LAB EXERCISE #2 Quantifying Patch Mosaics

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1 LAB EXERCISE #2 Quantifying Patch Mosaics Instructors: K. McGarigal Overview: In this exercise, you will learn to appreciate the challenges of quantifying patch mosaics and gain practical hands-on experience doing so with FRAGSTATS. Specifically, you will: 1) familiarize yourself with the pre-established pattern-process objective and associated landscape definition, 2) select a parsimonious suite of relevant metrics and justify your choices, 3) quantify and interpret the current landscape patterns, and 4) discuss the challenges in choosing and interpreting the landscape metrics. Primary objectives To gain an appreciation for the many challenges (and alternatives) in quantifying patch mosaics. To gain an understanding for how to select and interpret landscape metrics for a specific application. Part 1: Background information The case study landscape is the Lower Connecticut River watershed, a HUC6 (Hydrologic Unit Code) level watershed representing the lower half of the Connecticut River watershed. The landscape encompasses 1,295,924 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), goal and objective of the analysis for this exercise were pre-established, as follows: Goal: Quantify Anderson's level II habitat selection (home range selection) by blackburnian warblers within the lower Connecticut River watershed. Pattern: the composition and configuration of blackburnian warbler habitat within a local neighborhood, as defined below. Process: selection of home range centers by blackburnian warblers within the lower Connecticut River watershed based on habitat extent and configuration, as defined below. Lab 2 Page 1

2 Analysis objective: based on ebird presence/absence training data representing observations collected between within the lower Connecticut River watershed, quantify contemporary blackburnian warbler level II (home range) habitat selection within the lower Connecticut River watershed based on the extent and configuration of current habitat, as defined below, and measured by a comprehensive but parsimonious suite of patch mosaic metrics (to be defined). Step 2. Define the landscape The next step is to define the landscape in accordance with the objective. While there are many possible and potentially meaningful landscape definitions for meeting the objective, the landscape was pre-defined for this exercise as follows: Conceptual model.-for this exercise, we will use the patch mosaic model of landscape structure; accordingly, the landscape was classified into habitat and a variety of non-habitat classes. Thematic content.-the landscape was classified into land cover classes based on the dominant ecological setting and human land use. Thematic resolution.- the original DSLland cover map containing 65 classes was reclassified into 8 classes that were deemed meaningful for distinguishing focal habitat cover from the non-habitat cover types that were hypothesized to function differently as the spatial context for the habitat patches, as follows: 1. Developed 2. Agriculture 3. Water 4. Wetlands 5. Cliff & rock 6. Grasslands & shrublands 7. Forest (non-habitat types) 8. Habitat (selected forested types) Note, the cross-walk was based on a model for the blackburnian warbler developed by the DSL project. The cross-walk can be found in the following file:...\exercies\lab2\gisdata\dsllandkey.csv 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 2 Page 2

3 Spatial extent.-the landscape extent was the lower Connecticut River watershed, which encompasses 1,295,924 ha. Fragmenting features.-all major roads and medium/large rivers were treated as fragmenting features. Specifically, all roads classified as tertiary road, local road or track, and all streams classified as headwater/creek or small stream were removed from the dslland cover before the reclassification above and replaced with the majority surrounding class. boundary and context.-the watershed boundary is relatively arbitrary and "open" to the process of blackburnian warbler habitat selection. However, despite the fact that the local population of blackburnian warblers undoubtedly is influenced by the broader regional population, and that individuals undoubtedly both emigrate from the landscape and immigrate to the landscape, the landscape is sufficiently large given the internal heterogeneity in potential habitat for inferences on habitat selection to be valid. Step 3. Inspect the GIS data Now that we have defined the landscape, take some time to familiarize yourself with the GIS data. Open up in ArcMap the following project file:...\exercises\lab2\lab2.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 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. o coverblbw.tif - dslland reclassified into 8 major cover types based on ecological formation and human land use, but with the additional class of "habitat" for the ecosystem types purported to represent preferred habitat by blackburnian warblers. Blackburnian warbler - suite of layers representing the blackburnian warbler ebird data: o blbw.tif - raster with a unique integer value (including zero) for each presence or absence observation point; all other cells coded as nodata. Lab 2 Page 3

4 o blbw.shp - point shapefile with the ebird presence/absence observations; attribute Response = 0 for absent and 1 = present. o HUC10blbw.tif - raster with a unique integer value (including zero) for each HUC10 watershed; note, three watersheds containing no ebird observations were coded as nodata. o HUC10blbw.shp - polygon shapefile with attributes for the number of presences observed (blbwpre), number of absences observed (blbwabs) and the ratio of presences to total number of observations (blbwratio). Note, the symbology is set to display the blbwratio field. Watersheds - raster layers depicting watersheds at the HUC8, HUC10 and HUC12 levels. Vector overlays - a handful of shapefiles that may be useful as overlays, including: o DSLroads - roads layer with the symbology set to display road class. o NHDhighRes - streams layer derived from the National Hydrography data based on high resolution 1:24 k streams. o HUC8, 10, 12 and 6 level watersheds. o HUC6 buffer - HUC6 lower Connecticut River watershed with a 10 km buffer to serve as the clip extent for the land cover layers. Part 2. Analysis of pattern-process Step 4. Select landscape metrics The next step is to choose a parsimonious suite of landscape metrics to evaluate the structure of the landscape in the vicinity of the ebird presence/absence points. The choice of metrics is limited to those computed in the FRAGSTATS software. To simplify your choice, there is an abbreviated list of potentially useful metrics in Appendix 2A. To view the full list of available metrics and a complete description of each, open up the FRAGSTATS help files, as follows: Program files Fragstats Fragstats user manual In the left-hand window, click on (or expand) the item named Fragstats metrics and navigate to the desired metric. In your selection of metrics, consider the following: 1. You may want to select both landscape- and class-level metrics, or you may limit your consideration to class-level metrics for the focal habitat class. Patch metrics are probably not as relevant to the objective under consideration, so restrict your consideration to class and/or landscape metrics. Note, if you choose to use both landscape and class metrics, it is OK to choose the same metric at both levels. Lab 2 Page 4

5 2. You should include both composition and configuration metrics. Note, PLAND at the class level is generally considered an essential composition metric in all analyses. 3. You may want to include both structural and functional metrics, as the functional metrics allow you to taylor the analysis to your application, as long as the functional metrics and their parameterization can be justified. 4. Be sure not to include redundant metrics; i.e., those that measure the same thing but with a different mathematical formulation. 5. Keep in mind the focus of the analysis: blackburnian warbler habitat selection. Note, functional metrics require additional parameterization. Specifically, if you select any metrics dealing with core area, edge contrast, or similarity, refer to the tables in the fragstats directory (...exercises\lab2\fragstats\) that were created in advance. The instructor will guide you through the creation and interpretation of these tables. Assignment: Q1: For each metric selected, answer the following questions: Is it a class-, or landscape-level metric? Does it represent landscape composition or configuration? What aspect of configuration, if any, does it represent? Is it a structural or functional metric? Provide a brief justification of your choice of the metric using information provided in lecture and readings to support your case. Step 5. Conduct the FRAGSTATS analysis The next step is to compute the landscape metrics using FRAGSTATS a spatial pattern analysis program for categorical maps (i.e., patch mosaics). It is beyond the scope of this document to describe the FRAGSTATS software, but a complete user manual is included with the software. Consequently, the instructor will guide you through the use of FRAGSTATS for this exercise. There are two options made available (others exist) for analyzing pattern-process to meet the stated objective, and the detailed FRAGSTATS analysis varies accordingly. Choose one of the two following options: Option 1: presence versus absence observations.- In this option, the observations are the individual ebird survey locations, classified as either present or absent, or, more specifically, a local neighborhood (i.e., window) around each point location. Here, you will use FRAGSTATS to quantify the selected landscape metrics within each window and subsequently model the relationship between the landscape pattern metrics and probability of blackburnian warbler occurrence within the local neighborhood. Note, Lab 2 Page 5

6 you will need to choose a local window shape (square or circle) and size (side length or radius, respectively). Option 2: relative occurrence within HUC10 watershed.- In this option, the observations are the HUC10 watersheds, excluding the few that did not contain any ebird observations. Here, you will use FRAGSTATS to quantify the selected landscape metrics within each watershed and subsequently model the relationship between the landscape pattern metrics and blackburnian warbler relative occurrence (i.e., proportion of the observations within the watershed in which the warbler was recorded as present). Step 6. Quantify pattern-process relationship The next step is to quantify the relationship between the measured landscape patterns and the process; specifically, model blackburnian warbler habitat selection using logistic regression. The details of the analysis vary slightly between the two options in step5, as noted in the lab2.r script. As the focus of this exercise is not on statistical modeling, the instructor will guide you through the use of R to conduct the statistical analysis. Assignment: Q2: Summarize the results of the logistic regression analysis and interpret your findings with respect to the stated objective. Q3: Describe three of the greatest challenges you faced in selecting and interpreting the landscape metrics for this application and what you might be able to do to overcome these challenges if you had more time? Lab 2 Page 6

7 Appendix 2A. Brief description of a candidate set of FRAGSTATS metrics. The table below lists some of the metrics that may be useful in the context of this exercise. The table lists whether the metric can be applied at the patch, class and/or landscape level, whether it measures landscape composition or configuration, and whether it is a structural or functional measure. Note, there are other landscape metrics computed in Fragstats and you are free to choose them as needed. Metric Classification Description Percentage of landscape (PLAND) Patch density (PD) Largest patch index (LPI) Edge density (ED) Patch area (AREA) Mean patch size (AREA_MN) Class Composition Patch Composition Percentage of the landscape comprised of the focal class. Density of patches (# per 100 ha) of the focal class (or landscape); it represents a simple measure of the degree of class (or landscape) subdivision. Percentage of the landscape comprised of the single largest patch of the focal class (or landscape). Density of edge (m/ha) associated with the focal class (or landscape), where an edge is the boundary between adjacent patches. Size of the patch (hectares). Average patch size (ha) for a patch of the focal class (or landscape) selected at random. Note the subtle but important difference with the area-weighted mean patch size below. Lab 2 Page 7

8 Area-weighted mean patch size (AREA_AM) Radius of gyration (GYRATE/ GYRATE_AM) Shape index (SHAPE/ SHAPE_MN/ SHAPE_AM) Core area percentage of landscape (CPLAND) Core area (CORE/ CORE_MN/ CORE_AM) Disjunct core area (DCORE/ DCORE_MN/ DCORE_AM) Class Average patch size (ha) for a cell selected at random; each patch is weighted by the proportion of the total class (or landscape) area it represents; it is based on the probability that two randomly chosen places in the focal class (or landscape) will be situated in the same contiguous patch. Physical continuity of the patch, class or landscape based on a measure of the extensiveness of each patch (m), as measured by the radius of gyration (GYRATE), weighted by patch area; it can be interpreted as the average distance an organism might traverse the map from a random starting point and moving in a random direction without having to leave a patch. Index of patch shape complexity (unitless) for the focal patch, class or landscape; it is a normalized perimeterto-area ratio that equals one for a square and increases as the patch becomes increasingly non-euclidean. Percentage of the landscape comprised of core area of the focal class, where core area is defined as the area of the focal class (or landscape) greater than a userspecified distance from the nearest patch edge (i.e.., patch interior). Core area (ha) of the patch or of patches of the focal class (or landscape), where core area is defined as the patch interior based on user-specified edge depths. Disjunct core area (ha) for spatially disjunct core areas of the focal patch, class or landscape, where core area is defined as above. Lab 2 Page 8

9 Core area index (CAI/TCAI = CAI_AM) Proximity index (PROX/ PROX_MN) Percentage of the focal class (or landscape) that is designated as core (i.e., greater than a user-specified distance from the nearest patch edge), independent of the total area of the focal class (or landscape). Basically an edge-tointerior ratio like many shape indices, the main difference being that the core area index treats edge as an area of varying width and not as a line (perimeter) around each patch. Note, TCAI and CPLAND are equivalent at the landscape level. Patch-level measure of neighborhood isolation (unitless) that considers the size and proximity of all like patches whose edges are within a specified search radius of the focal patch; it can be averaged across all patches of the focal class (weighted by patch area) to provide a suitable class-level measure of patch isolation or averaged across all patches in the landscape to provide a landscapelevel measure of patch isolation. Note, this metric is sensitive to landscape boundary effects. CAUTION: computationally intensive Lab 2 Page 9

10 Similarity index (SIMILAR/ SIMILAR_MN) Euclidean nearest neighbor distance (ENN/ ENN_MN) Contrastweighted edge density (CWED) Patch-level measure of neighborhood similarity (unitless) that considers the size, proximity and similarity of all like and unlike patches whose edges are within a specified search radius of the focal patch; it can be averaged across all patches of the focal class (weighted by patch area) to provide a suitable classlevel measure of patch isolation or averaged across all patches in the landscape to provide a landscape-level measure of patch isolation. Note, this metric is sensitive to landscape boundary effects. CAUTION: computationally intensive Euclidean distance (m) between patches of the same class; perhaps the simplest measure of patch context used to quantify patch isolation. Nearest neighbor distance is defined using simple Euclidean geometry as the shortest straight-line distance between the focal patch and its nearest neighbor of the same class. CAUTION: computationally intensive Density of edge (m/ha) weighted by the degree of contrast between adjacent patch types, where contrast weights for each unique edge type are user-defined and represent the magnitude of difference between adjacent patch types; it represents the equivalent maximumcontrast edge density (meters of maximum contrast edge per unit area). Lab 2 Page 10

11 Total edge contrast index (TECI) Contagion (CONTAG) Interspersion & Juxtaposition index (IJI) Clumpiness (CLUMPY) Aggregation index (AI) Configuration Class Configuration Percentage of the maximum possible edge contrast, independent of the length of edge; for the average unit of edge, it gives the percentage of maximum edge contrast, where contrast weights for each unique edge type are user-defined and represent the magnitude of difference between adjacent patch types. Clumpiness of the landscape as a percentage of the maximum. Contagion is affected by both the dispersion and interspersion of patch types. High contagion equates to highly aggregated patch types that are poorly interspersed (spatially intermixed). Interspersion (spatial intermixing) of patch types (classes) as a percentage of the maximum possible interspersion given the number of classes; can be computed for each focal class or for the entire landscape mosaic. Normalized measure of class aggregation (-1 to 1); it measures the degree to which the focal class is aggregated or clumped compared to the expected for a spatially random distribution, given the amount of the focal class. Normalized measure of class aggregation as a percentage of the maximum, summarized for the focal class or the entire landscape mosaic; it measures the degree to which the focal class is aggregated relative to the maximum aggregation given the amount of the focal class; at the landscape level, it is simply the area-weighted mean class level aggregation index. Lab 2 Page 11

12 Connectance index (CONNECT) Simpson s diversity index (SIDI) Composition Percentage of all possible pairwise combinations of patches of the same class that are within a user-specified Euclidean distance of each other; reported as a percentage of the maximum possible connectance given the number of patches. CAUTION: computationally intensive Index of the diversity of patch types (classes) interpreted as the probability that any 2 cells selected at random would be different patch types. Popular diversity measure borrowed from community ecology less sensitive to the presence of rare types and more intuitive than the more familiar Shannon's diversity index. Lab 2 Page 12

LAB EXERCISE #4 Modeling Connectivity

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