Update for Revision

experimental/pdef/response_parameter/architecture_response_figures/KRS_results.csv

@@ -0,0 +1,29 @@
+krs_P0,krs_P1,krs_P2,krs_P3
+0.0,0.0,0.00017052912290617433,0.0002639360971845172
+0.00030060005993207334,0.0003582225540406684,0.0003787090107898672,0.0005878974281014213
+0.00044721850705771904,0.0005099354157625003,0.0005293977378133673,0.0007788794713755109
+0.0007076956728384689,0.0007912561003321214,0.0008179937456090164,0.001136797958505673
+0.0012439486905185975,0.001421589926878574,0.0014890102492802891,0.0021042846811132315
+0.0018322014804505213,0.002039472680292055,0.0021128653148593125,0.0029360538909289397
+0.002384477564170751,0.002621203931688025,0.0027151712043451816,0.003714215409340092
+0.0029074020173606757,0.0032334247218034993,0.003353539995024071,0.004661301125794276
+0.0033544799676322374,0.0037071827615338575,0.003818903007081381,0.005348097899723868
+0.003841456358814563,0.004238172138510451,0.004348583938850652,0.006065645409674563
+0.004448695408992153,0.0049252443784889135,0.0050497436571144425,0.007060388972815897
+0.0051922276392787434,0.005711097929493183,0.005858329743300231,0.008144736935591987
+0.006009119489235185,0.006589851708842187,0.0067629276347532016,0.009320885345363641
+0.006794111383041575,0.0074456784235855345,0.007626097894736625,0.010433750244366815
+0.00752066784488896,0.008247972167972666,0.00844678654315839,0.01148557970231376
+0.008227800762961176,0.009011227191472522,0.009234757201750394,0.012478658032991974
+0.008912014899166277,0.009743016804967312,0.009979565366570207,0.013421518633049518
+0.009553410227769076,0.010450037830167966,0.010704008547184339,0.014317611997971613
+0.010175522502462795,0.011119133674319517,0.011387487467945996,0.015166986031929347
+0.010766689797009423,0.011760686756671477,0.012050648632763188,0.015966920670494517
+0.011309126160439298,0.012362199220631118,0.012666534600045933,0.016723108825701893
+0.01184126749619731,0.012933082378686045,0.013247954249351076,0.017426580021822857
+0.01233037571802794,0.013468270029243643,0.013797743578893112,0.01808835937385587
+0.012792179805177314,0.013973060953707485,0.014316979646544674,0.01870558571242662
+0.013235633622205045,0.01445726780634857,0.014808268901562529,0.019278974377453337
+0.013657353280786327,0.014910090838265928,0.015273225137247189,0.019817931647864113
+0.01405346747054703,0.015338675964600684,0.015713917033839994,0.02032697924508732
+0.014429109558180718,0.015742803051402365,0.01613230045559699,0.02080321403822758

experimental/pdef/response_parameter/architecture_response_figures/KRS_results_sd.csv

@@ -0,0 +1,29 @@
+krs_P0,krs_P1,krs_P2,krs_P3
+0.0,0.0,1.9430892282840468e-05,2.2408180607858668e-05
+5.5027925338934905e-05,6.826716630416238e-05,5.320759985603394e-05,7.600784925532395e-05
+0.00011203395217468929,0.00011202883695429011,8.815328530471953e-05,9.226258911869482e-05
+0.00017030582106829597,0.00015726468298626946,0.00012233757082492677,0.00010778878403559057
+0.00022730754411132877,0.00021526480697424514,0.00018268410355329678,0.00019196498038546737
+0.0003058158425913934,0.0002754816042110409,0.00023375185812113172,0.0002467555643484848
+0.00037452300544379015,0.00032982375190437647,0.00028269002813894836,0.00029270551693698284
+0.00044742187621365115,0.00039218135696868293,0.00034221195797950365,0.00038320957754690074
+0.0005352897920579016,0.00045952171076536356,0.00040034239816824836,0.00045505926144392956
+0.000612699344329978,0.0005220390047803186,0.0004616909789997843,0.0005150404934228325
+0.0006825275095356637,0.0005937970232029881,0.0005246879607362602,0.0006074087509265712
+0.0007667786021254421,0.000656363613812417,0.0005841546498663003,0.0006850671611360848
+0.0008387394705520445,0.000714815098840523,0.0006479033086303231,0.0007489306664414173
+0.000908545057434724,0.0007687765629103327,0.0006982929802068331,0.0008192415237033828
+0.0009829909277261413,0.0008248930355557927,0.0007470888886993556,0.0008871461887639607
+0.0010482058506291574,0.0008754134805907748,0.0007962170696908162,0.0009427873356395898
+0.001112470660991921,0.0009273665831752263,0.0008463833640968701,0.0009975618088725118
+0.0011867469852760374,0.0009786612742115665,0.000885676872422532,0.0010435497517856677
+0.0012461236747786212,0.0010271001744623428,0.000925719743264234,0.0010835879639137362
+0.0013038731939664415,0.0010700551070369092,0.0009646139558703474,0.0011194749772571024
+0.0013648188022824654,0.0011138048910001959,0.001004352476126638,0.0011598376677215616
+0.00141989062262217,0.0011556916361055891,0.0010376690082156595,0.0011854866184403709
+0.00146972943819261,0.0011968355965489392,0.0010716148471816018,0.0012150109562342262
+0.0015262787346216064,0.0012386196775563258,0.0011022436672833583,0.0012359671188702656
+0.001574266180167995,0.0012760567717592247,0.0011237042520546216,0.0012569080906360093
+0.0016183514430844665,0.0013165830656533043,0.0011476324954841593,0.0012719659435486649
+0.0016704098046616944,0.0013453009399376835,0.0011729131158262887,0.0012806813570484905
+0.0017098060381409148,0.0013749576875269698,0.0011973396838137354,0.0012883717096179382

experimental/pdef/response_parameter/architecture_response_figure_3_and_5/figures_3_5.py → experimental/pdef/response_parameter/architecture_response_figures/figures.py

@@ -9,20 +9,53 @@
 import seaborn as sns
 from scipy.stats import linregress
 import os
+from statsmodels.stats.multicomp import pairwise_tukeyhsd
+import scikit_posthocs as sp
+from statannotations.Annotator import Annotator
+import pingouin as pg
+from scipy.stats import shapiro, levene
+from statsmodels.stats.anova import anova_lm
+from statsmodels.formula.api import ols
+import scikit_posthocs as sp
+from statannotations.Annotator import Annotator
 
 dir = os.path.dirname(os.path.abspath(__file__))
 
-'''FIGURE 3'''
+'''SUP. FIGURE 2'''
 # 'load data'
 idx = ['P0','P1','P2','P3']
+P_lvl = [1.8,3.3,4.6,7.7]
 a_axial = pd.read_csv(dir + '/a_axial.csv')
 r_crown = pd.read_csv(dir + '/r_crown.csv')
 r_leaves = pd.read_csv(dir + '/r_leaves.csv')
 rootshoot = pd.read_csv(dir + '/root_shoot_ratio.csv')
+response_data = pd.read_excel(dir + '/response_data.xlsx')
+krs_df = pd.read_csv(dir + '/KRS_results.csv')
+krs_sd_df = pd.read_csv(dir + '/KRS_results_sd.csv')
 
 '''PANEL A'''
+# Conduct Shapiro-Wilk test for normality
+stat, p = shapiro(a_axial['value'])
+print(f'Shapiro-Wilk test statistic: {stat}, p-value: {p}')
+shapiro_results = {}
+for group, values in a_axial.groupby('variable')['value']:
+    stat, p = shapiro(values)
+    shapiro_results[group] = (stat, p)
+    print(f'Group: {group}, Shapiro-Wilk test statistic: {stat}, p-value: {p}')
+
+# Levene test for homogeneity of variances
+grouped_values = [values for name, values in a_axial.groupby('variable')['value']]
+levene_stat, levene_p = levene(*grouped_values)
+print(f'Levene test statistic: {levene_stat}, p-value: {levene_p}')
+
+model = ols('value ~ C(variable)', data=a_axial).fit()
+anova_results = anova_lm(model)
+print(anova_results)
+
+#Plot
 plt.subplots(figsize=(6,4))
 tukey_df = posthoc_tukey(a_axial, val_col="value", group_col="variable")
+print(tukey_df)
 remove = np.tril(np.ones(tukey_df.shape), k=0).astype("bool")
 tukey_df[remove] = np.nan
 molten_df = tukey_df.melt(ignore_index=False).reset_index().dropna()
@@ -39,6 +72,26 @@
 plt.show()
 
 '''PANEL B'''
+# Conduct Shapiro-Wilk test for normality
+stat, p = shapiro(r_crown['value'])
+print(f'Shapiro-Wilk test statistic: {stat}, p-value: {p}')
+shapiro_results = {}
+for group, values in r_crown.groupby('variable')['value']:
+    stat, p = shapiro(values)
+    shapiro_results[group] = (stat, p)
+    print(f'Group: {group}, Shapiro-Wilk test statistic: {stat}, p-value: {p}')
+
+
+# Levene test for homogeneity of variances
+grouped_values = [values for name, values in r_crown.groupby('variable')['value']]
+levene_stat, levene_p = levene(*grouped_values)
+print(f'Levene test statistic: {levene_stat}, p-value: {levene_p}')
+
+model = ols('value ~ C(variable)', data=r_crown).fit()
+anova_results = anova_lm(model)
+print(anova_results)
+
+#Plot
 plt.subplots(figsize=(6,4))
 tukey_df = posthoc_tukey(r_crown, val_col="value", group_col="variable")
 remove = np.tril(np.ones(tukey_df.shape), k=0).astype("bool")
@@ -57,6 +110,23 @@
 plt.show()
 
 '''PANEL C'''
+# Conduct Shapiro-Wilk test for normality
+shapiro_results = {}
+for group, values in r_leaves.groupby('variable')['value']:
+    stat, p = shapiro(values)
+    shapiro_results[group] = (stat, p)
+    print(f'Group: {group}, Shapiro-Wilk test statistic: {stat}, p-value: {p}')
+
+# Levene test for homogeneity of variances
+grouped_values = [values for name, values in r_leaves.groupby('variable')['value']]
+levene_stat, levene_p = levene(*grouped_values)
+print(f'Levene test statistic: {levene_stat}, p-value: {levene_p}')
+
+model = ols('value ~ C(variable)', data=r_leaves).fit()
+anova_results = anova_lm(model)
+print(anova_results)
+
+#Plot
 plt.subplots(figsize=(6,4))
 tukey_df = posthoc_tukey(r_leaves, val_col="value", group_col="variable")
 remove = np.tril(np.ones(tukey_df.shape), k=0).astype("bool")
@@ -75,7 +145,7 @@
 plt.show()
 
 '''PANEL D'''
-rootshoot['root_shoot'] = rootshoot.root_dm / rootshoot.shoot_dm
+rootshoot['root_shoot'] = rootshoot.root_dm / (rootshoot.shoot_dm+rootshoot.root_dm)
 grouped = rootshoot.groupby('level')
 def standard_error(x):
     return np.std(x, ddof=1) / np.sqrt(x.count())
@@ -96,29 +166,30 @@ def standard_error(x):
     plt.text(x, result['root_shoot_mean'].loc[result['level'] == level].values[0] + 0.1, level, ha='center')
 plt.plot(x_values, y_fit, linestyle='--', color='red', label='Linear Regression Line')
 plt.text(
-    2.5, 1.3, f"R²: {r_value**2:.2f}", 
+    0.5, 0.6, f"R²: {r_value**2:.2f}", 
     bbox={'facecolor': 'white', 'alpha': 0.5, 'pad': 10})
 ax.set_xlabel('Pcal mg 100g$^{-1}$')
 ax.set_ylabel('[-]')
 plt.xlim(1.5, 8)
-plt.ylim(0.6, 2)
+plt.ylim(0.3, 0.7)
 plt.legend()
 plt.tight_layout()
 plt.show()
 
 
-'''FIGURE 5'''
+'''FIGURE 3'''
 '''PLOT PANEL A'''
-data = pd.read_excel('C:/Users/mobil/Desktop/Experiment_Rhizotrons/response_curves/response_data.xlsx')
-adjusted_data = data[(data['Pcal'] >= 1.8) & (data['Pcal'] <= 7.3)]
+#data = pd.read_excel('C:/Users/mobil/Desktop/Experiment_Rhizotrons/response_curves/response_data.xlsx')
+data = response_data
+adjusted_data = data[(data['Pcal'] >= 1.8) & (data['Pcal'] <= 7.7)]
 X = adjusted_data[['Pcal']]
 y = adjusted_data['a_axial']
 X_with_const = sm.add_constant(X)
 model = sm.OLS(y, X_with_const).fit()
 print(model.summary())
 p_value = model.pvalues[1]
 print(f"P-value for 'Pcal': {p_value}")
-pcal_values = np.linspace(1.8, 7.3, 500)
+pcal_values = np.linspace(1.8, 7.7, 500)
 X_plot = sm.add_constant(pcal_values)  # Remember to add constant for prediction
 predictions = model.predict(X_plot)
 r_squared = model.rsquared
@@ -129,7 +200,7 @@ def standard_error(x):
 plt.text(x=0.5, y=0.1, s=f'R² = {r_squared:.4f}\nP-value = {p_value:.4g}', fontsize=12, transform=plt.gca().transAxes)
 plt.xlabel('P$_{Cal}$ (g 100g$^{-1}$)')
 plt.ylabel('axial root radii (cm)')
-plt.xlim(1.6, 7.5)
+plt.xlim(1.6, 8)
 plt.ylim(0.02, 0.12)
 plt.show()
 
@@ -152,7 +223,7 @@ def standard_error(x):
 plt.xlabel('P$_{Cal}$/DM')
 plt.ylabel('crown root elongation (cm d^-1)')
 plt.xlim(0, 0.0011)
-plt.ylim(0, 7.8)
+plt.ylim(0, 8)
 plt.ticklabel_format(axis= 'both',style = 'scientific',scilimits=(-2,5))
 plt.show()
 
@@ -172,6 +243,49 @@ def standard_error(x):
 plt.plot(Pcal_observed_range, r_leaf_pred, color='black')
 plt.xlabel('P$_{Cal}$ (g 100g$^{-1}$)')
 plt.ylabel('leaf elongation rate (cm d^-1)$')
-plt.xlim(1.6, 7.5)
+plt.xlim(1.6, 8)
 plt.ylim(2.5, 15.5)
 plt.show()
+
+
+'''Supplementary Figure 4'''
+df = pd.DataFrame(rootshoot)
+df['shoot_dm + root_dm'] = df['shoot_dm'] + df['root_dm']
+level_mapping = {"P0": 1.8, "P1": 3.3, "P2": 4.6, "P3": 7.7}
+df['level'] = df['level'].map(level_mapping)
+df_grouped = df.groupby('level').agg(['mean', 'std'])
+
+plt.figure(figsize=(6,4))
+plt.plot(df_grouped.index, df_grouped['shoot_dm']['mean'], marker='o',label = 'Biomass shoot')
+plt.fill_between(df_grouped.index, df_grouped['shoot_dm']['mean'] - df_grouped['shoot_dm']['std'], 
+                 df_grouped['shoot_dm']['mean'] + df_grouped['shoot_dm']['std'], alpha=0.2)
+plt.plot(df_grouped.index, df_grouped['root_dm']['mean'], marker='o',  label = 'Biomass root')
+plt.fill_between(df_grouped.index, df_grouped['root_dm']['mean'] - df_grouped['root_dm']['std'], 
+                 df_grouped['root_dm']['mean'] + df_grouped['root_dm']['std'], alpha=0.2)
+plt.plot(df_grouped.index, df_grouped['shoot_dm + root_dm']['mean'], marker='o',  label = 'Biomass plant')
+plt.fill_between(df_grouped.index, df_grouped['shoot_dm + root_dm']['mean'] - df_grouped['shoot_dm + root_dm']['std'], 
+                 df_grouped['shoot_dm + root_dm']['mean'] + df_grouped['shoot_dm + root_dm']['std'], alpha=0.2)
+plt.xlabel('P$_{soil}$')
+plt.ylabel('Dry biomass [g]')
+plt.ylim(1,11)
+plt.xlim(1.6, 8)
+plt.legend()
+plt.show()
+
+'''Supplementary Figure 5'''
+
+rows_to_plot = [6, 13, 20, 27]
+line_styles = ['-', '--', '-.', ':']
+point_markers = ['o', 's', 'D', '^']
+legend_labels = ['7 DAS', '14 DAS', '21 DAS', '28 DAS']
+plt.figure(figsize=(10, 7))
+for i, row in enumerate(rows_to_plot):
+    values = krs_df.loc[row, ['krs_P0', 'krs_P1', 'krs_P2', 'krs_P3']].values
+    std_devs = krs_sd_df.loc[row, ['krs_P0', 'krs_P1', 'krs_P2', 'krs_P3']].values
+    plt.errorbar(P_lvl, values, yerr=std_devs, fmt=line_styles[i] + point_markers[i], capsize=5, color='black', label=legend_labels[i])
+plt.xlabel('P$_{soil}$')
+plt.ylabel('K$_{rs}$ (cm² d$^{-1}$)')
+plt.ylim(0, 0.025)  
+plt.grid()  
+plt.legend()
+plt.show()

experimental/pdef/response_parameter/architecture_response_figure_3_and_5/r_crown.csv → experimental/pdef/response_parameter/architecture_response_figures/r_crown.csv

@@ -2,31 +2,24 @@ variable,value
 P0,0.4069888296703522
 P0,0.20145526095972843
 P0,2.655896231882634
-P0,6.004191905485569
 P0,1.835820484698392
 P0,1.5983496513785964
-P0,7.137807992205309
 P1,0.2461394157898003
 P1,1.0849921000228604
 P1,0.7224329768288287
 P1,0.9130196835680513
 P1,1.0643374230432476
 P1,0.7861815374918731
-P1,2.1601997215510345
 P1,1.566359007372194
 P1,1.3008379043428926
 P2,0.2695678868844662
 P2,0.19231977713775533
 P2,0.7041493736558947
 P2,0.19578787288601868
-P2,4.904461163412021
 P2,2.006413428677393
-P2,2.7375809064243106
 P2,2.539780555797498
 P2,1.9164469200335799
 P3,0.7362927320999927
 P3,3.7840653191149354
 P3,3.1386825176005466
-P3,8.095972841254206
-P3,6.593720347825664
 P3,5.649212327296748

experimental/pdef/simulation/sim.py

@@ -1,5 +1,5 @@
 import sys;
-CPBdir = "../../.."
+CPBdir = "../.."
 sys.path.append(CPBdir + "/src");
 sys.path.append(CPBdir);
 sys.path.append("../../..");