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Outline

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Competition has a big effect on community structure- depth distribution, ... Benthic diatoms. Foraminiferans. Polychaete worms. crustaceans. pycnogonids. heart urchins ... – PowerPoint PPT presentation

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Title: Outline


1
Outline
Soft-sediment ecosystems
  • The deep sea
  • Processes in shallow water assemblages
  • Detecting impacts of multiple disturbances

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Sandy shores/beaches
4
Muddy shores/bays, estuaries, and lagoons
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Competition as a force in soft-sediments
  • Competition usually for food
  • Competition rarely causes displacement. Instead,
    effects growth, reproduction, and survival
  • Competition has a big effect on community
    structure- depth distribution, population
    dynamics but not species composition

6
Pete Peterson (1979)
Does the IDH hold in soft-sediments?
Worked in tidal mudflats and lagoons (Mugu Lagoon)
Intermediate disturbance hypothesis (IDH) Joe
Connell (1978)
Not in soft-sediments
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Shallow subtidal soft-bottoms
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Benthic diatoms
Foraminiferans
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Polychaete worms
crustaceans
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heart urchins
pycnogonids
brittle stars
scallops
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Many types of predators
Predators have a big effect on community
composition
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bat ray feeding pits
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Types and scales of disturbances in soft-sediments
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Physical disturbances in Antarctic soft-bottoms
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Soft-sediment infauna
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Natural physical disturbances
Iceberg scouring
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Recently scoured
Recovered
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Anchor ice formation and uplift
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Resulting zonation of species
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Process of recovery controlled by succession
  • Directional sequence of species replacement
    following disturbance
  • Influenced by species life histories, disturbance
    regime, and characteristics of free space
  • Models (1) Connell Slatyer (1977)
  • Facilitation
  • Inhibition
  • Tolerance

Models (2) Sutherland (1974) Multiple stable
states
25
Physical disturbance caused by organic enrichment
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Pollution a major problem in all marine systems
Two generic forms of contaminants
Eutrophication
Toxic contamination
Metals, organic compounds (PCBs, Dioxin,
pesticides)
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Assessing biological impacts of chemical
stressors in marine soft-sediment habitats
  • gt1 Billion US spent annually worldwide to
    monitor pollution and biological effects
  • Tripartite approach to assessments
  • Produced few models that predict community
    responses to specific contaminant types
  • Do not know the how to differentiate biological
    impacts of different contaminant types

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Two generic forms of marine contaminants often
covary in space and time
What is the effect of organic enrichment and
toxics on marine communities?
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Predictions based on life history traits of
soft-sediment organisms
30
Predictions
Annelids
Arthropods
Echinoderms
Organic enrichment
little change slight decrease
little change slight increase
Metal contamination
little change
Both
How will biotic interactions influence these
reponses?
31
Why test hypotheses in Antarctica?
  • Pristine habitats with little background
    contamination
  • Well-described and species rich communities
  • Sea ice cover drastically reduces disturbances
  • Few epibenthic predators reduces biological
    disturbance

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Maniuplative field experiment
0 TOC / 0 ppm Cu
0 TOC / 100 ppm Cu
0 TOC / 500 Cu
1 TOC / 0 Cu
1 TOC / 100 Cu
Same treatments But caged to exclude predators
1 TOC / 500 Cu
2 TOC /500 Cu
2 TOC / 0 Cu
2 TOC /100 Cu
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Community response
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Population demographic responses to stressors
Total annelids
No. Individuals/0.04 m2
Capitella capitata
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5
4
Growth (mm in length/month)
3
2
1
0
TOC
35
Preliminary model
Annelids
Arthropods
Echinoderms
Organic enrichment
Copper contamination
Both
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How general are responses of high taxa?Do
responses vary with environmental conditions?
37
Meta-analyses of data from pollution monitoring
stations
  • 414 US EPA, NOAA, and CSBPP monitoring stations
    located
  • in temperate latitudes
  • New York-Florida (Atlantic coast) and
    Oregon-California
  • (Pacific coast)
  • Bays, estuaries, and nearshore coastal habitat
  • 1997-98 data for 56 chemical contaminants,
    sediment characteristics,
  • and macrofauna
  • Multiple stepwise backward regression
  • analyses

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Applications
  • Community index based on high taxonomic responses
  • can streamline pollution monitoring programs
  • Reduce pollution monitoring costs
  • Improve understanding of cause and effect-
  • finally a model!
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