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Research about Illinois, Michigan

Source-linked reports with geographic coverage including Illinois, Michigan.

3 recordsLinked to original sources
USGS2026

MusselFinder: Automated dreissenid mussel density estimation via robotic benthic imaging

Despite the widespread need to map the abundance of invasive quagga and zebr mussels ( Dreissena spp.) in lakes and reservoirs worldwide, current methods remain limited by data collection and analysis bottlenecks. Autonomous underwater vehicles (AUVs) combined with machine learning (ML) offer a scalable remote sensing approach for estimating mussel density (individuals m −2 ) across large spatial domains and a range of habitats that are difficult to sample with current techniques. We present MusselFinder: a deep neural network (DNN)-based pipeline that detects and enumerates live mussels in AUV-collected benthic images. MusselFinder estimates live mussel percent cover and density in nadir monocular images captured at known altitudes above the lakebed. In images of suitable clarity, MusselFinder achieved strong pixel-level localization (mussel-class Intersection over Union [IoU] > 0.76) and density prediction (log-density R 2 = 0.79–0.95) over a variety of bottom types. AUV-based image acquisition is more than three orders of magnitude faster than physical sampling by divers or ponar grabs, and DNN-based analysis can proceed at rates comparable to image acquisition. We used MusselFinder to analyze over 50,000 spatially referenced images at a spawning reef in Lake Michigan, representing a shift from small-scale, diver-collected datasets to large-scale monitoring in unconstrained environments. The method can be adapted to new imaging conditions using a modest training set and a user-friendly point-labeling technique, supporting broader-scale monitoring of one of the world's most invasive invertebrates.

Illinois, Michigan↗
USGS2020

Identification of factors affecting predation risk for juvenile turtles using 3D printed models

Although it is widely accepted that juvenile turtles experience high levels of predation, such events are rarely observed, providing limited evidence regarding predator identities and how juvenile habitat selection and availability of sensory cues to predators affects predation risk. We placed three-dimensional printed models resembling juvenile box turtles ( Terrapene carolina ) across habitats commonly utilized by the species at three sites within their geographical range and monitored models with motion-triggered cameras. To explore how the presence or absence of visual and olfactory cues affected predator interactions with models, we employed a factorial design where models were either exposed or concealed and either did or did not have juvenile box turtle scent applied on them. Predators interacted with 18% of models during field trials. Nearly all interactions were by mesopredators (57%) and rodents (37%). Mesopredators were more likely to attack models than rodents; most (76%) attacks occurred by raccoons ( Procyon lotor ). Interactions by mesopredators were more likely to occur in wetlands than edges, and greater in edges than grasslands. Mesopredators were less likely to interact with models as surrounding vegetation height increased. Rodents were more likely to interact with models that were closer to woody structure and interacted with exposed models more than concealed ones, but model exposure had no effect on interactions by mesopredators. Scent treatment appeared to have no influence on interactions by either predator group. Our results suggest raccoons can pose high predation risk for juvenile turtles (although rodents could also be important predators) and habitat features at multiple spatial scales affect predator-specific predation risk. Factors affecting predation risk for juveniles are important to consider in management actions such as habitat alteration, translocation, or predator control.

Illinois, Michigan↗