The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

ArticleThis is the final version of the article. Available from Wiley via the DOI in this record.Eddy covariance (EC) continues to provide invaluable insights into the dynamics of Earth's surface processes. However, despite its many strengths, spatial replication of EC at the ecosystem scale is rare. High equipment costs are likely to be partially responsible. This contributes to the low sampling, and even lower replication, of ecoregions in Africa, Oceania (excluding Australia) and South America. The level of replication matters as it directly affects statistical power. While the ergodicity of turbulence and temporal replication allow an EC tower to provide statistically robust flux estimates for its footprint, these principles do not extend to larger ecosystem scales. Despite the challenge of spatially replicating EC, it is clearly of interest to be able to use EC to provide statistically robust flux estimates for larger areas. We ask: How much spatial replication of EC is required for statistical confidence in our flux estimates of an ecosystem? We provide the reader with tools to estimate the number of EC towers needed to achieve a given statistical power. We show that for a typical ecosystem, around four EC towers are needed to have 95% statistical confidence that the annual flux of an ecosystem is nonzero. Furthermore, if the true flux is small relative to instrument noise and spatial variability, the number of towers needed can rise dramatically. We discuss approaches for improving statistical power and describe one solution: an inexpensive EC system that could help by making spatial replication more affordable. However, we note that diverting limited resources from other key measurements in order to allow spatial replication may not be optimal, and a balance needs to be struck. While individual EC towers are well suited to providing fluxes from the flux footprint, we emphasize that spatial replication is essential for statistically robust fluxes if a wider ecosystem is being studied.Natural Environment Research Council. Grant Numbers: NE/J015644/1, NE/K002619/

The stabilisation potential of individual and mixed assemblages of natural bacteria and microalgae

It is recognized that microorganisms inhabiting natural sediments significantly mediate the erosive response of the bed ("ecosystem engineers") through the secretion of naturally adhesive organic material (EPS: extracellular polymeric substances). However, little is known about the individual engineering capability of the main biofilm components (heterotrophic bacteria and autotrophic microalgae) in terms of their individual contribution to the EPS pool and their relative functional contribution to substratum stabilisation. This paper investigates the engineering effects on a non-cohesive test bed as the surface was colonised by natural benthic assemblages (prokaryotic, eukaryotic and mixed cultures) of bacteria and microalgae. MagPI (Magnetic Particle Induction) and CSM (Cohesive Strength Meter) respectively determined the adhesive capacity and the cohesive strength of the culture surface. Stabilisation was significantly higher for the bacterial assemblages (up to a factor of 2) than for axenic microalgal assemblages. The EPS concentration and the EPS composition (carbohydrates and proteins) were both important in determining stabilisation. The peak of engineering effect was significantly greater in the mixed assemblage as compared to the bacterial (x 1.2) and axenic diatom (x 1.7) cultures. The possibility of synergistic effects between the bacterial and algal cultures in terms of stability was examined and rejected although the concentration of EPS did show a synergistic elevation in mixed culture. The rapid development and overall stabilisation potential of the various assemblages was impressive (x 7.5 and x9.5, for MagPI and CSM, respectively, as compared to controls). We confirmed the important role of heterotrophic bacteria in "biostabilisation" and highlighted the interactions between autotrophic and heterotrophic biofilm consortia. This information contributes to the conceptual understanding of the microbial sediment engineering that represents an important ecosystem function and service in aquatic habitats.</p

Differences between the first day of sampling (day 1) and day 14 where most of the variables showed their maximum value as well as differences between the given treatments (mixed: BD, Bacteria B, Diatom D); both times expressed as quotient/factors for EPS carbohydrates, EPS proteins, MagPI and CSM.

<p>Differences between the first day of sampling (day 1) and day 14 where most of the variables showed their maximum value as well as differences between the given treatments (mixed: BD, Bacteria B, Diatom D); both times expressed as quotient/factors for EPS carbohydrates, EPS proteins, MagPI and CSM.</p

Low-temperature scanning electron microscope images using different magnifications.

<p>A–B. The mixed assemblages bacteria + diatom. C–D. The diatom treatment. E–F. The bacteria treatment. G–H. The control substratum. Frozen water (ice) on the surface produces a solid matrix around the glass beads in the controls. In the other treatments with microorganisms, the EPS matrix is visible, heavily covering the glass beads and permeating the intermediate pore space.</p

Mean values of the different treatments: mixed assemblages (BD), diatoms (D), bacteria (B), control (C).

<p>A. chlorophyll <i>a</i> (n = 21). B. bacterial cell numbers (n = 24). C. bacterial division rates (n = 18). D. bacterial specific division rates (n = 18).</p

Mean values of MagPI and of CSM measurements and their relative assessment between the treatments.

<p>A. Mean values (n = 6) of MagPI over the course of the experiment. B. Mean values (n = 6) of CSM measurements over the course of the experiment. The different treatments were bacteria and diatoms (BD, ▴), diatoms (D, ♦), bacteria (B, □) and controls (C, •). Substratum stability by the mixed BD treatment relative to the stability of the single B and D treatments is given for MagPI (C) and CSM (D). Where the stability created by the mixed culture (BD) exceeds that of the added single cultures (B and D), the value is positive (synergistic effect) and vice versa (inhibitory effect). If the added values of the single cultures equals the mixed cultures then the effect measured is additive.</p

Oligonucleotides used in this study (^a Probe nomenclature as described by Alm et al. (1996).

<p>Oligonucleotides used in this study (^a Probe nomenclature as described by Alm et al. (1996).</p

Mean values of EPS concentrations and their relative assessment between treatments.

<p>A–B: Mean values (n = 3 per treatment, based on n = 3 replicates per box ± SE) of EPS concentrations in the treatments bacteria and diatoms (BD, ▴), diatoms (D, ♦), bacteria (B, □) and controls (C, •) for carbohydrates (A) and proteins (B). C–D: The EPS concentration of the mixed cultures (BD) relative to the contribution of the single cultures (B and D) such that the value “[BD]-[B+D]” is reported for carbohydrates (C) and proteins (D). Where the production of carbohydrate or protein from mixed cultures (BD) exceeds that of the added single cultures (B and D) the value is positive (synergistic effect) and vice versa (inhibitory effect). If the added values of the single cultures exactly equals the mixed cultures then there is an additive effect.</p

Relationships between sediment stability (MagPI, CSM) and EPS components.

<p>A–B. The relationships between surface adhesion (MagPI) and EPS carbohydrates and proteins concentrations. C–D. The relationships between substratum stability (CSM) and EPS carbohydrates and proteins concentrations.</p