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208 records · Page 12Linked to original sources
USGS2025

Do bighorn sheep use desert tortoise burrow spoil piles as mineral licks in southern California?

In a previous study, bighorn sheep ( Ovis canadensis ) were periodically photographed by trail cameras when they visited desert tortoise ( Gopherus agassizii ) burrows in the San Bernardino Mountains near Palm Springs, California, USA. The authors suggested that bighorn sheep may utilize the excavated spoil material from burrows as a mineral lick. To test that supposition, we collected soil samples from the spoil piles and undisturbed surface soil up to 1 m away at two desert tortoise study sites with bighorn sheep at the aforementioned site and another nearby site. We hypothesized that sodium concentrations would be greater in spoil than in nearby surface soil samples. Common major cations were quantified in soil samples using inductively coupled plasma-mass spectrometry following microwave-assisted digestion to solubilize the fraction of elements that could become biologically or environmentally available. Mean sodium concentrations were greater in burrow spoil samples than in undisturbed soil samples at both sites, likely due to aqueous leaching of sodium from surface to subsurface soil horizons. Although we did not collect evidence of geophagy by bighorn sheep, the behavior is well known. However, trail camera photographs of bighorn sheep with their head next to the entrance of tortoise burrows suggests that they may periodically utilize sodium and other elements brought to the surface by desert tortoises that are otherwise unavailable to bighorn sheep on the surface.

California↗
USGS2023

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables.

Frontiers in Water↗
USGS2026

Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations

Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.

conterminous United States↗
USGS2018

Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age

We collected blood and serum from 155 brown bears ( Ursus arctos ) inhabiting five locations in Alaska during 2013–16 and tested samples for evidence of prior exposure to a suite of bacterial, viral, and parasitic agents. Antibody seroprevalence among Alaska brown bears was estimated to be 15% for Brucella spp., 10% for Francisella tularensis , 7% for Leptospira spp., 18% for canine adenovirus type 1 (CAV-1), 5% for canine distemper virus (CDV), 5% for canine parvovirus, 5% for influenza A virus (IAV), and 44% for Toxoplasma gondii . No samples were seropositive for antibodies to Trichinella spp. Point estimates of prior exposure to pathogens among brown bears at previously unsampled locations generally fell within the range of estimates for previously or contemporaneously sampled bears in Alaska. Statistical support was found for variation in antibody seroprevalence among bears by location or age cohort for CAV-1, CDV, IAV, and Toxoplasma gondii . There was limited concordance in comparisons between our results and previous serosurveys regarding spatial and age-related trends in antibody seroprevalence among Alaska brown bears suggestive of temporal variation. However, we found evidence that the seroprevalence of CAV-1 antibodies is consistently high in bears inhabiting SW Alaska and the cumulative probability of exposure may increase with age. We found evidence for seroconversion or seroreversion to six different infectious agents in one or more bears. Results of this study increase our collective understanding of disease risk to both Alaska brown bear populations and humans that utilize this resource.

Alaska↗
USGS2023

Survey for selected parasites in Alaska brown bears (Ursus arctos)

To assess infection with or exposure to endo- and ectoparasites in Alaska brown bears ( Ursus arctos ), blood and fecal samples were collected during 2013–17 from five locations: Gates of the Arctic National Park and Preserve; Katmai National Park; Lake Clark National Park and Preserve; Yakutat Forelands; and Kodiak Island. Standard fecal centrifugal flotation was used to screen for gastrointestinal parasites, molecular techniques were used to test blood for the presence of Bartonella and Babesia spp., and an ELISA was used to detect antibodies to Sarcoptes scabiei , a species of mite recently associated with mange in American black bears ( Ursus americanus ). From fecal flotations ( n =160), we identified the following helminth eggs: Uncinaria sp. ( n =16, 10.0%), Baylisascaris sp. ( n =5, 3.1%), Dibothriocephalus sp. ( n =2, 1.2%), and taeniid-type eggs ( n =1, 0.6%). Molecular screening for intraerythrocytic parasites ( Babesia spp.) and intracellular bacteria ( Bartonella spp.) was negative for all bears tested. We detected antibodies to S. scabiei in six of 59 (10.2%) individuals. The relatively low level of parasite detection in this study meets expectations for brown bear populations living in large, relatively undisturbed habitats near the northern edge of the range. These results provide a contemporary understanding of parasites in Alaska brown bears and establish baseline levels of parasite presence to monitor for changes over time and relative to ecologic alterations.

Alaska↗
USGS2012

Bioenergy potential of the United States constrained by satellite observations of existing productivity

Background/Question/Methods Currently, the United States (U.S.) supplies roughly half the world’s biofuel (secondary bioenergy), with the Energy Independence and Security Act of 2007 (EISA) stipulating an additional three-fold increase in annual production by 2022. Implicit in such energy targets is an associated increase in annual biomass demand (primary bioenergy) from roughly 2.9 to 7.4 exajoules (EJ; 1018 Joules). Yet, many of the factors used to estimate future bioenergy potential are relatively unresolved, bringing into question the practicality of the EISA’s ambitious bioenergy targets. Here, our objective was to constrain estimates of primary bioenergy potential (PBP) for the conterminous U.S. using satellite-derived net primary productivity (NPP) data (measured for every 1 km2 of the 7.2 million km2 of vegetated land in the conterminous U.S) as the most geographically explicit measure of terrestrial growth capacity. Results/Conclusions We show that the annual primary bioenergy potential (PBP) of the conterminous U.S. realistically ranges from approximately 5.9 (± 1.4) to 22.2 (± 4.4) EJ, depending on land use. The low end of this range represents current harvest residuals, an attractive potential energy source since no additional harvest land is required. In contrast, the high end represents an annual harvest over an additional 5.4 million km2 or 75% of vegetated land in the conterminous U.S. While we identify EISA energy targets as achievable, our results indicate that meeting such targets using current technology would require either an 80% displacement of current croplands or the conversion of 60% of total rangelands. Our results differ from previous evaluations in that we use high resolution, satellite-derived NPP as an upper-envelope constraint on bioenergy potential, which removes the need for extrapolation of plot-level observed yields over large spatial areas. Establishing realistically constrained estimates of bioenergy potential seems a critical next step for effectively incorporating bioenergy into future U.S. energy portfolios.

Conference Paper↗