Open Access
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 418, 2017
Article Number 58
Number of page(s) 11
DOI https://doi.org/10.1051/kmae/2017044
Published online 27 November 2017
  • Ahmadi-Nedushan B, St-Hilaire A, Bérubé M, Robichaud É, Thiémonge N, Bobée B. 2006. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Res Appl 22: 503–523. [CrossRef]
  • Ali MM, Soltan MA. 2006. Expansion of Myriophyllum spicatum (Eurasian water milfoil) into Lake Nasser, Egypt: invasive capacity and habitat stability. Aquat Bot 84: 239–244. [CrossRef]
  • Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223–1232. [CrossRef]
  • Angradi TR, Pearson MS, Bolgrien DW, Bellinger BJ, Starry MA, Reschke C. 2013. Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary. J Great Lakes Res 39: 536–546. [CrossRef]
  • Araújo MB, Luoto M. 2007. The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16: 743–753. [CrossRef]
  • Austin MP. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157: 101–118. [CrossRef]
  • Austin MP. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200: 1–19. [CrossRef]
  • Austin MP, Meyers JA. 1996. Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity. For Ecol Manage 85: 95–106. [CrossRef]
  • Barko J, Adams M, Clesceri N. 1986. Environmental factors and their consideration in the management of submersed aquatic vegetation: a review. J Aquat Plant Manage 24: 1–10.
  • Beck KG, Zimmerman K, Schardt JD, et al. 2008. Invasive species defined in a policy context: recommendations from the Federal Invasive Species Advisory Committee. Invasive Plant Sci Manage 1: 414–421. [CrossRef]
  • Benda L, Andras K, Miller D, Bigelow P. 2004. Confluence effects in rivers: interactions of basin scale, network geometry, and disturbance regimes. Water Resour Res 40: W05402. [CrossRef]
  • Borchers DL, Buckland ST, Priede IG, Ahmadi S. 1997. Improving the precision of the daily egg production method using generalized additive models. Can J Fish Aquat Sci 54: 2727–2742. [CrossRef]
  • Bornette G, Puijalon S. 2011. Response of aquatic plants to abiotic factors: a review. Aquat Sci 73: 1–14. [CrossRef]
  • Bučas M, Bergström U, Downie A-L, et al. 2013. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: evaluating the scope for predictive mapping using different modelling approaches. ICES J Mar Sci 70: 1233–1243. [CrossRef]
  • Buchan LAJ, Padilla DK. 2000. Predicting the likelihood of Eurasian watermilfoil presence in lakes, a macrophyte monitoring tool. Ecol Appl 10: 1442–1455. [CrossRef]
  • Buisson L, Blanc L, Grenouillet G 2008. Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors. Ecol Freshwater Fish 17: 244–257. [CrossRef]
  • Bunn SE, Arthington AH. 2002. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ Manage 30: 492–507. [CrossRef] [PubMed]
  • Burnham K, Anderson D. 1998. Model selection and inference: a practical information-theoretic approach. New York: Springer. [CrossRef]
  • Camporeale C, Ridolfi L. 2006. Riparian vegetation distribution induced by river flow variability: a stochastic approach. Water Resour Res 42: W10415. [CrossRef]
  • Canfield D, Langeland K, Linda S, Haller W. 1985. Relations between water transparency and maximum depth of macrophyte colonization in lakes. J Aquat Plant Manage 23: 25–28.
  • Chambers PA, DeWreede RE, Irlandi EA, Vandermeulen H. 1999. Management issues in aquatic macrophyte ecology: a Canadian perspective. Can J Bot 77: 471–487.
  • Chappuis E, Gacia E, Ballesteros E. 2014. Environmental factors explaining the distribution and diversity of vascular aquatic macrophytes in a highly heterogeneous Mediterranean region. Aquat Bot 113: 72–82. [CrossRef]
  • Cheng YW, Gallinat MP. 2004. Statistical analysis of the relationship among environmental variables, inter-annual variability and smolt trap efficiency of salmonids in the Tucannon River. Fish Res 70: 229–238. [CrossRef]
  • Clayton J, Edwards T. 2006. Aquatic plants as environmental indicators of ecological condition in New Zealand lakes. Hydrobiologia 570: 147–151. [CrossRef]
  • Cook CDK, Lüönd R. 1982. A revision of the genus Hydrilla (Hydrocharitaceae). Aquat Bot 13: 485–504. [CrossRef]
  • Dar NA, Pandit AK, Ganai BA. 2014. Factors affecting the distribution patterns of aquatic macrophytes. Limnol Rev 14: 75–81.
  • Dawson FH, Raven PJ, Gravelle MJ. 1999. Distribution of the morphological groups of aquatic plants for rivers in the U.K. In Caffrey J, Barrett PRF, Ferreira MT, Moreira IS, Murphy KJ, Wade PM, eds. Biology, Ecology and Management of Aquatic Plants: Proceedings of the 10th International Symposium on Aquatic Weeds, European Weed Research Society. Dordrecht: Springer Netherlands, pp. 123–130. [CrossRef]
  • Dennison WC, Orth RJ, Moore KA, et al. 1993. Assessing water quality with submersed aquatic vegetation. Bioscience 43: 86–94. [CrossRef]
  • Dodkins IAN, Rippey B, Hale P. 2005. An application of canonical correspondence analysis for developing ecological quality assessment metrics for river macrophytes. Freshw Biol 50: 891–904. [CrossRef]
  • Downie AL, von Numers M, Boström C. 2013. Influence of model selection on the predicted distribution of the seagrass Zostera marina. Estuar Coast Shelf Sci 121: 8–19. [CrossRef]
  • Drexler M, Ainsworth CH. 2013. Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool. PLoS ONE 8: e 64458. [CrossRef] [PubMed]
  • Dynesius M, Nilsson C. 1994. Fragmentation and flow regulation of river systems in the Northern Third of the World. Science 266: 753–762. [CrossRef] [PubMed]
  • Eiswerth ME, Donaldson SG, Johnson WS. 2000. Potential environmental impacts and economic damages of Eurasian watermilfoil (Myriophyllum spicatum) in Western Nevada and Northeastern California. Weed Technol 14: 511–518. [CrossRef] [EDP Sciences]
  • Elith J, Graham CH, Anderson RP, et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151. [CrossRef] [EDP Sciences]
  • Fielding AH, Bell JF. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 38–49. [CrossRef]
  • Franklin P, Dunbar M, Whitehead P. 2008. Flow controls on lowland river macrophytes: a review. Sci Total Environ 400: 369–378. [CrossRef] [PubMed]
  • Freeman EA, Moisen GG. 2008. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Model 217: 48–58. [CrossRef]
  • Gallego I, Pérez-Martínez C, Sánchez-Castillo PM, Fuentes-Rodríguez F, Juan M, Casas JJ. 2015. Physical, chemical, and management-related drivers of submerged macrophyte occurrence in Mediterranean farm ponds. Hydrobiologia 762: 209–222. [CrossRef]
  • Gassmann A, Cock MJW, Shaw R, Evans HC. 2006. The potential for biological control of invasive alien aquatic weeds in Europe: a review. Hydrobiologia 570: 217–222. [CrossRef]
  • Gastón A, García-Viñas JI. 2013. Evaluating the predictive performance of stacked species distribution models applied to plant species selection in ecological restoration. Ecol Model 263: 103–108. [CrossRef]
  • Guisan A, Edwards Jr TC, Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157: 89–100. [CrossRef]
  • Heegaard E, Birks HH, Gibson CE, Smith SJ, Wolfe-Murphy S. 2001. Species-environmental relationships of aquatic macrophytes in Northern Ireland. Aquat Bot 70: 175–223. [CrossRef]
  • Järvelä J. 2005. Effect of submerged flexible vegetation on flow structure and resistance. J Hydrol 307: 233–241. [CrossRef]
  • Jiménez-Valverde A, Lobo JM. 2007. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol 31: 361–369. [CrossRef]
  • Jones RC, Walti K, Adams MS. 1983. Phytoplankton as a factor in the decline of the submersed macrophyte Myriophyllum spicatum L. in Lake Wingra, Wisconsin, USA. Hydrobiologia 107: 213–219. [CrossRef]
  • Jun KS, Kim JS. 2011. The four major rivers restoration project: impacts on river flows. KSCE J Civ Eng 15: 217–224. [CrossRef] [EDP Sciences]
  • Kennedy TL, Horth LA, Carr DE. 2009. The effects of nitrate loading on the invasive macrophyte Hydrilla verticillata and two common, native macrophytes in Florida. Aquat Bot 91: 253–256. [CrossRef]
  • Kenneth AL. 1996. Hydrilla verticillata (L.F.) Royle (Hydrocharitaceae), “The Perfect Aquatic Weed”. Castanea 61: 293–304.
  • Kiffney PM, Greene CM, Hall J, Davies J. 2006. Tributary streams create spatial discontinuities in habitat, biological productivity, and diversity in mainstem rivers. Can J Fish Aquat Sci 63: 2518–2530. [CrossRef]
  • Klippel S, Amaral S, Vinhas L. 2016. Development and evaluation of species distribution models for five endangered elasmobranchs in southwestern Atlantic. Hydrobiologia 779: 11–33. [CrossRef]
  • Kuhn M, Johnson K. 2013. Applied predictive modeling. New York: Springer. [CrossRef]
  • Lacoul P, Freedman B. 2006. Environmental influences on aquatic plants in freshwater ecosystems. Environ Rev 14: 89–136. [CrossRef]
  • Lah T, Park Y, Cho YJ. 2015. The four major rivers restoration project of South Korea: an assessment of its process, program, and political dimensions. J Environ Dev 24: 375–394. [CrossRef]
  • Landis JR, Koch GG. 1977. The measurement of observer agreement for categorical data. Biometrics 33 159–174. [CrossRef] [MathSciNet] [PubMed]
  • Lathrop RG, Styles RM, Seitzinger SP, Bognar JA. 2001. Use of GIS mapping and modeling approaches to examine the spatial distribution of seagrasses in Barnegat Bay, New Jersey. Estuaries 24: 904–916. [CrossRef]
  • Lauridsen TL, Jeppesen E, Declerck SAJ, et al. 2015. The importance of environmental variables for submerged macrophyte community assemblage and coverage in shallow lakes: differences between northern and southern Europe. Hydrobiologia 744: 49–61. [CrossRef]
  • Lee JW, Bae SI, Lee DR, Seo DI. 2014. Transportation modeling of conservative pollutant in a river with weirs − the Nakdong River case. J Korean Soc Environ Eng 36: 821–827. [CrossRef]
  • Lehmann A. 1998. GIS modeling of submerged macrophyte distribution using Generalized Additive Models. Plant Ecol 139: 113–124. [CrossRef]
  • Li X, Wang Y. 2013. Applying various algorithms for species distribution modelling. Integr Zool 8: 124–135. [CrossRef] [PubMed]
  • Liu C, White M, Newell G. 2009. Measuring the accuracy of species distribution models: a review. In: Proceedings 18th World IMACs/MODSIM Congress , Cairns, Australia, pp. 4241–4247.
  • Murase H, Nagashima H, Yonezaki S, Matsukura R, Kitakado T. 2009. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. ICES J Mar Sci 66: 1417–1424. [CrossRef]
  • Narumalani S, Jensen JR, Burkhalter S, Althausen JD, Mackey Jr HE. 1997. Aquatic macrophyte modeling using GIS and logistic multiple regression. Photogramm Eng Remote Sens 63: 41–49.
  • Nepf H, Ghisalberti M, White B, Murphy E. 2007. Retention time and dispersion associated with submerged aquatic canopies. Water Resour Res 43: W04422. [CrossRef]
  • Netherland MD. 1997. Turion ecology of hydrilla. J Aquat Plant Manage 35: 1–10.
  • Nichols SA, Shaw BH. 1986. Ecological life histories of the three aquatic nuisance plants, Myriophyllum spicatum, Potamogeton crispus and Elodea canadensis. Hydrobiologia 131: 3–21. [CrossRef]
  • Nieder WC, Barnaba E, Findlay SEG, Hoskins S, Holochuck N, Blair EA. 2004. Distribution and abundance of submerged aquatic vegetation and Trapa natans in the Hudson River Estuary. J Coast Res 150–161. [CrossRef]
  • Normile D. 2010. Restoration or devastation? Science 327: 1568–1570. [CrossRef] [PubMed]
  • O'Hare JM, O'Hare MT, Gurnell AM, Dunbar MJ, Scarlett PM, Laizé C. 2011. Physical constraints on the distribution of macrophytes linked with flow and sediment dynamics in British rivers. River Res Appl 27: 671–683. [CrossRef] [EDP Sciences]
  • Park HK, Cho KH, Won DH, Lee J, Kong DS, Jung DI. 2013. Ecosystem responses to climate change in a large on-river reservoir, Lake Paldang, Korea. Clim Change 120: 477–489. [CrossRef]
  • Patrick CJ, Weller DE, Li X, Ryder M. 2014. Effects of shoreline alteration and other stressors on submerged aquatic vegetation in subestuaries of Chesapeake Bay and the Mid-Atlantic Coastal Bays. Estuar Coast 37: 1516–1531. [CrossRef]
  • Pearce J, Ferrier S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133: 225–245. [CrossRef]
  • Peters J, Baets BD, Verhoest NEC, Samson R, Degroeve S, Becker PD, Huybrechts W. 2007. Random forests as a tool for ecohydrological distribution modelling. Ecol Model 207: 304–318. [CrossRef]
  • QGIS Development Team. 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/.
  • R Development Core Team . 2016. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.r-project.org/.
  • Rice SP, Ferguson RI, Hoey TB. 2006. Tributary control of physical heterogeneity and biological diversity at river confluences. Can J Fish Aquat Sci 63: 2553–2566. [CrossRef]
  • Riis T, Biggs BJF. 2003. Hydrologic and hydraulic control of macrophyte establishment and performance in streams. Limnol Oceanogr 48: 1488–1497. [CrossRef]
  • Riis T, Sand-Jensen K, Larsen SE. 2001. Plant distribution and abundance in relation to physical conditions and location within Danish stream systems. Hydrobiologia 448: 217–228. [CrossRef]
  • Rybicki NB, Landwehr JM. 2007. Long-term changes in abundance and diversity of macrophyte and waterfowl populations in an estuary with exotic macrophytes and improving water quality. Limnol Oceanogr 52: 1195–1207. [CrossRef]
  • Sanchez P, Demestre M, Recasens L, Maynou F, Martin P. 2008. Combining GIS and GAMs to identify potential habitats of squid Loligo vulgaris in the Northwestern Mediterranean. Hydrobiologia 612: 91–98. [CrossRef]
  • Segurado P, Araujo MB. 2004. An evaluation of methods for modelling species distributions. J Biogeogr 31: 1555–1568. [CrossRef]
  • Shin JH, Chung JY. 2011. The four major rivers restoration project in South Korea. Proc Inst Civ Eng Civ Eng 164: 19–26.
  • Shmueli G. 2010. To explain or to predict? Stat Sci 289–310. [CrossRef]
  • Sing T, Sander O, Beerenwinkel N, Lengauer T. 2005. ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940–3941. [CrossRef] [PubMed]
  • Solanki HU, Bhatpuria D, Chauhan P. 2016. Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea. Geocarto Int 32: 30–43. [CrossRef]
  • Sousa W. 2011. Hydrilla verticillata (Hydrocharitaceae), a recent invader threatening Brazil's freshwater environments: a review of the extent of the problem. Hydrobiologia 669: 1–20. [CrossRef]
  • Sousa WTZ, Thomaz SM, Murphy KJ, Silveira MJ, Mormul RP. 2009. Environmental predictors of the occurrence of exotic Hydrilla verticillata (L.f.) Royle and native Egeria najas Planch. in a sub-tropical river floodplain: the Upper River Paraná, Brazil. Hydrobiologia 632: 65–78. [CrossRef]
  • Strand JA, Weisner SEB. 1996. Wave exposure related growth of epiphyton: implications for the distribution of submerged macrophytes in eutrophic lakes. Hydrobiologia 325: 113–119. [CrossRef]
  • Swets J. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285–1293. [CrossRef] [PubMed]
  • Thomaz SM, Souza DC, Bini LM. 2003. Species richness and beta diversity of aquatic macrophytes in a large subtropical reservoir (Itaipu Reservoir, Brazil): the influence of limnology and morphometry. Hydrobiologia 505: 119–128. [CrossRef]
  • Thuiller W, Richardson DM, PyŠEk P, Midgley GF, Hughes GO, Rouget M. 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Change Biol 11: 2234–2250. [CrossRef]
  • Tian K, Liu G, Xiao D, Sun J, Lu M, Huang Y, Lin P. 2015. Ecological effects of dam impoundment on closed and half-closed wetlands in China. Wetlands 35: 889–898. [CrossRef]
  • Van TK, Wheeler GS, Center TD. 1999. Competition between Hydrilla verticillata and Vallisneria americana as influenced by soil fertility. Aquat Bot 62: 225–233. [CrossRef]
  • VanDerWal J, Falconi L, Januchowski S, Shoo L, Storlie C. 2014. SDMTools: Species Distribution Modelling tools: tools for processing data associated with species distribution modelling exercises. R package version, 1.1-221.
  • Wedding L, Yoklavich MM. 2015. Habitat-based predictive mapping of rockfish density and biomass off the central California coast. Mar Ecol Prog Ser 540: 235–250. [CrossRef]
  • Woo H. 2010. Trends in ecological river engineering in Korea. J Hydroenviron Res 4: 269–278.
  • Wood SN. 2000. Modelling and smoothing parameter estimation with multiple quadratic penalties. J R Stat Soc Ser B (Stat Method) 62: 413–428. [CrossRef]
  • Wood SN. 2006. Generalized additive models: an introduction with R. Boca Raton: CRC Press.
  • Xie D, Yu D, You WH, Wang LG. 2013. Morphological and physiological responses to sediment nutrients in the submerged macrophyte Myriophyllum spicatum. Wetlands 33: 1095–1102. [CrossRef]
  • Yee TW, Mitchell ND. 1991. Generalized additive models in plant ecology. J Veg Sci 2: 587–602. [CrossRef]
  • Yu H, Ye C, Song X, Liu J. 2010. Comparative analysis of growth and physio-biochemical responses of Hydrilla verticillata to different sediments in freshwater microcosms. Ecol Eng 36: 1285–1289. [CrossRef]
  • Zhao J, Cao J, Tian S, Chen Y, Zhang S, Wang Z, Zhou X. 2014. A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices. Aquat Ecol 48: 297–312. [CrossRef]
  • Zhou N, Hu W, Deng J, Zhu J, Xu W, Liu X. 2016. The effects of water depth on the growth and reproduction of Potamogeton crispus in an in situ experiment. J Plant Ecol 10: 546–558.
  • Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol 44: 1057–1067. [CrossRef] [PubMed]
  • Zuur AF, Pierce GJ. 2004. Common trends in northeast Atlantic squid time series. J Sea Res 52: 57–72. [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.