Adapting a dynamic vegetation model for regional biomass, plant biogeography, and fire modeling in the Greater Yellowstone Ecosystem: Evaluating LPJ-GUESS-LMfireCF

Emmett, Kristen; Renwick, Katherine M.; Poulter, Benjamin

doi:10.1016/j.ecolmodel.2020.109417

Cited by 7 publications

(2 citation statements)

References 92 publications

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“…But those that have been tested demonstrate considerable promise. For instance, mechanistic models have successfully been used to predict population declines, pest population dynamics, species distributions from phenological traits, forest carbon dioxide exchanges, and fire dynamics in nature (Wilder 1999, Brook et al 2000, Amthor et al 2001, Chuine and Beaubien 2001, Emmett et al 2021. These examples suggest that accurate predictions for biodiversity and ecosystems are possible especially when more mechanistic models and data are available.…”

Section: Implementing the Visionmentioning

confidence: 99%

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

et al. 2021

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Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

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Section: Implementing the Visionmentioning

confidence: 99%

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

et al. 2021

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“…While the modeling community has been improving mortality functions to better approximate observed patterns of mortality, there have been few efforts to better represent the effects of climate and disturbance on tree seedling establishment. Understanding how regeneration patterns in post‐fire conditions may change as a function of changing climate is necessary to improve our ability to project the spatial distribution of tree species, forest cover, and forest biomass following wildfire (Canelles et al, 2019; Emmett et al, 2021).…”

Section: Introductionmentioning

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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Modeling wildfire risk in western Iran based on the integration of AHP and GIS

Nasiri

Sadeghi

Bagherabadi

et al. 2022

Environ Monit Assess

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Adapting a dynamic vegetation model for regional biomass, plant biogeography, and fire modeling in the Greater Yellowstone Ecosystem: Evaluating LPJ-GUESS-LMfireCF

Cited by 7 publications

References 92 publications

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

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

Modeling wildfire risk in western Iran based on the integration of AHP and GIS

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