Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty. (13th January 2022)

• Record Type: Journal Article
• Authors: Heilman, Kelly A.; Dietze, Michael C.; Arizpe, Alexis A.; Aragon, Jacob; Gray, Andrew; Shaw, John D.; Finley, Andrew O.; Klesse, Stefan; DeRose, R. Justin; Evans, Margaret E. K.
• Is Part Of: Global change biology. Volume 28:Number 7(2022)
• Page Start: 2442
• Page End: 2460
• Publication Date: 2022-01-13
• Subjects: climate change -- data fusion -- ecological forecasting -- forest -- forest inventory -- ponderosa pine -- tree ring; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.16038
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall–spring maximum temperature, and a positive effect of water‐year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%–117%, while the combined effect of climate and size‐related trends results in a 56%–91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree‐ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill. Abstract : Great enthusiasm surrounds forest carbon sequestration as a way to offset carbon emissions and address the climate crisis, but ecological complexity contributes to scientific uncertainty about forest responses to climate change. Here, we fused together two important sources of information on tree growth – forest inventory and tree‐ring data – to quantify multiple drivers of Pinus ponderosa tree growth (climate sensitivity, competition, tree size, and site quality), and their interactions. Ecological forecasting approaches indicate that growth will decline by 56%–91% under future climate conditions and allow us to identify the causes of uncertainty surrounding future tree growth.