Spatial Dependency in Stubble-Borne Pyrenophora teres f. teres and Influence of Sample Support Size on DNA Concentration and Fungicide Resistance Frequency.

Fungicide resistance in foliar fungal pathogens is an increasing challenge to crop production. Yield impacts due to loss of fungicide efficacy may be reduced through effective surveillance and appropriate management intervention. For stubble-borne pathogens, off-season crop residues may be used to monitor fungicide resistance to inform pre-planting decisions; however, appropriate sampling strategies and support sizes for crop residues have not previously been considered. Here, we used Pyrenophora teres f. teres (Ptt) with resistance to demethylase inhibitor fungicides as a model system to assess spatial dependency and to compare the effects of different sampling strategies and support sizes on pathogen density (Ptt DNA concentration) and the frequency of fungicide resistance mutation. The results showed that sampling strategies (hand-picked versus raked) did not affect estimates of pathogen density or fungicide resistance frequency; however, sample variances were lower from raked samples. The effects of differing sample support size, as the size of the collection area (1.2, 8.6, or 60 m2), on fungicide resistance frequency were not evident (P > 0.05). However, measures of pathogen density increased with area size (P < 0.05); the 60 m2 area yielded the highest Ptt DNA concentration and produced the lowest number of pathogen-absent samples. Sample variances for pathogen density and fungicide resistance frequency were generally homogeneous between area sizes. The pattern of pathogen density was spatially independent; however, spatial dependency was identified for fungicide resistance frequency, with a range of 110 m, in one of the two fields surveyed. Collectively, the results inform designs for monitoring of fungicide resistance in stubble-borne pathogens.

Probabilistic approaches for investigating species co-occurrence from presence-absence maps.

Background: In this research, we propose probabilistic approaches to identify pairwise patterns of species co-occurrence by using presence-absence maps only. In particular, the two-by-two contingency table constructed from a presence-absence map of two species would be sufficient to compute the test statistics and perform the statistical tests proposed in this article. Some previous studies have investigated species co-occurrence through incidence data of different survey sites. We focus on using presence-absence maps for a specific study plot instead. The proposed methods are assessed by a thorough simulation study. Methods: A Chi-squared test is used to determine whether the distributions of two species are independent. If the null hypothesis of independence is rejected, the Chi-squared method can not distinguish positive or negative association between two species. We propose six different approaches based on either the binomial or Poisson distribution to obtain p-values for testing the positive (or negative) association between two species. When we test to investigate a positive (or negative) association, if the p-value is below the predetermined level of significance, then we have enough evidence to support that the two species are positively (or negatively) associated. Results: A simulation study is conducted to demonstrate the type-I errors and the testing powers of our approaches. The probabilistic approach proposed by Veech (2013) is served as a benchmark for comparison. The results show that the type-I error of the Chi-squared test is close to the significance level when the presence rate is between 40% and 80%. For extremely low or high presence rate data, one of our approaches outperforms Veech (2013)'s in terms of the testing power and type-I error rate. The proposed methods are applied to a tree data of Barro Colorado Island in Panama and a tree data of Lansing Woods in USA. Both positive and negative associations are found among some species in these two real data.

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.

Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. Clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.