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Effects of connectivity and watercourse distance on temporal coherence patterns in a tropical reservoir

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Abstract

Temporal coherence exists when environmental variables measured at different spatial locations vary synchronously over time. This is an important property to be analyzed because levels of coherence may indicate the role of regional and local processes in determining population and ecosystem dynamics. Also, studies on temporal coherence may guide the optimal allocation of sampling effort. We analyzed a dataset from a monitoring program undertaken at a tropical reservoir (Peixe Angical Reservoir, State of Tocantins, Brazil) to test three predictions. First, coherence should be a common pattern in the reservoir considering that sampling sites were distributed in a single water body and over a small spatial extent. Second, coherence was expected to decline with increasing watercourse distance and to increase with hydrological connectivity. Third, abiotic variables should exhibit higher coherence than biological variables. Twenty limnological variables were monitored at 14 sites and for 31 months. We found significant levels of coherence for all variables, supporting our first prediction. Watercourse distances, hydrological connectivity, or both were significant predictors of coherence for 17 environmental variables. In all these cases, the signs of the coefficients were in the direction predicted. Interestingly, for some environmental variables (color, turbidity, alkalinity, and total phosphorus), hydrological connectivity was even more important in predicting coherence than watercourse distance. The view that abiotic variables should exhibit higher coherence than biological variables was supported. Our analyses revealed that precipitation was an important factor inducing coherence of a key set of environmental variables, highlighting the role of regional processes in ecosystem dynamics.

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References

  • Anderson, T. L., Walter, J. A., Levine, T. D., Hendricks, S. P., Johnston, K. L., White, D. S., & Reuman, D. C. (2018). Using geography to infer the importance of dispersal for the synchrony of freshwater plankton. Oikos, 127(3), 403–414. https://doi.org/10.1111/oik.04705.

    Article  Google Scholar 

  • APHA. (2005). Standard methods for the examination of water and wastewater. American Water Works Association/American Public Works Association/Water Environment Federation.

  • Arnott, S. E., Keller, B., Dillon, P. J., Yan, N., Paterson, M., & Findlay, D. (2003). Using temporal coherence to determine the response to climate change in boreal shield lakes. Environmental Monitoring and Assessment, 88(1-3), 365–388. https://doi.org/10.1023/A:1025537628078.

    Article  CAS  Google Scholar 

  • Baines, S. B., Webster, K. E., Kratz, T. K., Carpenter, S. R., & Magnuson, J. J. (2000). Synchronous behavior of temperature, calcium, and chlorophyll in lakes of northern Wisconsin. Ecology, 81(3), 815–825. https://doi.org/10.1890/0012-9658(2000)081[0815:SBOTCA]2.0.CO;2.

    Article  Google Scholar 

  • Baron, J. S., & Caine, N. (2000). Temporal coherence of two alpine lake basins of the Colorado front range, U.S.A. Freshwater Biology, 43(3), 463–476. https://doi.org/10.1046/j.1365-2427.2000.00517.x.

    Article  Google Scholar 

  • Benson, B. J., Lenters, J. D., Magnuson, J. J., Stubbs, M., Kratz, T. K., Dillon, P. J., et al. (2000). Regional coherence of climatic and lake thermal variables of four lake districts in the Upper Great Lakes Region of North America. Freshwater Biology, 43(3), 517–527. https://doi.org/10.1046/j.1365-2427.2000.00572.x.

    Article  Google Scholar 

  • Bjørnstad, O. N., Stensetii, N. C., & Saitoh, T. (1999). Synchrony and scaling in dynamics of voles and mice in northern Japan. Ecology, 80(2), 622. https://doi.org/10.1890/0012-9658(1999)080[0622:SASIDO]2.0.CO;2.

    Article  Google Scholar 

  • Caliman, A., Carneiro, L. S., Santangelo, J. M., Guariento, R. D., Pires, A. P. F., Suhett, A. L., Quesado, L. B., Scofield, V., Fonte, E. S., Lopes, P. M., & Sanches, L. F. (2010). Temporal coherence among tropical coastal lagoons: a search for patterns and mechanisms. Brazilian Journal of Biology, 70(3), 803–814. https://doi.org/10.1590/S1519-69842010000400011.

    Article  CAS  Google Scholar 

  • Carmouze, J. P. (1994). O metabolismo dos ecossistemas aquáticos. In In Fundamentos teóricos, métodos de estudo e análises químicas. São Paulo: Edgard Blücher/FAPESP.

    Google Scholar 

  • Carneiro, F. M., Nabout, J. C., Vieira, L. C., Roland, F., & Bini, L. M. (2014). Determinants of chlorophyll-a concentration in tropical reservoirs. Hydrobiologia, 740(1), 89–99. https://doi.org/10.1007/s10750-014-1940-3.

    Article  CAS  Google Scholar 

  • Defriez, E. J., & Reuman, D. C. (2017). A global geography of synchrony for marine phytoplankton. Global Ecology and Biogeography, 26(8), 867–877. https://doi.org/10.1111/geb.12594.

    Article  Google Scholar 

  • Ganio, L. M., Torgersen, C. E., & Gresswell, R. E. (2005). A geostatistical approach for describing spatial pattern in stream networks. Frontiers in Ecology and the Environment, 3(3), 138–144. https://doi.org/10.1890/1540-9295(2005)003[0138:AGAFDS]2.0.CO;2.

    Article  Google Scholar 

  • George, D. G., Talling, J. F., & Rigg, E. (2000). Factors influencing the temporal coherence of five lakes in the English Lake District. Freshwater Biology, 43, 449–461. https://doi.org/10.1046/j.1365-2427.2000.00566.x.

    Article  Google Scholar 

  • Golterman, H. L., Clymo, R. S., & Ohmstad, M. A. M. (1978). Methods for physical and chemical analysis of fresh waters. Oxford: Blackwell Scientific.

    Google Scholar 

  • Goslee, S. C., & Urban, D. L. (2007). The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software, 22(7), 1–19 https://core.ac.uk/download/pdf/6303215.pdf.

    Article  Google Scholar 

  • Järvinen, M., Rask, M., Ruuhijärvi, J., & Arvola, L. (2002). Temporal coherence in water temperature and chemistry under the ice of boreal lakes (Finland). Water Research, 36(16), 3949–3956. https://doi.org/10.1016/S0043-1354(02)00128-8.

    Article  Google Scholar 

  • Jassby, A. D. (1998). Interannual variability at three inland water sites: Implications for sentinel ecosystems. Ecological Application, 8(2), 277–287. https://doi.org/10.1890/1051-0761(1998)008[0277:IVATIW]2.0.CO;2.

    Article  Google Scholar 

  • Kimmel, B. L., Lind, O. T., & Paulson, L. J. (1990). Reservoir primary production. In K. W. Thornton, B. L. Kimmel, & F. E. Payne (Eds.), Reservoir limnology: ecological perspectives (pp. 133–194). New York: John Wiley & Sons.

    Google Scholar 

  • Kling, G. W., Kipphut, G. W., Miller, M. M., & O’Brien, W. J. (2000). Integration of lakes and streams in a landscape perspective: the importance of material processing on spatial patterns and temporal coherence. Freshwater Biology, 43(3), 477–497. https://doi.org/10.1046/j.1365-2427.2000.00515.x.

    Article  Google Scholar 

  • Koenig, W. D. (1999). Spatial autocorrelation of ecological phenomena. Trends in Ecology & Evolution, 14(1), 22–26. https://doi.org/10.1016/S0169-5347(98)01533-X.

    Article  CAS  Google Scholar 

  • Koenig, W. D. (2002). Global patterns of environmental synchrony and the Moran effect. Ecography, 25(3), 283–288. https://doi.org/10.1034/j.1600-0587.2002.250304.x.

    Article  Google Scholar 

  • Kratz, T. K., Soranno, P. A., Baines, S. B., Benson, B. J., Magnuson, J. J., Frost, T. M., & Lathrop, R. C. (1998). Interannual synchronous dynamics in north temperate lakes in Wisconsin, USA. In Management of lakes and reservoirs during global climate change (Vol. 42, pp. 273–287). Kluwer Academic: Publishers.

    Chapter  Google Scholar 

  • Landeiro, V. L., Magnusson, W. E., Melo, A. S., Espírito-Santo, H. M. V., & Bini, L. M. (2011). Spatial eigenfunction analyses in stream networks: do watercourse and overland distances produce different results? Freshwater Biology, 56(6), 1184–1192. https://doi.org/10.1111/j.1365-2427.2010.02563.x.

    Article  Google Scholar 

  • Lansac-Tôha, F. A., Bini, L. M., Velho, L. F. M., Bonecker, C. C., Takahashi, E. M., & Vieira, L. C. G. (2008). Temporal coherence of zooplankton abundance in a tropical reservoir. Hydrobiologia, 614(1), 387–399. https://doi.org/10.1007/s10750-008-9526-6.

    Article  Google Scholar 

  • Lichstein, J. W. (2007). Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecology, 188(2), 117–131. https://doi.org/10.1007/s11258-006-9126-3.

    Article  Google Scholar 

  • Lindenmayer, D. B., & Likens, G. E. (2009). Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends in Ecology and Evolution, 24(9), 482–486. https://doi.org/10.1016/j.tree.2009.03.005.

    Article  Google Scholar 

  • Lindenmayer, D. B., & Likens, G. E. (2010). The science and application of ecological monitoring. Biological Conservation, 140, 1317–1328. https://doi.org/10.1016/j.biocon.2010.02.013.

    Article  Google Scholar 

  • Lindenmayer, D. B., Gibbons, P., Bourke, M. A. X., Burgman, M., Dickman, C. R., Ferrier, S., Fitzsimons, J., Freudenberger, D., Garnett, S. T., Groves, C., & Hobbs, R. J. (2012). Improving biodiversity monitoring. Austral Ecology, 37(3), 285–294. https://doi.org/10.1111/j.1442-9993.2011.02314.x.

    Article  Google Scholar 

  • Lodi, S., Velho, L. F. M., Carvalho, P., & Bini, L. M. (2014). Patterns of zooplankton population synchrony in a tropical reservoir. Journal of Plankton Research, 36(4), 966–977. https://doi.org/10.1093/plankt/fbu028.

    Article  Google Scholar 

  • Mackereth, F. J. H., Heron, J., & Talling, J. F. (1979). Water analysis: some revised methods for limnologists. Ambleside: Freshwater Biological Association. Retrieved from https://doi.org/10.1002/iroh.19790640404, F. J. H. Mackereth, J. Heron & J. F. Talling: Water Analysis: Some Revised Methods for Limnologists. – With 4 fig., 120 pp. Far Sawrey, Ambleside: Freshwater Biological Association Scientific Publication No. 36. 1978. I SBN 900386 31 2. £ 2. 50.

    Google Scholar 

  • Magnuson, J. J., Benson, B. J., & Kratz, T. K. (1990). Temporal coherence in the limnology of a suite of lakes in Wisconsin, U.S.A. Freshwater Biology, 23(1), 145–159. https://doi.org/10.1111/j.1365-2427.1990.tb00259.x.

    Article  Google Scholar 

  • Manly, B. F. J. (2007). Randomization, bootstrap and Monte Carlo methods in biology. Texts in statistical science: Chapman and Hall/CRC.

    Google Scholar 

  • Moran, P. A. P. (1953). The statistical analysis of the Canadian Lynx cycle. II synchronization and meteorology. Australian Journal of Zoology, 1(3), 291–298. https://doi.org/10.1071/ZO9530291.

    Article  Google Scholar 

  • Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., & Oksanen, M. J. (2013). Package “vegan”. Community ecology package, version, 2(9).

  • Pandit, S. N., Cottenie, K., Enders, E. C., & Kolasa, J. (2016). The role of local and regional processes on population synchrony along the gradients of habitat specialization. Ecosphere, 7(5). https://doi.org/10.1002/ecs2.1217.

    Article  Google Scholar 

  • Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439–473.

    Article  Google Scholar 

  • Peterson, E. E., & Urquhart, S. N. (2006). Predicting water quality impaired stream segments using landscape-scale data and a regional geostatistical model: a case study in Maryland. Environmental Monitoring and Assessment, 121(1–3), 613–636. https://doi.org/10.1007/s10661-005-9163-8.

    Article  CAS  Google Scholar 

  • Peterson, E. E., Merton, A. A., Theobald, D. M., & Urquhart, N. S. (2006). Patterns of spatial autocorrelation in stream water chemistry. Environmental Monitoring and Assessment, 121(1–3), 569–594. https://doi.org/10.1007/s10661-005-9156-7.

    Article  CAS  Google Scholar 

  • Core Team, R. (2018). R: a language and environment for statistical computing. In R foundation for statistical computing. Vienna: Austria https://www.R-project.org/.

    Google Scholar 

  • Rangel, L. M., Silva, L. H., Rosa, P., Roland, F., & Huszar, V. L. (2012). Phytoplankton biomass is mainly controlled by hydrology and phosphorus concentrations in tropical hydroelectric reservoirs. Hydrobiologia, 693(1), 13–28. https://doi.org/10.1007/s10750-012-1083-3.

    Article  CAS  Google Scholar 

  • Rhodes, J. R., & Jonzén, N. (2011). Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time? Ecography, 34(6), 1040–1048. https://doi.org/10.1111/j.1600-0587.2011.06370.x.

    Article  Google Scholar 

  • Rusak, J. A., Yan, N. D., & Somers, K. M. (2008). Regional climatic drivers of synchronous zooplankton dynamics in north-temperate lakes. Canadian Journal of Fisheries and Aquatic Sciences, 65(5), 878–889. https://doi.org/10.1139/f08-043.

    Article  Google Scholar 

  • Rusak, J. A., Yan, N. D., Somers, K. M., & McQueen, D. J. (1999). The temporal coherence of zooplankton population abundances in neighboring north-temperate lakes. The American Naturalist, 153(1), 46–58.10.1086/303147.

    Google Scholar 

  • Seebens, H., Einsle, U., & Straile, D. (2013). Deviations from synchrony: spatio-temporal variability of zooplankton community dynamics in a large lake. Journal of Plankton Research, 35(1), 22–32. https://doi.org/10.1093/plankt/fbs084.

    Article  Google Scholar 

  • Stoddard, J. L., Driscoll, C. T., Kahl, J. S., & Kellogg, J. H. (1998). Can site-specific trends be extrapolated to a region? An acidification example for the northeast. Ecological Applications, 8(2), 288–299. https://doi.org/10.1890/1051-0761(1998)008[0288:CSSTBE]2.0.CO;2.

    Article  Google Scholar 

  • Tedesco, P. A., Hugueny, B., Paugy, D., & Fermon, Y. (2004). Spatial synchrony in population dynamics of west African fishes: a demonstration of an intraspecific and interspecific Moran effect. Journal of Animal Ecology, 73(4), 693–705. https://doi.org/10.1111/j.0021-8790.2004.00843.x.

    Article  Google Scholar 

  • Tobler, W. R. (1970). A computer movie simulation urban growth in Detroit region. Economic Geography, 46(332), 234–240. https://doi.org/10.2307/143141.

    Article  Google Scholar 

  • Urquhart, N. S., Paulsen, S. G., & Larsen, D. P. (1998). Monitoring for policy-relevant regional trends over time. Ecological Applications, 8(2), 246–257. https://doi.org/10.1890/1051-0761(1998)008[0246:MFPRRO]2.0.CO;2.

    Google Scholar 

  • Vogt, R. J., Frost, P. C., Nienhuis, S., Woolnough, D. A., & Xenopoulos, M. A. (2016). The dual synchronizing influences of precipitation and land use on stream properties in a rapidly urbanizing watershed. Ecosphere, 7(9). https://doi.org/10.1002/ecs2.1427.

    Article  Google Scholar 

  • Vogt, R. J., Rusak, J. A., Patoine, A., & Leavitt, P. R. (2011). Differential effects of energy and mass influx on the landscape synchrony of lake ecosystems. Ecology, 92(5), 1104–1114.10 1890/10-1846.1.

    Article  Google Scholar 

  • Walter, J. A., Sheppard, L. W., Anderson, T. L., Kastens, J. H., Bjørnstad, O. N., Liebhold, A. M., & Reuman, D. C. (2017). The geography of spatial synchrony. Ecology Letters, 20(7), 801–814. https://doi.org/10.1111/ele.12782.

    Article  Google Scholar 

  • Webster, K. E., Soranno, P. A., Baines, S. B., Kratz, T. K., Bowser, C. J., Dillon, P. J., Campbell, P. L., FeE. ., E. J., & Hecky, R. E. (2000). Structuring features of lake districts: Landscape controls on lake chemical responses to drought. Freshwater Biology, 43(3), 499–515. https://doi.org/10.1046/j.1365-2427.2000.00571.x.

    Article  CAS  Google Scholar 

  • Wetzel, R. G., & Likens, G. E. (2000). Limnological Analyses (Vol. 3).

    Chapter  Google Scholar 

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Acknowledgments

We thank Msc. Carol Costa for helping with Fig. 1, and Katia Bittar Haddad and Wagner Batista Xavier for helping with the fieldwork, and Dr. Bruno Godoy for helpful suggestions throughout the study. This work was supported by the Coordination of higher education personnel (CAPES) and National Counsel of Technological and Scientific Development (CNPQ). This work was also developed in the context of the National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNpq (proc. 465610/2014-5) and FAPEG.

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Lodi, S., Machado-Velho, L.F., Carvalho, P. et al. Effects of connectivity and watercourse distance on temporal coherence patterns in a tropical reservoir. Environ Monit Assess 190, 566 (2018). https://doi.org/10.1007/s10661-018-6902-1

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