diff --git a/Main_analyse.Rmd b/Main_analyse.Rmd index 9fd34d83f0d8836fa1dcacf8a7ce10f855e93d3c..af267fe0aab3d7c14717b0d0c97f8dbc26bd2519 100644 --- a/Main_analyse.Rmd +++ b/Main_analyse.Rmd @@ -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$sp == "Rubus chamaemorus"), ] %>% diff --git a/Main_analyse.html b/Main_analyse.html index 74c02c90252d210f209a5ab4a9b991a657489723..b6166338e9978731e6bfb452113ddac69a838249 100644 --- a/Main_analyse.html +++ b/Main_analyse.html @@ -343,9 +343,9 @@ str(DATA$cover) ## $ GST_1989 : num NA NA NA NA NA ... ## [list output truncated] 
mostRecordedSP = DATA$cover %>% group_by(Name) %>% summarise(nobs = length(Name)) %>% arrange(desc(nobs))
-selection = as.vector(t(mostRecordedSP[1:22, "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"))]
-

N.B. I have quickly selected 10 species among the 22 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

+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"))] +

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

@@ -409,7 +409,33 @@ spDATA.check = lapply(spDATA.list, check.nas) ## $`Rubus chamaemorus` ## ## AA COLO EXT PP -## 315 34 22 191 +## 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

@@ -624,136 +650,95 @@ Rubus chamaemorus 0.50 - - -
kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-selected variables -
- -vars.colo - -vars.ext -
-Vaccinium vitis-idaea +Salix reticulata -GST:SoilMoist;GST;TotalCover:SoilMoist;GSL:SoilMoist;SoilMoist;TotalCover;GSL - -SoilMoist + +0.18
-Carex bigelowii + +0.75 -GSL;snowDays:SoilMoist;snowDays;TotalCover:SoilMoist;SoilMoist;TotalCover + +0.21 -SoilMoist;TotalCover:SoilMoist;snowDays;TotalCover + +0.73
-Ledum palustre - -SoilMoist;TotalCover:SoilMoist;GST;TotalCover +Geum rossii -snowDays + +0.25
-Betula nana + +0.70 -TotalCover:SoilMoist;snowDays;GSL;SoilMoist;TotalCover + +0.28 -TotalCover;snowDays + +0.86
-Eriophorum vaginatum - -SoilMoist +Empetrum nigrum -GSL;snowDays;TotalCover;GST + +0.16
-Salix pulchra + +0.77 -GSL;snowDays;SoilMoist;TotalCover:SoilMoist;TotalCover + +0.11 -TotalCover:SoilMoist;SoilMoist;TotalCover + +0.67
-Eriophorum chamissonis - -snowDays:SoilMoist;GSL:SoilMoist;TotalCover:SoilMoist;SoilMoist;TotalCover;snowDays;GSL +Bistorta bistortoides -snowDays:SoilMoist;GSL:SoilMoist;GSL;GST;snowDays;SoilMoist + +0.20
-Persicaria bistorta + +0.71 -SoilMoist;snowDays:SoilMoist;GST:SoilMoist;snowDays;GSL;GST + +0.04 -SoilMoist;snowDays;GSL + +0.59
-Dupontia fisheri +Hierochloe alpina -GSL:SoilMoist;snowDays;snowDays:SoilMoist;GST;GSL;SoilMoist;TotalCover - -snowDays;snowDays:SoilMoist;GSL;GSL:SoilMoist;TotalCover:SoilMoist;TotalCover;SoilMoist + +0.18
-Rubus chamaemorus + +0.77 + +0.28 + +0.77
+
# 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

@@ -770,31 +755,22 @@ Rubus chamaemorus return(coe) }) coeffs.all = do.call(rbind, coeffs.all.list) -str(coeffs.all) -
## 'data.frame':    79 obs. of  7 variables:
-##  $ Estimate  : num  -0.939 3.268 0.95 -1.496 -2.566 ...
-##  $ Std..Error: num  1.11 1.45 0.46 2.04 1.48 ...
-##  $ z.value   : num  -0.85 2.257 2.065 -0.733 -1.738 ...
-##  $ Pr...z..  : num  0.3955 0.024 0.039 0.4637 0.0822 ...
-##  $ sp        : chr  "Vaccinium vitis-idaea" "Vaccinium vitis-idaea" "Vaccinium vitis-idaea" "Vaccinium vitis-idaea" ...
-##  $ var       : chr  "GSL" "GST" "TotalCover" "SoilMoist" ...
-##  $ process   : chr  "colonisation" "colonisation" "colonisation" "colonisation" ...
+# str(coeffs.all)
-
-

Number of selection of variable per process

+
+

Number of selected variable per process, accross all species

data.frame(unclass(table(coeffs.all$var, coeffs.all$process)))  %>% 
-  mutate(
-    colonisation = cell_spec(colonisation, color = ifelse(colonisation > 5, "red", "black")),
-    extinction = cell_spec(extinction, color = ifelse(extinction > 5, "red", "black"))) %>%
   kable(.) %>%
   kable_styling("striped")
+ - @@ -802,90 +778,166 @@ extinction - + - + - + - + - + - + - + - + - +
+ colonisation + extinction
-<span style=" color: red !important;" >7</span> +GSL -<span style=" color: black !important;" >4</span> + +11 + +6
-<span style=" color: black !important;" >3</span> +GSL:SoilMoist -<span style=" color: black !important;" >2</span> + +5 + +3
-<span style=" color: black !important;" >4</span> +GST -<span style=" color: black !important;" >2</span> + +9 + +6
-<span style=" color: black !important;" >2</span> +GST:SoilMoist -<span style=" color: black !important;" >0</span> + +5 + +2
-<span style=" color: red !important;" >9</span> +SoilMoist -<span style=" color: red !important;" >6</span> + +13 + +10
-<span style=" color: red !important;" >7</span> +TotalCover -<span style=" color: black !important;" >5</span> + +11 + +7
-<span style=" color: red !important;" >6</span> +TotalCover:SoilMoist -<span style=" color: black !important;" >3</span> + +9 + +4
-<span style=" color: red !important;" >6</span> +snowDays -<span style=" color: red !important;" >7</span> + +10 + +10
-<span style=" color: black !important;" >4</span> +snowDays:SoilMoist -<span style=" color: black !important;" >2</span> + +7 + +4
+
+
+

Variable effect coefficients

+

This gives a summary of selected variables with significant impact on colonisation or extinction and their estimated coefficients.

coeffs.all[which(coeffs.all$process == "colonisation" & coeffs.all$Pr...z..<0.1),] %>%
   ggplot(aes(sp, Estimate, fill = var)) +
   geom_col(position = "dodge") +
   theme_bw() + facet_wrap(~sp,scales = "free_x", ncol=4) +
   ggtitle("Colonisation process")
-

+

coeffs.all[which(coeffs.all$process == "extinction" & coeffs.all$Pr...z..<0.1),] %>%
   ggplot(aes(sp, Estimate, fill = var)) +
   geom_col(position = "dodge") +
   theme_bw() + facet_wrap(~sp,scales = "free_x", ncol=4) +
   ggtitle("Extinction process")
-

+

+
+
+

Lambda (1-ext/colo)

+

An overview of the performance of populations by site of by species

+
all.sites = unique(DATA$cover[,c("SemiUniquePLOT", "YEAR", "SUBSITE", "SoilMoist", "TotalCover")])
+pDATA = proj.dat.shape(all.sites, DATA$climato)
+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)
+
+pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
+  ggplot(aes(YEAR, lambda)) +
+  geom_point(shape = 1) +
+  geom_smooth() +
+  facet_wrap( ~ sp, ncol = 4)
+
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
+

+
pred.all.together$SITE =  unlist( lapply(as.character(pred.all.together$SUBSITE), function(x){
+  strsplit(x, ":")[[1]][[1]]
+}))
+
+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 = 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() + ylim (-1, 1) + geom_hline(yintercept = 0) +
+  facet_wrap( ~ sp,scales = "free_x", ncol = 7)
+

+

Simplified version focusing only on the sign of lambda

+
pred.all.together$lambdaSign = ifelse(pred.all.together$lambda>0, "+", "-")
+
+pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
+  ggplot(aes(x=SITE, y=lambdaSign, fill= lambdaSign )) +
+  theme(axis.text.x = element_text(angle=90, hjust=1)) +
+  geom_bar(stat="identity")  + 
+  facet_wrap( ~ sp,scales = "free_x", ncol = 7)
+

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-- Load climate -- Load species cover data -- Filter out treatment plots -- filter out un-repeated plots -- filter out years > 2014 because of missing in climatologies - -``` r +--- +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 > 2014 because of missing in climatologies + + +```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