Dataset for: Integrated trophic position as a proxy for food-web complexity
Data files
Nov 01, 2023 version files 148.89 KB
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README.md
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TableS1.txt
Nov 21, 2023 version files 149.19 KB
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README.md
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TableS1.txt
Abstract
There are two distinct approaches to describing the distributions of biomass and species in food webs: one to consider them as discrete trophic levels (TLs); and the other to consider them as continuous trophic positions (TPs). Bridging the gap between these two perspectives presents a non-trivial challenge in integrating biodiversity and food-web structure.
Food Network Unfolding (FNU) is a technique used to bridge this gap by partitioning the biomass of species into integer TLs to compute three complexity indices, namely vertical (DV), horizontal (DH), and range (DR) diversity (D indices), through decomposition of Shannon’s index H’. Using FNU, the food web (a network of species with unique TPs) is converted to a linear food chain (a biomass distribution at discrete TLs). This enables us to expect that the unfolded biomass within species decreases exponentially as the TL increases. Under this condition, the mean TL value in unfolded food chains is hypothesized to have an exponential relationship with the vertical diversity, DV. To explore this, we implemented FNU and calculated D indices for food webs publicly available at EcoBase (n = 158) and calculated the integrated TP (iTP), defined as the biomass-weighted average TP of a given food web. The iTP corresponds to the mean TL in unfolded food chains and can be empirically measured through compound-specific isotope analysis of amino acids (CSIA-AA).
Although our analysis is biased towards marine ecosystems, we revealed an exponential relationship between iTP and DV, suggesting that iTP can serve as a measurable proxy for DV. Furthermore, we found a positive correlation between the iTP observed in the total communities (total iTP) and the iTPs of partial communities consisting only of species with 2.0 ≤ TP < 3.0 (partial iTP; r2 = 0.48), suggesting that DV can be predicted using partial iTP.
Our findings suggest that the net effect of species diversity, excluding the effect of biomass (corresponding to H’ − DV), on food-web complexity can be revealed by combining CSIA-AA with biodiversity analysis (e.g., environmental DNA).
README: Dataset for: Integrated trophic position as a proxy for food-web complexity
Version Record
- Published, November 1, 2023
- Software and Supplemental information reorganized, November 3, 2023
Description of the Data and file structure
- Data
- TableS1.txt
- A text file providing metadata of food-web models downloaded from EcoBase on 10 September 2021.
- Food web #
- Number of food webs in this study (continuous variable; unit-less variable)
- EcoBase original#
- Original number of food webs used in EcoBase but unnecessary in this study (continuous variable)
- .id
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.model_number
- Model number used in EcoBase but unnecessary in this study (continuous variable)
- model.model_name
- Model name used in EcoBase but unnecessary in this study (categorical variable)
- model.country
- Model country name used in EcoBase but unnecessary in this study (categorical variable)
- model.region
- Model region number used in EcoBase but unnecessary in this study (continuous variable)
- model.lme
- Variable used in EcoBase but unnecessary in this study (continuous variable)
- model.geographic_extent1
- Longitude western limit (continuous variable; unit: degree)
- model.geographic_extent2
- Latitude southern limit (continuous variable; unit: degree)
- model.geographic_extent3
- Longitude eastern limit (continuous variable; unit: degree)
- model.geographic_extent4
- Latitude northern limit (continuous variable; unit: degree)
- model.ecosystem_type
- Ecosystem type of the food web model (categorical variable; bay/fjord, continental shelf, open ocean, beach, Estuary, coastal lagoon, upwelling, channel/strait, marine-coastal, coral reef, river, Coastal & Pelagic, Fish farm, Reservoir)
- model.currency_units
- Unit of weight (categorical variable; DryWeight, WetWeight)
- model.num_group
- Model group number in EcoBase but unnecessary in this study (continuous variable)
- model.model_year
- Year when the model was made in EcoBase but unnecessary in this study (continuous variable)
- model.model_period
- Period the model was run in EcoBase but unnecessary in this study (continuous variable)
- model.author
- Author of the model in EcoBase but unnecessary in this study (categorical variable)
- model.contact
- Contact address of the author of the model in EcoBase but unnecessary in this study (categorical variable)
- model.url
- URL of the article where the model is reported in EcoBase but unnecessary in this study (categorical variable)
- model.depth_min
- Minimum water depth of the model in EcoBase (continuous variable; unit: m)
- model.depth_max
- Minimum water depth of the model in EcoBase (continuous variable; unit: m)
- model.reference
- Reference of the article where the model is reported in EcoBase but unnecessary in this study (categorical variable)
- model.ecosim
- Whether the model was run with Ecosim but unnecessary in this study (categorical variable)
- model.ecospace
- Whether the model was run with Ecospace but unnecessary in this study (categorical variable)
- model.whole_food_web
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.fisheries
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.aquaculture
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.environment_variability
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.ecosyst_functioning
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.pollution
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.dissemination_allow
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.area
- Relative area of the food web (continuous variable; unit-unknown variable in EcoBase but used as unit-less variable in this study)
- model.currency_units_custom
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.description
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.num_digits
- Variable used in EcoBase but unnecessary in this study (continuous variable)
- model.depth_mean
- Mean water depth of the model in EcoBase (continuous variable; unit: m)
- model.is_fitted
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.other_impact_assessment
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.temperature_mean
- Mean water temperature of the model in EcoBase (continuous variable; unit: degreeC)
- model.temperature_min
- Minimum water temperature of the model in EcoBase (continuous variable; unit: degreeC)
- model.temperature_max
- Maximum water temperature of the model in EcoBase (continuous variable; unit: degreeC)
- model.doi
- DOI of the article where the EcoBase model is reported but unnecessary in this study (categorical variable)
- model.comments_objectives
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.comments_objectives.1
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- model.modification_child
- Variable used in EcoBase but unnecessary in this study (categorical variable)
- Food web #
- A text file providing metadata of food-web models downloaded from EcoBase on 10 September 2021.
- TableS1.txt
- Software available at Zenodo (https://doi.org/10.5281/zenodo.10070281)
- pickupdata.R
- createcolormap.m
- A Matlab function necessary to draw color figures.
- script.m
- A Matlab code for data analysis, statistics, and graphics used in the present study.
- All figures except for Figure 1, which was drawn using Microsoft PowerPoint 2019, will be generated when this code is implemented on Matlab 2023a.
- It should be noted that Figure 7 in the main article was edited using Adobe Illustrator 2023.
- Supplemental information available at Zenodo (https://doi.org/10.5281/zenodo.10065328)
- Supporting_Information.pdf
- A PDF providing supporting text, supporting tables and figures, and references for the EcoBase dataset.
- Supporting_Information.pdf
Sharing/access Information
- The original data downloaded from EcoBase are not included in this dataset. The R code "pickupdata.R" should be first implemented to access the food web models.
- createcolormap.m
- Copyright (c) 2021 Takuya Miyashita Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Methods
The dataset was collected from EcoBase and processed using R and Matlab codes (pickupdata.R, createcolormap.m, and script.m).
Details are provided in the README file.