This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Harris G. Phytoplankton Ecology: Structure, Function and Fluctuation. London, New York: Chapman and Hall; 1986. DOI: 10.1007/978-94-009-3165-7.Search in Google Scholar
Chapman RL. Algae: the world’s most important “plants” - an introduction. Mitig Adapt Strateg Glob Change. 2013;18:5-12. DOI: 10.1007/s11027-010-9255-9.Search in Google Scholar
Geider RJ, Moore CM, Suggett DJ. Ecology of Marine Phytoplankton. In: Ecology and the Environment. New York: Springer; 2014:483-531. DOI: 0.1007/978-1-4614-7501-9_23.Search in Google Scholar
Lapointe BE, Burkholder JM, Alstyne KLV. Harmful Macroalgal Blooms in a Changing World: Causes, Impacts, and Management. Chapter 15 in Harmful Algal Blooms: A Compendium Desk Reference. 2018:515-60. DOI: 10.1002/9781118994672.ch15.Search in Google Scholar
Zhang Y, Whalen JK, Cai C, Shan K, Zhou H. Harmful cyanobacteria-diatom/dinoflagellate blooms and their cyanotoxins in freshwaters: A nonnegligible chronic health and ecological hazard. Water Res. 2023;233:119807. DOI: 10.1016/j.watres.2023.119807.Search in Google Scholar
Patino R, Christensen VG, Graham JL, Rogosch JS, Rosen BH. Toxic algae in inland waters of the conterminous United States - A review and synthesis. Water. 2023;15:2808. DOI: 10.3390/w15152808.Search in Google Scholar
Park J, Patel K, Lee WH. Recent advances in algal bloom detection and prediction technology using machine learning. Sci Total Environ. 2024;938:173546. DOI: 10. 1016/j.scitotenv.2024.173546.Search in Google Scholar
Igwaran A, Kayode AJ, Moloantoa KM, Khetsha ZP, Unuofin JO. Cyanobacteria harmful algae blooms: causes, impacts, and risk management. Water Air Soil Pollut. 2024;235:71. DOI: 10.1007/s11270-023-06782-y.Search in Google Scholar
Calomeni-Eck AJ, McQueen AD, Kinley-Baird CM, Clyde Jr T. Identification of cyanobacteria overwintering cells and environmental conditions causing growth: Application for preventative management. Ecol Solut Evid. 2024;5:e12326. DOI: 10.1002/2688-8319.12326.Search in Google Scholar
Yan Y, Xu Z, Yang B, Jiang, He S, Sheng H, et al. Spatio-temporal variations of water quality and planktonic algal communities in Qingshan Reservoir, China. Pol J Environ Stud. 2023;32:2405-16. DOI: 10.15244/pjoes/158907.Search in Google Scholar
Barkoh A, Fries LT. Aspects of the origins, ecology, and control of golden alga Prymnesium parvum: Introduction to the featured collection. JAWRA J Am Water Resources Assoc. 2010;46:1-5. DOI: 0.1111/j.1752-1688.2009.00394.x.Search in Google Scholar
Roelke D, Manning S. Harmful Algal Species Fact Sheet: Prymnesium parvum (Carter) Golden Algae: A Compendium Desk Reference. In: Harmful Algal Blooms. Hoboken, NJ: Wiley; 2018:629-32. DOI: 10.1002/9781118994672.ch16q.Search in Google Scholar
Kitsiou D, Karydis M. Coastal marine eutrophication assessment: A review on data analysis. Environ Int. 2011;37:778-801. DOI: 10.1016/j.envint.2011.02.004.Search in Google Scholar
Goovaerts P. Geostatistics for Natural Resources Evaluation. Oxford: University Press; 1997. DOI: 10.1093/oso/9780195115383.001.0001.Search in Google Scholar
Webster R, Oliver MA. Geostatistics for Environmental Scientists. 2nd ed. Chichester: Wiley; 2007: DOI: 10.1002/9780470517277.Search in Google Scholar
Gómez-Hernández JJ. Geostatistics for environmental applications. Math Geosci. 2016;48:1-2. DOI: 10.1007/s11004-015-9627-5.Search in Google Scholar
Christakos G. Modern Spatiotemporal Geostatisics. New York: Oxford University Press; 2000: ISBN: 0195138953.Search in Google Scholar
Zawadzki J, Fabijańczyk P. On the influence of the nugget effect on the efficiency of magnetometric soil surface screening. Ecol Chem Eng S. 2023;29:525-35. DOI: 10.2478/eces-2022-0038.Search in Google Scholar
Chilès JP, Delfiner P. Geostatistics: Modeling Spatial Uncertainty. 2nd ed. Hoboken, NJ: Wiley; 2012: ISBN: 9780470183151.Search in Google Scholar
Clark I, Harper WV. Practical Geostatistics 2000. Columbus Ohio: Ecosse North America; 2000. ISBN: 0970331746.Search in Google Scholar
Zawadzki J. Metody geostatystyczne: dla kierunków przyrodniczych i technicznych (Geostatistical Methods: For Natural and Technical Sciences). Warszawa: Ofic Wyd Polit Warszawskiej; 2011. ISBN: 8372079536 .Search in Google Scholar
Isaaks EH, Srivastava RM. An Introduction to Applied Geostatistics. New York: Oxford University Press; 1990. ISBN: 9780195050127.Search in Google Scholar
Chilès JP, Desassis N. Fifty Years of Kriging. In: Daya Sagar BS, Cheng Q, Agterberg F, editors. Handbook of Mathematical Geosciences: Fifty Years of IAMG. Cham: Springer Int Publishing; 2018:589-612. DOI: 978-0-19-505012-7.Search in Google Scholar
Hengl T. A Practical Guide to Geostatistical Mapping of Environmental Variables. EUR 22904 EN. Luxembourg: Office for Official Publications of the European Communities; 2007. ISBN: 9789279069048.Search in Google Scholar
Cressie N. Statistics for Spatial Data. NY, Chichester, Toronto, Brisbane, Singapore: John Wiley Sons; 2015. ISBN: 9781119115182.Search in Google Scholar
Varouchakis EA. 1 - Geostatistics: Mathematical and Statistical Basis. In: Corzo G, Varouchakis EA, editors. Spatiotemporal Analysis of Extreme Hydrological Events. Elsevier; 2019:1-38. ISBN: 9780128116890.Search in Google Scholar
Kleijnen J. Kriging: Methods and Applications. SSRN Electronic J. 2017; DOI: 10.2139/ssrn.3075151.Search in Google Scholar
Oliver MA, Webster R. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA 2014;113:56-69. DOI: 10.1016/j.catena.2013.09.006.Search in Google Scholar
Diggle P, Lophaven S. Bayesian geostatistical design. Scandinavian J Statistics. 2006;33:53-64. DOI: 10.1111/j.1467-9469.2005.00469.x.Search in Google Scholar
Pilz J, Spöck G. Why do we need and how should we implement Bayesian kriging methods. Stoch Environ Res Risk Assess. 2008;22:621-32. DOI:10.1007/s00477-007-0165-7.Search in Google Scholar
Montero J, Fernández-Avilés G, Mateu J. Spatial and Spatio-Temporal Geostatistical Modeling and Kriging. Wiley; 2015. DOI: 10.1002/9781118762387.Search in Google Scholar
Gómez-Rubio V, Rue H. Markov chain Monte Carlo with the Integrated Nested Laplace Approximation. Stat Comput. 2018;28:1033-51. DOI: 10.1007/s11222-017-9778-y.Search in Google Scholar
Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics Computing. 2000;10:325-37. DOI: 10.1023/A:1008929526011.Search in Google Scholar
Ntzoufras I. Bayesian Modeling Using WinBUGS. Hoboken, NJ: Wiley; 2009: DOI: 10.1002/9780470434567.Search in Google Scholar
Blangiardo M, Cameletti M. Spatial and Spatio-temporal Bayesian Models with R - INLA. Chichester, UK: Wiley; 2015. DOI: 10.1002/9781118950203.Search in Google Scholar
Van Niekerk J, Krainski E, Rustand D, Rue H. A new avenue for Bayesian inference with INLA. Computational Statistics Data Analysis. 2023;181:107692. DOI: 10.1016/j.csda.2023.107692.Search in Google Scholar
Wu X, Mitsch W. Spatial and temporal patterns of algae in newly constructed freshwater wetlands. Wetlands. 1998;18:9-20. DOI: 10.1007/BF03161438.Search in Google Scholar
Kawata M, Hayashi M, Hara T. Interactions between neighboring algae and snail grazing in structuring microdistribution patterns of periphyton. Oikos. 2001;92:404-16. DOI: 10.1034/j.1600-0706.2001.920302.x.Search in Google Scholar
Zhao B, Cai Q. Geostatistical analysis of chlorophyll a in freshwater ecosystems. J Freshwater Ecol. 2004;19:613-21. DOI: 10.1080/02705060.2004.9664742.Search in Google Scholar
Burrough P. Fractal dimensions of landscapes and other environmental data. Nature. 1981;294:240-2. DOI: 10.1038/294240a0.Search in Google Scholar
Welty L, Stein M. Modeling Phytoplankton: Covariance and Variogram Model Specification for Phytoplankton Levels in Lake Michigan. In: geoENV IV - Geostatistics for Environmental Applications. Barcelona: Cluver Academic Publishers; 2004:163-73. DOI: 10.1007/1-4020-2115-1_14.Search in Google Scholar
Falkowski PG, Dubinsky Z, Wyman K. Growth-irradiance relationships in phytoplankton. Limnology Oceanography. 1985;30:311-21. DOI: 10.4319/lo.1985.30.2.0311.Search in Google Scholar
Eadie BJ, Robbins JA, Klump JV, Schwab DJ, Edgington DN. Winter-spring storms and their influence on sediment resuspension, transport, and accumulation patterns in Southern Lake Michigan. Oceanography. 2008;21:118-35. DOI: 10.5670/oceanog.2008.09.Search in Google Scholar
Müller D. Estimation of algae concentration in cloud covered scenes using geostatistical methods. In: Proceedings of Envisat Symposium, 23-27 April 2007, Montreux, Switzerland. 2007. ISBN: 9789292912000.Search in Google Scholar
Wang XJ, Liu RM. Spatial analysis and eutrophication assesment for chlorophyll a in Taihu Lake. Environ Monit Assess. 2005;101:167-74. DOI: 10.1007/s10661-005-9154-9.Search in Google Scholar
Zhang F, Tang H, Jin G, Zhu Y, Zhang H, Stewart RA, et al. Evaluating nutrient distribution and eutrophication pattern in a shallow impounded lake: Exploring the influence of floods. Int J Sediment Res. 2024;39:375-85. DOI: 10.1016/j.ijsrc.2024.04.006.Search in Google Scholar
Ludovisi A, Minozzo M, Pandolfi P, Taticchiet MI. Modelling the horizontal spatial structure of planktonic community in Lake Trasimeno (Umbria, Italy) using multivariate geostatistical methods. Ecol Modelling. 2005;181:247-62. DOI: 10.1016/j.ecolmodel.2004.06.033.Search in Google Scholar
Bucas M. Distribution patterns and ecological role of the Red Alga Furcellaria Lumbricalis (Hudson) J.V.Lamouroux off the exposed Baltic Sea coast of Lithuania. [PhD Thesis]. Klaipėda 2009. Available from: https://www.ku.lt/uploads/documents/files/jti/studijos/MBucas2004-2009.pdf; Access: 16.12.2024.Search in Google Scholar
Diaz E, Erlandsson J, McQuaid C. Detecting spatial heterogeneity in intertidal algal functional groups, grazers and their co-variation among shore levels and sites. J Experimental Marine Biol Ecol. 2011;409. DOI: 10.1016/j.jembe.2011.08.013.Search in Google Scholar
Tapia O, Vilchis MI, Sentíes A, Dreckmann K. Mapping of algae richness using spatial data interpolation. International J Remote Sensing. 2015;XL-7/W3. DOI: 10.5194/isprsarchives-XL-7-W3-1005-2015.Search in Google Scholar
Buelo C, Carpenter S, Pace MA modeling analysis of spatial statistical indicators of thresholds for algal blooms. Limnology Oceanography Lett. 2018;3. DOI: 10.1002/lol2.10091.Search in Google Scholar
Serizawa H, Amemiya T, Itoh K. Patchiness in a minimal nutrient - phytoplankton model. J Biosci. 2008;33:391-403. DOI: 10.1007/s12038-008-0059-y.Search in Google Scholar
Fox JE. Utilising chlorophyll fluorescence to assess the variability of phytoplankton biomass and productivity in the north-west European shelf seas. PhD Thesis. University of Essex; 2018. Available from: https://repository.essex.ac.uk/21546/1/Fox_2017_corrected.pdf; Access: 16.12.2024.Search in Google Scholar
Kim JS, Seo IW, Baek D. Modeling spatial variability of harmful algal bloom in regulated rivers using a depth-averaged 2D numerical model. J Hydroenviron Res. 2018;20. DOI: 10.1016/j.jher.2018.04.008.Search in Google Scholar
Pinkerton M, Gall M, Wood S, Zeldis J. Measuring the effects of bivalve mariculture on water quality in northern New Zealand using 15 years of MODIS-Aqua satellite observations. Aquacult Environ Interactions. 2018;10:529-45. Available from: https://www.researchgate.net/publication/328024096_Measuring_the_effects_of_bivalve_mariculture_on_water_quality_in_northern_New_Zealand_using_15_years_of_MODIS-Aqua_satellite_observations; Access: 16.12.2024.Search in Google Scholar
Son G, Kim D, Kim Y, Lyu S, Kim S. A forecasting method for harmful algal bloom(HAB)-prone regions allowing preemptive countermeasures based only on acoustic doppler current profiler measurements in a large river. Water. 2020;12:3488. DOI: 10.3390/w12123488.Search in Google Scholar
Doney SC, Glover D, McCue S, Fuentes M. Mesoscale Variability of SeaWiFS Satellite Ocean Color: Global Patterns and Spatial Scales. Available from: https://www.researchgate.net/publication/2409753_Mesoscale_Variability_of_SeaWiFS_Satellite_Ocean_Color_Global_Patterns_and_Spatial_Scales; Access: 03.12.2024.Search in Google Scholar
Doney SC, Glover D, McCue S, Fuentes M. Mesoscale variability of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color: Global patterns in spatial scales. J Geophys Res (Oceans). 2003;108. DOI: 10.1029/2001JC000843.Search in Google Scholar
Wallis A, Doney SC, Glover D, Nelson NB. Characterizing Submesoscale Ocean Color Variability in the Sargasso Sea in the Vicinity of the Bermuda Atlantic Time-series Site (BATS): A Geostatistical Approach. Available from: https://www.researchgate.net/publication/252148091_Characterizing_Submesoscale_Ocean_Color_Variability_in_the_Sargasso_Sea_in_the_Vicinity_of_the_Bermuda_Atlantic_Time-series_Site_BATS_A_Geostatistical_Approach; Stand: 03.12.2024.Search in Google Scholar
Glover D, Doney SC, Oestreich W, Tullo A. Geostatistical analysis of mesoscale spatial variability and error in SeaWiFS and MODIS/Aqua Global Ocean Color Data. J Geophys Res: Oceans. 2017;123. DOI: 10.1002/2017JC013023.Search in Google Scholar
Chelton D, Gaube P, Schlax M, Early JJ, Samelson RM. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science. New York. 2011;334:328-32. DOI: 10.1126/science.1208897.Search in Google Scholar
Gaube P, Chelton D, Strutton P, Behrenfeld MJ. Satellite observations of chlorophyll, phytoplankton biomass, and Ekman pumping in nonlinear mesoscale eddies. J Geophys Res: Oceans. 2013;118. DOI: 10.1002/2013JC009027.Search in Google Scholar
Rohr T, Harrison C, Long MC, Gaube P, Doney SC. The simulated biological response to southern ocean eddies via biological rate modification and physical transport. Global Biogeochem Cycles. 2020;34:e2019GB006385. DOI: 10.1029/2019GB006385.Search in Google Scholar
Rohr T, Harrison C, Long MC, Gaube P, Doney SC. Eddy‐modified iron, light, and phytoplankton cell division rates in the simulated southern ocean. Global Biogeochem Cycles. 2020;34: e2019GB006380. DOI: 10.1029/2019GB006380.Search in Google Scholar
Eveleth R, Glover DM, Long MC, Lima ID, Chase AP, Doney SC. Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing. Front Marine Sci. 2021;8. DOI: 10.3389/fmars.2021.612764.Search in Google Scholar
Smith R, Jones P, Briegleb P, Brayan F, Danabasoglu G, Dennis J, et al. The Parallel Ocean Program (POP) reference manual: Ocean Component Community Climate System Model (CESM). 2010. Available from: https://www2.cesm.ucar.edu/models/cesm1.3/ocean/doc/sci/POPRefManual.pdf. Access: 03.12.2024.Search in Google Scholar
Fang S, Del Giudice D, Scavia D, Binding CE, Bridgeman TB, Chaffin JD, et al. A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent. Sci Total Environ. 2019;695:133776. DOI: 10.1016/j.scitotenv.2019.133776.Search in Google Scholar
Stein ML. Space-time covariance functions. J Am Statistical Assoc. 2005; DOI: 10.1198/016214504000000854.Search in Google Scholar
Sharp SL, Forrest AL, Bouma-Gregson K, Jin Y, Cortes A, Schladow SG. Quantifying scales of spatial variability of cyanobacteria in a large, eutrophic lake using multiplatform remote sensing tools. Front Environ Sci. 2021;9. DOI: 10.3389/fenvs.2021.612934.Search in Google Scholar
Wynne T, Stumpf R, Tomlinson M, Dyble J. Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data. Limnol Oceanography. 2010. DOI: 10.4319/lo.2010.55.5.2025.Search in Google Scholar
Lunetta RS, Schaeffer BA, Stumpf RP, Keith D, Scott AJ, Murphy MS. Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA. Remote Sensing Environ. 2015;157:24-34. DOI: 10.1016/j.rse.2014.06.008.Search in Google Scholar
Szatmári G, Kocsis M, Makó A, Pásztor L, Bakacsi Z. Joint spatial modeling of nutrients and their ratio in the sediments of Lake Balaton (Hungary): A multivariate geostatistical approach. Water. 2022;14:361. DOI: 10.3390/w14030361.Search in Google Scholar
Présing M, Preston T, Takátsy A, Kovács AW, Vörös L, Kenesi G, et al. Phytoplankton nitrogen demand and the significance of internal and external nitrogen sources in a large shallow lake (Lake Balaton, Hungary). Hydrobiologia. 2008;599:87-95. DOI: 10.1007/s10750-007-9191-1.Search in Google Scholar
Vörös L, Göde PN. Long term changes of phytoplankton in Lake Balaton (Hungary). Int Vereinigung theoretische angewandte Limnologie: Verhandlungen. 1993;25:682-6. DOI: 10.1080/03680770.1992.11900224.Search in Google Scholar
Sahu S, Sarkar S, Gogoi P, Naskar M. A geostatistical framework predicting zooplankton abundance in a large river: Management implications towards potamoplankton sustainability. Environ Manage. 2023;71. DOI: 10.1007/s00267-023-01784-2.Search in Google Scholar
Ivanova N. Global overview of the application of the Braun-Blanquet approach in research. Forests. 2024;15:937. DOI: 10.3390/f15060937.Search in Google Scholar