vignettes/archive/oxy_crit_disc.Rmd
oxy_crit_disc.RmdThis page has been archived and will not be updated. This is
because it was submitted as part of the publication of
respR in Methods in Ecology and Evolution, and has been
retained unchanged for reference. Any results and code outputs shown are
from respR v1.1 code. Subsequent updates to
respR should produce the same or very similar results.
Currently respR has implemented three methods of
calculating a breakpoint in the relationship of oxygen uptake rate to
oxygen concentration. It is in our plans to support several more
(e.g. Duggleby, 1984; Lighton and Turner, 2004, Marshall et al., 2013),
and additionally, metrics which describe the degree of oxyregulation of
a specimen in intermediate cases (e.g. Tang, 1933; Mueller &
Seymour, 2011). Note also, the classification of species into strict
oxyconformers and oxyregulators is simplistic. There is a continuum of
responses between these extremes, and many intermediate cases (Mueller
and Seymour, 2011).
Discussion of ‘breakpoint’ identification in time-series data is a whole branch of mathematics, which we do not have the expertise to discuss. The user should however be aware of the controversy around using the BSR method to estimate \(P_{crit}\).
Marshall et al. (2013) discussed the limitations of the BSR methods, and found them to be inaccurate and prone to error. This publication is a convincing critique of the BSR methods, and posits that use of them was understandable in the past when computing power and analytical processing was limited, but that this should no longer be a limitation, and better methods are available. A very valid point from this analysis is that BSR approaches provide no estimates of error, making it difficult to judge just how reliable they are.
Whether or not this is the definitive critique of this method is
still an open question; scientific methods persist through consensus,
and BSR is still in use by many prominent physiologists in the years
since this publication (Chu & Gale, 2017; Regan & Richards,2017;
Stoffels et al. 2017). It remains to be seen if the non-linear
regression (NLR) methods proposed by Marshall et al. will supplant other
methods of estimating \(P_{crit}\). It
is a rigorous method, which indeed appears to be more reliable, and we
plan to support this method in a future update to
respR.
As far as we are aware, there is no controversy around using the
segmented method of Muggeo (2008), and in our testing it
usually gives similar results to the BSR approach with data that is
high-resolution and not particularly noisy. Until we implement other
options such as the NLR methods of Marshall et al., we would encourage
users to use the segmented method when in doubt, and
carefully consider use of the BSR approach. As we saw in the examples
above, all three methods often give similar results, in which case it is
likely the BSR results would be appropriate to report. However, there
may be cases where the results from these different methods differ.
Different methods are discussed amongst the literature cited below.
It is important to note that \(P_{crit}\) is most frequently used as a
comparative metric. Since analytical options chosen by the
investigator (such as regression width) inherently affect the result, it
is arguably more important that these are kept the same amongst analyses
that will be the basis of comparisons, rather than consideration of the
ultimate values of \(P_{crit}\) per
se. So, it is important that investigators fully report the
parameters under which these analyses have been conducted. This allows
editors and reviewers to reproduce and assess analyses, and subsequent
investigators to know if comparisons to their own results are
appropriate. respR has been designed to make the process of
reporting these analyses straightforward (see open
science and reproducibility using respR).
Chu, J. W. F., & Gale, K. S. P. (2017). Ecophysiological limits to aerobic metabolism in hypoxia determine epibenthic distributions and energy sequestration in the northeast Pacific ocean. Limnology and Oceanog- raphy, 62(1), 59-74. https://doi.org/10.1002/lno.10370
Duggleby, R.G., 1984. Regression analysis of nonlinear Arrhenius plots: An empirical model and a computer program. Computers in Biology and Medicine 14, 447–455. https://doi.org/10.1016/0010-4825(84)90045-3
Lighton, J.R.B., Turner, R.J., 2004. Thermolimit respirometry: an objective assessment of critical thermal maxima in two sympatric desert harvester ants, Pogonomyrmex rugosus and P. californicus. Journal of Experimental Biology 207, 1903–1913. https://doi.org/10.1242/jeb.00970
Marshall, D.J., Bode, M., White, C.R., 2013. Estimating physiological tolerances - a comparison of traditional approaches to nonlinear regression techniques. Journal of Experimental Biology 216, 2176–2182. https://doi.org/10.1242/jeb.085712
Mueller, C.A., Seymour, R.S., 2011. The Regulation Index: A New Method for Assessing the Relationship between Oxygen Consumption and Environmental Oxygen. Physiological and Biochemical Zoology 84, 522–532. https://doi.org/10.1086/661953
Muggeo, V.M.R., 2008. Modeling temperature effects on mortality: multiple segmented relationships with common break points. Biostatistics 9, 613–620. https://doi.org/10.1093/biostatistics/kxm057
Regan, M. D., & Richards, J. G. (2017). Rates of hypoxia induction alter mechanisms of O2 uptake and the critical O2 tension of goldfish. The Journal of Experimental Biology, 220(14), 2536-2544. https://doi.org/ 10.1242/jeb.154948
Stoffels, R. J., Weatherman, K. E., & Allen-Ankins, S. (2017). Heat and hypoxia give a global invader, Gambusia holbrooki, the edge over a threatened endemic fish on Australian floodplains. Biological Inva- sions, 19(8), 2477-2489. https://doi.org/10.1007/s10530-017-1457-6
Tang, P.-S., 1933. On the rate of oxygen consumption by tissues and lower organisms as a function of oxygen tension. The Quarterly Review of Biology 8, 260–274. https://doi.org/10.1086/394439
Yeager, D.P., Ultsch, G.R., 1989. Physiological regulation and conformation: A BASIC program for the determination of critical points. Physiological Zoology 62, 888–907. https://doi.org/10.1086/physzool.62.4.30157935