Quantifying climate change effects on future forest biomass availability using yield tables improved by mechanistic scaling

Storms, Ilié; Verdonck, Sanne; Verbist, Bruno; Willems, Patrick; Geest, Pieterjan De; Gutsch, Martin; Cools, Nathalie; Vos, Bruno De; Mahnken, Mats; Lopez, Joachim; Orshoven, Jos Van; Muys, Bart

doi:10.1016/j.scitotenv.2022.155189

Cited by 4 publications

(1 citation statement)

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“…Our results indicate that not only do we need additional empirical data, but we also may need to explore individual species or trait‐based approaches (e.g., photosynthetic capacity and water stress response) to grouping species so we better capture observed species‐specific regeneration variability (Davis et al, 2023). Furthermore, there are other model responses, such as the effects of the interaction of temperature and higher CO 2 concentrations on tree growth (Jung & Hararuk, 2022; Schimel et al, 2015; Storms et al, 2022; Walker et al, 2019) that we need to evaluate for tree regeneration. Importantly, as we collect additional tree regeneration data that can be used to parameterize models, we need to ensure these data are freely available to support their integration in other models because of the wide range of landscape model structure diversity.…”

Section: Discussionmentioning

confidence: 99%

Topographic information improves simulated patterns of post‐fire conifer regeneration in the southwest United States

Jung

Keyser

Remy

et al. 2023

Global Change Biology

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The western United States is projected to experience more frequent and severe wildfires in the future due to drier and hotter climate conditions, exacerbating destructive wildfire impacts on forest ecosystems such as tree mortality and unsuccessful post‐fire regeneration. While empirical studies have revealed strong relationships between topographical information and plant regeneration, ecological processes in ecosystem models have either not fully addressed topography‐mediated effects on the probability of plant regeneration, or the probability is only controlled by climate‐related factors, for example, water and light stresses. In this study, we incorporated seedling survival data based on a planting experiment in the footprint of the 2011 Las Conchas Fire into the Photosynthesis and EvapoTranspiration (PnET) extension of the LANDIS‐II model by adding topographic and an additional climatic variable to the probability of regeneration. The modified algorithm included topographic parameters such as heat load index and ground slope and spring precipitation. We ran simulations on the Las Conchas Fire landscape for 2012–2099 using observed and projected climate data (i.e., Representative Concentration Pathway 4.5 and 8.5). Our modification significantly reduced the number of regeneration events of three common southwestern conifer tree species (piñon, ponderosa pine, and Douglas‐fir), leading to decreases in aboveground biomass, regardless of climate scenario. The modified algorithm decreased regeneration at higher elevations and increased regeneration at lower elevations relative to the original algorithm. Regenerations of three species also decreased in eastern aspects. Our findings suggest that ecosystem models may overestimate post‐fire regeneration events in the southwest United States. To better represent regeneration processes following wildfire, ecosystem models need refinement to better account for the range of factors that influence tree seedling establishment. This will improve model utility for projecting the combined effects of climate and wildfire on tree species distributions.

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