Thermal history mediates the ecological role of body size in a freshwater fish
Data files
Jul 03, 2025 version files 38.87 KB
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Meso_body_size.csv
36.30 KB
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README.md
2.56 KB
Abstract
Intraspecific trait variation among populations may strongly alter community and ecosystem structure and function. Body size is a fundamental trait of all organisms, affecting both organismal physiology and ecological effects, which may differ across populations. For example, body size is predicted to decline with warming. As such, in this study, we aim to determine how population differences in thermal history mediate the ecological role of body size in a freshwater fish. We conducted a mesocosm experiment in which we manipulated fish (Gambusia affinis [Baird and Girard, 1853]) source populations (ambient source vs warm source) and body size while holding their biomass constant. We monitored community and ecosystem response variables including, macroinvertebrate abundance, zooplankton biomass, phytoplankton abundance, and greenhouse gas flux. Changes in fish body size influenced most ecological responses, but these effects often depended on the thermal history of the fish population. For many responses, the effects of reduced fish body size were offset by a history of exposure to warm temperatures, suggesting that environmental factors (including thermal acclimation) and adaptation may offset the community and ecosystem effects of decreased consumer body size. Our research suggests that ecological changes will depend on changes in body size and environmental factors, as well as on other trait changes associated with warming. Experiments and models addressing the ecological effects of body-size decline alone may overestimate the ecological changes expected under warming.
Dataset DOI: 10.5061/dryad.qv9s4mwsd
Description of the data and file structure
Mescosm (experimental pond) data were collected over 6 time points (The first was before adding fish (T0) and then measurements were taken at 4 (T1), 8 (T2), 16 (T3), 24 (T4), and 32 (T5) days after adding fish. Experimental ponds contained small or large-sized fish from ambient or warm source populations. There are also control tanks. In total, there are 5 experimental treatments (Control, Small-warm, Small-cool, Large-ambient, Large-ambient).
Files and variables
File: Meso_body_size.csv
Description: Treatment description and data from the variables measured throughout the mesocosm experiment. Units for variables measured are described below. Where data were not collected for a response variable in a given week or for a treatment, cells are filled with n/a.
Variables
- Pop: Source population (warm or ambient)
- Size: Body size (small or large)
- Mesocosm: Mesocosm number
- Row: Row in which the mesocosm was in
- Treatment: Treatment description (Population, Size). PP refers to Waikato River Spring at Prawn Park which is the ambient site and AA refers to Akatarewa Hot Spring the warm site.
- Week: Sampling week
- Day: Sampling day
- Chla: chlorophyll-a abundance (µg/L)
- Periphyton: Periphyton (mg/m2)
- Bugs_pred: Macroinvertebrates predatory (count)
- Bugs_nonpred: Macroinvertebrates nonpredatory (count)
- N2O_am: Nitrous oxide, measured during the day (mg m2 min1)
- CO2_am: Carbon dioxide, measured during the day (mg m2 min1)
- CH4_am: Methane, measured during the day (mg m2 min1)
- NH3_am: Ammonia, measured during the day (mg m2 min1)
- N2O_pm: Nitrous oxide, measured at night (mg m2 min1)
- CO2_pm: Carbon dioxide, measured at night (mg m2 min1)
- CH4_pm: Methane, measured at night (mg m2 min1)
- NH3_pm: Ammonia, measured at night (mg m2 min1)
- ER: Ecosystem respiration (DO mg/L)
- NPP: Net Primary Production (DO mg/L)
- GPP: Gross Primary Producion (DO mg/L)
- NH4: Ammonia (NH4+ µg/L)
- pH: pH
- SPC: Temperature compensated (25°C) specific conductivity (mS/m)
- Zoop_bm: Zooplankton biomass (µg/L)
- Fish_biomass_g: Fish biomass (g)
Code/software
Data are stored in a .csv file and may be opened by any software. Analysis for the manuscript was carried out in R (R statistical software version 3.3.3; R Project for Statistical Computing, Vienna, Austria).
