Geology topics

Reesa Y. Conrey

Publications and source records attributed to Reesa Y. Conrey.

4 recordsLinked to original sources

USGS2026

Activity, but not size of Black-tailed Praire Dog colonies, is associated with higher Athene cunicularia hypugaea (Western Burrowing Owl) occupancy and reproductive success in the shortgrass prairie

Conservation in fragmented ecosystems, such as grasslands, has historically put more value on larger habitat patches but recent research suggests that small, high-quality habitat patches hold important conservation value. In many grassland systems, Athene cunicularia hypugaea (Western Burrowing Owl) relies on habitat patches created by Cynomys ludovicianus (Black-tailed Prairie Dog; hereafter prairie dog). Prairie dogs create important nesting habitat for A. c. hypugaea and other grassland birds. We examined the effect of size and characteristics of prairie dog colonies on A. c. hypugaea occupancy and reproductive success. We specifically looked at how colony size, prairie dog activity level, and vegetation characteristics influence these population parameters on 175 survey plots throughout eastern Colorado, U.S., across two sample years. Results are based on detections of adult and owlet A. c. hypugaea collected by paired observers traversing transects through study plots during the 2022 and 2023 A. c. hypugaea nesting seasons (May–August). Our top multistate occupancy model indicated that latitude affects A. c. hypugaea occupancy probabilities. Occupancy was higher in southern Colorado compared to northern Colorado. In addition, prairie dog activity was positively associated with A. c. hypugaea reproductive success. Colony size and vegetation characteristics were generally uninformative predictors of A. c. hypugaea occupancy and reproductive success. We compared our results to a previous A. c. hypugaea population assessment conducted within our study area in 2005 and found that active prairie dog colonies positively affected A. c. hypugaea local colonization while local extinction was driven by a transition of active prairie dog colonies to inactive. This study highlights the importance of high-quality prairie dog habitat patches for A. c. hypugaea nesting in fragmented grassland ecosystems, regardless of patch size.

Colorado↗

USGS2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

Ecology and Evolution↗

USGS2016

Extremes of heat, drought and precipitation depress reproductive performance in shortgrass prairie passerines

Climate change elevates conservation concerns worldwide because it is likely to exacerbate many identified threats to animal populations. In recent decades, grassland birds have declined faster than other North American bird species, a loss thought to be due to habitat loss and fragmentation and changing agricultural practices. Climate change poses additional threats of unknown magnitude to these already declining populations. We examined how seasonal and daily weather conditions over 10 years influenced nest survival of five species of insectivorous passerines native to the shortgrass prairie and evaluate our findings relative to future climate predictions for this region. Daily nest survival ( n = 870) was best predicted by a combination of daily and seasonal weather variables, age of nest, time in season and bird habitat guild. Within a season, survival rates were lower on very hot days (temperatures ≥ 35 °C), on dry days (with a lag of 1 day) and on stormy days (especially for those species nesting in shorter vegetation). Across years, survival rates were also lower during warmer and drier breeding seasons. Clutch sizes were larger when early spring temperatures were cool and the week prior to egg-laying was wetter and warming. Climate change is likely to exacerbate grassland bird population declines because projected climate conditions include rising temperatures, more prolonged drought and more intense storms as the hydrological cycle is altered. Under varying realistic scenarios, nest success estimates were halved compared to their current average value when models both increased the temperature (3 °C) and decreased precipitation (two additional dry days during a nesting period), thus underscoring a sense of urgency in identifying and addressing the current causes of range-wide declines.

Ibis↗

USGS2016

Vulnerability of shortgrass prairie bird assemblages to climate change

The habitats and resources needed to support grassland birds endemic to North American prairie ecosystems are seriously threatened by impending climate change. To assess the vulnerability of grassland birds to climate change, we consider various components of vulnerability, including sensitivity, exposure, and adaptive capacity (Glick et al. 2011). Sensitivity encompasses the innate characteristics of a species and, in this context, is related to a species’ tolerance to changes in weather patterns. Groundnesting birds, including prairie birds, are particularly responsive to heat waves combined with drought conditions, as revealed by abundance and distribution patterns (Albright et al. 2010). To further assess sensitivity, we estimated reproductive parameters of nearly 3000 breeding attempts of a suite of prairie birds relative to prevailing weather. Fluctuations in weather conditions in eastern Colorado, 1997-2014, influenced breeding performance of a suite of avian species endemic to the shortgrass prairie, many of which have experienced recent population declines. High summer temperatures and intense rain events corresponded with lower nest survival for most species. Although dry conditions favored nest survival of Burrowing Owls and Mountain Plovers (Conrey 2010, Dreitz et al. 2012), drought resulted in smaller clutch sizes and lower nest survival for passerines (Skagen and Yackel Adams 2012, Conrey et al. in review). Declining summer precipitation may reduce the likelihood that some passerine species can maintain stable breeding populations in this region of the shortgrass prairie.

Conference Paper↗