Forest production efficiency increases with growth temperature

Abstract

Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or—more generally—for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings—FPE declining with age—are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate.

Global patterns of forest autotrophic carbon fluxes

“…A shallower increase in NPP than in GPP with decreasing latitude would align with the suggestion that tropical forests tend to have low carbon use efficiency (CUE = NPP/GPP; Anderson-Teixeira et al, 2016;DeLucia et al, 2007;Malhi, 2012) but contrast with recent findings of the opposite pattern (Collalti et al, 2020). Such differences among C fluxes in their relationship to latitude have profound implications for our understanding of the C cycle and its climate sensitivity (e.g., Collalti et al, 2020). However, until recently, the potential to compare latitudinal trends across C fluxes has been limited by lack of a large database with standardization for methodology, stand history, and management (Anderson-Teixeira et al, 2018.…”

Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events

“…But an increased atmospheric CO 2 concentration necessarily implies an increase in mean air temperature which is in turn speculated to increase plants' respiration and should result in a levelled-off forest carbon use efficiency [83]. Recent studies indicate that the ability of intact tropical forests to remove carbon from the atmosphere may be already saturating [9,106] while others indicate for tropical species higher thermal acclimation capacities to buffer C-losses by respiration [51], thus, calling for more studies on the possible consequences of warming and increased atmospheric CO 2 concentration on forest dynamics. However, in the Amazon phosphorus is an important limiting nutrient over large parts and its low availability may limit positive CO 2 fertilization effects.…”

“…where Y is the carbon use efficiency (i.e., the fraction of GPP not used to support autotrophic respiration, known as CUE [49][50][51]). GPP is computed as:…”

“…The 3-PG calculates NPP as a constant fraction of GPP, using an NPP/GPP ratio (Y = 0.47) based on empirical evidence [47]. For Brazilian Amazon forests other studies suggest Y to be closer to 0.3 [57] while others report much higher values at some tropical sites, even including Amazonian ones (i.e., Y > 0.5; [51]). However, the issue of whether Y is a constant value, its actual value, even including its top-down limits, is a much-debated issue as described in [51,58].…”

Tackling unresolved questions in forest ecology: The past and future role of simulation models

“…Models can also prove useful to create benchmarks against which other methods to estimate carbon stocks and fluxes can be evaluated and improved (e.g., LiDAR, Knapp et al, 2018;eddy-flux tower, Jung et al, 2009). Plant respiration, tree mortality, and carbon allocation are key drivers of forest productivity and biomass (Bugmann & Bigler, 2011;Johnson et al, 2016) but remain poorly understood (Hartmann et al, 2018;Holzwarth, Kahl, Bauhus, & Wirth, 2013;Malhi et al, 2015;Merganičová et al, 2019;Collalti & Prentice, 2019;Collalti, Ibrom, et al, 2020), and future modelling studies should seek to foster our understanding of these critical processes for example, through model-data fusion approaches (Q9, Q10, Table 3).…”