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

2. Early plant succession in the Saxon (Germany) post brown coal mining area Chicken Creak 2005 2010 Succession starts with colonising species from a regional species pool and from the initial seed bank

3. Species co-occurrences How do patterns of species co-occurrences change in time? 2005A1-2A3-2A4-2A4-3B1-2B2-1B2-4 Agrostis_capillaris000.10000 Agrostis_stolonifer0.10000 Cirsium_arvense0.10.5000.100 2011A1-1A1-2A1-3A1-4A2-1A2-2A2-3 Achillea_pannonica00.5003.00.10 Agrostis_capillaris00.50 Agrostis_stolonifer000.50000 Agrostis_vinealis0002.0000 Ajuga_genevensis001.50000 Apera_spica-venti00.4000.700 Arenaria_serpyllifo00000.5 0.1 Early plant succession Starting hypothesis 1.Island biogeography predicts initial species occurrences to be random. 2.Compretition theory predicts equilibrium species occurrences to be not random but driven by interspecific competition. Basic questions 1.Which species colonise initially? 2.Is initial colonisation directional? 3.Is colonisation predictable?

4. How to test for (non-) randomness? Sites 12345678 Species A01000011 B00101011 C00001000 D10000111 E01000011 Clustered co-occurrence Joined co-occurrence SegregationReciprocal segregation Checkerboard Common ecological requirements Habitat filtering Niche conservatism Competition Reciprocal habitat requirements Facilitation, mutualism Habitat engineering by the earlier colonizer Joined absences Habitat filtering Niche conservatism is the tendency of closed related species to have similar ecological requirements and life history raits

5. Sites 12345678 Species A01000011 B00101011 C00011000 D10000111 E01000011 EV-0.7-0.60.32.41.4-0.7-0.4 EV -0.5 0.26 2.16 -0.6 -0.5 Sites 45378216 Species C110000002.16 B011110000.26 A00011100-0.5 E00011100 D00011011-0.6 2.41.40.3-0.4 -0.6-0.7 Sort according to the dominant eigenvector of correspondence analysis Spatial species turnover (  -diversity) Sorting a presence- absence matrix according to the dominant eigenvectors of corresondence analysis (seriation) maximizes the number of occurrences along the left to right diagonal. Metric for spatial species turnover 12345678 1 2 3 4 5 Squared correlation R 2 of row and column ranks of species occurrrences. RCPearson 110.75 12 22R2R2 230.56 24 25 34 35 36 44 45 46 54 55 57 57

6. Sites 12345678Sum Species A111111118 B111110005 C111000003 D110000002 E100000001 Sum54322111 A nested subset pattern Nestedness describes a situation where a species poorer site is a true subset of the next species richer site. A presence – absence matrix ordered according to total species richness (marginal totals, degree distributions) Sites 12345678Sum Species A111111118 B101110004 C111000003 D110001003 E100000001 Sum53322211 Unexpected absence Unexpected presence

7. The measurement of nestedness The distance concept of nestedness. Sort the matrix rows and columns according to some gradient. Define an isocline that divides the matrix into a perfectly filled and an empty part. The normalized squared sum of relative distances of unexpected absences and unexpected presences is now a metric of nestedness the nestedness temperature.

8. Nestedness based on Overlap and Decreasing Fill (NODF) NODF is a gap based metric and more conservative than temperature.

9. Back to the Huehnerwasser How does species co-occurrence change during early succession?

10. The number of reciprocal species co- occurrences increases in time The degree of nestedness is at an average level (neithert nested nor anti- nested) The degree of species spatial turnover decreases in time But are raw scores reliable? What do we expect if colonisation were a simple random process?

11. Statistical inference using null models What is random in ecology? Sites 12345678Sum Species A000000008 B000000005 C000000003 D000000002 E000000001 Sum54322111 1 1 1 1 1 1 1 Fill the matrix at random but proportional to observed marginal (row/column) totals until the observed total number of occurrences is reached The proportional- proportional null model Fill the matrix at random until for each row and each column the observed total number of occurrences is reached The fixed - fixed null model Sites 12345678Sum Species A111101016 B111110005 C110010103 D111000003 E100000001 Sum54322111 Take a checkerboard pair and swap. Do this 100000 times to randomize the matrix.

12. Statistical inference using null models Observed score 400 randomized matrices For each randomized matrix we calculate the respective metric (C-score, NODF, R 2 ). Standardized effect sizes (SES) are Z- transformed scores and can be linked to a normal distribution. P(-1 < X <+1) = 68% P(-1.65 < X < +1.65) = 90% P(-1.96 < X < +1.96) = 95% P(-2.58 < X < +2.58) = 99% P(-3.29 < X < +3.29) = 99.9% The Fisherian significance levels SES- SES

15. YearScore Average null distribution score Standard deviation Z-score (SES) FF null model P(Z) C-score 20050.010870.010850.000090.1930.847184 20060.001060.001040.000021.1060.26857 20070.001320.00130.000013.0360.002319 20080.003550.003490.000014.9956E-07 20090.004920.004870.000014.4547.9E-06 20100.007560.007470.000017.2730 20110.008130.0080.0000110.2730 NODF 20050.059310.059990.00575-0.120.904851 20060.487070.488340.00116-1.0950.27362 20070.538150.538380.00171-0.1380.890053 20080.464880.46860.00274-1.3580.174224 20090.498420.498320.001220.0830.933961 20100.521190.521590.00081-0.4980.618628 20110.496320.496270.000890.0530.95803 R2R2 20050.916080.927460.02176-0.5230.601039 20060.275260.282480.04284-0.1680.866252 20070.207230.177990.023611.2380.215409 20080.120740.054480.0116.0250 20090.061060.026950.005096.7050 20100.046780.019070.005075.4631E-07 20110.029640.017280.004972.4880.012634

16. SES scores (FF mull model) The degree of reciprocal species segregation constantly increases during early succession Local plant communities are not significantly nested during succession Species spatial turnover peaks at intermediate stages of succession In ecological research raw metrics are most often meaningless!!

17. Different null assumptions give different answers FF PP FF PP

18. FF PP The FF null model assumes that sites are filled with species and that each species occupies the maximal number of sites. The PP null model assumes that sites might differ dynamically in species richness and that each species might occupy a variable number of sites. The PP null model points often to a random pattern in species occupancy.

19. Idiosyncratic species are those that deviate in their patterns of occurrences Species 2005 Total Occ NODFExpNODFSpResultZ-SpResuDeltaSpC Cirsium_arvense1156.61863.4316.0870.864-2.229 Chenopodium_album_agg.769.11869.4025.9751.049-1.146 Agrostis_stolonifera_agg.67574.6155.409-0.529-1.452 Crepis_tectorum691.17679.3485.365-0.607-1.382 Echium_vulgare666.91280.9446.2453.091-0.536 Rumex_acetosella_var._te nuifoliu 386.02986.8525.9191.163-0.05 Calamagrostis_epigejos394.11885.9085.311-1.004-0.841 Arenaria_serpyllifolia_agg.292.64790.2935.8591.341-0.008 Festuca_rubra_agg.285.29490.7955.971.6830.075 Daucus_carota292.64792.8995.505-0.527-0.383 Agrostis_capillaris292.64791.2035.8311.133-0.092 Carex_ericetorum298.52991.1665.7921.035-0.133 Bromus_tectorum292.64791.3455.8220.806-0.092 Robinia_pseudoacacia192.64796.8055.8381.6720.075 Lupinus_luteus192.64796.9495.7891.1260.075 Poa_palustris192.64794.7875.8161.2690.075 Rubus_fruticosus_agg.0100 5.62900.075 Sites 12345678 Species A11111111 B11111000 C11110000 D11000110 E10000001 Cirsium arvense increases (DeltaC < 0) the degree of nestedness None of the species decreases the nestedness pattern. There are few unexpected occurrences. All species behave similar.