Linking root-associated fungal and bacterial functions to root economics

Reviewer #1 (Public Review):

Summary:
In this study, Wu et al. investigated the microbiome in the rhizosphere and roots of plant species along an elevational gradient. They found that: (i) plants with higher root nitrogen ("fast" strategy) were more likely to be associated with saprotrophic fungi, plant pathogenic fungi, and AM fungi, but plants with lower root nitrogen ("slow" strategy) were more likely to be associated with ectomycorrhizal fungi; (ii) bacterial functional guilds were associated with root-zone pH but not root traits.

Strengths:
This study is novel in the sense that it revealed the associations between microbiome and trait dimensions of plants. This has been rarely explored even though we acknowledge the importance of plant-microbe interactions.

Weaknesses:
The authors tried to include the relative abundances of bacterial and fungal guilds into the root economics framework, which I disagree with because they are just associated with the root economics framework. The title also states that the authors' aim is to link microbial functional guilds to root economics. Therefore, I would suggest that the analyses should be redone to elaborate on the relationships between microbiome and root functional traits.

Below I provide some critiques and comments that outline my concerns and provide recommendations to hopefully improve the current manuscript.

-Figures 2 and 3: The authors included soil properties, relative abundances of bacterial or fungal guilds, and root traits in the root economics spectrum. However, soil properties and relative abundances of bacterial or fungal guilds are not root traits, they are just associated with root traits. These bacterial or fungal guilds are the consequence of root traits. Also, the authors did not elaborate on the root trait dimensions of the plants. The only trait dimension they discussed is the "fast-slow" axis. Therefore, I would suggest the authors first analyze the trait dimensions of plants by only using the root traits (PCA), and then explore how the soil properties and relative abundances of bacterial or fungal guilds are associated with the trait dimensions (e.g., envfit in the vegan package).

-When exploring the associations between microbial functional guilds and root traits, it is unnecessary to analyze the bacterial and fungal functional guilds separately. The bacterial and fungal functional guilds can be included in the same models, and their relative importance and patterns can be compared.

-For fungi, the authors used FUNGuild to infer functional guilds from taxonomy. qPCR was also performed to validate the results of AMF. This is fantastic. For bacteria, the authors used FAPROTAX to infer functional guilds from taxonomy. However, archaea are also considered in some functions in FAPROTAX. For example, both bacteria (ammonia-oxidizing bacteria) and archaea (ammonia-oxidizing archaea) play critical roles in nitrification. I would assume the authors have removed archaea from the dataset because they stated that the functions of bacteria are inferred from FAPROTAX. Therefore, the importance of nitrification might be underestimated.

-Key methodological details are missing. First, maps of the sampling site and plots are missing. It would be great if the authors provided maps showing the location of the sampling site and the spatial distribution of the 11 plots. Second, in lines 304-306 the authors claimed that they sampled the most common species in the plots, but they did not provide the coverage or relative abundances of plant species in the plots.

Reviewer #2 (Public Review):

Summary:
The authors aimed to determine to what extent root morphology, chemistry, and soil characteristics explained the relative abundance of functional groups of bacteria and fungi associated with roots. To do so, they sample roots and rhizhospheric soil of trees along an elevation gradient. This type of work is common in the field of microbial ecology. The main novelties I see are two: a) a focus on the functional groups of bacteria and fungi rather than just taxonomic abundance. I think this approach is valuable because it provides information about the potential functions of these microorganisms; b) using the root economic spectrum to frame the findings. The root economic spectrum reflects a gradient along which plant roots can be allocated from 'short-lived that provide fast investment return' to 'long-lived that provide a slow investment return'. It is logical to expect (as the authors did) that variation along this gradient will be an important factor in explaining the variation in functional groups.

Strengths:
The main strength is using the root economic spectrum as a framework to interpret the data. There are countless studies addressing variation in the relative abundance of microbial communities along environmental gradients which tend to be more descriptive. I think using this framework advances the field by suggesting that while the root economic spectrum exists it is not a very important explanatory variable to predict changes in functional diversity. I also think the authors use state-of-the art methods to collect and process the sample (i.e. to obtain the data).

Weaknesses:
The main weakness is with the presentation of statistical methods as it currently stands. The authors use distance-based redundancy analysis as the main statistical method. However, my understanding is that this method is not advised for a relative abundance of communities. At least not with Euclidean distances which is the default option of the functions dbrda in vegan. The use of this distance would group together communities with no species in common as close to each other (which is an incorrect interpretation). I think the authors should specify what distance they use. My guess is that they actually used bray-curtis in which case this weakness does not apply. However, as it stands it is not specified what metric they use and if they indeed use Euclidean distances it may lead to inaccurate conclusions. In addition, they also mention they use PCA on the relative abundance of functional groups. By definition, PCA is also based on Euclidean distances, which gives a similar problem as dbrda. Thus, I encourage the authors to use bray-curtis distance and specify it in the text.

Reviewer #3 (Public Review):

Summary:
In this study, the authors collected a large set of data on root traits and root-associated microbes in the root endosphere and rhizosphere in order to integrate these important organisms in the root economics spectrum. By sampling a relatively large set of species from the subtropics along an elevation gradient, they tested whether microbial functions covary with root traits and root trait axes and if so, aimed to discuss what this could tell us about the (belowground) functioning of trees and forests.

Strengths:
The strengths of this study lie mostly in the impressive dataset set the authors compiled: they sampled belowground properties of a relatively large number of tree species from an understudied region: i.e., the subtropics, where species-level root data are notoriously scarce. Secondly, their extensive sampling of associated microbes to integrate them in the root economics space is an important quality, because of the strong associations between roots and fungi and bacteria: soil microbes are directly related to root form (e.g., mycorrhizal fungi and root diameter and SRL), and function (e.g., taking up soil nutrients from various sources). Thirdly, the PCA figures (Figures 2 and 3) look very nice and intuitive and the paper is very well written.

Weaknesses:
That said, this study also has several methodological weaknesses that make the results, and therefore the impact of this study difficult to evaluate and interpret.

(1) Design: The design of this study needs further explanation and justification in the Introduction and Methods sections in order to understand the ecological meaning of the results. Root traits and microbial community composition differ with their environment, and therefore (likely) also with elevation. Elevation is included in the redundancy analysis as a main effect, but without further environmental information, its impact is not ecologically meaningful. What is the rationale for including an elevation gradient in the design and as a main effect in the analyses? Do environmental conditions vary across altitudes and how, and if so, how would this impact the data?

What is the rationale behind sampling endosphere and rhizosphere microbial communities - why do both? And why also include pathogens - what are their expected roles in the RES? What do we know about this already? The introduction needs a more extensive literature review of these additional variables that are included in the analyses.

(2) Units of replication and analysis in the model: What are the units of replication and analyses, e.g., how many trees were sampled per species, how many species or trees per elevation, and how many plots per elevation? Were all 11 plots at different elevations and if so, which ones? The level of analysis for the redundancy analyses is not entirely clear: L. 404 mentions that the analyses were done 'across the rhizosphere and root tissue samples', but is that then at the individual-tree level? If so, it seems that these analyses should then also account for dependencies between trees from the same species and phylogeny (as (nested) covariates or random factors). With the information provided, I cannot tell whether there was sufficient replication for statistical interpretations.

(3) PCA: The results of the parallel analyses are not described: which components were retained? Because the authors aim to integrate microbial functions in a root economics space, I recommend first demonstrating the existence of a root economics space across the 52 subtropical species before running a PCA that includes the microbial traits. The PCA shown in this study does not exactly match the RES and this could be because traits of these species covary differently, but may also simply result from including additional traits to the PCA.

Also, the PCA's shown are carried out at the individual-tree level. I would recommend, however, including the species-level PCA's in the main text, because the individual-level PCA may not only reflect species-inherent ecological strategies (that e.g., the RES by Bergmann et al. 2020 describe) but also plasticity (Figures 2 and 3 both show an elevation effect that may be partly due to plasticity). While the results here are rather similar, intraspecific differences in root traits may follow different ecological principles and therefore not always be appropriate to compare with an interspecific RES (see for example Weemstra & Valverde-Barrantes, 2022, Annals of Botany).

I could not deduce whether tree species in the "fungal PCA" (Figure 2) were assigned as AM or EcM based on Table 1, or based on their observed fungal community composition. In the former case, the fungal functional guild gradient (from EcM to saprotrophs and AM) is partially an artificial one, because EcM tree species are not AM species (according to Table 1) and therefore, by definition, constitute a tradeoff or autocorrelation. And, as the authors also discuss, AM tree species may host EcM fungal species. Before I can evaluate the ecological meaning of PC1, and whether or not it really represents a mineral/organic nutrient gradient, information is needed on which data are used here.

I do not agree with the term 'gradient of bacterial guilds' (i.e., PC1 in Figure 3). All but 1 bacterial 'function' positively loaded on PC1 and 'fermentation' was only weakly negatively correlated with PC1. I do not think this constitutes a 'bacterial gradient'.

(4) Soil samples: Were they collected from the surrounding soil of each tree (L. 341), or from the root zone (L. 110). The former seems to refer to bulk soil samples, but the latter could be interpreted as rhizosphere soils. It is therefore not entirely clear whether these are the same soil samples, and if so, where they were sampled exactly.

Aims:
The authors aimed to integrate endospheric and rhizospheric microbial and fungal community composition in the root economics space. Owing to statistical concerns (i.e., lacking parallel analysis results and the makeup of the PCs (AM versus EcM classification), I am not sure the authors succeeded in this. Besides that, the interpretation of the axes seems rather oversimplified and needs some consideration.

Root N is discussed as an important driver of fungal functional composition. Indeed, it was one of the significant variables in the redundancy models predicting microbial community composition, but its contribution to community composition was small (2 - 3 %), and the mechanistic interpretation was rather speculative. Specifically, the role of root N in root (and tree) functioning remains highly uncertain: the link with respiration and exudation is increasingly demonstrated but its actual meaning for nutrient uptake is not well understood (Freschet et al. 2021. New Phytologist). If and how root economics (represented by root N) and the fungal-driven nutrient economy (EcM versus AM, saprotrophs) can indeed be integrated into a unified framework (L. 223 - 224) seems a relevant question that is worth pursuing based on this paper, but in my opinion, this study does not clearly answer it, because the statistical analyses might need further work (or explanation) and underlying mechanisms are not well explained and supported by evidence.

In addition, the root morphology axis was indeed independent of the "fungal gradient", but this is in itself not an interesting finding. What is interesting, but not discussed is that, generally, AM species are expected to have thicker roots than EcM tree species (Gu et al. 2014 Tree Physiology; Kong et al. 2014 New Phytologist). I am therefore curious to see why this is not the case here? Did the few EcM species sampled just happen to have very thick roots? Or is there a phylogenetic effect that influences both mycorrhizal type and root thickness that is not accounted for here (Baylis, 1975; Guo et al., 2008 New Phytologist; Kubisch et al., 2015 Frontiers in Plant Science; Valverde-Barrantes et al., 2015 Functional Ecology; 2016 Plant and Soil)?

I also do not agree with the conclusion that this integrated framework 'explained' tree distributions along the elevation gradient. First of all, it is difficult to interpret because the elevation gradient is not well explained (e.g., in terms of environmental variation). Secondly, the framework might coincide with the framework, but the framework does not explain it: an environmental gradient probably underlies the elevation gradient that may be selected for species with certain root traits or mycorrhizal types, but this is not tested nor clearly demonstrated by the data. It thus remains rather speculative, and it should be more thoroughly explained based on the data observed. Similarly, I do not understand from this study how root traits like root N can influence the abundance of EcM and pathogenic fungi (L. 242 - 243). Which data show this causality? It seems a strong statement, but not well supported (or explained).

Impact:
The data collected for this study are timely, valuable, and relevant. Soilborne microbes (fungi and bacteria; symbionts and pathogens) play important roles in root trait expressions (e.g., root diameter) and below-ground functioning (e.g., resource acquisition). They should therefore not be excluded from studies into the belowground functioning of forests, but they mostly are. This dataset therefore has the potential to improve our understanding of this subject. Making these data publicly available in large-scale datasets that have recently been initiated (e.g., FRED) will also allow further study in comparative (with other biomes) or global (across biomes) studies.

Technically, the methodology seems sound, although I lack the expertise to judge the Molecular Methods (L. 349 - 397). However, owing to some statistical uncertainties mentioned above (that the authors might well clarify or improve) and the oversimplified discussion, I am hesitant to determine the impact of the contents of this work. Statistical improvements and/or clearer explanation/justification of statistical choices made can make this manuscript highly interesting and impact, however.

Context:
As motivated above, I am not sure to what extent the EcM - AM/saprotroph presents a true ecological tradeoff. However, if it does, this work would fit very well in the context of the mycorrhizal-associated nutrient economy (Phillips et al. 2013 New Phytology). This theory postulates that EcM trees generally produce low-quality litter (associated with 'slow traits') that can be more readily accessed by EcM but not AM fungi, thereby slowing down nutrient cycling rates at their competitive advantage, and vice versa for AM tree species. This study did not aim to test the MANE, so it was beyond its scope to study litter quality, and the number of EcM and AM species was unbalanced (8 EcM versus 44 AM species): nonetheless, the denser roots of EcM species and higher root N of AM species indicates that the MANE may also apply to this subtropical forest and may be an interesting impetus for future work on this topic. It might also offer one way to bridge the root economics space and the MANE.

What I also found interesting is the sparse observations of EcM fungal taxa in the root endosphere of species typically identified as AM hosts (L. 212 - 214). While their functionality remains to be tested (fungal structures in the endosphere were not studied here), this observation might call for renewed attention to classifying species as AM, EcM, or both.

Reviewer #4 (Public Review):

Summary:
Recent progress in root economics has revealed global-scale axes of covaried root traits that reflect various root resource acquisition strategies. These covariance patterns are powerful tools for understanding root functional diversity. However, roots do not function in isolation for below-ground resource acquisition. Rather, symbiotic fungi and rhizosphere microorganisms often collaborate with plant roots, forming a root-microbial-soil continuum. This study seeks to provide novel insights into this continuum by extending the existing framework of root economics to include the structures of root-associated microorganisms. I find this topic highly relevant. Considering the role of soil microorganisms is undoubtedly crucial for a more comprehensive understanding of below-ground resource strategies.

Major comments:
A key finding of this study is a relationship between root N and the tendency for roots to associate with particular types of mycorrhizal associations (Line 27, Fig. 2). The authors concluded that this indicates "a linkage from simple root traits to fungal-mediated carbon nutrient cycling" (line 27) and integrates "microbial functions into the root economics framework," (line 32). If substantiated, this correlation could represent a significant discovery about the connection between root functional traits and root-associated fungi. It suggests that low root N, indicative of low metabolic activity within the root economics framework, is linked with forming EcM associations. However, I am not fully convinced this is the case based on the current data presentation and interpretation.

First, there is no biological interpretation of this relationship between root N and mycorrhizal type. It merely noted that root N is indicative of root metabolic activity, and thus by relating root N to fungal composition, "the trait-related root economics and fungal-driven nutrient economics may be integrated into a unified framework" (lines 221-224). Why would roots with low N and low metabolic activity tend to favor EcM associations? What are the potential mechanisms? Biological interpretation is essential for understanding whether a statistical correlation reflects a causal and meaningful relationship or is coincidental.

I am also concerned that this relationship may be spurious, especially when it lacks biological interpretation. EcM is underrepresented in this study (8 EcM species, of which more than half are conifers and oaks vs. 44 AM) and seems to cluster at higher elevations (line 231). Thus, the tree species/individual data points are not independent, but phylogenetically and geographically clustered. The unique properties at higher elevations (e.g., distinct plant community structures, low levels of mineral N) may drive both the lower root N and the prevalence of EcM associations. This scenario aligns with the observation that at higher elevations, AM roots also exhibited low root N (Line 231). In this case, root N may not directly relate to mycorrhizal type but is characteristic of certain locations (or closely related species), and it would be misleading to suggest that low root N/metabolic activity, a proxy in fast-slow root economics, is directly linked to the preference for a particular mycorrhizal type (lines 27-28, 220 - 224). In summary, because the studied tree species appear to be clustered both phylogenetically and geographically, these factors need to be carefully taken into account in the statistical analysis and data interpretation to understand the underlying causes of the apparent relationship and prevent overinterpretation. I also recommend, if possible, providing a visual presentation of the geographical and phylogenetic distribution of the studied tree species.

That being said, this dataset is undoubtedly valuable in revealing the shifts in the compositional structures of root-associated soil microorganisms. However, integrating the traits of microbial composition to root trait economics would require more caution and careful examination of the potential driving causes.