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Similar to Critical Open Water Habitat for Eastern Arctic Bowhead Whales
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Critical Open Water Habitat for Eastern Arctic Bowhead Whales
- 1. Determining Highly Suitable/Critical Open Water Habitat for the Eastern Arctic Bowhead Whale Benjamin Wheeler, Marianne Gilbert and Stephen Rowe 18 th Biennial Conference Society of Marine Mammalogy Québec City, QC October 16 th , 2009
- 5. Study Area: Eastern Canadian Arctic [ECA]
- 8. Sea Surface Temperature Chlorophyll Concentration Depth Ice Concentration
- 10. Inuit Knowledge
- 11. Historic Whaling Kills ~2100 total sighting locations
- 12. Private Sector Sightings ~400 total sighting locations
- 13. DFO Sightings August 2002-2003 118 total sighting locations
- 17. Average conditions Range of EGV condition Count Tolerance/Specialization EGV values at whale locations (niche) Def’n : Niche breadth More picky Not too picky
- 21. RESULTS: Models Produced Using ENFA and Monthly Data Used
- 22. Black = Unsuitable Habitat Blue = Marginal Red = Suitable Yellow = Highly Suitable Grey = land or no data RESULTS: Habitat Suitability Maps e.g. July Habitat Suitability Map for Historical Whaling Data Model
- 23. JUNE - High Suitability Combined Models
- 24. JULY - High Suitability Combined Models
- 25. AUGUST - High Suitability Combined Models
- 26. SEPTEMBER - High Suitability Combined Models
- 27. OCTOBER - High Suitability Combined Models
- 29. Cells Modeled as Highly Suitable for 1 or more Months
- 30. Delineation of Critical Open-Water Bowhead Habitat (3+ months High Suitability)
- 32. Validation using Inuit Traditional Knowledge (late spring to early fall)
- 37. Acknowledgements Alooloo Kautaq , Dr. Gill Ross, Bill Koski (LGL), Dr. Sue Cosens (DFO), Larry Dueck, Holly Cleator (DFO), Mads Peter Heide-Jorgensen, John Iacozza (UofW), Jeff Higdon (DFO) members of the Eastern Arctic Bowhead Recovery Team, and the many Inuit that contributed bowhead IQ. Pete Ewins and WWF Canada are thanked for their support and funding.
Editor's Notes
- Critical Habitat in SARA terms is the habitat identified by in the recovery strategy as Critical habitat based on the best available information. The objective of this project was to apply the power of GIS and statistical tools to previously collected information on bowhead whale location and associated habitat in order to identify areas that may be interpreted as ‘more important’ or ‘critical’ to bowhead recovery . Sub-objectives of the project included: -compiling a comprehensive GIS database of recorded whale positions and relevant environmental and geographical data; -using whale positions (determined from numerous sources), environmental and geographical data to predict areas where bowheads would be found at different times of year; and, -producing map(s) showing areas of predicted high habitat suitability for bowhead whales. The approach presented here was an attempt at using available data to guide selection of important habitat to bowhead (potential critical habitat).
- Environmental variables that we think may determine where whales are found because they likely influence the distribution of their food, their behaviour and/or were found to be related to whale locations in other similar studies include: depth, slope, ice, sst, chloro, distance from shore. Depth : Limit distribution/availability of prey (and perhaps predators) Slope : Limit distribution/availability of prey, can induce upwelling of nutrients Ice : Affect productivity, can affect movement of animals Sst : Often a signature of oceanographic features (e.g., river discharges, eddies…), can relate to primary productivity Chlorophyll : Proxy for phytoplankton biomass with which zooplankton (like copepods) can be associated and on which bowheads feed Distance from shore : Whales may find a refuge from predation close to land (in bays) or land features may affect currents and waves (whales resting in protected areas like Isabella Bay) allowing them to rest. Found to be related to marine mammal distribution in other studies
- Depth, Ice, sst and chlorophyll values were not measured in situ during the surveys so for August 2003 values were acquired from remote sensing (from satellite instruments) databases: sst, chloro = MODIS, monthly average Ice = Canadian Ice Service weekly average (week overlapping with survey) Depth = NOAA Slope was calculated from the bathymetry Distance was calculated using GIS tools All values were then averaged within the study area in 10km grid cells .
- (Just name them here) maps with details follow
- Based on the NWMB Inuit knowledge study: + Gives good idea where whales were traditionally seen and can be used to verify models (- Not quantitative)
- Collected positions of whale kills reported in papers based on captains’ journals from whaling era (between 1615-1988) + Large dataset, can get a better idea of their extent No idea where the ships spent most of their time (effort). Locations not very precise (not often lat and/or longitude). Lack of correspondence with measured environmental variables at the time the kills were made
- Locations of bowheads collected during aerial surveys of marine mammals conducted by LGL between 1975 and 1982 (span March to April). + good coverage, fairly precise locations, some overlap with measured environmental variables available - Have general coverage of flight pattern but not the number of times line was surveyed and exactly when
- Sighting from DFO aerial surveys of August 2002 and August 2003. Surveyed different areas each year (different colour transect lines) + good coverage, precise locations, can obtain corresponding values for environmental variables of those years, known effort (where transects were done and where no whales were seen)
- Marginality: how the location of the whales varies relative to average conditions Tolerance: how narrow the range of conditions relative to range of overall conditions
- Most models included all EGVs but we had some challenges in some months because of ice data (difficult to get both data for ice and SST and Chloro even using averaged products) Cross-validations and the Boyce index indicated that models varied in quality but they generally performed well in the higher habitat suitability classes.
- As indicated in cross-validation results, the use of four HS bins (or categories) produced the best models (highest Boyce Index); therefore all habitat suitability maps produced by ENFA were reclassified into four suitability classes: 0-25: unsuitable (black) 26-50: marginal (blue) 51-75: suitable (red) 76-100: highly suitable (yellow)
- Assumptions/decisions: gave equal weight to high HS habitat from models obtained from each dataset
- Assumptions/decisions: gave equal weight to each monthly high HS maps
- Mention that ≥3 months represents majority of open water season Delineation was done visually as a first attempt
- Areas in red have been predicted using all three datasets available (more confidence) whereas areas in yellow have been predicted using historical whaling data only (lower confidence).
- Model results strongly suggest that bowheads select less than average distances to shore, ice concentration and depth from June to October. Regarding slope, the majority of models (seven of eleven) suggest that greater than average conditions are preferred by bowheads but most of the values were small with many values <0.1 which suggests that this trend may not be significant. Similarly, the majority of models (seven of ten) indicate bowheads prefer greater than average conditions of chlorophyll concentration. Model results do not demonstrate a strong preference for greater or less than average sea surface temperatures from June to July. The primary variable accounting for marginality is clearly distance of whales from shore (10 of 11 models); however no one variable appears to mostly influence bowhead specialization from June to October. In this respect five ENFA models suggest ice is the dominant variable, four ENFA models suggest chlorophyll concentration is the dominant variable and at least one model suggest SST is the dominant. This would suggest that whales were usually located within a narrow range of ice concentrations and somewhat narrow range of chlorophyll and SST values compared to average values.