| 1 | = !AniMove Wish List = |
| 2 | |
| 3 | '''This is a list of metrics that can be useful. Feel free to comment, and add your own.''' |
| 4 | |
| 5 | Here we try to summarize what is necessary to have a full set of home range analyses in AniMove. Many of the following analyses are already available, check AnimoveHowto. |
| 6 | |
| 7 | == Summary Statistics == |
| 8 | |
| 9 | ''Number of bearings:'' Total number of bearings (angles) per dataset |
| 10 | |
| 11 | ''Mean bearing:'' Mean bearing per dataset (azimuth) |
| 12 | |
| 13 | ''R Concentration of angles:'' The concentration of angles 1-r is the "circular variance" |
| 14 | |
| 15 | ''Angular deviation:'' The angular equivalent of linear standard deviation |
| 16 | |
| 17 | ''Rayleigh's z for angles:'' The z value for Rayleigh's test for significant angles |
| 18 | |
| 19 | ''Duration of Study:'' Total number of days per dataset |
| 20 | |
| 21 | ''Primary axis angle:'' The angle which the primary axis is offset from the X axis (90 to -90) |
| 22 | |
| 23 | ''Cramer-von Mises:'' Test statistic for Complete Spatial Randomness (CSR)(see Cramer-von Mises) |
| 24 | |
| 25 | ''CM heterogeneity p:'' The probability value for rejecting the null hypothesis of CSR using the Cramer-von Mises |
| 26 | |
| 27 | ''Nearest-Neighbor R:'' The nearest neighbor test statistic (see Nearest-Neighbor) |
| 28 | |
| 29 | ''Nearest-Neighbor z:'' The z value of R |
| 30 | |
| 31 | ''Nearest-Neighbor p:'' The probability value for rejecting the null hypothesis of CSR using Nearest-Neighbor R |
| 32 | |
| 33 | == Alpha Hull == |
| 34 | |
| 35 | Code for generating alpha hulls. This example generates hulls for the dataset "Richards Pipit" which consists of two columns, |
| 36 | the first being the x coordinate, and the second being the y coordinate for each bird observation. Heuristic rules in this |
| 37 | version are 2, 3 and 4 times the average triangle edge length. |
| 38 | |
| 39 | Method proposed by '''Burgman, M.A. & Fox, J.C.''' (2003). Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Animal Conservation 6: 19- 28. |
| 40 | |
| 41 | == k-Nearest Neighbors Convex Hull == |
| 42 | |
| 43 | A k-NNCH covering for constructing UDs Given a set of specified |
| 44 | points the method begins by constructing the convex hull associated with each |
| 45 | point and its (k-1) nearest neighbors. We refer to the area covered by the |
| 46 | union of all these convex hulls as a k-NNCH covering. We then order the hulls |
| 47 | from the smallest to the largest. By progressively taking the union of these |
| 48 | from the smallest upwards, until x% of points are included (with some rounding |
| 49 | error), we construct the areas whose boundaries represent the x% isolpleth of |
| 50 | the densest set of points in our k-NNCH covering. Clément Calenge has written the R code (now included in Adehabitat) to determine the k-nearest-neighbor convex hull following the method by '''Getz, W.M. & Wilmers, C.C.''' (2004). //A local nearest-neighbor convex-hull construction of home ranges and utilization distributions//. Ecography 27: 489-505.[http://www.cnr.berkeley.edu/%7Egetz/Reprints04/Getz&WilmersEcography04.pdf PDF]. |
| 51 | |
| 52 | == MCP Sample Size Bootstrap == |
| 53 | |
| 54 | Bootstraps the points in a dataset given the user- selected parameters to test the effects of sample size on mcp area. |
| 55 | |
| 56 | == Nearest Neighbor Analysis Test For Complete Spatial Randomness == |
| 57 | |
| 58 | The Nearest Neighbor Analysis program tests for complete spatial |
| 59 | randomness using a selected graphic or polygon feature from a polygon |
| 60 | shapefile. Returns a message box displaying the area of the chosen |
| 61 | study site and the z and r values.\\ |
| 62 | This program references the FunNNDCSR, which implements the Clark and |
| 63 | Evans (1954, Ecology 35. pp445-453) algorithm and allows for either |
| 64 | points beyond the boundary to be used for correcting edge effects or |
| 65 | uses the correction of Donnely (1978, Holder (eds) Simulation methods |
| 66 | in archaeology. pp.91-95). This allows considerable flexibility. If the |
| 67 | population has been completely sampled, e.g. animal locations from |
| 68 | radio tracking or tag returns, then choose FALSE for correction and |
| 69 | ignore the boundary boolean (i.e. set it to TRUE or FALSE as it won't |
| 70 | matter). The program defaults to using the boundary correction for |
| 71 | anlaysis. If you have not sampled the complete population then select |
| 72 | either the edge correction or the boundary correction (if you have |
| 73 | sample points beyond the boundary). It checks to see if the sample size |
| 74 | is too small (from Donnely 1978) for the normal distribution. |
| 75 | |
| 76 | == Cramer-Von Mises Test for Complete Spatial Randomness == |
| 77 | |
| 78 | //Description//: Works from a menu on either a selected point theme and a |
| 79 | selected rectangle graphic or from a rectangle in a shape file. This |
| 80 | function is only valid with a rectangular study plot. The wait cursor |
| 81 | will appear and then a messagebox will tell you the W value and how |
| 82 | many features were accounted for in the analysis. W values relate how |
| 83 | clustered or dispersed points are within the graphic rectangle or |
| 84 | rectangle theme you specified. A W value of greater than .3 indicates a |
| 85 | tendency towards a clumped (clustered) pattern. A W value of 0.3-.06 |
| 86 | indicates a random distribution. An R value of less than .06 indicates |
| 87 | an organized (uniform) pattern. This statistical test |
| 88 | is insensitive to origin but highly sensitive to the upper right corner |
| 89 | of the study plot. Its primary usefulness is in detecting heterogeneity |
| 90 | that the nearest neighbor analysis (NNA) misses especially NNA base |
| 91 | purely on distance measurements. '''Zimmerman, D.L.''' 1993, A bivariate |
| 92 | Cramer- von Mises type of test for spatial randomness, Appl. Statist. |
| 93 | V. 42. pp. 43-54. |
| 94 | |
| 95 | == Circular Point Statistics == |
| 96 | |
| 97 | Conducts circular point statistics and outputs a graphic representation |
| 98 | of the bearings. The red line = mean bearing, which is scaled |
| 99 | proportionately to the r value of the angles; r value = 0 to 1, (1=all |
| 100 | angles the same) multiplied by the longest line segment.\\ |
| 101 | This implements circular statistics (Batschlet 1981) for the sequence |
| 102 | of points in a point coverage. Useful for determining the travel |
| 103 | directions from animal movement locations and the significance of |
| 104 | direction of travel.. This function requires that the data be ordered |
| 105 | in the sequence desired. The function works on the selected records (or |
| 106 | all if none are selected) which is useful for examining parts of the |
| 107 | movement path. The program will not tell the probability level of |
| 108 | rejecting the null hypothesis of directed movement but will give the Z |
| 109 | value and sample size which can be looked up in a Z table. |
| 110 | |
| 111 | == Site Fidelity Test == |
| 112 | |
| 113 | Creates random angles and uses distances between existing sequential points to determine walk points.\\ |
| 114 | Requires an active point theme. Prompts the user to select one of the |
| 115 | following starting points for the simulation: First Point, Last Point, |
| 116 | Arithmatic Mean, and Harmonic Mean (Defaults to the First Point).\\ |
| 117 | The simulation compares the observed pattern with a user selectable |
| 118 | number of random walks. This uses a Monte Carlo simulation and |
| 119 | parameters from the original data to determine if the observed movement |
| 120 | pattern has more site fidelity than should occur randomly, is a random |
| 121 | pattern or is overly dispersed. It is suggested that a 100 simulations |
| 122 | be run first and if the data is close to chosen probability level break |
| 123 | point then run it with a 1000 simulations which will more accurately |
| 124 | reflect the random walk distribution. Outputs a table with 3 fields, |
| 125 | Replicate, R2 and Linearity. Outputs a polyline theme containing |
| 126 | polylines of random walks. The polyline attribute table and the R2 |
| 127 | table are linked by Replicate and LinkID. If replicates < 100, |
| 128 | outputs a chart showing the r2 sorted in ascending order. To identify |
| 129 | which r2 belongs with which polyline, select the record from the output |
| 130 | table and view the selected polyline in the view. However,as the chart |
| 131 | is dynamically linked to the table, selecting individual records will |
| 132 | modify the charts color scheme. To save the original chart, add it to a |
| 133 | static layout before viewing individual records in the r2 table.The O |
| 134 | replicate in the output table and chart is the observed data the others |
| 135 | are replicates from simulation runs. Works on selected point records or |
| 136 | if none selected the entire table.A single selected polygon or polyline |
| 137 | may be use to limit the extent of the random walks. Progress bar tells |
| 138 | the progress through the simulation loop. |