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Record W6996697197

Spatial analysis of fungicide resistance mutations in Botrytis spp. populations

2013· dissertation· en· W6996697197 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicFungal Plant Pathogen Control
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsBotrytis cinereaFungicideSingle-nucleotide polymorphismResistance (ecology)BotrytisAmmiSpatial distributionAzoxystrobin
DOInot available

Abstract

fetched live from OpenAlex

The objectives of this project were: 1) to study the spatial interactions of single nucleotide polymorphisms (SNPs) related to fungicide resistance within Botrytis cinerea populations isolated from grapes; 2) to study the spatial distribution patterns of SNPs related to fungicide resistance within B. cinerea populations in grape and within B. squamosa populations in onion; and 3) to compute sampling curves relative to mean SNP incidence estimation. In a first experiment, B. cinerea isolates were collected following a quadrat-based design (100 10x10m quadrats) in two commercial vineyards. The presence of 9 SNPs related to resistance to iprodione, boscalid, azoxystrobin and fenhexamid were detected using PCR-RFLP, PIRA-PCR and RT-qPCR assays. These data were spatially referenced and considered as a multivariate point pattern in a given vineyard. Spatial point patterns were analyzed by pairs, using an extension of Diggle's procedure for the analysis of nearest-neighbor distances. In this randomization testing procedure, the cumulative relative frequency distribution of the inter-SNP distances was used to characterize the spatial relationship between SNPs related to fungicide resistance. In the second experiment, two SNPs known to be responsible for boscalid resistance and one SNP known to be responsible for dicarboximide resistance in B. cinerea on grape were studied, in addition to one SNP responsible for dicarboximide resistance in B. squamosa on onion. One onion field was sampled in 2009 and another one was sampled in 2010 for B. squamosa, and two vineyards were sampled in 2011 for B. cinerea, for a total of four sampled sites. Sampling was carried following the same design as in the first experiment, except 10 samples were collected in each quadrat. Samples were analyzed by RFLP-PCR. The characterization of spatial distribution patterns was made through the fitting of discrete probability distributions. The level of mutations obtained in the first experiment was 90%, 64%, 67%, 33% and 1% for G143A, I86S, H272R, H272Y and N230I, respectively. Our results show that three spatial relationships can arise when spatial point patterns representing the presence of SNPs related to fungicide resistance are compared by pairs: spatial exclusiveness (12%), spatial co-existence (31%) and absence of a spatial relationship (56%). Despite the fact that more than one half of the pairs of SNPs tested showed no spatial relationship, the presence of about a third of inclusive spatial relationships supports the models of co-existence between sensitive and resistant strains postulated in the literature, but suggests a higher level of complexity in the resistant-sensitive interactions. In the second experiment, the beta-binomial distribution was found to fit the data better than the binomial distribution for all data sets. This indicates local SNP aggregation among sampling units, as supported by estimates of the parameter θ of the beta-binomial distribution ranging from 0.09 to 0.23, with an overall median value of 0.20. On the basis of the spatial distribution patterns of SNP incidence that we found in Botrytis populations, sampling curves were developed for various levels of precision, emphasizing the importance of sampling for early detection of fungicide resistance in plant disease epidemiology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.229
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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