https://doi.org/10.4081/jlimnol.2021.2035
Hitting the sweet spot of complexity: Reasons why the development of new custom-tailored models is still warranted and should be encouraged in aquatic sciences
Submitted: 20 May 2021
Accepted: 11 September 2021
Published: 27 September 2021
Accepted: 11 September 2021
Abstract Views: 876
PDF: 256
HTML: 14
HTML: 14
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Process-based aquatic ecosystem models are increasingly being developed and used in freshwater ecology and other aquatic sciences, as they are powerful tools to gain a mechanistic understanding of ecological processes and inform policy and decision making in environmental management. Over the last decades, not only have these models increased considerably in number, but also in their degree of complexity, which can improve predictive capacity. Nevertheless, it is also because of the higher degree of complexity of many models of current widespread use, that not all the hypotheses and assumptions upon which they have been built are always met by the relatively simple experiments that characterise fundamental ecological research. This is true for both laboratory experiments and those carried out outdoors, under semi-controlled conditions. Examples of the latter are the mesocosms experiments through which several novel questions are nowadays being addressed. In this article, we present our views on why the development of new custom-tailored aquatic ecosystem models of varying degrees of complexity is still very much warranted and should, therefore, be encouraged despite arguments in favour of always increasing complexity and against the creation of new models that are largely based on previously published ones (‘reinventing the wheel’). Deciding on the right complexity level should be linked to the biological organisation levels that are relevant to the specific research questions, and to how much knowledge on the subject is already available. Spatial and temporal scales are additional factors that a modeller should weigh in when deciding on the complexity of a model. To address these needs in the long term, the modelling community needs to grow. Training a new generation of model developers will not only benefit other scientists to better design future experiments but will also facilitate interdisciplinary research and teamwork, approaches such as ensemble modelling, as well as the communication of science to managers and many other stakeholders.
Ahn C, Mitsch W J, 2002. Scaling considerations of mesocosm wetlands in simulating large created freshwater marshes. Ecol. Engin. 18:327-342.
DOI: https://doi.org/10.1016/S0925-8574(01)00092-1
Anderson TR, 2005. Plankton functional type modelling: running before we can walk? J Plankton Res 27:1073-1081.
DOI: https://doi.org/10.1093/plankt/fbi076
Arhonditsis GB, Brett TM, 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modelling. Mar. Ecol. Progr. Ser. 271:13-26.
DOI: https://doi.org/10.3354/meps271013
Bernacchi CJ. Bagley JE, Serbin SP, Ruiz-Vera UM, Rosenthal DM, VanLoocke A, 2013. Modelling C3 photosynthesis from the chloroplast to the ecosystem. Plant Cell Environ. 36:1641-1657.
DOI: https://doi.org/10.1111/pce.12118
Bjorndal KA, Bolten AB, Chaloupka MY, 2000. Green turtle somatic growth model: Evidence for density dependence. Ecol. Appl. 10:269-282.
DOI: https://doi.org/10.1890/1051-0761(2000)010[0269:GTSGME]2.0.CO;2
Bruggeman J, Bolding K, 2014. A general framework for aquatic biogeochemical models. Environ. Modell. Softw. 61:249-265.
DOI: https://doi.org/10.1016/j.envsoft.2014.04.002
Chang M, Teurlinx S, DeAngelis DL, Janse JH, Troost TA, Van Wijk D, Mooij WM, Janssen ABG, 2019. A Generically Parameterized model of Lake eutrophication (GPLake) that links field-, lab- and model-based knowledge. Sci. Total Environ. 695:133887.
DOI: https://doi.org/10.1016/j.scitotenv.2019.133887
Chen C-C, Petersen JE, Kemp WM, 1997. Spatial and temporal scaling of periphyton growth on walls of estuarine mesocosms. Mar. Ecol. Progr. Ser. 155:1-15.
DOI: https://doi.org/10.3354/meps155001
D'Alelio D, Libralato S, Wyatt T, Ribera d'Alcalà M, 2016. Ecological-network models link diversity, structure and function in the plankton food-web. Sci. Rep. 6:21806.
DOI: https://doi.org/10.1038/srep21806
Droop MR, 1983. 25 years of algal growth kinetics: A personal view. Bot. Mar. 26:99-112.
DOI: https://doi.org/10.1515/botm.1983.26.3.99
Duquesne F, Vallaeys V, Vidaurre PJ, Hanert E, 2021. A coupled ecohydrodynamic model to predict algal blooms in Lake Titicaca. Ecol. Model. 440:109418.
DOI: https://doi.org/10.1016/j.ecolmodel.2020.109418
Dürr D, Goldstein S, Lebowitz JL, 1981. A mechanical model of Brownian motion. Commun. Math. Phys. 78:507-530.
DOI: https://doi.org/10.1007/BF02046762
Edwards AM, 2001. Adding detritus to a nutrient-phytoplankton-zooplankton model: a dynamical-systems approach. J. Plankton Res. 23:389-413.
DOI: https://doi.org/10.1093/plankt/23.4.389
Elliot JA, 2021. Modelling lake phytoplankton communities: recent applications of the PROTECH model. Hydrobiologia 848:209-217.
DOI: https://doi.org/10.1007/s10750-020-04248-4
Fischer EK, Paglialonga L, Czech E, Tamminga M, 2016. Microplastic pollution in lakes and lake shoreline sediments – A case study on Lake Bolsena and Lake Chiusi (central Italy). Environ. Pollut. 213:648-657.
DOI: https://doi.org/10.1016/j.envpol.2016.03.012
Flynn KJ, 2005. Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modellers. J. Plankton Res. 27:1205-1210.
DOI: https://doi.org/10.1093/plankt/fbi099
Franks PJ, 2002. NPZ models of plankton dynamics: Their construction, coupling to physics, and application. J. Oceanogr. 58:379-387.
DOI: https://doi.org/10.1023/A:1015874028196
Frassl MA, Abell JM, Botelho DA, Cinque K, Gibbes BR, Jöhnk KD, Muraoka K, Robson B, Wolski M, Xiao M, Hamilton D, 2019. A short review of contemporary developments in aquatic ecosystem modelling of lakes and reservoirs. Environ. Model. Softw. 117:181-187.
DOI: https://doi.org/10.1016/j.envsoft.2019.03.024
Fulton EA, Boschetti F, Sporcic M, Jones T, Little R, Dambacher JM, Gray R, Scott R, Gorton R, 2015. A multi-model approach to engaging stakeholder and modellers in complex environmental problems. Environ. Sci. Policy 48:44-56.
DOI: https://doi.org/10.1016/j.envsci.2014.12.006
Fulton EA, Link JS, Kaplan IC, Savina‐Rolland M, Johnson P, Ainsworth C, et al., 2011. Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish Fisher. 12:171-188.
DOI: https://doi.org/10.1111/j.1467-2979.2011.00412.x
Gaston KJ, Visser ME, Hölker F, 2015. The biological impacts of artificial light at night: The research challenge. Philos Trans R Soc Lond B Biol Sci 370:20140133.
DOI: https://doi.org/10.1098/rstb.2014.0133
Guo WJ, Wang YX, Xie MX, Cui YJ, 2009. Modeling oil spill trajectory in coastal waters based on fractional Brownian motion. Mar. Pollut. Bull. 58:1339-1346.
DOI: https://doi.org/10.1016/j.marpolbul.2009.04.026
Hairston NG, Ellner SP, Geber MA, Yoshida T, Fox JA. 2005. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8:1114-1127.
DOI: https://doi.org/10.1111/j.1461-0248.2005.00812.x
Harfoot MB, Newbold T, Tittensor DP, Emmott S, Hutton J, Lyutsarev V, et al., 2014. Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model. PLoS Biol. 12:e1001841.
DOI: https://doi.org/10.1371/journal.pbio.1001841
Havens KE, 1995. Secondary nitrogen limitation in a subtropical lake impacted by non-point source agricultural pollution. Environ. Pollut. 89:241-246.
DOI: https://doi.org/10.1016/0269-7491(94)00076-P
Hellweger FL, 2015. 100 years since Streeter and Phelps: It is time to update the biology in our water quality models. Environ. Sci. Technol. 49: 6372-6373.
DOI: https://doi.org/10.1021/acs.est.5b02130
Hellweger FL, 2017. 75 years since Monod: It is time to increase the complexity of our predictive ecosystem models (opinion). Ecol. Model. 346:77-87.
DOI: https://doi.org/10.1016/j.ecolmodel.2016.12.001
Herb WR, Stefan HG, 2004. Temperature stratification and mixing dynamics in a shallow lake with submersed macrophytes. Lake Reserv. Manage. 20:296-308.
DOI: https://doi.org/10.1080/07438140409354159
Hipsey MR, Bruce LC, Boon C, Busch B, Carey CC, Hamilton DP, et al., 2019. A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON). Geosci. Model Dev. 12:473-523.
DOI: https://doi.org/10.5194/gmd-12-473-2019
Hipsey MR, Gal G, Arhonditsis GB, Carey CC, Elliott JA, Frassl MA, et al., 2020. A system of metrics for the assessment and improvement of aquatic ecosystem models. Environ. Model. Softw. 128:104697.
DOI: https://doi.org/10.1016/j.envsoft.2020.104697
Hoeting JA. Madigan D, Raftery EA, Volinsky CT, 1999. Bayesian model averaging: A tutorial. Stat. Sci. 14:382-401.
DOI: https://doi.org/10.1214/ss/1009212519
Hölker F, Breckling B, 2005. A spatiotemporal individual-based fish model to investigate emergent properties at the organismal and the population level. Ecol. Model. 186:406-426.
DOI: https://doi.org/10.1016/j.ecolmodel.2005.02.010
Hölker F, Vanni MJ, Kuiper JJ, Meile C, Grossart H-P, Stief P, et al., 2015. Tube-dwelling invertebrates: tiny ecosystem engineers have large effects in lake ecosystems. Ecol. Monogr. 85:333-351.
DOI: https://doi.org/10.1890/14-1160.1
Holling CS, 1959. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91:385-398.
DOI: https://doi.org/10.4039/Ent91385-7
Hootsmans M, 1994. A growth analysis model for Potamogeton pectinatus L., p. 250-286. In: W. Van Vierssen, M. Hootsmans and J. Vermaat (Eds.), Lake Veluwe, a macrophyte-dominated system under eutrophication stress. Dordrecht: Springer.
DOI: https://doi.org/10.1007/978-94-011-2032-6_14
Hu F, Bolding K, Bruggeman J, Jeppesen E, Flindt MR, van Gerben L, et al., 2016. FABM-PCLake – linking aquatic ecology with hydrodynamics. Geosci. Model Dev. 9:2271-2278.
DOI: https://doi.org/10.5194/gmd-9-2271-2016
Irwin AJ, Finkel ZV, Müller-Karger FE, Troccoli Ghinaglia L, 2015. Phytoplankton adapt to changing ocean environments. P. Natl. Acad. Sci. USA 112:5762-5766.
DOI: https://doi.org/10.1073/pnas.1414752112
Jackson MC, Loewen CJ, Vinebrook RD, Chimimba CT, 2016. Net effects of multiple stressors in freshwater ecosystems: a meta-analysis. Global Change Biol. 22:180-189.
DOI: https://doi.org/10.1111/gcb.13028
Janse JH, 1997. A model of nutrient dynamics in shallow lakes in relation to multiple stable states, p. 1-18. In: L. Kufel, A. Prejs and J. I. Rybak (Eds.), Shallow Lakes '95. Dordrecht: Springer.
DOI: https://doi.org/10.1007/978-94-011-5648-6_1
Janse J, Scheffer M, Lijklema L, Van Liere L, Sloot J, Mooij W, 2010. Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty. Ecol. Model. 221: 654-665.
DOI: https://doi.org/10.1016/j.ecolmodel.2009.07.023
Janssen AB, Arhonditsis GB, Beusen A, Bolding K, Bruce L, Bruggeman J, et al., 2015. Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective. Aquat. Ecol. 49:513-548.
DOI: https://doi.org/10.1007/s10452-015-9544-1
Janssen AB, Hilt S, Kosten S, de Klein JJ, Paerl HW, Van de Waal DB, 2020. Shifting states, shifting services: Linking regime shifts to changes in ecosystem services of shallow lakes. Freshwater Biol. 66:1-12.
DOI: https://doi.org/10.1111/fwb.13582
Janssen AB, Teurlincx S, Beusen AH, Huijbregts MA, Rost J, Schipper AM, et al., 2019. PCLake+: A process-based ecological model to assess the trophic state of stratified and non-stratified freshwater lakes worldwide. Ecol. Model. 396:23-32.
DOI: https://doi.org/10.1016/j.ecolmodel.2019.01.006
Jenkinson IR, Sun J, 2011. A model of pycnocline thickness modified by the rheological properties of phytoplankton exopolymeric substances. J. Plankton Res. 33:373-383.
DOI: https://doi.org/10.1093/plankt/fbq099
Jenkinson IR, Sun J, Seuront L, 2015. Thalassorheology, organic matter and plankton: towards a more viscous approach in plankton ecology. J. Plankton Res. 37:1100-1109.
DOI: https://doi.org/10.1093/plankt/fbv071
Jeppesen E, Meerhoff M, Jacobsen BA, Hansen RS, Søndergaard M, Jensen J, et al., 2007. Eutrophication in Lakes: Restoration of shallow lakes by nutrient control and biomanipulation—the successful strategy varies with lake size and climate. Hydrobiologia 581:269-285.
DOI: https://doi.org/10.1007/s10750-006-0507-3
Johnson KA, Goody RS, 2011. The original Michaelis constant: Translation of the 1913 Michaelis–Menten paper. Biochemistry 50:8264-8269.
DOI: https://doi.org/10.1021/bi201284u
Kragt ME, Robson BJ, Macleod CJ, 2013. Modellers' roles in structuring integrative research projects. Environ. Model. Softw. 39:322-330.
DOI: https://doi.org/10.1016/j.envsoft.2012.06.015
Kuiper JJ, Verhofstad MJ, Louwers EL, Bakker ES, Brederveld RJ, van Gerven LP, et al., 2017. Mowing submerged macrophytes in shallow lakes with alternative stable states: Battling the good guys? Environ. Manage. 59:619-634.
DOI: https://doi.org/10.1007/s00267-016-0811-2
Litchman E, Ohman MD, Kiørboe T, 2013. Trait-based approaches to zooplankton communities. J. Plankton Res. 35:473-484.
DOI: https://doi.org/10.1093/plankt/fbt019
Liu X, Chen L, 2003. Complex dynamics of Holling type II Lotka–Volterra predator–prey system with impulsive perturbations on the predator. Chaos Solitons Fract. 16:311-320.
DOI: https://doi.org/10.1016/S0960-0779(02)00408-3
López Moreira M., GA, Hinegk L, Salvadore A, Zolezzi G, Hölker F, Monte Domecq RA, et al., 2018. Eutrophication, research and management history of the shallow Ypacaraí Lake (Paraguay). Sustainability 10:2426.
DOI: https://doi.org/10.3390/su10072426
Messer JJ, Brezonik PL, 1984. Laboratory evaluation of kinetic parameters for lake sediment denitrification models. Ecol. Model. 21:277-286.
DOI: https://doi.org/10.1016/0304-3800(84)90064-4
Meyer MF, Zwart JA, 2020. Virtual summit: Incorporating data science and open science in aquatic research. Limnol. Oceanogr. Bull. 29:144-146.
DOI: https://doi.org/10.1002/lob.10411
Mintram KS, Maynard SK, Brown AR, Boyd R, Johnston AS, Thorbek P, Tyler CR, 2020. Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations. Aquat. Toxicol. 224:105483.
DOI: https://doi.org/10.1016/j.aquatox.2020.105483
Monod J, 1942. [Recherches sur la croissance des cultures bactériennes].[Book in French]. Paris: Hermann et cie.
Mooij MW, van Wijk D, Beusen AH, Brederveld RJ, Chang M, Cobben MMP, et al., 2019. Modeling water quality in the Anthropocene: directions for the next-generation of aquatic ecosystem models. Curr. Opin. Environ. Sustain. 36:85-95.
DOI: https://doi.org/10.1016/j.cosust.2018.10.012
Mooij MW, De Senerpont Domis LN, Janse JH, 2009. Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model. Ecol. Model. 220:3011-3020.
DOI: https://doi.org/10.1016/j.ecolmodel.2009.02.003
Mooij MW, Janse JH, De Senerpont Domis LN, Hülsmann S, Ibelings BW, 2007. Predicting the effect of climate change on temperate shallow lakes with the ecosystem model PCLake. Hydrobiologia 584:443-454.
DOI: https://doi.org/10.1007/s10750-007-0600-2
Mooij MW, Trolle D, Jeppesen E, Arhonditsis G, Belolipetsky PV, Chitamwebwa DB, et al., 2010. Challenges and opportunities for integrating lake ecosystem models. Aquat. Ecol. 44:633-667.
DOI: https://doi.org/10.1007/s10452-010-9339-3
Moore TN, Mesman JP, Ladwig R, Feldbauer J, Olsson F, Pilla RM, et al., 2021. LakeEnsemblR: An R package that facilitates ensemble modelling of lakes. Environ. Model. Softw. 143:105101.
DOI: https://doi.org/10.1016/j.envsoft.2021.105101
Pal A, Yew-Hoong Gin K, Yu-ChenLin A, Reinhard M, 2010. Impacts of emerging organic contaminants on freshwater resources: Review of recent occurrences, sources, fate and effects. Sci. Total Environ. 408:6062-6069.
DOI: https://doi.org/10.1016/j.scitotenv.2010.09.026
Peeters F, Straile D, 2018. Trait selection and co-existence of phytoplankton in partially mixed systems: Trait based modelling and potential of an aggregated approach. PLoS One 13:e0194076.
DOI: https://doi.org/10.1371/journal.pone.0194076
Perkin EK, Hölker F, Richardson JS, Sadler JP, Wolter C, Tockner K, 2011. The influence of artificial light on stream and riparian ecosystems: questions, challenges, and perspectives. Ecosphere 2:1-16.
DOI: https://doi.org/10.1890/ES11-00241.1
Persson I, Jones ID, 2008. The effect of water colour on lake hydrodynamics: a modelling study. Freshwater Biol. 53:2345-2355.
DOI: https://doi.org/10.1111/j.1365-2427.2008.02049.x
Péry AR, Mons R, Garric J, 2005. Modelling of the life cycle of Chironomus species using an energy-based model. Chemosphere 59: 247-253.
DOI: https://doi.org/10.1016/j.chemosphere.2004.11.083
Purves D, Scharlemann JP, Harfoot M, Newbold T, Tittensor DP, Hutton J, Emmott S, 2013. Time to model all life on earth. Nature 493:295-297.
DOI: https://doi.org/10.1038/493295a
Radinger J, Hölker F, Horký P, Slavík O. Dendoncker N, Wolter C, 2016. Synergistic and antagonistic interactions of future land use and climate change on river fish assemblages. Global Change Biol. 22:1505-1522.
DOI: https://doi.org/10.1111/gcb.13183
Reynolds CS, Irish AE, Elliot JA, 2001. The ecological basis for simulating phytoplankton responses to environmental change (PROTECH). Ecol. Model. 140:271-291.
DOI: https://doi.org/10.1016/S0304-3800(01)00330-1
Rinke K, Yeates P, Rothhaupt K-O, 2010. A simulation study of the feedback of phytoplankton on thermal structure via light extinction. Freshwater Biol. 55:1674-1693.
DOI: https://doi.org/10.1111/j.1365-2427.2010.02401.x
Schäfer RB, von der Ohe PC, Kühne R, Schüürmann G, Liess M, 2011. Occurrence and toxicity of 331 organic pollutants in large rivers of north Germany over a decade (1994 to 2004). Environ. Sci. Technol. 45:6167-6174.
DOI: https://doi.org/10.1021/es2013006
Scheffer M, Hosper SH, Meijer ML, Moss B, Jeppesen E, 1993. Alternative equilibria in shallow lakes. Trends Ecol. Evol. 8:275-279.
DOI: https://doi.org/10.1016/0169-5347(93)90254-M
Schuwirth N, Borgwardt F, Domisch S, Friedrichs M, Kattwinkel M, Kneis D, et al., 2019. How to make ecological models useful for environmental management. Ecol. Model. 411:108784.
DOI: https://doi.org/10.1016/j.ecolmodel.2019.108784
Senar OE, Creed IF, Trick CE, 2021. Lake browning may fuel phytoplankton biomass and trigger shifts in phytoplankton communities in temperate lakes. Aquat. Sci. 83:21.
DOI: https://doi.org/10.1007/s00027-021-00780-0
Serlet AJ, López Moreira M. GA, Zolezzi G, Wharton G, Hölker F, et al., 2020. SMART Research: Toward interdisciplinary river science in Europe. Front. Environ. Sci. 8:00063.
DOI: https://doi.org/10.3389/fenvs.2020.00063
Shatwell T, Adrian R, Kirillin G, 2016. Planktonic events may cause polymictic-dimictic regime shifts in temperate lakes. Sci. Rep. 6:24361.
DOI: https://doi.org/10.1038/srep24361
Shimoda Y, Arhonditsis GB, 2016. Phytoplankton functional type modelling: Running before we can walk? A critical evaluation of the current state of knowledge. Ecol. Model. 320:9-43.
DOI: https://doi.org/10.1016/j.ecolmodel.2015.08.029
Sommer U, 1991. A comparison of the Droop and the Monod models of nutrient limited growth applied to natural populations of phytoplankton. Funct. Ecol. 5:535-544.
DOI: https://doi.org/10.2307/2389636
Søndergaard M, Lauridsen TL, Johansson LS, Jeppesen E, 2017. repeated fish removal to restore lakes: Case study of Lake Væng, Denmark - Two biomanipulations during 30 years of monitoring. Water 9:43.
DOI: https://doi.org/10.3390/w9010043
Stewart RIA, Dossena M, Bohan DA, Jeppesen E, Kordas RL, Ledger ME, et al., 2013. Chapter Two - Mesocosm experiments as a tool for ecological climate-change research, p. 71-181. In: G. Woodward and EJ O’Green (eds.), Advances in ecological research. Academic Press.
DOI: https://doi.org/10.1016/B978-0-12-417199-2.00002-1
Strand E, Jørgensen C, Huse G, 2005. Modelling buoyancy regulation in fishes with swimbladders: bioenergetics and behaviour. Ecol. Model. 185:309-327.
DOI: https://doi.org/10.1016/j.ecolmodel.2004.12.013
Tawfik AA, Rong H, Choi I, 2015. Failing to learn: towards a unified design approach for failure-based learning. Educ. Technol. Res. Dev. 63:975-994.
DOI: https://doi.org/10.1007/s11423-015-9399-0
Trolle D, Elliott JA, Mooij WM, Janse JH, Bolding K, Hamilton DP, Jeppesen E, 2014. Advancing projections of phytoplankton responses to climate change through ensemble modelling. Environ. Model. Softw. 61:371-379.
DOI: https://doi.org/10.1016/j.envsoft.2014.01.032
Trolle D, Hamilton DP, Hipsey MR, Bolding K, Bruggeman J, Mooij WM, et al., 2012. A community-based framework for aquatic ecosystem models. Hydrobiologia 683:25-34.
DOI: https://doi.org/10.1007/s10750-011-0957-0
Turchini GM, Francis DS, De Silva SS, 2006. Modification of tissue fatty acid composition in Murray cod (Maccullochella peelii peelii, Mitchell) resulting from a shift from vegetable oil diets to a fish oil diet. Aquacult. Res. 37:570-585.
DOI: https://doi.org/10.1111/j.1365-2109.2006.01465.x
Van Nes EH, Lammens EH, Scheffer M, 2002. PISCATOR, an individual-based model to analyze the dynamics of lake fish communities. Ecol. Model. 152:261-278.
DOI: https://doi.org/10.1016/S0304-3800(02)00005-4
Vollenweider RA, 1968. Scientific fundamentals of the eutrophication of lakes and flowing waters, with particular reference to phosphorus and nitrogen as factors in eutrophication. OECD Technical Report DAS/CS1/68.27. Paris: Organisation for Economic Co-operation and Development.
Ward NK, Fitchett L, Hart JA, Shu L, Stachelek J, Weng W, et al., 2019. Integrating fast and slow processes is essential for simulating human–freshwater interactions. Ambio 48:1169-1182.
DOI: https://doi.org/10.1007/s13280-018-1136-6
Wollrab S, Diehl S, De Roos AM, 2012. Simple rules describe bottom-up and top-down control in food webs with alternative energy pathways. Ecol. Lett. 15:935-946.
DOI: https://doi.org/10.1111/j.1461-0248.2012.01823.x
Wuchty S, Jones BF, Uzzi B, 2007. The increasing dominance of teams in production of knowledge. Science 316:1036-1039.
DOI: https://doi.org/10.1126/science.1136099
Supporting Agencies
Education, Audiovisual and Culture Executive Agency (EACEA) of the European CommissionHow to Cite
López Moreira M., Gregorio A., Marco Toffolon, and Franz Hölker. 2021. “Hitting the Sweet Spot of Complexity: Reasons Why the Development of New Custom-Tailored Models Is Still Warranted and Should Be Encouraged in Aquatic Sciences”. Journal of Limnology 80 (3). https://doi.org/10.4081/jlimnol.2021.2035.
Copyright (c) 2021 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Similar Articles
- Lorenzo Traversetti, Simona Ceschin, Alessandro Manfrin, Massimiliano Scalici, Co-occurrence between macrophytes and macroinvertebrates: towards a new approach for the running waters quality evaluation? , Journal of Limnology: Vol. 74 No. 1 (2015)
- Oscar Wembo Ndeo, Torsten Hauffe, Diana Delicado, Alidor Kankonda Busanga, Christian Albrecht, Mollusk communities of the central Congo River shaped by combined effects of barriers, environmental gradients, and species dispersal , Journal of Limnology: Vol. 76 No. 3 (2017)
- Alexey A. Kotov, Derek J. Taylor, Daphnia lumholtzi Sars, 1885 (Cladocera: Daphniidae) invades Argentina , Journal of Limnology: Vol. 73 No. 2 (2014)
- Rodrigo L. Arrieira, Geziele M. Alves, Leilane T. F. Schwind, Fabio A. Lansac-Tôha, Local factors affecting the testate amoeba community (Protozoa: Arcellinida; Euglyphida) in a neotropical floodplain , Journal of Limnology: Vol. 74 No. 3 (2015)
- Janaina A. Santos, Rozane V. Marins, José E. Aguiar, Guillermo Challar, Francisco A.T.F. Silva, Luiz D. Lacerda, Hydrochemistry and trophic state change in a large reservoir in the Brazilian northeast region under intense drought conditions , Journal of Limnology: Vol. 76 No. 1 (2017)
- M. Carolina Bernal, Lunhui Lu, Carmen Sabio y García, María Laura Sánchez, M. Solange Vera, Sol Porcel, Rodrigo Sinistro, Zhe Li, Irina Izaguirre, Spatial variation of picoplankton communities along a cascade reservoir system in Patagonia, Argentina , Journal of Limnology: Vol. 80 No. 3 (2021): Celebratory Issue - 80th Anniversary of the Journal of Limnology
- Yamila S. Battauz, Susana B. José de Paggi, Juan C. Paggi, Marcelo Romano, Ignacio Barberis, Zooplankton characterisation of Pampean saline shallow lakes, habitat of the Andean flamingoes , Journal of Limnology: Vol. 72 No. 3 (2013)
- Mariano Bresciani, Claudia Giardino, Rosaria Lauceri, Erica Matta, Ilaria Cazzaniga, Monica Pinardi, Andrea Lami, Martina Austoni, Emanuela Viaggiu, Roberta Congestri, Giuseppe Morabito, Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes , Journal of Limnology: Vol. 76 No. s1 (2017): Aquatic biomonitoring: Lessons from the past, challenges for the future
- Kenneth W. Cummins, Combining taxonomy and function in the study of stream macroinvertebrates , Journal of Limnology: Vol. 75 No. s1 (2016): Proceedings of the 6th National Congress of Limnology
- Daniela Ghia, Gianluca Fea, Fabio Ercoli, Roberto Sacchi, Escape rooms: behavioural response of two invasive crayfish species under water decline scenarios , Journal of Limnology: Vol. 83 (2024)
<< < 32 33 34 35 36 37 38 39 40 41 > >>
You may also start an advanced similarity search for this article.
List of Cited By :
