Testing across vegetation types for common environmental dependencies of Gross Primary Production
Authors: Bloomfield Keith , Stocker Benjamin , Prentice Colin 
Published: March 2020

&lt;p&gt;Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions. &amp;#160;The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established. &amp;#160;At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO&lt;sub&gt;2&lt;/sub&gt;. &amp;#160;Models of GPP make a variety of different assumptions when &amp;#8216;scaling-up&amp;#8217; the standard model of photosynthesis. &amp;#160;As a troublesome consequence, they make a variety of different predictions about how GPP responds to contemporary environmental change.&lt;/p&gt;&lt;p&gt;This problem can be tackled by theoretically based modelling, or by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements. &amp;#160;Theoretical modelling has provided an explanation for why &amp;#8216;light-use efficiency&amp;#8217; (LUE) models work well at time scales of a week or longer. &amp;#160;The same logic provides a justification for the use of LUE as a key metric in an empirical analysis. &amp;#160;By focusing on LUE, we can isolate the controls of GPP that are distinct from its over-riding control by absorbed light. &amp;#160;We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time). &amp;#160;These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network. &amp;#160;We have combined the flux data with a satellite product (FPAR from MODIS) that provides spatial estimates of the fraction of incident light absorbed by green vegetation. &amp;#160;Soil moisture at flux sites was estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids. &amp;#160;LUE was then calculated as the amount of carbon fixed per unit of absorbed light. &amp;#160;We then considered additive models (incorporating multiple explanatory factors) that support non-linear responses, including a peaked response to temperature. &amp;#160;Recognising that our longitudinal data are not fully independent, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.&lt;/p&gt;&lt;p&gt;In arriving at a final parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content, are all salient predictors of LUE. &amp;#160;The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months. &amp;#160;Model performance was not significantly improved by inclusion of additional variables such as rainfall, site elevation or vegetation category (e.g. Plant Functional Type, PFT). &amp;#160;This empirical analysis supports the notion that GPP is predictable using a single model structure that is common to different PFTs.&lt;/p&gt;