Title: Fieberg, John R. (2015). R Code and Output Supporting: Do Capture and Survey Methods Influence Whether Marked Animals are Representative of Unmarked Animals?

Author: John Fieberg, University of Minnesota


General Description:  This collection contains R code (along with associated output from running the code) supporting all results reported in Fieberg et al. in press. In addition, it contains resighting data collected from moose in Minnesota from 2004-2007.


Files:
1. WA_elk.R = R code used to analyze elk resighting data in Fieberg et al. (in press).
2. WA_elk.html = html output resulting from running the R code in WA_elk.R.  
3. Mtg_AK_WA.R = R code used to analyze mountain goat resighting data in Fieberg et al. (in press).  
4. Mtg_AK_WA.html = html output resulting from running the R code in Mtg_AK_WA.R.   
5. Moose_MN.R = R code used to analyze moose resighting data in Fieberg et al. (in press).  
6. Moose_MN.html = html output resulting from running the R code in Moose_MN.R.   
7. sightdat.csv = resighting data collected from moose in Minnesota between 2004 and 2007. Data set contains 6 columns with the following variables:
  a. id = unique animal identifier
  b. observed = an indicator if the moose was observed (1) or not (0) 
  c. age = age of the animal
  d. year = year of the survey 
  e. capyear = year the animal was captured
  f. voc = visual obstruction surrounding the animal
  g. NA = missing value	
8. MTG_Sight_Alaska.csv = resighting data collected from mountain goats in Alaska from 2005–2008. Data set contains 18 columns with the following variables:
  a. ID = unique animal identifier
  b. Seen = an indicator if the mountain goat was observed (1) or not (0) 
  c. Capture.Date = date animal was captured
  d. Survey.Date = date of survey 
  e. Day.Since.Capture = number of days between capture and survey = Survey.Date-Capture.Date
  f. Time = time of observation (missing if the animal was not observed)
  d. Group.Size = number of animals present with the focal individual (missing if the animal was not observed)
  e. Behaviour = behavior of the animal at the time of the survey (missing if the animal was not observed) 
  f. Landform = landform where the animal was observed (missing if the animal was not observed)
  g. Slope = slope where the animal was observed (missing if the animal was not observed)
  h. Terrain = terrain where the animal was observed (missing if the animal was not observed)
  i. Habitat = habitat where the animal was observed (missing if the animal was not observed)
  j. Lighting = lighting when the animal was observed (missing if the animal was not observed)
  h. Aircraft = type of aircraft used to survey the animals.
  i. Observers = number of observers present when surveying the animal.
  j. Obs_ID = Observers initials
  k. l. Area1 and Area2 = area where the survey occurred.
  l. Note:  blanks and "NA" indicate missing values	

9. NE_MN_Map.pdf = map of collection region for moose resighting data.

For more information on the moose survey data, see Giudice et al. (2012) and Fieberg et al. (2013).
 
The htlm files ([2], [4], and [6], above) were created using the knitr package of program R (R Core Team 2013, Yihui 2013).

R session info for code:
This code was created and run with R 3.1.1 and on a x86_64-w64-mingw32/x64 (64-bit) platform. 


Geographic Location of Data Collection and Methods:
1. Mountain goats, Alaska: Mountain goats (n = 92) were captured using standard helicopter darting methods during August–October 2005–2008 (see White et al. 2011, White et al. 2012 for details) in eastern Lynn Canal, Alaska (59 N, 135 W).
2. Mountain goats, Washington (from Rice et al. 2009): Sightability trials were conducted in mountain goat habitat throughout the Cascade and Olympic ranges in Washington. Areas were divided to survey for mountain goats into blocks of contiguous suitable habitat of about 500 ha (Olympics) or a size that could be surveyed in 30–45 minutes (Cascades). In the Olympics, survey blocks contained all terrain above 1,525 m, whereas in the Cascades, block boundaries were determined on the basis of elevation, habitat maps, and local expert knowledge.
3. Moose, Minnesota: The data come from Northeastern Minnesota. See the grided area on the map “NE_MN_Map.pdf” (Giudice et al. 2012, Fieberg 2013).
4. Elk, Nooksack Elk Herd (from McCorquodale et al. 2013): The Nooksack elk herd area includes part or all of Whatcom, Skagit, and Snohomish Counties and consists of Washington State Game Management Units (GMUs) 407 (North Sound),418 (Nooksack), 437 (Sauk), 448 (Stillaguamish), and 450 (Cascade; approx. 48.598N, 122.078W). The herd’s current core area represents about 1,230 km2 of the historic range within GMU 418, and the primary study area was approximately the southern half of this GMU (McCorquodale eta l. 2013)
5. Elk, Mount St. Helens Elk Herd (from McCorquodale et al. 2014): The Mount St. Helens elk herd area covers much of southwest Washington, east of Interstate 5, consisting of 14 Game Management Units (GMUs) defining 5 Population Management Units (PMUs). This large area (  4,710 mi2 ) extends north to south from almost south Puget Sound to the Columbia River Gorge and west to east from I-5 to US Highway 97 (more than 40 miles east of the Cascade Crest). The scale of the defined herd area made it impractical to serve as a formal study area, so a 5 GMU core area was selected as the area of study; the GMUs selected were: Winston (GMU 520), Loowit (GMU 522), Margaret (GMU 524), Coweeman (GMU 550), and Toutle (GMU 556) (Fig. 1). These GMUs represent a large swath of the herd’s core range, including an extensive area of industrial and state-managed forest, as well as that part of the landscape still impacted by the 1980 eruption of the volcano (North Fork of the Toutle River and the Mount St. Helens National Volcanic Monument).




License/Restriction Info:
These data are protected under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States license. 



References:

Fieberg, J., K. Jenkins, S. McCorquodale, C. G. Rice, G. C. White, and K. White. In press. Do Capture and Survey Methods Influence Whether Marked Animals are Representative of Unmarked Animals?  Wildlife Society Bulletin.

Fieberg, J., M. Alexander, S. Tse, and K. St. Clair.  (2013).  Abundance estimation with sightability data: a Bayesian data augmentation approach.  Methods in Ecology and Evolution 4:854–864.

Giudice, J., J. Fieberg, and M. Lenarz.  (2012).  Spending degrees of freedom in a poor economy:  a case study of building a sightability model for Moose in northeastern Minnesota.  Journal of Wildlife Management 76:75-87.

McCorquodale, S. M., P. J. Miller, S. M. Bergh and E. W. Holman.  2014.  Mount St. Helens elk population assessment: 2009-2013.  Washington Department of Fish and Wildlife, Olympia, Washington, USA 

McCorquodale, Scott M., Knapp, S.M., Davison, M. A., Bohannon, J.S., Danilson, C.D., Madsen, W. C. 2013. Mark-Resight and Sightability Modeling of a Western Washington Elk Population. The Journal of Wildlife Management 77:359–371.

R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

Rice, C. G., K. J. Jenkins, and W.-Y. Chang. 2009. A sightability model for mountain goats. The Journal of Wildlife Management 73:468–478.  

White, K. S., G. W. Pendleton, D. Crowley, H. Griese, K. J. Hundertmark, T. McDonough, L. Nichols, M. Robus, C. A. Smith and J. W. Schoen. 2011. Mountain goat survival in coastal Alaska: effects of age, sex and climate. Journal of Wildlife Management, 75: 1731-1744.

White, K. S., D. P. Gregovich, G. W. Pendleton, N. L. Barten, R. Scott, A. Crupi and D. N. Larsen. 2012. Mountain goat population ecology and habitat use along the Juneau Access road corridor, Alaska. Research Final Report. Alaska Department of Fish and Game. Juneau, AK. 76pp.

Yihui, Xie (2013). knitr: A general-purpose package for dynamic report generation in R. R package version 1.4.1.