m bird survey radius, excluding water pixels. We also computed image texture as the second-order standard deviation of NDVI, capturing the variability in pixel value greenness across a defined area. We first calculated the standard deviation of pixel values within a 5 × 5-pixel moving window and assigned this value to the center pixel. We then calculated the standard deviation of those values within the 100-m survey radius. We computed the NDVI and habitat heterogeneity calculations using the image analysis and focal stats tools in ArcGIS 10.5.1. To quantify the cover of vegetation classes from the NAIP image, we first defined 4 classes that broadly characterized floodplain vegetation and were relevant for giant reed management and for quantifying riparian wildlife habitat use. These classes were giant reed, xeric woody vegetation, mesic woody vegetation, and low herbaceous vegetation (i.e., forbs and grasses, excluding giant reed). The cover of grass and forb vegetation are important butterfly habitat features that were difficult to distinguish from one another with remote sensing. Therefore, we included these 2 ground-based habitat measures in butterfly analyses, replacing the image-based herbaceous cover estimates. We estimated the proportional cover of forbs and grasses (excluding giant reed) within the butterfly transects by visual estimation in the field using a relevé method. Observers walked the length of each transect and sketched the cover of grass and forb vegetation within the 5-m gridded rectangular outline of the transect, from which we estimated cover.

采样设计
本研究采用空间替代时间采样设计，在现有洪泛平原区域内开展采样，以覆盖未焚烧且有、无巨芦苇的位点，以及末次焚烧后不同时间的焚烧位点。我们在BIBE区域内的里奥格兰德河洪泛平原中共选定167个采样位点，分布范围从园区西部的圣埃琳娜峡谷至东部的博奎拉斯峡谷。我们依据以下标准将位点划分为4个管理组别，作为后续分析的基础：
近期焚烧组（n=21）：采样前≤3年发生过焚烧的位点，特征为裸地分布、植被稀疏，伴生部分萌蘖的巨芦苇；
远期焚烧组（n=19）：采样前≥4年发生过焚烧的位点，特征为河岸植被已恢复，仍存在部分萌蘖的巨芦苇；
未焚烧洪泛平原组（n=16）：未发生焚烧、非森林生境且巨芦苇覆盖度无显著升高（<3%）的位点，用于表征典型的非森林洪泛平原生境。
原始167个位点中，共有73个符合上述标准并纳入本研究分析，剩余94个位点主要分布于高地森林洪泛平原（以蜜牧豆树廊道林为主），如前所述，这类生境并非本次管理活动的目标对象。

蝴蝶与鸟类调查
2016年与2017年的5-7月期间，我们在每个采样位点开展鸟类与蝴蝶调查。每年在每个位点开展3次计数，2017年额外增加1次蝴蝶计数以覆盖夏季季风期的活动高峰。鸟类调查采用5分钟点计数法，记录100米半径范围内通过目视或鸣听发现的所有鸟类。蝴蝶调查则设置10×100米的带状样带，样带中心与鸟类点计数位点重合，且大致平行于河流走向。观察者沿样带中心线缓慢行进，记录自身周围5米网格范围内的蝴蝶个体。

生境表征：遥感与野外调查
本研究采用2016年采集的无云NAIP彩色红外航空影像镶嵌图（空间分辨率1平方米，图2）量化洪泛平原生境特征。基于该影像，我们计算了植被绿度指数——归一化差异植被指数（normalized difference vegetation index, NDVI）：首先对每个影像像素计算NDVI值，随后在每个100米鸟类调查半径范围内计算NDVI平均值，并排除水体像素。我们同时通过NDVI的二阶标准差计算影像纹理，以表征特定区域内像素绿度值的空间变异性：首先在5×5像素的移动窗口内计算像素NDVI值的标准差，并将该结果赋值给窗口中心像素，随后在100米调查半径范围内计算这些窗口标准差的整体标准差。上述NDVI与生境异质性的计算均通过ArcGIS 10.5.1的影像分析与焦点统计工具完成。

为从NAIP影像中量化植被类别的覆盖度，我们首先定义了4大类可概括洪泛平原植被特征、且与巨芦苇管理及河岸野生动物生境利用量化相关的植被类型，分别为巨芦苇、旱生木本植被、湿生木本植被以及低草本植被（即除巨芦苇外的阔叶草本与禾草）。由于禾草与阔叶草本的覆盖度是重要的蝴蝶生境特征，但遥感手段难以对二者进行有效区分，因此我们在蝴蝶分析中采用两项地面生境实测指标替代基于影像的草本覆盖度估算结果。我们通过样方调查法（relevé method）在野外目视估算蝴蝶样带内阔叶草本与禾草（不含巨芦苇）的比例覆盖度：观察者沿每条样带行进，在样带的5米网格矩形轮廓内手绘草本与阔叶草本的覆盖分布情况，据此完成覆盖度估算。