Geology topics

Matthew J. Butler

Publications and source records attributed to Matthew J. Butler.

4 recordsLinked to original sources

USGS2025

Variation in detection distance of Eastern Black Rail (Laterallus jamaicensis jamaicensis) vocalizations by autonomous recording units

Autonomous recording units (ARUs) are an emerging technology that allows for passive monitoring of soniferous animals and soundscapes. Over the past decade, ARUs have become a popular tool for monitoring birds for their potential to reduce the labor and costs of traditional in-person sampling procedures. However, uncertainty surrounding factors affecting detection of avian taxa using ARUs can inhibit their monitoring efficacy. Eastern Black Rails ( Laterallus jamaicensis jamaicensis ) are a secretive marsh bird listed as a federally threatened species in the U.S.A. Eastern Black Rail vocalizations are difficult to detect by field personnel, and numerous in-person surveys can be required to confirm their presence at a site. While ARUs are an alternative for detecting Eastern Black Rails, it is unknown at what maximum distance an ARU can detect their vocalizations. We evaluated factors affecting the detection distance of simulated vocalizations for ARUs in four marsh vegetation types under a range of environmental conditions. Detection distances varied across models, vocalization and vegetation types, and call volume. Kickeedo vocalizations were detected at greater distances, and detection distances increased for all vocalization types in open vegetation. High relative humidity increased detection distances, while louder background noise decreased detection distances. High wind speeds in cordgrass ( Spartina spp .) decreased detection probability disproportionately relative to other vegetation types. Based on these results, considerations of survey area, vegetation type, and site condition can allow land managers and researchers to optimize Eastern Black Rail monitoring using ARUs. Given the substantial staff time needed to monitor this species, ARUs may increase the likelihood of detection and provide an efficient alternative to in-person monitoring.

Waterbirds↗

USGS2024

Management implications of habitat selection by whooping cranes (Grus americana) on the Texas coast

Effective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes ( Grus americana ) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat.

Texas↗

USGS2022

The abundance and persistence of Caprinae populations

Stable or growing populations may go extinct when their sizes cannot withstand large swings in temporal variation and stochastic forces. Hence, the minimum abundance threshold defining when populations can persist without human intervention forms a key conservation parameter. We identify this threshold for many populations of Caprinae , typically threatened species lacking demographic data. Doing so helps triage conservation and management actions for threatened or harvested populations. Methodologically, we used population projection matrices and simulations, with starting abundance, recruitment, and adult female survival predicting future abundance, growth rate (λ), and population trend. We incorporated mean demographic rates representative of Caprinae populations and corresponding variances from desert bighorn sheep ( Ovis canadensis nelsoni ), as a proxy for Caprinae sharing similar life histories. We found a population’s minimum abundance resulting in ≤ 0.01 chance of quasi-extinction ( QE ; population ≤ 5 adult females) in 10 years and ≤ 0.10 QE in 30 years as 50 adult females, or 70 were translocation (removals) pursued. Discovering the threshold required 3 demographic parameters. We show, however, that monitoring populations’ relationships to this threshold requires only abundance and recruitment data. This applied approach avoids the logistical and cost hurdles in measuring female survival, making assays of population persistence more practical.

Scientific Reports↗

USGS2022

Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes

The expansion of human infrastructure has contributed to novel risks and disturbance regimes in most ecosystems, leading to considerable uncertainty about how species will respond to altered landscapes. A recent assessment revealed that whooping cranes ( Grus americana ), an endangered migratory waterbird species, avoid wind-energy infrastructure during migration. However, uncertainties regarding collective impacts of other types of human infrastructure, such as power lines on migration, variable drought conditions, and continued construction of wind energy infrastructure may compromise ongoing recovery efforts for whooping cranes. Droughts are increasing in frequency and severity throughout the whooping crane migration corridor, and the impacts of drought on stopover habitat use are largely unknown. Moreover, decision-based analyses are increasingly advocated to guide recovery planning for endangered species, yet applications remain rare. Using GPS locations from 57 whooping cranes from 2010 through 2016 in the United States Great Plains, we assessed habitat selection and avoidance of potential disturbances during migration relative to drought conditions, and we used these results in an optimization analysis to select potential sites for new wind energy developments that minimize relative habitat loss for whooping cranes and maximize wind energy potential. Drought occurrence and severity varied spatially and temporally across the migration corridor during our study period. Whooping cranes rarely used areas <5 km from human settlements and wind energy infrastructure under both drought and non-drought conditions, and <2 km from power lines during non-drought conditions, with the lowest likelihood of use near wind energy infrastructure. Whooping cranes differed in their selection of wetland and cropland land cover types depending on drought or non-drought conditions. We identified scenarios for wind energy expansion across the migration corridor and in select states, which are robust to uncertain drought conditions, where future loss of highly selected stopover habitats could be minimized under a common strategy. Our approach was to estimate functional habitat loss while integrating current disturbances, potential future disturbances, and uncertainty in drought conditions. Therefore, dynamic models describing potential costs associated with risk-averse behaviors resulting from future developments can inform proactive conservation before population impacts occur.

Kansas, Montana, Nebraska, North Dakota, South Dak↗