Abstract
The response of aquatic organisms to anthropogenic drivers varies over space and time and, as a result, the cumulative effects of these drivers is often heterogeneous and context-dependent (De Senerpont Domis et al. 2013; Nogués-Bravo et al. 2018; Barouillet et al. 2023). By quantifying trends in abiotic and biotic factors before, during, and after major anthropogenic events, we can better identify the timing and severity of these cumulative effects on species responses (e.g., whether driver-response relationships are indicative of an abrupt ecosystem transition; Vermaire et al. 2017; Zhao et al. 2022), and possibly curtail delayed and unforeseeable outcomes (Eastwood et al. 2022).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6(2):343–351. https://doi.org/10.1038/ismej.2011.119
Barouillet C, Monchamp M-E, Bertilsson S, Brasell K, Domaizon I, Epp LS, Ibrahim A, Mejbel H, Nwosu EC, Pearman JK, Picard M, Thomson-Laing G, Tsugeki N, Von Eggers J, Gregory-Eaves I, Pick FR, Wood SA, Capo E (2023) Investigating the effects of anthropogenic stressors on lake biota using sedimentary DNA. Freshw Biol n/a(n/a). https://doi.org/10.1111/fwb.14027
Blackman RC, Ho H-C, Walser J-C, Altermatt F (2022) Spatio-temporal patterns of multi-trophic biodiversity and food-web characteristics uncovered across a river catchment using environmental DNA. Commun Biol 5(1):1–11. https://doi.org/10.1038/s42003-022-03216-z
Capo E, Debroas D, Arnaud F, Guillemot T, Bichet V, Millet L, Gauthier E, Massa C, Develle A-L, Pignol C, Lejzerowicz F, Domaizon I (2016) Long-term dynamics in microbial eukaryotes communities: a palaeolimnological view based on sedimentary DNA. Mol Ecol 25(23):5925–5943. https://doi.org/10.1111/mec.13893
Capo E, Debroas D, Arnaud F, Perga M-E, Chardon C, Domaizon I (2017) Tracking a century of changes in microbial eukaryotic diversity in lakes driven by nutrient enrichment and climate warming. Environ Microbiol 19(7):2873–2892. https://doi.org/10.1111/1462-2920.13815
Cardini A, Polly PD (2020) Cross-validated between group PCA scatterplots: a solution to spurious group separation? Evol Biol 47(1):85–95. https://doi.org/10.1007/s11692-020-09494-x
Chen W, Ficetola GF (2020) Numerical methods for sedimentary-ancient-DNA-based study on past biodiversity and ecosystem functioning. Environ DNA 2(2):115–129. https://doi.org/10.1002/edn3.79
Christensen EM, Harris DJ, Ernest SKM (2018) Long-term community change through multiple rapid transitions in a desert rodent community. Ecology 99(7):1523–1529. https://doi.org/10.1002/ecy.2373
de Senerpont Domis LN, Elser JJ, Gsell AS, Huszar VLM, Ibelings BW, Jeppesen E, Kosten S, Mooij WM, Roland F, Sommer U, van Donk E, Winder M, Lürling M (2013) Plankton dynamics under different climate conditions in tropical freshwater systems (a reply to the comment by Sarmento, Amado & Descy). Freshw Biol 58(10):2211–2213. https://doi.org/10.1111/fwb.12203
Deng Y, Jiang Y-H, Yang Y, He Z, Luo F, Zhou J (2012) Molecular ecological network analyses. BMC Bioinf 13(1):113. https://doi.org/10.1186/1471-2105-13-113
Doncaster CP, Alonso Chávez V, Viguier C, Wang R, Zhang E, Dong X, Dearing JA, Langdon PG, Dyke JG (2016) Early warning of critical transitions in biodiversity from compositional disorder. Ecology 97(11):3079–3090. https://doi.org/10.1002/ecy.1558
Eastwood N, Stubbings WA, Abou-Elwafa Abdallah MA, Durance I, Paavola J, Dallimer M, Pantel JH, Johnson S, Zhou J, Hosking JS, Brown JB, Ullah S, Krause S, Hannah DM, Crawford SE, Widmann M, Orsini L (2022) The Time Machine framework: monitoring and prediction of biodiversity loss. Trends Ecol Evol 37(2):138–146. https://doi.org/10.1016/j.tree.2021.09.008
Ellegaard M, Clokie MRJ, Czypionka T, Frisch D, Godhe A, Kremp A, Letarov A, McGenity TJ, Ribeiro S, John Anderson N (2020) Dead or alive: sediment DNA archives as tools for tracking aquatic evolution and adaptation. Commun Biol 3(1):1–11. https://doi.org/10.1038/s42003-020-0899-z
Fortin M-J, Dale MRT, Brimacombe C (2021) Network ecology in dynamic landscapes. Proc R Soc B Biol Sci 288(1949):20201889. https://doi.org/10.1098/rspb.2020.1889
Griffiths K, Jeziorski A, Paquette C, Taranu ZE, Baud A, Antoniades D, Beisner B, Hamilton PB, Smol JP, Gregory-Eaves I (2021) Multi-trophic level responses to environmental stressors over the past ~150 years: insights from a lake-rich region of the world. Ecol Indic 127:107700. https://doi.org/10.1016/j.ecolind.2021.107700
Heathcote AJ, Taranu ZE, Tromas N, MacIntyre-Newell M, Leavitt PR, Pick FR (2023) Sedimentary DNA and pigments show increasing abundance and toxicity of cyanoHABs during the Anthropocene. Freshw Biol n/a(n/a). https://doi.org/10.1111/fwb.14069
Hui F (2021) boral: Bayesian Ordination and Regression AnaLysis. R package version 2.0
Hui FKC, Taskinen S, Pledger S, Foster SD, Warton DI (2015) Model-based approaches to unconstrained ordination. Methods Ecol Evol 6(4):399–411. https://doi.org/10.1111/2041-210X.12236
Kara EL, Hanson PC, Hu YH, Winslow L, McMahon KD (2013) A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J 7(3):680–684. https://doi.org/10.1038/ismej.2012.118
Kisand V, Talas L, Kisand A, Stivrins N, Reitalu T, Alliksaar T, Vassiljev J, Liiv M, Heinsalu A, Seppä H, Veski S (2018) From microbial eukaryotes to metazoan vertebrates: wide spectrum paleo-diversity in sedimentary ancient DNA over the last ~14,500 years. Geobiology 16(6):628–639. https://doi.org/10.1111/gbi.12307
Legendre P, Legendre LF (2012) Numerical Ecology. (Vol. 20). Elsevier, The Netherlands, pp. 209–213, 406–411
Letten AD, Keith DA, Tozer MG, Hui FKC (2015) Fine-scale hydrological niche differentiation through the lens of multi-species co-occurrence models. J Ecol 103(5):1264–1275. https://doi.org/10.1111/1365-2745.12428
Li F, Zhang X, Xie Y, Wang J (2019) Sedimentary DNA reveals over 150 years of ecosystem change by human activities in Lake Chao, China. Environ Int 133:105214. https://doi.org/10.1016/j.envint.2019.105214
Magurran AE, McGill BJ (2011) Biological diversity: frontiers in measurement and assessment, 1st edn. Oxford University Press, Oxford/New York
Mayfield RJ, Dearing JA, Doncaster CP, Langdon PG (2022) Stability of chironomid community structure during historic climatic and environmental change in subarctic Alaska. Limnol Oceanogr 67(S1):S444–S460. https://doi.org/10.1002/lno.12007
McKay DIA, Dyke JG, Doncaster CP, Dearing JA, Wang R (2020) Network-based metrics of resilience and ecological memory in lake ecosystems. https://doi.org/10.1101/810762
Monchamp M-È, Bruel R, Frossard V, McGowan S, Lavrieux M, Muschick M, Perga M-É, Dubois N (2021) Paleoecological evidence for a multi-trophic regime shift in a perialpine lake (Lake Joux, Switzerland). Anthropocene 35(100301). https://doi.org/10.1016/j.ancene.2021.100301
Nogués-Bravo D, Rodríguez-Sánchez F, Orsini L, de Boer E, Jansson R, Morlon H, Fordham DA, Jackson ST (2018) Cracking the code of biodiversity responses to past climate change. Trends Ecol Evol 33(10):765–776. https://doi.org/10.1016/j.tree.2018.07.005
Orsini L, Schwenk K, De Meester L, Colbourne JK, Pfrender ME, Weider LJ (2013) The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments. Trends Ecol Evol 28(5):274–282. https://doi.org/10.1016/j.tree.2013.01.009
Ovaskainen O, Tikhonov G, Norberg A, Guillaume Blanchet F, Duan L, Dunson D, Roslin T, Abrego N (2017) How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol Lett 20(5):561–576. https://doi.org/10.1111/ele.12757
Popovic GC, Hui FKC, Warton DI (2022) Fast model-based ordination with copulas. Methods Ecol Evol 13(1):194–202. https://doi.org/10.1111/2041-210X.13733
Simpson GL (2018) Modelling palaeoecological time series using generalised additive models. Front Ecol Evol 6. https://doi.org/10.3389/fevo.2018.00149
Simpson GL, Anderson NJ (2009) Deciphering the effect of climate change and separating the influence of confounding factors in sediment core records using additive models. Limnol Oceanogr 54(6 part 2):2529–2541. https://doi.org/10.4319/lo.2009.54.6_part_2.2529
Taranu ZE, Carpenter SR, Frossard V, Jenny J-P, Thomas Z, Vermaire JC, Perga M-E (2018) Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records? Ecosphere 9(10):e02438. https://doi.org/10.1002/ecs2.2438
Tse TJ, Doig LE, Tang S, Zhang X, Sun W, Wiseman SB, Feng CX, Liu H, Giesy JP, Hecker M, Jones PD (2018) Combining high-throughput sequencing of sedaDNA and traditional paleolimnological techniques to infer historical trends in cyanobacterial communities. Environ Sci Technol 52(12):6842–6853. https://doi.org/10.1021/acs.est.7b06386
van der Veen B, Hui FKC, Hovstad KA, O’Hara RB (2023) Concurrent ordination: simultaneous unconstrained and constrained latent variable modelling. Methods Ecol Evol 14(2):683–695. https://doi.org/10.1111/2041-210X.14035
Vermaire JC, Taranu ZE, MacDonald GK, Velghe K, Bennett EM, Gregory-Eaves I (2017) Extrinsic vs. intrinsic regimes shifts in shallow lakes: long-term response of cyanobacterial blooms to historical catchment phosphorus loading and climate warming. Front Ecol Evol 5. https://doi.org/10.3389/fevo.2017.00146
Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So many variables: joint modeling in community ecology. Trends Ecol Evol 30(12):766–779. https://doi.org/10.1016/j.tree.2015.09.007
Zhao Y, Wang R, Yang X, Dearing JA, Doncaster CP, Langdon P, Dong X (2022) Reconstruction of ecological transitions in a temperate shallow lake of the middle Yangtze River Basin in the last century. Water 14(7):1136. https://doi.org/10.3390/w14071136
Acknowledgements
We acknowledge the following funding sources for salary support: Environment and Climate Change Canada (supporting ZET), Canada Research Chairs program (awarded to IGE), and an NSERC Post-Doctoral Fellowship (awarded to MEM). We are also grateful for fruitful comments and revisions provided by Drs. Susanne McGowan, Luke Holman and Eric Capo, John Smol and Cécilia Barouillet.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Taranu, Z.E., Gregory-Eaves, I., Monchamp, ME. (2023). An Overview of Biodiversity and Network Modeling Approaches: Applications to Sedimentary DNA Records. In: Capo, E., Barouillet, C., Smol, J.P. (eds) Tracking Environmental Change Using Lake Sediments. Developments in Paleoenvironmental Research, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-031-43799-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-43799-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43798-4
Online ISBN: 978-3-031-43799-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)