Theories and Methods in Spatial Community Modelling: An Overview. Manuela D Amen & Antoine Guisan.

Transcription

1 Theories and Methods in Spatial Community Modelling: An Overview Manuela D Amen & Antoine Guisan manuela.damen@unil.ch University of Lausanne Ecospat group

2 What is a community? DEFINITION. A community is composed by a group of species that co-occur in space and time and that have the potential to interact. It can be defined at any size, scale or level within a hierarchy of habitats based on different criteria e.g. taxonomy, functional (Begon, Harper & Townsend, 1996) 2

3 What shapes species assemblages? Lortie et al 2004 Oikos 3

4 What shapes species assemblages? SESAM framework SPECIES PRODUCTION (global pool) STEPS Source Species Pool Regional Source Pool Evolutionary History & Dispersal filtering Local Source Pool Habitat suitability models Se Abiotic Habitat Filtering Abiotic Habitat Pool Macroecological constraints Ecological assembly rules (EAR) Biotic Filtering REALIZED ASSEMBLAGE Guisan & Rahbek 2011 J Biogeogr 4

5 What shapes species assemblages? Hille Ris Lambers et al 2012 Annu Rev Ecol Evol Syst 5

6 What shapes species assemblages? Mittelbach & Schemske 2015 TREE 6

7 Drivers of community assembly 1. HISTORICAL & EVOLUTIONARY DRIVERS 2. ENVIRONMENTAL DRIVERS 3. BIOTIC DRIVERS 4. STOCHASTIC DRIVERS D Amen et al Biol Rev, in review 7

8 1. Historical and evolutionary drivers Short timescale Geological times EVOLUTIONARY PROCESSES e.g., speciation, extinction, radiation, adaptation, drift HISTORICAL EVENTS i.e., modifications of the earth surface and climate change Species Source pools: the set of species that could potentially colonize by dispersal and establish within a community over large spatio-temporal scales Metacommunity dynamics Human impacts Dispersal filter Local unit Phylogenetic structure of species assemblage Ricklefs & Schluter 1993; Vellend et al 2010 Quart Rev Biol; Lessard et al 2012 Proc Biol Sci 8

9 2. Environmental drivers The environment acts like a filter on the regional species pool on all species lacking specified combinations of functional traits (i.e., physiological and morphological properties). ENVIRONMENTAL FILTER Trait-based community assembly Keddy 1992 J Veg Science 9

10 2. Environmental drivers SPECIES PRODUCTION (global pool) Regional Source Pool The environment can also act like a constraint on the maximum number of species that may co-occur in the local community (carrying capacity of the local environment), determining the limits to community saturation Source Species Pool Habitat suitability models Macroecological constraints Local Source Pool Abiotic Habitat Pool Ecological assembly rules (EAR) REALIZED COMMUNITY Loreau 2000 PNAS; Brown et al 2004 Ecology; Guisan & Rahbek 2011 J Biogeogr 10

11 3. Biotic drivers Competition for limiting resources Resource partitioning Competitive exclusion Gause 1934; Hutchinson 1959 Am Nat; MacArthur 1964 Am Nat, 1965 Biol Rev; MacArthur & Levins 1967 Am Nat 11

12 3. Biotic drivers A greater variety of different interactions can influence coexistence patterns among species within and across trophic levels predation +/- mutualism +/+ commensalism +/0 parasitism +/- competition -/- Communities where local interactions are low or absent have been observed Some of the patterns previously attributed to interspecific competition could be simulated by null models that do not account for competitive forces Real communities lie on a continuum of processes from interactive to non-interactive Hairston et al 1960 Am Nat; Connell 1975; Turelli 1978 PNAS; Ricklefs 1987; Cornell & Lawton 1992 J Anim Ecol 12

13 Environmental and biotic drivers: Trait: any morphological, physiological, phenological or behavioral feature measurable at the individual level Underdispersion similar traits co-occurring more often than expected by chance Overdispersion greater range of traits or trait spacing than expected by chance Wheier & Keddy

14 Environmental and biotic drivers: Importance of interspecific variability in trait values External and Internal filters are factors acting respectively at a larger or smaller spatial scale than the scale of the community. Violle et al 2012 TREE 14

15 4. Stochastic drivers Many patterns in nature have a stochastic component species demographic fluctuations and genetic drift Neutral theory predicts that regional variation in species richness results from a balance between speciation and stochastic extinction events caused by random drift in population size external factors reflecting underlying environmental variability Neutral models: species are assumed to be ecologically and demographically equivalent The relative importance of stochastic vs deterministic processes on community assembly continues to be debated Strong et al 1984; Hubbel 2001; Tilman 2004 PNAS; Rosindell et al 2011 TREE 15

16 Spatial Community Modelling To what extent can we move from theoretical description of the community assembly to spatially predict spatial patterns of community attributes? If environmental conditions are modified, can we predict resulting changes in community attributes? 16

17 State of the art of community-level models STRATEGY Based on 1. conceptual basis 2. modelling procedure 3. expected outputs Assemble first Predict later Inspired by the Clementsonian (1916) view of communities, as combinations of a fixed set of co-occurring species Predict first Assemble later Inspired by the Gleasonian (1939) view of communities as resulting from the coincidental collections of individualistic species Assemble and predict together Intermediate theoretical view: communities are not completely fixed units, species interaction are recognized, but only implicitly accounted Ferrier & Guisan 2006 J Appl Ecol; D Amen et al Biol Rev, in review 17

18 State of the art of community-level models STRATEGY PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT HISTORICAL / EVOLUTIONARY Richness STATIC Processes are not explicitly modelled, e.g. correlative ENVIRONMENTAL approach Species relative Abundance DYNAMIC Processes are explicitly BIOTIC modelled Composition STOCHASTIC Other community attributes (e.g. RAD, vegetation types) Ferrier & Guisan 2006 J Appl Ecol; D Amen et al Biol Rev, in review 18

19 STRATEGY: Assemble first Assemble first Predict Predict later later PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT Correlative Macroecological models Correlative trait model STATIC HISTORICAL / EVOLUTIONARY ENVIRONMENTAL Richness Species relative Abundance Composition DYNAMIC BIOTIC Dynamic global vegetation models STOCHASTIC Other community attributes (e.g. RAD, vegetation types) Explicitely accounted Implicitely accounted Potential extensions Fischer, 1960; Currie & Paquin, 1987; Cramer et al., 2001; Sitch et al., 2003; Douma et al., 2012; Dubuis et al., 2013 Scheiter, et al., 2013 for new extensions on DGVM D Amen et al Biol Rev, in review 19

20 Correlative Macroecological Models Data collection Field data Field richness data Statistical modelling Spatial predictions Environmental variables Response curves Predicted distribution of species richness Variation in species richness Assumption: The environment determines the number of species (or other property) that a unit or community can hold e.g. Fischer 1960 Evolution; Currie & Paquin 1987 Nature 20

21 STRATEGY: Assemble first Assemble first Predict Predict later later PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT Correlative Macroecological models Correlative trait model STATIC HISTORICAL / EVOLUTIONARY ENVIRONMENTAL Richness Species relative Abundance Composition DYNAMIC BIOTIC Dynamic global vegetation models STOCHASTIC Other community attributes (e.g. RAD, vegetation types) Explicitely accounted Implicitely accounted Potential extensions Fischer, 1960; Currie & Paquin, 1987; Cramer et al., 2001; Sitch et al., 2003; Douma et al., 2012; Dubuis et al., 2013 Scheiter, et al., 2013 for new extensions on DGVM D Amen et al Biol Rev, in review 21

22 Dynamic global vegetation models (DGVMs) PFTs: plant functional types NEE: net ecosystem exchange GPP: gross primary production NPP: net primary production RE: respiration ET: evapotranspiration Arneth et al 2014 Nature Climate Change 22

23 STRATEGY: Predict first Assemble later Predict first Assemble later PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT HISTORICAL / EVOLUTIONARY Richness Stacked species distribution models STATIC ENVIRONMENTAL Species relative Abundance Stacked single species dynamic models DYNAMIC BIOTIC Composition STOCHASTIC Other community attributes (e.g. RAD, vegetation types) Explicitely accounted Implicitely accounted Potential extensions Guisan & Zimmermann, 2000; Only approximations of the second approach e.g., Gap models, Bugmann, 2001, "mechanistic SDMs"(e.g. Buckley, 2008; Kearney et al., 2009; Kearney & Porter, 2009) D Amen et al Biol Rev, in review 23

24 Stacked Species Distribution Models (S-SDM) Single Species Modelling (SDM) Presence absence data Stacking many species Σ Guisan & Zimmermann 2000 Ecol Mod; D Amen et al 2015 J Biog Plant species richness (SR) modelled with S-SDM 24

25 STRATEGY: Predict first Assemble later Predict first Assemble later PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT HISTORICAL / EVOLUTIONARY Richness Stacked species distribution models STATIC ENVIRONMENTAL Species relative Abundance Stacked single species dynamic models DYNAMIC BIOTIC Composition STOCHASTIC Other community attributes (e.g. RAD, vegetation types) Explicitely accounted Implicitely accounted Potential extensions Guisan & Zimmermann, 2000; Only approximations of the second approach e.g., Gap models, Bugmann, 2001, "mechanistic SDMs"(e.g. Buckley, 2008; Kearney et al., 2009; Kearney & Porter, 2009) D Amen et al Biol Rev, in review 25

26 STRATEGY: Assemble and Predict together Assemble and predict together PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT Multispecies extension from SDMs HISTORICAL / EVOLUTIONARY Richness General Dissimilarity Models STATIC ENVIRONMENTAL Species relative Abundance MaxEnt Traitspace DYNAMIC BIOTIC Composition Landscape simulation model STOCHASTIC Other community attributes (e.g. RAD, vegetation types) D Amen et al Biol Rev, in review Explicitely accounted Implicitely accounted Potential extensions Botkin et al., 1972; Pacala et al., 1993; Glonek & McCullagh, 1995; Hastie & Tibshirani, 1996; Fulton et al., 2004; Lischke et al., 2006; Shipley et al., 2006; Ferrier et al., 2007; Shipley, 2010; Shipley et al., 2012; Laughling et al., 2012; see Bugmann 2001 for a review on Landscape simulation models 26

27 Multiresponse models Data collection Statistical modelling Spatial predictions x x x x x x x x x x x x x x x x x x x x Field x data x 0/1 data for multiple species Response curves Single species x x x OUTPUT: Environmental variables Predicted distribution of single species and community attributes (species richness) presence absence Species richness e.g. Yee & Mackenzie 2002 Ecol Mod; Olden 2003 Cons Biol; Leathwick et al 2005 Fresh Biol 27

28 STRATEGY: Assemble and Predict together Assemble and predict together PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT Multispecies extension from SDMs HISTORICAL / EVOLUTIONARY Richness General Dissimilarity Models STATIC ENVIRONMENTAL Species relative Abundance MaxEnt Traitspace DYNAMIC BIOTIC Composition Landscape simulation model STOCHASTIC Other community attributes (e.g. RAD, vegetation types) D Amen et al Biol Rev, in review Explicitely accounted Implicitely accounted Potential extensions Botkin et al., 1972; Pacala et al., 1993; Glonek & McCullagh, 1995; Hastie & Tibshirani, 1996; Fulton et al., 2004; Lischke et al., 2006; Shipley et al., 2006; Ferrier et al., 2007; Shipley, 2010; Shipley et al., 2012; Laughling et al., 2012; see Bugmann 2001 for a review on Landscape simulation models 28

29 New solutions and perspectives: INTEGRATIVE FRAMEWORKS Integrative Frameworks: methodological procedures made of a series of sequential analytical and/or modelling steps to integrate multiple drivers of community assembly Strengths Ease the management of assembly complexity Strong theoretical backgroud for each step Possible implementation with pre-existing methods indipendently developed to predict communities Possibility to promptly incorporate any technical advance May include at the same time static and dynamic approaches Ad hoc implementation for each case study D Amen et al Biol Rev, in review 29

30 INTEGRATIVE FRAMEWORKS PREDICTIVE APPROACH ASSEMBLY DRIVERS PREDICTION OUTPUT HISTORICAL / EVOLUTIONARY Richness Dynamic metacommunity model M-SET STATIC ENVIRONMENTAL Species relative Abundance Size-Spectrum Dynamic Bioclimate Envelope Models SESAM Spatially explicit species assemblage modelling DYNAMIC BIOTIC STOCHASTIC Composition Other community attributes (e.g. RAD, vegetation types) Explicitely accounted Implicitely accounted Potential extensions Guisan & Rahbek 2011; Mokany et al. 2011; Fernandes et al D Amen et al Biol Rev, in review 30

31 M-SET Dynamic metacommunity model Mokani et al 2011 Ecol Lett; Mokani & Ferrier 2011 Div & Distr 31

32 Size-Spectrum Dynamic Bioclimate Envelope Models (SS-DBEM) Fernandes et al 2013 Global Change Biology 32

33 SESAM - Spatially explicit species assemblage modelling SESAM framework SPECIES PRODUCTION (global pool) STEPS Source Species Pool Regional Source Pool Evolutionary History & Dispersal filtering Local Source Pool Habitat suitability models Se Abiotic Habitat Filtering Abiotic Habitat Pool Macroecological constraints Ecological assembly rules (EAR) Biotic Filtering Guisan & Rahbek 2011 J Biogeogr 33

34 SESAM - Spatially explicit species assemblage modelling Count data Macroecological Model (MEM) RICHNESS PREDICTION MACROECOLOGICAL CONSTRAINT ON THE TOTAL NUMBER OF SPECIES Species pool Statistical models + use of GIS predictors (e.g. climate) Ecological Assembly Rules (EARs) based on biotic interactions COMMUNITY PREDICTION Single species pres/abs data Species Distribution Models (SDMs) HABITAT POOL ENVIRONMENTAL FILTER ON THE SPECIES POOL D Amen et al 2015 J Biogeogr; D Amen et al GEB in review; Di Febbraro, D Amen et al GCB in review 34

35 SESAM - Spatially explicit species assemblage modelling Count data Macroecological Model (MEM) RICHNESS PREDICTION MACROECOLOGICAL CONSTRAINT ON THE TOTAL NUMBER OF SPECIES Species pool Statistical models + use of GIS predictors (e.g. climate) Ecological Assembly Rules (EARs) based on biotic interactions COMMUNITY PREDICTION Single species pres/abs data Species Distribution Models (SDMs) MaxKappa MaxPCC ObsPrev ROC PCC 5 BINARY HABITAT POOL ENVIRONMENTAL FILTER ON THE SPECIES POOL D Amen et al 2015 J Biogeogr; D Amen et al GEB in review; Di Febbraro, D Amen et al GCB in review 35

36 SESAM - Spatially explicit species assemblage modelling Count data Macroecological Model (MEM) RICHNESS PREDICTION BIOTIC RULES: MACROECOLOGICAL CONSTRAINT ON THE TOTAL NUMBER OF SPECIES Species pool Statistical models + use of GIS predictors (e.g. climate) Based on SDM probabilities PROBABILITY RANKING RULE Based on Co-occurrece patterns COMMUNITY PREDICTION Single species pres/abs data Species Distribution Models (SDMs) HABITAT POOL ENVIRONMENTAL FILTER ON THE SPECIES POOL D Amen et al 2015 J Biogeogr; D Amen et al GEB in review; Di Febbraro, D Amen et al GCB in review 36

37 SESAM - Spatially explicit species assemblage modelling Taxon N species N plots Resolution Plants m Butterflies m Grasshoppers m Birds Km Comparison SESAM prediction vs bs-sdm (sum of binary SDMs) D Amen et al 2015 J Biogeogr; D Amen et al GEB in review; Di Febbraro, D Amen et al GCB in review 37

38 SESAM test on PLANT DATASET Different SESAM implementations Test of the Probability Ranking Rule (PRR) Richenss prediction error Prediction Success Sorensen Index SESAM rand ps-sdm rand MEM PRR ps-sdm PRR MEM bs-sdm ps-sdm: Sum of probabilities from SDMs bs-sdm: Sum of binary SDMs (1 threshold) MEM: direct richness prediction PPPPPPPPPPPPPPPPPPPP ssssppppssssss = TTTT + TTTT SSSS SP: species pool TP: true positive SSSSSSSSSSSSSSS iiiiiiiiii = FN: false negative FP: false positive 2TTTT 2TTTT + FFFF + FFFF TN: true negative D Amen et al 2015 J Biogeogr 38

39 SESAM test on BIRD DATASET Richness estimates Mean Error Mean Absolute Error r Pearson bs-sdm ps-sdm MEM ps-sdm: Sum of probabilities from SDMs bs-sdm: Sum of binary SDMs (1 threshold) MEM: direct richness prediction RESULTS Different SESAM implementations bs-sdm Richenss prediction error Sorensen Index Prediction Success SESAM ProbRankRule MEM ProbRankRule ps-sdm PPPPPPPPPPPPPPPPPPPP ssssppppssssss = TTTT + TTTT SSSS SSSSSSSSSSSSSSS iiiiiiiiii = 2TTTT 2TTTT + FFFF + FFFF SP: species pool TP: true positive FN: false negative FP: false positive TN: true negative Di Febbraro, D Amen et al GCB in review 39

40 SESAM test on INSECT DATASETS SESAM implemented with all combinations from 5 Binary Habitat Pool X 2 Richness constraints X 5 Biotic rules (PRR and 4 from co-occurrence patterns) in two insect groups Agreement in the results from Butterflies and Grasshoppers RESULTS Richness estimates: NO SIGNIFICANT DIFFERENCES AMONG MEM, ps-sdm and the 5 bs-sdm Moderate degree of overprediction using only the Obs Prev threshold D Amen et al GEB in review 40

41 SESAM test on INSECT DATASETS Combination PCC & ps-sdm SESAM SESAM SESAM implemented with all combinations from 5 Binary Habitat Pool X 2 Richness constraints X 5 Biotic rules (PRR and 4 from co-occurrence patterns) in two insect groups CoocBioticRule ProbRankRule Combination ObsPrev & MEM bs-sdm CoocBioticRule ProbRankRule Richenss prediction error Sorensen Index Prediction Success Richenss prediction error Sorensen Index Prediction Success RESULTS bs-sdm CASE OF RICHNESS OVERPREDICTION D Amen et al GEB in review 41

42 Community ecology has innate complexity, mutidimensionality, multiple causality with extensive scale in space and time involved (Pianka, 1999) Alessio Morelli Weiher & Keddy 1999 Ecological Assembly Rules: Perspectives, Advances, Retreats

43 Thanks for the attention And thanks to the Spatial Ecology Group University of Lausanne

44 44

45 SESAM PROBABILITY RANKING RULE Species Prob SDM Sp Sp Sp Sp Sp Sp MACROECOLOGICAL CONSTRAINT Richness prediction S=3 COMMUNITY PREDICTION Species Prob SDM Sp Sp Sp Sp Sp Sp S=3

46 BIOTIC RULES BASED ON CO-OCCURRENCE PATTERNS Method: Co-occurrence analysis coupled with environmentally weighted null models N Checkerboard Units to quantify pairwise interactions Empirical Bayes approach to control for false discovery rates Assumption: Processes structuring the community leave an imprint on the spatial distributions of species Binary Abiotic Habitat Pool Species HS Sp1 1 Sp2 1 Sp3 1 Sp4 1 Sp5 1 Sp6 0 S=5 Macroecological constraint S=4 Results from co-occurrence analysis Sp.A Sp.B CU-Obs CU- Exp P.greater sp1 sp sp1 sp sp1 sp sp1 sp sp1 sp sp1 sp sp1 sp Probabilities from SDM Species HS Sp Sp Sp Sp Sp Sp COMMUNITY PREDICTION Species HS Sp1 1 Sp2 1 Sp3 1 Sp4 0 Sp5 1 Sp6 0 S=4 46