Geospatial dynamics of Northwest. fisheries in the 1990s and 2000s: environmental and trophic impacts

Transcription

1 Geospatial dynamics of Northwest Atlantic ti cod and crustacean fisheries in the 1990s and 2000s: environmental and trophic impacts Matthew J.S. WINDLE 1, George A. ROSE 2, Rodolphe DEVILLERS 3, and Marie-Josée FORTIN 4 1 PhD Candidate, Memorial University, Canada 2 Professor, Fisheries Conservation Group, Memorial University 3 Assistant Professor, Department of Geography, Memorial University 4 Professor, Department of Ecology and Evolutionary Biology, University of Toronto

2 Collapse of the Northern Cod Rose, 2007 DFO SSR 2004/011

3 Fall 1983

4 Fall 1984

5 Fall 1985

6 Fall 1986

7 Fall 1987

8 Fall 1989

9 Fall 1990

10 Fall 1991

11 Fall 1992

12 Fall 1993

13 Fall 1994

14 Fall 1995

15 Northern Shrimp (Pandalis borealis) DFO SSR Report 2004/022

16 Snow Crab (Chionoecetes opilio) DFO SSR 2003/021

17 GeoCod Project Study Area Environmental Data Temperature Salinity Remote Sensing > 300 GB! Fisheries Data Scientific Surveys Industry Data Four Species > 800,000 records!

18 Study Objectives 1. To model the spatial relationships between cod biomass and environmental and trophic variables 2. To compare Odi Ordinary Least ts Squares (OLS) regression and Geographically Weighted Regression (GWR) model performance 3. To investigate spatial non-stationarity of the regression parameters a e within the study area

19 Data Description Source: Canadian Department of Fisheries and Oceans Stratified Random Bottom Trawl Surveys (Newfoundland Region) Timeline: Variables: Atlantic cod (dependent) [log(kg)] g)] snow crab [log(kg)] northern shrimp [log(kg)] depth (m) bottom temperature ( C) bottom salinity (ppt) slope (degrees) distance from shore (m)

20 Newfoundland Fall Multispecies Trawl Surveys ( ) Labrador Newfoundland

21 Regression Models Ordinary Least Squares (OLS) γ β + β x β + ε = x p p Geographically Weighted Regression (GWR) γ = β μ, ν ) + β ( μ, ν ) x β ( μ, ν ) x + ε 0 ( p p ε Coordinates of Samples Model Type: Gaussian Bandwidth Selection: Akaike Information Criterion (AIC) Kernel Type: Fixed, Bi-Square Software: GWR 3.0 for regression (Fotheringham et al.), ArcGIS (v. 9.2) for mapping

22 Pre-Regression Data Analysis High correlation between independent variables makes it difficult to identify the individual contribution of each variable Collinearity it Diagnostics: Correlation matrix Helps identify pairwise collinearity VIF (Variance Inflation Factor) Collinearity is present when VIF for at least one independent variable is large (>5 or >10) Variables retained: Bottom salinity, distance from shore, crab and shrimp

23 Adjusted R 2 Comparison 06 0,6 0,5 Ad djusted R2 0,4 0,3 0,2 0, OLS GWR

24 Local (GWR) R 2 Fall 1995 Survey Fall 2000 Survey

25 Global Coefficient Estimates 0,2 0,1 0-0,1-0,2 02-0,3-0,4 04-0, B_SAL Distance Log_Crab Log_Shrimp Salinity and Shrimp significant predictors for cod biomass

26 Model Performance Comparison Akaike Information Criterion (AIC) Scores The lower the AIC, the closer the approximation of the model to reality AIC values that differ by more than 3 units considered significantly different AIC Sco ore OLS GWR

27 Model Performance Comparison Improvement of GWR over OLS model assessed using F-test F P value < Significant improvement in all years!

28 Observed Cod Weight (Fall 1995) OLS Predicted (Fall 1995) GWR Predicted (Fall 1995)

29 Model Residuals Global (OLS) residuals - Fall 1995 Local (GWR) residuals - Fall 1995

30 Spatial Autocorrelation of Residuals Global Moran s I (ArcMap) OLS Residuals GWR Residuals Fall 1995

31 Spatial Non-Stationarity of Parameter Estimates with GWR Spatial non-stationarity of GWR coefficients tested with Monte Carlo significance test All variables consistently show significant spatial non- stationarity in relationship with cod biomass Examples (maps)

32 Spatial Non-Stationarity of Parameter Estimates with GWR Salinity - Fall 1995 Shrimp - Fall 1995

33 Spatial Non-Stationarity in NAFO Management Divisions Salinity - Fall 1995 NAFO Division 3K Shrimp - Fall 1995 NAFO Division 3L

34 Conclusions Global regression models not completely satisfactory for fisheries analyses at large scales Local spatial regression models relatively new in fisheries research, can better explore spatial variability of fisheries data GWR increased model fit and precision i in current study Additional 20% to 45% of variation explained over OLS Large reduction in prediction error of GWR residuals GWR revealed non-stationarity of relationship between cod and environmental/trophic variables within study area Next steps Time-lagged analysis

35 Acknowledgements GEOIDE Network Canadian Center for Fisheries Innovations Fisheries and Oceans Canada Fisheries and Aquaculture Newfoundland World Wildlife Fund CIDCO St. Lawrence Observatory IFREMER

36 GeoCod Team Researchers Students Dr. Rodolphe Devillers Matthew Windle (PhD) Project Leader Valérie Carette (MSc) Dr. Mir Abolfazl Mostafavi Jonathan Ruppert (BSc) Deputy-leader Dr. Marie-Josée Fortin Dr. George Rose International Collaborators Dr. Stewart Fotheringham (National University of Ireland) Dr. Geoff Meaden (Canterbury Christ Church University) Krista Jones (BSc) René Enguehard (BSc) Steve Hill (BSc) Brad Durdle (BSc) Jennifer Guy (BSc) Research Professionals Philippe LeBlanc Randy McVeigh Memorial University University of Toronto Université Laval

37 For more information visit the GeoCod website:

38 Obrigado! Thank you!