Dataset Information

Low offspring survival in mountain pine beetle infesting the resistant Great Basin bristlecone pine supports the preference-performance hypothesis.

ABSTRACT: The preference-performance hypothesis states that ovipositing phytophagous insects will select host plants that are well-suited for their offspring and avoid host plants that do not support offspring performance (survival, development and fitness). The mountain pine beetle (Dendroctonus ponderosae), a native insect herbivore in western North America, can successfully attack and reproduce in most species of Pinus throughout its native range. However, mountain pine beetles avoid attacking Great Basin bristlecone pine (Pinus longaeva), despite recent climate-driven increases in mountain pine beetle populations at the high elevations where Great Basin bristlecone pine grows. Low preference for a potential host plant species may not persist if the plant supports favorable insect offspring performance, and Great Basin bristlecone pine suitability for mountain pine beetle offspring performance is unclear. We infested cut bolts of Great Basin bristlecone pine and two susceptible host tree species, limber (P. flexilis) and lodgepole (P. contorta) pines with adult mountain pine beetles and compared offspring performance. To investigate the potential for variation in offspring performance among mountain pine beetles from different areas, we tested beetles from geographically-separated populations within and outside the current range of Great Basin bristlecone pine. Although mountain pine beetles constructed galleries and laid viable eggs in all three tree species, extremely few offspring emerged from Great Basin bristlecone pine, regardless of the beetle population. Our observed low offspring performance in Great Basin bristlecone pine corresponds with previously documented low mountain pine beetle attack preference. A low preference-low performance relationship suggests that Great Basin bristlecone pine resistance to mountain pine beetle is likely to be retained through climate-driven high-elevation mountain pine beetle outbreaks.

SUBMITTER: Eidson EL

PROVIDER: S-EPMC5929522 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Low offspring survival in mountain pine beetle infesting the resistant Great Basin bristlecone pine supports the preference-performance hypothesis.

Eidson Erika L EL Mock Karen E KE Bentz Barbara J BJ

PloS one 20180501 5

The preference-performance hypothesis states that ovipositing phytophagous insects will select host plants that are well-suited for their offspring and avoid host plants that do not support offspring performance (survival, development and fitness). The mountain pine beetle (Dendroctonus ponderosae), a native insect herbivore in western North America, can successfully attack and reproduce in most species of Pinus throughout its native range. However, mountain pine beetles avoid attacking Great Ba ...[more]

PMID: 29715269

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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

Low offspring survival in mountain pine beetle infesting the resistant Great Basin bristlecone pine supports the preference-performance hypothesis.

Publications

Low offspring survival in mountain pine beetle infesting the resistant Great Basin bristlecone pine supports the preference-performance hypothesis.

Similar Datasets

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