Increasing nutrient stress reduces the efficiency of energy transfer through planktonic size spectra

Abstract

Size-spectral approaches quantify the efficiency of energy transfer through food webs, but theory and field studies disagree over how changes in temperature, nutrients, and extreme weather impact on this efficiency. We address this at two scales: via 6 years of weekly sampling of the plankton size spectrum at the Plymouth L4 shelf sea site, and via a new, global-scale, meta-analysis of aquatic size spectra. The time series showed that with summertime nutrient starvation, the energy transfer efficiency from picoplankton to macroplankton decreased (i.e., steepening slopes of the size spectra). This reflected increasing dominance by small cells and their microbial consumers. The extreme storms in winter 2013/2014 caused high metazoan mortality, steep size-spectral slopes, and reduced plankton biomass. However, recovery was within months, demonstrating an inbuilt resilience of the system. Both L4 and our meta-analysis showed steep slopes of normalized size spectra (median −1.11). This reflects much lower values, either of trophic transfer efficiency (3.5%) or predator-prey mass ratio (569), compared to commonly quoted values. Results from the meta-analysis further showed that to represent energy transfer faithfully, size spectra are best constructed in units of carbon mass and not biovolume, and span a mass range of > 10 7. When this range is covered, both the meta-analysis and time series show a dome-shaped relationship between spectral slopes and plankton biomass, with steepening slopes under increasingly oligotrophic and eutrophic conditions. This suggests that ocean warming could decrease the efficiency of energy transfer through pelagic food webs via indirect effects of increasing stratification and nutrient starvation. Climate change and the efficiency of energy transfer through the plankton In a warming climate, increases in ocean temperature, stratification, nutrient shortage, and the frequency of extreme

The global ocean size-spectrum from bacteria to whales

“…Although the ‘Sheldon hypothesis’ has been validated locally among plankton groups, often with striking similarity in reported exponents (near −1; Fig. S2) ( 1 – 4 ), it has not been examined globally, and has been limited to much smaller size ranges than the 23 orders of magnitude originally envisioned. Empirical size-spectra have typically averaged 6 orders of magnitude range in body mass, and have never exceeded 14 (Ref: ( 4 ); Table S3).…”

“…S2) ( 1 – 4 ), it has not been examined globally, and has been limited to much smaller size ranges than the 23 orders of magnitude originally envisioned. Empirical size-spectra have typically averaged 6 orders of magnitude range in body mass, and have never exceeded 14 (Ref: ( 4 ); Table S3). Furthermore, the extent to which human activities may have impacted the whole-ocean size-spectrum has not been investigated.…”

“…B. Biomass estimates in A are shown on a linear scale to highlight differences of bacteria and whales from the overall trend. C. Frequency histograms of biomass spectrum slopes for i ) resampled data incorporating our biomass uncertainty ( n = 10,000 simulations); and ii ) published slope values for n = 248 measured biomass spectra (from 41 separate studies; Ref: ( 4 )).…”

“…One major assumption is what constitutes “ocean” life. Like Sheldon and the many size-spectra studies that came after, we take oceanic life to refer largely to the pelagic environment (organisms living suspended in the salt water column of the ocean)( 1, 4, 30 ). We therefore did not consider benthic organisms explicitly or in great depth, though aggregate measures of organisms that live on the seafloor are included in estimates of “non-mesopelagic fish” from the FishErIes Size and functional Type model (FEISTY; ( 14 )) and the BiOeconomic mArine Trophic Size-spectrum model (BOATS; ( 13 )).…”

“…Individual size may be in units of length, volume, wet or dry mass, and counted over an area or volume, which may be transformed into biomass or energy density. Individuals may be binned into any number of logarithmic or linear size-classes, or represented as a probability or cumulative density function, and fit using various methods such as least squares or maximum likelihood ( 2, 4, 6 ). These varied published representations can obscure commonalities among observed patterns, which, when expressed as a size-frequency distribution, tend to be strikingly similar with exponents near a value of −1, as revealed by a recent meta-analysis ( 4 ), and shown in Table S3 and Fig.…”