Community Ecology –

transcript

Community Ecology –

Descriptive and functional approches

Distinction between Population a Community Ecology is rather

• “Papuan canaca” counting (one, two, three, many)

• Community – when I am not able to study each population separately (~ many)

• Classical trade-off – I can either study very limited number of populations, each in detail, or study many populations together, but some details must be neglected

Community ecology vs. [Pflanzen]sociologie, =

Phytocoenologia = Phytosociology

• Community ecology (functional approach): e.g. Mechanisms of (many) species coexistence, interspecific interactions in community context

• Phytosociology – description and classification of plant communities in landscape, vegetation maps, Z-M community classification

• Historically separated – now more and more communicating

Zurich-Montpellier (Z-M) approach

• Braun Blanquet 1932 Plant Sociology

Z-M classification of plant communities

• Originated in Alps (relatively sharp boundaries between communities)

• Standardized way of recording vegetation relevés (Braun-Blanquet scale)

• Hierarchical system of communities

Hierarchy - Syntaxonomy

• Association: Dentario-Fagetum

• Alliance: Fagion

• Order: Fagetalia

• Class: Querco – Fagetea

• Vegetation maps – Actual vegetation

• Concepts of potential vegetation, „vegetation reconstruction“

Legacy of phytosociology

• Databases of phytosociological relevés

• Contain broad-scale patterns of species composition

• Use with caution (non-random/intentional selection of locations to record), but they contain incredibly large (140 000+ in Czech phytosociological database) number of compositional records

Pattern and process

• Observation and manipulative experiment

• The goal of ecology is to explain observed patters by mechanisms (processes); so the good description of pattern is the first step

• Temporal and spatial scales for observation and experiment

• Selection of model communities:

species poor – easier to study

species rich – more interesting

Methodological constrains

• Ability to identify (and subsequently quantify abundance of) species; compare: vascular plants in temperate zone [well know], tropical insects [much worse known], soil bacteria [difficult to identify, quantification problematic]

• Species names are only labels – knowledge of life history of species, species traits

Community Ecology – complex of causal relationships, causal

chainsBritish imperium saved by old maids

Removal of seed eating rodent increased abundance of other seedeaters, but not of insectivors

Rodent removal in Sonoran desert

Both, ants and rodents eat seeds (ants prefer smaller seeds), but partial overlap

The higher density of Erodium, the higher is percented of plants infected by fungus – in fact, the fungus and rodents compete for a plant (plant is their common resource)

Removal of rodents – increase of large-seeded plants, but on the expense of decrease of small-seeded plants, which are suppressed by competition.

Net effect of rodents on ants depends also on the time scale

Community as a biotic component of ecosystem

• Composed of individual populations• We are never able to study all the species =>

inclusion criteria• Functional (community of faeces decomposers) –

compare with “guild”• Spatial… • Taxonomic (plant community - usually means

vascular plants / and sometimes bryophytes)„Species assemblage“ - not necessarily functional relationships (species assemblages from light traps) – ?rather terminological problem

The „horizontal communities“

• Communities composed of organisms of the same trophic level

• Thus, the trophic interactions are „external“ – the main interspecific interaction is competition (typical for plant communities)

The big one

Diversity

(Species diversity, Richness, Biodiversity)

Ecologists are fascinated by diversity. Questions:

• Why they are so many (so few) species

• What is diversity determined by (local ecological interactions vs. historical factors)

• Changes of diversity along environmental gradients (what are diversity determinants)

• Effect of diversity on community functioning

We must be able to define and subsequently measure the

diversity

To be abe to provide answer to any of these (and many other) question about diversity

How to characterize the population structure (which species are there and how are they represented) of a

community? • Number of species (=species richness)

• Diversity, reflecting not only number, but also relative representation of species populations

• Eveness, Equitability as o component of diversity

• Spatial aspects of diversity

Species-area (Species - no of individuals) relationship – often SAR

Area (or number of individuals)

Number of species

The same equations are used for within community species area, and for the dependence in archipelago

Methodological note – independent quadrats, or „collectors curve“ (nested quadrats)

Nested quadrats – individual values are not independent

Independent quadrats of varying sizes – probably more laborious

Two most often used equations

Pover curve (Arrhenius)

S=c.Az , fitted usually as log S = log c + z log A

fitted as log(S) vs log(A)

assumption: by increasing area two times, species number will increase 2z times – usually z ~ 0.2 to 0.35]

[Semi]Logarithmic curve (Gleason)

S=a+b.log(A) - fitted as S vs log(A)

assumption: by increasing area two times, species number will increase by b.log(2) species, Disadvantage - Negative for small A

Similar relationship also for dependence on number of sampled individuals

Some recommend three parameter functions - ???

Causes of SAR – with increasing spatial scale

• Increased number of individuals

• Environmental heterogeneity (at various spatial scales, from, e.g. small scale heterogeneity within a meadow, up to heterogeneity among habitats on a landscape scale)

• Biogeographical divides, evolutionary differences

Area increases faster than no. of species

• Typical conservationist’s slogan: Our island comprises only 5% of land of the Earth, but hosts 20% of all vascular plants

• You can say similarly: the rubbish dump comprises only 0.1% area of the whole Ceske Budejovice (1 ha out of 10 km2), but hosts 10% of all its species (say 70 out of 700)

Concept of minimal area (historically used in plant ecology)

Attempt to find an upper asymptote (which, in my view does not exist), and identify Area (= minimal area of a community) when it is “nearly reached” (sometimes, more sophisticated methods, looking for decrease in heterogeneity, e.g. Moravec – today mostly of historical relevance)

Comparing “samples” of varying size (i.e. varying no. of individuals)

(compare meaning of sample in statistics and community ecology)

Rarefaction – estimates expected number of species in a sample of reduced size:

E(S) – expected no. of species

SO – no of species in original sample

N – no of individuals in original sample (each species represented by Ni individuals)

n – number of individuals in reduced sample

Take care!

Everything is calculated under the assumption that the reduced sample is random selection of individuals from the larger sample. It is usually not the case. The no. of species is so (slightly) overestimated in comparison with samples taken in the field.

Comparing species richness of area of different size

• Compare species richness of protected areas under different management (but these are of different size) / take care of another problem – protected areas are usually selected because they are species rich

• Compare number of species on islands with and without an invasive species (again, take care about causality)

In each comparison• The dependence of species number on the area

must be taken into account

• Various possibilities how to statistically filter out the effect of area

• You will either work with residuals on the species area curve or will use area as covariate

• if S=cAz expected, fit log(S) on log(A) – both approaches, however, expect common value of z

Diversity (taking into account species proportions)

Higher here

iiPdomSimpson

Pi – relative species representation - Usually, Pi = Ni/N , N=Σni

Shannon diversity index

Simpson dominance index

(Diversity=1/Simpson or Diversity = 1-Simpson, i.e. probability that two randomly drawn individuals will belong to different species) – assumption – P is proportion of individuals in infinitely large community

ii PPH ln.´

Diversity indices – attempt to reflect both, number of species and their relative proportions

Simpson

Usually, Pi = Ni/N , N=Σni

And then Pi2 = Ni

2/N2 - i.e. probability that two randomly drawn individuals will belong to species i

With number of individuals [and finite sample], Simpson index uses

P’i2 = Ni (Ni-1))/(N (N-1))

i.e. the probability, that species i will be selected in two subsequent random draws without replacement

otherwise Pi routinely calculated from biomass, cover, etc.

Do not subtract 1 there!

Free software available at:

http://folk.uio.no/ohammer/past/index.html

Shannon formula (based on information theory, sometimes Shannon - Weaver, Shannon - Wiener [complicated history of various papers]

ii PPH ln.´

Various log are used, originally log2.

It is useful to use antilog, i.e. eH´ (for ln) 2H´(for log2) or 10H´ for log10 – the values are the same (meaning – number of species forming the same diversity when equally represented

General formula for diversity (Hill notation), series of increasing importance of representation of dominants with increasing a

aia pN

According to a value, we get

N0 – number of species

N1 - eH´ (asymptotic)

N2 - 1/Simpson dominance

Ninfinity - 1/relative representation of the most abundant species

(relative representation of most abundant =Berger-Parker index)

Evenness, equitability, = vyrovnanost

Pielou (ratio of actual diversity to maximum diversity with given number of species)

Ep = H´/ H'max = H´/ ln S;

Partially problematic

In my view better

Buzas and Gibson's evenness = eH/S

Graphical representation

of community population structure

Diversity - dominance curves

0.00001

0.0001

Species sequence

R emoval non-fert ilised

Control non-fertilised

Control fertilised Removal fertilised

Models of species abundance distribution

Good web page is

http://www.columbia.edu/itc/cerc/danoff-burg/MBD%203.ppt

Four basic models -

Geometric Series – each species x% of previous (e.g. half, then 80, 40, 20, 10, 5,….) Biological explanation – niche pre-emption –

Log Series – number of species with 1, 2, 3 individuals are αx, αx2/2, αx3/3, αx4/4 [α and x parameters, α sometimes considered good index of diversity – useful for numbers of individuals]

Log-Normal Series – see figure

Broken-Stick Model (a stick is broken in random S-1 points)

Dominance-diversity curves pro 4 modely

10 20 30 40

Geometric SeriesLog Series

Log-Normal Series

Broken Stick Model

Relative abundance (plotted at log scale]

from Donoff-Burg

248163264128256512

Number of

species

With log-normal distribution, this graphical representation gives normal curve

Preston - octaves

Left truncarted – Species with low abundance are missing on the left side

(less than 1 individual, so that they are actually not found).

Number of individuals of a species

Less than

from Donoff-Burg

Functional and phylogenetic diversity

(more in Traits in ecology)

• Representation of life forms

• Diveristy of genera, families etc.• Example: community composed of 37 species of dandelions from the

Taraxacum officinale agg. will have lower phylogenetic and functional diversity of community composed of “normal” species.

• Functional diversity should not be affected by the ability of “splitters taxonomists” to distinguish several functionally identical species

First posibility – Functional groups

• Problem – how to define functional group, what to do with hierarchical classifications, relevance of traits used for functional classification

• jak definovat funkční skupiny, co když je ta klasifikace hierarchická (phanerophyty mohou být dále děleny do několika podskupin), co když zrovna dané znaky nejsou úplně relevantní (schopnost fixovat dusík není vázaná na žádnou životní formu, ale může být funkčně velmi důležitá)

Rao index Shimatani 2001

• Shimatani K 2001: On the measurement of species diversity incorporating species differences OIKOS 93: 135-147

• Functional (or phylogenetic) diversity reflect the dissimilarity of two randomly selected individuals from the community

• qi,j – dissimilarity of two species

• pi – relative representation of a species

• If qi,j = 1 for all species pairs, FD equals to Simpson diversity, i.e.. 1-Simpson dominance

ijjiji ppqFD

Macro na http://botanika.bf.jcu.cz/suspa/FunctDiv.php

See also: Leps J., de Bello F., Lavorel S., Berman S. (2006): Quantifying and interpreting functional diversity of natural communities: practical considerations matter. Preslia 78: 481-501.

Usually [but not necessarily]

• Two functionally identical species: q=0

• Two completely different species: q=1

• Acceptable dissimilarity measure [qualitative traits]

• 1-(no. of identical traits/no. of all traits)

• Similar scaling useful also for phylogenetic dissimilarity

Alpha, beta and gamma diversity• Diversity in space (compare with species-

area relationship)

• Alpha – diversity of single habitat, of singe quadrat, etc.

• Beta diversity – variability among the basic units in space

• Total diversity of an area, landscape – gamma diversity

Relationship between alpha, beta, gamma

• Both forms were used:• Gamma = alpha + beta• Gamma = alpha (beta+1)

• E.g. Whittaker • beta = S/a – 1, where S is the total number of species in the

habitat complex studied (called sometimes γ diversity) and a is the a-diversity, expressed as the mean number of species per fixed sample size

• Problems with estimate of S

Characteristics of beta-diversity

• Average dissimilarity of basic units (should not be dependent on the number of basic units)

• Special methods when the units are on a gradient – use of multivariate methods (e.g. ordinations) and estimation of length of the gradient (as in DCA)

Functional beta diversity

• Measure of trait convergence – divergence

• Use of null models in community ecology

Recommended reading:• Maguran A.E. 2004. Measuring biological diversity.

Blackwell.

• Rosenzweig M.L. 1995. Species diversity in space and time. Cambridge Univ. Press

• Huston M. A. 1997. Biological Diversity: The Coexistence of Species. Cambridge Univ. Press

• My chapter in van der Maarel (2013) Vegetation Ecology. Blackwell.

• Vellend, M. (2016). The theory of ecological communities (MPB-57). Princeton University Press.

Community Ecology –

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