Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems
Version of Record online: 14 OCT 2010
© 2010 Blackwell Publishing Ltd
Global Change Biology
Volume 17, Issue 4, pages 1681–1694, April 2011
How to Cite
YVON-DUROCHER, G., MONTOYA, J. M., TRIMMER, M. and WOODWARD, G. (2011), Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems. Global Change Biology, 17: 1681–1694. doi: 10.1111/j.1365-2486.2010.02321.x
- Issue online: 28 FEB 2011
- Version of Record online: 14 OCT 2010
- Accepted manuscript online: 24 AUG 2010 07:53AM EST
- Received 14 June 2010 and accepted 27 July 2010
Figure S1. Frequency distributions of individual body mass for (a) all individuals measured, (b) a random sample of 400 (i.e. the number of individuals actually measured in a sample) from a, (c) a random sample of 2000 from a, (d) a random sample of 100 from a. Data highlight that a sample of 400 individuals is sufficient to estimate the variance in the distribution of body size comparable to the whole community. When measuring the phytoplankton a minimum of 400 individuals from any given pond were measured over the number of fields of view required to count 400 from the sample in the sedimentation chamber. It is also clear that a sample of 100 is not sufficient to accurately reproduce the variance in the body mass distribution of the whole community. Assuming that organisms of a given body mass are Poisson distributed (Figure S2, table S3) on the surface of the sedimentation chamber, the measurement of 400 individuals should be sufficient to attain an error of 5% [if error=1/sqrt(n)].
Figure S2. Size-frequency distribution for phytoplankton in pond 14 from April 2007. Panels show the size-frequency distribution after analysing all fields of view (FOV) taken to measure ˜400 individuals in the sedimentation chamber, 1 FOV, 2 FOVs, 3 FOVs and 4 FOVs. Data highlight the equitable distribution of body size among fields of view which reflects the random settlement of phytoplankton cells in the sedimentation chamber. Tests for dispersion were carried for all samples and settlement conformed to Poisson statistics in every case (data not shown).
Figure S3. Seasonality of inorganic nutrients in the warmed (red lines) and ambient (black lines) mesocosms. (a) Nitrite, (b) Nitrate, (c) Ammonium, (d) Silicate, (e) Phosphate, (f) the stoichiometry of the inorganic nutrient pool, N:P. Water samples for measuring dissolved inorganic nutrient concentrations were collected from mid depth in the mesocosm at 9am on each sampling occasion. Samples were filtered (Whatmann GF/F) and stored frozen (-20°C) for subsequent determination of NO3-, NO2-, NH4+, PO43- and dissolved silica (Si) using a segmented flow auto-analyser (Skalar, San++, Breda, Netherlands), according to (Kirkwood, 1996). Inorganic nutrients (NO3-, NO2-, NH4+, PO43- & Si) exhibited strong seasonal trends. For example, NO3- concentrations peaked in spring and declined progressively throughout the summer, when rates of primary production were maximal (Yvon-Durocher et al., 2010), and were depleted to ≅0.005 μmol L-1 by October, before regeneration in the winter. Concentrations of NO3-, NO2-, NH4+ and PO43- showed identical seasonal patterns in the warmed and ambient treatments, with no significant differences in the overall mean annual concentrations of these nutrients (Table S4). Furthermore, the stoichiometry of the inorganic nutrient pool exhibited remarkable similarity between treatments, with a mean annual ratio of total inorganic N to P of ≅11 : 1 in both heated and ambient mesocosms.
Table S4. Results of the linear mixed effects model testing for differences in the concentration of inorganic nutrients between heated and ambient mesocosms. A linear mixed effects model was conducted with restricted maximum likelihood methods using the lme (linear mixed-effects model) function in R, treatment (heated or unheated) was the fixed effect, and temporal pseudo-replication from repeated sampling of the mesocosms over the year was accounted for by including mesocosm identity nested with sampling occasion as random effects.
Table S5. Regression statistics for the community size spectrum of each mesocosm for the relationship: log (Ni)=b*log (Mi)+a. Where Ni is the abundance of the size class i and is the mass at the centre of the ith size bin, b and a are the slope and the intercept, respectively. These data highlight that the size spectrum was linear for each of the mesocosms and that the individual size distribution was a power law.
Table S6. Regression statistics for the phytoplankton size spectrum of each mesocosm for the relationship: log (Ni)=b*log (Mi)+a. Where Ni is the abundance of the size class i and is the mass at the centre of the ith size bin, b and a are the slope and the intercept respectively.
Figure S7. Quotient of benthic to ecosystem metabolism. On average over the course of the year benthic metabolism represented ˜35% of whole ecosystem metabolism measured using the dissolved oxygen change technique (see Yvon-Durocher et al. (2010) for details). Benthic metabolism was measured using dark in-situ benthic chambers which enclosed a sample of 500 mL at the sediment-water interface. A magnetic stirrer in the chamber ensured that the sample was evenly mixed. Benthic respiration was measured by the removal of 25 mL samples at the beginning and the end of the 6 h incubations. The samples were gently discharged into gas-tight vials (12 mL, Exetainers, Labco Ltd, High Wycombe, UK) and allowed to overflow twice (to minimize atmospheric gas exchange), and fixed for Winkler analysis. The samples were immediately fixed and stored in a fridge at 5°C to minimize light and temperature fluctuations until they could be titrated in the laboratory (<5 d). To ensure linearity of oxygen uptake a timed series of samples were taken, subsequently only T=0 and T=final samples were taken to limit sample extraction from the chambers.
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