Community EcologyBSC 405

Fall 2010

Steven Juliano

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

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

Hierarchy

• Individuals

• Populations

• Communities

• Ecosystems

Individuals

• Physiology

• Behavior

• Reproductive schedules

• Homeostasis

• Adaptation, evolutionary ecology

Populations

• Dynamics

• Regulation

• Age structure

• Spatial structure, metapopulations

• Sex ratio, Mating system

Communities

• Properties & patterns– Number of species– Relative abundances– Morphology– Trophic links– Succession

• Processes– Disturbances– Trophic interactions– Competition – Mutualism– Indirect effects

Ecosystems

• Energy flow

• Cycles of matter

• Global change, climate

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

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?

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

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

Community processes: causes of patterns

• Tolerances to the physical environment and disturbance

• Species interactions: population / individual effects

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

Methods in community ecology

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

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

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

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

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

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)

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

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

The process: each activity is judged

Observation

Experiment Theory

phenomena patterns hypotheses

predictionsalternatives

refutations qualificationsnew phenomena

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

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

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

spray

field B

control

Measure yield / plant on n=15 plants each

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

What experiments can tell you

• Manipulative– Laboratory– Field

• Natural• hypothetical example:

altitudinal distributions of terrestrial salamanders Plethodon jordani (pj) & Plethodon glutinosus (pg)– Experiments by N. Hairston

http://163.238.8.180/~fburbrink/Field%20Work/AlabamaMississippi/index.htm

http://www.apsu.edu/~amatlas/images/PgluAFS1copy.jpg

A natural experiment - multiple mountains

pj

pj

pg

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

pj

pgCONTROL

pj

pg

REMOVAL

pj

pg

REMOVALOUTCOME 1

pj

pg

REMOVALOUTCOME 2

Interpreting removal outcomes

• Removal outcome 1– some interaction with

P. glutinosus sets lower limit for P. jordani

– mechanism?

pj

pg

REMOVALOUTCOME 1

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

pg

REMOVALOUTCOME 2

pj

CONTROL

pj ADDITIONOUTCOME 1

pj

pg

ADDITIONOUTCOME 2

pj

ADDITION

pg

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 ADDITIONOUTCOME 1

Interpreting addition outcomes

• Addition outcome 2– interaction with P.

glutinosus sets lower limit on P. jordani

– mechanism?

pj

pg

ADDITIONOUTCOME 2

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

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