diff --git a/manuscript/appendix.lof b/manuscript/appendix.lof deleted file mode 100644 index 5d998e6..0000000 --- a/manuscript/appendix.lof +++ /dev/null @@ -1,9 +0,0 @@ -\contentsline {figure}{\numberline {1}{\ignorespaces Figure S1: Maps showing distribution of samples for the nine forest C fluxes analyzed\relax }}{10}{figure.caption.18}% -\contentsline {figure}{\numberline {2}{\ignorespaces Figure S2: Scatterplots and Pearson's R values for relationships among climate variables and latitude\relax }}{12}{figure.caption.20}% -\contentsline {figure}{\numberline {3}{\ignorespaces Figure S3: Ratios among forest C fluxes as a function of latitude and climate variables. Regressions test variation in allocation to component fluxes for three relationships: (1) $GPP = NPP + R_{auto}$, (2) $NPP = ANPP + BNPP$, and (3) $ANPP = ANPP_{foliage} + ANPP_{stem}$. We interpret analysis of variation in $NPP:R_{auto}$ in the context of CUE (GPP/NPP), as variation in CUE will be directly related to variation in $NPP:R_{auto}$.\relax }}{14}{figure.caption.22}% -\contentsline {figure}{\numberline {4}{\ignorespaces Figure S4: Individual plots of forest C fluxes in relation to mean annual climate, part 1.\relax }}{16}{figure.caption.24}% -\contentsline {figure}{\numberline {5}{\ignorespaces Figure S5: Individual plots of forest C fluxes in relation to mean annual climate, part 2.\relax }}{18}{figure.caption.26}% -\contentsline {figure}{\numberline {6}{\ignorespaces Figure S6: Individual plots of forest C fluxes in relation to mean climate seasonality, part 1.\relax }}{20}{figure.caption.28}% -\contentsline {figure}{\numberline {7}{\ignorespaces Figure S7: Individual plots of forest C fluxes in relation to mean climate seasonality, part 2.\relax }}{22}{figure.caption.30}% -\contentsline {figure}{\numberline {8}{\ignorespaces Figure S8: Growing season length-standardized forest C fluxes in relation to mean growing season climate, part 1.\relax }}{23}{figure.caption.32}% -\contentsline {figure}{\numberline {9}{\ignorespaces Figure S9: Growing season length-standardized forest C fluxes in relation to mean growing season climate, part 2.\relax }}{24}{figure.caption.34}% diff --git a/manuscript/appendix.lot b/manuscript/appendix.lot deleted file mode 100644 index e69de29..0000000 diff --git a/manuscript/manuscript_draft.log b/manuscript/manuscript_draft.log deleted file mode 100644 index 2131ac7..0000000 --- a/manuscript/manuscript_draft.log +++ /dev/null @@ -1,1058 +0,0 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019/W32TeX) (preloaded format=pdflatex 2019.6.6) 12 SEP 2019 12:16 -entering extended mode - 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387 PDF objects out of 1000 (max. 8388607) - 338 compressed objects within 4 object streams - 72 named destinations out of 1000 (max. 500000) - 74 words of extra memory for PDF output out of 10000 (max. 10000000) - diff --git a/manuscript/word_count.Rmd b/manuscript/word_count.Rmd deleted file mode 100644 index 8fc364b..0000000 --- a/manuscript/word_count.Rmd +++ /dev/null @@ -1,238 +0,0 @@ ---- -title: "" -author: "" -output: - word_document: -bibliography: library.bib -csl: global-change-biology.csl - ---- - -Carbon (C) cycling in Earth's forests provides the energetic basis for sustaining the majority of Earth's terrestrial biodiversity and many human populations [@millennium_ecosystem_assessment_ecosystems_2005], while strongly influencing atmospheric carbon dioxide (CO~2~) and climate [@bonan_forests_2008]. -Forests' autotrophic carbon fluxes -- that is, carbon fixation, allocation, and metabolism by trees and other primary producers -- sets the energy ultimately available to heterotrophic organisms (including microbes), in turn influencing their abundance [@zak_plant_1994; @niedzialkowska_species_2010] and possibly diversity [@waide_relationship_1999; @chu_direct_2018]. -They are linked to cycling of energy, water, and nutrients and, critically, influence all C stocks and define forest interactions with Earth's changing climate. -Each year, over 69 Gt of C cycle through Earth's forests [@badgley_terrestrial_2019] -- a flux more than seven times greater than that of recent anthropogenic fossil fuel emissions [9.5 Gt C yr^-1^; @friedlingstein_global_2019]. -As atmospheric CO~2~ continues to rise, driving climate change, forests will play a critical role in shaping the future of Earth's climate [@cavaleri_urgent_2015; @rogelj_mitigation_2018]. -However, our understanding of the climate dependence of forest C cycling on a global scale has been limited by analyses typically considering only one or a few variables at a time, insufficient parsing of related variables, and the mixing of data from forests that vary in stand age, disturbance history, and management status, all of which affect C cycling [@litton_carbon_2007; @gillman_latitude_2015; @simova_enigma_2017]. - -Forest C fluxes decrease with latitude [e.g., @luyssaert_co_2007; @gillman_latitude_2015; @li_mapping_2019]. -However, studies have differed in their conclusions regarding the shape of this relationship -- quite possibly because of lack of standardization with respect to methodology and stand history. -For instance, studies agree that gross primary productivity ($GPP$) increases continuously with decreasing latitude and is indisputably highest in tropical forests [@luyssaert_co_2007; @beer_terrestrial_2010; @jung_global_2011; @badgley_terrestrial_2019; @li_mapping_2019]. -In contrast, some studies have suggested that net primary productivity ($NPP$), or its aboveground portion ($ANPP$), exhibits a less distinct increase from temperate to tropical forests [@luyssaert_co_2007] -- or even a decrease [@huston_global_2009, but see @gillman_latitude_2015]. -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 [$CUE$= $NPP$/$GPP$; @de_lucia_forest_2007; @malhi_productivity_2012; @anderson-teixeira_carbon_2016]. -Such differences among C fluxes their relationship to latitude could have profound implications for our understanding of the C cycle and its climate sensitivity. 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_forc_2018]. - -The latitudinal gradient in forest C flux rates, along with altitudinal gradients [@girardin_net_2010; @malhi_variation_2017], is driven primarily by climate, which is a significant driver of C fluxes across broad spatial scales [@luyssaert_co_2007; @cleveland_relationships_2011; @wei_forest_2010]. -However, there is little consensus as to the shapes of these relationships or the best predictor variables. -The majority of studies have focused on exploring the relationships of C fluxes to mean annual temperature ($MAT$) and mean annual precipitation ($MAP$), as the most commonly reported site-level climate variables. -C fluxes increase strongly with $MAT$ on the global scale, but whether they saturate or potentially decrease at higher temperatures remains disputed. -Some studies have detected no deceleration or decline in $GPP$ [@luyssaert_co_2007], $NPP$ [@schuur_productivity_2003], or root respiration [$R_{root}$; @piao_forest_2010; @wei_forest_2010] with increasing $MAT$. -In contrast, others have found evidence of saturation or decline of C flux in the warmest climates; @luyssaert_co_2007 found $NPP$ saturating at around 10$^\circ$C MAT; @larjavaara_temperature_2012 found that increases in $GPP$ saturate at approximately 25$^\circ$C $MAT$, and @sullivan_long-term_2020 found that, within the tropics, $ANPP_{stem}$ decreases at the highest maximum temperatures. -C fluxes generally saturate at high levels of $MAP$, though the saturation points identified vary from $MAP$ of ~1000 mm for $R_{root}$ [@wei_forest_2010] up to 2,445 mm for $NPP$ [@schuur_productivity_2003]. -Interactions between $MAT$ and $MAP$ are also possible; within the tropics, there is a positive interaction between $MAT$ and $MAP$ in shaping $ANPP$, such that high rainfall has a negative effect on productivity in cooler climates, compared to a positive effect in warmer climates [@taylor_temperature_2017]. -There is also evidence that C fluxes also respond to climate variables such as temperature and precipitation seasonality [@wagner_climate_2016], cloud cover [@taylor_temperature_2017], solar radiation [@beer_terrestrial_2010; @fyllas_solar_2017], and potential evapotranspiration [@kerkhoff_plant_2005]; however, these are not typically assessed in global-scale analyses of annual forest C flux. - -As metrics of annual climate, $MAT$ and $MAP$ fail to capture variation in climate on an intra-annual scale, including temperature and precipitation seasonality and growing season length. -Some studies have suggested that the apparently strong relationship between $MAT$ and C fluxes is actually a consequence of the correlation between $MAT$ and growing season length [@kerkhoff_plant_2005; @michaletz_convergence_2014; @michaletz_drivers_2018]. -@kerkhoff_plant_2005 and @michaletz_convergence_2014 found no significant relationship between growing season temperature and $ANPP$ or $NPP$ standardized to growing season length [but see @chu_does_2016]. -While this suggests that the influence of temperature may be limited to determining the length of the frost-free growing season, analysis with carefully standardized variables and forest ages would be necessary to test the the veracity and generality of this hypothesis. - -The recent development of the Global Forest Carbon database (ForC), which synthesizes multiple variables and includes records of stand history [@anderson-teixeira_carbon_2016; @anderson-teixeira_forc_2018], opens up the possibility for a standardized analysis of global scale variation in multiple C fluxes and the principle climatic drivers of these patterns. -In order to approach this broad topic, we simplify the major gaps in our knowledge to five broad questions and corresponding predictions (Table 1). -First, we ask how nine forest autotrophic carbon fluxes in ForC vary with latitude (*Q1*). -We then test how these fluxes relate to $MAT$ and $MAP$ (*Q2*), and additionally how they respond to other, less well-studied, climate variables (*Q3*). -Finally, we consider the relationship between C flux and seasonality, considering the role of seasonality in explaining variation in carbon fluxes (*Q4*), and the influence of climate on C flux standardized by growing season length (*Q5*). - -### Materials and Methods - -*Forest carbon flux data* - -This analysis focused on nine C flux variables included in the open-access ForC database [Table 2; @anderson-teixeira_carbon_2016; @anderson-teixeira_forc_2018]. -ForC contains records of field-based measurements of forest carbon stocks and annual fluxes, compiled from original publications and existing data compilations and databases. -Associated data, such as stand age, measurement methodologies, and disturbance history, are also included. The database was significantly expanded since the publication of @anderson-teixeira_forc_2018 through integration with the Global Soil Respiration Database [@bond-lamberty_global_2010]. -Additional targeted literature searches were conducted to identify further available data on the fluxes analyzed here, with particular focus on mature forests in temperate and boreal regions, which were not included in the review of @anderson-teixeira_carbon_2016. -We used ForC v3.0, archived on Zenodo with DOI 10.5281/zenodo.3403855. -This version contained 29,730 records from 4,979 plots, representing 20 distinct ecozones across all forested biogeographic and climate zones. -From this, we drew 1,319 records that met our criteria, as outlined below (Fig. 1). - -This analysis focused on mature forests with no known history of significant disturbance or management. -There is evidence that stand age influences patterns of C flux and allocation in forest ecosystems, and can confound relationships between latitude and primary productivity [@de_lucia_forest_2007; @gillman_latitude_2015]. To reduce any biasing effects of stand age, we included only stands of known age $\ge$ 100 years and those described by terms such as "mature", "intact", or "old-growth". -Since management can alter observed patterns of C cycling [@simova_enigma_2017], sites were excluded from analysis if they were managed, defined as plots that were planted, managed as plantations, irrigated, fertilised or included the term "managed" in their site description. -Sites that had experienced significant disturbance within the past 100 years were also excluded. Disturbances that qualified sites for exclusion included major cutting or harvesting, burning, flooding, drought and storm events with site mortality >10% of trees. Grazed sites were retained. - -\newpage -*Climate data* - -ForC contains geographic coordinates associated with each measurement record and, when available, $MAT$ and $MAP$ as reported in the primary literature [@anderson-teixeira_forc_2018]. Based on the geographic co-ordinates for each site, data on twelve climate variables -- including $MAT$, $MAP$, temperature and precipitation seasonality, annual temperature range, solar radiation, cloud cover, annual frost and wet days, potential evapotranspiration ($PET$), aridity ($MAP/PET$), and vapor pressure deficit ($VPD$) -- were extracted from five open-access climate datasets: WorldClim [@hijmans_very_2005], WorldClim2 [@fick_worldclim_2017], the Climate Research Unit time-series dataset (CRU TS v4.03 [@harris_updated_2014], the Global Aridity Index and Potential Evapotranspiration Climate Database [@trabucco_global_2019], and TerraClimate [@abatzoglou_terraclimate_2018] (Table S1). From these data, we derived maximum $VPD$, defined as the $VPD$ of the month with the largest deficit, and the number of water stress months, defined as the number of months annually where precipitation was lower than $PET$. Where site-level data was missing for $MAT$ or $MAP$, we used values from the WorldClim dataset. - -Following the previous studies whose hypothesis we were evaluating [@kerkhoff_plant_2005; @michaletz_convergence_2014], length of the growing season was estimated to the nearest month, where growing season months were defined as months with mean minimum temperature > 0.5$^\circ$C. -We experimented with a definition of growing season months including a moisture index, defined as $(MAT - PET)/PET > -0.95$ [@kerkhoff_plant_2005; see also @michaletz_convergence_2014]. -However, we found that including a moisture index had minimal effect on the estimates of growing season length for the sites included here, and so chose to exclude it. -Monthly data for $PET$, precipitation, and temperature from CRU v 4.03 [@harris_updated_2014] and solar radiation from WorldClim2 [@fick_worldclim_2017] were used to calculate mean monthly $PET$, precipitation, temperature and solar radiation during the growing season. - -*Analyses* - -The effects of latitude and climate on C fluxes were analysed using mixed effects models using the package 'lme4' [@bates_fitting_2015] in R v.3.5.1 [@r_core_team_2018]. -The basic model for all analyses included a fixed effect of latitude or climate and a random effect of plot nested within geographic area. -Geographic areas--*i.e.*, spatially clustered sites--were defined within ForC using a hierarchical cluster analysis on the distance matrix of the sites and a cutoff of 25km [@anderson-teixeira_forc_2018]. -We experimented with inclusion of altitude as a fixed effect, but excluded it from the final models because it added very little explanatory power -- that is, the difference in AIC ($\Delta AIC$) relative to models excluding altitude was generally small (often $\Delta AIC$<2). -Effects were considered significant when inclusion of the fixed effect of interest resulted in p $\le$ 0.05 and $\Delta AIC$ $\ge$ 2.0 relative to a corresponding null model. -All $R^2$ values presented here are marginal $R^2$ values, and refer to the proportion of variation explained by only the fixed effects. -Specific analyses are as described below. - -We first examined the relationship between latitude and C fluxes (*Q1*; Table 1). -We tested models with latitude as a first-order linear, second-order polynomial, and logarithmic term. -For brevity, we henceforth refer to first-order linear models as "linear" and second-order polynomial models as "polynomial". -We selected as the best model that with the highest $\Delta$ AIC relative to a null model with no fixed term, with the qualification that a polynomial model was considered an improvement over a linear model only if it reduced the AIC value by 2.0 or more. -In addition, pairwise comparisons of $R^2$ values were carried out for a selection of pairs of C fluxes to test for differences among variables in the proportion of variation explained by latitude and climate. Models were run on data from sets of sites that were common to each pair, in order to account for variation in the number of data points included. - -To test whether trends in component fluxes across latitude sum to match those of larger fluxes, regression lines for smaller component fluxes were summed to generate new estimates of larger fluxes. -Because no fluxes were significantly better predicted by a logarithmic or polynomial fit than by a linear fit, we used linear fits for all fluxes in this analysis. -We then determined whether these summed predictions fell within the 95% CI for the larger flux across the entire latitudinal range. -Confidence intervals for the line of best fit for the larger flux were estimated using the 'bootMer' function, a parametric bootstrapping method for mixed models [@bates_fitting_2015]. This function carried out 2000 simulations estimating the line of best fit, using quantiles at 0.025 and 0.975 to estimate 95% CIs. -This analysis was applied to the following sets of fluxes: (1) $GPP = NPP + R_{auto}$, (2) $NPP = ANPP + BNPP$, and (3) $ANPP = ANPP_{foliage} + ANPP_{stem}$. In addition, we estimated total belowground C flux (TBCF, not analyzed due to limited data) as $TBCF = BNPP + R_{root}$. - -Variation in allocation to component carbon fluxes was explored for three groupings: (1) $GPP = NPP + R_{auto}$, (2) $NPP = ANPP + BNPP$, and (3) $ANPP = ANPP_{foliage} + ANPP_{stem}$. For each group, measurements taken at the same site and plot, and in the same year, were grouped together. For groups (1) and (2), where 2 of the 3 flux measurements were available for a given site, plot, and year, these measurements were used to calculate the third. The ratio of each pair of component fluxes was calculated. The log of these ratios were regressed against latitude and climate variables, using the linear model specified above. Cook's distance analyses were carried out for each of the models, and extreme outliers removed. - -We next examined the relationships of C fluxes to climate variables (*Q2-Q4*; Table 1). -We tested first-order linear, second-order polynomial, and logarithmic fits for each climate variable. -Again, polynomial fits were considered superior to first-order linear fits only if inclusion of a second-order polynomial term resulted in $\Delta$AIC $\ge$ 2.0 relative to a first-order linear model. -We tested relationships of each C flux (Table 2) against each climate variable (Table S1). Variables which were not significant explanatory variables or which explained <20% of variation in C fluxes are only presented in SI. - -Multivariate models were used to investigate the potential joint and interactive effects of $MAT$ and $MAP$ on carbon fluxes. An additive model including $MAP$ in addition to $MAT$ was accepted when $\Delta$AIC >2 relative to a null including only $MAT$ as a fixed effect. An interactive model including an $MAT$ x $MAP$ interaction was accepted when $\Delta$AIC >2 relative to a null including $MAT$ and $MAP$ as fixed effects. - -To test whether and how C fluxes varied with climate when standardised by growing season length (*Q5*; Table 1), we first standardized all annual C fluxes by dividing by growing season length (as defined above). We then derived four variables to describe growing season climate, specifically growing season temperature, precipitation, solar radiation, and PET (Table S1). We tested for correlations between these standardised fluxes and growing season climate variables, using only first-order linear models. - -All analyses were conducted in R v.3.5.1 [@r_core_team_2018]. Code and data necessary to reproduce all results are available through GitHub (https://github.com/forc-db/Global_Productivity) and archived in Zenodo (DOI: TBD). - -### Results - -In total, we analyzed 1,319 records from nine forest autotrophic C flux variables taken from forests that had experienced no major anthropogenic disturbances within the past 100 years. These records represented a total of 255 plots in 154 distinct geographic areas across all forested biogeographic and climate zones (Figs. 1, S1; Table 2). - -*Q1. How does C flux vary with latitude?* - -All major carbon fluxes decreased with latitude (Fig. 2; Table S2). Latitude was a strong predictor for many of the carbon fluxes, particularly the larger fluxes (Table S2, S6). Specifically, latitude explained 64% of variation in GPP (n = 243, p<0.0001), 50% in NPP (n = 161, p<0.0001) and 44% in ANPP (n = 278, p<0.0001). The C fluxes that were most poorly predicted by latitude were $BNPP_{fine.root}$ ($R^2$=0.17) and $ANPP_{stem}$ ($R^2$=0.18). The relationship with latitude was best fit by the first-order linear model, with the exception of NPP and $R_{root}$, for which a logarithmic model was a slightly -- but not significantly -- better fit. - -Smaller component fluxes summed approximately to larger fluxes across the latitudinal gradient (Fig. 2). That is, modeled estimates of $GPP$, generated from the sum of $NPP$ and $R_{auto}$; $NPP$, generated from the sum of $ANPP$ and $BNPP$; and $ANPP$, generated from the sum of $ANPP_{foliage}$ and $ANPP_{stem}$, fell almost completely within the confidence intervals of the regressions of field estimates of $GPP$, $NPP$, and $ANPP$, respectively. - -We found no evidence of systematic variation in C allocation with latitude or climate (Fig. S3). Of 12 relationships tested (3 ratios among C flux variables regressed against latitude, $MAT$, $MAP$ and temperature seasonality), none were significant. - -*Q2. How does C flux relate to $MAT$ and $MAP$?* - -All fluxes increased with $MAT$ (all p<0.05; Figs. 3-4, S4-S5, Table S2). -For eight of the nine fluxes, this relationship was linear. -For only one variable, $BNPP$, did a lognormal fit provide an improvement over a first-order linear relationship, though this was not significant ($\Delta$AIC <2). -As with latitude, $MAT$ tended to explain more variation in the larger fluxes ($GPP$, $NPP$, $ANPP$, $R_{auto}$) and $ANPP_{foliage}$ (all $R^2$> 0.4) than in subsidiary and belowground fluxes ($ANPP_{stem}$, $R_{root}$, $BNPP_{fine.root}$; all $R^2$< 0.25; Table S6). - -$MAP$ was a significant (p<0.05) predictor of all fluxes (Figs. 4a, S4-S5; Table S2). -However, it explained little variation: with the exception of $R_{auto}$, $MAP$ explained at most 25% of variation in C flux. All fluxes increased with $MAP$ up to at least 2000 mm, above which responses were variable (Figs. 4, S4-S5). - -There was a significant additive effect of $MAT$ and $MAP$ on $GPP$, $ANPP$ and $R_{auto}$ (Fig. 3, Table S3), and a significant interactive effect between $MAT$ and $MAP$ for $NPP$ and $ANPP_{stem}$ (Fig. 3, Table S3). The interaction was negative for $NPP$ and positive for $ANPP_{stem}$. For $ANPP_{foliage}$, $BNPP$, $BNPP_{fine.root}$, and $R_{root}$, $MAP$ did not have a significant effect when accounting for $MAT$ (Fig. 3, Table S3). - -*Q3. How does C flux relate to other annual climate variables?* - -All C flux variables showed a significant relationship with annual $PET$. The relationship was logarithmic for $ANPP_{foliage}$, $BNPP_{fine.root}$ and $R_{root}$, and polynomial for all other fluxes (Fig. 4c, S4-5; Table S2). We found strong evidence for a saturation point or peak with $PET$: C fluxes tended to increase at values below 1000mm, before saturating between 1200 and 1700mm. There was also evidence that some C fluxes begin to decrease at values above 1800mm PET. - -Mean annual $VPD$ was a significant predictor of all C fluxes. $ANPP_{foliage}$, $BNPP_{fine.root}$ and $R_{root}$ showed a logarithmic relationship with $VPD$, but all other fluxes showed a polynomial relationship (Figs. 4d, S4-5; Table S2). -C fluxes initially increased with $VPD$, before saturating at around 0.8 kPa, after which point they began to decrease. - -All fluxes, with the exception of $R_{root}$, showed a significant positive relationship with solar radiation (Figs. S4-S5, Table S2). -Solar radiation explained a low proportion of variability (<30%) in all C fluxes. - -Annual wet days, cloud cover, and aridity were poor or non-significant predictors of variation in C fluxes, explaining less than 20% of the variation in each of the carbon fluxes (Figs. S4-S5; Table S2). - -*Q4. What is the role of seasonality in explaining C fluxes?* - -Variables describing temperature seasonality -- temperature seasonality, annual temperature range, annual frost days, and length of growing season -- were strongly correlated with both latitude and $MAT$ (all r$\ge$ 0.2; Fig. S2), and were consistently identified as strong univariate predictors of C fluxes (Figs. 4, S4-S7). - -All fluxes decrease with increasing temperature seasonality, though the shape of this relationship varies (all p<0.05; Figs. 4e, S6-7; Table S2). -Temperature seasonality was strongly correlated with annual temperature range, which was likewise a similarly strong predictor of C fluxes (Table S2). -C fluxes were highest where temperature seasonality = 0, and at an annual temperature range of 15$^\circ$C or lower (*i.e.*, in the tropics). - -In contrast, there was no significant effect of precipitation seasonality on C fluxes at this global scale. -Both maximum vapour pressure deficit and water stress months were poor or non-significant predictors of variation in C fluxes (Figs. S6-S7; Table S2). - -We found a significant relationship between length of growing season and C fluxes, with all fluxes showing a positive relationship with length of growing season (Figs. 4e, S6-S7; Table S2). Length of growing season was a strong predictor of C fluxes, explaining 53% of variation in GPP, 38% of variation in NPP, and 34% of variation in ANPP (all p<0.05; Table S2), but it was a weaker predictor than $MAT$ for all fluxes analysed (Table S4). - -*Q5. Within the growing season, how do C fluxes vary with climate?* - -When annual C fluxes were standardized by growing season length (in monthly increments), correlations with growing season climate were generally weak (Figs. S8-S9). -$ANPP$ increased with growing season temperature ($R^2$ = 0.09, p<0.001) and precipitation ($R^2$ = 0.04, p<0.05). -Similarly, $ANPP_{foliage}$ increased slightly with growing season temperature ($R^2$ = 0.16, p<0.01) and precipitation ($R^2$ = 0.09, p<0.05). -Growing season solar radiation was positively correlated with on $BNPP$ ($R^2$ = 0.17, p<0.001) and $BNPP_{fine.root}$ ($R^2$ = 0.13, p<0.01). -Growing season PET had a positive influence on $GPP$ ($R^2$ = 0.15, p<0.01), $NPP$ ($R^2$ = 0.07, p<0.01), $BNPP$ ($R^2$ = 0.23, p<0.0001), $BNPP_{fine.root}$ ($R^2$ = 0.10, p<0.05), and $ANPP_{stem}$ ($R^2$ = 0.06, p<0.05). -All other relationships were non-significant. - -### Discussion - -Our analysis of a large global database (ForC) clarifies how autotrophic C fluxes in mature forests vary with latitude and climate on a global scale. -We show that, across all nine variables analyzed, annual C flux decreases continually with latitude (Fig. 2), a finding that confirms multiple previous studies but contradicts the idea that productivity of temperate forests rivals or even exceeds that of tropical forests [@luyssaert_co_2007; @huston_global_2009]. -At this global scale, C fluxes increase approximately in proportion to one another, with component fluxes summing appropriately to larger fluxes and no detectable differences in allocation across latitude or climates (Figs. 2, 4, S3). -Similarly, we show broad - *albeit* not complete - consistency of climate responses across C fluxes, with the observed latitudinal variation primarily attributable to temperature and its seasonality (Figs. 3-4). -Water availability is also influential, but of secondary importance across the climate space occupied by forests (Figs. 3-4). -Contrary to prior suggestions that the majority of variation in C cycling is driven primarily by the length of the growing season [@kerkhoff_plant_2005; @enquist_adaptive_2007; @michaletz_convergence_2014], we find modest explanatory power of growing season length and small but sometimes significant influence of climate within the growing season (Figs. 4f,S6-S9). -Together, these findings yield a unified understanding of climate's influence on forest C cycling. - -Our findings indicate that, among mature, undisturbed stands, forest C fluxes are unambiguously highest in the tropical regions, and the relationship with both latitude and $MAT$ is approximately linear (Table 1, *Q1*,*Q2*; Figs. 2, 4). -This contrasts with the suggestion that C fluxes (e.g., $NPP$, $ANPP$, $ANPP_{stem}$) of temperate forests are similar to or even greater than that of tropical forests [@luyssaert_co_2007; @huston_global_2009]. -Previous indications of such a pattern may have been an artifact of differences in stand age across biomes. -Compared to tropical forests, the temperate forest biome has experienced more widespread anthropogenic disturbance and has a larger fraction of secondary stands [@potapov_mapping_2008; @poulter_global_2018], so analyses comparing across latitudinal gradients without controlling for stand age risk confounding age with biome effects. -Because carbon allocation varies with stand age [@de_lucia_forest_2007; @anderson-teixeira_altered_2013; @doughty_what_2018], age differences may introduce systematic biases into analyses of C fluxes across latitude or global climatic gradients. -For example, woody productivity tends to be higher in rapidly aggrading secondary stands than in old-growth forests, where proportionally more C is allocated to respiration and non-woody productivity [@de_lucia_forest_2007; @piao_forest_2010; @doughty_what_2018; @kunert_understanding_2019]. -Thus, findings that temperate forest productivity rivals that of tropical forests are likely an artifact of different forest ages across biomes. - - -We show that C fluxes are broadly consistent in their responses to climate drivers on the global scale, with no trends in C allocation among the variable pairs tested (Figs. 2, 4, S3). -This parallels the observation that C allocation across multiple C fluxes varies little with respect to climate along a steep tropical elevational gradient [@malhi_variation_2017; but see @moser_elevation_2011], -and is not surprising given that carbon allocation within forest ecosystems is relatively constrained [@enquist_global_2002; @litton_carbon_2007; @malhi_allocation_2011]. -We find no trend in the allocation of $GPP$ between production and respiration across latitude or climate ($NPP$:$R_{auto}$; Fig. S3), refuting the idea that tropical forests have anomalously low $CUE$ [@de_lucia_forest_2007; @malhi_productivity_2012; @anderson-teixeira_carbon_2016]. -Rather, differences in $CUE$ between old-growth tropical forests relative to (mostly younger) extratropical forests are likely an artifact of comparing stands of different age, as $CUE$ is known to decline with forest age [@de_lucia_forest_2007; @piao_forest_2010; @collalti_is_2019]. -Another previously observed pattern for which we find no support is a tendency for belowground C allocation to decrease with increasing temperature [@moser_elevation_2011; @gill_belowground_2016]; rather, we observe no trends in allocation between $ANPP$ and $BNPP$ across latitudes. -Failure to detect significant tends in C allocation with respect to climate in this analysis does not imply that none exist; rather, it suggests that, at this global scale, differences are subtle and/or that more careful methodological standardization is required to detect them. - -Despite the broad consistency of climate responses across C fluxes, climate explains lower proportions of variability among some of the subsidiary C fluxes (*e.g.*, $ANPP_{stem}$, $BNPP$, $BNPP_{fine.root}$; Fig. 2; Tables S2, S6). -There are two, non-exclusive, potential explanations for this. -First, it may be that methodological variation is larger relative to flux magnitude for some of the subsidiary fluxes. -Belowground fluxes in particular are difficult to quantify, and measurement methods for the belowground fluxes considered here may use fundamentally different approaches in different sites (*e.g.*, minirhizotrons, ingrowth cores, or sequential coring for $BNPP_{fine.root}$; root exclusion, stable isotope tracking, or gas exchange of excised roots for $R_{root}$), and sampling depth is variable and often insufficient to capture the full soil profile. -$ANPP_{stem}$, which is also poorly explained by latitude or climate, is more straightforward to measure but is subject to variability introduced by differences such as biomass allometries applied and minimum plant size sampled [@clark_measuring_2001]. -However, methodological variation and uncertainty affect all of fluxes considered here, and some of the larger fluxes that vary more strongly with respect to climate ($ANPP$, $NPP$) are estimated by summing uncertain component fluxes. -Second, differences among variables in the proportion of variation explained by climate may be attributable to more direct climatic control over $GPP$ than subsidiary fluxes. -That is, subsidiary fluxes may be shaped by climate both indirectly through its influence on $GPP$ and respiration and directly through any climatic influence on C allocation, as well as many other local- and regional-scale factors [e.g., @moser_elevation_2011]. - -Temperature and its seasonality were the primary drivers of C fluxes on the global scale (Table 1, *Q2*,*Q4*; Figs. 2-4), consistent with a long legacy of research identifying temperature as a primary driver of forest ecosystem C cycling [e.g., @lieth_primary_1973; @luyssaert_co_2007; @wei_forest_2010]. -We find little evidence of any non-linearity in temperature's influence on C fluxes. -The relationship of all fluxes to $MAT$ as an individual driver were best described by a linear function (Table S2) -- with the exception of $BNPP$, whose response to $MAT$ was close to linear (Fig. 4a). -This result contrasts with the idea that fluxes saturate with $MAT$ below approximately 25$^\circ$C $MAT$ [@luyssaert_co_2007; @huston_global_2009]. -It remains possible that fluxes decline above this threshold [@larjavaara_temperature_2012; @sullivan_long-term_2020], as is also consistent with tree-ring records indicating that tropical tree growth declines at high temperatures [e.g., @vlam_temperature_2014]. -However, these higher temperatures also tend to be associated with high $PET$ and $VPD$, both of which are associated with reduced C fluxes (Figs. 4c-d, S4-S5). - -Indeed, while temperature responses dominate at this global scale and within the climate space occupied by forests, the effects of temperature are moderated by moisture availability (Table 1, *Q2*,*Q3*; Figs 3-4). -Specifically, C fluxes are reduced under relatively dry conditions (*i.e.*, low $MAP$; high $VPD$) and sometimes under very high precipitation (Figs. 3-4). -The observed positive interaction between $MAT$ and $MAP$ for $ANPP_{stem}$ on the global scale (Fig. 3) is consistent with an analysis showing a similar interaction for $ANPP$ in tropical forests, also with a cross-over point at ~20^$\circ$C [@taylor_temperature_2017]. -However, we detect no such interaction for $ANPP$ or most other C fluxes, and we find a contrasting negative interaction for $NPP$ (Fig. 3), suggesting that more data are required to sort out potential differences in the interactive effects of $MAT$ and $MAP$ on C fluxes in the tropics. - -Forest C fluxes decline with temperature seasonality (Table 1, *Q4*; Fig. 4e), and are minimal during cold- or dry- dormant seasons. -To account for this, a number of analyses seeking to characterize global-scale effects of climate on productivity have examined the relationship of C flux per month of the growing season with growing season climatic conditions [Table 1, *Q5*; @kerkhoff_plant_2005; @anderson_temperature-dependence_2006; @enquist_adaptive_2007; @michaletz_convergence_2014]. -The sort of simple metric that has been used to define growing season at a global scale [@kerkhoff_plant_2005] is coarse with respect to temperature because it is calculated on a monthly timescale and problematic with respect to moisture because it doesn't capture temporal lags between precipitation and plant water use caused by storage in soil or snow. -We found that a temperature-defined growing season length had strong positive correlation with C fluxes (Fig. 4f), but was never the best univariate predictor. -Dividing annual fluxes by growing season length to yield average flux per growing season month removed the majority of climate-related variation, supporting the idea that the latitudinal gradient in carbon flux is attributable more to shorter growing seasons at high latitudes than to inherently lower rates of photosynthesis or respiration by high-latitude forests [@enquist_adaptive_2007]. -However, there remained a number of significant correlations with growing season climatic conditions, indicating that climatic conditions remain influential within the growing season (Figs. S8-S9). -We conclude that while correcting for growing season length takes analyses a step closer to mechanistic linkage of instantaneous C flux rates to environmental conditions, it remains crude relative to the timescales on which climate affects plant metabolism, and does not advance statistical predictive power. -Rather, mechanistic accounting for climatic effects on global forest carbon flux patterns requires models representing physiologically meaningful timescales [e.g., @medvigy_mechanistic_2009; @longo_biophysics_2019]. - -Our analysis clarifies how annual forest autotrophic C fluxes vary with latitude and climate on a global scale, with some important implications for how forest C cycling relates to climate and, by extension, how it is likely to respond to climatic warming. -To the extent that patterns across broad scale climatic gradients can foretell how ecosystem responses to climate change, our findings suggest that higher temperatures with similar moisture availability would result in a generalized acceleration of forest C cycling (Figs. 2-3). -This is consistent with observations of continental- to global-scale increases over time in $GPP$ [@li_mapping_2019] and $ANPP_{stem}$ [@brienen_long-term_2015; @hubau_asynchronous_2020], along with some C cycle components not considered here--tree mortality [@brienen_long-term_2015; @mcdowell_drivers_2018], soil respiration [@bond-lamberty_global_2010], and heterotrophic soil respiration [@bond-lamberty_globally_2018]. -However, increasing C flux rates are by no means universal [e.g., @rutishauser_testing_2020; @hubau_asynchronous_2020], likely because other factors are at play, including changes to other aspects of climate, atmospheric pollution (CO~2~, SO~2~, NO~x~), and local disturbances. -Moreover, forest ecosystem responses to climatic changes outside the temperature range to which forest communities are adapted and acclimatized will not necessarily parallel responses across geographic gradients in climate. -Indeed, tree-ring studies from forests around the world indicate that tree growth rates -- along with $ANPP_{stem}$ and possibly other ecosystem C fluxes -- respond negatively to temperature [@sniderhan_growth_2016;@helcoski_growing_2019]. -Furthermore, in the tropics, climate change will push forests beyond any contemporary climate, and there are some indications that this could reduce C flux rates [@mau_temperate_2018; @sullivan_long-term_2020]. -Further research is required to understand the extent to which forest responses to climate change will track the observed global gradients, and the time scale on which they will do so. -In the meantime, understanding the fundamental climatic controls on annual C cycling in Earth's forests sets a firmer foundation for understanding forest C cycle responses to accelerating climate change. - - -### Acknowledgements -We gratefully acknowledge all authors of the original studies and data compilations included in this analysis, their funding agencies, and the various networks that support ground-based measurements of C fluxes. -We also thank the numerous researchers who have contributed to the building of ForC. -This study was funded by a Smithsonian Scholarly Studies grant to KJAT and HCML and by Smithsonian's Forest Global Earth Observatory (ForestGEO). -Original compilation of the ForC database was funded by DOE grants DE-SC0008085 and DE-SC0010039 to KAT. \ No newline at end of file