Fragments were visualized by silver staining with a commercial kit . Scoring for each marker was double checked, and any ambiguous accessions were rerun, or scored as missing data. In the later part of the study, fragments were separated and sized on an ABI 3130 Genetic Analyzer . Products from up to four primers were analyzed in one injection by using different fluorescent labels on different primers and taking into account the expected fragment size. PCR products were added to an 11 μl: 0.2 μl mixture of HD-formamide and GeneScan HD 400 ROX as the internal size standard, respectively. The fragments were denatured for 2 min at 92°C then injected into a 36 cm capillary filled with the polymer POP-7 . Fragment sizes were determined and rounded using Genotyper 2.5 software . Four to six common V. vinifera cultivars were used as an internal control and to ensure allele calls were consistent with samples run on silver stained sequencing gels.Allele size data for 133 accessions from the INRA Domaine de Vassal germplasm collection were generated in France for 20 SSR markers following procedures described by Laucou et al.. Allele sizes were transformed to match the allele sizes from the UCD data set based on known references and samples common to both group’s data sets. Multi-locus accessions from the INRA set were compared to the UCD data set to identify synonymous samples. Unique accessions from the INRA set were analyzed at UCD with an additional 14 markers to increase marker overlap with the UCD data set.Powdery mildew resistance evaluations were made on selected accessions in a field nursery trial under unsprayed conditions. These trials were carried out in the summer of 2009 and 2010. Accessions that carry either one or both flanking SSR marker alleles linked to the Ren1 locus were evaluated.
Powdery mildew resistant and susceptible accessions from the UCD breeding program, flower bucket and well known resistant interspecific hybrids and highly susceptible V. vinifera cultivars were used as positive and negative controls . A total of 65 accessions were screened in 2009. Five to six replicates of each accession were propagated from hardwood cuttings and planted in three field nursery rows with 30 cm between plants and rows. Powdery mildew symptoms were evaluated based on the extent of infection following the Organisation Internationale de la Vigne et du Vin criteria and scored from 0 to 5: 0 ; 1 one or two very small spots; 2 limited patches of powdery mildew infection; 3 patches of infection wider than 5 cm in diameter; 4 many powdery mildew infection spots and abundant mycelium growth; and 5 where leaves and other Thissue types were covered with unlimited patches of powdery mildew infection. In 2009, disease evaluations were carried out twice on the same plant during the last week of August and last week of September. Each observation was considered as one replicate for that accession. A total of 43 accessions including positive and negative controls were screened in 2010 . The majority of these accessions were Chinese or Central Asian species that were difficult to propagate by hardwood cuttings. These accessions were propagated from herbaceous cuttings that were dipped in rooting hormone and rooted under intermittent mist with bottom heat. Rooted cuttings were planted into small plastic pots and once established were planted into the field nursery. Disease symptoms were evaluated twice during the first week of September and first week of October as described above. Four of the wild V. vinifera subsp. sylvestris accessions with the resistance allele of the closely linked marker SC8-0071-014 were screened in 2012 under the conditions described above. Accessions maintained in the INRA collection that had Ren1 linked alleles were evaluated for powdery mildew resistance in an unsprayed greenhouse with susceptible controls and artificial inoculum in 2012. Because powdery mildew disease symptoms were recorded as discrete categories, the ordinal logistic regression model platform of JMP was used to estimate the effectiveness of the screen by comparing the significance level of genotype, date, field nursery bed and year.
Two SSR markers were reported to co-segregate with Ren1 locus in ‘Kishmish vatkana’ and ‘Karadzhandal’. A sequence fragment was obtained using the PN40024 genome by aligning the sequence of SC8-0071-014. Primers were designed around the region of SC8-0071-014 that generated a 625 bp ampliftication product. PCR products were cloned for 16 accessions using the pGEM®-T Easy vector system using standard protocols. ‘Khwangi’ was the seventeenth accession that had the 143 bp allele, but it was powdery mildew susceptible in the field trial and was not included for sequencing. Eight to twelve positive colonies were selected for each accession and DNA was extracted using the Qiagen plasmid mini kit. PCR ampliftications were carried out with the SC8-0071-014 primers in order to identify two alleles of each accession using standard protocols. Sequencing with SP6 primer was carried out only on those samples that represent 143 allele haplotype. Sequences were aligned with the Clustal V method by using the MegAlign application of DNASTAR Lasergene V8.1.Accessions with seven or more missing data were not included in the genetic diversity analysis. Next, two different data sets were prepared: the first set consisted of 394 unique accessions that included interspecific hybrids, European reference wine grape varieties and North American species; there were 380 accessions in the second set after the 14 samples of hybrids, North American species and European wine grape varieties were excluded. Simple matching distance was calculated with 19 and 34 SSR markers on both data sets. Hierarchical clustering and principal coordinate analysis were carried out with DARWIN V5.0.158 to determine the number of groups. Following these analyses, STRUCTURE V2.3.1 was used to infer the number of clusters with 19 and 34 markers, and with both data sets of 394 and 380 accessions. The membership of each accession was run for a range of genetic clusters with K values of 1 to 10 using the admixture model, and it was replicated 10 times for each K. Each run was implemented with a burn-in period of 100,000 steps followed by 400,000 Monte Carlo Markov Chain replicates using no prior information and assuming correlated allele frequencies.
The posterior probability was then calculated for each value of K using the estimated log-likelihood of K to choose the optimal K . The results from STRUCTURE were displayed by DISTRUCT software. The microsatellite tool kit software was used to calculate standard parameters of genetic variability: expected heterozygosity ; allele frequencies ; and observed heterozygosity . The deviation from Hardy-Weinberg equilibrium at each locus was examined by calculating the inbreeding coefficient ‘FIS’ for each group, and the overall differentiation index ‘FST’ with FSTAT V2.9.3.2 software. The probability of identity , probability of exclusion and LOD likelihood ratios for potential parent-progeny relationships were calculated with FAMOZ software. The 10,000 simulated pairs were performed to identify a log of the odds ratios score threshold to assess a potential parent pair with 34 SSR markers. A discrepancy of a maximum of two loci was allowed to cover possible data errors, null alleles, and clonal mutations as previously described. Potential parental pairs were further evaluated if a discrepancy in the allelic data was observed. They were ampliftied and repeated either on denaturing polyacrylamide gels or using the ABI 3130 Genetic Analyzer. Additional markers were also added on putative parental pairs.Managing the benefits people receive from nature, or ecosystem services, requires a detailed understanding of ecosystem processes. In particular, biodiversity-driven services, such as pest control on farms, requires knowledge of cropping systems, the habitats in and around croplands, square flower bucket and the interactions among the many organisms that inhabit them. Interactions are complex and often change over space and time ; therefore, a critical first step is identifying the species and populations that provide benefits to society . Identifying service providers, however, may not be straightforward. For example, predation is rarely witnessed directly, making it difficult to identify the predators of crop pests. Pest control is a critical service; in the United States, insect predators save farmers billions of dollars annually in avoided pest damage . Several different techniques have been utilized to identify predator–prey interactions. An indirect approach is using stable isotopes to determine trophic positions . A direct approach for identifying predators is visual identification of prey remains in predators’ guts or feces . While visual identification of prey gut contents can sometimes yield the necessary taxonomic resolution to identify insect pests, the necessary inspection labor is considerable and sampling techniques often result in high mortality rates among study subjects. Molecular identification techniques, however, offer great potential to yield insight into predator–prey interactions . These techniques often rely on targeting and sequencing a standardized DNA region across species to facilitate identifications . Applications of this approach are diverse; for example, detecting diet shifts in ancient humans , characterizing biological communities in hydrothermal vents , identifying illegal trade in endangered species , and surveying rare mammals with DNA from leeches . Similarly, molecular identification in feces,regurgitate, and stomach contents from carnivores, insectivores, and herbivores of diverse taxa has been used to infer diet . While the application of molecular diet analysis is becoming widespread, the technique is not without limitations. First, predators vary in gut retention times and digestion processes, which may affect detection rates and complicate comparisons among species . Second, DNA assays can misattribute diet in the presence of intraguild predation— that is, if the DNA of the prey of an intermediate predator is found in the fecal samples of a top predator . Finally, digestion degrades prey DNA, making fecal analysis more sensitive than other PCR procedures to DNA quantity . Despite these shortcomings, several studies have used molecular techniques to identify suites of pest predators, largely through DNA analysis of arthropod predators’ gut contents . Less work has focused on vertebrate insectivores, despite their great potential to control pest infestations .
Those that have studied vertebrate predators of insect pests tend to analyze single predator species rather than communities . Further, analyses have neglected the biologically diverse, tropical countries that may stand to benefit most from conservation-minded pest-management plans . We used molecular fecal analysis to identify bird predators of coffee’s most damaging insect pest— the coffee berry borer beetle . Coffee is cultivated across the tropics, with a total export value over US$20 billion and twenty million households involved in its production . The borer has invaded almost every coffee-producing country in recent years. In fact, the borer invaded Costa Rica in 2000 and our study sites in 2005. It spends the majority of its liftecycle within coffee berries, overwintering in unharvested berries and undergoing a major dispersal event several months after the first rains . Previous exclusion experiments have shown that birds consume the borer, likely during the primary dispersal event or secondary movements to adjacent berries throughout the year . The borer’s small size makes directly witnessing predation unlikely . Our work builds on Karp et al. , which used exclosures to quantify bird-mediated borer control. Here, we sought to characterize more completely which species are borer predators, supplementing their analysis with an additional 961 fecal samples and 33 bird species . In addition, we verified this approach through feeding trials with three insectivorous bird species. Finally, we compiled a database of bird conservation and functional traits to make a preliminary determination of the traits associated with borer consumption and to assess whether species that important for controlling damaging insect pests are also conservation targets.Our investigation cenThered on coffee plantations in southern Costa Rica, near the Las Cruces Biological Station of the Organization for Tropical Studies. We worked on two coffee plantations—a small family plantation and a large commercial operation. Both are situated at ~1100 m asl, and cultivate coffee under sun. We collected fecal samples from birds in April-May of 2010 and 2011, when borers were at peak dispersal. All animals were treated humanely, in accordance with the Institutional Animal Care and Use Committee guidelines and approved by the Administrative Panel on Laboratory Animal Care of Stanford University . We placed three mist-netting stations at each plantation and visited each station three times per year. Each station was composed of 20 12 m × 2.5 m mist nets, located between rows of coffee and within patches of forest next to plantations. Our surveys began at sunrise, continuing for 5–6 hours until bird activity subsided. All birds were placed in breathable cotton bags until they could be identified and uniquely marked with a metal leg band.