Giant reed (Arundo donax) is a prevalent invasive plant in desert riparian ecosystems that threatens wildlife habitat. From 2008 to 2018, under a United States–Mexico partnership, prescribed burns and herbicide applications were used to remove giant reed and promote native revegetation along the Rio Grande – Río Bravo floodplain in west Texas, USA, and Mexico. Our goal was to explore the effects of the removal efforts on butterfly and bird communities and their habitat along the United States portion of the Rio Grande – Río Bravo floodplain in Big Bend National Park, Texas. During spring and summer, 2016–2017, we surveyed butterflies, birds, and their habitat using ground-collected and remotely sensed data. Using a variety of generalized linear and N-mixture modeling routines and multivariate analyses, we found that the initial giant reed removal efforts removed key components of riparian habitat leading to reduced butterfly and bird communities. Within several years following management, giant reed levels remained low, while riparian habitat conditions and butterfly and bird communities largely rebounded, including many disturbance-sensitive butterfly species and riparian-associated bird species. Butterflies were most consistently associated with forb and grass cover, and birds with a remotely sensed index of greenness (the normalized difference vegetation index), several vegetation cover types, and habitat heterogeneity, habitat elements that were most common in locations that had the longest time to recover following management actions. Our results suggest that prescribed burns and herbicide applications, when used following protocols to minimize risk to wildlife, can limit the spread of giant reed in desert riparian systems and introduce habitat conditions that support diverse and abundant 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.

We used R for all data analyses. We have attached R files, with code, adjoining the datasets used for analyses related to our objectives.

R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.