1. C. Novaglio, F. Ferretti, A.D.M. Smith, S.D. Frusher
SPECIES-AREA RELATIONSHIPS AS INDICATORS OF HUMAN IMPACT ON FISH COMMUNITIES
C. Novaglio, F. Ferretti, A.D.M. Smith, S.D. Frusher

2. OUTLINE 1. Description of Species Area Relationship (SAR) and its use
2. SAR application to South East Australia
3. Outcome
4. Future research

3. SAR - Species Area Relationships
Power curves S = cAz Logarithmic functions S = c + z log(A) S = number of species A = area
In linear space z is the rate of species accumulation within the sampled area

4. SAR - Species Area Relationships
Power curves S = cAz Logarithmic functions S = c + z log(A) S = number of species A = area
In linear space z is the rate of species accumulation within the sampled area

5. SAR - Applications
Ecological properties of the sampled community determine z:
Community richness
Species abundances
Spatial patterns
Anthropogenic disturbance modifies community structure
The relationship between z and an index of human pressure is used to study the effects of human use on biotic communities

6. SAR - Applications Lower slope (z) Depleted community
effort
Pacific and indian, arrow with fishing pressure. Tittensor uses the power law
effort
Tittensor et al. 2007

7. SAR - Applications Bottom trawl fishing impacts marine communities:
Removal of target species
By-catch of non-commercial species
Habitat modification

8. SAR - Applications
Bottom trawl fishing impacts marine communities:
Removal of target species
By-catch of non-commercial species
Habitat modification
Are community impacted by trawl fishing characterised by lower z then pristine communities?

9. SAR - Applications Short history of commercial exploitation
shelf
1970s - slope
1940s - shelf
1970s - slope
Anni exploitation and survey

10. SAR - Applications
Scientific investigation precedes commercial exploitation
shelf
1970s - slope
shelf
1970s - slope
Anni exploitation and survey

11. Unique windows of observation
SAR - Applications
Short history of commercial exploitation
Scientific investigation precedes commercial exploitation
Anni exploitation and survey
Unique windows of observation

12. DATA
Geographic and temporal distribution of survey data: (a) Tow positions; (b) years represented within the dataset in longitudinal bins of 0.1 degree; colors indicate different surveys

13. METHODS – Models and simulations
We build SAR for each survey
We tested models used to describe SAR (Tjørve 2003)
Curve name
Model
Power
a * x b
Logarithmic
a + b * log(x)
Monod
a * (x / (b + x))
Negative exponential
a * (1 – exp (- b*x))
Rational function
(a + b * x)/(1 + c * x)
Graph on 12 in slide 11

14. METHODS – Best fit We selected the model best describing SAR Power
Logarithmic
Monod
Negative Exponential
Rational Function
Repetition
Models fit for the demersal community surveyed by Kapala in 1976

15. METHODS – Models and simulations
We explore the effect of trawling effort on z
zijkl= α + βRegionj + γDepthk + δCumulativeEffortl + εijk
Region = New South Wales, Tasmania
Depth = continental shelf, upper slope, mid-slope
CumulativeEffort = cumulative meters trawled in the survey area up to the year the survey was carried out
Explain better cumulative effort. Indice intensita’ sfruttamento con dimensione temporale. Indice di quanto l’area e’ stat sfruttata fino a quel momento

16. RESULTS – SAR slope Increasing pattern in z
Slope of the logarithmic SAR fitted to each community sampled versus the cumulative area trawled at the time of the survey

17. RESULTS – Model predictions
zijkl= α + βRegionj + γDepthk + δCumulativeEffortl + εijk
Significative effects:
CumulativeEffort
Region
Predicted slopes at increasing cumulative area trawled for the communities of New South Wales and Tasmania

18. DISCUSSION
The slope of the logarithmic SAR increases as trawling effort increases
Structural change in South East Australia demersal fish communities
Pattern in contrast with the findings of similar studies on other exploited communities

19. DISCUSSION Proposed mechanisms explaining increase in SAR slope:
We may have detected early stages of community change missed in other studies
Intermediate level of exploitation may enhance richness and/or increase evenness (Intermediate disturbance hypothesis
Changes in species behavior, reflected in their spatial patterns
Changes in z are masked by other factors:
Changes in fishing efficiency
Changes in data (e.g. taxonomic resolution of surveys)

20. DISCUSSION Future directions:
Which changes in community properties may explain changes in SAR slope?
Numeric Simulations:
Community richness
Absolute abundance
Evenness of species abundances
Intra-specific aggregation

21. THANKS FOR YOUR ATTENTION