DOI: 10.5061/dryad.0rxwdbsd8
title: Data from: What is regulating chironomid populations? The influence of food supply and interference competition on development and mortality in <em>Chironomus riparius</em>

description: Density-dependent processes are important for a fundamental understanding
 of the regulation of populations, as well as for understanding responses
 to and recovery from stressors. While exploitative competition is well
 studied, interference competition is rather difficult to investigate, but
 has been regularly observed to occur in many aquatic insect populations.
 We conducted laboratory experiments with the non-biting midge Chironomus
 riparius (Diptera: Chironomidae) to investigate the impact of different
 combinations of food supply and larval densities on development and
 mortality at a constant temperature of 20°C. The chosen two-factorial
 experimental design allowed a separate evaluation of exploitative (food)
 and interference (mortality) competition across a gradient of larval
 densities. The use of different vessel sizes between 50 cm2 and 600 cm2
 made it possible to quantify the functional response at different food
 densities. To test mechanistic explanations for the statistically
 significant empirical relationships found in this study and to predict
 density-dependent processes, we used a dynamic process-oriented modeling
 approach. We extended a recently developed DEB-IBM full life cycle model
 for C. riparius and successfully applied it under variable food conditions
 at the population-level under laboratory conditions. Our study showed that
 chironomid development and reproduction are primarily dependent on food
 supply, whereas larval density drives the density-dependent mortality
 rate. The interaction of food availability and interference competition
 determined the effective mortality over time. Killing by conspecifics was
 the most likely mechanism responsible for the intraspecific mortality of
 the larval stages. Combining data generated using a tailor-made
 experimental design with a mechanistic model provided insights into and
 quantified regulation mechanisms of chironomid populations, allowing
 future uses of this information in the context of population-level risk
 assessment from exposure to chemicals.
description: # Data from: What is regulating chironomid populations? The influence of
 food supply and interference competition on development and mortality in
 *Chironomus riparius* Dataset DOI:
 [10.5061/dryad.0rxwdbsd8](10.5061/dryad.0rxwdbsd8) ## Description of the
 data and file structure **Supplementary data files for: What is regulating
 chironomid populations? The influence of food supply and interference
 competition on development and mortality in *Chironomus riparius.*** This
 compilation includes experimental raw data as well as R scripts and input
 data for calculating EmT~50~ values (the time until emergence of 50% of
 the surviving larvae) and for the statistical analysis of several
 observational endpoints regarding food availability and larval density.
 **Experimental design of the study**: First instar larvae &lt; 12h of
 *Chironomus riparius* were added to glass beakers of different sizes (50,
 180, and 600 cm^2^) containing Elendt M4 medium and artificial sediment
 (OECD guideline 219). The tests were conducted in the laboratory at 20°C
 without water exchange up to 62 days. Larval densities ranging from 10 to
 200 per beaker and daily feed additions ranging from 0.05 to 0.5 mg dw
 larva⁻^1^ and day⁻^1^ were investigated. TetraPhyll was used as food for
 the larvae. Four replicates were prepared for all density-food
 combinations (further details can be found in Strauss et al. 2025). ###
 Files and variables #### File: Experimental_rawdata.zip The experimental
 data is available as CSV files (semicolon- and tab-separated) and can be
 displayed using e.g., Microsoft Excel. **Description:** This folder
 provides the following experimental raw data and calculations: emergence
 data over time; calculated time to 50% emergence (EmT~50~); surviving
 larvae and emerged individuals as well as calculated mortality at the end
 of the test; calculated relative emerging success; numbers of eggs per egg
 rope; oxygen and pH measurements; dry weight measurements of surviving
 larvae at the end of the test; criteria for outliers in emergence data. 
 Column headings for the test design, applicable to all CSV files
 containing experimental data: ·       Food level (mg dw larva⁻^1^
 day⁻^1^): Daily food amount per larva expressed in mg dw TetraPhyll
 ·       Initial density larvae (#): Total number of larvae per beaker at
 the start of the experiment (day 0) ·       Area (cm^2^): Sediment area of
 the respective beakers ·       Density (larvae / 50 cm^2^): Initial
 density of larvae, converted to individuals per 50 cm^2^ for all beaker
 sizes ·       Additional information is provided on the replicate number,
 the date of sampling, and the respective experimental day. **Overview of
 the CSV-files with their names and contents:** ·       Raw data
 emergence.csv **Description:** Number of emerged adults per day and
 replicate. Please note: 5 replicates were classified as outliers and
 excluded from further analysis. For the criteria, see the data file
 „Criteria for outliers.csv“. **Reported results:** Emerged males (daily
 number), Emerged females (daily number), Sum of emerged adults (daily
 number), Cumulative emerged adults, Cumulative emerged males, Cumulative
 emerged females, % cumulative total emergence per initial number of
 larvae. ·      Development replicates.csv **Description:** Results of
 fitting a two-parameter log-logistic model to the cumulative emergence
 data over time for individual replicates. The development speed EmT~50~ is
 expressed in days until emerging of 50 % of the surviving larvae. Please
 note: 5 replicates were classified as outliers and excluded from further
 analysis. For the criteria, see the corresponding spreadsheet. **Reported
 results**: EmT~50~ (d): Days to 50 % emergence; slope b: Slope of the
 two-parameter log-logistic model; R^2^: coefficient of determination of
 the two-parameter log-logistic model. ·       Development.csv
 **Description:** The development speed EmT~50~ expressed in days until
 emerging of 50 % of the surviving larvae, mean values, and standard
 deviation (M ± SD) of replicates. **Reported results:** EmT~50~ (d) - mean
 values of replicates; EmT~50~ (d) - SD of replicates; Number of replicates
 (n): Number of replicates evaluated without outliers. ·      Rawdata at
 Test End.csv **Description:** Overview of the results regarding the
 combinations of larval density and food supply after completion of the
 experiments for each individual replicate. **Reported results:** Vital
 larvae (#); Vital pupae (#); Total emergence (#); Calculated mortality
 (#); Mortality (%): mortality based on initial larvae number; Comments on
 mortality (%). ·       Mortality.csv **Description:** Mortality (% of
 initial larvae) at the test end (M ± SD). **Reported results:** Total
 daily food (mg dw beaker⁻^1^ d⁻^1^); Dead animals at test end (#/beaker);
 Daily mortality rate (% / d) - mean values of replicates; Daily mortality
 rate (% / d) - SD of replicates; Mortality at test end (%) - mean value of
 replicates; Mortality at test end (%) – SD of replicates; Number of
 replicates (n): Number of replicates evaluated without outliers. ·      
 Emergence success.csv **Description:** Emergence success (% emerged adults
 per initial number of larvae) at the test end (M ± SD). **Reported
 results:** % Emergence success - mean values of replicates; % Emergence
 success - SD of replicates; Number of replicates (n): Number of replicates
 evaluated without outliers. ·       Reproduction.csv **Description:** Egg
 number per rope oviposited by female midges (M ± SD). **Reported
 results:** Food level (mg dw larva⁻^1^ d⁻^1^); Density (larvae / 50
 cm^2^); Eggs per rope - Mean values of evaluated egg clutches; Eggs per
 rope – SD of evaluated egg clutches; Number of clutches (n): Number of egg
 clutches evaluated. ·       Oxygen and pH values.csv **Description:**
 Oxygen and pH measurements in the experimental beakers **Reported
 results:** Total daily food (mg dw beaker⁻^1^ d⁻^1^); pH-value (-); Oxygen
 (mg O~2~/L); Oxygen saturation (%). ·       Larval dry weight.csv
 **Description:** Dry weight measurements of surviving larvae at the end of
 the experiments. **Reported results:** Number of vital larvae (#); Dry
 weight of pooled larvae (mg); mean dry weight per individual (mg/larva).
 ·       Criteria for outliers.csv **Description:** Criteria for data
 outliers in emergence data, specifying the replicas affected #### **File:
 Figures_Development_replicates.zip** **Description:** This folder contains
 all figures (as PNG files) of the cumulative emergence of adults based on
 the data in the file „Development replicates.csv“ and the fitted
 regression curves, each for every replicate evaluated. The PNG files are
 named as: Figure number \_ X Food level … larval density … replicate
 number. #### File: R-Calculation_of_EmT50_values.zip
 **Description:** Folder contents: Data and scripts for calculating EmT~50~
 values from emergence data for all combinations of food and density using
 a two-parameter log-logistic model. Comment: The results for all
 replicates can be found in the files ‘Development_replicates.csv’ and
 graphically in the zip folder ‘Figures_Development_replicates.zip’. **File
 names and contents:** ·       Legend_EmT50_calculation.txt: Legend for the
 R input data file ·       food_dep_emerg_data.csv: R input data for
 EmT~50~ calculations ·       food_dep_emerg_drc_analyses.r: R script for
 EmT~50~ calculations ·       food_dep_emerg_data.docx: R script for
 EmT~50~ calculations as a word file #### File: R-Statistical_analysis.zip
 **Description:** Folder contents: Data and scripts for statistical
 analysis of mortality, development, and reproduction data with regard to
 food and density dependency. **File names and contents:** ·      
 Legend_statistics.txt: Legend for all R input data files for statistical
 analysis ·       Mortality.csv: R input data for mortality statistics
 ·       mortality_food_density.r: R script for mortality statistics
 ·       Mortality vs food and density.docx: R script for mortality
 statistics as a Word file ·       EmT50.csv: R input data for EmT~50~
 statistics ·       analyse_EmT50.r: R script for EmT~50~ statistics
 ·       EmT~50~ vs food and density.docx: R script for EmT~50~ statistics
 as a Word file ·       Egg_number.csv: R input data for reproduction
 statistics ·       analyse_egg_numbers.r: R script for reproduction
 statistics ·       Egg numbers vs food and density.docx: R script for
 reproduction statistics as a Word file ## Code/software R packages used
 for statistical analysis: All statistical analyses were performed using R
 4.3.2 (R Core Team 2023). Calculation of EmT~50~ values from emergence
 data: The sigmoidal cumulative emergence curves over time adjusted for
 mortality could be well described with a two-parameter log-logistic model
 based on the Hill equation. Regression equation: y = 1 / (1 + (x~0~/x)^b^,
 with: x~0~: time of 50 % emergence (EmT~50~) (d); b: the slope of the
 cumulative emergence curve; y: fraction of emerged individuals as a
 function of the experimental time x (d). This model was fitted to each
 cumulative emergence curve, individually, to determine the time to 50%
 emergence success (EmT~50~) for each replicate. The model was fitted using
 the function ‚drm‘ (settings: fct=LL.2(), type="binomial") from
 package ‚drc‘ (Ritz et al. 2015) in R (R Core Team 2023), and was then
 used to interpolate the median time to emergence (EmT~50~) for each
 respective curve. Mortality: Since mortality is a binominal variable, we
 used a generalized linear model (GLM) with beta distribution to describe
 the mortality rate and mortality at the end of the tests (ratio of dead to
 initial number of larvae) as a function of food per larva and larval
 density using the function ‚betareg´ from the R package betareg (Vers.
 3.1.4, Cribari-Neto and Zeileis 2010). Reproduction: The egg number per
 clutch was assumed to be Poisson distributed (discrete, (0,+inf)). Data
 were tested for overdispersion (function ‚dispersiontest´ from R package
 AER, Cameron and Trevedi 1990), and two alternative GLM models (Poisson
 and quasi-Poisson, fitted with function ‚glm´ (McCullagh and Nelder
 1989)).