There are four broad methodological approaches in respirometry:

  • Closed chamber
  • Intermittent flow
  • Flowthrough
  • Open tank or open arena

In closed chamber respirometry, oxygen decrease is measured within a hermetically sealed chamber of known volume, sometimes situated within a closed loop to allow mixing of the environment within the chamber. Oxygen recordings may be continuous through use of an oxygen probe, periodic through withdrawing water or gas samples at set intervals, or a two-point measurement consisting of the initial and final concentrations. Metabolic rates are estimated from the oxygen timeseries by fitting a linear regression of oxygen against time to all or part of the data. Estimates of metabolic rate are made using the equation: \[VO_2 = \dot O_2V\] where \(\dot O_2\) is the slope of the regression that describes the rate of change in oxygen against time, or in the case of a two-point measurement the difference in oxygen divided by the time elapsed, and \(V\) is the volume of fluid in the container (Lighton 2008).

In intermittent flow respirometry, oxygen is measured as described above, but the chamber is periodically flushed with new water or air, returning it to initial conditions, resealed, and the experiment repeated (Svendsen et al. 2016). This technique is essentially the same as closed respirometry, but with the ability to conduct multiple replicates relatively easily. Depending on the metabolic rate metric being investigated, the final rate can be calculated as the mean of multiple measures (e.g. Carey et al. 2016), the lowest or highest rates recorded in some portion of the trials (e.g. Stoffels 2015), or some other way of summarising the multiple rates recorded.

Flowthrough respirometry (also known as “open flow” or “continuous flow” respirometry) involves a closed chamber, but with a regulated flow of air or water through it at a precisely determined flowrate. After equilibrium has been reached, the oxygen concentration differential (or ‘oxygen delta’) between the inflowing and outflowing water, along with the flowrate, allows calculation of the oxygen extracted from the flow volume per unit time: \[\dot{V}O_2 = (C_iO_2 - C_eO_2)FR\] where \(\dot{V}O_2\) is the rate of oxygen consumption over time, \(C_iO_2\) and \(C_eO_2\) are the inflow and outflow oxygen concentrations, and \(FR\) is the flow rate through the system (Lighton 2008).

A final method is open tank or open arena respirometry, in which a tank or semi-enclosed area open to the atmosphere is used, but the input or mixing rate of oxygen from the surroundings has been quantified or found to be negligible relative to oxygen consumption of the specimens (Leclercq et al. 1999). It is seldom used, but for some applications it is a sufficient and practical methodology (Gamble et al. 2014). The common equation used for open respirometry is: \[\dot{V}O_2 = \dot O_2V + \phi_d\] where \(\dot O_2V\) is the slope of the regression of oxygen against time, \(V\) is the volume of the arena, and \(\phi_d\) is the oxygen flux at the surface as determined by Fick’s Law (Leclercq et al. 1999).

The respR package

respR is a package for R designed to process the data from all of these types of respirometry experiment. It is designed primarily for aquatic respirometry. However, because the majority of the functions are unitless it is adaptable for use with gas respirometry, and indeed analysis of other data where a parameter may change over time.

When working with respirometry data, you will often need to:

  1. Ensure that the data or a subset of the data is representative towards the research question of interest, and free of errors.
  2. Determine the rate of change in oxygen across the data or subset.
  3. Depending on the experiment, adjust rates for background extraction of oxygen by micro-organisms, or for oxygen flux from the air.
  4. Convert the resulting unitless rate to an absolute (i.e. whole specimen or chamber) rate, a mass-specific rate, or an area-specific rate in appropriate units.
  5. Select and summarise the resulting rates according to various criteria for reporting a final rate.

The respR package contains several functions in a logical workflow to make this process straightforward:

  • It provides visual feedback and diagnostic plots to help you explore, subset and analyse your data.
  • It uses computational techniques such as rolling regressions and kernel density estimates to determine the highest, lowest or most linear rates within timeseries data.
  • It uses an object-oriented approach, with functions outputting objects which can be read by subsequent functions, reducing the need for additional inputs.
  • Output objects can be saved or exported, and contain raw data, parameters used in calculations, and results, allowing for fully documented and reproducible analyses.

Vignettes and documentation

Help files for the functions in respR can be viewed in the Documentation link at the top of this page, via invoking the ? command in R, or by searching in the Help panel in RStudio.

In addition, this site contains multiple vignettes (i.e. user guides) describing typical analysis workflows, and looking at several functions in more depth. These can be found under Vignettes.

Under More you can find release notes, contact details, how to support development, how to get help or provide feedback, how respR is being used by the scientific community, and more.

To get started check out one of these vignettes describing typical analysis workflows:

Closed-chamber respirometry
Even if you are not conducting this particular type of experiment, this is the best place to start to understand the full functionality of respR. It describes an entire workflow to process and analyse a closed-chamber respirometry dataset.

auto_rate: Automatic detection of metabolic rates
Here we detail the function auto_rate() which automatically identifies the highest, lowest and most linear rates in a dataset.

Intermittent-flow respirometry: Short experiment
Intermittent-flow respirometry: Long experiment
How to analyse and extract rates from relatively simple and much longer intermittent-flow respirometry experiments.

Flowthrough respirometry
Several complete analyses of different types of flowthrough respirometry experiments.

Critical oxygen values
Determine critical oxygen values, for example \(P_{crit}\).

Adjusting rates for background
How adjust_rate allows you to perform all sorts of different adjustments to account for the background oxygen use of microbial organisms.