DOI: 10.5061/dryad.31zcrjdqn
title: Removing invasive giant reed reshapes desert riparian butterfly and bird communities

description: 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. 
description: 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 (&lt;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.
description: 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/.