Ann M. Raiho's research while affiliated with University of Notre Dame and other places

Publications (17)

Preprint
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The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a dynamic statistical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes in a hierarch...
Article
Changes in woody biomass over centuries to millennia are poorly known, leaving unclear the magnitude of terrestrial carbon fluxes before industrial-era disturbance. Here, we statistically reconstructed changes in woody biomass across the upper Midwestern region of the United States over the past 10,000 years using a Bayesian model calibrated to pre...
Article
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The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on...
Article
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Aim Climate change is occurring at accelerated rates in high latitude regions such as Alaska, causing alterations in woody plant growth and associated ecosystem patterns and processes. Our aim is to assess the magnitude and speed that climate‐induced changes in woody plant distribution and volume may be reduced and/or slowed by relatively static la...
Article
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Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when at...
Preprint
Full-text available
The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on...
Article
Full-text available
A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments...
Article
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Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses i...
Article
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Forecasts of future forest change are governed by ecosystem sensitivity to climate change, but ecosystem model projections are under‐constrained by data at multidecadal and longer timescales. Here, we quantify ecosystem sensitivity to centennial‐scale hydroclimate variability, by comparing dendroclimatic and pollen‐inferred reconstructions of droug...
Article
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In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at...
Preprint
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Predictions from ecological models necessarily include five different uncertainties: demographic stochasticity, initial conditions, external forcing (i.e., drivers/covariates), parameters, and model processes. However, most predictions from process-based ecological models only account for a subset of these uncertainties (e.g. only demographic stoch...
Preprint
Full-text available
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the infor...
Article
Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multi-model comparisons with data have largely focused on individual processes at sub-annual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO2 at multi...
Article
Full-text available
Overabundant populations of ungulates have caused environmental degradation and loss of biological diversity in ecosystems throughout the world. Culling or regulated harvest is often used to control overabundant species. These methods are difficult to implement in national parks, other types of conservation reserves, or in residential areas where p...
Data
Supporting appendices, figures, and tables. (ZIP)

Citations

... Our hierarchical framework aids in integrating various data sources, some of which may arise from other studies on Alaskan vegetation change. For example, Scharf et al. (2022) and Raiho et al. (2022) ...
... Soil moisture (SM), the moisture content in the soil, is a crucial component in the hydrological cycle; it links atmospheric precipitation and underground water and is also an important parameter of energy exchange between the land surface and the atmosphere [1][2][3][4]. Consequently, SM is recognized as an essential element in studies aimed at analyzing and understanding Earth system processes, such as climate change and ecological evolution. Specifically, the available water content, which is essential for vegetation growth, is one of the most important components of soil and has crucial guiding significance for agricultural production. ...
... Our hierarchical framework aids in integrating various data sources, some of which may arise from other studies on Alaskan vegetation change. For example, Scharf et al. (2022) and Raiho et al. (2022) ...
... Moreover, we evaluate not only carbon and water fluxes such as in eddy covariance MIPs (e.g., Dietze et al. 2011;Schaefer et al. 2012;Stoy et al. 2013;Richardson et al. 2013;Huntzinger et al. 2013, Wei et al. 2014) but also evaluate the forest structure which is the key target of forest management operations. Likewise, we go beyond comparison of models to tree-ring reconstruction data to evaluate growth (e.g., Rollinson et al. 2017;Klesse et al. 2018;Rollinson et al. 2021) by assessing BA, DBHinc and Hinc, although on shorter time scales. ...
... This new openaccess dataset, implemented in an R package, subsampled the vegetation plots from sPlot to ensure a more environmentally balanced version for global comparisons (sPlotOpen; Sabatini et al. 2021). More efforts need to be redirected to coordinate not only filling the data gaps, but also to generate platforms and cyber-structures that will help identify issues and devote the resources in the necessary direction (Fer et al. 2020, Urban et al. 2022. ...
... Observations were filtered based on u * , and effective sample size was corrected for autocorrelation following Fer et al. (2018). Keeping with previous works (Fer et al., 2018;Reich and Cotter, 2015), we used an asymmetric heteroscedastic Laplace likelihood that accounts for the increase in observation error with the magnitude of the flux and error bias. In our case, these errors are larger in the positive direction (nighttime respiration) than the negative. ...
... Moreover, we evaluate not only carbon and water fluxes such as in eddy covariance MIPs (e.g., Dietze et al. 2011;Schaefer et al. 2012;Stoy et al. 2013;Richardson et al. 2013;Huntzinger et al. 2013, Wei et al. 2014) but also evaluate the forest structure which is the key target of forest management operations. Likewise, we go beyond comparison of models to tree-ring reconstruction data to evaluate growth (e.g., Rollinson et al. 2017;Klesse et al. 2018;Rollinson et al. 2021) by assessing BA, DBHinc and Hinc, although on shorter time scales. ...
... The emergence of ecological forecasting using the Bayesian framework (Dietz 2017) has provided tools for adaptive management of wildlife (e.g., , Raiho et al. 2015, Ketz et al. 2016, Andrén et al. 2020) that were computationally infeasible until relatively recently. We use the term forecast to mean predictions of the future accompanied by rigorous quantification of uncertainty. ...