Optimum temperature for photosynthesis: from leaf- to ecosystem-scale

Arndt

et al. 2021

Global Change Biology

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%–75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary‐line analysis to eddy covariance flux observations to (a) derive ecosystem GPP–Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP–Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down‐welling short‐wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP–Ta relationship. GPP–Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna—wet and dry seasons). The shape of GPP–Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2: 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

Section: Introductionmentioning

confidence: 99%

“…Understanding these relationships is needed to evaluate and improve Earth System models (Liu, 2020), decrease uncertainty in modelling GPP under current and future climates (Booth et al, 2012), and improve knowledge of how ecosystems adjust (i.e. adapt and acclimate) to their environment (Drake et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Thermal optima of gross primary productivity are closely aligned with mean air temperatures across Australian wooded ecosystems

Arndt

et al. 2021

Global Change Biology

“…For instance, Green et al (2020) reported that ecosystem photosynthesis increases in the central Amazon when VPD increased from 0.1 to 10 hPa. In tropical rainforests, the optimum temperature for photosynthesis is about 29 °C (Liu, 2020), with decreasing photosynthetic rates at higher temperatures. This can help explain the decline in tree growth with rising T max .…”

Section: Discussionmentioning

confidence: 99%

Principal component regression analysis demonstrates the collinearity-free effect of sub estimated climatic variables on tree growth in the central Amazon

Marenco

Antezana-Vera

2021

RBT

Introduction: Climatic variables show a seasonal pattern in the central Amazon, but the intra-annual variability effect on tree growth is still unclear. For variables such as relative humidity (RH) and air vapor pressure deficit (VPD), whose individual effects on tree growth can be underestimated, we hypothesize that such influences can be detected by removing the effect of collinearity between regressors. Objective: This study aimed to determine the collinearity-free effect of climatic variability on tree growth in the central Amazon. Methods: Monthly radial growth was measured in 325 trees from January 2013 to December 2017. Irradiance, air temperature, rainfall, RH, and VPD data were also recorded. Principal Component Regression was used to assess the effect of micrometeorological variability on tree growth over time. For comparison, standard Multiple Linear Regression (MLR) was also used for data analysis. Results: Tree growth increased with increasing rainfall and relative humidity, but it decreased with rising maximum VPD, irradiance, and maximum temperature. Therefore, trees grew more slowly during the dry season, when irradiance, temperature and VPD were higher. Micrometeorological variability did not affect tree growth when MLR was applied. These findings indicate that ignoring the correlation between climatic variables can lead to imprecise results. Conclusions: A novelty of this study is to demonstrate the orthogonal effect of maximum VPD and minimum relative humidity on tree growth.

“…Much of our knowledge of T opt , however, is derived from plant autecological studies (Kattge & Knorr, 2007). While such autecological analyses are vital for mechanistic understanding of plant photosynthesis and its temperature control, they do not directly translate into information that can be used for the modelling and prediction of photosynthetic production at regional or global level, the scale that is more relevant to policies (Field et al, 1995; Liu, 2020). Recently, Huang et al (2019) made the first attempt to estimate ecosystem‐level T opt across the global land with remote sensing and eddy covariance data.…”

Section: Introductionmentioning

confidence: 99%

“…A synthesis analysis of 16 manipulative warming experiments across the TP shows an average temperature sensitivity of 23 ± 10 g m −2 °C −1 , with large variations across different warming systems (Piao et al, 2019). However, it remains unclear whether or not this vegetation productivity enhancement will persist under further warming, which is critical for carbon cycle and climate change predictions (Liu, 2020; Lu et al, 2019). A key to this question is to quantify the difference between projected future temperature and ecosystem T opt .…”

Section: Introductionmentioning

confidence: 99%

Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau

Chen

Huang

Liu

et al. 2021

Global Change Biology

Vegetation productivity first increases and then decreases with temperature; and temperature corresponding to the maximum productivity is called optimal temperature (Topt). In this study, we used satellite derived near‐infrared reflectance of vegetation (NIRv) data to map Topt of vegetation productivity at the spatial resolution of 0.1° on the Tibetan Plateau (TP), one of most sensitive regions in the climate system. The average Topt of non‐forest vegetation on the TP is about 14.7°C, significantly lower than the Topt value used in current ecosystem models. A remarkable geographical heterogeneity in Topt is observed over the TP. Higher Topt values generally appear in the north‐eastern TP, while the south‐western TP has relatively lower Topt (<10°C), in line with the difference of climate conditions and topography across different regions. Spatially, Topt tends to decrease by 0.41°C per 100 m increase in elevation, faster than the elevational elapse rate of growing season temperature, implying a potential CO2 regulation of Topt in addition to temperature acclimation. Topt increases by 0.66°C for each 1°C of rising mean annual temperature as a result of vegetation acclimation to climate change. However, at least at the decadal scale, there is no significant change in Topt between 2000s and 2010s, suggesting that the Topt climate acclimation may not keep up with the warming rate. Finally, future (2091–2100) warming could be close to and even surpass Topt on the TP under different RCP scenarios without considering potential climate acclimation. Our analyses imply that the temperature tipping point when the impact of future warming shifts from positive to negative on the TP is greatly overestimated by current vegetation models. Future research needs to include varying thermal and CO2 acclimation effects on Topt across different time scales in vegetation models.