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Building Customer Loyalty on Online Food Platforms: Sharing Economy Practices in Indonesia
Open the record for dataset details and reuse information.
Data for: Food Web Structure Significantly Affects the Body Condition and Survival of an Amphibian Intraguild Predator
<p>Data for upcoming publication</p>
Figure 1 from: Koleva P, Tsanova-Savova S, Paneva S, Velikov S, Savova Z (2021) Polyphenols content of selected medical plants and food supplements present at Bulgarian market. Pharmacia 68(4): 819-826. https://doi.org/10.3897/pharmacia.68.e71460
Figure 1 Chromatogram of Standard mix of (+)-catechin, (-)-epicatechin and rutin at 275 nm (a) and 355 nm (b).
The novel concept of diversity at giving-up density (DivGUD) in experimental ressource landscapes of 9 food patches explored by Norway rats
<p>Foraging by consumers acts as a biotic filtering mechanism for biodiversity at the trophic level of resources. Variation in foraging behaviour have cascading effects on abundance, diversity, and functional trait composition of the community of resource species. Here we propose diversity at giving-up density (DivGUD), when foragers quit exploring a patch, as a novel concept and simple measure to quantify these effects at multiple spatial scales. In experimental landscapes, patch residency of wild rodents decreased local α-DivGUD (via elevated mortality of species with large seeds) and regional γ-DivGUD, while dissimilarity among patches in a landscape (ß-DivGUD) increased. Thus, DivGUD provides a framework linking theories of adaptive foraging behaviour with community ecology allowing to investigate cascading indirect predation effects across multiple trophic levels e.g. the ecology-of-fear framework; feedbacks between functional trait composition of resource species and consumer communities; and effects of inter-individual differences among foragers on the biodiversity of resource communities.</p>
Supplementary material 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
SourceAttributionModel.fskx
Figure 6 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 6 Bar plot of number of human cases of Salmonella infection attributed to different sources. Subfigure A shows the result for the baseline data 2004–2007 (the so-called defaultSimulation in Table 2) and Subfigure B the results for themonitoring data 2010/2011 (the so-called SimulationTable3 in Table 2).
Figure 4 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 4 Starting points for the Markov chains of Parameter scenario 3 and their effect on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0.18) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points the Markov chains do not converge within 30,000 iterations for the parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} or \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_3\), \(q_2\) and \(q_3 \end{equation*} \end{varwidth} \end{document} , where types 2 and 3 correspond to S. enterica serotype Enteritidis PT 14b and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour). The error bars of the predicted source attribution are large (see the bar plot).
Figure 2 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 2 Starting points for the Markov chains of Parameter scenario 1, their effects on the convergence behaviour and the model predictions. The starting points are evenly spaced in the lower fifth of the space of possible starting points (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge quickly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) \end{equation*} \end{varwidth} \end{document} and \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).
Figure 3 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 3 Starting points for the Markov chains of Parameter scenario 2 and their effects on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge slowly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) and \(q_3\) \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).
Figure 5 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 5 Model-data fit when setting \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M_j \end{equation*} \end{varwidth} \end{document} =1 and using different parameterizations. Each point in the figure corresponds to one bacterial subtype. Subfigure A shows a consistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_j \sim uniform(0,30000) \end{equation*} \end{varwidth} \end{document} . I.e. the logarithm of the number of cases as found in the data corresponds well to the logarithm of predicted number of cases. Subfigure B shows an inconsistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a\sim uniform(0,20) \end{equation*} \end{varwidth} \end{document} . Here, the model systematically underestimates the number of cases for the subtypes as the points gather well below the identity line.
Figure 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 1 The posterior distributions for the fifth entry in the list of Salmonella subtypes (q5), which is S. enterica serotype Enteritidis PT 21, as a function of the possible values of q5. The shown posterior distributions are calculated by the Gibbs-Sampler software OpenBUGS using the Bayes DB model presented in Jabin et al. (2019) for the monitoring data and a sample size of 1e5. Subfigure A) shows the posterior distribution calculated for \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,0.2) \end{equation*} \end{varwidth} \end{document} . The chosen limits of the uniform distribution lead to a cut off in the posterior distribution. When enlarging the interval defining the prior distribution to \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,1) \end{equation*} \end{varwidth} \end{document} , then a complete posterior distribution is produced by OpenBUGS (see Subfigure B)).
Supplemental data. Experimentally manipulated food availability affects offspring quality but not quantity in zebra finch meso-populations
<p>Data used for the publication: " Experimentally manipulated food availability affects offspring quality but not quantity in zebra finch meso-populations "</p>
Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.
Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .
Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".
The paradox of food production, consumption, poverty and malnutrition in Tanzania
<p><span><b>Background</b>: There have been claims amongst nutrition stakeholders in Tanzania that the food basket regions are leading in stunting prevalence. However, we could not find evidence that combines food production and stunting levels to substantiate this claim. Therefore, this study aims to compare data of stunting, food production and consumption at administrative regions in Tanzania Mainland.</span></p> <p><span><b>Methods</b>: The study used an ecological study design to show the relationship between stunting, poverty, and food production and consumption across administrative regions in Tanzania. The study used data from three national wide surveys: 2017/2018 Household Budget Survey (HBS), Tanzania National Nutrition Survey (TNNS) 2018 and Agriculture Statistics for Food Security report 2018/2019.</span></p> <p><span><b>Results</b>: The study showed that there is a positive relationship between the prevalence of stunting and food production (r=0.43, p=0.03) while there is a negative relationship between stunting and the level of both the average monthly household consumption expenditure (r = -0.48, p = 0.01) and average monthly household food consumption expenditure (r = -0.509, p = 0.01). It was further found that some regions which have higher levels of stunting such as Njombe have the lowest level of basic need poverty.</span></p> <p><span><b>Conclusion</b>: The study found a positive relationship between food production and the prevalence of stunting using data across regions in Mainland Tanzania. This is an indication that regional food security may not entail nutrition security hence a call for more advocacy on nutrition-sensitive agriculture.</span></p>
Supplementary material to "Food and habitats requirements of the Scops Owl (Otus scops) in Switzerland revealed by very high-resolution multi-scale models"
<p><strong>Abstract</strong></p> <p>In Europe, agricultural practices have progressively evolved towards high productivity leading either to the intensification of productive and accessible areas or to the abandonment of less profitable sites. Both processes have led to the degradation of semi-natural habitats like extensive grasslands, threatening species such as the Eurasian Scops Owl <em>Otus scops</em> that rely on extensively managed agricultural landscapes. In this work, we aimed to assess the habitat preferences of the Scops Owl using habitat suitability models combined with a multi-scale approach. We generated a set of multi-scale predictors, considering both biotic and abiotic variables, built on two newly developed vegetation management and orthopteran abundance models. To select the variables to incorporate in a ‘best multi-scale model’, we chose the best spatial scale for each variable using univariate models and by calculating their relative importance through multi-model inference. Next, we built ensembles of small models (ESMs) at 10 different scales from 50 to 1000 m, and an additional model with each variable at its best scale (‘best multi-scale model’). The latter performed better than most of the other ESMs and allowed the creation of a high-resolution habitat suitability map for the species. Scops Owls showed a preference for dry sites with extensive and well-structured habitats with 30–40% bush cover, and relied strongly on semi-extensive grasslands covering at least 30% of the surface within 300 m of the territory centre and with high orthopteran availability near the centre (50-m radius), revealing a need for good foraging grounds near the nest. At a larger spatial scale within a radius of 1000 m, the habitat suitability of Scops Owls was negatively related to forest cover. The resulting ESM predictions provide valuable tools for conservation planning, highlighting sites in need of particular conservation efforts together with offering estimates of the percentage of habitat types and necessary prey abundance that could be used as targets in future management plans to ensure the persistence of the population.</p>
Data from: Darwin's small and medium ground finches might have taste preferences, but not for human foods
<p>Urbanization is rapidly changing ecological niches. On the inhabited Galapagos Islands, Darwin's finches consume human-introduced foods preferentially; however, it remains unclear why. Here we presented pastry with flavour profiles typical of human foods (oily, salty, sweet) to small ground finches (<em>Geospiza fuliginosa</em><i>) </i>and medium ground finches (<em>Geospiza fortis</em><i>)</i> to test if latent taste preferences might drive selection of human foods. If human-food flavours were consumed more than a neutral or bitter control only at sites with human foods, then we predicted tastes were acquired after urbanization; however, if no site-differences were found then this would indicate latent taste preferences. Contrary to both predictions, we found little evidence that human-food flavours were preferred compared to control flavours at any site. Instead, finches showed a weak aversion to oily foods, but only at remote (no human foods present) sites. This was further supported by behavioural responses, with beak wiping occurring more often at remote sites after finches tasted flavours associated with human foods. Our results suggest, therefore, that while Darwin's finches exposed to human foods might have acquired a tolerance human food flavours, latent taste preferences are unlikely to have played a major role in their dietary response to increased urbanization.</p>
Food restriction delays breeding and affects insulin-like growth factor-1, oxidative damage, and haematocrit value before egg-laying in female canaries
<p>Environmental challenges, such as fluctuations in food availability, could influence reproductive investment. If resource availability is poor, individuals need to decide in which life-history process they invest more energy and vital compounds, which determine the cost of reproduction. In resource allocation, the physiological pathways have important roles. The aim of our study was to examine whether food availability influenced physiological traits (insulin-like growth factor-1 (IGF-1) concentration, antioxidant capacity (OXY), level of oxidative damage (ROM) and haematocrit value) prior to egg-laying in female canaries (<i>Serinus canaria</i>). We also tested whether these physiological traits were associated with traits reflecting the reproductive investment in egg macro- (egg mass and yolk mass) and micronutrient content (eggshell biliverdin- and protoporphyrin-based colouration). To test these questions we conducted a food restriction experiment with control and food-restricted (72% of control food) groups. Our study showed that food-restricted canary females delayed egg-laying and the physiological traits differed between the groups. At the pre-laying period, after 9-11 days of treatment, a reduction in plasma IGF-1 concentration, ROM level, and haematocrit value was detected in the control group, whereas in the food-restricted group plasma IGF-1 concentration increased, while ROM level and haematocrit value did not change. Plasma level of OXY was not influenced by treatment or breeding period. Plasma concentration of IGF-1, haematocrit value and oxidative status before egg laying did not affect the egg characteristics. Our study highlights the importance of breeding stage when studying and interpreting the effects of food restriction on physiological traits of breeding birds. Moreover, our data suggest that nutritional limitation had an effect on the timing of egg-laying that could be mediated by changes in physiological variables.</p>
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