the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Dec 2024
The fungal collaboration gradient drives root trait distribution and ecosystem processes in a tropical montane forest
Abstract. Plant roots have a large diversity of form and function, which is also related to their degree of mycorrhizal association. This is known as the fungal collaboration gradient, where thin roots acquire resources by themselves and thicker roots depend on mycorrhizas. In this study, we, for the first time, implement the fungal collaboration gradient in a trait-based Dynamic Vegetation Model (DVM, LPJ-GUESS-NTD). We test if the DVM can predict root trait distributions, and estimate the effects of mycorrhizae-mediated nutrient uptake on ecosystem processes along an elevation gradient in a tropical montane forest in southern Ecuador. The model reproduces the observed root traits specific root length (SRL) and AMF colonization along the elevation gradient, which ranges from high SRL and low AMF colonization at 1,000 m to low SRL and high AMF colonization at 3,000 m. When AMF-mediated nutrient uptake is deactivated site average biomass values are reduced by up to 80 %. Accounting for AMF-related belowground traits also affects simulated community leaf traits, suggesting linkages between below- and aboveground traits. The model suggests that the collaboration gradient has a substantial influence on vegetation diversity and functioning in the study system. We thus advocate more explicit treatment of root traits and mycorrhizae in DVMs. The model scheme here is based on general trade-offs and could be implemented in other DVMs and be tested for other study regions.
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Status: open (until 12 Feb 2025)
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RC1: 'Comment on egusphere-2024-3259', Anonymous Referee #1, 26 Dec 2024
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General comment:
The manuscript presents a comprehensive study on the interactions between arbuscular mycorrhizal fungi (AMF) and root traits in a tropical montane forest ecosystem. Using a dynamic vegetation model (DVM), the authors explore how these interactions influence aboveground vegetation traits and ecosystem processes. This research is timely and highly relevant, given the increasing interest in understanding both above- and below-ground interactions in the context of global change.
The study significantly advances our understanding of the role of AMF in shaping plant communities by integrating a novel “fungal collaboration gradient” (FCG) within a trait-based DVM. This approach enables nuanced exploration of how root traits and mycorrhizal associations, particularly AMF, influence ecosystem dynamics in tropical montane forests.
The inclusion of empirical data on specific root length (SRL) and AMF colonization across an altitudinal gradient enhances the model’s credibility and relevance. One key finding is the significant impact of deactivating AMF-mediated nutrient uptake, with biomass values potentially reduced by up to 80%, emphasizing the critical role of mycorrhizal associations in nutrient acquisition.
While the results are compelling, the discussion would benefit from deeper integration with existing literature, particularly through comparisons with previous studies on AMF and root traits. Additionally, exploring the practical applications of the findings, particularly for ecosystem management, would strengthen the study’s relevance.
In summary, the manuscript is well-conceived and addresses a significant gap in ecological research. However, revisions are needed to improve clarity, structure, and depth of analysis. Given the scope of the requested revisions—reorganization, additional data, expanded literature integration, methodological elaboration, and enhanced analysis—this report constitutes a major revision request. Addressing these revisions aims to deepen the manuscript's analysis and elevate its scientific standards prior to publication.
Specific comments:
Abstract (Section 1):
The acronym AMF should be spelled out on its first mention (line 22).
While the abstract mentions "linkages between below- and aboveground traits" (line 25), elaborating on the nature or significance of these associations would better inform readers of their ecological implications.
Numerical results on SRL and AMF colonization along the altitudinal gradient are missing. Including key statistical results would add weight to the claims.
Although specific to a tropical montane forest in southern Ecuador, expanding on the study’s contributions to global ecological or climate models would strengthen its impact.
Introduction (Section 2):
The introduction provides a solid background on belowground processes, fine root characteristics and the role of mycorrhizae in ecosystem functioning. The references are diverse and robust, effectively highlighting the novelty in modelling of modelling fine root characteristics and FCG. However, the section could benefit from improved clarity and flow:
- Simplifying dense sentences:
- Lines 32-33 could be rephrased as, e.g.: “Soils at depths of up to 200 cm store an estimated 2400 Pg C globally, highlighting their importance in the carbon cycle (Batjes, 1996). This is nearly nine times the amount stored in global forests (Santoro et al., 2021)”.
- Lines 38-39 could be simplified to, e.g.: "Fine root traits - such as branching patterns, root depth and diameter - play a critical role in nutrient and water uptake. These traits may also shape species coexistence in specific environments (Nie et al., 2013)".
- Restructuring dense paragraphs:
- The paragraph in lines 47-53 could be condensed to, e.g.: “Advances in belowground phenotyping have enabled researchers to synthesize fine-root traits within the global spectrum of plant form and function (Weemstra et al., 2016, 2022; Weigelt et al., 2021). Similar efforts have been made for leaf and wood traits (Wright et al., 2004; Chave et al., 2009). This framework highlights how trade-offs in physiological and morphological traits influence species coexistence (Shipley et al., 2006). By analyzing the co-occurrence of plant traits, researchers have identified new trade-off gradients (Guerrero-Ramírez et al., 2020; Kattge et al., 2020)”.
- Trade-offs and gradients appear to be over-explained in lines 53-59, and could be condensed and focused to, e.g.: “Plant traits often exhibit trade-offs, such as the conservation gradient seen in leaves, where traits range from high productivity to high longevity (Wright et al., 2013; Díaz et al., 2016). Similarly, root traits show trade-offs, though their patterns differ from those observed in leaves (Carmona et al., 2021)”.
- Lines 67-69 appear overly complex and could be simplified and broken down, e.g.: "Although plants must transfer carbon to fungi as part of their partnership, the fungi’s extensive hyphae networks significantly boost nutrient and water absorption. This collaboration offers thick-rooted plants an alternative strategy to relying solely on fine roots (Kakouridis et al., 2022)".
- Improving transition:
- The sentence in line 60 should belong to the previous paragraph, followed by a sentence of the type “Capturing such dynamics is crucial for process-based dynamic vegetation models (DVMs), which rely on generalized ecological representations to simulate plant and soil processes”. This means that the concepts currently expressed from line 75 onwards should appear earlier to ensure a smooth transition between topics like fine root traits, fungal collaboration gradients and DVMs.
- Lines 78-82 could also be smoothed, e.g. “Aboveground plant traits are often analyzed using the 'leaf economics spectrum,' a framework that classifies leaves based on a trade-off between rapid growth and resource conservation (Wright et al., 2004). Including such frameworks in DVMs has provided insights into nutrient dynamics and community resilience (Sakschewski et al., 2015, 2016; Dantas de Paula et al., 2021). By contrast, belowground processes, despite their critical ecological roles, remain underrepresented in these models (Langan et al., 2017; Sakschewski et al., 2021)”.
- Similarly, to ensure a smooth transition from DVM limitations to study objectives, the authors might consider something like “Existing models simplify or omit critical variations in root traits and mycorrhizal dynamics, limiting their ability to capture site-specific belowground processes. To address these gaps, this study develops a dynamic approach that integrates detailed root trait and fungal interaction data into DVMs for broader ecological applications”.
- Explaining concepts:
- The authors could further describe the "fungal collaboration gradient" (line 60) as a spectrum of strategies that plants use to interact with mycorrhizal fungi: at one end, plants invest heavily in root traits that enhance independent nutrient uptake, while at the other end they rely more on fungal partners to exchange nutrients for carbon.
- Similarly, the authors can briefly introduce and support DVMs as simulation tools that predict how plant communities respond to environmental changes by incorporating simplified representations of ecological processes, such as growth, nutrient cycling and competition.
- Introducing hypotheses:
The current introduction does include hypotheses (from line 98 onwards), but they can be made more explicit, with a clear framing sentence following the rationale for focusing on root traits and fungal interactions. For example: "In this study, we hypothesize that incorporating the FCG into a trait-based DVM will improve predictions of root trait distributions, biomass, and productivity across nutrient gradients. Specifically, we predict that …". This approach would make the goals and hypotheses prominent and easier to follow.
- Synthesizing related studies:
While the introduction includes citations from relevant and recent studies on several topics, i.e. fine root traits (e.g. Nie et al., 2013; Bardgett et al., 2014), mycorrhizal interactions (e.g. Van Der Heijden et al., 2008; Hawkins et al., 2023), modeling approaches and limitations (e.g., Langan et al., 2017; Dantas de Paula et al., 2021), a broader review of (a) root-mycorrhizal interactions in different ecosystems (e.g., temperate or boreal forests) for comparative insights, and (b) previous attempts to incorporate belowground traits into models (even outside of DVMs), would contextualize the novel contributions of this study.
Material and methods (Section 3):
The Materials and Methods section is generally well-written, providing a solid outline of the model's structure and integration of the FCG. However, certain aspects require clarification or expansion to ensure transparency and reproducibility. Specifically, a dedicated sub-section (e.g. “3.1. Land use and vegetation cover”) should be created to include detailed information about historical and current land use, vegetation types, conservation status, and anthropogenic impacts. The current description does not specify the vegetation type at each elevation site (e.g., primary forest, secondary forest, or disturbed areas), which is crucial because vegetation types significantly influence nutrient cycling, organic matter decomposition, and fungal associations. It remains unclear whether the sites are pristine or subject to human activity. For instance: Are these sites located within protected areas? Do they feature continuous forest cover, fragmented landscapes, or mixed land use? The authors should describe the dominant vegetation types or species at each elevation, such as tropical montane cloud forests, mixed forests, or other specific plant communities. A brief mention of historical land use (e.g., deforestation or reforestation efforts) would provide context for the current ecosystem conditions.
This considered, the following changes are recommended to improve organization and clarity:
Section 3.1 (to be renumbered 3.2 under a new title, e.g. “Abiotic characteristics and nutrient gradients”) could benefit from including quantitative data on environmental factors, such as soil nutrient levels and moisture content, and elaborate on how these variables were incorporated into the model.
Section 3.2 (to be renumbered 3.3) could anticipate a discussion on specific algorithms and assumptions used to simulate root growth dynamics and mycorrhizal interactions, which would add transparency to the mechanics of the model.
Section 3.3 (to be renumbered 3.4) could expand on the interactions between the FCG and external drivers (e.g., climate, competition, disturbances) to provide a more comprehensive view of their role in ecosystem dynamics.
Section 3.4 (to be renumbered 3.5) could include a more detailed explanation of how the datasets used were used. This could include descriptions of the variables measured and how they were incorporated into the model to improve transparency and reproducibility.
Section 3.5 (to be renumbered 3.6) could enhance the ecological relevance of the AMF-on and AMF-off scenarios by linking them to practical applications (e.g., conservation strategies and ecosystem management).
Section 3.6 (to be renumbered 3.7) could provide a more detailed statistical analysis, including performance indicators like root mean square error (RMSE) or Nash-Sutcliffe Efficiency (EF), to assess the model accuracy.
Section 3.7 (to be renumbered 3.8) could discuss the ecological implications of the sensitivity analysis findings, particularly highlighting their relevance to management practices and identifying areas for future research.
Results (Section 4):
Section 4.1 could elaborate on the ecological significance and practical applications of the optimal rmax value (0.5). For example, how does varying rmax inform decisions on ecological restoration or nutrient cycling management?
Section 4.2 could provide details on the statistical significance of observed patterns (e.g., p-values) and clarify whether the AMF-off scenario exhibited an exact uniform distribution or simply a lack of observed patterns. In addition, the paragraph on SLA traits under the AMF-off scenario requires further explanation of the ecological implications. What does a "conservative" (lines 308) trait like lower SLA mean for plant strategy or ecosystem processes? Further discussion of these traits in terms of resource allocation or plant adaptation would strengthen the argument.
Section 4.3 requires further interpretation of the reduction in biomass at the 3,000 m site (80.6%) under the AMF-on scenarios. A brief discussion of the ecological mechanisms behind this pattern would enrich the results and help to explain why such a large reduction in biomass occurs at higher altitudes (possibly in terms of nutrient availability, plant strategy or other environmental factors).
Discussion (Section 5):
The discussion briefly touches on potential ecological mechanisms, in particular nutrient limitation and mycorrhizal requirements at higher elevations. However, there is limited discussion of the biological mechanisms driving the large biomass differences observed in the AMF-on scenario, particularly at higher elevations. As noted above, the section should expand on the biological mechanisms underlying biomass differences, particularly at higher elevations, and explore interactions between FCG and other factors (e.g., climate, competition, disturbance).
Conclusion (Section 6):
The conclusion could be strengthened by addressing interactions between the FCG and other factors, such as climate or soil type. While the authors acknowledge the need for future research on belowground traits and their interactions with AMF and aboveground traits, the discussion could be expanded to include how this understanding can be applied to ecosystem management and conservation practices.
The implications of rising atmospheric CO2 on belowground processes and mycorrhizal interactions (lines 478-479) also warrant further development.
In addition, exploring the potential for extending this approach to other ecosystems, such as temperate forests, grasslands or drylands, would increase the impact of the study.
Finally, a more explicit link between the results of the study and broader ecological theory would strengthen the conclusions. This could include discussion of how the integration of root traits and mycorrhizal cooperation into DVMs advances our understanding of plant-soil feedback mechanisms, nutrient cycling and ecosystem resilience.
Appendix A (Section 7):
The appendix significantly enhances the transparency and reproducibility of the study by providing additional information, including data tables and analyses that help interpret the model results. However, the authors may consider omitting the heading 7.1, as it pertains to a single section.
Citation: https://doi.org/10.5194/egusphere-2024-3259-RC1
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