#+TITLE: Integrated Models, Scenarios and Dynamics of Climate, Land Use and Common Birds for France: Dynamic Maps
#+AUTHOR: Jean-Sauveur Ay $<$ jsay.site@gmail.com $>$
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#+LANGUAGE: en
#+LaTeX_CLASS: ManueStat
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This page contains the dynamic maps associated to the following
*research paper*:
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#+BEGIN_LaTeX...
* Presentation
** Framework
We present here only a brief overview of the methods used in this
research, a more complete picture can be found in the [[./WPMOBILIS.pdf][last working
paper version]] and the associated online ressources.
The modeling framework is structured in 3 blocks:
- Species Distribution Models :: relating climate, land use and
environmental variables (elevation, slope, etc.) to common
birds abundances. They are calibrated on FBBS survey 2001--2011
through negative binomial Generalized Additive Models.
- Land Use Change Econometric Models :: relating climate, returns
from land (in euros) and environmental variables (slope, land
quality, etc.) to land use choices. They are calibrated on
TERUTI survey 1993--2003 through multinomial models.
- Ricardian Models :: relating climate and environmental variables
(slope, geographical coordinates, etc.) to the economic returns
from land (approximated by land prices). They are calibrated on
land price data from the French Ministry of Agriculture
1990--2005 through gaussian Generalized Additive Models.
Because some of our data have a restricted access, not all our work
is reproducible from this page. From here, only the output data
frames from our simulations are available. This suffices
nevertheless to reproduce all the Figures of the paper and to
produce some dynamic maps that reflect more precisely than the
published paper the dynamic content of our results.
** Scenarios
As illustrated by the following Figure, we simulate 5 different
scenarios in order to disentangle the respective effects of the
integrated modeling blocks.
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#+ATTR_LaTeX: :options scale= .3
#+Caption: Modelling structure and differences between scenarios
./output/schema.png
In the Figure, *CC* counts for climate change, *SDM* for species
distribution models, *RIC* for Ricardian models of returns from
land, *LU* for land use and *CP* for conservation
payments. Simulations of bird population by SDM pursue the observed
2001--2009 trends and integrate climate change in all scenarios. In
scenario S0, land use is constant. In scenario S1, the model of LUC
is used to extrapolate the temporal trends to obtain a kind of
business-as-usual scenario. In scenario S2, the effects of climate
change on the returns from land and, consequently, on LUC are taken
into account. Scenario S3 and S4 are respectively equivalent to S1
and S2 with a conservation policy providing uniform payments for
pastures.
** Content
The rest of this file first presents the Land Use Changes (section
LUC) and the Bird Abundances (section BAB) associated to each
scenario. Each section contains the data from simulations (in
compressed =.Rda= format for the R software). Sections AKN and ADM
are the Acknowledgements and some Additional Material required to
run the R codes (they are tangled in =myFunctions.R= and are loaded
in the workspace with =source("myFunctions.R")=) that allow to
produce the dynamic maps. Moreover, this HTML page is exported from
an Org Mode file than can be opened with GNU Emacs and is also
available as a =.pdf= file for easy print. More details on this work
flow is available at the following webpage. If you see any errors or
strange results, you can contact me at
=jsay_dot_site_at_gmail_dot_com=.
* <<LUC>> Land Use Changes
** Scenario S0
Land use is constant in this scenario, only birds are impacted by
climate change, see section 1.2 for a description of scenarios.
** Scenario S1
This scenario is a kind of "business as usual" scenario in terms of
land use changes. The dynamics 1990--2005 of returns from land is
extrapolated to 2053. In particular, it does not integrate the
climate change effects on returns.
You can download the data from this scenario simulation here. See
Section MAP for the functions.
This scenario presents an increase in annual crops, forests and
urban area and a decrease in pastures and perennial crops. The
following Table contains the links to the R Codes and the
Animations 2003--2053 at the national scale.
#+CAPTION: Land Use Changes according to scenario S1
| <l> | <c> | <c> | <c> |
| Land Use | Variations 2003--2053 | R Code | Animation |
|-----------------+-----------------------+------------+------------|
| Annual Crops | + 3.17% | Click here | Click here |
| Pastures | - 17.7% | Click here | Click here |
| Perennial Crops | - 17.7% | Click here | Click here |
| Forests | + 9.11% | Click here | Click here |
| Urban Area | + 33.4% | Click here | Click here |
/More interpretations to come./
** Scenario S2
This scenario integrates climate-induced land use changes. The
dynamics 1990--2005 of returns from land are modeled through
climate variables, and the IPCC projection A1B is used to estimate
the future returns from land. Then, the econometric model of land
use allows to establish the consequences in terms of land use
changes.
You can download the data from this scenario simulation here. See
Section MAP for the functions.
This scenario presents an increase in annual crops, perennial
crops, forests and urban area. The pastures are proven to
potentially suffer from climate change, with a strong decrease of
the acreages. The following Table contains the links to the R Codes
and the Animations 2003--2053 at the national scale.
#+CAPTION: Land Use Changes according to scenario S2
| <l> | <c> | <c> | <c> |
| Land Use | Variations 2003--2053 | R Code | Animation |
|-----------------+-----------------------+------------+------------|
| Annual Crops | + 27.2% | Click here | Click here |
| Pastures | - 55.5% | Click here | Click here |
| Perennial Crops | + 177 % | Click here | Click here |
| Forests | + 1.71% | Click here | Click here |
| Urban Area | + 60.1% | Click here | Click here |
/More interpretations to come./
** Scenario S3
This scenario corresponds to S1 coupled with a payment of \EUR{}
200 per hectare for pastures.
You can download the data from this scenario simulation here. See
Section MAP for the functions.
This scenario presents an increase in pastures and urban area and a
decrease in annual crops, perennial crops and forests. For this
scenario, the animations of the following Table are not the
absolute land use changes but the land use change relatively to S1,
to show the net effect of the policy of payments for pastures.
#+CAPTION: Land Use Changes from scenario S3 relatively to S1
| <l> | <c> | <c> | <c> |
| Land Use | Variations 2003--2053 | R Code | Animation |
|-----------------+-----------------------+------------+------------|
| Annual Crops | - 20.8% | Click here | Click here |
| Pastures | + 22.6% | Click here | Click here |
| Perennial Crops | - 27.6% | Click here | Click here |
| Forests | - 2.15% | Click here | Click here |
| Urban Area | + 17.5% | Click here | Click here |
/More interpretations to come./
** Scenario S4
This scenario corresponds to S2 coupled with a payment of \EUR{}
200 per hectare for pastures.
You can download the data from this scenario simulation here. See
Section MAP for the functions.
This scenario presents an increase in annual crops, perennial crops
and urban area and a decrease in pastures and forests. With
climate-induced land use changes, the payments are not sufficient
to reverse the decreasing trend of pastures. For this scenario, the
animations of the following Table are not the absolute land use
changes but the land use change relatively to S2, to show the net
effect of the policy of payments for pastures.
#+CAPTION: Land Use Changes according to scenario S4 relatively to scenario S2
| <l> | <c> | <c> | <c> |
| Land Use | Variations 2003--2053 | R Code | Animation |
|-----------------+-----------------------+------------+------------|
| Annual Crops | + 16.2% | Click here | Click here |
| Pastures | - 19.4% | Click here | Click here |
| Perennial Crops | + 83.7% | Click here | Click here |
| Forests | - 9.43% | Click here | Click here |
| Urban Area | + 23.5% | Click here | Click here |
/More interpretations to come./
* <<BAB>> Bird Abundances
** Scenario S0
This scenario is with constant land use, it shows the direct
response of birds' distributions from the climate projections IPCC
A1B.
Download the data for the simulation here. The dynamic map
2003--2053 for the aggregate bird index as presented in the working
paper (equation 9) is here with the corresponding R code.
Species names are available from this =.csv= file from which we
build the following tabular containing the animations 2003--2053 of
bird abundances from all the species studied in this research. The
R Code using to generate the simulations from the raw data is
here. The Table is simply obtained from the following R script.
#+begin_src R :results value table :colnames yes...
#+LaTeX: {\footnotesize
#+ATTR_LaTeX: :environment longtable
#+CAPTION: Links to the animations 2003--2053 for scenario S0 and each bird species
#+RESULTS:...
#+LaTeX: }
/More interpretations to come./
** Scenario S1
This scenario corresponds to a "business-as-usual" scenario for
land use, with climate effects on birds as in S0.
Download the data for the simulation here. The dynamic map
2003--2053 for the aggregate bird index as presented in the working
paper (equation 9) is here with the corresponding R code.
Species names are available from this =.csv= file from which we
build the following tabular containing the animations 2003--2053 of
bird abundances from all the species studied in this research. The
R Code using to generate the simulations from the raw data is
here. The Table is simply obtained from the following R script.
#+begin_src R :results value table :colnames yes...
#+LaTeX: {\footnotesize
#+ATTR_LaTeX: :environment longtable
#+CAPTION: Links to the animations 2003--2053 for scenario S1 and each bird species
#+RESULTS:...
#+LaTeX: }
/More interpretations to come./
** Scenario S2
This scenario corresponds to a climate-induced scenario for land
use, with climate effects on birds as in S0.
Download the data for the simulation here. The dynamic map
2003--2053 for the aggregate bird index as presented in the working
paper (equation 9) is here with the corresponding R code. Some
explanations.
Species names are available from this =.csv= file from which we
build the following tabular containing the animations 2003--2053 of
bird abundances from all the species studied in this research. The
R Code using to generate the simulations from the raw data is
here. The Table is simply obtained from the following R script.
#+begin_src R :results value table :colnames yes...
#+LaTeX: {\footnotesize
#+ATTR_LaTeX: :environment longtable
#+CAPTION: Links to the animations 2003--2053 for scenario S2 and each bird species
#+RESULTS:...
#+LaTeX: }
/More interpretations to come./
** Scenario S3
Scenario S1 coupled with a payment of \EUR{} 200 per hectare for
pastures, with climate effects on birds as in S0.
Download the data for the simulation here. The dynamic map
2003--2053 for the aggregate bird index as presented in the working
paper (equation 9) is here with the corresponding R code. Some
explanations.
Species names are available from this =.csv= file from which we
build the following tabular containing the animations 2003--2053 of
bird abundances from all the species studied in this research. The
R Code using to generate the simulations from the raw data is
here. The Table is simply obtained from the following R script.
#+begin_src R :results value table :colnames yes...
#+LaTeX: {\footnotesize
#+ATTR_LaTeX: :environment longtable
#+CAPTION: Links to the animations 2003--2053 for scenario S3 and each bird species
#+RESULTS:...
#+LaTeX: }
/More interpretations to come./
** Scenario S4
Scenario S2 coupled with a payment of \EUR{} 200 per hectare for
pastures, with climate effects on birds as in S0.
Download the data for the simulation here. The dynamic map
2003--2053 for the aggregate bird index as presented in the working
paper (equation 9) is here with the corresponding R code. Some
explanations.
Species names are available from this =.csv= file from which we
build the following tabular containing the animations 2003--2053 of
bird abundances from all the species studied in this research. The
R Code using to generate the simulations from the raw data is
here. The Table is simply obtained from the following R script.
#+begin_src R :results value table :colnames yes...
#+LaTeX: {\footnotesize
#+ATTR_LaTeX: :environment longtable
#+CAPTION: Links to the animations 2003--2053 for scenario S4 and each bird species
#+RESULTS:...
#+LaTeX: }
/More interpretations to come./
* <<AKN>> Acknowledgements
This research has been founded by the FRB (/Fondation de Recherche
sur la Biodiversité/) and GDF--SUEZ through the MOBILIS
project. R. Chakir also acknowledges the financial support from
French /Agence Nationale de la Recherche/ through the ModULand
project (ANR--11--BSH1--005). The authors also acknowledge volunteer
ornithologists, French Ministry of Agriculture (/Service de la
Statistique et de la Prospective/), IGN, INRA InfoSol, and Météo
France for the production of data that allow such work. We are
grateful to Laurent Terray, Christian Pagé and Julian Boé for the
regional climate scenarios, Vincent Badeau for the development of
the 8km soil data set and Christophe Fran\c{c}ois for his assistance
in the use of climate and soils data sets.
* <<ADM>> Additional Material
** <<MAP>> Geographic files
The compressed geographical shapefiles are available here.
#+begin_src R :results silent :tangle ./myFunctions.R
library(sp) ; library(rgdal)
MAP <- readOGR("./Data", "GrMaille", verbose= FALSE)
CRD <- data.frame(MAP, coordinates(MAP))
F2C <- readOGR("./Data", "F2C" , verbose= FALSE)
FD.CRT <- list("sp.polygons", F2C, lwd= 10)
#+end_src
** <<LAP>> Linear Approx.
#+begin_src R :results silent :tangle ./myFunctions.R
LinApprox <- function(pdat, nc= 2: 52){
prd <- matrix(0, ncol= length(nc))
for (i in unique(pdat$MAILLE)){
yop <- approx(pdat$time[pdat$MAILLE== i],
pdat$value[pdat$MAILLE== i], n= length(nc))$y
prd <- rbind(prd, yop)
}
DAT <<- data.frame(unique(pdat$MAILLE), prd[-1, ], row.names= NULL)
names(DAT) <<- c("MAILLE", paste("N", 2003: 2053, sep= ""))
DAT[, nc] <<- (DAT[, nc]- DAT[, 2])* 100
}
#+end_src
** <<ANM>> Animations
#+begin_src R :results silent :tangle ./myFunctions.R
library(RColorBrewer) ; library(classInt) ; library(animation)
AnimHTML <- function(SPDF, ttle, brks, pal, t1, t2, dir, name){
IC <- classIntervals(-100: 100, n= length(brks)+ 1,
style= "fixed", fixedBreaks= brks)
CR <- attr(findColours(IC, brewer.pal(5, pal)), "palette")
saveHTML({
oopt <- ani.options(interval= 0.15, nmax= 100, title= ttle)
opar <- par(mar= c(3, 3, 1, 0.5), mgp= c(2, .5, 0),
tcl= -0.3, cex.axis= 1.5, cex.lab= 1.5, cex.main= 2)
for(i in 2: 52){
dev.hold()
mp <- spplot(SPDF[, i], cuts= IC$brks, col.regions= CR,
cex= 1, pch= 15, colorkey= T, sp.layout= FD.CRT,
main= paste(t1, substr(names(SPDF)[ i], 2, 5),
t2, sep= ""),
par.settings= list(panel.background=
list(col="grey")))
mp$legend$right$args$key$at <- IC$brks ; print(mp)
ani.pause()
}
}, autoplay= FALSE, loop= FALSE, verbose= FALSE, outdir = dir,
htmlfile = name, autobrowse= FALSE, single.opts=
"'controls': ['first', 'previous',
'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")
}
#+end_src