Integrated Models, Scenarios and Dynamics of Climate, Land Use and Common Birds for France: Dynamic Maps

We present here only a brief overview of the methods used in this research, a more complete picture can be found in the 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.

As illustrated by the following Figure, we simulate 5 different scenarios in order to disentangle the respective effects of the integrated modeling blocks.

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.

The rest of this file first presents the Land Use Changes (section 2) and the Bird Abundances (section 3) associated to each scenario. Each section contains the data from simulations (in compressed .Rda format for the R software). Sections 4 and 5 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.

Land use is constant in this scenario, only birds are impacted by climate change, see section 1.2 for a description of scenarios.

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 5.1 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.

More interpretations to come.

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 5.1 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.

More interpretations to come.

This scenario corresponds to S1 coupled with a payment of € 200 per hectare for pastures.

You can download the data from this scenario simulation here. See Section 5.1 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.

More interpretations to come.

This scenario corresponds to S2 coupled with a payment of € 200 per hectare for pastures.

You can download the data from this scenario simulation here. See Section 5.1 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.

More interpretations to come.

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.

NDO <- read.csv("Data/NomsDoizos.csv")
(OIZO <- data.frame("CODE"= NDO$SPEC, "French"= NDO$NOM, "English"= NDO$Anglais,
                    "Latin"= paste("/",NDO$ESP1," ",NDO$ESP2,"/",sep= ""),
                    "Animation"= paste("[[./output/", NDO$SPEC, "S0/",
                                       NDO$SPEC,"S0.html][Click here]]",sep="")))

More interpretations to come.

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.

Outliers <- c("ALAARV", "APUAPU")
NDO <- subset(read.csv("Data/NomsDoizos.csv"), !SPEC %in% Outliers)
(OIZO <- data.frame("CODE"= NDO$SPEC, "French"= NDO$NOM, "English"= NDO$Anglais,
                   "Latin"= paste("/", NDO$ESP1, " ", NDO$ESP2, "/", sep= ""),
                   "Animation"= paste("[[./output/", NDO$SPEC, "S1/",
                                      NDO$SPEC,"S1.html][Click here]]", sep= "")))

More interpretations to come.

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.

Outliers <- c("ALAARV", "APUAPU", "MOTFLA", "SYLMEL")
NDO <- subset(read.csv("Data/NomsDoizos.csv"), !SPEC %in% Outliers)
(OIZO <- data.frame("CODE"= NDO$SPEC, "French"= NDO$NOM, "English"= NDO$Anglais,
                   "Latin"= paste("/", NDO$ESP1, " ", NDO$ESP2, "/", sep= ""),
                   "Animation"= paste("[[./output/", NDO$SPEC, "S2/",
                                      NDO$SPEC,"S2.html][Click here]]", sep= "")))

More interpretations to come.

Scenario S1 coupled with a payment of € 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.

Outliers <- c("ALAARV", "APUAPU", "SYLMEL")
NDO <- subset(read.csv("Data/NomsDoizos.csv"), !SPEC %in% Outliers)
(OIZO <- data.frame("CODE"= NDO$SPEC, "French"= NDO$NOM, "English"= NDO$Anglais,
                   "Latin"= paste("/", NDO$ESP1, " ", NDO$ESP2, "/", sep= ""),
                   "Animation"= paste("[[./output/", NDO$SPEC, "S3/",
                                      NDO$SPEC,"S3.html][Click here]]", sep= "")))

More interpretations to come.

Scenario S2 coupled with a payment of € 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.

Outliers <- c("ALAARV", "APUAPU", "SYLMEL")
NDO <- subset(read.csv("Data/NomsDoizos.csv"), !SPEC %in% Outliers)
(OIZO <- data.frame("CODE"= NDO$SPEC, "French"= NDO$NOM, "English"= NDO$Anglais,
                   "Latin"= paste("/", NDO$ESP1, " ", NDO$ESP2, "/", sep= ""),
                   "Animation"= paste("[[./output/", NDO$SPEC, "S4/",
                                      NDO$SPEC,"S4.html][Click here]]", sep= "")))

More interpretations to come.

The compressed geographical shapefiles are available here.

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)