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Systematic Review

Climate Seasonality Mediates Global Patterns of Foliar Carbon and Nitrogen Isotopes

by
Lan Du
1,2,3,
Yan Li
1,3 and
Xinjun Zheng
1,3,*
1
State Key Laboratory of Desert and Oasis Ecology, Chinese Academy of Sciences, Urumqi 830011, China
2
University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
3
Fukang Station of Desert Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Fukang 831505, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(3), 461; https://doi.org/10.3390/f14030461
Submission received: 5 January 2023 / Revised: 15 February 2023 / Accepted: 20 February 2023 / Published: 24 February 2023
(This article belongs to the Special Issue Analyses of Stress Impact on Forest Ecosystems Connected with Global Climate Changes)
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Abstract

:
Frequent extreme climate events have significantly affected plant intrinsic water-use efficiency (iWUE) and forest nitrogen (N) availability. Understanding the coupling between climate seasonality and plant water, carbon, and nitrogen may provide insights into how plants respond to climate change. Here, we integrated Δ13C and δ15N in woody plant leaves as a probe to elucidate the iWUE and N availability patterns of plants under global change and found that woody plants from sites with high climate seasonality, especially precipitation seasonality, tend to have improved iWUE and N availability compared with those with low seasonality. Specifically, high potential evapotranspiration, solar radiation, vapor pressure deficit, and low precipitation during the growth season are the driving factors. The intra-annual and annual climate explained 43% and 49% of Δ13C and 40% and 53% of δ15N, respectively, suggesting that the intra-annual climate is at least as important as the annual climate. These results suggest that not only the direction (decrease vs. increase) of decadal climate should be counted but also the abnormal fluctuation of intra-annual should be considered. Climate seasonality is a more suitable ecological filter for determining plant distribution across terrestrial ecosystems.

1. Introduction

During leaf photosynthesis, stomata absorb CO2, which leads to loss of water vapor from leaves [1]. The foliar N concentration is strongly related to the amount of RuBisCO and, therefore, to the light-saturated rate of RuBP carboxylation [2,3]. Thus, the trade-off between water, C, and N cycles can be reflected by plant intrinsic water-use efficiency (iWUE) and the N availability of the ecosystem [4,5], which is subject to great variation due to climatic and edaphic heterogeneity [6,7,8]. An in-depth study of iWUE and N availability will provide insights into variations of the water, C, and N cycles under climate change and improve our ability to predict the feedback of ecosystems to the climate.
One common consensus is that foliar Δ13C indicates the stomatal opening (intercellular: atmospheric CO2 concentration ratio (Ci/Ca)) of the natural vegetation and is tightly linked to iWUE [9,10,11,12]. N availability is typically assessed using δ15N, with a high δ15N indicating high N availability in forest ecosystems [13,14,15,16]. Therefore, foliar Δ13C and δ15N values may reveal the temporal and spatial patterns of the plant water, C, and N cycles and explain alterations due to disturbances.
Variation in the isotopic signature of leaves exists at a series of nested scales: within individuals, among individuals of a species, and among species [10,17]. Furthermore, variation among species exists at a given site and across climatic gradients [7,17].
How to enhance existing knowledge at the leaf and species levels about ecosystems or even the global scale is the key question. To date, trends on the effect of annual climate variables on foliar Δ13C and δ15N have been relatively clear. For example, global meta-analyses have found that plant δ15N is positively correlated with mean annual temperature (MAT) and N deposition but is negatively correlated with mean annual precipitation (MAP) [13,14]. Murphy and Bowman [7] found that plants in low water-availability environments maintain high iWUE and process less Δ13C in their leaves [12]. Numerous other climatic indices, e.g., vapor pressure deficit (VPD) and potential evapotranspiration (PET), can have individually small but cumulatively substantial impacts on plant Δ13C and δ15N [18,19]. In addition, soil properties may also influence Δ13C through soil water conditions [12], silt, and pH (soil pH may affect plant WUE via changing plant photosynthesis) [9,20].
Frequent extreme climate events and prolonged growing seasons are the main manifestations of global climate change [21]. These intra-annual climate variations, an overlooked category, are important in determining plant iWUE and N availability [22,23]. For instance, longer growth seasons due to global warming may increase plant N demand more than supply in some ecosystems, thus reducing δ15N [16]. Additionally, growth-season water availability also decreases δ15N but increases Δ13C of C3 grasses [7]. However, a comprehensive understanding of how these intra-annual environmental indices affect plant growth is lacking, and whether the effect is to compound or to counteract each other remains largely unknown. Although the integration study from Cornwell et al. [9] compared the seasonal and annual climates, their results lack an exploration of the synergistic relationship between environmental factors, which is often crucial. Similarly, the interaction of environmental factors was rarely considered in the study of plant δ15N [13,14,15]. In particular, how seasonality and growth-season climate affect plant δ15N have not been reported. Comprehensive assessments of different factors across the soil–atmosphere interface (i.e., spanning ‘traditional’ and other climate variables and soil factors) on plant WUE and N availability are lacking.
To assess the forest ecosystem status under global changes, a basic trajectory of how plant Δ13C and δ15N respond to climate and its seasonality properties is required. In this study, we compiled a dataset of 474 woody species from 148 sites worldwide to address how climate seasonality and growth-season climate indices affect plant iWUE and N availability and explain their relative contributions by comparing them with annual climate and soil properties across biomes.

2. Materials and Methods

2.1. Foliar δ13C and δ15N Values of Woody Plants

We extracted data from published datasets from 1990 to 2020 that contain leaf δ13C and δ15N by searching the Web of Science for papers that include terms such as ‘leaf 13C’ or ‘foliar 13C’ and ‘leaf 15N’ or ‘foliar 15N’. In total, 478 papers matched. We filtered the articles according to the following two criteria: (1) sites affected by human activities (e.g., grazing and farming) were excluded; (2) only observations with a reported coordinates were extracted. In addition, we selected sampling years using the following two criteria: (1) when the sampling year lasted for two years, the first year was selected; (2) if the sampling year lasted for more than three years, an average was calculated and selected. Ultimately, 41 papers, 149 sites, and 474 woody plants were included in the dataset (Figure 1).

2.2. Climate and Soil Properties Matched with Each Site

All climatic and edaphic variables were extracted using ArcGIS ArcGIS Desktop (Version 10.7, Redlands, CA, USA). For each site, we first extracted potential evapotranspiration (PET) and aridity index (AI = MAP/PET) from the Global-PET and Global-AI datasets [24]. Climate variables were then extracted from the WorldClim version 2 database [25]. We extracted 18 climate variables at an annual scale from WorldClim: the highest and lowest precipitation in the year (Pmax and Pmin, respectively), precipitation in the warmest and coldest quarters (Pwarm and Pcold, respectively), precipitation in the wettest and driest quarters (Pwet and Pdry, respectively), MAP, precipitation seasonality (Ps), MAT, the highest and lowest temperatures in the year (Tmax and Tmin, respectively), temperature during the warmest and coldest quarter temperatures (Twarm and Tcold, respectively), temperature during the wettest and driest quarters (Twet and Tdry, respectively), temperature seasonality (Ts), solar radiation (SR), and water vapor pressure (VP). We also extracted precipitation, temperature, SR, PET, and VP for each month at each site (1–12, corresponding to each of the 12 months). Subsequently, VPD was calculated using the following formula: VPD = VPsat − VP, where VPsat and VP are the indicators of atmospheric potential and actual evaporation, respectively. Monthly VPsat was calculated using monthly air temperature T as a × exp[b × T/(c + T)], where a, b, and c are constants with values of 0.611 kPa, 17.502 (unitless), and 240.97 °C, respectively [26]. If possible, the elevation (El) at each site was extracted from the original article. Missing values were matched with the geographic coordinates using WorldClim version 2 database. Soil properties, including two categories of the physical soil (soil available water capacity (AWC), soil clay (Clay), and soil bulk density (Bulk)) and the chemical soil (soil pH (pH), soil organic carbon (SOC); soil total nitrogen (SN), and soil cation exchange capacity (CEC)) were extracted from World Soil Database [27]. Environmental predictors are summarized in Table 1.

2.3. Data Analyses

The advantage of iWUE as a term is that it allows direct comparison of intrinsic physiological considerations, while excluding the confounding effects of temperature and humidity gradient differences between plants [28]. iWUE was defined as the ratio of atmosphere to stomatal conductance of CO2 [11]:
iWUE = C a − C i 1.6 = C a 1.6 ( 1 − C i C a )
where Ci is the intercellular CO2 concentration, and Ca is the ambient CO2 concentration.
Considering the remarkable decrease in the 13C/12C value of atmospheric CO2 concentration in recent decades, Δ13C was preferred to express leaf carbon isotope discrimination [10] in the following manner:
Δ 13 C = δ 13 C a − δ 13 C p 1 + δ 13 C p / 1000
where δ13Cp and δ13Ca are the δ13C of leaf and atmosphere, respectively. δ13Ca was calculated by Feng [29] as follows:
δ13Ca = −6.429 − 0.0060 exp [0.0217(t − 1740)]
where t is the sample year.
A linear form of Δ13C that does not include effects due to mesophyll conductance and photorespiration commonly relates Δ13C linearly to Ci/Ca [10]:
C i C a = Δ 13 C − a b − a = δ 13 C a − δ 13 C p − a b − a
since the linear relationship between Δ13C and Ci/Ca, iWUE is represented by Δ13C in this study.
Nitrogen availability is indicated by foliar δ15N values, with high δ15N values showing high N availability in forest ecosystems [30]. The indications of foliar δ15N values on N availability are considered reliable [13,14,15,16].

2.4. Determining Whether Climate Variables Are Related to Foliar Δ13C and δ15N

We conducted principal component analysis (PCA) on 21 annual climate indicators using the PCA ‘prcomp’ function in R statistical software (Version 3.6.3, Vienna, Austria) [31]. Another monthly PCA of 60 monthly climate indicators was conducted based on the annual results. Owing to the opposing climate patterns between the Northern and Southern Hemispheres (Figure S1), we transformed the limited observations loaded on the Southern Hemisphere (65 observations) into the Northern Hemisphere (409 observations). Our transformation corresponds to the seasons of the year (i.e., July in the Southern Hemisphere corresponds to January in the Northern Hemisphere, see Figure S1). To understand how climate variables influence leaf isotope signals, we regressed the first two principal components (PCs) of both annual and monthly scales against Δ13C and δ15N using standardized major axis (SMA) regression. Analysis was performed using the ‘sma’ function in the R package SMATR [32].

2.5. Determining Whether Climate Seasonality Explains Foliar Δ13C and δ15N

Considering the results of the PCA, four climate indicators (Ts, Ps, Ts and Ps, and VPD and Pmin) were selected for further analysis. Pairwise indices of Δ13C and δ15N were employed to compare seasonal patterns from low to high using the SMA models. First, for each observation in our dataset, we assigned ‘low’, ‘middle’, and ‘high’ seasonality groups according to Ts, Ps, Ts and Ps, and VPD and Pmin. We considered the first (<25th) and last quarter (≥75th) as ‘low’ and ‘high’ seasonality, respectively, and the ‘middle’ seasonality fell in between. The median was adopted as the group criterion for the Ts and Ps and VPD and Pmin groups, since very few samples fell beyond the 25th and 75th percentiles.
Then, for testing the seasonality pattern designed above, we performed an SMA model that contained three steps. First, we tested the slopes (A) based on the likelihood ratio test between two groups of ‘low’ and ‘high’ seasonality. Second, if the slopes were equal (B), the Wald test was performed to examine the hypothesis that the two groups have the same intercepts. Finally, if both tests performed above were equal (C), we also used the Wald test to observe the position changes along x-axis of each group.
Finally, to quantify the relative importance of climate seasonality on WUE and N availability, predictors including annual climate, intra-annual climate, and soil physical and chemical indictors were selected to conduct structural equation models (SEMs). Model establishment was accomplished through piecewise SEM packages [33]. Model estimation was completed using Fisher’s C (p > 0.05 indicates that the model is acceptable) and AIC.

3. Results

3.1. Effect of Annual Climate on Foliar Δ13C and δ15N

Principal components (PC1 and PC2) based on 21 annual climate variables show the climatic patterns of the 474 woody observations (Figure 2a,b). In Figure 2a,b, PC1 and PC2 explain 71.21% of the total variation, with Ts loading positively and Tmin and Tcold loading negatively on PC2. Ps, VPD, SR, PET, and Tmax are positively loaded, while AI, Pdry, and Pmin are negatively loaded on PC1. Biomes of desert (DES), tropical rainforest (TRR), and tropical seasonal forest (TRS) are distinguished by Ts (Figure 2b), whereas woodland/shrubland (WDS), DES, boreal forest (BOR), and temperate rain forest (TMR) are distinguished by climate patterns of high Pmin, Pdry, and AI, as well as low Ps, VPD, SR, PET, and Tmax (Figure 2b). Foliar Δ13C and δ15N show opposite correlation patterns with PC1, meaning that high Ps, VPD, SR, PET, and Tmax, together with low Pmin, Pdry, and AI, are loaded on PC1 enriched with both 13C (p < 0.001) and 15N (p < 0.001) in the leaves (Figure 2e,i). Both Δ13C and δ15N are negatively correlated with PC2.

3.2. Effect of Growth-Season Climate Variables on Foliar Δ13C and δ15N

The PC1 and PC2 of the 60 monthly climate variables based on the 474 woody observations were employed to summarize the monthly climate indices (Figure 3a,b). In Figure 2c,d, growth-season precipitation (P5–10) loaded positively, while SR (6–8), VPD (6–8), and PET (6–8) loaded negatively on PC2. All biomes, except for WDS, can be clearly distinguished. For example, wet season SR and VPD (SR6–8, VPD6–8) loaded negatively and precipitation (P5–10) loaded positively on PC2, mainly as a precipitation axis separating the desert, temperate, and tropical biomes (Figure 2c,d). Foliar Δ13C and δ15N show opposite correlation patterns with PC2. Thus, growth-season precipitation together with growth-season low PET, SR, and VPD drive both 13C (p < 0.001) and 15N (p < 0.001) depletion in leaves (Figure 2h,l).

3.3. Effect of Seasonality on Determining Foliar Δ13C and δ15N

SMA models of low and high climate seasonality groups exhibit significantly different slopes, shifts, and intercepts along the x and y axes, respectively (Figure 3, Table 2). Observations of the high Ts group are closer to the lower-left corner, whereas observations of the high Ps group exhibit a more positive shift along the x axis (Figure 3a,b; Table 2). The SMA models fitted using the Ts and Ps group reveal not only the slope and shift change along the x axis but also a significant intercept decline of the high Ts and Ps group (Figure 3c, Table 2). Comparing with low VPD and high Pmin group, high VPD and low Pmin group has a less negative slope and a more positive shift along x axis (Figure 3d, Table 2).

3.4. Effect of Intra-Annual Climate Compared with Annual Climate and Soil Properties

SEMs based on intra-annual and annual climate combined with soil physical and chemical properties indicated that leaf Δ13C and δ15N significantly responded to environmental changes (Figure 4). In Figure 4a, the intra-annual climate fluctuations (Ps, Ts, and GSC) are equivalent to the annual climate in regulating leaf Δ13C, while soil factors had little effect. In addition, increased intra-annual climate fluctuations can indirectly affect foliar Δ13C by weakening the soil chemistry, while the annual climate still positively affected the soil chemistry (Figure 4a). In Figure 4b, intra-annual climate fluctuations and annual climate were also the main driving factors of foliar δ15N. Different from foliar Δ13C, soil chemical properties had a significant influence on foliar δ15N (Figure 4b).

4. Discussion

This study explored the concordance between climate, seasonality, and forest C sequestration and N retention strategies. Our findings suggest that there is not a simple mapping of Δ13C and δ15N to one or a few macroclimatic variables, such as annual precipitation. Instead, numerous aspects of climate and soils affect the supply of water to the plant, the balance of the CO2 supply through the stomata, and the CO2 demand at the sites of photosynthesis within the leaf. Specifically, for woody plants, intra-annual climate fluctuation (Ps, Ts, and GSC) were significantly correlated with foliar Δ13C and δ15N (Figure 2). Woody plants at sites associated with high water-related seasonality (Ps, Ts and Ps, and VPD and Pmin) tended to obtain higher δ15N but lower Δ13C than those of low seasonality ones (Figure 3). Intra-annual climate showed the same important driving effect on the leaf isotope signals as annual climate (Figure 4), so understanding intra-annual seasonal fluctuations is important for explaining how plants respond to climate change at finer time scales.

4.1. Effect of Climate Seasonality on Foliar Δ13C and δ15N

Physiological and ecological factors reinforce the finding that climate abnormal fluctuation shapes plant isotope signals and causes tree mortality [7,34,35]. For example, at the species level, stomatal regulation in plants is inextricably linked by two dynamic supply–demand functions: the first is the supply of water to plants in the soil and the evaporative demand for that water from the atmosphere; the second is the supply of CO2 from the atmosphere to chloroplasts through the stomata and the enzymatic demand for that CO2 at the chloroplast. The supply and demand of both water and CO2 change on the order of seconds, and plants must adjust to constantly changing conditions by closing the stomata to slow water loss. At the macro-scale, the geographical distribution also revealed that plants tend to increase light-saturated photosynthesis (Amax) and WUE in drier ecosystems by decreasing foliar evaporation (smaller, thicker leaves, with a lower specific leaf area) and increasing N availability [36]. Thus, annual seasonal precipitation and drought could explain the systematic schemes of the plant Δ13C (Figure 2). However, soil properties vary only slightly compared with the dramatic seasonal and annual fluctuations of the climate [37]. This may be the reason why Δ13C is mainly affected by climate and seasonality rather than soil properties (Figure 4a).
Numerous plausible explanations involving plant–soil interfaces, such as rooting depth, source differences or availability, and soil texture, have been invoked to explain the N isotope patterns in plants [15,38,39]. For example, Adams et al. [40] suggested that the additional Narea (area-weighted leaf nitrogen) is often in the form of a higher concentration of RuBisCO, which will lower the internal CO2 concentration and, thus, lower Δ13C, assuming that all else, including stomatal conductance, is held constant. Subsequently, Cornwell et al. [9] established evidence of a negative correlation between Narea and Δ13C, which supported the conclusion of Adams, although there remains a lack of direct comparison between Δ13C and δ15N. Our study, based on leaf isotope integration, showed that Δ13C and δ15N were not only closely responsive to climate and its seasonality (Figure 3 and Figure 4) but also correlated with each other (Figure S2). In addition to climate, soil δ15N was significantly correlated with plant δ15N; thus, interpreting plants δ15N through soil is feasible [41,42]. Further, soil texture (e.g., soil clay content and moisture) also affects soil δ15N in the Chinese Loess Plateau [38] and globally [15]. However, these soil physical properties may affect plant δ15N indirectly by influencing soil chemical properties (Figure 4b). For example, the forms of N uptaken by plants, such as the two main forms of ammonium and nitrate nitrogen [17] as well as other forms of dissolved organic N [43], hydrolyzed amino acids [44], and amino sugars [45] all tightly correlated with soil physical properties (e.g., AWC and soil bulk density). In addition to the framework of this study, the presence of mycorrhiza and root depth is also an important factor for the identification of plant δ15N [17]. A more comprehensive consideration of these factors is the key to interpreting plant δ15N.
Compared with other leaf traits, foliar Δ13C and δ15N convergence within climate zones is remarkable. In other words, when compared with global variation, in a given climate, species often have relatively different leaf morphologies and chemistries but relatively similar CO2 concentrations at the site of carboxylation and a relatively stable nitrate to ammonium ratio of soil and, thus, Δ13C and δ15N, respectively.

4.2. Effect of Growth-Season Climate on Foliar Δ13C and δ15N

Precipitation is an important source of external water replenishment of terrestrial ecosystems, and its abnormal fluctuations can cause drought and consequent tree mortality [33]. SR (representing atmospheric drought) and VPD (to some extent) influenced plant growth, survival, and distribution, and the effects of these meteorological traits are not as well understood as those of precipitation and temperature [46,47]. Temperature is correlated to SR, VPD, and PET. Thus, PET, VPD, SR, Tdry, and Tmax were clustered together in the PCA plots (Figure 2a,c). VPD of 1.5 kPa is an approximate threshold for the onset of stomatal closure in forest species. Thus, VPD is an important factor in determining carbon metabolism and growth in plants [48,49]. Both VPD [50] and SR [51] participate in hydraulic transport through their effects on stomatal and evaporative demand. Therefore, higher VPD and SR might drive an evolutionary divergence between isohydric species and anisohydric species, with the former avoid drought-induced hydraulic failure through stomatal closure and the latter tolerate large fluctuations in operating water potential [52]. This may result from the fact that anisohydric species adhering the tolerance strategies, whereas isohydric species do not, as they are more likely to store adequate water in their tissues and maintain high levels of metabolism [53]. The distribution pattern of natural terrestrial plants suggests this phenomenon. For example, plants are generally aligned with lower water conductivity, thinner conduits, and lower water potential in arid environments, whereas the opposite is true in humid regions [47,54]. Meanwhile, the plants showed enhanced photosynthesis and nitrogen metabolism in the wet season [7,55,56], but, in arid or experienced dry/hot growth seasons, more conservative metabolic efficiency (e.g., lower xylem hydraulic) was selected, though for survival [47]. Our results may reflect the divergence between the above two strategies. For example, species in wet habitats (high Pmin and Pdry) might be forced to draw down δ15N but increase Δ13C in leaves towards the ‘incompetent’ side (with both low iWUE and N availability). Whereas a high PET, SR, and VPD in the growth season (similar to SR and PET, see Figure 2) together with a low Pmin may drive low Δ13C but high δ15N in plants, which promotes more conservative C and N strategies.
This may explain why woody plants are subject to seasonal changes in climate gradients ranging from humid to arid environments, and, to some extent, introducing these growth-season climate variables and considering their interactions revealed the relationship between plant water, C, and N cycles, which could not be obtained from the annual climate perspective (e.g., MAP and MAT). In addition, the seasonal variations reported here are also confirmed the water–energy hypothesis, which not only affects plant ecological adaptation strategies at the regional level [57] but may also affect plant diversity on a global scale [58].

4.3. Knowledge Gap and Perspectives

To date, the impacts of global climate change on terrestrial ecosystems are poorly understand, that is, there are major divergence in the modeled and measured scenarios for the past and current transformation of terrestrial ecosystems [59]. In particular, these divergences arise due to the difficulties of embodying ecosystem responses to changes in drought, rising CO2 levels, and warming in a single framework [60]. There remains a need for quantitative methods to elaborate the structure between plant and soil microbial communities during biogeochemical and biophysical processes to predict changes in ecosystem functions (e.g., C, N, and water cycling). Gaps in understanding that hamper progress in this field stem from the fact that measurements at plot–level or infrequently repeated may bring conflicting ecological knowledge. Such a view, constrained by both temporal and spacial, is vulnerable to address the sustainable development under global climate change. For example, agriculture and forestry have involved management plans that attempt to push the limits of carbon–water–nutrient tradeoffs by reducing competition for soil resources by assuming steady climate and growing seasons, with no realized carbon costs, for decades [61,62]. Such managrment is problematic, because managed landscapes tend to use more water and nutrients but sequent less carbon than unmanaged landscapes [63,64]. Moreover, anthropogenic management will, to a certain extent, amplify the climate–driven disturbances [65,66], with significant impacts on ecological resilience in response to drought and warming stress [67]. Considering the integration of plant isotopes with climate and its seasonality may provide a finer time-scale analysis of plant nutrient use and restriction, which is crucial for specifying management modalities. Explicitly incorporating the influence of plant isotopes in ecosystem models may reduce the uncertainty when assessing the ecological effects of global changing environments.
There have been several successful attempts to define WUE. For example, species-specific iWUE derived from leaf δ13C ratios can be normalized by leaf morphology (e.g., specific leaf area (SLA)) and/or divided by downscaled VPD (WUE = A/T = iWUE/VPD) to the level of a stand for which pedogenic development is known [68]. In addition, this can occur in cases where net primary productivity can be quantified for the same period for which WUE is calculated, e.g., annual tree-ring basal area increments coupled with Δ13C time series [62,69]. However, for N availability, although the empirical relationships amongst leaf δ15N and N availability and N contents were established [13,14], studies on the mechanism (e.g., a quantitative model) of δ15N and N processes in leaves remain warranted. In addition, plants’ N-use forms, rooting depth, mycorrhizal symbiosis [70,71], forest age, and land-use types [72,73] are crucial to plant iWUE and N availability. Unfortunately, most studies focus on these properties separately and measure only once, which cannot inform the long-term impacts and show partial results. Establishing long-term observation plots based on multiple ecological indicators may be the key to addressing this issue.

5. Conclusions

When the intra-annual climate fluctuations were considered, a completely different perspective was obtained from previously reported climate–trait relationships. We found that the optimal allocation of Δ13C and δ15N in woody plants is driven by growth season, climate, and seasonality. Water limitation in the wet season (high VPD, PET, and SR and low Pmin) is critical for the bidirectional selection between plants and climate, which determines the ecological distribution of plants. In addition, intra-annual climate fluctuations may be at least as important as annual climate in shaping foliar isotopes. Our results emphasize the need to explicitly incorporate the influence of intra-annual climate indices in ecosystem models to reduce the uncertainty when assessing the ecological effects (such as forest N retention and C sequestration) of global changing environments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14030461/s1. Table S1: The full database used in this study; Figure S1: The opposite climate seasonal patterns between Northern and Southern Hemispheres; Figure S2: The SMA aggression of foliar Δ13C and δ15N.

Author Contributions

L.D. and X.Z. conceived the study; L.D. compiled the data; L.D., X.Z. and Y.L. analyzed the data; L.D. led the manuscript writing with substantial contributions by X.Z and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA2006030102); the Key Research Project of Frontier Sciences of the Chinese Academy of Sciences (No. QYZDJ-SSW-DQC014); the ‘Western Light’ program of the Chinese Academy of Sciences (2019-XBQNXZ-B-003); and the National Natural Sciences Foundation of China (No. 41730638 and No. 42171068).

Data Availability Statement

The data presented in this study are openly available in the Supplementary MS Access Database included with this submission: Table S1.

Acknowledgments

We thank Li-song Tang for his suggestions on manuscript writing to improve this submission. We are appreciative of the scientists and technicians who collected the data upon which these analyses were based.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Global distribution of 149 study sites based on precipitation seasonality. Each black circle represents an independent study site.
Figure 1. Global distribution of 149 study sites based on precipitation seasonality. Each black circle represents an independent study site.
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Figure 2. PCA of 474 woody plants based on 21 annual (a,b) and monthly (c,d) climate variables and the liner relationship between principal component (PC1 and PC2) and foliar isotope signals (Δ13C and δ15N) using SMA (e–l). (a,c) PC loadings and (b,d) observation scores with different colored symbols representing various biomes (DES, deserts = black; BOR, boreal forest = red; TMR, temperate rainforest = yellow; TMS, temperate seasonal forest = carmine; TRR, tropical rainforest = olive green; TRS, tropical seasonal forest = navy blue; WDS, woodland/shrubland = brown). Percentages explained by first two principal components were provided on the coordinate axis (a–d). Only significantly fitted results of the standardized major axis (SMA) regressions were reported in (e–l). In (a–d), the red (negative PC) and black (positive PC) circles correspond to (e–l). Significance of SMA regressions were indicated by *** p < 0.001 and ** p < 0.01.
Figure 2. PCA of 474 woody plants based on 21 annual (a,b) and monthly (c,d) climate variables and the liner relationship between principal component (PC1 and PC2) and foliar isotope signals (Δ13C and δ15N) using SMA (e–l). (a,c) PC loadings and (b,d) observation scores with different colored symbols representing various biomes (DES, deserts = black; BOR, boreal forest = red; TMR, temperate rainforest = yellow; TMS, temperate seasonal forest = carmine; TRR, tropical rainforest = olive green; TRS, tropical seasonal forest = navy blue; WDS, woodland/shrubland = brown). Percentages explained by first two principal components were provided on the coordinate axis (a–d). Only significantly fitted results of the standardized major axis (SMA) regressions were reported in (e–l). In (a–d), the red (negative PC) and black (positive PC) circles correspond to (e–l). Significance of SMA regressions were indicated by *** p < 0.001 and ** p < 0.01.
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Figure 3. SMA estimation of driving intensity and direction of foliar Δ13C and δ15N signals by climate seasonality. Seasonality of temperature ((a), Ts), precipitation ((b), Ps), temperature and precipitation ((c), Ts and Ps) and relative evaporative demand correspond to precipitation ((d), VPD and Pmin) all classified into high and low groups. Red dots/lines and black dots/lines represent high and low seasonality, respectively.
Figure 3. SMA estimation of driving intensity and direction of foliar Δ13C and δ15N signals by climate seasonality. Seasonality of temperature ((a), Ts), precipitation ((b), Ps), temperature and precipitation ((c), Ts and Ps) and relative evaporative demand correspond to precipitation ((d), VPD and Pmin) all classified into high and low groups. Red dots/lines and black dots/lines represent high and low seasonality, respectively.
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Figure 4. SEM estimation of the driving intensity of foliar Δ13C (a) and δ15N (b) signals by soil, climate, and climate seasonality. The first principal component among multiple variables was used to denote the effect of a specific category. Ps, precipitation seasonality; Ts, temperature seasonality; GSC, growth-season climate (the PC2 of monthly climate principal component analysis); MAP, mean annual precipitation; MAT, mean annual temperature; AI, aridity index; SR, solar radiation; AWC, soil available water capacity; Clay, soil clay; Bulk, soil bulk density; SOC, soil organic carbon; SN, soil total nitrogen; CEC, soil cation exchange capacity; pH, soil pH. Significant positive relationships are indicated by green continuous arrows, while negative relationships are indicated by red dashed arrows. The width of the lines is proportional to the significance (*** p < 0.001, ** p < 0.01, and * p < 0.05) (and effect size) of standardized model coefficient estimates. Coefficients of determination (R2) for explained variables are listed below the variable names.
Figure 4. SEM estimation of the driving intensity of foliar Δ13C (a) and δ15N (b) signals by soil, climate, and climate seasonality. The first principal component among multiple variables was used to denote the effect of a specific category. Ps, precipitation seasonality; Ts, temperature seasonality; GSC, growth-season climate (the PC2 of monthly climate principal component analysis); MAP, mean annual precipitation; MAT, mean annual temperature; AI, aridity index; SR, solar radiation; AWC, soil available water capacity; Clay, soil clay; Bulk, soil bulk density; SOC, soil organic carbon; SN, soil total nitrogen; CEC, soil cation exchange capacity; pH, soil pH. Significant positive relationships are indicated by green continuous arrows, while negative relationships are indicated by red dashed arrows. The width of the lines is proportional to the significance (*** p < 0.001, ** p < 0.01, and * p < 0.05) (and effect size) of standardized model coefficient estimates. Coefficients of determination (R2) for explained variables are listed below the variable names.
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Table
Table 1. Environmental predictors used in this study.
Table 1. Environmental predictors used in this study.
Full NameUnitAbbreviationNote
Annual climatic variables
Elevationm a.s.l.El
Aridity indexunitlessAIMAP/PET
Mean annual precipitationmmMAP
Precipitation seasonalityunitlessPsSD/mean of 12 months
Precipitation of wettest quartermmPwet
Precipitation of driest quartermmPdry
Precipitation of wettest monthmmPmax
Precipitation of driest monthmmPmin
Precipitation of warmest quartermmPwarm
Precipitation of coldest quartermmPcold
Mean annual temperature°CMAT
Temperature seasonalityunitlessTsSD of monthly temperature
Temperature of warmest month°CTmax
Temperature of coldest month°CTmin
Temperature of warmest quarter°CTwarm
Temperature of coldest quarter°CTcold
Temperature of wettest quarter°CTwet
Temperature of driest quarter°CTdry
Vapor pressure deficitkPaVPD
Potential evapotranspirationmm·y−1PET
Mean annual solar radiationkJ·m−2·d−1SR
Monthly climatic variables
Monthly temperature°CT1~T121~12 corresponding to 12 months
Monthly precipitationmmP1~P12months
Monthly solar radiationkJ·m−2·d−1SR1~SR12
Monthly vapor pressure deficitkPaVPD1~VPD12
Monthly potential evapotranspirationmm·yr−1PET1~PET12
Physical soil
Soil available water capacitycm·m−1AWC
Soil clay%Clay
Soil bulk densitykg·dm−3Bulk
Chemical soil
Soil pHunitlesspH
Soil organic carbong·kg−1SOC
Soil total nitrogeng·kg−1SN
Soil cation exchange capacitycmolc·kg−1CEC
Table
Table 2. Summary of SMA regression estimated the driving intensity and direction of foliar Δ13C and δ15N signals by climate seasonality.
Table 2. Summary of SMA regression estimated the driving intensity and direction of foliar Δ13C and δ15N signals by climate seasonality.
High Seasonality Low Seasonality Model ComparisonChanging Patterns
SlopeInter.R2pSlopeInter.R2pA-SlopeB-Inter.C-Shift
Ts−0.5120.530.28<0.001−0.5622.980.21<0.001LR= 0.4528F = 45.33F = 0.01High Ts group has a smaller y intercept
p = 0.501p < 0.001p = 0.91
Ps−0.5120.690.080.008−0.6721.290.040.01LR = 3.294F = 1.172F = 153.6High Ps group has a more positive shift along x axis
p = 0.07p = 0.28p < 0.001
Ts and Ps−0.4620.290.36<0.001−0.6221.880.140.005LR = 5F = 13.06F = 49.89High Ts and Ps group has a smaller intercept, a less negative slope, and a more positive shift along x axis
p = 0.03p < 0.001p < 0.001
VPD and Pmin−0.4420.480.050.02−0.5820.880.10<0.001LR = 4.59F = 1.273F = 152.5High VPD and Pmin group has a less negative slope and a more positive shift along x axis
p = 0.03p = 0.26p < 0.001
Notes: Inter. means the y intercept of the SMA model. A, B, and C represent three SMA comparison types, respectively, with detailed descriptions in the data analysis. p values for determining changing patterns are in bold. LR, likelihood ratio; Ts, temperature seasonality; Ps, precipitation seasonality; Ts and Ps, temperature seasonality co-variate with precipitation seasonality; VPD and Pmin, relative evaporation demand with low precipitation.
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Du, L.; Li, Y.; Zheng, X. Climate Seasonality Mediates Global Patterns of Foliar Carbon and Nitrogen Isotopes. Forests 2023, 14, 461. https://doi.org/10.3390/f14030461

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Du L, Li Y, Zheng X. Climate Seasonality Mediates Global Patterns of Foliar Carbon and Nitrogen Isotopes. Forests. 2023; 14(3):461. https://doi.org/10.3390/f14030461

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Du, Lan, Yan Li, and Xinjun Zheng. 2023. "Climate Seasonality Mediates Global Patterns of Foliar Carbon and Nitrogen Isotopes" Forests 14, no. 3: 461. https://doi.org/10.3390/f14030461

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