Paper Review: Liu (2019). SAM->NEE across biomes. ELE

1 May, 2020 (Liu, ELE, SAM->NEE across biomes)

Liu, Y., Schwalm, C.R., Samuels‐Crow, K.E. and Ogle, K. (2019), Ecological memory of daily carbon exchange across the globe and its importance in drylands. Ecol Lett, 22: 1806-1816. doi:10.1111/ele.13363

Why did I read this paper?

To apply the SAM framework for my paper, I needed a reference. Professor Ogle recommended this paper for me as I and Liu et al. have the same type of data.

This paper would be much more important than I expected because 1) it contains sample R code in which the SAM was implemented; I would directly refer to it given I will use the same kind of data (i.e. observation with a fixed timestep), and 2) it also proposes several critical questions related to my future PhD study at MPI-bgc (i.e. water-carbon interactions across scales).

What did they do?

The authors quantified the antecedent effect of some key factors on NEE. They used observations from 42 FLUXNET sites, where met some criteria (e.g. data availability). The factors included 1) environmental factors (shortwave radiation, temperature, VPD, current SWC, antecedent SWC, PPT), and 2) biological factors (unexplained NEE residuals by the environment factors).

Key findings

Environmental memory is necessary to explain variation in daily NEE throughout the year
- various time scales (shortest by shortwave radiation and SWC to longest by precipitation)
- different responses by biomes (dry shrub lands versus forests)
- insensitive to the level of leaf growing
Another evidence for the local compensatory effect of water availability on photosynthesis and respiration (Jung et al., 2017)

the insensitivity to the level of leaf growing might be due to this local compensatory effect.
Scaling relationship between environmental memory and water stress
Drylands may be more vulnerable to the future droughts
- Drylands showed more conservative strategy against resource deficit (i.e. longer-term memory)

Possible limitations

The biological memory effect should be directly quantified: The authors successfully considered the biological effects even without parameterizing them in the formula. The results however showed that the major contributor of the effect were forest practices by human and natural disturbances (i.e. insects). I could not fully agree that they are the biological factors the authors originally intended to investigate and I think a method that can directly take the biological factors into account is required rather than dealing with the unexplained residual by environmental factors.

Notes

How does ecological memory at the daily scale propagate across other (and particularly, longer) time-scales to influence ecosystem carbon metabolism?
How to assess ecological memory of component fluxes (e.g. photosynthesis and respiration)?
- But, the overall carbon exchange could be understood without this assessment given those component fluxes are compensated out (the local compensatory effect).
The ability to predict land-carbon responses to future environmental changes rests upon an adequate mechanistic understanding of how multiple processes and their interactions give rise to memory effects.
Environmental memory integrates multiple processes, therefore it could provides new insight into ecological responses at various time scales.
How does the scale relationship (i.e. memory vs. aridity) form across sites within multiple biomes?
- Physical delays driven by the movement of water?

I may further explore papers in which refer to the project paper (Jung et al., 2017) to get some idea for my PhD study.