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30 results for “mountain pine beetle”

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dryad28/100

Translocation experiment reveals capacity for mountain pine beetle persistence under climate warming

<p>Predicting species response to climate change is a central challenge in ecology, particularly for species that inhabit large geographic areas. The mountain pine beetle (MPB) is a significant tree mortality agent in western North America with a distribution limited by climate. Recent warming has caused large-scale MPB population outbreaks within its historical distribution, in addition to migration northward in western Canada. The relative roles of genetic and environmental sources of variation governing MPB capacity to persist-in-place in a changing climate, and the migratory potential at its southern range edge in the United States, have not been investigated. We reciprocally translocated MPB populations taken from the core and southern edge of their range, and simultaneously translocated both populations to a warmer, low-elevation site near the southern range boundary where MPB activity has historically been absent despite suitable hosts. We found genetic variability and extensive plasticity in multiple fitness traits that would allow both populations to persist in a warming climate that resembles the thermal regime of our low-elevation site. We demonstrate, for the first time, that supercooling points in MPBs are influenced both by genetic and environmental factors. Both populations reproduced with seasonally appropriate univoltine generation times at all translocated sites, and bivoltinism was not observed. The highest reproductive success occurred at the warmest, out-of-range low-elevation site, suggesting that southward migration may not be temperature-limited.</p>

opencc-zeroAug 2020View details →
dryad28/100

Data from: Adaptive and neutral markers both show continent-wide population structure of mountain pine beetle (Dendroctonus ponderosae)

Assessments of population genetic structure and demographic history have traditionally been based on neutral markers while explicitly excluding adaptive markers. In this study, we compared the utility of putatively adaptive and neutral single-nucleotide polymorphisms (SNPs) for inferring mountain pine beetle population structure across its geographic range. Both adaptive and neutral SNPs, and their combination, allowed range-wide structure to be distinguished and delimited a population that has recently undergone range expansion across northern British Columbia and Alberta. Using an equal number of both adaptive and neutral SNPs revealed that adaptive SNPs resulted in a stronger correlation between sampled populations and inferred clustering. Our results suggest that adaptive SNPs should not be excluded prior to analysis from neutral SNPs as a combination of both marker sets resulted in better resolution of genetic differentiation between populations than either marker set alone. These results demonstrate the utility of adaptive loci for resolving population genetic structure in a nonmodel organism.

opencc-zeroDec 2015View details →
dryad28/100

Data from: Field studies demonstrate bivoltinism in the mountain pine beetle (a reply to Bentz and Powell)

[No abstract entered]

opencc-zeroDec 2013View details →
dryad28/100

Lignin concentrations in phloem and outer bark are not associated with resistance to mountain pine beetle in high elevation pines

<p>A key component to understanding plant-insect interactions is the nature of host defenses. Research on defense traits among <i>Pinus</i> species has focused on specialized metabolites and axial resin ducts, but the role of lignin in defense within diverse systems is unclear. We investigated lignin levels in the outer bark and phloem of <i>P. longaeva</i>, <i>P. balfouriana</i>,<i> </i>and<i> P. flexilis</i>; high elevation species in the western United States known to differ in susceptibility to mountain pine beetle (<i>Dendroctonus ponderosae</i>; MPB). Relative to <i>P. flexilis</i>, <i>P. longaeva</i> and <i>P. balfouriana</i> are attacked by MPB less frequently, and MPB brood production in <i>P. longaeva</i> is limited. Because greater lignification of feeding tissues has been shown to provide defense against bark beetles in related genera, such as <i>Picea</i>, we hypothesized that <i>P. longaeva</i> and <i>P. balfouriana</i> would have greater lignin concentrations than <i>P. flexilis</i>. Contrary to expectations, we found that the more MPB-susceptible <i>P. flexilis</i> had greater phloem lignin levels than the less susceptible <i>P. longaeva</i> and <i>P. balfouriana</i>. No differences in outer bark lignin levels among the species were found. We conclude that lignification in <i>Pinus</i> phloem and outer bark is likely not adaptive as a physical defense against MPB.</p>

opencc-zeroSep 2021View details →
dryad28/100

Data from: Field studies demonstrate bivoltinism in the mountain pine beetle (a reply to Bentz and Powell)

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publicJul 2014View details →
dryad28/100

Data from: Adaptive and neutral markers both show continent-wide population structure of mountain pine beetle (Dendroctonus ponderosae)

Open the record for dataset details and reuse information.

publicAug 2017View details →
dryad28/100

Translocation experiment reveals capacity for mountain pine beetle persistence under climate warming

Open the record for dataset details and reuse information.

publicAug 2020View details →
dryad28/100

Lignin concentrations in phloem and outer bark are not associated with resistance to mountain pine beetle in high elevation pines

Open the record for dataset details and reuse information.

publicSep 2021View details →
geo24/100

Functional genomics of mountain pine beetle (Dendroctonus ponderosae) midguts and fatbodies

GEO Series GSE17858. Dendroctonus ponderosae. 44 samples. Type: Expression profiling by array.

openGEO-OpenAug 2009View details →
geo24/100

Quantitative transcriptomics of pheromone biosynthetic midgut and fat body tissues in the mountain pine beetle, Dendroctonus ponderoase Hopkins

GEO Series GSE69061. Dendroctonus ponderosae. 32 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2016View details →

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