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Biodiversity

  • Cang Hui
  • Pietro Landi
  • Henintsoa Onivola Minoarivelo
  • Andriamihaja Ramanantoanina
Chapter
Part of the SpringerBriefs in Ecology book series (BRIEFSECOLOGY)

Abstract

Biodiversity is the most striking phenomenon in nature but perhaps also the most difficult to monitor and hypothesise. This chapter introduces key concepts and metrics for describing biodiversity patterns, as well as changes in these patterns. It starts with introducing the concepts of occupancy and aggregation across spatial scales for single species, followed by measures of species association and co-occurrence. It then discusses biodiversity patterns based on the manipulation of species-by-site matrices, from occupancy frequencies to species turnover and partitioning. It ends with the effects of imperfect detection and sampling on observed biodiversity patterns. This chapter lays the platform for understanding concepts and models of other chapters.

References

  1. Anselin L (1995) Local indicators of spatial association - LISA. Geogr Anal 27:93–115CrossRefGoogle Scholar
  2. Arita HT, Christen JA, Rodriguez P, Soberon J (2008) Species diversity and distribution in presence-absence matrices: mathematical relationships and biological implications. Am Nat 172:519–532CrossRefPubMedGoogle Scholar
  3. Brown AM, Warton DI, Andrew NR, Binns M, Cassis G, Gibb H (2014) The fourth-corner solution–using predictive models to understand how species traits interact with the environment. Methods Ecol Evol 5:344–352CrossRefGoogle Scholar
  4. Burgman MA, Fox JC (2003) Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Anim Conserv 6:19–28CrossRefGoogle Scholar
  5. Chao A (1984) Nonparametric estimation of the number of classes in a population. Scand J Stat 11:265–270Google Scholar
  6. Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion, LondonGoogle Scholar
  7. Crist TO, Veech JA (2006) Additive partitioning of rarefaction curves and species–area relationships: unifying α-, β- and γ-diversity with sample size and habitat area. Ecol Lett 9:923–932CrossRefPubMedGoogle Scholar
  8. Diserud OH, Ødegaard F (2007) A multiple-site similarity measure. Biol Lett 3:20–22CrossRefPubMedGoogle Scholar
  9. Ellis EC, Ramankutty N (2008) Putting people in the map: anthropogenic biomes of the world. Front Ecol Environ 6:439–447CrossRefGoogle Scholar
  10. Faith DP, Minchin PR, Belbin L (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69:57–68CrossRefGoogle Scholar
  11. Ferrier S, Manion G, Elith J, Richardson K (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers Distrib 13:252–264CrossRefGoogle Scholar
  12. Gotelli NJ, Graves GR (1996) Null models in ecology. Smithsonian Institute Press, LondonGoogle Scholar
  13. Gotelli NJ, McCabe DJ (2002) Species co-occurrence: a meta-analysis of J. M. Diamond’s assembly rules model. Ecology 83:2091–2096CrossRefGoogle Scholar
  14. Hanski I (1982) Dynamics of regional distribution: the core and satellite species hypothesis. Oikos 38:210–221CrossRefGoogle Scholar
  15. Harte J (2011) Maximum entropy and ecology: a theory of abundance, distribution and energetics. Oxford University Press, OxfordCrossRefGoogle Scholar
  16. Harte J, Kinzig A, Green J (1999) Self-similarity in the distribution and abundance of species. Science 284:334–336CrossRefPubMedGoogle Scholar
  17. He F, Gaston KJ (2000) Estimating species abundance from occurrence. Am Nat 156:553–559CrossRefPubMedGoogle Scholar
  18. He F, Gaston KJ (2003) Occupancy, spatial variance, and the abundance of species. Am Nat 162:366–375CrossRefPubMedGoogle Scholar
  19. Hill MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54:427–432CrossRefGoogle Scholar
  20. Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography. Princeton University Press, PrincetonGoogle Scholar
  21. Hui C (2009a) A Bayesian solution to the modifiable areal unit problem. In: Hassanien AE, Abraham A, Herrera F (eds) Foundations of computational intelligence, vol 2.: Approximate Reasoning. Springer, Berlin, pp 175–196Google Scholar
  22. Hui C (2009b) On the scaling pattern of species spatial distribution and association. J Theor Biol 261:481–487CrossRefPubMedGoogle Scholar
  23. Hui C (2011) Forecasting population trend from the scaling pattern of occupancy. Ecol Model 222:442–446CrossRefGoogle Scholar
  24. Hui C (2012) Scale effect and bimodality in the frequency distribution of species occupancy. Community Ecol 13:30–35CrossRefGoogle Scholar
  25. Hui C, Li ZZ (2004) Distribution patterns of metapopulation determined by Allee effects. Popul Ecol 46:55–63CrossRefGoogle Scholar
  26. Hui C, McGeoch MA (2007a) A self-similarity model for the occupancy frequency distribution. Theor Popul Biol 71:61–70CrossRefPubMedGoogle Scholar
  27. Hui C, McGeoch MA (2007b) Modelling species distributions by breaking the assumption of self-similarity. Oikos 116:2097–2107CrossRefGoogle Scholar
  28. Hui C, McGeoch MA (2008) Does the self-similar species distribution model lead to unrealistic predictions? Ecology 89:2946–2952CrossRefPubMedGoogle Scholar
  29. Hui C, McGeoch MA (2014) Zeta diversity as a concept and metric that unifies incidence-based biodiversity patterns. Am Nat 184:684–694CrossRefPubMedGoogle Scholar
  30. Hui C, McGeoch MA, Warren M (2006) A spatially explicit approach to estimating species occupancy and spatial correlation. J Anim Ecol 75:140–147CrossRefPubMedGoogle Scholar
  31. Hui C, McGeoch MA, Reyers B, le Roux PC, Greve M, Chown SL (2009) Extrapolating population size from the occupancy-abundance relationship and the scaling pattern of occupancy. Ecol Appl 19:2038–2048CrossRefPubMedGoogle Scholar
  32. Hui C, Veldtman R, McGeoch MA (2010) Measures, perceptions and scaling patterns of aggregated species distributions. Ecography 33:95–102CrossRefGoogle Scholar
  33. Hui C, Foxcroft LC, Richardson DM, MacFadyen S (2011a) Defining optimal sampling effort for large-scale monitoring of invasive alien plants: a Bayesian method for estimating abundance and distribution. J Appl Ecol 48:768–776CrossRefGoogle Scholar
  34. Hui C, Richardson DM, Robertson MP, Wilson JRU, Yates CJ (2011b) Macroecology meets invasion ecology: linking the native distributions of Australian acacias to invasiveness. Divers Distrib 17:872–883CrossRefGoogle Scholar
  35. Hui C, Boonzaaier C, Boyero L (2012) Estimating changes in species abundance from occupancy and aggregation. Basic Appl Ecol 13:169–177CrossRefGoogle Scholar
  36. Hui C, Richardson DM, Pyšek P, Le Roux JJ, Kučera T, Jarošík V (2013) Increasing functional modularity with residence time in the co-distribution of native and introduced vascular plants. Nat Commun 4:2454CrossRefPubMedPubMedCentralGoogle Scholar
  37. Jaccard P (1900) Contribution au proble`me de l’immigration postglaciaire de la flore alpine. Bull Soc Vaud Sci Nat 36:87–130Google Scholar
  38. Jaynes ET (1968) Prior probabilities. IEEE Trans Syst Sci Cybern 4:227–241CrossRefGoogle Scholar
  39. Jost L, Chao A, Chazdon RL (2011) Compositional similarity and b (beta) diversity. In: Magurran AE, McGill BJ (eds) Biological diversity: frontiers in measurement and assessment. Oxford University Press, Oxford, UK, pp 66–84Google Scholar
  40. Koch LF (1957) Index of biotal dispersity. Ecology 38:145–148CrossRefGoogle Scholar
  41. Kunin WE (1998) Extrapolating species abundance across spatial scales. Science 281:1513–1515CrossRefPubMedGoogle Scholar
  42. Kunin WE, Harte J, He F, Hui C, Jobe RT, Ostling A, Polce C, Šizling A, Smith AB, Smith K, Smart SM, Storch D, Tjørve E, Ugland KI, Ulrich W, Varma V (2018) Upscaling biodiversity: estimating the species-area relationship from small samples. Ecol Monogr 88:170–187CrossRefGoogle Scholar
  43. Lande R (1996) Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos 76:5–13CrossRefGoogle Scholar
  44. Latombe G, McGeoch MA, Nipperess DA, Hui C (2018) zetadiv: Functions to compute compositional turnover using zeta diversity. R package, version 1.1.1, cran.r-project.orgGoogle Scholar
  45. Latombe G, Hui C, McGeoch MA (2017) Multi-site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species. Methods Ecol Evol 8:431–442CrossRefGoogle Scholar
  46. Legendre P, Legendre L (1998) Numerical ecology, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  47. Lloyd M (1967) Mean crowding. J Anim Ecol 36:1–30CrossRefGoogle Scholar
  48. MacArthur RH (1957) On the relative abundance of bird species. Proc Natl Acad Sci U S A 43:293–295CrossRefPubMedPubMedCentralGoogle Scholar
  49. MacKenzie DI, Nichols JD, Hines JE, Knutson MG, Franklin AB (2003) Estimating site occupancy, colonization and local extinction probabilities when a species is detected imperfectly. Ecology 84:2200–2207CrossRefGoogle Scholar
  50. McGeoch MA, Gaston KJ (2002) Occupancy frequency distributions: patterns, artefacts and mechanisms. Biol Rev 77:311–331CrossRefPubMedGoogle Scholar
  51. McGlinn DJ, Hurlbert AH (2012) Scale dependence in species turnover reflects variance in species occupancy. Ecology 93:294–302CrossRefPubMedGoogle Scholar
  52. Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: synthesis. Island Press, Washington DCGoogle Scholar
  53. Mora C, Tittensor DP, Adl S, Simpson AGB, Worm B (2011) How many species are there on Earth and in the Ocean? PLoS Biol 9:e1001127CrossRefPubMedPubMedCentralGoogle Scholar
  54. Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23CrossRefPubMedGoogle Scholar
  55. Morisita M (1962) Id-index, a measure of dispersion of individuals. Res Popul Ecol 4:1–7CrossRefGoogle Scholar
  56. Myers N, Mittermeier RA, Mittermeier CG, da Fonseca Gustavo AB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858CrossRefGoogle Scholar
  57. Olson DM, Dinerstein E (1998) The Global 200: a representation approach to conserving the Earth’s most biologically valuable ecoregions. Conserv Biol 12:502–515CrossRefGoogle Scholar
  58. Openshaw S (1984) The modifiable areal unit problem. GeoBooks, NorwichGoogle Scholar
  59. Papp L, Izsák J (1997) Bimodality in occurrence classes: a direct consequence of lognormal or logarithmic series distribution of abundances – a numerical experimentation. Oikos 79:191–194CrossRefGoogle Scholar
  60. Park SY, Bera AK (2009) Maximum entropy autoregressive conditional heteroskedasticity model. J Econ 150:219–230CrossRefGoogle Scholar
  61. Peleg S, Werman M, Rom H (1989) A unified approach to the change of resolution: space and gray-level. IEEE Trans Pattern Anal Mach Intel 11:739–742CrossRefGoogle Scholar
  62. Perry JN (1995) Spatial analysis by distance indexes. J Anim Ecol 64:303–314CrossRefGoogle Scholar
  63. Qian H, Ricklefs RE (2012) Disentangling the effects of geographic distance and environmental dissimilarity on global patterns of species turnover. Glob Ecol Biogeogr 21:341–351CrossRefGoogle Scholar
  64. Rapoport EH (1982) Aerography. Permagon Press, Oxford, UKGoogle Scholar
  65. Raunkiaer C (1934) The life forms of plants and statistical plant geography being the collected papers of C. Raunkiaer. Clarendon Press, OxfordGoogle Scholar
  66. Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Probab 13:255–266CrossRefGoogle Scholar
  67. Royle JA, Dorazio RM (2008) Hierarchical modelling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press, New YorkGoogle Scholar
  68. Royle JA, Nichols JD (2003) Estimating abundance from repeated presence absence data or point counts. Ecology 84:777–790CrossRefGoogle Scholar
  69. Shannon CE (1948) A mathematical theory of communication. Bell Labs Tech J 27:379–423CrossRefGoogle Scholar
  70. Simpson EH (1949) Measurement of diversity. Nature 163:688CrossRefGoogle Scholar
  71. Sørensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter 5:1–34Google Scholar
  72. Stone L, Roberts A (1990) The checker board score and species distributions. Oecologia 85:74–79CrossRefPubMedGoogle Scholar
  73. Taylor LR (1961) Aggregation, variance and the mean. Nature 189:732–735CrossRefGoogle Scholar
  74. Tilman D (2004) Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc Natl Acad Sci U S A 101:10854–10861CrossRefPubMedPubMedCentralGoogle Scholar
  75. Tokeshi M (1990) Niche apportionment or random assortment: species abundance patterns revisited. J Anim Ecol 59:1129–1146CrossRefGoogle Scholar
  76. Ugland KI, Gray JS, Ellingsen KE (2003) The species–accumulation curve and estimation of species richness. J Anim Ecol 72:888–897CrossRefGoogle Scholar
  77. Weiss MC, Sousa FL, Mrnjavac N, Neukirchen S, Roettger M, Nelson-Sathi S, Martin WF (2016) The physiology and habitat of the last universal common ancestor. Nat Microbiol 1:16116CrossRefPubMedGoogle Scholar
  78. Whittaker RH (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecol Monogr 30:279–338CrossRefGoogle Scholar
  79. Wilson RJ, Thomas CD, Fox R, Roy DB, Kunin WE (2004) Spatial patterns in species distributions reveal biodiversity change. Nature 432:393–396CrossRefPubMedGoogle Scholar
  80. World Conservation Union (2014) IUCN Red List of Threatened Species, 2014.3. Summary Statistics for Globally Threatened Species. Table 1: Numbers of threatened species by major groups of organisms (1996–2014). International Union for Conservation of Nature, SwitzerlandGoogle Scholar
  81. Wright DH (1991) Correlations between incidence and abundance are expected by chance. J Biogeogr 1:463–466CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cang Hui
    • 1
  • Pietro Landi
    • 1
  • Henintsoa Onivola Minoarivelo
    • 1
  • Andriamihaja Ramanantoanina
    • 1
  1. 1.Department of Mathematical SciencesStellenbosch UniversityStellenboschSouth Africa

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