Mesoscopic analysis of ecological networks using Hill numbers

Size: px

Start display at page:

Download "Mesoscopic analysis of ecological networks using Hill numbers"

Gerald Harrison
5 years ago
Views:

1 Mesoscopic analysis of ecological networks using Hill numbers Marc Ohlmann PhD supervisor : Wilfried Thuiller Jump In Jackson Pollock

Introduction Examples and simulations Conclusion Aim : assess the diversity of one or several ecological communities that are interacting through an ecological network α-diversity : richness of a

2 Introduction Examples and simulations Conclusion Aim : assess the diversity of one or several ecological communities that are interacting through an ecological network α-diversity : richness of a community β-diversity : the extent of change in community composition, or degree of community differentiation, in relation to a complex-gradient of environment, or a pattern of environments Whittaker 1960 Classic diversity index omit interactions Pocock et al., 2012

3 Introduction Examples and simulations Conclusion Measuring diversity of a community? A diversity of metrics Shannon entropy set of organisms colours are species Simpson index Unifying framework : True diversity (Hill numbers, 1973) Suppose that (E) is composed of N individuals, belonging to S distinct species, with relative abundances : Generalized mean of order q-1

4 Introduction Examples and simulations Conclusion Hill number of order q

5 Introduction Examples and simulations Conclusion Hill number of order q Species richness Shannon entropy (exp) Inverse of Simpson index

6 Introduction Examples and simulations Conclusion Hill number of order q Species richness Shannon entropy (exp) Inverse of Simpson index α-diversity

7 Introduction Examples and simulations Conclusion Comparing two communities β-diversity concept : The extent of change in community composition, or degree of community differentiation, in relation to a complexgradient of environment, or a pattern of environments Whittaker, 1960 Meta-community community 1 community 2

8 Introduction Introduction Examples and simulations Conclusion Comparing two communities β-diversity concept : The extent of change in community composition, or degree of community differentiation, in relation to a complexgradient of environment, or a pattern of environments Whittaker, 1960 ratio of generalised means in presence of two classifications (species and space) cf Tuomisto, 2010, Ecography β-diversity

9 Introduction Examples and simulations Conclusion Problem : interactions Biotic interactions Interaction network

10 Introduction Examples and simulations ExemplesConclusion and simulations

11 Introduction Examples and simulations ExemplesConclusion and simulations Connectance Connectance does not take into account species identity Not a measure of diversity, since there is only one group

12 Introduction Connectance Not a measure of diversity, since there is only one group Examples and simulations ExamplesConclusion and simulations Link number Measure of α-diversity

13 Introduction Examples and simulations Conclusion Link number Connectance scale (size of Q) Macroscopic scale Microscopic scale

14 Introduction Examples and simulations Conclusion Link number Connectance scale (size of Q) Microscopic scale Macroscopic scale Mesoscopic scale?

15 Introduction Examples and simulations Conclusion group proportion link proportion (connecting group k and l) connectance between class k and l If there is only one class, then it's a scalar equal to connectance

16 Introduction Examples and simulations Conclusion Hill number on group proportion Hill number on links proportion Hill number on connectance matrix

Introduction Examples and simulations ExamplesConclusion and simulations The network dissimilarity problem Macroscopic comparison Q : set of groups

17 Introduction Examples and simulations ExamplesConclusion and simulations The network dissimilarity problem Macroscopic comparison Q : set of groups of the metaweb Elton niches= species that have a similar position in the network Q={q} Compare connectance of the two networks Charles Elton ( )

18 Introduction Examples and simulations Examples Conclusion and simulations Microscopic comparison What already exists : Poisot, 2012

19 Introduction Examples and simulations Examples Conclusion and simulations Microscopic comparison What already exists : Poisot, 2012 Both species turnover and plasticity of interactions (at a species level) contribute to β WN Aim : seperate these two effects

20 Introduction Examples and simulations ExamplesConclusion and simulations Microscopic comparison Total dissimilarity Dissimilarity due to plasticity of interactions Dissimilarity due to species turnover

21 Introduction Examples and simulations Conclusion Microscopic comparison Total dissimilarity Dissimilarity due to plasticity of interactions Dissimilarity due to species turnover Mesoscopic comparison?

22 Introduction Examples and simulations G1 Conclusion G2

23 Introduction Examples and simulations G1 Conclusion G2

24 Introduction Examples and simulations G1 Conclusion G2

25 Introduction Examples and simulations G1 Conclusion G2

26 Introduction Examples and simulations G1 A particular case : Tim Poisot case (microscopic turnover) Easy to show that : Conclusion G2

27 Introduction Examples and simulations G1 Conclusion G2 A particular case : Tim Poisot case (microscopic turnover) Easy to show that : Do change of link diversity reflect change in connectivity? Yes, but there is a group size effect too!

28 Introduction Here, we work on : and simulations Change ofexamples connectivity Conclusion

29 Introduction and simulations Change ofexamples connectivity Here, we work on : A particular case : Tim Poisot case (microscopic scale) Conclusion

30 Introduction Change Examples of connectivity and simulations Examples Conclusion and simulations Macroscopic scale C=0.19 Mesoscopic scale β α =0 β L =0 β π =0 Microsocopic scale β S =0.5 β WN =2/3 β OS =0 β ST =2/3

31 Introduction Change of Examples connectivity and simulations Conclusion A trait perspective Forbidden links between species? Sp. A 1 Sp. B At individual level : Sp.C Sp. D

Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Forbidden links between species? 1 Sp. A Sp. B Sp.C Sp.

32 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Forbidden links between species? 1 Sp. A Sp. B Sp.C Sp. D These binary relations neglect the intraspecific trait variability compared to interspecific trait variability Taken from : The labile limit of forbidden interactions Gonzalez-Varo Trends in Ecology and Evolution, 2016 Sp. A π Sp. B

33 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Can we neglect intra-specific traits variability? A mono-trophic view of traits intra/interspecific variability Taken from : A multi-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits Albert et al., Functionnal Ecology, 2010

34 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Can we neglect intra-specific traits variability? A mono-trophic view of traits intra/interspecific variability Taken from : Amulti-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits Albert et al., Functionnal Ecology, 2010 For trophic networks : Interaction is the resultant of a match between a vulnerability trait (prey) and a foraging trait (predator) see : Gravel et al., 2016

35 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective For trophic networks : Interaction is the resultant of a match between a vulnerability trait (prey) and a foraging trait (predator) see : Gravel et al., 2016 Vulnerability trait Sp. A Sp. B Sp. C Sp. D

36 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective For trophic networks : Interaction is the resultant of a match between a vulnerability trait (prey) and a foraging trait (predator) see : Gravel et al., 2016 Vulnerability Trait Sp. A Sp. B Sp. P Foraging trait Sp. C Sp. D

37 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Taken from : Amulti-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits Albert et al., Functionnal Ecology, 2010 Using functionnal groups might be relevant in that case

38 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Traits group A high plasticity of interactions Traits group B high plasticity of interactions Traits group C

39 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective? high plasticity of interactions? high plasticity of interactions - high plasticity of interactions - environment slightly modifying the between class connectivity but not the global connectance

40 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations A trait perspective Macroscopic scale : no variations Microscopic scale : too much variations Mesoscopic scale : strong pattern

41 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations Rarefactions curves Aim : Test the robustness of mesoscopic metrics to incomplete sampling Using a model of food web : the niche model (Williams and Martinez, 2000) Inferring classes of nodes using Stochastic Block Model ( a model of community detection) Taken from : Simple rules yield complex food webs, Williams and Martinez, 2000, Nature Groups inferred using SBM are almost functionnal groups

42 Introduction Change of Examples connectivity and simulations Examples Conclusion and simulations Rarefactions curves Rarefaction curves : how robust are our metrics to incomplete species sampling?

43 Introduction Examples and simulations Conclusion A framework using Hill numbers that allows network comparison at different scales, from macroscopic scale to microscopic scale Ecological networks might evolve at different Elton niches scales, especially if you're interested in microbial/soil ecology Mesoscopic analysis is more robust to incomplete sampling Key question : how to determine Elton niches? Using network topology? Traits?

Studying the effect of species dominance on diversity patterns using Hill numbers-based indices

Studying the effect of species dominance on diversity patterns using Hill numbers-based indices Loïc Chalmandrier Loïc Chalmandrier Diversity pattern analysis November 8th 2017 1 / 14 Introduction Diversity