Fish and Wildlife Population Ecology  Dr. Edward O. Garton


Use the links below to download the installation program for Animal Space Use software available in beta test versions. This software will allow you to apply information theoretic methods to the analysis of space use: home range analysis and resource selection. You'll have to use the link below which passes the file as a .zip file which must be passed through a decompression program (e.g. Winzip) to decompress it first into the .exe file and then run the .exe file to actually install the program on your computer. If you are using a computer running Windows 7 or Vista you will need to download 3 .ocx files in a zipped archive with short instructions on placing them in the correct directories and registering them. Earlier versions of Windows included these .ocx files but they are no longer included and will cause various operations in Animal Space Use to hang up without them. Animal Space Use 1.2  Includes Brownian Bridges Animal Space Use 1.3  Includes Brownian Bridges Windows 7 & Vista Fixes files  Download these files and follow instructions Animal Space Use Tutorial (Manual) Recommended citation: Horne, Jon S. and Edward O. Garton. 2009. Animal Space Use 1.3 <http://www.cnr.uidaho.edu/population_ecology/animal_space_use>. This software performs calculations described in a series of papers published, currently in press or review as follows: Horne, J. S. and E. O. Garton. 2006. Selecting the Best Home Range Model: An Information Theoretic Approach. Ecology 87:1146–1152 Abstract: Choosing an appropriate home range model is important for describing space use by animals and understanding the ecological processes affecting animal movement. Traditional approaches for choosing among home range models have not resulted in general, consistent, and unambiguous criteria that can be applied to individual data sets. We present a new application of informationtheoretic model selection that overcomes many of the limitations of traditional approaches, as follows. (1) It alleviates the need to know the true home range to assess home range models, thus allowing performance to be evaluated with data on individual animals. (2) The best model can be chosen from a set of candidate models with the proper balance between fit and complexity. (3) If candidate home range models are based on underlying ecological processes, researchers can use the selected model not only to describe the home range, but also to infer the importance of various ecological processes affecting animal movements within the home range. Horne, J. S. and E. O. Garton. 2006. Likelihood Crossvalidation versus Least Squares Crossvalidation for Choosing the Smoothing Parameter in Kernel Home Range Analysis. Journal of Wildlife Management 70:641648. Abstract: Fixed kernel density analysis with least squares crossvalidation (LSCVh) choice of the smoothing parameter is currently recommended for homerange estimation. However, LSCVh has several drawbacks, including high variability, a tendency to undersmooth data, and multiple local minima in the LSCVh function. An alternative to LSCVh is likelihood crossvalidation (CVh). We used computer simulations to compare estimated home ranges using fixed kernel density with CVh and LSCVh to true underlying distributions. Likelihood crossvalidation generally performed better than LSCVh, producing estimates with better fit and less variability, and it was especially beneficial at sample sizes <50. Because CVh is based on minimizing the KullbackLeibler distance and LSCVh the integrated squared error, for each of these measures of discrepancy, we discussed their foundation and general use, statistical properties as they relate to homerange analysis, and the biological or practical interpretation of these statistical properties. We found 2 important problems related to computation of kernel homerange estimates, including multiple minima in the LSCVh and CVh functions and discrepancies among estimates from current homerange software. Choosing an appropriate smoothing parameter is critical when using kernel methods to estimate animal home ranges, and our study provides useful guidelines when making this decision. Horne, J. S., E. O. Garton, and K. A. Sager. 2007. Correcting Home Range Models For Observation Bias. Journal of Wildlife Management: 71:9961001. Abstract: Homerange models implicitly assume equal observation rates across the study area. Because this assumption is frequently violated, we describe methods for correcting homerange models for observation bias. We suggest corrections for 3 general types of homerange models including those for which parameters are estimated using leastsquares theory, models utilizing maximum likelihood for parameter estimation, and models based on kernel smoothing techniques. When applied to mule deer (Odocoileus hemionus) location data, we found that uncorrected estimates of the utilization distribution were biased low by as much as 18.4% and biased high by 19.2% when compared to corrected estimates. Because the magnitude of bias is related to several factors, future research should determine the relative influence of each of these factors on homerange bias. Horne, Jon S., Edward O. Garton, Stephen S. Krone, and Jesse L. Lewis. 2007. Analyzing animal movements using Brownian bridges. Ecology 88:23542363. Abstract: By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding populationlevel dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal’s mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal’s probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluatingthe influence of finescale resource selection on animal movement patterns. Horne, Jon S., Edward O. Garton, and Janet L. Rachlow. 2008. A synoptic model of animal space use: simultaneous analysis of home range, habitat selection, and inter/intraspecific relationships. Ecological Modelling 214:338348. Synoptic Modeling Software and detailed examples from IWMC workshop at Durban, South Africa on 9 July, 2012 are available through this hotlink. 