Geology topics

Paul C. Stoy

Publications and source records attributed to Paul C. Stoy.

8 recordsLinked to original sources

USGS2022

Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Idaho, Montana, Nebraska, North Dakota, South Dako↗

USGS2022

Multiple UAV flights across the growing season can characterize fine scale phenological heterogeneity within and among vegetation functional groups

Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6% to 61.4% at the drier site, while at the more mesic/densely vegetated site, we found an increase of 59.0% to 64.4% between a single and multiple flights over the growing season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had only a short detectable window of active phtosynthesis; therefore, a single flight could not capture all vegetation that was active across the growing season. The multi-temporal analyses identified differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex mixed grass/shrub vegetation.

Montana↗

USGS2021

Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management

Vegetation phenology—the seasonal timing and duration of vegetative phases—is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual-resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season-long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season-long productivity. Annual precipitation and temperature had strong explanatory power for productivity-related phenology measures but predicted date-based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing-season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land-use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.

Ecology and Evolution↗

USGS2021

Towards an urgent yet deliberate conservation strategy: Sustaining social-ecological systems in rangelands of the Northern Great Plains, Montana

Urgency and deliberateness are often at odds when executing conservation projects, especially as the scale and complexity of objectives increases. The pace of environmental degradation supports immediate and measurable action. However, best practices for adaptive governance and building resilient social-ecological systems call for more deliberate efforts and participatory processes, which can be slow. We explore conflicts between urgency and deliberateness and the potential for their reconciliation through a case study of the challenges of conserving native rangelands in North America’s Northern Great Plains, an ecoregion targeted for global conservation initiatives. This region is undergoing a significant social-ecological transition, which underscores a need to rethink conservation strategies in light of the social-ecological system dynamics and potential future trajectories. Based on a structured narrative literature review process and iterative engagement with key regional stakeholders, we identify three interrelated factors critical to the system’s future outcomes that illustrate system complexity as well as trade-offs between urgent and deliberate action and unilateral and multilateral approaches to conservation: (1) influences of land management on biodiversity, (2) economic restructuring and shifting land use priorities, and (3) changing climate and disturbance regimes. We identify key gaps in the literature for each factor and across the factors—an effort that informs our call for research and practice agendas that address uncertainty and complexity at regional scales through more inclusive and future-oriented approaches.

Montana↗

USGS2018

Toward a social-ecological theory of forest macrosystems for improved ecosystem management

The implications of cumulative land-use decisions and shifting climate on forests, require us to integrate our understanding of ecosystems, markets, policy, and resource management into a social-ecological system. Humans play a central role in macrosystem dynamics, which complicates ecological theories that do not explicitly include human interactions. These dynamics also impact ecological services and related markets, which challenges economic theory. Here, we use two forest macroscale management initiatives to develop a theoretical understanding of how management interacts with ecological functions and services at these scales and how the multiple large-scale management goals work either in consort or conflict with other forest functions and services. We suggest that calling upon theories developed for organismal ecology, ecosystem ecology, and ecological economics adds to our understanding of social-ecological macrosystems. To initiate progress, we propose future research questions to add rigor to macrosystem-scale studies: (1) What are the ecosystem functions that operate at macroscales, their necessary structural components, and how do we observe them? (2) How do systems at one scale respond if altered at another scale? (3) How do we both effectively measure these components and interactions, and communicate that information in a meaningful manner for policy and management across different scales?

Forests↗

USGS2018

On the exchange of sensible and latent heat between the atmosphere and melting snow

The snow energy balance is difficult to measure during the snowmelt period, yet critical for predictions of water yield in regions characterized by snow cover. Robust simplifications of the snowmelt energy balance can aid our understanding of water resources in a changing climate. Research to date has demonstrated that the net turbulent flux ( F T ) between a melting snowpack and the atmosphere is negligible if the sum of atmospheric vapor pressure ( e a ) and temperature ( T a ) equals a constant, but it is unclear how frequently this situation holds across different sites. Here, we quantified the contribution of F T to the snowpack energy balance during 59 snowmelt periods across 11 sites in the FLUXNET2015 database with a detailed analysis of snowmelt in subarctic tundra near Abisko, Sweden. At the Abisko site we investigated the frequency of occurrences during which sensible heat flux ( H ) and latent heat flux ( λE ) are of (approximately) equal but opposite sign, and if the sum of these terms, F T , is therefore negligible during the snowmelt period. H approximately equaled - λE for less than 50% of the melt period and F T was infrequently a trivial term in the snowmelt energy balance at Abisko. The reason is that the relationship between observed e a and T a is roughly orthogonal to the “line of equality” at which H equals - λE as warmer T a during the melt period usually resulted in greater e a . This relationship holds both within melt periods at individual sites and across different sites in the FLUXNET2015 database, where F T comprised less than 20% of the energy available to melt snow, Q m , in 44% of the snowmelt periods studied here. F T / Q m was significantly related to the mean e a during the melt period, but not mean T a , and F T tended to be near 0 W m −2 when e a averaged ca . 0.5 kPa. F T may become an increasingly important term in the snowmelt energy balance across many global regions as warmer temperatures are projected to cause snow to melt more slowly and earlier in the year under conditions of lower net radiation ( R n ). Eddy covariance research networks such as Ameriflux must improve their ability to observe cold-season processes to enhance our understanding of water resources and surface-atmosphere exchange in a changing climate.

Agricultural and Forest Meteorology↗

USGS2016

Preface: Impacts of extreme climate events and disturbances on carbon dynamics

The impacts of extreme climate events and disturbances (ECE&D) on the carbon cycle have received growing attention in recent years. This special issue showcases a collection of recent advances in understanding the impacts of ECE&D on carbon cycling. Notable advances include quantifying how harvesting activities impact forest structure, carbon pool dynamics, and recovery processes; observed drastic increases of the concentrations of dissolved organic carbon and dissolved methane in thermokarst lakes in western Siberia during a summer warming event; disentangling the roles of herbivores and fire on forest carbon dioxide flux; direct and indirect impacts of fire on the global carbon balance; and improved atmospheric inversion of regional carbon sources and sinks by incorporating disturbances. Combined, studies herein indicate several major research needs. First, disturbances and extreme events can interact with one another, and it is important to understand their overall impacts and also disentangle their effects on the carbon cycle. Second, current ecosystem models are not skillful enough to correctly simulate the underlying processes and impacts of ECE&D (e.g., tree mortality and carbon consequences). Third, benchmark data characterizing the timing, location, type, and magnitude of disturbances must be systematically created to improve our ability to quantify carbon dynamics over large areas. Finally, improving the representation of ECE&D in regional climate/earth system models and accounting for the resulting feedbacks to climate are essential for understanding the interactions between climate and ecosystem dynamics.

Biogeosciences↗

USGS2011

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site‐level synthesis

[1] Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

Journal of Geophysical Research: Biogeosciences↗