Preparing and processing environmental data for Species Distribution Modeling

Transcription

1 Preparing and processing environmental data for Species Distribution Modeling using FOSS software () Instituut voor Biodiversiteit en Ecosysteem Dynamica Universiteit van Amsterdam July 12, 2010

2 Programme 9:00 11:00 Importance of environmental maps for SDM; 11:00 12:15 Overview of publicly available global data sets; 13:15 14:00 Obtaining and preparing data using FOSS; Installation and first (baby) steps (intro.r). Building, running and editing models in R.. 14:00 16:30 Processing worldmaps; Optional: preparation of ecological predictors and metadata for your own case study; 16:30 17:00 Discussion and closing remarks

3 Outline 1 2 3

4 Bivand, R., Pebesma, E., Rubio, V., Applied Spatial Data Analysis with R. Use R Series, Springer, Heidelberg, 378 p. Hengl, T., A Practical Guide to Geostatistical Mapping, 2nd edition. University of Amsterdam, 291 p. ISBN Kabacoff, R.I., Data Analysis and Graphics with R. Manning publications, 375 p. Phillips, S.J., Anderson, R.P., Schapire, R.E., Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190:

5 The ASDA(R)-book.org

6 The ASDA(R) team

7 A Practical Guide to Geostatistical Mapping

8 Outline 1 2 3

9 Objectives Where to obtain auxiliary environmental maps for SDM? Where to obtain and how to use the R+OSGeo software? How to combine GIS and SDM operations?

10 Objectives Where to obtain auxiliary environmental maps for SDM? Where to obtain and how to use the R+OSGeo software? How to combine GIS and SDM operations?

11 Objectives Where to obtain auxiliary environmental maps for SDM? Where to obtain and how to use the R+OSGeo software? How to combine GIS and SDM operations?

12 Definitions Species Distribution Model: Analytical model that generates predictions of distribution of a species in an area of interest, given field records of occurrences and list of environmental maps. The main input to SDM is a map; the main output of SDM analysis is a map; There are two main groups of maps we produce: 1 Habitat-maps (potential distribution) 2 Density-maps (actual distribution)

13 Definitions Species Distribution Model: Analytical model that generates predictions of distribution of a species in an area of interest, given field records of occurrences and list of environmental maps. The main input to SDM is a map; the main output of SDM analysis is a map; There are two main groups of maps we produce: 1 Habitat-maps (potential distribution) 2 Density-maps (actual distribution)

14 Definitions Species Distribution Model: Analytical model that generates predictions of distribution of a species in an area of interest, given field records of occurrences and list of environmental maps. The main input to SDM is a map; the main output of SDM analysis is a map; There are two main groups of maps we produce: 1 Habitat-maps (potential distribution) 2 Density-maps (actual distribution)

15 Definitions Species Distribution Model: Analytical model that generates predictions of distribution of a species in an area of interest, given field records of occurrences and list of environmental maps. The main input to SDM is a map; the main output of SDM analysis is a map; There are two main groups of maps we produce: 1 Habitat-maps (potential distribution) 2 Density-maps (actual distribution)

16 Definitions Species Distribution Model: Analytical model that generates predictions of distribution of a species in an area of interest, given field records of occurrences and list of environmental maps. The main input to SDM is a map; the main output of SDM analysis is a map; There are two main groups of maps we produce: 1 Habitat-maps (potential distribution) 2 Density-maps (actual distribution)

17 Space / domains

18 Bittervoorn

19 Outline 1 2 3

20 maps (environmental predictors) are equally important input to species distribution modeling as the occurrence records. If we are lucky, we will be able to explain distribution of a species by using a small sample of species occurrence/attributes. can be of different type (remote sensing images, topographic, land cover data, proximity and sources of food and water, proximity to human population etc.). The publicly available global data sets are in general under-used.... for various reasons (unpopular formats, missing metadata, significant processing required to put it to a usable format etc.).

21 maps (environmental predictors) are equally important input to species distribution modeling as the occurrence records. If we are lucky, we will be able to explain distribution of a species by using a small sample of species occurrence/attributes. can be of different type (remote sensing images, topographic, land cover data, proximity and sources of food and water, proximity to human population etc.). The publicly available global data sets are in general under-used.... for various reasons (unpopular formats, missing metadata, significant processing required to put it to a usable format etc.).

22 maps (environmental predictors) are equally important input to species distribution modeling as the occurrence records. If we are lucky, we will be able to explain distribution of a species by using a small sample of species occurrence/attributes. can be of different type (remote sensing images, topographic, land cover data, proximity and sources of food and water, proximity to human population etc.). The publicly available global data sets are in general under-used.... for various reasons (unpopular formats, missing metadata, significant processing required to put it to a usable format etc.).

23 maps (environmental predictors) are equally important input to species distribution modeling as the occurrence records. If we are lucky, we will be able to explain distribution of a species by using a small sample of species occurrence/attributes. can be of different type (remote sensing images, topographic, land cover data, proximity and sources of food and water, proximity to human population etc.). The publicly available global data sets are in general under-used.... for various reasons (unpopular formats, missing metadata, significant processing required to put it to a usable format etc.).

24 maps (environmental predictors) are equally important input to species distribution modeling as the occurrence records. If we are lucky, we will be able to explain distribution of a species by using a small sample of species occurrence/attributes. can be of different type (remote sensing images, topographic, land cover data, proximity and sources of food and water, proximity to human population etc.). The publicly available global data sets are in general under-used.... for various reasons (unpopular formats, missing metadata, significant processing required to put it to a usable format etc.).

25 Global repository of publicly available maps

26 worldmaps I ve been collecting and sorting/processing various publicly available data sets over the years (at 1 5 km resolution, there is a lot of free data). The result is a repository with cca 100 unique rasters, that can be obtained directly from Each gridded map consists of 7200 columns and 3600 rows; the cell size is 0.05 arcdegrees, which corresponds to about 5.6 km; all maps fall on the same grid. Maps are projected in the Latitude-Longitude WGS84 system +proj=longlat +ellps=wgs84. These maps are ideal for SDM applications at continental and national levels.

27 The happy triangle guy Statistical computing GDAL GIS analysis ground overlays, time-series KML Storage and browsing of geo-data

28 Setting up R basics R basic and add-on packages; R syntax; R objects and methods ; R FAQs; getting help and the most important literature; Scripting in R data management; creating and debugging scripts (scripting editors: Tinn-R); automating analysis making functions and packages; publication quality outputs (using R+Sweave); Advanced topics GDAL and R; spatial classes and packages; export of maps to Google ;

29 Outline 1 2 3

30 Getting the right motivation What is R, and why should you invest time to learn it? What can it do? (and what it can t do?) How does the R community works (what are its shared principles)? Is R suited for spatio-temporal data analysis?

31 Quote R has really become the second language for people coming out of grad school now, and there s an amazing amount of code being written for it. Max Kuhn

32 Packages of interest to SDM: Pure habitat analysis packages: adehabitat package for habitat analysis for animals; BiodiversityR for community ecology analysis; spatstat for analysis of point patterns; and gstat for geostatistical analysis; Integration packages: RODBC package; XML package; Geographical analysis: Open Source Desktop and Server-based GIS: SAGA GIS, GRASS GIS and ILWIS GIS; openmodeller an open source plugin that contains an extensive Niche modeling library (Muñoz et al. 2007); MaxEnt the Maximum Entropy niche analysis algorithm (Phillips et al. 2008);

33 Type of SDM output maps

34 R code > library(spatstat); library(geor) # unconditional gaussian simulations (psill=1, mean=0): > s <- grf(100, grid="reg", cov.pars=c(1, 0.2), + cov.model="mat", kappa=1.5) # define your own model, e.g. poisson: > lambda <- 0.2*exp(0.5 +s$data) > y <- rpois(length(s$data), lambda=lambda) > image(s, col=gray(seq(1, 0.5, l=21))) > text(s$coords, label=y, pos=3, offset=-0.2, cex=1.5) # simulate a point pattern: > sm <- list(x=seq(0, 1, l=10), y=seq(0, 1, l=10), + z=matrix(y, nrow=10)) > y.p <- rpoint(n=sum(y), f=as.im(sm)) > image(s, col=gray(seq(1, 0.5, l=21))) > points(y.p, pch="+", cex=1.5)

35 Why make scripts? It s easy to use: Because S (and its implementation R) is a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities, existing functions can be modified. In R we all become programmers (but much faster than with C++ or Java). The basic approach to using R is to generate scripts that define the data processing steps (workflows?). Documenting the analysis process is a good thing, so programming scripts are not just a burden, certainly for users doing original research and repetitive work, arguably for student classes too. Point-and-click operations are for amateurs.

36 Why make scripts? It s easy to use: Because S (and its implementation R) is a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities, existing functions can be modified. In R we all become programmers (but much faster than with C++ or Java). The basic approach to using R is to generate scripts that define the data processing steps (workflows?). Documenting the analysis process is a good thing, so programming scripts are not just a burden, certainly for users doing original research and repetitive work, arguably for student classes too. Point-and-click operations are for amateurs.

37 Why make scripts? It s easy to use: Because S (and its implementation R) is a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities, existing functions can be modified. In R we all become programmers (but much faster than with C++ or Java). The basic approach to using R is to generate scripts that define the data processing steps (workflows?). Documenting the analysis process is a good thing, so programming scripts are not just a burden, certainly for users doing original research and repetitive work, arguably for student classes too. Point-and-click operations are for amateurs.

38 Why make scripts? It s easy to use: Because S (and its implementation R) is a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities, existing functions can be modified. In R we all become programmers (but much faster than with C++ or Java). The basic approach to using R is to generate scripts that define the data processing steps (workflows?). Documenting the analysis process is a good thing, so programming scripts are not just a burden, certainly for users doing original research and repetitive work, arguably for student classes too. Point-and-click operations are for amateurs.

39 we will use Not all software is required to follow the exercises R v2.11 (Windows OS) including a list of packages; Tinn-R v2.3 (code editor); Optional: FWTools v2.4 a list of utilities to handle ; SAGA GIS v2.0.4 a light GIS excellent for educational purposes.

40 Installing the add-on packages > install.packages("ctv") > library(ctv) > install.views("spatial") This will install all connected packages listed at R views Spatial.

41 Check your installation > Sys.getenv(c("OS", "COMPUTERNAME", "R_HOME", "R_LIBS_USER", + "PROCESSOR_IDENTIFIER")) OS "Windows_NT" COMPUTERNAME "PC-IBED193" R_HOME "C:\\PROGRA~1\\R\\R-210~1.1" R_LIBS_USER "n:/r/win-library/2.10" PROCESSOR_IDENTIFIER "x86 Family 6 Model 15 Stepping 6, GenuineIntel"

42 Outline 1 2 3

43 Tinn-R

44 JaGuaR

45 Customizing link between R and Tinn-R: 1 Open Tinn-R select Options R Path and check that the path is correct; 2 Tinn-R requires R to run either Rterm or Rgui in SDI mode: In Rgui, select Edit GUI preferences and set SDI and click on Save. 3 To add the CRTL+R shortcut to Tinn-R: First open Options Shortcuts and replace the existing CRTL+R shortcut to e.g. CRTL+M; Then, open R menu and select Hotkeys ; add a CRTL+R shortcut to Send line ; To send blocks of text you will need to edit your Rprofile.site file under R/etc/ directory; 4 To learn more about how to customize Tinn-R, read the user_guide.html file under the Tinn-R/doc/English/user guide/ directory.

46 Outline 1 2 3

47 Spatial objects An advantage of R (as compared to e.g. Matlab) is that you can create your own formats and structures for data. But if there are too many formats you can easily get lots. In addition, we want to have smooth links to external formats (R is open!). To reduce this problem, Bivand et al. (2008) developed new-style classes to represent.

48 Spatial class The foundation object is the Spatial class, with just two basic slots (new-style S classes have pre-defined components called slots): a bounding box mostly used for setting up plots; a CRS class object defining the coordinate reference system, and may be set to CRS(as.character(NA)); Operations on Spatial* objects should update or copy these values to the new Spatial* objects being created. The most basic object is a point, which may have 2 or 3 dimensions.

49 Spatial classes for point features: SpatialPoints; SpatialPointsDataFrame; for line features: SpatialLines, SpatialLinesDataFrame; polygons: SpatialPolygons, SpatialPolygonsDataFrame; rasters: SpatialPixels, SpatialPixelsDataFrame, SpatialGrid, SpatialGridDataFrame;

50 SpatialPoints > library(sp) > data(meuse) > coords <- SpatialPoints(meuse[, c("x", "y")]) > summary(coords) Object of class SpatialPoints Coordinates: min max x y Is projected: NA proj4string : [NA] Number of points: 155

51 SpatialPointsDataFrame We can add the tabular data to make a SpatialPointsDataFrame object: > meuse1 <- SpatialPointsDataFrame(coords, meuse) > str(meuse1, max.level = 2) Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots..@ data :'data.frame': 155 obs. of 14 variables:..@ coords.nrs : num(0)..@ coords : num [1:155, 1:2] attr(*, "dimnames")=list of 2..@ bbox : num [1:2, 1:2] attr(*, "dimnames")=list of 2..@ proj4string:formal class 'CRS' [package "sp"] with 1 slots

52 Basic methods spplot plotting of spatial objects (maps); spsample sample points from a set of polygons, on a set of lines or from a gridded area; bbox get the bounding box; proj4string get or set the projection (coordinate reference system); coordinates set or retrieve coordinates; sptransform transform coordinates from one CRS to another; overlay combine two different spatial objects;

53 Plotting a SpatialPoints object > plot(as(meuse1, "Spatial"), axes = TRUE) > plot(meuse1, add = TRUE) > plot(meuse1[meuse1$ffreq == 1, ], col = "green", add = TRUE)

54 Combining statistical and GIS operations Because the Spatial*DataFrame family objects behave in most cases like data frames, most of what we are used to doing with standard data frames just works (but no merge, etc., yet). These objects are very similar to typical representations of the same kinds of objects in geographical information systems, so they do not suit that is not geographical (like medical imaging) as such. Because now sp classes for GIS data exits, this opens the door for fusing GIS and statistical operations (this has not been possible in e.g. 2002).

55 Spatial packages R now offers a range of contributed packages in spatial statistics and increasing awareness of the importance of spatial data analysis in the broader community. Current contributed packages with spatial applications: point patterns: spatstat, VR:spatial, splancs; geostatistics: gstat, geor, georglm, fields, spbayes, RandomFields, VR:spatial, sgeostat, vardiag; lattice/area data: spdep, DCluster, spgwr, ade4; links to GIS: rgdal, spgrass, RPy, RSAGA;

56 Let s create spatial objects! We can create spatial objects from scratch! For example a DEM: > dem <- expand.grid(x = seq(100, 600, 100), y = seq(100, + 600, 100)) > dem$z <- as.vector(c(23, 24, 34, 38, 45, 51, 24, 20, + 20, 28, 18, 49, 22, 20, 19, 14, 38, 45, 19, 15, 13, + 21, 23, 25, 14, 11, 18, 11, 18, 19, 10, 16, 23, 16, + 9, 6)) > gridded(dem) <- ~x + y > dem <- as(dem, "SpatialGridDataFrame") > str(dem)

57 Outline 1 2 3

58 Controlling SAGA from R > library(rsaga) > rsaga.env() $workspace [1] "." $cmd [1] "saga_cmd.exe" $path [1] "C:/PROGRA~1/R/R-210~1.1/library/RSAGA/saga_vc" $modules [1] "C:/PROGRA~1/R/R-210~1.1/library/RSAGA/saga_vc/modules"

59 Getting list of modules > rsaga.get.modules("ta_channels") $ta_channels code name interactive 1 0 Channel Network FALSE 2 1 Watershed Basins FALSE 3 2 Watershed Basins (extended) FALSE 4 3 Vertical Distance to Channel Network FALSE 5 4 Overland Flow Distance to Channel Network FALSE 6 5 D8 Flow Analysis FALSE 7 6 Strahler Order FALSE

60 Run stream extraction > rsaga.geoprocessor(lib = "ta_channels", module = 5, + param = list(dem = "dem6.sgrd", + DIRECTION = "channels.sgrd", CONNECTION = "route.sgrd", + NETWORK = "channels.shp")) SAGA CMD library path: C:/PROGRA~1/R/R-210~1.1/library/RSAGA/... library name: ta_channels module name : D8 Flow Analysis author : (c) 2003 by O.Conrad Load grid: dem6.sgrd... ready Parameters Grid system: 100; 6x 6y; 100x 100y DEM: dem6 Flow Direction: Flow Direction Flow Connectivity: Flow Connectivity Flow Network: Flow Network Minimum Connectivity: 0...

61 Read back to R > dem$route <- readgdal("route.sdat")$band1 route.sdat has GDAL driver SAGA and has 6 rows and 6 columns > channels <- readogr("channels.shp", "channels") OGR data source with driver: ESRI Shapefile Source: "channels.shp", layer: "channels" with 32 features and 2 fields Feature type: wkblinestring with 2 dimensions

62 Plot the final result > dem.plt <- spplot(dem[1], main = "DEM", col.regions = topo.colors(25)) > channels.plt <- spplot(dem[2], col.regions = rev(gray(0:20/20)), + main = "Flow connectivity", sp.layout = list("sp.lines", + channels, col = "red")) > print(dem.plt, split = c(1, 1, 2, 1), more = T) > print(channels.plt, split = c(2, 1, 2, 1), more = F) DEM Flow connectivity

63 Outline 1 2 3

64 Exercise 1 Create a working directory called NL, then download the makerdc.r script. Open new session in R and run the script from Tinn-R. This will download a land cover map of NL and resample it to the Dutch coordinate system.

65 Exercise 1 Create a working directory called NL, then download the makerdc.r script. Open new session in R and run the script from Tinn-R. This will download a land cover map of NL and resample it to the Dutch coordinate system.

66 Exercise 1 Create a working directory called NL, then download the makerdc.r script. Open new session in R and run the script from Tinn-R. This will download a land cover map of NL and resample it to the Dutch coordinate system.

67 Preparing FWTools There is still no package to control FWTools from R, but we can simply send command lines using the system command. Before we can use FWTools from R, we need to locate it on our PC: > gdalwarp <- gsub("/", "\\\\", dir(path="c:/progra~2/fwtool~1.7", + pattern="gdalwarp.exe", recursive=true, full.names=true)) > gdalwarp [1] "C:\\PROGRA~2\\FWTOOL~1.7\\bin\\gdalwarp.exe" > workd <- paste(gsub("/", "\\\\", getwd()), "\\", sep="")

68 MODIS data Now we can download some GIS data from web: > MOD12Q1 <- + MOLT/MOD12Q1.004/ /" > download.file(paste(mod12q1, + "MOD12Q1.A h18v hdf", sep=""), + destfile=paste(getwd(), + "MOD12Q1.A h18v hdf", sep="/"), + mode='wb', method='wget') Resolving e4ftl01u.ecs.nasa.gov Connecting to e4ftl01u.ecs.nasa.gov :21... connected. Logging in as anonymous... Logged in! ==> SYST... done. ==> PWD... done. ==> TYPE I... done. ==> CWD /MOLT/MOD12Q1.004/ done. ==> SIZE MOD12Q1.A h18v hdf ==> PASV... done. ==> RETR MOD12Q1.A done. Length: (22M) 0K % 64.9K 5m48s K % 501K 0s 22600K % 503K=65s

69 Reprojecting grids We can reproject/resample the map to our local coordinate system using the gdalwarp functionality (this combines several processing steps in one function): > NL.prj <- "+proj=sterea +lat_0= lon_0= k= x_0= y_0= ellps=bessel +units=m +no_defs + +towgs84= , , , , , ,4.0812" > system(paste(gdalwarp, " HDF4_EOS:EOS_GRID:\"", workd, + "\\MOD12Q1.A h18v hdf\" + :MOD12Q1:Land_Cover_Type_1 -t_srs \"", NL.prj, "\" + IGBP2004NL.tif -r near -te tr ", sep="")) Creating output file that is 560P x 650L. Processing input file HDF4_EOS:EOS_GRID:\\MOD12Q1.A Using internal nodata values (eg. 255) for image HDF4_EOS:EOS_ done.

70 Plot the final result In this case we have produced a MODIS-based land cover map for the whole Netherlands in resolution of 500 m (in local coordinate system).

71 Exercise 2 & 3 Create a working directory called worldmaps, then download a selection of worldmaps from the repository (or get a copy from the USB stick) and unzip the maps that you need to complete the exercises. Use any GIS that you find suitable to answer the questions. I recommend first testing SAGA GIS, then running the analysis in R. These maps are Large so it could take time until you import/open a map.

72 Exercise 2 & 3 Create a working directory called worldmaps, then download a selection of worldmaps from the repository (or get a copy from the USB stick) and unzip the maps that you need to complete the exercises. Use any GIS that you find suitable to answer the questions. I recommend first testing SAGA GIS, then running the analysis in R. These maps are Large so it could take time until you import/open a map.

73 Exercise 2 & 3 Create a working directory called worldmaps, then download a selection of worldmaps from the repository (or get a copy from the USB stick) and unzip the maps that you need to complete the exercises. Use any GIS that you find suitable to answer the questions. I recommend first testing SAGA GIS, then running the analysis in R. These maps are Large so it could take time until you import/open a map.

74 Exercise 4 We focus on extracting land surface parameters (or geomorphometric parameters) using a DEM. First create a new working directory on your computer, then Download the map of world countries (countries) and the global Digital Elevation Model at 5.6 km resolution (globedem). Resample and subset the DEMs to local coordinate systems for Germany use the European ETRS89 coordinate system (EPSG:3035), and for Bolivia use the South America Albers Equal Area Conic coordinate system (ESRI:102033). Try also to derive these parameters using the RSAGA package, i.e. by sending the commands to SAGA from R.

75 Outline 1 2 3

76 Controlling MaxEnt from R MaxEnt is by many considered to be the most robust approach to species distribution modeling. It can work with both continuous and categorical predictors and has very extensive and flexible possibilities for analysis of biodiversity data. MaxEnt is not available as an R package, therefore you will first need to request and download it from the MaxEnt homepage. The complete algorithm is contained in a single maxent.jar (Java ARchive) file, which is basically a zipped Java (class file) code. More flexible way to writing KML files is by using loops.

77 Prepare grids # Location of MaxEnt and directories: > Sys.chmod(getwd(), mode="7777") # write permissions > MaxEnt <- "C:\\MaxEnt\\maxent.jar" > dir.create(path="meout"); dir.create(path="mesamples") > MaxEnt.layers <- paste(gsub("/", "\\\\", getwd()), "\\grids", sep="") > MaxEnt.out <- paste(gsub("/", "\\\\", getwd()), "\\MEout", sep="") > MaxEnt.samples <- paste(gsub("/", "\\\\", getwd()), + "\\MEsamples", sep="") where MEout is the directory where MaxEnt will write the results of analysis (plots, grids and table data), and MEsamples is a directory containing the input samples.

78 Prepare grids Next, copy the grids of interest to some working directory e.g. /grids: > dir.create(path="grids") > for(j in c("dem.asc", "grad.asc", "twi.asc", "achan.asc")) { > file.copy(j, paste("grids/", j, sep=""), overwrite=true) > } > asc.list <- list.files(paste(getwd(), "/grids", sep=""), + pattern="\\.asc$", recursive=true, full=false) > asc.list [1] "achan.asc" "dem.asc" "twi.asc"

79 Preparing the occurrence only records Before we can run MaxEnt, we still need to prepare the occurrence records in the required format (.csv): # Write records to a csv file (species, longitude, latitude): > bei.csv <- data.frame(sp=rep("bei", bei$n), gx=bei$x, gy=bei$y) > write.csv(bei.csv[,c("sp","gx","gy")], "MEsamples/bei.csv", + quote=false, row.names=false)

80 Sending batch process to MaxEnt We can now run MaxEnt using the system command in R: # Run a batch process (opens a process window): > system(command=paste("java -mx1000m -jar ", MaxEnt, + " environmentallayers=", MaxEnt.layers, " samplesfile=", + MaxEnt.samples, "\\bei.csv", " outputdirectory=", + MaxEnt.out, " randomtestpoints=25 maximumiterations=100 + redoifexists autorun nowarnings notooltips", sep="")) where randomtestpoints=25 will randomly take 25% points for cross-validation, redoifexists will replace the existing files, autorun, nowarnings and notooltips will force MaxEnt to run without waiting. For more information about MaxEnt batch mode flags, look in the MaxEnt Help documentation.

81 Reading the results After the analysis, open the /MEout directory and browse the generated files. We are most interested in two files: bei.asc the Habitat Suitability Index map (0 1), and bei.html the complete report from the analysis.

82 MaxEnt window

83 Exercise (for ambitious) We focus on mapping the potential habitat for Sturnella magna (Eastern Meadowlark) in USA based on the occurrence records obtained from AKN. First create a new working directory on your computer, then download the worldmap.r script and open it in Tinn-R. Note that the worldmaps need to be already available in the working directory, and a link to MaxEnt needs to be established via the directory address e.g. C:\\MaxEnt\\maxent.jar. Otherwise the script will not run.

84 Modeling distribution of Bigfoot (?!) Lozier et al. (2009)

85 Outline 1 2 3

86 Writing to KML There are two possibilities to export maps to KML: (a) using existing packages, and (b) by writing KML files by-hand. To export point or line features to KML, use the writeogr method that is available in R package rgdal. More flexible way to writing KML files is by using loops.