Grazing preferences of three species of amoebae on cyanobacteria and green algae.

Twenty species/isolates of cyanobacteria and green algae were isolated from cyanobacterial bloom samples in lakes associated with the upper Qu'Appelle River drainage system in southern Saskatchewan, Canada. Three amoebae species (Cochliopodium sp., Vannella sp. and Vermamoeba vermiformis) were also isolated from one of these samples, and were subjected to grazing assays to determine which species of cyanobacteria or algae could potentially serve as a food source. Amoeba grazing rates were quantified based on the diameter of the plaque after 12 days on agar plate assays, and by estimation of the amoeba population growth rate from the rate of increase of plaque area. The common cyanobacterial bloom-formers Dolichospermum sp. and Aphanizomenon flos-aquae supported high growth rates for all three amoebae, while green algae, with the exception of one green alga/amoeba combination, did not support growth of the tested amoebae. Many of the cyanobacterial and algal isolates that did not support amoebae growth were ingested, suggesting that ingestion did not determine grazing success. Overall, while the cyanobacteria Dolichospermum sp. and Aphanizomenon flos-aquae were suitable food sources for the amoebae, the other cyanobacteria were grazed in an unpredictable manner, with some species/strains grazed by some amoebae and some species not grazed at all.

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials.

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.