Determining Highly Suitable/Critical Open Water Habitat for the

Eastern Arctic Bowhead Whale

Benjamin Wheeler, Marianne Gilbert and Stephen Rowe18th Biennial Conference

Society of Marine MammalogyQuébec City, QC

October 16th, 2009

Outline• Introduction

– Background– Research objectives– Approach

• Methods– Available information– Model, concepts, tools/GIS– Assumptions

• Results– Mapping– Definition of Critical Habitat– Habitat relationships

• Discussion • Future Work• Summary

Background

• Bowhead whales– Large, slow moving, slow

growing baleen whale

– Circumpolar

– Highly migratory (fasts in winter, feeds in summer)

– Pagophilic (ice loving)

– Historically heavily exploited

– 1 vs 2 groups in eastern Canada?

– Conservation status • 1 group endangered; 1

threatened 2004

• 1 group special concern (COSEWIC 2009)

• Recovery Planning– Inuit Traditional Bowhead

Study (1995-2000)– Bowhead Conservation

Strategy (NWMB, DFO, WWF) (~1999 – 2003)

– National Eastern Arctic Bowhead Recovery Team (2005 - ?)

Objectives

1. Address information gap in Bowhead Conservation Strategy

2. Address Species at Risk mandate of critical habitat

Main Goals:

A. produce map(s) of coastal areas that are Critical Habitat (patterns)

B. describing relationships between known bowhead locations and environmental variables (processes)

Sub Goals:

Study Area: Eastern Canadian Arctic [ECA]

Approach

• What have others done?- Gregr & Trites 2001- Mandleberg 2004- Compton 2004- Cañadas et al. 2005

• Consulted expert panel (SMM San Diego, 2006)– “look to your data for answers”

• Obtained eco-geographical variable [EGV] and whale location datasets

• Looked to our data for answers• Selected suitable modeling method based on available data• Produced models and conducted statistical analyses• Validated results and developed synthesized map of high suitability

habitat

METHODS: Eco-geographical Variables (EGVs)

• Sea Surface Temperature• Chlorophyll Concentration• Ice• Water Depth• Slope• Distance to Shore

Sea Surface Temperature Chlorophyll Concentration

Depth Ice Concentration

METHODS: Available Whale Location Data

• Inuit Knowledge (NWMB)• Historical Whaling Data• Industry Sector Survey Data• DFO Aerial Survey Data

Inuit KnowledgeInuit Knowledge

Historic Whaling KillsHistoric Whaling Kills

~2100 total sighting locations

Private Sector SightingsPrivate Sector Sightings

~400 total sighting locations

DFO Sightings August 2002-2003DFO Sightings August 2002-2003

118 total sighting locations

Methods: Modeling & Analyses

• Needed a model that could handle data that weren’t associated with ‘absence’ information

• Chose ENFA (Ecological Niche Factor Analysis): Biomapper v. 3.2 (Hirzel et al. 2006a):

• Doesn’t require presence & absence data – only presence• Has been used with datasets similar to the historic whaling• Is GIS based and includes statistical tools to build habitat suitability models and maps• Downside: can only predict Highly Suitable Habitat within the general study area in which whale locations were collected

ENFA Theory

• Ecological niche: how an organism or population responds to the distribution of resources and competitors

• Habitat Suitability maps computed by fitting some statistical or numerical model on environmental data and species distribution data

• ENFA’s principle is to compare EGV distribution for the presence data set (bowhead distributions) to that of the whole area

• Like Principal Component Analysis, the ENFA summarises many EGV into a few uncorrelated factors retaining most of the information. But here, the factors have an ecological meaning.– Marginality

– Tolerance/Specialization

Def’n: The location of bowheads as it relates to an EGV; relative to the

average EGV condition [0 = low marginality; 1 = high marginality]

Marginality

Average conditions

Range of EGV condition

Co

unt

EGV values at whale locations (niche)

Average conditions

Range of EGV condition

Co

unt

Tolerance/Specialization

EGV values at whale locations (niche)

Not too picky

Def’n: Niche breadth

More picky

Key Assumptions

• Open-water whale distribution related to resource availability

• Distribution of bowhead prey governed by oceanographic processes

• Bowheads not typically found in heavy ice during open water period

• Available whale positions of sufficient accuracy to reflect large-scale trends

• Spatial patterns of whale positions not artifacts of whaling or survey effort

• Recent environmental conditions were the same for the entire timeframe of whale locations used

Technical Methods

• Monthly datasets for whale location information

• Normalized environmental data using Box-Cox algorithm (Hirzel and Arlettaz 2003) where possible

• IDRISI raster, 10-km grid

• Assigned values of each EGV to grid cells in study areas using ArcGIS

• Biomapper distance geometric mean algorithm

• 4 bins used in Biomapper for definition of HS habitat– 0-25% = unsuitable habitat– 25-50% = marginal habitat– 50-75% = suitable habitat– 75-100% = highly suitable habitat

Model Execution, Validation

• Model execution– Ran all EGV datasets with

bowhead location datasets– June, July, August,

September & October

• Cross-validation– Jack-knife procedure; used

bowhead locations, Spearman rank coefficient/Boyce Index

– Visual validation with Inuit Traditional Knowledge

RESULTS: Models Produced Using ENFA and Monthly Data Used

Black = Unsuitable HabitatBlue = MarginalRed = Suitable Yellow = Highly SuitableGrey = land or no data

RESULTS: Habitat Suitability Mapse.g. July Habitat Suitability Map for Historical

Whaling Data Model

JUNE - JUNE - High Suitability Combined ModelsHigh Suitability Combined Models

JULYJULY - High Suitability Combined Models - High Suitability Combined Models

AUGUSTAUGUST - High Suitability Combined Models - High Suitability Combined Models

SEPTEMBERSEPTEMBER - High Suitability Combined Models - High Suitability Combined Models

OCTOBEROCTOBER - High Suitability Combined Models - High Suitability Combined Models

Definition of Critical Open-Water Bowhead Habitat

• Premise: habitat more regularly used is more important to bowheads• Each 10x10km grid cell coded per month noted as highly suitable (of

5 months)• Areas denoted as Highly Suitable for 3+ months of open-water

period are Critical Habitat

Number of Months cell coded as Highly Suitable

Relative Value of Highly Suitable Habitat

0

1

2

3

4

5

Cells Modeled as Highly Suitable for 1 or more Cells Modeled as Highly Suitable for 1 or more MonthsMonths

Delineation of Critical Open-Water Bowhead HabitatDelineation of Critical Open-Water Bowhead Habitat(3+ months High Suitability)(3+ months High Suitability)

Validation using Inuit Traditional Knowledge (late Validation using Inuit Traditional Knowledge (late spring to early fall)spring to early fall)

• Values for 11 models (June to October) ranged from 0.447 to 0.926

• Suitable bowhead habitat differs considerably from average conditions

• Primary variable governing marginality:• Distance from shore

Results: Marginality Trends

• Ice and chlorophyll main EGVs governing specialization (not always consistently)

• Suitable bowhead habitat potentially within narrower ranges of ice and chlorophyll conditions than for other EGVs

Results: Tolerance Trends

• Majority of the 11 models validated as good• Missing EGV data, due to ice coverage, under-

represents HS habitat (i.e., Foxe Basin, PR Inlet)• Distance to shore identified as key EGV - may be

biased by survey/whaling effort• Critical Habitat likely under-represented: 1 or 2

months• Scientific literature supports several areas identified

as critical habitat• Critical habitat identified in south of the ECA not

supported by recent observations of bowheads or literature (may over-represent Critical Habitat)

Discussion

Summary• Most models validated as good – therefore good confidence in

results• Practical definition of Critical Habitat based on ecological theory • Useful approach and tool for management and planning

Future Work• Integrate satellite tagging results & future sources of bowhead

location data• Integrate oceanography (tides, currents)• Use to predict effects of climate change (i.e., change SST based

on GCMs)• “zoom in” resolution and use to understand finer scale ecological

and oceanographic processes• Weight whale location datasets

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.