The availability of nutrients is one of the factors that regulate
terrestrial carbon cycling and modify ecosystem responses to environmental
changes. Nonetheless, nutrient availability is often overlooked in
climate–carbon cycle studies because it depends on the interplay of various
soil factors that would ideally be comprised into metrics applicable at large
spatial scales. Such metrics do not currently exist. Here, we use a Swedish
forest inventory database that contains soil data and tree growth data for
> 2500 forests across Sweden to (i) test which combination of
soil factors best explains variation in tree growth, (ii) evaluate an
existing metric of constraints on nutrient availability, and (iii) adjust
this metric for boreal forest data. With (iii), we thus aimed to provide an
adjustable nutrient metric, applicable for Sweden and with potential for
elaboration to other regions. While taking into account confounding factors
such as climate, N deposition, and soil oxygen availability, our analyses
revealed that the soil organic carbon concentration (SOC) and the ratio of
soil carbon to nitrogen (C : N) were the most important factors explaining
variation in “normalized” (climate-independent) productivity (mean annual
volume increment – m
Nutrients determine structure and functioning at all levels of biological
organization. The availability of mineral elements influences plant growth
(von Liebig, 1840), patterns of biodiversity (Fraser et al., 2015), and
ecosystem processes (e.g., Janssens et al., 2010; Vicca et al., 2012;
Fernández-Martínez et al., 2014). Moreover, nutrient availability
can modify ecosystem responses to global atmospheric and climatic changes,
such as nitrogen (N) deposition (Nohrstedt, 2001; Hyvönen et al., 2008;
Vadeboncoeur, 2010), increasing
Comparing nutrient availability among terrestrial ecosystems is difficult for two reasons: comprehensive and harmonized data on soil properties and nutrients are not usually available from experimental and observational sites, and no standardized quantitative metric exists to compare the nutrient statuses of terrestrial ecosystems at the global scale, or even at a national scale (e.g., for Sweden, which is considered in this study). In the absence of a standardized nutrient availability metric, studies comparing nutrient availability across sites commonly use soil-fertility-related approximations such as the height of 100-year-old trees (which, however, also depends on other factors such as soil depth and hydrology – Hägglund and Lundmark, 1977) or manually classify sites as low, medium, and high nutrient availability based on existing site information (Vicca et al., 2012; Fernández-Martínez et al., 2014). The absence of a more nuanced expression impedes elucidating the role of nutrient availability in ecosystem processes and functioning (Cleveland et al., 2011) and how these respond to global change, and it precludes investigating nonlinear effects of nutrient availability.
Although various proxies exist to estimate soil N and phosphorus (P)
availability at the local scale (e.g., “snapshots” of extractable pools), no
perfect method exists to quantify N and P availability in a comparable way
across ecosystems (Binkley and Hart, 1989; Holford, 1997; Neyroud and
Lischer, 2003). This limits the potential for inter-site comparisons based on
these data alone (Cleveland et al., 2011). Soil properties like soil texture,
soil organic matter (SOM) quantity and quality, and pH, however,
are more indicative of the general nutrient status because together with
environmental factors (temperature and moisture – Binkley and Hart, 1989),
they control (1) the total amount of nutrients in soil solution, (2) ion
exchange sites, and (3) unavailable pools of soil nutrients, as well as fluxes
among these three (Roy et al., 2006). For instance, a high clay fraction
corresponds to a high cation exchange capacity (CEC), i.e., the soil's
potential to retain positively charged, exchangeable ions such as
NH
Only a few exploratory attempts to find an expression for nutrient
availability at the global scale have been made. The most recent one was
developed by IIASA and FAO, who provide a simple index in their Global
Agro-ecological Zones report of 2012 (IIASA and FAO, 2012). It is a
worldwide-applicable metric for constraints on nutrient availability,
principally meant for agricultural purposes. This metric represents, for a
particular crop species, the percentage of the maximum attainable
productivity that could be reached given constraints imposed by
environmental characteristics such as climate, rooting conditions, and soil
oxygen availability but absent nutrient limitation:
To the best of our knowledge, the accuracy of the IIASA metric has not yet been tested against data from natural ecosystems, and it is not known to what extent the metric – aimed at describing constraints on nutrient availability – can describe variation in nutrient availability of nonagricultural soils. Evaluation of the IIASA metric, and further development of a widely applicable metric of nutrient availability, requires datasets that combine the necessary information on soil properties and nutrients with data on plant productivity, while also covering a substantial variation in nutrient availability. Such a unique dataset – which comprises > 2500 conifer forest plots and thus provides sufficient statistical power for an evaluation of the metric – is provided by the Swedish forest inventory service. Moreover, it contains additional variables of interest related to N availability, such as total soil N stock and concentration, and especially the soil C : N ratio, which we expected to be an important factor in explaining variation in nutrient availability. This large dataset also allows the evaluation of our country-scale findings against local gradients in nutrient availability that avoid confounding effects of covarying factors such as climate and N deposition.
Specifically, we used the Swedish dataset to address the following
questions:
Which single soil variables can explain variation in normalized
(i.e.,
climate-independent) productivity across Sweden? Which combination of soil
factors best explains variation in normalized productivity? Can the IIASA metric of constraints on nutrient availability explain
variation in normalized productivity? Are the soil variables already
included in the metric (SOC, soil texture, TEB, and pH Can the IIASA metric be adjusted to characterize nutrient availability in
Swedish forests?
We combined a Swedish forest soil (Olsson, 1999; Lundin, 2011) and inventory
database for the period 2003–2012 (Lundin, 2011) with a database for soil
texture and climate information across Sweden. Precipitation data were
extracted from the European Commission Joint Research Centre Monitoring
Agricultural Resources dataset (EC–JRC–MARS, based on ECMWF model outputs
and a reanalysis of ERA-Interim; see
Many of the (mostly managed) forest plots were not monocultures, but
contained both Norway spruce (
In order to facilitate comparisons among sites and to allow the calculation
of the nutrient availability metric, we converted the soil measurements (SOC,
soil texture, TEB, pH
Conversions of soil data (“variables”) per horizon to data per depth
interval (layer
The IIASA metric of constraints on nutrient availability, originally meant
for use on arable land, incorporates four crop-specific scores (estimated for
SOC, soil texture, TEB, and pH
The total score for nutrient availability, which can be interpreted as the
expected actual yield (i.e., aboveground productivity) proportional to the
maximum attainable yield (i.e., without nutrient constraints), was then
calculated as follows (IIASA and FAO, 2012):
IIASA soil scores for soil organic carbon concentration (SOC),
texture, total exchangeable bases (TEB), and pH measured in water (pH
Overview of variables of the database used in the current study.
Each plot for soil and vegetation analyses had a 10 m radius and was sampled
once during the period 2003–2012. The (mostly managed) forests in the
inventory represent a random sample of Swedish forests. Abbreviations:
MAP: mean annual precipitation; TSUM: growing season temperature
sum; SOC: soil organic carbon concentration; TEB: total
exchangeable bases; pH
Forest productivity across Sweden depends not only on soil nutrient availability but also on climate, soil wetness, and N deposition. Before evaluating the metric, we removed the influence of climate on forest productivity (“PRE” in Fig. 2). The influence of soil moisture and N deposition are considered in further analyses (see Sect. 2.3.1). Normalized productivity was calculated in two alternative ways: (1) as the residuals of the regression model (of PRE; from here on referred to as “method 1”; Figs. 3a and S1a, b, Tables S1 and S2, and Eq. S1) and (2) as the ratio of the original productivity relative to the theoretical maximum productivity (from here on referred to as “method 2”; Figs. 3b and S1b, c). This theoretical maximum productivity, which was extracted from a map provided by Bergh et al. (2005) with ArcGIS (ESRI, 2011), indicates the productivity that could be obtained under non-nutrient-limited conditions and is further referred to as attainable productivity. The second method is thus very similar to the IIASA approach (see Eq. 1), but because an estimate for attainable productivity was only available for spruce, it could only be applied for this species. The two alternative methods for normalizing productivity were used to verify the robustness of the analyses, and because each method has its own advantages and disadvantages. The main disadvantage of method 1 is that not only the direct influence of climate on productivity is removed but also its indirect effect through nutrient availability, so that only effects of regional variation in nutrient availability on productivity remain. Method 2, however, involves an extrapolation based on the results of only a few fertilization experiments and thus comes with high uncertainty on the estimates of attainable productivity.
Regression analysis was then used to elucidate how the different soil variables were related to normalized productivity (Question 1). In addition, normalized productivity was fitted against the IIASA metric to test its performance. The correlation between the residuals of this relationship and each of the four variables of the metric then indicated whether or not the variables were well implemented (Question 2). Finally, the associations found in Question 1 indicated how the metric could be adjusted (Question 3). Two adjusted metrics were then evaluated in the same way as the original IIASA metric in Question 2, and by investigating if they could explain variation in productivity for five local gradients in nutrient availability. An overview of the methodology is presented in Fig. 2.
As explained in the paragraphs above, productivity was normalized using two methods. Method 1 considers the residuals to reflect deviations in productivity imposed by spatial variation in nutrient availability and in the absence of climate effects. However, residuals deviated more strongly from zero towards the warmer south (Fig. 3a), thus causing heteroscedasticity and a potential bias in the further analyses if not properly accounted for. For further analyses, we therefore split the database into three TSUM groups (north, middle, and south; Fig. 3a). For method 2, considering the ratio of actual to attainable productivity, this separation of different regions was not required.
Objectives and methods followed in the current paper. PRE refers to a regression model of productivity vs. climate and species (spp.); Question 1, Question 2, and Question 3 refer to the research questions. Performance of the adjusted nutrient metrics was evaluated against the entire database, and against five nutrient availability gradients, selected from the database (Fig. S2).
In order to understand the correlation structure of the database, and avoid
multicollinearity in the subsequent analyses, we examined correlations among
the soil variables (SOC, TN, total N stock, soil C : N ratio, sand
fraction, clay fraction, TEB, pHw, and pH
Soil moisture and soil type (available as categorical variables) may act as confounding factors for associations between productivity and other soil properties (e.g., in wet soils, the rooting environment is anoxic and decomposition is inhibited (Olsson et al., 2009), leading to reduced productivity and accumulation of SOM). We therefore tested if the selected soil variables and normalized productivity differed among soil moisture classes (dry, fresh, fresh-moist, and moist, as available from the database and derived from a combination of indicators such as groundwater depth – Olsson, 1999; Olsson et al., 2009) and the most common World Reference Base for Soil Resources-based soil types (Histosols, Gleysols, Regosols, Leptosols, and Podzols) using two-way ANOVA with soil moisture or type and tree species as fixed factors.
Numerous studies have shown the strong influence of N deposition on forest
productivity (e.g., Laubhann et al., 2009; Solberg et al., 2009; de Vries et
al., 2014; Binkley and Högberg, 2016; Wang et al., 2017). Although N
deposition can influence the soil properties considered in our analyses, it
may also influence productivity without immediate changes in these soil
properties (i.e., there is a time lag – Novotny et al., 2015). In other
words, for a given set of soil characteristics and climate, productivity may
vary depending on N deposition, which would weaken the link between soil
properties and normalized productivity. To verify whether N deposition
confounded our analyses, we extracted N deposition data of 2015 from a map
available at
Simple regression analysis was used to determine the relationship between single soil variables and normalized productivity. To test the robustness of the observed relationships in the absence of potentially confounding effects of soil moisture and type, we performed these analyses on all data, and on the data stratified by soil moisture and soil type. Then, we tested which combination of continuous soil variables best explained variation in normalized productivity across Sweden (multiple regression analysis). Starting from the full model containing all explanatory variables, the least significant term was removed, resulting in a simplified model. Performance of the full and simplified models was then compared using the mean squared error (mse), based on cross validation (package DAAG – Maindonald and Braun, 2015). We repeated this model simplification procedure until mse stopped decreasing. Interaction effects up to the first order were added if suggested by regression trees (package tree – Ripley, 2015). For method 1 (Fig. 3), first-order interactions of continuous variables with region as a factor (levels: N, M, S) were included in the selection procedure (i.e., an ANCOVA was used for this approach).
Normalized productivity was calculated in two alternative ways.
Irrespective of the method applied, a well-functioning nutrient availability
metric would be recognized by a clear, positive relationship with
productivity. We used linear model analysis to test the significance of the
relationship between the metric and normalized productivity, and to
determine its explanatory power (
Outcomes of Question 1 indicated which soil variables best explained
variation in normalized productivity. This information was further used to
(i) assess if the relationships for variables already included in the
IIASA metric should be altered, (ii) remove soil variables from the metric if
their empirical associations with normalized productivity were opposite from
their relationships in the original IIASA metric, which would complicate
parameterization, and (iii) include additional soil variables to improve
performance of the metric. Two new metrics were developed: “adjusted
metric 1” and “adjusted metric 2”, referring to the respective methods of
normalizing productivity (Fig. 3). As a starting point for adjusted metric 1,
half of the dataset from southern Sweden (where productivity varied most; see
Fig 3a) was used as a calibration set to derive regression equations, while
half of the complete national dataset for spruce served as a calibration set
for adjusted metric 2. The best predictors of normalized productivity as
indicated by the analyses in Question 1 were then adopted as partial metric
scores (see the original Eqs. 6–9). Moreover, for adjusted metric 1, the
minimum and maximum normalized productivities observed in southern Sweden
were included as lower and upper boundaries to the partial metric scores to
avoid possible unrealistic values for future applications to other datasets.
For method 2, the minima and maxima were, as in the IIASA metric, set to 0
and 100 %, respectively (units for this metric (%) remained the same
as in the original IIASA metric, while for new metric 1, the unit was
(m
Correlation structure of a set of potential key soil variables for a
soil depth of 0–20 cm. Panel
Normalized productivity per soil moisture class.
Normalized productivity per soil type.
Performance of the adjusted metrics was evaluated by (i) testing normalized
productivity in the database against the metrics and inspecting the
implementation of the variables, and by (ii) testing productivity against the
metrics and examining variable implementation for five manually selected
local gradients in nutrient availability. For (i), the metrics were thus
evaluated as described for the IIASA metric under Question 2, with the
exceptions that validation datasets were used (i.e., the data that were used
for developing the metrics were not included for the evaluations) and that
the same analyses were also performed after stratifying by soil moisture and
type to assess robustness. For (ii), two gradients with spruce, and three
gradients with pine (locations indicated in Fig. S2) were selected in ArcGIS
(ESRI, 2011). Each of these gradients included at least 40 data points from
the Swedish database that (i) were located in the same region, without
showing substantial spatial variation in climate and (ii) showed high
spatial variation in soil moisture, TEB, or productivity (we also
searched specifically for clear soil C : N gradients, but found none for
which climate did not vary; variables like soil C : N or SOC did however
sufficiently vary within the five selected gradients:
We examined the validity of the linear models' assumptions (linearity,
normality of residuals, no influential outliers, homoscedasticity) with
standard functions of R (R Core Team, 2015), including diagnostic plots.
Moreover, for all regressions, potential nonlinearities were detected with
histograms of all variables' distributions and generalized additive models
from the mgcv package (Wood, 2006). Data were log transformed if their
distribution was right-skewed, while polynomial (e.g., quadratic) functions
were included in the model selection procedure where the general additive
models suggested nonlinear patterns. The variance inflation factor (package
car – Fox and Weisberg, 2011) assessed possible multicollinearity. Whenever
confidence intervals are given, they represent standard errors of the mean (s.e.m.).
For all analyses,
Correlations among soil properties and nutrients were investigated to verify
if any of the variables could be excluded in the subsequent analyses due to
redundancy. In this database, pH
Relationships between soil variables and normalized productivity might vary
depending on factors such as soil moisture and soil type. Therefore, we first
examined how these factors influence soil properties and normalized
productivity. Soil moisture, for example, may influence nutrient availability
of ecosystems by – among others – affecting the rate of decomposition, and
consequently change other soil characteristics. In the database, each forest
was originally assigned to a soil moisture category. Using these categories,
we found that SOC and the soil C : N ratio increased from dry to moist. A
similar trend was observed for TEB, while the sand fraction and
pH
Soil properties not only differed among soil moisture classes, but also among
soil types. Especially Histosols and Podzols could be distinguished from the
other soils: Histosols (which largely overlapped with the wet soil moisture
classes) were characterized by a low pH
Relationship between normalized productivity following method 1
(residual mean annual increment – MAI);
In addition to soil moisture and soil type, N deposition may also confound
associations between normalized productivity and soil data. In our Swedish
database, N deposition correlated significantly with all soil variables.
Especially TN and SOC correlated positively with N deposition (Fig. 4b). N
deposition was also strongly positively correlated with productivity
(Pearson's
Relationship between normalized productivity following method 2
(actual
Associations between single soil variables and
normalized productivity for Swedish spruce and pine forests. Significance
(
N/a: not applicable.
Estimates
N/a: not applicable.
In order to elucidate how soil variables affect nutrient availabilities
across Sweden, we used their single and combined relationships with
normalized productivity. For method 1, we found that most single soil
variables were significantly related to normalized productivity (Table 2;
Results of method 2 were qualitatively similar to those of the other approach
for SOC (Fig. 8a), N stock, soil C : N ratio (Fig. 8b), clay fraction, and
TEB, although the N stock explained a larger proportion of the variation here
and the curve for actual
Evaluation of the IIASA metric of constraints on nutrient
availability for Swedish conifer forests.
Evaluation of adjusted nutrient availability metric 1 for Swedish
conifer forests.
Evaluation of adjusted nutrient availability metric 2 for Swedish
conifer forests.
Since soil moisture and soil type influenced both soil properties and
normalized productivity, we also stratified the analyses above by these
factors. In general, these separate analyses confirmed the robustness of the
observed patterns across the database (despite low
Both methods agreed on the poor performance of the IIASA metric to elucidate
patterns in nutrient availability, as the weakly positive correlation between
normalized productivity and the metric was rarely significant, and explained
< 1 % of the variation in normalized productivity in northern
Sweden for method 1 (Fig. 9). Residual values of the relationship between
normalized productivity of method 1 and the metric score (Fig. 9a) were
significantly associated with all four input variables of the metric (SOC,
soil texture, TEB, and pH
From the statistical analyses for Question 1, we deduce that SOC, soil
C : N, and pH each play a role in influencing nutrient availability in
Sweden. Based on their relationships with normalized productivity in southern
Sweden according to method 1 (Table S10), and in all of Sweden according to
method 2 (Table S11), the following formulae were implemented in two adjusted
nutrient availability metrics (Figs. S5 and S6):
In the same way as for the IIASA metric, Eqs. (11)–(13) and (14)–(16) were combined in Eq. (10) to calculate the final nutrient availability score for each metric. Soil texture and exchangeable bases were not included here, as their empirical relationships with normalized productivity showed opposite trends compared to their implementation in the IIASA metric (Fig. 1 vs. Tables 2 and S9), likely due to indirect effects of soil moisture and related organic matter accumulation.
Evaluation of adjusted nutrient availability metric 1 for selected
nutrient availability gradients in Sweden (Fig. S2). Statistics indicate the
relationship between productivity (mean annual increment –
m
N/a: not applicable
In contrast to the IIASA metric of constraints on nutrient availability, the
adjusted metrics were significantly related with normalized productivity
(Figs. 10 and 11), albeit with low
Five nutrient availability gradients were selected to evaluate the
performance of the adjusted metrics in the absence of confounding climate and
N deposition effects (Fig. S2). Both metrics were capable of describing
variation in productivity for all gradients, with
Soil moisture varies between dry and very wet across Sweden and may obfuscate associations between nutrient-related soil properties and (normalized) productivity. Across our database, we indeed observed that certain soil properties (SOC, soil C : N ratio, TEB) were related with soil moisture (Fig. S3), and also normalized productivity depended on soil wetness (Fig. 5): productivity was highest for intermediate soil moisture levels, and was significantly reduced for the most dry and wet soils. The influence of soil moisture on productivity can be explained as follows: at high water content, the anoxic rooting environment inhibits root and microbial respiration. Tree productivity is thus suppressed, both directly due to the lack of oxygen for the tree itself and because nutrient supply is limited due to the inhibition of mineralization (Gorham, 1991). For relatively dry soils, however, productivity is reduced because of water limitation (which has been shown to occur in southern Sweden – Bergh et al., 1999), lower nutrient inputs through groundwater, fewer periods with easily available nutrients in the soil solution (Qian and Schoenau, 2002), and lower retention (Larcher, 2003; Roy et al., 2006) and supply (Binkley and Hart, 1989) of nutrients by organic matter. In summary, any associations between a soil variable and productivity should be interpreted in view of the fact that soil moisture may act as a factor influencing both this soil variable and productivity. We therefore performed our analyses not only for the complete set of data but also for the data stratified by soil moisture to assess whether relationships between soil properties and productivity would change.
In the same way as for soil moisture, stratification by soil type might help in resolving nutrient–productivity relationships. Soil properties and productivity differed among the five most common soil types in the database (i.e., Histosols, Gleysols, Regosols, Leptosols, and Podzols – Fig. S4). To some extent, these differences among soil types overlapped with those observed for soil moisture classes (e.g., wet Histosols had the highest SOC, soil C : N, and the lowest productivity), but additional patterns emerged as well (e.g., Podzols had a particularly low TEB stock). Although actual differences in nutrient availability among soil types will in part underlie the variations in productivity, other factors related to soil type (e.g., wetness, soil depth, or the rooting environment) may also influence productivity (Binkley and Hart, 1989). The main analyses of the current study were therefore stratified by both soil moisture and type to test the robustness of associations between nutrient-related soil properties and normalized productivity.
Many studies have shown the strong influence of N deposition on forest
productivity (e.g., Laubhann et al., 2009; Solberg et al., 2009; de Vries et
al., 2014; Binkley and Högberg, 2016; Wang et al., 2017). As expected, N
deposition correlated to some extent with some of the soil variables
considered in the present study, such as the total soil N stock and
concentration (Fig. 4b). Furthermore, N deposition was strongly positively
related to productivity. However, this effect of N deposition on productivity
cannot be separated from the influence of climate and light, as all these
factors increase together in the north–south direction. Nevertheless, we
argue that for the goals of this study, i.e., investigating soil
nutrient–productivity relationships across Sweden and developing a nutrient
metric, the spatially varying N deposition is not problematic since the
normalization for climate and species according to method 1 (Fig. 3a) at the
same time also removed the influence of the confounding N deposition on
productivity. Accordingly, residual productivity was generally not
correlated with N deposition (Table S3). The response variable derived from
method 2 (i.e., actual
Evaluation of adjusted nutrient
availability metric 2 for selected nutrient availability gradients in Sweden
(Fig. S2). Statistics indicate the relationship between productivity (mean
annual increment – m
N/a: not applicable
Soil C : N ratio had a negative effect on normalized productivity for both methods (Figs. 7b and 8b). Apart from high N concentrations at low C : N, increased productivities with decreasing C : N ratio can follow from its influence on litter decomposition and mineralization, and thus on nutrient availability: when the ratio in organic matter is high, microbes more strongly immobilize N to adjust their internal C to N stoichiometry. As a consequence, N is not easily released and made available for plant uptake. A low C : N ratio, however, facilitates N mineralization (Roy et al., 2006) and thus enhances N availability (Wilkinson et al., 1999).
The relationship of ln SOC with normalized productivity, which showed an optimum (Figs. 7a and 8a), is partly explained by the role of SOM in storing and exchanging nutrients, but also partly by the confounding effect of soil moisture. At high moisture levels, SOC most likely increases because decomposition is reduced in water-saturated soils, leading to organic matter accumulation (Fig. S3a). Anoxic soils impede productivity because of the aforementioned prevention of root respiration and reduced supply of newly available nutrients through mineralization. At low SOC, however, productivity supposedly decreases with decreasing SOC because of water limitation and low availability of organic matter, which acts as a nutrient store. Together, these results suggest that the empirical relationship between SOC and productivity might have an optimum below which soil fertility is reduced due to a lack of sufficient organic matter, and above which high SOC indicates hostile rooting conditions and limited nutrient supply through slow mineralization. The first aspect is thus included in the IIASA metric (Fig. 1), while the decreasing part of the curve should be included in the empirical relationship of SOC with nutrient availability if the effect of reduced decomposition is not captured by any of the other soil variables in an updated metric.
Soil factors other than the soil C : N ratio and SOC either exhibited only a marginal influence on normalized productivity or their effect depended on the approach (Table 2). N stocks could explain variation across both methods, but their explanatory power was rather modest for method 1. We anticipate that if we aim to develop metrics applicable beyond the boreal biome, including N stock will be of limited value, as this variable is only loosely related to N availability (Högberg et al., 2017).
Mineral soil clay fractions had a weak but significantly positive effect on
normalized productivity. Even though clay particles can protect SOM from
decomposition (Xu et al., 2016), clay soils in the Swedish database in all
likelihood positively influence nutrient availability by means of their
negative charges that serve as cation exchange sites (i.e., for NH
All equations resulting from multiple regression analysis combining different
soil variables contained the soil C : N ratio and SOC (Table 3), confirming
that, in the absence of direct soil nutrient data, these are key and
complementary determinants of nutrient availability in northern coniferous
forests. Qualitatively considered, associations of C : N ratio (
Although the IIASA metric of constraints of nutrient availability was originally designed for arable lands, we opted to start with this metric for a few reasons. Apart from the fact that to our knowledge it represents the only attempt so far to develop a generic nutrient metric, the structures of its formulas (Eqs. 6–9) reflect general mechanisms that link soil properties to nutrient availability, which are also valid for nonagricultural ecosystems. Soil pH for example shows a typical optimum effect on nutrient availability, while SOC and TEB have a direct positive nonlinear influence (IIASA and FAO, 2012). The final weighing of the four partial scores (Eq. 10) finds its rationale in the idea that if a certain soil property is particularly suboptimal, it will be the most important nutrient-related determinant of productivity, with less influence of the other soil properties that are closer to or within their optimal range. This way of weighing can be considered a type of interaction, but one that cannot be implemented in a simple linear regression model. Hence, our main reason for adopting the IIASA metric as a starting point is that, in spite of its simplicity, it is based on theoretical considerations. Moreover, adopting this structure allows for updating with other datasets – something that can probably not be achieved with multiple regression equations (see Sect. 4.4).
The IIASA metric of constraints on nutrient availability does not clarify
much variation in normalized productivity among Swedish forests. Moreover,
SOC, soil texture, TEB, and pH
Based on results of the analyses for Question 1, the nutrient availability metric was adjusted by (i) including an empirical optimum in the influence of SOC on normalized productivity, and (ii) including soil C : N, thus more explicitly incorporating the availability of N. In the current analysis, soil texture and TEB were excluded from the metrics, as they exhibited negative instead of the expected positive associations with normalized productivities (IIASA and FAO, 2012), probably due to indirect effects of low soil oxygen, reduced decomposition, and suppressed productivity where the proportion of sand is low and TEB is high.
In contrast to the original metric developed by IIASA, the adjusted metrics described some variation across all approaches using the full database (Figs. 10 and 11). Variables were generally properly implemented, at least for the adjusted metric 1 (Table S16). For metric 2, significant (but normalization-method-dependent) associations emerged between residuals of normalized productivity and SOC and pH (Table S17). The stratified analyses confirm that the metrics are an improvement, at least for those soil moisture classes and soil types with sufficient data points (Tables S12–15). Moreover, each metric could describe spatial variation in productivity for five manually selected local nutrient availability gradients (Tables 4 and 5). The coefficients of determination were generally higher for these gradients than for the database analyses, likely because the gradients did not require a normalization for climate (the latter increased the uncertainty on the response variable; see Sect. 4.5 on sources of uncertainty and future challenges). Lastly, the gradients generally confirmed the correct implementation of soil variables in adjusted metric 1 (Table S18), whereas for metric 2, scores for high SOC might be overestimated (Table S19).
Variation in normalized productivity explained by the adjusted metrics
(
Even though normalized productivity was significantly related to soil
properties, and to our adjusted metrics, much of the variation in normalized
productivity remains unexplained. The considerable unexplained variation may
have multiple reasons. Apart from a possible lack of soil and nutrient data
more closely related to N availability than the ones available in our
database, another possible factor reducing
The similar and significant results for the different methods (1 and 2) and subsets of the database (regions, soil moisture classes, and soil types) indicate that the findings about the soil properties and nutrients are generally robust. The adjusted metrics explained up to 21 % of the variation in normalized productivity. It is unclear to what degree the influence of nutrient availability is covered by this percentage. Future studies, in which additional soil data (e.g., P) can be included, will need to verify this. In any case, the significant relationships with normalized productivity, the better implementation of the soil variables, and the capability of the metrics to explain up to 38 % of the variation in productivity across different gradients imply a significant improvement compared to the original IIASA metric for this database.
A key challenge in the further development of a metric describing spatial variation in nutrient availability both within and outside the boreal biome is differential nutrient limitation. Eventually, we want to be able to compare for example N-limited and P-limited systems. The original structure of the IIASA metric, which was kept in our adjusted metrics, facilitates this by allowing the inclusion of multiple soil variables such as soil C : N (mainly relating to N availability), pH (among others a critical factor controlling P availability), and TEB in one single metric. In fact, the IIASA metric is particularly useful in this regard, as it gives more weight to the soil factor with the lowest score. This corresponds to reality and enables accounting for the type of nutrient limitation. For instance, if soil C : N is high, indicating N limitation, the metric score will be substantially reduced by this high C : N, while at low C : N other limiting factors can dominate the metric score.
In our database, the soil properties explaining most variation in tree productivity across Swedish conifer forests were SOC and the soil C : N ratio. The empirical relationship between SOC and normalized productivity showed an optimum, reflecting the soil characteristic's direct positive effect on nutrient availability only at low soil carbon concentrations, whereas at high SOC, its effect was masked by other environmental factors (soil moisture and oxygen, and temperature), affecting both SOC and productivity through their role in regulating organic matter formation and decomposition rates. The soil C : N ratio showed the expected negative correlation with normalized productivity in the present database. Based on the resulting regression equations, we adjusted the IIASA metric for Swedish conifer forests by modifying the relationship between SOC and nutrient availability, and by incorporating soil C : N.
The current nutrient availability metrics were developed based on data from Swedish conifer forests only, and can therefore not be extrapolated outside the boreal biome. In order to verify if development of a metric that compares the nutrient status across sites also beyond the boreal biome is feasible, the adjusted metrics developed in this study will need to be validated (and if necessary further modified) based on other forests elsewhere for which the necessary soil information is available. In a later stage, this approach can then be expanded to other ecosystem types.
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SV and KVS conceived the study. KVS performed the analyses and wrote the paper. JAH provided statistical advice and JS provided data. All authors contributed to the discussions and the writing of the paper.
The authors declare that they have no conflict of interest.
This research was supported by the Fund for Scientific Research – Flanders (FWO aspirant grant to KVS; FWO postdoctoral fellowship to SV) and by the European Research Council grant ERC-SyG-610028 IMBALANCE-P. We also acknowledge support from the ClimMani COST Action (ES1308). The Swedish Forest Soil Inventory is part of the national environmental monitoring commissioned by the Swedish Environmental Protection Agency. EC–JRC–MARS provided precipitation data. We thank Ivan Janssens for his valuable comments on earlier versions of the paper. Published with support from the Belgian University Foundation. Edited by: Edzo Veldkamp Reviewed by: three anonymous referees