Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)

详细信息    查看全文

• 作者：Rosa M. Chefaoui ; Jorge Assis ; Carlos M. Duarte ; Ester A. Serrão
• 关键词：Coastal morphology ; Cymodocea nodosa ; Environmental niche ; Landscape metrics ; Seagrass ; Species distribution modelling
• 刊名：Estuaries and Coasts
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：39
• 期：1
• 页码：123-137
• 全文大小：1,224 KB
• 参考文献：Alberto, F., S. Massa, P. Manent, E. Diaz‐Almela, S. Arnaud‐Haond, C.M. Duarte, and E.A. Serrão. 2008. Genetic differentiation and secondary contact zone in the seagrass Cymodocea nodosa across the Mediterranean–Atlantic transition region. Journal of Biogeography 35: 1279–1294.
  Allouche, O., A. Tsoar, and R. Kadmon. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 1223–1232.CrossRef
  Araújo, M.B., and M. New. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22: 42–47.CrossRef
  Araújo, M.B., and A.T. Peterson. 2012. Uses and misuses of bioclimatic envelope modeling. Ecology 93: 1527–1539.CrossRef
  Barbet-Massin, M., F. Jiguet, C.H. Albert, and W. Thuiller. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution 3: 327–338.CrossRef
  Bryan, T.L., and A. Metaxas. 2007. Predicting suitable habitat for deep-water gorgonian corals on the Atlantic and Pacific continental margins of North America. Marine Ecology Progress Series 330: 113–126.CrossRef
  Cabaço, S., Ó. Ferreira, and R. Santos. 2010. Population dynamics of the seagrass Cymodocea nodosa in Ria Formosa lagoon following inlet artificial relocation. Estuarine, Coastal and Shelf Science 87: 510–516.CrossRef
  Calenge, C. 2006. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling 197: 516–519.CrossRef
  Calenge, C., G. Darmon, M. Basille, A. Loison, and J.M. Jullien. 2008. The factorial decomposition of the Mahalanobis distances in habitat selection studies. Ecology 89: 555–566.CrossRef
  Cancemi, G., M.C. Buia, and L. Mazzella. 2002. Structure and growth dynamics of Cymodocea nodosa meadows. Scientia Marina 66: 365–373.
  Chefaoui, R.M. 2014. Landscape metrics as indicators of coastal morphology: a multi-scale approach. Ecological Indicators 45: 139–147.CrossRef
  Chefaoui, R.M., and J.M. Lobo. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling 210: 478–486.CrossRef
  Chen, Z., F.E. Muller-Karger, and C. Hu. 2007. Remote sensing of water clarity in Tampa Bay. Remote Sensing of Environment 109: 249–259.CrossRef
  Clark, J.D., J.E. Dunn, and K.G. Smith. 1993. A multivariate model of female black bear habitat use for a geographic information system. The Journal of Wildlife Management 57: 519–526.CrossRef
  Costanza, R., R. d’Arge, R. De Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R.V. O’Neill, J. Paruelo, R.G. Raskin, P. Sutton, and M. van den Belt. 1998. The value of the world’s ecosystem services and natural capital. Ecological Economics 25: 3–15.CrossRef
  Cunha, A.H., and A. Araújo. 2009. New distribution limits of seagrass beds in West Africa. Journal of Biogeography 36: 1621–1622.CrossRef
  Cunha, A.H., J.F. Assis, and E.A. Serrão. 2013. Seagrasses in Portugal: a most endangered marine habitat. Aquatic Botany 104: 193–203.CrossRef
  Cushman, S.A., K. McGarigal, and M.C. Neel. 2008. Parsimony in landscape metrics: strength, universality, and consistency. Ecological Indicators 8: 691–703.CrossRef
  Duarte, C.M. 1995. Submerged aquatic vegetation in relation to different nutrient regimes. Ophelia 41: 87–112.CrossRef
  Duarte, C. M. 2011. Seagrass meadows. http://​www.​eoearth.​org/​view/​article/​155952 .
  Duarte, C.M., and K. Sand-Jensen. 1996. Nutrient constraints on establishment from seed and on vegetative expansion of the Mediterranean seagrass Cymodocea nodosa. Aquatic Botany 54: 279–286.CrossRef
  Duarte, C. M., J. Borum, F. Short, and D. Walker 2008. Seagrass ecosystems: Their global status and prospects. In Aquatic Ecosystems, ed N. Polunin, 281–294. Cambridge University Press.
  Duarte, C.M., I.E. Hendriks, T.S. Moore, Y.S. Olsen, A. Steckbauer, L. Ramajo, J. Carstensen, J.A. Trotter, and M. McCulloch. 2013. Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH. Estuaries and Coasts 36: 221–236.CrossRef
  Elith, J., S. Ferrier, F. Huettmann, and J. Leathwick. 2005. The evaluation strip: A new and robust method for plotting predicted responses from species distribution models. Ecological Modelling 186: 280–289.CrossRef
  Enriquez, S., N. Marbà, J. Cebriàn, and C.M. Duarte. 2004. Annual variation in leaf photosynthesis and leaf nutrient content of four Mediterranean seagrasses. Botanica Marina 47: 295–306.CrossRef
  Espino, F., F. Tuya, A. Brito, and R.J. Haroun. 2011. Ichthyofauna associated with Cymodocea nodosa meadows in the Canarian Archipelago (central eastern Atlantic): community structure and nursery role. Ciencias Marinas 37: 157–174.CrossRef
  Fernández-Torquemada, Y., and J.L. Sánchez-Lizaso. 2006. Effects of salinity on growth and survival of Cymodocea nodosa Ucria Ascherson and Zostera noltii Hornemann. Biologia Marina Mediterranea 13: 46–47.
  Fielding, A.H., and J.F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38–49.CrossRef
  Gartner, A., F. Tuya, P.S. Lavery, and K. McMahon. 2013. Habitat preferences of macroinvertebrate fauna among seagrasses with varying structural forms. Journal of Experimental Marine Biology and Ecology 439: 143–151.CrossRef
  GEBCO General Bathymetric Chart of the Oceans 2010. Br. Oceanogr. Data Cent., Liverpool, U. K. http://​www.​gebco.​net/​data_​and_​products/​gridded_​bathymetry_​data/​ .
  GEODAS-NG Software version 1.1.1.1. National Geophysical Data Center, Boulder, Colorado. http// www.​ngdc.​noaa.​gov/​mgg/​gdas/​gx_​announce.​html Accessed Aug 2013.
  Gohin, F., S. Loyer, M. Lunven, C. Labry, J.M. Froidefond, D. Delmas, M. Huret, and A. Herbland. 2005. Satellite-derived parameters for biological modelling in coastal waters: illustration over the eastern continental shelf of the Bay of Biscay. Remote Sensing of Environment 95: 29–46.CrossRef
  Green, E. P. and F. T. Short (eds) 2003. World atlas of seagrasses. Univ of California Press.
  GSHHG. A global self-consistent, hierarchical, high-resolution geography database version 2.2.2. National Geophysical Data Center, Boulder, Colorado. http://​www.​ngdc.​noaa.​gov/​mgg/​shorelines/​gshhs.​html . Accessed Aug 2013.
  Guidetti, P., and S. Bussotti. 2000. Fish fauna of a mixed meadow composed by the seagrasses Cymodocea nodosa and Zostera noltii in the Western Mediterranean. Oceanologica Acta 23: 759–770.CrossRef
  Guiry, M. D., and G. M. Guiry 2014. AlgaeBase. World-wide electronic publication, National University of Ireland, Galway. http://​www.​algaebase.​org . Accessed 13 Feb 2014.
  Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993–1009.CrossRef
  Hendriks, I.E., Y.S. Olsen, L. Ramajo, L. Basso, A. Steckbauer, T. Moore, J. Howard, and C.M. Duarte. 2014. Photosynthetic activity buffers ocean acidification in seagrass meadows. Biogeosciences 11: 333–346.CrossRef
  Hirzel, A.H., J. Hausser, D. Chessel, and N. Perrin. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 7: 2027–2036.CrossRef
  Huot, Y., C.A. Brown, and J.J. Cullen. 2005. New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products. Limnology and Oceanography: Methods 3: 108–130.CrossRef
  Jiménez-Valverde, A., J.M. Lobo, and J. Hortal. 2008. Not as good as they seem: The importance of concepts in species distribution modelling. Diversity and Distributions 14: 885–890.CrossRef
  Lee, K.S., S.R. Park, and Y.K. Kim. 2007. Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: a review. Journal of Experimental Marine Biology and Ecology 350: 144–175.CrossRef
  Liaw, A., and M. Wiener. 2002. Classification and regression by randomForest. R News 2: 18–22.
  Lobo, J.M., A. Jiménez-Valverde, and J. Hortal. 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33: 103–114.CrossRef
  Malea, P., T. Kevrekidis, and M. Potouroglou. 2013. Seasonal variation of trace metal Mn, Zn, Cu, Pb, Co, Cd concentrations in compartments of the seagrass Cymodocea nodosa. Botanica Marina 56: 169–184.CrossRef
  Marbà, N., J. Cebrián, S. Enriquez, and C.M. Duarte. 1996. Growth patterns of Western Mediterranean seagrasses: species-specific responses to seasonal forcing. Marine Ecology Progress Series 133: 203–215.CrossRef
  Mateo, R.G., T.B. Croat, A.M. Felicísimo, and J. Muñoz. 2010. Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections. Diversity and Distributions 16: 84–94.CrossRef
  McGarigal K., Cushman S. A., and Ene E. 2012. FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. University of Massachusetts, Amherst. http://​www.​umass.​edu/​landeco/​research/​fragstats/​fragstats.​html . Accessed Aug 2013.
  Olivé, I., J.J. Vergara, and J.L. Pérez-Lloréns. 2013. Photosynthetic and morphological photoacclimation of the seagrass Cymodocea nodosa to season, depth and leaf position. Marine Biology 160: 285–297.CrossRef
  Olsen, Y.S., M. Sánchez-Camacho, N. Marbà, and C.M. Duarte. 2012. Mediterranean seagrass growth and demography responses to experimental warming. Estuaries and Coasts 35: 1205–1213.CrossRef
  Orth, R.J., T.J. Carruthers, W.C. Dennison, C.M. Duarte, J.W. Fourqurean, K.L. Heck, A.R. Hughes, G.A. Kendrick, W.J. Kenworthy, S. Olyarnik, F.T. Short, M. Waycott, and S.L. Williams. 2006. A global crisis for seagrass ecosystems. Bioscience 56: 987–996.
  Pagès, J.F., M. Pérez, and J. Romero. 2010. Sensitivity of the seagrass Cymodocea nodosa to hypersaline conditions: a microcosm approach. Journal of Experimental Marine Biology and Ecology 386: 34–38.CrossRef
  Pérez, M., and J. Romero. 1992. Photosynthetic response to light and temperature of the seagrass Cymodocea nodosa and the prediction of its seasonality. Aquatic Botany 43: 51–62.CrossRef
  Pérez, M., J. Romero, C.M. Duarte, and K. Sand-Jensen. 1991. Phosphorus limitation of Cymodocea nodosa growth. Marine Biology 109: 129–133.CrossRef
  Pérez, M., C.M. Duarte, J. Romero, K. Sand-Jensen, and T. Alcoverro. 1994. Growth plasticity in Cymodocea nodosa stands: the importance of nutrient supply. Aquatic Botany 47: 249–264.CrossRef
  Poloczanska, E.S., C.J. Brown, W.J. Sydeman, W. Kiessling, D.S. Schoeman, P.J. Moore, K. Brander, J.F. Bruno, L. Buckley, M.T. Burrows, C.M. Duarte, B.S. Halpern, J. Holding, C.V. Kappel, M.I. O’Connor, J.M. Pandolfi, C. Parmesan, F. Schwing, S.A. Thompson, and A.J. Richardson. 2013. Global imprint of climate change on marine life. Nature Climate Change 3: 919–925.CrossRef
  R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://​www.​R-project.​org/​ Accessed Mar 2013.
  Schaeffer, P., Y. Faugere, J.F. Legeais, A. Ollivier, T. Guinle, and N. Picot. 2012. The CNES_CLS11 global mean sea surface computed from 16 years of satellite altimeter data. Marine Geodesy 35: 3–19.CrossRef
  Stark, J.D., C.J. Donlon, M.J. Martin and M.E. McCulloch 2007. OSTIA: An operational, high resolution, real time, global sea surface temperature analysis system. In: OCEANS 2007-Europe, IEEE, 1-4.
  Thuiller, W., D. Georges, and R. Engler 2013. biomod2: Ensemble platform for species distribution modeling. R package version 3.1-25.
  Tittensor, D.P., C. Mora, W. Jetz, H.K. Lotze, D. Ricard, E.V. Berghe, and B. Worm. 2010. Global patterns and predictors of marine biodiversity across taxa. Nature 466: 1098–1101.CrossRef
  Turner, M.G., R.V. O’Neill, R.H. Gardner, and B.T. Milne. 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecology 3: 153–162.CrossRef
  Tuya, F., H. Hernandez-Zerpa, F. Espino, and R. Haroun. 2013. Drastic decadal decline of the seagrass Cymodocea nodosa at Gran Canaria eastern Atlantic: interactions with the green algae Caulerpa prolifera. Aquatic Botany 105: 1–6.CrossRef
  Tuya, F., R. Haroun, and F. Espino. 2014a. Economic assessment of ecosystem services: monetary value of seagrass meadows for coastal fisheries. Ocean & Coastal Management 96: 181–187.CrossRef
  Tuya, F., L. Png-Gonzalez, R. Riera, R. Haroun, and F. Espino. 2014b. Ecological structure and function differs between habitats dominated by seagrasses and green seaweeds. Marine Environmental Research 98: 1–13.CrossRef
  Tyberghein, L., H. Verbruggen, K. Pauly, C. Troupin, F. Mineur, and O. De Clerck. 2012. Bio‐ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography 21: 272–281.CrossRef
  Valle, M., G. Chust, A. del Campo, M.S. Wisz, S.M. Olsen, J.M. Garmendia, and Á. Borja. 2014. Projecting future distribution of the seagrass Zostera noltii under global warming and sea level rise. Biological Conservation 170: 74–85.CrossRef
  Van Katwijk, M.M., A.R. Bos, V.N. De Jonge, L.S.A.M. Hanssen, D.C.R. Hermus, and D.J. De Jong. 2009. Guidelines for seagrass restoration: importance of habitat selection and donor population, spreading of risks, and ecosystem engineering effects. Marine Pollution Bulletin 58: 179–188.CrossRef
  Venables, W.N., and B.D. Ripley. 2002. Modern applied statistics with S. New York: Springer.CrossRef
  Verdiell-Cubedo, D., F.J. Oliva-Paterna, and M. Torralva-Forero. 2007. Fish assemblages associated with Cymodocea nodosa and Caulerpa prolifera meadows in the shallow areas of the Mar Menor coastal lagoon. Limnetica 26: 341–350.
  Waycott, M., C.M. Duarte, T.J. Carruthers, R.J. Orth, W.C. Dennison, S. Olyarnik, A. Calladine, J.W. Fourqurean, K.L. Heck Jr., A.R. Hughes, G.A. Kendrick, W.J. Kenworthy, F.T. Short, and S.L. Williams. 2009. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proceedings of the National Academy of Sciences 106: 12377–12381.CrossRef
  Welch, B.L. 1947. The generalization of student’s’ problem when several different population variances are involved. Biometrika 34: 28–35.
  Zaniewski, A.E., A. Lehmann, and J.M. Overton. 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling 157: 261–280.CrossRef
  Zarranz, M.E., N. González-Henríquez, P. García-Jiménez, and R.R. Robaina. 2010. Restoration of Cymodocea nodosa seagrass meadows through seed propagation: germination in vitro, seedling culture and field transplants. Botanica Marina 53: 173–181.
  
• 作者单位：Rosa M. Chefaoui (1)
  Jorge Assis (1)
  Carlos M. Duarte (2)
  Ester A. Serrão (1)
  
  1. Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Campus de Gambelas, 8005-139, Faro, Portugal
  2. Red Sea Research Center (RSRC) Building 2, Level 3, Room 3219, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Environment
  Ecology
  Geosciences
  Environmental Management
  Nature Conservation
  
• 出版者：Springer New York
• ISSN：1559-2731

文摘

Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. Here, we predict the realized and potential distribution for the species Cymodocea nodosa modelling its environmental niche in the Mediterranean and adjacent Atlantic coastlines. We use a combination of environmental variables and landscape metrics to perform a suite of predictive algorithms which enables examination of the niche and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 °C to 26.4 °C and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We found potential suitable areas not occupied by the seagrass mainly in coastal regions of North Africa and the Adriatic coast of Italy. The present study describes the realized and potential distribution of a seagrass species, providing the first global model of the factors that can be shaping the environmental niche of C. nodosa throughout its range. We identified the variables constraining its distribution as well as thresholds delineating its environmental niche. Landscape metrics showed promising prospects for the prediction of coastal species dependent on the shape of the coast. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems.