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Advanced analytical approaches in ecological data analysis The world comes in fragments.

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Presentation on theme: "Advanced analytical approaches in ecological data analysis The world comes in fragments."— Presentation transcript:

1 Advanced analytical approaches in ecological data analysis The world comes in fragments

2 Biogeography Species occurrences across a fragmented landscape 1.Islands 2.Lakes 3.River bed and irrigation systems 4.Mainlands continental distributions habitat islands mountain tops and valleys fragmented landscapes scattered distributed host plants 5.Cities and anthropogenic habitats 6.Routes of species invasion 7.Experimental plots (natural, macro-, meso-cosm experiments)

3 Galapagos Islands The Darwin finches

4 The Darwin finches (Sanderson, Am. Scient. 2000) A presence – absence matrix reflects the distribution of species across sites

5 The distribution of ground beetles across Mazuran lake islands In presence – absence matrices zeros denote species absence, ones denote species presences. Absences might be caused either by real absences of species or by incomplete detection.

6 Species abundance matrix M Phylogenetic distance matrix P Species trait matrix T Site GPS location matrix D Environmental variable matrix V Interdepen- dence matrix X Species Sites Variables Traits Multivariate approaches to biodiversity Why are species abundant or rare? What determines community composition? How does a community function in space and time? L

7 SA1-2C2-4C3-1D3-2D3-4 CaCO30. Sand82.5476.7188.5679.02 pH8.167.978.088.18 SA1-2C2-4C3-1D3-2D3-4 Achillea_pannonica00000 Agrostis_capillaris0.100.1500 Agrostis_stolonifera00000.3 Agrostis_vinealis00.900.20 Ajuga_genevensis00000 Apera_spica-venti000.400 A1-2C2-4C3-1D3-2D3-4 Longitude171.38205.61227.46239.65233.65 Latitude295.09275.55293.74271.88 Species Leaf mass [mg] Leaf size [mm 2 ] Life span Achillea_pannonica82.33567.845 Agrostis_capillaris60.979861147.9345 Agrostis_stolonifera60.979861147.9345 Agrostis_vinealis1.4122.755 Ajuga_genevensis17.99365.55 Apera_spica-venti60.979861147.9341 Arctium_minus_agg.60.979861147.9342 S Achillea_p annonica Agrostis_c apillaris Agrosti s_stolo nifera_ agg. Agrosti s_vine alis Ajuga_ geneve nsis Apera_ spica_ venti Achillea_pannonica0.0179.0 117.0179.0 Agrostis_capillaris179.00.02.5 179.06.7 Agrostis_stolonifera179. Agrostis_vinealis179.02.5 0.0179.06.7 Ajuga_genevensis117.0179.0 0.0179.0 Apera_spica_venti179.06.7 179.00.0 Observed species abundances Phylogenetic data from Genbank or own sequencing Plant species trait data from Bioflor and Leda GPS longitude and latitude data Measured environmental data WordClim, BioClim Species and environmental data in primary plant succession




11 Biogeographic matrices are static descriptions of colonization patterns. Colonization and extinction are permanent processes. In reality presence – absence pattern change whole the time. It makes therefore a difference if we use temporal point data to construct our matrices or a time series. Time series data contain much more entries but might be ecologically unrealistic.

12 Dispersion Extinction Time axis Time series matrices have too many entries and do not reflect real ecological patterns. They do not give information on real species interactions For a proper assessment of ecological patterns we need point data. The comparison of point and time series matrices gives information about dispersion rates.

13 The distribution of ground beetles across Mazuran lake islands Abundance matrices contain additional information. Abundance matrices might be based on point or averaged time series data.

14 Mutual interaction matrices Food web example 1.Food webs 2.Host – parasite networks 3.Plant – herbivore networks 4.Pollination networks 5.Predator – prey networks 6.Competition networks 7.Species impact networks Translation of a food web into a matrix. Ones denote direct links. Generalist predator Specialist predator Typical terrestrial food web

15 Interaction strength is expressed by probabilities or by frequencies of interaction A quantitative food web

16 Interaction matrices Pollination networks Plants Bees From Kratochwil et al. 2009 From Ollerton et al. 2003

17 How to present a presence – absence matrix? Unsorted raw dataSorted according to marginal totals Sorted to maximize species turnover Correspondence analysis Reciprocal averaging (seriation)

18 Ecological gradients Sorting of matrix columns according to ecological gradients allows for an assessment of the importance of environmental variables. Spatial or ecological distance

19 Species turnover Species turnover or beta diversity is a special case of species segregation where there is an ordering change in species composition across the sites. Raw matrix Ordinated presence – absence matrix Unexpected occurrences Spatial distance between species Ecological distance between sites Basic patterns

20 Small scale spatial variability in phylogenetic signals during early plant succession depends on soil properties Werner Ulrich, Marcin Piwczyński, Markus Klemens Zaplata, Susanne Winter, Wolfgang Schaaf, Anton Fischer Phylogenetic distance matrix according to the classification contained in APG III (Angiosperm Phylogeny Group 2009) S Achillea _pannon ica Agrostis _capillari s Agrostis _stolonif era_agg. Agrostis _vinealis Ajuga_g enevensi s Apera_s pica_ven ti Arctium_ minus_a gg. Arenaria _serpyllif olia_agg. Achillea_pannonica0.0179.0 117.0179.044.0122.0 Agrostis_capillaris179.00.02.5 179.06.7179.0 Agrostis_stolonifera_agg. Agrostis_vinealis179.02.5 0.0179.06.7179.0 Ajuga_genevensis117.0179.0 0.0179.0117.0122.0 Apera_spica_venti179.06.7 179.00.0179.0 Arctium_minus_agg.44.0179.0 117.0179.00.0122.0 Arenaria_serpyllifolia_agg.122.0179.0 122.0179.0122.00.0 Artemisia_campestris_agg.32.7179.0 117.0179.044.0122.0 Artemisia_vulgaris_agg.32.7179.0 117.0179.044.0122.0 Berteroa_incana127.0179.0 127.0179.0127.0 Betula_pendula127.0179.0 127.0179.0127.0 The data of our lecture

21 Small scale spatial variability in phylogenetic signals during early plant succession depends on soil properties Werner Ulrich, Marcin Piwczyński, Markus Klemens Zaplata, Susanne Winter, Wolfgang Schaaf, Anton Fischer Database containing a total of 33 plant functional, genetic and morphological traits complied from the Leda (Kleyer et al. 2008) and BioFlor (Klotz et al. 2002) trait bases. Species Leaf mass [mg] Leaf size [mm 2 ] Life span Min releasin g height [m] Max releasin g height [m] Stem erect % Stem ascendi ng to prostrat e % Emerge nts attache d to substrat e Termina l velocity m/s Achillea_pannonica82.33567.8450.20.810000 1.93471 7 Agrostis_capillaris 60.9798 6 1147.93 450.20.8350 00.98 Agrostis_stolonifera_agg. 60.9798 6 1147.93 450.10.75025 1.11 Agrostis_vinealis1.4122.7550.20.450 01.59 Ajuga_genevensis17.99365.550.070.375250 1.93471 7 Apera_spica-venti 60.9798 6 1147.93 410.31100001.31

22 Software Niche – A Fortran program for meta- community analysis. Turnover – A Fortran program for the analysis of species associations. NODF – a Fortran program for nestedness analysis. Past Matrix Excel add in Literature Zaplata M. K., Winter S., Fischer A., Kollmann J., Ulrich W. 2013. Species-driven phases and increasing structure in early- successional plant communities. Am. Nat. 181: E17-E27. Ulrich W., Gotelli N. J. 2013. Pattern Detection in Null Model Analysis. Oikos 122: 2-18. Gotelli N. J., Ulrich W. 2012. Statistical challenges in null model analysis. Oikos 121: 171-180. Ulrich W., Gotelli N. J. 2012. A null model algorithm for presence – absence matrices based on proportional resampling. Ecol. Modell. 244: 20-27. Ulrich W., Piwczyński, M., Maestre F. T., Gotelli N. J. 2012. Null model tests for niche conservatism, phylogenetic assortment and habitat filtering. Meth. Ecol. Evol. 3: 930-939. Ulrich W., Almeida-Neto M., Gotelli N. G. 2009. A consumer’s guide to nestedness analysis. Oikos 118: 3-17.

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