Skip to main content

Advertisement

SpringerLink
Log in

Are Northern Lakes in Relatively Intact Temperate Forests Showing Signs of Increasing Phytoplankton Biomass?

  • Published:
Ecosystems Aims and scope Submit manuscript

Abstract

Increased reports of phytoplankton blooms in remote pristine lakes provide the perception that climate-driven fundamental changes may be occurring in lake ecosystems. There is a crucial need for detailed surveys of lakes covering large spatial and temporal scales to determine whether phytoplankton biomass is on the rise in northern forested landscapes. To characterize spatial and temporal variation in chlorophyll-a (Chl-a) as a proxy of phytoplankton biomass in lakes located in the relatively intact northern temperate forest in the Great Lakes-St. Lawrence forest region, we developed a regression model that relates Landsat 4–5 TM and 7 ETM + optical reflectance in blue, green, and red bands to Chl-a from 45 lake water samples. Reflectance from the Landsat three-band algorithm ((B1-B3)/B2) showed the strongest correlation with in situ data explaining 78% of the variance in Chl-a. We applied this model to produce a 28-year time series of Chl-a in 12,644 lakes within the northern temperate forest. By applying a two-way analysis of variance, we were able to quantify the different components of variation in Chl-a. We found that space and time components combined explained 55.9% of the total variation in Chl-a (35.6% for space and 20.3% for time), while the space × time interaction component explained 44.1% of the variation. The spatial pattern revealed relatively low Chl-a in headwater lakes to higher Chl-a in lower reaches of watersheds. Only a minority of lakes were found to have a temporal trend in Chl-a; some lakes displayed positive trends while others displayed negative trends. The trends were substantial enough to indicate changes in trophic states (that is, oligotrophic lakes becoming eutrophic, or vice versa). These spatial and temporal patterns indicate the complex interactions of local and regional catchment characteristics and lake-specific properties that filter the response of northern lakes to climate change. The large proportion of the space × time interaction component shows the importance of processes occurring within lakes that affect water mixing and nutrient concentrations and therefore availability of the nutrients to phytoplankton.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1

(modified from Schalles 2006).

Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • Allan MG, Hamilton DP, Hicks BJ, Brabyn L. 2011. Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand. Int J Remote Sens 32:2037–2055.

    Article  Google Scholar 

  • Allan MG, Hamilton DP, Hicks B, Brabyn L. 2015. Empirical and semi-analytical chlorophyll a algorithms for multi-temporal monitoring of New Zealand lakes using Landsat. Environ Monit Assess 187:1–24.

    Article  CAS  Google Scholar 

  • Anderson NJ. 2014. Landscape disturbance and lake response: temporal and spatial perspectives. Freshw Rev 7:77–120.

    Article  Google Scholar 

  • Arar EJ, Collins GB (1997) Method 445.0, In vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence. In: Methods for the Determination of Chemical Substances in Marine and Estuarine Environmental Matrices, 2nd ed. National Exposure Research Laboratory, Office of Research and Development, USEPA, Cincinnati, OH, USA, pp. 132–154.

  • Baldwin DJB, Desloges JR, Band LE. 2000. Physical geography of Ontario. In: Perera A, Euler D, Thompson ID, Eds. Ecology of a managed terrestrial landscape: Patterns and processes of forest landscapes in Ontario. Vancouver, BC: University of British Columbia Press. pp 141–162.

    Google Scholar 

  • Baines SB, Webster KE, Kratz TK, Stephen R, Ecology S, Mar N. 2000. Synchronous behavior of temperature, calcium, and chlorophyll in lakes of Northern Wisconsin. Ecology 81:815–825.

    Article  Google Scholar 

  • Bonansea M, Bazán R, Ledesma C, Rodriguez C, Pinotti L. 2015. Monitoring of regional lake water clarity using Landsat imagery. Hydrol Res 46(5):661–670.

    Article  Google Scholar 

  • Bergmann M, Peters RH. 1980. A simple reflectance method for the measurement of particulate pigment in lake water and its application to phosphorus-chlorophyll-seston relationships. Can J Fisher Aquat Sci 37:111–114.

    Article  CAS  Google Scholar 

  • Bird RE, Riordan C. 1986. Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at the earth’s surface for cloudless atmospheres. J Appl Meteorol Climatol 25:87–97.

    Article  Google Scholar 

  • Blenckner T, Adrian R, Livingstone DM, Jennings E, Weyhenmeyer GS, George DG, Jankowski T, Jarvinen M, Aonghusa CN, Nõges T, Strailes D, Teubner K. 2007. Large-scale climatic signatures in lakes across Europe: a meta-analysis. Global Change Biol 13:1314–1326.

    Article  Google Scholar 

  • Brivio PA, Giardino C, Zilioli E. 2001. Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images. Int J Remote Sens 22:487–502.

    Article  Google Scholar 

  • Brezonik P, Menken KD, Bauer M. 2005. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake Reserv Manag 21:373–382.

    Article  Google Scholar 

  • Boucher J, Kathleen W, Hamidreza N, Bethel S. 2018. Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring. Ecol Appl 28:1044–1054.

    Article  PubMed  Google Scholar 

  • Bucholtz A. 1995. Rayleigh-scattering calculations for the terrestrial atmosphere. Appl Opt 34:2765–2773.

    Article  CAS  PubMed  Google Scholar 

  • Bush EJ, Loder JW, James TS, Mortsch LD, Cohen SJ. 2014. An overview of Canada’s changing climate. In: Warren FJ, Lemmen DS, Eds. Canada in a Changing Climate: Sector Perspectives on Impacts and Adaptation. Ottawa: Government of Canada. pp 23–64.

    Google Scholar 

  • Canada Soil Survey Committee (1978) Proceedings of the eighth meeting of the Canada Soil Survey Committee of Canada. Mimeographed report of the Canada Department of Agriculture. Ottawa, Canada. 354 p.

  • Capon S, Bunn S. 2015. Assessing climate change risks and prioritising adaptation options using a water ecosystem services-based approach. In: Martin-Ortega J, Ferrier R, Gordon I, Khan S, Eds. Water Ecosystem services: A global perspective. Cambridge: Cambridge University Press. pp 17–25.

    Chapter  Google Scholar 

  • Carlson RE, Simpson J. 1996. A coordinator’s guide to volunteer lake monitoring methods. Madison: North American Lake Management Society. p 96.

    Google Scholar 

  • Carpenter SR, Brock WA, Cole JJ, Pace ML. 2014. A new approach for rapid detection of nearby thresholds in ecosystem time series. Oikos 123:290–297.

    Article  Google Scholar 

  • Center for International Earth Science Information Network - CIESIN - Columbia University (2018) Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center. Accessed: 10 June 2020.

  • Chander G, Markham B, Helder D. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903.

    Article  Google Scholar 

  • Chandrasekhar S. 1960. Radiative transfer. New York, USA: Dover Publications Inc. p 393.

    Google Scholar 

  • Creed IF, Band LE, Foster NW, Morrison IK, Nicolson JA, Semkin RS, Jeffries DS. 1996. Regulation of nitrate-N release from temperate forests: a test of the N flushing hypothesis. Water Resour Res 32:3337–3354.

    Article  CAS  Google Scholar 

  • Creed IF, Bergström AK, Trick CG, Grimm NB, Hessen DO, Karlsson J, and others 2018. Global change-driven effects on dissolved organic matter composition: Implications for food webs of northern lakes. Global Change Biol 24(8):3692–3714. https://doi.org/10.1111/gcb.14129.

    Article  Google Scholar 

  • Creed IF, Sanford SE, Beall FD, Molot LA, Dillon PJ. 2003. Cryptic wetlands: integrating hidden wetlands in regression models of the export of dissolved organic carbon from forested landscapes. Hydrolog Process 17:3629–3648. https://doi.org/10.1002/hyp.1357.

    Article  Google Scholar 

  • Creed IF, Beall FD, Clair TA, Dillon PJ, Hesslein RH. 2008. Predicting export of dissolved organic carbon from forested catchments in glaciated landscapes with shallow soils. Global Biogeochem Cycles 22:GB4024. https://doi.org/10.1029/2008GB003294.

    Article  CAS  Google Scholar 

  • Creed IF, Beall FD. 2009. Distributed topographic indicators for predicting nitrogen export from headwater catchments. Water Resour Res 45:W10407.

    Article  CAS  Google Scholar 

  • Cressie N, Wikle CK. 2011. Statistics for spatio-temporal data. Hoboken: Wiley. p 571.

    Google Scholar 

  • Dalu T, Dube T, Froneman PW, Mwazvita TB, Clegg BW, Nhiwatiwa T. 2015. An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data, in a small tropical reservoir. Geocarto Int 6049:1–14.

    Google Scholar 

  • Dekker AG, Vos RJ, Peters SWM. 2002. Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int J Remote Sens 23:15–35.

    Article  Google Scholar 

  • Dekker AG, Hestir EL. 2012. Evaluating the feasibility of systematic inland water quality monitoring with satellite remote sensing. Canberra: CSIRO: Water for a Healthy Country National Research Flagship. p 123.

    Google Scholar 

  • Dakos V, Carpenter SR, Brock WA, Ellison AM, Guttal V, Ives AR, Kefi S, Livina V, David A, Seekell D, van Nes EH, Scheffer M. 2012. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7:e38410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 climatic conditions in space and time. Freshw Biol 58:463–482.

    Article  Google Scholar 

  • Downing JA, Prairie YT, Cole JJ, Duarte CM, Tranvik LJ, Striegl RG, McDowell WH, Kortelainen P, Caraco NF, Melack JM, Middelburg J. 2006. The global abundance and size distribution of lakes, ponds, and impoundments. Limnol Oceanogr 51(5):2388–2397.

    Article  Google Scholar 

  • Duan H, Zhang Y, Zhang B, Song K, Wang Z. 2007. Assessment of chlorophyll-a concentration and trophic state for Lake Chagan using Landsat TM and field spectral data. Environ Monitor Assess 129(1):295–308.

    Article  CAS  Google Scholar 

  • de Wit HA, Valinia S, Weyhenmeyer GA, Futter MN, Kortelainen P, Austnes K, Hessen DO, Räike A, Laudon H, Vuorenmaa J. 2016. Current browning of surface waters will be further promoted by wetter climate. Environ Sci Technol 3(12):430–435.

    Google Scholar 

  • Favot EJ, Rühland KM, DeSellas AM, Ingram R, Paterson AM, Smol JP. 2019. Climate variability promotes unprecedented cyanobacterial blooms in a remote, oligotrophic Ontario lake: evidence from paleolimnology. J Paleolimnol 62:31–52.

    Article  Google Scholar 

  • Foley J, DeFries R, Asner G, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK. 2005. Global consequences of land use. Science 309:570–574.

    Article  CAS  PubMed  Google Scholar 

  • Fraser RN. 1998. Hyperspectral remote sensing of turbidity and chlorophyll-a among Nebraska Sand Hills lakes. Int J Remote Sens 19:1579–1589.

    Article  Google Scholar 

  • Fraterrigo JM, Downing JA. 2008. The influence of land use on lake nutrients varies with watershed transport capacity. Ecosystems 11:1021–1034.

    Article  CAS  Google Scholar 

  • Frazier P, Page K, Louis J, Briggs S, Robertson AI. 2003. Relating wetland inundation to river flow using Landsat TM data. Int J Remote Sens 24:3755–3770.

    Article  Google Scholar 

  • Freeman EC, Creed IF, Jones B, Bergstrom A-K. 2020. Global changes may be promoting a rise in select cyanobacteria in nutrient-poor northern lakes. Global Change Biol 26:4966–4987.

    Article  Google Scholar 

  • Garrison Investigative Board (1977) Garrison Diversion Study: report to the International Joint Commission.

  • Gerten D, Adrian R. 2002. Effects of climate warming, North Atlantic Oscillation, and El Niño-Southern Oscillation on Thermal conditions and plankton dynamics in northern hemispheric lakes. Sci World J 2:586–606.

    Article  Google Scholar 

  • Gilabert MA, Conese C, Maselli F. 1994. An atmospheric correction method for the automatic retrieval of surface reflectances from TM images. Int J Remote Sens 15:2065–2086.

    Article  Google Scholar 

  • Gitelson A, Garbuzov G, Szilagyi F, Mittenzwey K-H, Karnieli A, Kaiser A. 1993. Quantitative remote sensing methods for real-time monitoring inland water quality. Int J Remote Sens 14:1269–1295.

    Article  Google Scholar 

  • Gitelson AA, Yacobi YZ, Schalles JF, Rundquist DC, Han L, Stark R, Etzion D. 2000. Remote estimation of phytoplankton density in productive waters. Archiv Für Hydrobiol 55:121–136.

    CAS  Google Scholar 

  • Glibert PM, Anderson DM, Gentien P, Granéli E, Sellner KG. 2005. The global, complex phenomena of harmful phytoplankton blooms. Oceanography 18:136–147.

    Article  Google Scholar 

  • Guanter L, Ruiz-Verdu A, Odermatt D, Giardino C, Simis S, Heege T, Domínguez-Gómez JA, Moreno J. 2010. Atmospheric correction of ENVISAT/MERIS data over inland waters: validation for European Lakes. Remote Sens Environ 114:467–480.

    Article  Google Scholar 

  • Gunn J, Sein R, Keller B, Beckett P. 2001. Liming of acid and metal contaminated catchments for the improvement of drainage water quality. Water Air Soil Pollut 130:1439–1444.

    Article  Google Scholar 

  • Hakanson L. 2005. The importance of lake morphometry and catchment characteristics in limnology–ranking based on statistical analyses. Hydrobiologia 541:117–137.

    Article  CAS  Google Scholar 

  • Hamed KH. 2011. The distribution of Kendall’s tau for testing the significance of cross-correlation in persistent data. Hydrol Sci J 56(5):841–853.

    Article  Google Scholar 

  • Han L, Jordan K. 2005. Estimating and mapping chlorophyll a concentration in Pensacola Bay, Florida using Landsat ETM data. Int J Remote Sens 26:5245–5254.

    Article  Google Scholar 

  • Havens EK. 2008. Cyanobacterial harmful algal blooms: state of the science and research needs. Adv Exp Med Biol 619:733–748.

    Article  PubMed  Google Scholar 

  • Hipsey MR, Hamilton DP, Hanson PC, Carey CC. 2015. Predicting the resilience and recovery of aquatic systems. Water Resouces Res 51:7023–7043.

    Article  Google Scholar 

  • Ho JC, Michalak AM, Pahlevan N. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574(7780):667–670. https://doi.org/10.1038/s41586-019-1648-7.

    Article  CAS  PubMed  Google Scholar 

  • Ho JC, Stumpf RP, Bridgeman TB, Michalak AM. 2017. Using Landsat to extend the historical record of lacustrine phytoplankton blooms: a Lake Erie case study. Remote Sens Environ 191:273–285.

    Article  Google Scholar 

  • Huber V, Rita A, Dieter G. 2008. Phytoplankton response to climate warming modified by trophic state. Limnol Oceanogr 53:1.

    Article  Google Scholar 

  • Huisman J, Matthijs HCP, Visser PM. 2005. Harmful cyanobacteria. Berlin: Springer. p 241.

    Book  Google Scholar 

  • Huot Y, Brown CA, Potvin G, Antoniades D, Baulch HM, Beisner BE, Bélanger S, Brazeau S, Cabana H, Cardille JA, del Giorgio PA, Gregory-Eaves I, Fortin M-J, Lang A, Laurion I, Maranger R, Prairie YT, Rusak JA, Segura PA, Siron R, Smol JP, Vinebrooke RD, Walsh DA. 2019. The NSERC Canadian lake pulse network: a national assessment of lake health providing science for water management in a changing climate. Sci Total Environ 695:133668.

    Article  CAS  PubMed  Google Scholar 

  • Isles PDF, Creed IF, Bergström A-K. 2018. Recent synchronous declines in DIN:TP in Swedish lakes. Global Biogeochem Cycl 32:208–225.

    Article  CAS  Google Scholar 

  • Jeppesen E, Kronvang B, Meerhoff M, Søndergaard M, Hansen KM, Andersen HE, Lauridsen TL, Liboriussen L, Olesen JE. 2009. Climate change effects on runoff, catchment phosphorus loading and lake ecological state, and potential adaptations. J Environ Qual 38(5):1930–1941.

    Article  CAS  PubMed  Google Scholar 

  • Jorge DSF, Barbosa CCF, De Carvalho LAS, Affonso AG, Lobo FDL, Novo EMLDM. 2017. SNR (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex Amazon lakes. Remote Sens 9:644.

    Article  Google Scholar 

  • Keith D, Rover J, Green J, Zalewsky B, Charpentier M, Thursby G, Bishop J. 2018. Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager. Int J Remote Sens 39:2818–2846.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kendall MG. 1975. Rank correlation methods, 4th edn. London, UK: Griffin. p 202.

    Google Scholar 

  • Keller W, Heneberry J, Edwards BA. 2018. Recovery of acidified Sudbury, Ontario, Canada, lakes: a multi-decade synthesis and update. Environ Rev 16:1–16.

    Google Scholar 

  • Kloiber SM, Brezonik PL, Olmanson LG, Bauer ME. 2002. A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens Environ 82:38–47.

    Article  Google Scholar 

  • Kosten S, Huszar VLM, Bécares E, Costa LS, van Donk E, Hansson LA, Jeppesen E, Kruk C, Lacerot G, Mazzeo N, De Meester L, Moss B, Lürling M, Nõges T, Romo S, Scheffer M. 2012. Warmer climates boost cyanobacterial dominance in shallow lakes. Global Change Biol 18:118–126.

    Article  Google Scholar 

  • Kuhn C, Butman D. 2021. Declining greenness in Arctic-boreal lakes. Proc Natl Acad Sci 118(15).

  • Leckner B. 1978. The spectral distribution of solar radiation at the earth’s surface—elements of a model. Solar Energy 20:143–150.

    Article  Google Scholar 

  • Lin S, Novitski LN, Qi J, Stevenson RJ. 2018. Landsat TM/ETM+ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes. J Appl Remote Sens 12:1–17.

    CAS  Google Scholar 

  • Lobo FL, Costa MPF, Novo EMLM. 2015. Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sens Environ 157:170–184.

    Article  Google Scholar 

  • Lunetta RS, Schaeffer BA, Stumpf RP, Keith D, Jacobs SA, Murphy MS. 2015. Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA. Remote Sens Environ 157:24–34.

    Article  Google Scholar 

  • Maeda EE, Lisboa F, Kaikkonen L, Kallio K, Koponen S, Brotas V, Kuikka S. 2019. Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data. Remote Sens Environ 221:609–620.

    Article  Google Scholar 

  • Marshall CT, Peters RH. 1989. General patterns in the seasonal development of chlorophyll a for temperate lakes. Limnol Oceanogr 34:856–867.

    Article  CAS  Google Scholar 

  • Matthews MW. 2011. A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int J Remote Sens 32:6855–6899.

    Article  Google Scholar 

  • Mayo M, Gitelson A, Yacobi Y, Ben-Avraham Z. 1995. Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data. Int J Remote Sens 16:175–182.

    Article  Google Scholar 

  • McKenney DW, Hutchinson MF, Papadopol P, Lawrence K, Pedlar J, Campbell K, Milewska E, Hopkinson RF, Price D, Owen T. 2011. Customized spatial climate models for North America. Bull Am Meteorol Soc 92:1611–1622.

    Article  Google Scholar 

  • Mengistu SG, Creed IF, Webster KL, Enanga E, Beall FD. 2014. Searching for similarity in topographic controls on carbon, nitrogen and phosphorus export from forested headwater catchments. Hydrol Process 28:3201–3216.

    Article  CAS  Google Scholar 

  • Mengistu SG, Quick CG, Creed IF. 2013. Nutrient export from catchments on forested landscapes reveals complex nonstationary and stationary climate signals. Water Resource Res 49:1–18.

    Article  Google Scholar 

  • Mishra D, Ogashawara I, Gitelson A. 2017. Bio-optical modeling and remote sensing of inland waters. USA: Elsevier Inc. p 332.

    Google Scholar 

  • Mosley LM. 2015. Drought impacts on the water quality of freshwater systems; review and integration. Earth-Science Rev 140:203–214.

    Article  CAS  Google Scholar 

  • Mustard JF, Staid MI, Fripp WJ. 2002. A semi-analytical approach to the calibration of AVIRIS data to reflectance over water application in a Temperate Estuary. Remote Sens Environ 75:335–349.

    Article  Google Scholar 

  • Nõges T. 2009. Relationships between morphometry, geographic location and water quality parameters of European lakes. Hydrobiologia 633:33–43.

    Article  CAS  Google Scholar 

  • Odermatt D, Gitelson A, Brando VE, Schaepman M. 2012. Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote Sens Environ 118:116–126.

    Article  Google Scholar��

  • Oliver SK, Collins SM, Soranno PA, Wagner T, Stanley EH, Jones JR, Stow CA, Lottig NR. 2017. Unexpected stasis in a changing world: lake nutrient and chlorophyll trends since 1990. Global Change Biol 23:5455–5467.

    Article  Google Scholar 

  • Olmanson LG, Brezonik PL, Finlay JC, Bauer ME. 2016. Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes. Remote Sens Environ 185:119–128.

    Article  Google Scholar 

  • O’Neil JM, Davis WT, Burford AM, Gobler JC. 2012. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae 14:313–334.

    Article  CAS  Google Scholar 

  • Ontario Geological Survey (2003) Surficial geology of southern Ontario, Misc. Release-Data 128, Toronto, Canada. 52 p.

  • Orihel DM, Parsons CT, Venkiteswaran JJ, Casson NJ, Seckar DCM, Baulch HM, Casson NJ, North RL, Parsons CT, Seckar DCM, Venkiteswaran JJ. 2017. Internal phosphorus loading in Canadian fresh waters: a critical review and data analysis. Can J Fish Aquatic Sci 74:2005–2029.

    Article  CAS  Google Scholar 

  • Östlund C, Flink P, Strömbeck N, Pierson D, Lindell T. 2001. Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper. Sci Total Environ 268:139–154.

    Article  PubMed  Google Scholar 

  • Paerl HW, Huisman J. 2008. Blooms like it hot. Science 320:57–58.

    Article  CAS  PubMed  Google Scholar 

  • Pálffy K, Vörös L. 2019. Phytoplankton functional composition shows higher seasonal variability in a large shallow lake after a eutrophic past. Ecosphere 10(5):e02684.

    Article  Google Scholar 

  • Palmer SCJ, Kutser T, Hunter PD. 2015. Remote sensing of inland waters: challenges, progress and future directions. Remote Sens Environ 157:1–8.

    Article  Google Scholar 

  • Paterson AM, Rühland KM, Anstey CV, Smol JP. 2017. Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada. Lake Reserv Manag 33(4):403–414.

    Article  CAS  Google Scholar 

  • Paul VJ. 2008. Global warming and cyanobacterial harmful phytoplankton blooms. Adv Exp Med Biol 619:239–257.

    Article  CAS  PubMed  Google Scholar 

  • Pearson D, Gunn JM, Keller W. 2002. The past, present, and future of Sudbury’s lakes. In: Rousell DH, Jansons K, Eds. Physical Environment of the Sudbury Region, Geological Survey Special Volume. Sudbury, Ontario: Ontario Geological Survey. pp 195–215.

  • Pelster D, Burke JM, Couling K, Luke SH, Smith DW, Prepas EE. 2008. Water and nutrient inputs, outputs, and storage in Canadian boreal forest wetlands: a review. J Environ Eng Sci 7:35–50.

    Article  Google Scholar 

  • Posch T, Köster O, Salcher MM, Pernthaler J. 2012. Harmful filamentous cyanobacteria favoured by reduced water turnover with lake warming. Nat Clim Change 2:809–813.

    Article  CAS  Google Scholar 

  • Randsalu-Wendrup L, Conley DJ, Carstensen J, Fritz SC. 2016. Paleolimnological records of regime shifts in lakes in response to climate change and anthropogenic activities. J Paleolimnol 56:1–4.

    Article  Google Scholar 

  • Read EK, Patil VP, Oliver SK, Hetherington AL, Brentrup JA, Zwart JA, Winters KM, Corman JR, Nodine ER, Woolway RI, Dugan HA, Jaimes A, Santoso AB, Hong GS, Winslow LA, Hanson PC, Weathers KC. 2015. The importance of lake-specific characteristics for water quality across the continental United States. Ecol Appl 25:943–955.

    Article  PubMed  Google Scholar 

  • Richardson L. 1996. Remote sensing of algal bloom dynamics. BioScience 46(7):492–501.

    Article  Google Scholar 

  • Richardson J, Miller C, Maberly SC, Taylor P, Globevnik L, Hunter P, Jeppesen E, Mischke U, Moe J, Pasztaleniec A, Søndergaard M, Carvalho L. 2018. Effects of multiple stressors on cyanobacteria abundance vary with lake type. Global Change Biol 24:5044–5055.

    Article  Google Scholar 

  • Rigosi A, Carey CC, Ibelings BW, Brookes JD. 2014. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol Oceanogr 59:99–114.

    Article  Google Scholar 

  • Rivera A. (2014) Canada’s groundwater resources. Geological Survey of Canada. Fitzhenry and Whiteside, Markham, ON, 803 p.

  • Ross MRV, Topp SN, Appling AP, Yang X, Kuhn C, Butman D, Simard M, Pavelsky TM. 2019. AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resour Res 55:10012–10025.

    Article  Google Scholar 

  • Sass GZ, Creed IF, Bayley SE, Devito KJ. 2007. Understanding variation in trophic status of lakes on the Boreal Plain: a 20 year retrospective using Landsat TM imagery. Remote Sens Environ 109:127–141.

    Article  Google Scholar 

  • Schalles JF. 2006. Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal waters with varying suspended matter and CDOM concentrations. In: Richardson L, LeDrew E, Eds. Remote sensing of aquatic coastal ecosystem processes. Dordrecht: Springer. pp 27–79.

    Chapter  Google Scholar 

  • Scheffer MA, Van Nes EH. 2007. Shallow lakes theory revisited: various alternative regimes driven by climate, nutrients, depth and lake size. Hydrobiologia 584:455–466.

    Article  CAS  Google Scholar 

  • Sabo JL, Post DM. 2008. Quantifying periodic, stochastic, and catastrophic environmental variation. Ecol Monogr 78:19–40.

    Article  Google Scholar 

  • Salem SI, Higa H, Kim H, Kobayashi H, Oki K, Oki T. 2017. Assessment of chlorophyll-a algorithms considering different trophic statuses and optimal bands. Ah S Sens 17(8):1746.

    Article  CAS  Google Scholar 

  • Semkin RG, Kramer JR. 1976. Lakes of sudbury area geochemistry sediment. Can Mineral 14:73–90.

    Google Scholar 

  • Sen PK. 1968. Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63(324):1379–1389.

    Article  Google Scholar 

  • Senar OE, Creed IF, Webster KL. 2018. Catchment-scale shifts in the magnitude and partitioning of carbon export in response to changing hydrologic connectivity in a northern hardwood forest. J Geophys Res Biogeosci 123:2337–2352.

    Article  CAS  Google Scholar 

  • Simis SG, Peters SW, Gons H. 2005. Remote Sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol Oceanogr 50:237–245.

    Article  CAS  Google Scholar 

  • Singh K, Ghosh M, Sharma SR, Kumar P. 2014. Blue–red–NIR model for chlorophyll-a retrieval in hypersaline–alkaline water using Landsat ETM+ sensor. IEEE J Sel Topics Appl Earth Observ Remote Sens 7(8):3553–3559.

    Article  Google Scholar 

  • Sinha E, Michalak AM, Balaji V. 2017. Eutrophication will increase during the 21st century as a result of precipitation changes. Science 357:1–5.

    Article  Google Scholar 

  • Smith RC, Baker KS. 1981. Optical properties of the clearest natural water (200–800 nm). Appl Opt 20:177–184.

    Article  CAS  PubMed  Google Scholar 

  • Smol PJ, Cumming BF. 2000. Tracking long-term changes in climate using algal indicators in lake sediments. J Phycol 36:986–1011.

    Article  CAS  Google Scholar 

  • Sobek S, Tranvik LJ, Prairie YT, Cole JJ. 2007. Patterns and regulation of dissolved organic carbon: An analysis of 7,500 widely distributed lakes. Limnol Oceanogr Methods 52:1208–1219.

    Article  CAS  Google Scholar 

  • Song K, Li L, Tedesco LP, Li S, Duan H, Liu D, Hall BE, Du J, Li Z, Shi K, Zhao Y. 2013. Remote estimation of chlorophyll-a in turbid inland waters: three-band model versus GA-PLS model. Remote Sens Environ 136:342–357.

    Article  Google Scholar 

  • Sorichetti RJ, Creed IF, Trick CG. 2014. Evidence for iron-regulated cyanobacterial predominance in oligotrophic lakes. Freshw Biol 59:679–691.

    Article  CAS  Google Scholar 

  • Spry DJ, Weiner JG. 1991. Metal bioavailability and toxicity to fish in low alkalinity lakes: a critical review. Environ Pollut 71:243–304.

    Article  CAS  PubMed  Google Scholar 

  • Stadelmann TH, Brezonik PL, Kloiber S. 2001. Seasonal patterns of chlorophyll a and secchi disk transparency in lakes of east-central Minnesota: Implications for design of ground- and satellite-based monitoring programs. Lake Reserv Manag 17(4):299–314.

    Article  CAS  Google Scholar 

  • Staehr PA, Baastrup-Spohr L, Sand-Jensen K, Stedmon C. 2012. Lake metabolism scales with lake morphometry and catchment conditions. Aquatic Sci 74:155–169.

    Article  CAS  Google Scholar 

  • Stoddard JL, Van Sickle J, Herlihy AT, Brahney J, Paulsen S, Peck DV, Mitchell R, Pollard A. 2016. Continental-Scale increase in lake and stream phosphorus: are oligotrophic systems disappearing in the United States? Environ Sci Technol 50(7):3409–3415.

    Article  CAS  PubMed  Google Scholar 

  • Stomp M, Huisman J, Mittelbach GG, Litchman E, Klausmeier CA. 2011. Large-scale biodiversity patterns in freshwater phytoplankton. Ecology 92(11):2096–2107.

    Article  PubMed  Google Scholar 

  • Strömbeck N, Pierson DC. 2001. The effects of variability in the inherent optical properties on estimations of chlorophyll a by remote sensing in Swedish freshwaters. Sci Total Environ 268:123–137.

    Article  PubMed  Google Scholar 

  • Stumpf RP, Davis TW, Wynne TT, Graham JL, Loftin KA, Johengen TH, Gossiaux D, Palladino D, Burtner A. 2016. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae 54:160–173.

    Article  CAS  PubMed  Google Scholar 

  • Sudheer KP, Chaubey I, Garg V. 2006. Lake water quality assessment from Landsat thematic mapper data using neural network: an approach to optimal band combination selection. J Am Water Resour Assoc 42:1683–1695.

    Article  Google Scholar 

  • Tan W, Liu P, Liu Y, Yang S, Feng S. 2017. 30-year assessment of phytoplankton blooms in Erhai Lake using Landsat imagery: 1987 to 2016. Remote Sens 9:1265.

    Article  Google Scholar 

  • Taranu ZE, Gregory-Eaves I, Leavitt PR, Bunting L, Buchaca T, Catalan J, Domaizon I, Guilizzoni P, Lami A, McGowan S, Moorhouse H, Morabito G, Pick FR, Stevenson MA, Thompson PL, Vinebrooke RD. 2015. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol Lett 18:375–384.

    Article  PubMed  Google Scholar 

  • Tebbs EJ, Remedios JJ, Harper DM. 2013. Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hyper-eutrophic, saline–alkaline, flamingo lake, using Landsat ETM+. Remote Sens Environ 135:92–106.

    Article  Google Scholar 

  • Tetzlaff D, Carey SK, Laudon H, McGuire K. 2010. Catchment processes and heterogeneity at multiple scales—benchmarking observations, conceptualization and prediction. Hydrol Process 24:2203–2208.

    Article  Google Scholar 

  • Tomlinson MC, Stumpf RP, Wynnea TT, Dupuy D, Burks R, Hendrickson J, Fulton RS. 2016. Relating chlorophyll from cyanobacteria-dominated inland waters to a MERIS bloom index. Remote Sens Lett 2:141–149.

    Article  Google Scholar 

  • Topp SN, Pavelsky TM, Jensen D, Simard M, Ross MRV. 2020. Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications. Water 12:169.

    Article  CAS  Google Scholar 

  • Torbick N, Hession S, Hagen S, Wiangwang N, Becker B, Qi J. 2013. Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery. Int J Remote Sens 34:7607–7624.

    Article  Google Scholar 

  • Valois AE, Keller WB, Ramcharan CW. 2011. Recovery in a multiple stressor environment: using the reference condition approach to examine zooplankton community change along opposing gradients. J Plankton Res 33:1417–1429.

    Article  Google Scholar 

  • Van Nes EH, Rip WJ, Scheffer MA. 2007. Theory for cyclic shifts between alternative states in shallow lakes. Ecosystems 10:17–27.

    Article  CAS  Google Scholar 

  • Verpoorter C, Kutser T, Tranvik L. 2012. Automated mapping of water bodies using Landsat multispectral data. Limnol Oceanogr Methods 10:1037–1050.

    Article  Google Scholar 

  • Vincent R, Xiaoming Q, McKay R, Miner J, Czajkowski K, Savino J, Bridgeman T. 2004. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sens Environ 89:381–392.

    Article  Google Scholar 

  • Wasserstein RL, Schirm AL, Lazar NA. 2019. Moving to a World Beyond “p < 0.05”. Am Stat 73:1–19.

    Article  Google Scholar 

  • Wiley MJ, Kohler SL, Seelbach PW. 1997. Reconciling landscape and local views of aquatic communities: lessons from Michigan trout streams. Freshw Biol 37:133–148.

    Article  Google Scholar 

  • Winder M, Sommer U. 2012. Phytoplankton response to a changing climate. Hydrobiologia 698:5–16.

    Article  Google Scholar 

  • Winter JG, DeSellas AM, Fletcher R, Heintsch L, Morley A, Nakamoto L, Utsumi K. 2011. Algal blooms in Ontario, Canada: Increases in reports since 1994. Lake Reserv Manag 27:107–114.

    Article  Google Scholar 

  • Whitehead PG, Wilby RL, Battarbee RW, Kernan M, Wade AJ. 2009. A review of the potential impacts of climate change on surface water quality. Hydrol Sci J 54:101–123.

    Article  Google Scholar 

  • Yacobi YZ, Gitelson AA, Mayo M. 1995. Remote sensing of chlorophyll in Lake Kinneret using high spectral resolution radiometer and Landsat thematic mapper: Spectral features of reflectance and algorithm development. J Plankton Res 17(11):2155–2173.

    Article  CAS  Google Scholar 

  • Yan ND, Keller W, Somers KM, Pawson TW, Girard RE. 1996. Recovery of crustacean zooplankton communities from acid and metal contamination: comparing manipulated and reference lakes. Can J Fish Aquatic Sci 53:1301–1327.

    Article  Google Scholar 

  • Yeung ACY, Paltsev A, Daigle A, Duinker PN, Creed IF. 2019. Atmospheric change as a driver of change in the Canadian boreal zone. Environ Rev 27(3):346–376.

    Article  Google Scholar 

  • Yip HD, Johansson J, Hudson JJ. 2015. A 29-year assessment of the water clarity and chlorophyll-a concentration of a large reservoir: Investigating spatial and temporal changes using Landsat imagery. J Great Lakes Res 41:34–44.

    Article  CAS  Google Scholar 

  • Zhang H, Wang M. 2010. Evaluation of sun glint models using MODIS measurements. J Quant Spectrosc Ra 111(3):492–506.

    Article  CAS  Google Scholar 

  • Zhu Z, Woodcock CE. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ 118:83–94.

    Article  Google Scholar 

  • Zhu W, Yu Q, Tian YQ, Becker BL, Zheng T, Carrick HJ. 2014. An assessment of remote sensing algorithms for colored dissolved organic matter in complex freshwater environments. Remote Sens Environ 140:766–778.

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 06579-2014 to IFC and an NSERC Collaborative Research and Training Experience (CREATE) Grant 448172-2014 to IFC that funded AP. The authors are grateful to David Aldred and Francesco Tonini for assistance with remote sensing and kriging procedures, and to Claire Holeton and Jennifer Winter for sharing the Ontario Ministry of the Environment, Conservation and Parks phytoplankton bloom reports data.

Author information

Author notes

  1. IFC conceived the study and AP designed the study. AP performed the research, including compiling, analyzing, and modelling data, determining the image analysis protocols, and creating the tables and figures. AP wrote the paper, and IFC edited the paper.

Authors and Affiliations

  1. Department of Biology, Western University, 1151 Richmond Street, London, Ontario, N6A 5B7, Canada

    Aleksey Paltsev & Irena F. Creed

  2. School of Environment and Sustainability, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada

    Irena F. Creed

  3. Office of the Vice-Principal Research & Innovation, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, M1C 1A4, Canada

    Irena F. Creed

Authors
  1. Aleksey Paltsev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irena F. Creed
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IFC conceived the study and AP designed the study. AP performed the research, including compiling, analyzing, and modeling data, determining the image analysis protocols, and creating the tables and figures. AP wrote the paper, and IFC edited the paper.

Corresponding author

Correspondence to Irena F. Creed.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 356 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paltsev, A., Creed, I.F. Are Northern Lakes in Relatively Intact Temperate Forests Showing Signs of Increasing Phytoplankton Biomass?. Ecosystems 25, 727–755 (2022). https://doi.org/10.1007/s10021-021-00684-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10021-021-00684-y

Keywords

Search

Navigation