The Cooperative Wildlife Research Unit and wildlife education: Historic development, future challenges

John A. Bissonette; Cynthia S. Loftin; David M. Leslie; L. A. Nordstrom; W. James Fleming

Home/Archive/USGS

USGS · 70023186

The Cooperative Wildlife Research Unit and wildlife education: Historic development, future challenges

John A. Bissonette·Cynthia S. Loftin·David M. Leslie·L. A. Nordstrom·W. James Fleming

View original source ↗Export metadata ↓

Abstract

In 1932, J. N. 'Ding' Darling proposed a 3-year tripartite arrangement between the Iowa Fish and Game Commission, Iowa State University, and himself to establish the first Cooperative Wildlife Research Unit. Three years later, the Cooperative Wildlife Research Unit Program was broadened to include 9 land-grant colleges representing recognized ecoregions in the United States. In 1960, the Units were given statutory recognition by Public Law 86-686 that also included provision for establishing Cooperative Fishery Units. The Cooperative Research Unit idea has evolved to 39 Units in 2000. Today, the main cooperators of the Unit program are the land-grant university, the state fish and game or conservation agency, the U.S. Geological Survey, and the Wildlife Management Institute. The Cooperative Units mission, as stated in Public Law 86-686, remains: 'To facilitate cooperation between the Federal Government, colleges and universities, and private organizations for cooperative unit programs of research and education relating to fish and wildlife and for other purposes.' Graduate research and education continue to be the program's primary missions. In any given year >600 graduate and post-graduate students are involved. Post-graduate employment of Unit-afffiliated students is >90%. Perhaps the primary benefit to the education process is the Units' formal connection to the state cooperator and to their federal agency that might not otherwise be available to university faculty and students. Units are conduits to state and federal funding for research projects conducted by university faculty and students. The CRU program is well positioned to educate a multitalented, ethnically diverse cadre of graduate students who will be prepared not only for their first professional job but also for their career by having been instilled with a desire for life-long professional accomplishment.

Explore related subjects

Wildlife Society Bulletin Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

John A. Bissonette, Cynthia S. Loftin, David M. Leslie, L. A. Nordstrom, W. James Fleming. 2000. The Cooperative Wildlife Research Unit and wildlife education: Historic development, future challenges. https://pubs.usgs.gov/publication/70023186

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

KEEP EXPLORING

Related USGS reports

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↗

USGS2026

Abundance, trends, and challenges facing mountain goats throughout their North American distribution

Recent declines among some mountain goat ( Oreamnos americanus ) populations have heightened concern about their current status and ability to cope with future challenges. We conducted a range-wide assessment of the status of mountain goats across their distribution to understand the extent and patterns of change in recent years. We queried states, provinces, National Parks, and Indigenous governments with territories and reserves containing local wild populations, requesting updates on estimated mountain goat abundance and trends over time, as well as qualitative assessments of conservation challenges. We supplemented the questionnaires with existing literature (both published and available from agency websites). We subjected a subset of mountain goat population time−series to exploratory meta-analyses, examining covariates associated with rates of change. Respondents identified 203 units within which estimates were documented, a majority of which were raw counts from aircraft; fewer jurisdictions used site-specific sightability models, mark-recapture statistics, or other approaches to account for imperfect detection. Jurisdictions updated unit-specific estimates on average every 4.0 years (SD = 2.9, Min–Max = 1–24 years). Among 152 qualitative estimates of trends of native populations, 3% were reported as having increased substantially, 10% as having increased slightly, 24% as having declined slightly, and 14% as having declined substantially (49% reported either no detectable trend or insufficient data to identify a trend). Among 133 estimates of trends of introduced or pioneering populations, 5% increased substantially, 24% increased slightly, 20% declined slightly, and 11% declined substantially (40% reported no detectable trend). Jurisdiction-wide abundance estimates were lower than those previously published in all jurisdictions except Colorado and Oregon, and in Alaska, where trends were unclear. Intrinsic rates of increase ( r ) were positively associated with the population being introduced or pioneering, negatively associated with heavy snow and, to a lesser extent, with drought, but were not associated with abundance or presence of hunter harvest. Existing evidence suggested that many local populations, especially native populations, have declined, but understanding of specific causes has been constrained by limited monitoring capacity. Greater spatiotemporal monitoring effort and detailed studies would inform appropriate strategies to address threats and future challenges to mountain goats.

Wildlife Society Bulletin↗