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7 results for “Brown Food Webs”
Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs"
<p>Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs".</p> <p>This includes estimations of various properties of food webs, such as stocks of compartments, fluxes between compartments, and conversion efficiencies.</p>
Fig. 3 in The role of cladocerans in green and brown food web coupling
Fig. 3. The proportion of the contribution of POC, phytoplankton, and biofilm to cladoceran biomass in the three PIAP lagoons. Boxplots show the quartiles of 97.5%, 75%, 50%, 25%, and 2.5%. The lower and upper boxes indicate the 25% and 50% quartiles. Likewise, the lower and upper vertical lines indicate the 97.5% and 2.5% quartiles and the horizontal lines show the mode of contribution.
Data for: Herbivores' impacts cascade through the brown food web in a dryland
<p><span><span>Food webs can be conceptualized as being powered by energy derived from living and dead vegetation, respectively. Most food web research has focused on "green food webs" which begin with the consumption of living vegetation by herbivores. However, "brown food webs" which stem from the consumption of senescent vegetation by detritivores are also an important channel of energy transfer. In theory, herbivores have the potential to disrupt brown food webs by consuming plant material before it has the opportunity to senesce and become available for detritivores. Here we investigate the effects that grazing by kangaroos whose population had irrupted in the absence of an apex predator has on the brown food web in an arid environment. We compared the cover of living and dead vegetation and the abundances of detritivorous termites and their predators inside one ha herbivore exclosures and nearby control plots. Results show there was more cover of living and dead vegetation inside the exclosures. Similarly, abundances of termites and small vertebrate predators of termites were greater inside the exclosures. Our study provides evidence that consumption of plant material by irruptive herbivores can disrupt the functioning of the brown food web by reducing the flow of energy from plant biomass to termites which in turn translates to reduced abundances of termites and small vertebrates that feed on termites. Our findings have implications for conservation and management because they shed light on a previously unconsidered threat to the functioning of arid ecosystems, disruption of brown food webs by irruptive herbivores. </span></span></p>
Data for: Herbivores’ impacts cascade through the brown food web in a dryland
Open the record for dataset details and reuse information.
Fig. 2 in The role of cladocerans in green and brown food web coupling
Fig. 2. Mean and standard error for values of δ13C and δ15N for the three lagoons analyzed.
Fig. 1 in The role of cladocerans in green and brown food web coupling
Fig. 1. Map of the sampling locations. Font: PEREIRA, Jaime Luiz Lopes, 2021.
Prey identity affects fitness of a generalist consumer in a brown food web
<p>These are datasets for the manuscript entitled "Prey identity affects fitness of a generalist consumer in a brown food web". Each data file contains counts of protists per 100uL of water when grown with individual strains (protist_counts_strains.csv) and mixtures of strains (protist_counts_mixed_strains.csv). </p> <p>Columns descriptions, protist_counts_strains.csv:</p> <p>1: Name - the name of the bacterial strain, combined with the protist genotype that it was grown with<br> 2: Bacterial_strain - the unique identifier for each bacterial strain<br> 3: strain_genus - the bacterial genus determined by 16S rRNA sequencing and NCBI BLAST<br> 4: Protist_genotype - the genotype of protist that the bacteria were grown with<br> 5: count_100uL - the number of protists that were counted per 100uL using a Palmer cell. <br> 6: Avg_area - the average cell area as measured using ImageJ software<br> 7: Biomass - the count_100uL x Avg_area to get some metric for biomass</p> <p>Columns descriptions, protist_counts_mixed_strains.csv:</p> <p>1: Mixture_name - the name of the bacterial mixture<br> 2: Protist_genotype - the genotype of protist that the bacteria were grown with<br> 3: count_per100uL - the number of protists that were counted per 100uL using a Palmer cell. <br> 4: mixture_ID - a descriptor name for each bacterial mixture based on how well the strains in the mixture performed in isolation<br> 5: description - a list of the bacterial strains added to the mixture</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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