ABSTRACT

Intra-annual distribution of precipitation in central Amazonia often leads to a short mild dry season and an increase in irradiance, temperature, and vapor pressure deficit; however, the accurate effect of intra-annual microclimatic variability on stem growth is still under investigation. The objective of this study was to determine how stem growth responds to monthly variations in microclimatic factors in the central Amazon. During five years (2008-2012) we measured diameter stem growth of 109 trees (26 species) and used principal component regression to evaluate the effect of microclimatic variability on stem growth in diameter. We found that the mean stem growth in diameter across species increased in response to an increase in rainfall and reference evapotranspiration, but it decreased with a rise in mean and minimum vapor pressure deficit. A contribution of this study is to show that even when irradiance and temperature had no significant effect on stem growth, small changes in vapor pressure deficit significantly affect stem growth. If the dry season becomes longer, as predicted by models, trees currently more sensitive to microclimatic variability associated with droughts would be the most affected by climate changes.

Keywords:
Amazonia; stem growth; tropical rainforest; random forests; microclimatic variability

Introduction

The Amazon rainforest is of paramount importance for the global carbon cycle because of the large amount of carbon stored in the forest biomass ‒a total of about 86 Pg of carbon over the Amazon basin, including dead and belowground biomass (Saatchi et al. 2007Saatchi SS, Houghton RA, Alvalá ARCS, Soares JV, Yu Y. 2007. Distribution of aboveground live biomass in the Amazon basin. Global Change Biology 13: 816-837. doi: 10.1111/j.1365-2486.2007.01323.x
https://doi.org/10.1111/j.1365-2486.2007... ). Tree growth (defined as biomass gain) is a major component of net primary production, and hence it has been used to infer forest productivity. Tree growth is the result of a myriad of biochemical reactions and processes of which photosynthesis, a light-and water dependent process, is of special importance (Kozlowski & Pallardy 1997Kozlowski TT, Pallardy SG. 1997. Physiology of woody plants. 2nd. edn. London, Academic Press. ).

Several factors have been associated with variations in stem growth, photosynthesis, and vegetation greenness in the Amazon region including precipitation, solar radiation, and temperature (Zhao et al. 2017 Zhao W, Zhao X, Zhou T et al. 2017. Climatic factors driving vegetation declines in the 2005 and 2010 Amazon droughts. PloS One 12: e0175379. doi: 10.1371/journal.pone.0175379
https://doi.org/10.1371/journal.pone.017... ; Yang et al. 2018Yang J, Tian H, Pan S, Chen G, Zhang B, Dangal S. 2018. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology 24: 1919-1934. doi: 10.1111/gcb.14056
https://doi.org/10.1111/gcb.14056... ; Elias et al. 2020Elias F, Ferreira J, Lennox GD et al. 2020. Assessing the growth and climate sensitivity of secondary forests in highly deforested Amazonian landscapes. Ecology 101: e02954. doi: 10.1002/ecy.2954
https://doi.org/10.1002/ecy.2954... ). Studies that aim to assess the effect of the dry season on stem growth in the Amazon have led to contradictory conclusions. For instance, Silva et al. (2003Silva RP, Nakamura S, Azevedo CP et al. 2003. Use of metallic dendrometers for individual diameter growth patterns of trees at Cuieiras river basin. Acta Amazonica 33: 67-84. doi: 10.1590/1809-4392200331084
https://doi.org/10.1590/1809-43922003310... ) and Dias et al. (2022Dias DP, Antezana-Vera SA, Marenco RA. 2022. Variation in bark water content in three Amazonian species in response to rainfall seasonality. Ciência Florestal 32: 2057-2073. doi: 10.5902/1980509866232
https://doi.org/10.5902/1980509866232... ) found no effect of precipitation on stem growth in the central Amazon, while Yang et al. (2018)Yang J, Tian H, Pan S, Chen G, Zhang B, Dangal S. 2018. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology 24: 1919-1934. doi: 10.1111/gcb.14056
https://doi.org/10.1111/gcb.14056... found that photosynthesis, over the entire Amazon region, can decline in the dry season. Likewise, Méndez (2018Méndez CR. 2018. Influência do El Nino 2015- 2016 no incremento diamétrico das árvores da Amazônia Central. Msc. Thesis, Instituto Nacional de Pesquisas da Amazônia, Brazil.) and Souza & Marenco (2022Souza AP, Marenco RA. 2022. Stem growth of multipurpose tree species: Net effect of micrometeorological variability assessed by principal component regression. Acta Amazonica 52: 277-284. doi: 10.1590/1809-4392202103390
https://doi.org/10.1590/1809-43922021033... ) reported a decline in stem growth during the dry season in a terra-firme rainforest of the central Amazon.

Besides precipitation, other microclimatic variables such as temperature (Ryan 2010Ryan MG. 2010. Temperature and tree growth. Tree Physiology 30: 667-668. doi: 10.1093/treephys/tpq033
https://doi.org/10.1093/treephys/tpq033... ; Slot & Winter 2016Slot M, Winter K. 2016. The effects of rising temperature on the ecophysiology of tropical forest trees. In: Goldstein G, Santiago LS (eds.). Tropical Tree Physiology . Cham, Springer. p. 385-412.; Zhao et al. 2017 Zhao W, Zhao X, Zhou T et al. 2017. Climatic factors driving vegetation declines in the 2005 and 2010 Amazon droughts. PloS One 12: e0175379. doi: 10.1371/journal.pone.0175379
https://doi.org/10.1371/journal.pone.017... ; Méndez 2018Méndez CR. 2018. Influência do El Nino 2015- 2016 no incremento diamétrico das árvores da Amazônia Central. Msc. Thesis, Instituto Nacional de Pesquisas da Amazônia, Brazil.), vapor pressure deficit and relative humidity can also affect stem growth of tropical trees (Méndez 2018Méndez CR. 2018. Influência do El Nino 2015- 2016 no incremento diamétrico das árvores da Amazônia Central. Msc. Thesis, Instituto Nacional de Pesquisas da Amazônia, Brazil.; Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ). It is known that climatic variables are often correlated (e.g. Clark et al. 2003Clark DA, Piper SC, Keeling CD, Clark DB. 2003. Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984-2000. Proceedings of the National Academy of Sciences U S A 100: 5852-5857. doi: 10.1073/pnas.0935903100
https://doi.org/10.1073/pnas.0935903100... ; Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ; Souza & Marenco 2022Souza AP, Marenco RA. 2022. Stem growth of multipurpose tree species: Net effect of micrometeorological variability assessed by principal component regression. Acta Amazonica 52: 277-284. doi: 10.1590/1809-4392202103390
https://doi.org/10.1590/1809-43922021033... ), which makes it rather difficult to assess the individual effects of microclimatic factors on tree growth.

In this context, Principal Component Regression has proved to be a valuable tool for dealing with the collinearity problem, whereby a new set of independent variables can be extracted from the original data by principal component analysis (Montgomery et al. 2012Montgomery DC, Peck EA, Vining GG. 2012. Principal component regression. In: Montgomery DC, Peck EA, Vining GG (eds.). Introduction to Linear Regression Analysis. 5th edn. Hoboken, Wiley. p. 313-319. ). Principal component regression has been used for a long time to assess the climate-tree growth relationship (e.g. Fritts et al. 1971Fritts HC, Blasing TJ, Hayden BP, Kutzbach JE. 1971. Multivariate techniques for specifying tree-growth and climate relationships and for reconstructing anomalies in paleoclimate. Journal of Applied Meteorology 10: 845-864, doi: 10.1175/1520-0450(1971)010<0845:MTFSTG>2.0.CO;2
https://doi.org/10.1175/1520-0450(1971)0... ; Marquardt et al. 2019Marquardt PE, Miranda BR, Jennings S, Thurston G, Telewski FW. 2019. Variable climate response differentiates the growth of Sky Island ponderosa pines. Trees 33: 317-332. doi: 10.1007/s00468-018-1778-9
https://doi.org/10.1007/s00468-018-1778-... ; Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ), and although much research has been carried out to assess the effect of environmental conditions on tree growth of tropical rainforests, there is still no consensus about the relative importance of individual microclimatic variables on stem growth. Understanding the effect of microclimatic variability on stem growth of Amazonian trees is particularly important because of the large influence of the Amazon forest on the global carbon balance and regional climate.

Thus, the main objective of this study was to determine the effect of intra-annual variation in precipitation, temperature, and irradiance on stem growth at a terra-firme rainforest site in the central Amazon. We hypothesized that stem growth would vary reflecting the availability of water and changes in irradiance and temperature, and expected that the trees would grow faster following an increase in irradiance and temperature. Because in the central Amazon the dry season is often mild and the roots can extract water from deeper layers of the soil (Broedel et al. 2017Broedel E, Tomasella J, Cândido LA, Von Randow C. 2017. Deep soil water dynamics in an undisturbed primary forest in central Amazonia: Differences between normal years and the 2005 drought. Hydrological Processes 31: 1749-1759. doi: 10.1002/hyp.11143
https://doi.org/10.1002/hyp.11143... ), we did not predict an effect of intra-annual precipitation variability on stem growth.

Materials and methods

Study site

The research was conducted at the Tropical Silviculture Experiment Station (ZF2 Reserve), located about 60 km north of Manaus. The study area is a terra-firme forest on a plateau (centered at 02° 36´ 21" S, 60° 08' 11" W, 110‒120 m above sea level). In this region tree density can reach up to 637 trees per hectare (Rankin-De-Merona et al. 1992Rankin-De-Merona JM, Prance GT, Hutchings RW, Silva MF, Rodrigues WA, Uehling ME. 1992. Preliminary results of a large-scale tree inventory of upland rain forest in the Central Amazon. Acta Amazonica 22: 493-534. doi: 10.1590/1809-43921992224534
https://doi.org/10.1590/1809-43921992224... ), and species diversity is high ‒up to 179 species ha^‒1 (Prance et al. 1976Prance GT, Rodrigues WA, Silva MF. 1976. Inventário florestal de um hectare de mata de terra firme km 30 de Estrada Manaus-Itacoatiara. Acta Amazonica 6: 9-35. doi: 10.1590/1809-43921976061009
https://doi.org/10.1590/1809-43921976061... ). However, it is not uncommon that trees of the same species are hundreds of meters apart. Thus, for this study, tree species were selected based on the availability of at least three trees of the same species, each of them with stem diameter (at 1.3 m above the ground ‒diameter at breast height, DBH) of at least 10 cm. In the experimental site, the annual rainfall is about 2,540 mm with a mild dry season from June through October (Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ; Dias et al. 2022Dias DP, Antezana-Vera SA, Marenco RA. 2022. Variation in bark water content in three Amazonian species in response to rainfall seasonality. Ciência Florestal 32: 2057-2073. doi: 10.5902/1980509866232
https://doi.org/10.5902/1980509866232... ), being July-September the driest months (INMET 2021INMET - Instituto Nacional de Meteorologia. 2021. Gráficos climatológicos [Weather charts]. http://clima.inmet.gov.br/graficosclimatologicos/am/82331. 27 May 2021.
http://clima.inmet.gov.br/graficosclimat... ‒ climate data for the nearby city of Manaus). During 2013‒2017, reference evapotranspiration (ET_o) was 120.8 mm month^‒1, mean temperature 26.5 °C, and mean relative humidity (RH) 78.9% (Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ). In this area, the predominant soil type is a clayed Oxisol (Yellow Latosol in the Brazilian classification) with low fertility, and pH (in water) of 4.0-4.3 at 0-20 cm depth (Chauvel 1982Chauvel A. 1982. Os latossolos amarelos, álicos, argilosos dentro dos ecossistemas das bacias experimentais do INPA e da região vizinha. Acta Amazonica 12: 47-60. doi: 10.1590/1809-43921982123S047
https://doi.org/10.1590/1809-43921982123... ; Magalhães et al., 2014Magalhães NS, Marenco RA, Camargo MAB. 2014. Do soil fertilization and forest canopy foliage affect the growth and photosynthesis of Amazonian saplings? Scientia Agricola 71: 58-65. doi: 10.1590/S0103-90162014000100008
https://doi.org/10.1590/S0103-9016201400... ).

Physical environment, plant material and stem growth

During the period of January 2008 to December 2012, air temperature (T), photosynthetically active radiation (PAR), RH, and rainfall data were daily recorded above the forest canopy, at the top of a 40-m-tall observation tower (02°35´21"S, 60°06´53"W). Temperature and RH were measured with specific sensors (Humitter 50y, Oy Vaisala, Finland) and PAR with a quantum sensor (Li-190SA, Li-Cor, NE, USA) connected to a data logger (Li-1400, Li-Cor, Lincoln, NE). Data were logged at 15 min (PAR) or 30 min intervals (T and RH). PAR data were integrated over time to obtain daily PAR values (mol m^‒2 day^‒1). Rainfall data were recorded using a rain gauge (Em5b, Decagon, WA, USA). We used RH (%) and temperature (T °C) to calculate vapor pressure deficit (D) as: D (hPa) = e_s - (e_s × RH), where e_s is the saturated vapor pressure in hectopascal (Buck 1981Buck AL. 1981. New equations for computing vapor-pressure and enhancement factor. Journal of Applied Meteorology 20: 1527-1532. doi: 10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2
https://doi.org/10.1175/1520-0450(1981)0... ). The daily reference evapotranspiration (ET_o, mm day^‒1) was computed as (Hargreaves & Samani 1985Hargreaves GH, Samani ZA. 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1: 96-99. doi: 10.13031/2013.26773
https://doi.org/10.13031/2013.26773... ):

where R_a represents the extraterrestrial radiation (expressed in mm day^‒1), and T _mean, T _min and T _max (in °C) the mean, minimum and maximum temperature, respectively. Then, from daily data, the monthly ET_o was obtained.

In this study we collected data from 109 trees of 26 evergreen species (located on the above- mentioned plateau), which are described in the Supplementary Material (^{Tab. S1-A} Table S1 - A. Family and species used in the study. There are also shown stem growth in diameter (SG mm month‒1 ± standard error), number of tree per species (n), mean diameter at breast height (DBH, in cm), height (H, in m), and wood density (WD, in g cm‒3). Tree height (H) was computed using the equation of Souza & Marenco (2022), being H (m) = -11.387 + 11.504ln(DBH). ). In these trees we measured the breast-height stem growth in diameter (S _G) at monthly intervals during 60 months (January 2008 ‒ December 2012), as previously described (Dias et al. 2022Dias DP, Antezana-Vera SA, Marenco RA. 2022. Variation in bark water content in three Amazonian species in response to rainfall seasonality. Ciência Florestal 32: 2057-2073. doi: 10.5902/1980509866232
https://doi.org/10.5902/1980509866232... ). The S _G was measured using stainless steel dendrometer bands, which were installed at least two years before the beginning of data collection.

Statistical analyses

The effect of microclimatic variability (PAR, rainfall, temperature, vapor pressure deficit, and ET_o) on the mean diameter stem growth across species (S _G-mean) was evaluated using Principal Component Regression (PCR). Prior to conducting PCR, the S _G-mean data were centered (observed value minus the mean) and the microclimatic variables standardized (observed value minus the mean divided by standard deviation), and then microclimatic data subjected to principal component analysis (PCA) to extract orthogonal factors. The PCR is performed in several steps (Montgomery et al. 2012Montgomery DC, Peck EA, Vining GG. 2012. Principal component regression. In: Montgomery DC, Peck EA, Vining GG (eds.). Introduction to Linear Regression Analysis. 5th edn. Hoboken, Wiley. p. 313-319. ), and briefly summarized as follows (equations 2-9):

These equations describe the standard multiple linear regression (MLR, Eq. 2) and the PCR model (Eq. 3), and following the classic notation, Y denotes a vector of observations (dependent variable), X a matrix of regressors, b and α vectors of coefficients, and ϵ the random errors. In equation 4, the columns of Z represent a new set of orthogonal components ‒z _i (hereafter termed principal components), while T is a matrix whose columns represent eigenvectors. The computation of α (coefficients of the PCR model) is described in equation 5, and that of b _pc in equation 6. The values of $\widehat{\alpha }$ (estimator of α) are obtained after regressing Y on the principal components (z _i). In equation 6, the “pc” subscript indicates that only a reduced k number of z _i components has been retained in the model (i.e. PCR reduced model). The variance (var) of b _pc, its standard error (SE) and the t values are computed as described in Eq. 7 ‒ 9. To determine the number of principal components to be used in the PCR reduced model, we used the adjusted coefficient of determination (R ² _ajd), after Jolliffe (2002Jolliffe IT. 2002. Principal components in regression analysis. In: Jolliffe IT. Principal component analysis. 2nd edn. New York, Springer. p. 167-198, ). While the significance of b _pc was tested on individual coefficients using t-test, and n - k - 1 degree of freedom (DF), where n is number of observations (i.e. 60 months in this study) and k the number of principal components in the reduced model (the DF in the PCR analysis of variance). In addition, we also described the relationship between S _G-mean and rainfall, temperature and vapor pressure deficit to illustrate the trend of microclimatic variables. For further information, we used the Random Forest machine learning technique to rank the microclimatic variables (predictors) according to their importance in predicting stem growth (S _G-mean). The analyses were performed using R v.4.0.5 (R Core Team, 2021R Core Team. 2021. R: A language and environment for statistical computing. https://www.R-project.org/. 15 May 2021.
https://www.R-project.org/... ). The PCR was performed using PLS (Liland et al. 2021Liland KH, Mevik B-H, Wehrens R. 2021. PLS: Partial Least Squares and Principal Component Regression. R package. version 2.8-0. https://cran.r-project.org/package=pls
https://cran.r-project.org/package=pls... ), while the randomForest package was used for computing random forest algorithm (Liaw & Wiener 2002Liaw A, Wiener M. 2002. Classification and Regression by randomForest. R News 2: 18-22. https://cran.r-project.org/doc/rnews/rnews_2002-3.pdf
https://cran.r-project.org/doc/rnews/rne... ). In all analyses, we used p ≤ 0.05 to define statistical significance.

Results

Microclimatic variability and its relationship with stem growth: a heuristic approach

We found no correlation between the stem growth of species (S _G) and wood density (p = 0.44, Tab. 1), and although the largest trees tended to grow faster, the correlation between stem diameter (DBH) and S _G was not significant (p = 0.06, Tab. 1). Thus, data were pooled to assess the effect of microclimatic variability on stem growth over species (S _G-mean).

The PCR showed that the maximum R ² _adj value (0.1458) was found when the fifth principal component was added to the model (Tab. 2). Hence, the first five components (z ₁‒z ₅) were used for PCR analysis. Furthermore, it is shown in Tab. 2 that although the fifth component (z ₅) was associated with a rather low-magnitude eigenvalue (λ₅ = 0.40, Fig. 1) it had a significant effect on stem growth (p = 0.0172), whose p value was even larger than that associated with the first component ‒z ₁ (p = 0.0752). As the first and the fifth components were more closely associated with stem growth (inferred from p values), only Factor 1 and Factor 5 are shown in Fig. 1. It is worth noting, that the first five factors extracted by PCA from microclimatic data combined accounted for 97.1 % of the total variance [i.e. 100×(6.14 + … 0.40)/9.0] in microclimatic data (Fig. 1). That is, by discarding very small eigenvalues (λ < 0.4) only a small fraction (3%) of the total microclimatic variance was disregarded.

Figure 1
Principal component analysis of microclimatic variables, with the mean stem growth in diameter (S _G-mean) as a supplementary variable. The eigenvalues (λ_i) of orthogonal factors are shown in the inset. Note that the first five factors account for 97.1% of total variance (100×8.74/9.0). Abbreviations as described in Tab. 1.

It is displayed in Fig. 1 that S _G-mean (indicated by the square symbol) shares the same quadrant with rainfall. Therefore, it can be expected that precipitation positively affects stem growth. Because both S _G-mean and ET_o (in Fig. 1) are negatively correlated with Factor 5, ET_o may be positively correlated with stem growth. The Fig. 1 also shows that D _min and S _G-mean (square symbol) are in opposite quadrants indicating that they are negatively correlated. Although the other microclimatic variables (temperature, PAR and D _mean and D _max) are closely related to Factor 1, it is difficult to infer (from Fig. 1) how these variables can affect stem growth, as the p value of z ₁ did not reach a significant level (p = 0.0752). In the next section, by using PCR we examine the growth-microclimatic relationship in more detail.

Effect of microclimatic variability on stem growth based on PCR

During the study period mean temperature (T _mean) was 25.6 °C, D _mean 5.15 hPa and monthly rainfall 242.1 mm month^-1 (2,905 mm yr^-1, Fig. 2, 3). The PCR analysis showed that S _G-mean responded positively to both rainfall and ET_o (Fig. 2A, Tab. 3), whereas D _min and D _mean had a negative effect on the mean stem growth (Fig. 3, Tab. 3). Temperature, on the other hand, had a neutral effect on S _G-mean (Fig. 2B, Tab. 3). Thus, using the coefficients shown in Tab. 3, the centered S _G-mean as a function of the standardized microclimatic variables can be represented as follows (equation 10):

Figure 2
Mean stem growth in diameter (S _G-mean) as a function of rainfall (A) and mean temperature (T _mean, B). In panel A, the solid line shows the trend. Each symbol represents the mean stem growth across species (26 species) for a given month. Data were collected from January 2008 to December 2012. The means (± SE) of rainfall and T _mean were 242.1 ± 18.3 mm month^‒1 and 25.6 ± 0.14 °C, respectively.

Figure 3
Mean stem growth in diameter (S _G-mean) as a function of vapor pressure deficit (D), mean D (D _mean, A) and minimum D (D _min, B). Further information as described in Fig. 2. The means (± SE) of D _mean and D _min were 5.15 ± 0.34 and 0.79 ± 0.11 hPa, respectively; whereas mean D _max was 16.5 ± 0.70 hPa (data not shown).

Comparison of PCR with the Random Forest model

The PCR model showed that the coefficient associated with rainfall has the largest beta (0.020837). In this respect, the prediction based on Random Forest concurs with the outcome of the PCR model, as Random Forest showed that rainfall was the most important variable (Tab. 1). On the other hand, the Random Forest model predicted that D _max, ET_o and T _mean performed similarly (importance of 20-22%); whereas the PCR showed that D _max and T _mean had no significant effect on stem growth, which is an important aspect to consider when the performance of these models is compared. With respect to the effect of PAR on stem growth, both models converged, as PAR was ranked as non-important by Random Forest, and likewise the PCR model showed that PAR has no significant effect on stem growth.

Discussion

In this study we found that the minimum and mean vapor pressure deficit had a constraining effect on S _G-mean, while stem growth increased with increasing ET_o and rainfall. The reduced stem growth with a rise in D _min indicates an effect of nocturnal atmospheric conditions on tree growth, as the lowest values of D _min often occur at night when relative humidity is high (INMET 2021INMET - Instituto Nacional de Meteorologia. 2021. Gráficos climatológicos [Weather charts]. http://clima.inmet.gov.br/graficosclimatologicos/am/82331. 27 May 2021.
http://clima.inmet.gov.br/graficosclimat... ). This is important, as it shows that stem growth can be affected not only by environmental factors that limit photosynthesis, but also by nocturnal conditions that tend to increase transpiration and thereby to lower leaf water potential. Indeed, it is widely accepted that changes in expansive growth are related to changes in leaf water potential (Bradford & Hsiao 1982Bradford KJ, Hsiao TC. 1982. Physiological responses to moderate water stress. In: Lange OL, Nobel PS, Osmond CB, Ziegler H. (eds). Physiological Plant Ecology II - Water Relation and Carbon Assimilation. Heidelberg, Springer-Verlag. p. 263-324. doi: 10.1007/978-3-642-68150-9_10
https://doi.org/10.1007/978-3-642-68150-... ; Kozlowski & Pallardy 1997Kozlowski TT, Pallardy SG. 1997. Physiology of woody plants. 2nd. edn. London, Academic Press. ).

The negative effect of D _mean can be explained by considering the effect of vapor pressure deficit on stomatal conductance, as the most common response to a rise in vapor pressure deficit is a decrease in stomatal conductance and hence in carbon uptake by photosynthesis (McDowell & Allen 2015Mcdowell NG, Allen CD. 2015. Darcy's law predicts widespread forest mortality under climate warming. Nature Climate Change 5: 669-672. doi: 10.1038/nclimate2641
https://doi.org/10.1038/nclimate2641... ; Vinod et al. 2023Vinod N, Slot M, McGregor IR et al. 2023. Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications. New Phytologist 237: 22-47. doi: 10.1111/nph.18539
https://doi.org/10.1111/nph.18539... ). In fact, Yang et al. (2018Yang J, Tian H, Pan S, Chen G, Zhang B, Dangal S. 2018. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology 24: 1919-1934. doi: 10.1111/gcb.14056
https://doi.org/10.1111/gcb.14056... ) reported a decrease in ecosystem photosynthesis in the dry season (over the forests of the Amazon basin) when temperature and vapor pressure deficit often increase (Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ). The constraining influence of an increase in vapor pressure deficit on stem growth is relevant in the face of global climate change, especially taking into account that in the Amazon region temperature has increased (around 0.16 °C per decade) and the rainfall pattern altered (e.g. decreased rainfall in the eastern and southern Amazon, Marengo et al. 2018Marengo JA, Souza CM, Thonicke K et al. 2018. Changes in climate and land use over the Amazon region: current and future variability and trends. Frontiers in Earth Science 6: 228. doi: 10.3389/feart.2018.00228
https://doi.org/10.3389/feart.2018.00228... ), which combined may lead to changes in vapor pressure. In this regard, it is known that the dry season is associated with an increase in irradiance, temperature and vapor pressure deficit (Méndez 2018Méndez CR. 2018. Influência do El Nino 2015- 2016 no incremento diamétrico das árvores da Amazônia Central. Msc. Thesis, Instituto Nacional de Pesquisas da Amazônia, Brazil.; Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ; INMET 2021INMET - Instituto Nacional de Meteorologia. 2021. Gráficos climatológicos [Weather charts]. http://clima.inmet.gov.br/graficosclimatologicos/am/82331. 27 May 2021.
http://clima.inmet.gov.br/graficosclimat... ), which may lead to a decline in photosynthesis and tree growth (Yang et al. 2018Yang J, Tian H, Pan S, Chen G, Zhang B, Dangal S. 2018. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology 24: 1919-1934. doi: 10.1111/gcb.14056
https://doi.org/10.1111/gcb.14056... ; Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ).

The positive effect of rainfall variability on stem growth suggests that even during a relative mild dry season, Amazonian trees tend to respond to variation in water availability, even when it has been found that in the central Amazon root water uptake can occur even below 4.8 m depth, during severe dry periods (Broedel et al. 2017Broedel E, Tomasella J, Cândido LA, Von Randow C. 2017. Deep soil water dynamics in an undisturbed primary forest in central Amazonia: Differences between normal years and the 2005 drought. Hydrological Processes 31: 1749-1759. doi: 10.1002/hyp.11143
https://doi.org/10.1002/hyp.11143... ), which eventually can help to withstand the effect of water stress in mild dry seasons. Even when the enhancing effect of T _max on S _G-mean did not reach a significant level effect (p = 0.07), S _G-mean increased with increasing ET_o (p = 0.02, Tab. 3). At first glance, it seems unexpected to record a positive effect of ET_o and at the same time a neutral effect of temperature (T _min, T _mean and T _max) on stem growth (Tab. 3). This can be explained by taking into account that ET_o is a function of solar radiation and temperature, including T _max (Eq. 1), and T _max as mentioned above tended to have a positive on stem growth (Tab. 3). The direct effect of temperature on biomass gain can occur through its effect on photosynthesis, via the direct effect of temperature on enzyme activity, electron transport chain, and stomatal conductance (Lloyd & Farquhar 2008Lloyd J, Farquhar GD. 2008. Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 363: 1811-1817. doi: 10.1098/rstb.2007.0032
https://doi.org/10.1098/rstb.2007.0032... ; Marenco et al. 2014Marenco RA, Nascimento HC, Magalhães ND. 2014. Stomatal conductance in Amazonian tree saplings in response to variations in the physical environment. Photosynthetica 52: 493-500. doi: 10.1007/s11099-014-0056-3
https://doi.org/10.1007/s11099-014-0056-... ; Vinod et al. 2023Vinod N, Slot M, McGregor IR et al. 2023. Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications. New Phytologist 237: 22-47. doi: 10.1111/nph.18539
https://doi.org/10.1111/nph.18539... ). In addition, root-zone temperature regulates photosynthesis via its influence on water absorption and stomatal conductance, because temperature affects water viscosity and root permeability to water (Kaufmann 1975Kaufmann MR. 1975. Leaf water stress in Engelmann spruce: Influence of the root and shoot environments. Plant Physiology 56: 841-844. doi: 10.1104/pp.56.6.841
https://doi.org/10.1104/pp.56.6.841... ; Delucia 1986Delucia EH. 1986. Effect of low root temperature on net photosynthesis, stomatal conductance and carbohydrate concentration in Engelmann spruce (Picea engelmannii Parry ex Engelm.) seedlings. Tree Physiology 2: 143-154. doi: 10.1093/treephys/2.1-2-3.143
https://doi.org/10.1093/treephys/2.1-2-3... ). The neutral effect of T _min on S _G-mean suggests that variations in night temperatures did not alter significantly metabolic processes, such as root, leaf and stem respiration. This is important, as respiration provides energy and carbon intermediates for growth and maintenance of tissues (Kozlowski & Pallardy 1997Kozlowski TT, Pallardy SG. 1997. Physiology of woody plants. 2nd. edn. London, Academic Press. ). The absence of an effect of temperature on stem growth does not support our working hypothesis, as we had expected that trees would grow faster under warmer conditions, as reported by Elias et al. (2020Elias F, Ferreira J, Lennox GD et al. 2020. Assessing the growth and climate sensitivity of secondary forests in highly deforested Amazonian landscapes. Ecology 101: e02954. doi: 10.1002/ecy.2954
https://doi.org/10.1002/ecy.2954... ). The optimum temperature for photosynthesis in tropical rainforests is about 29 °C (Liu 2020Liu Y. 2020. Optimum temperature for photosynthesis: From leaf-to ecosystem-scale. Science Bulletin 65: 601-604. doi: 10.1016/j.scib.2020.01.006
https://doi.org/10.1016/j.scib.2020.01.0... ). Thus, the lack of an effect of temperature on S _G-mean indicates that changes in temperatures over the year were not high enough to alter stem growth. The importance of irradiance on tree growth is indisputable via its effect on photosynthesis. Notwithstanding, we found no effect of PAR variability on stem growth, which indicates absence of adverse photochemical effects associated with the increase in irradiance which often occurs during the dry season (Antezana-Vera & Marenco 2021Antezana-Vera SA, Marenco RA. 2021. Intra-annual tree growth responds to micrometeorological variability in the central Amazon. iForest - Biogeosciences and Forestry 14: 242-249. doi: 10.3832/ifor3532-014
https://doi.org/10.3832/ifor3532-014... ). In fact, leaf photochemistry tends to remain constant in plants subjected to moderate water stress as shown by Rascher et al. (2004Rascher U, Bobich EG, Lin GH et al. 2004. Functional diversity of photosynthesis during drought in a model tropical rainforest-the contributions of leaf area, photosynthetic electron transport and stomatal conductance to reduction in net ecosystem carbon exchange. Plant Cell Environment 27: 1239-1256. doi: 10.1111/j.1365-3040.2004.01231.x
https://doi.org/10.1111/j.1365-3040.2004... ).

We have shown that PCR proved to be a useful approach to separate the individual contribution of microclimatic variability on stem growth. Even though Random Forest was effective to identify rainfall as one of the most important variables influencing stem growth and PAR as the least, by using this technique it is rather difficult to assess the individual contribution of microclimatic variables due to the effect of collinearity. Indeed, Random Forest is a powerful algorithm for prediction, but when the primary objective is explanation, principal component regression seems to perform better than Random Forest.

Conclusions

In this study we show that the mean diameter stem growth decreased in response to an increase in mean and minimum vapor pressure deficit, while rainfall and the reference evapotranspiration had a positive effect on S _G-mean. Irradiance and temperature, on the other hand, had no significant effect on the mean stem growth. These results do not support our working hypothesis, as we had expected that stem growth would increase with an increase in temperature and irradiance, and that a slight decrease in precipitation during the dry season would not affect stem growth. A contribution of this study is to show that even when temperature and irradiance intra-annual variability did not significantly affect stem growth, besides precipitation, environmental factors related to atmospheric dryness can influence stem growth. These results are important in the context of the current climate changes and enhance our understanding on the drivers of Amazonian trees’ radial growth.

Acknowledgements

To Ministério da Ciência, Tecnologia e Inovação - Instituto Nacional de Pesquisas da Amazônia (MCTI-INPA, PRJ15.120), Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES code 0001) and Conselho Nacional de Desenvolvimento Científico e Tecnológico ‒ CNPq (303913/2021-5). Conflict of Interest: The authors declare that they have no conflict of interest. Authorship contribution: Collected data and collaborated with data analysis and writing of manuscript original draft (MABC); secured funding, collaborated with data analysis, and wrote the article with contributions of the first author (RAM). We thank the Editor and reviewers for their valuable comments and suggestions, which greatly improved the quality of the manuscript.

References

Supplementary material

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Table S1 - A. Family and species used in the study. There are also shown stem growth in diameter (SG mm month^‒1 ± standard error), number of tree per species (n), mean diameter at breast height (DBH, in cm), height (H, in m), and wood density (WD, in g cm‒3). Tree height (H) was computed using the equation of Souza & Marenco (2022), being H (m) = -11.387 + 11.504ln(DBH).

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