The success of the horse-chestnut leaf-miner, Cameraria ohridella, in the UK revealed with hypothesis-led citizen science

Citizen science is an increasingly popular way of undertaking research and simultaneously engaging people with science. However, most emphasis of citizen science in environmental science is on long-term monitoring. Here, we demonstrate the opportunities provided by short-term hypothesis-led citizen science. In 2010, we ran the ‘Conker Tree Science’ project, in which over 3500 people in Great Britain provided data at a national scale of an insect (horse-chestnut leaf-mining moth, Cameraria ohridella) undergoing rapid range-expansion. We addressed two hypotheses, and found that (1) the levels of damage caused to leaves of the horse-chestnut tree, Aesculus hippocastanum, and (2) the level of attack by parasitoids of C. ohridella larvae were both greatest where C. ohridella had been present the longest. Specifically there was a rapid rise in leaf damage during the first three years that C. ohridella was present and only a slight rise thereafter, while estimated rates of parasitism (an index of true rates of parasitism) increased from 1.6 to 5.9% when the time C. ohridella had been present in a location increased from 3 to 6 years. We suggest that this increase is due to recruitment of native generalist parasitoids, rather than the adaptation or host-tracking of more specialized parasitoids, as appears to have occurred elsewhere in Europe. Most data collected by participants were accurate, but the counts of parasitoids from participants showed lower concordance with the counts from experts. We statistically modeled this bias and propagated this through our analyses. Bias-corrected estimates of parasitism were lower than those from the raw data, but the trends were similar in magnitude and significance. With appropriate checks for data quality, and statistically correcting for biases where necessary, hypothesis-led citizen science is a potentially powerful tool for carrying out scientific research across large spatial scales while simultaneously engaging many people with science

Biochemical characteristics of fresh roots of transgenic high Beta-carotene cassava

Recent research efforts have developed high beta carotene or yellow cassava roots through genetic engineering and conventional breeding. Beta-carotene pigments are biosynthesized through a cellular isoprenoid pathway. In this study, biochemical techniques and microscopy were employed in determining beta-carotene distribution and metabolic isoprenoid pathway in fresh transgenic and conventionally bred high beta-carotene cassava roots. Transgenic high beta-carotene cassava varieties (EC20-7 and EC20-8) were compared with two conventionally bred high beta-carotene cassava varieties (UMUCASS 38 and UMUCASS 45) and an indigenous white fleshed cassava variety (TME-7). These five cassava varieties were planted in environmentally controlled greenhouse and harvested after four months. Results showed that the dry matter content of UMUCASS 38 (35.61 %) and UMUCASS 45 (36.96 %) were significantly (P<0.05) different from EC20-7 (24.23 %), and EC20-8 (23.18 %). β-carotene content on dry matter basis of the fresh yellow cassava roots (50.93 - 80.45µg/g) was not significantly (P>0.05) different between the transgenic and conventional varieties The observed carotenoids in this study were biosynthesized from isopentenyl diphosphate and its isomer dimethylallyl diphosphate. All the varieties, especially the EC20-8 transgenic variety, had appreciable levels of methyl-D-erythritol 4-phosphate. Microscopic analysis showed the distribution of carotenoids in the cortex, parenchyma and the xylem bundles. Carotenoids were not observed in the sclerenchyma, therefore, peeling of yellow cassava varieties during processing will not necessarily cause any loss in the carotenoid contents Keywords: Cassava, Beta-carotene, fresh root

Herbicide Control of Tall Larkspurs on Mountain Rangeland

Tall larkspurs kill more cattle on mountain rangelands than any other plant or disease. Tall larkspurs are principal components of tall forb communities and occur in patches associated with snow drifts in mountain big sagebrush, aspen and subalpine plant communities. Controlling larkspur patches can substantially reduce cattle deaths (3). Larkspur will never be eradicated, but if its density could be reduced to where a cow could not eat enough larkspur, fast enough, death losses can be reduced

Real Forms of Non-abelian Toda Theories and their W-algebras

We consider real forms of Lie algebras and embeddings of sl(2) which are consistent with the construction of integrable models via Hamiltonian reduction. In other words: we examine possible non-standard reality conditions for non-abelian Toda theories. We point out in particular that the usual restriction to the maximally non-compact form of the algebra is unnecessary, and we show how relaxing this condition can lead to new real forms of the resulting W-algebras. Previous results for abelian Toda theories are recovered as special cases. The construction can be extended straightforwardly to deal with osp(1|2) embeddings in Lie superalgebras. Two examples are worked out in detail, one based on a bosonic Lie algebra, the other based on a Lie superalgebra leading to an action which realizes the N=4 superconformal algebra.Comment: 11 pages, LaTex; minor errors corrected, extra references adde

On the Classification of Real Forms of Non-Abelian Toda Theories and W-algebras

We consider conformal non-Abelian Toda theories obtained by hamiltonian reduction from Wess-Zumino-Witten models based on general real Lie groups. We study in detail the possible choices of reality conditions which can be imposed on the WZW or Toda fields and prove correspondences with sl(2,R) embeddings into real Lie algebras and with the possible real forms of the associated W-algebras. We devise a a method for finding all real embeddings which can be obtained from a given embedding of sl(2,C) into a complex Lie algebra. We then apply this to give a complete classification of real embeddings which are principal in some simple regular subalgebra of a classical Lie algebra.Comment: 42 pages, LaTeX; Minor corrections to ensure consistent conventions; some references adde

Why do people (not) engage in social distancing? Proximate and ultimate analyses of Norm-Following during the COVID-19 pandemic

COVID-19 has had a profound negative effect on many aspects of human life. While pharmacological solutions are being developed and implemented, the onus of mitigating the impact of the virus falls, in part, on individual citizens and their adherence to public health guidelines. However, promoting adherence to these guidelines has proven challenging. There is a pressing need to understand the factors that influence people’s adherence to these guidelines in order to improve public compliance. To this end, the current study investigated whether people’s perceptions of others’ adherence predict their own adherence. We also investigated whether any influence of perceived social norms was mediated by perceptions of the moral wrongness of non-adherence, anticipated shame for non-adherence, or perceptions of disease severity. One hundred fifty-two Australians participated in our study between June 6, 2020 and August 21, 2020. Findings from this preliminary investigation suggest that (1) people match their behavior to perceived social norms, and (2) this is driven, at least in part, by people using others’ behavior as a cue to the severity of disease threat. Such findings provide insight into the proximate and ultimate bases of norm-following behavior, and shed preliminary light on public health-related behavior in the context of a pandemic. Although further research is needed, the results of this study—which suggest that people use others’ behavior as a cue to how serious the pandemic is and as a guide for their own behavior—could have important implications for public health organizations, social movements, and political leaders and the role they play in the fight against epidemics and pandemics

Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems

Summary 1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ‘nested tagging’ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree–insect–parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change

Amyloid-β oligomerization monitored by single-molecule stepwise photobleaching

A major hallmark of Alzheimer’s disease is the misfolding and aggregation of the amyloid- β peptide (Aβ). While early research pointed towards large fibrillar- and plaque-like aggregates as being the most toxic species, recent evidence now implicates small soluble Aβ oligomers as being orders of magnitude more harmful. Techniques capable of characterizing oligomer stoichiometry and assembly are thus critical for a deeper understanding of the earliest stages of neurodegeneration and for rationally testing next-generation oligomer inhibitors. While the fluorescence response of extrinsic fluorescent probes such as Thioflavin-T have become workhorse tools for characterizing large Aβ aggregates in solution, it is widely accepted that these methods suffer from many important drawbacks, including an insensitivity to oligomeric species. Here, we integrate several biophysics techniques to gain new insight into oligomer formation at the single-molecule level. We showcase single-molecule stepwise photobleaching of fluorescent dye molecules as a powerful method to bypass many of the traditional limitations, and provide a step-by-step guide to implementing the technique in vitro. By collecting fluorescence emission from single Aβ(1–42) peptides labelled at the N-terminal position with HiLyte Fluor 555 via wide-field total internal reflection fluorescence (TIRF) imaging, we demonstrate how to characterize the number of peptides per single immobile oligomer and reveal heterogeneity within sample populations. Importantly, fluorescence emerging from Aβ oligomers cannot be easily investigated using diffraction-limited optical microscopy tools. To assay oligomer activity, we also demonstrate the implementation of another biophysical method involving the ratiometric imaging of Fura-2-AM loaded cells which quantifies the rate of oligomer-induced dysregulation of intracellular Ca2+ homeostasis. We anticipate that the integrated single-molecule biophysics approaches highlighted here will develop further and in principle may be extended to the investigation of other protein aggregation systems under controlled experimental conditions

Greenhouse gas emission factors associated with rewetting of organic soils

Drained organic soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils may reduce GHG emissions and could also create suitable conditions for return of the carbon (C) sink function characteristic of undrained organic soils. In this article we expand on the work relating to rewetted organic soils that was carried out for the 2014 Intergovernmental Panel on Climate Change (IPCC) Wetlands Supplement. We describe the methods and scientific approach used to derive the Tier 1 emission factors (the rate of emission per unit of activity) for the full suite of GHG and waterborne C fluxes associated with rewetting of organic soils. We recorded a total of 352 GHG and waterborne annual flux data points from an extensive literature search and these were disaggregated by flux type (i.e. CO2, CH4, N2O and DOC), climate zone and nutrient status. Our results showed fundamental differences between the GHG dynamics of drained and rewetted organic soils and, based on the 100 year global warming potential of each gas, indicated that rewetting of drained organic soils leads to: net annual removals of CO2 in the majority of organic soil classes; an increase in annual CH4 emissions; a decrease in N2O and DOC losses; and a lowering of net GHG emissions. Data published since the Wetlands Supplement (n = 58) generally support our derivations. Significant data gaps exist, particularly with regard to tropical organic soils, DOC and N2O. We propose that the uncertainty associated with our derivations could be significantly reduced by the development of country specific emission factors that could in turn be disaggregated by factors such as vegetation composition, water table level, time since rewetting and previous land use history

Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems

1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ‘nested tagging’ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree–insect–parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change