Aerial lakes photo

1. Aerial lakes photo

2. Two parallel histories in ecology Regional School MacArthur-Wilson (Island Biogeography) Levins (Metapopulations) Neutral Theory (Hubbell-Bell) Source-Sink theory (Shmida-Ellner, Pulliam) Assembly rules brouhaha (Diamond, Strong, Simberloff) MacArthur (Geographical Ecology) MacArthur (Niche theory) Hutchinson (Plankton paradox) Local School Mechanistic competition theory (Tilman) Primordial ooze (Gausse, Lotka, Volterra, Elton) 1980 1990 2000 1960 1970

3. Four ways of modeling spatially-structured species interactions(from Leibold et al. 2004, Smith and Shurin in press) • Patch dynamics(Metapopulation, Island Biogeography) • Source-sink • Neutral • Species sorting(non-spatial) Key Differences- Dispersal rate, Interaction intensity, Spatial heterogeneity

8. What are the roles of local and regional processes in structuring zooplankton communities?

9. Zooplankton photograph

10. What are the roles of local and regional processes in zooplankton communities? • Observational test • global patterns of local and regional richness • Experimental tests • measuring community invasibility • measuring dispersal rates • The importance of scale • modeling spread of invaders

11. Regional richness Regional richness Regional vs. local control of local diversity • Regional processes • colonization • local extinction • Local processes • abiotic constraints • species interactions if colonization  extinction if colonization >> extinction Local richness

12. “In a clear majority of studies, possibly the great majority, the main driver of local species richness appears to be the size of the regional species pool… Strong dependence of local richness on regional richness may arise because species interactions- the stuff of traditional community ecology- are weak” or… J.H. Lawton (1999). Are there general laws in ecology?Oikos 84: 177-192.

13. 17 11,12 17 20 4,14 23 19 1,3,5-10, 15,16,18, 22,24,25 21 13 2,832 lakes Shurin et al. 2000. Ecology 81: 3062

14. A- Cladocerans B- Cyclopoids 8 4 7 3 6 5 4 2 3 1 2 1 0 Local richness 0 -20 -10 0 10 20 30 -10 -5 0 5 10 15 D- Crustaceans C- Calanoids 3 15 2 10 1 5 0 0 -20 0 20 40 -10 -5 0 5 10 Residual regional richness Local vs. residual regional richness

15. Zooplankton show linear patterns of local and regional richness • Predictions • Local communities are open to invasion from the regional pool • Species interactions have little impact on invasion success

16. What are the roles of local and regional processes in zooplankton communities? • Observational test • global patterns of local and regional richness • Experimental tests • measuring community invasibility • measuring dispersal rates • The importance of scale • modeling spread of invaders

22. Invasion experiment questionsShurin 2000. Ecology 81: 3074 • Are zooplankton dispersal limited? • species will colonize the invasion treatment • What are the effects of relaxing dispersal limitation? • zooplankton diversity and biomass will increase • Does invasibility relate to native diversity? • invasibility will decline at high native diversity

23. 1.2 1 0.8 0.6 0.4 0.2 0 8 7 6 5 4 3 2 1 0 Hut Ent. Lab Shaw Bird 1 Bird 2 Rock Upton Norris L.A. 11 L.A. 12 Invasion experiment results Exotic biomass [log (ug/L) + 1] % introduction success

24. Why would more diverse communities be harder to invade?(Elton’s 1958 hypothesis) • Fewer potential invaders in regional pool • more likely a species is already present • Less available niche space • prediction: lower per invader success rate • prediction: lower invader biomass once established

25. Invasibility vs. native diversity 8 rs = -0.70 P < 0.025 6 Exotic biomass (ug/L) 4 2 EXPERIMENT 0 1 2 8 6 rs = -0.57 P < 0.05 % introduction success 4 2 0 2 1 3 Native diversity (Fisher’s alpha)

26. No effects of invasion on • total zooplankton diversity or biomass • biomass or extinction of native species • phytoplankton biomass

27. How important are interspecific interactions to invasion success? Invasion treatment Resistance treatment 11 ponds 4 Ponds

28. 2.5 Biological resistance to invasion 2 1.5 Resistance= 16X Invasion Exotic biomass [log (ug/L) + 1] 1 0.5 0 7 6 5 INVASION 4 % introduction success Resistance= 4X Invasion 3 RESISTANCE 2 1 0 Bird Shaw Upton L.A.

29. Conclusions from invasion experiment • Weak dispersal limitation • Dispersal does not limit biomass or diversity • Invasibility declines at high diversity • Interspecific interactions play a major role • big effect of resistance treatment Suggests colonization >> extinction for most species

31. Experimental design:pools at 5, 10, 30 and 60m from 2 real ponds plus a non-dispersal limited control Cohen and Shurin 2003, Oikos 103: 603

32. Fast colonization: one new species every four days

33. A paradox? • Observational evidence: strong regional control • linear patterns of local and regional diversity • Experimental evidence: strong local control • resistance to invasion • strong species interactions • rapid dispersal

34. Theoretical and empirical resolution? • Facilitation versus inhibition • The effects of predators on local and regional coexistence • Shurin and Allen 2001. American Naturalist 158: 624 • Shurin 2001. Ecology 82: 3404 • The influence of scale • Modeling the spread of exotic species • Havel, Shurin and Jones 2002. Ecology  83: 3306 • Shurin and Havel 2002. Biological Invasions 4: 431

38. Modeling spread of exotics with John Havel and Jack Jones (Ecology 83:3306) How can we use spread to estimate dispersal at broad scales? Is invasion limited by dispersal or local constraints?

39. invasion probability local variables dispersal potential  + surface area temperature conductivity total nitrogen total phosphorus chlorophyll volatile solids non-volatile solids secchi depth turbidity How do we include dispersal potential? Modeling invasion probability

40. source 1 source 2 target Modeling dispersal potential from spatial position P 1T P 2T * * d2,T d1,T # propagules = P 1T + P 2T What is the shape of the dispersal function?

41. # propagules # propagules = e(-d/a) 0.4 0.6 0.8 0.2 0 1 0 a=10 20 20 30 40 100 Distance (km) 60 80 100 The shapes of different dispersal functions What is the most likely value for a?

42. 52 The most likely a = 17 51 50 -2 log likelihood 49 1995 48 47 100 80 20 60 0 40 a The likelihood profile approach to estimate a

43. 0.12 0.09 Pi = exp(-d/17) # dispersal events 0.06 0.03 0 0 20 40 60 80 100 The dispersal function for 1995 Distance (km)

44. invasion probability local variables dispersal potential  + 90 80 70 60 50 AIC 40 30 20 10 0 local dispersal local + dispersal Do spatial or local models best predict invasion? 1995

45. invasion probability local variables dispersal potential  + Do spatial or local models best predict invasion? 90 80 70 local 60 dispersal 50 AIC local + dispersal 40 30 20 10 0 1996 1995 1993 1994 year

46. Conclusions from invasion model Dispersal of Daphnia lumholtzi localized up to 20-30 km Invasion limited by local environment and dispersal Local control more important than dispersal

47. How does dispersal ability vary among aquatic taxa?How does dispersal ability affect patterns of diversity at different scales? with Karl Cottenie and Helmut Hillebrand (in review)

48. How should β-diversity change with distance? bad dispersers good dispersers Tilman, Lehman and Kareiva. 1997. Spatial Ecology

49. β-diversity versus distance for different taxa

50. Vertebrates show higher β-diversity, clumpier distributions Slope (β vs. distance)