Global Patterns and Controls of Nutrient Immobilization On Decomposing Cellulose In Riverine Ecosystems

Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature

Supplement 1. Non-transformed data used in statistical analysis of the effects of temperature and resource utilization on bacterial production in two rivers.

<h2>File List</h2><blockquote> <p><a href="EEA_data.txt">EEA_data.txt</a></p> </blockquote><h2>Description</h2><blockquote> <p>The EEA data.txt file is a tab-separated ASCII file. The file contains the non-transformed data used in statistical analyses of the effects of temperature and resource utilization on bacterial production in two rivers.</p> <p>Rates measured in the lab at 20˚C were converted to rates at ambient temperature using the following equations: BP(T) = BP(T₀)e^-Ea/k^(1/T0-1/T) and ^AppVmax(T) = ^AppVmax(T₀)e^-Ea/k^(1/T0-1/T), where BP represents bacterial production, ^AppVmax is the observed maximum extracellular enzyme activity (EEA) rate, T denotes ambient stream temperature (in K), and T₀ equals 293 K (or 20 ˚C); E_a is the activation energy (eV) of the process, and k is Boltzmann's constant (8.62 x 10^-5 eV K^-1). The activation energy (E_a) was assumed to be 0.5 eV for bacterial production and maximum EEA rates.</p> <p>Column definitions:</p> <p>1 = Study name; more detailed information can be found at Sinsaubaugh et al. 1997. Limnology and Oceanography 42:29–38.</p> <p>2 = Ambient temperature (˚C)</p> <p>3 = Inverse absolute temperature (1/kT) where k is Boltzmann's constant (8.62 x 10^-5 eV K^-1) and T is ambient temperature in K</p> <p>4 = Bacterial production (BP, nmol h^-1 L^-1)</p> <p>5 = Acetyl-esterase (AE) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>6 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for AE activity</p> <p>7 = The turnover rate (S_t, h^-1) of AE defined as ^AppVmax/2^AppKm </p> <p>8 = Endoprotease (EP) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>9 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for EP activity</p> <p>10 = The turnover rate (S_t, h^-1) of EP defined as ^AppVmax/2^AppKm</p> <p>11 = Leucyl-aminopeptidase (LAP) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>12 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for LAP activity</p> <p>13 = The turnover rate (S_t, h^-1) of LAP defined as ^AppVmax/2^AppKm</p> <p>14 = alpha-1,4-glucosidase (AG) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>15 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for AG activity</p> <p>16 = The turnover rate (S_t, h^-1) of AG defined as ^AppVmax/2^AppKm</p> <p>17 = beta-1,4-glucosidase (BG) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>18 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for BG activity</p> <p>19 = The turnover rate (S_t, h^-1) of BG defined as ^AppVmax/2^AppKm</p> <p>20 = Alkaline phosphatase (AP) maximum activity rate (^AppVmax) (nmol h^-1 L^-1)</p> <p>21 = The half saturation constant (the substrate concentration at which the rate of substrate conversion is equal to Vmax/2) (^AppKm, nmol) for AP activity</p> <p>22 = The turnover rate (S_t, h^-1) of AP defined as ^AppVmax/2^AppKm</p> <p>23 = Carbohydrate turnover rate (S_t, h^-1); the sum of BG and AG turnover rates</p> <p>24 = Protein turnover rate (S_t, h^-1); the sum of LAP and EP turnover rates</p> <p>25 = Ratio of carbohydrate:protein turnover (dimensionless)</p> <p>26 = Resource pools contributing to bacterial production defined as the sum of ^AppVmax/2 for all extracellular enzyme activities (EEA)</p> <p>Missing values are represented as “.”.</p> <p>Check sum values:</p> <p>Column 2: check sum = 962, 0 missing values<br> Column 3: check sum = 2197, 0 missing values<br> Column 4: check sum = 194769, 4 missing values<br> Column 5: check sum = 183177, 1 missing value<br> Column 6: check sum = 1359360, 0 missing values<br> Column 7: check sum = 4.615, 0 missing values<br> Column 8: check sum = 246091, 14 missing values<br> Column 9: check sum = 3954300, 14 missing values<br> Column 10: check sum = 1.59, 14 missing values<br> Column 11: check sum = 50756, 0 missing values<br> Column 12: check sum = 2945410, 0 missing values<br> Column 13: check sum = 0.54, 0 missing values<br> Column 14: check sum = 73194, 4 missing values<br> Column 15: check sum = 3.91, 4 missing values<br> Column 16: check sum = 4733, 1 missing value<br> Column 17: check sum = 124638, 1 missing value<br> Column 18: check sum = 5.96, 1 missing value<br> Column 19: check sum = 24782, 1 missing value<br> Column 20: check sum = 291090, 0 missing values<br> Column 21: check sum = 3.04, 0 missing values<br> Column 22: check sum = 9.87, 0 missing values<br> Column 23: check sum = 2.13, 0 missing values<br> Column 24: check sum = 408, 0 missing values<br> Column 25: check sum = 225418, 14 missing values </p></blockquote

Scaling Microbial Biomass, Metabolism, and Resource Supply

The microbiome concept has drawn attention to the complex signal and syntrophic networks that underlie microbial community organization. This self-organization may lead to patterns in the allometric scaling of microbial community metabolism that differ from those of macrobial communities. Using meta-analyses, we analyzed the power scaling relationships between community production, respiration, extracellular enzyme activity and biomass for bacteria and fungi across aquatic and terrestrial ecosystems. The scaling exponents for community production versus biomass for fungi and bacteria were 0.85 ± 0.06 (95 % CI) and 0.72 ± 0.07, respectively. The scaling exponent for fungal respiration versus production was 0.61 ± 0.06. Previous studies reported exponents of 0.41, 0.44 and 0.58 for bacterial respiration versus production. Carbon use efficiency increased with biomass for both fungi and bacteria with an exponent of 0.27 ± 0.06. The potential activities of four widely measured extracellular enzymes were directly related to community production with power scaling exponents of 1.0–1.2. The frequency distribution of biomass turnover times (median 112 h for bacteria and 1,128 h for fungi) overlapped substantially with those for environmental substrate turnover, presented in a prior analysis of extracellular enzyme kinetics. These metabolic relationships, which have scaling exponents of 0.5, are linked by the ratio of assimilation to carbon use efficiency. This connection ties ecological stoichiometry and metabolic theory to microbial community homeostasis. At the ecosystem scale, allometry of microbial communities has similarities to that of eusocial insects but differs from that of plant communities, perhaps as a result of proto-cooperative processes that contribute to microbial community organization

Appendix B. Daily temperature in the microenvironment of litter bags in floodplain surface, floodplain subsurface, and river channel.

Daily temperature in the microenvironment of litter bags in floodplain surface, floodplain subsurface, and river channel

Appendix C. PCA factor loading scores for the two species of litter decomposing among three habitats.

PCA factor loading scores for the two species of litter decomposing among three habitats

Appendix A. Characteristics of soils across study sites.

Characteristics of soils across study sites

Extracellular Enzyme Kinetics Scale With Resource Availability

Microbial community metabolism relies on external digestion, mediated by extracellular enzymes that break down complex organic matter into molecules small enough for cells to assimilate. We analyzed the kinetics of 40 extracellular enzymes that mediate the degradation and assimilation of carbon, nitrogen and phosphorus by diverse aquatic and terrestrial microbial communities (1160 cases). Regression analyses were conducted by habitat (aquatic and terrestrial), enzyme class (hydrolases and oxidoreductases) and assay methodology (low affinity and high affinity substrates) to relate potential reaction rates to substrate availability. Across enzyme classes and habitats, the scaling relationships between apparent Vmax and apparent Km followed similar power laws with exponents of 0.44 to 0.67. These exponents, called elasticities, were not statistically distinct from a central value of 0.50, which occurs when the Km of an enzyme equals substrate concentration, a condition optimal for maintenance of steady state. We also conducted an ecosystem scale analysis of ten extracellular hydrolase activities in relation to soil and sediment organic carbon (2,000–5,000 cases/enzyme) that yielded elasticities near 1.0 (0.9 ± 0.2, n = 36). At the metabolomic scale, the elasticity of extracellular enzymatic reactions is the proportionality constant that connects the C:N:P stoichiometries of organic matter and ecoenzymatic activities. At the ecosystem scale, the elasticity of extracellular enzymatic reactions shows that organic matter ultimately limits effective enzyme binding sites. Our findings suggest that one mechanism by which microbial communities maintain homeostasis is regulating extracellular enzyme expression to optimize the short-term responsiveness of substrate acquisition. The analyses also show that, like elemental stoichiometry, the fundamental attributes of enzymatic reactions can be extrapolated from biochemical to community and ecosystem scales

Global patterns and controls of nutrient immobilization on decomposing cellulose in riverine ecosystems

Abstract Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter

Global Patterns and Controls of Nutrient Immobilization on Decomposing Cellulose in Riverine Ecosystems

International audienceMicrobes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low‐nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization arepoorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low‐nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, andwere strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature‐dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter