We were unable to do this because of our study design, which did not examine seed set from single bee visits. Nevertheless, this is the first sunflower seed set study to detect an interspecific interactive effect at the community-level rather than at the individual-level. However, despite the importance of these interactive effects on sunflower yield, company was the factor that most strongly influenced seed set. Although there was little variation in head size between sunflower companies , using company as a classification may mask other differences, such as genetic differences between varieties and variation in field management techniques. By pairing control and hedgerow sites by company, variety and landscape context, we sought to minimize these potential differences,pe grow bag and the few differences in management practice were noted between companies. It is hypothesized that the effectiveness of field-edge vegetation re-diversification is maximized in landscapes that retain a small percentage of natural areas that can facilitate recolonization of restored habitats . The added benefits of diversification efforts may be minimal in complex landscapes with high proportions of natural habitat since ecosystem service providers are often already supported.
Diversification efforts may not support ecosystem providers in highly intensified landscapes with no remaining natural habitat, either because there are no source areas to colonize the new habitats or because the new habitats alone cannot support populations of ecosystem service providers . Although the landscape where we conducted our study constitutes a “cleared” landscape, and we did not detect landscape effects, other studies in the same location have found that hedgerows increase wild bee abundance, richness and population persistence and promote rare and/or more specialized species . Nevertheless we did not find evidence that these biodiversity benefits translated into higher rates of pollination services in adjacent sunflower crop fields. Although both wild bee richness and abundance were important factors contributing to sunflower seed set, these contributions may be attributable to factors other than hedgerows. For example, wild bee visitors to sunflower were predominately sunflower specialists; the amount of sunflower maintained in the landscape over time could therefore influence sunflower pollinator populations more strongly than hedgerow plantings that do not contain floral resources suitable for the specialists’ dietary requirements , as we found was true in the independent dataset. It is important to balance the conservation value of field-edge plantings with ecosystem service delivery objectives. While conservation and ecosystem service outcomes can be synergistic, win–win scenarios are challenging to achieve .
Hedgerows augment pollinator populations, which can be important for achieving wild bee conservation goals ; however, they may not be a “silver bullet” strategy for increasing crop pollination. Both the scale of the re-diversification effort relative to the farming system and the adjacent crop type could limit the effectiveness of hedgerow plantings. Hedgerows occupy <1% of our study landscape and contain 175 times less area than a typical average crop field in our study area. The intensity of bloom in hedgerows is also minimal in comparison to the hundreds of thousands of blooms in a single MFC field . Increasing the size of hedgerows relative to fields or introducing a suite of diversification techniques could increase the effectiveness of re-diversification efforts .Alternately, the configuration of habitat could impact pollinator populations. For example, when Morandin and Winston examined the optimal spatial distribution of a MFC, canola , they found that both profits and pollination services would be maximized if a central field was left fallow or allowed to revert to semi-natural habitat. The size, configuration and quality of habitat may all interact to influence pollinator communities . The benefits of field-edge diversifications may also differ based on crop identity and landscape context . For example, sunflower has easily accessible florets that attract both generalist and specialist pollinators. However, in systems where flowers have specific requirements, such as high bush blueberry that requires buzz-pollination, the identity of pollinator species may be of more importance .
Further, species-specific responses to habitat features may differ. Carvell et al. found bumble bees had differential responses to wildflower patch size and landscape heterogeneity, indicating that local and landscape habitat factors can also interact with one another, and with crop-specific attributes, to affect crop pollination. In a tropical region, Carvalheiro et al. found that wildflower plantings worked in concert with natural habitat to heighten mango production. There are a paucity of studies on the ecosystem service benefits from field-edge plantings, therefore the complex range of factors, including farming type, crop system, landscape context, and region , influencing their performance is still relatively unknown .Molecular networking1 , introduced in 2012, was one of the first data organization approaches to visualize the relationships between tandem mass spectrometry fragmentation spectra. In molecular networking, relationships between similar MS/MS spectra are visualized as edges. As MS/MS spectral similarity implies chemical structural similarity1 , chemical structural information can thus be represented as a network and chemical relationships can be visualized. This approach forms the basis for the web-based mass spectrometry infrastructure, Global Natural Products Social Molecular Networking2 which sees ~200,000 new accessions per month. Molecular networking has successfully been used for a range of applications in drug discovery, natural products research, environmental monitoring, medicine, and agriculture.
To tap into the chemistry of complex samples through metabolomics, a subset of MS/MS spectra can be annotated by spectral library matching or by using in silico approaches. While molecular networking facilitates the visualization of closely related molecules in molecular families, the inference of chemical relationships at a dataset-wide level and in the context of diverse sample metadata requires complementary representation strategies. To address this need, we developed an approach that uses fragmentation trees4 and machine learning5 to calculate all pairwise chemical relationships. These chemical relationships are represented as a chemicaltree that can be visualized in the context of sample metadata and molecular annotations obtained from spectral matching and in silico annotation tools. We show that such a chemical tree representation enables the application of various tree-based tools, originally developed for analyzing DNA sequencing data, for exploring mass-spectrometry data. Here, we introduce Qemistree software that constructs a chemical tree based on predicted molecular fingerprints from MS/MS fragmentation spectra10 . Molecular fingerprints are vectors where each position encodes a sub-structural property of the molecule, and recent methods allow us to predict molecular fingerprints from tandem mass spectra. In Qemistree, we use SIRIUS16 and CSI:FingerID to obtain predicted molecular fingerprints. Users can first perform feature detection to generate a list of observed ions with associated peak areas and MS/MS fragmentation spectra,growing bags referred to as chemical features henceforth, to be analyzed by Qemistree . Only chemical features with MS/MS data are included; features with only MS1 are not considered. SIRIUS then determines the molecular formula of each feature using the isotope and fragmentation patterns and estimates the best fragmentation tree explaining the fragmentation spectrum. Subsequently, CSI:FingerID operates on the fragmentation trees using kernel support vector machines to predict molecular properties . We use these molecular fingerprints to calculate pairwise distances between chemical features and hierarchically cluster the fingerprint vectors to generate a tree representing their chemical structural relationships. Although alternative approaches to hierarchically cluster features based on cosine similarity of fragmentation spectra exist19–21, we use molecular fingerprints predicted by CSI:FingerID for this. Previous work has shown that CSI:FingerID outperforms other tools for automatic in silico structural annotation. Therefore, we leverage it to search molecular structural databases to provide complementary insights into structures when no match is obtained against spectral libraries. Subsequently, we use ClassyFire23 to assign a 5-level chemical taxonomy to all molecules annotated via spectral library matching and in silico prediction . Phylogenetic tools such as iTOL24 can be used to visualize Qemistree trees interactively in the context of sample information and feature annotations for easy data exploration. The outputs of Qemistree can also be plugged into other workflows in QIIME 2 or in R, Python, etc. for system-wide metabolomic data analyses.
In this study, we apply Qemistree to perform chemically informed comparisons of samples in the presence of technical variation such as chromatographic shifts that commonly affect mass spectrometry data analysis. Additionally, we exemplify the use of a tree-based representation to visualize and explore chemical diversity using a heterogeneous collection of food products. Qemistree can be used iteratively to incorporate multiple datasets without the need for cumbersome reprocessing , allowing for large-scale dataset comparisons. Qemistree is available to the microbiome community as a QIIME 2 plugin and the metabolomics community as a workflow on GNPS2 . The chemical tree from the GNPS workflow can be explored interactively using the Qemistree GNPS dashboard. . To verify that molecular fingerprint-based trees correctly capture the chemical relationships between molecules, we designed an evaluation dataset using four distinct biological specimens: two human fecal samples, a tomato seedling sample, and a human serum sample. Samples were prepared by combining them in binary, tertiary, and quaternary mixtures in various proportions to generate a set of diverse but related metabolite profiles . Untargeted tandem mass spectrometry was used to analyze the chemical composition of these samples and obtain fragmentation spectra. The mass spectrometry experiments were performed twice using different chromatographic elution gradients, causing a retention time shift between the two runs . Processing the data of these two experiments with traditional LC-MS-based pipelines leads to the same molecules being detected as different chemical features in downstream analysis. Figure 1 shows the analysis of pure samples to demonstrate this. In Extended Data Figure 4, we highlight how these technical variations make the same samples appear chemically disjointed. Using Qemistree, we mapped each of the spectra in the two chromatographic conditions to a molecular fingerprint, and organized these in a tree structure . Because molecular fingerprints are independent of retention time shifts, spectra are clustered based on their chemical similarity. It is noteworthy that the structural information from chemical features with spectral library matches or other forms of annotation could also be used to compare the chemical composition of samples across different mass spectrometry runs. Qemistree improves upon this by enabling the use of all MS/MS spectra with molecular fingerprints for downstream comparative analyses, by not constraining analysis to the chemical features with spectral matches only. This tree structure can be decorated using sample type descriptions, chromatographic conditions, spectral matches obtained from molecular networking in GNPS , and any other chemical annotations. Figure 1 shows that similar chemical features were detected exclusively in one of the two batches. However, based on the molecular fingerprints, these chemical features were arranged as neighboring tips in the tree regardless of the retention time shifts. This result shows how Qemistree can reconcile and facilitate the comparison of datasets acquired on different chromatographic gradients. On the southwestern Oregon Coast lies Coos Bay. This bay is important to the economy of Oregon; functioning as its southernmost deep water port . It has also historically been a center of trade and commerce among the Indigenous American peoples of the southwestern Oregon Coast. It is home to the eponymous Hanis and Miluk Coos peoples, as well as the Siuslaw. It has also frequently hosted peoples of the Tututni, Alsea, Lower Umpqua, and Coquille tribes. It is the languages of these first two peoples, the Hanis and the Miluk, that is the focus of this paper: hanis kuukwiisand miluk kuukwiis .According to oral history, the Miluk and Hanis people came to settle in what is now known as Coos Bay between 12,000 and 15,000 years ago, with archaeological evidence of permanent habitation at least 3,300 years ago . As Whereat points out, it is unlikely that there will be significantly older archaeological evidence found due to the natural geography and the nature of the ebb tide in Coos Bay. Starting in 1850 with the Oregon Land Donation Act, the Hanis, Miluk, and Siuslaw peoples were forced to cede their lands to the United States. This was followed by forced internment, conquest, and occupation of their lands that continue until this day . Both languages are currently near-silent, with only a few community members that know some words, phrases, and stories, though there is not yet language fluency at this time. The Miluk and Hanis peoples were confederated by President Reagan into the Confederated Tribes of Coos, Lower Umpqua, & Siuslaw Indians . In recent years, there has been a push towards language reclamation, led by tribal councilor Enna Helms and Patricia Whereat, Troy Anderson, and Dr. Lawrence Morgan.