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NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2017

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Sammendrag

The European winter moth, Operophtera brumata, is a non-native pest in the Northeastern USA causing defoliation of forest trees and crops such as apples and blueberries. This species is known to hybridize with O. bruceata, the Bruce spanworm, a native species across North America, although it is not known if there are hybrid generations beyond F1. To study winter moth population genetics and hybridization with Bruce spanworm, we developed two sets of genetic markers, single nucleotide polymorphisms (SNPs) and microsatellites, using genomic approaches. Both types of markers were validated using samples from the two species and their hybrids. We identified 1216 SNPs and 24 variable microsatellite loci. From them we developed a subset of 95 species-diagnostic SNPs and ten microsatellite loci that could be used for hybrid identification. We further validated the ten microsatellite loci by screening field collected samples of both species and putative hybrids. In addition to confirming the presence of F1 hybrids reported in previous studies, we found evidence for multi-generation asymmetric hybridization, as suggested by the occurrence of hybrid backcrosses with the winter month, but not with the Bruce spanworm. Laboratory crosses between winter moth females and Bruce spanworm males resulted in a higher proportion of viable eggs than the reciprocal cross, supporting this pattern. We discuss the possible roles of population demographics, sex chromosome genetic incompatibility, and bacterial symbionts as causes of this asymmetrical hybridization and the utility of the developed markers for future studies.

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Sammendrag

Several mathematical models have been proposed for describing particle‐size distribution (PSD) data, but their characteristics and accuracy have not been investigated for the < 0.002, 0.002–0.05 and 0.05–2.0‐mm fractions separately. Therefore, the primary objective of this study was to examine the characteristics of various PSD models and to evaluate the accuracy of fitting to the entire PSD curve and to each of the three fractions separately. Thirty‐six PSD models were fitted to the experimental data of 160 soil samples from Iran. The beerkan estimation of soil transfer (BEST), Fredlund unimodal and bimodal, two‐ and three‐parameter Weibull, Rosin–Rammler and van Genuchten models provided the best fit to the experimental data of the three size fractions above, but with a different order of performance for the different fractions. For all textural fractions, the following models performed substantially less well than the other models: the offset‐non‐renormalized lognormal, simple lognormal, S‐curve, Schuhmann, Yang, Turcotte and Gompertz models. A comparison of the overall accuracy and simplicity of the models indicated that the BEST, two‐ and three‐parameter Weibull and Rosin–Rammler models provided the best fit to the experimental data for the entire curve, which is similar but does not correspond fully to the findings of a similar, earlier study. We found that the number of model parameters and the type of equation did not explain the models' fitting capabilities. We also found that the iterated function system (IFS) model performed better than the PSD models for all fractions. Comprehensive comparisons of PSD models will be of value to future model users, but it is important to note that such comparisons will remain dataset dependent. This is likely to continue until they are tested on a near‐infinite synthetic dataset that covers all possible data options.