Lagos et al. 2017, Fig. 2

Marine invertebrates metabolism

Novel technologies (e.g. PreSens SensorDish Reader) have greatly advanced our capacity to measure respiratory rates of living organisms. These high throughput equipment also dump large amounts of data, and we still lack automated software capable of processing it. Olito et al. 2017 have proposed an empirical, reproducible method to estimate biological rates which are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. The package allows the user to automate rate estimation across a list of files (dumped from hardware) with very few lines of code. This is now published as an R package named LoLinR, and it is freely available for download on GitHub.

Marine artificial structures are proliferating worldwide and provide a haven for marine invasive species. Such structures disrupt local hydrodynamics, which can lead to the formation of oxygen-depleted microsites. The extent to which native fauna can cope with such low oxygen conditions, and whether invasive species, long associated with artificial structures in flow-restricted habitats, have adapted to these conditions remains unclear. Lagos et al. 2017 measured water flow and oxygen availability in marinas and piers at the scales relevant to sessile marine invertebrates (mm). We then proposed a novel statistical method based on Michaelis-Menten curves to estimate critical oxygen levels of marine organisms. The code is freely available on GitHub.

Publications

(2017). Estimating monotonic rates from biological data using local linear regression. Journal of Experimental Biology, 220: 759–764. doi: 10.1242/jeb.148775.

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