The modern geological survey: a model for research, innovation, synthesis. A USGS perspective

Kimball, Suzette; Goldhaber, Martin B.; Labson, Victor F.

doi:10.1144/sp499-2019-250

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…We use measures of greenwave processes that have a long period-of-record (>30 years) and low-latency at the daily time step with a goal of examining temporal trends in migration initiation and demonstrating potential for prediction of migration at the daily time step. These purposes are beneficial to practical applications such as traffic safety, land-use planning, herd management, migration fencing management, and predicting climate change effects on migrations, but also work towards a broader goal of the USGS of building a multi-disciplinary integrated data and knowledge platform [24]. We hypothesized that the timing of the spring pulse measured at a streamgage would closely correspond to the timing of onset of spring migration of mule deer in a cold-desert river basin and would demonstrate potential for linkages between data being measured at streamgages and animal migrations.…”

Section: Introductionmentioning

confidence: 99%

Testing the potential of streamflow data to predict spring migration of an ungulate herds

2022

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In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the ‘spring pulse.’ Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.

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Section: Introductionmentioning

confidence: 99%

Testing the potential of streamflow data to predict spring migration of an ungulate herds

2022

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“…The increasing use of technologies that offer in-situ data for field research, e.g., mobile robotic platforms, have the potential to transform and advance data collection practices beyond existing expert capabilities (Gil et al, 2018 ; Kimball et al, 2020 ; Shipley & Tikoff, 2019 ). In-situ data not only provide scientists the opportunity to make real-time adjustments to data collection strategies, but technologies may also be leveraged as decision support systems that (a) package and present data to users in a manner that improves processing fluency, and (b) provide guidance in the form of behavioral nudges (Wilson et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Spatially and temporally distributed data foraging decisions in disciplinary field science

et al. 2021

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How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.

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“…With state surveys taking on these direct public good and land use-related roles, the US Geological Survey has consciously adopted a broader, more integrative approach. As described by Kimball et al (2020), the US Geological Survey (USGS) has emphasized interdisciplinary science that addresses 'grand' issues of Earth system science through an innovative programme structure that includes mechanisms to enhance data integration, incorporation of technological innovation and crossdisciplinary synthesis of knowledge on complex public issues. The Community for Data Integration nurtures expertise in data generation, management and integration through proposal-driven projects on information technology.…”

Section: Changing Roles Of Geological Survey Organizationsmentioning

confidence: 99%

“…As mentioned by several authors in this volume, collaboration across disciplines and borders is essential to address Earth systems science and global issues such as environmental degradation and climate change (Kimball et al 2020). Looking forward, there is the likelihood of deep sea mining for rare minerals in international waters and the very real prospect of exploitation of the Moon and other planets (Ludden 2020), which will require significant international collaboration and agreement.…”

Section: International Collaborationmentioning

confidence: 99%

The changing role of Geological Surveys: introduction

Hill

Lebel

Hitzman

et al. 2020

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This volume is a collection of papers authored by senior managers and heads of Geological Survey organizations (GSOs) from around the world in an attempt to provide a benchmark on how GSOs are responding to national and international needs in a rapidly changing world. GSOs face an uncertain future and need to understand global trends. Whereas population trends are somewhat predictable, societal responses to change are much less so and technological change is fundamentally disruptive and chaotic. As countries adopt sustainable development principles and the public becomes increasingly (but not necessarily reliably) informed about environmental issues using social media, the integration of resource development and environmental stewardship becomes increasingly important. GSOs will continue to provide key information about Earth systems, natural hazards and climate change in this context. This introduction comprises a short review of the global trends affecting GSOs, a snapshot of the state of GSOs, examples of how GSOs are adapting their activities to the modern world, including the growing use of big data, and an examination of international collaboration between GSOs. The time is perhaps ripe to reinforce international collaborations through a global network of GSOs. To achieve this will require leadership and a focus on the big picture of global sustainability.

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The modern geological survey: a model for research, innovation, synthesis. A USGS perspective

Cited by 5 publications

References 15 publications

Testing the potential of streamflow data to predict spring migration of an ungulate herds

Testing the potential of streamflow data to predict spring migration of an ungulate herds

Spatially and temporally distributed data foraging decisions in disciplinary field science

The changing role of Geological Surveys: introduction

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