1 / 11

Monitoring data poor fisheries using a self starting scheme

Monitoring data poor fisheries using a self starting scheme. Deepak George Pazhayamadom University College Cork, Ireland. SAFE. DANGER. Indicator based management using traffic light approach. Empirical indicators e.g. Mean length or Mean weight.

jacqueline-clay
Télécharger la présentation

Monitoring data poor fisheries using a self starting scheme

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Monitoring data poor fisheries using a self starting scheme Deepak George Pazhayamadom University College Cork, Ireland SAFE DANGER

  2. Indicator based management using traffic light approach • Empirical indicators e.g. Mean length or Mean weight • Reference directions e.g. Increased or decreased? • Reference limit e.g. Whether management required or not? Limit - 18cm Precautionary - 25cm Acceptable - 28cm

  3. Statistical Process Control (SPC)- Shewhart control chart (A Statistical framework for traffic light approach) True Positive True Negative False Positive False Negative

  4. Statistical Process Control (SPC)- CUSUM control chart [zt=(D-µ)/σ] D = Indicator(Time Series) µ = Control Mean (Target) σ = Standard Deviation

  5. Self starting CUSUM control chart (SS-CUSUM) Is it useful to monitor data poor fisheries?

  6. Methods - Fisheries Simulation SS-CUSUM

  7. Methods - Stock Indicators • Mean Age 2. Mean Length 3. Mean Weight 4. Large Fish Catch Numbers (LFCN) e.g. LFCN = 30/100 5. Large Fish Catch Weight (LFCW) n=1 n=4 n=25 n=35 n=20 n=10 n=5 Age 1 Age 2 Age 3 Age 4 Age 5 Age 6 Age 7

  8. Methods - An example scenario • Monitored 20 years • Fixed parameters (k=0.5, h=0) • Collected data on TP, TN, FP, FN • Repeated 1000 times Repeated for control limit (h) ranging from 0 to 6 with 0.1 interval

  9. Results - Performance Measures • Receiver Operator Characteristic (ROC) Curve (Sensitivity Vs 1-Specificity) Sensitivity – Probability of True Positive signals Specificity – Probability of True Negatives 2. Optimal Performance (Sensitivity=Specificity)

  10. Acknowledgements Emer Rogan University College Cork, Ireland Ciaran Kelly Marine Institute, Ireland Edward A. Codling University of Essex, UK Thank You Any questions? deepakgeorgep@student.ucc.ie

More Related