Geology topics

Timothy A. Wertz

Publications and source records attributed to Timothy A. Wertz.

4 recordsLinked to original sources

USGS2025

Deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in fish communities of the Chesapeake Bay watershed, USA: A regional assessment and potential landscape drivers

Fish diseases in freshwater ecosystems pose significant ecological and socioeconomic challenges, yet monitoring them in wild populations is complex due to interactions between pathogens, hosts, and environmental conditions. We examine the prevalence and watershed-scale landscape drivers of external deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in 57 riverine fish species using a large dataset (577,266 individuals collected 2008–2019) from the Chesapeake Bay watershed that originated from state and federal agencies. Overall, DELT prevalence was low (1.4%), but was higher in larger, longer-lived species, including Channel Catfish ( Ictalurus punctatus ) (18.9%), Rock Bass ( Ambloplites rupestris ) (7.6%), Smallmouth Bass ( Micropterus dolomieu ) (7.3%), Brown Bullhead ( Ameiurus nebulosus ) (5.6%), and Yellow Bullhead ( Ameiurus natalis ) (5.1%), signifying their potential as regional environmental health indicators. Spatial analysis indicated warmer temperatures increased the estimated probability of DELT occurrence, whereas higher precipitation often mitigated the probability of DELT occurrence. Conservation strategies (e.g., best management practices) had mixed effectiveness in reducing DELT occurrence probability across agricultural and urban landscapes. Across the landscape, various drivers, including harvested forest, impervious land, and pesticide use, influenced DELT occurrence probability differently across species. However, uncertainty remains partly due to low prevalence and variability in sampling methods across agencies. Despite low overall prevalence, DELT occurrence is a rapid fish health indicator. Future research could emphasize species-specific responses and longitudinal studies that incorporate life stages and health indicators. Understanding these intricate, multi-scale interactions is vital for effective monitoring, conservation, and adaptive management of freshwater ecosystems.

Chesapeake Bay watershed↗

USGS2024

Predicted potential for aquatic exposure effects of per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania’s statewide network of streams

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios ( Σ EARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters.

Pennsylvania↗

USGS2024

Quantifying the relative importance of biotic and abiotic factors in landscape-based models of stream fish distributions

Lotic fish species distributions are frequently predicted using remotely sensed habitat variables that characterize the adjacent landscape and serve as proxies for instream habitat. Recent advancements in statistical methodology, however, allow for leveraging fish assemblage data when predicting distributions. This is important because assemblage composition likely provides better information about instream habitat compared to landscape-derived metrics and therefore may improve predictions. To better understand the value of using multi-species fish data in species distribution modeling, we fit two conditional random fields (CRF) models to quantify the relative importance of fish assemblage co-occurrence, landscape-derived habitat variables, and interactions between these two predictor groups (i.e., effects of co-occurrence could be context-dependent) at over 1200 stream catchments in Pennsylvania, USA. We first compared predictive performance of CRF models against traditionally used single-species logistic regressions (generalized linear models; GLMs) and found that inclusion of fish assemblage data often improved predictive performance. The multi-species CRF models performed significantly better at predicting occurrence for 63% of species with an average percent increase in AUC of 25% compared to GLMs. Furthermore, the CRF identified species co-occurrences as more informative, and thus relatively more important, at predicting occurrence than the other effect types. The CRF also suggested that allowing these biotic effects to be context-dependent was important for predicting occurrence of many species. These findings illustrate the value of fish assemblage data for landscape-scale species distribution modeling and leveraging this information can improve predictions and inferences to help inform the management and conservation of freshwater fishes.

Pennsylvania↗

USGS2023

Per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania surface waters: A statewide assessment, associated sources, and land-use relations

The objectives of this study are to identify per- and polyfluoroalkyl substances (PFAS) in Pennsylvania surface waters, corresponding associations with potential sources of PFAS contamination (PSOC) and other parameters, and compare raw surface water concentrations to human and ecological benchmarks. Surface water samples from 161 streams were collected in September 2019 and were analyzed for 33 target PFAS and water chemistry. Land use and physical attributes in upstream catchments and geospatial counts of PSOC in local catchments are summarized. The hydrologic yield of the sum of 33 PFAS (∑PFAS) for each stream was computed by normalizing each site's load by the drainage area of the upstream catchment. Utilizing conditional inference tree analysis, the percentage of development (>7.58 %) was identified as a primary driver of the ∑PFAS hydrologic yields. When percentage of development was removed from analysis, ∑PFAS yields were closely related to surface water chemistry associated with landscape alteration (e.g., development or agricultural cropland), such as concentrations of total nitrogen, chloride, and ammonia, but also to count of water pollution control facilities (agricultural, industrial, stormwater, and/or municipal waste pollution abatement facilities). In oil and gas development regions, ∑PFAS yields were associated with combined sewage outfalls. Sites surrounded by ≥2 electronic manufacturing facilities had elevated ∑PFAS yields (median = 241 ng/s/km 2 ). Study results are critical to guide future research, regulatory policy, best practices that will mitigate PFAS contamination, and the communication of human health and ecological risks associated with PFAS exposure from surface waters.

Pennsylvania↗