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Matthew L. Carlson

Publications and source records attributed to Matthew L. Carlson.

3 recordsLinked to original sources
USGS2018

Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides

Codominant marker systems are better suited to analyze population structure and assess the source of an individual in admixture analyses. Currently, there is no codominant marker system using microsatellites developed for the sea sandwort, Honckenya peploides (L.) Ehrh., an early colonizer in island systems. We developed and characterized novel microsatellite loci from H. peploides , using reads collected from whole genome shotgun sequencing on a 454 platform. The combined output from two shotgun runs yielded a total of 62,669 reads, from which 58 loci were screened. We identified 12 polymorphic loci that amplified reliably and exhibited disomic inheritance. Microsatellite data were collected and characterized for the 12 polymorphic loci in two Alaskan populations of H. peploides : Fossil Beach, Kodiak Island ( n = 32) and Egg Bay, Atka Island ( n = 29). The Atka population exhibited a slightly higher average number of alleles (3.9) and observed heterozygosity (0.483) than the Kodiak population (3.3 and 0.347, respectively). The overall probability of identity values for both populations was PID = 2.892e −6 and PID sib = 3.361e −3 . We also screened the 12 polymorphic loci in Wilhelmsia physodes (Fisch. ex Ser.) McNeill, the most closely related species to H. peploides , and only one locus was polymorphic. These microsatellite markers will allow future investigations into population genetic and colonization patterns of the beach dune ruderal H. peploides on new and recently disturbed islands.

Alaska↗
USGS2017

Cross-scale assessment of potential habitat shifts in a rapidly changing climate

We assessed the ability of climatic, environmental, and anthropogenic variables to predict areas of high-risk for plant invasion and consider the relative importance and contribution of these predictor variables by considering two spatial scales in a region of rapidly changing climate. We created predictive distribution models, using Maxent, for three highly invasive plant species (Canada thistle, white sweetclover, and reed canarygrass) in Alaska at both a regional scale and a local scale. Regional scale models encompassed southern coastal Alaska and were developed from topographic and climatic data at a 2 km (1.2 mi) spatial resolution. Models were applied to future climate (2030). Local scale models were spatially nested within the regional area; these models incorporated physiographic and anthropogenic variables at a 30 m (98.4 ft) resolution. Regional and local models performed well (AUC values > 0.7), with the exception of one species at each spatial scale. Regional models predict an increase in area of suitable habitat for all species by 2030 with a general shift to higher elevation areas; however, the distribution of each species was driven by different climate and topographical variables. In contrast local models indicate that distance to right-of-ways and elevation are associated with habitat suitability for all three species at this spatial level. Combining results from regional models, capturing long-term distribution, and local models, capturing near-term establishment and distribution, offers a new and effective tool for highlighting at-risk areas and provides insight on how variables acting at different scales contribute to suitability predictions. The combinations also provides easy comparison, highlighting agreement between the two scales, where long-term distribution factors predict suitability while near-term do not and vice versa.

Alaska↗