Dataset Information

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies.

ABSTRACT: Widespread tree mortality caused by outbreaks of native bark beetles (Circulionidae: Scolytinae) in recent decades has raised concern among scientists and forest managers about whether beetle outbreaks fuel more ecologically severe forest fires and impair postfire resilience. To investigate this question, we collected extensive field data following multiple fires that burned subalpine forests in 2011 throughout the Northern Rocky Mountains across a spectrum of prefire beetle outbreak severity, primarily from mountain pine beetle (Dendroctonus ponderosae). We found that recent (2001-2010) beetle outbreak severity was unrelated to most field measures of subsequent fire severity, which was instead driven primarily by extreme burning conditions (weather) and topography. In the red stage (0-2 y following beetle outbreak), fire severity was largely unaffected by prefire outbreak severity with few effects detected only under extreme burning conditions. In the gray stage (3-10 y following beetle outbreak), fire severity was largely unaffected by prefire outbreak severity under moderate conditions, but several measures related to surface fire severity increased with outbreak severity under extreme conditions. Initial postfire tree regeneration of the primary beetle host tree [lodgepole pine (Pinus contorta var. latifolia)] was not directly affected by prefire outbreak severity but was instead driven by the presence of a canopy seedbank and by fire severity. Recent beetle outbreaks in subalpine forests affected few measures of wildfire severity and did not hinder the ability of lodgepole pine forests to regenerate after fire, suggesting that resilience in subalpine forests is not necessarily impaired by recent mountain pine beetle outbreaks.

SUBMITTER: Harvey BJ

PROVIDER: S-EPMC4210318 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies.

Harvey Brian J BJ Donato Daniel C DC Turner Monica G MG

Proceedings of the National Academy of Sciences of the United States of America 20140929 42

Widespread tree mortality caused by outbreaks of native bark beetles (Circulionidae: Scolytinae) in recent decades has raised concern among scientists and forest managers about whether beetle outbreaks fuel more ecologically severe forest fires and impair postfire resilience. To investigate this question, we collected extensive field data following multiple fires that burned subalpine forests in 2011 throughout the Northern Rocky Mountains across a spectrum of prefire beetle outbreak severity, p ...[more]

PMID: 25267633

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Project description:Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate-term future, e.g., 5-year. Machine-learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance-measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1-, 3-, 5- and 7-year infestations. We train nine machine-learning models, including two generalized boosted regression trees (GBM) that predict future 1- and 3-year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5- and 7-year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year-based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k-nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year-based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history-length increases, particularly for future 1- and 3-year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies.

Dataset Information

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies.

Publications

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies.

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