Geology topics

Katherina D. Gieder

Publications and source records attributed to Katherina D. Gieder.

4 recordsLinked to original sources

USGS2025

Observations of tear-drinking by lepidopterans on moose ( Alces alces americana ) in northeastern North America

Lepidoptera have long been known to feed on the tears of vertebrates as a presumed source of minerals or nutrients. While this unusual behavior has been observed in a variety of species, only a single previous record has been documented outside of the tropics. Here, we present the first documentation of moths visiting the eyes of a bull moose ( Alces americanus americanus ), captured via trail camera in Green Mountain National Forest, Vermont, United States. We discuss the biogeography of this behavior, how it may differ between tropical and temperate climates, and its potential impact on moose health.

Vermont↗

USGS2025

Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse

Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse ( Bonasa umbellus ). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.

Vermont↗

USGS2021

Effects of winter ticks and internal parasites on moose survival in Vermont, USA

Moose ( Alces alces ) have experienced considerable declines along the periphery of their range in the northeastern United States. In Vermont, the population declined 45% from 2010 to 2017 despite minimal hunter harvest and adequate habitat. Similarly, nearby populations recently experienced epizootics characterized by >50% mortality. Declines have largely been associated with the effects of winter ticks ( Dermacentor albipictus ), but uncertainty exists about the effects of environmental and other parasite-related conditions on moose survival. We examined patterns of moose survival among a radio-collared population ( n = 127) in Vermont from 2017 to 2019. Our objectives were to estimate causes of mortality and model survival probability as a function of individual and landscape variables for calves (<1 yr) and adults (≥1 yr). Observed adult survival was 90% in 2017, 84% in 2018, and 86% in 2019, and winter calf survival was 60% in 2017, 50% in 2018, and 37% in 2019. Winter tick infestation was the primary cause of mortality (91% of calves, 25% of adults), and 32% of all mortalities had evidence of meningeal worm ( Parelaphostrongylus tenuis ). Other sources of mortality such as vehicles, harvest, predation, deep snow, and other parasitic infections were negligible. The best supported calf model included sex differences and negative effects of tick engorgement (%/week) and parasite level (roundworm and lungworm). The best supported adult model included the effect of cumulative tick engorgement (cumulative %/week), which negatively affected survival. Our results indicate that winter tick engorgement strongly affects survival, and is probably compounded by the presence of meningeal worm and other parasites. Reduced tick effects may be achieved by decreasing moose density through harvest and managing late winter habitat to minimize tick density. Management of white-tailed deer ( Odocoileus virginianus ) density may also affect the transmission of meningeal worm.

Vermont↗

USGS2014

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover ( Charadrius melodus ) using barrier island features

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Maryl↗