Analisis de la riqueza especifica. Dan Cogălniceanu

Transcription

1 Analisis de la riqueza especifica Dan Cogălniceanu

2 Steps in data analysis

3 Contenido Muestrear Analisis preliminar Curba de acumulacion de especies Rarefaction Estimadores de riqueza especifica Relacion riqueza especifica-superficie

4 BIOSPHERE Complexity level BIOM BIOCENOSIS COMMUNITY GUILD TROPHIC LEVEL TAXA Species Como estudiar la riqueza especifica? Populations

5 Sampling species diversity Species Sample 1 Sample 2 Sample 3 A B C D E F G H Sample 1 Sample 2 Sample 3 I Total %

6 Exploratory analysis Ranking species according to abundance

7 Exploratory analysis Ranking species according to cumulative abundance

8 Diversity and evenness measures

9 Each forest has the same species richness. but they differ in eveness.

10 Scale dependent species diversity Gamma diversity 4-6

11 SITE 1 SITE 2 SITE 3 ALPHA DIVERSITY BETA DIVERSITY High Low Low High Low Low High High The relationship between alpha and beta diversity based on four transects with three sites each. Each number corresponds to a species. There are four species in each site.

12 Abundance Low species richness High eveness Species Abundance Low species richness Low eveness Species Abundance High species richness Low eveness Species

13 Low eveness High eveness Abundance Abundance Species Species Community Number of species Number of individuals Species 1 Species 2 Species 3 Species 4 Species 5 A B

14 Biodiversity Indices Shannon-Wiener index: measures the order/disorder in a particular system. The proportion of species i relative to the total number of species (p i ) is calculated. and then multiplied by the natural logarithm of this proportion (lnp i ). The resulting product is summed across species. and multiplied by -1. Simpson's Reciprocal index: measures the probability that two randomly selected individuals belong to two different species/categories. The proportion of species i relative to the total number of species (p i ) is calculated and squared. The squared proportions for all the species are summed. and the reciprocal is taken.

15 Other indices all with strengths and weaknesses!! Brillouin index (HB) Berger-Parker index (d) McIntosh index Margalef index (D Mg ) Menhinick index (D Mn ) Hill's (N 1 ) Sequential Comparison Index

16 Species accumulation curve (SAC)

17 Smoother curve

18 Sampling effort Number of species Numar de specii

19 Sampling effort Number of persons Unit times Unit of area/volume or distance Unit of effort (number of boats, liters of gasoline, number of cars, meters of nets, number of traps, etc.)

20 Number of species (S) Unsaturated SAC S 2 S 2 -S 1 >0 S 1 S 1 /S 2 S 2 -S 1 =0 Saturated SAC E 1 E 2 Sampling effort

21 Number of samples Species accumulation curve Rarefaction curve Number of species of Chironomids

22 Use of rarefaction

23 Initial data Use of rarefaction 2 Parameter Control Impacted Number of individuals Number of taxa Comparable data after rarefaction Parameter Control Impacted Number of individuals Number of taxa 23 18

24 60 50 Number of individuals Ranking species according to abundance (rank order)

25 60 Number of individuals Common species Rare species 0 Ranking species according to abundance (rank order)

26 SAC Number of species What happens if we continue the inventory? Inventory effort

27 Estimators of species richness Based on the premise that only part of the species are inventoried (S observed ). to which an estimator of the rare and very rare species is added. S estimated = S observed + S rare

28 Number of individuals S observed S rare Species rank

29 Estimation of asymptotic species richness by fitting a log-normal distribution to a species abundance distribution. The graph shows the number of species of ants in each of seven logarithmically-scaled abundance categories (a total of 435 species collected) in a long-term rainforest inventory in Costa Rica. The number of undetected species (21 additional species) is estimated by the area marked with horizontal hatching. yielding a predicted complete richness of 456 species.

30 EstimateS SPADE Recommended software for computing species accumulation curves, rarefaction and estimators of species richness

31 Chao 1 Estimator Chao1 F 1 F 2 S Chao1 S obs - number of species with a single individual (singleton); - number of species with two individuals (doubleton). F 2 1 2F 2 The higher the difference between the number of singletons compared to doubletons. the higher the difference between real and observed species richness The estimator S Chao1 reaches his maximum value in the situation when there is a single doubleton and all the rest are singletons.

32 Chao 2 Chao2 is based on incidence data (presence/absence): S Chao2 S obs Q 2 1 2Q 2 Q 1 number of uniques (species that occur in a only one sample); Q 2 number of duplicates. (species that occur in exacly two samples).

33 60 50 Species observed Chao1 Chao2 40 Number of species Number of samples

34 Species area relationships

35 The relationship between the area of the islands from Galapagos Archipelago and the plant species richness.

36 Species - area curve is a particular case of species accumulation curve, in this case based not on the number of individuals or samples, but on surface. Already in the first half of the nineteenth century it was observed that the number of species increases with increasing surface.

37 Hypotheses: (i) Larger areas host more individuals. (ii) Larger areas have a higher habitat and ecosystem diversity. (iii) Larger areas cover more biogeographical realms.

38 The shape of the species-area curve varies with the spatial scale (A) On local scales. the species accumulation curve (SAC) is most sensitive to the relative abundance of species. (B) On regional spatial scales. the SAC is sensitive to the encounter of the ranges of species at steady state between speciation. dispersal and extinction. (C) On macroregional spatial scales. sampling is done among biogeographical provinces with separate evolutionary histories.

39 Rosenzweig (1995) described at least four species space-scales relationships 1. Small patches within a single biota, where below a threshold area, species richness is not a simple function of area. 2. Large patches of a singe biota whose specific pattern results from larger areas containing more habitats. 3. The pattern among islands of one archipelago results from the different rates of immigration and extinction of species on islands. 4. The pattern among geographical provinces is the result of a greater rate of speciation and a lower rate of extinction in larger areas.

40 Species-area relationships The relationship between species richness and area is not a linear one. The standard way to plot species-area curves for analysis is to transform both the area (A) and the number of species (S) into logarithms (most often in base 10). The log-log plot aligns the data along a straight line. which can be described by the equation: y = ax + b Log S = z log A + log c where z describes the slope of the log-log relationship and log c its intercept.

41 Species-area relationships Since we are not interested in log S but in S we can transform the equation: S = ca z Note that c is scale dependent while z is not.

42 The significance of z If c is held constant and z is variable. the higher slope (z 1 ) corresponds to a species-area curve amongst separate biogeographical regions or islands while the lower slope (z 2 ) corresponds to a species-area curve of neighbouring areas with a low level of endemics.

43 Significance of c If z is held constant (thus the slope is the same) c is variable. depending on species richness. with higher values (c 1 ) corresponding to higher species richness.

44 Study case (1) Island/ Archipelago Area (km 2 ) Plant genera Bird genera Cook Tonga Society Samoa New Hebrides Fiji New Caledonie Solomon

45 Study case (2) Island/ Archipelago Log Area Log Plant genera Log Bird genera Cook Tonga Societatii Samoa Noile Hebride Fiji Noua Caledonie Solomon

46 3.00 Calculând ecuaţia dreptei de regresie dintre logaritmul suprafeţei şi logaritmul numărului de 2.50 genuri obţinem următoarele ecuaţii: Log number of genera y = x R² = Log S gen plante = 0.31 Log A R 2 = 0.92 Log S gen păsări = 0.46 Log A 0.15 R 2 = y = x R² = De unde obţinem z şi c pentru cele două 1.00 categorii: Genuri plante: z = 0.31 iar c = Genuri păsări: z = 0.46 iar c = Log Area Plant genera Plant genera

47 Species-area curves allow to estimate the number of species impacted by habitat destruction log S The number of species that will disappear log A Deforestation diminishes the available habitat

48 How does the slope (z) affect species loss? Higher species losses log S Lower species losses log A Habitat reduction

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50 Extinction debt At equilibrium, species richness in a habitat patch is high. After an environmental perturbation, habitat is lost but this does cause immediate extinction to reach a new equilibrium. The difference between the number of species remaining after habitat loss and a new theoretical equilibrium represents a possible extinction debt. Relaxation time is the time elapsed since the habitat was lost until the new equilibrium is attained.

51 Extinction debt In a continuous habitat, subsamples of different areas show a shallower species area relationship (blue dots) than those in a fragmented habitat (orange dots). The difference in species numbers between large and small subsamples provides an estimate of the deterministic species loss in the initial phase of habitat loss. Since patch-level extinctions tend to occur faster in small patches, after some time the remaining extinction debt can be higher in large patches.