GEOG 566






         Advanced spatial statistics and GIScience

April 7, 2017

Habitat use of blue whales New Zealand’s industrial South Taranaki Bight region

Filed under: My Spatial Problem 2017 @ 12:44 pm

Research objectives

My research focuses on the ecology of blue whales (Balaenoptera musculus brevicauda) in New Zealand’s South Taranaki Bight region (STB). Despite the recent documentation of a foraging ground in the STB (Torres et al. 2015), blue whale distribution remains poorly understood in the southern hemisphere. The STB is New Zealand’s most industrially active marine region, and the site of active oil and gas extraction and exploration, busy shipping traffic, and proposed seabed mining (Torres 2013). This potential space-use conflict between endangered whales and industry warrants further investigation into the spatial and temporal extent of blue whale habitat in the region. My goals are to investigate the relationship between blue whale presence and their environment, and subsequently to examine how their space-use overlaps with industry presence. Specifically, I intend to:

  • Quantify the relationship between sea-surface temperature (SST), chlorophyll-a (chl-a), krill density, and blue whale presence
  • Investigate the spatial overlap between blue whale presence and oil and gas extraction platforms, the Trans-Tasman Resources Ltd. proposed seabed mining site, and shipping traffic

Map of New Zealand with the South Taranaki Region indicated by the white box.

 

A blue whale surfaces in front of an oil rig in the South Taranaki Bight. Photo by Kristin Hodge.

Dataset

I will be working with data collected during vessel-based surveys in February of 2014, 2016, and 2017 in the STB. Blue whale sighting location and group size were recorded by observers during the surveys. To record the oceanographic conditions throughout the water column, profiles of water column depth, temperature, and salinity were recorded using a Sea-Bird microCAT (SBE 911plus) Conductivity, Temperature and Depth (CTD) sensor approximately every hour during survey and at every blue whale sighting. Krill density and patch size will be quantified from hydroacoustic backscatter data collected with a Simrad EK60 echosounder (Simrad ES120-7DD splitbeam transducer, 120kHz transceiver, 250 W, 1.024 ms pulse length, 0.5 s ping rate). The echosounder data have not yet been processed, however I hope to do so this term so that the prey data can be included in these analyses.

In addition to the in situ data collected during our surveys, I plan to incorporate satellite imagery of SST and chl-a concentration for the region. I will use satellite data generated from NASA Moderate Resolution Imaging Spectrometer (MODIS).

I have been provided with the locations of the oil and gas drilling platforms and the proposed site for the iron sands seabed mine. I will use ship automatic identification system (AIS) data for the shipping traffic layer.

Hypotheses

  • Blue whale presence will show a positive relationship with chl-a concentration and krill density
  • Blue whale presence will show a negative relationship with SST
  • There will be apparent inter-annual differences in blue whale sighting distribution, reflecting the strong El Nino conditions seen in 2016
  • Blue whale presence will overlap spatially with industrial activities

Approaches

I plan to use ArcMap to visualize blue whale presence and the layers of oceanographic data I have described previously (in situ SST and prey density, remote-sensed SST and chlorophyll-a). I will then use R to compute either a generalized linear model (GLM) or generalized additive model (GAM) to evaluate the association between blue whales and these oceanographic variables, with blue whale presence as the response variable.

For the overlap between blue whale presence and industry, I intend to use ArcMap to visualize this spatial overlap by creating buffers around the stationary platforms and the proposed mining site and examining how often blue whale sightings took place within those buffers.

Outcomes

I intend to produce map figures that will show oceanographic measurements interpolated over our study area, overlaid with our blue whale sighting locations for each year of study. I hope to be able to report the model results that quantify the impact of our measured oceanographic conditions on blue whale presence.

My priority for this course is the habitat analysis and modeling. The industry overlap will be more of a visual examination at this stage, and more quantitative analyses of impacts will take place subsequently once a foundational understanding of blue whale habitat use in the region has been established.

 Significance

Despite their enormous size and once-large global population, relatively little is known about blue whale distribution and habitat use due to their elusive nature and relative inaccessibility for study. Blue whales have extremely high metabolic demands in addition to employing an energetically expensive lunge-feeding foraging strategy (Croll et al. 1998, Goldbogen et al. 2011, Hazen et al. 2015). The ability to consistently locate dense patches of prey is therefore critical to blue whale survival, making the documentation of foraging grounds such as the STB region important. The STB region presents a unique opportunity for studying this species in a location where they seem to be consistently found in high abundance and relatively close to shore. But beyond their relative accessibility, the understanding of blue whale habitat use on a foraging ground will contribute to the body of knowledge on these endangered and little-studied whales.

Under the New Zealand threat classification system, blue whales are currently listed as ‘Migrant’ in New Zealand waters. However, our preliminary analyses point toward the possibility of a resident population of blue whales in New Zealand. Observations of foraging, breeding, and nursing behaviors demonstrate the likelihood of the STB form multiple critical life history functions. The strong industrial presence in the region and the ongoing push for industry expansion makes gaining an understanding of the spatial and temporal extent of blue whale habitat critical for management decisions.

A blue whale mom and calf surface in the South Taranaki Bight. Photo by Dawn Barlow.

Experience

I have some experience with Arc and R from coursework and my own preliminary analyses. I think that with more time and more practice I will grow more confident in my ability to use them. I have used MATLAB some, but mostly as a platform for running more specialized software programs specific to my field. I have no experience in python.

References

Croll DA, Tershy BR, Hewitt RP, Demer DA, Fiedler PC, Smith SE, Armstrong W, Popp JM, Kiekhefer T, Lopez VR, Urban J, Gendron D (1998) An integrated approach to the foraging ecology of marine birds and mammals. Deep Res II 45:1353–+

Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE (2011) Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J Exp Biol 214:131–146

Hazen EL, Friedlaender AS, Goldbogen JA (2015) Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci Adv 1:e1500469–e1500469

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248

Torres LG, Gill PC, Graham B, Steel D, Hamner RM, Baker S, Constantine R, Escobar-Flores P, Sutton P, Bury S, Bott N, Pinkerton M (2015) Population, habitat and prey characteristics of blue whales foraging in the South Taranaki Bight, New Zealand.

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