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An ecological disturbance is a temporary disruption in the environment resulting from abiotic, biotic, or anthropogenic factors, causing a pronounced change in an ecosystem. The impact of an ecological disturbance, which can depend on its intensity, frequency, and spatial distribution, plays a significant role in shaping the species diversity within the ecosystem.
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Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Confronting terrestrial biosphere models with forest inventory data.

Jeremy W Lichstein, Ni-Zhang Golaz, Sergey Malyshev

    Ecological Applications : a Publication of the Ecological Society of America
    |July 4, 2014
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    Summary
    This summary is machine-generated.

    Ignoring driver errors in terrestrial biosphere models (TBMs) using forest inventory data can bias results. Accounting for these errors, particularly in climate and soil data, improves model accuracy and parameter estimation for better ecosystem predictions.

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    Area of Science:

    • Ecology
    • Climate Science
    • Computational Biology

    Background:

    • Terrestrial biosphere models (TBMs) are crucial for understanding ecosystem responses to environmental change.
    • Forest inventories offer extensive data but are underutilized in TBM development due to driver uncertainties.
    • Uncertainty in environmental drivers like climate and soil can introduce bias in model-data fusion.

    Purpose of the Study:

    • To assess the impact of driver errors on TBM-data fusion using forest inventory data.
    • To evaluate how ignoring driver uncertainties affects model parameterization and ecosystem response estimation.
    • To propose a novel approach for TBM-data fusion that accounts for driver errors.

    Main Methods:

    • Estimated climate and soil driver errors at U.S. Forest Inventory and Analysis (FIA) plots.
    • Performed model-data fusion using the Geophysical Fluid Dynamics Laboratory LM3V dynamic global vegetation model.
    • Conducted a power analysis to determine the number of intensive study sites needed to overcome driver errors.

    Main Results:

    • Ignoring driver errors led to biased estimates of biomass production responses to precipitation and soil water.
    • Parameter estimates for fine-root allocation were biased when driver errors were not considered.
    • A large number of intensive study sites (>=40) would be needed to accurately quantify precipitation-biomass relationships without accounting for driver errors.

    Conclusions:

    • Driver errors in forest inventory data significantly impact TBM performance and parameterization.
    • Existing methods to handle driver errors are computationally expensive.
    • A new approach fitting TBM functional responses to driver-error-corrected data offers a computationally feasible alternative.