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Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

  • RICHARD J. CAMP (a1), KEVIN W. BRINCK (a1), P. MARCOS GORRESEN (a1) and EBEN H. PAXTON (a2)

Summary

Estimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.

Copyright

Corresponding author

*Author for correspondence; email: rick_camp@usgs.gov

References

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Albert, J. (2012) LearnBayes: Functions for learning Bayesian inference. R package version 2.12.http://CRAN.R-project.org/package=LearnBayes.
Alldredge, M. W., Pacifici, K., Simons, T. R. and Pollock, K. H. (2008) A novel field evaluation of the effectiveness of distance and independent observer sampling to estimate aural avian detection probabilities. J. Appl. Ecol. 45: 13491356.
Alldredge, M. W., Pollock, K. H., Simons, T. R., Collazo, J. A. and Shriner, S. A. (2007a) Time-of-detection methods for estimating abundance from point-count surveys. Auk 124: 653664.
Alldredge, M. W., Simons, T. R. and Pollock, K. H. (2007b) Factors affecting aural detections of songbirds. Ecol. Applic. 17: 948955.
Alldredge, M. W., Simons, T. R. and Pollock, K. H. (2007c) A field evaluation of distance measurement error in auditory avian point count surveys. J. Wildl. Manage. 71: 27592766.
Anderson, D. R. (2001) The need to get the basics right in wildlife field studies. Wildl. Soc. Bull. 29: 12941297.
Banko, W. E. and Banko, P. C. (2009) Historic decline and extinction. Pp. 25–58 in T. K. Pratt, C. T. Atkinson, P. C. Banko, J. D. Jacobi and B. L. Woodworth, eds. Conservation biology of Hawaiian forest birds: Implications for island avifauna. New Haven, CT, USA: Yale University Press.
Barker, R. J. and Sauer, J. R. (1995) Statistical aspects of point count sampling. Pp. 125–130 in C. J. Ralph, J. R. Sauer and S. Droege, eds. Monitoring bird populations by point counts. Albany, CA: U.S. Forest Service, Pacific Southwest Research Station. General Technical Report PSW-149.
Besbeas, P., Freeman, S. N., Morgan, B. J. T. and Catchpole, E. A. (2002) Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58: 540547.
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. and Thomas, L. (2001) Introduction to distance sampling: Estimating abundance of biological populations. Oxford, UK: Oxford University Press.
Burnham, K. P. (1981) Summarizing remarks: Environmental influences. Stud. Avian Biol. 6: 324325.
Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: A practical information-theoretic approach, second edition. New York, NY, USA: Springer-Verlag.
Camp, R. J., Pratt, T. K., Gorresen, P. M., Jeffrey, J. J. and Woodworth, B. L. (2010) Population trends of forest birds at Hakalau Forest National Wildlife Refuge, Hawai`i. Condor 112: 196212.
Camp, R. J., Pratt, T. K., Gorresen, P. M., Woodworth, B. L. and Jeffrey, J. J. (2014) Hawaiian forest bird trends: Using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013). Condor 116: 97101.
Camp, R. J., Reynolds, M. H., Gorresen, P. M., Pratt, T. K. and Woodworth, B. L. (2009) Monitoring Hawaiian forest birds. Pp. 83–107 in T. K. Pratt, C. T. Atkinson, P. C. Banko, J. D. Jacobi, and B. L. Woodworth, eds. Conservation biology of Hawaiian forest birds: Implications for island avifauna. New Haven, CT, USA: Yale University Press.
Camp, R. J., Seavy, N. E., Gorresen, P. M. and Reynolds, M. H. (2008) A statistical test to show negligible trend: Comment. Ecology 89: 14691472.
Chandler, R. B., Royle, J. A. and King, D. I. (2011) Inference about density and temporary emigration in unmarked populations. Ecology 92: 14291435.
Dennis, B., Ponciano, J. M., Lele, S. R., Taper, M. L. and Staples, D. F. (2006) Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76: 323341.
Dudley, N. (2008) Guidelines for applying protected area management categories. Gland, Switzerland: International Union for Conservation of Nature.
Freed, L. A. and Cann, R. L. (2010) Misleading trend analysis and decline of Hawaiian forest birds. Condor 112: 213221.
Freed, L. A. and Cann, R. L. (2013) More misleading trend analysis of Hawaiian forest birds. Condor 115: 442447.
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004) Bayesian data analysis, second edition. New York, USA: Chapman & Hall.
Gorresen, P. M., Camp, R. J., Reynolds, M. H., Woodworth, B. L. and Pratt, T. K. (2009) Status and trends of native Hawaiian songbirds. Pp. 108–136 in T. K. Pratt, C. T. Atkinson, P. C. Banko, J. D. Jacobi and B. L. Woodworth, eds. Conservation biology of Hawaiian forest birds: Implications for island avifauna. New Haven, CT, USA: Yale University Press.
Hess, S. C., Jeffrey, J. J., Ball, D. L. and Babich, L. (2006) Efficacy of feral pig removals at Hakalau Forest National Wildlife Refuge, Hawai‘i. Trans. West. Sec. The Wildl. Soc. 42: 5367.
Hess, S. C., Jeffrey, J. J., Pratt, L. W. and Ball, D. L. (2010) Effects of ungulate management on vegetation at Hakalau Forest National Wildlife Refuge, Hawai‘i Island. Pac. Conserv. Biol. 16: 144150.
Humbert, J-Y, Mills, L. S., Horne, J. S. and Dennis, B. (2009) A better way to estimate population trends. Oikos 118: 19401946.
Ives, A. R., Abbott, K. C. and Ziebarth, N. L. (2010) Analysis of ecological time series with ARMA(p,q) models. Ecology 91: 858871.
Johnson, L., Camp, R. J., Brinck, K. W. and Banko, P. C. (2006) Long-term population monitoring: Lessons learned from an endangered passerine in Hawai`i. Wildl. Soc. Bull. 34: 10551063.
Juvik, S. P. and Juvik, J. O., eds. (1998) Atlas of Hawai‘i. Third edition. Honolulu, HI, USA: University of Hawai‘i Press.
Kepler, C. B. and Scott, J. M. (1981) Reducing bird count variability by training observers. Stud. Avian Biol. 6: 366371.
Kéry, M. and Schaub, M. (2012) Bayesian population analysis using WinBUGS. Waltham, USA: Academic Press.
Knape, J. (2008) Estimability of density dependence in models of time series data. Ecology 89: 29943000.
Knape, J., Jonzén, N. and Sköld, M. (2011) On observation distributions for state space models of population survey data. J. Anim. Ecol. 80: 12691277.
Krebs, C. K. (1989) Ecological methodology. New York, NY, USA: HarperCollins Publishers, Inc.
Maindonald, J. and Braun, J. (2006) Data analysis and graphics using R – an example-based approach. Cambridge, UK: Cambridge University Press.
Maxfield, B. (1998) Hakalau Forest National Wildlife Refuge. Endang. Sp. Bull. 23: 2627.
Paxton, E. H., Gorresen, P. M. and Camp, R. J. (2013) Abundance, distribution, and population trends of the iconic Hawaiian Honeycreeper, the Iiwi (Vestiaria coccinea) throughout the Hawaiian Islands. U.S. Geological Survey Open-File Report 2013-1150. [http://pubs.usgs.gov/of/2013/1150/].
Pratt, T. K. (2009) Origins and evolution. Pp. 3–24 in T. K. Pratt, C. T. Atkinson, P. C. Banko, J. D. Jacobi and B. L. Woodworth, eds. Conservation biology of Hawaiian forest birds: Implications for island avifauna. New Haven, CT, USA: Yale University Press.
R Core Team (2014) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0, URLhttp://www.R-project.org/.
Ramsey, F. L. and Scott, J. M. (1979) Estimating population densities from variable circular plot surveys. Pp. 155–181 in R. M. Cormack, G. P. Patil and D. S. Robson, eds. Sampling biological populations. Fairland, MD, USA: Co-op. Publishing House. (Stat. Ecol. Ser., vol. 5).
Scott, J. M., Jacobi, J. D. and Ramsey, F. L. (1981a) Avian surveys of large geographical areas: A systematic approach. Wildl. Soc. Bull. 9: 190200.
Scott, J. M., Ramsey, F. L. and Kepler, C. B. (1981b) Distance estimation as a variable in estimating bird numbers from vocalizations. Stud. Avian Biol. 6: 334340.
Scott, J. M., Mountainspring, S., Ramsey, F. L. and Kepler, C. B. (1986) Forest bird communities of the Hawaiian Islands: Their dynamics, ecology, and conservation. Stud. Avian Biol. 9: 1431.
Scott, M. J. (2008) Report on U.S. Fish and Wildlife Service’s implementing recovery for endangered forest bird species in Hawai‘i workshop. Summary of workshop held in Hilo, Hawai‘i, 8–9 October 2008. Summary submitted to U.S. Fish and Wildlife Service, 14 November 2008.
Simon, J. C., Pratt, T. K., Berlin, K. E., Kowalsky, J. R., Fancy, S. G. and Hatfield, J. S. (2002) Temporal variation in bird counts within a Hawaiian rainforest. Condor 104: 469481.
Skalski, J. R., Ryding, K. E. and Millspaugh, J. J. (2005) Wildlife demography: Analysis of sex, age, and count data. Oxford, UK: Elsevier Academic Press.
Stan Development Team (2014) Stan: A C++ Library for probability and sampling, version 2.2.0. URLhttp://mc-stan.org/.
Thomas, L. (1996) Monitoring long-term population change: Why are there so many analysis methods? Ecology 77: 4958.
Thomas, L, Buckland, S. T., Rexstad, E. A., Laake, J. L., Strindberg, S., Hadley, S. L., Bishop, J. R. B., Marques, T. A. and Burnham, K. P. (2010) Distance software: Design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47: 514.
Thomas, L., Burnham, K. P. and Buckland, S. T. (2004) Temporal inferences from distance sampling surveys. Pp. 71–105 in S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers and L. Thomas, eds. Advanced distance sampling: Estimating abundance of biological populations. Oxford, UK: Oxford University Press.
Tummons, P. (2009) UH professor takes long-running feud with Feds into court of public opinion. Environment Hawaii volume 19, number 10 April(http://www.environment-hawaii.org).
U.S. Fish and Wildlife Service (1996) Feral ungulate management plan. Region 1, Portland, OR.
Urquhart, N. S. and Kincaid, T. M. (1999) Designs for detecting trend from repeated surveys of ecological resources. J. Ag. Biol. Environ. Stat. 4: 404414.
Urquhart, N. S., Paulsen, S. G. and Larsen, D. P. (1998) Monitoring for policy-relevant regional trends over time. Ecol. Applic. 8: 246257.

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Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

  • RICHARD J. CAMP (a1), KEVIN W. BRINCK (a1), P. MARCOS GORRESEN (a1) and EBEN H. PAXTON (a2)

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