Biodiversity Flashcards
1
Q
Diversity (types)
A
Genetic diversity
Species diversity
Ecological diversity
Functional diversity
2
Q
Biodiversity movement
A
Movement addressing environmental concerns in relation to politics, biology, and ethics
3
Q
Ecological diversity
A
Concept relating to variability of trophic levels, life cycles, and biological resources
4
Q
Biodiversity
A
Term analogous to biological diversity; popularized in the 80s by E.O. Wilson
5
Q
Biological diversity
A
Concept referring to the variety and abundance of species in a spatial unit
6
Q
Taxonomic distinctness
A
Measure of diversity similar to Simpson’s index
7
Q
Diversity measures
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Measure taking into account richness and abundance
8
Q
Richness
A
Simply the number of species in a spatial unit
9
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Evenness
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Measure of differences in abundance between species
10
Q
Diversity index
A
A single statistic conveying information on richness and abundance
11
Q
Heterogeneity
A
Concept incorporating both richness and evenness
12
Q
a-diversity
A
Diversity within a defined spatial area
13
Q
b-diversity (concept)
A
The extent to which the diversity of two or more spatial units differ; Concept conceived by Whittaker (1960) originally to measure diversity changes between samples along transects, but commonly used for changes along other spatial arrangements and over time.
14
Q
Dominance
A
The extent to which one or a few species are primarily conform a community
15
Q
Abundance (and evenness) visualization
A
1) Histogram: abundance (y axis) against ranked species (x axis)
2) Rank/abundance plot (aka Whittaker): line graph of relative abundance (in log10 scale) against ranked species; the steeper the curve, the lower the evenness of the assemblage
3) k-dominance plot: line graph of cumulative relative abundance (y) against ranked species (x); The more elevated the line is from the x axis, the less diverse diverse the assemblage
4) Abundance/biomass comparison (ABC plot): Same as k-dominance, but cumulative relative biomass also plotted
5) Fisher plot: Number of species (y) plotted against number of individuals (x); looks like a very lef-skewed histogram
6) Preston plot: Number of species (y) plotted against number of individuals in a log2 scale arranged in “octaves” (x); can use other log base; looks like a bell-shape (i.e., log normal model)
16
Q
Pattern vs process
A
Pattern refers to the way a system behaves
Process refers to the reasons why a system behaves in a particular way
17
Q
Biodiversity measurement (assumptions)
A
- All species are equal
- All individuals are equal
- Abundance is expressed in comparable units
18
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Biodiversity (nature as a science)
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Biodiversity is a comparative science by nature
19
Q
Community
A
Group of organisms in a defined area that interact among themselves
20
Q
Guild
A
A group of organisms using the same resources
21
Q
Assemblage
A
Group of organisms in a communiy that share a certain phylogeny
22
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Local guild
A
Group of organisms using the same resources and co-ocurring in an area
23
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Ensemble
A
Group of organisms sharing geographic distribution, resources, and phylogeny
24
Q
Self-similarity
A
A pattern of diversity maintained across different spatial scales
25
Geographic boundary of a community
Set arbitrarily by te researcher
26
Statistical vs biological models
Statistical models aim to describe observed patterns, while bioloical models aim to explain those patterns
27
Logarithmic series model
Statistical model proposed by Fisher (1940s) to describe the relationship between number of species and number of individuals in those species;
αx, αx2/2, αx3/3...αx/*n*
αx is the number of species predicted to have one individual, and so on
the paremeter α is a good index of diversity
28
Log normal model
Statistical model proposed by Preston (1940s); Describes species abundance against species ranks (log2) resulting in classes called octaves. Can use other bases (e.g., 3 or 10).
Most large assemblages follow this distribution.
Incroporates γ as an additional parameter when a number of individuals curve is superimposed on the number of species curve.
The distribution is canonical when γ=1, in which case the mode of the individuals curve coincides with the upper tail of the species curve.
29
Log normal: Statistical explanation for its commonness in real data
Log normal distribution might be common as a result of the central limit theorem, wich states that when the amount of a variable is determined by a large number of factors, random variation in the factors will result in a normal distribution.
Additional support: more specious assemblages tend to be canonicl compared to less specious
30
Log normal: Biological explanation for its commonness in real data
Log normal distribution can be a consequence of random niche division/invasion.
31
Log normal distribution: Veil line
Truncation point of a log normal curve resulting as a consequence of difficulty sampling the less abundant species.
Note that veiled distributions are more difficult to fit.
32
Log normal (fitting and nature of data)
Technically, a log normal distribution should only be fitted to continuous abundance data such as biodiversity, but large discrete datasets work fine because they often act as continuous
33
Poisson log normal
A variation of the log normal distribution that assumes discrete abundance classes.
Uses the Poisson parameter λ, which is also a robust index of diversity.
34
Log normal: visual method for seeing fit of data
Plotting cumulative frequency of species (y) against log2 rank classes (x) should look like a straight line
35
Log normal: implication of departure
Departure of an assemblage from a log normal distribution is often used as a sign of environmental disturbance (though this is a challenged view)
36
Overlapping distributions (explain)
Data are often described by more than one distribution model
37
Overlapping of log series and log normal distribution
Potentially explained by a difference between permanent species (log normal distribution) and occasional species (log series distribution); when plotted together, the result is a left-skewed distribution.
38
Other statistical models besides log series and log normal
Negative binomial; Zipf-Mandelbrot
39
Fitting a model to data
Goodness of fit tests (usually *X*2) are used to evaluate the relationship between observed and expected frequencies.
*X*2 = [(observed - expeted)2/expected]
A *P*\<0.05 means the model does not describe the observed pattern.
40
Goodnes of fit test for small datasets
Kolmogorov-Smirnov goodness of fit test may be applied to small datasets and can also be used to compare two datasets
41
Niche appointment models
Noche appintmt models are based on the assumption that a community has a property called niche space that is divided in a certain way among species.
Different biological models vary in the way they propose niche fragmentation/filling occurs.
42
Niche filling
An alternative mehcanism to niche fragmentation in which additional niche space is accomodated (e.g., newly formed habitats like island and lakes).
43
Deterministic vs stochastic statistical diversity models
Deterministic models assume tat a set number of individuals, *N*, will be distributed among species, *S*, in a particular way.
Stochastic models establish that replicated communities will vary in their species abundances.
Most statistical models are deterministic, while most biological models are stochastic
44
Geometric series model
The geometric series is a biological model that assumes dominance-based niche appointment undrer limiting factors, and a relationship between allocated niche and abundance.
Usually observed in data from species-poor and harsh environments.
45
Broken stick model
Biological model proposed by McArthur (late 1950's). The model proposes niche division occurs like a stick is simultaniously being divided at random. Imporant in the development of ecological thought but rarely fits empirical data.
46
Tokeshi's models
Niche appointment models (biological models) proposed by Tokeshi (90's). They all assume niche space is occupied by species proportionally to abundance, but propose different mechanisms of niche division.
1) *Dominance pre-emption*: niche of least abundant species is subdivided
2) *Random fracion*: niche to be subdivided is selected at random
3) *Power fraction*: probabiltiy of division is moslty random but slighlty influenced by size
4) *McArthur fraction model*: probability of division proportional to size
5) *Dominance decay*: largest niche invariably split
6) *Random assortment*: No relationship between niche appointment and species abundance
7) *Composite*: small niches divided randomly and large niches divided following a rule
47
Caswell's neutral model
An alternative biological model, proposed by Caswell (late 1970's). Operates by examining how species abundance would occur in a community if all interactions were removed.
48
Hubbell's neutral theory of biodiversity and biogeography
A more ambitious neutral model than Caswell's. Proposed y Hubbel (2000). In this bioloical model, metacommunities are composed of a set of local communities that are always saturated, so there is no place for new individuals until others die.
The relative abundance of a species in a local community is dictated by its aundance in the metacommunity.
Explains a great number of empirical systems. One complication is that it requires simulations.
49
Fitting stochastic niche appointment models to empirical data
Communities are repetedly constructed using simulation, which allows to produce confidence limits on expected abundances. If the observed model fits within the confidence intervals, a model is said to fit the data.
Does not work if the variance is greater than that predicted.
Replication of observations in required (increases power) but not necessary (i.e., when detecting community changes over time).
50
Comparing abundance patterns between communities
The Kolmogorov-Sminrov two-sample test allows to determine whether two communities have the same pattern of abundance.
51
Criteria for classifying species as rare
1) Relative criteria: Example: species in the first quartile of the distribution are considered rare. This will vary depending on the measure used to quantify abundance (e.g., number of individuals vs biomass).
2) Absolute criteria: Example, all species with only one individual (singletons) are considered rare.
52
Rarity (mechanistic components)
Rarity occurs as a function of:
geograhpic distribution, habitat specificity, and local population size.
53
Rarity (definition according to the International Union for Conservation of Nature and Natural Resources
Taxa with small populations and restricted geographic areas that are often scattered.
54
Biggest obstacles for measuring biodiversity
1. The variety of species conepts and lack of consensus
2. Taxonomic bias
3. Sampling issues (rare = lower detectability); sampling scale (spatial and temporal); sampling effort
55
Morphospecies (aka morphotypes)
Morphospecies are taxa that are distinguishable on a morphological basis, without necessary taxonomic knowledge.
56
Ways of expressing richness measures
1. Numerical (i.e., number of species per abundance or biomass category)
2. Species density (number of species per area unit)
3. Incidence (number of sample units in which a species is present)
57
Richness indices
Richness indices attempt to compensate for sampling effects by dividing *S* by *N* in some way. Examples include Margalef's diversity index and Menhinick's index. These indeces are still influenced by sampling effort.
58
Methods to estimate true richness from samples
1) Extrapolation
2) Examining shape of species abundane curves
3) Nonparametric estimators (most powerful approach)
59
Species accumulation curves
Examines the rate at which new species are added by simulating the the samples in a randomized way a certain number of times. Mean and SD are also calculated.
The product is a plot of cumulative numer of species (y) by sampling effort (x).
Species accumulation curves produce total values of richness.
Can be used to 1) compre communities
2) evaluate sample size adequacy.
60
Species area curves
Similar to species accumulation curves, examines the rate at which new species are added to an inventory, but uses area units instead of sequential sampling. Widely used in botanical surveys.
61
Rarefaction curves
Rarefaction curves are similar to species accumulation curves, but instead of explaining richness as samples are added, it explains it by examining what pattern would have been produced if sampling effort had been reduced.
Rarefaction curves produce mean values of richness and can be viewed as the statistical product of a species accumulation curve.
Rarefaction curves are mostly used to compare assemblages.
62
Richness extrapolation (conditions)
To extrapolate richness, sampling needs to:
1) occur in a systematic way as opposed to *ad hoc* (as needed)
2) occur in a homogenous habitat
63
Richness extrapolation (types)
Functions of richness extrapolation can be:
1) asymptotic (e.g., negaive exponential, Michaelis-Menten equation)
2) nonasymptotic (e.g., log linear models)
64
Abundification
A parametric method to estimate the overall species richness in an assemblage or the increase in *S* expected from additional sampling.
α is estimated from the observed *S* and *N*, and used to deduce *S* given a larger *N*.
Requires that the data conform to a log series distribution.
Also allows to compare communities with unequal sampling effort.
65
Log normal: Issues for fitting to data
1) assumes continuous data
2) requires a large sample size (N \> 1,000)
3) choosing abundance classes is problematic
4) no method for condifence intervals
5) Poisson log normal is harder to fit
66
Nonparametric estimators of richness
Not based on any particular species abundance model and therefore avoid issued related to fitting parametric models.
67
Chao 1
Chao 1 is a nonparametric estimator for absoule richness based on a ratio between singletons and doubletons:
*S*Chao1 = *S*obs + (*F*12/2*F*2)
* F*1: number of singletons
* F*2: number of doubletons
Chao 1 assumes homogeneity among samples.
68
Chao 2
Nonparametric estimator of richness. Variation of Chao 1 for presence/absence data:
*S*Chao1 = *S*obs + (*Q*12/2*Q*2)
* Q*1: species present in only 1 sample
* Q*2: species present in 2 samples
69
Coverage estimators
Based on the idea that abundant species are likely to be included in any sample and thus provide little information about a community. In constrast, information about rare species is more useful.
70
Abundance-based coverage estimator (ACE)
A coverage estimator based on species with 1 - 10 individuals.
71
Incidence-based coverage estimator (ICE)
A coverage estimator based on species found in ≤10 samples.
72
Other nonparametric estimators
Jacknife 1: uses the numbr of species found that occur one sample.
Jacknife 2: uses the numbr of species found that occur in one and two samples.
Bootstrap estimator: also uses incidence data.
73
Stopping rule
Stopping rule is an indicator that further sampling is not required.
Two examples of approaches to determine the sampling stopping point:
1) Empirical species accumulation curve crosses curve generated by Michealis-Menten.
2) No singletons are left.
3) Subdivide sample in two (at random) and check that both subsamples follow the same pattern.
4) Estimators become stable and stop rising.
74
Richness estimators: desirable qualities
1) can use a sample size as small as posible.
2) Accuracy
3) allow an estimation of variance
4) not biased
5) computationally efficient
75
Species surrogacy tpes
1) Cross-taxon: richness of one taxon used to infer richness of another
2) Within-taxon: familiar or generic richness used as a surrogate for species richness
3) Environmenal: relatonships between environmenal factors and species richness
76
Heterogeneity measures (categories)
Heterogeneity measures are classified as parametric or nonparametric
77
α
Parametric measure of the log series model used to fit the distribution; Can be used as a measure of diversity even when the log series model doesn't fit the data; It is possible to attach confidence intervals; It's unaffected by sample size.
78
λ
Parametric measure of the log normal distribution; Calculated as S/σ.
(σ is standard deviation); λ is in itself an effective measure of biodiversity.
79
Q statistic
Parametric measure that uses the inerquartile slope of a cumulative abundance curve; Not biased by rare or very abundant species; Similar use to α from log series.
80
Shannon's diversity index
Nonparametric measure of diversity; Calculated by the equation:
*H'* = - Σ*pi* ln*pi*
where *pi* is the proportion of individuals in the *i*th species.
It's value constrain (1.5. and 3.5) makes it diffcult to interpret; Also, it confounds richness and evenness.
81
Shannon's evenness measure
A nonparametric measure of evenness, separate of *H* that uses the ratio of observed diversity to maximum possible diversity. Symbolized by *J'*
82
Heip's index of evenness
An alternative to *J'* (Shannon's evenness measure) that is less senitive to species richness; Calculated with the formula:
E*Heip* = (e*H'* - 1)/(*S* - 1)
83
SHE analysis
Nonparametric diversity measure based on the relationship between species richness, *H* (Shannon's index), and eveness; allowing a better interpetation than *H*.
Graphing SHE can be used to distinguish between broken stick, log series, and log normal as an alternative to using a goodness of fit test.
84
The Brillourin index
A nonparametric diversity measure similar to *H* that is appropriate to use when randomness of sample is not achievable (e.g., pitfall traps); Cannot be used with abundance or productivity measures.
85
Simpson's index (*D*)
Nonparametric measure of diversity that uses the proportion of individuals in each species to determine the probability of any two individuals to be drawn from the sample.
Because diversity decreases as *D* increases, a reciprocal (1 - *D* or 1/*D*) is often reported to make things more intuitive.
One of the most robust and meaningful measures of diversityl, but less popular than *H*.
86
Simpson's measure of evenness (*E*)
A separate measure of evenness from *D* (which is not strictly one). The reciprocal of D is divided by total number of species:
*E*1/D = (1/*D*)/S
Ranges from 0 to 1 and is not sensitive to richness.
87
McIntosh's measure of diversity
A nonparametric measure of diversity that uses the Euclidean distance of the assemblage from its origin, reresented as *U*.
88
Nee, Harvey, and Cotgreave's evenness measure
Nonparametric measure of eveness; slope (*b*) of a rank/abundance plot with log abundance;
Falls between ∞ and 0 (0 is perfect eveness); hard to interpret due to value range.
89
Smith and Wison's evenness index
*Evar*, A nonparametric measure of evenness that measures the variance in species abundance.
90
Four requirements for a good diversity measure, according to Smith and Wilson
1) indepndent of richness
2) decreases along with abundance of the least abundant species
3) decreases if a rare species is added
4) unaffected by measure units
91
Taxonomic diversity
Taxonomic diversity is thought to make an assemblage more resilient and diverse. It's a useful measure in conservation, but can hinder the identification of vulnerable assemblages and is sensitive to sampling effort.
92
Clark and Warwick's taxonomic distinctness index
Clark and Warwick's taxonomic distinctness index describes the average taxonomic distance (measured as a path's length) between species.
has two forms:
1) Taxonomic diversity Δ: Average path length between two random individuals.
2) Taxonomic distinctness Δ*: Special case of Δ where each individual is drawn from a different species.
Each taxonomic level receives a different weighting.
Can be compared to simulated values and is independent of richness.
93
Taxonomic distinctivness
Refers to a species of particular conservation importance.
94
Sample size sensitivity
Sample size sensitivity is a common problem of diversity measurements that complicates comparison of assemblages. One solution is sample standarization across sites.
95
Functional diversity
Can be measured by branch length from dendrograms constructed from a number of traits;
A powerful technique for evaluating the functional consequences of extinction.
The positive relationship between species richness and ecosystem function is often attributed to a greater number of functional groups.
96
Body size and diversity
Avrage body size, togeter with guild identity allows to infer about abundance.
97
Sampling for biodiversity (issues)
1) Sample size and sampling effort: The number of species and therefore diversity will increase with number of samples. Thus, sample size must be standarized or adjusted for. Ideally, the number of samples should be so to achieve an asympote when plotted with a measure of diversity or eveness.
2) Nonrandomness: Sampling is assumed to be random, but in reality might not be (e.g., environmental heterogeneity, individual behavior).
98
Replication/pseudoreplication
True replicates must be spatially independent.
99
Methodological edge effects
When sampling inefficiency results in concluding one or more species is absent or rare (e.g., sampling methods, sampling time)
100
Measuring abundance: Modular units
Sampling units regarded as different individuals from one larger clonal individual (e.g., fungi, trees, corals). The use of modular units is justified by sub-individuals acting as true individuals ecologically.
101
Measuring abundance: Biomass as an abundance measure
Biomass can be a good surrogate of abundance, and provides information on resource use.
It can also reduce numerical differences between species that vary in their abundances by orders of magnitude.
102
Measuring abundance: Cover area
Cover area can be used as a surrogate of abudnance but is problematic, as organisms can overlap.
103
Measuring abundance: Frequency or incidence
The number of sampling units in which a species occurs. It can be an useful measure for richness.
However, the abundance of widespread species can be underestimated.
104
Comparison of communities: Species abundance distributions
Species abundance patterns can be compared between communities through hypothesis testing (e.g., Kolmogorov-Smirnov test) and slope comparison.
105
Comparison of communities: Rarefaction
Method to measure species richness. A good way of comparing two communities with confidence intervals.
Allows the adjustment of sample size between communties by calculating what richness would be if less samples would have been taken.
Assumes that sampling was truly random.
Note the shape of rarefaction curve is expected to vary depending on the species abundance distribution type.
106
Comparison of communities: Statistical tests
because thr Shannon, Simpson, and other statistics are often normally distributed, they can be compared with *t* tests and ANOVA.
107
Comparison of communities: Jackknifing
Becaue jackknifing is a nonparametric method, one of its biggest advantages is that it makes no assumption about the underlying distribution of data.
1) diversity (i.e., and index) is estimated for all samples tgether: *St*
2) diversity is recalculated *n* times leaving out one sample at a time: *St*-i
3) pseudovalues are then calculated for each sample
4) jackknifed estimate is the mean of all pseudovalues
5) SE used to perform *t* tests
108
Comparison of communities: Bootstrapping
Bootstrapping is a method similar to jackknifing (and considered an improvement). The original data set is repeatedly sampled to produce many combinations of observations and these are used to calculate a standard error.
109
Comparison of communities: Null models
A fairly recent approach to compare a community in question against a series of random draws from a regional pool. Can also be used to test if differences in diversity are an artifact of sampling.
110
Environmental assessment
Environmental assessment evaluates the status of potentially impacted assemblages against a benchmark expectation.
1) **Species/abundance distributions**: undisturbed assemblaes usually follow a log normal pattern.
2) **ABC curves**: Placement of individual/biomass curves used to infer on disturbances. *W* statistic (a measure linking biomass and abundace) can be compared with a sttistical test.
3) **Richness**: Number of species simply used to infer on disturbance
4) **Taxonomic distinctness**: Δ+ used to infer on ecosystem function.
5) Indices of biotic integrity (**IBI's**): integrate a number of metrics; requires extensive background information; not intended for measures of diversity.
111
Turnover
The number of species eliminated and replaced per unit time.
Term is also used sometimes for spatial changes, though β is more appropriate in that situation.
112
Levels of diversity
_Inventory diversity:_
α diversity: the diversity of a set of samples form one spatial unit
γ diversity: the diversity of a landscape
ε diversity: the diversity of a geographic region
_Differentiation diversity:_
pattern diversity: variation in diversity between samples from a site
β diversity: the difference in diversity between two spatial units or points in time
𝛿 diversity: change in diversity betwee units of γ (landscapes)
Note: no concensus on how to define a landscape or geographic region.
113
β-diversity and area sampled
Contrary to α, β-diversity decreases as the area sampled increases.
114
Levels of richness
According to Gray (2000):
_Scale_
point species richness: richness of a sampling unit
sample species richness: richness from a number of samples from one site
large area richness: richness from an area including a variety of habbitats
biogeographic province species richness: richness of a biogeograpic province
_Independent of scale_
habitat species richness: richness in a habitat
assemblage species richness: richness of an assemblage
115
β-diversity formula
, where *ST* is the species richness of the landscape;
* Sj* is the species richness of the assemblage *j*;
* qj* is the proportional weight of assemblage *j* based on its sample size.
116
Measuring β-diversity (trhee main approaches)
1) Difference between 2 areas of α-diversity in respect to γ-diversity (where γ-div is measured as richness); e.g., Whittaker and Lande
2) Similarity/dissimilarity between 2 areas of α-diversity(e.g., Jaccard and Bray-Curtis coefficients)
3) Measuring turnover in terms of the slope in a species-area realtionship
117
Whittaker's βW
One of the simplest and most effective measures of β-diversity;
βW = S/(mean α)
or
Dβ = ST/(mean Sj) in Lande;s notation
Values range from 1 to 2; often reported as β - 1 to set the range from 0 to 1
118
Harrison's modifications of βW
1) βH1 = {[(S/mean α) - 1]/(N - 1)} \* 100
sets range from 0 - 100
2) βH2 = {[(S/αmax) - 1]/(N - 1)} \* 100
αmax is the maximum within-taxon richness per sample;
Often used to compare turnover of different taxa
119
Other measures of β-diversity
1) Cody's measure βC: Focuses on lost vs gaines species
2) Routledge's measures βR (takes overlapping distributions into consideration)
βI (based on information theory)
βE (the exponential form of βI)
3) Wilson's and Smida's measure βT (focuses on loss and gain like βC, but uses average sample richnes like βW).
120
Wilson and Shmida's criteria for a good β-diversity measure
1) Number of community changes
2) Additivity
3) Independece from α diversity
4) Independence from excessive sampling
βW considered the best measure under these criteria
121
Complementarity
Describes the difference between sites in terms of species they support; used for conservation;
Very similar to β-diversity; The more complementary two sites
122
Complementarity measures
Complementarity measres combine three variables: a (species present in both samples), b (species present in sample 1), c (species present in sample 2).
123
Maczewski-Steinhaus measure
Complementarity measure calculated as:
CMS = 1 - a/(a+b+c)
124
Jaccard's measure
Complementarity measure calculated as:
CJ = a/(a+b+c)
125
Sorensen's measure
Complementarity measure calculated as:
CS = 2a/(2a+2b+2c)
126
Lennon's measure
Complementarity measure that focuses on differences in composition; calculated as:
βsim = 1 - [a/a+min(b,c)]
smallest values of b and c are used to avois inflating value
127
CN = 2jN/(Na + Nb);
a version of Sorensen that takes abundance into account
A ccomplementarity measure taking abundance into account; calculated as:
CN = 2jN/(Na + Nb)
Regarded as the best complementarity measure.
128
Morisita-Horn
Popular complementarity measure that is very sensitive to the abundance of the most abundant species. A way of dealing with that is square transforming the raw data.
129
Estimating the true number of shared species
*V* is an index to estimate complementarity that focuses on the rare species providing more information than abundant ones.
130
Cluster analysis
A method to compare betwee communties in terms of β-dibversity; Works by constructing dendrograms based on site similarities, where distance is measured. Boostrap values can be added.
Includes dentrograms and ordination.
131
ANOSIM
Analysis of similarities; a one-way nonparametric test using permutations to test a null hypothesis;
Comparisons are made among localities with a number of replicates.
132
Null expectation contrasting
A method to compare communities in terms of β-dibersity.
An observed pattern of β-biodiversity is contrasted against a null expectation.
133
β measure distribution comparison
134
Whittaker's multiplicative law
Gamma diversity is a product of Alpha and Betta diversity
135
Phytosociology
The science that aims to clasify plant communities into fixed units
136
Niche
An n-dimensional hypervolume composed by environmental and resource characteristics that a species requires to persist (Hutchinson 1959).
137
Assembly rule (Diamond 1975)
A restricted species combination to which competitive ineractions can lead.
