# Shifts in soil and plant functional diversity along an altitudinal gradient in the French Alps

Alexia Stokes, Guillermo Angeles, Fabien Anthelme, Eduardo

Aranda-Delgado, Isabelle Barois, Manon Bounous, Nereyda Cruz-Maldonado,

Thibaud Decaëns, Stéphane Courtier, Gregoire T. Freschet, et al.

► To cite this version:

Alexia Stokes, Guillermo Angeles, Fabien Anthelme, Eduardo Aranda-Delgado, Isabelle Barois, et al. Shifts in soil and plant functional diversity along an altitudinal gradient in the French Alps. BMC

Research Notes, 2021, 14, 4 p. 10.1186/s13104-021-05468-0. hal-03137074

HAL Id: hal-03137074

https://hal.inrae.fr/hal-03137074

Submitted on 10 Feb 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
DATA NOTE

Open Access

<figure><img src="image_2.png" /></figure>

Check for
updates

# Shifts in soil and plant functional diversity along an altitudinal gradient in the French Alps

Alexia Stokes¹*, Guillermo Angeles², Fabien Anthelme¹, Eduardo Aranda-Delgado³, Isabelle Barois², Manon Bounous¹, Nereyda Cruz-Maldonado¹, Thibaud Decaëns⁴, Stéphane Fourtier¹, Grégoire T. Freschet⁵, Quentin Gabriac⁴, Daniel Hernández-Cáceres¹, Leonor Jiménez², Jing Ma⁶, Zhun Mao¹, Beatriz Eugenia Marín-Castro⁷, Luis Merino-Martín¹,⁴,¹², Awaz Mohamed¹, Christian Piedallu⁸, Carlos Pimentel-Reyes², Hans Reijnen⁴, Frédérique Reverchon⁹, Hervé Rey¹, Lavinia Sellı¹, Christina Desireé Siebe-Grabach¹⁰, Katrin Sieron¹¹, Monique Weemstra¹,⁴ and Catherine Roumet⁴

**Abstract**

**Objectives:** Altitude integrates changes in environmental conditions that determine shifts in vegetation, including temperature, precipitation, solar radiation and edaphogenetic processes. In turn, vegetation alters soil biophysical properties through litter input, root growth, microbial and macrofaunal interactions. The belowground traits of plant communities modify soil processes in different ways, but it is not known how root traits influence soil biota at the community level. We collected data to investigate how elevation affects belowground community traits and soil microbial and faunal communities. This dataset comprises data from a temperate climate in France and a twin study was performed in a tropical zone in Mexico.

**Data description:** The paper describes soil physical and chemical properties, climatic variables, plant community composition and species abundance, plant community traits, soil microbial functional diversity and macrofaunal abundance and diversity. Data are provided for six elevations (1400–2400 m) ranging from montane forest to alpine prairie. We focused on soil biophysical properties beneath three dominant plant species that structure local vegetation. These data are useful for understanding how shifts in vegetation communities affect belowground processes, such as water infiltration, soil aggregation and carbon storage. Data will also help researchers understand how plant communities adjust to a changing climate/environment.

**Keywords:** Aggregate stability, Alpine ecosystems, Carbon, Community, Elevation, Environmental gradient, Infiltration, Macroinvertebrates, Microorganisms, Plant community traits, Soil biophysical properties

**Objective**

Elevational gradients can be used as a space-for-time substitution to provide insights into the response of communities to climatic changes and the impact on the local environment [1, 2]. In particular, altitude integrates changes in the diverse conditions that determine soil

biophysical properties [3], including temperature, soil moisture, solar radiation and input from vegetation [4]. In an attempt to disentangle the mechanisms through which vegetation affects soil biotic and abiotic processes at different elevations, we collated data on the biophysical environment, soil physico-chemical properties, plant community traits, microbial functional diversity and soil macroinvertebrate indicators.

Soil biophysical properties are heavily influenced by input rates and decomposability of organic matter, transport to deeper soil horizons and physical protection in

<footnote>*Correspondence: alexia.stokes@cirad.fr
¹AMAP, Univ Montpellier, INRAE, IRD, CIRAD, CNRS, 34000 Montpellier, France
Full list of author information is available at the end of the article</footnote>
aggregate complexes. Therefore, our dataset includes data from infiltration experiments in the field and the measurement of soil aggregate stability at different depths. Our data also allow researchers to explore the impact of plant communities and soil macroinvertebrates on soil properties and vice-versa. With a particular focus on three plant species that structure local vegetation communities, belowground community-level traits were measured to determine how plant community composition impacts soil biophysics and microbial activity. To our knowledge, this dataset represents the largest freely available collection of data on plant traits and soil variables along a 1000 m elevational gradient in a temperate climate. Methods for each measurement are provided in the data files and supplementary materials (Table 1).

## Data description

We present data collected in 2018, at six altitudes along an elevational gradient (1400–2400 m), at Massif de Belledonne, France (Table 1, file 1, [5]). Bedrock was composed of Variscan metamorphic rocks and ophiolitic complexes. Soils were umbrisols and cambisols and laminar erosion was present (Table 1, file 2, [6]). Climatic data (Table 1, file 3) were estimated over 2004–2014, using the Aurelhy model [7]. Soil water potential and temperature were measured at each altitude for two growing seasons (2018–2019, Table 1, file 4).

Five 400 m² plots were chosen at each altitude, so that two or three dominant and community-structuring species were present: *Picea abies* (L.) H. Karst, *Vaccinium myrtillus* L. and *Juniperus communis* L. A botanical survey was performed in the plot (Table 1, file 5) and one adult individual of each structuring species was selected. At the limit of the individual’s canopy on the downslope

side, infiltration tests were performed to estimate water flow through soil [8] and hydraulic conductivity of the quasi-steady phase was calculated (Table 1, file 6). Soil samples were collected for an overall description of soil type per horizon (Table 1, file 2) and aggregate stability measurements (Table 1, file 7 [9]).

To investigate the relationships between soil biophysical properties and vegetation, plant community composition was measured in a 1.0 m² subplot within each plot and close to each structuring species (Table 1, file 8). Rhizospheric soil attached to fine roots of the three structuring plant species was collected. A soil monolith (0.25 m × 0.25 m × 0.15 m depth) was excavated within each subplot (n=70). Above the monolith, litter layer thickness (Table 1, file 9) and aboveground biomass per species were measured (Table 1, file 10).

Soil texture, cationic exchange capacity, pH, organic carbon, nitrogen content, nitrate and ammonium were determined on pooled soil samples harvested within each monolith (Table 1, file 9). The MicroResp™ system [10] was used to characterize microbial activity and functional diversity on air-dried bulk and rhizospheric samples, through the community level physiological profiles of the soil microbial communities (Table 1, file 11).

Soil macroinvertebrates were hand sorted from each monolith and fixed in 100% ethanol. Macroinvertebrates were identified at the order level, except for earthworms for which morphological diagnoses were combined with DNA barcoding to obtain species level assignations. Invertebrates within each taxa were counted and weighed (Table 1, file 12, [11]).

Roots were hand sorted, washed and sorted into categories according to their diameter and functionality: rhizomes, very coarse roots with diameter > 5 mm, coarse

<table><caption>**Table 1** Overview of data files/data sets</caption><thead><tr><td>Label</td><td>Name of data file/data set</td><td>File type (file extension)</td><td>Data repository and identifier (DOI or accession number)</td></tr></thead><tbody><tr><td>Data file 1</td><td>Plots and map</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/E3SAPW (2020) [15]</td></tr><tr><td>Data file 2</td><td>Description of soil profiles</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/ZTCHEG (2020) [16]</td></tr><tr><td>Data file 3</td><td>Plot climate interpolated base</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/ZDODW2 (2020) [17]</td></tr><tr><td>Data file 4</td><td>Soil water potential 2018–2019</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/PJKD06 (2020) [18]</td></tr><tr><td>Data file 5</td><td>Plot vegetation 20 × 20 m</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/UGFNKE (2020) [19]</td></tr><tr><td>Data file 6</td><td>Stable state infiltration</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/PWWMYV (2020) [20]</td></tr><tr><td>Data file 7</td><td>Monolith aggregate stability</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/UOU4TO (2020) [21]</td></tr><tr><td>Data file 8</td><td>Monolith 1 × 1 m vegetation</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/FZ7RA7 (2020) [22]</td></tr><tr><td>Data file 9</td><td>Ecopics soil analysis</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/B3FP79 (2020) [23]</td></tr><tr><td>Data file 10</td><td>Ecopics aboveground biomass</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/V70ZST (2020) [24]</td></tr><tr><td>Data file 11</td><td>Ecopics community level physiological profiles</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/KFBNR8 (2020) [25]</td></tr><tr><td>Data file 12</td><td>Monolith earthworms</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/UBFC6D (2020) [26]</td></tr><tr><td>Data file 13</td><td>Ecopics monolith root traits</td><td>MS Excel file (.xlsex)</td><td>Portail Data INRAE. https://doi.org/10.15454/RARBD1 (2020) [27]</td></tr></tbody></table>
roots with diameter 2–5 mm and fine roots (<2 mm). Fine roots were separated into absorptive and transport roots [12]. Two subsamples of roots <5 mm were scanned and analysed using Winrhizo Pro (Regent Instruments, Canada). Several root traits were measured, including root length density and mass, diameter, specific root length and tissue density (Table 1, file 13, [13]. Chemical traits were measured on absorptive and transport roots (Table 1, file 13), including nitrogen, carbon content and hydrosoluble compounds, hemicellulose, cellulose and lignin content [14].

## Limitations

Although this dataset comprises a large number of field and laboratory data collated from 70 monoliths in 30 plots, long-term climatic data were not available for each of the six altitudinal levels. Therefore we used the Aurelhy model, that estimates climatic data to a resolution of 1 km [7]. As some altitudinal bands were located within 1 km of each other, data will be the same for those plots. Also, datasets refer specifically to three dominant and community-structuring plant species, limiting generalization at larger scales.

## Acknowledgements

Thanks are due to the Mairie de Chamrousse, Isère, France, for access to field sites.

## Authors' contributions

AS prepared the manuscript. AS, GA, FA, IB, TD, GTF, LJ, ZM, BEM-C, LM-M, FR, KS, MW, CR designed and conducted fieldwork. MB, EA-D, NC-M, SF, QG, DH-C, JM, AM, CPR, HR, HRey conducted fieldwork. CDS-G designed fieldwork. MB, GTF, NC-M, QG, DH-C, LM-M, CPR, HR, LS, CR, MW, performed laboratory analyses. MB, TD, GTF, QG, DH-C, BEM-C, LM-M, AM, CP, KS, MW, CR analysed data. AS, GA, FA, IB, TD, GTF, ZM, BEM-C, LM-M, AM, FR, KS, MW, CR conceived and reviewed the final manuscript before submission. All authors read and approved the final manuscript.

## Funding

Funding was provided by the French and Mexican governments (ECOPICS project, ANR-16-CE03-0009 and CONACYT-2 73659).

## Availability of data and materials

The data described in this Data note can be freely and openly accessed on Portail Data INRAE under https://data.inrae.fr/dataverse/ecopics. Please see Table 1 for details and links to the data. Supplementary material can be accessed at: https://doi.org/10.15454/1S15ZS.

## Ethics approval and consent to participate

Not applicable.

## Consent for publication

Not applicable.

## Competing interests

The authors declare that they have no competing interests.

## Author details

Received: 7 November 2020 Accepted: 28 January 2021

Published online: 08 February 2021

## References

1. Fukami T, Wardle DA. Long-term ecological dynamics: reciprocal insights from natural and anthropogenic gradients. Proc R Soc B Biol Sci. 2005;272:2105–15.

2. Read QD, Moorhead LC, Swenson NG, Bailey JK, Sanders NJ. Convergent effects of elevation on functional leaf traits within and among species. Funct Ecol. 2014;28:37–45.

3. Targalian VO, Krasilnikov PV. Soil system and pedogenic processes: self-organization, time scales and environmental significance. CATENA. 2007;71:373–81.

4. Körner C. The use of 'altitude' in ecological research. Tree. 2007;22:569–74.

5. Weemstra M, Freschet G, Stokes A, Roumet C. Patterns in intraspecific variation in root traits are species-specific along an elevation gradient. Funct Ecol. 2021; 35:342–56.

6. WRB. World Reference Base for Soil Resources 2014, update 2015, International soil classification system for naming soils and creating legends for soil maps. 2015; IUSS Working Group World Soil Resources Reports No. 106. FAO, Italy.

7. Piedallu C, Gégout JC, Lebourgeois F, Seynave I. Soil aeration, water deficit, nitrogen availability, acidity and temperature all contribute to shaping tree species distribution in temperate forests. J Veg Sci. 2016;27:387–99.

8. Marín-Castro BE, Negrete-Yankelevich S, Geissert D. Litter thickness, but not root biomass, explains the average and spatial structure of soil hydraulic conductivity in secondary forests and coffee agroecosystems in Veracruz, Mexico. Sci Tot Environ. 2017;607–608:1357–66.

9. Le Bissonnais Y. Aggregate stability and assessment of soil crust stability and erodibility: I. Theory and methodology. Eur J Soil Sci. 1996;47:425–37.

10. Campbell CD, Chapman SJ, Cameron CM, Davidson MS, Potts JM. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl Environ Microbiol. 2003;69:3593–9.

11. Anderson JM, Ingram JSI. Tropical soil biology and fertility. In: A handbook of methods. 2nd edn. UK: CAB International; 1993.

12. Freschet GT, Roumet C, Comas LH, Weemstra M, Bengough AG, Rewald B, Bardgett RD, De Deyn GB, Johnson D, Klimešová J, Lukac M, McCormack L, Meier IC, Pagès L, Poorter H, Prieto I, Wurzburger N, Zadworny M, Bagniewska-Zadworna A, Blancaflor EB, Brunner I, Gessler A, Hobbie SE, Iversen CM, Mommer L, Picon-Cochard C, Postma JA, Rose L, Ryser P, Scherer-Lorenzen M, Soudzilovskaia NA, Sun T, Valverde-Barrantes OJ, Weigelt A, York LA, Stokes A. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. New Phytol. 2021. https://doi.org/10.1111/nph.17072.

13. Freschet GT, Pagès L, Iversen CM, Comas LH, Rewald B, Roumet C, Klimešová J, Zadworny M, Poorter H, Postma JA, Adams TS, Bagniewska-Zadworna A, Blancaflor EB, Brunner I, Cornelissen JHC, Garnier E, Gessler A, Hobbie SE, Lambers H, Meier IC, Mommer L, Picon-Cochard C, Rose L, Ryser P, Scherer-Lorenzen M, Soudzilovskaia NA, Stokes A, Sun T, Valverde-Barrantes OJ, Weemstra M, Weigelt A, Wurzburger N, York LM, Batterman SA, Bengough AG, Gomes de Moraes M, Janeček S, Salmon V, Tharayil N, McCormack ML. A starting guide to root ecology: strengthening ecological concepts and standardizing root classification, sampling,

<footnote n="1">¹ AMAP, Univ Montpellier, INRAE, IRD, CIRAD, CNRS, 34000 Montpellier, France.</footnote>

<footnote n="2">² Red de Ecología Funcional, Instituto de Ecología, A.C., Xalapa, Veracruz, Mexico. ³ Terranova Lombricultores, Coatepec, Veracruz, Mexico. ⁴ CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier,</footnote>

<footnote>France. ⁵ Station d'Ecologie Théorique et Expérimentale, CNRS, 09200 Moulis, France. ⁶ Low Carbon Energy Institute, China University of Mining and Technology, Xuzhou 221008, China. ⁷ Laboratorio de Edafología Ambiental, Instituto de Geología, Universidad Nacional Autónoma de México, Mexico City, Mexico.</footnote>

<footnote n="8">⁸ Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France. ⁹ Red de Estudios Moleculares Avanzados, Instituto de Ecología, A.C., Pátzcuaro, Michoacán, Mexico. ¹⁰ Laboratorio de Edafología Ambiental, Instituto de Geología, Universidad Nacional Autónoma de México, Mexico City, Mexico.</footnote>

<footnote n="11">¹¹ Centro de Ciencias de la Tierra, Universidad Veracruzana, 91090 Xalapa, Veracruz, Mexico. ¹² Departamento de Biología y Geología, Física y Química inorgánica, ESCET, Universidad Rey Juan Carlos, C/Tulipán s/n, Móstoles, 28933 Madrid, Spain.</footnote>
processing and trait measurements. New Phytologist. 2020; Accepted.
[hal-02918834].

14. Van Soest PJ. Use of detergents in the analysis of fibrous feeds. II A rapid method for the determination of fiber and lignin. Off Agric Chem. 1963;46:829.

15. Sieron K. Ecopics plots and map. Portail Data INRAE. https://doi.org/10.15454/E3SAPW. 2020

16. Marin Castro BE. Description of soil profiles. Portail Data INRAE. https://doi.org/10.15454/ZTCHEG. 2020

17. Piedallu C. Plot climate interpolated base. Portail Data INRAE. https://doi.org/10.15454/ZDODW2. 2020

18. Merino-Martin L, Mohamed A. Soil water potential 2018–2019. Portail Data INRAE. https://doi.org/10.15454/PJKD06. 2020

19. Bounous M, Anthelme F. Plot Vegetation 20 × 20 m. Portail Data INRAE. https://doi.org/10.15454/UGFNKE. 2020

20. Stokes A. Stable state infiltration. Portail Data INRAE. https://doi.org/10.15454/PWWMYV. 2020

21. Merino-Martin L. Monolith aggregate stability. Portail Data INRAE. https://doi.org/10.15454/UOU4TO. 2020

22. Bounous M, Anthelme F. Monolith 1 × 1 m vegetation. Portail Data INRAE. https://doi.org/10.15454/FZ7RA7. 2020

23. Stokes A. Soil analysis. Portail Data INRAE. https://doi.org/10.15454/B3FP79. 2020

24. Roumet C. Monolith aboveground biomass. Portail Data INRAE. https://doi.org/10.15454/V70ZST. 2020

25. Hernández-Cáceres D. Microbial community level physiological profiles. Portail Data INRAE. https://doi.org/10.15454/KFBNR8. 2020

26. Decaëns T. Monolith earthworms. Portail Data INRAE. https://doi.org/10.15454/UBFC6D. 2020

27. Roumet C. Monolith root traits. Portail Data INRAE. https://doi.org/10.15454/RARBD1. 2020

**Publisher's Note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research? Choose BMC and benefit from:

* fast, convenient online submission

* thorough peer review by experienced researchers in your field

* rapid publication on acceptance

* support for research data, including large and complex data types

* gold Open Access which fosters wider collaboration and increased citations

* maximum visibility for your research: over 100M website views per year

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

BMC