Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia SUPPLEMENTARY MATERIAL

Similar documents
SOIL: DEFINITION, FORMATION! & LAYERS"

Why should I use a Kruskal-Wallis test? (With Minitab) Why should I use a Kruskal-Wallis test? (With SPSS)

Difference in two or more average scores in different groups

Biomes Section 1. Chapter 6: Biomes Section 1: What is a Biome? DAY ONE

p of increase in r 2 of quadratic over linear model Model Response Estimate df r 2 p Linear Intercept < 0.001* HD

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis

Evapotranspiration. Andy Black. CCRN Processes Workshop, Hamilton, ON, Sept Importance of evapotranspiration (E)

Table 9. FAI accession log

sentinel-2 COLOUR VISION FOR COPERNICUS

Table S1. Concentrations of dissolved inorganic nitrogen (DIN) and phosphate (DIP)

Two-Way ANOVA. Chapter 15

What Does the F-Ratio Tell Us?

Independent Samples ANOVA

Ecosystem classification in the Central Rocky Mountains, Utah

Crossword puzzles! Activity: stratification. zonation. climax community. succession. Match the following words to their definition:

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES

Degrees of freedom df=1. Limitations OR in SPSS LIM: Knowing σ and µ is unlikely in large

Data Fusion and Multi-Resolution Data

THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADAR CAPABILITIES OVER A FORESTED AREA

7/8/2010 Olga N. Krankina, OSU

Multiple Comparisons

Modelling atmospheric transport and deposition of ammonia and ammonium. Willem A.H. Asman Danish Institute of Agricultural Sciences

Supplementary material: Methodological annex

1 Electronic Supporting Information. 2 The transformation and fate of silver nanoparticles in a paddy soil:

Evaluation of photosynthesis rates of introduced and native species in a mixed grassland ecosystem

ANOVA Analysis of Variance

Gene flow favours local adaptation under habitat choice in ciliate microcosms

MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA:

Remote Sensing for Ecosystems

Periglacial Geomorphology

SUPPLEMENTARY INFORMATION

Chapter 20 : Two factor studies one case per treatment Chapter 21: Randomized complete block designs

Ganbat.B, Agro meteorology Section

Links between Plant and Fungal Diversity in Habitat Fragments of Coastal Sage Scrub

Stat 5303 (Oehlert): Tukey One Degree of Freedom 1

ECOLOGICAL PLANT GEOGRAPHY

A Concept to Assess the Performance. with a Climate Model

Using SPSS for One Way Analysis of Variance

DATA REPOSITORY FIGURES AND TABLES

Supplementary Material: Crop & Pasture Science, 2013, 64(12),

Stat 6640 Solution to Midterm #2

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA:

LANDSAF SNOW COVER MAPPING USING MSG/SEVIRI DATA

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means

Determining the Optimal Grid Size of Local Climate Zones for Spatial Mapping in High-density Cities

Waithe et al Supplementary Figures

Predicting Elk Nutritional Resources and Habitat Use Across Large Landscapes: the Westside Model

Spatial Non-Cellular Automata

Note: The problem numbering below may not reflect actual numbering in DGE.

Supplementary Material for: Scientific Uncertainty and Climate Change: Part I. Uncertainty and Unabated Emissions

Workshop 7.4a: Single factor ANOVA

Introduction to Analysis of Variance. Chapter 11

PLSC PRACTICE TEST ONE

Integrating Imagery and ATKIS-data to Extract Field Boundaries and Wind Erosion Obstacles

N J SS W /df W N - 1

Stat 579: Generalized Linear Models and Extensions

An Old Research Question

Directorate E: Sectoral and regional statistics Unit E-4: Regional statistics and geographical information LUCAS 2018.

C. J. Schwarz Department of Statistics and Actuarial Science, Simon Fraser University December 27, 2013.

SUPPORTING MATERIAL Tracing the origin of dioxins in Baltic air using an atmospheric modeling approach

Analysis of variance

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA:

Vincent BADEAU, Renaud RABASTENS (INRA Nancy) Manuel NICOLAS, Erwin ULRICH (ONF Fontainebleau)

Current LCLUC challenges in SCERIN: Assessing Ecosystem Function and Processes

Comparing Several Means: ANOVA

MULTIVARIATE ANALYSIS OF VARIANCE

Cloud Analysis Image: Product Guide

How can flux-tower nets improve weather forecast and climate models?

Homogeneity Assessment for Grass Samples Used for Organically Bound Tritium Proficiency Test

2 Hand-out 2. Dr. M. P. M. M. M c Loughlin Revised 2018

Introduction. Chapter 8

Supplementary Figure S1

13: Additional ANOVA Topics

Warner, R. M. (2008). Applied Statistics: From bivariate through multivariate techniques. Thousand Oaks: Sage.

and soils characterizing would be defined.

EVALUATION OF MIGRATION OF HEAVY METAL CONTAINING SEDIMENT RESULTING FROM WATER EROSION USING A GEO- INFORMATION MODEL

REMOTE SENSING ACTIVITIES. Caiti Steele

Monitoring and modelling hydrological fluxes in support of nutrient cycling studies in Amazonian rain forest ecosystems Tobon-Marin, C.

Large divergence of satellite and Earth system model estimates of global terrestrial CO 2 fertilization

Responses of N 2 O, CH 4 and CO 2 emissions and vegetable production to biochar application. Junxiang Jia, Zubin Xie, Zhengqin Xiong*

Use of climate reanalysis for EEA climate change assessment. Blaz Kurnik. European Environment Agency (EEA)

Chapter 14. One-Way Analysis of Variance for Independent Samples Part 2

Supplement of Upside-down fluxes Down Under: CO 2 net sink in winter and net source in summer in a temperate evergreen broadleaf forest

The Tropical Rainforest Ecosystem

Green Space Services for Local Monitoring

TOPIC 9 SIMPLE REGRESSION & CORRELATION

M A N O V A. Multivariate ANOVA. Data

Plant Appearance. Name: Class:

NR402 GIS Applications in Natural Resources. Lesson 9: Scale and Accuracy

It is relatively simple to comprehend the characteristics and effects of an individual id fire. However, it is much more difficult to do the same for

5. Explain how and why the Eastern High Country and the Western High Country vary from each other.

Factorial Independent Samples ANOVA

Detection of surface heterogeneity in eddy covariance data

DOI: /jcprm

SIRS NEESPI megaproject :

What determines: 1) Species distributions? 2) Species diversity? Patterns and processes

Wyoming Big Sagebrush Sites Fire/Land Treatment Study Overview

Global Patterns Gaston, K.J Nature 405. Benefit Diversity. Threats to Biodiversity

Mean Ellenberg indicator values as explanatory variables in constrained ordination. David Zelený

Neuendorf MANOVA /MANCOVA. Model: MAIN EFFECTS: X1 (Factor A) X2 (Factor B) INTERACTIONS : X1 x X2 (A x B Interaction) Y4. Like ANOVA/ANCOVA:

Transcription:

Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia SUPPLEMENTARY MATERIAL Félix Brédoire 1,2,, Polina Nikitich 3,4, Pavel A Barsukov 5, Delphine Derrien 3, Anton Litvinov 6, Helene Rieckh 7, Olga Rusalimova 5, Bernd Zeller 3, and Mark R Bakker 2,1 1 INRA, UMR 1391 ISPA, F-33140 Villenave d Ornon, France 2 Bordeaux Sciences Agro, UMR 1391 ISPA, F-33170 Gradignan, France 3 INRA, UR 1138 BEF, F-54280 Champenoux, France 4 Tomsk State University, Tomsk, Russia 5 Institute of Soil Sciences and Agrochemistry, Novosibirsk, Russia 6 Novosibirsk State Pedagogical University, Novosibirsk, Russia 7 Institute of Soil Landscape Research, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany Last Update: 29/09/2015 Corresponding author: felix.bredoire@bordeaux.inra.fr; Tel.: +33 5 57 12 26 55; Fax: +33 5 57 12 25 15 1

Supplementary Tle 1: Results of all the one-way ANOVA testing a site effect. Site effect tested on Veg. Cover Species Diameter F df p sig. lev. 1 Roots bottom pit Forest overall coarse 4.5922 5 0.01424 * overall fine 3.5211 5 0.03435 * Grassland overall coarse 0.7514 4 0.5793 overall fine 1.4598 4 0.2852 FRL in litter Forest overall < 0.8 1.6188 5 0.2287 aspen < 0.8 2.6003 5 0.08126. non-aspen < 0.8 1.915 5 0.1654 Grassland overall < 0.8 3.3321 4 0.05582. FRM in litter Forest overall < 0.8 1.3484 5 0.3096 aspen < 0.8 1.5792 5 0.239 non-aspen < 0.8 2.1616 5 0.1272 Grassland overall < 0.8 1.9599 4 0.1317 beta FRL Forest overall < 0.8 8.4154 5 0.001276 ** aspen < 0.8 1.8589 5 0.1757 non-aspen < 0.8 2.2469 5 0.1223 Grassland overall < 0.8 2.7179 4 0.091. Total FRL Forest overall < 0.8 4.8921 5 0.01135 * aspen < 0.8 7.3919 5 0.002233 ** non-aspen < 0.8 2.0074 5 0.1497 Grassland overall < 0.8 12.21 4 0.00073 *** FRL top 30 cm Forest overall < 0.8 7.5791 5 0.002008 ** aspen < 0.8 2.8304 5 0.06488. non-aspen < 0.8 2.1838 5 0.1302 Grassland overall < 0.8 3.8992 4 0.03683 * beta FRM Forest overall < 0.8 3.5493 5 0.03351 * aspen < 0.8 3.6112 5 0.03175 * non-aspen < 0.8 1.4347 5 0.2864 Grassland overall < 0.8 1.5297 4 0.2663 Total FRM Forest overall < 0.8 4.7124 5 0.01299 * aspen < 0.8 3.7766 5 0.02754 * non-aspen < 0.8 2.3462 5 0.105 Grassland overall < 0.8 8.1198 4 0.003487 ** FRM top 30 cm Forest overall < 0.8 4.7898 5 0.01225 * aspen < 0.8 7.0496 5 0.002725 ** non-aspen < 0.8 1.8805 5 0.1778 Grassland overall < 0.8 1.7126 4 0.2232 1 Significance levels: *** p < 0.001; ** p < 0.01; * p < 0.05;. p < 0.1. 2

Supplementary Tle 2: Length and mass of fine roots of a diameter < 0.8 mm in the litter layer. Mean of 3 replicates per site ± standard error of the mean. Different letters denote significant differences at p < 0.05 level using a Tukey post-hoc comparison. ANOVA results are given in 1. FRL (m m 2 ) FRM (g m 2 ) Veg. Cover Species Site mean se stat mean se stat Forest Overall FS1 17.81 5.46 a 1.04 0.34 a FS2 38.87 19.15 a 4.80 3.40 a FS3 31.87 3.84 a 2.85 0.54 a FS4 23.20 9.58 a 1.12 0.72 a ST1 11.50 3.16 a 0.49 0.16 a ST2 8.23 2.55 a 0.63 0.33 a Aspen FS1 1.66 1.66 a 0.18 0.18 a FS2 21.67 11.90 a 3.29 2.42 a FS3 2.12 1.06 a 0.39 0.22 a FS4 4.15 3.46 a 0.35 0.30 a ST1 0.74 0.25 a 0.23 0.11 a ST2 0.00 0.00 a 0.00 0.00 a Non-aspen FS1 16.15 4.62 a 0.87 0.28 a FS2 17.20 8.74 a 1.51 1.02 a FS3 29.75 4.60 a 2.46 0.56 a FS4 19.05 6.58 a 0.77 0.44 a ST1 10.76 2.95 a 0.26 0.05 a ST2 8.23 2.55 a 0.63 0.33 a Grassland Overall FS1 5.13 3.34 a 0.11 0.09 a FS2 0.89 0.89 a 0.06 0.06 a FS3 15.93 9.90 a 0.29 0.18 a FS4 18.68 1.57 a 0.44 0.20 a ST2 0.00 0.00 a 0.00 0.00 a 3

Supplementary Tle 3: Structure of the total fine root length calculated over 120 cm. Mean and standard error of the mean of 3 pits per site. Results are expressed in % of total FRL, diameters are in mm. FS1 FS2 FS3 FS4 ST1 ST2 Veg. Cover Species Diameter mean se mean se mean se mean se mean se mean se Forest Aspen overall 78.44 14.00 31.51 4.50 65.63 8.21 37.13 10.84 54.08 11.35 52.16 8.57 Non-aspen overall 21.56 14.00 68.49 4.50 34.37 8.21 62.87 10.84 45.92 11.35 47.84 8.57 Overall Aspen < 0.2 58.44 1.16 68.13 5.98 70.42 5.30 66.68 6.46 49.50 6.66 61.29 4.06 0.2-0.4 26.69 1.42 24.27 6.20 17.87 4.07 25.61 6.09 38.02 5.66 29.50 3.05 0.4-0.8 11.00 1.81 6.18 1.03 9.64 1.91 6.48 1.97 6.95 0.84 8.09 2.87 0.8-1.2 3.65 2.15 1.41 0.77 1.86 0.41 0.80 0.80 5.52 2.80 0.43 0.43 1.2-2.0 0.22 0.22 0.00 0.00 0.21 0.21 0.43 0.43 0.00 0.00 0.68 0.68 Non-aspen < 0.2 86.63 10.17 78.01 8.95 88.52 4.95 89.62 2.99 69.10 17.00 67.94 17.12 0.2-0.4 12.98 10.30 11.33 3.52 11.44 4.95 9.52 3.56 16.35 9.16 24.79 16.98 0.4-0.8 0.14 0.14 6.33 1.52 0.04 0.04 0.63 0.46 10.32 5.21 7.27 4.77 0.8-1.2 0.25 0.25 2.67 2.59 0.00 0.00 0.24 0.24 4.23 4.23 0.00 0.00 1.2-2.0 0.00 0.00 1.66 1.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Overall < 0.2 65.71 5.72 74.40 7.72 77.96 1.39 81.75 4.63 54.22 7.18 61.22 7.86 0.2-0.4 22.47 4.06 15.69 4.21 14.56 2.01 14.47 3.90 30.02 5.26 30.54 7.74 0.4-0.8 9.20 2.72 6.22 1.31 6.10 0.96 3.06 1.15 9.24 1.87 7.71 3.65 0.8-1.2 2.41 1.25 2.45 1.69 1.21 0.29 0.53 0.29 6.52 1.15 0.28 0.28 1.2-2.0 0.21 0.21 1.24 1.24 0.17 0.17 0.18 0.18 0.00 0.00 0.24 0.24 Grassland < 0.2 90.12 2.48 91.42 2.40 80.64 1.00 81.24 0.24 87.01 2.48 0.2-0.4 7.81 1.28 6.94 1.65 17.35 1.86 15.53 0.99 9.61 2.63 0.4-0.8 1.51 0.93 0.88 0.28 1.43 0.73 2.64 0.86 2.36 0.93 0.8-1.2 0.56 0.35 0.76 0.50 0.58 0.42 0.50 0.25 1.02 0.83 1.2-2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.09 0.00 0.00 overall 100.00 100.00 100.00 100.00 100.00 4

Supplementary Tle 4: Structure of the total fine root mass calculated over 120 cm. Mean and standard error of the mean of 3 pits per site. Results are expressed in % of total FRM, diameters are in mm. FS1 FS2 FS3 FS4 ST1 ST2 Veg. Cover Species Diameter mean se mean se mean se mean se mean se mean se Forest Aspen overall 86.08 9.95 36.95 12.89 83.18 4.07 52.80 12.15 62.75 22.48 70.13 8.53 NonAspen overall 13.92 9.95 63.05 12.89 16.82 4.07 47.20 12.15 37.25 22.48 29.87 8.53 Overall Aspen < 0.8 78.95 12.90 85.87 7.29 74.91 2.02 89.05 10.95 79.58 10.87 81.06 10.1 0.8-2 21.05 12.90 14.13 7.29 25.09 2.02 10.95 10.95 20.42 10.87 18.94 10.1 NonAspen < 0.8 96.63 3.37 74.67 21.58 100.00 0.00 86.75 11.60 76.41 23.59 100 0 0.8-2 3.37 3.37 25.33 21.58 0.00 0.00 13.25 11.60 23.59 23.59 0 0 Overall < 0.8 83.21 9.51 71.45 16.17 78.97 2.78 89.62 5.92 63.11 11.26 86.11 8.44 0.8-2 16.79 9.51 28.55 16.17 21.03 2.78 10.38 5.92 36.89 11.26 13.89 8.44 Grassland Overall < 0.8 93.45 4.26 90.47 4.86 83.97 11.64 87.73 6.14 77.92 18.09 0.8-2 6.55 4.26 9.53 4.86 16.03 11.64 12.27 6.14 22.08 18.09 overall 100.00 100.00 100.00 100.00 100.00 5

Forest Grassland a Species aspen non aspen 0.6 a overall kg m 2 0.4 a 0.2 b b b 0.0 FS1 FS2 FS3 FS4 ST1 ST2 FS1 FS2 FS3 FS4 ST1 ST2 Supplementary Figure 1: Total fine root mass over 120 cm in forest (left panel) and grassland (right panel). Mean and standard error of the mean of 3 replicates per site. In forest, total fine root mass is detailed for aspen (dark grey) and non-aspen (light grey). Results presented for roots with a diameter < 0.8 mm. Different letters denote significant differences at p < 0.05 level using a Tukey post-hoc comparison. ANOVA results are given in Supplementary Tle 1. 6

Forest Grassland Depth (cm) Site (mean β) FS1 (0.981) FS2 (0.962) FS3 (0.965) FS4 (0.963) ST1 (0.948) ST2 (0.945) Site (mean β) FS1 (0.962) FS2 (0.955) FS3 (0.957) FS4 (0.946) ST2 (0.911) Cumulative Fine Root Mass (Y) Supplementary Figure 2: Cumulative fine root mass (cumulative proportion) as a function of soil depth in forest (left panel) and grassland (right panel) for the six sites. The figure shows the differences between sites. Species are not sorted, diameter < 0.8 mm. The line was generated with the mean β (of 3 pits) from Eq. 6: Y = 1 β d (Gale_1987). 0 25 FS1 FS2 FS3 FS4 ST2 0.911 0.955 0.957 0.946 0.962 0.963 0.945 Depth (cm) 50 75 0.981 0.962 0.965 Veg. cover 100 Forest Grassland 125 0.0 0.5 1.00.0 0.5 1.00.0 0.5 1.00.0 0.5 1.00.0 0.5 1.0 Cumulative Fine Root Mass (Y) Supplementary Figure 3: Cumulative fine root mass (cumulative proportion) as a function of soil depth in forest and grassland for the six sites. The figure shows the differences between forest and grassland within sites and the quality of model fitting. Species are not sorted, diameter < 0.8 mm. Points are field measurements (3 per site and depth) and line was generated with the mean β (of 3 pits) from Eq. 6: Y = 1 β d (Gale_1987). 7

Depth (cm) 0 25 50 75 100 FS1 FS2 FS3 FS4 ST1 ST2 0.945 0.932 0.884 0.933 0.941 0.972 0.967 0.944 0.946 0.963 0.971 0.981 Species aspen non aspen 125 0.0 0.5 1.00.0 0.5 1.00.0 0.5 1.00.0 0.5 1.00.0 0.5 1.00.0 0.5 1.0 Cumulative Fine Root Mass (Y) Supplementary Figure 4: Cumulative fine root mass (cumulative proportion) as a function of soil depth in forest for the six sites. The figure shows the differences between aspen and non-aspen fine root systems within forest sites and the quality of model fitting. Aspen and non-aspen (understorey vegetation) are sorted, diameter < 0.8 mm. Points are field measurements (3 per site and depth) and line was generated with the mean β (of 3 pits) from Eq. 6: Y = 1 β d (Gale_1987). 8