Biology subjects

Finley, A. O.

Publications and source records attributed to Finley, A. O..

3 recordsLinked to original sources

bioRxiv2025

Quantifying impacts of natural gas development on forest carbon

As energy demands continue to rise, energy production from sources including natural gas is expected to rapidly accelerate in the coming decades, potentially leading to substantial land-use changes. In the Appalachian region of the United States, natural gas development often occurs in forested areas, which can cause high levels of forest disturbance and loss. Here, we use nationwide forest inventory and remotely sensed data in a Bayesian model to quantify the impacts of natural gas development at fine spatial resolutions between 2008-2021. Based on well permit locations in the states of Ohio, Pennsylvania, and West Virginia, the analysis quantifies disturbance area, forest carbon loss, and opportunity cost with associated levels of uncertainty at the pixel-level. Overall, we estimate 10,854 ha of forest land were disturbed, resulting in 542,675 Mg ({+/-} 4,275) of forest carbon loss. The opportunity cost associated with these disturbances is estimated to be 575,246 Mg ({+/-} 30,774). Pixel-level estimates are generated for individual well sites, which can be aggregated to the county-level to highlight regional patterns. Specifically, we observe greater levels of disturbance in Northern West Virginia, while opportunity costs are greatest for large forested counties in Northeastern Pennsylvania. This study demonstrates the importance of quantifying balances and tradeoffs between energy production and forest ecosystem services, and provides important insights into the impacts of energy development on forest carbon.

ecology↗

bioRxiv2025

Connecting growth and yield models to continuous forest inventory data to better account for uncertainty

Models of forest growth and yield are frequently used to inform adaptive management decisions aimed at increasing forest resilience or promoting long-term carbon storage. Despite the increasing ecological detail represented in growth and yield models, there remains large variability (uncertainty) in predictions of forest dynamics under global change. Quantifying this uncertainty and accounting for it when making management decisions is integral to sustainable management in the face of changing conditions. However, the structure and complexity of modern growth and yield models make it challenging to quantify uncertainty and propagate it to predictions of forest dynamics under alternative management strategies. To address this challenge, we develop a Bayesian dynamical model informed by continuous forest inventory data that supports the quantification and propagation of uncertainty in predictions of forest dynamics at a stand scale. The model predicts the temporal evolution of the size-species distribution using a matrix projection process model approximating growth, mortality, and regeneration. Disturbance is integrated through its effect on the size-species distribution within a stand providing a flexible framework to represent adaptive management. We apply the model to long-term inventory data from the Penobscot Experimental Forest in Maine, USA to predict multi-decadal biomass dynamics under five alternative management strategies. Predictions are used to identify the management strategy maximizing live aboveground biomass growth and yield over the model period. We conclude by discussing the benefits and challenges of connecting the model to large-scale inventory data and how its predictions can be used to better inform adaptive management decisions.

ecology↗

bioRxiv2024

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

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 spatio-temporal models (DSTMs) are a particularly powerful tool in this setting given they quantify uncertainty associated with process-based models of forest demography, the parameters upon which those models depend, and the forest data used to inform them. Further, DSTMs propa-gate this uncertainty to predictions of forest dynamics allowing for its formal integration within adaptive management decision frameworks. 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 McKendrick-von Foerster partial differential equation for size-structured population dynamics within a Bayesian hierarchical DSTM informed by 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, accounting for uncertainty in inventory observations, process-based predictions, and model parameters for nine Acadian Forest species. We find that variability in inventory observations associated with heterogeneous stand conditions drives uncertainty in predictions of forest dynamics. We conclude with a discussion of how the initial DSTM can be refined and extended to better represent forest dynamics under global change and inform adaptive management.

ecology↗