Advertisement

Environmental and Ecological Statistics

, Volume 20, Issue 2, pp 271–284 | Cite as

Determining patterns of variability in ecological communities: time lag analysis revisited

  • Christian Kampichler
  • Henk P. van der Jeugd
Article

Abstract

All ecological communities experience change over time. One method to quantify temporal variation in the patterns of relative abundance of communities is time lag analysis (TLA). It uses a distance-based approach to study temporal community dynamics by regressing community dissimilarity over increasing time lags (one-unit lags, two-unit lags, three-unit lags). Here, we suggest some modifications to the method and revaluate its potential for detecting patterns of community change. We apply Hellinger distance based TLA to artificial data simulating communities with different levels of directional and stochastic dynamics and analyse their effects on the slope and its statistical significance. We conclude that statistical significance of the TLA slope (obtained by a Monte Carlo permutation procedure) is a valid criterion to discriminate between (i) communities with directional change in species composition, regardless whether it is caused by directional abundance change of the species or by stochastic change according to a Markov process, and (ii) communities that are composed of species with population sizes oscillating around a constant mean or communities whose species abundances are governed by a white noise process. TLA slopes range between 0.02 and 0.25, depending on the proportions of species with different dynamics; higher proportions of species with constant means imply shallower slopes; and higher proportions of species with stochastic dynamics or directional change imply steeper slopes. These values are broadly in line with TLA slopes from real world data. Caution must be exercised when TLA is used for the comparison of community time series with different lengths since the slope depends on time series length and tends to decrease non-linearly with it.

Keywords

Community change Markov process Species composition Stochasticity Temporal dynamics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

10651_2012_219_MOESM1_ESM.doc (98 kb)
ESM 1 (DOC 98kb)

References

  1. Angeler DG, Viedma O, Moreno JM (2009) Statistical performance and information content of time lag analysis and redundancy analysis in time series modelling. Ecology 90: 3245–3257. doi: 10.1890/07-0391.1 PubMedCrossRefGoogle Scholar
  2. Blanchet FG, Legendre P, Maranger R, Monti D, Pepin P (2011) Modelling the effect of directional spatial ecological processes at different scales. Oecologia 166: 357–368. doi: 10.1007/s00442-010-1867-y PubMedCrossRefGoogle Scholar
  3. Borcard D, Legendre P, Avois-Jacquet C, Tuomisto H (2004) Dissecting the spatial structure of ecological data at multiple scales. Ecology 85: 1826–1832. doi: 10.1890/03-3111 CrossRefGoogle Scholar
  4. Collins SL, Micheli F, Hartt L (2000) A method to determine rates and patterns of variability in ecological communities. Oikos 91: 285–293. doi: 10.1034/j.1600-0706.2000.910209.x CrossRefGoogle Scholar
  5. Cowpertwait PSP, Metcalfe AV (2009) Introductory time series with R. Springer, Heidelberg. doi: 10.1007/978-0-387-88698-5 Google Scholar
  6. Enemar A, Sjöstrand B, Andersson G, von Proschwitz T (2004) The 37-year dynamics of a subalpine passerine bird community, with special emphasis on the influence of environmental temperature and Epirrita autumnata cycles. Ornis Svecica 14: 63–106Google Scholar
  7. Hall GA (1984) A long-term bird population study in an Appalachian spruce forest. Wilson Bull 96: 228–240Google Scholar
  8. Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography. Princeton University Press, PrincetonGoogle Scholar
  9. Inchausti P, Halley J (2002) The long-term temporal variability and spectral colour of animal populations. Evol Ecol Res 4: 1033–1048Google Scholar
  10. Kampichler C, Geissen V (2005) Temporal predictability of soil microarthropod communities in temperate forests. Pedobiologia 49: 41–50. doi: 10.1016/j.pedobi.2004.07.011 CrossRefGoogle Scholar
  11. Kampichler C, van Turnhout CAM, Devictor V, van der Jeugd HP (2012) Large-scale changes in community composition: determining land use and climate change signals. Plos One 7(4): e35272. doi: 10.1371/journal.pone.0035272 PubMedCrossRefGoogle Scholar
  12. Kendeigh SC (1982) Bird populations in east central illinois: fluctuations, variations, and development over a half-century. Illinois Biological Monographs 52. University of Illinois Press, ChampaignCrossRefGoogle Scholar
  13. Legendre P, Gallagher ED (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129: 271–280. doi: 10.1007/s004420100716 CrossRefGoogle Scholar
  14. Legendre P, Legendre L (1998) Numerical ecology, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  15. Lundberg P, Ranta E, Ripa J, Kaitala V (2000) Population variability in space and time. Trends Ecol Evol 15: 460–464. doi: 10.1016/S0169-5347(00)01981-9 PubMedCrossRefGoogle Scholar
  16. Magurran AE, Baillie SR, Buckland ST, Dick JMcP, Elston DA, Scott EM, Smith RI, Somerfield PJ, Watt AD (2010) Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol Evol 25: 574–582. doi: 10.1016/j.tree.2010.06.016 PubMedCrossRefGoogle Scholar
  17. Magurran AE, Henderson PA (2010) Temporal turnover and the maintenance of diversity in ecological assemblages. Phil Trans R Soc B 365: 3611–3620. doi: 10.1098/rstb.2010.0285 PubMedCrossRefGoogle Scholar
  18. McArthur RH, Wilson EO (1967) The theory of Island biogeography. Princeton University Press, PrincetonGoogle Scholar
  19. Meyn SP, Tweedie RL (1993) Markov chains and stochastic stability. Springer, LondonCrossRefGoogle Scholar
  20. Muggeo VMR (2003) Estimating regression models with unknown break-points. Stat Med 22: 3055–3071PubMedCrossRefGoogle Scholar
  21. Muggeo VMR (2008) Segmented: an R package to fit regression models with broken-line relationships. R News 8(1):20–25 URL:http://cran.r-project.org/doc/Rnews/
  22. Pfister CA (2006) Concordance between short-term experiments and long-term censuses in tide pool fishes. Ecology 87: 2905–2914. doi: 10.1890/0012-9658(2006)87[2905:CBSEAL]2.0.CO;2 PubMedCrossRefGoogle Scholar
  23. Rao CR (1995) A review of canonical coordinates and an alternative to correspondence analysis using Hellinger distance. Qüestiió 19: 23–63Google Scholar
  24. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. URL:http://www.R-project.org
  25. Ricklefs RE (2003) A comment on Hubbell’s zero-sum ecological drift model. Oikos 100: 185–192. doi: 10.1034/j.1600-0706.2003.12018.x CrossRefGoogle Scholar
  26. Rull V, Vegas-Vilarrúbia T (2011) What is long-term in ecology?. Trends Ecol Evol 26: 3–4. doi: 10.1016/j.tree.2010.10.002 PubMedCrossRefGoogle Scholar
  27. Silvertown J, Poulton P, Johnston E, Edwards G, Heard M, Biss PM (2006) The Park Grass experiment 1856-2006: its contribution to ecology. J Ecol 94: 801–814. doi: 10.1111/j.1365-2745.2006.01145.x CrossRefGoogle Scholar
  28. Svensson S (2006) Species composition and population fluctuations of alpine bird communities during 38 years in the Scandinavian mountain range. Ornis Svecica 16: 183–210Google Scholar
  29. Thibault KM, White EP, Ernest SKM (2004) Temporal dynamics in the structure and composition of a desert rodent community. Ecology 85: 2649–2655. doi: 10.1890/04-0321 CrossRefGoogle Scholar
  30. Wesołowski T, Mitrus C, Czeszczewik D, Rowiński P (2010) Breeding bird dynamics in a primeval temperate forest over thirty-five years: variation and stability in the changing world. Acta Ornithol 45: 209–232. doi: 10.3161/000164510X551354 CrossRefGoogle Scholar
  31. White EP, Adler PB, Lauenroth WK, Gill RA, Greenberg D, Kaufman DM, Rassweiler A, Rusak JA, Smith MD, Steinbeck JR, Waide RB, Yao J (2006) A comparison of the species/time relationship across ecosystems and taxonomic groups. Oikos 112: 185–195. doi: 10.1111/j.0030-1299.2006.14223.x CrossRefGoogle Scholar
  32. Williams BK, Nichols J, Conroy M (2002) Analysis and management of animal populations. Academic Press, San DiegoGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Christian Kampichler
    • 1
    • 2
    • 3
  • Henk P. van der Jeugd
    • 1
  1. 1.Vogeltrekstation—Dutch Centre for Avian Migration and DemographyNIOO-KNAWWageningenThe Netherlands
  2. 2.División Académica de Ciencias Biológicas, Universidad Juárez Autónoma de TabascoCarretera Villahermosa-Cárdenas Km. 0.5 s/nVillahermosaMexico
  3. 3.Sovon Dutch Centre for Field OrnithologyNijmegenThe Netherlands

Personalised recommendations