Coastal fish functional diversity in the Baltic Sea.pdf

Functional diversity and assembly rules of the Baltic Sea fish assemblages

laupe@aqua.dtu.dk

PhD projectLaurène Pécuchet

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1st study:Environmental filtering driving the traitscomposition of the Baltic fish community

Laurène Pécuchet, Martin Lindegren and Anna Törnroos

12-02-2015 laupe@aqua.dtu.dk

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Mouillot et al., 2007

Trait-based approach

Species enter and persist in local communities because of their ecological fit to local conditions. Mechanisms influencing patterns of community assembly act on the ecological similarities and/or differences of organisms

Mechanisms influencing patterns of community assembly

12-02-2015 laupe@aqua.dtu.dk

The Baltic Sea – a strong salinity gradient

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Hypothesis: Abiotic control of the biological community

Studied area – bottom salinity contour (PSU) from CTD data, and representations of the ICES squares in the Baltic Sea

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Baltic International Trawl Survey (BITS)

Number of hauls performed from 2003 to 2014

From this dataset we derived the occurence and mean CPUE of Species per ICES square for the period 2003-2014

2003-2014, February - March

For each haul, the species are sorted and counted (CPUE/h) by length class

512-02-2015 laupe@aqua.dtu.dk

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Species and traits database

DIET

MEAN LENGTH

BODY SHAPE

CAUDAL SHAPE

FECUNDITY

AGE AT MATURITY

6 traits characterising the diet, the demography, the habitat and the morphology.

3 traits are categorical. 3 traits are continuous.

Traits values from literature primarily and secondarily through species fact sheet (FAO/FishBase)

42 strictly demersal (10 pelagics eliminated)

laupe@aqua.dtu.dk

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Investigate potential abiotic drivers of the diversity indices:• Salinity• Oxygen• Temperature• Depth• Sediment

Diversity pattern of the Baltic Sea: Species vs Functional richness

Metrics Best gam model Dev.expl

Species Richness Temp.* + Salinity*** + Oxy*** + habitat*** 92.5% Salinity 83.3

Functional Rich. Salinity*** + Oxygen* + habitat*** 67.1% Salinity 51.8

Functional Richness : The amount of niche space filled by species in the community (Convex Hull)

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Limiting similarity

Niche filtering

Neutral

Species Richness

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Limiting similarity

Functional Dissimilarity

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Niche filtering

Functional Dissimilarity

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Functional Dissimilarity

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Functional Richness : The amount of niche space filled by species in the community (Convex Hull)

Investigating the assembly rules- Observed Functional Richness vs null model

Null model : for each species richness level, mean Fric calculated from random assemblages of species from the ecosystem pool

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Observed Functional Richness vs null model

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Species Richness

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46Environmental filteringNeutralLimiting similarity

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What about the biomass distribution in the communities?

Calculation of communities weighted Functional Dissimilarities

Functional Dissimilarity: Quantify how similar/dissimilar are two species based on their traits

Ecologically analogous species Ecologically different species

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What about the biomass distribution in the communities?

Mean Functional dissimilarity per hauls of the pairwise functional dissimilarities of the two species with the highest abundance in each haul.

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Conclusion

• Strong environmental control : Salinity high explanatory power of species and functional richness in the Baltic.

• In general, Environmental filtering appears to drive the Baltic fish assemblages composition and is especially strong in the Western Baltic, which corresponds to the salinity transition zone.

• The fish communities in the Baltic have a lower functional richness than expected by random. The communities are composed of functionally similar species.

1212-02-2015 laupe@aqua.dtu.dk

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2nd study:Functional diversity of coastal fish

assemblages along a salinity gradient

Laurène Pécuchet, Martin Lindegren, Jens Olsson, Anna Gårdmark and friends

12-02-2015 laupe@aqua.dtu.dk

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Why a new study on Baltic fish assemblages?

• Studying a longer salinity gradient• Permits to include the freshwater species – absent from BITS

survey• Sensitive to other environmental pressures, e.g. eutrophication• Datasets available

Wetzel, 200112-02-2015 laupe@aqua.dtu.dk

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What type of data?Example. Gillnet survey from Sweden

If possible environmental information:• Salinity• Oxygen• Temperature• Depth• Sediment type/habitat

& Gear type, mesh size

laupe@aqua.dtu.dk

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Which hypothesis and analysis?

• Would be depending of the datasets available• Spatial analyses and not temporal – no need of long time-series• Due to different surveys catchability, CPUE is risky. Use of

presence/absence only?• Functional diversity patterns – other traits could describe better

ecological niche of the coastal fish?

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Who wants to join?

12-02-2015 laupe@aqua.dtu.dk