Does urbanization explain differences in interactions between an insect herbivore and its natural enemies and mutualists?

Urbanization can alter the composition of arthropod communities. However, little is known about how urbanization affects ecological interactions. Using experimental colonies of the black bean aphid Aphis fabae Scopoli reared on Vicia faba L, we asked if patterns of predator-prey, host-parasitoid and ant-aphid mutualisms varied along an urbanization gradient across a large town in southern England. We recorded the presence of naturally occurring predators, parasitoid wasps and mutualistic ants together with aphid abundance. We examined how biotic (green areas and plant richness) and abiotic features (impervious surfaces and distance to town center) affected (1) aphid colony size, (2) the likelihood of finding predators, mutualistic ants and aphid mummies (indicating the presence of parasitoids), and (3) how the interplay among these factors affected patterns of parasitoid attack, predator abundance, mutualistic interactions and aphid abundance. The best model to predict aphid abundance was the number of mutualistic ants attending the colonies. Aphid predators responded negatively to both the proportion of impervious surfaces and to the number of mutualistic ants farming the colonies, and positively to aphid population size, whereas parasitized aphids were found in colonies with higher numbers of aphids and ants. The number of mutualistic ants attending was positively associated with aphid colony size and negatively with the number of aphid predators. Our findings suggest that for insect-natural enemy interactions, urbanization may affect some groups, while not influencing others, and that local effects (mutualists, host plant presence) will also be key determinants of how urban ecological communities are formed

Diversity array technology markers : genetic diversity analyses and linkage map construction in rapeseed (Brassica napus L.)

We developed Diversity Array Technology (DArT) markers for application in genetic studies of Brassica napus and other Brassica species with A or C genomes. Genomic representation from 107 diverse genotypes of B. napus L. var. oleifera (rapeseed, AACC genomes) and B. rapa (AA genome) was used to develop a DArT array comprising 11 520 clones generated using PstI/BanII and PstI/BstN1 complexity reduction methods. In total, 1547 polymorphic DArT markers of high technical quality were identified and used to assess molecular diversity among 89 accessions of B. napus, B. rapa, B. juncea, and B. carinata collected from different parts of the world. Hierarchical cluster and principal component analyses based on genetic distance matrices identified distinct populations clustering mainly according to their origin/pedigrees. DArT markers were also mapped in a new doubled haploid population comprising 131 lines from a cross between spring rapeseed lines \u2018Lynx-037DH\u2019 and \u2018Monty-028DH\u2019. Linkage groups were assigned on the basis of previously mapped simple sequence repeat (SSRs), intron polymorphism (IP), and gene-based markers. The map consisted of 437 DArT, 135 SSR, 6 IP, and 6 gene-based markers and spanned 2288 cM. Our results demonstrate that DArT markers are suitable for genetic diversity analysis and linkage map construction in rapeseed.Peer reviewed: YesNRC publication: N

Box-plot showing variation for resistance to pod shatter in population of DHs from BLN2762/Surpass400, grown under three different environments: Experiment 1, 2010, Birdcage; Experiment 2, 2011, Birdcage; and Experiment 3, 2011, Field conditions).

<p>Box-plot showing variation for resistance to pod shatter in population of DHs from BLN2762/Surpass400, grown under three different environments: Experiment 1, 2010, Birdcage; Experiment 2, 2011, Birdcage; and Experiment 3, 2011, Field conditions).</p

QTL detected with linkage (whole average genome interval mapping – WGAIM [DH-QTL], statistical machine learning-SML [DH-QTL], and genome-wide association analysis [GWAS] in <i>Brassica napus</i> germplasm.

<p>Marker sequences were aligned with the sequenced genomes of <i>B. rapa</i> and <i>B. oleracea</i> and their physical positions are shown <i>with dotted lines</i> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101673#pone.0101673.s008" target="_blank">Tables S1</a>& S4). Putative candidate genes (marked with red lines) that were localised within the physical map intervals are listed. Only QTL consistent across environments are shown (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101673#pone-0101673-t001" target="_blank">Table 1</a>): a = <i>Qrps.wwai-A03a</i> and <i>Qrps.wwai-A03b</i>; b = <i>Qrps.wwai-C03</i>; c = <i>Qrps.wwai-A09a</i>, and <i>Qrps.wwai-A09; b</i> and d = <i>Qrps.wwai-C08a</i>, <i>Qrps.wwai-C08b</i> and <i>Qrps.wwai-C08c</i>.</p

Significant QTL associated with resistance to pod shatter (pod strength or rupture energy) identified in a doubled haploid population derived from BLN2762/Surpass400

<p>Flanking markers that show the LOD score ≥2 are only shown, the additive effect refers to the parental allele that showed an increased effect and, the percentage of genotypic variation (R²) explained, and size of QTL effect (pod strength in millijules -mJ). The QTL analysis was carried out using a whole genome average interval mapping approach in R software. Bold letters indicate consistent QTL detected across different experiments/Pod shatter resistance attributes. H²: Broad sense heritability, C.V: Coefficient of variation. #: Estimations of RELSQ are natural logarithmic transformation therefore C. V. values are not provided.</p

Distribution of shatter resistance, as measured with the pendulum test, among DH lines from the BLN2762/Surpass 400 population grown under three environments: experiment 1 (2011, screenhouse, SHT11); experiment 2 (2012, screenhouse, SHT12); and experiment 3 (2012, screenhouse, SHTWW12).

<p>Pair-plots of EBLUPS from DH lines and parental lines showing correlations are presented. Rupture energy (RE) was measured in mJ. Data from experiments SHT11, SHT12 and SHTWW12 were RE, whereas for experiments RELSQ11, RELSQ12 and RELSQWW12 the data were RE (adjusted for pod length) from the same lines.</p

Additional file 4: Table S2. of Multi-environment QTL studies suggest a role for cysteine-rich protein kinase genes in quantitative resistance to blackleg disease in Brassica napus

Survival and internal infection of control B. napus lines in each environment. (DOCX 16 kb

Additional file 2: Table S1. of Multi-environment QTL studies suggest a role for cysteine-rich protein kinase genes in quantitative resistance to blackleg disease in Brassica napus

All single- and multi-environment QTL (LOD >2.5). (DOCX 43 kb

Genetic and physical mapping of flowering time loci in canola (Brassica napus L.)

We identified quantitative trait loci (QTL) underlying variation for flowering time in a doubled haploid (DH) population of vernalisation-responsive canola (Brassica napus L.) cultivars Skipton and Ag-Spectrum and aligned them with physical map positions of predicted flowering genes from the Brassica rapa genome. Significant genetic variation in flowering time and response to vernalisation were observed among the DH lines from Skipton/Ag-Spectrum. A molecular linkage map was generated comprising 674 simple sequence repeat, sequence-related amplified polymorphism, sequence characterised amplified region, Diversity Array Technology, and candidate gene based markers loci. QTL analysis indicated that flowering time is a complex trait and is controlled by at least 20 loci, localised on ten different chromosomes. These loci each accounted for between 2.4 and 28.6 % of the total genotypic variation for first flowering and response to vernalisation. However, identification of consistent QTL was found to be dependant upon growing environments. We compared the locations of QTL with the physical positions of predicted flowering time genes located on the sequenced genome of B. rapa. Some QTL associated with flowering time on A02, A03, A07, and C06 may represent homologues of known flowering time genes in Arabidopsis; VERNALISATION INSENSITIVE 3, APETALA1, CAULIFLOWER, FLOWERING LOCUS C, FLOWERING LOCUS T, CURLY LEAF, SHORT VEGETATIVE PHASE, GA3 OXIDASE, and LEAFY. Identification of the chromosomal location and effect of the genes influencing flowering time may hasten the development of canola varieties having an optimal time for flowering in target environments such as for low rainfall areas, via marker-assisted selection

Molecular mapping of qualitative and quantitative loci for resistance to Leptosphaeria maculans causing blackleg disease in canola (Brassica napus L.)

Blackleg, caused by Leptosphaeria maculans, is one of the most important diseases of oilseed and vegetable crucifiers worldwide. The present study describes (1) the construction of a genetic linkage map, comprising 255 markers, based upon simple sequence repeats (SSR), sequence-related amplified polymorphism, sequence tagged sites, and EST-SSRs and (2) the localization of qualitative (race-specific) and quantitative (race non-specific) trait loci controlling blackleg resistance in a doubled-haploid population derived from the Australian canola (Brassica napus L.) cultivars Skipton and Ag-Spectrum using the whole-genome average interval mapping approach. Marker regression analyses revealed that at least 14 genomic regions with LOD ≥ 2.0 were associated with qualitative and quantitative blackleg resistance, explaining 4.6–88.9 % of genotypic variation. A major qualitative locus, designated RlmSkipton (Rlm4), was mapped on chromosome A7, within 0.8 cM of the SSR marker Xbrms075. Alignment of the molecular markers underlying this QTL region with the genome sequence data of B. rapa L. suggests that RlmSkipton is located approximately 80 kb from the Xbrms075 locus. Molecular marker-RlmSkipton linkage was further validated in an F2 population from Skipton/Ag-Spectrum. Our results show that SSR markers linked to consistent genomic regions are suitable for enrichment of favourable alleles for blackleg resistance in canola breeding programs.Rosy Raman, Belinda Taylor, Steve Marcroft, Jiri Stiller, Paul Eckermann, Neil Coombes, Ata Rehman, Kurt Lindbeck, David Luckett, Neil Wratten, Jacqueline Batley, David Edwards, Xiaowu Wang, Harsh Rama