LAB EXERCISE #4 Modeling Connectivity

Transcription

1 LAB EXERCISE #4 Modeling Connectivity Instructor: K. McGarigal Overview: In this exercise, you will learn to appreciate the challenges of modeling connectivity and gain practical hands-on experience doing so with R. Specifically, you will: 1) choose among two pre-established applications, 2) examine various connectivity modeling approaches on neutral landscapes, 3) apply various connectivity modeling approaches to your chosen real-world application, and 3) discuss the challenges in choosing and interpreting the results of these analyses. Primary objectives To learn how to model connectivity based using a few common approaches as implemented in R. To gain an appreciation for the many challenges (and alternatives) in quantifying connectivity for a specific application. Part 1: Background information The case study landscape is the Massachusetts portion of the Lower Connecticut River watershed, a HUC6 (Hydrologic Unit Code) level watershed representing the lower half of the Connecticut River watershed. The landscape encompasses 704,882 ha and, as noted in lab1, supports a range of ecological systems and human land uses. Step 1. Establish the objective of the analysis Given a user-specified overall goal pertaining to a pattern-process of interest, the first step of any landscape pattern analysis is to establish a clear objective; specifically, a Specific, Measurable, Attainable, Relevant, and Time-based (SMART) statement of the pattern and process to quantify. Unfortunately, because of the data preparation required for landscape pattern analysis, it is not practical in this brief exercise for you to define your own unique goal and objective and associated landscape definition (step 2). Consequently, while there are numerous possibilities (as you hopefully discovered in lab 1), the goal and objective of the analysis for this exercise were pre-established, as follows. Option 1: Potential vernal pools Goal: Evaluate connectivity of the landscape, as defined below, for vernal pool amphibians at multiple scales and identify and prioritize vernal pools and the associated upland habitat and connections between pools for land protection Pattern: distribution of potential vernal pools and the spatial structure of the intervening landscape, as defined below, within the defined landscape extent. Lab 4 Page 1

2 Process: amphibian seasonal migration (to and from breeding pools), dispersal to non-natal pools, and gene flow across multiple generations. Analysis objective: quantify connectivity of vernal pools to their associated uplands and neighboring vernal pools at the local (i.e., dispersal) and regional (i.e., gene flow) scales within the defined landscape extent in order to prioritize locations for land protection. Option 2: Blackburnian warbler habitat Goal: Evaluate connectivity of the landscape, as defined below, for blackburnian warblers and associated species at multiple scales and identify and prioritize corridors for northward movements to support range shifts under a warming climate scenarios, and locations along the major transportation infrastructure for potential wildlife overpass structures. Pattern: distribution of suitable habitat to support blackburnian warbler movement among potential home ranges and during north-south migration within the defined landscape extent. Process: blackburnian warbler (and associated species) within and among home range movement, seasonal migration, and range shift under a warming climate scenario. Analysis objective: quantify connectivity of quality blackburnian warbler (and associated species) habitat within the defined landscape extent in order to identify priorities for land protection and potential road crossing structures. Step 2. Define the landscape The next step is to define the landscape in accordance with the objective. Option 1: Potential vernal pools Conceptual model.-we will use the patch mosaic model to represent landscape conductance/resistance for vernal pool amphibians. Thematic content.- -the landscape was classified into land cover classes based on the dominant ecological setting and human land use (as represented in the DSLland cover map). Thematic resolution.- the original DSLland cover map containing 65 classes was reclassified into "resistance" values based on previous work (although this can be modified if you are so inclined). Spatial grain.- the landscape was represented as a raster at 30 m resolution - the original dslland resolution. Thus, each 30x30 m cell was classified into one of the 8 land cover classes. Note, for this exercise we maintained the 1 cell minimum mapping unit (i.e., minimum patch size) and did not kernel smooth the raster. Lab 4 Page 2

3 However, for some of the connectivity analyses it was necessary to aggregate the raster to a coarser resolution (e.g., 90 m) based on a focal mean of resistance. Spatial extent.-the landscape extent was defined as an arbitrary rectangular area centered on the Pioneer Valley (see GIS layers below), with the extent chosen to be small enough to allow for processing efficiency. Fragmenting features.- all roads and rivers were treated as major impediments (but not complete barriers) to movement in the reclassification of the DSLland cover into resistance values. Landscape boundary and context.-the boundary was arbitrary, as noted above, and thus offers no meaningful ecological unit for this analysis, other than to provide an area small enough to render the processing practical for this exercise. Option 2: Blackburnian warbler habitat Conceptual model.-we will use the landscape gradient model to represent landscape conductance/resistance for blackburnian warbler habitat. Thematic content.- -the landscape was modeled in a previous lab to produce a resource selection function that generated a continuous surface representing the predicted probability of occurrence. Here, we will use the RSF value as an estimate of conductance for movement, even though in this case the RSF was not based on movement data per se (more on this later). Thematic resolution.- the precision of the RSF probability of occurrence. Spatial grain.- the landscape was represented as a raster at 30 m resolution - the original dslland resolution. Thus, each 30x30 m cell was given a predicted probability of occurrence (0-1), which we interpreted as conductance. However, for some of the connectivity analyses it was necessary to aggregate the raster to a coarser resolution (e.g., 90 m) based on a focal mean of conductance. Spatial extent.-the landscape extent was defined as an arbitrary rectangular area centered on the Pioneer Valley (see GIS layers below), with the extent chosen to be small enough to allow for processing efficiency. Fragmenting features.- no fragmenting features per se were identified other than as already incorporated in the RSF function. Landscape boundary and context.-the boundary was arbitrary, as noted above, and thus offers no meaningful ecological unit for this analysis, other than to provide an area small enough to render the processing practical for this exercise. Step 3. Inspect the GIS data Now that we have defined the landscape, take some time to familiarize yourself with the GIS data associated with the two applications. Open up in ArcMap the following project file: Lab 4 Page 3

4 ...\exercises\lab3\lab4.mxd Take some time to review each of the data layers. The ArcMap table of contents is organized into four groups of layers, from bottom to top, as follows: Lower Connecticut watershed - suite of layers encompassing the Lower Connecticut River HUC6 watershed. These layers provide a spatial context for the focal landscape: o hillshade.tif - backdrop for other layers if desired. o dslland_2010.tif - original 30m resolution land cover map from the DSL project containing 65 classes within the project area. o cover7_2010.tif - dslland reclassified into 7 major cover types based on ecological formation and human land use. Blackburnian warblers - couple of layers with blackburnian warbler point observations and resource selection function: o blbwpred.tif - 30 m raster representing the resource selection function generated from a previous lab, in which the cell values represent the probability of blackburnian warbler occurrence. o blbw - point shapefile containing the ebird sample locations, with the symbology present to display present versus absent locations. Potential vernal pools - point shapefile depicting potential vernal pools, given for the Massachusetts portion of the lower Connecticut River watershed (PVP). Vector overlays - a handful of shapefiles that may be useful as overlays, including: o boundary - boundary of the arbitrary project area centered on the Pioneer Valley in the lower Connecticut River watershed within Massachusetts. o NHDhighRes - streams layer derived from the National Hydrography data based on high resolution 1:24 k streams. o DSLroads - roads layer with the symbology set to display road class. Part 2: Data prep In this section, we will prepare the spatial data we will need for the functions below. Note, the detailed process, and thus the R scripting, differs slightly between the two applications owing to differences in the source data and analysis objective. The detailed steps below will be presented for the potential vernal pool application and significant modifications for the blackburnian warbler application will be noted, but separate R scripts are provided for the two applications to simplify your execution. In both cases, you will need to source the landeco.r package and install (if need be) and load the other required libraries, as given in the provided lab3 R scripts. Lab 4 Page 4

5 Step 2a. read shapefiles First, read in the boundary (polygon) shapefile: setwd('c:\\work\\landeco-umass\\exercises\\lab4\\gisdata\\') boundary<-readogr('.','boundary') Next, read in the PVP (points) shapefile and clip to the boundary extent: pvppoints<-readogr('.','pvp') pvppoints<-pvppoints[boundary,] Next, given the large number of pools, for computation efficiency it is necessary to randomly sample a subset of the pools, otherwise you will be processing for weeks: pvppoints<-pvppoints[sample(1:nrow(pvppoints),size=10,replace=false),] Finally, plot the boundary with the randomly sampled pools: plot(boundary) points(pvppoints) Step 2b. create resistance surface First, read in the dslland cover layer and crop to the boundary extent, and optionally write the resulting surface to disk for viewing in ArcMap: temp<-raster('dslland_2010.tif') temp<-crop(temp,boundary) #temp<-mask(temp,boundary) #only need if boundary irregular writeraster(temp,'dslland_2010_boundary.tif',datatype='int2u',overwrite=true) Next, reclass land cover into resistance values based on the pre-established cross-walk provided in the dsllandkey table (feel free to change the cross-walk): reclass<-read.csv('dsllandkey.csv',header=true) rclmat<-as.matrix(subset(reclass,select=c('export_value','reclasspvp'))) temp<-reclassify(temp,rclmat,include.lowest=true) Next, coarsen resistance raster for processing efficiency with the gdistance functions below, and optionally write the resulting raster to disk for viewing in ArcMap. Note, here the fact=3 argument results in a 3x3 focal window (or 90 m) and the fun=mean says to replace the coarse cell with the mean of the 9 subsumed cells: pvpresist<-aggregate(temp,fact=3,fun=mean,na.rm=true) writeraster(pvpresist,'pvpresist.tif',datatype='flt4s',overwrite=true) Finally, plot the resistance surface with the randomly sampled pools: plot(pvpresist) points(pvppoints,pch=19) Lab 4 Page 5

6 Step 2c. select random sample points [blackburnian warbler application] Note, for the potential vernal pool application we randomly sampled a subset of the preexisting PVP points (see above). However, for the blackburnian warbler application we may not want to use the ebird point locations, as they are somewhat arbitrary and their density and distribution don't really reflect meaningful locations for blackburnian warblers; they merely reflect where the ebirders decided to conduct a survey. It may be preferable in this case to randomly sample the RSF surface so that the sample point locations at least reflect the expected density and distribution of blackburnian warblers. To select points randomly based on inclusion probabilities derived from the RSF surface, we can use the probpointselect() function in the landeco.r library. Specifically, designate the surface containing relative inclusion probabilities (although the surface does not have to already have a probability scale) and the number of points to select: blbwpoints<-probpointselect(r=blbwrsf,n=100) Also, because we are interested in potential corridors for south to north movement, either during seasonal migration or range shift under a warming climate scenario, we may also want to create a set of "from" points on the southern boundary of the landscape and a set of "to" points on the northern boundary of the landscape and ask all the southern points to go visit all of the northern points. The following script will create a set of 20 evenly spaced points along each of the southern and northern boundary of the landscape: xcoord<-seq(xmin(blbwrsf),xmax(blbwrsf),length=20) ycoord<-rep(ymin(blbwrsf),length=20) blbwsouth<-spatialpoints(cbind(xcoord,ycoord)) ycoord<-rep(ymax(blbwrsf),length=20) blbwnorth<-spatialpoints(cbind(xcoord,ycoord)) Finally, plot the resistance surface with the randomly sampled points and the southern and northern boundary points plot(blbwrsf) points(blbwpoints,pch=19) points(blbwsouth,pch=19,col='red') points(blbwnorth,pch=19,col='blue') Part 3: gdistance functions In this section, we will derive least cost paths and random low cost paths using functions in the gdistance library. To begin, we need to create a conductance transition matrix from the resistance or conductance raster. Note, conductance and is the reciprocal of resistance. In the case of the potential vernal pools, we created a resistance matrix, so here we have to convert resistance to conductance by taking the reciprocal of resistance in the transition function that follows: Lab 4 Page 6

7 pvpconduct<-transition(pvpresist,function(x) 1/mean(x,na.rm=TRUE), direction=8) Note, because we used an 8-neighbor rule in the transition matrix, we need to make a geographic distance correction for the diagonals. In addition, it is usually helpful to scale the conductance values using the scl=true argument to allow for more efficient computations: pvpconduct<-geocorrection(pvpconduct,scl=true) Step 3a. least cost surface First, create a least cost surface; specifically, a surface that depicts the accumulated least cost from every cell to the least costly point location. Hence, the cell value represents the accumulated cost along the least cost path to the "closest" point location: pvpcostsurf<-acccost(pvpconduct,pvppoints) Next, plot the least cost surface with the randomly sampled point locations, and optionally write the resulting raster to disk for viewing in ArcMap: plot(pvpcostsurf) points(pvppoints,pch=19) writeraster(pvpcostsurf,'pvpcostsurf.tif', datatype='flt4s',overwrite=true) Step 3b. least cost paths Next, compute least cost paths among the point locations. First, compute the least cost patch between two arbitrarily selected points (1 and 10 in the example below) and plot the result along with the points: lcp<-shortestpath(pvpconduct,pvppoints[1,],pvppoints[10,], output='spatiallines') plot(pvpresist) points(pvppoints,pch=19) points(pvppoints[1,],pch=19,col='red') points(pvppoints[10,],pch=19,col='blue') lines(lcp) Next, compute factorial least cost paths by computing and overlaying the least cost path between every pairwise combination of point locations, and optionally write the resulting raster to disk for viewing in ArcMap (CAUTION, this takes a long time to run, so you might want to let it run while you go for a run): pvpfactlcp<-factoriallcp(r=pvpresist,points=pvppoints,conduct=pvpconduct) writeraster(pvpfactlcp,'pvpfactlcp.tif',datatype='int2u',overwrite=true) Step 3c. Random low cost paths Next, compute random low cost paths among the point locations. Note, in the passage() function used below, the theta= argument controls the magnitude of random deviations Lab 4 Page 7

8 from the straight-line patch between two points, with a theoretical range of close to zero (but not zero) to a maximum of 20. Values close to zero, the default, result in a completely random walk (i.e., a "drunkards" walk) and is analogous to flow in an electrical conduction surface (as implemented in the software Circuitscape). Larger values result in walks that are increasingly focused on the straight-line path between two points. However, I have found that with values above 1 to 5, depending on the landscape, that the resulting passage values are all zero and thus provide no useful output. Consequently, it warrants some experimenting beforehand, but likely values between will work best. First, compute random low cost paths between two arbitrarily selected points (1 and 10 in the example below) and plot the result along with the points. pvppassage<-passage(pvpconduct,pvppoints[1,],pvppoints[10,],theta=0.05) plot(pvppassage) points(pvppoints[1,],pch=19,col='red') points(pvppoints[10,],pch=19,col='blue') Next, compute factorial random low cost paths; i.e., between all pairwise combinations of points and summing the result (CAUTION: this takes bloody forever to run, so it is probably best to run this overnight or over the weekend), and optionally write the resulting raster to disk for viewing in ArcMap: pvpfactrlcp<-factorialrlcp(r=pvpresist,points=pvppoints, conduct=pvpconduct,theta=0.05) writeraster(pvpfactrlcp,'pvpfactrlcp.tif',datatype='flt4s',overwrite=true) Part 4: Resistant kernels In this section, we will compute resistant kernels and derive several different metrics. To begin, we must switch to 32-bit R in order to use the gridio library functions. This can be done in RStudio by selecting Tools Global options General R version and change to 32-bit, and then closing and restarting RStudio. Once you are back in RStudio you will need to resource the landeco.r library and load a few other libraries as indicated in the R script provided. Step 4a. Read shapefiles First, read in the boundary shapefile: setwd('c:\\work\\landeco-umass\\exercises\\lab4\\gisdata\\') boundary<-readogr('.','boundary') Next, read in the points shapefile and clip to the project boundary. Note, here we are NOT going to randomly sample a subset of the points as we did before because the resistant kernel function is efficient enough to work rapidly with thousands of points: pvppoints<-readogr('.','pvp') pvppoints<-pvppoints[boundary,] Lab 4 Page 8

9 Finally, plot the boundary with the points to verify: plot(boundary) points(pvppoints,cex=0.5) Step 4b. Create resistance surface First, read in the dlsland raster and reclass it to represent resistance values based on the cross-walk in the dsllandkey table. Note, here we are NOT going to coarsen the grid as we did before because the resistant kernel function is efficient enough to work with the 30 m resolution data: temp<-raster('dslland_2010_boundary.tif') reclass<-read.csv('dsllandkey.csv',header=true) rclmat<-as.matrix(subset(reclass,select=c('export_value','reclasspvp'))) pvpresist<-reclassify(temp,rclmat,include.lowest=true) Finally, plot the resistance surface with the points to verify? plot(pvpresist) points(pvppoints,cex=0.5) Step 4c. Compute resistant kernels First, in order to use the resistant kernel function spread() in the gridio library, we need to initialize the gridio library with any of the rasters having the desired template (i.e., extent and cell size) as follows: gridinit() pvpresist<-as.grid(pvpresist) setwindow(pvpresist) Next, compute a suite of resistant kernel metrics using the rkernel() function in the landeco.r package, which makes use of the spread() function in gridio: result<-rkernel(r=pvpresist,p=pvppoints,sd=1000) Note, in the function above, the resistant kernel is Gaussian and the sd=1000 specifies a standard deviation of 1,000 m. The resistant kernel extends outward from the focal cell to a maximum of 3 times the standard deviation by default, although this can be changed with the sd.threshold= argument. The rkernel() function returns a list object with two components. The first component is named "cumrk" and contains the cumulative resistant kernel surface in grid form. The following lines extract the cumrk grid from the result object, converts the grid to a raster class object, and then reassigns the original coordinate reference system back to the resulting raster object: pvpcumrk1000<-raster(result$cumrk) crs(pvpcumrk1000)<-crs(pvpresist) Lab 4 Page 9

10 Next, plot the cumulative resistant kernel surface, and optionally write the resulting raster to disk for viewing in ArcMap: plot(pvpcumrk1000) writeraster(pvpcumrk1000,'pvpcumrk1000.tif',datatype='flt4s', overwrite=true) Finally, extract the SpatialPointsDataFrame object from the second list component named "points", and optionally write the resulting object as a shapefile to disk for viewing in ArcMap: writeogr(result$points,dsn='c:\\work\\landeco-umass\\exercises\\lab4\\gisdata', layer='pvprkern',driver='esri Shapefile') The SpatialPointsDataFrame object contains the original point attributes plus three additional attributes: rkern = the raw sum of the resistant kernel derived for the focal point traverse = traversability index, defined as rkern divided by rkern for a corresponding standard kernel (i.e., resistant kernel applied to a uniform nonresistant surface); hence, traverse ranges from 0 for a focal point completely disconnected from neighboring cells to a theoretical maximum of 1 for a focal point surrounded by non-resistant surface out to a distance defined by three times (by default) the specified standard deviation (sd) of the Gaussian kernel, which in the example above equates to 3,000 m. isolate = isolation index (or, inversely, connectedness index), defined as the sum of neighboring resistant kernels minus the resistant kernel derived from the kernel built for the focal point; hence, isolate measures the total flow from neighboring points to the focal point. Isolate ranges from 0 for a completely isolated cell to increasing larger values for focal points that have increasing numbers of connected nearby points. The theoretical maximum is achieved when every neighboring cell is a point and the neighborhood is non-resistant. Assignment: Option 1: Potential vernal pools Q1. Based on the multi-metric connectivity assessment, identify and prioritize vernal pools and associated uplands and intervening matrix lands for land protection. Hint, use all forms of connectivity analysis, including least cost paths, random low cost paths and the various resistant kernel products to help answer this question. Be sure to graphically show your results with respect to priority locations. In deriving your solution, be sure to discuss and interpret what each connectivity measure means to demonstrate your understanding. Q2. Discuss the major challenges and limitations in conducting a meaningful connectivity analysis to meet your goal. What could you do differently or additionally to further address your goal? Lab 4 Page 10

11 Option 2: Blackburnian warbler habitat Q1. Based on the multi-metric connectivity assessment, identify and prioritize lands with high connectivity value for blackburnian warblers and associated species. Specifically, identify and prioritize potential corridors for northward movements to support range shifts under a warming climate scenarios, and locations along the major transportation infrastructure for potential wildlife overpass structures. Hint, use all forms of connectivity analysis, including least cost paths, random low cost paths and the various resistant kernel products to help answer this question. Be sure to graphically show your results with respect to priority locations. In deriving your solution, be sure to discuss and interpret what each connectivity measure means to demonstrate your understanding. Q2. Discuss the major challenges and limitations in conducting a meaningful connectivity analysis to meet your goal. What could you do differently or additionally to further address your goal? Lab 4 Page 11