Community Ecology BSC 405

1. Community EcologyBSC 405 Fall 2010 Steven Juliano

2. Access to course materials Assigned readings: Either email of pdf or photocopy Lecture notes: Power points Posted on my web page Emailed to you You print

3. What is Community ecology? • One level in the hierarchical levels of organization in Ecology. • Ecology -- The science of how organisms interact with their living and non-living environment • Ecology -- The distribution and abundance of organisms

4. Hierarchy • Individuals • Populations • Communities • Ecosystems

5. Individuals • Physiology • Behavior • Reproductive schedules • Homeostasis • Adaptation, evolutionary ecology

6. Populations • Dynamics • Regulation • Age structure • Spatial structure, metapopulations • Sex ratio, Mating system

7. Communities • Properties & patterns • Number of species • Relative abundances • Morphology • Trophic links • Succession • Processes • Disturbances • Trophic interactions • Competition • Mutualism • Indirect effects

8. Ecosystems • Energy flow • Cycles of matter • Global change, climate

9. Definitions / Jargon(see also Morin, chapter 1) Community: Organisms living in one place, at one time, and actually or potentially interacting Metacommunity: set of local communities that are linked by dispersal of multiple potentially interacting species Taxocene: Organisms of a particular taxon occurring together in one place (e.g., “plant community”) Component community: species occupying, e.g., one plant species, and drawing part of their resource needs from that plant

10. Time scale of study Ecological: How a community functions now How do contemporary processes act to maintain observed community structure? Evolutionary History of how a community came to its present state over evolutionary time How do species evolve in response to selection due to community processes?

11. Ecological vs. Evolutionary questions Ecological studies much more readily done Evolutionary studies rely less on direct experiment and more on comparative, observational, & theoretical methods Evolutionary questions imply ecological questions Ecological questions do not necessarily imply evolutionary questions

12. Investigating communities Investigation and description of community pattern Any study of interacting species is a community level study Investigations of the processes that determine community properties

13. Community processes: causes of patterns Tolerances to the physical environment and disturbance Species interactions: population / individual effects

14. Community processes: causes of patterns Spatial or landscape effects proximity effects: patterns in a community depend on proximity of that community to others metacommunities Regional processes community pattern is driven not by local processes (competition, tolerance, etc.) but regional floristic/faunistic effects

15. Methods in community ecology

16. Required reading Salt 1983 (pdf by e-mail) J.H. Brown 1997. An Ecological Perspective on the Challenge of Complexity http://webcache.googleusercontent.com/search?q=cache:Krq4MRo4aRkJ:www.nceas.ucsb.edu/nceas-web/projects/resources/ecoessay/brown/ P. Kareiva 1997. Why worry about the maturing of a science? http://www.nceas.ucsb.edu/nceas-web/projects/resources/ecoessay/brown/kareiva.html

17. Goals of community ecology Finding patterns, laws, & generalizations that apply to diverse systems and convey understanding about those systems in general. Gain sufficient understanding of communities to be able to predict community properties & processes under certain conditions

18. Research Methods Ecology (and community ecology in particular) began with inductive approaches to science Accumulate observations, e.g., on diversity of local communities. Generalizations will result from such accumulation [Morin Table 1.1, Figs. 1.1, 1.2] Result: Reams of data; Descriptions of patterns. No hypotheses, no increased understanding of mechanisms – how systems work

19. Research methods Next step: Hypothetico-deductive approach (phase 1). Using simple mathematical models and observations. Determine general properties & hypothesize relationships among components Formulate hypotheses into a simple mathematical model Manipulate model, deduce new predictions Attempt to verify prediction by observation (usually qualitative) Niche width models and resource overlap – see pp. 57-58

20. Problems Tended to look for confirmation of predictions Predictions were often not risky Observational data involve multiple processes that may also produce similar predicted results Requires an assumption that all else is equal Theory became esoteric and complex, data gathering and handling was rudimentary

21. Two approaches, two problems Induction little in the way of generality “… much al fresco hackwork…” (Salt 1983) H-D approach phase 1 general theory rarely confirmed Mechanisms lacking theory that was “… true but trivial, or false but profound…” (Henry Horn)

22. H-D approach phase 2: experimental ecology Rigorous definition of “pattern” Experimental tests of predictions Control of other variables Falsification of hypotheses Multiple hypotheses Salt (1983): three roles in science

23. Three roles Observer: Formulate hypotheses about how nature works Theoretician: Convert verbal explanations into mathematical model yielding new predictions Experimentalist: Design experimental tests of predictions, falsify some hypotheses

24. The process: each activity is judged Observation refutations qualifications new phenomena phenomena patterns hypotheses Experiment Theory predictions alternatives

25. Experiments Action or operation undertaken to collect observations under a prearranged plan and defined conditions in order to discover something unknown or to test a hypothesis Natural: ambient conditions; measure phenomena as they exist Manipulative: create conditions; measure phenomena under known conditions

26. Manipulative experiments Experimental units (e.u.) : smallest unit to which a manipulation (=treatment) is applied Randomization: every e.u. has an equal & independent chance to receive each treatment eliminate bias e.u.’s on average alike, except for treatments Replication: >1 e.u. receives each treatment independently

27. Manipulative experiments Pseudoreplication: in data analysis, treating something that is not an e.u. as if it were example: effect of pesticide on plant growth field A field B         spray control • Measure yield / plant on n=15 plants each

28. Manipulative experiments Control: treatment incorporating all natural variation except the factor of interest (treatment) untreated sham treated Independence: response of 1 e.u. is unrelated to the response of another Interspersion: spatial independence

29. What experiments can tell you Manipulative Laboratory Field Natural hypothetical example: altitudinal distributions of terrestrial salamanders Plethodonjordani (pj) & Plethodonglutinosus(pg) Experiments by N. Hairston http://163.238.8.180/~fburbrink/Field%20Work/AlabamaMississippi/index.htm http://www.apsu.edu/~amatlas/images/PgluAFS1copy.jpg

30. A natural experiment - multiple mountains pj pj pg

31. Hypotheses P. glutinosus excludes P. jordani P. jordani & P. glutinosus do best in different climates or on different substrates range of P. jordani dependent on some other species (e.g., predator) Does P. glutinosus affect P. jordani? Cannot answer without manipulation

32. CONTROL REMOVAL pj pj pg pg pj pj REMOVAL OUTCOME 1 REMOVAL OUTCOME 2 pg pg

33. Interpreting removal outcomes Removal outcome 1 some interaction with P. glutinosus sets lower limit for P. jordani mechanism? pj REMOVAL OUTCOME 1 pg

34. Interpreting removal outcomes Removal outcome 2 P. glutinosus has no effect on range of P. jordani some other factor limits distribution does not establish which other factor pj REMOVAL OUTCOME 2 pg

35. pj pj CONTROL ADDITION pg pj pj ADDITION OUTCOME 1 ADDITION OUTCOME 2 pg

36. Interpreting addition outcomes Addition outcome 1 P. glutinosus has no effect on P. jordani P. jordani inhibits P. glutinosus? some aspect of the environment excludes P. glutinosus ? pj ADDITION OUTCOME 1

37. Interpreting addition outcomes Addition outcome 2 interaction with P. glutinosus sets lower limit on P. jordani mechanism? pj ADDITION OUTCOME 2 pg

38. Criticisms of experimental ecology Experiments are unrealistic that is their function control multiple factors & focus on hypothesis Field experiments don’t control all variables true, but irrelevant no experiment controls all variables Experimental units are not identical if they were, no need to replicate

39. Natural experiments Snap shot experiments find sites that differ and compare e.g., observed salamander distributions Trajectory experiments find sites at which some perturbation occurs and compare change over time with that at sites where that perturbation has not occurred known timing of change