migdat

migda

Appendix A. Simulated movement path using Euler's method.

Simulated movement path using Euler's method

Supplement 1. R source code for parameter estimation, model simulations, and calculation of Sobol' indices.

<h2>File List</h2><blockquote> <h4> <a href="FitUnstructuredModel.R">FitUnstructuredModel.R </a> <br> <a href="IterateUnstructuredModel.R">IterateUnstructuredModel.R </a> <br> <a href="BootstrapDistributionsStageModel.R">BootstrapDistributionsStageModel.R</a> <br> <a href="IterateStageModel.R">IterateStageModel.R </a> <br> <a href="SobolandCI.R">SobolandCI.R </a> <br> <a href="EllnerFiebergSourceCode.zip">All files (WinZip archive)</a> <br> </h4> </blockquote><h2>Description</h2><p> These are text files (readable by, e.g., Notepad or Wordpad on PCs or by any ASCII text-file editor). They contain R-language source code for the data analyses and model simulations in the paper. Because we are not at liberty to redistribute all of the original data used in the analyses, only some portions of these programs can be run by users. The purpose of presenting the complete source code is to document in full detail calculations that are described conceptually in the paper. A WinZip archive containing all the files is also provided. </p> <p> FitUnstructuredModel.R contains the statistical analyses behind the models for the distributions of Et and p4t for the unstructured models. As noted in the paper, we considered a number of models for p4t and only the final one is presented in the code here (a truncated logistic distribution). Users cannot run any portions of this code.</p> <p> IterateUnstructuredModel.R contains code for iterating the structured model, including iterations using bootstrap parameter distributions to account for parameter uncertainty. Users can run portions of this code. The top part of the code (which reads in data and fits models for Et and p4t) cannot be run by users. However at the comment line 'USERS START HERE' we insert 'by hand' the necessary numerical results from the analysis so that the rest of the code can be run, to replicate the process leading to Fig. 2 in the paper. </p> <p> BootstrapDistributionsStageModel.R contains code for generating the bootstrap distribution of model parameters for the stage-structured model, that is used for Sobol sensitivity indices and confidence interval calculations. Users cannot run any portions of this code. </p> <p> IterateStageModel.R contains code for an R function that simulates the stage-structured model using user-supplied parameters, and estimates the population growth rate as a function of parameter values by doing multiple simulations of the model. Users can run this file (which will load the model function into the current R workspace), but must supply parameter values when calling the function. </p> <p> SobolandCI.R contains code to calculate confidence intervals on population growth rate, and Sobol' sensitivity indices, for the structured population model. Users cannot run this code. </p

Appendix A. Table summarizing winter severities, crude mortality rates, and hunting hazards for female white-tailed deer (≥0.6 yr old), north-central Minnesota, USA, 1 January 1991–31 December 1996.

Table summarizing winter severities, crude mortality rates, and hunting hazards for female white-tailed deer (≥0.6 yr old), north-central Minnesota, USA, 1 January 1991–31 December 1996

Appendix B. Plot of Schoenfeld residuals for the effect of site 4 (relative to site 1) vs. time in the age-based semi-parametric proportional hazards model.

Plot of Schoenfeld residuals for the effect of site 4 (relative to site 1) vs. time in the age-based semi-parametric proportional hazards model

Appendix B. An explanation of calculating the main and total Sobol' indices by the Winding Stairs algorithm.

An explanation of calculating the main and total Sobol' indices by the Winding Stairs algorithm

Supplement 1. R code used for simulation.

<h2>File List</h2><blockquote> <p><a href="Simulation code.r">Simulation code.r</a> – Source code to run simulation analysis</p> </blockquote><h2>Description</h2><blockquote> <p>This is the complete code for simulating usage under different environmental scenarios of food/cover availability and fitting GFR models to the synthetic data. The end of the listing contains the following three functions:</p> <p>environ(d,x): Creates a square dxd arena containing a total of x units of resource. The function introduces spatial autocorrelation via kernel smoothing of the resource units. Kernel smoothed map is re-normalized to ensure that x is conserved.</p> <p>movement(d, env1, env2) : This function contains the movement simulation. The parameters pertaining to animal behavior are locally defined, so the function operates as a wrapper. Its input is the dimensionality d of the square arena and the two environmental layers (env1 and env2). Its output is a map of usage.</p> <p>predict.lmer(mod, newdat) : Generates predictions from the fixed effects of mixed model.</p> <p>The main body of the code has the following parts:</p> <p>1. Initialization</p> <p> Parameters regulating the arena size, number of environmental scenarios and overall availabilities of each resource in the arena for each scenario.</p> <p>2. Simulation</p> <p>The functions environ() and movement() are used to generate resource distributions and resulting usage for each environmental scenario.</p> <p>3. Model fitting</p> <p>Here, the data frame is extended with columns for expectations of the two resources in each scenario and four GFR models are fit to the augmented data frame.</p> <p>4. Model validation</p> <p> This part first inspects the goodness-of-fit of the models. It then generates data for a new environmental scenario with unobserved availabilities. The model fitted in part 3 is then used to make predictions about the new scenario. Then follow several different outputs that aim to visualize and quantify the quality of the resulting predictions.</p> </blockquote

Appendix C. Model comparisons for individual wolves.

Model comparisons for individual wolves

Sightability models and data

Data and JAGS models associated with the following paper published in Methods in Ecology and Evolution: Fieberg, J., Alexander, M., Tse, S,, and K. St. Clair. 2013. Abundance estimation with sightability data: a Bayesian data augmentation approach. Methods in Ecology and Evolution

Thinking Like a Duck: Fall Lake Use and Movement Patterns of Juvenile Ring-Necked Ducks before Migration

<div><p>The post-fledging period is one of the least studied portions of the annual cycle in waterfowl. Yet, recruitment into the breeding population requires that young birds have sufficient resources to survive this period. We used radio-telemetry and generalized estimating equations to examine support for four hypotheses regarding the drivers of landscape scale habitat use and movements made by juvenile ring-necked ducks between the pre-fledging period and departure for migration. Our response variables included the probability of movement, distances moved, and use of different lake types: brood-rearing lakes, staging lakes, and lakes with low potential for disturbance. Birds increased their use of staging areas and lakes with low potential for disturbance (i.e., without houses or boat accesses, >100 m from roads, or big lakes with areas where birds could sit undisturbed) throughout the fall, but these changes began before the start of the hunting season and their trajectory was not changed by the onset of hunting. Males and females moved similar distances and had similar probabilities of movements each week. However, females were more likely than males to use brood-rearing lakes later in the fall. Our findings suggest juvenile ring-necked ducks require different lake types throughout the fall, and managing solely for breeding habitat will be insufficient for meeting needs during the post-fledging period. Maintaining areas with low potential for disturbance and areas suitable for staging will ensure that ring-necked ducks have access to habitat throughout the fall.</p></div