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

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

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

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

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

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

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

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?

SAR - Applications Short history of commercial exploitation 1915 - shelf 1970s - slope 1940s - shelf 1970s - slope Anni exploitation and survey

SAR - Applications Scientific investigation precedes commercial exploitation 1898 - shelf 1970s - slope 1909 - shelf 1970s - slope Anni exploitation and survey

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

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

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

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

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

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

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

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

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)

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

THANKS FOR YOUR ATTENTION