Species diversity - Misplaced Pages

(Redirected from Taxonomic biodiversity) Number of distinct species in a biological community

Species diversity is the number of different species that are represented in a given community (a dataset). The effective number of species refers to the number of equally abundant species needed to obtain the same mean proportional species abundance as that observed in the dataset of interest (where all species may not be equally abundant). Meanings of species diversity may include species richness, taxonomic or phylogenetic diversity, and/or species evenness. Species richness is a simple count of species. Taxonomic or phylogenetic diversity is the genetic relationship between different groups of species. Species evenness quantifies how equal the abundances of the species are.

Calculation of diversity

Species diversity in a dataset can be calculated by first taking the weighted average of species proportional abundances in the dataset, and then taking the inverse of this. The equation is:

{}^{q}\!D={1 \over {\sqrt{\sum _{i=1}^{S}p_{i}p_{i}^{q-1}}}}

The denominator equals mean proportional species abundance in the dataset as calculated with the weighted generalized mean with exponent q - 1. In the equation, S is the total number of species (species richness) in the dataset, and the proportional abundance of the ith species is $p_{i}$ . The proportional abundances themselves are used as weights. The equation is often written in the equivalent form:

{}^{q}\!D=\left({\sum _{i=1}^{S}p_{i}^{q}}\right)^{1/(1-q)}

The value of q determines which mean is used. q = 0 corresponds to the weighted harmonic mean, which is 1/S because the $p_{i}$ values cancel out, with the result that D is equal to the number of species or species richness, S. q = 1 is undefined, except that the limit as q approaches 1 is well defined:

\lim _{q\rightarrow 1}{}^{q}\!D=\exp \left(-\sum _{i=1}^{S}p_{i}\ln p_{i}\right),

which is the exponential of the Shannon entropy.

q = 2 corresponds to the arithmetic mean. As q approaches infinity, the generalized mean approaches the maximum $p_{i}$ value. In practice, q modifies species weighting, such that increasing q increases the weight given to the most abundant species, and fewer equally abundant species are hence needed to reach mean proportional abundance. Consequently, large values of q lead to smaller species diversity than small values of q for the same dataset. If all species are equally abundant in the dataset, changing the value of q has no effect, but species diversity at any value of q equals species richness.

Negative values of q are not used, because then the effective number of species (diversity) would exceed the actual number of species (richness). As q approaches negative infinity, the generalized mean approaches the minimum $p_{i}$ value. In many real datasets, the least abundant species is represented by a single individual, and then the effective number of species would equal the number of individuals in the dataset.

The same equation can be used to calculate the diversity in relation to any classification, not only species. If the individuals are classified into genera or functional types, $p_{i}$ represents the proportional abundance of the ith genus or functional type, and D equals genus diversity or functional type diversity, respectively.

Diversity indices

Often researchers have used the values given by one or more diversity indices to quantify species diversity. Such indices include species richness, the Shannon index, the Simpson index, and the complement of the Simpson index (also known as the Gini-Simpson index).

When interpreted in ecological terms, each one of these indices corresponds to a different thing, and their values are therefore not directly comparable. Species richness quantifies the actual rather than effective number of species. The Shannon index equals log(D), that is, q approaching 1, and in practice quantifies the uncertainty in the species identity of an individual that is taken at random from the dataset. The Simpson index equals 1/D, q = 2, and quantifies the probability that two individuals taken at random from the dataset (with replacement of the first individual before taking the second) represent the same species. The Gini-Simpson index equals 1 - 1/D and quantifies the probability that the two randomly taken individuals represent different species.

Sampling considerations

Depending on the purposes of quantifying species diversity, the data set used for the calculations can be obtained in different ways. Although species diversity can be calculated for any data-set where individuals have been identified to species, meaningful ecological interpretations require that the dataset is appropriate for the questions at hand. In practice, the interest is usually in the species diversity of areas so large that not all individuals in them can be observed and identified to species, but a sample of the relevant individuals has to be obtained. Extrapolation from the sample to the underlying population of interest is not straightforward, because the species diversity of the available sample generally gives an underestimation of the species diversity in the entire population. Applying different sampling methods will lead to different sets of individuals being observed for the same area of interest, and the species diversity of each set may be different. When a new individual is added to a dataset, it may introduce a species that was not yet represented. How much this increases species diversity depends on the value of q: when q = 0, each new actual species causes species diversity to increase by one effective species, but when q is large, adding a rare species to a dataset has little effect on its species diversity.

In general, sets with many individuals can be expected to have higher species diversity than sets with fewer individuals. When species diversity values are compared among sets, sampling efforts need to be standardised in an appropriate way for the comparisons to yield ecologically meaningful results. Resampling methods can be used to bring samples of different sizes to a common footing. Species discovery curves and the number of species only represented by one or a few individuals can be used to help in estimating how representative the available sample is of the population from which it was drawn.

Trends

The observed species diversity is affected not only by the number of individuals but also by the heterogeneity of the sample. If individuals are drawn from different environmental conditions (or different habitats), the species diversity of the resulting set can be expected to be higher than if all individuals are drawn from a similar environment. Increasing the area sampled increases observed species diversity both because more individuals get included in the sample and because large areas are environmentally more heterogeneous than small areas.

Notes

^ Hill, M. O. (1973) Diversity and evenness: a unifying notation and its consequences. Ecology, 54, 427–432
^ Tuomisto, H. (2010) A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography, 33, 2-22. doi:10.1111/j.1600-0587.2009.05880.x
^ Tuomisto, H. 2010. A consistent terminology for quantifying species diversity? Yes, it does exist. Oecologia 4: 853–860. doi:10.1007/s00442-010-1812-0
Xu, S., Böttcher, L., and Chou, T. (2020). Diversity in biology: definitions, quantification and models. Physical Biology, 17, 031001. doi:10.1088/1478-3975/ab6754
Krebs, C. J. (1999) Ecological Methodology. Second edition. Addison-Wesley, California.
Magurran, A. E. (2004) Measuring biological diversity. Blackwell Publishing, Oxford.
^ Jost, L. (2006) Entropy and diversity. Oikos, 113, 363–375
Jost, L. (2007) Partitioning diversity into independent alpha and beta components. Ecology, 88, 2427–2439.
Tuomisto, H. (2010) A diversity of beta diversities: straightening up a concept gone awry. Part 2. Quantifying beta diversity and related phenomena. Ecography, 33, 23-45. doi:10.1111/j.1600-0587.2009.06148.x
Colwell, R. K. and Coddington, J. A. (1994) Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions: Biological Sciences, 345, 101-118.
Webb, L. J.; Tracey, J. G.; Williams, W. T.; Lance, G. N. (1969), Studies in the Numerical Analysis of Complex Rain-Forest Communities: II. The Problem of Species-Sampling. Journal of Ecology, Vol. 55, No. 2, Jul., 1967, pp. 525-538, Journal of Ecology, British Ecological Society, JSTOR 2257891
Good, I. J. and Toulmin, G. H. (1956) The number of new species, and the increase in population coverage, when a sample is increased. Biometrika, 43, 45-63.
Chao, A. (2005) Species richness estimation. Pages 7909-7916 in N. Balakrishnan, C. B. Read, and B. Vidakovic, eds. Encyclopedia of Statistical Sciences. New York, Wiley.

External links

Harrison, Ian; Laverty, Melina; Sterling, Eleanor. "Species Diversity". Connexions (cnx.org). William and Flora Hewlett Foundation, the Maxfield Foundation, and the Connexions Consortium. Retrieved 1 February 2011. (Licensed under Creative Commons 1.0 Attribution Generic).

v t e Ecology: Modelling ecosystems: Trophic components
General	Abiotic component Abiotic stress Behaviour Biogeochemical cycle Biomass Biotic component Biotic stress Carrying capacity Competition Ecosystem Ecosystem ecology Ecosystem model Green world hypothesis Keystone species List of feeding behaviours Metabolic theory of ecology Productivity Resource Restoration
Producers	Autotrophs Chemosynthesis Chemotrophs Foundation species Kinetotrophs Mixotrophs Myco-heterotrophy Mycotroph Organotrophs Photoheterotrophs Photosynthesis Photosynthetic efficiency Phototrophs Primary nutritional groups Primary production
Consumers	Apex predator Bacterivore Carnivores Chemoorganotroph Foraging Generalist and specialist species Intraguild predation Herbivores Heterotroph Heterotrophic nutrition Insectivore Mesopredators Mesopredator release hypothesis Omnivores Optimal foraging theory Planktivore Predation Prey switching
Decomposers	Chemoorganoheterotrophy Decomposition Detritivores Detritus
Microorganisms	Archaea Bacteriophage Lithoautotroph Lithotrophy Marine Microbial cooperation Microbial ecology Microbial food web Microbial intelligence Microbial loop Microbial mat Microbial metabolism Phage ecology
Food webs	Biomagnification Ecological efficiency Ecological pyramid Energy flow Food chain Trophic level
Example webs	Lakes Rivers Soil Tritrophic interactions in plant defense Marine food webs cold seeps hydrothermal vents intertidal kelp forests North Pacific Gyre San Francisco Estuary tide pool
Processes	Ascendency Bioaccumulation Cascade effect Climax community Competitive exclusion principle Consumer–resource interactions Copiotrophs Dominance Ecological network Ecological succession Energy quality Energy systems language f-ratio Feed conversion ratio Feeding frenzy Mesotrophic soil Nutrient cycle Oligotroph Paradox of the plankton Trophic cascade Trophic mutualism Trophic state index
Defense, counter	Animal coloration Anti-predator adaptations Camouflage Deimatic behaviour Herbivore adaptations to plant defense Mimicry Plant defense against herbivory Predator avoidance in schooling fish

v t e Ecology: Modelling ecosystems: Other components
Population ecology	Abundance Allee effect Consumer-resource model Depensation Ecological yield Effective population size Intraspecific competition Logistic function Malthusian growth model Maximum sustainable yield Overpopulation Overexploitation Population cycle Population dynamics Population modeling Population size Predator–prey (Lotka–Volterra) equations Recruitment Small population size Stability Resilience Resistance Random generalized Lotka–Volterra model
Species	Biodiversity Density-dependent inhibition Ecological effects of biodiversity Ecological extinction Endemic species Flagship species Gradient analysis Indicator species Introduced species Invasive species / Native species Latitudinal gradients in species diversity Minimum viable population Neutral theory Occupancy–abundance relationship Population viability analysis Priority effect Rapoport's rule Relative abundance distribution Relative species abundance Species diversity Species homogeneity Species richness Species distribution Species–area curve Umbrella species
Species interaction	Antibiosis Biological interaction Commensalism Community ecology Ecological facilitation Interspecific competition Mutualism Parasitism Storage effect Symbiosis
Spatial ecology	Biogeography Cross-boundary subsidy Ecocline Ecotone Ecotype Disturbance Edge effects Foster's rule Habitat fragmentation Ideal free distribution Intermediate disturbance hypothesis Insular biogeography Land change modeling Landscape ecology Landscape epidemiology Landscape limnology Metapopulation Patch dynamics r/K selection theory Resource selection function Source–sink dynamics
Niche	Ecological niche Ecological trap Ecosystem engineer Environmental niche modelling Guild Habitat marine habitats Limiting similarity Niche apportionment models Niche construction Niche differentiation Ontogenetic niche shift
Other networks	Assembly rules Bateman's principle Bioluminescence Ecological collapse Ecological debt Ecological deficit Ecological energetics Ecological indicator Ecological threshold Ecosystem diversity Emergence Extinction debt Kleiber's law Liebig's law of the minimum Marginal value theorem Thorson's rule Xerosere
Other	Allometry Alternative stable state Balance of nature Biological data visualization Ecological economics Ecological footprint Ecological forecasting Ecological humanities Ecological stoichiometry Ecopath Ecosystem based fisheries Endolith Evolutionary ecology Functional ecology Industrial ecology Macroecology Microecosystem Natural environment Regime shift Sexecology Systems ecology Urban ecology Theoretical ecology
Outline of ecology

Categories: