Journal of Paleolimnology

, Volume 57, Issue 2, pp 205–212 | Cite as

Quantitative assessment of the reliability of chironomid remains in paleoecology: effects of count density and sample size

  • Victor Frossard
  • Valérie Verneaux
  • Patrick Giraudoux


Random distributions for a wide range (1–100,000) of chironomid head capsules (HC) were simulated on a 1-m2 surface. The number of HC found in circular surfaces equivalent to standard core diameters (90 and 63 mm) was estimated 1000 times, over the range of tested densities. For each number of HC found in the samples, the range of simulated densities was estimated using a threshold probability (p > 0.95). This enabled us to develop equations to infer HC density from sample counts. Because of the threshold probability for comparable sample counts, the equations yield higher estimated densities under a random distribution than for a regular distribution. The probability of sampling at least one HC was >0.95 for densities of 900 HC m−2 for the 90-mm core and 1400 HC m−2 for the 63-mm core. For a specific sample count, the range of actual densities was ~10 times higher for the 63-mm core than the 90-mm core. Comparison with field larval densities revealed that most densities were too low to be suitable for annually resolved reconstruction of a quantitative signal, using current corer sizes, although a large number of populations can support sub-decadal analyses. Nonetheless, some lakes exhibit population sizes large enough to reconstruct robust quantitative estimates of past chironomid abundances. This work provides guidance to reconstruct species dynamics and fine-scale time series analyses in paleoecology.


Paleoecology Representativity Chironomidae Sampling effort Population size 


  1. Ali A, Mulla M (1976) Chironomid larval density at various depths in a southern California water-percolation reservoir. Environ Entomol 5:1071–1074CrossRefGoogle Scholar
  2. Battarbee RW (1986) Diatom analysis. In: Berglund BE (ed) Handbook of holocene palaeoecology and palaeohydrology. Wiley, Chichester, pp 527–570Google Scholar
  3. Battarbee R, Jones V, Flower R, Cameron N, Bennion H, Carvalho L, Juggins S (2001) Diatoms. In: Smol JP, Birks HJB, Last WM, Bradley RS, Alverson K (eds) Tracking environmental change using lake sediments, vol 3., Terrestrial, algal, and siliceous indicatorsSpringer, The Netherlands, pp 155–202CrossRefGoogle Scholar
  4. Belle S, Millet L, Gillet F, Verneaux V, Magny M (2015) Assemblages and paleo-diet variability of subfossil chironomidae (Diptera) from a deep lake (Lake Grand Maclu, France). Hydrobiologia 755:145–160CrossRefGoogle Scholar
  5. Bennion H, Battarbee R (2007) The European union water framework directive: opportunities for palaeolimnology. J Paleolimnol 38:285–295CrossRefGoogle Scholar
  6. Birks HJB (1998) D.G. Frey and E.S. Deevey Review 1: numerical tools in palaeolimnology—progress, potentialities, and problems. J Paleolimnol 20:307–332Google Scholar
  7. Birks HJB, Birks HH (1980) Quaternary palaeoecology. Arnold, LondonGoogle Scholar
  8. Carter CE (2001) On the use of instar information in the analysis of subfossil chironomid data. J Paleolimnol 25:493–501CrossRefGoogle Scholar
  9. Dakos V, Carpenter S, Brock W, Ellison A, Guttal V, Ives A, Kéfi S, Livina V, Seekell D, van Nes E, Scheffer M (2012) Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7:e41010CrossRefGoogle Scholar
  10. Drake P, Arias A (1995) Distribution and production of Chironomus salinarius (Diptera: Chironomidae) in a shallow coastal lagoon in the Bay of Cadiz. Hydrobiologia 299:195–206CrossRefGoogle Scholar
  11. Edgar WD, Meadows PS (1969) Case construction, movement, spatial distribution and substrate selection in the larvae of Chironomus riparius Meigen. J Exp Biol 50:247–253Google Scholar
  12. Gardarsson A, Snorrason SS (1993) Sediment characteristics and density of benthos in Lake Myvatn, Iceland. Verh Internat Verein Limnol 25:452–457Google Scholar
  13. Gerstmeier R (1989) Phenology and bathymetric distribution of the profundal chironomid fauna in Starnberger See (F.R. Germany) Diptera, Chironomidae. Hydrobiologia 184:29–42CrossRefGoogle Scholar
  14. Giraudoux P (2013) pgirmess: data analysis in ecology. R package version 1.5.7.
  15. Heiri O, Lotter A (2001) Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids. J Paleolimnol 26:343–350CrossRefGoogle Scholar
  16. Hirabayashi K, Yoshizawa K, Yoshida N, Kazama F (2004) Progress of eutrophication and change of chironomid fauna in Lake Yamanakako, Japan. Limnology 5:47–53CrossRefGoogle Scholar
  17. Holt A, Gaston K, He F (2002) Occupancy-abundance relationships and spatial distribution: a review. Basic Appl Ecol 3:1–13CrossRefGoogle Scholar
  18. Jenny JP, Arnaud F, Dorioz JM, Giguet-Covex C, Frossard V, Sabatier P, Millet L, Reyss JL, Tachikawa K, Bard E, Pignol C, Soufi F, Romeyer O, Perga ME (2013) A spatiotemporal investigation of varved sediments highlights the dynamics of hypolimnetic hypoxia in a large hard-water lake over the last 150 years. Limnol Oceanogr 58:1395–1408CrossRefGoogle Scholar
  19. Kitagawa N (1973) Studies on the bottom fauna of the Fuji Five Lakes and Lake Ashino (in Japanese). Rikusui Fueiyouka no Kisotekikenkyu 2:32–37Google Scholar
  20. Kuhns LA, Berg MB (1999) Benthic invertebrate community responses to round goby (Neogobius melanostomus) and zebra mussel (Dreissena polymorpha) invasion in southern Lake Michigan. J Great Lakes Res 25:910–917CrossRefGoogle Scholar
  21. Larocque I (2001) How many chironomid head capsules are enough? A statistical approach to determine sample size for palaeoclimatic reconstructions. Palaeogeogr Palaeoclim Palaeoecol 172:133–142CrossRefGoogle Scholar
  22. Larocque-Tobler I, Oberli F (2011) The use of cotton blue stain to improve the efficiency of picking and identifying chironomid head capsules. J Paleolimnol 45:121–125CrossRefGoogle Scholar
  23. Lindegaard C (1994) The role of zoobenthos in energy flow in two shallow lakes. Hydrobiologia 275(276):313–322CrossRefGoogle Scholar
  24. Lobinske R, Arshad A, Frouz J (2002) Ecological studies of spatial and temporal distributions of larval Chironomidae (Diptera) with emphasis on Glyptotendipes paripes (Diptera: Chironomidae) in three central Florida lakes. Comm Ecosyst Ecol 31:637–647Google Scholar
  25. Maher LJ Jr (1972) Nomograms for computing 0.95 confidence limits of pollen data. Rev Palaeobot Palynol 13:85–93CrossRefGoogle Scholar
  26. Maher LJ, Heiri O, Lotter A (2011) Assessment of uncertainties associated with palaeolimnological laboratory methods and microfossil analysis. In: Birks J, Lotter A, Juggins S, Smol J (eds) Tracking environmental change using lake sediments. Springer, The Netherlands, pp 143–166Google Scholar
  27. Miyadi D (1932) Studies on the bottom fauna of Japanese lakes. 5. Five lakes at the north foot of Mt. Hudi and Lake Asi. Asi. Jpn J Zool 4:81–125Google Scholar
  28. Mosimann JE (1965) Statistical methods for the pollen analyst. In: Kummel B, Raup D (eds) Handbook of paleontological techniques. Freeman and Company, San Francisco, pp 636–673Google Scholar
  29. Ojala AEK, Francus P, Zolitschka B, Besonen M, Lamoureux SF (2012) Characteristics of sedimentary varve chronologies—a review. Quat Sci Rev 43:45–60CrossRefGoogle Scholar
  30. Quinlan R, Smol JP (2001) Setting minimum head capsule abundance and taxa deletion criteria in chironomid-based inference models. J Paleolimnol 26:327–342CrossRefGoogle Scholar
  31. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0.
  32. Rowlingson B, Diggle P (2013) splancs: spatial and space-time point pattern analysis. R package version 2.01-32.
  33. Rull V (2014) Time continuum and true long-term ecology: from theory to practice. Front Ecol Evol 2:1–7CrossRefGoogle Scholar
  34. Sagova-Mareckova M (2002) Distribution of benthic macroinvertebrates in relationship to plant roots, sediment type and spatial scale in fishponds and slow streams. Arch Hydrobiol 156:63–81CrossRefGoogle Scholar
  35. Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, van de Koppel J, van de Leemput IA, Levin SA, van Nes EH, Pascual M, Vandermeer J (2012) Anticipating critical transitions. Science 338:344–348CrossRefGoogle Scholar
  36. Stevens M, Helliwell S, Cranston P (2006) Larval chironomid communities (Diptera: Chironomidae) associated with establishing rice crops in southern New South Wales, Australia. Hydrobiologia 556:317–325CrossRefGoogle Scholar
  37. Sugihara G, May R, Ye H, Hsieh CH, Deyle E, Fogarty M, Munch S (2012) Detecting causality in complex ecosystems. Science 338:496–500CrossRefGoogle Scholar
  38. Takacs V, Tokeshi M (1994) Spatial distribution of two chironomid species in the bottom sediment of Lough Neagh, Northern Ireland. Aquat Insect 16:125–132CrossRefGoogle Scholar
  39. Taylor L, Woiwod I, Perry J (1978) The density-dependence of spatial behaviour and the rarity of randomness. J Anim Ecol 47:383–406CrossRefGoogle Scholar
  40. Tylmann W, Szpakowska K, Ohlendorf C, Woszczyk M, Zolitschka B (2012) Conditions for deposition of annually laminated sediments in small meromictic lakes: a case study of Lake Suminko (northern Poland). J Paleolimnol 47:55–70CrossRefGoogle Scholar
  41. van Hardenbroek M, Heiri O, Lotter A (2009) Efficiency of different mesh sizes for isolating fossil chironomids for stable isotope and radiocarbon analyses. J Paleolimnol 44:721–729CrossRefGoogle Scholar
  42. Velle G, Larocque I (2008) Assessing chironomid head capsule concentrations in sediment using exotic markers. J Paleolimnol 40:165–177CrossRefGoogle Scholar
  43. Wagner A, Volkmann S, Dettinger-Klemm PMA (2012) Benthic–pelagic coupling in lake ecosystems: the key role of chironomid pupae as prey of pelagic fish. Ecosphere 3:2–17CrossRefGoogle Scholar
  44. Walker I (2002) Midges: Chironomidae and related Diptera. In: Smol JP, Birks HJB, Last WM (eds) Tracking environmental change using lake sediments. Vol 4: zoological indicators. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 43–66Google Scholar
  45. Wang R, Dearing J, Langdon P, Zhang E, Yang X, Dakos V, Scheffer M (2012) Flickering gives early warning signals of a critical transition to a eutrophic lake state. Nature 492:419–422CrossRefGoogle Scholar
  46. Warnock J, Scherer R (2015) A revised method for determining the absolute abundance of diatoms. J Paleolimnol 53:157–163CrossRefGoogle Scholar
  47. Wolfe AP (1997) On diatom concentrations in lake sediments: results of an inter-laboratory comparison and other experiments performed on a uniform sample. J Paleolimnol 18:61–66CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Victor Frossard
    • 1
  • Valérie Verneaux
    • 2
  • Patrick Giraudoux
    • 2
    • 3
  1. 1.Laboratoire CARRTEL-UMR42Université de Savoie Mont-BlancLe Bourget du LacFrance
  2. 2.Laboratoire Chrono-environnement-UMR6249Université de Bourgogne Franche-ComtéBesançonFrance
  3. 3.Institut Universitaire de FranceParisFrance

Personalised recommendations