Seasonal variation in the relative strength of bottom-up vs top-down effects in pelagic cladoceran populations identified through contribution analysis of birth rate

By Nicholas A. Tonelli from Pennsylvania, USA - Lacawac Sanctuary (1), CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=21215360
Submitted: 24 April 2023
Accepted: 5 August 2023
Published: 7 September 2023
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Pelagic freshwater communities are characterized by the presence of strong trophic interactions, with the dominance of either food limitation (bottom-up effect) or predation pressure (top-down effect) alternating in time. Though this temporal variation should have a major impact on zooplankton dynamics, few studies have examined it. We consider here an approach that involves identifying signatures of food availability and size-selective fish predation based on the demographic characteristics of cladoceran populations. The relative strength of top-down vs bottom-up effects was assessed on short sampling intervals as contributions of the proportion of adults and fecundity, respectively, to the resulting change in birth rate. The dominant effect on each sampling interval was determined based on the absolute value of the ratio of those contributions (R). From previous experiments, we expected that under the dominant effect of food, R should be less than unity, while under fish predation it should be 1 < R < 3.4. We analyzed two datasets – one collected for a pelagic cladoceran community of three species, and another retrieved from the published data for Daphnia catawba in 1986. In the former case, the temporal variation in the dominant effect was assumed from the pattern of cladoceran populationdynamics as well as limited data on zooplankton consumption by planktivorous fish; in the latter case, the seasonal change in predation pressure by fish on the daphnids was known from the original data. Our results show that the probability density functions for R values from the two datasets indicate an increased probability of the ratio values associated with the abovementioned ranges, suggesting that both bottom-up and top-down effects should have been driving cladoceran population dynamics during the study periods. Based on the results of the Generalized Additive Models (GAMs), the fitted R values for the most abundant species from the first dataset - Bosmina longirostris - changed from the values indicative of strong food effect at the beginning of the study period to those indicative of strong top-down effect when fish with substantial numbers of bosminids in the gut were caught. In the second dataset, for the two time intervals associated with increased predation pressure by fish, the fitted R values were predominantly located between 1 and 3.4, as expected. For both datasets, our results indicate that contribution analysis of birth rate can be used as an informative, albeit preliminary, tool to identify trophic interactions driving zooplankton seasonal population fluctuations in freshwater communities.

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Abrams PA, 2001. Describing and quantifying interspecific interactions: a commentary on recent approaches. Oikos 94:209-218. DOI: https://doi.org/10.1034/j.1600-0706.2001.940201.x
Allesina S, Tang S, 2012. Stability criteria for complex ecosystems. Nature 483:205-208. DOI: https://doi.org/10.1038/nature10832
Benoit-Bird KJ, McManus MA, 2012. Bottom-up regulation of a pelagic community through spatial aggregations. Biol Lett 8:813-816. DOI: https://doi.org/10.1098/rsbl.2012.0232
Berninger UG, Caron DA, Sanders RW, 1992. Mixotrophic algae in three ice-covered lakes of the Pocono Mountains, USA. Freshwater Biol 28:263-272. DOI: https://doi.org/10.1111/j.1365-2427.1992.tb00583.x
Berlow EL, 1999. Strong effects of weak interactions in ecological communities. Nature 398:330-334. DOI: https://doi.org/10.1038/18672
Berlow EL, Neutel AM, Cohen JE, de Ruiter PC, Ebenman B, Emmerson M, et al., 2004. Interaction strengths in food webs: issues and opportunities. J Anim Ecol 73:585-598. DOI: https://doi.org/10.1111/j.0021-8790.2004.00833.x
Borer ET, Seabloom EW, Shurin JB, Anderson KE, Blanchette CA, Broitman B, et al., 2005. What determines the strength of a trophic cascade? Ecology 86:528-537. DOI: https://doi.org/10.1890/03-0816
Bottrell HH, Duncan A, Gliwicz ZM, Grygierek E, Herzig A, Hillbright Ilkowska A, et al., 1976. A review of some problems in zooplankton production studies. Norw J Zool 24:419-456.
Carpenter SR, Kitchell JF, Hodgson JR, 1985. Cascading trophic interactions and lake productivity. BioScience 35:634-639. DOI: https://doi.org/10.2307/1309989
Collie JS, Richardson K, Steele JH, 2004. Regime shifts: can ecological theory illuminate the mechanisms? Prog Oceanogr 60:281-302. DOI: https://doi.org/10.1016/j.pocean.2004.02.013
de Ruiter PC, Neutel AM, Moore JC, 1995. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269:1257-1260. DOI: https://doi.org/10.1126/science.269.5228.1257
Downing AL, Jackson C, Plunkett C, Ackerman Lockhart J, Schlater SM, Leibold MA, 2020. Temporal stability vs. community matrix measures of stability and the role of weak interactions. Ecol Lett 23:1468-1478. DOI: https://doi.org/10.1111/ele.13538
Edmondson WT, 1968. A graphical model for evaluating the use of the egg ratio for measuring birth and death rates. Oecologia 1:1-37. DOI: https://doi.org/10.1007/BF00377252
Elser JJ, Fagan WF, Denno RF, Dobberfuhl DR, Folarin A, Huberty A, et al., 2000. Nutritional constraints in terrestrial and freshwater food webs. Nature 408:578-580. DOI: https://doi.org/10.1038/35046058
Furnass TI, 1979. Laboratory experiments on prey selection by perch fry (Perca fluviatilis). Freshwater Biol 9:33-43. DOI: https://doi.org/10.1111/j.1365-2427.1979.tb01484.x
Gellner G, McCann KS, 2016. Consistent role of weak and strong interactions in high-and low-diversity trophic food webs. Nat Commun 7:1-7. DOI: https://doi.org/10.1038/ncomms11180
George DG, 2021. Top-down versus bottom-up control in planktonic systems: some case studies from the English Lake District. Hydrobiologia 848:219-236. DOI: https://doi.org/10.1007/s10750-020-04357-0
Gliwicz ZM, 2003. Between hazards of starvation and risk of predation: the ecology of offshore animals. In: Kinne O (ed.), Excellence of ecology. International Ecology Institute, Oldendorf/Luhe: 379 pp.
Gsell AS, Scharfenberger U, Özkundakci D, Walters A, Hansson L-A, Janssenb ABG, et al., 2016. Evaluating early-warning indicators of critical transitions in natural aquatic ecosystems. Proc Natl Acad Sci USA 113:E8089-E8095. DOI: https://doi.org/10.1073/pnas.1608242113
Hastings A, Powell T, 1991. Chaos in a three-species food chain. Ecology 72:896-903. DOI: https://doi.org/10.2307/1940591
Heinze AW, Truesdale CL, DeVaul SB, Swinden J, Sanders RW, 2013. Role of temperature in growth, feeding, and vertical distribution of the mixotrophic chrysophyte Dinobryon. Aquat Microb Ecol 71:155-163. DOI: https://doi.org/10.3354/ame01673
Jeppesen E, Jensen JP, Søndergaard M, Fenger-Grøn M, Bramm ME, Sandby K, et al., 2004. Impact of fish predation on cladoceran body weight distribution and zooplankton grazing in lakes during winter. Freshwater Biol 49:432-447. DOI: https://doi.org/10.1111/j.1365-2427.2004.01199.x
Krebs CJ, 1972. Ecology: The experimental analysis of distribution and abundance. Harper and Row, New York: 694 pp.
Lampert W, 1988. The relative importance of food limitation and predation in the seasonal cycle of two Daphnia species. Vehr Internat Verein Limnol 23:713-718. DOI: https://doi.org/10.1080/03680770.1987.11899698
Lampert W, 1989. The adaptive significance of diel vertical migration of zooplankton. Funct Ecol 3:21-27. DOI: https://doi.org/10.2307/2389671
Liu B, Wu J, Hu Y, Wang G, Chen Y, 2022. Seven years study of the seasonal dynamics of zooplankton communities in a large subtropical floodplain ecosystem: A test of the PEG model. Int J Environ Res Public Health 19:956. DOI: https://doi.org/10.3390/ijerph19020956
Matveev V, 1995. The dynamics and relative strength of bottom-up vs top-down impacts in a community of subtropical lake plankton. Oikos 73:104-108. DOI: https://doi.org/10.2307/3545731
May RM, 1972. Will a large complex system be stable? Nature 238:413-414. DOI: https://doi.org/10.1038/238413a0
McCann K, Hastings A, Huxel GR, 1998. Weak trophic interactions and the balance of nature. Nature 395:794-798. DOI: https://doi.org/10.1038/27427
McQueen DJ, Johannes MR, Post JR, Stewart TJ, Lean DR, 1989. Bottom-up and top-down impacts on freshwater pelagic community structure. Ecol Monogr 59:289-309. DOI: https://doi.org/10.2307/1942603
Moeller R, Williamson C, 1994. Lake Lacawac: report on limnological conditions in 1993. Unpublished Report to the Lacawac Sanctuary. Department of Earth and Environmental Sciences, Lehigh University: 85 pp.
Moustaka-Gouni M, Michaloudi E, Sommer U, 2014. Modifying the PEG model for Mediterranean lakes – no biological winter and strong fish predation. Freshwater Biol 59:1136-1144. DOI: https://doi.org/10.1111/fwb.12335
Novak M, Yeakel JD, Noble AE, Doak DF, Emmerson M, Estes JA, et al., 2016. Characterizing species interactions to understand press perturbations: what is the community matrix. Annu Rev Ecol Evol Syst 47:409-432. DOI: https://doi.org/10.1146/annurev-ecolsys-032416-010215
Oksiyuk OP, VN Zhukinsky VN, Braginsky LP, Linnik PN, Kuzmenko MI, Klenus VG, 1993. Complex ecological classification of quality of the land surface water. Hydrobiol J 29:62-76.
Paine RT, 1992. Food-web analysis through field measurement of per capita interaction strength. Nature 355:73-75. DOI: https://doi.org/10.1038/355073a0
Paloheimo JE, 1974. Calculation of instantaneous birth rate 1. Limnol Oceanogr 19:692-694. DOI: https://doi.org/10.4319/lo.1974.19.4.0692
Pfaff B, 2008. Analysis of integrated and cointegrated time series with R. Second Edition. Springer, New York: 190 pp. DOI: https://doi.org/10.1007/978-0-387-75967-8
Pimm S, Lawton JH, 1980. Are food webs divided into compartments? J Anim Ecol 49:879-898. DOI: https://doi.org/10.2307/4233
Polishchuk LV, 1995. Direct positive effect of invertebrate predators on birth rate in Daphnia studied with a new method of birth rate analysis. Limnol Oceanogr 40:483-489. DOI: https://doi.org/10.4319/lo.1995.40.3.0483
Polishchuk LV, Kasparson AA, 2023. Temporal resolution of birth rate analysis in zooplankton and its implications for identifying strong interactions in ecology. Ecol Evol 13:e10341. DOI: https://doi.org/10.1002/ece3.10341
Polishchuk LV, Vijverberg J, Voronov DA, Mooij WM, 2013. How to measure top-down vs bottom-up effects: a new population metric and its calibration on Daphnia. Oikos 122:1177-1186. DOI: https://doi.org/10.1111/j.1600-0706.2012.00046.x
R Core Team. R, 2022. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
Riede JO, Brose U, Ebenman B, Jacob U, Thompson R, Townsend CR, Jonsson T, 2011. Stepping in Elton’s footprints: a general scaling model for body masses and trophic levels across ecosystems. Ecol Lett 14:169-178. DOI: https://doi.org/10.1111/j.1461-0248.2010.01568.x
Rip JMK, McCann KS, 2011. Cross-ecosystem differences in stability and the principle of energy flux. Ecol Lett 14:733-740. DOI: https://doi.org/10.1111/j.1461-0248.2011.01636.x
Rooney N, McCann K, Gellner G, Moore JC, 2006. Structural asymmetry and the stability of diverse food webs. Nature 442:265-269. DOI: https://doi.org/10.1038/nature04887
Rooney N, McCann KS, 2012. Integrating food web diversity, structure and stability. Trends Ecol Evol 27:40-46. DOI: https://doi.org/10.1016/j.tree.2011.09.001
Sarnelle O, 2003. Nonlinear effects of an aquatic consumer: causes and consequences. Am Nat 161:478-496. DOI: https://doi.org/10.1086/367881
Shurin JB, Gruner DS, Hillebrand H, 2006. All wet or dried up? Real differences between aquatic and terrestrial food webs. Proc Royal Soc B 273:1-9. DOI: https://doi.org/10.1098/rspb.2005.3377
Simpson GL, 2018. Modelling palaeoecological time series using generalised additive models. Front Ecol Evol 6:149. DOI: https://doi.org/10.3389/fevo.2018.00149
Sommer U, Gliwicz ZM, Lampert W, Duncan A, 1986. The PEG-model of seasonal succession of planktonic events in fresh waters. Arch Hydrobiol 106:433-471. DOI: https://doi.org/10.1127/archiv-hydrobiol/106/1986/433
Sommer U, Adrian R, De Senerpont Domis L, Elser JJ, Gaedke U, Ibelings B, et al., 2012. Beyond the plankton ecology group (PEG) model: mechanisms driving plankton succession. Annu Rev Ecol Evol Syst 43:429-448. DOI: https://doi.org/10.1146/annurev-ecolsys-110411-160251
Strong DR, 1992. Are trophic cascades all wet? Differentiation and donor-control in speciose ecosystems. Ecology 73:747-754. DOI: https://doi.org/10.2307/1940154
Tessier AJ, 1983. Coherence and horizontal movements of patches of Holopedium gibberum (Cladocera). Oecologia 60:71-75. DOI: https://doi.org/10.1007/BF00379322
Tessier AJ, 1986. Comparative population regulation of two planktonic cladocera (Holopedium gibberum and Daphnia catawba). Ecology 67:285-302. DOI: https://doi.org/10.2307/1938573
Urmy SS, Warren JD, 2019. Seasonal changes in the biomass, distribution, and patchiness of zooplankton and fish in four lakes in the Sierra Nevada, California. Freshwater Biol. 64:1692-1709. DOI: https://doi.org/10.1111/fwb.13362
Wahl CM, Mills EL, McFarland WN, DeGisi JS, 1993. Ontogenetic changes in prey selection and visual acuity of the yellow perch, Perca flavescens. Can J Fish Aquat Sci 50:743-749. DOI: https://doi.org/10.1139/f93-085
Wang SC, Liu X, Liu Y, Wang H, 2020. Benthic-pelagic coupling in lake energetic food webs. Ecol Modell 417:108928. DOI: https://doi.org/10.1016/j.ecolmodel.2019.108928
Wood SN, 2017. Generalized additive models: an introduction with R. Boca Raton, FL. CRC Press: 476 pp.
Wootton JT, Emmerson M, 2005. Measurement of interaction strength in nature. Annu Rev Ecol Evol Syst 36:419-444. DOI: https://doi.org/10.1146/annurev.ecolsys.36.091704.175535
Wootton KL, Stouffer DB, 2016. Many weak interactions and few strong; food-web feasibility depends on the combination of the strength of species’ interactions and their correct arrangement. Theor Ecol 9:185-195. DOI: https://doi.org/10.1007/s12080-015-0279-3
Wu L, Culver DA, 1994. Daphnia population dynamics in western Lake Erie: regulation by food limitation and yellow perch predation. J Great Lakes Res 20:537-545. DOI: https://doi.org/10.1016/S0380-1330(94)71170-9
Yelnikov AN, Zhukova KA, Kozlov ES, Ponomareva VY, Rybakov AA, 2006. [Species composition and biological characteristics of the Sterlyazhii Pond ichthyofauna].[Field project report in Russian].
Zeileis A, 2019. dynlm: Dynamic Linear Regression. R package version 0.3-6. Available from: https://CRAN.R-project.org/package=dynlm

Edited by

Federico Marrone, Dept. of Biological, Chemical, and Pharmaceutical Sciences and Technologies, University of Palermo, Italy

Supporting Agencies

Russian Foundation for Basic Research , Interdisciplinary Scientific and Educational School “The Future of the Planet and Global Environmental Changes”, Lomonosov Moscow State University

How to Cite

Kasparson, Anna A., and Leonard V. Polishchuk. 2023. “Seasonal Variation in the Relative Strength of Bottom-up <em>vs< em> Top-down Effects in Pelagic Cladoceran Populations Identified through Contribution Analysis of Birth Rate”. Journal of Limnology 82 (1). https://doi.org/10.4081/jlimnol.2023.2142.

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