diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5b6a0652566d10360493952aec6d4a4febc77083
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+++ b/.gitignore
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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
diff --git a/Analysis.Rproj b/Analysis.Rproj
new file mode 100755
index 0000000000000000000000000000000000000000..8e3c2ebc99e2e337f7d69948b93529a437590b27
--- /dev/null
+++ b/Analysis.Rproj
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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
diff --git a/LICENSE b/LICENSE
new file mode 100755
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
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+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
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+ END OF TERMS AND CONDITIONS
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+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
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+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
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+ but WITHOUT ANY WARRANTY; without even the implied warranty of
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+ along with this program. If not, see <https://www.gnu.org/licenses/>.
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+ <program> Copyright (C) <year> <name of author>
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diff --git a/README.md b/README.md
old mode 100644
new mode 100755
index 88edfdbee57a14f72353cd19812bd94859217ea0..b8aa31d66402225f7e718dd60eef5fac6ac0f6c6
--- a/README.md
+++ b/README.md
@@ -1,92 +1,106 @@
-# Monteux 2023 Plant Removal
-
-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://git.bolin.su.se/monteuxs/monteux-2023-plant-removal.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://git.bolin.su.se/monteuxs/monteux-2023-plant-removal/-/settings/integrations)
-
-## Collaborate with your team
-
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-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
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-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
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-## Contributing
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-
-## Authors and acknowledgment
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+# Data analysis and figure generation for a gradient design to account for biases in plant removal experiments
+
+## Overview
+
+Code and data underlying "[Controlling biases in targeted plant removal experiments](https://doi.to-do)" by Sylvain Monteux, Gesche Blume-Werry, Konstantin Gavazov, Leah K. Kirchhoff, Eveline J. Krab, Signe Lett, Emily Pickering Pedersen, Maria Vaisanen, New Phytologist, 2023. This repository stores the entire analysis pipeline: statistical analyses and figure generation.
+
+The experimental design consists in 5 spatially-replicated blocks containing each one of the following treatments: undisturbed control (CTL), targeted removal of ericoid- (-ERM), ecto- (-ECM) or both ericoid- and ecto-mycorrhizal plants (-ECM/-ERM) in a subarctic tundra heath at the forest-tundra ecotone in northern Sweden. A gradient of indiscriminate biomass removal based on area was implemented alongside the targeted removal treatments to account for biases induced by the removal of plant biomass. The repository describes how the data was analysed based on results observed in the gradient, and depending on that whether the response variables in the treatment plots were adjusted for potential confounding effects.
+
+We show how non-specific biomass removal can result in false discoveries or false negatives based on data obtained within this experiment.
+
+In short, the scripts in this repository:
+
+- Create the conceptual Figure 1
+- Assess the significance of relationships between response variables and biomass removed in the gradient (gradient regression)
+- When these gradient regressions are significant:
+ - Adjust response variables to account for the confounding effect of removed biomass
+ - Simulate variation to assess how variability in the gradient regression
+- Analyze the treatment effects on the observed, adjusted or simulated data
+- Create Figure 4 to illustrate the procedure and its effect on three response variables
+- Create Supplementary Figures S1 and S2
+
+## Prerequisites
+
+R scripts were tested on R4.1.3 on Windows 10 and R4.3.1 on Linux Mint 21.2.
+No intensive resource use.
+
+
+## Language
+
+R.
+
+## File description
+
+- Files not contained in `code` or `data` are not necessary to initiate the analysis and are provided for record. Such files will be created and/or overwritten as the scripts are executed.
+- `data/`
+ - `harmonized_data.csv` - Contains sample information, biomass removed and response variables
+- `results/` - Final result files
+ - `Fig1_conceptual.pdf` - Figure 1
+ - `Fig3_assembled.pdf` - Figure 3
+ - `SI_Fig_1_biomass_cover.png` - Supplementary Figure S1
+ - `SI_Fig_2_Bnana_growth_nonegatives.pdf` - Supplementary Figure S2
+- `code/`
+ - `Manuscript_figures.Rmd` and `Manuscript_figures.html` - R-markdown and HTML output of the figure-generation script
+- `sessionInfo.txt` - R environment file
+- `LICENCE` - Licence file
+- `./README.md` - This document
+
+## Citation
+
+Monteux S (2023) Data analysis for adjusting effects of biomass removal in a mycorrhizal association-based plant removal experiment. Software version 1.0.0. Bolin Centre Code Repository. <https://doi.org/10.57669/monteux-2023-plant-removal-1.0.0>
+
+## References
+
+Monteux S., Blume-Werry G., Gavazov K., Kirchhoff L.K., Krab E.J., Lett S., Pickering Pedersen E. and Vaisanen M., Controlling for biases in targeted plant removal experiments, journaljournaljournal, vol nr pp. nr. <https://doi.org/placeholder>
+
+## Comments
+
+
+### Acknowledgements
+
+We thank the Swedish Polar Research Secretariat and SITES for the logistical support of the work done at the Abisko Scientific Research Station.
+
+This study was financially supported by the Bolin Centre for Climate Research, Helge Ax:son Johnsons stiftelse and Arcum (SM); Swiss Polar Institute Exploratory Grant (SPIEG-2020-001) and Swiss National Science Foundation (grant PZ00P2_174047) (KG); Waldemar von Frenckells stiftelse, Oskar Oflunds stiftelse, the Osk. Huttunen Foundation and The University of Oulu & The Academy of Finland PROFI4 (Grant 318930) (MV).
+
+We thank James T. Weedon for critical input on statistical data processing, Anika Mayr for obtaining and processing data on Betula nana growth and isotopic nitrogen content, and Sonia Meller for lending us the SEAR scanner. We also thank Anna Miettinen, Elisa Jung, Henrike Lange, Janne Welling, Lorenzo Masini, Luca Dettmers, Margaux Chadanson, Paul Schulz and Yannick Bernard for their help in the maintenance of the experiment and data collection.
+
+
+## Creators
+
+Sylvain Monteux
+
+## Contact information
+
+Sylvain Monteux
+
+Department of Environmental Science, Stockholm University, SE-106 91 Stockholm, Sweden
+
+sylvain.monteux [at] aces.su.se
+
+UiT The Arctic University Museum of Norway, NO-9013 Tromsø, Norway
+
+sylvain.monteux [at] uit.no
+
+## Publisher
+
+Bolin Centre Code Repository
+
+## Version
+
+1.0.0
+
+## Project URL
+
+<https://abisko-lter.weebly.com/>
+
+## DOI
+
+to-do
+
+## License
+
+Distributed under the GNU General Public License v3.0. See LICENSE text for more information.
+
+## Publication date
+
+placeholder
diff --git a/code/Monteux_NP_biomass_removal.Rmd b/code/Monteux_NP_biomass_removal.Rmd
new file mode 100755
index 0000000000000000000000000000000000000000..b8dcc145a7d651b17cd631252d96f35cf8127859
--- /dev/null
+++ b/code/Monteux_NP_biomass_removal.Rmd
@@ -0,0 +1,842 @@
+---
+title: "Controlling biases in targeted plant removal experiments"
+author: "Sylvain Monteux"
+date: "2023-01-31"
+output:
+ html_document:
+ code_folding: hide
+---
+
+This script supports the manuscript "*Controlling biases in targeted plant removal experiments*" by Sylvain Monteux, Gesche Blume-Werry, Konstantin Gavazov, Leah K. Kirchhoff, Eveline J. Krab, Signe Lett, Emily Pickering Pedersen, Maria Väisänen
+
+*New Phytologist* Special Issue "Mycorrhizal research now: from the micro- to the macro-scale", x, xx-xx, doi: xxxxxx-xxxxx
+
+The script details our approach to adjust response variables for unwanted effects of non-specific biomass removal, and reproduces the figures presented throughout the manuscript.
+
+## 1. Libraries and initial data loading
+
+```{r libraries, include=FALSE}
+knitr::opts_chunk$set(
+ echo = TRUE,
+ message = FALSE,
+ warning = FALSE
+)
+library(ggplot2); packageVersion("ggplot2")
+library(patchwork); packageVersion("patchwork")
+library(tidyverse); packageVersion("tidyverse")
+library(dplyr); packageVersion("dplyr")
+library(sciplot); packageVersion("sciplot")
+library(lmerTest); packageVersion("lmerTest")
+library(ggside); packageVersion("ggside")
+library(car); packageVersion("car")
+writeLines(capture.output(sessionInfo()), "../sessionInfo.txt")
+```
+
+```{r data-preparation, echo=TRUE, class.source = 'fold-show'}
+# Load data file
+dat<-read.csv("../data/harmonized_data.csv",sep=";")
+dat$block <- as.factor(dat$block)
+# block: Spatial blocks in the experiment [text]
+# Treatment: Experimental conditions [text]
+## either "gradient" for the non-specific biomass removal gradient plots or one of:
+## ECM: Ectomycorrhizal plants removal
+## ERM: Ericoid mycorrhizal plants removal
+## EE: Ecto- and Ericoid-mycorrhizal plants removal
+## control: Undisturbed control
+
+# plot: treatment disambiguation within a given block [text]
+## contains %_removal information for non-specific biomass removal gradient plots
+# Betula_d15N: Delta 15N values from Betula nana leaves [per mil]
+# Betula_growth: Growth of Betula nana shoots between July 1st and August 25th 2021, averaged over four pseudoreplicates within each plot [mm]
+# MUX: Reaction rate of extracellular β-xylosidase with 4-Methylumbelliferyl-β-D-xylopyranoside (MUX) in the Soil Enzymatic Activity Reader SEAR (Digit Soil, Zürich, Switzerland) [pmol.min-1]
+# biomass_removed_kg: Sum of biomass removed from the beginning of the experiment until 2021 within each plot [g]
+# ERM_rem: Removal of Ericoid mycorrhizal plants [boolean]
+# ECM_rem: Removal of Ectomycorrhizal plants [boolean]
+
+# Convert removed biomass from g to kg for readability
+dat$biomass_removed_kg<-dat$biomass_removed_g / 1000
+
+# Make a subset containing only observations from the non-specific biomass removal gradient
+grad<-subset(dat,Treatment=="gradient")
+grad$plot <- as.numeric(grad$plot)
+
+# Make a subset containing only targeted plant removal treatments and undisturbed controls
+trt<-subset(dat,Treatment!="gradient")
+
+# Set global parameters
+n_sim<-5000 # Set number of Monte Carlo simulations
+```
+
+## 2. Figure 1: Hypothetical and observed effects of treatments and biomass removal
+
+```{r Fig1-panels, echo=F}
+# Generate noisy flat line for non-significant slope case
+set.seed(1)
+dummyns<-data.frame(x=c(1:100),
+ y=rnorm(100,sd=50))
+# Generate noisy significant slope
+dummysig<-data.frame(x=c(0:100), # x coordinates
+ y=rnorm(101,sd=100)) # noise for y variable
+dummysig$y_noised<-50+1.3*dummysig$x+dummysig$y # y variable
+
+# Plotting command for no-artefact scenario (a)
+p2a<-ggplot(data=dummyns,mapping=aes(x=x,y=y))+
+ geom_smooth(method="lm",col="black",lty=3)+
+ theme_bw()+
+ theme(panel.grid = element_blank(),
+ axis.text=element_blank(),
+ axis.ticks = element_blank())+
+ ylab("Response variable")+
+ xlab("Biomass removed")+
+ ggtitle("No artefact")
+
+# Create data points for scenario (b)
+dummy15n<-data.frame(x=c(0,80),
+ y=c(55,160),
+ ymin=c(40,180),
+ ymax=c(70,140),
+ xmin=c(0,77),
+ xmax=c(0,83))
+# Plotting command for false discovery scenario (b)
+p2b<-ggplot(data=dummysig,mapping=aes(x=x,y=y_noised))+
+ geom_smooth(method="lm",col="black")+
+ theme_bw()+
+ theme(panel.grid = element_blank(),
+ axis.text=element_blank(),
+ axis.ticks = element_blank())+
+ geom_pointrange(data=dummy15n,
+ aes(x=x,y=y,ymin=ymin,ymax=ymax),
+ col=c("black","#4d9221"),
+ lwd=0.6)+
+ geom_errorbarh(data=dummy15n,
+ aes(x=x,
+ y=y,
+ xmin=xmin,
+ xmax=xmax),
+ col=c("black","#4d9221"),
+ lwd=0.6,
+ height=0)+
+ ylab("Response variable")+
+ xlab("Biomass removed")+
+ ggtitle("False positive")
+
+# Create data points for scenario (c)
+dummy_bet_gr<-data.frame(x=c(0,80),
+ y=c(55,60),
+ ymin=c(40,75),
+ ymax=c(70,45),
+ xmin=c(0,77),
+ xmax=c(0,83))
+# Plotting command for false negative scenario (c)
+p2c<-ggplot(data=dummysig,mapping=aes(x=x,y=y_noised))+
+ geom_smooth(method="lm",col="black")+
+ theme_bw()+
+ theme(panel.grid = element_blank(),
+ axis.text=element_blank(),
+ axis.ticks = element_blank())+
+ geom_pointrange(data=dummy_bet_gr,
+ aes(x=x,y=y,ymin=ymin,ymax=ymax),
+ col=c("black","#4d9221"),
+ lwd=0.6)+
+ geom_errorbarh(data=dummy_bet_gr,
+ aes(x=x,
+ y=y,
+ xmin=xmin,
+ xmax=xmax),
+ col=c("black","#4d9221"),
+ lwd=0.6,
+ height=0)+
+ ylab("Response variable")+
+ xlab("Biomass removed")+
+ ggtitle("False negative")
+
+# Create data points for scenario (d)
+dummy_bis_nit<-data.frame(x=c(0,80,80),
+ y=c(55,40,230),
+ ymin=c(40,25,215),
+ ymax=c(70,55,245),
+ xmin=c(0,77,76),
+ xmax=c(0,83,84))
+# Plotting command for misestimates scenario (d)
+p2d<-ggplot(data=dummysig,mapping=aes(x=x,y=y_noised))+
+ geom_smooth(method="lm",col="black")+
+ theme_bw()+
+ theme(panel.grid = element_blank(),
+ axis.text=element_blank(),
+ axis.ticks = element_blank())+
+ geom_pointrange(data=dummy_bis_nit,
+ aes(x=x,y=y,ymin=ymin,ymax=ymax),
+ col=c("black","#4d9221","#4d9221"),
+ pch=c(16,16,17),
+ lwd=0.6)+
+ geom_errorbarh(data=dummy_bis_nit,
+ aes(x=x,
+ y=y,
+ xmin=xmin,
+ xmax=xmax),
+ col=c("black","#4d9221","#4d9221"),
+ lwd=0.6,
+ height=0)+
+ ylab("Response variable")+
+ xlab("Biomass removed")+
+ ggtitle("Biased effect size")
+```
+
+```{r compile-fig-1, echo=F, fig.height=8}
+# Add panels together in one column (patchwork)
+p2a+p2b+p2c+p2d+plot_layout(ncol=1)
+ggsave("../results/Fig1_conceptual.pdf",width=10,height=20,unit="cm")
+```
+
+## 3. Analysis of non-specific biomass removal and treatment effects for different variables
+
+a. For all the response variables, a linear-mixed effect model `lmer` is carried out on the non-specific biomass removal gradient plots, using amount of removed biomass as fixed factor and spatial block as random factor.
+b. The response variable is analyzed in the treatment plots, using a 2-way ANOVA on a linear mixed model with ericoid- and ectomycorrhizal-removal as fixed factors and block as random factor (or one-way ANOVA if the variable is only present in one of the removal treatments).
+c. Depending on **a**:
+
+- If there is no significant relationship between the response variable and amount of removed biomass in the gradient plots **a**, data analysis from **b** is considered correct.
+- If there is a significant relationship **a**, the response variable has to be adjusted for biomass removal effect. The gradient regression model **a** is used to predict values based on biomass removed in treatment plots (and block, if relevant). The adjusted response variable is calculated by subtracting the biomass-dependent component of the predicted values from the observed values: `adjusted_response = model_intercept + (observed_value - predicted_value)`
+
+d. The adjusted response variable is tested for treatment effects as in **b**
+
+### 3.1. β-glucosidase activity
+#### a. Gradient regression
+```{r beta-glucosidase-a}
+# a. gradient regression
+beta_lmer_grad<-lmer(MUX~biomass_removed_kg+(1|block),data=grad,na.action = na.omit)
+coef(beta_lmer_grad) # Random factor coefficients properly estimated, no reason to simplify the model
+## check assumptions
+# plot(beta_lmer_grad)
+# shapiro.test(residuals(beta_lmer_grad))
+# summary(beta_lmer_grad)
+anova(beta_lmer_grad)
+# biomass_removed effect n.s. P=0.478
+# use non-adjusted response variable
+```
+
+#### b. Non-adjusted response variable
+```{r beta-glucosidase-b}
+# b. Non-adjusted treatment analysis
+beta_lmer_trt<-lmer(MUX~ERM_rem*ECM_rem+(1|block),data=trt,na.action=na.omit) # singular
+## check assumptions
+# plot(beta_lmer_trt)
+# shapiro.test(residuals(beta_lmer_trt))
+# summary(beta_lmer_grad)
+anova(beta_lmer_trt) # ECM_rem significant *
+```
+
+### 3.2. *Betula nana* delta-^15^N
+#### a. Gradient regression
+
+```{r betula-delta-15n-a}
+# a. Gradient regression
+bnd15n_lmer_grad<-lmer(Betula_d15N~biomass_removed_kg+(1|block),data=grad,na.action=na.omit)
+coef(bnd15n_lmer_grad) # Random factor coefficients estimated with 0 variance, use a linear model instead
+bnd15n_lm_grad<-lm(Betula_d15N~biomass_removed_kg,data=grad,na.action = na.omit)
+## check assumptions
+# plot(bnd15n_lmer_grad)
+# shapiro.test(residuals(bnd15n_lmer_grad))
+# summary(bnd15n_lmer_grad)
+anova(bnd15n_lmer_grad)# biomass_removed effect P < 0.01
+```
+
+#### b. Non-adjusted response variable
+
+```{r betula-delta-15n-b}
+# b. Non-adjusted ANOVA for 15N
+naB15n<-lmer(Betula_d15N~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(naB15n) # Random factor coefficients properly estimated, no reason to simplify the model
+# # check assumptions
+# plot(naB15n)
+# shapiro.test(resid(naB15n)) # deviation from normality
+# hist(resid(naB15n)) # deviation seems due to chance because of few observations, rather than e.g. bimodal or skewed distribution
+anova(naB15n) # Significant difference between Control and -ERM before adjustment
+```
+
+#### c. Adjust response variable
+
+```{r betula-delta-15n-c, class.source = 'fold-show'}
+# c. Adjust response variable
+trt$Betula_d15N_adj<-summary(bnd15n_lmer_grad)$coefficients[1] + (trt$Betula_d15N - predict(bnd15n_lmer_grad,newdata = trt)) # Create adjusted variable: intercept + (observed - predicted)
+# In this case the intercept value is identical across blocks, however this code would work with a model where the intercept differs across the random factor:
+# the first component is the mean intercept across different levels of the random factor
+# the variability across blocks is included in the prediction term which takes the block of the new data into account
+
+#trt$Betula_d15N_adj<-bnd15n_lmer_grad$coefficients[1] + (trt$Betula_d15N - predict(bnd15n_lmer_grad,newdata = trt)) # This would work for a linear model
+
+# A GL(M)M would require creating a null model to get the intercept, see section 4.7
+```
+
+#### d. Adjusted response variable
+
+```{r betula-delta-15n-d}
+# d. Analyse adjusted response variable
+bnd15n_lmer_trt<-lmer(Betula_d15N_adj~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bnd15n_lmer_trt) # Random factor coefficients properly estimated, no reason to simplify the model
+# # check assumptions
+# plot(bnd15n_lmer_trt)
+# shapiro.test(resid(bnd15n_lmer_trt))
+anova(bnd15n_lmer_trt) # no significant differences between Control and -ERM after adjusting for biomass removal
+```
+
+### 3.3. *Betula nana* growth
+#### a. Gradient regression
+
+```{r betula-growth-a}
+# a. Gradient regression
+bg_lmer_grad<-lmer(Betula_growth~biomass_removed_kg+(1|block),data=grad,na.action=na.omit)
+coef(bg_lmer_grad) # Singular fit, no intercept variation, equivalent to using lm
+bg_lm_grad<-lm(Betula_growth~biomass_removed_kg,data=grad,na.action = na.omit)
+# # check assumptions
+# plot(bg_lmer_grad)
+# shapiro.test(resid(bg_lmer_grad))
+anova(bg_lmer_grad) # biomass removed effect
+```
+
+#### b. Non-adjusted response variable
+
+```{r betula-growth-b}
+# b. Non-adjusted ANOVA for growth
+nabg<-lmer(Betula_growth ~ ERM_rem+ (1|block),data=trt,na.action=na.omit)
+coef(nabg) # Singular fit, no intercept variation, equivalent to using lm
+# # check assumptions
+# plot(nabg)
+# shapiro.test(resid(nabg))
+anova(nabg) # No significant difference between Control and -ERM before adjustment
+```
+
+#### c. Adjust response variable
+
+```{r betula-growth-c}
+# c. Adjust response variable
+trt$Betula_growth_adj<-summary(bg_lmer_grad)$coefficients[1] + (trt$Betula_growth - predict(bg_lmer_grad,newdata = trt)) # Create adjusted variable: intercept + (observed - predicted)
+```
+
+#### d. Adjusted response variable
+
+```{r betula-growth-d}
+# d. Analyse adjusted response variable
+bg_lmer_trt<-lmer(Betula_growth_adj~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bg_lmer_trt) # Singular fit, no intercept variation, equivalent to using lm
+# # check assumptions
+# plot(bg_lmer_trt)
+# shapiro.test(resid(bg_lmer_trt))
+anova(bg_lmer_trt) # Significant difference between Control and -ERM after adjusting for biomass removal
+100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj)/mean(subset(trt,Treatment=="control")$Betula_growth_adj)) # % lower growth in -ERM
+```
+
+#### e. Adjusted response variable without negative values
+
+```{r betula-growth-e}
+# d.bis. Remove negative growth values before analysing adjusted response variable
+trt$Betula_growth_adj2<-ifelse(trt$Betula_growth_adj<=0,0,trt$Betula_growth_adj)
+bg_lmer_trt2<-lmer(Betula_growth_adj2~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bg_lmer_trt2) # Singular fit, no intercept variation, equivalent to using lm
+# # check assumptions
+# plot(bg_lmer_trt2)
+# shapiro.test(resid(bg_lmer_trt2))
+anova(bg_lmer_trt2) # Significant difference between Control and -ERM after adjusting for biomass removal
+100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj2)/mean(subset(trt,Treatment=="control")$Betula_growth_adj2)) # 78% lower growth in -ERM
+
+```
+
+```{r compile-means-SE}
+# Compute means and SE of the covariate, non-adjusted and adjusted response variables, for plotting purposes
+trt_mean_se <- trt%>%
+ group_by(Treatment,
+ ECM_rem,
+ ERM_rem) %>%
+ summarize_at(c("MUX","Betula_d15N","Betula_d15N_adj","Betula_growth","Betula_growth_adj","Betula_growth_adj2","biomass_removed_kg"),
+ .funs = list(mean = mean, se = se), na.rm = TRUE) # sciplot::se()
+
+```
+
+## 4. Figure 4
+
+### 4.1. Figure 4a - β-xylosidase activity
+
+```{r Fig4a-betaxyl, echo=FALSE}
+p3a<-ggplot(data=grad,mapping=aes(x=biomass_removed_kg,y=MUX))+
+ geom_smooth(method="lm",col="black",lty=3)+
+ geom_point(col="darkgrey")+
+ theme_bw()+
+ theme(panel.grid = element_blank())+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=MUX_mean,
+ ymin=MUX_mean-MUX_se,
+ ymax=MUX_mean+MUX_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=MUX_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6,
+ height=0)+
+ ylab(expression(paste(beta*"-xylosidase activity [pmol.min"^-1*"]")))+
+ xlab("Biomass removed [kg]")
+```
+
+### 4.2. Figure 4b - Non-adjusted *Betula nana* delta_15N
+
+```{r Fig4b-15N}
+p3b<-ggplot(data=grad,mapping=aes(x=biomass_removed_kg,y=Betula_d15N))+
+ geom_smooth(method="lm",col="black")+
+ geom_point(col="grey")+
+ theme_bw()+
+ theme(panel.grid=element_blank())+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_mean,
+ ymin=Betula_d15N_mean-Betula_d15N_se,
+ ymax=Betula_d15N_mean+Betula_d15N_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6,
+ height=0)+
+ ylab(expression(paste(italic("Betula nana "),delta*"15N [‰]")))+
+ xlim(c(0,4))+
+ ylim(c(-10,-1))+
+ xlab("Biomass removed [kg]")
+```
+
+### 4.3. Fig 4c: Adjusted *Betula nana* delta-^15^N
+
+#### 4.3.1. Monte Carlo simulations
+
+```{r MC-15N}
+# Monte Carlo simulations for Betula nana delta_15N variable adjustment
+set.seed(1234)
+# Create subset of observed data on which to base the prediction
+prediction_data_betulaERM<-subset(trt,Treatment == "ERM") %>% select(Treatment,block,Betula_d15N,Betula_growth,biomass_removed_kg)
+
+# Create subset of control data to which the predicted values will be compared
+control_data<-subset(trt,Treatment=="control") %>% select(Treatment,block,Betula_d15N,Betula_growth)
+
+
+# Create simulated slopes
+simpar<-data.frame(slope=rnorm(n_sim,
+ summary(bnd15n_lmer_grad)$coefficients[2,1],
+ summary(bnd15n_lmer_grad)$coefficients[2,2]))
+# slope: n_sim points from normal distribution centered on slope intercept and spread by slope SE
+
+# Initialize variables
+simdat_15n<-NULL
+results<-data.frame()
+treatment<-c(rep("ERM_rem",5),rep("Control",5))
+block<-c(rep(1:5,2))
+# Run MC simulations
+for (i in 1:n_sim)
+{
+ simdat_15n[[i]]<-c((prediction_data_betulaERM$Betula_d15N-(simpar$slope[i]*prediction_data_betulaERM$biomass_removed_kg)),# Create n_sim sets of adjusted variable
+ control_data$Betula_d15N)
+ results[i,1]<-mean(simdat_15n[[i]][1:5]) # Extract means
+ results[i,2]<-sd(simdat_15n[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_15n[[i]][1:5] , simdat_15n[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ results[i,3]<-t_temp$statistic # Extract t-statistic from tests
+ results[i,4]<-t_temp$p.value # Extract P value from tests
+ lme_temp<-lmer(simdat_15n[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ results[i,5]<-anova_temp$`F value`
+ results[i,6]<-anova_temp$`Pr(>F)`
+}
+names(results)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+results$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+sigdiff<-100*round(length(which(results$P_t<0.05))/length(results$P_t),3) # Calculate proportion of t-tests where P < 0.05
+sigdiff_F<-100*round(length(which(results$P_F<0.05))/length(results$P_F),3) # Calculate proportion of ANOVAs where P < 0.05
+```
+
+#### 4.3.2. Plotting command
+
+```{r 4c-MC-15N-plot}
+p3c<-ggplot(results,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_d15N),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_d15N),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=results,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_adj_mean,
+ ymin=Betula_d15N_adj_mean-Betula_d15N_adj_se,
+ ymax=Betula_d15N_adj_mean+Betula_d15N_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(sigdiff_F,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=-8.5,
+ size=3,
+ col="#5ab4ac")+
+ ylab(expression(paste(italic("Betula nana "),delta*"15N [‰]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4))+
+ ylim(c(-10,-1))
+```
+
+### 4.4. Figure 4d: Non-adjusted *Betula nana* growth
+
+```{r panel-4d-Betula_growth_unadj}
+p3d<-ggplot(data=grad,mapping=aes(x=biomass_removed_kg,y=Betula_growth))+
+ geom_smooth(method="lm",col="black")+
+ geom_point(col="darkgrey")+
+ theme_bw()+
+ theme(panel.grid=element_blank())+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_mean,
+ ymin=Betula_growth_mean-Betula_growth_se,
+ ymax=Betula_growth_mean+Betula_growth_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6,
+ height=0)+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4))+
+ ylim(c(-5,15))
+```
+
+### 4.5. Figure 4e: Adjusted *Betula nana* growth
+
+#### 4.5.1. Monte Carlo simulations
+
+```{r MC-growth}
+# Monte Carlo simulations for 15N variable adjustment
+# Create simulated slopes
+set.seed(1234)
+gsimpar<-data.frame(slope=rnorm(n_sim,
+ summary(bg_lmer_grad)$coefficients[2,1],
+ summary(bg_lmer_grad)$coefficients[2,2]))
+# slope: n_sim points from normal distribution centered on slope intercept and spread by slope SE
+
+# Initialize variables
+simdat_growth<-NULL
+gresults<-data.frame()
+
+# Run MC simulations
+for (i in 1:n_sim)
+{
+ simdat_growth[[i]]<-c((prediction_data_betulaERM$Betula_growth-(gsimpar$slope[i]*prediction_data_betulaERM$biomass_removed_kg)), # Create n_sim sets of adjusted variable
+ control_data$Betula_growth)
+ gresults[i,1]<-mean(simdat_growth[[i]][1:5]) # Extract means
+ gresults[i,2]<-sd(simdat_growth[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_growth[[i]][1:5] , simdat_growth[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ gresults[i,3]<-t_temp$statistic # Extract t-statistic from t-tests
+ gresults[i,4]<-t_temp$p.value # Extract P values
+ lme_temp<-lmer(simdat_growth[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ gresults[i,5]<-anova_temp$`F value`
+ gresults[i,6]<-anova_temp$`Pr(>F)`
+}
+names(gresults)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+gresults$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+gsigdiff<-100*round(length(which(gresults$P_t<0.05))/length(gresults$P_t),3) # Calculate proportion of t-tests where P < 0.05
+gsigdiff_F<-100*round(length(which(gresults$P_F<0.05))/length(gresults$P_F),3) # Calculate proportion of ANOVAs where P < 0.05
+```
+
+```{r remove-negative-growth-values}
+simdat_growth_corr<-NULL
+gresults_corr<-data.frame()
+
+for (i in 1:n_sim)
+{
+ simdat_growth_corr[[i]]<-ifelse(simdat_growth[[i]]<0,1e-5,simdat_growth[[i]])
+ gresults_corr[i,1]<-mean(simdat_growth_corr[[i]][1:5]) # Extract means
+ gresults_corr[i,2]<-sd(simdat_growth_corr[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_growth_corr[[i]][1:5] , simdat_growth_corr[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ gresults_corr[i,3]<-t_temp$statistic # Extract t-statistic from t-tests
+ gresults_corr[i,4]<-t_temp$p.value # Extract P values
+ lme_temp<-lmer(simdat_growth_corr[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ gresults_corr[i,5]<-anova_temp$`F value`
+ gresults_corr[i,6]<-anova_temp$`Pr(>F)`
+}
+names(gresults_corr)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+gresults_corr$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+gsigdiff_corr<-100*round(length(which(gresults_corr$P_t<0.05))/length(gresults_corr$P_t),3) # Calculate proportion of t-tests where P < 0.05
+gsigdiff_F_corr<-100*round(length(which(gresults_corr$P_F<0.05))/length(gresults_corr$P_F),3) # Calculate proportion of ANOVAs where P < 0.05
+
+```
+
+#### 4.5.2. Plotting command
+
+```{r 4e-MC-growth-plot}
+p3e<-ggplot(gresults,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=gresults,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ ymin=Betula_growth_adj_mean-Betula_growth_adj_se,
+ ymax=Betula_growth_adj_mean+Betula_growth_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(gsigdiff_F,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=-2,
+ size=3,
+ col="#5ab4ac")+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4.000))+
+ ylim(c(-5,15))
+```
+
+### 4.6. Figure 4 - Plotting command
+
+```{r compile-fig-4, fig.height=8}
+p3a+ggplot()+p3b+p3c+p3d+p3e+plot_layout(ncol=2,widths = c(2,3))
+ggsave("../results/Fig4_assembled.pdf",width=18,height=20,unit="cm")
+```
+
+### 4.7. Betula nana growth GLMM
+#### a. Gradient regression
+
+```{r GLMM-betula-growth-a}
+# a. Gradient regression
+grad3<-subset(grad,!is.na(Betula_growth))
+bg_glm_grad<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="inverse"))
+bg_glm_grad2<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="identity"))
+bg_glm_grad3<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="log"))
+
+# bg_glm_grad;summary(bg_glm_grad)
+# bg_glm_grad2;summary(bg_glm_grad2)
+# bg_glm_grad3;summary(bg_glm_grad3)
+## slightly lower AIC/residual variance than identity or log link, keep inverse
+
+# summary(bg_glm_grad) # significant slope
+Anova(bg_glm_grad) # significant slope
+```
+
+#### b. Non-adjusted response variable
+
+```{r GLMM-betula-growth-b}
+# b. Non-adjusted ANOVA for growth
+nabgl<-glmer(Betula_growth ~ ERM_rem +(1|block),data=trt,na.action=na.omit,family=Gamma())
+## check assumptions
+# plot(nabgl)
+# shapiro.test(resid(nabgl))
+# summary(nabgl) # no treatment effect
+Anova(nabgl) # no treatment effect
+```
+
+#### c. Adjust response variable
+
+```{r GLMM-betula-growth-c}
+# c. Adjust response variable
+nabgl_null<-glm(Betula_growth ~ 1 ,data=trt,na.action=na.omit,family=Gamma()) # create null model for intercept
+
+trt$Betula_growth_adj_glm<-(1/coef(nabgl_null)) + (trt$Betula_growth - predict(bg_glm_grad,newdata = trt,type="response",na.action=na.omit)) # Create adjusted variable: intercept + (observed - predicted)
+trt$Betula_growth_adj2_glm<-replace(trt$Betula_growth_adj_glm,which(trt$Treatment=="control"),subset(trt,Treatment=="control")$Betula_growth) # replace control values with observations: we don't adjust controls where no removal was carried out
+trt$Betula_growth_adj3_glm<-ifelse(trt$Betula_growth_adj2_glm<=0,1e-5,trt$Betula_growth_adj2_glm) # replace negative adjusted values with 1e-5 to fit GLMM
+```
+
+#### d. Adjusted response variable
+
+```{r GLMM-betula-growth-d}
+# d. Analyse adjusted response variable
+nabgl_adj<-glmer(Betula_growth_adj3_glm ~ ERM_rem +(1|block),data=trt,na.action=na.omit,family=Gamma())
+1/coef(nabgl_adj)$block # singular fit and identical coefs, drop random factor
+nabgl_adj<-glm(Betula_growth_adj3_glm ~ ERM_rem ,data=trt,na.action=na.omit,family=Gamma())
+1/coef(nabgl_adj) # coefficients identical to GLMM above
+Anova(nabgl_adj)
+
+100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj3_glm)/mean(subset(trt,Treatment=="control")$Betula_growth_adj3_glm)) # 83% lower growth in -ERM
+```
+
+
+## 5. Supplementary Figure S1: Relationship between removed biomass and removed surface in non-specific biomass removal gradient plots
+
+```{r SI-Fig-1-gradient-cover-biomass}
+gradsi1<-subset(grad,plot!="0")
+trt$Treatment<-as.factor(trt$Treatment)
+levels(trt$Treatment)
+trt$Treatment<-fct_recode(trt$Treatment,"Control" = "control", "Ectomycorrhiza removal" = "ECM", "Ecto- and ericoid mycorrhiza removal" = "EE", "Ericoid mycorrhiza removal"="ERM")
+bmlmer<-lmer(biomass_removed_kg ~ 0+ plot+(1|block),data=gradsi1)
+# keeping the random effect would not alter the findings: slope estimate = 43.9
+# here we use a linear model for simpler plotting purposes
+
+bmlm<-lm(biomass_removed_g~0+plot,data=gradsi1)
+# # check assumptions
+# par(mfrow=c(2,2));plot(bmlm);par(mfrow=c(1,1))
+# shapiro.test(resid(bmlm))
+summary(bmlm)
+
+# Extract model parameters for annotation
+slop<-round(bmlm$coefficients[1],2)
+r2<-round(summary(bmlm)$r.squared,3)
+R<-cor.test(gradsi1$biomass_removed_g,gradsi1$plot)
+
+# Plotting command
+ggplot(gradsi1,aes(x=plot,y=biomass_removed_g))+
+ geom_point(col="grey")+
+ scale_color_manual(values=c("black","#d73027","#8c510a","#3288bd"))+
+ geom_smooth(method="lm",
+ formula= y~0+x,
+ col="black",
+ fill="grey80",
+ lwd=0.5)+
+ theme_bw()+
+ annotate("segment",
+ x=trt$biomass_removed_g/slop,
+ xend=trt$biomass_removed_g/slop,
+ y=-Inf,
+ yend=trt$biomass_removed_g,
+ lty=3,
+ lwd=0.5)+
+ geom_point(data=trt,aes(y=biomass_removed_g,x=biomass_removed_g/slop,col=Treatment),size=2)+
+ # geom_vline(data=trt,aes(xintercept=biomass_removed_g/slop,col=Treatment),lty=3)+
+ theme(panel.grid = element_blank())+
+ annotate("text",
+ x=40,
+ y=4500,
+ label=paste("Biomass =",slop,"* Area removed \n Adjusted-R2 =",r2,"\nr=",round(R$estimate,3),"; P < 0.001"))+
+ ylab("Biomass removed [g]")+
+ xlab("Area removed [%]")
+ggsave("../results/SI_Fig_1_biomass_cover.pdf",width=12,height=10,unit="cm")
+```
+
+## 6. Supplementary Fig S2: Betula nana growth adjusted to convert negative values to 0
+
+```{r 3e-MC-growth-plot-corr}
+# Compute means and SE of the GLMM-adjusted Betula nana growth, for plotting purposes
+glm_mean_se <- trt%>%
+ group_by(Treatment,
+ ECM_rem,
+ ERM_rem) %>%
+ summarize_at("Betula_growth_adj3_glm",
+ .funs = list(mean = mean, se = se), na.rm = TRUE)
+glm_adj_mean<-as.numeric(glm_mean_se[3,4])
+glm_adj_se<-as.numeric(glm_mean_se[3,5])
+ggplot(gresults_corr,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=gresults_corr,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ ymin=Betula_growth_adj_mean-Betula_growth_adj_se,
+ ymax=Betula_growth_adj_mean+Betula_growth_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(gsigdiff_F_corr,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=1,
+ size=3,
+ col="#5ab4ac")+
+ annotate("pointrange",
+ x=3.2,
+ y=glm_adj_mean,
+ ymin=glm_adj_mean-glm_adj_se,
+ ymax=glm_adj_mean+glm_adj_se,
+ size=0.5,
+ pch=15,
+ col="#800080ff")+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4.000))+
+ ylim(c(-1,15))+
+ geom_hline(yintercept=0,lty=3,col="grey")
+ggsave("../results/SI_Fig_2_Bnana_growth_nonegatives.pdf",width=12,height=10,unit="cm")
+```
diff --git a/code/Monteux_NP_biomass_removal.html b/code/Monteux_NP_biomass_removal.html
new file mode 100755
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--- /dev/null
+++ b/code/Monteux_NP_biomass_removal.html
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+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Sylvain Monteux" />
+
+<meta name="date" content="2023-01-31" />
+
+<title>Controlling biases in targeted plant removal experiments</title>
+
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) 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+};
+</script>
+<style>h1 {font-size: 34px;}
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+ "use strict";
+ $.fn.rmarkdownStickyTabs = function() {
+ var context = this;
+ // Show the tab corresponding with the hash in the URL, or the first tab
+ var showStuffFromHash = function() {
+ var hash = window.location.hash;
+ var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+ var $selector = $(selector, context);
+ if($selector.data('toggle') === "tab") {
+ $selector.tab('show');
+ // walk up the ancestors of this element, show any hidden tabs
+ $selector.parents('.section.tabset').each(function(i, elm) {
+ var link = $('a[href="#' + $(elm).attr('id') + '"]');
+ if(link.data('toggle') === "tab") {
+ link.tab("show");
+ }
+ });
+ }
+ };
+
+
+ // Set the correct tab when the page loads
+ showStuffFromHash(context);
+
+ // Set the correct tab when a user uses their back/forward button
+ $(window).on('hashchange', function() {
+ showStuffFromHash(context);
+ });
+
+ // Change the URL when tabs are clicked
+ $('a', context).on('click', function(e) {
+ history.pushState(null, null, this.href);
+ showStuffFromHash(context);
+ });
+
+ return this;
+ };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+ // build a tabset from a section div with the .tabset class
+ function buildTabset(tabset) {
+
+ // check for fade and pills options
+ var fade = tabset.hasClass("tabset-fade");
+ var pills = tabset.hasClass("tabset-pills");
+ var navClass = pills ? "nav-pills" : "nav-tabs";
+
+ // determine the heading level of the tabset and tabs
+ var match = tabset.attr('class').match(/level(\d) /);
+ if (match === null)
+ return;
+ var tabsetLevel = Number(match[1]);
+ var tabLevel = tabsetLevel + 1;
+
+ // find all subheadings immediately below
+ var tabs = tabset.find("div.section.level" + tabLevel);
+ if (!tabs.length)
+ return;
+
+ // create tablist and tab-content elements
+ var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+ $(tabs[0]).before(tabList);
+ var tabContent = $('<div class="tab-content"></div>');
+ $(tabs[0]).before(tabContent);
+
+ // build the tabset
+ var activeTab = 0;
+ tabs.each(function(i) {
+
+ // get the tab div
+ var tab = $(tabs[i]);
+
+ // get the id then sanitize it for use with bootstrap tabs
+ var id = tab.attr('id');
+
+ // see if this is marked as the active tab
+ if (tab.hasClass('active'))
+ activeTab = i;
+
+ // remove any table of contents entries associated with
+ // this ID (since we'll be removing the heading element)
+ $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+ // sanitize the id for use with bootstrap tabs
+ id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+ tab.attr('id', id);
+
+ // get the heading element within it, grab it's text, then remove it
+ var heading = tab.find('h' + tabLevel + ':first');
+ var headingText = heading.html();
+ heading.remove();
+
+ // build and append the tab list item
+ var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+ a.attr('href', '#' + id);
+ a.attr('aria-controls', id);
+ var li = $('<li role="presentation"></li>');
+ li.append(a);
+ tabList.append(li);
+
+ // set it's attributes
+ tab.attr('role', 'tabpanel');
+ tab.addClass('tab-pane');
+ tab.addClass('tabbed-pane');
+ if (fade)
+ tab.addClass('fade');
+
+ // move it into the tab content div
+ tab.detach().appendTo(tabContent);
+ });
+
+ // set active tab
+ $(tabList.children('li')[activeTab]).addClass('active');
+ var active = $(tabContent.children('div.section')[activeTab]);
+ active.addClass('active');
+ if (fade)
+ active.addClass('in');
+
+ if (tabset.hasClass("tabset-sticky"))
+ tabset.rmarkdownStickyTabs();
+ }
+
+ // convert section divs with the .tabset class to tabsets
+ var tabsets = $("div.section.tabset");
+ tabsets.each(function(i) {
+ buildTabset($(tabsets[i]));
+ });
+};
+
+</script>
+<script>
+window.initializeCodeFolding = function(show) {
+
+ // handlers for show-all and hide all
+ $("#rmd-show-all-code").click(function() {
+ $('div.r-code-collapse').each(function() {
+ $(this).collapse('show');
+ });
+ });
+ $("#rmd-hide-all-code").click(function() {
+ $('div.r-code-collapse').each(function() {
+ $(this).collapse('hide');
+ });
+ });
+
+ // index for unique code element ids
+ var currentIndex = 1;
+
+ // select all R code blocks
+ var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable');
+ rCodeBlocks.each(function() {
+ // skip if the block has fold-none class
+ if ($(this).hasClass('fold-none')) return;
+
+ // create a collapsable div to wrap the code in
+ var div = $('<div class="collapse r-code-collapse"></div>');
+ var showThis = (show || $(this).hasClass('fold-show')) && !$(this).hasClass('fold-hide');
+ var id = 'rcode-643E0F36' + currentIndex++;
+ div.attr('id', id);
+ $(this).before(div);
+ $(this).detach().appendTo(div);
+
+ // add a show code button right above
+ var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Show') + '</span>');
+ var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>');
+ showCodeButton.append(showCodeText);
+ showCodeButton
+ .attr('data-toggle', 'collapse')
+ .attr('data-bs-toggle', 'collapse') // BS5
+ .attr('data-target', '#' + id)
+ .attr('data-bs-target', '#' + id) // BS5
+ .attr('aria-expanded', showThis)
+ .attr('aria-controls', id);
+
+ var buttonRow = $('<div class="row"></div>');
+ var buttonCol = $('<div class="col-md-12"></div>');
+
+ buttonCol.append(showCodeButton);
+ buttonRow.append(buttonCol);
+
+ div.before(buttonRow);
+
+ // show the div if necessary
+ if (showThis) div.collapse('show');
+
+ // update state of button on show/hide
+ // * Change text
+ // * add a class for intermediate states styling
+ div.on('hide.bs.collapse', function () {
+ showCodeText.text('Show');
+ showCodeButton.addClass('btn-collapsing');
+ });
+ div.on('hidden.bs.collapse', function () {
+ showCodeButton.removeClass('btn-collapsing');
+ });
+ div.on('show.bs.collapse', function () {
+ showCodeText.text('Hide');
+ showCodeButton.addClass('btn-expanding');
+ });
+ div.on('shown.bs.collapse', function () {
+ showCodeButton.removeClass('btn-expanding');
+ });
+
+ });
+
+}
+</script>
+<style type="text/css">.hljs-literal {
+color: #990073;
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+.hljs-number {
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+.hljs-comment {
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+font-style: italic;
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+
+<h1 class="title toc-ignore">Controlling biases in targeted plant
+removal experiments</h1>
+<h4 class="author">Sylvain Monteux</h4>
+<h4 class="date">2023-01-31</h4>
+
+</div>
+
+
+<p>This script supports the manuscript “<em>Controlling biases in
+targeted plant removal experiments</em>†by Sylvain Monteux, Gesche
+Blume-Werry, Konstantin Gavazov, Leah K. Kirchhoff, Eveline J. Krab,
+Signe Lett, Emily Pickering Pedersen, Maria Väisänen</p>
+<p><em>New Phytologist</em> Special Issue “Mycorrhizal research now:
+from the micro- to the macro-scaleâ€, x, xx-xx, doi: xxxxxx-xxxxx</p>
+<p>The script details our approach to adjust response variables for
+unwanted effects of non-specific biomass removal, and reproduces the
+figures presented throughout the manuscript.</p>
+<div id="libraries-and-initial-data-loading" class="section level2">
+<h2>1. Libraries and initial data loading</h2>
+<pre class="r fold-show"><code># Load data file
+dat<-read.csv("../data/harmonized_data.csv",sep=";")
+dat$block <- as.factor(dat$block)
+# block: Spatial blocks in the experiment [text]
+# Treatment: Experimental conditions [text]
+## either "gradient" for the non-specific biomass removal gradient plots or one of:
+## ECM: Ectomycorrhizal plants removal
+## ERM: Ericoid mycorrhizal plants removal
+## EE: Ecto- and Ericoid-mycorrhizal plants removal
+## control: Undisturbed control
+
+# plot: treatment disambiguation within a given block [text]
+## contains %_removal information for non-specific biomass removal gradient plots
+# Betula_d15N: Delta 15N values from Betula nana leaves [per mil]
+# Betula_growth: Growth of Betula nana shoots between July 1st and August 25th 2021, averaged over four pseudoreplicates within each plot [mm]
+# MUX: Reaction rate of extracellular β-xylosidase with 4-Methylumbelliferyl-β-D-xylopyranoside (MUX) in the Soil Enzymatic Activity Reader SEAR (Digit Soil, Zürich, Switzerland) [pmol.min-1]
+# biomass_removed_kg: Sum of biomass removed from the beginning of the experiment until 2021 within each plot [g]
+# ERM_rem: Removal of Ericoid mycorrhizal plants [boolean]
+# ECM_rem: Removal of Ectomycorrhizal plants [boolean]
+
+# Convert removed biomass from g to kg for readability
+dat$biomass_removed_kg<-dat$biomass_removed_g / 1000
+
+# Make a subset containing only observations from the non-specific biomass removal gradient
+grad<-subset(dat,Treatment=="gradient")
+grad$plot <- as.numeric(grad$plot)
+
+# Make a subset containing only targeted plant removal treatments and undisturbed controls
+trt<-subset(dat,Treatment!="gradient")
+
+# Set global parameters
+n_sim<-5000 # Set number of Monte Carlo simulations</code></pre>
+</div>
+<div id="figure-1-hypothetical-and-observed-effects-of-treatments-and-biomass-removal" class="section level2">
+<h2>2. Figure 1: Hypothetical and observed effects of treatments and
+biomass removal</h2>
+<p><img 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" width="672" /></p>
+</div>
+<div id="analysis-of-non-specific-biomass-removal-and-treatment-effects-for-different-variables" class="section level2">
+<h2>3. Analysis of non-specific biomass removal and treatment effects
+for different variables</h2>
+<ol style="list-style-type: lower-alpha">
+<li>For all the response variables, a linear-mixed effect model
+<code>lmer</code> is carried out on the non-specific biomass removal
+gradient plots, using amount of removed biomass as fixed factor and
+spatial block as random factor.</li>
+<li>The response variable is analyzed in the treatment plots, using a
+2-way ANOVA on a linear mixed model with ericoid- and
+ectomycorrhizal-removal as fixed factors and block as random factor (or
+one-way ANOVA if the variable is only present in one of the removal
+treatments).</li>
+<li>Depending on <strong>a</strong>:</li>
+</ol>
+<ul>
+<li>If there is no significant relationship between the response
+variable and amount of removed biomass in the gradient plots
+<strong>a</strong>, data analysis from <strong>b</strong> is considered
+correct.</li>
+<li>If there is a significant relationship <strong>a</strong>, the
+response variable has to be adjusted for biomass removal effect. The
+gradient regression model <strong>a</strong> is used to predict values
+based on biomass removed in treatment plots (and block, if relevant).
+The adjusted response variable is calculated by subtracting the
+biomass-dependent component of the predicted values from the observed
+values:
+<code>adjusted_response = model_intercept + (observed_value - predicted_value)</code></li>
+</ul>
+<ol start="4" style="list-style-type: lower-alpha">
+<li>The adjusted response variable is tested for treatment effects as in
+<strong>b</strong></li>
+</ol>
+<div id="β-glucosidase-activity" class="section level3">
+<h3>3.1. β-glucosidase activity</h3>
+<div id="a.-gradient-regression" class="section level4">
+<h4>a. Gradient regression</h4>
+<pre class="r"><code># a. gradient regression
+beta_lmer_grad<-lmer(MUX~biomass_removed_kg+(1|block),data=grad,na.action = na.omit)
+coef(beta_lmer_grad) # Random factor coefficients properly estimated, no reason to simplify the model</code></pre>
+<pre><code>## $block
+## (Intercept) biomass_removed_kg
+## 1 32.71970 -1.352249
+## 2 54.14939 -1.352249
+## 3 31.00143 -1.352249
+## 4 21.85180 -1.352249
+## 5 22.32594 -1.352249
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code>## check assumptions
+# plot(beta_lmer_grad)
+# shapiro.test(residuals(beta_lmer_grad))
+# summary(beta_lmer_grad)
+anova(beta_lmer_grad)</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## biomass_removed_kg 45.446 45.446 1 8.2712 0.5529 0.4777</code></pre>
+<pre class="r"><code># biomass_removed effect n.s. P=0.478
+# use non-adjusted response variable</code></pre>
+</div>
+<div id="b.-non-adjusted-response-variable" class="section level4">
+<h4>b. Non-adjusted response variable</h4>
+<pre class="r"><code># b. Non-adjusted treatment analysis
+beta_lmer_trt<-lmer(MUX~ERM_rem*ECM_rem+(1|block),data=trt,na.action=na.omit) # singular
+## check assumptions
+# plot(beta_lmer_trt)
+# shapiro.test(residuals(beta_lmer_trt))
+# summary(beta_lmer_grad)
+anova(beta_lmer_trt) # ECM_rem significant *</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 3.68 3.68 1 14 0.0636 0.80448
+## ECM_rem 344.58 344.58 1 14 5.9595 0.02852 *
+## ERM_rem:ECM_rem 0.08 0.08 1 14 0.0013 0.97156
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+</div>
+<div id="betula-nana-delta-15n" class="section level3">
+<h3>3.2. <em>Betula nana</em> delta-<sup>15</sup>N</h3>
+<div id="a.-gradient-regression-1" class="section level4">
+<h4>a. Gradient regression</h4>
+<pre class="r"><code># a. Gradient regression
+bnd15n_lmer_grad<-lmer(Betula_d15N~biomass_removed_kg+(1|block),data=grad,na.action=na.omit)
+coef(bnd15n_lmer_grad) # Random factor coefficients estimated with 0 variance, use a linear model instead</code></pre>
+<pre><code>## $block
+## (Intercept) biomass_removed_kg
+## 1 -7.220721 1.042507
+## 2 -7.220721 1.042507
+## 3 -7.220721 1.042507
+## 4 -7.220721 1.042507
+## 5 -7.220721 1.042507
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code>bnd15n_lm_grad<-lm(Betula_d15N~biomass_removed_kg,data=grad,na.action = na.omit)
+## check assumptions
+# plot(bnd15n_lmer_grad)
+# shapiro.test(residuals(bnd15n_lmer_grad))
+# summary(bnd15n_lmer_grad)
+anova(bnd15n_lmer_grad)# biomass_removed effect P < 0.01</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## biomass_removed_kg 20.562 20.562 1 12 11.551 0.005283 **
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+<div id="b.-non-adjusted-response-variable-1" class="section level4">
+<h4>b. Non-adjusted response variable</h4>
+<pre class="r"><code># b. Non-adjusted ANOVA for 15N
+naB15n<-lmer(Betula_d15N~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(naB15n) # Random factor coefficients properly estimated, no reason to simplify the model</code></pre>
+<pre><code>## $block
+## (Intercept) ERM_remTRUE
+## 1 -6.176360 2.4984
+## 2 -5.940357 2.4984
+## 3 -6.069561 2.4984
+## 4 -7.234267 2.4984
+## 5 -7.130455 2.4984
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code># # check assumptions
+# plot(naB15n)
+# shapiro.test(resid(naB15n)) # deviation from normality
+# hist(resid(naB15n)) # deviation seems due to chance because of few observations, rather than e.g. bimodal or skewed distribution
+anova(naB15n) # Significant difference between Control and -ERM before adjustment</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 15.605 15.605 1 4 44.673 0.002605 **
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+<div id="c.-adjust-response-variable" class="section level4">
+<h4>c. Adjust response variable</h4>
+<pre class="r fold-show"><code># c. Adjust response variable
+trt$Betula_d15N_adj<-summary(bnd15n_lmer_grad)$coefficients[1] + (trt$Betula_d15N - predict(bnd15n_lmer_grad,newdata = trt)) # Create adjusted variable: intercept + (observed - predicted)
+# In this case the intercept value is identical across blocks, however this code would work with a model where the intercept differs across the random factor:
+# the first component is the mean intercept across different levels of the random factor
+# the variability across blocks is included in the prediction term which takes the block of the new data into account
+
+#trt$Betula_d15N_adj<-bnd15n_lmer_grad$coefficients[1] + (trt$Betula_d15N - predict(bnd15n_lmer_grad,newdata = trt)) # This would work for a linear model
+
+# A GL(M)M would require creating a null model to get the intercept, see section 4.7</code></pre>
+</div>
+<div id="d.-adjusted-response-variable" class="section level4">
+<h4>d. Adjusted response variable</h4>
+<pre class="r"><code># d. Analyse adjusted response variable
+bnd15n_lmer_trt<-lmer(Betula_d15N_adj~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bnd15n_lmer_trt) # Random factor coefficients properly estimated, no reason to simplify the model</code></pre>
+<pre><code>## $block
+## (Intercept) ERM_remTRUE
+## 1 -6.256174 -0.5489086
+## 2 -6.188411 -0.5489086
+## 3 -6.240052 -0.5489086
+## 4 -7.089060 -0.5489086
+## 5 -6.777302 -0.5489086
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code># # check assumptions
+# plot(bnd15n_lmer_trt)
+# shapiro.test(resid(bnd15n_lmer_trt))
+anova(bnd15n_lmer_trt) # no significant differences between Control and -ERM after adjusting for biomass removal</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 0.75325 0.75325 1 4 1.5146 0.2859</code></pre>
+</div>
+</div>
+<div id="betula-nana-growth" class="section level3">
+<h3>3.3. <em>Betula nana</em> growth</h3>
+<div id="a.-gradient-regression-2" class="section level4">
+<h4>a. Gradient regression</h4>
+<pre class="r"><code># a. Gradient regression
+bg_lmer_grad<-lmer(Betula_growth~biomass_removed_kg+(1|block),data=grad,na.action=na.omit)
+coef(bg_lmer_grad) # Singular fit, no intercept variation, equivalent to using lm</code></pre>
+<pre><code>## $block
+## (Intercept) biomass_removed_kg
+## 1 5.699793 1.349211
+## 2 5.699793 1.349211
+## 3 5.699793 1.349211
+## 4 5.699793 1.349211
+## 5 5.699793 1.349211
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code>bg_lm_grad<-lm(Betula_growth~biomass_removed_kg,data=grad,na.action = na.omit)
+# # check assumptions
+# plot(bg_lmer_grad)
+# shapiro.test(resid(bg_lmer_grad))
+anova(bg_lmer_grad) # biomass removed effect</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## biomass_removed_kg 34.44 34.44 1 12 5.4951 0.0371 *
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+<div id="b.-non-adjusted-response-variable-2" class="section level4">
+<h4>b. Non-adjusted response variable</h4>
+<pre class="r"><code># b. Non-adjusted ANOVA for growth
+nabg<-lmer(Betula_growth ~ ERM_rem+ (1|block),data=trt,na.action=na.omit)
+coef(nabg) # Singular fit, no intercept variation, equivalent to using lm</code></pre>
+<pre><code>## $block
+## (Intercept) ERM_remTRUE
+## 1 4.448 0.4816667
+## 2 4.448 0.4816667
+## 3 4.448 0.4816667
+## 4 4.448 0.4816667
+## 5 4.448 0.4816667
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code># # check assumptions
+# plot(nabg)
+# shapiro.test(resid(nabg))
+anova(nabg) # No significant difference between Control and -ERM before adjustment</code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 0.58001 0.58001 1 8 0.1255 0.7323</code></pre>
+</div>
+<div id="c.-adjust-response-variable-1" class="section level4">
+<h4>c. Adjust response variable</h4>
+<pre class="r"><code># c. Adjust response variable
+trt$Betula_growth_adj<-summary(bg_lmer_grad)$coefficients[1] + (trt$Betula_growth - predict(bg_lmer_grad,newdata = trt)) # Create adjusted variable: intercept + (observed - predicted)</code></pre>
+</div>
+<div id="d.-adjusted-response-variable-1" class="section level4">
+<h4>d. Adjusted response variable</h4>
+<pre class="r"><code># d. Analyse adjusted response variable
+bg_lmer_trt<-lmer(Betula_growth_adj~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bg_lmer_trt) # Singular fit, no intercept variation, equivalent to using lm</code></pre>
+<pre><code>## $block
+## (Intercept) ERM_remTRUE
+## 1 4.448 -3.462156
+## 2 4.448 -3.462156
+## 3 4.448 -3.462156
+## 4 4.448 -3.462156
+## 5 4.448 -3.462156
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code># # check assumptions
+# plot(bg_lmer_trt)
+# shapiro.test(resid(bg_lmer_trt))
+anova(bg_lmer_trt) # Significant difference between Control and -ERM after adjusting for biomass removal </code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 29.966 29.966 1 8 7.3726 0.02644 *
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<pre class="r"><code>100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj)/mean(subset(trt,Treatment=="control")$Betula_growth_adj)) # % lower growth in -ERM</code></pre>
+<pre><code>## [1] 77.83623</code></pre>
+</div>
+<div id="e.-adjusted-response-variable-without-negative-values" class="section level4">
+<h4>e. Adjusted response variable without negative values</h4>
+<pre class="r"><code># d.bis. Remove negative growth values before analysing adjusted response variable
+trt$Betula_growth_adj2<-ifelse(trt$Betula_growth_adj<=0,0,trt$Betula_growth_adj)
+bg_lmer_trt2<-lmer(Betula_growth_adj2~ERM_rem+(1|block),data=trt,na.action=na.omit)
+coef(bg_lmer_trt2) # Singular fit, no intercept variation, equivalent to using lm</code></pre>
+<pre><code>## $block
+## (Intercept) ERM_remTRUE
+## 1 4.448 -3.147727
+## 2 4.448 -3.147727
+## 3 4.448 -3.147727
+## 4 4.448 -3.147727
+## 5 4.448 -3.147727
+##
+## attr(,"class")
+## [1] "coef.mer"</code></pre>
+<pre class="r"><code># # check assumptions
+# plot(bg_lmer_trt2)
+# shapiro.test(resid(bg_lmer_trt2))
+anova(bg_lmer_trt2) # Significant difference between Control and -ERM after adjusting for biomass removal </code></pre>
+<pre><code>## Type III Analysis of Variance Table with Satterthwaite's method
+## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
+## ERM_rem 24.77 24.77 1 8 7.2255 0.02758 *
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<pre class="r"><code>100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj2)/mean(subset(trt,Treatment=="control")$Betula_growth_adj2)) # 78% lower growth in -ERM</code></pre>
+<pre><code>## [1] 70.76725</code></pre>
+<pre class="r"><code># Compute means and SE of the covariate, non-adjusted and adjusted response variables, for plotting purposes
+trt_mean_se <- trt%>%
+ group_by(Treatment,
+ ECM_rem,
+ ERM_rem) %>%
+ summarize_at(c("MUX","Betula_d15N","Betula_d15N_adj","Betula_growth","Betula_growth_adj","Betula_growth_adj2","biomass_removed_kg"),
+ .funs = list(mean = mean, se = se), na.rm = TRUE) # sciplot::se()</code></pre>
+</div>
+</div>
+</div>
+<div id="figure-4" class="section level2">
+<h2>4. Figure 4</h2>
+<div id="figure-4a---β-xylosidase-activity" class="section level3">
+<h3>4.1. Figure 4a - β-xylosidase activity</h3>
+</div>
+<div id="figure-4b---non-adjusted-betula-nana-delta_15n" class="section level3">
+<h3>4.2. Figure 4b - Non-adjusted <em>Betula nana</em> delta_15N</h3>
+<pre class="r"><code>p3b<-ggplot(data=grad,mapping=aes(x=biomass_removed_kg,y=Betula_d15N))+
+ geom_smooth(method="lm",col="black")+
+ geom_point(col="grey")+
+ theme_bw()+
+ theme(panel.grid=element_blank())+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_mean,
+ ymin=Betula_d15N_mean-Betula_d15N_se,
+ ymax=Betula_d15N_mean+Betula_d15N_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6,
+ height=0)+
+ ylab(expression(paste(italic("Betula nana "),delta*"15N [‰]")))+
+ xlim(c(0,4))+
+ ylim(c(-10,-1))+
+ xlab("Biomass removed [kg]")</code></pre>
+</div>
+<div id="fig-4c-adjusted-betula-nana-delta-15n" class="section level3">
+<h3>4.3. Fig 4c: Adjusted <em>Betula nana</em> delta-<sup>15</sup>N</h3>
+<div id="monte-carlo-simulations" class="section level4">
+<h4>4.3.1. Monte Carlo simulations</h4>
+<pre class="r"><code># Monte Carlo simulations for Betula nana delta_15N variable adjustment
+set.seed(1234)
+# Create subset of observed data on which to base the prediction
+prediction_data_betulaERM<-subset(trt,Treatment == "ERM") %>% select(Treatment,block,Betula_d15N,Betula_growth,biomass_removed_kg)
+
+# Create subset of control data to which the predicted values will be compared
+control_data<-subset(trt,Treatment=="control") %>% select(Treatment,block,Betula_d15N,Betula_growth)
+
+
+# Create simulated slopes
+simpar<-data.frame(slope=rnorm(n_sim,
+ summary(bnd15n_lmer_grad)$coefficients[2,1],
+ summary(bnd15n_lmer_grad)$coefficients[2,2]))
+# slope: n_sim points from normal distribution centered on slope intercept and spread by slope SE
+
+# Initialize variables
+simdat_15n<-NULL
+results<-data.frame()
+treatment<-c(rep("ERM_rem",5),rep("Control",5))
+block<-c(rep(1:5,2))
+# Run MC simulations
+for (i in 1:n_sim)
+{
+ simdat_15n[[i]]<-c((prediction_data_betulaERM$Betula_d15N-(simpar$slope[i]*prediction_data_betulaERM$biomass_removed_kg)),# Create n_sim sets of adjusted variable
+ control_data$Betula_d15N)
+ results[i,1]<-mean(simdat_15n[[i]][1:5]) # Extract means
+ results[i,2]<-sd(simdat_15n[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_15n[[i]][1:5] , simdat_15n[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ results[i,3]<-t_temp$statistic # Extract t-statistic from tests
+ results[i,4]<-t_temp$p.value # Extract P value from tests
+ lme_temp<-lmer(simdat_15n[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ results[i,5]<-anova_temp$`F value`
+ results[i,6]<-anova_temp$`Pr(>F)`
+}
+names(results)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+results$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+sigdiff<-100*round(length(which(results$P_t<0.05))/length(results$P_t),3) # Calculate proportion of t-tests where P < 0.05
+sigdiff_F<-100*round(length(which(results$P_F<0.05))/length(results$P_F),3) # Calculate proportion of ANOVAs where P < 0.05</code></pre>
+</div>
+<div id="plotting-command" class="section level4">
+<h4>4.3.2. Plotting command</h4>
+<pre class="r"><code>p3c<-ggplot(results,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_d15N),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_d15N),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=results,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_adj_mean,
+ ymin=Betula_d15N_adj_mean-Betula_d15N_adj_se,
+ ymax=Betula_d15N_adj_mean+Betula_d15N_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_d15N_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(sigdiff_F,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=-8.5,
+ size=3,
+ col="#5ab4ac")+
+ ylab(expression(paste(italic("Betula nana "),delta*"15N [‰]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4))+
+ ylim(c(-10,-1))</code></pre>
+</div>
+</div>
+<div id="figure-4d-non-adjusted-betula-nana-growth" class="section level3">
+<h3>4.4. Figure 4d: Non-adjusted <em>Betula nana</em> growth</h3>
+<pre class="r"><code>p3d<-ggplot(data=grad,mapping=aes(x=biomass_removed_kg,y=Betula_growth))+
+ geom_smooth(method="lm",col="black")+
+ geom_point(col="darkgrey")+
+ theme_bw()+
+ theme(panel.grid=element_blank())+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_mean,
+ ymin=Betula_growth_mean-Betula_growth_se,
+ ymax=Betula_growth_mean+Betula_growth_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#3288bd","black"),
+ lwd=0.6,
+ height=0)+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4))+
+ ylim(c(-5,15))</code></pre>
+</div>
+<div id="figure-4e-adjusted-betula-nana-growth" class="section level3">
+<h3>4.5. Figure 4e: Adjusted <em>Betula nana</em> growth</h3>
+<div id="monte-carlo-simulations-1" class="section level4">
+<h4>4.5.1. Monte Carlo simulations</h4>
+<pre class="r"><code># Monte Carlo simulations for 15N variable adjustment
+# Create simulated slopes
+set.seed(1234)
+gsimpar<-data.frame(slope=rnorm(n_sim,
+ summary(bg_lmer_grad)$coefficients[2,1],
+ summary(bg_lmer_grad)$coefficients[2,2]))
+# slope: n_sim points from normal distribution centered on slope intercept and spread by slope SE
+
+# Initialize variables
+simdat_growth<-NULL
+gresults<-data.frame()
+
+# Run MC simulations
+for (i in 1:n_sim)
+{
+ simdat_growth[[i]]<-c((prediction_data_betulaERM$Betula_growth-(gsimpar$slope[i]*prediction_data_betulaERM$biomass_removed_kg)), # Create n_sim sets of adjusted variable
+ control_data$Betula_growth)
+ gresults[i,1]<-mean(simdat_growth[[i]][1:5]) # Extract means
+ gresults[i,2]<-sd(simdat_growth[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_growth[[i]][1:5] , simdat_growth[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ gresults[i,3]<-t_temp$statistic # Extract t-statistic from t-tests
+ gresults[i,4]<-t_temp$p.value # Extract P values
+ lme_temp<-lmer(simdat_growth[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ gresults[i,5]<-anova_temp$`F value`
+ gresults[i,6]<-anova_temp$`Pr(>F)`
+}
+names(gresults)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+gresults$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+gsigdiff<-100*round(length(which(gresults$P_t<0.05))/length(gresults$P_t),3) # Calculate proportion of t-tests where P < 0.05
+gsigdiff_F<-100*round(length(which(gresults$P_F<0.05))/length(gresults$P_F),3) # Calculate proportion of ANOVAs where P < 0.05</code></pre>
+<pre class="r"><code>simdat_growth_corr<-NULL
+gresults_corr<-data.frame()
+
+for (i in 1:n_sim)
+{
+ simdat_growth_corr[[i]]<-ifelse(simdat_growth[[i]]<0,1e-5,simdat_growth[[i]])
+ gresults_corr[i,1]<-mean(simdat_growth_corr[[i]][1:5]) # Extract means
+ gresults_corr[i,2]<-sd(simdat_growth_corr[[i]][1:5])/sqrt(4) # Extract SE
+ t_temp<-t.test(simdat_growth_corr[[i]][1:5] , simdat_growth_corr[[i]][6:10]) # t.test against control values, provided as comparison to ANOVA
+ gresults_corr[i,3]<-t_temp$statistic # Extract t-statistic from t-tests
+ gresults_corr[i,4]<-t_temp$p.value # Extract P values
+ lme_temp<-lmer(simdat_growth_corr[[i]]~treatment+(1|block))
+ anova_temp<-anova(lme_temp)
+ gresults_corr[i,5]<-anova_temp$`F value`
+ gresults_corr[i,6]<-anova_temp$`Pr(>F)`
+}
+names(gresults_corr)<-c("Mean","SE","t_stat","P_t","F_stat","P_F") # Rename columns
+gresults_corr$biomass_removed_kg_mean<-mean(subset(trt,Treatment=="ERM")$biomass_removed_kg) # Calculate x-axis value around which to center simulations (mean of removed biomass in a given treatment)
+gsigdiff_corr<-100*round(length(which(gresults_corr$P_t<0.05))/length(gresults_corr$P_t),3) # Calculate proportion of t-tests where P < 0.05
+gsigdiff_F_corr<-100*round(length(which(gresults_corr$P_F<0.05))/length(gresults_corr$P_F),3) # Calculate proportion of ANOVAs where P < 0.05</code></pre>
+</div>
+<div id="plotting-command-1" class="section level4">
+<h4>4.5.2. Plotting command</h4>
+<pre class="r"><code>p3e<-ggplot(gresults,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=gresults,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ ymin=Betula_growth_adj_mean-Betula_growth_adj_se,
+ ymax=Betula_growth_adj_mean+Betula_growth_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(gsigdiff_F,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=-2,
+ size=3,
+ col="#5ab4ac")+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4.000))+
+ ylim(c(-5,15))</code></pre>
+</div>
+</div>
+<div id="figure-4---plotting-command" class="section level3">
+<h3>4.6. Figure 4 - Plotting command</h3>
+<pre class="r"><code>p3a+ggplot()+p3b+p3c+p3d+p3e+plot_layout(ncol=2,widths = c(2,3))</code></pre>
+<p><img src="data:image/png;base64,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width="672" /></p>
+<pre class="r"><code>ggsave("../results/Fig4_assembled.pdf",width=18,height=20,unit="cm")</code></pre>
+</div>
+<div id="betula-nana-growth-glmm" class="section level3">
+<h3>4.7. Betula nana growth GLMM</h3>
+<div id="a.-gradient-regression-3" class="section level4">
+<h4>a. Gradient regression</h4>
+<pre class="r"><code># a. Gradient regression
+grad3<-subset(grad,!is.na(Betula_growth))
+bg_glm_grad<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="inverse"))
+bg_glm_grad2<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="identity"))
+bg_glm_grad3<-glmer(Betula_growth~biomass_removed_kg+(1|block),data=grad3,family=Gamma(link="log"))
+
+# bg_glm_grad;summary(bg_glm_grad)
+# bg_glm_grad2;summary(bg_glm_grad2)
+# bg_glm_grad3;summary(bg_glm_grad3)
+## slightly lower AIC/residual variance than identity or log link, keep inverse
+
+# summary(bg_glm_grad) # significant slope
+Anova(bg_glm_grad) # significant slope</code></pre>
+<pre><code>## Analysis of Deviance Table (Type II Wald chisquare tests)
+##
+## Response: Betula_growth
+## Chisq Df Pr(>Chisq)
+## biomass_removed_kg 5.3691 1 0.0205 *
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+<div id="b.-non-adjusted-response-variable-3" class="section level4">
+<h4>b. Non-adjusted response variable</h4>
+<pre class="r"><code># b. Non-adjusted ANOVA for growth
+nabgl<-glmer(Betula_growth ~ ERM_rem +(1|block),data=trt,na.action=na.omit,family=Gamma())
+## check assumptions
+# plot(nabgl)
+# shapiro.test(resid(nabgl))
+# summary(nabgl) # no treatment effect
+Anova(nabgl) # no treatment effect</code></pre>
+<pre><code>## Analysis of Deviance Table (Type II Wald chisquare tests)
+##
+## Response: Betula_growth
+## Chisq Df Pr(>Chisq)
+## ERM_rem 0.139 1 0.7093</code></pre>
+</div>
+<div id="c.-adjust-response-variable-2" class="section level4">
+<h4>c. Adjust response variable</h4>
+<pre class="r"><code># c. Adjust response variable
+nabgl_null<-glm(Betula_growth ~ 1 ,data=trt,na.action=na.omit,family=Gamma()) # create null model for intercept
+
+trt$Betula_growth_adj_glm<-(1/coef(nabgl_null)) + (trt$Betula_growth - predict(bg_glm_grad,newdata = trt,type="response",na.action=na.omit)) # Create adjusted variable: intercept + (observed - predicted)
+trt$Betula_growth_adj2_glm<-replace(trt$Betula_growth_adj_glm,which(trt$Treatment=="control"),subset(trt,Treatment=="control")$Betula_growth) # replace control values with observations: we don't adjust controls where no removal was carried out
+trt$Betula_growth_adj3_glm<-ifelse(trt$Betula_growth_adj2_glm<=0,1e-5,trt$Betula_growth_adj2_glm) # replace negative adjusted values with 1e-5 to fit GLMM</code></pre>
+</div>
+<div id="d.-adjusted-response-variable-2" class="section level4">
+<h4>d. Adjusted response variable</h4>
+<pre class="r"><code># d. Analyse adjusted response variable
+nabgl_adj<-glmer(Betula_growth_adj3_glm ~ ERM_rem +(1|block),data=trt,na.action=na.omit,family=Gamma())
+1/coef(nabgl_adj)$block # singular fit and identical coefs, drop random factor</code></pre>
+<pre><code>## (Intercept) ERM_remTRUE
+## 1 4.448 0.9089207
+## 2 4.448 0.9089207
+## 3 4.448 0.9089207
+## 4 4.448 0.9089207
+## 5 4.448 0.9089207</code></pre>
+<pre class="r"><code>nabgl_adj<-glm(Betula_growth_adj3_glm ~ ERM_rem ,data=trt,na.action=na.omit,family=Gamma())
+1/coef(nabgl_adj) # coefficients identical to GLMM above</code></pre>
+<pre><code>## (Intercept) ERM_remTRUE
+## 4.4480000 0.9089208</code></pre>
+<pre class="r"><code>Anova(nabgl_adj)</code></pre>
+<pre><code>## Analysis of Deviance Table (Type II tests)
+##
+## Response: Betula_growth_adj3_glm
+## LR Chisq Df Pr(>Chisq)
+## ERM_rem 6.5807 1 0.01031 *
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<pre class="r"><code>100*(1-mean(subset(trt,Treatment=="ERM")$Betula_growth_adj3_glm)/mean(subset(trt,Treatment=="control")$Betula_growth_adj3_glm)) # 83% lower growth in -ERM</code></pre>
+<pre><code>## [1] 83.03278</code></pre>
+</div>
+</div>
+</div>
+<div id="supplementary-figure-s1-relationship-between-removed-biomass-and-removed-surface-in-non-specific-biomass-removal-gradient-plots" class="section level2">
+<h2>5. Supplementary Figure S1: Relationship between removed biomass and
+removed surface in non-specific biomass removal gradient plots</h2>
+<pre class="r"><code>gradsi1<-subset(grad,plot!="0")
+trt$Treatment<-as.factor(trt$Treatment)
+levels(trt$Treatment)</code></pre>
+<pre><code>## [1] "control" "ECM" "EE" "ERM"</code></pre>
+<pre class="r"><code>trt$Treatment<-fct_recode(trt$Treatment,"Control" = "control", "Ectomycorrhiza removal" = "ECM", "Ecto- and ericoid mycorrhiza removal" = "EE", "Ericoid mycorrhiza removal"="ERM")
+bmlmer<-lmer(biomass_removed_kg ~ 0+ plot+(1|block),data=gradsi1)
+# keeping the random effect would not alter the findings: slope estimate = 43.9
+# here we use a linear model for simpler plotting purposes
+
+bmlm<-lm(biomass_removed_g~0+plot,data=gradsi1)
+# # check assumptions
+# par(mfrow=c(2,2));plot(bmlm);par(mfrow=c(1,1))
+# shapiro.test(resid(bmlm))
+summary(bmlm)</code></pre>
+<pre><code>##
+## Call:
+## lm(formula = biomass_removed_g ~ 0 + plot, data = gradsi1)
+##
+## Residuals:
+## Min 1Q Median 3Q Max
+## -974.06 -387.26 -87.13 154.87 1014.64
+##
+## Coefficients:
+## Estimate Std. Error t value Pr(>|t|)
+## plot 42.045 2.153 19.53 1.49e-11 ***
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+##
+## Residual standard error: 467.6 on 14 degrees of freedom
+## Multiple R-squared: 0.9646, Adjusted R-squared: 0.9621
+## F-statistic: 381.3 on 1 and 14 DF, p-value: 1.486e-11</code></pre>
+<pre class="r"><code># Extract model parameters for annotation
+slop<-round(bmlm$coefficients[1],2)
+r2<-round(summary(bmlm)$r.squared,3)
+R<-cor.test(gradsi1$biomass_removed_g,gradsi1$plot)
+
+# Plotting command
+ggplot(gradsi1,aes(x=plot,y=biomass_removed_g))+
+ geom_point(col="grey")+
+ scale_color_manual(values=c("black","#d73027","#8c510a","#3288bd"))+
+ geom_smooth(method="lm",
+ formula= y~0+x,
+ col="black",
+ fill="grey80",
+ lwd=0.5)+
+ theme_bw()+
+ annotate("segment",
+ x=trt$biomass_removed_g/slop,
+ xend=trt$biomass_removed_g/slop,
+ y=-Inf,
+ yend=trt$biomass_removed_g,
+ lty=3,
+ lwd=0.5)+
+ geom_point(data=trt,aes(y=biomass_removed_g,x=biomass_removed_g/slop,col=Treatment),size=2)+
+ # geom_vline(data=trt,aes(xintercept=biomass_removed_g/slop,col=Treatment),lty=3)+
+ theme(panel.grid = element_blank())+
+ annotate("text",
+ x=40,
+ y=4500,
+ label=paste("Biomass =",slop,"* Area removed \n Adjusted-R2 =",r2,"\nr=",round(R$estimate,3),"; P < 0.001"))+
+ ylab("Biomass removed [g]")+
+ xlab("Area removed [%]")</code></pre>
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" width="672" /></p>
+<pre class="r"><code>ggsave("../results/SI_Fig_1_biomass_cover.pdf",width=12,height=10,unit="cm")</code></pre>
+</div>
+<div id="supplementary-fig-s2-betula-nana-growth-adjusted-to-convert-negative-values-to-0" class="section level2">
+<h2>6. Supplementary Fig S2: Betula nana growth adjusted to convert
+negative values to 0</h2>
+<pre class="r"><code># Compute means and SE of the GLMM-adjusted Betula nana growth, for plotting purposes
+glm_mean_se <- trt%>%
+ group_by(Treatment,
+ ECM_rem,
+ ERM_rem) %>%
+ summarize_at("Betula_growth_adj3_glm",
+ .funs = list(mean = mean, se = se), na.rm = TRUE)
+glm_adj_mean<-as.numeric(glm_mean_se[3,4])
+glm_adj_se<-as.numeric(glm_mean_se[3,5])
+ggplot(gresults_corr,aes(x=biomass_removed_kg_mean,y=Mean))+
+ geom_smooth(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ method="lm",col="black")+
+ geom_point(data=grad,
+ aes(x=biomass_removed_kg,y=Betula_growth),
+ col="grey")+
+ geom_ysidedensity(aes(x = stat(density)),
+ alpha=0.3,
+ fill="black")+
+ geom_pointrange(data=gresults_corr,
+ aes(x=biomass_removed_kg_mean,
+ y=Mean,
+ ymin=Mean-SE,
+ ymax=Mean+SE,
+ col=P_F<0.05),
+ alpha=0.05,
+ position=position_jitter(0.1),
+ size=0.2)+
+ geom_pointrange(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ ymin=Betula_growth_adj_mean-Betula_growth_adj_se,
+ ymax=Betula_growth_adj_mean+Betula_growth_adj_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ pch=c(17,17,17,19),
+ lwd=0.6)+
+ geom_errorbarh(data=trt_mean_se,
+ aes(x=biomass_removed_kg_mean,
+ y=Betula_growth_adj_mean,
+ xmin=biomass_removed_kg_mean-biomass_removed_kg_se,
+ xmax=biomass_removed_kg_mean+biomass_removed_kg_se),
+ col=c("#d73027","#8c510a","#800080ff","black"),
+ lwd=0.6,
+ height=0)+
+ theme_bw()+
+ theme(panel.grid=element_blank(),
+ legend.position="none")+
+ annotate("text",
+ label=paste(gsigdiff_F_corr,"% simulated P<0.05",sep = ""),
+ x=1.500,
+ y=1,
+ size=3,
+ col="#5ab4ac")+
+ annotate("pointrange",
+ x=3.2,
+ y=glm_adj_mean,
+ ymin=glm_adj_mean-glm_adj_se,
+ ymax=glm_adj_mean+glm_adj_se,
+ size=0.5,
+ pch=15,
+ col="#800080ff")+
+ ylab(expression(paste(italic("Betula nana "),"growth [mm]")))+
+ scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)+
+ xlab("Biomass removed [kg]")+
+ xlim(c(0,4.000))+
+ ylim(c(-1,15))+
+ geom_hline(yintercept=0,lty=3,col="grey")</code></pre>
+<p><img 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xOJRMQnxhmNxoqKio0bN2o0mnRPh6xCwAMAAOQW0h2QnUqlys/Pz8/PFwShtrY23eMgaxHwAAAAOYFuR5ISicTc3Jy4UVyj0eh0unRPBOQcAh4AACDLke5I3szMzOTk5MW3atNoNAUFBeI5ZwCpQcADAABkLdIdspicnFz8pLRYLDY+Pj43N1dcXJyWqYAcRMADAABkG7odMopEIks853x6elqv13OjdSA1CHgAAIDsQbpDdtPT00sfMDk5ScADqUHAAwAAKB7djlWSSCSi0ejSx8zOziYSCZVKlZqRgFxGwAMAACgY6Y5VlUgkEonEFQ+LxWJaLWUBrDr+MwMAAFAk0h0psMzz6px+B1KDgAcAAFASuh2ppFKpNBrNxU+Pu9wxKRsJyGUEPAAAgDKQ7kgLo9G4xF3oxQNSNgyQ4wh4AACAjEa3I73y8/NnZmYudxJeo9EUFhameCQgZxHwAAAAGYp0RyZQq9WlpaVjY2Ozs7ML3tJoNKWlpWq1Oi2DATmIgAcAAMgsdDsyjVarLSsrC4fD4XB4bm5OfEWv1xuNRm5fB6QSAQ8AAJApSHdkLJVKZTQaudwdSC8CHgAAIM3odgDAchDwAAAAaUO6AwCWj4AHAABIA9IdALBSBDwAAEDq0O0AAMkIeAAAgFQg3YGs19/fn5eXV1tbm+5BkLUIeAAAgFVEtwPZ7ezZs8Fg0O12Hzp06PTp05/4xCduueWWdA+FrEXAAwAArArSHchWQ0NDfr/f7Xa73e5QKHTxWx6PJ11TIRcQ8AAAAHKi24GsNDw83Nra6vV6vV7v8ePHL3fY8ePHT58+vWHDhlTOhtxBwAMAAMiDdAeyzPT0dEdHh3imvbu7Ox6PX/FLEonEW2+99YlPfCIF4yEHEfAAAABJoduBbBIOh4PBYCAQCAQCra2tc3NzK12BgMfqIeABAAAkIt2B7BCJRDo6OsTt8Z2dnRKiXRAEo9HY1NRkt9vvu+8+2ScERAQ8AADAytDtQBaIxWI9PT3i9vhgMBgOhyUsotFoampqXC6Xy+VqamrS6XSCIPAYOaweAh4AAGC5SHdA0eLxeH9/fyAQcLvdLS0tExMTEhaZj/bGxka73V5QUCD7nMDlEPAAAABXQLcDypVIJHp7e71er8fj8fv94+PjEhZRq9W1tbV2u93hcFit1vz8fNnnBJaDgAcAALg0uh1QrlAo5Ha7A4GA1+sdHByUtojJZBK3xzudzpKSEnknBCQg4AEAABYi3QElOnfunLg9/u233z516pS0RUwmU2Njo8Vi2b179/r16+WdEEgSAQ8AAPC/6HZAcc6dOyfePd7n84VCIWmLbNiwwXEB0Y5MRsADAACQ7oCSjIyMtLe3B4NBt9vd1dWVSCQkLFJWVmaz2Vwul8Viqa6uln1IYDUQ8AAAIHfR7YBSTE9Pd3R0iE996+7ujsfjEhYpKCgwm83iZe11dXUqlUr2OYFVRcADAIBcRLoDmS8cDvv9fp/P5/F4uru7Y7GYhEUKCwutVqvdbnc6nTt37lSr1bLPCaQMAQ8AAHII3Q5kuFgs1tPTI55pb2tri0ajEhYxGAx1dXUWi8Xlclmt1ry8PNnnBNKCgAcAANmPbgcy2Xy0BwIBv98/OTkpYRGNRlNTUyNuj29qatLpdLLPCaQdAQ8AALIZ6Q5kpng8fuTIEfEG8n6/f2pqSsIiGo3m2muvFe8e39jYaDAYZJ8TyCgEPAAAyEJ0O5CZQqGQuD3e6/WOjo5KW8RkMoln2pubm4uKiuSdEMhkBDwAAMgedDuQgd5///35R7UPDQ1JW6S6ulo8026z2UpKSuSdEFAKAh4AAGQD0h3IKKdPnxaj3ePxDA4OSltky5YtDofDbrc7HI7y8nJ5JwSUiIAHAAAKRrcDmWN4eLi1tVW8e3xfX5+0RSorK8W7x+/atWvTpk3yTggoHQEPAAAUiXQHMsHU1FRnZ6d4WXtXV1cikZCwSFlZmc1mc7lcFoulurpa9iGBrEHAAwAAJaHbgbSbnJz0+/3iDvmjR4/G43EJi5SUlNhsNofD4XQ6r7rqKtmHBLISAQ8AABSAbgfSKxwOB4PBQCAgPqp9dnZWwiJGo7GhoUE8015fX6/RaGSfE8huBDwAAMhopDuQLrFYrKenR9we39bWFo1GJSxiMBjq6urEy9qtVmteXp7scwK5g4AHAACZiG4H0iIWi7377rs+n8/r9QaDwXA4LGGRvLw8s9nsdDrtdnt9fb1Op5N9TiA3EfAAACCD0O1A6sXj8SNHjng8Hp/P5/f7p6amJCyi0WiuueYa8VHtFovFYDDIPicAAh4AAKQf3Q6kXigUErfHe73e0dFRaYuYTCbXBcXFxfJOCGABAh4AAKQT6Q6k0gcffOC9YGhoSNoiV111lXim3WazrVmzRt4JASyBgAfwf2ZnZycnJ2OxmF6vLywsVKlU6Z4IQNai24GUOXPmjFjsHo/nzJkz0hYxmUziNe0Oh6OiokLeCQEsEwEPQBAEYWZmpre3d2hoKJFIiK/k5eVt3rzZZDKR8QBkRLcDqTE8PNza2trW1hYMBg8fPixtkYqKiqamJpfL1dzcXFVVJe+EACQg4AEIY2NjHR0dsVjs4hdnZ2f7+vpGRkauu+46tVqdrtkAZAe6HUiBsbGx1tZW8WR7b2+vtEXKy8vF0+wOh2PLli3yTgggSQQ8kOvm5ubee++9BfU+b3h4eGBgYPv27SmeCkDWIN2BVTU1NeX3+30+n8fjOXLkSDwel7BIcXGxzWZzOp0Oh+Oqq65i8x2QsVYW8O+//77sE1RVVWk0GtmXBbBMp0+fjkajSxwQCoW2bNnCf6cAVoRuB1ZPOBzu6uoKBAJut9vv98/OzkpYxGg0NjQ0uFwui8ViNpu1Wk7sAQqwsv9Qt27dKvsEAwMDbM4B0mh4eHjpA+Lx+OjoaHl5eWrmAaBodDuwSmZnZzs7Oz0ej9fr7ezsXPqX75djMBgaGhocDofdbjebzfx2HlAcftMG5LpIJHLFY8LhcAomAaBcdDuwGmKx2OHDh8Vr2gOBgLRvx1qt1mw2izeQb2ho0Ol0ss8JIGWkBPwPf/jDtWvXJv/Zd911V/KLAEjScq5z4zf0AC6JbgdWQygUcl8wPj4uYQWNRlNTU9PY2NjU1NTc3FxUVCT7kADSQkrA33LLLWx6B7JGQUHB5OTk0sfk5+enZhgASkG6A/Lq6+vzeDw+n6+1tXV0dFTCCmq1eseOHeLd461WK9EOZCW20AO5bu3atYODg0scYDQa+SEAgIhuB2QUCoXEaPd6vefPn5e2yPbt28Vot9lspaWl8k4IINOsLOB/9atfCYJQWVkpy2fLuxoAaSoqKkpLS0dGRi75rkqlqq6u5nEyQI6j2wG5nD9/vq2tze12t7S0nDhxQtoilZWVFovF5XLdcMMNGzZskHdCAJlsZQF/zz33yPjZ8q4GQLJrr732vffeW9zw4ma8ioqKtEwFIO3odkAWw8PD3gskP5V53bp1jgs2btwo74QAlEKeLfSTk5Pd3d0DAwPxeHzpI++9915ZPnHem2+++dxzzz399NPr169f/G44HP7pT3/q8XjGx8evuuqqhoaGvXv3cjsuYAGtVltfX3/27NkzZ85MTEzEYjG9Xl9WVlZVVcXV70AOotuB5I2Pj7e2torR3tvbm0gkJCxSVlZmt9vFaF+NxzkDUBwZAv6ZZ5756le/esWbYIlkD/jXX3/9ck/UOH/+/BNPPHH8+HFBEEpKSg4fPnz48OGOjo6vfvWrXNALLKBSqdatW7du3bp0DwIgbeh2IEkzMzPt7e1utzsQCHR2ds7NzUlYJD8/v76+3uVyuVyu2tpatVot+5wAlCvZgP/d7363f/9+WUZZqenp6Zdffvnw4cOXO+A//uM/jh8/fs011/zDP/xDRUXFqVOnvvGNb7z33ns//vGPH3zwwVSOCgBAxqLbgWSEw+H29nbxTPu7774bi8UkLJKfn9/U1CQ+qr2mpobtogAuJ9mA/853viMIglqtvv/++z/+8Y9XVVWl4NeEb7zxxmuvvXb8+PFIJHK5Y3p6et59993CwsIDBw6I59s3btz4+OOP33///W+99dYnP/lJbp4HAMhldDsg2ezs7LvvvuvxeLxeb0dHRzQalbCIXq9vaGgQt8ebzWaiHcByJBvwPT09giDs37//qaeekmOeZRkYGDh16pRer9fr9RMTE5e8pqilpUUQhAXPwFy3bl1tbe17773n8Xg++tGPpmxgAAAyBN0OSBOLxXp6egKBQCAQaGlpmZiYkLCIRqOpqakRt8dbLBa9Xi/7nACyW7IBPzY2JgjC3r175Rhmufbt27dv3z7xz5/61KfGx8cXH9PX1ycIgsViWfC6xWJ57733xHcBAMgRdHsmm5ubC4fD0Wg0FotpNJq8vLz8/HzOx2aCeDx+7NgxcXu83++XHO21tbXimfampiaj0Sj7nAByR7IBbzKZjh07NjMzI8s0Mjp16pQgCIsffyW+cvLkycVfMjExwc83AIBswve1zDc1NXVxFsZisWg0OjU1VVRUVFBQkMbBcll/f78Y7a2trYufsbocKpVq586ddrvd6XQu2BAKAMlINuDvvvvuJ5988pe//OXNN98sy0BymZqaEgRh8V+XhYWFgiBMT08v/pLe3t4vfvGLi1/n71wAgLLQ7UoxPT19uZO64us0fMqcPXs2GAy63e5Dhw6dPn1a2iImk8nlcjU2NjqdTh7sAmA1JBvwBw4c+MUvfvH888+bzeYHHnhApVLJMlbyZmdnBUFYvElJfKj1Ene/AwBAoeh2ZYnH40s/hXdyctJoNPIUsdUzODjovUDcvClBVVXV/KPa165dK++EALBAsgFfXFz85ptv7tq1a//+/S+88MKHPvShLVu2LPGd5oEHHkjyE5epqKhodHR08d5+8dz7JU+qV1RU3H333Ytff/3111djQgAAZEG3K1QkEonH40sckEgkwuGweO4BchkeHvb5fB6Px+fzDQwMSFtk7dq1YrHb7faqqipZBwSApSQb8IIgPP/882fOnBEEIRgMBoPBpQ9OWcCXlZWNjo4u/sW2+EppaeniLzGZTAcOHFj8+htvvLEaEwIAkAy6XenE3YLJH4Mrmp6e7ujocLvdbre7u7t76d+bXE5paWl9fb3FYnG5XHV1dZmz7RRATkk24H/yk59861vfkmUUea1Zs0YQhOHh4QWvi3ciKS8vT8NMAAAkjW7PGpd8Di7kMj093dbWJm6P7+npicViEhYpKiqyWq1Op9Nut+/cuZNoB5B2yQb897//fUEQ8vLyHn/88b17927atClDrtTavHlzIBBob2+/4YYbLn69o6NDEIRt27alaS4AAKSg27PPcn5kypAfq5QiEom0t7d7vV6fz9fR0SEt2o1GY1NTk7hDvra2luf5AcgoyQb8kSNHBEF4+OGHH330UTnmkY3dbn/11Vf9fn8kEtHr9eKL4+Pj7777rk6nu/HGG9M7HgAAV0S0Zze9Xi8+NGcJOp0uNcMoVywW6+npEbfHBwIBaTcq1mg0NTU1LpfL5XI1NTXxPzuAjJVUwE9PT4uXlO/Zs0emeWRz7bXXXn311UeOHHn66ae/8pWvaDSaSCTy7W9/e3Z29sMf/jAPZQEAZCy6PUfk5eXl5eUtcZW7VqulJC8pFot1d3eL2+Pb2toW37R4ObRardlsFm8g39DQMH++BwAyWVIBPzc3p1KpEomEeGF5pvnKV77yT//0T4cOHWpra9u8eXN/f380Gt24ceNnP/vZdI8GAMBCdHuuUalUJSUlw8PDl7ynmlqtXrNmDRddz0skEkePHhVvIO/3+ycmJiQsotFoamtr7Xa70+m0WCzc4R+A4iQV8MXFxU6n0+12/+xnP7v99tvlmkkuGzZs+O53v/uzn/2sra2tt7e3rKysubn54x//OH9ZAwAyB92ey7RabXl5+fj4+IKN3zqdrqSkhKuvBUEIhULi3niv1zs4OChtEZPJJG6PdzqdJSUl8k4IAKmU7DXwzz333K5du6Vr6xYAACAASURBVF566aW6urqHHnoo9d9pfvrTny7xbllZ2Ze+9KWUDQMAwDLR7RBpNJrS0tJYLBaNRuPxuFqt1ul0OZ7uZ8+eDQaDbrf70KFDp0+flraIGO2NjY0Oh2P9+vXyTggA6ZJswIdCoYcffvhrX/vaI4888uKLL+7evXvdunVLHP/EE08k+YkAACgU0Y7L0Wg0RqMx3VOk09mzZ70XnDx5UtoiGzdudFyw9E+kAKBQyQb8Rz7ykfk/HzlyRLwp/RIIeABArqHbgUsaHh7u6OgQT7Z3dXUlEgkJi5SXl1utVpfLZbFYqqurZR8SADJKsgFfWFgoyxwAAGQZuh1YbHp6uqOjQ3zqW3d39yVv4HdFBQUFZrNZvKy9rq6OW/0ByB3JBry0W4ACAJCViHZgsZmZmba2No/H4/P5uru7Y7GYhEWKioqsVqu4PX7Hjh1qtVr2OQEg8yUb8AAAgG4HFojFYj09PeKZ9ra2tmg0KmERg8HQ2NhosVgsFovNZtNq+cEVQK7j70EAAKQg2oEF5qM9EAi0trZOTU1JWESj0dTU1Ijb45uamnQ6nexzAoByyRPwk5OT3d3dAwMDV7yQ6d5775XlEwEASAu6HbiYGO0+n8/r9fr9/unpaQmLaDQas9lst9sdDkdDQ4PBYJB9TgDIDjIE/DPPPPPVr351cnJyOQcT8AAAxSHagQVCoZC4Pd7j8YyNjUlYQa1Wb9u2rampyeVyNTc3FxUVyT4kAGSfZAP+d7/73f79+2UZBQCAjEK3AxcToz0QCHi93sHBQWmLmEwmcXu80+ksKSmRd0IAyHrJBvx3vvMdQRDUavX999//8Y9/vKqqipuCAgCUi2gHLnby5Elxe3wy0b5t2zZxe7zdbi8rK5N3QgDIKckGfE9PjyAI+/fvf+qpp+SYBwCANKDbgXlDQ0N+v1+8e3xfX5+0RSorKy0Wi8vluv766zdu3CjvhACQs5INePGqp71798oxDAAAqUO0A/NGRkba29uDwaDb7e7q6kokEhIWKSsrs9lsLpfLYrFUV1fLPiQAINmAN5lMx44dm5mZkWUaAABWFdEOzJuYmPD7/eL2+KNHj0qL9jVr1thsNofD4XQ6t2/fLvuQAICLJRvwd99995NPPvnLX/7y5ptvlmUgAABkR7cDonA4HAwGA4GA+Kj2ubk5CYsYjcaGhgbxXnS1tbXc/wgAUibZgD9w4MAvfvGL559/3mw2P/DAAyqVSpaxAABIEtEOiMRHtYtPfWtra4tGoxIWMRgMdXV14mXtVqs1Ly9P9jkBAFeUbMAXFxe/+eabu3bt2r9//wsvvPChD31oy5YtS/wi9oEHHkjyEwEAuByiHRDFYrHOzk7xBvLt7e3hcFjCIjqdrr6+3m63O51Os9lMtANA2iUb8IIgPP/882fOnBEEIRgMBoPBpQ8m4AEA8iLaAVE8Hu/v7w8EAm63u6WlZWJiQsIiGo2mpqbG5XI1NjbabLbCwkLZ5wQASJZswP/kJz/51re+JcsoAAAsE9EOzAuFQuL2eI/HIz4eSAKTySRe097c3FxUVCTvhAAAuSQb8N///vcFQcjLy3v88cf37t27adMmbmQCAFgNRDsw7/333/deMDw8LG2R6upqh8PhcDhsNltJSYm8EwIAVkOyAX/kyBFBEB5++OFHH31UjnkAAPg/RDsw79y5c+L2+HfeeefkyZPSFjGZTI2NjRaLZffu3evXr5d3QgDAaksq4KenpycnJwVB2LNnj0zzAAByHdEOzBseHm5tbRXvHt/X1ydtkYqKiqamJpfLtWvXrk2bNsk7IQAglZIK+Lm5OZVKlUgkRkZG5BoIAJCDiHZg3ujoqM/nE28gn0y0OxwOu93ucDg2b94s74QAgHRJKuCLi4udTqfb7f7Zz352++23yzUTACAXEO3AvJmZmfb2dvFedN3d3fF4XMIiBQUFZrNZvBddXV2dSqWSfU4AQHolew38c889t2vXrpdeeqmuru6hhx7SaDSyjAUAyEpEOzAvHA4Hg8FAIBAIBPx+/+zsrIRFjEZjQ0ODGO21tbXcSxgAsluyAR8KhR5++OGvfe1rjzzyyIsvvrh79+5169YtcfwTTzyR5CcCABSEYgcuFo1GOzo6fD6fx+Pp7OyUFu0Gg6GxsVG8gfy1117L6RMAyB3JBvxHPvKR+T8fOXJEvCn9Egh4AMh6RDtwsVgs1tPT43a7xTPt4g2AV0qj0dTU1Ihn2i0Wi16vl31OAEDmSzbgCwsLZZkDAKBoRDuwQCgUEq9pb2lpmZiYkLDCfLQ3NjbabDZ+6AIAJBvw0r4hAQCUjmIHFuvr6/N4POI95MfGxiSsoFard+7c6XQ67Xa71Wol2gEAF0s24AEAuYNoBxYLhUKBQCAYDB48ePDMmTPSFjGZTOL2eIfDsWbNGnknBABkjZUF/K9//WtBEPbs2ZOfn5/8Z8u7GgBAdhQ7cEnnz59va2tzu93vvPPOyZMnpS1SWVlpsVhcLtfu3bvXr18v74QAgKy0soDfu3evIAgDAwNbtmxJ/rPlXQ0AkDyKHbicoaEhr9cr3kD+gw8+kLbIunXrnE6neAP5DRs2yDshACDrsYUeAHIaxQ4sYWxsrLW11ev1er3e3t5eaYuUlZU5LuCkBQAgGVIC/g9/+MPSD3sHAGQyoh1YwszMTHt7u3gD+e7u7ng8LmGR/Pz8+vp68bL2uro6lUol+5wAgBwkJeA/97nPyT4HAGD1UOzA0sLhcDAYFM+0v/fee7FYTMIi+fn5VqvV4XDY7fba2lq1Wi37nACAHMcWegDIQhQ7cEWzs7OdnZ0ej8fr9XZ2dkajUQmLGAyGhoYGMdrNZrNGo5F9TgAA5q0s4AcGBmSfoKqqSvY1ASCnkOvAMsVisZ6eHrfbHQgE/H7/5OSkhEU0Gk1NTY24Pd5isej1etnnBADgklYW8Nx5BQAyAcUOLF88Hj969KjH4/H5fMlEe11dnXimvampyWg0yj4nAABXxBZ6AFAAih1Yqb6+PvGa9tbW1tHRUQkrqNXqHTt2iHePt1qtRUVFsg8JAMCKEPAAkHHIdUCas2fPBoNBt9t98ODBM2fOSFvEZDK5XK7GxkaXy7V27Vp5JwQAIBkEPACkH8UOSHbmzBmfz+f1ej0ez+nTp6UtYjKZ7Ha7eLK9srJS3gkBAJALAQ8AqUauA0kaHh4Wt8f7fD7Jd9hdt26d44KNGzfKOiAAAKuCgAeA1UWuA7KYnp7u6Ohwu91ut7urqyuRSEhYpKyszGw2WywWl8tVV1enUqlknxMAgNVDwAOAnMh1QEbT09N+v188097d3R2PxyUsUlxcbLVanU6nw+Gorq4m2gEAykXAA4B05Dogu3A43N7eLkZ7Z2dnLBaTsEh+fn5TU5O4Pb6mpkaj0cg+JwAAqUfAA8BykevAKonFYj09PeL2+EAgEIlEJCyi0WhqampcLpfL5WpqatLpdLLPCQBAehHwAHAJtDqw2mKxWFdXl3imva2tbWZmRsIiWq3WbDY7HA673d7Q0KDX62WfEwCAzCFbwJ88ebKvr++Kd5S58cYb5fpEAJALuQ6kTCgUcl8wPj4uYQXxTHtjY2NTU1Nzc3NRUZHsQwIAkJlkCHi3233ffff19vYu52Bp94wFALnQ6kDqzUe7z+cbGRmRtojJZBK3xzudzpKSEnknBABAEZIN+P7+/ptuuikajcoyDQDIiFYH0igUCvl8PvFp7efOnZO2yLZt28Qb0dlstrKyMnknBABAcZIN+H/+538W6/3uu+++7777Nm/erFar5RgMAFaAVgcywdmzZ71er8fj8Xq9p06dkrZIVVWV3W4Xu33t2rXyTggAgKIlG/Ber1cQhI997GO//vWv5ZgHAJZCqAOZZmRkpL29PRgMut3urq4uadfKlZeXW61Wl8tlsViqq6tlHxIAgOyQbMAPDAwIgvD5z39ehlkA4CK0OpCxpqenOzo6xMvau7u74/G4hEVKS0vr6+stFovL5aqrq1OpVLLPCQBAlkk24IuKisLhcEVFhSzTAMhBhDqgCNPT04FAQLymvbu7OxaLSVikqKjIarU6nU673b5z506iHQCAFUk24K1W6+9///tgMGiz2WQZCEC2ItQBxYnFYj09PeKZ9ra2Nmn3rDUYDI2NjRaLxWKx2Gw2rVa2R9gCAJBrkv0mun///t///vdPP/30Jz7xicLCQllmAqBohDqgaHNzc52dnV6v1+fztbe3RyIRCYsYDIb6+nrxXnRms5loBwBAFsl+Q92zZ89DDz305JNP3nHHHc8880xdXZ0sYwHIcFR6TkkkErOzs7Ozs/F4XK1W6/V6eizLxGKx7u5ucXt8W1vbzMyMhEW0Wq3ZbHY4HHa7vaGhQa/Xyz4nAAA5bmU/gX3jG99Y/GJhYWF1dfVbb7113XXXXX/99Tt37jSZTBqN5pIrPPLII1LGBJAOVDoEQZidnR0bG5ubm5t/ZWJiQqfTlZSUXO6veihFKBQSt8d7PJ6xsTEJK2g0mpqamsbGxqampubm5qKiItmHBAAA81YW8I8++ugS78bj8YMHDx48eHCJYwh4INNQ6VhCJBIZGRlZ/Ho0Gh0aGiovL6fhFae/v9/n83m93tbW1uHhYQkrqFSqHTt2OBwOp9NptVqJdgAAUmZlAc9V7oBCUemQIB6PL3FWVny3rKwslSNBmhMnTojR7vV6z549K22RrVu32u128Qby/HsHACAtVhbwExMTqzQHgCSR6JBdOBxe+vne0Wh0bm6O6+Ez09DQkN/vF3fIh0IhaYtUVlaKz2m//vrrN27cKO+EAABgpfipC1AMEh0pNjs7e8VjIpEIAZ85RkZG2tvbg8Gg2+3u6upKJBISFikrK7PZbC6Xy2KxVFdXyz4kAACQLNmfur773e8KgvDlL3956cNOnz79yiuvVFRUfPKTn0zyE4FsRZ8j08RisSses/QpeqTAxMSE3+/3eDw+n+/o0aPSor20tNRmszkcDofDsX37dtmHBAAAskg24B988EFhGQE/PDz84IMPVlVVEfDIZSQ6lEWtVl/xGJVKlYJJsEA4HA4Gg4FAIBAItLa2XvyMgOXLz8+vr693uVwul6u2tnY5/7oBAEB6pWjf45EjRwRBOHfuXGo+Dkg94hzZJy8vLxwOX/GY1AyDSCTS3t4u3ouus7NTWrQbjcampia73e5wOOrq6niIAAAAyrLigG9ra7vtttsWvFhZWbnEl8RisdHRUUEQNmzYsNKPAzIEfY4cZDAYJicnl9iSrdFo9Hp9KkfKNbFYrLOz0+fzeTyejo6OK/4+5ZJ0Ol1DQ4N4A/nrrruO37kAAKBcKw74ubm58+fPL3hx8SuX9IUvfGGlHwekAHEOXJJGoyksLFzi+SPFxcWpnCdHxOPx/v7+QCDgdrtbWlqkPf9Fo9HU1NS4XK7Gxka73V5QUCD7nAAAIPVWHPCbNm169NFH5//xX/7lXwRBuPiVS3+MVmuxWD760Y+u9OOAZFDmQJLE8Ft8Hl6tVhcXF3P6XUahUEh85JvH4xkbG5Owglqt3rZtW1NTk8vlam5uLioqkn1IAACQXisO+KqqqieeeGL+H8WAv/gVIAUocyBlCgoKjEbj9PT07OxsIpFQq9U6nc5gMHDPs+QNDAx4vV6v1+vz+YaHhyWsoFKpqqurxbvH22w29kQAAJDdkr2J3f333y/LHIAgKcvj8fjMzMzs7Gw8Hler1Xl5efn5+dwWG5CXWq0uLCxM9xRZ4ty5c+L2+LfffvvUqVPSFjGZTI2NjRaLZffu3evXr5d3QgAAkLGSDfgf/OAHssyBLLZ6Z8tnZmbGx8cv3tkbDocnJydLSkoMBsMqfSgArNTQ0JDf73e73W1tbX19fdIWqaystFgsLpdr165dmzZtkndCAACgCMkG/Oc///k9e/b8+Z//eUlJiSwDIcusar1f8jLRRCIhvk7DA0ijkZGR1tZWcYd8f3+/tEUqKiocDod4A3mTySTvhAAAQHGSDfgf/ehHP/rRj7Rarcvl2rNnz2233dbQ0MAGZqy2eDy+xJ2ZE4nE+Pi4Xq/n/4oAUml6erqjo0O8F113d3c8HpewSEFBgdlsdrlcLperrq6Ov8cAAMC8ZANeNDc3d+jQoUOHDh04cGDdunW33nrrnj17br755vLyclnWBxYIh8NL/2Qcj8cjkQgn4QGstnA4HAwGA4FAIBBobW2dm5uTsIjRaGxoaBCjvba2lhsEAgCAS0o24AcGBg4ePPinP/3p4MGDx44dEwRhcHDwxRdffPHFF9Vqtc1mu+222/bs2WOz2fhxBDJazo/Is7OzBDyA1RCJRDo6Onw+n8fj6ezslBbtBoPBYrGIN5C/5pprNBqN7HMCAIAsk2zAb9my5dOf/vSnP/1pQRBOnz49H/NdXV3xeFy89u/rX/96WVnZLbfcctttt/31X/+1HGMj1y1nY+qCx1YDQDJisVhPT4/b7RbPtE9NTUlYRKPR1NTUiGfam5qadDqd7HMCAIAsJs8WetGGDRvuvffee++9VxCE8+fPHzp0SIz5jo6O4eHhl19++eWXXybgIYvlbOhg0weAJMXj8f7+fvGpby0tLUvcemMJ89He2Nhos9l4IB8AAJBMzoC/WElJyfr16zds2LBhw4Zjx45NTk6u0gchN+l0uunp6aWPycvLS80wALJJIpHo6+vzer0ej6e1tXV8fFzCImq1+uqrr3Y6nXa73Wq1FhQUyD4nAADIQXIG/MzMjNfrFc+6u93umZmZi9+trKzcvXu3jB+HXKbX6zUaTSwWu9wBWq2WvakAli8UCgUCgWAw+Kc//WlwcFDaIiaTSdwe73A41qxZI++EAAAAyQb8+Ph4S0vLwYMHDx482NraGo1GL3533bp1N95444033njTTTfV1dUl+VnAPJVKVVJSMjIycskL3dVq9Zo1a3j2EoClnTt3Ttwe/84775w8eVLaIiaTqbGx0WKx7N69e/369fJOCAAAcLFkA76srGzBWdCNGzfeeEFNTU2S6wOXo9PpysrKxsbGFtz/OS8vr6SkRKtdrctDkMXi8XgsFlOpVBqNhl8AZavz5897PB6fz+f1ekOhkLRF1q9f73Q6xRvIE+0AACBlko2c+XovLy9/8MEH77333h07diQ9FbAseXl5FRUV0Wh0dnY2Ho+r1WqdTsel75AgHA5PTk7O/zJIpVIZDIaioiJuhZgdpqamOjs73W632+3u6uqS9oiKsrIym81msVgsFss111wj+5AAAABXJNtZyqGhoccee+zFF1+84YYbrr/++htuuKG6ulquxYEl6HQ6LndHMsbHxxfcEzGRSMzMzITD4bKyMn4lpFAzMzPt7e1itHd3dy/n2ZOLFRQUmM1m8bL2uro69mUAAID0SjbgOzo63n777bfffvvQoUMnTpw4duzYsWPHXnjhBUEQ1q1bNx/z9fX1Go1GjoEBQE7T09OXe6JBIpEYGRmpqKjgPLxShMPhQCDg9Xq9Xu/hw4eXuM/lEgoKCqxWq91udzgcNTU1/NsHAACZI9mAN5vNZrP5i1/8oiAI77///tsXHD58eHBw8Fe/+tWvfvUrQRAKCwubm5uvv/76xx57TIapAUAOiURi6YdcxuPx6elpHtydyWKxWE9Pj3imva2tbcG9VJfJYDDU1dVZLBaXy2W1Wtl2AQAAMpOcN/rasmXLli1bPvnJTwqCMDw83NLSIsa83++fnJx87bXXXnvtNQIeQOaYm5u74s5qaUGIVTUf7YFAQPwWI2ERjUZTU1Mjbo+3WCx6vV72OQEAAOS1WnfqLigoKCoqMhqNnMcAkLGWs8Va2jZsyC4ej/f09Hi9Xp/P5/f7p6amJCyi0WiuueYa8e7xFovFYDDIPicAAMDqkTPgI5GIz+f74x//+NZbb7nd7nA4PP+WWq22WCy33nqrjB8HAMh6oVBI3B7v9XpHR0elLWIymVwXFBcXyzshAABAyiQb8NFotLW19a233vrjH//Y0tIyMzNz8bvr16+/5ZZbbrvttptvvrmioiLJzwIAeWm1V/47kBtwpl4oFAoEAsFg8ODBg2fOnJG2yHy0OxyONWvWyDshAABAWiQb8KWlpQtu4KzT6Xbt2nXbbbfdeuut9fX1Sa4PAKtHq9Vqtdr5x79fErusU+PMmTPi3eM9Hk8y0e64gN8aAwCA7JNswM/Xe3V1tRjtf/Znf1ZQUJD0YACQCkVFRSMjI5d7V6vVGo3GVM6TU4aHh1tbW9va2oLB4OHDh6UtUlFR0dTU5HK5mpubq6qq5J0QAAAgoyQb8Hfeeeett9566623bt++XZaBACCV9Hp9cXHx+Pj44re0Wm1paalKpUr9VFlsamqqs7NTvKy9q6srkUhIWKSsrMxms1ksFovFUldXx78jAACQI5IN+FdffVWWOQAgXfLz8/Py8qampiKRiNiTWq3WYDAUFBRQhrKYmpry+/0+n8/j8Rw5cuSKj+67pJKSEpvNJm6Pv+qqq/hXAwAAcpBsd6GPxWI///nP33nnnb6+vr6+vnA4vGPHjp07dzqdzvvuu285d4oCgHTJy8sT73MWj8dVKhVxmLxwONzV1RUIBNxut9/vn52dlbCI0WhsaGgQn9NuNpv5VgIAAHKcPD8MvfrqqwcOHOju7r74xVOnTv3pT396/vnnn3zyyX/913+98847ZfksAFg9arU63SMoWCwW6+npEbfHBwKBSCQiYRGDwVBXV2exWFwul9VqzcvLk31OAAAAhZIh4J966qm///u/F/9cWFi4Y8eOrVu3arXa48ePHz16dHx8/OjRo3fddddvfvObu+66K/mPAwBkjlgsdvjwYY/H4/P5AoFAOByWsIhWqzWbzU6n0263NzQ06HQ62ecEkCKJhCAkBEEQVPw+FNmvtrY23SMg5yQb8D09Pf/4j/8oCEJZWdkjjzzyhS98obCwcP7dmZmZH/7wh0888cTQ0NC+ffvsdvvGjRuT/EQAQNqFQiH3BZe8BeAVaTSampqaxsbGpqam5ubmoqIi2YcEkAaxmCoRFwQhoc0TuBwJGY8Ch+IkG/Df+9735ubmdDrdf//3f+/atWvBu0aj8ctf/rLdbr/pppuGhoa+/e1vP/3000l+IgAgLeaj3ev1jo6OSlvEZDK5LiguLpZ3QgBArqHAkWuSDfg//OEPgiD8zd/8zeJ6n+dyufbt2/fss8++/vrrSX4cACCVPvjgA6/X6/P5vF7v+fPnpS2yfft28e7xNputtLRU3gkBAFmADgeWKdmAP3HihCAILpdr6cOam5ufffZZ8WAAQCYbHBz0er1er9fj8Zw+fVraIiaTyW63i91eWVkp74QAgMxEhwOrLdmAF5+ZvMzDlnkwACDFhoeHOzo6gsGg2+3u6uqS9td1eXm51Wp1uVzNzc1VVVWyDwkASA06HMhYyQb8pk2bjh075na777vvviUOc7vdgiCYTKYkPw4AIJeJiYnW1lbxBvLHjh2TFu1lZWXzZ9q3bt0q94wAgGRR40A2STbgb7755mPHjv3kJz+57777LreR3uv1/vjHPxYE4cMf/nCSHwcASMb09LTf7xcva+/p6YnFYhIWKS4utlqtTqfT4XBUV1eruNE0AKQKNQ7kuGQD/uGHH/75z38+MjJyxx13fO1rX9u3b5/BYJh/NxwO//jHP/76178eiUTy8vL279+f5McBAFYqHA53dXUFAgG32+33+2dnZyUsYjQaGxoaXC6XxWIxm81abbLfPgAAAkEOYIWS/QnMZDK98MILH/vYx86fP79///7HHnuspqZG3EX5/vvvd3d3zz9q6Dvf+c7OnTuT/DgAwHLMzs6+++674vb49vb2aDQqYRGDwdDQ0OBwOOx2u9ls1mg0ss8JANmHJgewemQ4hXLXXXe9+uqrDz300JEjR0ZHRz0ej8fjufiAq6+++tvf/vZdd92V/GcBAC4nFov19PQEAoFAINDS0jIxMSFhEY1GU1NTIz6n3WKx6PV62ecEAMWhyQFkCHn2QN55550f+chH/uu//qulpaW3t7e3t1cQhOrq6urqapfL9elPf5rNlgCwSkKhkPuC8fFxCSuI0d7Y2NjU1NTc3FxUVCT7kACQgchyAIojW1drNJrPfOYzn/nMZ+RaEABwOf39/eKj2ltbW0dGRiSsoFKpduzYId6Izmq1Eu0AsgZZDiCLJRXwZ8+e/c1vfiMIwh133LFhwwaZRgIAXMKJEyd8Pp/H4/F6vefOnZO2yLZt28RHvtlstrKyMnknBIBVRZkDQFIBX15e/qUvfWlubq6iouKee+6RayZkgWg0eubMmfHx8bGxMY1Gk5eXZzQaedYUsFJDQ0N+v1/cHh8KhaQtUllZabFYXC7X9ddfv3HjRnknBJDT4jGVIAgqVUKlTmYZyhwAlimpgNdoNHV1dZ2dneJF74Do5MmTvb298Xh8/pWZmZnJycni4uKLnzII4JJGRkba29uDwaDb7e7q6kokEhIWKS8vt1qt4o3oqqurZR8SAARBUMXjQiIhCCoh77IBT5wDgIySvQb+8ccf37t37/e+972/+7u/Ky4ulmUmKNqJEyeOHTu2+PV4PD46OlpWVqbT6VI/FZDhJiYm/H6/+NS3o0ePSov2srIym80mPvVt+/btsg8JAAtcffXVYsCrLjyxIjE7K8RjgiCodHqBnXcAILdkA/6ee+753ve+9+CDD959991PP/10XV2dLGNBoSKRSH9//xIHjI2NVVRUsJceEAQhHA6Lp9kDgUBnZ+fc3JyERfLz8+vr68WnvtXW1qrVSe1iBYCLXfHkeSIaSc0kAABRsgH/P//zPzt27HjkkUe++c1vXnfddbt27dq+fbvJZLrcTulHHnkkyU9EJjt79mwsFlvigFgsFo1GebI0clYkEmlvb/d6vT6fL5lot1gs4r3oamtrNRqN7HMCyAVsbv9/7N1rbF3V2h1CUwAAIABJREFUmTfw/7PW3ucc27kHcoE4qbkpMZDEl/iSklTqMKRqoWhKOlKhoVMhjZqqnaqq4MNoKjFDI3W4VJQX6Je2VANDM+UytKNCI6gYyJTEju04l+IkBUMukAvFIRf7nL33Wut5Pyz7xOQen+Pj2/PTqNrneHvvxbQcn/9eaz2PEEKMOYUG+C984QuDX27cuHHjxo3nOV8C/Ph24sSJC56TJIkEeDGhGGO2b9/uQ3tnZ2cUDWXCKp1OL1261If2xYsXS2gXQlyQ5HMhhBh/Cg3wQVC0TvJiHLiY6cShbe4VYmyx1u7ataujo6Ojo+Ott966mGdbZ9JaL1y40C+Pr62tlSdfQojTSEQXQoiJptD4nSRJUcYhxocwDC94juzRFeMVM+/Zs8e3am9raxtyaF+0aFFDQ0NTU1NtbW15eXnRxymEGEMkogshhBhM5s9FMU2bNu3QoUPnP+diQr4QY0h3d3dra2tLS8uWLVt6enqGcAUiuuaaa/zy+GXLlklHDyEmGknpQgghLpIEeFFMs2bN6u7ujuP4XCeEYSht5MQ4cOTIEV9AfuPGjQcPHhzaRSorK2tqampra1euXDlnzpzijlAIMdpIShdCCFG4QgP84cOHL+a0VCpVXl4uGzjHPb/6d/v27Wfd6K6Umjp1aulHJURRfPzxx21tbZs2bdq0adP+/fuHdpHLL7+8tra2ubn5pptuuuKKK4o7QiHEiJOUPkw4jsAMgNJn73MkhBATRKEB/pJmjcrKyq6++ura2to77rjjtttuk2bg49KMGTOWLl26e/fuvr6+we+nUqkpU6ZI1UMxtvT09GzZsqWlpaWlpeW9994b2kUuv/xyvzy+oaGhsrKyuCMUQpSepHQhhBAjpaRpKpvN7ty5c+fOnf/xH/9RX1//m9/8pqqqqpQDEKUxbdq0hoaGTz755Pjx44cPH1ZKpVIp2fouxoq+vr5t27b5mfauri7n3BAuUlFRsXjxYl9Avrq6Wp5XCjEWSVA/D05iMDiJKZC/70IIUTqFBvhdu3a99957f/d3f5fL5Yjob/7mb66//vp58+YFQbB///6urq4NGzY45xobG++8886PPvro8OHDL7/88gcffNDW1vaFL3xh+/btsq5+XCKi6dOnT58+/bR5eCFGp2w2297e7mfau7q6rLVDuMjkyZPr6+v9ZPu1114rDReEGCskqA+FY4AhrWGFEKK0Cg3wV1555Ve/+tVcLve3f/u3TzzxxLXXXnvaCd3d3d/+9rc3bNjQ0NDw2GOPAbDWPv300/fcc8+ePXueeuqpb33rWwWOQQghhiCKom3btvnQvn37dmPMEC5SVlZWV1fX0NDQ2NhYXV2ttS76OIUQxSJBXQghxFhXaIB/4IEHduzYceONN/73f/93RUXFmSdcddVVL774YnNz8//7f/+vqanpzjvv1Fr/wz/8w+7du3/84x+/9NJLEuCFECVjrd21a5dfHr9169ZcLjeEi2itFy5c6JfH19XVSWMFIUYbCepCCCHGq0ID/IsvvgjgzjvvPGt698rLy++6667t27c/9thjd955p3/z1ltv/fGPf7xt27YCByCEEOfnQ7ufaW9vbx/atg6t9eLFi/1M+9KlSzMZKYMsxKggWX2sypcXkd1GQghxKQoN8L6X0nXXXXf+0xYuXAjgz3/+c/4dv9j+6NGjBQ5ACCHOxMzvvPNOS0vL5s2b29rajh8/PoSLKKUWLlzo97TX19eXl5cXfZxCiIsnWX08YZOAGUSUklpIQghxCQoN8NOmTTt8+HBnZ+dXvvKV85zW2dkJYPCcVU9PD4C5c+cWOAAhhMjbv3//pk2bOjo6WlpaDh8+PLSLVFZW+uXxjY2N06ZNK+4IhRAXQ7K6EEKMFflWOyxVLUui0AC/YsWK559//le/+tW3vvWtK6644qznHDp06Je//CWAz372s/k3X3jhBQDXXHNNgQMQQkxwH330UUdHx6ZNm/7v//7vww8/HNpFKisra2pqamtrV65cOWfOnOKOUAhxHpLVhRBCiItXaID/wQ9+8OKLL+7fv//mm29+8MEHv/jFLw7unMTMr7zyyn333bd3714iuu+++wB8+OGHzzzzzL/9278B+PrXv17gAIQQE9BHH33k97S3trb6jTxDMHfu3MYBEtqFKAHJ6kIIIUSBCg3wTU1NP/7xj++7776urq7bbrutsrJy0aJF8+bNA/DBBx90dXXt27fPn/nv//7vy5cvB7Bq1aqdO3cCWLp06Zo1awocgBBigjh69GhnZ+fWrVs3bdr09ttvD22Z1owZM5YtW9bc3FxbWysrgIQYVhLXhRBiQpEl9KVRaIAHcO+991533XX/8i//snPnzv379585GzZ16tQnn3wyX3/eu/XWW5955hklpUeFEOfW19e3bds23/Wtq6vL5asWX4qKiorFixf7be3V1dX5nVpCiGKRrC6EEEKURhECPIDbb7/9y1/+8vPPP79hw4Z33nnnnXfeyWaz11xzzXXXXVdXV/fNb35z6tSp+ZN/9KMf3XjjjVdddVVRbi2EGGey2Wx7e3tra2tLS8vbb79trR3CRSZNmlRfX9/Q0NDU1HTdddfJs0IhikjiuhBCiDwpYldixQnwAIjoq1/96le/+tULnnn77bcX66ZCiPHBt2r3M+3t7e1xHA/hIplMprq6ura2trm5ub6+PgzDoo9TiAlI4roQQojzyE+TSIAvjaIFeCGEuCT50N7R0bFly5be3t4hXERrvXDhQr88vra2Np2WfsJCFETiuhBCCDGaSYAXQpSOc27Xrl2+gHx7e/uQQ/sNN9zgq8fX1NRkMpmij1OICULiuhgaNgZJDDBkwk2ICS+/hH5otYrEpZIAL4QYdvv37/fL41taWj755JOhXaSystLPtC9fvnzy5MnFHaEQE4QkdlEUBHbOAZLehRDQWvsDCfClIQFeCDEs9u/f39HRsXXr1jfeeOPw4cNDu0g+tDc2Nk6bNq24IxRi3JO4LsYyRpIAACkE8n1ViDFA9sCXhnwgCiGK5uDBg355/ObNm4cc2ufPn9/U1NTQ0NDY2Dhz5szijlCI8U0SuxgpbAycBYo2K88MOAeAlPT+FGJUyxexkxn40pAAL4QoSE9Pz5YtW3z1+HfffXdoF7nsssvq6uqam5s/+9nPXnnllcUdoRDjmCR2MUpQfvKNSFbWCzGh5JfQD631r7hUEuCFEJest7d3+/btflv722+/PbQVUzNmzFi2bFltbW1tbe31119f9EEKMf5IXBcjiOPIl6yjdDFKhzKzc6eKX532I2Zmlpl3IcYE6QNfYkUI8L29vY8//viePXuWLl3a3Ny8ZMmSM9svx3Gcy+WmTJlS+O2EECPi5MmTbW1tfoX8nj17hrZKaurUqcuWLfMF5K+55pqiD1KIcUYSuxgbfEE7a6A0zhbJz8RJDOdAhEzZWX7EDOaLvJQQYmTJDHyJFRrgc7ncihUrtm7dmn+nrKysvr6+qampubm5qalp7ty5ALq7u6urq1etWvXKK68UeEchRMnkcrmtW7d2dHR0dHS0tbUlvp7QJSorK/NP92pra5csWZL/lBdCnEkSuxiTrGUHtobSQ/piyczWwM/jaVkcKsQYI3vgS6zQT8knn3xycHoHkM1mN27cuHHjRv9ywYIFjY2Nhw4dYubXX3+9wNsJIYabtXbXrl1+eXx7e3scx0O4SCaTqa6urq2tbW5urq+vP3NVjhDCk8QuBABYC4CVInnGK8RYIwG+xAoN8C+99BKAdDr92muv1dTUvP7669/4xjd6enrmzp178OBBAHv37t27d68/2c/GCyFGG2vtzp07/fL4rVu35nK5IVwkDMPFixf7AvJLlixJpVJFH6cQ44OEdjFuWctgWAcCaQ0dyDJ4IcY96QNfYoUG+Pfeew/A2rVrb7rpJgC33nrr2rVr161b99JLL82dO/fNN9986KGHtm3bBqCqquqnP/1p4SMWQhTL/v37Nw04fvz4EK6gtV64cGFNTU1dXd1nP/vZSZMmFX2QQowDktjFmMbWkv/PgXm2s3AOSczwVegZDDYG1kIHJF3chRjXZAa+xAr9SP3oo48A1NXV5d/xx2+88ca999571113fe1rX/viF7/45ptvvvDCCzU1NQXeTghRoHxob2lp+eSTT4Z2kcrKyuYBUpxSiDNJYhfjijXMvkbdOZZWMbM1vm58vlg9mClTBmsYkAwvxDiWD/BSxK40Cv08nT179r59+zKZUw1FFixYAGDXrl3+pVLq17/+9YIFC1avXt3V1SWraoUovf3797e0tLS2tm7evPmvf/3r0C5y9dVX++rxy5YtmzZtWnFHKMQ4IKFdTFzGAAyf3getmecoR+kMrIG+2Or0QogxR5bQl1ihAb6ysnLfvn27d+/OvzNnzhwA3d3d+XemT59+9913P/HEE+vXr7/77rsLvKMQ4mL89a9/bW9v37Rp01tvvXXgwIGhXeTyyy/3hehWrlzp/9UWQuRJYhcCgG/pDgLHEaUzFIR+FT0pzex8hmdrZRJeiPFKltCXWKEfpo2NjX/605+eeuqpe++918+uz549OwzDrq6uwaetWrXqiSeeePnllyXACzF8Pv74Yz/T3tLSki8eealmz57d1NTkJ9ul8KQQp5HQLsRpeNBXdiIFpfpbuAcBkjh/0jl+mcHsZ++FEGOULKEvsUID/He+851HH3303Xffveeeex599NGZM2dqrauqqvbs2XPo0KH8lN3VV18NoLW1tdDxCiE+7dixY1u2bPEF5N95552hXWTmzJk+sTc0NPhdMEIITxK7EOdHIB704uzOmtCZ2SQcR0Tnro0nhBj1ZAl9iRUa4Kuqqr7//e8/8sgjzzzzzKZNm3x+qK+v37Nnz3PPPffd737XnxZFEYBDhw4VeDshBIBsNtvZ2elr0XV1dQ3t47KiomLx4sW+EF11dTXJ7kQhBkhoFxOaMcwOAIUXVbdocDb3dez6jwfPzCv5EyPEuCVL6EusCPuRHn744XQ6/eCDD+ZXTaxevfrZZ5994IEHbr75Zv816NVXXwUgha+EGLJcLvf22293dHRs2rSpra0tSZIhXKSsrGzp0qU+tC9atEidpyGQEBOMhHYhPHa2v4y8fxnHSOLzLHInpZioP8g7xybxh8QMIkpnADhminIASAeQzfBCjC/5SSAJ8KVRnM/QdevWfe9739uwYYN/efvtt99www07d+5cvnz5bbfdZox58cUXAVRXVxfldkJMEEmSbN++ffPmzS0tLdu3b4/j+MK/c4ZMJrN06VK/Qv7GG2/ML3MSQkhoF+IiMDsHBs4zi64UnKVUGgCc68//WlMqBWtBREEA+WYvxDglS+hLrGgPQWfNmrVmzRp/rJRav379ihUrjh49+vTTT+fPWbt2bbFuJ8R4Za3dtWvXpk2bOjo62traTp48OYSLaK0XLlzoZ9pra2vT6XTRxynEWCSJXYjhQDpgduysL0FPSoOIUmlYw85RmBq8tF4IMc7IEvoSG65VTNdff317e/t99933u9/9Lo7jefPm3X///Xfccccw3U6IsW7//v2bBhw/fnwIV/Chvaampq6ubvny5ZMnTy76IIUYiyS0C1ECFKbIxHAOREzar6glrRGEIz00IcTwkgBfYsO4Damqquq5556z1vb29k6ZMmX4biTEGPXuu+/66vFbtmz55JNPhnAFpdS1117rl8fX19dLaBfCk9AuROmRDqADEEFrUprSGVgDaSslxHiXX0IvbeRKowgBfs+ePQ8//PD27dsnTZp04403NjQ0NDQ0+L5xALTWkt6FyDty5MjWrVs3bdr05ptvDrkvQ2VlZXNzc01NTVNT0+zZs4s7QiHGKAntQpSacwDDGFj7qR3ypFCUzia+nB4zQql7J8ToJTPwJVboB2JnZ+fnPve5/IrfP/7xj/5gxowZy5Yt82F+2bJlkjHERPbXv/61vb1906ZNb7311oEDB4Z2kcsvv7y2tra5uXnFihVz584t7giFGKMktAsxItg5TmKwAwClYS0cYBIoPTi6c5JwHDGDysuGchvn2Foohjt3EXwhxEiTAF9ihQb4H/7whz69V1RUVFdX792798iRIwB6eno2bNiQr0s/f/58H+bvvffeAu8oxJjQ09PTMmDv3r1Du8js2bMbB1xxxRXFHaEQY5fkdiFGEDsHk/hS8xxHAGAspdP+/dO6x7NJwMyxolDKqQoxPuWX0BtjRnYkE0ShAb6lpQVATU3NH/7wh1mzZgHYt29fa2vrli1btmzZ0t7e7uP9vn379u3b9/zzz0uAF+PY8ePHt2zZ4kP7O++8wzyUGYMZM2Y0NDT40P6Zz3ym2GMUYqyS0C7EaNGfySNgcLv4CACY2ZrTMrwQYnwLgv5EKXvgS6PQAH/ixAkA69at8+kdwPz58+fPn7969WoAzLx79+58nu/s7CzwdkKMNtlstrOz03d92759+9AePZaXly9ZssR3fVu0aFF+JZIQE5yEdiFGHXbAwB8pIlKagpAZYMdRjsoCOMfOkfwhE2LCkCJ2JVZogJ8zZ877778/b968s/6UiBYuXLhw4cK7774bQJIkBd5OiNEgl8t1dna2tLS0trZu3759aJ9W5eXldXV1fqZ94cKF+c8+ISY4Ce1CjGrOQSs/305KQ2soRTpgZwBwHFE60783/oIYMAmSmFUALYFfiLFKltCXWKEB/qabbnr//fd37Nhx4403XvDkMJReoGKsstbu2rXL92nv6OiIomgIF/Gt2v1Me11dXSoliwyF6Ce5XYixYfDmsMGl5gcfX/QGMvbT9TRoVl8IMdbIEvoSKzTAr1mz5plnnnn22WfvvPPOogxIiNHDh/aOjo6Ojo633nrLbxi5VINDe21tbTotVXyE6CehXYix51Pt4U4ldRpc96UYLeSEEGNFPsDLDHxpFBrgb7nllltuueX3v//9U0899c1vfrMoYxJiBDnn/vKXv/hCdG1tbUMO7YsWLfLL4+vq6srKhtQ+R4jxSEK7EGOSSRhEIGgNgFJpZLNsLTHgLKxhZ/vf90vrhRATRj7Ay3bp0ig0wP/kJz9Zs2ZNa2vrPffc093dff/998tWXjEWdXd3+9C+ZcuWo0ePDuEKRHTttdc2NjY2NTXV19dPnjy56IMUYuyS3C7EmMbMIGZnKQxJKQYoleY4x9bAaFgL6k/vIPWp5fRFubtzyGWJnQPpVLro1xdCFEJm4Eus0AD/gx/8IH/8ox/96Be/+MXXv/7122+/vaampry8vMCLCzGsjhw5snXr1k2bNm3cuPHgwYNDu0hlZWVzc3NNTU1TU9Ps2bOLO0IhxjQJ7UKMT0GIJMbAfHv/AYHjHEAUFPrd8lOYkcsyO+SyrBSIOI5AisJQYrwQo4QE+BIr9EO2pqZm586d+fUSBw8efOihhx566CGtdXV1dV1dXX19fX19/ZIlSzKZTMGjFaJQhw8fbhnw4YcfDu0i8+bNy7dqzzdQFEJ4ktuFGOeIKEyBsrAWRJTOwFgAxc/VzGwStgY2ZGdhEkpnAIAdxzGlUpLhhRgN8nXKZQl9aRQa4H057m3btrUNePvtt6211todO3bs2LHjV7/6FYAgCK6//vr6+vqf//znRRi1EJeip6entbV18+bNra2t77///tAuMmvWLJ/YGxoaztU3UYgJS0K7EBMFM5sEcQRmELk4VuwARelM0WfFOUnA7KfcOY4oCDmOOMpROgMwJwlJMxchRoF8gJcZ+NIowjKndDrd0NDQ0NDgX/b19XV2dubz/O7du51zxpht27Zt27ZNArwojb6+vm3btvmub11dXc5dXE/aT5s+ffqSJUtqa2ubm5urq6tJnvQL8WmS24WYWJg5iU/9NSRSWkNpxAmQhc/0xVpCz+5UlXsiBAGCgEAA+jM8O3/D4txOCDFUMgNfYkXdpwQAKC8vX758+fLly/3LkydPdnR05PN80W8nRF5fX197e3tLS0tra2tXV9fQelFOnjy5vr6+qampoaHhuuuuk9AuxGkktAsxYbExFIYAOI44jgCGMXCWmNhadkaFKQBgBl90L/hzcc7fCL6svdKkNIgoCNiY/gzvHKR2shAjTQJ8iRU/wJ9m0qRJK1euXLly5XDfSExMURR1dnb60L59+/ahLd0pKyurq6vzK+QXLVoknRSEOJPkdiEmuoFYzlEOAPm/lcwMcBxRJgPnwAxn2TooIn2BL5l8/pA/6Ic86GE6f+rBesGPCYQQBUsNbGaJ43hkRzJBFCHA9/b2Pv7443v27Fm6dGlzc/OSJUvyj2Hy4jjO5XJTpkwp/HZCWGt37drll8f7KgxDuIjWeuHChc3Nzc3NzXV1dSnZRyfE2UhuF0J47Cz5veipNJSGDgAeWMQenzpHa7YGxrHSZ1/fbi1bw3EEYzmOOJUmEM5ceD/oV2lQ1h98LOvnhRgNZAa+xAoN8LlcbsWKFVu3bs2/U1ZW5lcgNzc3NzU1zZ07F0B3d3d1dfWqVateeeWVAu8oJiZrbVdXl68e397ens1mh3CRIAgWL17sC8gvXbo0nU4XfZxCjAMS2oUQg3GScByxszTQg5204oEv6kTkJ+GhCHyWRM3MlF9UnySs6PQfW4OEKPz0k3Sl4LvNZ/vYWXaWnAYAawH0l6MnVdx/UiHEEOSnwYY2qSYuVaEB/sknnxyc3gFks9mNGzdu3LjRv1ywYEFjY+OhQ4eY+fXXXy/wdmJCYeY9e/b4AvJtbW0nTpwYwkW01osWLfKhva6urry8vOjjFGJ8kNwuhDiPwbPd7E7Ngn96Rvwsa9qJmZOYGexYhSFUwFGufwY+iTmOqCyAczAGg1fDkTo1C88MYxgEImbuT+9KyQy8EKNBEARaa2utBPjSKDTAv/TSSwDS6fRrr71WU1Pz+uuvf+Mb3+jp6Zk7d+7BgwcB7N27d+/evf5kPxsvxPnt37/fr41vaWk5fPjw0C5SWVnpl8c3NTVNnTq1uCMUYjyR3C6EuBgMEECpNACyltkBADtisJ8qNzGfq/Srcz6iMzvO9fWXo9PaR3SOIyorZ2sIn96DGQTw0/6pNOd6/XsDc+9EwekbNoUQIyWdTvf19UmAL41CA/x7770HYO3atTfddBOAW2+9de3atevWrXvppZfmzp375ptvPvTQQ9u2bQNQVVX105/+tPARi3HpwIEDra2tfoX8kSNHhnaRz3zmMw0NDb6A/IwZM4o7QiHGEwntQohLRYP2tDOYk6S/Cv3A9nUiInX2KrDsLEc555xWGTCTIgpTMJbC0C+V72fd4AhPSrEO+tfMp9IUDPSZV+rUsRBiFCgrK+vr68vlciM9kAmh0AD/0UcfAairq8u/44/feOONe++996677vra1772xS9+8c0333zhhRdqamoKvJ0YT44cOeITe2tr64EDB4Z2kSuuuKJxwOzZs4s7QiHGGcntQoiLx86StUhiuIG2rFrDz4GbBAPFaCiV9lvfWevzRGq/tp7jCEEArWEt2LExCCwR+9p4zKfvoSelEKYQhkgCKO1fkpKt70KMLplMBsDQalSJS1VogJ89e/a+ffv8f2feggULAOzatcu/VEr9+te/XrBgwerVq7u6uqTW9wR39OjRzs7OrVu3btq06e23375AC5lzmDlzZn19fXNzc21t7TXXXFP0QQoxzkhuF0IMBYOdY+fy3dpIaQpT/XXsUmmA4afc/YJ2d9GdXPN//NkRMw/UvjvnnLpS0JqCAESS3oUYhSTAl1KhAb6ysnLfvn27d+/OvzNnzhwA3d3d+XemT59+9913P/HEE+vXr7/77rsLvKMYc06cONHW1rZ58+aWlpa//OUvQwvt06dPX7ZsmZ9pv+qqq4o+SCHGH8ntQojiU4rCFJihImKwZtIBAkApuLOdzwx2gCPo/q3yzrK1/UXpwXAWWvmt9VJVXogxqqKiAoCvYydtnoZboQG+sbHxT3/601NPPXXvvff62fXZs2eHYdjV1TX4tFWrVj3xxBMvv/yyBPgJpbu7++///u+7urqstRc++wyTJ0+ur6/3of3aa69V8tBdjA5JkmSzWWMMM2utU6lUWVnZuSo3lZ7kdiHE8CKC/8gjBhG0hjnfX3lOEoZjDYBgDdjBEicxGcNJwkGa9KnLlmL8Qohi8wEeQG9vrwT44VZogP/Od77z6KOPvvvuu/fcc8+jjz46c+ZMrXVVVdWePXsOHTrkZ+MBXH311QBaW1sLHa8YU6644oq//OUvl5TeM5lMTU1NbW1tbW3tsmXLgqDQ/4kKUUTMfPz48cErxJIkyeVyvb29U6dOHdktQpLbhRBFwwyTwCSwlp097UdsEkQ5l8v6Lm90kX+piUCarUUQsLUY1H+uv7K9DiTACzFG5QP8yZMnpZL0cCs0HVVVVX3/+99/5JFHnnnmmU2bNr3zzjsA6uvr9+zZ89xzz333u9/1p/mmAocOHSrwdmJsyWQyS5YsueCDm1QqtXTpUl9A/sYbbwxDaQwjRqnT0nuetfbo0aMzZ84s/SMnye1CiCJj5jhyuT6QUjqgwb3dneM4IqL+BfDMALMxZAwu4m83aU1MfiE9nAUpClOUSsE/BdBnq2A/pG13QogSmzRpkj84efLkyI5kIijCd82HH344nU4/+OCD+YnW1atXP/vssw888MDNN9/sv1y++uqrAKZNm1b47cTY0tDQcNYAr7VeuHBhc3NzTU1NQ0ND/rmdEKNWHMfnqc7CzMeOHZs5c2YJRiKhXQgxApxjk5BSnMsijjj2DZ+ZUhl2DtaA+VTeZuYoByIkn65spwPSGgQwAX7NvCIdnNZAnq2BtWwSthbWQWu4s+6wF0KMCpMnT/YHx48fH9mRTATFmSxat27d9773vQ0bNviXt99++w033LBz587ly5ffdtttxpgXX3wRQHV1dVFuJ8aQxsbGxx9/3B8rpaqqqurq6pqbm5cpCPPtAAAgAElEQVQvX57/V12IMeGCtVWTJLHW6rNOIhWD5HYhxAhia6Ed956gMEUm9tXjQeRyfWQZJgYYRHAajhk412p4ZkcMMJPWFISA65/DT2n/YyQxrB3UUo7hmE1CWiOQNXpCjEZTpkzxBydOnBjZkUwERVvtOWvWrDVr1vhjpdT69etXrFhx9OjRp59+On/O2rVri3U7MVYsXrz4hhtuWLp0aWNj47Jly/L/egsx5iRJcsFzjDFFD/CS24UQI4+ZrYHV5JyfPGcGKQXFbCzHMaXTcM5PwrO1xI5J0aV/HnKSsHPwHeN9vXprfXV6tpbUcD0hFUIUIv8N/9ixYyM7kolguLZrXn/99e3t7ffdd9/vfve7OI7nzZt3//3333HHHcN0OzFqhWH4m9/8ZqRHIUQRXEwHRFe8RZ6S24UQowOzSTiX5Wwv5yLABVNDjiI2MSvlK9hxEoPAcZYcI9BIZcAMY3Cu9jHOwloY66KcYhANfB1lB2thDUc5ZsBZspaNQWg5jigI2V50q3khRAnlA7wsoS+BYay3VFVV9dxzz1lre3t7Zd5VCDHWKaUu2FKh8GaHktuFEKMKJ8nA/nPmJKIwYDCcY2vIaWjNUZatIavYOjhLjjiOKAgAPr2CPcDO+Sbw7AayehI7FWhXxtYiiUHksn1gJqWIyDlLpE49PfXb7KVYvRCjjAT4UipCgN+zZ8/DDz+8ffv2SZMm3XjjjQ0NDQ0NDb5vHACttaR3IcQ4kEqlzr+KnoiG3ENBcrsQYhRik/i6dGwTThLogJPYnTwBZziOmJnIwTlihtJQipQGgY1h5yhTBvfphUvMcBYEmMRl+8g4jiMoTUnsek9QmAJAKnC5LJFCEJDWYLA1BHwqw0uAF2KUkQBfSoUG+M7Ozs997nP5/6r++Mc/+oMZM2YsW7bMh/lly5bNnj27wBsJIcTIKi8v7+vrO89C+rKyskudgZfcLk7DzCdPnvTFFCZNmlT4mg4hho4ZzoGI4xggCgJWAdgyGH29vlA8hQEbC9IwCZzjJKagDGzg/Fr3T31g9kfxJIYx/vpsDXJZl4JKBQBBa076YA2TpjDgOOIkJqXZl7gXQoxWUsSulAoN8D/84Q99eq+oqKiurt67d++RI0cA9PT0bNiwIV+Xfv78+T7M33vvvQXeUQghRoRfT3Su6ixBEOSboF6Q5HZxJmvt+++//+GHHxrTv8tXaz1nzpyrrroqCIZxv5sQ5zS4qAcpClIAKEwBmgHO5SgIXRSrTFn/dnelMJDPnbUIUipTduoKfvW7fyZFgA5gHCkNIo4jjjQCx9ZQEECFRGDHROTL0XMuovyzLJl+F2L0yTeE7u3tHdmRTASFfidoaWkBUFNT84c//GHWrFkA9u3b19raumXLli1btrS3t/t4v2/fvn379j3//PMS4IUQY5efYz9x4kQ+YuXfnzx58gUnSyW3i3NJkqSzs/PkyZOD37TWfvDBBx9//HFtbW06nR6psYkJzTmXy7IzcMzOMAJiprIM+gIEAYUhwoAI0IFKlzkAimCtiyOlg4G17gMz5+7U0nfSmowFACJ2DlBsEkqlySpSCnBgIIocrO07Scw0dTp8UzkiCfBCjEL55tCn/SETw6HQAO+XSaxbt86ndwDz58+fP3/+6tWrATDz7t2783m+s7OzwNsJIcTISqfTfjN8kiTMHARBGIYXbB0n0V2c3+7du8/1pSeXy/35z3+ura0t8ZCEgDW+4ByYmR2YyX/UsetP0UqDNJVNQi7iOOYkpjDNbOEn6plJKbb90/hMDBDHEZTvQscEcJJQEMI3hHdMqRBBgDiiVIptQobzK+dVOg2AZDWKEKNSfgZeAnwJFPo5OGfOnPfff3/evHln/SkRLVy4cOHChXfffTcurouyEEKMckSUSqVSqdQFz5TcLi5Gb2/vRx99dJ4Tjh07dvTo0enTp5dsSEL46O4nuzmJ2RgYg8CRLmdjQEqVlaswjSDwC+M5ify56F9mDygFpYH+9UoEYoBSaTYxswMPrM9n+O7ulEqxYwSB3yFPYQpIKAyp/5igA5BUhRBiNCovL/cH2Wx2ZEcyERT6OXjTTTcB2LFjx8WcPOT6zEIIMYYsGjDSAxFjw8cff1yUc4QoHoY1IGKTj9+KrWVjXK4XAJwF2PfeYGfBTGGKwhR00J/eiVTw6W99NLANHoB1bA2cY+d8a/eBzA9yTGAizc53oSOVSpMOkErRhdY6CSFGSllZf8ELCfAlUGiAX7NmDYBnn322GIMRQogxTHK7GJooii54Ti6XK8FIhOg3sO6dtIYiVVZOmQyFIRQBoFSaUmkwSGsQgYlNzHHWmRiOoRRlyikIoQd9yXSOkwTWsjWUSgOAYxflkOTYmvw+eZUph7PsHLMlRSpMqfIKBCGCgLQsnhdi9MpkMv7gYv6iiQIVGuBvueWWW2655fe///1TTz1VlAEJIcTYIrldFOhiesVJPzlRGuwcrHWJgXWcy4EIOmBr2SSkFaUzFKY4joiAMA1SlEqRLyynNOmAgkCVlZNSpFT/cne/Wt4XtFd6YPM8kdZgR+ly8tdPkv7m8GHKJ3xKZyiV9jP2kt6FGOXylVYlwJdAoR+IP/nJT9asWdPa2nrPPfd0d3fff//9FyzmJIQQ44AkdlEs+a2DBZ4jREGY2fdyNwnYskk4idkaClMg5QiKCIpIaQoCuBS5GAN71wdW1INSKQCkVL5WPJuE49iZiHRI2lEQQCWwzmd+jvooSLGxYIYOwA6OfW5ndhzHiAxCzdZKhhdiNMsXBpIAXwKFfhr+4Ac/yB//6Ec/+sUvfvH1r3/99ttvr6mpkW8bQojxR3K7KLqZM2cqpdzgntufRkT5Vi9CDAtmjuP8UnZOYo5yzOySGElCWhODnWPjKBVQphxxBCLSmsIQbJmof3M7EXQAKDgLAOw4idkkYAdrGGB2UCkCwViVTluboyCksIxSKRCpskkqTLFzBLJJDmwdEmUdzv1vhxBiNNBaExEzn9ZnVwyHQgN8TU3Nzp078+XlDx48+NBDDz300ENa6+rq6rq6uvr6+vr6+iVLluS3RgghxFgk0V0Mk1QqtWDBgvfee+9cJ1xxxRXyTFwML9O/EZ2jHMcRMbOznBiwA1nHFsykCKTZJuwsERERAHZMikhpthaOwZYAtg5sOGFOYl/B3kVZ0ppU4ACiKbAxsyMV6EwFBSFU2N/gnRSU7m9BZxMGsUlYOUoSZC7c+EMIMYK01sYYCfAlUGiA7+joiKJo27ZtbQPefvtta621dseOHTt27PjVr34FIAiC66+/vr6+/uc//3kRRi2EEKUiuV2UwGc+85kkSQ4cOHDmj2bPnn3ttdeWfkhiAmFf7x0c5QCQ1kxEKnDRSShNAeAYzGwNWKnyclgLrWAIADnLxnEUMRsmsLEIjIv6yFoKUhRoAOyneRhsDDRx73EidgyVJgAgRWGKMhlSisIQQXCqw5wQYuyQAF8yRdhQlE6nGxoaGhoa/Mu+vr7Ozs58nt+9e7dzzhizbdu2bdu2SYAXQowJkttFiV177bWXX375gQMHjh07FsdxEARTp0698sorZ86cOdJDE+Pd4AXqOoBzUIrCkHQItlBKpTNsDVnDQQj4tu39W9zBzEnsIzcnMeuA0d8rzmV7KUy5vl42FsQghSCEdYDlJKEg5ChidiBFqTL61IAov5hfCDFW+GKr1tqRHsj4V/yKIOXl5cuXL1++fLl/efLkyY6OjnyeL/rthBCiiCS3ixE0bdq0adOmjfQoxITDA4vnAZBS/S+TGFopHbDSUAqOWGtSCs5Ba8rvlnfWH3IS+1BP1rkk5ihHQQjnQIDWoIHzFQPcH9f973L/EgDKt1ogAjM7x86ys8wgN9BAXggxWvkAf556LqJYhr2k56RJk1auXLly5crhvpEQQgyZ5HYhxARGACid4SjHzuWTMvsWbkFAQQhSxJZJQylgIE0zI3Fgx3HkgzrpgLQma8BwUVazc7ksWCHUzACDUoFyDmHIDLaOnWWlEOWUAkBkLbSF1kgSmATWwjooYpuwSUgFCKQWvRCjlK+LIQG+BORzUAgxoUl0F0JMcKTUqdlta9haP/UN55iVClJgS8QwDoGmMIWBb+rsHNiwcWAHpUlpAExgAieRy+U4irm3j1IBKA12MAbBJMesiChMIcqBQdbBWU5i0sple3UYchT5Vf2cxPBF7ID+Tfh09n8EIcSIkwBfMhLghRATkeR2IYToRwSl4ayfhCd2bCwpDWY4hyRmGDYgWCA89SvAp5a1+3eIwA7GchwhjmAtMSOXBVvoEGy59xilMwwwKR2EpBQFKQpTFIYASGtw/2p5jk91k+Yk5lhTkIK1YM43mRdCjB6yhL5kJMALISYQye1CCHEWQYDYAqB0Bo7hjpEClWUIYGYGwVko+Ol3kBoU1/OX6O8Dz85yksAkBDgTEzMrRdmYKEYmA0dsra6YwjZhVmwMBSFApDQFAXTQXw8/juDzvA6gFJHyb1I6w9aSLKQXYvSRGfiSkU9AIcSEINFdCCHOhYg4lSZj2FkGUybjclkCVCqDIMWcMAyp/ohOSsEYjrIIMiDXX5HeORgLZ2ETNgZBAGsVBXCGSLmAiB0YCELSAZxjFQxauA/2V+mfficAUApKg4hIsdanTpV4IMSoJFXoS0YCvBBiPJPcLoQQF4OIEIbEAZKYdKhSKZsoVooGTbOT1hSEDGZr2VpSjkJiCwpTbBNmy8YATMRsDFIpX88eDBUGzEzMDE2KAFB/8Xv0N41zlg2D4NfSAwCpT43tFClHL8RoJAG+ZCTACyHGIcntQggxFESkNWvNzESkVIAwhGFokAaUOm3/OSnNzgGgMAXya+yZMuyyfWQNZSqYsmCAmJwFK1IgImYGEScJm4STGDoAMekMrGPnfDG8wTPt7BygBwaoIIQYfbTWAIwxIz2Q8U8C/Jj06KOPRlEEYOXKlc3NzSM9HCFGC8ntQghRXMyOrWFrwAwVEAFB+KkztCZDzACxb+EOgMIQSgGKiXxlO9IBnG/wDtLgJCH4+nn9DwU4jqmsHNbCB3h2bBJ2jgGyBhxSEIIZSirYCTEa+QCfJMlID2T8kwA/Jv3rv/7rJ598AmDdunUS4IWARHchhBgOxoBo8Kp1ZyyyfRxl2br+mvTsGAwi+D7tNmaGypRztg82ATM5x3BwQCoDRaQVEgsCBZo4IB2QPvV1lBSBmVJp5HrBTNawJcQRpzSbFJRiY4jBWpPUohdiNAmCAIBzzlqrB9etEMVWhADf29v7+OOP79mzZ+nSpc3NzUuWLAnD8LRz4jjO5XJTpkwp/HZCCJEnuV0IIYaLtawJ7Fwuy4lVikCKQUpNgrXE7rSTQQxrKZ2hbAKbgBRcRFEEpYkYWsFZx0opBV/QLr8Y3s/Dg+EYpH1JO0qlOcpyHFGY6p+iT2KVKQPA1hA7BKH0kxNi9EilUv4gm81OmjRpZAczvhUa4HO53IoVK7Zu3Zp/p6ysrL6+vqmpqbm5uampae7cuQC6u7urq6tXrVr1yiuvFHhHIYSQ3C6EEMOLmZ2FDlyU7Y/rKmTrAHa5PmJma2ANWLF1cNZZR5o4ieES30vKB2yOIgQBiKE1q5TSBKVhnb9F/m6+BD2bmE1IqQx8oftcRFoD4CjiUBMrhyz1nqSKSewcrG9BJ4QYFSTAl0yhAf7JJ58cnN4BZLPZjRs3bty40b9csGBBY2PjoUOHmPn1118v8HZCiAlOorsQQpQAD0rXIIIOSGuoAOwY4CjmOLaUBTkwIU5gHbMl0s4pBlM6gzgCE4chghQUACYdINCkBibe82XqmNm5U7PpisAMIgo02CGxCDJwlm1CqYCdRV8vlVfA2tN34wshRk46nfYHvb29l19++cgOZnwrNMC/9NJLANLp9GuvvVZTU/P6669/4xvf6OnpmTt37sGDBwHs3bt37969/mQ/Gy+EEJdKcrsQQpSSL0bHcQRSBHL5PE8KzMzON353uT6bi8gZSpXDsouyFIRMioJApcugAxcbSoUgBgM6REAAKExxHLFzbAybBDqgVNjf/p0UQLAW1rooh8QgCCkVQgdQGo6hNFvjwz5bS7LVVojRoayszB+cOHFiZEcy7hXaiuO9994DsHbt2ptuuqmiouLWW29du3YtgJdeemnfvn3PPPPMkiVL/JlVVVWPPfZYgbcTQkw0ixYtkvQuhBAl1p/X2TEbZ32VeQBgGkjvgE0iTmJyDs5x3Me5PjBzFHE2crkcW8PMpBWUhtID+9XJHwz0e+eBqvUp8utvtYa1AJwxcA4EBAEHKQpDP3Xv/5OzffDN5IUQo0N5ebk/OH78+MiOZNwrNMB/9NFHAOrq6vLv+OM33nijsrLyrrvu6ujoWLVqVVlZ2QsvvHDbbbcVeDshxASxaMBID0QIISYcthbsOI6IFBsLZ0/tV7cOzlIQIknIWeig/7tklMAkLtvrohxneznb63pPcC7HUcTWDr446YBIA6AwpKD//8DMzKSD/i7xAJuEo4jjBFHsHx+wL2UX5TCwwl/iuxCjR0VFhT/wrbLE8Ck0wM+ePRtAJpPJv7NgwQIAu3bt6r+BUr/+9a+DIFi9enUcxwXeTggxvkluF0KIkccOjmGtsxbWsHUuzrlczuWysMbnZmZLSoMdSCmtQQTrOOpDLou+XsSRy0acREgijiMwg7l/wbxjBJqCEKRAYGa2lp0jpdgaTmKyBmAiYgZUAF8wzzli35TeAlDpNACiQr/HCiGKZfLkyf6gp6dnZEcy7hX6wVdZWQlg9+7d+XfmzJkDoLu7O//O9OnT77777u7u7vXr1xd4OyHEeCW5XQghRhXSmqyFUnAWjuEsnOMkGWj/pvwBE4EIxOwMRQlbAxfDGDIx57JwDknsTp5wuT7OZtlYZsfMYIYm3wSetM5vZWdjbLbPnTzGzoEBdn6ZPduEkxyM6V9pT2qg+ZwQYlTI9wv3C7TF8Cn0g6+xsRHAU089lZ9dnz17dhiGXV1dg09btWoVgJdffrnA2wkhxhmZchdCiNGImZ07MyFTKgV2BCbfud2f6ywcU5SDc+QAViDfFi5Bro/YwRoyFtbCRC7KcRK5OGZrOYnZGrhBa+zZkbOcGDgmrcHc32MuSdgxAEqlKZ0BkfSQE2JUmTp1qj84cuTIyI5k3Cs0wH/nO99RSr377rv33HPPxx9/DEBrXVVVdfjw4UOHDuVPu/rqqwG0trYWeDshxLghuV0IIUYtthbMUArMIE2kQCCl2DkKQwY5MPyCdnZsnMv1wlp2lslxoKEUKyIdsGPuPUnZXu49TrleJAmbhE3Uv5yez9jGbi33b7lnBCE7sLOwjsIUhSlVMQkA6RCplEy/CzGqTJs2zR9IgB9uhX72VVVVff/73wfwzDPP+Nl4APX19QCee+65/GlRFAEYHOmFEBOTTLkLIcRoxwzr2DkX5TiJAccaqqwMgSZFfhc66RAA2JExHEcKDGZylkAqCMAAiNkhjmBi9hvnraFsFlFk+7JsEo4Tl8u6JGZjeKAnPDsH6wCQ0gRQqClMUSZN6YxKZ6C1ypRRWRkp6R4nxOgyffp0f+BbiYvhU2gfeAAPP/xwOp1+8MEH7UCV0dWrVz/77LMPPPDAzTff7L+mv/rqqxj0YKY0/uu//us///M/z/qjn/3sZ1deeWUpByPEBCeJXQghxgZmTmK2CZz1jdoIDONcYqA0W0uZsH/2OwhVOnAnT/gG7iCCY1aKggDWwjmKIscga0kz4pgpADkyMRFc30mVLqM4QZDlMEXWMDEGF6VTmoj8Sn5iJhVQOkOZDFLpkfl/ixDivGbMmOEPPvzww5EdybhXhAAPYN26dd/73vc2bNjgX95+++033HDDzp07ly9ffttttxljXnzxRQDV1dVFud1Fkgl/IUYDie5CCDGGcJL0z4ebhMkBDMUgDWvZ+Q3pIKUQaN+SHZlyMJBErDWRBpFfVw92ACgx0KGzlgmkFFRAziEIlIPryypmJDHHMSoq4JhdQsGpr6as+svjgQiKoPPN5IUQo046nZ40adLJkyc/+OCDkR7LOFecAA9g1qxZa9as8cdKqfXr169YseLo0aNPP/10/py1a9cW63YXwwf4Rx55xJfKHyydlse3Qgwvye1CCDHm9G99t45zfTAGgSJfqS6X4yBkthQGYMfWwTFSGkrBRgBxmCEFJAnZxCUJKY1sH8dZZyONFGnlrCWCdVDGgh07q4j6nwcwOIkpnQGYnTt7RlcaSsOXtZMYL8SoNHPmzJMnT/b09GSz2bKyspEezrg1XPU/rr/++vb29tWrV6dSKQDz5s37+c9/fscddwzT7c7KB/jKysrMGUg++sWYksvlPvnkk48//rinp+fYsWO+qMSoJVvchRBijCLn2MSu7zjnspzEzA7OsjFwluMcrOEkBimwIyIwSCvokIg4FUIrl8pwGCqlHBsCE4OccwAz/LEyEUyCOCFrHTOCADogTZzELnuSTUJ+8t8XsftUhTuGs/DPF4QQo9LMmTMBMLNMwg+rS5uBv/LKK5cNqK+vz291OKuqqqrnnnvOWtvb25tvDFgycRz39PRMmzYtk8mU+NZCFJG19ujRo8aYwW9ms9l0Oj116lQ1mmrwSmgXQoixjdlme+3Hf3VHj6IvIk1ghzgiYo4NBQoKCMvBzsWxbxQH48BMRESEIA1jKVBwICZWFoF2RCpJAIa1TIY0wSjAOSalNJuYkIHS+ZLyDCYGO+PiyMVZWAMiNn5NvkR3IUa1yy67zB8cOHDgmmuuGdnBjGOXFuA//PDD3/72t7/97W/9y6uvvjqf52traysqKs78Fa116dM7gMOHDzPznDlzSn9rIYrFWtvT05MvDzlYFEVHjx71TzpHnER3IYQY85g520fZXrAFABuxZWIGwIFik8AEVFYGdjbqI1i2lgJitoiy/kwOiVJpVszKUMLEFgxSAbNjsIpjOGfDIAjTnIv8SkhWgDVwji1IEwB2jo0BO1gLa9la0gGc48Swikm2QAoxiuW/l8oM/LC6tAD/3e9+d8uWLZ2dnblcDsC777777rvvrl+/HoBSqrq62s/ML1u2bMmSJX7x/Ejx6+enTZv2wgsv/O///u/hw4cvu+yyqqqqO+6446qrrjrrr5w4caKrq6u0wxTifE6ePHnW9O4lSdLX11deXl7KIQ0muV0IIcYNTiIXR+yc6+tF70kYAziHgOBgQXHCyoE1ZcopDNgaX3MOluAcGws/XU8BwGSBQLMKgARaqyAgx44IcGSdT/7IxqwVKepvJg/HrMAJpRQRyVy7EGNRPsAfOHBgZEcyvl1agH/ssccAGGN27NixZcCf//xnY4xzbufOnTt37nzqqacApFKpxYsX5+fnFy1apHVJO3b6AL958+bNmzcHQTBlypQDBw4cOHDgrbfe+uY3v/nlL3/5zF955513vv3tb5/5/uTJk4d9uEKcgZn9k7LzyGazIxLgJboLIcR4wtayMbCOk4RNwkQ2pXWiFcHGBtaQNSoI2DpOYkSKmBFlYQ2nNIwBA0ywziHWKQ1FbAyBmUBKswrgLLEFBUilqXwyTMSxgXGsFeVyTvVSJk06DTBbR4P3vjP3r5x3DOcw0C5eCDEK5QO8dJIbVkOpQh8EQU1NTU1NzT/+4z8CyGazW7du9WG+ra1tz549zBzHcVtbW1tb289+9jMAFRUVtbW1y5Yte+SRR4r8T3AOPsBPmjTpn/7pn+rr64Mg6O3tXb9+/W9/+9tf/vKX1dXVsjFDjHLWWr7QHMRpe+OHm+R2IYQYn5yFcxxnYS2URkWFNjHZhBUIjm0Cm3AUAsfhKtjGiGOKI6TKwClnYrIgDgiKwNDgbI41AYAO2ToVaHLKJQGRYiKYnMtliZkTQxoUxi7OUhgAYGtJM5sYWrO1bA2cRRxxLuf6NCnNtqR/9YQQlyRfH+3gwYMjO5LxrQht5MrKypYvX758+XL/8tixY+3t7fn5+X379gHo7e3duHHjxo0bSxbgb7755rq6uvnz5+erKVRUVNxzzz1Hjx598803f/Ob3/zzP//zab9y2WWXfeUrXznzUq+++uqwD1eIM7iLmGdgZmYuQVcFie5CCDG+UX+Hd1KplM32QQcIGEpxHMNEZB0FkeOUYiYmxJHryyrHgCNjCQRYhiYm6BSIyF9PKdaKicAJFJFj1op1ChwxFMhAhay0CjOkFJsEIITOxQkFDs7RwGNsgnMmoThWVmbghRi9pk+f7g9kBn5YFa0PfN7UqVM///nPf/7zn/cvjxw5kg/zbW1tRb/duVRVVVVVVZ35/qpVq958883u7u4zf1RZWXlmqgfw2muvFX98QlzIxVSYV0oNa3qX3C6EEBMFgUj1B2alLEODEcdIEhjjooRIKWTZGgo0mAjOxomCIQNkyjiKmENSGjETM1NAKmAbERGxBRQxsbPQitkyOWUSYnbGkDUwCVtNoWYLRDkYCyK2BnHExpBmxBE4Zh24vrSW/fFCjFa+QZJzzi+FFsOk0AD/P//zP/X19XPnzj3XCbNmzfrSl770pS99qcAbFcvs2bMB9PT8f/buPciysjob+POsd5/T3XMHRAa5RS6DAuEiA2PiLUoiJJYaoxZgqRGt0hhTpSbGSiw1ZcWKibEskkrUxASLiqLRAiOgRBEICdHgICgh3JGMIDDIzWF6us/e71rP98fu6fChIDPdMz09s35/ULtPn9N7ears6ee877vWQztn3TKl7dY0TSnlSZrYARgMBjvo7hndU0ppD0KCBpLDIUqBuynCHbUzKFwyWCnRFNDAwtE0WCyEfkU8HEKYFSsAJVcEBVihEIgiSBJC09MshTBGxWBcpDVFFAW4QwVRQUlC1yIEQDQQFNS2ElQ77ty2Simlp8jMVq5c+fDDD2eA36HmGuD7bnDPeMYz+v7za9euPfHEE/fdd9/5qG37bdmy5bLLLjOz3/iN33hcSo2GsicAACAASURBVH/ggQcAHHzwwZne065v6dKlmzZtevInzO8dM7enlNIeiFZUii1Z6ps3IQIgaseuY0S4UApBWWNNo0CECgLRgQXV4K0YGkyQVDhpEBAVMHiVwgZDyDVorA3R6FURsK0hvAzBIu8QBQSswKu6Du6hoAJREQqqRADIDvUp7cpWrVr18MMPT05OTk5Ozvufqak3P1voHzcf/pBDDlm71XOe85zZfgY7zcTExBe/+MWf/OQnq1evPvHEEx/7rSuuuALAkUceuZNLSmk7LFmypG3bJ+pFv2zZsnkc1pjRPaWU9lxmLEXNwJYs43AMzQDDMdQOY2PqKtopACwFnWNQ0BS1AVAKtSOGjIVFESKJpmHXobaAEWBIEkuDdhQS21ZjQxjQDIRiRaodrYkAOcJYIw94hYdqZxHhzhC6DgGNubpOEbkCk9Iua6+99rrzzjsB3HfffYcddthCl7N7mmuAv+WWW6655pr+fPt11103OTkJYMOGDRs2bDj//PP75xx22GGzef5XfuVX5njHp4Lky172svPOO+/ss89+z3vec9xxxwGYnp4+//zzv/GNbyxfvvzMM8/cCWWkNHerVq3avHnz5OTkYzvSm9ny5csnJibm/vMzt6eUUgKAZgCJpC1Z5o9Oqmk4GIOCZgGgFCJAoLo1Tisi2AxRqyJExGhkwwYRqBVb58Cp6wACdCNLwzGqa1WMKoBz2IRXA9BOcTgGicViyyS7FhFQoJ1G16IMUFsLIgRItSMEZIpPaVe0cuXK/uL+++/PAL+DzDXAr1mzZs2aNa973esAuPtNN93Uh/lrrrnm2muv7Wdc3XHHHXfcccc///M/A/i5Y7Hmy2tf+9qpqakvf/nLH/jAB/qo8+Mf/1jS8uXLf//3f3/VqlU7p4yU5m7ZsmVLly4djUb9efimaYbD4dzPgGR0Tyml9FgcDNEExydsfBilRDM0OIZjiBajViY2BVXoHCIJ1Q4KawYUCEGCwlXNDDaUgHB4MIIyGVBDg0FhESEjJY6NgybvaBQN4VAonJ3TO0kAFcHaoSmYnkI4JIRgGeBT2hXNhqyNGzcubCW7sfnsQl9KOeaYY4455pizzjoLwMaNG88999yPfvSjDz744IoVK1avXv0ze7/vIKWUs84665hjjrn44otvv/32TZs2HXHEEWvWrDn99NNnPxlKabEgOT4+Pi8/KnN7SimlJ0Rjadg0LA3HhM4wcEzDKHQSKkrDQSMGVWDGMHnAAySnt2gwLBxGiIOG1Z2G6CDBHBWwArnMMDZguADVTu4moAaGgiqKcTRCaRABCBDR53jBGNNbYKYIPoVBLSmlnW92klwG+B1n/sfIzdpvv/3e+973nn766a9+9auvu+66j33sY6eddtqOu93PdNJJJ5100kk7+aYp7ZoyuqeUUnpKFPLKrqIUeYAN2GFmyJw4GKAZCFsgEAa4yoBNgwi0rcaGVhqZycRmDFu2aGqK42P94jkJSYgOzUAKiARk5GBowwkZIQ93lIalobcCZtrd9Yk9xKaBchp8Sruo2RX4e++9d2Er2Y3twADfO+SQQy688MKjjz76rLPOuuOOO3L1O6WdLHN7SimlbeCuWlUrvdIKmyJKZhwObHx5INCMoWtVioVULKwYCUBGWKEi3GUoZj6aNh8BY5ieVuf9PvlChFfUMLOgwYylkdFKAUK1JaW++53ZTNd5WigYATJPv6e0K5ttXp6T5HacnbEB6RnPeMZ73/veBx988BOf+MROuF1KqffsZz8703tKKaVtoq6NqS2c3gJv4RW1C0BESGHE2BINDWNjVsZQCksxKyyNzDAYIqQOcKd36qaLVwjyDlFJwKtqJ68EaSYITYGZAJYhBwMJgFCr2pHaTl2VV0RFN4XRJNoRawcJzP3zKe2iZgP8Pffcs7CV7MZ20m/A5z//+QAuvPDCnXO7lPZwGd1TSiltDwm1g9eZbepeARhgMBsMSJiRZUgrGAwABhC0MCPNo6JzdVtQR+g6ucMdXlm78IAEFirgrnCUgjIEDSQ47I+7QyHv1I7YjqBqtUPXarQFk1PsAl03U2EegE9pV/W0pz2tv7jrrrsWtpLd2Fy30P/RH/3RunXr1q1b94xnPONJntb337rtttvmeLuU0pPI0J5SSmm7SULXSpKHaocINJJXhMOAYigNpBCpgEQFQ4jOvKiYGaiKaNS1sAnAQaIGjIhKEQjAfGqKpYRV2RCDAQGAQMhd3rFrYUXtNLzCHW1nxcQOrWPpctWOQAb4lHZZExMTS5cunZyc/OEPf7jQtey25hrg/+Iv/qK/OOCAA9atW3fyySevW7fuxBNPXL58+WOf9s1vfhPAo48+OsfbpZR+pozuKaWU5iocEepajqboAXfBGaFwuINUrRwaqyuqRUiUxBoqghuFqC2aAcxofcAPGaGAwmXFK8BmGmqMzXg0o2ibKGbWAOqn0HtU6zp1Hcsg4DIKkMDaatSydt62ZelCv1EppSe27777Tk5OPvTQQ48++ujjImGaF3MN8Pvuu++Pf/xjAD/60Y8uuOCCCy64AICZHXXUUSeffPLJJ598+OGHX3nllR/5yEcA7LfffnOvOKU0K3N7SimleROh2qkdAejHrRsQACMQQe/EIQBUJwyo8A7dCDWogsEwBJJeR8KggIBQCgKA5CoQAogaILrKUrl8ObxaCE2BBpIDtNYF0AxGG5+IcChojQR6hRm9IiIX4VPaZe23337/+7//C+DOO+889thjF7qc3dBcA/z999//gx/84Oqrr7766qu/853vXHfdddPT0xFxww033HDDDeecc85jn7zzx8iltLvK6J5SSml+qWsRoQi10woRQDjkqpUAIiBXCCY0jUZTFGAzPerBAtSImVPtItEG3dG2KKARkkKqtTSNA4jQls02rNEMoM6qq21BSDAySqPSoJDNgIoYDCEZCVDhgBb4nUopPbHVq1f3F7fffnsG+B1hHsbIHXrooYceeuiZZ54JoOu673//+7N5/tZbb5VmfskeccQRf/qnfzr32+3h7r///osuumhqaqr/8rLLLnv+85//vOc9r5SysIWlnSaje0oppR3CHQDaViRJmclMXataVV1bprF0gImlUD9qroUJNShQkCBJBOGogSnDaDraDt6penRgGVoESxMkbQCv3oWR5h4OMIhlqp3a6WhHkDhu4ECkJFAWQARqCxLKAJ/Srmu2M9rtt9++sJXsruYa4N/ylresW7furW99a//lYDBYu3bt2rVr3/GOdwB45JFH1q9fv2HDhn333fe0004bGxuba717sA0bNnzgAx/43Oc+FxGzD15++eWXX375QQcd9KEPfei3f/u3LXeU7b4yt6eUUtqBJPT73gGyyAAZSCqMJEBKIiRJBgCGfg5cUE2jgaEjwxmS05pOcrbTbNsgUAxyFQowWnhXJSsNjIrKruOwgTu9qnYmd9BGW6hxdRXuXicV3sBQO5pJOQs+pV3XbIDP/uU7yFwD/DnnnHPeeefNBvjHWbVq1a/92q/N8RYJwL/+67+eccYZP/nJT37md++66643v/nN559//nnnnbdixYqdXFva0TK6p5RS2uFIlhJthSRvNWphnNmrzj4v0xRRu60b5gMByBFCVKsWELvOC8yJdoTpEaKNqAiABe6KxgYAOkhF6LpRqR2aolIwPQ0KpNHgQQ+ZYbSJ1UmwGUCuwZBN4WBsaz0ppV3RgQce2F/cdNNNC1vJ7mqbA/zVV1995plnPnerHVFTepxLLrnk5S9/ufcb257YV7/61VNPPfWKK67oh/alxS5ze0oppZ1HUji8AsEgEFIDr/CuPw8f4aiVdaSQSkEX4R3MGA4J3kU7otO8kiUkAmIjtCiEO5siuGqoGwJijQKxBIOyTl5kjQwwAiQIiSFFyApJWiEAG6AU5n7DlHZh++yzz/j4+PT0dAb4HWR7fgPeeeedn//859/5zneuW7cOQNu2v/u7v3vuuefedNNNj93d/VjXXXfdG97whn/6p3+aU7F7pA0bNpx55pk/N733/uu//utd73rXji4p7WjPfvazM72nlFLamVQ7ABwM5RXtSFHhLcxQGgAiyQaKaFuORmhHbFsLmAdCaKu61rwKHRWgrBn42JBNA6OoICDJRchqVdvJA+4IRwighgObGLfhOFlQTAEVAwEjDUEKwNhgpp5cgU9pF0byoIMOAvDQQw9t3LhxocvZDW1zgD/44IPf9ra3HX/88U0zs3ofEZ/85Cff9KY3HXXUUXvttdev/uqvvu997/vKV75y7733zr5q+fLln/3sZzNbbof3v//9T7Rz/mf69Kc/ff311++4etIOldE9pZTSzid3RLAUjaYAKgIjx1SrdkqQCJhBIGjuUmhqEj4td7hTIIIB0IiCUkTKnaCIvhseAQlmkhCkgQGHQiwSETJrECINTYEZDATUDFgYTcOxcQ7HYcZS2MxDA+aU0g51yCGH9BeZSnaEbf4luP/++3/qU58CsGXLlu9+97svfOEL+09ZfvjDHwLYtGnTZZdddtlll/VPPvDAA9etW3fSSSf1TQg3b948r8Xv/u67777zzjtvm14SEWefffbjBvilXVyG9pRSSgspHIBGI4SslBiOYVAUYjMGuZUC71ioECIAgYogS4AWiiKG0asIhxgKkzOAtlU4UGAmUKCMA5o3pEMsLEQZEFQx9s3xQohgAw2GqFXWlAjvuigqVmA5cyelReCxAT4bos277f8Uc8mSJS94wQsAjI2Nbdiw4b777vvOd77TD5Bbv379pk2bANx999133333+eef37/k6KOPnpei9xwXX3zxE51K+Lmvyo70i0JG95RSSguvnwLnFU3DUjDztwflFQgBRoZXAqIEAWbFI0IRrFIhITBYQw2LgK5GdXgNgSaCMAo0WShi0DQsiAoWACHY9EjFWIgaDIVAa2ABmlRBsp8vn1JaDJ75zGf2F9///vcXtpLd0ly3If3hH/5hvzVi9erVr3jFK17xilcAkHTzzTdfvdV///d/11r32Wefj3/84/NQ8p5k+7ad/PjHP7733nsPOOCAea8nzZfM7SmllHYdBBQxM0muFJRGpQRiADpEd7ir62iSgWYoBV01CYqQK0DQionGqHInS6mjGpVeGYaJoYkojUIcDEAGpcGQBBC0QjlG04ArWkhE2VoXMrentOgceuihJCVde+21C13LbmiuAf6jH/3oTz9Isj/K+6Y3vQnA9PT0xo0bDzrooFwT3lb33Xff9r3wnnvuyQC/a3qK0d3d77vvvocffng0GpnZsmXLVq9evXz58h1dXkoppT2QSCgAqJ2WxKaBZJK8EhHujK74UAE29AiD1FWI8g4VbIxkSNaNRJkAtuFeSAkxaEg6rZHCTKAJRlaaDQ0oEtkM0BAy1toPjQcCNhPdJSmg8JmO9ymlXdvy5cuf/vSnb9y48eabb968efOyZcsWuqLdys5oBDI+Pj57ECJtkyVLlmzfC5cuXTq/laS5e+qr7o888siNN944Go0e+8jdd9+9//77r1mzJj8ISymlNM/6HnXDMbUj1IqQaiACDdRVhjOkrqoxVmcpVDEg2s4inPKuWmkY0be7Ew1sgE6CjAyBKDRJBC3CIbD/18woCyP6w/WFKsW6Gv3iuwfliKBEiJIi1I5IIlvZpbRrO+KIIzZu3Oju3/3ud1/0ohctdDm7lflJAt/73vdOP/30X/iFXxgfHz/ggANe9KIXveMd75iXn7yHO/DAA7fjVSS374VpR3j2Vk/x+Zs3b77++usfm95n3XvvvTfffPO8VpdSSimBVma2qw/HAMg7UqSrVtDYn4OHgIhS4BFT075lWt1U9L3o5Yr/m3dLdxi9WBjDAoQ7VQZohhoMNBygadg0ACFibBjDoUyiUBqURiygEM7aoatyh3eIgDtqB0Be+6F3KaVd1pFHHtlffPvb317YSnY/8xDgzz333Oc+97lf/OIXN2zYMBqN7rnnnn//93//xCc+ga0d0X9mFElPxSmnnLIdrzrppJNWrFgx78WkbbV9M+Fuu+02d3+i727cuPHhhx+eW10ppZTS/4/k+ARADsfQNILkoSrBpAgRRhARNDHc0Y4MleEWomoRzB3uBgUZTaNC2oBWLBrC2I+F60e702AWETRTYwBJYzPgcMzGJ9AYzShqNB0e8qAH3LW1p6/aEQC4Y9u7/KaUdprZv4Gvuuqqha1k9zPXAL9hw4a3v/3tfUQ/4ogj3vSmN73mNa+Z/a6kd7/73WvWrMkZgNvnBS94wXYcZT/99NN3RDHpqdvuce5TU1OPPPLIkz/n3nvv3a6iUkoppSdmheNjINF1mJ5m7VA7TU+rdcgJyWiFaluTMNOUXgGhc3Q1vFXXCbJwshiMFB00BEUA7FM8ZIXNAGZCAUwRqK6ugyK6Di7BVSu7lm2LOkJ0VNAdokgAGk0DeOyaf0ppV3PYYYeNjY0B+M///M8nWZpK22GuAf7DH/7w1NRU0zTnnnvurbfe+pnPfObss89+7BMmJiZ++MMfvvSlL73tttvmeK89UNM0f/Inf7JNLznwwAPf/va376B60pPb1t3yP23z5s3z8pyUUkpp2/Sj2iJghU2BGSJkVmxmchzdwQJRAMMDigiCImUExFpZa4TUTkU7Ta9iyBoYA1Dr6irMWAwusYFJ7nShdvRguHUj1YotI42m1VVFC0W/HQAhjUZ8bAO7XIFPaRfWNM2znvUsAI888sh111230OXsVuYa4K+55hoAb3nLW974xjf+9HdLKevXr3/mM5+5cePG17/+9XO8157pzW9+82mnnfYUn1xK+cd//MeJiYkdWlL6aXPM7bOeyieUtda53yillFJ6LLn3m+XVjRCBWhWOqAqFJEhQdCNSQIg0GEsD0gzoqrpQdXSddZUhhru7XOFhFQ2IpqAYRXU1FOraaCtrF9NbVFvVzrdMxpYpTk1yNKlRh9E0amUoQojat7JDl/8CprRoHHvssf3F5ZdfvrCV7GbmGuBvvfVWPOlR7aOPPvqiiy4aHx//zne+881vfnOOt9sDlVK+8IUvnHzyyU/lmZ/4xCde+tKX7oSq0qz5iu69wWDwc5/T70dKKaWU5lPtIGk0YjOQQez3qxsQZqQHPUwREDsnJIilsDAqUWjh7Dq1rbpR1CqpAFAYHU2/T75j18kramveWTgx015eIXVTNj1iuwVtG1GtdnRJiIDJJUet8E61Qzfbvi7nw6e0SzvuuOP6i0svvXRhK9nNzDXAr1q1CsD09PSTPOfoo49+3eteB+Ccc86Z4+32TCtXrvy3f/u3t7zlLU8yP+yAAw746le/+ta3vnVnFrYnm/tu+Z9p5cqVP3dKXP9/upRSSmne9NvRI/pMTJA0K0OW0veeFxnVAZpEAoKMERHVzWigYEEPSBEGMRwhLwQAryAIwgqNMENX1bXsWnWtIlg7eCiEqSmMpth26FvihRMOsHhEbdVOazRSOMfGAbDkUNWUdmlHHHHE8uXLAfzHf/zH5OTkQpez+5jr7741a9YAuPrqq5/8aS95yUsA3HTTTXO83R5rYmLiH/7hH6655po3vvGN++yzz+zjJNeuXfuxj33s1ltvPfXUUxewwj3Hjsjts5qm2X///Z/kCWa2HX0NU0oppSch9YfLBUDuIMOAEAEYFQJEU0CCyUwEPVhDzjA6ALhkhACi8/AKYxFAAuZVrhCEtmo0UnTwLrqWbWU42xa1NXdAIGSUIQpCgZDca8gAVidi5rMGElYW7P1KKT0FZnb88ccDGI1GuYt+Hs01wL/2ta8FcM4559x5551P8rS99toLwO233z7H2+3hTjjhhHPPPXfjxo2zU+Le//73r1+//g/+4A+WLFmysLXt9nbQkvtPO/TQQ5ctW/ZE3z3yyCPHx8d3dA0ppZT2QAJAggbIAqAgMBwQwgnCGg2IUlgamQEwunnXd7MDAZhs6KWxMpCCNSIg0oqMpBTh5hVdJUgUGRGSxBBq6woJBBmEy0B4pTs85C4rpKkZAOBgAOYW+pR2dWvXru0vvva1ry1sJbuTuQb4173udU9/+tOnpqZOP/30+++//4me1regf9rTnjbH2yUApZTZXdaZ5XaCnZPbZzVNc8IJJ+y///78//80mZiYOPbYY1evXr3TKkkppbSH6P/FIQ2SWdGoRTiihneuFhJBsFAxczi+GPt18kBABnopGg5UBkAlAZFSFEDeN8GjHBIgKSCHV8oZAXdGpQQXgyLRFADGUDdCDQSoIALeISq9cjgG5v75lBaBE088sf/1cvHFF2/d6ZPmah7OwP/t3/4tgPXr15988slf/vKXf7pFdtu2n/rUpwDszBSU0tzt5Og+qx+88Uu/9EtHHXXUoYceevjhh59wwgnr1q177OmJlFJKad70CwOkahe1Va3wDl7pbXFubWgnBtAYBkU09Xm+kFYKZf2wN/cSQQW7aYy6qGEhqqNXr46o8KpaFRERkEJwH0W4aqtwUSH1G/pDihpQJ3chQv0geSoiNj+aM+RSWhRWrlx55JFHArj77rtzmNx8mYfPL1/zmtf8zd/8DckNGzb81m/91rp16/rHN27c+MADD1x++eUvfvGLb775ZgBve9vb5n67lHa0nbZb/smNjY3tt99+hxxyyEEHHbRq1SrmXsGUUko7DJtBbNkMAaMtFhVRo4ZHhAT3UCgUZnAXGSEogg1CHnBFQAYYBFVUpwxgw0DIBAWNEf5//e0giCRhJAApEG6SgV77se8UAjBQxmJWFFUegOCudoSnMHg1pbTgZmdpXXTRRQtbyW5jfjYgveMd77j00kv7wLNx48b+wdWrV++7776nnHLKt771LQBnnHHGK1/5ynm5XUo7yK6Q21NKKaWdTYotm9F1gMAGIcFgMsFqBc1AGOgRXWVbEZUSEWYopKyYEIAjJHqMIBc8Qgp3F4zsV899Zj89XawdwulGkkYQIhheXE2tqF30w+r61vfATP/7qS0aTSlCtUPuyE1plze7uPuVr3xlYSvZbczbCaJTTjnl+uuvv+CCC0477bTHzbJumubDH/7w5z73ufm6V0rzLqN7SimlPZa6Vu0Ipfijm6KbloDaoQt4lbtG06iuGohapPCObRuqkKs/tw7IxVoNIYShqSBEgShFJELykACBVRh1shACUcWAwitRCr2imNRH9YBEuFd0NAFsTEJMT8trH92Vi/Ap7fIOPvjgfsTS9773vbvuumuhy9kdNPP5s5rmVa961ate9arJycmrrrrqlltueeCBB4455pgXvOAFTz4ZK6UFlLk9pZTSHk3SaBpSbH6UkEQV0ozo0AZcRA1XMQjm1qi2rNUCREjGxhCCQTTBaAYrVhovYWb98DhFYFAomMIRJsKrSoliDegqZo0GhrFxTU+DMgkQCQcaVPkQFqph0bFpAMTk5rJyFZQn4VNaBE466aQLL7xQ0sUXX/z2t799octZ9OYzwM9aunTpqaeemmPJ064sc3tKKaUEANo6XF1S7TgcMiAhnAwPVMKkLlqCFSM3BYAIN0Ow0gsAESw0b0IEiUFTavWQIcLMqBoYsLhHUSeU8LBwdkDTSCIZRmNRoQCSxDDglIuOGMmH6GqMWiLklaUoPLvDpLQonHzyyRdeeCGADPDzYpsDvKRPf/rTF1544d1337333nu/+MUvfuMb33jIIYfsiOJS2hEyuqeUUkr/J2ZOkitcZpiaVB1FbQGnwoCgU0Q4urCCKDLIFAwzFLCfIyd5BLooYyAksBg8IDIUwTIA5EQIJoIoonFg/V54CWy7KJU0lkagWFGtoQNmolGAo1ASRiMrDTzQZIBPaRE4+uijly5dOjk5ecUVV2zZsmXJkiULXdHits0B/owzzvjiF784++UVV1zxkY985M/+7M/e9a539Y984xvfOP/88zdt2nTUUUf94i/+4imnnLJ8+fJ5qzelOcjonlJKKT2OCJAwYwRrB8kigKI6HQgIBAUVIOpIIXIMrgBQjQOXaDIPQf1wdspKQ0QnUWIAZEOwSMGAER5iocxQFXBCsgCIAAVRgkgZICIUDEWNpro86sMP2d5PMwBtZ+MTC/vWpZSeiqZpTjjhhKuuumpqauqyyy57+ctfvtAVLW7bFuC/8IUvPDa9k5Q0NTX17ne/u9b6nve858///M//+I//+LEvWb169V/+5V++/vWvn596U9p2mdtTSimlJ0KaAFiRJAHVvXbhFQgCYKjveawqjgFgO1IZmAwl4CKBziExugAUHWVSgI6QIoxEMYIIBGEKAnSgVrL0nw3QW9ogwkNBgRCCgVoEAqEoMBgtAi5MPooVewE+M74+pbTLO+mkk6666ioAX/3qVzPAz9G2/eL767/+6/7iQx/60IMPPrhp06arrrrqFa94BYD3ve99//Iv//L+97//cS+577773vCGN7zyla+stc5LxSk9ddlbPqWUUvo5zDgY0syWLoUAb62rxasAhwADQiIAyqFQyNuqcHSSAHdHhFdh5ki6gL6VHZvGUIKNMAjBPaK6R6giImpACpjCgzDvPLouQBCUZjbfIwyggWBI8sqxBs0gHv0JSGUTu5QWiRNPPLFvWvG1r31toWtZ9LYtwN94440AfvmXf/mDH/zg3nvvvWzZsuc973lf+cpX3vOe93Rd9+pXv9rdV65c+clPfvJ//ud/vv3tb5999tkHHngggAsvvPD3fu/3dsj/gpR+lozuKaWU0lPEsXGQNr7MVu0lSVJEJVDAgAgrQL8WznAoCIR3VGt1OjxQa5HQt8ILw8wWeER1qT+97kaY0RAWIXSQF4MYFGAMCIaGJASPiJC7BMAECIZ+6LwVtS0ImBFAjoFPaZFYuXLlmjVrANx1113XX3/9QpezuG1bgN+0aROAl7zkJY97/MMf/vCaNWsiAsDHP/7x3/md3znqqKOe+9znvvOd77ztttte9apXAfj7v//79evXz1PZKf1sJwvNQQAAIABJREFUz95qoQtJKaWUFg8zW7IMwwGbAYZjoAH9KHYAdCGIgAICCDRAa/BAFQRUg8ACNgUmK4YGgmZazwlFQADOWgkPgAAQCI9g0GBGUV7lLbwGBCMAbk3oxqKmMSssDYdjCGEwVASRK/ApLRpr167tL3IRfo62LcBLArDffvs97vGxsbF+Iz2Al73sZY/91vj4+Gc/+9kDDjhA0jnnnDOHUlN6MpnbU0oppe1XCpetKEtWkI0gQAIKVNCnaRjQAI4CBmGg9XvmZxrB0wAPkBbuhAFBUYgATKIcAKBioETIGDIjJYVLHdwRCloxqtKAQF+HBUWKkswkkihLlmJmU0BKaXE48cQT+4tLLrlkYStZ7Lan+cdgMPjpBw8++OD+4qfj/ZIlS1784hcDuPzyy7fjdik9uYzuKaWU0twR4nCAJWMzTelhHdhB/aR4Qg7ACiQgJFGosH55HSaTAU4P0SlArikHgt0IFAVo5slGEO5eVTsg2IkuVsXIMd2qjSKXYBKh6Bf+qyBn10EKD0EgmU3sUlo8Dj/88FWrVgH41re+9cgjjyx0OYvYvP3im5h4skkexx9/PIB77rlnvm6XEjK6p5RSSvNFUttGbeERjQECZWIBKRImsEExijTJDDCygQiyDKogBAFJFpJ7uFt0QBUIdzNSIQPACgdQQIZFPxxejjoynyJcEX0nepAEi4H9oHgpImZa7nkFS3ahT2kRIdkvwtdaL7300oUuZxGbt1985JPtYuo/btm8efN83S7tyfKge0oppTTPwtVOo+vQubkUdIEEAVGEAHlDWUFjZAQQoIwsDcxEF0IAIDIIMUQLKhABUQowGBBkgKSIVt6KRFs16hgRQbgIMYIE1f88yJ0lBECO2tnYOErDxpBb6FNaVPIY/LzYtjnwKS2sDO0ppZTSjhBTW7RlS0xtQW1FWtOw6xz97HY6MA5GBQYzLeZmGsAHgSDVOLwf7EZXVGAIwCUDASIinPAgHP2iPRVqbFCAJqyjQwjWCgMZDkHhgIBCGEJVKgipLF0Go0BrhtDs6LqU0iJw/PHHl1Lc/ZJLLokIy00022V73rXp6el5ryOlJ5dL7imllNIOIq+xZfPMf7sOCkqCmc2schNwQIOGTbFCWgPA4IEALWpQIWwdJSeqdqpeABFAJ5OFExVQAK6ZoXQhuSQiKAJCiAqogACNZAhCwGhEYbGCWtF11gz6YXcL/MallLbFsmXL+r/nN27ceO211y50OYvV9gT4d73rXQcffPCrX/3qj3zkI5deeulDDz0072Wl1Mvd8imllNKOJaHr4K62VdfKSCsqILZ2iwMA0IZmAxarQoQAECyA1AFtv9+d7P+0DLQtUSvUBIWGKv2KvYACn1nBN8gGRMfqUA0oEO4hD+/n1kEwBCE4RhW1QzsNDwAxNamundljn1JaPGZ70ecu+u22nVvo77rrrrvuuuuCCy7ovzz00EOXLVvWX2/evHn2OqXtlqE9pZRS2hkk1I606FoYSSPoFYAREgURFKn+jHu/a71fkyfYb6sPBcUwGgIMqc/yrAZDlZqwMICQYP0ivQXhVaWI3ud7AgUywKEACGIm6Ycoa4rGBtFNc7rhCrBW5Bz4lBabtWvXnnvuuQAuueSSD37wgwtdzqK0bQH+yiuvXL/VD37wg9nHH3u9cuXKo4466qStjj322OFwOG/1pj1ARveUUkpp51FIkncAaAMJoUqEZgaxE0SA8EpWoViEUAAvgKABSECEUYQc1kgzW+4lUoJBTmcf+AMCUMAoAB2VAANoEAA1k9jp/Sl7hcFEzkymd2fbakLaMonBALkCn9Jic8ghhzztaU974IEH1q9f/+CDD+6zzz4LXdHis20B/oUvfOELX/jC/vqhhx5a/xj33ntv/3hE3HDDDTfccMNnPvMZAMPh8LjjjvuZo+NTepyM7imllNICkGgNvFNtzahaI/puc5IYVCM4Az5tdUm/s10zi9+KmXVy6wO3QQIDQaFQlSBgUQmDGOwnwRcCxT1YJKeCovej6ggqnBRUIJDqP0SAywkSJMIBSFBEtrBLaXHph8l9/etfd/dLL730jDPOWOiKFp/t70K/9957n3rqqaeeemr/5Y9+9KP169dfc801/X9nD8a3bbt+/fp5qDTtvjK3p5RSSguGBgGDRgCaBrUVgDDRAZaZIexmQAjRVjSK8D45BwCo3xsfEGAzB+ZBISgNZCAqgtDW1G8OFMABhDFIgIBg0d8NhagEO8CigxWgEawfES8Q3gFkKbkCn9Ji9JznPOfrX/86gK9//esZ4LfDvI2RO+CAAw444IDf/M3f7L+84447Zhfnr7322snJyfm6UdqdZHRPKaWUFhgJBEDA1E3X1gMCZQE3BERyaywPACEU0PuVdMi3nlRHn8mB0v8sWgUoQRL7bfRwsMzshgeAogjIIaMBMoSDhf1SuwAYB4ACIhRdcMsUtvwEtoKlAADLTn+zUkpzdeyxx5pZRFx66aULXcuitKPmwB922GGHHXZY/5lKRNx44419mN9Bt0uLTkb3lFJKaZdAoDToWg4GmEKRQjBEmLaeTocDfW85ytE5AEEGCmZ9epdAEkHA0S+qExAo37osb0B/AN4EkgFAFehnxfc/n4RcMs58IhCU9e3q4d6OmhhndVWPdtpiBUoOkU5p8Vm2bNkRRxxxyy23/OhHP7rxxhuPOuqoha5okdlRAf6xzOyYY4455phjzjrrrJ1wu7SLy+ieUkop7UJCHAzVjhDRrFjVLltSiokFcgAGCLT+HDpAKWZ2y6NGmBX1WZuEQKjSTAjSBBOqod/pHmBABLaemUcB+mP2AZgYhAGSGqoTRRln+uKJDIDoEELXoVZIMmzdGZBSWmSOP/74W265BcBll12WAX5b5SeXaSfJie4ppZTSrkiCOyI4MRGjkUZTHpBc6tfQQUBUP00OdEH9qXUYDAGJENBvfCcAowwIAlSZ6UwHAxqAAiUhpH4X/cx3t46mEwmXFdJESY4gi4AqCoMQ5JVmGA5JyzPwKS1Sxx13XH9x+eWXL2wli9HOWIFPe7gM7SmllNIuSwDCORjGIw+B4GB8Jp9TBEMzmRsgDKF+Zz1NKEQHFiIgUzQQ2LeV70+wU8TM3nqw71cvQogGBskBogDRIAg50C/RG6HZEXaC003ou88rQuMTXLYc09PhXiIDfEqL0rOe9azhcNi27ZVXXunupWQ/i22QK/Bpx8r0nlJKKe3KaKYIjUaMkLeoI8wMYwcAI2ePo1Pg1lgOQuBAAQiiyH7me9DKzOD4kAiQhKPpl/MJGSyg/oh8KTLQQYf62wUkhYjCKIoBUYIkQRNEFRZDCICZzezeTyktNoPBoM8IDz/88HXXXbfQ5SwyGeBTSimllPZojFBtURqNWsosvE/GhASYyL4hHQCgbP3zMaAgRBjRL7nP7KEXYFQAZEAhNVvHwgkwiFD/0zrvO9JrZiAdZrfsO1GCpYLBfno8CLIovLIpHAylmd33KaXFaHYX/WWXXbawlSw6GeBTSimllPZoIYBUO2WDQRAuUHSw61M6JLHMTHpTgAEKLCBkW8+hG71fjQ8QhGg0yEgD+w52A8hmJsgx+hV4iDKBBRAiBIEOgMX756nvbGfqy1QUQSAnJqw0sPw7NqXFajbA5zC5bZW/+FJKKaWU9lySWIylIazfwA4aCQoWKgDJfh99J4RmJrTP9KSnKKDfy14MqtRM/7uZVXWFBLI0NuiH0vXL+ZwZHR+g91PlCZLqfyQUBlk/T079WXjNNMIfX0YFmyEGA1qem01psTr88MOXLVsG4KqrrpqcnFzochaTDPAppZRSSnsuAiwNQI6NRwSiM7gQoMz6DvSQFPA+xkuh/rB6/3opCEABgBZmMGztQCf2h+iFoBFGEVBVkEag710HoEAVMLCf+i5aIJwEKJJ933sO0RQMBhybsCVLbTieW+hTWrzM7PjjjwcwGo2yF/02yQCfUkoppbQHI1EKSoluRMCmK2sA/co6CBMhGvrJ7QRYKiJERAREM/Sr5UI/Fi5Egv2kdgACxWKDYYjq1+BhRQoAMMACAWEAeL+ULxJhbBp1gs9m9GANQBMTtmpvDAYYDBbm7UopzZOTTjqpv7jooosWtpLFJQN8SimllNIejISZDQeMGl2LOq2oQhggUTGz4d1g6jM8NOgTuBlBAab+7Lv6pneianhQAYRCbMwAOWgUTTQyJMx8QBAminBQUBB9wzxFQEVCR3m/OG+DUsbKqn3KxASbQR6AT2mxW7t2bT9A7sILL3T3hS5n0cjffSmllFJKezSWJmrF2BIbG6dZVPWT242ASYBggkJyQIJj68y4mbZ2AAppAkoEgMJSYEK/Ft85CXeoilHJgDSzsN5Pcpcgoj9gD/Ub9Qk3C7AADQSJBg0G8MBgyOHYQr1XKaX5smLFiqOPPhrAxo0br7zyyoUuZ9FoFrqAtD0efvjhhS4hpZRSSrsLM7Oi4ZhXDwFm/dQ3AgU0CECAhaqIQhJwsum71IkAq+RUI8ho0a8Qieh31xNy54AE+4BvEqwfH1cIkaU/DE8GooFJlWSVF1oBHBRRzDgcshlwMETOkEtpt/D85z//+uuvB/D5z3/+JS95yUKXszjkCnxKKaWU0p4tQs2Ag6EtmRBplCQTrJ8hBxSo/5OxzPzpKEiChSiw32VfZrbdR99Hvu8Zv7WpfKB2fbt6M/UD5gec6YJHMUCi30CrCpg1FiygIfpu9ZC8KurIxpfATF53+nuUUpp/z3ve85qmAfClL31pampqoctZHDLAp5RSSint4QSz6EZwL6WgP9vOMNDgCtdMT7ut4RsGihIJ0Q0qhPcn4FkQEaAjBBf7vfEGskDsx7wHRVaRREiiLCCZQSYzweXRt58HjFFmmt0FXL5lE7ZsRgb4lHYLK1asWLt2LYCf/OQnX/rSlxa6nMUhA3xKKaWU0h6OJOmumeFwBhBiAN6vxENQVIj9ifioNjPp3Ygi9V3u1B9dVzELb7C1pR1AdFR1CAqKgJskBIQCCHILYxgUJBgGQegb3VVY7QuMMBIRPj0dk5tRM8OntDt46Utf2l/83d/93cJWslhkgE8ppZRS2qOJVDfSYMgyUDiq903sqqKP9n3CbwAgUAVDgCKh6nAxQoQUUIgmyIoDoAFSwCAiBOun04ESYDQQYD9m3qL/1AARMNDE/sacOewuEEDtYsskalXXRju9kG9ZSmmenHjiiU9/+tMBfOtb37ruuusWupxFIAN8SimllNIejRAFM0OEAWAJiWBDhVeTAuH9fDfSmwJY34JeKOYUVMIVZiDYb7b3mR8MVut7zMNmVuQDKAQJQXKAKED0zw+QqLPd6QMgQhAIAfLAaEpdywh1nSLnTqW06JnZr//6r/fXf/VXf7WwxSwKGeBTSimllPZsIViJqSmQdXyJA4b/x969xdqaVWW//z+tvWPMuVZVUfpxECgghEBVKUggxHAQDwGCXhA5FIhUBBNuNGrAGCUEiEGJiUgQxESRGySgaNwxEpU74sa4LxSDQCKCJ9hKIcWpqFXrMOd4396effGu8nOrn3IoaizWaL+rt+aca86WfjFqPKP33hpFGVk5BziCGq4EqHRIFkImPIHFFDDWee+2zdpkHm+g7m4YHwa7bKsKLKdrQChco3BaSRZOvLncJc+GqLJKiM3kMdfpKTaj9rhmrbV7yjOf+cztdgu8+93v/td//dd9l3Ol6wDfWmuttXbQ7PJY2Gw0bXKedTcjMaZCcpAhFQgNgWNmJKTCgCLKFciJJcWkglpYW95Rtd6Al1Do8qn4hBKThymLMPIyU1qqgp1UIRC4Lh+1HxHrVHhL9By51q4O97nPfdYZcrvd7i1vecu+y7nSdYBvrbXWWjtsVUjY6x32oAY2FIRx2KVRrgoPRIhKakLgGYdSi9cWc1LVkNFy+fI7Xg/Lr2PhbVOuMiDGmtkJyFwP2keGpBRjfZNahEQMUThctbtkW/q3QXWttavBc57znPWTvbe+9a3nzp3bdzlXtA7wrbXWWmsHTQYb22MEXm+lQ4SLjDCE0UIUaUYJ8PqPFOUZecIeEzFUGQospdZL7RgpJaS1JV78++hthywRipJteYxhKQmqDGMIpwGPGsxDIR0drWl/TwvWWruH3XDDDU9+8pOBL33pS7/xG7+x73KuaB3gW2uttdYOmu8+pV6XLmKYXUbYQlXIKZVCiMqKwjVEkJZBk4eqKrRAmtISBF7CY4QKyS5RdkHhJaMQZeMkCmMvOAqoVGBUMdZO+FKtU+ywxkDSZqtITRui38e2dvV4/vOfvz686U1vunTp0n6LuZL1C19rrbXW2mGTiGQscXRcNaoWoWBtU5celC/vmWesJ9dlWFvHX57/nuJynp6CqVwoIEMhqu4+8B61yMoqXA4FlGposikJOWREpKRFxKQIxeVL9pYHRMQ0MW3i+Ozelqu19nXwqEc96vGPfzxw++23v+1tb9t3OVeuDvCttdbaQTt//vwnPvGJj370ox/96Ec/+clPXrhwYd8VtXudZAqr5h3LHKyn2QNsjcuX2y2qlnVQnAzlspFQmYV1vz6GF7MgC5UW4N9OuhtXhrT+QV2+8+5IG1UwTNhhXFCV4KLMYtlgkKqWWdtjpsnqG/CtXW1+8Ad/cH14wxvecHp6ut9irlgd4FtrrbUDtSzL3/zN33zgAx/45Cc/efvtt99+++2f+MQn/vIv//KjH/3oGD1h+4Aokt2s7Ua7RbtTe17DMwC27aqShwgGniGEUqyX2cNsCtVsV2gKcp3ingRgGDhxQdoGIhacNmbReqxe4QnKlBzlqqCQSBHGXkfRxaTtsauUyTLva7laa18nj3nMYx796EcDt91229vf/vZ9l3OF6gDfWmutHaKq+vCHP/zZz372P3/r9ttv/8hHPrJGrXYQbE3JOp7dBIVDCkOQkySmwCJGsWgqLGtZ98BHKVCIyISQiyGvx+wj1m12tMNxeaIcosKgGEK2sFRex8IJ26mwRxp7vRivlL0e25ddRU5rz/w9rllr7evhh37oh9aH17/+9fPcn9P9FzrAt9Zaa4foU5/61H8zqudLX/rSbbfddm/W0/bINdgc+9JJ7U7RenK+AFm2F4aihFyVio0lZDkQjErKXmpGIqAEKckOs1QZXHhjhOySWRy6e6S8iAEmgoLAQRk7EDJSCRlXYoA8uoYqTk8AqgN8a1ebxz3ucTfffDPwyU9+8l3vete+y7kSdYBvrbXWDtGnP/3pr/EH2lXCxlaK+VSXLnJyaSC78JDWkeuxMKosMTSQqTJy2Z6ELNIpewwGrNvoMGStQ+BlhsJUBIg1owMqSwMSPBvWqK6ylaGilvUciIzwOmRuuwW8LAB9RqS1q9ELX/jC9eGXfumX+j7Xf9YBvrXWWjs48zz/j0N6Lly40O+cDsI6Af78+fncHV5GjDntdVh7re8UZwcpiVCVDQpECUIWSoIMgYSCCXt9UsZ69F1DS0Eu5Nr4zmWBZKOgBg4hj9D6EzWoghQSA5VlFAkXz1MDFzbdxq61q9ETnvCERzziEcDf/d3f/f7v//6+y7nidIBvrbXWDs6XebGw7x8eBMm73bjzi+x2PrnIWIyDKFcAdm0kI4mKy3fYjQlHLFU2osogLKBOwVUuYwKCMKkp7AFDUOvl+PUnyMWSpXKEjNe4HiYLSlGEFRpFVeHdzqcnSB5Lz4Fv7aok6QUveMH6/Mu//Mv7LeYK1C98rbXW2sHZbDb34I+1b2yST0/GySXvLur00ihLMSC9bs7XdPfANqkmYqYQWRZjkiTKku0aMhQ1bDEHSIVLa7s5ax0HJ01WlUtDEoxJlajsUQiYnGYIhwgW2Rqs+/w2y0lEeCyeZ9TvY1u7Oj3lKU958IMfDPz1X//1+973vn2Xc2XpF77WWmvt4Gw2m7Nnz/73P3Pttddm5r1TT9unKu9OmGcGLENTGgsvGZZKqqpax76rZDYSxSzCDGqdz76G7UJD2mYgNghswDUBSEEKY2OUQXiUURWz7YghbGXFIqsku5gBEetmvcIcXzt2O+aZkPuKR2tXqYh4znOesz6/+c1v3m8xV5oO8K211tohuuGGG77GH2hXDbvs0slF12DaGIWZzDBSlHz3HnwOmbJDSIssp7XeWR9lRS0BZqGsf+tU53V8XC1ojDIsIkwsiqRcoZokqnIdF2dnRAkU4QgFOPLylXmfXHDgZaboUfCtXcWe9rSnXXfddcB73/vef/iHf9h3OVeQDvCttdbaIbrhhhu++Zu/+f/03fve974PetCD7s162h5FWTYhjo9Vi7FlucKush322lC+EiMwuc5+k102jsy1M11V4QhRVtq4BsIunAxlYCgU5XA5wuG1113EejtekswkhCU8yoULQ1maNuH0WCTUO/CtXb2Ojo6+7/u+D6iq3/zN39x3OVeQDvCttdbaIZL07d/+7Q9+8IMl/YevP+QhD3nMYx7zH77ermIlPOzcalwez2aGlfZIyJA1CLPG/PVWO04KRQTlKhdCGREChyVqMcUUkteWdZ40RiqmYLGqsIgwMKOyRRQ2ZXsxYFsKRaAkbAlOTzxmEUBVB/jWrmbf//3fv/6f6B3veMdut9t3OVeKad8FtNZaa20/MvOmm2562MMe9vnPf36dKnf27Nn73e9+x8fH+y6t3Yuk2Bxpu2WTa2SWoKbSiFj33Qkmh6wxsMbaQz7KVeGwoipDp6PWbvUlLSo5rZEsQxsbK7IGkmGIIJwlvEChrSsEDJNS2JJy52UjuTxLU9mEhtmd1jKznK7z6NZ6972CrbWvi2/5lm95/OMf/8EPfvBzn/vcH//xHz/vec/bd0VXhN6Bb6211g7amTNnHvrQh95444033njjQx7ykE7vB0hHZ+Joq4GOz8R6cV2SVWvgDhcFlXZYUkYZDNaan3Ma6168wU48WaWRphRyBUoYoaGcGWmKwjE50ovKkqoYRHisidzsNmUXSBMxXKiY57X/vHJyDWV2dm/t6vaMZzxjfXjnO9+530quHB3gW2uttdYOmMQ0ceaszx4ViWO9A19CqFCgJDXCUhkJR6VUyJJdUWXbJcYQolxG1hBpEhklCiKjpuFhDxt5RxVhrZ8FkOVQmCo7mRwbpzUGGtIkolRCqrKNDaK331u7qj3xiU+85pprgPe+97133HHHvsu5InSAb6211lo7aDo6jukorvtfHG0rU0RQqZItqooKV8oVBbikKDw5ikrZQcoZOCcCTalwKCarVDAGnqFqcXkoFdpYQiEkhQVhFofGesE+wkiqNNIkS3KBipp3ZMbRGU2bngPf2lVvu90+6UlPAna73R/8wR/su5wrQr/wtdZaa60dNE1JSNsjnb1W0xbWVnIBBIoYhvKMNIWSMFEuNHLEsBY0VMVSUYKlVGS5lgBkBATOUFVssKFw1TCBCXwKJGFZcffgucK1MIZHFR4VgEu5jbNnVYMIot/Htnb1+57v+Z714Xd/93f3W8kVol/4WmuttdYOmsGbozhzVpmkvN6B53I7ulokPJFWGc2qYJRiF66YQaEKEjIpg7wYUmyhHEaJhRZUyQ6lK0LryXmkwZgoOYe8uKpkWdhEIImgCMkY67rr7fK0YbNV9Pn51q5+j33sY6+//nrgT//0Tz/zmc/su5z96wDfWmuttXbQhHK7ie2RNludOQMYSppMlCqxKYEVowKWcprNnMEUsoejLCxtWP+xqfIpihombA0cEFaGyzFMWMIBsoqyJDuQElxGEkkEoQhQyWw2tZzGZptnr5HUR+hbOwSZ+Z3f+Z3AGKM34ekA31prrbV24AyKJKSjM44NYM/pZWhYqEKEKwIkbBxMwlmFMBEqbKGlPGQ5tCiY7F2EPYTDKrAskJxrq3mrDGSu33CIshUS9hheXKWlDOsHCLgKbY4BxnB3sGvtMHzv937v+vCOd7xjr4VcETrAt9Zaa60dNEVYQYSOjxlzOKRJiiCkclijIuru+XLaLuwYdgrbHgNHRrmoTKS0w4TkWDvQe/07tssjPKJGjSSkKNU6970q5ADL4URWkngoBK4ANJSbacwneO1B31o7CDfffPMNN9wAfOhDH/qrv/qrfZezZx3gW2uttdYO3jRRzuuuH8Mli6DW+exYZvIMZiyUSwuxIRxGWkKR4RolSWEcGmDjBYeBURGLwERphGsyMalkinSiwCWGF4ssKopSlBdFWFDOAfKEjEztdsiu2veqtdbuDZKe+cxnrs+//uu/vt9i9q4DfGuttdbaoYvtUUTUpYvTmWNlCpbLO9+BQ/aGQAoUWTHJJiA8AkSFykWIAoEZxum1GV6EnV6nxWW6NgiWiTGsAvDOsxUZGyOksTaol7Qe2ldMZRjkRKjG8O7EJyfY+1621tq95BnPeMZ2uwV+53d+59Of/vS+y9mnDvCttdZaawevimkT01S7mTFmijHCAQ5sCVe4sMayhmoPvCCBkJWSSpha7FCApMAYhpBmcKgkFRVoREyLagCx0TY9at3795Ka5KoRsfbDY1jIItKbrWpQZllEB/jWDsV97nOfpz3tacDp6ekb3vCGfZezTx3gW2uttdYOnZdl1FKXLgZr6I4ImQIkcCHmylCl5UJ4ksUQjLJth9KsXepMrNvjhQOlPSwKuwBbni3LU4VreIQ1SkICIoftCGUNqiCJIoY8JM0zY5BpVy1jv4vWWrs3Pe95z8tM4K1vfeu//Mu/7LucvekA31prrbV22GowlrDHfFqXThKyPK894gx22TiOgjJzFiqjMkUUlWEVMcYISpIsLNsQYmBLgRQKybbMMlWqtA54J5ARtTa5L23K0uwiBeuUOxQkVRauqpOLiugudq0dlAc96EFPf/rTgZOTk1e+8pX7LmdvOsC31lprrR22Ks+75dydPnfXmE8HhtoaUYvHMESVaqcqV5Zsm5A1KUwO1tFwUxSMMlkmI7DtwiUCFPZig4Ycw1UmmrfOAAAgAElEQVRCUxRBVZGXr9BL4Uqyprv/zBigoHAuw3feoZyUGyRF7nvhWmv3qltvvfX4+Bh497vf/f73v3/f5exHB/jWWmuttYPmqnF6Uufu5NKFrGEgVLjIyWlRI9ILhCMzAk2wSLGAcBEwqGWHpySqoAYKZCuEGWUXDtJBGmVKl8fGl1WqESkjmFmb4I153WK3bFdVYcuhqIvnFAGhadrzwrXW7l33ve99X/CCFwC2f/RHf/Tk5GTfFe1BB/jWWmuttcNW9oXz3p3U6aVa5rCLQCl5yFQotTiDymIpZNL2MIyABJwEG1cRlevw9lpkIkQUECCtA+qKSoSdgSJCFZI0D1FyWgEm0iDNJYVqrO3pF4I4PqvNRn2AvrWD9LznPe+hD30o8PGPf/zVr371vsvZgw7wrbXWWmuHzcXpJeZd7E5xlcwwwzVmcKSjHAEOS5LtshJm0IJlY+OUEpATV4GRB2FXYZAi8DKG5MJDWVXAUIRVFdQi1tb2ygRss4l1oFyE7RTH1xbBZsumt99bO0TTNL385S+PCODNb37z+973vn1XdG/rAN9aa621dtB8eslVzDtctcwQTpaUYjNJWAQDrEGoBFELMefkGhvkMmWZGkOuzTImpYgJKxgiQ4DsNBlQE0VqHT1HEIVCOAIrpwG4KlIOZIZLMMkMyqXtFNsjoOfAt3aYbrrppuc///lAVb3kJS/57Gc/u++K7lUd4FtrrbXWDpqr2GxqGRBR1kAozGSGMtYedDgIj3katcyRLFuTmnZlUjXFSBwBzJmLh6GqhLNkV3gYLcIWYQvVqEGl8ShVirAsVOvnBLHDjCpJBtWCoFhONS8176Q+RN/a4XrRi1504403Ap/+9KdvvfXWMQ5oqGQH+NZaa621A2ZT1nQUGRWugYPJSFEMedQyKGLRzBByTpnILIIxMgiYatizRAEmQViZFCMKBaSpMLKHFGKweLKwy6qiZCM0K+wa4cnGFiWtnyqgeebceY/yySWPojfgWztU0zT97M/+7DXXXAO8733ve+1rX7vviu49HeBba6211g5bRp45ZrNl4HJKQyVKlevVddXwRDJlSB4Z61fRJEpGJjdO7CyFa+1DN8pDMo67T7sPSi6hAfaULjucIeUQjvLwxEBGIzQJmRCVUQFCnJyvC+c4veQxu4/Qt3bAHvjAB/7UT/2UJOAXf/EX3/Oe9+y7ontJB/jWWmuttYOmTGLy9kydnqaWYMhUURmICi0ROypGQYkse1lb3RGKdaO8FpUFGUMIJUxSKmRRWAAhPE1UqTzSIoZHliVPohA5hhNsi/IQ69dlCRXmdNHJxd0XP+/TU1z7XrnW2j496UlPuuWWWwDbL3nJSz7+8Y/vu6J7Qwf41lprrbUDJunoGDEJbUNmWEMQJc/rnnwFWzTQwjSwjMLlObBKQFnJRAiPsMx6IdXgMawwlkgTxiGKTFOlTSTJMOXauOQKFqzQNIIposD2gm0ClDCGdvO4dIHRAb61Q/fiF7/4cY97HHDu3LnnPOc5586d23dFX3cd4FtrrbXWDltOBYsXYmvLMiasUlCWNTmqrDA11j70DIY2hhEGRVUsVeu/CxuLaXGhmiaBKlweLkTBSEbYFaPwQglKuTiGEkWpKGcxm6qSnCgxTMP4wnm8eD5lOd33wrXW9iwiXvGKVzzgAQ8APvaxj73kJS+56i/XdIBvrbXWWjtoypArp40CGduIhTB2amgsYoRUDlMVBSVtPCAGsSuf5HQSk9BSNZwLIZZ1DNyyNrTDIUmMJVzhWPvbbwJriGKsJ+8dhCAqcyeVIp0uSrK1MAAvA5SIusrfprfWvhzXXXfdq1/96qOjI+A973nP6173un1X9PXVAb611lpr7aB5uTx63VU15SXlCSzCBGvwBsMyYqQWmF2pnO1RY3iMu3P3KYVwjEFdooZz2BAzQ+YEnSpOJy/WCZo9TlmWoZExpxzYIJ0OIE5tuyZ8Gj5Vndo7MB4giWUYXfX7bK21L9MjHvGIl73sZevzz//8z//Jn/zJfuv5uuoA31prrbV22GyW0uYoYlJcHudWsIghDU07J4S3UlGy0AifWiOipCkkxc6RMMe0GLMtNoVmsYPZmmMyQxWgSoVqhBYvp6kqAiKcwc5jne6ew9LaFk9HmmZwcArY3iaUjs+gHgTfWrvsu7/7u5/73OcCVfXiF7/4H//xH/dd0ddLB/jWWmuttcM2hkJj7Cw7FBhX1qiqjdcD7KLA7FK2T8mdM3IyBLmMYZwxUJqCTRVCgyorGYWXy83tan3zaTIgnFrmEjNjseb1k4MgGaQkDeUcXlyTM6DAu5nYsJni6Ngd4Ftr/86P/MiPPPaxjwXuuOOOW2655dKlS/uu6OuiA3xrrbXW2kFzlV1aFisgLNZmcqFpVrocReFSMuyJSFG1zoobDKUgBjVqHfi+TNhVKIxmwpomtLgU3uAaBMhKwt4ELlJaMJtSjioHcuDCaVCIKirshZLQ0XWEYpr2vXKttStIZr7iFa+43/3uB3z4wx/+iZ/4iX1X9HXRAb611lpr7bBVeSwxbTSKMRfasPaNwzEy5LBQjEUpljALwZCNN8RkmTE5RckBXjSclANd7oo329vIgoEjbXlUjFBuAmkCPIHniKWiBMOF0kIyQcTiQK5p6+1WIUtMm30vXGvtynL99de/8pWvnKYJePvb3/5bv/Vb+67ontcBvrXWWmvtoIUQGqPKMwDe4bQUYC0UNRyMAEdEBdsiNwZLaEZajBxKRykSySbkyYUEIcWCXWUoAosoG1yJAVwlJa4synIMUyo7wTNMa1M9W7ulVJq2e12z1toV6qabbnrpS1+6Pv/kT/7kxz72sf3Wc4/rAN9aa621duAEsDtRbE4iIYRn1YKGUyMrpihvFVGelIMBzKgUM7JMRpkKB5LBIVdZA8kjVNSAxCGoGnhYoHUbX3PtKiKRbZNDjMQYq1S4wAs2BmvZTUohqd/Httb+C8961rOe/OQnAxcuXHjRi150enq674ruSf3C11prrbV20EqqGnF0hpQqDIGiMmsBb1xCFTrBI6OqNla4Qoiyh1wADldQGkuJstngEAXD6ycEtb7xzEhHBN6aaahqZGxtzyrQJEpKOyTLOY+AWEY4wprnU6R5PsWD7PexrbX/gqSXvexlD3jAA4APfehDr371q/dd0T2pX/haa6211g6apDDabiU8STIGFTHJeErjtAVRtZSWy8PhrQKVbUS4DAhNgZWh2VWQkAoihKEwJXv2BLNYEkWWnRQminE59cfwEGLScDCFsbHWcklclw8OtNbaf3Lttdf+9E//dEQAb3rTm97//vfvu6J7TAf41lprrbWDpkzAZSmUSVEREoVDjBpRYxZpLZY3FFRpvd0uR8RUdiUTtkKG8CCnkIoNoRqy7VLEpKAqNrFAQZgiUODAqnSFEokKbWxJIihFyUNypEcpN0bY+1651tqV69GPfvQtt9wCVNVLX/rSCxcu7Luie0YH+NZaa621wxapKeLMGSo9Ctk1qxQgWeTaIn4RKQcpu+yiSgNTlCIwi0wttrGCGoWCWeuJeMSk0sxwpH35QrsFkHZ5pAYGSsWQZAsPEyAsZCMF08Q2lT1DrrX2P7j11lsf/vCHA//0T//0mte8Zt/l3DM6wLfWWmutHbZpqs02YtI118yyEZqW0LBHZS2V6SzCVYtjzGkySoQdBAEqSrZlVZrE5QoAu3IIYMDQiNIGh9KkKkzIJTzFVM4q5FHYw1WVCpdV6057WZBbNpNyY6r331tr/71pml7+8pdnJvBrv/ZrH/zgB/dd0T2gA3xrrbXW2mGz8/haB56mQLDOeVMqCGKrpYaIlDRRMrHujiskoXIihzMUwWZJWymmEVEOR2kUKEHWiBpYWK5NVGKh2dPsChGZR9IIZRLOsqeUKSPIxCynzozNFkLqO/Cttf/BIx/5yGc/+9nAGOPHf/zH/Y1/9aYDfGuttdbaQbMN1pkzjiASU7YpY0YNlDEtsFiKqWKzQASGKoFR2REUEGI9VD9pSS+iACeyJYVCTBBjzCEV4BqyXGEtJXmZHVFhIFxylYnAUq2H+0ExTi8qp74D31r7crzoRS+6//3vD/zFX/zFu971rn2X87XqAN9aa621dtAEjAGZm02BJClBgTxGuBb7CATlnYysQUi19otPG5UFYzGWUqhGlKNAhTFJUV7AC4UjXZSnhZCBiogID6tsB0OEKotSmAiZiAlrPuHSJeYTTk+9jq9rrbX/1vHx8Utf+tL1+VWvetWlS5f2W8/XqAN8a6211trBW3aSXFKEsVVUCbHdMEAMgSvYloqwGLiWGIlLBVGmwNhlREURsiKItFxGJq0KhY0RjAWMmIKhylKGjBOBxnpCXga0lL1+dIAvnOfSbjl3h3oDvrX25XnqU5/6bd/2bcCnPvWpX/3VX913OV+TDvCttdZaawfNVVYwFnuxF5ArpBguU0iybcIqL1rbzK935MkFQbK4ZCJrMDEoVM7dIru0lFyBHAhSo0gRippyPYy/WFOpwswQsZg0IiqQnUVNiQagMTi5aI+6dJ7Tk32vXGvtG8a/bcK/4Q1vuPPOO/dbzNeiA3xrrbXW2mGrIWqcv0snJxmTsTAIh0agQiGoFExMTiaEXeEKwpZyTESKgEVT2RWqDGSQSyKFYwg71h54VKyT5CyH58AKx0IVMCRcEChGlCg5FrAkqPN3qqpOvrHPwbbW7k033XTTE5/4ROCLX/ziW97yln2X89XrAN9aa621dtBco5bhkxOqxhgCUWZY5SxE4ZLLUlgjrDFVpIUSlygIDw8XqVQpSClEQJCILC94hGwYa0gHV6jWk/Gy5FExhQIRxq5yiSgo/L/nxlV5/e+x6z52rbUv36233rpOr3jzm998/vz5fZfzVeoA31prrbV22Mbg9KQuXfDuYoCNJGFEoEAxjJiq5JrCm/UkfVlVRq4sMSWJCmQmB7YDO4YssgKGkR1YkjQINFExEFiUnZRiMFUYCylCmPU4AEqIZdSyxOaIKpH7XrjW2jeSRzziEd/xHd8BfPGLX3zb296273K+Sh3gW2uttdYOmM08j5NLCO66UIBUSBFTKWDYCgVUSIoFzSIt5XpHfWSU7B2DcqAFBgVZTgEEVBgyoLKYVCEL8KgYVlkKcFSma1KFy3ZJHsVIX55GF1BRebxlPlVumKbef2+tfUVe8IIXrA+/8iu/stvt9lvMV6cDfGuttdbaQavdqRDzzkkuA4yrhlENOyICIEzYCoLFVtRwWiENbDLYSHYtKCwbSnOiGPO6w54DVVRokSxEWTHVtP4SS0ajsEs4a8QECKscEZZEWZpKqUxdc6wI7XnlWmvfYG6++ebHPOYxwG233fbbv/3b+y7nq9EBvrXWWmvtgNkIVbHbxbyYCpACabHAtoe8uFQ7WMziqZAIChekKgTUAGKSh03VPDl2Npnr7xhBxVDV3dvyYqmBpdlLmVIVESgNym05oiKRtTMhhJCHtkdsNzFtNW32u3KttW9Et9xyy/rwhje8oS435PhG0gG+tdZaa+2gCVUtrhrLTLmMsRQOEklSEaiYCplIbQYjQhAWHuAKm8jwAImYyBpruygsUIAZUqQwVcakjMSRFTgiIooqXEaFagSFQgljti9KdXQ2rrkWHDlZvQHfWvuKPeEJT3j4wx8O/O3f/u0f/uEf7rucr1gH+NZaa621g+ZlsYJCY1fU5VxspzUUxXBESlMSLpR4UDZVsk1FiFElMcYa+e2REbJMYWQzJhOZYr1gr7p8rd2iUkioamgOnIEsDYertFsH2u3EzCYKNhs2xyUpgs7wrbWvkKR/uwn/ute97htuE74DfGuttdbaAbPBGeGLd1kbhkCqElVyUFOF7EKLbQdVw6EKCI0B3mBpnQ4fKTOIMJ4rRJghKNgMcthmnQFnIiQpXFIJXZ79PoUMMWSnysBReW1KH8CSYgwdnzUwdRf61tpX46lPfeoNN9wAfOhDH/q93/u9fZfzlekA31prrbV2wGxiWnYnYNVgLLKJlKU13AelwXptPZBsQziWispJYWJyySoYOMVCSNvLW+8hkUFJl4e/s9iWcBUUlMNVZlJuggUPAEQ4QmXZIo4oMRRJbsgppo3UAb619tWIiFtvvXV9fuUrX3nx4sX91vMV6QDfWmuttXbITC2+687YbArjxSrjcXmUuwBZw2WBlYpN2pIn1cZLUdKOkkaZRA6HsYcNhKSyPKjhRGVFCKUpRyGNCKvK8qjZCuU0Ko3tBMusvxMlNUd4u8mzZzRt+vx8a+2r9l3f9V033ngj8M///M+vfe1r913OV6ADfGuttdbaARs1dqfEti6d5+QUCa977eupdS1EEhMVxsGu5sVOCkJoI9JlJhsrXBoMlxGZKlEgUZMibYyKMCxJhHPIIJEKKkiPcM2ZJSMPRsgOotYDAaiQg+0Rma6x77VrrX2jkvRjP/Zja6fNN77xjX/2Z3+274q+XB3gW2uttdYOV4Vit+PSOU5OwxWWJOxAjokqeZRswhGQoQzFkKAweBlViW3JlNBIYn0uM7CFolTKwiZM2nJxuds9HiirECOyWMfGh4l0lJ24wqyj5Koscf7OWnbgfS9ea+0b2KMe9ajnPve5QFW98IUvvO222/Zd0ZelA3xrrbXW2uESYp49F4DLLAaIGrKrQqIGMTAmxuIqMC6XE4YyFRDIXi+6p6As/T9/9E3/9+//rz/9v+7zD39zBEyuFCoHlVDhjS5Pggs0IspOl6goBo6aw4YoO4ahBJPKy+nYDc6fpzrAt9a+Jj/8wz/8yEc+EvjMZz7zAz/wA3feeee+K/qfdYBvrbXWWjtkHstOSLgiTQqHhgNV4UBT2KFIa1E4JJRkBbisGKLACouNKzFk2P/vx7f/9NHjT3z0+EufzaJKaUepMFbFUDlcSGWXUTqKsiJlwYhYgjAoKgIHZslNnFzyfOJRPrm076VrrX1j22w2r3rVq66//nrggx/84LOe9ay77rpr30X9DzrAt9Zaa60dsAgiuOZYsmqBNAZZtWGxZtl4jehjCgktVaUIa4mIAmlUhZVmjGmpFFX+9x3mhCiqJCRLrqywhSwKS4EHI5ViWAgSZ61b8EMCeVawLOPSSV08wYtPT/e2aK21q8X973//n/u5nztz5gzw53/+509/+tM///nP77uo/04H+NZaa621wyUFmWyPHJtZKVzEIHDuYguTJSnkKmdRAkgVJc34/CbPxebSdnNh2l7MzelGqCrS+vfn252Lg8CFtMBJ6CSmU1ypoRAqQNOCimmgQQxyRDg0YnKxgKHmU3Yn0qhlQfQ1+Nba1+7GG298zWtec3x8DHzgAx94ylOe8rGPfWzfRf0fdYBvrbXWWjtg9nR8FuLS0dkLxLmc7oQLsBM7OI8uESfSKdOpNJsihEfGxZzO5fFFpkuK884TdEF5IaYLMc1m/v8NadeYphm+pO3nvfkMR5/RmS8oP5fHn1PehRY8EzOaYYdnVFAVJ+gE3QXnpBMY5CydC+3mMWpos+383lq7Rzz2sY/9hV/4hWuvvRb4+7//+yc96Ul/9Ed/tO+i/msd4FtrrbXWDliEr7lutz2+dHR0sj26FDKxwDl0ns0O3aXNefKiOKe4Q9vPaHPb5ppP6sw/6Jp/5ehf4vgzcfS5PPPJuPaf4vrP+vj22P6rznyBbXH5FP0p3AXn0Sk+IT+3ufaO6cydOnOO7UU2t+vMZzl7F3lBnGc6RTu4g+lzMX2R4zs5OkUnbC74zEVtTuAkjs/HdMK2Qj0KvrV2T/nWb/3W17/+9Q984AOBO++889nPfvbP/MzPzPO877r+o2nfBbTWWmuttf2R6uw1u6Pju85+0y6/cNe03c6L4LyOSpzP6dh1htPtmM/rmrti2k1xLqYIHw9/YXN8NJYNXET3GYuCc3Ft4DH5umUZd6frmemLnF2cd2ynnXLAhWmq6Ux5nF18tPhzwTct89mxiGUid0wQhU9JwxzHubV2EEQczXAfIjd5PB31G9nW2j3oYQ972Bvf+MbXv/71H/nIR2y/8Y1vfP/73//Od77z5ptv3ndp/1u/7rXWWmutHbSxPTq59tovnrn24vbsybQ55ogjLiq/MB1NxQXVdmyddX5z9IXNdo7p09PxScSCjmqcqeV+tVyIvDCf3rnZfkvNg1q0ve7/Y+/Ow+W6qjvvf9fa+1TdWfNkSx7kCVue5BkbsDFDAg0BJzghDSQkTd50GgdI24kfArx5AwRCt4lDxxCGQGggzRBGNxCwMTFgbAsLI3mWrdGapat7r+5cdc7e6/2jroQ8yMianbs+j/+oOqqzz6mSnuv7q733WqmRZbQ1/pgUO+qhgr5QBInb6/V2K7MxGtp764il7qoaSrGzLHuqUhPD7aE95x1FfTwoWRAJlmNNMJNaZwr1Rr1DQ1EX6TiyH5xz7j+cnp6e973vfV/84he//OUvm9nSpUvPO++8D37wg3/6p3+qelSsXvcA75xzzjk3qSWNA0Vn2dmzrWfq+MDOjvZUqoxKbaCoGyLClNTArDe2b4gdO2N7b1FHLSMxW2fK6yVptukyrjAai+lVs6FhhHrBmGDAQD3kWhyPxbC0NUQLS2PaUbOc0B0Wx4oaMU3J1fTa+IzcCLk5qG21bCaaRUVSAy21ltGhqDOKtvGOKTNivaPe0ZHJZuqr6J1zB5WqvuENbzjrrLNuvPHG3t7esbGxd7zjHV/72tc+85nPtJrGH1ke4J1zzjnnJrWMDITYF+vr27pzUZc8pU5uaOyTWBHaKYdj1MxwLLa0dWypt49rMCFTREulaCB35XLneK0zJQ02EJqduexAekRaAX5E61ar9Uttp2hnqvqlXmCINtFRre2MbVVKo1qNWuiVoqbZNBimSA2LOe3UWilxZ6ylKOMxdtfardY5R8Nwykf6k3PO/Yd19tln33TTTf/4j//4ox/9CPjJT35yzjnnvPe9733HO94RQviVpx86HuCdc8455ya1puXhLFtDbVWtLbVP60xDWahRmUnC+kO7GDu66uOi64qpoxrGRQ3NCK0qckZ/hE6ZlYc6rMyVjjWLqeVI967xx9FtoW2sKKpSh+ttCY1lGi9igrIt7AyhKfVBS/257BltG2ivzRwfL0MoUoqSzHSoaKuUpkhTtbfeNYdaQ/TkzFjOPv3unDt0Ojs7r7vuussuu+xjH/vYwMDA6Ojodddd96//+q+f/vSnFy1adKTuygO8c84559yk1qzySMobsuyQoq99WpTQnqp2tTGkQgSyhIbEraFjFBkpomUlZ8NMQsIyFpAkVNLVKTZSG63FNNos5rK9NUu1o2jb1t7dKLStSAkZijFazqhYVpFkQU2a1JqhfWdH05TBzqIKdDayKhkzkwodqrdV0BHrVaxR0ZfTLMSX0DvnDrXnP//5Z5555ic/+cnbb78dWLJkyfnnn/+e97zn+uuvj/EIpGkP8M4555xzk1cyy1gz5YbIQGjf1j5kqaejKNUEMLFxtClhGKmMhoQqBww0YBlVzBAyilWDyCAyJp1dWg20pXNEIQFDoVhdTO2iOaQyGGIpWqIiiFGKtFUVrccEi3URSyZisrVuIAVpRKOZViBQhFBZmCeytWIRAp7enXOHXHd397XXXvuiF73oYx/7WG9vb6PRePe73/3Nb37zc5/73Omnn36Yb8YDvHPOOefc5JWN8ZS7ijBQ5qbqUIyDMdQoipxBDCrIKlXUJMGqTDIwTKgrzQorEINEhKzkNEAYUG0z271DfViLPg291mHRBJqqTVMTqYtJTiMxCmJmhorQCEoOWUSsMoCAoIiYhUAVa33oNpPRqsyCYJ7hnXOHx4UXXnjTTTf98z//8y233NIqUH/++ed/6EMfuuaaa+QwLgU6KkrhO+ecc865IyWrVjkHgFCpVMioyE6JOzUMoqOxNk5RZbVKCREN1CIaqQxRQgaZOBIUDQDRxgXb3QdedCgUY/XYqNWH6/XRoqhEUxFGa7XRohiPtYbIuBbjMQ4HLaVWBkkqVb0tIUk1QYk0VcayjhEbIkONssqknA/nL83OOdfZ2XnNNde8973vnTlzJjA2Nva2t73tqquu6uvrO2z34AHeOeecc27yUqFMKYgYOefKKsEMKRBFIzFgCQEVMCRTF1BqRlCklecBpVSAEGirUcY9l3lmkbJeb1gxXgsVAQLtBQgmVq9VsWgWbVWbphiIEYWsmFBWFIEgxBqEiXuNRTKwPGo5WToyH5lzbnI799xzb7rppiuuuKL19Fvf+tYFF1zw4IMPHp6re4B3zjnnnJu8gohClclYVlERNGKJIFgiZ0KkUDJEAyUbOZMFCQQjVQio0WZUQoKqQiHYEy6TM9kwQYwgZKUQstHIaAKjKqgEE8wgA9QME0yRTKgoapggTRMhSFvQpvn0u3PuyOjs7Lz22mv/7M/+rK2tDVizZs2ll1566623HoZLe4B3zjnnnJu8sllHjKM5dccoWZIaIUEiV6CEQDIsESApVSYbNcXADBSESkiJsdbe+EwweGJ6F0ggiTJDBCMbSQEUKgGImZDJmQRiFIlKaVXBM0WVsgQhiZoFsTYTMTOzp3lLzjl3WFx55ZU33njjMcccAwwODr761a+++eabD/VFPcA755xzzk1iInWVQqRRWUnKZJIhkRwIAckYSEANSaiShCqTMmQsUyiFkQSFuhAiFQRhz+nxVqLXGgSCUYJkJFNAMIIQAzlTZoJSU6KCEpTcWjtvmCIBTYhGkUJjEu2KAd8D75w7oubPn3/DDTecffbZQKPRuPrqq2+77bZDekUP8M4555xzk1c2kll31EwSy8ECIaAGgiUqBRivaEBrQtwMUYpIDmQjgwoBBCqjaD1Swh6/ZBrEOoUQDDWCkVrT8kIWmkZpZKFeQJj4msAUqxAmvgioSnLGCrCIdYt2F8E8vjvnjgLd3d1/9Vd/df755wPNZiaJgk8AACAASURBVPPqq69esWLFobucB3jnnHPOucnLMGA8WxYVNFsroguFEYQ6RKgJQBCCEowAVSLIxN74BBUkI0EqUaHKUP7yGgq5oky75tKVIFjAQIQOkIS0ttZXmJCMmKgCJoRMyoRIEHKK2dqCdBfF9KII6vndOXdUqNVq73rXuxYtWgT09/e/8Y1vrKrqEF3LA7xzzjnn3ORlZkFkOFlpqEpu1ZAzo8pUmVah9wRBsYwIlVAl1CgzObe2tKMQDTVyRpW6keq/vEYGrQiCCM2KZgah0aSWsUxp1Oto7Zdr6Vv77WOrmp0QlKSIUmghMrtWn15om1hN1HfAO+eOEkVR/OVf/mWrvdzSpUs/8pGPHKILeYB3zjnnnJu8VCRDTjlDyomgWKRKSEAEEUyIAoYKKUEr1QuSMQGjygTQgiAolEaDJ8zAG1jAMlUm1qhDztSULKiQhMY4jSaVUiVUJ2rdJ5uoZl+CGCmDBdNAbiJdtSL6DLxz7mjS09Pztre9rfX4b//2b4eHhw/FVTzAO+ecc85NXmoMV1WFScpVloSQM/X6xP7zCjRRgkGVEKgFBCJoq+WboEoWykQ2spIqEGSPXzIjWMKUqKSKpKigcdf+eUGEulBkIiQYrzBAyZEs1JQYiGA5xZwlBqOsKhE8wTvnjiqLFy++8MILgd7e3s9//vOH4hIe4J1zzjnnJq8sNLLVQwgiCEVOmNLIZKAiVAQltJrAR1QRSEpWEkSZqEUP1FrF56Gu1OwJ2ToDNlHWTjOWEaPRBEgZAZTMxIQ8reJ0hraK1UOWiZn5LJXoYHN8oJn6UvYSds65o9BVV13VevDFL37xUIzvAd4555xzbvIqs+VsU2MIQXqCSlakIhuFESJNaCQqo5XIBUqQhBnRyGABVSxTGRImGsVnfUIbORVMqJpQUCkFZCNGxEAojboRlWg0wYQCJGKtrfgQE0pr5CrnMtkoeaiZxqt0pD4055zbmzPPPHPatGnAXXfdNTIyctDH9wDvnHPOOTd5GWaCqPTEoIQcAwJiZKhsYl69JhhYogHRKATLIJQZMVq15ERImXYlsat/+y5VJgghkpsUUApRqRIIIhRQKWWmmVGbmHUPNjHbL9DMWCJnRJU8aDaac5nTeMLMy9g5544uInLGGWcAVVUtX778oI/vAd4555xzbvJq1Y9POY9nG5WsIlhr7XrEMrkgBMyoDBXEUCWDGSbEXYvtsyJCkIn6cwp7rqEXocykjAjSqnIPRQDDhCaUiQoqIQtakICMgraaygdUiIGaZIIhubSxnJspme+Cd84dfRYsWNB6sGrVqoM+uAd455xzzrnJK4jWYxhPOVuumzVTmojoadc0uhmqRCUbE0vpW43jjJyoFxhIK0pnykRdCU/I76jSVkeFMpMhGaVBICnV7vXzrZ32QEVkomxeNspdXxZkWlcXqLBhyxUGPgPvnDvqtJrJAZs3bz7og3uAd84555ybvKJKyjae8lhp45rNjNwK301iJpUkpWkTSVlgPKOtaXZFArWCmlEIGSooAgplK//vZuQSMWqBZAQlCpZIRt3IQlZCxjJZsIAZCqUSlDpoQQpEBbMMWDZro0hm6oXsnHNHn87OztaDwcHBgz54POgjOufcUe7R/h3rh3a+5LiFh/Oim0eGfrx+3ctOOGl6W/sBDvX1xx7+2qMP/eFZiw/PW/ju6sf+5eH73nD62a9ceMphuNzT+sTypT/esO4vL37hopmz9/aaP7rl5tGyfNLBKfX63M7uS+bNf+nxC/0XfeeeVjIrgopIVJOsIkprsluUctfOdm1NureKwytADVKFBsbHIFJlJBMFrUhKzkTp6m9OXMNsopg8CRVEqAyF9kDDiIZUZNBAM1NkNKIgNlHETpoEIdNanK+YauwIVlefiHLOHY3q9XrrQaPROOiDe4B3zj3nmdn31q788YZ1W0aGp9TbTps247dPO3NG+15z8pcfeWDb6Mi+p999Gf+uTeu/u+axTcNDczu7Xnr8whcvOPFJg/zLQ/ftbDZ++3lnPtt391yULD+yo7ce48lTpx/mS09ra4u7uk8ns/7G+M5G74q+3rs2rX/38y8Pzz7DDzTGv/HYw4/09Q6Mj580ddrzj1nwwvnHH6wTP/fgsu+vfZrdcfO7ez70opc921t1bv+UmShSYYWEusWAoRkTTNAaUhKUaFQBEqUhRgxIwgIiZCgyKoSCqmIMRKgJ8ssqdpd9dd0drz8RS2hBUKgQozKAAClTKCZYRS2QlYZQzwQYh5rQMOpCM1FTTE2sTaxAuoIHeOfc0agoitYDD/DOOfdkVc7/uPyeuzdtEJjX1T3QGL9j4+NLt256/wuunNfZ/dTX/2LblhV9vdP2eRp8X8b/9qpHv/jI/XM6u86fc8yanf3/dN+920ZGfmePrP7wju2/2Lbl3Ze86KBMAZ83Z970tvZTp804GIMdEmNl9YElPzm+Z+oHXviSw3zpv7jwBcf1TNn9tMz5R+vXfu7B5Y/27/j+mpXPdhHBusGBG+65s298rBbClHrb8u1bl2/funKg781nLn7mv8p9PHHLyDCg8uTvFXyxgDucMrkyi0jTckUl7FobnypCObERPWeCUWXqtYmC8NmwhNRAMIFEMgpBA2ZY/fqrvv+EywiEgBhliTDReS4LRSYHKiUaZYBW0XuoDDJtARL1AJkgqFCVbUVRDypBu2rFEfnEnHPumcU4kbLHx8cP/uAHfUTnnDuc/un+e+/etOHMmbP/dPHFXbVamfMXHlr+g3WrP75s6V9f9uLdL9s0PLRyoO/+7VuXbN74rEoe/crxh5vNf330wXNnz/3vFzw/iGazj/7iZ/939aNXHr9wVntHa5AvPvLA4tlzT58x66C85RN6pp7QM/WgDPUfXqH60uMX7hgfvXnlirs3b3hWAb7K+R/uXdI3PvaKE0/5needWahuGBr8m7t//IN1qxdOmXb5ghMO/MQtoyMCn/6119RC2Ntozh1qKjKeUs7ZUFVFFMuUFTUlJwKUSqs0fY7khBhBMCEUSEIgJwhEwyC1kvyTf9Be/1u3f+irV2KJoLR6vxWBlLECSVCSIwKh1VI+Y4EqIYIquSQHUBRibIgUQduEZPlp3o9zzh1p7bvWaQ4NDR30wX3pkXPuOax/fPzOjeunt7Vfe8GlXbUaUKj+/qJzp7W1rxzo2z46svuVX17xwCeWL71z0/pn9Qvfvoy/Zmd/lfPLTzgpiAIq8msnnmxmK/t3tAa5e/OG1Tv7X/+8sw7iG9+bZFbmdBgu9Nxy9sw5wKbhZ/c/0aVbN20eGV44Zdobzji7UAXmd/f8P+dcAHx79aMHfmIy6x0dmdHe4endHVk1kZEyNczGch6uUm5VmK/VMMGULEhGW8XnMgIKjUQyLJMMMXRXYfkEIVDT63/z3596oetf90OCIgZCBIwo5EQBOUyUmk+t6A4EakI2SkMKCsUSiWi5o2A8VfUQmp7fnXNHpe7uiUWa27ZtO+iD+wy8c+457IePr06WXzD/uD0jkIp84IUvaabUU6vvPnjVKae3Nr0PNhv/uOyegzh+K9j3jY3tfsHA+DjQVdSAZPkrKx580fzj53f37Pv72jY6cvOqFSv6envHRrtr9eO6p/zGyaftXjP/zZWP/OuKB3cXsbt13arPPrDsmsUXbRkZ/s7qx8aqckZ7+ynTZvzWKWcc09V99+YNP16/dvXOgSBy6vQZ//n0s3evC/jyIw/cvGrFfzv3wsuOPW7Pq//XW789XlWffcVrn+EO79j4+E83Pr5peGiw2ZhWbztp6vT/tPDUE6ZMBW78+V1Lt2wC1g0OvOE7Xzt56vTdSyE2DQ/dvGrFmp3920ZHZrZ3nDdn3itOPGVqvW3Pkc3s39asvHfb5nU7B2a0d5w6fcbVp56x7x/d00pmgD3LdlOtd3HJMfP3XM5+9qzZHUWxaXho88jwvM6uAzmxd3Qkmc3repqNHs4dToJky+NGV9BsIiKkCknQmnhX4q7S8VVFLZCEekAEMk1AUKHNqCAD6frX3b7Xiyk0oQbVrnZ0QakUTaiRMzFOJHwrUSVnFFJFikQFLVRrQLbKKJN/X+mcOxrNmDEjxlhV1WOPPXbQB/cA75x7Drtv+1bgnFlzn3R8z+jesnvNef/4GPtsX8Y/rmfKvM7ur6x4cHp7+/Omz1w90P+Fh5dPb2s/ZdoM4LZ1a/rHx173bCLo+qGd/9+dt49XVU+tPr+rp78x9ottm5dt3/LOi1+4aO+L8L+9+tG1OwfmdnadPG366oH+uzdteGRH74sWHH/zyhXHdvUc3zNl5UDfzzZv3Dg09IEXviQeQOlmg79beue9WzeryLzO7mO76ttGR+7ctH7p1k3vef7lC6dMO2/2vCm1ttseX91Tq1967IJZ7ROdVH668fF/uv/e1hcfczu7towMf3vVo3dsePwvLrrs+F1/O82UPrz0zgd6twHT2tpGyuZt61Y/2LvtAEv3txL1Mb8qKhvct33rrWtXvuGMc+Z1dq0bHABOn/6EzzyInjptxrJtW9YP7txbgN/HE7eMDgNzO7p+tmXjsm1b+sfH5nV2nTJtxiXHLPAd8O5wSmaqIeU8UmXLqZI4kaUtEyKq0CoRD/VABSoImDFutEMFZiRDAlS7WtA9vetf++8f+tIVmBGgFKIQlDIjRilgpIxFQgKjmYlGEopAaShYosoN1Sm1WjMlbwLvnDs6hRAWLFiwZs2a9evXb9y48dhjjz2Ig3uAd849h7VqgE2rt922bvWSLRvX7OzvqdUXTpn2m6eesbdwddDHD6LXnHfRh++583/87KetIz21+jvOv6QtxvGq+sZjD//6CSc/q/z5pUceGK+qq09b9JqTThMRg5tXPvKVFQ9+/dGHFj3/8r2dtXbnwJvOOOfXTzwZGGw23vnjHww0xm9eueItZ5/XKom/fXTknT+5bePw4LrBgZMOoDj8sm2b7926eXZH57suedHM9g4gWf6Xh+7//tqVt69f29rjff6cY257fPW0tvY3nXFO66wdY2P/dP+9KvL28y65aN6xQDOlLz3ywPfXrvyHe3/2wRe9tLXU/NurH32gd9uM9o63nXdxq4L9o/07/v7ndz20Y3g/bjWbbR8bvf3xNT9Ytwp44bF7rR4/WpU/Xr/u1nWrtowMB5HfFwF6x0aBnvqTvwzqrtV3/+nT2scTt4yMAD/asPbWdROF6O/bvvX7a1fdsnbVNYsvfoY2Cs7tI8vW2PmrCyA3ci4HxouhamxgrG2k7EjjjeHWJssMgiSsgkChjCQUmhUhIFAGyoQpkjFFK0ze/nt3PPPl2kZLGgWSMEOFXIKBoJAglEhFxcRWfDOCkKAGY4ZqLVVtTasXVTOUGqvx9KsLRGmhta7aPn5ozjl3UJxxxhlr1qwBvv3tb//xH//xQRzZA7xz7jlstCqBrz760J2b1rfHYkZ7e+/Y6J2b1t+zZdM1iy+6YO4xh2f8E3qmfujyl/1s88ZNw0NzOjsvnju/ta7+/65akbFXn3zas7ro44M7gRfNP75Vm1zg1044uZlSPT7TT+wzZsxqpXegp1a/aN6xt6xdtXj2vN0N7WZ1dJ47e+5dm9ZvHxs9kADfTGnx7HmXHbtg5q6l+EH0igUnfH/tyq0je43ZX3vsoWZKf3jW4lZ6B2ohvGnROWsHB1b09S7ftuWCuceMV9V3Vj8K7E7vwKnTZrzlrPM/vPTOfby9d/7kB097/Pw5x7zshJOeenzD0OAt61bdseHxRqpmtHdcfdqiKxacMLXeZmbNlNi1FWJP3bUaMJ6qp73Qvp+4dWQIEOQtZ5131qw5bTGu7O/7l4fve7R/x8eX3/OuS160j2/Zub0Z3DD498f//b6//lw499DdzS5v/8OfHpRxdsCX9+2VC1+28E23vOmgXNQ55/bRJZdc8p3vfAf43//7f3uAd845gGQ5mwFLNm98y9nnXbHgRIFkdvPKR7766EOfuv/np06f8dS19Ido/I5YXPHEsuQDjfHvrnnsd047syMWQJXz0i2bHh/aOaOtffGcec8wJ39sd0/f+NjHl93zm6eeccq0GUGkLcarT1v0zHd7yhO7yrUme0+b/qSDNTjQNacXz5t/8bz5ex4ZLcvb16955rMe7etVkecfs2DPgwKLZ89d0de7or/3grnHbBoeGq+qhVOnPal7/OI582a0t+8Y26e9D3v2gQeihnldXRfPm/+CJ+7zT2Y/37rplrWrHt6xXUTOmTXnJcctXDx77i8buokE0WcoebjXVcL7fOJZs+bO7uhaNHPWgu6Jvnfnzp67cOq0626/5aEd25dt23Lu7Cfv3XDOOefcc8LZZ589a9as7du333XXXXfccccLXvCCgzWyB3jn3HNVEBURM3v1SafunmcOIledcvqj/Tvu2771ns0bX3L8wiM1/lcffWhKrf7S4xcCO8bG/sc9d2wYGmz9UfsjD/zXcy7Y2wKBPzhz8f/6+d0P7tj+4F0/qoVw4pRpZ8yYdfG8Y3fHvKf1tNva255x0n6/jVbl3Zs2rOjr3TIyvH1sZGfjV6zRTZa3jY5ksz/6/s1P+4LWCJtHhoBju55c7U/gmM7ufQzwT+oD/7S+ufKRH6xb3T8+NqVef83Jz7vyuBN3rybY86JT6vW+8bGRsvmkj3G42QSm7eUrmH0/cfHT5fOeWv0Fxy74/tpVqwb6PMA755xzz1Gq+hu/8Ruf/vSngb/4i7/46U9/+stJggPjAd459xzWXdQGm43Fc+Y96fiZM2fft33r2sGBIzX+xuGhH61f+yfnXtjK1Z+4b+nWkZH/du6F5885ZtPI0CeWL/348qX/c+rLnjYEzunofP8Lrny4r3f5ti0P9/WuGuhb0df7zcce/rUTT969pfyQeuZq7SsH+v7nPT8dbjZntnecPG362bPmzO3smtbW/jd3/3hvp6Rs2awWwpXHnfi0L2iVGAx7L63X8ZTl6Afi648+lMzOmTXnmsUXdxTF3l42ra29b3xsqNmc8cR4P1w+U4A/kBNb5nZ284x77J3bRx0zO67+ytW/8mXZ2FGWP9sxuGxodMN4OZQrGhVAzmjAmCg4H8AENXKAiqQEI4G0trLbaz780LO6vW/9+SKyogIJUyyjRhaktRleMMOMEEkJg5igqNXjjBAWtrdfPKN7Vi0uaP/Vy6w653Q+qxtzzrmD4hWveMW3vvWt3t7eu+666zOf+cx/+S//5aAM6wHeOfccNr+756Ed26v85OXKrbZh7XGv2exQj/+lR+4/rmdKa8X4cLP5YO+2V590Wqtb28Ip0/74nAvec8cPl23fsnti/0lE5IwZs86YMQtoprRs25ZP3Lf0e2tWXjDnmNP3Xoj+oGikaqTZjLrXzuSfvv/e4Wbz9xad8/ITTt79TfIzt1ivhTC9vWNgfOwNp5+te//6eW5HF7BxePCpf7Rl77vr98NrTzn9tnWrl2/f+vZ//7cXzT/+pcef9LQlD+d1dq0a6FvRv6PVHq/FzFb29wFzO/YaCfblxLGqXL5taz2GxbOf/PXQYLMBeHs5d+CKjuKMq/epBcZjQ2OytX/Ltt6Vw2V/apATY60O7YIKZaJQBLIhYErMjEGhE/Xngzxt4/dn9silswkFzZLCaApqRKUyxJBIWWFCMKqM7LpW0dFZD9ZeLJjSNWX29HntxfO6Ow+gq4Zzzh1C9Xr9zW9+8w033ABce+21L3/5yxcsWPArz/qV/Geec+457MXHnQj8bPPGPQ8a/GLrZmDP+HQ4x1/R13vv1s3/+XlntaLq0FPmXVsLtgefbuX59rHRd/zw39571492H6mFcNG8Yy+ceyywbXTkAN/RU7WmhXdbvm3rM8y/Z7P1gzsL1T3TO/sQsBdOmZbMlmze8KTj//CLJW/9wXce698BzOvqao/F6oH+lQN9e75m9UD/4we8mGJPv3nK6f/rJa+4ZvFF87t6vrdm5Z/f/v2/XfKTpVs2tUoe7PbC+ccD92x5wt/+I329g83GqdNmzNl7m4N9ObHQ8Kn7f/7he+5sbRzYrbU5H1g4ZdqBvk/n9o3BeM41xXIMIhDIRrSJxF4mRCaqN0gmFJiRA1EJGTGK1iz6s3b9Vf9OCdFIkUIISgXRKAKSMKWtNeevREGgEHKpQqdIzhmSmIn/JuucO4pdfvnlF154IbBz5843vvGNKaUDH9N/7DnnnsMumTd/VkfnLetWfX/tqlb2KnP63IPLHu3fcWxXdyv07jsz++nGx3+68fHdq5f3b/wvPvLAObPmLJo5u/V0bmdXd61++/o149VE+fHvrVnJU8rOtcxsax+pyhV9vbeuW2W78uSGocEHercCB1I9/qlmd3QCP3x8zWhZto6sGxz4/EPLn+EUFZnR3lHm/NCO7bsP/mLblk/ffy/QeOL/lkb3+GrgdaeeoSKfuu/en23e2HpXyfI3Hnv47k0b2mM8edoMoB7iq046Fbjp3iVrdva3Tlw/tPOmZT876L2eg+jzj1nwV5de8TcvfMnlC05Y0b/jxp/f9Y5//7dvrnxk95b+RTNnz+nsenjH9tbfFzDYbHzqvp8DL92j9sFT/9nsy4lR9fL5xxt85OdLdlfvHy3LT93388cHd549a86Zu/79OHeolWYiFGgIGKCBUEAiAEIhhDCxVD4HUkaULGgiKwiq17/2h/t5balQJSRyRjOaMCElVGgTshDyxLcDWUjQJmTQoKLZpD3u1zcHzjl3GF1zzTVTpkwBfvzjH7/vfe878AF9Cb1z7jlMRf77+c//0M/u+NyDy7766IMz2zu2jAw3U5pab/tv515UPMuFlZXZx5bdA/zp4otbk+T7Mf7PtmxcOdD3gRe8ZPcRgTcvOvcffrHkz390y6KZszcPD60c6Lto3rFnPN1ieBF54+lnf/K+n3/2gWVff/ThWR2dY1W5eXjI4FULT53f/eQCbwfi/LnHfO2xhzYMDf7Zv3/v5GnTh5rNtTsHiqCzOzr7x/faWvlVJ5362QeWfXDJT06dNqMjFptGhraODF++4IQ7Nqxb2b/jI/fe/XtnnNtdqwXR7WOj77/7xydOmfqG08+e393zn08/64sP3/+Re+/uKIq5nV3bRkeGm82uovZnF1y6+1fwV5x48iM7tt/fu+3dd/xwZntHENk6OtJZ1F52/Em7m6UfXCf0TP2js8//3dPP+tH6tbeuW/2vKx78xmMP33D5y2d1dAr82fmX/O2SOz7/0PJb162a3tb+WH9fmdOvnXDSpXvUtH/qP5t9PPH1zztrqNm8c9P66350y9zOLkG2jAwnywu6p7zlrPMOxZt17mk1UlaRIcuFimGQMaW126U1CZ8TEaqAJiyTDBVEUcESYf9LVFz/mz/80DeuBEPqMI6CRKQkZ5KgoIooVUaMEKnQwpqW2oKYWE+x180+zjl3lJg+ffrb3/72973vfWb2/ve//4orrrjiiisOZEAP8M6557bjeqb89WUv/tIjDzy0Y9uWkeH5XT3PmzHzqpNPf4biZIdu/GT2lUceeMGxxz2pEPolx8zvrNW+8djDS7dsmt7W9tunLXrVSXttDn/5ghNmtHd8d/WjG4eHHh8cmFJvO3vW3JefcNJBr0neU6u/5/lXfHXFg4/09S7ftsVgWlvbW8+96JsrH3mGAP+y40/qLGrfW7Py8cGdXUVxwpRpv7/onHNmzZ3X2XXL2lUP9G5rpipq2+8tOucbjz28sr+vY1elgFeceMqp02Z8Z/Vjjw8ObBganN3R+cJjj/+Nk0/bs9VfPcTrL3rBv61Zee+2zet2DqjIJfPm/87zzly+fcvBfe9P0lXU/tPCU1954im/2LbllnWrql1rHxZ0T/l/L73iiw/f/3Df9h1jY3M6O19y3MKXP10/+SfZlxNrIbx18UVnzpx9+/q1G4YGReTU6TPOnjXnPy08JfiyYHd4lWbDVapBHUF37ULXTNNoDxhYRJuYYIGQCHVokowQKOVD//flACXEEmpYRaqI8tbfv7Orvwn89HdOvOP1xyGCKGZkRTNilAVkEsQKCnKTUGEBSQiIoGCJYBitcyvRrhCmFkGQKXH/G4U659xhc+GFF77mNa/55je/mVJ605vetHz58unT939NpZgd9JWJ/9FceeWVv/u7v/tHf/RHR/pGnHPuUGmm1D8+Nquj8xmKzDnn/kMartKygaFf7Bz55paBjY3xx8tMoyQlakaG0lAoIqkVvANmBKNKRKUqkYJapDFOjOTcmrXHDOStf3DHRID/7ePv+N0TJk6PYAmrQZMkRCEliIQEkZyIgdKIRpUISoJcEQMhYHFaLZ43tf3ymd2XTO0+s6dr6qFplumc29MrX/nKV77ylddcc83uI2vXrn3d6173sY997KKLLmod+fa3v32E7u65oaqqP//zP1+5ciXwpje96XOf+1zreLPZvPTSS9/97ne/9rWv3ceh/Dt+55xz1EKY09nl6d25SUhFVLUjRNFcJciJEAhCiohS1AgFlWARBUpUQBAjV0iBGamBBkpFIiKoogVi7P6JIoEg1AosUSVEkYzWiIopUREQa8V+MLTEMiGSlEKpBaRVx06DWI0YLES0mX0Wyjn33BBjvO666+r1OvCFL3zh9ttv3++hPMA755xzzk1erUJwYzl1ETJGUiwhYaL4vBoZgqEV7I7uGcmYEhKFkQKFEg3JSCADJWbsztdmVIoaIRKAAgPJZEENqxGEKqCCZHLA2jElJ0LCFIRQYJEyCwQYtTxuKe1xBeecO8ode+yxr3/96wEze9e73rXf43iAd84555ybzFrz5tBajq7Vrs3nSkogKIRi4oFB1UAUKbCCHMiKGrk1215hCTEqI+yxQV2gCGQQIQeqcYBkKIiSM9ZK8gYRKQkVJLTAwBQCzUwwlEQetNRXloOlNVM+/B+Wc87tt9e+9rWzZ88G7rzzziVLluzfIB7gnXPOOecmr2zWHiWiYlU05ZU6kwAAIABJREFURQJBsYRmggKYUCVINA1lospdUiKoEMCEVFFlCOQKBA3YHulalGSYIUKA2JrkV5IABMgZEXIiCESshtYApAaZVBGVKmN53EJvozHayP1VlfZYpO+cc0e/GOOrXvWq1uP9rhrgAd4555xzbvJSoa5Bg5QpV5ogUxlVojIMrNWJXbGCQsiG1TABIyWCUoKUiE40lkuKCaogv4zXltFMErKRhdxan2/EDImUQbBELrCMGVZiiZBQwQRq0CqPZ6UwUuadlY1VVfA28M6555rFixe3Hixbtmz/RvAA75xzzjk3ebXm0bPJUGY8C6oUYDVCwEACZpChQhTqSIYSESKkhCpSo2qNAgqWsQp0jx3qgtQIrUX4huZfDpiVoIgRAzR3V7DHEsmwJikhJQjBoCgtNRJbqmaZTC0dwc/NOef2Q0dHR+tBo9HYvxE8wDvnnHPOTV5Bta5xR9ksy1QiZCEJdTADpbJdneED5Tg00YC0ISXW+jWyQdUkGgbJCEqMqEz0cm8RIyVQTAglJmSBRAlBIZMDZSJEEIJOfCnQ+gqgtfFeEkmwBLEplGU5kjISjtin5pxz+2X16tWtB/Pnz9+/ETzAO+ecc85NXgKjZTlcVQkTmJgPTzbRsD0aYmAkowioQRNrEgtSIoEoUkcEMkFASK1TeEIV+ol+7xXUSQGNWMfE+vkqU2Ni4b0WJIEGWRDFWnP6gZQxI0YkJ6weQsPyeFUdsU/NOef2y80339x6cMUVV+zfCB7gnXPOOecmtaHKxpIRQJiIyjkBhIhGsiIFIlgmKxYxKIUoxAyBmLGABBBUyIlKSdaqbT/BjJAQJSe0QjKSCIpkQqJSYsSMcoxUQkFhJNACBc0TnedSJlnAykR71CGvQu+ce0757ne/e//99wPz5s37rd/6rf0bxAO8c84559yk1lc2FBQJVSLniXLxOWMNGuOIkZoEyIokNCKBICQjK2UiK5rR1q71TEgEJsrRTQgAiYn27wgoYjQTWQhthAZWYkZQikAyshGM0sgGkQiFtArji0lHIXWCYdkbwTvnniOWLFnyyU9+svX4b/7mbzo7O/dvnHjwbsk555xzzj33VCYZKy1bVBIkoEQLciREyoqglE2KDnLCMrSCupIzGkglKaKCllDDIhh7loi3jAkSUaGhxIwJKVBEJFEJEpHGRBk8qZCCsiQURJuob5+biGISlZrk9hDrIj4N5Zx7rrj11ls/+tGPppSAN7/5zX/wB3+w30N5gHfOOeecm7zMUJGxCkVrrW5tSdCCVJIgQtHqDBcmJtVTiSrU0IpSEKNWp2xCgEiVCAZK2GODehAEgtGEulEVxEwuyUqIaIUFFCrDlGCoYRHJBCVBbgJIQoPksl6rRawtxqgq3kjOOXd0azabn/rUp773ve+1nv76r//6xz/+8QMZ0AO8c84559wkJrSpZLMpRVw/VgKokYWimOjlFgQTFFJJEKpINMqMRqJNpPpgVCUhUKtRNtBEM+zRBx4QUiYYOUKGhBqpVaYOxJBWe3nBIChWoQkKpCIrakhADA0R2kNBTtHzu3Pu6LZ69eobb7xx7dq1radvfOMbP/OZzxRFcSBjeoB3zjnnnJu8BIqobTUbG01tys4sFNA0GhUm1ARaXdyUWJANSVAQjDSGtmMZIlVsTcCTK9SwQC3uUYU+Q6YwcsAyqmRFBIWciEYOlOXEbLwqOYNBnWZJqJCCkEmKaESJ1KBEO9XbyDnnjlLNZvMrX/nK1772taqqgBjjBz7wgeuuu04O+ItHD/DOOeecc5NaM1UL2jqWpmFFyIBgRhEZH4MaOWERGpiiFVLHEmZoxBISsIoikipKQwIWICPplxcQwRKpjjYoIepEvXoziJQJMSKQkUCjpIgg5AoEq6GtXfcVFgwjCYHuQvboU+ecc0eRpUuXfvKTn9y8eXPr6Yknnvj5z3/+sssuOyiDe4B3zjnnnJu8DMSkQ2RaLW5utIrJV4QAmbY6VUUtUyWogSGB1ICCqJiQDRVMSCWiRCVXqFIpJuw5z6QRM7SONrFEiJghGZQQEUMCQSkhFGQICQ0QsEQGqVEDECRhA2VZiDSSB3jn3NFl8+bNn/70p5csWdJ6qqpvfetbP/jBD+53zfmn8gDvnHPOOTd5JbP2GFUZy1IXgqRkgag0m2QhRMpW/7dEDmBYJAqpJESiUGVUKYQEOZOUAAFy2mMJvWBGbm19V8wom4SIFMSKSglKqsgFVqIFZEwxRQ0gC3lXw3kJVTYzto03q872I/exOefcE4yNjX3lK1/51re+VZZl68i555770Y9+9NJLLz24F/IA75xzzjk3eZnRHXRHVQa1hKVSEcOMUJDGsAIBCURjTNCANsk6sXI+KwLamiRPEw3bUWwc3bNKk2KgGZhYoh+UKhMDzQytufo6KRMLMlirb3wmC6aQCSUhYJIlpaxkequUvIadc+4oYGY/+MEPPv/5z/f397eOTJ069a//+q/f+ta3hnDwS3V4gHfOOeecm7xECKKSRKCZmagwnzIWKOJEZfgUsEwoSQUEABPE0EiVqQmpIgmasEjIVAVS7rGE3pBWdFeikYSoBCEbldIGGGQMVMEmmtKpICAZFbKQDZFgKqodMY5nAzOzA68I5Zxz++3hhx/+xCc+sWrVqtbTEMJb3vKW97///TNnzjxEV/QA75xzzjk3eQWR8VyZMp4oARUISMRKspCMupAhl4RIlYiBDBgSIWFGCQEsTEzLl5kgUN+jxpxhkIUIORMKrCIHqkQhGFhGKmKdxii0UbSRMxlCJtRIJfUCVUolaBTGcurUoK0N/Efsk3POTWp9fX2f/exnb7/9drOJH3YvfvGLb7zxxnPOOeeQXtcDvHPOOefc5CUwVuZs1IIVIiRBql2T4a3QnpFMioSAZpIRoQRJZCUoolgTMayEOlpigfTEKvStFnGttfFmoGCIooYKTSNAMmINlJzJiaIgGY0mEcYrCiEQs3S3haA6py0K+PS7c+7wyzl/5zvf+cIXvjA6Oto6snDhwhtuuOGqq646DFf3AO+cc845N6llRcVGmrk1Gz4xwa6QAhEqJQjtiYYRlWaAVqe3iCZKCEYOiKBKZVAjtCbhdzGwJkSqklhD8kT5+pBBaCTqcaKUXS7JFUFRIWUEQkRBI5oha6Zd22fWY08RClWP7865w2zdunV///d/v3LlytbTzs7Od77znddee21bW9vhuQEP8M4555xzk1c2wxhNlsWsVV5OjAxVhQQQNIFNBGzLSMIKUkIzEhDIidIoFAuE1lLSJzZpb+1mLxMKOSFMzOFboqgRAyixSS5RxVpXaQ1gaDVR/V4DhoTYXuSTO+vtqlPr/nusc+7wyTl//etf/z//5//srjN/1VVXfeQjH1mwYMHhvA3/weecc845N3mJiKqAFKiSSQkxshEClZEqNGJAIgnBiJAqQoRMMgwkUssk2ZW6MyFgzV9eI4OEifn21mbRhqCgShYsYxmLmFI1UaGmNIHWnHzGFC0hIKEd2iS0iUwtYrce/PLOzjn3tHbs2PHhD3/4/vvvbz097rjjPvrRj77qVa86/HfiAd4555xzbhIzyzm3B6kXGsYpRMuUCYmyAFBDlFyhgWBYQEpUUSMrAaSaKGunYBUWSa259z1+yVSjaYQCaUIiC0WERBkIRgGlEQQxpA2D8RKNiJFsVzf4CIJIR2RKiG0SZhUx+Ap659xhsWzZshtuuGHnzp2AiPzhH/7h3/3d3/X09ByRm/EA75xzzjk3eZlIEKkh3aIRomqZElagmZxByEzUkCdNtJSLSmWoYQKtxJ6plEKJRiUTteF3V5gzCJBLRGDXHngCAcRISqgwmViNH1or7A0xLIORBYwAaE21pwjdNe2KsTKLXsTOOXeIfeMb3/jsZz+bcwZmzJjxz//8z69+9auP4P14gHfOOeecm9TaJXSE0B2LrnpzZyOPiWA28R+KVWgmgyaoIQmRiXQtQADBIFQkRQQFmZiRn7iACAZSQRtUSMRAEiaoYq0O8BXU0QqLSIkKIuSA5YnF9qS2HNuDzu8ocibtbtzknHOHRlVVN91002233dZ6etlll33pS1+aP3/+kb0rPbKXd84555xzR5BAPejseq2u0lMU9YnDhgaiUZWoIhEVtI4aIhOF6LQARSEaGEVADBWiQsb2aCNnhrROz5CwDICSDRNKyEKOWBOEXCKQhZRBkYBMfJWQUzW9iG0agooG9QjvnDt0BgcH3/Oe9+xO73/yJ3/ywx/+8Iind3wG3jnnnHNuMhPojDKtCFODFoJgBKXKWCILQSe2vueKnP//9u48Pooi/R/4U909R47JRSAcMXJIIEA4I8glKOCBwiKwgMqugIgLP8IivBZXRcRFBHEjcogXiOyCKILcyKWiIAYlSEhCOBISIIQjIfcxV3f9/iiczTczCSEkGSZ83i//GKsr1U/PFJM8XdVVxNiN0XWNSCFSOTFOdpl0Etk04jJpjDSNdMqNcXVB4yTbycZIUojLJHFiRKQRk0hWiUtEjJiVuEKMExOL3okZ9XZiRJpCOk5c43q5VCWJSJYkIxM1AABqXkZGxr/+9a/Lly8TkaIoy5cvf/HFF90d1A1I4AEAAADuav46nZdk9TXKDaw6H15KGiem3cirJSI7EdNIYyT9MXKuk8muEuekyKTaSBPZvJ4kjYiTwkjjVHaJeEkiu0IyJyYWq5eJa8QYcRvZ9cTsRDJxhTgnphDTiNmJcVLpxvJ4jEhVSVZ0JJfatUJVM2t2hRHWsAOA2vDbb7/FxMQUFxcTUUBAwNdffz1w4EB3B/U/SOABAAAA7mo6iQL0UoCklySrXZKIaWSTiFSSFNJsRDriKskycZVIIkUjm40YJ64Qt5FOJhsnq0TMTrJMnBFxYnaylnlOk3FSONmJmEKylTRGGpHESacnjROTiDSSiFROXL3x6DuppJdJ5STLpNmIy6RJds2u8zFInHsz6brF1sLHy33vGQDUQ6qqrl+/fsOGDeIJnZYtW27fvr1du3bujuv/qOcJvNlsXrt2bWxsbEFBQatWrTp37jxy5EhZxq6hAAAAAA5MJknHiGukSVy2y6psJzuRTCQbyV5KTEeaSgoR10gVW7jLxCViMtkYMYmYSopEdjspOrJz8Wz8B+seurHavKQSETGJiEgi0hRS+I2t6chOJBHnpDFiGnGNiJHMiTGyMdJpxG1ECuk52UlPpDKep9oMklSsaSrXJIblnACgZly6dOm99947c+aM+N+HH354w4YNDRo0cG9UzupzAp+dnT1v3ry0tDQi8vf3T0pKSkpKio+Pf+WVV0wmk7ujAwAAALgjmDVNJZ6vql46ycjYjZF2RmTnpKn/mxtvZ8Rl0mnEGGkScU5cIsZJkUgT+8zJxMX2bzLJjDSNJE5i3r3YhU5mpEpEdpIUIkZ2GzFGMpGdE2NEMhGRpBKTyC6RIha3k0km0iRFIh2TTEyyqlKBzdbYqC9WtQAJCTwA3C6r1fr1119/8803VquViBhjM2fOXLBggaLcicnynRhTTVm8eHFaWlr79u1nzpwZHBycmZk5f/78xMTEVatWTZ8+3d3RAQAAANwRzHYt12KzqjzPauMaqZyTXSWJExFxRlwmbiMbIx2RphFnZKUbA/Ikk2y/seGcSiQR2TXSxLp3nKxEeo2snBSNiBFjpGqkaSQpZLGQJJEik2Ynq9j1XSWNSBJ70Ukk2cmqJ4WTZiWukMztxGySVKpxG+cXLPYWvtyqctK5+a0DAI+mquqBAwe++OKLa9euiZJmzZqtXr160KBB7g2sEvX2tuWpU6cSEhJ8fX1fffXV4OBgImratOmcOXNkWf7hhx+ysrLcHSAAAACA+6mcF3PVzEllvMBqzdE4I4l0elIU0kskSSQRkY70MikKSYw4I1kmo0JMJpnIrpDKiSQyKMQU4hJJCjFGXCE9I9KTXiZZIUkhWSJNJllPmkQ6HSkS2a0kKaQQ6YgYJ51Cip4kA6kycYUMnEgiWSJFJqYnSVIl6Uqp5Vxh6RWLucBql2SsYgcA1WS327///vspU6a8//77InuXJOmFF144ceLEnZy9Uz0egT98+DARRUVFlZ0tHxISEhERkZiYGBsbO2TIEPdFBwAAAHBHUDm3qGThPM9sy1XtnBgX897tREwiWSJuJ86I042xca4S48QU4nbiMilikjwjOyeJkyaTRMQZyUSM3ZhCzyViKnEihciukiTduBGg0/+x6J1MjIirpBExCzGZiIjrSOakSsSINI2YVGrneQrPslpzLfr0EmtUYL0diAKA2pObm7t79+7du3fn5OQ4Crt3775s2bLu3bu7MbAqqrcJfGpqKhF17dq1XHnXrl0TExPFUQAAAIC7nMq5xjVVU7NtmtUuknFGxG9k4HaVGCOZkZWTXiMrkSKRJJFdu3FUkUljJDPi/Mb+7ZyIcVI5SRKpKmkSyXbiCikq2WVSGJFI4DXSOBEnTqQxYkQyI043bgGQQqqdJIWYSiSRxonbSTGUEM+2a8Wqdt1utWr8ZhcHAHCDqqq///77d999Fxsba7fbHeVdunSZO3fukCFDGPOMST31NoHPzMwkIjF5vixRcunSJecfKSwsTE5Odi7nnJeWlubl5QUEBLg8V2lpqdlsJiJfX1+dzsXDWKqqFhQUEJFOp/P19XXZSEFBgaqqRBQQEOCy91gslpKSEiLy8vIyGo0u48zLyyMiWZb9/PxcnqW4uFiszWAymVyuymCz2YqKiojIYDB4e3u7bCQ/P1/TNCIKDAx0WcHxhvj4+Oj1eucKmqbl5+dTpW9IYWGh+Kfl7+8vuVqixmq1iu0ZK3pDiCg3N5eIJEny9/d3WaGkpMRisVDFb4jdbi8sLCQivV7v4+PjspGbviFms7m0tJSIvL29DQaDc4WqfHZFRUU2m42I/Pz8XO6k4PjsjEajl5frnXXy8vI454yxO7wz0x+fnQd1ZkVRKlod04M6s+OzQ2cW7pDO7PjsbqczOz67uunMlXx2Ve/MFX126Mzl3GZn1ogxohKzpZG91MbsNpIKNIk0lXScNI0YkcZIIibzzppKermUa6dsGkmcuI50nKwq6SXSVGJyGFMbSJw4nbZpJTqFVI24RhIjVSI9kUq+zNZalkizZ3O6qHKSZNKIOCNiN3aG11g7phoU0jRbPOdks5NOubHJnCQTcZVTENNM1pIAC7teXNxA7+KjqUpndnx2HtSZK/oiQmcup+rfzJV8dh70zVw3nVlsq+ahUlNTDxw48OOPP4rLdHjooYemT5/uQam7UG8TeNFNnTu6+C4Q/6rLSUlJmTJlinO5t7f3lStXkpKSevfu7fJcmZmZFy5cIKKOHTu63GnAbDYfP36ciBo2bNihQweXjZw9e1Z8YfXt29fl10R2drbY1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+<pre class="r"><code>ggsave("../results/SI_Fig_2_Bnana_growth_nonegatives.pdf",width=12,height=10,unit="cm")</code></pre>
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diff --git a/data/harmonized_data.csv b/data/harmonized_data.csv
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diff --git a/results/SI_Fig_1_biomass_cover.pdf b/results/SI_Fig_1_biomass_cover.pdf
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diff --git a/results/SI_Fig_2_Bnana_growth_nonegatives.pdf b/results/SI_Fig_2_Bnana_growth_nonegatives.pdf
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diff --git a/sessionInfo.txt b/sessionInfo.txt
new file mode 100755
index 0000000000000000000000000000000000000000..a916c9b12a408f954fa42b0860f762348bf46612
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+++ b/sessionInfo.txt
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+R version 4.3.1 (2023-06-16)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Linux Mint 21.2
+
+Matrix products: default
+BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+
+locale:
+ [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
+ [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
+ [5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=en_US.UTF-8
+ [7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C
+ [9] LC_ADDRESS=C LC_TELEPHONE=C
+[11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C
+
+time zone: Europe/Stockholm
+tzcode source: system (glibc)
+
+attached base packages:
+[1] stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] car_3.1-2 carData_3.0-5 ggside_0.2.2 lmerTest_3.1-3
+ [5] lme4_1.1-34 Matrix_1.6-0 sciplot_1.2-0 lubridate_1.9.2
+ [9] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.2
+[13] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
+[17] patchwork_1.1.3 ggplot2_3.4.3
+
+loaded via a namespace (and not attached):
+ [1] sass_0.4.7 utf8_1.2.3 generics_0.1.3
+ [4] stringi_1.7.12 lattice_0.21-8 hms_1.1.3
+ [7] digest_0.6.33 magrittr_2.0.3 evaluate_0.21
+[10] grid_4.3.1 timechange_0.2.0 fastmap_1.1.1
+[13] jsonlite_1.8.7 fansi_1.0.4 scales_1.2.1
+[16] numDeriv_2016.8-1.1 jquerylib_0.1.4 abind_1.4-5
+[19] cli_3.6.1 rlang_1.1.1 munsell_0.5.0
+[22] splines_4.3.1 withr_2.5.0 cachem_1.0.8
+[25] yaml_2.3.7 tools_4.3.1 tzdb_0.4.0
+[28] nloptr_2.0.3 minqa_1.2.5 colorspace_2.1-0
+[31] boot_1.3-28 vctrs_0.6.3 R6_2.5.1
+[34] lifecycle_1.0.3 MASS_7.3-60 pkgconfig_2.0.3
+[37] pillar_1.9.0 bslib_0.5.1 gtable_0.3.4
+[40] Rcpp_1.0.11 glue_1.6.2 xfun_0.40
+[43] tidyselect_1.2.0 rstudioapi_0.15.0 knitr_1.43
+[46] htmltools_0.5.6 nlme_3.1-162 rmarkdown_2.24
+[49] compiler_4.3.1