Search USGS⌕ Search
Home/Archive/USGS

USGS · 5223065

Bird use of agricultural fields under reduced and conventional tillage in the Texas Panhandle

Edward L. Flickinger·G.W. Pendleton
View original source ↗Read source document ↗Export metadata ↓

Abstract

We conducted bird surveys in reduced-tillage and conventional tillage fields in spring, summer, fall, and winter from 1987 to 1991 in the Texas Panhandle. Eastern meadowlarks, longspurs, and savannah sparrows were more common in reduced-tillage (sorghum and wheat stubble) fields than in conventionally tilled (plowed) fields in at least 1 season. Other species also had patterns suggestive of greater abundance in reduced-tillage fields. Hornedlarks, which prefer habitat with sparse vegetation, were more abundant in plowed fields in all seasons except summer. Bird diversity was greater in reduced-tillage fields than in conventionally tilled fields in summer. Cover density and height were greater in reduced tillage fields in all seasons except spring. Cover density and height rather than cover composition (e.g.,grain stubble or live plants) seemed to be the important factors affecting bird distribution. Patterns of bird abundance between sorghum and wheat stubble fields also were dependent on cover. Herbicide use was not greater in reduced-tillage fields than in conventionally tilled fields. Reduced-tillage agriculture for sorghum and wheat farming should be encouraged in the southern Great Plains as a means of improving the attractiveness of agricultural land to many bird species.

Explore related subjects

Wildlife Society BulletinArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Edward L. Flickinger, G.W. Pendleton. 1994. Bird use of agricultural fields under reduced and conventional tillage in the Texas Panhandle. https://pubs.usgs.gov/publication/5223065

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Considerations for using autonomous recording units to monitor northern bobwhite occupancy at large spatial scales

Northern bobwhites ( Colinus virginianus ) have experienced widespread declines across their range, increasing the need for population monitoring across broad spatial and temporal scales. Yet large-scale monitoring of post-breeding bobwhites remains limited by the time-intensive nature of fall (i.e., autumn) covey counts. Passive acoustic monitoring with autonomous recording units (ARUs) and automatic detection software may help overcome these constraints but requires further study. From 2017 to 2021, we paired in-person observer point counts with ARU surveys during the fall in southern Iowa, USA to 1) evaluate how ARU detections compare to observer detections, 2) assess the accuracy of automatic classification software for covey calls, 3) gauge the influence of weather and ordinal date on detection, and 4) offer best practices for processing ARU acoustic data. We found high correspondence (89.2%) in detection and non-detection outcomes between paired ARU and observer surveys. Across manually processed recordings, 95.7% of covey call detections occurred within a 35-min window (50–15 min before sunrise). Increasing wind speed and cloud cover were associated with reduced detection probabilities. Wind speeds ≥5 m/s resulted in detection probabilities <0.5. The cumulative probability of detecting bobwhites at a site exceeded 0.995 (95% Credible Interval [CrI] = 0.987, 0.999) after 4 survey days when technicians processed the files. Manual processing had the highest mean detection probability (0.75, 95% CrI = 0.66, 0.82) compared with automatic detection software with 2 different confidence thresholds (0.46 [95% CrI = 0.39, 0.53] and 0.45 [95% CrI = 0.36, 0.55]). Automatic detection software allowed for substantially faster processing with relatively few false positives (false positive rate of 0.014 to 0.038). Autonomous recording units coupled with automatic detection software provided a promising alternative to in-person observer point counts of bobwhite covey calls for determining site occupancy and have the potential to greatly increase capacity for landscape-level bobwhite monitoring.

Iowa↗
USGS2026

Developing a low-cost drone-based method for deploying and retrieving autonomous recording units in inaccessible areas

Autonomous Recording Units (ARUs) are increasingly used in research to support passive acoustic monitoring, but deployment in remote or inaccessible locations remains logistically challenging. Traditional manual placement is labor-intensive, time-consuming, potentially hazardous, and often limited to habitat edges, creating sampling biases and restricting spatial coverage. To address placement limitations in wetland systems, we developed and field-tested a low-cost, lightweight floating platform paired with a drone-based deployment and retrieval system. The drone-deployable ARU platform, constructed from inexpensive, off-the-shelf materials (~US $21 per unit), weighed ~560 g and was mounted with an ARU (AudioMoth) at ~1.2 m above water. The platform was designed for stability, portability, and compliance with Federal Aviation Administration Part 107 regulations. Field trials were conducted across 50 operations in various types of historical rice-field impoundments along the South Carolina coast, which provide critical habitat for secretive marsh birds such as rails (e.g ., Rallus, Laterallus , and Porzana ) and bitterns (e.g ., Botaurus and Ixobrychus ). All 50 deployments and retrievals were successful, demonstrating the robustness of the method under diverse hydrologic and vegetative conditions. Deployment times were significantly shorter than retrieval times (median 6 vs. 9 min), with retrieval requiring greater precision for hook engagement. Operation times scaled predictably with distance: deployment increased by 0.93 min/100 m (R 2 = 0.94) and retrieval by 1.17 min/100 m (R 2 = 0.95). No platform damage or displacement was observed, as emergent vegetation was likely providing natural anchoring. Our approach offers an affordable and effective solution for expanding ARU coverage in large, inaccessible wetlands, reducing sampling bias and enhancing biodiversity monitoring. With continued advances in drone payload capacity, battery endurance, and beyond-visual-line-of-sight flight regulations, our workflow could be adapted for deploying multi-sensor platforms (e.g., ARUs, eDNA samplers, water and air quality sensors, plant sample collectors, insect traps, and trail cameras) to support integrated biodiversity monitoring in challenging landscapes worldwide.

South Carollina↗
USGS2026

Comparison of data handling techniques for modeling bat acoustic activity

With the proliferation of acoustic sampling to investigate bat distribution and ecology, researchers have implemented a myriad of statistical modeling approaches to interpret findings. Bats are taxa of high conservation concern; therefore, ensuring the accuracy of species-level habitat association models is critical for informing management. We sought to determine prediction differences among statistical approaches to modeling counts of acoustic detections, using generalized linear mixed models with 8 acoustic data-handling techniques. We applied each approach or combination of approaches to a rare species, the northern long-eared bat ( Myotis septentrionalis ), and a common species, the eastern red bat ( Lasiurus borealis ), from summer survey results on a landscape in south-central Pennsylvania, USA, 2024. We evaluated the accuracy of habitat association models of bat acoustic activity at the species level using cross-validation and compared resulting predictions of models using spatial correlations. We determined that filtering data by automated identification software (Kaleidoscope Pro), the maximum likelihood estimate (MLE) P -value thresholds reduced relative mean absolute error (rMAE) in cross-validation of northern long-eared bat models. Using the MLE-retained data produced the most accurate predictions over using raw data or the overly conservative match ratio data. However, for the eastern red bat, the results from the most conservative approach of only retaining data with at least a 90% match ratio from software development training sets had the lowest rMAE. We have provided evidence that the current standard of filtering data by nightly MLE can result in more accurate and informative habitat-use acoustic activity models for rare bat species.

Pennsylvania↗