Transcript Program Mark

Using Free Software to Analyze T&E Monitoring Data
Wood Turtle (Glyptemys insculpta)
• State Species of Concern (G3, S3S4)
• Monitored since 2003
• Annual Mark-Recapture Surveys
• Eastern Box Turtle (G5, S3S4)
• Spotted Turtle (G5, S3S4)
• Snapping Turtle (G5, S5)
Eastern Regal Fritillary Butterfly (Speyeria idalia idalia)
• State Species of Concern (G3, S1)
• Monitored since 1998
• 3 Subpopulations
• Pollard-Walk Transects
• Mark-Recapture: 4-5 years
• Proposed for ESA listing in 2013
• Former Category 2
• Subspecies???
Common Data Types
• Open and Closed Capture Population
Estimation
• Survival and Capture/Recapture
Probability
Other types
• Dead Recoveries
• Known Fate
• Mark-Resight
• Robust Design
• Multi-State Models
• Recruitment
• Occupancy
• Radio Telemetry
http://www.phidot.org/software/mark/
Other Features
• Tests: GOF, median c-hat, Likelihood
Ratios, etc.
• Data simulations
• Make adjustment
Program Mark Data File
• Starts as a text file
• Encounter history----1010100010101 2;
• Histories can be as individuals or
condensed into groups:
1010100010101 1;
1010100010101 2;
or
1010100010101 1;
Change extension from .txt to .inp
•Can handle additional information
• time interval variation
• groups (i.e. gender)
• covariates (i.e. size, age, etc)
Analysis selection
File information and storage location
Check text file
Set additional
information
POPAN Jolly-Seber Estimated Parameters
• phi= Survival Prob. (between sessions)
• p= Recapture Prob. (each session)
• PENT= Prob. of Entry (between sessions)
• N= Pop. Est. of dataset
Building Models
• Control how parameters are considered
• Time variation
=phi(t)p(t)pent(t)N(t)
• Group variation
=phi(g)p(g)pent(g)N(g)
• Both=phi(g*t)p(g*t)N(g*t)
• fully saturated
• No variation =phi(.)p(.)pent(.)N(.)
• Many combinations
• Common sense is important in model
selection
• LINK functions used to accommodate
calculations
• Some analysis require numerical
transformations
• Parameter specific link
• Control individual model
parameters
• Extras
• Model Name
• Fix Parameters
• Numerical Options
• RTFM=Read the F@$%#&@
manual
• not my saying, it’s in the
manual
Comparing different models
• AIC-Akaike’s information Criterion
• Recent-1970’s
• Find a balance
• Precision vs. Fit
• Lowest value=Best fit
• Other considerations
• Deviance
• Alternatives (i.e. BICc)
• Common sense
• Valid Model?
• Reasonable estimates?
Is what you’re looking at really any good?
• Tests tab
• Goodness-of-fit-test
• Observed/Expected (i.e. chi-squared)
• Compare models to each other
• Likelihood ratio, ANODEV, chi-squared, F-stat
• Not all test work for all data types
Model 1=Phi(.)p(t)pent(t)N(.)
• AICc= 398.1408
• Weight=0. 41939 (42%)
• N=519.76 (SE=160.51)
Model 2=Phi(.)p(.)pent(t)N(.)
• AICc= 398.2854
• Weight=0.39013 (39%)
• N=446.96 (SE=67.71)
Which one is better?
• Why choose one when you can average like parameters together
• In this case, phi, pent, and N could be averaged
Insect Count Analyzer (INCA)
Uses transect data to create
estimates
• i.e. Pollard-Walk
Estimates
• alpha=death rate
• beta=spread of death rate
• mu=peak emergence
• N=population estimate
within surveyed area
Can handle adjustments
• Prior information
• Initial estimates
• Adjust algorithms
INCA: Insect Count Analyzer. 2002. A user-friendly program to analyze transect count data. The
Urban Wildlands Group, http://www.urbanwildlands.org/INCA/
MS excel-type format
Data information
• Julian dates automatically
convert into # of days
• Dates can be ignored if
needed
• Saves as an .xml
Flight Phenogram
Parameter estimates,
errors terms and 95% CI’s
Phenogram with best fit
curve
Correlation terms
• relationship between
parameters
Likelihood profiles and 95% CI’s
Usefulness as a Conservation Tool?
• Trends in space and time
• Tracking survival and detection rates
• Sites comparisons
• Sampling years
•Example: Declining regal subpopulation at FIG
• Model metapopulation “winking out”
PM Survival
(Phi) Estimates
Male
2005
90%
2009
96%
2014
24%
Female
92%
95%
36%
2011 - Population crash
PM population
estimates
INCA
population
extrapolations
2005
106.83
2009
52.93
2014
36.25
81.06
63.78
45.03
Male
2012-2014: Increase in death rate ~3x
Combining Methods-Fill in Gaps
MRR-M
INCA estimate
2005
106.83
81.06
2006
42.52
2007
28.43
2008
52.92
2009
52.93
63.78
2010
60.46
2011
15.36
2012
38.89
2013
49.98
2014
36.25
90
80
INCA Population Estimate
Year
70
60
50
40
30
20
10
45.03
0
2005
MRR-M
INCA estimate
2001
600.00
579.52
2002
290.78
2003
459.45
2004
352.74
2005
487.12
332.28
2006
132.92
2007
307.98
2008
467.24
2009
1062.86
958.30
2010
850.45
2011
368.56
2012
838.34
2013
2056.04
2014
3568.56
?
2007
2008
2009
Year
2010
2011
2012
2013
2014
2500
INCA Population Estimate
Year
2006
2000
1500
1000
500
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Other Free Programs
• Distance (http://distancesampling.org/)
– Estimates abundance using # of objects and distance from centerline
(i.e. transect line)
• R (http://www.r-project.org/)
– Free and very powerful statistical package
– Requires some computer programming knowledge
• Rmark (http://www.phidot.org/software/mark/rmark)
– Program Mark for Program R
• USGS Patuxent Wildlife Research Center (http://www.mbrpwrc.usgs.gov/software.html)
– Contains many of Program Mark’s data types but in separate
packages
• Many programs can run straight off the website (no downloading or
installing)
• U-Care (http://www.cefe.cnrs.fr/fr/recherche/bc/bbp/264-logiciels)
– In depth GOF testing
– Has installation issues
Software in the literature
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Program Mark
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Hemerik, L., Geertsma, M., Waasdorp, S., Middelveld, R. P., van Kleef, H., & Klok, C. 2015.
Survival, reproduction, and immigration explain the dynamics of a local Red-backed Shrike
population in the Netherlands. Journal of Ornithology, 156(1), 35-46.
Rebenack, J. J., Ricker, S., Anderson, C., Wallace, M., & Ward, D. M. 2015. Early Emigration of
Juvenile Coho Salmon: Implications for Population Monitoring. Transactions of the American
Fisheries Society, 144:163-172.
Jungers, J. M., Arnold, T. W., & Lehman, C. 2015. Effects of Grassland Biomass Harvest on Nesting
Pheasants and Ducks. The American Midland Naturalist, 173:122-132.
Little, I. T., Hockey, P. A., & Jansen, R. 2015. Assessing biodiversity integrity for the conservation
of grazed and burnt grassland systems: avian field metabolic rates as a rapid assessment tool.
Biodiversity and Conservation 1-29.
Hammond, R. L., Crampton, L. H., & Foster, J. T. 2015. Breeding biology of two endangered forest
birds on the island of Kauai, Hawaii. The Condor, 117:31-40.
INCA
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Longcore, T., R. Mattoni, C. Zonneveld, and J. Bruggeman. 2003. INsect Count Analyzer: a tool to
assess responses of butterflies to habitat restoration. Ecological Restoration 21(1):60–61.
Haddad, N.M., B. Hudgens, C. Damiani, K. Gross, D. Kuefler, and K. Pollock. 2008. Determining
optimal monitoring for rare butterfly populations. Conservation Biology 22:929-940.
Marschalek, D. A., & Deutschman, D. H. 2008. Hermes copper (Lycaena [Hermelycaena] hermes:
Lycaenidae): life history and population estimation of a rare butterfly. Journal of Insect
Conservation, 12(2), 97-105.
Ferster, B., & Vulinec, K. 2010. Population size and conservation of the last eastern remnants of
the regal fritillary, Speyeria idalia (Drury)[Lepidoptera, Nymphalidae]; implications for temperate
grassland restoration. Journal of insect conservation, 14(1), 31-42.
Calabrese, J. M. 2012. How emergence and death assumptions affect count-based estimates of
butterfly abundance and lifespan. Population ecology, 54(3), 431-442.