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The effect of sampling effort on spatial autocorrelation in macrobenthic intertidal invertebrates

Hydrobiologia volume 811, pages 239–250 (2018)Cite this article

Abstract

The importance of sampling effort in the statistical exploration of spatial autocorrelation is demonstrated for benthic macroinvertebrate assemblages within the intertidal warm-temperate Knysna estuary, South Africa. While the role of spatial scale in determining autocorrelation patterns in ecological populations has been noted, the effects of changing sampling effort (e.g., sample size) have rarely been explored; neither have the nature of any changes with sample size. Invertebrate assemblages were sampled from a single grid lattice comprised of 48 sampling stations at four sample sizes (0.0015, 0.0026, 0.0054 and 0.01 m2). Four metrics were investigated: assemblage abundance, frequency (species density), and numbers of the two most abundant species in the area Simplisetia erythraeensis and Prionospio sexoculata. Spatial autocorrelation was estimated for each sample size from the global Moran’s I. For a range of distance classes, Moran’s I correlograms were constructed, these plotted autocorrelation estimates as a function of the separation distance between point samples. Spatial autocorrelation was present in three of the metrics (assemblage abundance frequency and Prionospio abundance), but not for Simplisetia abundance. The estimated magnitude of spatial autocorrelation varied across sampling units for all four assemblage and species metrics (global Moran’s I ranged from 0.5 to − 0.07). Correlograms indicated that optimal sampling interval distances fell in the region of 8 m for Simplisetia and 19 m for the remaining three metrics. These distances indicate the dimensions of the processes (both biotic and abiotic) that determine spatial patterning in the microbenthic intertidal invertebrates sampled.

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References

  • Albano, P., B. Sabelli & P. Bouchet, 2011. The challenge of small and rare species in marine biodiversity surveys: microgastropod diversity in a complex tropical coastal environment. Biodiversity and Conservation 20(13): 3223–3237.

    Article  Google Scholar 

  • Alongi, D. M. & J. H. Tietjen, 1980. Population growth and trophic interactions among freeliving marine nematodes. In Tenore, K. R. & B. C. Coull (eds.), Marine Benthic Dynamics. University of South Carolina Press, Columbia, SC: 151–166.

    Google Scholar 

  • Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical analysis 27(2): 93–115.

    Article  Google Scholar 

  • Barnes, R., 2014. The nature and location of spatial change in species assemblages: a new approach illustrated by the seagrass macrofauna of the Knysna estuarine bay, South Africa. Transactions of the Royal Society of South Africa 69(2): 75–80.

    Article  Google Scholar 

  • Barnes, R., 2016. Spatial homogeneity of benthic macrofaunal biodiversity across small spatial scales. Marine Environmental Research 122: 148–157.

    Article  CAS  PubMed  Google Scholar 

  • Barnes, R. & M. Barnes, 2014. Biodiversity differentials between the numerically-dominant macrobenthos of seagrass and adjacent unvegetated sediment in the absence of sandflat bioturbation. Marine Environmental Research 99: 34–43.

    Article  CAS  PubMed  Google Scholar 

  • Barnes, R. & M. Ellwood, 2011. The significance of shore height in intertidal macrobenthic seagrass ecology and conservation. Aquatic Conservation: Marine and Freshwater Ecosystems 21(7): 614–624.

    Article  Google Scholar 

  • Barnes, R. & M. Ellwood, 2012. Spatial variation in the macrobenthic assemblages of intertidal seagrass along the long axis of an estuary. Estuarine, Coastal and Shelf Science 112: 173–182.

    Article  Google Scholar 

  • Barnes, R. & S. Hamylton, 2013. Abrupt transitions between macrobenthic faunal assemblages across seagrass bed margins. Estuarine, Coastal and Shelf Science 131: 213–223.

    Article  Google Scholar 

  • Barnes, R. & S. Hamylton, 2016. On the very edge: faunal and functional responses to the interface between benthic seagrass and unvegetated sand assemblages. Marine Ecology Progress Series 553: 33–48.

    Article  Google Scholar 

  • Cliff, A. D. & J. K. Ord, 1981. Spatial Processes: Models & Applications, Vol. 44. Pion, London.

    Google Scholar 

  • Cole, R., T. Healy, M. Wood & D. Foster, 2001. Statistical analysis of spatial pattern: a comparison of grid and hierarchical sampling approaches. Environmental Monitoring and Assessment 69(1): 85–99.

    Article  CAS  PubMed  Google Scholar 

  • Cooke, B. C., I. D. Goodwin & M. J. Bishop, 2014. Small-scale spatial structuring of interstitial invertebrates on three embayed beaches, Sydney, Australia. Estuarine, Coastal and Shelf Science 150: 92–101.

    Article  Google Scholar 

  • Cressie, N. A. & N. A. Cassie, 1993. Statistics for spatial data, Vol. 900. Wiley, New York.

    Google Scholar 

  • Dale, M. R. & M.-J. Fortin, 2014. Spatial Analysis: A Guide for Ecologists. Cambridge University Press, Cambridge.

    Book  Google Scholar 

  • Dauer, D., 1985. Functional morphology and feeding behavior of Paraprionospio pinnata (Polychaeta: Spionidae). Marine Biology 85(2): 143–151.

    Article  Google Scholar 

  • Davidson, I. C., A. C. Crook & D. K. Barnes, 2004. Quantifying spatial patterns of intertidal biodiversity: is movement important? Marine Ecology 25(1): 15–34.

    Article  Google Scholar 

  • Dowd, M., J. Grant & L. Lu, 2014. Predictive modeling of marine benthic macrofauna and its use to inform spatial monitoring design. Ecological Applications 24(4): 862–876.

    Article  PubMed  Google Scholar 

  • Dungan, J. L., J. Perry, M. Dale, P. Legendre, S. Citron-Pousty, M. J. Fortin, A. Jakomulska, M. Miriti & M. Rosenberg, 2002. A balanced view of scale in spatial statistical analysis. Ecography 25(5): 626–640.

    Article  Google Scholar 

  • Fleecer, J., M. Palmer & E. Moser, 1990. On the scale of aggregation of Meio-benthic copepods on a Tidal Mudflat. Marine Ecology 11(3): 227–237.

    Article  Google Scholar 

  • Fortin, M.-J., 1994. Edge detection algorithms for two-dimensional ecological data. Ecology 75: 956–965.

    Article  Google Scholar 

  • Fortin, M.-J., 1999. Effects of sampling unit resolution on the estimation of spatial autocorrelation. Ecoscience 6: 636–641.

    Article  Google Scholar 

  • Gaudêncio, M. J. & H. Cabral, 2007. Trophic structure of macrobenthos in the Tagus estuary and adjacent coastal shelf. Hydrobiologia 587(1): 241–251.

    Article  Google Scholar 

  • Gingold, R., S. E. Ibarra-Obando & A. Rocha-Olivares, 2011. Spatial aggregation patterns of free-living marine nematodes in contrasting sandy beach micro-habitats. Journal of the Marine Biological Association of the United Kingdom 91(03): 615–622.

    Article  Google Scholar 

  • Griffith, D. A., 1987. Spatial Autocorrelation. A Primer. Association of American Geographers, Washington DC.

    Google Scholar 

  • Hamylton, S., 2013. Five practical uses of spatial autocorrelation for studies of coral reef ecology. Marine Ecology Progress Series 478: 15–25.

    Article  Google Scholar 

  • Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6: 65–70.

    Google Scholar 

  • Jelinski, D. E. & J. Wu, 1996. The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology 11(3): 129–140.

    Article  Google Scholar 

  • Klumpp, D. W. & S. N. Kwak, 2005. Composition and abundance of benthic macrofauna of a tropical sea-grass bed in north Queensland, Australia. Pacific Science 59(4): 541–560.

    Article  Google Scholar 

  • Kraan, C., G. Aarts, J. Van Der Meer & T. Piersma, 2010. The role of environmental variables in structuring landscape-scale species distributions in seafloor habitats. Ecology 91(6): 1583–1590.

    Article  PubMed  Google Scholar 

  • Kraan, C., J. van der Meer, A. Dekinga & T. Piersma, 2009. Patchiness of macrobenthic invertebrates in homogenized intertidal habitats: hidden spatial structure at a landscape scale. Marine Ecology Progress Series 383(6): 211–224.

    Article  Google Scholar 

  • Legendre, P., 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74(6): 1659–1673.

    Article  Google Scholar 

  • Legendre, P. & M. J. Fortin, 1989. Spatial pattern and ecological analysis. Vegetatio 80(2): 107–138.

    Article  Google Scholar 

  • Legendre, P. & L. F. Legendre, 2012. Numerical Ecology, Vol. 24. Elsevier, Amsterdam.

    Google Scholar 

  • Levin, S. A., 1992. The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73(6): 1943–1967.

    Article  Google Scholar 

  • Lewis III, F. G. & A. W. Stoner, 1981. An examination of methods for sampling macrobenthos in seagrass meadows. Bulletin of Marine Science 31(1): 116–124.

    Google Scholar 

  • McGarvey, R., J. E. Feenstra, S. Mayfield & E. V. Sautter, 2010. A diver survey method to quantify the clustering of sedentary invertebrates by the scale of spatial autocorrelation. Marine and Freshwater Research, 61(2): 153–162.

  • Pashley, H., 1985. Feeding and Optimization: The Foraging Behaviour of Nereis diversicolor (Polychaeta). University of Cambridge, Cambridge.

    Google Scholar 

  • Pinckney, J. & R. Sandulli, 1990. Spatial autocorrelation analysis of meiofaunal and microalgal populations on an intertidal sandflat: scale linkage between consumers and resources. Estuarine, Coastal and Shelf Science 30(4): 341–353.

    Article  Google Scholar 

  • Qi, Y. & J. Wu, 1996. Effects of changing spatial resolution on the results of landscape pattern analysis using spatial autocorrelation indices. Landscape Ecology 11(1): 39–49.

    Article  Google Scholar 

  • Riisgård, H. U., 1991. Suspension feeding in the polychaete Nereis diversicolor. Marine Ecology Progress Series 70: 29–37.

    Article  Google Scholar 

  • Rodil, I., T. Compton & M. Lastra, 2014. Geographic variation in sandy beach macrofauna community and functional traits. Estuarine, Coastal and Shelf Science 150: 102–110.

    Article  CAS  Google Scholar 

  • Rossi, R. E., D. J. Mulla, A. G. Journel & E. H. Franz, 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs 62(2): 277–314.

    Article  Google Scholar 

  • Sandulli, R. & J. Pinckney, 1999. Patch sizes and spatial patterns of meiobenthic copepods and benthic microalgae in sandy sediments: a microscale approach. Journal of Sea Research 41(3): 179–187.

    Article  Google Scholar 

  • Snelgrove, P., J. Grassle & R. Petrecca, 1994. Macrofaunal response to artificial enrichments and depressions in a deep-sea habitat. Journal of Marine Research 52(2): 345–369.

    Article  Google Scholar 

  • Tobler, W. R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46: 234–240.

    Article  Google Scholar 

  • Yamakita, T. & M. Nakaoka, 2009. Scale dependency in seagrass dynamics: how does the neighboring effect vary with grain of observation? Population Ecology 51(1): 33–40.

    Article  Google Scholar 

  • Yamakita, T. & M. Nakaoka, 2011. Importance of considering grain and extent for the analysis on spatial dynamics: perspectives from comparison between theory and empirical example on seagrass bed dynamics in Tokyo Bay. Procedia-Social and Behavioral Sciences 21: 177–183.

    Article  Google Scholar 

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Acknowledgements

RSKB is grateful to: the Smuts Memorial Fund, managed by the University of Cambridge in memory of Jan Christiaan Smuts, and Rhodes University Research Committee for financial support of the fieldwork; and the Rondevlei Scientific Services Offices of SANParks and the Knysna Area Manager, Johan de Klerk, for permission to undertake research in the Knysna Section of the Garden Route National Park.

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Authors and Affiliations

  1. School of Earth and Environmental Sciences, University of Wollongong, New South Wales, NSW, 2522, Australia

    S. M. Hamylton

  2. Department of Zoology and Entomology, Rhodes University, Grahamstown, Eastern Cape, 6140, Republic of South Africa

    R. S. K. Barnes

  3. Knysna Basin Project, Knysna, Western Cape, 6570, Republic of South Africa

    R. S. K. Barnes

  4. Department of Zoology and Conservation Research Institute, University of Cambridge, Cambridge, UK

    R. S. K. Barnes

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  1. S. M. Hamylton
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Correspondence to S. M. Hamylton.

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Handling editor: K. W. Krauss

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Hamylton, S.M., Barnes, R.S.K. The effect of sampling effort on spatial autocorrelation in macrobenthic intertidal invertebrates. Hydrobiologia 811, 239–250 (2018). https://doi.org/10.1007/s10750-017-3491-x

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  • DOI: https://doi.org/10.1007/s10750-017-3491-x

Keywords

  • Sampling effort
  • Sample size
  • Moran’s I correlogram
  • Moran’s I