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Application of Regression Models in Bird Population Data: An Example of Haçlı Lake

Year 2020, Volume: 10 Issue: 2, 788 - 798, 01.06.2020
https://doi.org/10.21597/jist.649180

Abstract

In this study, the effects of habitat, ordo, UTM frame, seasons and number of species on bird populations and distribution in Haçlı Lake were investigated. Bird population data were obtained using point counts and transect observation methods. Poisson regression is typically used in such data sets. The basic principle of Poisson regression assumes that the variance is equal to the mean. Failure to achieve this equality causes incorrect parameter estimates and standard errors. In practice, the variance is often higher than the mean (variance > mean). This is called over-dispersion, where the value of over-dispersion is greater than 1.0. The population status of the data set used in the study was over-dispersed. Negative binomial regression is the most common method used to eliminate the over-dispersion effect. In this case, the preferred method is the negative binomial regression method. The over-dispersion value in the Poisson regression was considerably greater than 1.0 (54.937) while the over-dispersion value was very close to 1.0 (1.588) in the negative binomial regression. The results indicated that the use of negative binomial regression method is more appropriate. Therefore, parameter estimations were interpreted according to negative binomial regression method. Herein, climatic factors including temperature and humidity exhibited significant impacts on population density and number of species.

References

  • Adızel Ö, Özdemir K, Durmuş A, Akın G, 2010. Genetiği Değiştirilmiş Organizmaların (GDO) Doğa ve İnsana Etkileri. Yüzüncü Yıl Üniversitesi, Fen Bilimleri Enstitüsü Dergisi, 15 (2): 148-153.
  • Agresti A, 1997. Categorical Data Analysis. John and Wiley & Sons, Incorporation, New Jersey, Canada
  • Aksan Ş, Özdemir İ, Oğurlu İ, 2014. Modeling the distributions of some wild mammalian species in Gölcük Natural Park/Turkey. Biological Diversity and Conservation, 7 (1): 1-15.
  • Beerens JM, Gawlik DE, Herring G, Cook MI, 2011. Dynamic habitat selection by two wading bird species with divergent foraging strategies in a seasonally fluctuating wetland. The Auk, 128 (4): 651-662.
  • Bibby CJ, Burgess DN, Hill AD, Mustoe S, 2000. Bird Census Techniques, Second Edition, Academic Press, ISBN 0-12-095831-7, London, United Kingdom, 86.
  • Boyce MS, Johnson CJ, Merrill EH, Nielsen SE, Solberg EJ, Van Moorter B, 2016. Can habitat selection predict abundance?. Journal of Animal Ecology, 85 (1): 11-20.
  • Cameron AC, Trivedi PK, 2013. Regression analysis of count data (Vol. 53). Cambridge University press.
  • Clark RG, Shutler D, 1999. Avian habitat selection: pattern from process in nest‐site use by ducks? Ecology, 80 (1): 272-287.
  • Çelik E, Durmuş A, 2020. Nonlinear Regression Applications in Modeling Over-dispersion of Bird Populations. The Journal of Animal & Plant Sciences, 30(2): 345-354.
  • Dalrymple ML, Hudson IL, Ford RPK, 2003. Finite mixture, zero-inflated poisson and hurdle models with application to SIDS. Computational Statistics and Data Analysis, 41: 491-504
  • Durmuş A, Yeşilova A, Çelik E, Kara R, 2018. Using Poisson and Negative Binomial Regression Models on Birds Population in Dönemeç Delta. Yuzuncu Yıl Unıversıty Journal of Agricultural Sciences, 28 (1): 78-85.
  • Famoye F, Karan PS, 2006. Zero- inflated generalized poisson regression model with an application to domestic violence data. Journal of Data Science, 5: 117-130.
  • Girma Z, Mamo Y, Mengesha G, Verma A, Asfaw T, 2017. Seasonal abundance and habitat use of bird species in and around Wondo Genet Forest, south‐central Ethiopia. Ecology and Evolution, 7 (10): 3397-3405.
  • Gonçalves GR, Santos MPD, Cerqueira PV, Juen L, Bispo AÂ, 2017. The relationship between bird distribution patterns and environmental factors in an ecotone area of northeast Brazil. Journal of Arid Environments, 140: 6-13
  • Graham CH, Ferrier S, Huettman F, Morit C, Peterson AT, 2004. New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19: 497–503.
  • Guisan A, Graham CH, Elith J, Huettmann F, NCEAS. Species Distribution Modelling Group, 2007. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13 (3): 332-340.
  • Guisan A, Zimmermann NE, 2000. Predictive Habitat Distribution Models İn Ecology. Ecological Modelling, 135: 147–186.
  • Hilbe JM, 2007. Negative Binomial Regression. Cambridge, U.K.
  • Johnston A, Fink D, Reynolds MD, Hochachka WM, Sullivan BL, Bruns NE, Kelling S, 2015. Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications, 25 (7): 1749-1756.
  • Kéry M, Royle JA, Schmid H, 2005. Modeling avian abundance from replicated counts using binomial mixture models. Ecological applications, 15 (4): 1450-1461.
  • Kiziroğlu İ, 2008. Türkiye Kuşları. Tür Listesi ve Türkiye Kuşları Kırmızı Listesi. Hacettepe Üniversitesi, Çevre Eğitimi, Kuş Araştırmaları ve Halkalama Merkezi, Ankara. 86.
  • Knape J, Arlt D, Barraquand F, Berg Å, Chevalier M, Pärt T, Żmihorski M, 2018. Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit. Methods in Ecology and Evolution, 9 (10): 2102-2114.
  • Li P, Martin TE, 1991. Nest-Site Selection and Nesting Success of Cavity Nesting Birds in High Elevation Forest Drainages. Auk, 108: 405-418.
  • Lindén A, Mäntyniemi S, 2011. Using the negative binomial distribution to model overdispersion in ecological count data. Ecology, 92 (7): 1414-1421.
  • Luo J, Qu Y, 2015. Estimation of group means when adjusting for covariates in generalized linear models. Pharm Stat., 14 (1): 56-62.
  • Mathews EA, Pendleton GW, 2006. Declines in harbor seal (Phoca vitulina) numbers in Glacier Bay National Park, Alaska, 1992–2002. Marine Mammal Science, 22: 167–189.
  • McCarthy MA, Burgman MA, Ferson S, 1995. Sensitivity analysis for models of population viability. Biological Conservation, 73 (2): 93-100.
  • Milsom TP, Langton SD, Parkin WK, Peel S, Bishop JD, Hart JD, Moore NP, 2000. Habitat Models of Bird Species' Distribution: an Aid to The Management of Coastal Grazing Marshes. Journal of Applied Ecology, 37: 706-727.
  • Muthén LK, Muthén B, 2006. Mplus: User’s guide. Los Angeles, CA: Muthén & Muthén
  • O’Hara RB, 2005. Species richness estimators: How many species can dance on the head of a pin? Journal of Animal Ecology, 74: 375–386.
  • O'Hara RB, Kotze DJ, 2010. Do not log-transform count data. Methods in Ecology and Evolution. 1: 118–122.
  • Onmuş O, 2008. Gediz Deltası'nda üreyen su kuşu türlerinin yuvalama alanlarının izlenmesi ve bu kolonilerin yönetilmesi Ege Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi (Basılmamış), İzmir.
  • Rékási J, Rozsa L, Kiss BJ, 1997. Patterns in the distribution of avian lice (Phthiraptera: Amblycera, Ischnocera). Journal of Avian Biology, 150-156.
  • Ridout M, Clarice GBD, John H, 1998. Models for count data with many zeros. International Biometric Conference. Cape Town.
  • Small RJ, Pendleton GW, Pitcher KW, 2003. Trends in abundance of Alaska harbor seals, 1983-2002. Marine Mammal Science, 19: 344–362.
  • Ver Hoef JM, Boveng PL, 2007. Quasi Poisson vs. negative binomial regression: how should we model overdispersed count data?. Ecology, 88 (11): 2766-2772.
  • Wang P, Putterman ML, 1998. Mixed logistic regression models. Journal of Agriculture Biological and Environmental Statistics, 3 (2): 175-200.
  • Wedderburn RWM, 1974. Quasi-likelihood functions,generalized linear models, and the Gauss-Newton method. Biometrika, 61: 439–447.
  • Yesilova A, Denizhan E, 2016. Modeling mite counts using poisson and negative binomial regressions. Fresenıus Envıronmental Bulletın, 25 (11): 5062-5066
  • Yeşilova A, Özgökçe MS, Atlıhan R, Polat Yıldız Ş, Karaca İ, Ser G, 2016. Modeling of the arthropod population densities in the coastal band of Lake Van using mixture poison regression. Fresenius Environmental Bulletin, 25: 1768-1778.
  • Yuan Y, Zeng G, Liang J, Li X, Li Z, Zhang C, Yu X, 2014. Effects of landscape structure, habitat and human disturbance on birds: a case study in East Dongting Lake wetland. Ecological Engineering, 67: 67-75.

Application of Regression Models in Bird Population Data: An Example of Haçlı Lake

Year 2020, Volume: 10 Issue: 2, 788 - 798, 01.06.2020
https://doi.org/10.21597/jist.649180

Abstract

In this study, the effects of habitat, ordo, UTM frame, seasons and number of species on bird populations and distribution in Haçlı Lake were investigated. Bird population data were obtained using point counts and transect observation methods. Poisson regression is typically used in such data sets. The basic principle of Poisson regression assumes that the variance is equal to the mean. Failure to achieve this equality causes incorrect parameter estimates and standard errors. In practice, the variance is often higher than the mean (variance > mean). This is called over-dispersion, where the value of over-dispersion is greater than 1.0. The population status of the data set used in the study was over-dispersed. Negative binomial regression is the most common method used to eliminate the over-dispersion effect. In this case, the preferred method is the negative binomial regression method. The over-dispersion value in the Poisson regression was considerably greater than 1.0 (54.937) while the over-dispersion value was very close to 1.0 (1.588) in the negative binomial regression. The results indicated that the use of negative binomial regression method is more appropriate. Therefore, parameter estimations were interpreted according to negative binomial regression method. Herein, climatic factors including temperature and humidity exhibited significant impacts on population density and number of species.

References

  • Adızel Ö, Özdemir K, Durmuş A, Akın G, 2010. Genetiği Değiştirilmiş Organizmaların (GDO) Doğa ve İnsana Etkileri. Yüzüncü Yıl Üniversitesi, Fen Bilimleri Enstitüsü Dergisi, 15 (2): 148-153.
  • Agresti A, 1997. Categorical Data Analysis. John and Wiley & Sons, Incorporation, New Jersey, Canada
  • Aksan Ş, Özdemir İ, Oğurlu İ, 2014. Modeling the distributions of some wild mammalian species in Gölcük Natural Park/Turkey. Biological Diversity and Conservation, 7 (1): 1-15.
  • Beerens JM, Gawlik DE, Herring G, Cook MI, 2011. Dynamic habitat selection by two wading bird species with divergent foraging strategies in a seasonally fluctuating wetland. The Auk, 128 (4): 651-662.
  • Bibby CJ, Burgess DN, Hill AD, Mustoe S, 2000. Bird Census Techniques, Second Edition, Academic Press, ISBN 0-12-095831-7, London, United Kingdom, 86.
  • Boyce MS, Johnson CJ, Merrill EH, Nielsen SE, Solberg EJ, Van Moorter B, 2016. Can habitat selection predict abundance?. Journal of Animal Ecology, 85 (1): 11-20.
  • Cameron AC, Trivedi PK, 2013. Regression analysis of count data (Vol. 53). Cambridge University press.
  • Clark RG, Shutler D, 1999. Avian habitat selection: pattern from process in nest‐site use by ducks? Ecology, 80 (1): 272-287.
  • Çelik E, Durmuş A, 2020. Nonlinear Regression Applications in Modeling Over-dispersion of Bird Populations. The Journal of Animal & Plant Sciences, 30(2): 345-354.
  • Dalrymple ML, Hudson IL, Ford RPK, 2003. Finite mixture, zero-inflated poisson and hurdle models with application to SIDS. Computational Statistics and Data Analysis, 41: 491-504
  • Durmuş A, Yeşilova A, Çelik E, Kara R, 2018. Using Poisson and Negative Binomial Regression Models on Birds Population in Dönemeç Delta. Yuzuncu Yıl Unıversıty Journal of Agricultural Sciences, 28 (1): 78-85.
  • Famoye F, Karan PS, 2006. Zero- inflated generalized poisson regression model with an application to domestic violence data. Journal of Data Science, 5: 117-130.
  • Girma Z, Mamo Y, Mengesha G, Verma A, Asfaw T, 2017. Seasonal abundance and habitat use of bird species in and around Wondo Genet Forest, south‐central Ethiopia. Ecology and Evolution, 7 (10): 3397-3405.
  • Gonçalves GR, Santos MPD, Cerqueira PV, Juen L, Bispo AÂ, 2017. The relationship between bird distribution patterns and environmental factors in an ecotone area of northeast Brazil. Journal of Arid Environments, 140: 6-13
  • Graham CH, Ferrier S, Huettman F, Morit C, Peterson AT, 2004. New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19: 497–503.
  • Guisan A, Graham CH, Elith J, Huettmann F, NCEAS. Species Distribution Modelling Group, 2007. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13 (3): 332-340.
  • Guisan A, Zimmermann NE, 2000. Predictive Habitat Distribution Models İn Ecology. Ecological Modelling, 135: 147–186.
  • Hilbe JM, 2007. Negative Binomial Regression. Cambridge, U.K.
  • Johnston A, Fink D, Reynolds MD, Hochachka WM, Sullivan BL, Bruns NE, Kelling S, 2015. Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications, 25 (7): 1749-1756.
  • Kéry M, Royle JA, Schmid H, 2005. Modeling avian abundance from replicated counts using binomial mixture models. Ecological applications, 15 (4): 1450-1461.
  • Kiziroğlu İ, 2008. Türkiye Kuşları. Tür Listesi ve Türkiye Kuşları Kırmızı Listesi. Hacettepe Üniversitesi, Çevre Eğitimi, Kuş Araştırmaları ve Halkalama Merkezi, Ankara. 86.
  • Knape J, Arlt D, Barraquand F, Berg Å, Chevalier M, Pärt T, Żmihorski M, 2018. Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit. Methods in Ecology and Evolution, 9 (10): 2102-2114.
  • Li P, Martin TE, 1991. Nest-Site Selection and Nesting Success of Cavity Nesting Birds in High Elevation Forest Drainages. Auk, 108: 405-418.
  • Lindén A, Mäntyniemi S, 2011. Using the negative binomial distribution to model overdispersion in ecological count data. Ecology, 92 (7): 1414-1421.
  • Luo J, Qu Y, 2015. Estimation of group means when adjusting for covariates in generalized linear models. Pharm Stat., 14 (1): 56-62.
  • Mathews EA, Pendleton GW, 2006. Declines in harbor seal (Phoca vitulina) numbers in Glacier Bay National Park, Alaska, 1992–2002. Marine Mammal Science, 22: 167–189.
  • McCarthy MA, Burgman MA, Ferson S, 1995. Sensitivity analysis for models of population viability. Biological Conservation, 73 (2): 93-100.
  • Milsom TP, Langton SD, Parkin WK, Peel S, Bishop JD, Hart JD, Moore NP, 2000. Habitat Models of Bird Species' Distribution: an Aid to The Management of Coastal Grazing Marshes. Journal of Applied Ecology, 37: 706-727.
  • Muthén LK, Muthén B, 2006. Mplus: User’s guide. Los Angeles, CA: Muthén & Muthén
  • O’Hara RB, 2005. Species richness estimators: How many species can dance on the head of a pin? Journal of Animal Ecology, 74: 375–386.
  • O'Hara RB, Kotze DJ, 2010. Do not log-transform count data. Methods in Ecology and Evolution. 1: 118–122.
  • Onmuş O, 2008. Gediz Deltası'nda üreyen su kuşu türlerinin yuvalama alanlarının izlenmesi ve bu kolonilerin yönetilmesi Ege Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi (Basılmamış), İzmir.
  • Rékási J, Rozsa L, Kiss BJ, 1997. Patterns in the distribution of avian lice (Phthiraptera: Amblycera, Ischnocera). Journal of Avian Biology, 150-156.
  • Ridout M, Clarice GBD, John H, 1998. Models for count data with many zeros. International Biometric Conference. Cape Town.
  • Small RJ, Pendleton GW, Pitcher KW, 2003. Trends in abundance of Alaska harbor seals, 1983-2002. Marine Mammal Science, 19: 344–362.
  • Ver Hoef JM, Boveng PL, 2007. Quasi Poisson vs. negative binomial regression: how should we model overdispersed count data?. Ecology, 88 (11): 2766-2772.
  • Wang P, Putterman ML, 1998. Mixed logistic regression models. Journal of Agriculture Biological and Environmental Statistics, 3 (2): 175-200.
  • Wedderburn RWM, 1974. Quasi-likelihood functions,generalized linear models, and the Gauss-Newton method. Biometrika, 61: 439–447.
  • Yesilova A, Denizhan E, 2016. Modeling mite counts using poisson and negative binomial regressions. Fresenıus Envıronmental Bulletın, 25 (11): 5062-5066
  • Yeşilova A, Özgökçe MS, Atlıhan R, Polat Yıldız Ş, Karaca İ, Ser G, 2016. Modeling of the arthropod population densities in the coastal band of Lake Van using mixture poison regression. Fresenius Environmental Bulletin, 25: 1768-1778.
  • Yuan Y, Zeng G, Liang J, Li X, Li Z, Zhang C, Yu X, 2014. Effects of landscape structure, habitat and human disturbance on birds: a case study in East Dongting Lake wetland. Ecological Engineering, 67: 67-75.
There are 41 citations in total.

Details

Primary Language English
Subjects Structural Biology
Journal Section Biyoloji / Biology
Authors

Emrah Çelik 0000-0003-1274-4122

Atilla Durmuş 0000-0002-5116-9581

Publication Date June 1, 2020
Submission Date November 20, 2019
Acceptance Date December 28, 2019
Published in Issue Year 2020 Volume: 10 Issue: 2

Cite

APA Çelik, E., & Durmuş, A. (2020). Application of Regression Models in Bird Population Data: An Example of Haçlı Lake. Journal of the Institute of Science and Technology, 10(2), 788-798. https://doi.org/10.21597/jist.649180
AMA Çelik E, Durmuş A. Application of Regression Models in Bird Population Data: An Example of Haçlı Lake. J. Inst. Sci. and Tech. June 2020;10(2):788-798. doi:10.21597/jist.649180
Chicago Çelik, Emrah, and Atilla Durmuş. “Application of Regression Models in Bird Population Data: An Example of Haçlı Lake”. Journal of the Institute of Science and Technology 10, no. 2 (June 2020): 788-98. https://doi.org/10.21597/jist.649180.
EndNote Çelik E, Durmuş A (June 1, 2020) Application of Regression Models in Bird Population Data: An Example of Haçlı Lake. Journal of the Institute of Science and Technology 10 2 788–798.
IEEE E. Çelik and A. Durmuş, “Application of Regression Models in Bird Population Data: An Example of Haçlı Lake”, J. Inst. Sci. and Tech., vol. 10, no. 2, pp. 788–798, 2020, doi: 10.21597/jist.649180.
ISNAD Çelik, Emrah - Durmuş, Atilla. “Application of Regression Models in Bird Population Data: An Example of Haçlı Lake”. Journal of the Institute of Science and Technology 10/2 (June 2020), 788-798. https://doi.org/10.21597/jist.649180.
JAMA Çelik E, Durmuş A. Application of Regression Models in Bird Population Data: An Example of Haçlı Lake. J. Inst. Sci. and Tech. 2020;10:788–798.
MLA Çelik, Emrah and Atilla Durmuş. “Application of Regression Models in Bird Population Data: An Example of Haçlı Lake”. Journal of the Institute of Science and Technology, vol. 10, no. 2, 2020, pp. 788-9, doi:10.21597/jist.649180.
Vancouver Çelik E, Durmuş A. Application of Regression Models in Bird Population Data: An Example of Haçlı Lake. J. Inst. Sci. and Tech. 2020;10(2):788-9.