Geology topics

Austen Lorenz

Publications and source records attributed to Austen Lorenz.

13 recordsLinked to original sources

USGS2025

Potential impacts of 2.3.4.4b highly pathogenic H5N1 avian influenza virus infection on Snow Goose (Anser caerulescens) movement ecology

While wild waterfowl are known reservoirs of avian influenza viruses and facilitate the movement of these viruses, there are notable differences in the response to infection across species. This study explored differential responses to infection with highly pathogenic avian influenza in Snow Geese ( Anser caerulescens ) located in the California Central Valley. Though H5 antibody prevalence was high across years among birds sampled in the winter (75% in both years via hemagglutination inhibition), these values were even higher among birds sampled in summer that failed to migrate (i.e., August 2023 = 100% and August 2024 = 93% via hemagglutination inhibition). Birds that failed to migrate were also generally lighter than birds sampled in the winter and presented notable damage to cerebrum and cerebellum. In December 2022, a single individual positive for infection with H5N1 at the time of sampling indicated reduced movement during the 14 days following sampling but completed spring migration comparably with uninfected conspecifics. However, while no birds were actively infected during sampling and marking in 2023, two marked geese departed for migration late and one did not migrate at all. Additional banded birds marked in August have been reencountered in scenarios ranging from hunter harvest at a different site over a year later to found dead shortly after banding. Our data indicate that Snow Geese infected with HPAI have the potential to express variable outcomes following infection with highly pathogenic H5N1, ranging from rapid recovery within a migratory season to death. These data also suggest that the abnormal failure of some Snow Geese to migrate from the Central Valley is likely driven by HPAI infection.

California↗

USGS2024

Mitigating risk: Predicting H5N1 avian influenza spread with an empirical model of bird movement

Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (Dec 2023), poses a serious global threat to animals and public health. We combined a large, long-term waterfowl GPS tracking dataset (16 species) with on-ground disease surveillance data (county-level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted non-migratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multi-species tracking to highlight potential high-risk disease spread locations and more effectively manage outbreaks.

Transboundary and Emerging Diseases↗

USGS2024

Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk

Interactions between wildlife and livestock can lead to cross-species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long-distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine-scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.

California↗

USGS2023

Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California

In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail ( Anas acuta ), Green-winged Teal ( Anas crecca ), and Northern Shoveler ( Anas clypeata ), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.

California↗

USGS2023

AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Journal of Environmental Management↗

USGS2022

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl

Background Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”. Methods Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models. Results Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting). Conclusions Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.

Movement Ecology↗

USGS2022

Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape

Zoonotic diseases are of considerable concern to the human population and viruses such as avian influenza (AIV) threaten food security, wildlife conservation and human health. Wild waterfowl and the natural wetlands they use are known AIV reservoirs, with birds capable of virus transmission to domestic poultry populations. While infection risk models have linked migration routes and AIV outbreaks, there is a limited understanding of wild waterfowl presence on commercial livestock facilities, and movement patterns linked to natural wetlands. We documented 11 wild waterfowl (three Anatidae species) in or near eight commercial livestock facilities in Washington and California with GPS telemetry data. Wild ducks used dairy and beef cattle feed lots and facility retention ponds during both day and night suggesting use for roosting and foraging. Two individuals (single locations) were observed inside poultry facility boundaries while using nearby wetlands. Ducks demonstrated high site fidelity, returning to the same areas of habitats (at livestock facilities and nearby wetlands), across months or years, showed strong connectivity with surrounding wetlands, and arrived from wetlands up to 1251 km away in the week prior. Telemetry data provides substantial advantages over observational data, allowing assessment of individual movement behaviour and wetland connectivity that has significant implications for outbreak management. Telemetry improves our understanding of risk factors for waterfowl–livestock virus transmission and helps identify factors associated with coincident space use at the wild waterfowl–domestic livestock interface. Our research suggests that even relatively small or isolated natural and artificial water or food sources in/near facilities increases the likelihood of attracting waterfowl, which has important consequences for managers attempting to minimize or prevent AIV outbreaks. Use and interpretation of telemetry data, especially in near-real-time, could provide key information for reducing virus transmission risk between waterfowl and livestock, improving protective barriers between wild and domestic species, and abating outbreaks.

Transboundary and Emerging Diseases↗

USGS2021

Megafires and thick smoke portend big problems for migratory birds

In 2020, the fire season affecting the western United States reached unprecedented levels. The 116 fires active in September consumed nearly 20,822 km 2 ( https://inciweb.nwcg.gov/accessible-view/ Accessed 2020-09-29) with eighty percent of this footprint (16,567 km 2 ) from 68 fires occurring within California, Oregon, and Washington. Although the 2020 fire season was the most extreme on record, it exemplified patterns of increased wildfire size, number, timing, return frequency, and extent which are linked to climate-driven changes in precipitation and temperature affecting fire ignition and severity (Westerling 2016, Goss et al. 2020, Weber and Yadav 2020).

California, Oregon, Washington↗

USGS2021

Waterfowl use of wetland habitats informs wetland restoration designs for multi‐species benefits

Extensive global estuarine wetland losses have prompted intensive focus on restoration of these habitats. In California, substantial tracts of freshwater, brackish and tidal wetlands have been lost. Given the anthropogenic footprint of development and urbanization in this region, wetland restoration must rely on conversion of existing habitat types rather than adding new wetlands. These restorations can cause conflicts among stakeholders and species that win or lose depending on identified restoration priorities. Suisun Marsh on the San Francisco Bay Estuary is the largest brackish marsh on the U.S. Pacific coast. To understand how conversion of brackish managed wetlands to tidal marsh would impact waterfowl populations and whether future tidal marsh restorations could provide suitable habitat for dabbling ducks, we examined waterfowl wetland use with a robust GPS‐GSM tracking dataset (442,017 locations) from six dabbling duck species (N=315). Managed wetlands, which comprise 47% of Suisun Marsh, were consistently and strongly selected by waterfowl over tidal marshes, with use ~98% across seasons and species. However, while use of tidal marsh (only 14% of Suisun Marsh) was generally <2%, almost half our ducks (~44%) spent some time in this habitat and exhibited strong utilization of pond‐like features. Ponds only comprise ~10% of this habitat but attracted 44% use (~4.5 times greater than availability). Synthesis and applications : Managed wetlands were vital to dabbling ducks, but losses from conversion of these habitats may be partially mitigated by incorporating pond features that are more attractive to waterfowl, and likely to offer multi‐species benefits, into tidal marsh restoration designs. While waterfowl are presently a common taxon, previously seen calamitous population declines can be avoided through informed ecosystem‐based management that promotes species richness, biodiversity and helps “keep common species common”.

California↗

USGS2021

Informing wetland management with waterfowl movement and sanctuary use responses to human-induced disturbance

Long-term environmental management to prevent waterfowl population declines is informed by ecology, movement behavior and habitat use patterns. Extrinsic factors, such as human-induced disturbance, can cause behavioral changes which may influence movement and resource needs, driving variation that affects management efficacy. To better understand the relationship between human-based disturbance and animal movement and habitat use, and their potential effects on management, we GPS tracked 15 dabbling ducks in California over ~4-weeks before, during and after the start of a recreational hunting season in October/November 2018. We recorded locations at 2-min intervals across three separate 24-h tracking phases: Phase 1) two weeks before the start of the hunting season (control (undisturbed) movement); Phase 2) the hunting season opening weekend; and Phase 3) a hunting weekend two weeks after opening weekend. We used GLMM models to analyze variation in movement and habitat use under hunting pressure compared with ‘normal’ observed patterns prior to commencement of hunting. We also compared responses to differing levels of disturbance related to the time of day (high - shooting/~daytime); moderate - non-lethal (~crepuscular); and low - night). During opening weekend flight (% time and distance) more than doubled during moderate and low disturbance and increased by ~50% during high disturbance compared with the pre-season weekend. Sanctuary use tripled during moderate and low disturbance and increased ~50% during high disturbance. Two weeks later flight decreased in all disturbance levels but was only less than the pre-season levels during high disturbance. In contrast, sanctuary use only decreased at night, although not to pre-season levels, while daytime doubled from ~45% to >80%. Birds adjust rapidly to disturbance and our results have implications for energetics models that estimate population food requirements. Management would benefit from reassessing the juxtaposition of essential sanctuary and feeding habitats to optimize wetland management for waterfowl.

Journal of Environmental Management↗

USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing

Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

California↗

USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through remote sensing

Across the Central Valley of California, millions of wintering waterfowl rely on moist soil seed (MSS) plants that grow in managed seasonal wetlands as a critical source of food. Estimates of MSS plant production are used to set waterfowl habitat targets yet this information is not well known. We created the first Central Valley-wide time series maps of MSS plant distributions and productivity. We found that MSS plant seed yield declined in critical drought years, which corresponded with reduced water delivery to managed wetlands. Our results provide improved food resource estimates and information to help managers prioritize actions as water supply becomes more uncertain with climate change.

California↗

USGS2020

Good prospects: High-resolution telemetry data suggests novel brood-site selection behavior in waterfowl

Breeding success should increase with prior knowledge of the surrounding environment, which is dependent upon an animal’s ability to evaluate habitat. Prospecting for nesting locations and migratory stop-over sites are well-established behaviors among bird species. We assessed whether ducks in Suisun Marsh, California, USA, a brackish marsh, prospect for suitable wetlands in the week prior to brooding. K-means cluster analyses grouped 29 mallard and gadwall hens into 3 groups. One group (n=13) demonstrated evidence of brood site prospecting with fewest and latest pre-brooding wetland visits. Of these hens, seven visited their future brood pond an average of 1.14 times and only shortly before brooding (1.29 days), obtaining current information on habitat suitability. For the remaining 6 hens, we did not detect a brooding wetland visit which may be due to data limitations or the need to prospect the specific brood pond was precluded by having acquired sufficient familiarity with the wetland habitat during nest breaks in adjacent wetlands. The second identified group of hens (n=11) visited the brooding wetland most frequently (on 4.55 days), farther in advance (5.27 days), with the fewest unique wetland visits and the earliest brooding date (May 26). The final group of hens (n=5) were the latest to brood (Jun 21) and visited the most wetlands, possibly due to less water or more broods present across the landscape. Brood ponds were always farther from the nest than the nearest ponds indicating that habitat suitability or presence of conspecifics is more important to brood-site selection. Prospecting provides hens with knowledge about current habitat conditions and allows them to ‘crowdsource’ public information regarding use of that habitat by other brooding hens. Prospecting may therefore, benefit ducks inhabiting ephemeral habitats like those within Suisun Marsh, where brood habitat is limited, and water cover changes rapidly during the breeding season.

California↗