Community Ecology –

1. Community Ecology – Descriptive and functional approches

2. Distinction between Population a Community Ecology is rather fuzzy • “Mamooth hunter” 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

3. 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

4. Zurich-Montpellier (Z-M) approach • Braun Blanquet 1932 Plant Sociology

6. Z-M classification of plant communities • Originated in Alps (relatively sharp boundaries between communities) – often connected with Clements “superorganismal view”as opposed to Gleasonian continualistic concept • Standardized way of recording vegetation relevés (Braun-Blanquet scale) • Hierarchical system of communities

7. Hierarchy - Syntaxonomy • Association: Dentario-Fagetum • Alliance: Fagion • Order: Fagetalia • Class: Querco – Fagetea • Vegetation maps – Actual vegetation • Concepts of potential vegetation, „vegetation reconstruction“

8. Map of potencial natural vegetation (what it might be???) http://www.infodatasys.cz/lesypraha/mapa/grm.htm

9. 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

10. 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

11. 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

12. Community Ecology – complex of causal relationships, causal chains British imperium saved by old maids

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

14. Rodent removal in Sonoran desert Both, ants and rodents eat seeds (ants prefer smaller seeds), but partial overlap

15. 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)

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

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

18. 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

19. 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)

21. The big one Diversity (Species diversity, Richness, Biodiversity)

22. 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

23. 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

24. 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 (and what is the measure of species representation?) • Eveness, Equitability as o component of diversity • Spatial aspects of diversity

25. Species-area (Species - no of individuals) relationship – often SAR Number of species Area (or number of individuals)

26. 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)

27. Nested quadrats – individual values are not independent

28. Independent quadrats of varying sizes – probably more laborious

29. 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 2ztimes– 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 - ???

31. 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

32. 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)

33. 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)

34. 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

35. 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. Take care!

36. 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)

37. 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

38. Diversity (taking into account species proportions) Higher here

39. Diversity indices – attempt to reflect both, number of species and their relative proportions Pi – relative species representation - Usually, Pi = Ni/N , N=Σni 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 Shannon diversity index

40. Simpson Usually, Pi = Ni/N , N=Σni And then Pi2 = Ni2/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!

41. Free software available at: http://folk.uio.no/ohammer/past/index.html

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

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

44. 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

45. Graphical representation of community population structure

46. Diversity - dominance curves

47. Models of species abundance distribution Good web page is http://www.columbia.edu/itc/cerc/danoff-burg/MBD%203.ppt

48. 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)

49. Broken Stick Model Log-Normal Series Log Series Geometric Series Dominance-diversity curves pro 4 modely 100 from Donoff-Burg 10 1 Relative abundance (plotted at log scale] 0.1 0.01 0.001 10 20 30 40 Rank

50. 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). Less than Number of species from Donoff-Burg Number of individuals of a species