Comparing biotic drivers of litter breakdown across stream compartments

Abstract Litter breakdown in the streambed is an important pathway in organic carbon cycling and energy transfer in the biosphere that is mediated by a wide range of streambed organisms. However, most research on litter breakdown to date has focused on a small fraction of the taxa that drive it (e.g. microbial vs. macroinvertebrate‐mediated breakdown) and has been limited to the benthic zone (BZ). Despite the importance of the hyporheic zone (HZ) as a bioreactor, little is known about what, or who, mediates litter breakdown in this compartment and whether breakdown rates differ between the BZ and HZ. Here, we explore the relationship between litter breakdown and the variation in community structure of benthic and hyporheic communities by deploying two standardized bioassays (cotton strips and two types of commercially available tea bags) in 30 UK streams that encompass a range of environmental conditions. Then, we modelled these assays as a response of the streambed compartment and the biological features of the streambed assemblage (Prokaryota, Protozoa and Eumetazoa invertebrates) to understand the generality and efficiency of litter processing across communities. Litter breakdown was much faster in the BZ compared with the HZ (around 5 times higher for cotton strips and 1.5 times faster for the tea leaves). However, differences in litter breakdown between the BZ and the HZ were mediated by the biological features of the benthos and the hyporheos. Biomass of all the studied biotic groups, α‐diversity of Eumetazoa invertebrates and metabolic diversity of Prokaryota were important predictors that were positively related to breakdown coefficients demonstrating their importance in the functioning of the streambed ecosystem. Our study uses a novel multimetric bioassay that is able to disentangle the contribution by Prokaryota, Protozoa and Eumetazoa invertebrates to litter breakdown. In doing so, our study reveals new insights into how organic matter decomposition is partitioned across biota and streambed compartments.


Comparing biotic drivers of litter breakdown across streams compartments
Ignacio Peralta-Maraver, Daniel M. Perkins, Murray S. A. Thompson, Katarina Fussmann, Julia Reiss, Anne L. Robertson.  Table S1. Physicochemical characteristics of the 30 studied rivers: pH, temperature (Temp, o C), altitude (alt, m), latitude (Lat, degree), longitude (Long, degree), channel width (Width, m), water depth (cm), days of exposure (days), nitrate (N, mg/ L), orthophosphate (P, mg/ L), dissolved organic carbon (DOC, mg/L), canopy cover (Canopy), quantity of cobbles, quantity of gravel, amount of sand, quantity of silt, quantity of leaf litter in the open channel, quantity of submerged plants, quantity of submerged wood. Canopy cover, sediment morphology (cobbles, gravel, sand and silt) and the quantity of leaf litter, submerged plants (Sub plant) and submerged wood (Sub wood) were characterized semi-quantitatively in situ at each site (giving values ranging from 0 when no presence to a maximum of 3). Physicochemical measurements of pH, altitude, latitude, longitude, dissolved organic carbon, dissolved inorganic nitrogen, ammonium, nitrate and phosphate were obtained from the UK Environment Agency as annual averages when available.        Decay coefficients of green and rooibos-tea bioassays from sites in two of the study catchments. The negative exponential dacay model is fitted to the data (where possible) highlighting that the k used in the analysis is generally supported for these assays. Data were collected by volunteers from the Eden Trust and Surrey Wildlife Trust as part of a Citizen Science Project: http://www.riverflies.org/scratching-below-surface-monitoring-functioning-underriver-bed.  Judd and Kenny (1981).  Table S2.

Supplementary Methods.
Preparing and processing of cotton-strips and tea bags -Tensile strength of all cottonstrips was measured with an Instron Series IX tensiometer (Instron Corporation,Canton, Ohio) at 20 °C and 65% relative humidity in a climate-controlled room. Mean and standard deviation of pre-incubation tensile strength (631.0 ± 17 kg) was measured using 5 new cotton-strips. The green and rooibos-tea bags were dried for 2 days at 55°C and weighed (total bag weight) before incubation in the field. Initial bag weights were 2.12 g (SD = 0.02 g) and 2.15 g (SD = 0.02 g) for green-tea bags and rooibos-tea bags, respectively.
Samples preparation for organisms processing -Once in the laboratory, falcon vials containing bioassays collected in the field were shaken continuously for 1 min at 2500 rpm using a compact vortex shaker (SciQuip Vortex Mixers). Immediately after shaking, 10 ml water was collected with a pipette. From the collected water, 5 ml were filtered using cellulose acetate membrane filters (45 µm) to remove Protozoa and Eumetazoa invertebrates from the medium for later measurements of prokaryote biomass diversity and potential metabolic activity. The remaining water was kept unfiltered to process Protozoa. Both filtered and unfiltered water samples were stored in sterile conditions at 4 °C. The remaining content of the vials was retained on a 40µm sieve for identification. Tea-bags and cotton-strips were stored and the remaining sieve contents were preserved in 4% formalin containing Bengal-rose stain so that invertebrates could be processed at a later time.
Body size-dry carbon content conversions -Body dimensions of all counted Protozoa and meiofauna (Eumetazoa invertebrates whose body size is into the range of 0.45-500.00 µm) were transformed to biovolume after Reiss and Schmid-Araya (2010).
Protozoa individual biovolume was directly converted to dry carbon content assuming 0.14 pg C/µm 3 (Putt & Stoecker, 1996). For meiofauna individual biovolume was first converted into fresh mass using published gravity values (Feller & Warwik, 1998) following the approach of previous studies ( where OD i is the corrected OD value of each substrate containing well and N is the number of substrates (31) (Gryta, Frąc, & Oszust 2014). The average well colour development values for each substrate (Substrate AWCD) were also obtained using equation 2. For that propose, OD i represented the corrected OD value of the substrates within the substrate category and N was the number of substrates in the category (Kenarova, Radeva, Traykov, & Boteva, 2014).

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where Sr is the number of wells with color development and P i is the proportional colour development of the well over total color development of all wells of a plate.
Statistical analysis: models selection, fitting and models validation -Biomass of all identified groups and standardized decay coefficients (coefficients / standing stock biomass) were first Log 10 transformed to solve heterogeneity of the residuals in the ANOVA tests and the regression models, but this was not necessary for the rest of the responses. Continuous covariates in the regression models were first centered by subtracting the mean and dividing by the standard deviation. Collinearity problems were detected among plate-AWCD (metabolic potential to utilize all carbon sources in EcoPlates) and individual substrate-AWCD (metabolic potential to utilize each carbon sources in EcoPlates) during data exploration. Therefore, plate-AWCD was maintained as a potential covariate of the regression models, while substrate-AWCD was not included. Intra-class correlation effects of the studied responses with the study site (samples collection from the same streams) and with catchment (streams sampled from the same catchment) were also detected during data exploration. Therefore, study site