Species Richness and Community Dynamics: A Conceptual Framework

Abstract

The study of animal communities has a long history in many branches of ecology, for instance in community ecology, biogeography and conservation biology. Furthermore, characterizing the size, composition and dynamics of animal communities is also important from a management perspective. For instance, community characteristics such as total size (species richness) or the size of certain subsets (e.g., number of rare or Red listed species) are often used to direct conservation efforts or to monitor their effectiveness. Camera traps can be used to study the size, composition and dynamics of animal communities, especially for large and medium-sized mammals and birds, terrestrial animals and particularly for nocturnal species. Although camera trap data can be treated in much the same way as data from other methods of sampling animal communities, it is particularly suited for capture–recapture-type analyses, given the ease with which discrete capture periods are defined. One important feature of camera-trap data as used for community inference is that the surveyed communities are typically not very large. Hence, the inferential challenges caused by the possible presence of a very large number of very rare or elusive species (Mao and Colwell 2005) are presumably greatly alleviated.

References

  1. Alpizar-Jara, R., J. D. Nichols, J. E. Hines, J. R. Sauer, K. H. Pollock, and C. S. Rosenberry. 2004. The relationship between species detection probability and local extinction probability. Oecologia 141:652–660PubMedCrossRefGoogle Scholar
  2. Bailey, L. L., J. E. Hines, J. D. Nichols, and D. I. MacKenzie. 2007. Sampling design trade-offs in occupancy studies with imperfect detection: examples and software. Ecological Applications 17:281–290PubMedCrossRefGoogle Scholar
  3. Boulinier, T., J. D. Nichols, J. R. Sauer, J. E. Hines, and K. P. Pollock. 1998a. Estimating species richness: the importance of heterogeneity in species detectability. Ecology 79:1018–1028CrossRefGoogle Scholar
  4. Boulinier, T., J. D. Nichols, J. E. Hines, J. R. Sauer, C. H. Flather, and K. P. Pollock. 1998b. Higher temporal variability of forest breeding bird communities in fragmented landscapes. Proceedings of National Academy of Sciences USA 95:7497–7501PubMedCrossRefGoogle Scholar
  5. Boulinier, T., J. D. Nichols, J. E. Hines, J. R. Sauer, C. H. Flather, and K. P. Pollock. 2001. Forest fragmentation and forest bird dynamics: inference at regional scales. Ecology 82:1159–1169CrossRefGoogle Scholar
  6. Bunge, J. and M. Fitzpatrick. 1993. Estimating the number of species: a review. Journal of the American Statistical Association 88:364–373Google Scholar
  7. Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information theoretic approach, Second edition. Springer, New YorkGoogle Scholar
  8. Burnham, K. P. and W. S. Overton. 1979. Robust estimation of population size when capture probabilities vary among animals. Ecology 60:927–936CrossRefGoogle Scholar
  9. Cam, E., J. D. Nichols, J. R. Sauer, and J. E. Hines. 2002a. On the estimation of species richness based on the accumulation of previously unrecorded species. Ecography 25:102–108CrossRefGoogle Scholar
  10. Cam, E., J. D. Nichols, J. E. Hines, J. R. Sauer, R. Alpizar-Jara, and C. H. Flather. 2002b. Disentangling sampling and ecological explanations underlying species-area relationships. Ecology 83:1118–1130Google Scholar
  11. Chao, A. 1987. Estimating the population size for capture–recapture data with unequal catchability. Biometrics 43:783–791PubMedCrossRefGoogle Scholar
  12. Chao, A. 2005. Species estimation and applications. Pages 7907–7916 in N. Balakrishnan, C.B. Read, and B. Vidakovic, editors. Encyclopedia of statistical sciences, Vol. 12, Second edition. Wiley, New YorkGoogle Scholar
  13. Chao, A. and S.-M. Lee. 1992. Estimating the number of classes via sample coverage. Journal of the American Statistical Association 87:210–217CrossRefGoogle Scholar
  14. Chao, A., R. L. Chazdon, R. K. Colwell, and T.-J. Shen. 2006. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62:361–371PubMedCrossRefGoogle Scholar
  15. Colwell, R. K. and J. A. Coddington. 1994. Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society B: Biological Sciences 345:101–118CrossRefGoogle Scholar
  16. Conn, P. B., A. D. Arthur, L. L. Bailey, and G. R. Singleton. 2006. Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods. Ecological Applications 16:829–837PubMedCrossRefGoogle Scholar
  17. Coull, B. A. and A. Agresti. 1999. The use of mixed logit models to reflect heterogeneity in capture–recapture studies. Biometrics 55:294–301PubMedCrossRefGoogle Scholar
  18. Doherty, P. F., Jr., G. Sorci, J. A. Royle, J. E. Hines, J. D. Nichols, and T. Boulinier. 2003. Sexual selection affects local extinction and turnover in bird communities. Proceedings of National Academy of Sciences USA 100:5858–5862PubMedCrossRefGoogle Scholar
  19. Dorazio, R. M. and J. A. Royle. 2003. Mixture models for estimating the size of a closed population when capture rates vary among individuals. Biometrics 59:351–364PubMedCrossRefGoogle Scholar
  20. Dorazio, R. M. and J. A. Royle. 2005. Estimating size and composition of biological communities by modeling the occurrence of species. Journal of the American Statistical Association 100:389–398Google Scholar
  21. Dorazio, R. M., J. A. Royle, B. Söderström, and A. Glimskär. 2006. Estimating species richness and accumulation by modeling species occurrence and detectability. Ecology 87:842–854PubMedCrossRefGoogle Scholar
  22. Dorazio, R. M., M. Kéry, J. A. Royle, and M. Plattner. 2010. Models for inference in dynamic metacommunity systems. Ecology (in press)Google Scholar
  23. Gelfand, A. E., A. M. Schmidt, S. Wu, J. A. Silander, Jr., A. Latimer, and A. G. Rebelo. 2005. Modelling species diversity through species level hierarchical modelling. Applied Statistics 54:1–20Google Scholar
  24. Gotelli, N. J. and R. K. Colwell. 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4:379–391CrossRefGoogle Scholar
  25. Greenwood, R. J., A. B. Sargeant, and D. H. Johnson. 1985. Evaluation of mark-recapture for estimating striped skunk abundance. Journal of Wildlife Management 49:332–340CrossRefGoogle Scholar
  26. Hines, J. E., T. Boulinier, J. D. Nichols, J. R. Sauer, and K. P. Pollock. 1999. COMDYN: software to study the dynamics of animal communities using a capture–recapture approach. Bird Study 46(suppl.):S209–S217Google Scholar
  27. Jiguet, F., O. Renault, and A. Petiau. 2005. Estimating species richness with capture–recapture models: choice of models when sampling in heterogeneous conditions. Bird Study 52:180–187CrossRefGoogle Scholar
  28. Karr, J. R. 1990. Biological integrity and the goal of environmental legislation: lessons for conservation biology. Conservation Biology 4:244–250CrossRefGoogle Scholar
  29. Kendall, W. L. 1999. Robustness of closed capture–recapture methods to violation of the closure assumption. Ecology 80:2517–2525Google Scholar
  30. Kendall, W. L., and G. C. White. 2009. A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy. Journal of Applied Ecology 46:1182–1188Google Scholar
  31. Kéry, M. 2002. Inferring the absence of a species – A case study of snakes. Journal of Wildlife Management 66:330–338CrossRefGoogle Scholar
  32. Kéry, M. 2010. Introduction to WinBUGS for ecologists: a Bayesian approach to regression, ANOVA, mixed models and related analyses. Academic Press, Burlington, MAGoogle Scholar
  33. Kéry, M. and M. Plattner. 2007. Species richness estimation and determinants of species detectability in butterfly monitoring programs. Ecological Entomology 32:53–61CrossRefGoogle Scholar
  34. Kéry, M. and J. A. Royle. 2008. Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology 45:589–598CrossRefGoogle Scholar
  35. Kéry, M. and J. A. Royle. 2009. Inference about species richness and community structure using species-specific occupancy models in the national Swiss breeding bird survey MHB. Pages 639–656 in D. L. Thomson, E. G. Cooch, and M. J. Conroy, editors. Modeling demographic processes in marked populations, series: environmental and ecological statistics, Vol. 3. Springer, BerlinGoogle Scholar
  36. Kéry, M. and H. Schmid. 2004. Monitoring programs need to take into account imperfect species detectability. Basic and Applied Ecology 5:65–73Google Scholar
  37. Kéry, M. and H. Schmid. 2006. Estimating species richness. Calibrating a large avian monitoring program. Journal of Applied Ecology 43:101–110Google Scholar
  38. Kéry, M. and B. R. Schmidt. 2008. Imperfect detection and its consequences for monitoring for conservation. Community Ecology 9:207–216CrossRefGoogle Scholar
  39. Kéry, M., J. A. Royle, and H. Schmid. 2008. Importance of sampling design and analysis in animal population studies: a comment on Sergio et al. Journal of Applied Ecology 45:981–986CrossRefGoogle Scholar
  40. Kéry, M., J. A. Royle, M. Plattner, and R. M. Dorazio. 2009. Species richness and occupancy estimation in communities subject to temporary emigration. Ecology 90:1279–1290Google Scholar
  41. Lekve, K., T. Boulinier, N. C. Stenseth, J. Gjosaeter, J.-M. Fromentin, J. E. Hines, and J. D. Nichols. 2002. Spatio-temporal dynamics of species richness in coastal fish communities. Proceedings of the Royal Society B: Biological Sciences 269:1781–1789CrossRefGoogle Scholar
  42. Link, W. A. 1999. Modeling pattern in collections of parameters. Journal of Wildlife Management 63:1017–1027CrossRefGoogle Scholar
  43. Link, W. A. 2003. Nonidentifiability of population size from capture–recapture data with heterogeneous detection probabilities. Biometrics 59:1123–1130PubMedCrossRefGoogle Scholar
  44. Link, W. A. and R. J. Barker. 2010. Bayesian inference with ecological applications. Academic Press, LondonGoogle Scholar
  45. MacKenzie, D. I. and J. A. Royle. 2005. Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology 42:1105–1114CrossRefGoogle Scholar
  46. MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probability rates are less than one. Ecology 83:2248–2255CrossRefGoogle Scholar
  47. MacKenzie, D. I., J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200–2207CrossRefGoogle Scholar
  48. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. P. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic, New YorkGoogle Scholar
  49. Manning, T., W. D. Edge, and J. O. Wolf. 1995. Evaluating population-size estimators: an empirical approach. Journal of Mammalogy 76:1149–1158CrossRefGoogle Scholar
  50. Mao, C. X. and R. K. Colwell. 2005. Estimation of species richness: mixture models, the role of rare species, and inferential challenges. Ecology 86:1143–1153CrossRefGoogle Scholar
  51. McCoy, E. D. and K. L. Heck, Jr., 1987. Some observations on the use of taxonomic similarity in large-scale biogeography. Journal of Biogeography 14:79–87CrossRefGoogle Scholar
  52. Nichols, J. D. and Conroy, M. J. 1996. Estimation of species richness. Pages 226–234 in D. E. Wilson, F. R. Cole, J. D. Nichols, R. Rudran, and M. Foster, editors. Measuring and monitoring biological diversity. Standard methods for mammals. Smithsonian Institution Press, Washington, DCGoogle Scholar
  53. Nichols, J. D., T. Boulinier, J. A. Hines, K. P. Pollock, and J. R. Sauer. 1998a. Estimating rates of local species extinction, colonization, and turnover in animal communities. Ecological Applications 8:1213–1225Google Scholar
  54. Nichols, J. D., T. Boulinier, J. A. Hines, K. P. Pollock, and J. R. Sauer. 1998b. Inference methods for spatial variation in species richness and community composition when not all species are detected. Conservation Biology 12:1390–1398Google Scholar
  55. Nichols, J. D., L. L. Bailey, A. F. O’Connell, Jr., N. W. Talancy, E. H. C. Grant, A. T. Gilbert, E. M. Annand, T. P. Husband, and J. E. Hines. 2008. Multi-scale occupancy estimation and modeling using multiple detection methods. Journal of Applied Ecology 45:1321–1329CrossRefGoogle Scholar
  56. Norris, J. L. and K. H. Pollock. 1996. Nonparametric MLE under two closed-capture models with heterogeneity. Biometrics 52:639–649CrossRefGoogle Scholar
  57. Otis, D. L., K. P. Burnham, G. C. White, and D. R. Anderson. 1978. Statistical inference from capture data on closed animal populations. Wildlife Monographs 62:1–135Google Scholar
  58. Pielou, E. C. 1977. Mathematical ecology. Wiley, New YorkGoogle Scholar
  59. Pledger, S. 2000. Unified maximum likelihood estimates for closed capture–recapture models using mixtures. Biometrics 56:434–442PubMedCrossRefGoogle Scholar
  60. Pledger, S. 2005. The performance of mixture models in heterogeneous closed population capture–recapture. Biometrics 61:868–876CrossRefGoogle Scholar
  61. Pollock, K. H. 1982. A capture–recapture sampling design robust to unequal catchability. Journal of Wildlife Management 46:752–757CrossRefGoogle Scholar
  62. Pollock, K. H., J. D. Nichols, T. R. Simon, G. L. Farnsowrth, L. L. Bailey, and J. R. Sauer. 2002. Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics 13:105–119CrossRefGoogle Scholar
  63. R Development Core Team. 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org
  64. Rota, C. J., R. J. Fletcher Jr, R. M. Dorazio, and M. G. Betts. 2009. Occupancy estimation and the closure assumption. Journal of Applied Ecology 46:1173–1181Google Scholar
  65. Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115PubMedCrossRefGoogle Scholar
  66. Royle, J. A. 2006. Site occupancy models with heterogeneous detection probabilities. Biometrics 62:97–102PubMedCrossRefGoogle Scholar
  67. Royle, J. A. 2009. Analysis of capture–recapture models with individual covariates using data augmentation. Biometrics 65:267–274PubMedCrossRefGoogle Scholar
  68. Royle, J. A. and R. M. Dorazio. 2008. Hierarchical modeling and inference in ecology. Academic, AmsterdamGoogle Scholar
  69. Royle, J. A. and M. Kéry. 2007. A Bayesian state-space formulation of dynamic occupancy models. Ecology 88:1813–1823PubMedCrossRefGoogle Scholar
  70. Royle, J. A. and W. A. Link. 2006. Generalized occupancy models allowing false positive and false negative errors. Ecology 87:835–841PubMedCrossRefGoogle Scholar
  71. Royle, J. A. and J. D. Nichols. 2003. Estimating abundance from repeated presence–absence data or point counts. Ecology 84:777–790CrossRefGoogle Scholar
  72. Royle, J. A., R. M. Dorazio, and W. A. Link. 2007. Analysis of multinomial models with unknown index using data augmentation. Journal of Computational and Graphical Statistics 16:67–85Google Scholar
  73. Russell, R. E., J. A. Royle, V. A. Saab, J. F. Lemkuhl, W. M. Block, and J. R. Sauer. 2009. Modeling the effects of environmental disturbance on wildlife communities: avian responses to prescribed fire treatments in a coniferous forest in Washington. Ecological Applications 19:1253–1263Google Scholar
  74. Schmidt, B. R. 2005. Monitoring the distribution of pond-breeding amphibians when species are detected imperfectly. Aquatic Conservation: Marine and Freshwater Ecosystems 15:681–692CrossRefGoogle Scholar
  75. Seber, G. A. F. 1982. The estimation of animal abundance and related parameters. Charles Griffin, LondonGoogle Scholar
  76. Soberón, M. J. and B. J. Llorente. 1993. The use of species accumulation functions for the prediction of species richness. Conservation Biology 7:480–488Google Scholar
  77. Spiegelhalter, D., A. Thomas, and N. G. Best. 2003. WinBUGS user manual, version 1.4. MCR Biostatistics Unit, CambridgeGoogle Scholar
  78. Thompson, S. K. 2002. Sampling. Wiley, New YorkGoogle Scholar
  79. Thompson, W. L. 2004. Sampling rare and elusive species. Island, Washington, DCGoogle Scholar
  80. White, G. C. and K. B. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 (suppl.):S120–S138Google Scholar
  81. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of animal populations. Academic, San Diego, CAGoogle Scholar
  82. Yoccoz, N. G., J. D. Nichols, and T. Boulinier. 2001. Monitoring of biological diversity in space and time. Trends in Ecology and Evolution 16:446–453CrossRefGoogle Scholar
  83. Zipkin, E. F., A. DeWan, and J. A. Royle. 2009. Impacts of forest fragmentation on species richness: a hierarchical approach to community modeling. Journal of Applied Ecology 46: 815–822Google Scholar

Copyright information

© Springer 2011

Authors and Affiliations

  1. 1.Swiss Ornithological InstituteSempachSwitzerland

Personalised recommendations