Bridging implementation gaps to connect large ecological datasets and complex models

Raiho, Ann; Nicklen, E. Fleur; Foster, Adrianna C.; Roland, Carl A.; Hooten, Mevin B.

doi:10.1002/ece3.8420

Cited by 3 publications

(1 citation statement)

References 121 publications

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“…Integrating these complex models into Markov chain Monte Carlo (MCMC) routines commonly used to fit Bayesian DSTMs is generally intractable particularly over large spatio-temporal domains. Recent research has focused on the use of statistical emulators to reduce this computational challenge while still allowing for improved uncertainty quantification (Fer et al, 2018;Raiho et al, 2021).…”

Section: Dynamical Statistical Modelsmentioning

confidence: 99%

Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change

Itter,

Finley

2024

Preprint

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Models of forest dynamics are an important tool to understand and predict forest responses to global change. Despite recent model development, predictions of forest dynamics under global change remain highly variable reflecting uncertainty in future conditions, forest demographic processes, and the data used to parameterize and validate models. Quantifying this uncertainty and accounting for it when making adaptive management decisions is critical to our ability to conserve forest ecosystems in the face of rapidly changing conditions. Dynamical spatiotemporal models (DSTMs) are a particularly powerful tool in this setting given they quantify and partition uncertainty in demographic models and noisy forest observations, propagate uncertainty to predictions of forest dynamics, and support refinement of predictions based on new data and improved ecological understanding. A major challenge to the application of DSTMs in applied forest ecology has been the lack of a scalable, theoretical model of forest dynamics that generates predictions at the stand level—the scale at which management decisions are made. We address this challenge by integrating a matrix projection model motivated by the well-known McKendrick-von Foerster partial differential equation for size-structured population dynamics within a Bayesian hierarchical DSTM informed by continuous forest inventory data. The model provides probabilistic predictions of species-specific demographic rates and changes in the size-species distribution over time. The model is applied to predict long-term dynamics (60+ years) within the Penobscot Experimental Forest in Maine, USA, quantifying and partitioning uncertainty in inventory observations, process-based predictions, and model parameters for nine Acadian Forest species. We find that uncertainty in inventory observations drives variability in predictions for most species and limits the inclusion of ecological detail within the DSTM. We conclude with a discussion of how DSTMs can be used to reduce uncertainty in predictions of forest dynamics under global change through informed model refinement and the assimilation of multiple forest data sources.

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Section: Dynamical Statistical Modelsmentioning

confidence: 99%

Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change

Itter,

Finley

2024

Preprint

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Integrating remotely sensed imagery in a forest gap model to study North American boreal forests in a changing world

Sundquist,

Lutz,

Foster

et al. 2024

Environ. Res.: Ecology

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Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g., trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up'. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine (Pinus contorta) remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change.

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Process Line Diagrams (PLDs): An Approach for Modular Process Modeling

Shivhare

Joshi

2023

16th Innovations in Software Engineering Conference

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Bridging implementation gaps to connect large ecological datasets and complex models

Cited by 3 publications

References 121 publications

Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change

Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change

Integrating remotely sensed imagery in a forest gap model to study North American boreal forests in a changing world

Process Line Diagrams (PLDs): An Approach for Modular Process Modeling

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