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Commit 34b85f25 authored by Boulangeat Isabelle's avatar Boulangeat Isabelle
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rebuild main analysis output document

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Main_analyse.Rmd
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......@@ -3,9 +3,9 @@ title: "Main_analyse.Rmd"
author: "Isabelle Boulangeat"
date: "23/01/2020"
output:
md_document:
variant: markdown_github
html_document:
keep_md: yes
variant: markdown_github
editor_options:
chunk_output_type: console
always_allow_html: true
......@@ -97,7 +97,7 @@ res.tab.eval = as.data.frame(do.call(rbind, lapply(eval.models, function(x)unlis
res.tab.vars = as.data.frame(do.call(rbind, lapply(eval.models, function(x)unlist(x[5:6]))))
kable(res.tab.eval, caption = "model evaluation", digits = 2) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width=FALSE)
kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
# kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
```
N.B. Some models do not fit well. The variables explain more or less depending on species but also on processes (extinction and colonisation).
......@@ -133,7 +133,7 @@ data.frame(unclass(table(coeffs.all$var, coeffs.all$process))) %>%
kable_styling("striped")
```
## Variables effect coefficients
## Variable effect coefficients
This gives a summary of selected variables with significant impact on colonisation or extinction and their estimated coefficients.
......@@ -153,18 +153,15 @@ coeffs.all[which(coeffs.all$process == "extinction" & coeffs.all$Pr...z..<0.1),]
## Lambda (1-ext/colo)
An overview of the performance of populations by site of by species
```{r lambda, fig-TRUE, fig.width=10}
```{r lambda, fig-TRUE, fig.width=10, warning=FALSE, fig.height=7}
all.sites = unique(DATA$cover[,c("SemiUniquePLOT", "YEAR", "SUBSITE", "SoilMoist", "TotalCover")])
pDATA = proj.dat.shape(all.sites, DATA$climato)
str(pDATA)
summary(pDATA)
selectVars = c("snowDays", "GSL", "GST", "SoilMoist", "TotalCover")
pDATA.all = na.omit(pDATA[,c(selectVars,"SemiUniquePLOT", "YEAR", "SUBSITE") ])
pred.all = lapply(spDATA.fit, predictions, pred.data = pDATA.all, selectVars=selectVars)
pred.tab = lapply(pred.all, function(x)data.frame(sp = x$sp, lambda = x$lambda, YEAR = x$YEAR, SUBSITE = x$SUBSITE))
pred.all.together = do.call(rbind, pred.tab)
head(pred.all.together)
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(YEAR, lambda)) +
......@@ -180,7 +177,7 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(SITE, lambda, sp)) +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
geom_boxplot() + geom_hline(yintercept = 0) +
facet_wrap( ~ sp,scales = "free_x", ncol = 4)
facet_wrap( ~ sp,scales = "free_x", ncol = 7)
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(SITE, lambda, sp)) +
......@@ -190,7 +187,7 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
```
Simplified version focusing only on the sign of lambda
```{r lambda_sign, fig=TRUE, fig.width=10}
```{r lambda_sign, fig=TRUE, fig.width=10, warning=FALSE}
pred.all.together$lambdaSign = ifelse(pred.all.together$lambda>0, "+", "-")
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
......
Main_analyse.html
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README.md
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Load data
---------
---
title: "Main_analyse.Rmd"
author: "Isabelle Boulangeat"
date: "23/01/2020"
output:
html_document:
keep_md: yes
variant: markdown_github
editor_options:
chunk_output_type: console
always_allow_html: true
---
## Load data
- Load climate
- Load species cover data
- Filter out treatment plots
- filter out un-repeated plots
- filter out years &gt; 2014 because of missing in climatologies
- filter out years > 2014 because of missing in climatologies
``` r
```r
DATA = load.datasets()
str(DATA$cover)
```
## 'data.frame': 32793 obs. of 43 variables:
## $ SUBSITE : chr "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" ...
## $ SemiUniquePLOT : chr "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" ...
## $ YEAR : num 1995 1995 1995 1995 1995 ...
## $ Name : chr "Cassiope tetragona" "Papaver radicatum" "Oxyria digyna" "Luzula confusa" ...
## $ SITE : chr "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" ...
## $ TRTMT : chr "CTL" "CTL" "CTL" "CTL" ...
## $ PLOT : chr "Cas.c.c.10new" "Cas.c.c.10new" "Cas.c.c.10new" "Cas.c.c.10new" ...
## $ GFNARROWwalker : chr "SEVER" "FORB" "FORB" "RUSH" ...
## $ GFNARROWarft : chr "ESHRUB" "FORBSV" "FORBSV" "GRAMINOID" ...
## $ COVER : num 22 0 1 0 6 3 7 5 0 8 ...
## $ UniquePLOT : chr "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" ...
## $ NumSurveysPerSubsite: int 3 3 3 3 3 3 3 3 3 3 ...
## $ NumSurveysPerPlot : int 3 3 3 3 3 3 3 3 3 3 ...
## $ MinYearSubsite : num 1995 1995 1995 1995 1995 ...
## $ MaxYearSubsite : num 2007 2007 2007 2007 2007 ...
## $ DurationSubsite : num 13 13 13 13 13 13 13 13 13 13 ...
## $ MinYearPlot : num 1995 1995 1995 1995 1995 ...
## $ MaxYearPlot : num 2007 2007 2007 2007 2007 ...
## $ DurationPlot : num 13 13 13 13 13 13 13 13 13 13 ...
## $ SiteOnly : chr "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" ...
## $ SubsiteOnly : chr "CASSIOPE" "CASSIOPE" "CASSIOPE" "CASSIOPE" ...
## $ MultipleSubsites : num 1 1 1 1 1 1 1 1 1 1 ...
## $ RepeatedSubsites : num 1 1 1 1 1 1 1 1 1 1 ...
## $ RepeatedPlots : num 1 1 1 1 1 1 1 1 1 1 ...
## $ MixedPlots : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Morphosp : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Genus : chr "Cassiope" "Papaver" "Oxyria" "Luzula" ...
## $ Family : chr "Ericaceae" "Papaveraceae" "Polygonaceae" "Juncaceae" ...
## $ SppInPlot : num 1 1 1 1 1 1 1 1 1 1 ...
## $ TotalCover : num 44 44 44 44 44 44 44 44 44 46 ...
## $ RelCover : num 0.5 0 0.0227 0 0.1364 ...
## $ Latitude : num 78.9 78.9 78.9 78.9 78.9 ...
## $ Longitude : num -75.8 -75.8 -75.8 -75.8 -75.8 ...
## $ MaxTemp : num 70.5 70.5 70.5 70.5 70.5 ...
## $ MAT : num -164 -164 -164 -164 -164 ...
## $ MinTemp : num -367 -367 -367 -367 -367 ...
## $ WarmQuarterTemp : num 25.6 25.6 25.6 25.6 25.6 ...
## $ ColdQuarterTemp : num -321 -321 -321 -321 -321 ...
## $ WClimGrid : num 57684506 57684506 57684506 57684506 57684506 ...
## $ CRUGrid : num 16049 16049 16049 16049 16049 ...
## $ WClimGroup : num 2 2 2 2 2 2 2 2 2 2 ...
## $ SoilMoist : chr "MOIST" "MOIST" "MOIST" "MOIST" ...
## $ WClimGroupSM : num 3 3 3 3 3 3 3 3 3 3 ...
```
## 'data.frame': 32793 obs. of 43 variables:
## $ SUBSITE : chr "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" ...
## $ SemiUniquePLOT : chr "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" ...
## $ YEAR : num 1995 1995 1995 1995 1995 ...
## $ Name : chr "Cassiope tetragona" "Papaver radicatum" "Oxyria digyna" "Luzula confusa" ...
## $ SITE : chr "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" ...
## $ TRTMT : chr "CTL" "CTL" "CTL" "CTL" ...
## $ PLOT : chr "Cas.c.c.10new" "Cas.c.c.10new" "Cas.c.c.10new" "Cas.c.c.10new" ...
## $ GFNARROWwalker : chr "SEVER" "FORB" "FORB" "RUSH" ...
## $ GFNARROWarft : chr "ESHRUB" "FORBSV" "FORBSV" "GRAMINOID" ...
## $ COVER : num 22 0 1 0 6 3 7 5 0 8 ...
## $ UniquePLOT : chr "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL_1995" ...
## $ NumSurveysPerSubsite: int 3 3 3 3 3 3 3 3 3 3 ...
## $ NumSurveysPerPlot : int 3 3 3 3 3 3 3 3 3 3 ...
## $ MinYearSubsite : num 1995 1995 1995 1995 1995 ...
## $ MaxYearSubsite : num 2007 2007 2007 2007 2007 ...
## $ DurationSubsite : num 13 13 13 13 13 13 13 13 13 13 ...
## $ MinYearPlot : num 1995 1995 1995 1995 1995 ...
## $ MaxYearPlot : num 2007 2007 2007 2007 2007 ...
## $ DurationPlot : num 13 13 13 13 13 13 13 13 13 13 ...
## $ SiteOnly : chr "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" "ALEXFIORD" ...
## $ SubsiteOnly : chr "CASSIOPE" "CASSIOPE" "CASSIOPE" "CASSIOPE" ...
## $ MultipleSubsites : num 1 1 1 1 1 1 1 1 1 1 ...
## $ RepeatedSubsites : num 1 1 1 1 1 1 1 1 1 1 ...
## $ RepeatedPlots : num 1 1 1 1 1 1 1 1 1 1 ...
## $ MixedPlots : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Morphosp : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Genus : chr "Cassiope" "Papaver" "Oxyria" "Luzula" ...
## $ Family : chr "Ericaceae" "Papaveraceae" "Polygonaceae" "Juncaceae" ...
## $ SppInPlot : num 1 1 1 1 1 1 1 1 1 1 ...
## $ TotalCover : num 44 44 44 44 44 44 44 44 44 46 ...
## $ RelCover : num 0.5 0 0.0227 0 0.1364 ...
## $ Latitude : num 78.9 78.9 78.9 78.9 78.9 ...
## $ Longitude : num -75.8 -75.8 -75.8 -75.8 -75.8 ...
## $ MaxTemp : num 70.5 70.5 70.5 70.5 70.5 ...
## $ MAT : num -164 -164 -164 -164 -164 ...
## $ MinTemp : num -367 -367 -367 -367 -367 ...
## $ WarmQuarterTemp : num 25.6 25.6 25.6 25.6 25.6 ...
## $ ColdQuarterTemp : num -321 -321 -321 -321 -321 ...
## $ WClimGrid : num 57684506 57684506 57684506 57684506 57684506 ...
## $ CRUGrid : num 16049 16049 16049 16049 16049 ...
## $ WClimGroup : num 2 2 2 2 2 2 2 2 2 2 ...
## $ SoilMoist : chr "MOIST" "MOIST" "MOIST" "MOIST" ...
## $ WClimGroupSM : num 3 3 3 3 3 3 3 3 3 3 ...
```
``` r
```r
str(DATA$climato)
```
## 'data.frame': 257 obs. of 172 variables:
## $ SITE : Factor w/ 62 levels "ABISKO","AKUREYRI",..: 3 3 3 3 3 3 3 3 3 38 ...
## $ SUBSITE : Factor w/ 254 levels "A","ABISKODRY",..: 1 37 46 64 67 68 82 122 123 215 ...
## $ COMMTYPE : Factor w/ 5 levels "","DRY","MIXED",..: 5 5 5 5 2 2 5 2 2 4 ...
## $ LAT : num 78.9 78.9 78.9 78.9 78.9 ...
## $ LONG : num -75.7 -75.7 -75.7 -75.7 -75.9 ...
## $ Checked_Coords : Factor w/ 2 levels "","x": 1 1 1 1 2 2 1 2 2 2 ...
## $ HasCover : int 0 0 0 0 1 1 0 1 1 1 ...
## $ ELEV : int 30 30 30 30 540 540 30 540 540 580 ...
## $ AZONE : Factor w/ 4 levels "ALPINE","ANT",..: 3 3 3 3 3 3 3 3 3 4 ...
## $ PI : Factor w/ 42 levels "","Blok","COOPER",..: 12 12 12 12 12 12 12 22 22 24 ...
## $ DomGrazer : Factor w/ 10 levels "","LARGE","Small",..: 8 8 8 8 8 8 8 5 5 7 ...
## $ GrazerIntensity : Factor w/ 5 levels "","LOW","high",..: 4 4 4 4 4 4 4 4 4 5 ...
## $ CAVM : Factor w/ 22 levels "","B1","B3","B4",..: 21 21 21 21 4 3 21 4 3 8 ...
## $ CAVMBROAD : Factor w/ 7 levels "","B","G","P",..: 7 7 7 7 2 2 7 2 2 3 ...
## $ PlotSize_m2 : Factor w/ 16 levels "","0.04","0.0531",..: 1 1 1 1 12 12 1 16 16 6 ...
## $ SurveyMethod : Factor w/ 13 levels "","BraunBlanquet",..: 4 4 4 4 8 8 4 3 3 2 ...
## $ HitsPerPlot : Factor w/ 10 levels "","100","137",..: 1 1 1 1 2 2 1 NA NA NA ...
## $ Comments : Factor w/ 20 levels "","\"0.25 m2 plots in 1992; 0.49 m2 in 2009\"",..: 1 1 1 1 1 1 1 2 2 1 ...
## $ CHELSA_snow_days_1979 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1980 : int 365 365 365 365 365 365 365 365 365 298 ...
## $ CHELSA_snow_days_1981 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1982 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1983 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1984 : int 365 365 365 365 365 365 365 365 365 321 ...
## $ CHELSA_snow_days_1985 : int 365 365 365 365 365 365 365 365 365 271 ...
## $ CHELSA_snow_days_1986 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1987 : int 365 365 365 365 365 365 365 365 365 311 ...
## $ CHELSA_snow_days_1988 : int 365 365 365 365 365 365 365 365 365 312 ...
## $ CHELSA_snow_days_1989 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1990 : int 365 365 365 365 365 365 365 365 365 340 ...
## $ CHELSA_snow_days_1991 : int 365 365 365 365 365 365 365 365 365 334 ...
## $ CHELSA_snow_days_1992 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1993 : int 365 365 365 365 355 355 365 355 355 355 ...
## $ CHELSA_snow_days_1994 : int 365 365 365 365 365 365 365 365 365 351 ...
## $ CHELSA_snow_days_1995 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1996 : int 365 365 365 365 365 365 365 365 365 292 ...
## $ CHELSA_snow_days_1997 : int 365 365 365 365 365 365 365 365 365 317 ...
## $ CHELSA_snow_days_1998 : int 365 365 365 365 346 346 365 346 346 365 ...
## $ CHELSA_snow_days_1999 : int 365 365 365 365 354 354 365 354 354 308 ...
## $ CHELSA_snow_days_2000 : int 365 365 365 365 365 365 365 365 365 311 ...
## $ CHELSA_snow_days_2001 : int 365 365 365 365 365 365 365 365 365 307 ...
## $ CHELSA_snow_days_2002 : int 365 365 365 365 365 365 365 365 365 306 ...
## $ CHELSA_snow_days_2003 : int 365 365 365 365 365 365 365 365 365 303 ...
## $ CHELSA_snow_days_2004 : int 365 365 365 365 365 365 365 365 365 299 ...
## $ CHELSA_snow_days_2005 : int 365 365 365 365 316 316 365 316 316 328 ...
## $ CHELSA_snow_days_2006 : int 365 365 365 365 340 340 365 340 340 318 ...
## $ CHELSA_snow_days_2007 : int 338 338 338 338 324 324 338 324 324 354 ...
## $ CHELSA_snow_days_2008 : int 334 334 334 334 297 297 334 297 297 302 ...
## $ CHELSA_snow_days_2009 : int 303 303 303 303 298 298 303 298 298 342 ...
## $ CHELSA_snow_days_2010 : int 365 365 365 365 344 344 365 344 344 283 ...
## $ CHELSA_snow_days_2011 : int 313 313 313 313 306 306 313 306 306 365 ...
## $ CHELSA_snow_days_2012 : int 312 312 312 312 295 295 312 295 295 337 ...
## $ CHELSA_snow_days_2013 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ GSL_1979 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1980 : int 0 0 0 0 0 0 0 0 0 61 ...
## $ GSL_1981 : int 0 0 0 0 0 0 0 0 0 15 ...
## $ GSL_1982 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1983 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1984 : int 0 0 0 0 0 0 0 0 0 50 ...
## $ GSL_1985 : int 0 0 0 0 0 0 0 0 0 53 ...
## $ GSL_1986 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1987 : int 0 0 0 0 0 0 0 0 0 54 ...
## $ GSL_1988 : int 0 0 0 0 0 0 0 0 0 50 ...
## $ GSL_1989 : int 0 0 0 0 0 0 0 0 0 18 ...
## $ GSL_1990 : int 0 0 0 0 0 0 0 0 0 19 ...
## $ GSL_1991 : int 0 0 0 0 0 0 0 0 0 37 ...
## $ GSL_1992 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1993 : int 0 0 0 0 5 5 0 5 5 13 ...
## $ GSL_1994 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1995 : int 0 0 0 0 0 0 0 0 0 12 ...
## $ GSL_1996 : int 0 0 0 0 0 0 0 0 0 70 ...
## $ GSL_1997 : int 0 0 0 0 0 0 0 0 0 43 ...
## $ GSL_1998 : int 0 0 0 0 0 0 0 0 0 26 ...
## $ GSL_1999 : int 0 0 0 0 10 10 0 10 10 57 ...
## $ GSL_2000 : int 0 0 0 0 0 0 0 0 0 57 ...
## $ GSL_2001 : int 0 0 0 0 0 0 0 0 0 68 ...
## $ GSL_2002 : int 0 0 0 0 0 0 0 0 0 61 ...
## $ GSL_2003 : int 0 0 0 0 0 0 0 0 0 67 ...
## $ GSL_2004 : int 0 0 0 0 0 0 0 0 0 55 ...
## $ GSL_2005 : int 0 0 0 0 17 17 0 17 17 35 ...
## $ GSL_2006 : int 0 0 0 0 17 17 0 17 17 58 ...
## $ GSL_2007 : int 3 3 3 3 23 23 3 23 23 68 ...
## $ GSL_2008 : int 26 26 26 26 43 43 26 43 43 60 ...
## $ GSL_2009 : int 32 32 32 32 41 41 32 41 41 24 ...
## $ GSL_2010 : int 0 0 0 0 10 10 0 10 10 85 ...
## $ GSL_2011 : int 17 17 17 17 34 34 17 34 34 0 ...
## $ GSL_2012 : int 19 19 19 19 44 44 19 44 44 0 ...
## $ GSL_2013 : int 0 0 0 0 0 0 0 0 0 8 ...
## $ GST_1979 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1980 : num NA NA NA NA NA ...
## $ GST_1981 : num NA NA NA NA NA ...
## $ GST_1982 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1983 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1984 : num NA NA NA NA NA ...
## $ GST_1985 : num NA NA NA NA NA ...
## $ GST_1986 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1987 : num NA NA NA NA NA ...
## $ GST_1988 : num NA NA NA NA NA ...
## $ GST_1989 : num NA NA NA NA NA ...
## [list output truncated]
```
## 'data.frame': 257 obs. of 172 variables:
## $ SITE : Factor w/ 62 levels "ABISKO","AKUREYRI",..: 3 3 3 3 3 3 3 3 3 38 ...
## $ SUBSITE : Factor w/ 254 levels "A","ABISKODRY",..: 1 37 46 64 67 68 82 122 123 215 ...
## $ COMMTYPE : Factor w/ 5 levels "","DRY","MIXED",..: 5 5 5 5 2 2 5 2 2 4 ...
## $ LAT : num 78.9 78.9 78.9 78.9 78.9 ...
## $ LONG : num -75.7 -75.7 -75.7 -75.7 -75.9 ...
## $ Checked_Coords : Factor w/ 2 levels "","x": 1 1 1 1 2 2 1 2 2 2 ...
## $ HasCover : int 0 0 0 0 1 1 0 1 1 1 ...
## $ ELEV : int 30 30 30 30 540 540 30 540 540 580 ...
## $ AZONE : Factor w/ 4 levels "ALPINE","ANT",..: 3 3 3 3 3 3 3 3 3 4 ...
## $ PI : Factor w/ 42 levels "","Blok","COOPER",..: 12 12 12 12 12 12 12 22 22 24 ...
## $ DomGrazer : Factor w/ 10 levels "","LARGE","Small",..: 8 8 8 8 8 8 8 5 5 7 ...
## $ GrazerIntensity : Factor w/ 5 levels "","LOW","high",..: 4 4 4 4 4 4 4 4 4 5 ...
## $ CAVM : Factor w/ 22 levels "","B1","B3","B4",..: 21 21 21 21 4 3 21 4 3 8 ...
## $ CAVMBROAD : Factor w/ 7 levels "","B","G","P",..: 7 7 7 7 2 2 7 2 2 3 ...
## $ PlotSize_m2 : Factor w/ 16 levels "","0.04","0.0531",..: 1 1 1 1 12 12 1 16 16 6 ...
## $ SurveyMethod : Factor w/ 13 levels "","BraunBlanquet",..: 4 4 4 4 8 8 4 3 3 2 ...
## $ HitsPerPlot : Factor w/ 10 levels "","100","137",..: 1 1 1 1 2 2 1 NA NA NA ...
## $ Comments : Factor w/ 20 levels "","\"0.25 m2 plots in 1992; 0.49 m2 in 2009\"",..: 1 1 1 1 1 1 1 2 2 1 ...
## $ CHELSA_snow_days_1979 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1980 : int 365 365 365 365 365 365 365 365 365 298 ...
## $ CHELSA_snow_days_1981 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1982 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1983 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1984 : int 365 365 365 365 365 365 365 365 365 321 ...
## $ CHELSA_snow_days_1985 : int 365 365 365 365 365 365 365 365 365 271 ...
## $ CHELSA_snow_days_1986 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1987 : int 365 365 365 365 365 365 365 365 365 311 ...
## $ CHELSA_snow_days_1988 : int 365 365 365 365 365 365 365 365 365 312 ...
## $ CHELSA_snow_days_1989 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1990 : int 365 365 365 365 365 365 365 365 365 340 ...
## $ CHELSA_snow_days_1991 : int 365 365 365 365 365 365 365 365 365 334 ...
## $ CHELSA_snow_days_1992 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1993 : int 365 365 365 365 355 355 365 355 355 355 ...
## $ CHELSA_snow_days_1994 : int 365 365 365 365 365 365 365 365 365 351 ...
## $ CHELSA_snow_days_1995 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ CHELSA_snow_days_1996 : int 365 365 365 365 365 365 365 365 365 292 ...
## $ CHELSA_snow_days_1997 : int 365 365 365 365 365 365 365 365 365 317 ...
## $ CHELSA_snow_days_1998 : int 365 365 365 365 346 346 365 346 346 365 ...
## $ CHELSA_snow_days_1999 : int 365 365 365 365 354 354 365 354 354 308 ...
## $ CHELSA_snow_days_2000 : int 365 365 365 365 365 365 365 365 365 311 ...
## $ CHELSA_snow_days_2001 : int 365 365 365 365 365 365 365 365 365 307 ...
## $ CHELSA_snow_days_2002 : int 365 365 365 365 365 365 365 365 365 306 ...
## $ CHELSA_snow_days_2003 : int 365 365 365 365 365 365 365 365 365 303 ...
## $ CHELSA_snow_days_2004 : int 365 365 365 365 365 365 365 365 365 299 ...
## $ CHELSA_snow_days_2005 : int 365 365 365 365 316 316 365 316 316 328 ...
## $ CHELSA_snow_days_2006 : int 365 365 365 365 340 340 365 340 340 318 ...
## $ CHELSA_snow_days_2007 : int 338 338 338 338 324 324 338 324 324 354 ...
## $ CHELSA_snow_days_2008 : int 334 334 334 334 297 297 334 297 297 302 ...
## $ CHELSA_snow_days_2009 : int 303 303 303 303 298 298 303 298 298 342 ...
## $ CHELSA_snow_days_2010 : int 365 365 365 365 344 344 365 344 344 283 ...
## $ CHELSA_snow_days_2011 : int 313 313 313 313 306 306 313 306 306 365 ...
## $ CHELSA_snow_days_2012 : int 312 312 312 312 295 295 312 295 295 337 ...
## $ CHELSA_snow_days_2013 : int 365 365 365 365 365 365 365 365 365 365 ...
## $ GSL_1979 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1980 : int 0 0 0 0 0 0 0 0 0 61 ...
## $ GSL_1981 : int 0 0 0 0 0 0 0 0 0 15 ...
## $ GSL_1982 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1983 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1984 : int 0 0 0 0 0 0 0 0 0 50 ...
## $ GSL_1985 : int 0 0 0 0 0 0 0 0 0 53 ...
## $ GSL_1986 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1987 : int 0 0 0 0 0 0 0 0 0 54 ...
## $ GSL_1988 : int 0 0 0 0 0 0 0 0 0 50 ...
## $ GSL_1989 : int 0 0 0 0 0 0 0 0 0 18 ...
## $ GSL_1990 : int 0 0 0 0 0 0 0 0 0 19 ...
## $ GSL_1991 : int 0 0 0 0 0 0 0 0 0 37 ...
## $ GSL_1992 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1993 : int 0 0 0 0 5 5 0 5 5 13 ...
## $ GSL_1994 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GSL_1995 : int 0 0 0 0 0 0 0 0 0 12 ...
## $ GSL_1996 : int 0 0 0 0 0 0 0 0 0 70 ...
## $ GSL_1997 : int 0 0 0 0 0 0 0 0 0 43 ...
## $ GSL_1998 : int 0 0 0 0 0 0 0 0 0 26 ...
## $ GSL_1999 : int 0 0 0 0 10 10 0 10 10 57 ...
## $ GSL_2000 : int 0 0 0 0 0 0 0 0 0 57 ...
## $ GSL_2001 : int 0 0 0 0 0 0 0 0 0 68 ...
## $ GSL_2002 : int 0 0 0 0 0 0 0 0 0 61 ...
## $ GSL_2003 : int 0 0 0 0 0 0 0 0 0 67 ...
## $ GSL_2004 : int 0 0 0 0 0 0 0 0 0 55 ...
## $ GSL_2005 : int 0 0 0 0 17 17 0 17 17 35 ...
## $ GSL_2006 : int 0 0 0 0 17 17 0 17 17 58 ...
## $ GSL_2007 : int 3 3 3 3 23 23 3 23 23 68 ...
## $ GSL_2008 : int 26 26 26 26 43 43 26 43 43 60 ...
## $ GSL_2009 : int 32 32 32 32 41 41 32 41 41 24 ...
## $ GSL_2010 : int 0 0 0 0 10 10 0 10 10 85 ...
## $ GSL_2011 : int 17 17 17 17 34 34 17 34 34 0 ...
## $ GSL_2012 : int 19 19 19 19 44 44 19 44 44 0 ...
## $ GSL_2013 : int 0 0 0 0 0 0 0 0 0 8 ...
## $ GST_1979 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1980 : num NA NA NA NA NA ...
## $ GST_1981 : num NA NA NA NA NA ...
## $ GST_1982 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1983 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1984 : num NA NA NA NA NA ...
## $ GST_1985 : num NA NA NA NA NA ...
## $ GST_1986 : num NA NA NA NA NA NA NA NA NA NA ...
## $ GST_1987 : num NA NA NA NA NA ...
## $ GST_1988 : num NA NA NA NA NA ...
## $ GST_1989 : num NA NA NA NA NA ...
## [list output truncated]
```
``` r
```r
mostRecordedSP = DATA$cover %>% group_by(Name) %>% summarise(nobs = length(Name)) %>% arrange(desc(nobs))
selection = as.vector(t(mostRecordedSP[1:30, "Name"]))
selection = selection[-which(selection %in% c("Arctagrostis latifolia", "Persicaria vivipara", "Carex aquatilis", "Cassiope tetragona", "Luzula confusa", "Luzula nivalis", "Salix arctica", "Poa arctica", "Eriophorum angustifolium", "Petasites frigidus", "Vaccinium uliginosum", "Dryas integrifolia", "Stellaria longipes", "Saxifraga cernua", "Festuca brachyphylla"))]
......@@ -171,12 +189,29 @@ selection = selection[-which(selection %in% c("Arctagrostis latifolia", "Persica
N.B. I have quickly selected 14 species among the 30 having the most observations, with no problem with zero-days growing season in the previous years before an observation, and no problem in the dataset... **selection procedure to discuss**
Prepare species specific datasets
---------------------------------
Step 1: `create_dataset`: <br>- only subsites where the species have been seen (then ok for N=1) <br>- extract plot info <br>- build transition table <br>- merge with plot info <br>- calculate interval (years) <br>- tag transition types <br>- combine with climate (5 years earlier average) <br>- merge all <br>Step 2: adjustments <br>- Add anomalies <br>- remove intervals&gt;=10 years <br>- select variables c("snowDays", "GSL", "GST", "SoilMoist", "TotalCover") <br>- modify SoilMoist levels (-1, 0, 1) <br>Step 3: prepare dataset to fit <br>- separate colonisation and extinction datasets <br>- scale data (not SoilMoist) <br>- prepare data for LaplacesDemon fit (optional) <br>- add event column for glm fit
``` r
## Prepare species specific datasets
Step 1: `create_dataset`:
<br>- only subsites where the species have been seen (then ok for N=1)
<br>- extract plot info
<br>- build transition table
<br>- merge with plot info
<br>- calculate interval (years)
<br>- tag transition types
<br>- combine with climate (5 years earlier average)
<br>- merge all
<br>Step 2: adjustments
<br>- Add anomalies
<br>- remove intervals>=10 years
<br>- select variables c("snowDays", "GSL", "GST", "SoilMoist", "TotalCover")
<br>- modify SoilMoist levels (-1, 0, 1)
<br>Step 3: prepare dataset to fit
<br>- separate colonisation and extinction datasets
<br>- scale data (not SoilMoist)
<br>- prepare data for LaplacesDemon fit (optional)
<br>- add event column for glm fit
```r
spDATA.list = lapply(selection, function(SPECIES){
print(SPECIES)
spDATA = dat.species(SPECIES, DATA)
......@@ -186,101 +221,99 @@ names(spDATA.list) = selection
spDATA.check = lapply(spDATA.list, check.nas)
```
Visualisation of transition tables
----------------------------------
## Visualisation of transition tables
``` r
```r
lapply(spDATA.list, function(x) table(x$spdat$trType))
```
## $`Vaccinium vitis-idaea`
##
## AA COLO EXT PP
## 168 18 13 647
##
## $`Carex bigelowii`
##
## AA COLO EXT PP
## 194 53 38 490
##
## $`Ledum palustre`
##
## AA COLO EXT PP
## 147 22 17 513
##
## $`Betula nana`
##
## AA COLO EXT PP
## 191 19 5 510
##
## $`Eriophorum vaginatum`
##
## AA COLO EXT PP
## 199 34 27 419
##
## $`Salix pulchra`
##
## AA COLO EXT PP
## 287 50 31 376
##
## $`Eriophorum chamissonis`
##
## AA COLO EXT PP
## 81 40 46 251
##
## $`Persicaria bistorta`
##
## AA COLO EXT PP
## 296 77 60 183
##
## $`Dupontia fisheri`
##
## AA COLO EXT PP
## 214 29 38 218
##
## $`Rubus chamaemorus`
##
## AA COLO EXT PP
## 315 34 22 191
##
## $`Salix reticulata`
##
## AA COLO EXT PP
## 491 14 24 161
##
## $`Geum rossii`
##
## AA COLO EXT PP
## 16 4 8 153
##
## $`Empetrum nigrum`
##
## AA COLO EXT PP
## 448 18 23 133
##
## $`Bistorta bistortoides`
##
## AA COLO EXT PP
## 43 20 22 96
##
## $`Hierochloe alpina`
##
## AA COLO EXT PP
## 603 39 36 61
```
## $`Vaccinium vitis-idaea`
##
## AA COLO EXT PP
## 168 18 13 647
##
## $`Carex bigelowii`
##
## AA COLO EXT PP
## 194 53 38 490
##
## $`Ledum palustre`
##
## AA COLO EXT PP
## 147 22 17 513
##
## $`Betula nana`
##
## AA COLO EXT PP
## 191 19 5 510
##
## $`Eriophorum vaginatum`
##
## AA COLO EXT PP
## 199 34 27 419
##
## $`Salix pulchra`
##
## AA COLO EXT PP
## 287 50 31 376
##
## $`Eriophorum chamissonis`
##
## AA COLO EXT PP
## 81 40 46 251
##
## $`Persicaria bistorta`
##
## AA COLO EXT PP
## 296 77 60 183
##
## $`Dupontia fisheri`
##
## AA COLO EXT PP
## 214 29 38 218
##
## $`Rubus chamaemorus`
##
## AA COLO EXT PP
## 315 34 22 191
##
## $`Salix reticulata`
##
## AA COLO EXT PP
## 491 14 24 161
##
## $`Geum rossii`
##
## AA COLO EXT PP
## 16 4 8 153
##
## $`Empetrum nigrum`
##
## AA COLO EXT PP
## 448 18 23 133
##
## $`Bistorta bistortoides`
##
## AA COLO EXT PP
## 43 20 22 96
##
## $`Hierochloe alpina`
##
## AA COLO EXT PP
## 603 39 36 61
```
Here we can see how many transitions we have for each species.
Fit models
----------
## Fit models
``` r
```r
spDATA.fit = lapply(spDATA.list, fit.species)
```
Summary of model evaluation
---------------------------
## Summary of model evaluation
``` r
```r
eval.models = lapply(spDATA.fit, function(glm.fit){
eval.colo = eval.fit(glm.fit$colo, glm.fit$dat$colo, "colo.event")[-1]
coeffs.colo = sort(abs(glm.fit$colo$coefficients[-1]), dec=T)
......@@ -298,477 +331,135 @@ kable(res.tab.eval, caption = "model evaluation", digits = 2) %>% kable_styling(
```
<table class="table table-striped table-hover table-responsive" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
model evaluation
</caption>
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
colo.R2
</th>
<th style="text-align:right;">
colo.AUC
</th>
<th style="text-align:right;">
ext.R2
</th>
<th style="text-align:right;">
ext.AUC
</th>
</tr>
</thead>
<caption>model evaluation</caption>
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:right;"> colo.R2 </th>
<th style="text-align:right;"> colo.AUC </th>
<th style="text-align:right;"> ext.R2 </th>
<th style="text-align:right;"> ext.AUC </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Vaccinium vitis-idaea
</td>
<td style="text-align:right;">
0.42
</td>
<td style="text-align:right;">
0.89
</td>
<td style="text-align:right;">
0.03
</td>
<td style="text-align:right;">
0.61
</td>
</tr>
<tr>
<td style="text-align:left;">
Carex bigelowii
</td>
<td style="text-align:right;">
0.31
</td>
<td style="text-align:right;">
0.79
</td>
<td style="text-align:right;">
0.08
</td>
<td style="text-align:right;">
0.64
</td>
</tr>
<tr>
<td style="text-align:left;">
Ledum palustre
</td>
<td style="text-align:right;">
0.14
</td>
<td style="text-align:right;">
0.67
</td>
<td style="text-align:right;">
0.04
</td>
<td style="text-align:right;">
0.60
</td>
</tr>
<tr>
<td style="text-align:left;">
Betula nana
</td>
<td style="text-align:right;">
0.29
</td>
<td style="text-align:right;">
0.85
</td>
<td style="text-align:right;">
0.18
</td>
<td style="text-align:right;">
0.88
</td>
</tr>
<tr>
<td style="text-align:left;">
Eriophorum vaginatum
</td>
<td style="text-align:right;">
0.02
</td>
<td style="text-align:right;">
0.53
</td>
<td style="text-align:right;">
0.07
</td>
<td style="text-align:right;">
0.70
</td>
</tr>
<tr>
<td style="text-align:left;">
Salix pulchra
</td>
<td style="text-align:right;">
0.18
</td>
<td style="text-align:right;">
0.75
</td>
<td style="text-align:right;">
0.03
</td>
<td style="text-align:right;">
0.59
</td>
</tr>
<tr>
<td style="text-align:left;">
Eriophorum chamissonis
</td>
<td style="text-align:right;">
0.22
</td>
<td style="text-align:right;">
0.74
</td>
<td style="text-align:right;">
0.06
</td>
<td style="text-align:right;">
0.65
</td>
</tr>
<tr>
<td style="text-align:left;">
Persicaria bistorta
</td>
<td style="text-align:right;">
0.11
</td>
<td style="text-align:right;">
0.67
</td>
<td style="text-align:right;">
0.08
</td>
<td style="text-align:right;">
0.63
</td>
</tr>
<tr>
<td style="text-align:left;">
Dupontia fisheri
</td>
<td style="text-align:right;">
0.24
</td>
<td style="text-align:right;">
0.80
</td>
<td style="text-align:right;">
0.25
</td>
<td style="text-align:right;">
0.80
</td>
</tr>
<tr>
<td style="text-align:left;">
Rubus chamaemorus
</td>
<td style="text-align:right;">
0.00
</td>
<td style="text-align:right;">
0.50
</td>
<td style="text-align:right;">
0.00
</td>
<td style="text-align:right;">
0.50
</td>
</tr>
<tr>
<td style="text-align:left;">
Salix reticulata
</td>
<td style="text-align:right;">
0.18
</td>
<td style="text-align:right;">
0.75
</td>
<td style="text-align:right;">
0.21
</td>
<td style="text-align:right;">
0.73
</td>
</tr>
<tr>
<td style="text-align:left;">
Geum rossii
</td>
<td style="text-align:right;">
0.25
</td>
<td style="text-align:right;">
0.70
</td>
<td style="text-align:right;">
0.28
</td>
<td style="text-align:right;">
0.86
</td>
</tr>
<tr>
<td style="text-align:left;">
Empetrum nigrum
</td>
<td style="text-align:right;">
0.16
</td>
<td style="text-align:right;">
0.77
</td>
<td style="text-align:right;">
0.11
</td>
<td style="text-align:right;">
0.67
</td>
</tr>
<tr>
<td style="text-align:left;">
Bistorta bistortoides
</td>
<td style="text-align:right;">
0.20
</td>
<td style="text-align:right;">
0.71
</td>
<td style="text-align:right;">
0.04
</td>
<td style="text-align:right;">
0.59
</td>
</tr>
<tr>
<td style="text-align:left;">
Hierochloe alpina
</td>
<td style="text-align:right;">
0.18
</td>
<td style="text-align:right;">
0.77
</td>
<td style="text-align:right;">
0.28
</td>
<td style="text-align:right;">
0.77
</td>
</tr>
<tr>
<td style="text-align:left;"> Vaccinium vitis-idaea </td>
<td style="text-align:right;"> 0.42 </td>
<td style="text-align:right;"> 0.89 </td>
<td style="text-align:right;"> 0.03 </td>
<td style="text-align:right;"> 0.61 </td>
</tr>
<tr>
<td style="text-align:left;"> Carex bigelowii </td>
<td style="text-align:right;"> 0.31 </td>
<td style="text-align:right;"> 0.79 </td>
<td style="text-align:right;"> 0.08 </td>
<td style="text-align:right;"> 0.64 </td>
</tr>
<tr>
<td style="text-align:left;"> Ledum palustre </td>
<td style="text-align:right;"> 0.14 </td>
<td style="text-align:right;"> 0.67 </td>
<td style="text-align:right;"> 0.04 </td>
<td style="text-align:right;"> 0.60 </td>
</tr>
<tr>
<td style="text-align:left;"> Betula nana </td>
<td style="text-align:right;"> 0.29 </td>
<td style="text-align:right;"> 0.85 </td>
<td style="text-align:right;"> 0.18 </td>
<td style="text-align:right;"> 0.88 </td>
</tr>
<tr>
<td style="text-align:left;"> Eriophorum vaginatum </td>
<td style="text-align:right;"> 0.02 </td>
<td style="text-align:right;"> 0.53 </td>
<td style="text-align:right;"> 0.07 </td>
<td style="text-align:right;"> 0.70 </td>
</tr>
<tr>
<td style="text-align:left;"> Salix pulchra </td>
<td style="text-align:right;"> 0.18 </td>
<td style="text-align:right;"> 0.75 </td>
<td style="text-align:right;"> 0.03 </td>
<td style="text-align:right;"> 0.59 </td>
</tr>
<tr>
<td style="text-align:left;"> Eriophorum chamissonis </td>
<td style="text-align:right;"> 0.22 </td>
<td style="text-align:right;"> 0.74 </td>
<td style="text-align:right;"> 0.06 </td>
<td style="text-align:right;"> 0.65 </td>
</tr>
<tr>
<td style="text-align:left;"> Persicaria bistorta </td>
<td style="text-align:right;"> 0.11 </td>
<td style="text-align:right;"> 0.67 </td>
<td style="text-align:right;"> 0.08 </td>
<td style="text-align:right;"> 0.63 </td>
</tr>
<tr>
<td style="text-align:left;"> Dupontia fisheri </td>
<td style="text-align:right;"> 0.24 </td>
<td style="text-align:right;"> 0.80 </td>
<td style="text-align:right;"> 0.25 </td>
<td style="text-align:right;"> 0.80 </td>
</tr>
<tr>
<td style="text-align:left;"> Rubus chamaemorus </td>
<td style="text-align:right;"> 0.00 </td>
<td style="text-align:right;"> 0.50 </td>
<td style="text-align:right;"> 0.00 </td>
<td style="text-align:right;"> 0.50 </td>
</tr>
<tr>
<td style="text-align:left;"> Salix reticulata </td>
<td style="text-align:right;"> 0.18 </td>
<td style="text-align:right;"> 0.75 </td>
<td style="text-align:right;"> 0.21 </td>
<td style="text-align:right;"> 0.73 </td>
</tr>
<tr>
<td style="text-align:left;"> Geum rossii </td>
<td style="text-align:right;"> 0.25 </td>
<td style="text-align:right;"> 0.70 </td>
<td style="text-align:right;"> 0.28 </td>
<td style="text-align:right;"> 0.86 </td>
</tr>
<tr>
<td style="text-align:left;"> Empetrum nigrum </td>
<td style="text-align:right;"> 0.16 </td>
<td style="text-align:right;"> 0.77 </td>
<td style="text-align:right;"> 0.11 </td>
<td style="text-align:right;"> 0.67 </td>
</tr>
<tr>
<td style="text-align:left;"> Bistorta bistortoides </td>
<td style="text-align:right;"> 0.20 </td>
<td style="text-align:right;"> 0.71 </td>
<td style="text-align:right;"> 0.04 </td>
<td style="text-align:right;"> 0.59 </td>
</tr>
<tr>
<td style="text-align:left;"> Hierochloe alpina </td>
<td style="text-align:right;"> 0.18 </td>
<td style="text-align:right;"> 0.77 </td>
<td style="text-align:right;"> 0.28 </td>
<td style="text-align:right;"> 0.77 </td>
</tr>
</tbody>
</table>
``` r
kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
```
<table class="table table-striped table-hover table-responsive" style="font-size: 10px; margin-left: auto; margin-right: auto;">
<caption style="font-size: initial !important;">
selected variables
</caption>
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:left;">
vars.colo
</th>
<th style="text-align:left;">
vars.ext
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Vaccinium vitis-idaea
</td>
<td style="text-align:left;">
GST:SoilMoist;GST;TotalCover:SoilMoist;GSL:SoilMoist;SoilMoist;TotalCover;GSL
</td>
<td style="text-align:left;">
SoilMoist
</td>
</tr>
<tr>
<td style="text-align:left;">
Carex bigelowii
</td>
<td style="text-align:left;">
GSL;snowDays:SoilMoist;snowDays;TotalCover:SoilMoist;SoilMoist;TotalCover
</td>
<td style="text-align:left;">
SoilMoist;TotalCover:SoilMoist;snowDays;TotalCover
</td>
</tr>
<tr>
<td style="text-align:left;">
Ledum palustre
</td>
<td style="text-align:left;">
SoilMoist;TotalCover:SoilMoist;GST;TotalCover
</td>
<td style="text-align:left;">
snowDays
</td>
</tr>
<tr>
<td style="text-align:left;">
Betula nana
</td>
<td style="text-align:left;">
TotalCover:SoilMoist;snowDays;GSL;SoilMoist;TotalCover
</td>
<td style="text-align:left;">
TotalCover;snowDays
</td>
</tr>
<tr>
<td style="text-align:left;">
Eriophorum vaginatum
</td>
<td style="text-align:left;">
SoilMoist
</td>
<td style="text-align:left;">
GSL;snowDays;TotalCover;GST
</td>
</tr>
<tr>
<td style="text-align:left;">
Salix pulchra
</td>
<td style="text-align:left;">
GSL;snowDays;SoilMoist;TotalCover:SoilMoist;TotalCover
</td>
<td style="text-align:left;">
TotalCover:SoilMoist;SoilMoist;TotalCover
</td>
</tr>
<tr>
<td style="text-align:left;">
Eriophorum chamissonis
</td>
<td style="text-align:left;">
snowDays:SoilMoist;GSL:SoilMoist;TotalCover:SoilMoist;SoilMoist;TotalCover;snowDays;GSL
</td>
<td style="text-align:left;">
snowDays:SoilMoist;GSL:SoilMoist;GSL;GST;snowDays;SoilMoist
</td>
</tr>
<tr>
<td style="text-align:left;">
Persicaria bistorta
</td>
<td style="text-align:left;">
SoilMoist;snowDays:SoilMoist;GST:SoilMoist;snowDays;GSL;GST
</td>
<td style="text-align:left;">
SoilMoist;snowDays;GSL
</td>
</tr>
<tr>
<td style="text-align:left;">
Dupontia fisheri
</td>
<td style="text-align:left;">
GSL:SoilMoist;snowDays;snowDays:SoilMoist;GST;GSL;SoilMoist;TotalCover
</td>
<td style="text-align:left;">
snowDays;snowDays:SoilMoist;GSL;GSL:SoilMoist;TotalCover:SoilMoist;TotalCover;SoilMoist
</td>
</tr>
<tr>
<td style="text-align:left;">
Rubus chamaemorus
</td>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Salix reticulata
</td>
<td style="text-align:left;">
SoilMoist;GST:SoilMoist;TotalCover:SoilMoist;snowDays;GSL;GST;TotalCover
</td>
<td style="text-align:left;">
GSL:SoilMoist;GST:SoilMoist;SoilMoist;snowDays:SoilMoist;TotalCover:SoilMoist;GSL;snowDays;TotalCover;GST
</td>
</tr>
<tr>
<td style="text-align:left;">
Geum rossii
</td>
<td style="text-align:left;">
GST
</td>
<td style="text-align:left;">
snowDays;snowDays:SoilMoist;SoilMoist;TotalCover
</td>
</tr>
<tr>
<td style="text-align:left;">
Empetrum nigrum
</td>
<td style="text-align:left;">
GSL:SoilMoist;snowDays:SoilMoist;GST:SoilMoist;SoilMoist;snowDays;GST;GSL;TotalCover:SoilMoist;TotalCover
</td>
<td style="text-align:left;">
SoilMoist;snowDays;GST
</td>
</tr>
<tr>
<td style="text-align:left;">
Bistorta bistortoides
</td>
<td style="text-align:left;">
snowDays;GSL;GST;TotalCover;snowDays:SoilMoist;SoilMoist
</td>
<td style="text-align:left;">
GST
</td>
</tr>
<tr>
<td style="text-align:left;">
Hierochloe alpina
</td>
<td style="text-align:left;">
GSL:SoilMoist;snowDays:SoilMoist;GSL;snowDays;SoilMoist;GST:SoilMoist;GST;TotalCover:SoilMoist;TotalCover
</td>
<td style="text-align:left;">
SoilMoist;GST:SoilMoist;GST;GSL
</td>
</tr>
</tbody>
</table>
```r
# kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
```
N.B. Some models do not fit well. The variables explain more or less depending on species but also on processes (extinction and colonisation).
Selected variables and coefficient, sumnmary table
--------------------------------------------------
``` r
## Selected variables and coefficient, sumnmary table
```r
coeffs.all.list = lapply(spDATA.fit, function(x){
coe.colo = data.frame(summary(x$colo)$coefficients)
coe.colo[,"sp"] = x$dat$sp
......@@ -785,136 +476,77 @@ coeffs.all = do.call(rbind, coeffs.all.list)
# str(coeffs.all)
```
Number of selected variable per process, accross all species
------------------------------------------------------------
## Number of selected variable per process, accross all species
``` r
```r
data.frame(unclass(table(coeffs.all$var, coeffs.all$process))) %>%
kable(.) %>%
kable_styling("striped")
```
<table class="table table-striped" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
colonisation
</th>
<th style="text-align:right;">
extinction
</th>
</tr>
</thead>
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:right;"> colonisation </th>
<th style="text-align:right;"> extinction </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
GSL
</td>
<td style="text-align:right;">
11
</td>
<td style="text-align:right;">
6
</td>
</tr>
<tr>
<td style="text-align:left;">
GSL:SoilMoist
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
3
</td>
</tr>
<tr>
<td style="text-align:left;">
GST
</td>
<td style="text-align:right;">
9
</td>
<td style="text-align:right;">
6
</td>
</tr>
<tr>
<td style="text-align:left;">
GST:SoilMoist
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
SoilMoist
</td>
<td style="text-align:right;">
13
</td>
<td style="text-align:right;">
10
</td>
</tr>
<tr>
<td style="text-align:left;">
TotalCover
</td>
<td style="text-align:right;">
11
</td>
<td style="text-align:right;">
7
</td>
</tr>
<tr>
<td style="text-align:left;">
TotalCover:SoilMoist
</td>
<td style="text-align:right;">
9
</td>
<td style="text-align:right;">
4
</td>
</tr>
<tr>
<td style="text-align:left;">
snowDays
</td>
<td style="text-align:right;">
10
</td>
<td style="text-align:right;">
10
</td>
</tr>
<tr>
<td style="text-align:left;">
snowDays:SoilMoist
</td>
<td style="text-align:right;">
7
</td>
<td style="text-align:right;">
4
</td>
</tr>
<tr>
<td style="text-align:left;"> GSL </td>
<td style="text-align:right;"> 11 </td>
<td style="text-align:right;"> 6 </td>
</tr>
<tr>
<td style="text-align:left;"> GSL:SoilMoist </td>
<td style="text-align:right;"> 5 </td>
<td style="text-align:right;"> 3 </td>
</tr>
<tr>
<td style="text-align:left;"> GST </td>
<td style="text-align:right;"> 9 </td>
<td style="text-align:right;"> 6 </td>
</tr>
<tr>
<td style="text-align:left;"> GST:SoilMoist </td>
<td style="text-align:right;"> 5 </td>
<td style="text-align:right;"> 2 </td>
</tr>
<tr>
<td style="text-align:left;"> SoilMoist </td>
<td style="text-align:right;"> 13 </td>
<td style="text-align:right;"> 10 </td>
</tr>
<tr>
<td style="text-align:left;"> TotalCover </td>
<td style="text-align:right;"> 11 </td>
<td style="text-align:right;"> 7 </td>
</tr>
<tr>
<td style="text-align:left;"> TotalCover:SoilMoist </td>
<td style="text-align:right;"> 9 </td>
<td style="text-align:right;"> 4 </td>
</tr>
<tr>
<td style="text-align:left;"> snowDays </td>
<td style="text-align:right;"> 10 </td>
<td style="text-align:right;"> 10 </td>
</tr>
<tr>
<td style="text-align:left;"> snowDays:SoilMoist </td>
<td style="text-align:right;"> 7 </td>
<td style="text-align:right;"> 4 </td>
</tr>
</tbody>
</table>
Variables effect coefficients
-----------------------------
## Variable effect coefficients
This gives a summary of selected variables with significant impact on colonisation or extinction and their estimated coefficients.
``` r
```r
coeffs.all[which(coeffs.all$process == "colonisation" & coeffs.all$Pr...z..<0.1),] %>%
ggplot(aes(sp, Estimate, fill = var)) +
geom_col(position = "dodge") +
......@@ -922,9 +554,9 @@ coeffs.all[which(coeffs.all$process == "colonisation" & coeffs.all$Pr...z..<0.1)
ggtitle("Colonisation process")
```
![](Main_analyse_files/figure-markdown_github/plot_coeffs-1.png)
![](Main_analyse_files/figure-html/plot_coeffs-1.png)<!-- -->
``` r
```r
coeffs.all[which(coeffs.all$process == "extinction" & coeffs.all$Pr...z..<0.1),] %>%
ggplot(aes(sp, Estimate, fill = var)) +
geom_col(position = "dodge") +
......@@ -932,80 +564,22 @@ coeffs.all[which(coeffs.all$process == "extinction" & coeffs.all$Pr...z..<0.1),]
ggtitle("Extinction process")
```
![](Main_analyse_files/figure-markdown_github/plot_coeffs-2.png)
Lambda (1-ext/colo)
-------------------
![](Main_analyse_files/figure-html/plot_coeffs-2.png)<!-- -->
## Lambda (1-ext/colo)
An overview of the performance of populations by site of by species
``` r
```r
all.sites = unique(DATA$cover[,c("SemiUniquePLOT", "YEAR", "SUBSITE", "SoilMoist", "TotalCover")])
pDATA = proj.dat.shape(all.sites, DATA$climato)
str(pDATA)
```
## 'data.frame': 3088 obs. of 11 variables:
## $ SUBSITE : chr "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" "ALEXFIORD:CASSIOPE" ...
## $ YEAR : num 1995 1995 1995 1995 1995 ...
## $ snowDays : num 311 311 311 311 311 ...
## $ GSL : num 45.6 45.6 45.6 45.6 45.6 45.6 45.6 45.6 45.6 45.6 ...
## $ GST : num 3.49 3.49 3.49 3.49 3.49 ...
## $ SemiUniquePLOT: chr "ALEXFIORD:CASSIOPE_Cas.c.c.10new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.3new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.7new_CTL" "ALEXFIORD:CASSIOPE_Cas.c.c.11new_CTL" ...
## $ SoilMoist : chr "MOIST" "MOIST" "MOIST" "MOIST" ...
## $ TotalCover : num 44 28 29 38 36 44 31 22 49 34 ...
## $ snow_days_av : num 309 309 309 309 309 ...
## $ GSL_av : num 45.8 45.8 45.8 45.8 45.8 ...
## $ GST_av : num NA NA NA NA NA NA NA NA NA NA ...
``` r
summary(pDATA)
```
## SUBSITE YEAR snowDays GSL
## Length:3088 Min. :1981 Min. :108.0 Min. : 0.00
## Class :character 1st Qu.:1997 1st Qu.:261.8 1st Qu.: 79.00
## Mode :character Median :2002 Median :270.2 Median : 88.40
## Mean :2002 Mean :273.7 Mean : 83.73
## 3rd Qu.:2008 3rd Qu.:274.8 3rd Qu.: 97.20
## Max. :2013 Max. :365.0 Max. :238.80
## NA's :1 NA's :1
## GST SemiUniquePLOT SoilMoist TotalCover
## Min. : 2.632 Length:3088 Length:3088 Min. : 1.00
## 1st Qu.: 3.949 Class :character Class :character 1st Qu.: 40.00
## Median : 6.873 Mode :character Mode :character Median : 57.85
## Mean : 6.319 Mean : 62.47
## 3rd Qu.: 7.860 3rd Qu.: 79.30
## Max. :10.295 Max. :319.00
## NA's :195
## snow_days_av GSL_av GST_av
## Min. :163.2 Min. : 0.00 Min. : 3.334
## 1st Qu.:264.5 1st Qu.: 73.97 1st Qu.: 5.251
## Median :271.0 Median : 88.37 Median : 7.431
## Mean :277.1 Mean : 80.11 Mean : 6.487
## 3rd Qu.:280.1 3rd Qu.: 94.20 3rd Qu.: 7.724
## Max. :365.0 Max. :186.54 Max. :10.107
## NA's :505
``` r
selectVars = c("snowDays", "GSL", "GST", "SoilMoist", "TotalCover")
pDATA.all = na.omit(pDATA[,c(selectVars,"SemiUniquePLOT", "YEAR", "SUBSITE") ])
pred.all = lapply(spDATA.fit, predictions, pred.data = pDATA.all, selectVars=selectVars)
pred.tab = lapply(pred.all, function(x)data.frame(sp = x$sp, lambda = x$lambda, YEAR = x$YEAR, SUBSITE = x$SUBSITE))
pred.all.together = do.call(rbind, pred.tab)
head(pred.all.together)
```
## sp lambda YEAR SUBSITE
## Vaccinium vitis-idaea.1 Vaccinium vitis-idaea -12.294198594 1997 ANWR:ATIGUN-A
## Vaccinium vitis-idaea.2 Vaccinium vitis-idaea 0.008901113 2002 ANWR:ATIGUN-A
## Vaccinium vitis-idaea.3 Vaccinium vitis-idaea -2.338651699 1997 ANWR:ATIGUN-A
## Vaccinium vitis-idaea.4 Vaccinium vitis-idaea 0.429771952 2002 ANWR:ATIGUN-A
## Vaccinium vitis-idaea.5 Vaccinium vitis-idaea -14.843617900 1997 ANWR:ATIGUN-A
## Vaccinium vitis-idaea.6 Vaccinium vitis-idaea -1.173857289 2002 ANWR:ATIGUN-A
``` r
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(YEAR, lambda)) +
geom_point(shape = 1) +
......@@ -1013,77 +587,13 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
facet_wrap( ~ sp, ncol = 4)
```
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 1990
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 5.04
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.6627e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 9
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 1990
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 5.04
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.6627e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 9
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 1990
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 5.04
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.6627e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 9
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 1990
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 5.04
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.6627e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 9
## Warning: Removed 16 rows containing missing values (geom_point).
```
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
```
![](Main_analyse_files/figure-markdown_github/lambda,%20fig-TRUE-1.png)
![](Main_analyse_files/figure-html/lambda, fig-TRUE-1.png)<!-- -->
``` r
```r
pred.all.together$SITE = unlist( lapply(as.character(pred.all.together$SUBSITE), function(x){
strsplit(x, ":")[[1]][[1]]
}))
......@@ -1092,14 +602,12 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(SITE, lambda, sp)) +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
geom_boxplot() + geom_hline(yintercept = 0) +
facet_wrap( ~ sp,scales = "free_x", ncol = 4)
facet_wrap( ~ sp,scales = "free_x", ncol = 7)
```
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
![](Main_analyse_files/figure-html/lambda, fig-TRUE-2.png)<!-- -->
![](Main_analyse_files/figure-markdown_github/lambda,%20fig-TRUE-2.png)
``` r
```r
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
ggplot(aes(SITE, lambda, sp)) +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
......@@ -1107,13 +615,11 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
facet_wrap( ~ sp,scales = "free_x", ncol = 7)
```
## Warning: Removed 3055 rows containing non-finite values (stat_boxplot).
![](Main_analyse_files/figure-markdown_github/lambda,%20fig-TRUE-3.png)
![](Main_analyse_files/figure-html/lambda, fig-TRUE-3.png)<!-- -->
Simplified version focusing only on the sign of lambda
``` r
```r
pred.all.together$lambdaSign = ifelse(pred.all.together$lambda>0, "+", "-")
pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
......@@ -1123,4 +629,7 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
facet_wrap( ~ sp,scales = "free_x", ncol = 7)
```
![](Main_analyse_files/figure-markdown_github/lambda_sign-1.png)
![](Main_analyse_files/figure-html/lambda_sign-1.png)<!-- -->
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