Geology topics

Sarah McCarthy-Neumann

Publications and source records attributed to Sarah McCarthy-Neumann.

3 recordsLinked to original sources

USGS2024

Assessing the risk of climate maladaptation for Canadian polar bears

The Arctic is warming four times faster than the rest of the world, threatening the persistence of many Arctic species. It is uncertain if Arctic wildlife will have sufficient time to adapt to such rapidly warming environments. We used genetic forecasting to measure the risk of maladaptation to warming temperatures and sea ice loss in polar bears ( Ursus maritimus ) sampled across the Canadian Arctic. We found evidence for local adaptation to sea ice conditions and temperature. Forecasting of genome-environment mismatches for predicted climate scenarios suggested that polar bears in the Canadian high Arctic had the greatest risk of becoming maladapted to climate warming. While Canadian high Arctic bears may be the most likely to become maladapted, all polar bears face potentially negative outcomes to climate change. Given the importance of the sea ice habitat to polar bears, we expect that maladaptation to future warming is already widespread across Canada.

Ecology Letters↗

USGS2022

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology↗

USGS2022

A forested wetland at a climate-induced tipping-point: 17-year demographic evidence of widespread tree recruitment failure

Regeneration and survival of forested wetlands are affected by environmental variables related to the hydrologic regime. Climate change, specifically alterations to precipitation patterns, may have outsized effects on these forests. In Tennessee, USA, precipitation has increased by 15% since 1960. The goal of our research was to assess the evidence for whether this change in precipitation patterns resulted in shorter growing seasons and recruitment failure in common canopy trees for a forest wetland. In 2001 and 2018, the density of Quercus lyrata (overcup oak), Liquidambar styraciflua (sweetgum), Quercus phellos (willow oak), and Betula nigra (river birch) seedling, sapling and adult density were mapped in an area of 2.3 ha within a seasonally flooded karst depression. Overall, the percentage of the growing season experiencing inundation was 26% greater in the deep than in shallow areas between 2001 and 2018. Saplings and small adults of all four species were restricted to shallow areas, and their abundance has declined substantially. Overcup oak and sweetgum individuals that were recruited into the adult life history stage were repelled from the deep zone. Overcup oak and sweetgum adults experienced lower mortality across the 2.3-ha study area (11% and 26%, respectively) relative to willow oak (56%) and river birch (64%) over the 17-year study. Growing-season inundation showed no relation to mortality in adult sweetgum and willow oak, a positive relation to mortality among adult river birch across size classes and among small adult overcup oak, and an inverse relation to mortality among large adult overcup oak. In shallow regions, overcup oak and sweetgum adults had greater basal area increment relative to the intermediate and deep regions of the pond. Results of hydrologic modeling for the study area, based on rainfall and temperature records covering 1855–2019, show ponding durations after 1970 considerably longer than the historical baseline, across ponding-depth classes. Our results strongly suggest that climate change is a driving factor suppressing tree regeneration since 1970 in this seasonally flooded karst depression.

Tennessee↗