Geology topics

Lara S. Katz

Publications and source records attributed to Lara S. Katz.

4 recordsLinked to original sources

USGS2026

Habitat-based predictions of bridle shiner ( Notropis bifrenatus ) in the northeastern U.S.

We sought to assess bridle shiner ( Notropis bifrenatus ) habitat associations at local and regional scales across southern Maine and New Hampshire. We used local habitat data at 95 Maine sites to predict occupancy with classification and regression trees (CART). We then used ensemble species distribution models (SDMs) to model the historical (1898–2008) and current (2009–2022) ranges of the species. We used the BIOMOD platform to model the association between 35 environmental variables and bridle shiner presence during both time periods and at fine (pseudo-HUC14) and coarse (HUC12) spatial scales. We then calculated the change in predicted occupied drainages to estimate the change in the species' distribution at both scales. Within a site, bridle shiners were associated with submerged aquatic vegetation, organic substrate, and watermilfoil ( Myriophyllum spp.). SDMs revealed an association with Appalachian (Hemlock-)Northern Hardwood Forest, sand substrate, and low-elevation terrain (at both spatial scales). Ensemble fine-scale SDMs suggest a substantial loss of historical bridle shiner habitat in both Maine (36% of drainages) and New Hampshire (16%), with comparable described losses (of 21% and 14%) at a coarse scale. Our local and regional models may be used to focus surveys on areas with high predicted habitat suitability or to inform habitat restoration efforts.

Maine, New Hampshire↗

USGS2025

Assessing American eel (Anguilla rostrata) distribution in a heavily dammed watershed using eDNA : The Penobscot River watershed, Maine, USA

Catadromous American eel ( Anguilla rostrata ) are native to Maine's Penobscot River watershed and historically have migrated through many of its tributaries prior to extensive damming. Recent restoration efforts, including dam removals, have improved connectivity in the lower reaches of the Penobscot River. Characterizing the extent of the American eel's distribution is important to inform restoration and identify extant barriers to migrations within the watershed. In the summer of 2023, we conducted eDNA surveys throughout the Penobscot River watershed to estimate the current distribution of the American eel and identify barriers to inland waters. Water samples were collected from 70 sites representing 37 rivers and streams; the presence or absence of American eel genetic markers within those samples was assessed using qPCR. We have shown that American eel are present in virtually the full extent of the area surveyed (68/70 sites). The results suggest that the majority of the main-stem dams may be passed by American eels at some level, with eel DNA being confirmed upstream of six dams. We confirmed the presence of American eels throughout the lower watershed with just 1 week of eDNA sampling and have highlighted this method for determining the species' access to habitat upstream of dams. The use of eDNA to sample locally (or regionally) for American eel may provide cost-effective information in data deficient areas and help assess the permeability of dam structures to diadromous species.

Maine↗

USGS2024

An integrative approach to assessing bridle shiner (Notropis bifrenatus) distribution using environmental DNA and traditional techniques

The bridle shiner ( Notropis bifrenatus ) is a small cyprinid native to the eastern United States and Canada. Bridle shiner populations have declined across their range, and the species now receives concern status or legal protection in 13 states and two provinces. Bridle shiners were historically found in southern and western Maine in densely vegetated, shallow habitats along the shorelines of streams and ponds. We surveyed areas of Maine that supported historical bridle shiner populations using environmental DNA (eDNA) and traditional seine netting methods, and then used eDNA sampling to survey areas with unknown bridle shiner presence. We rediscovered bridle shiner populations at 11 of 32 historically occupied waterbodies and documented bridle shiners in four additional waterbodies. We determined that both eDNA and seine net surveys are viable options for monitoring bridle shiners in Maine and identified ways to streamline the eDNA methods used in this study to reduce the time and cost of future surveys.

Maine↗

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↗