Category: Migration

Studies involving migratory patterns of cetaceans.

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Distribution patterns of migrating humpback whales (Megaptera novaeangliae) in Jervis Bay, Australia: A spatial analysis using geographical citizen science data

Bruce and colleagues used Volunteered Geographic Information (VGI) to investigate the east Australian humpback whale population over four years spanning from 2007 to 2010. VGI is incredibly useful due to its availability and consistency over time. Within ArcGIS, a rectangular celled fishnet and spatial join were used to employ spatial clustering of VGI sighting data.

Bruce found an increase of occurrence of individuals between August and November, during which southern migration occurs. There was a clear relationship and geographic variability between groups with calves and groups without calves, likely due to the decrease depth of Jervis Bay which is hypothesized to have acted as a resting point or point of reprieve for mother and calf pairs. Maps presented here show the relationship between space, time, and whale occurrences using raster layers and heat maps to represent sighting frequency.

This study was a unique example of using VGI to conserve and detect changes in whale populations. Given the increase in accessibility to location-recording devices such as smart phones, VGI and VGI analysis may become more popular for whale conservation.

Bruce E, Albright L, Sheehan S, Blewitt M (2014) Distribution patterns of migrating humpback whales (Megaptera novaeangliae) in Jervis Bay, Australia: A spatial analysis using geographical citizen science data. Applied Geography 54:83–95. https://doi.org/10.1016/j.apgeog.2014.06.014

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Co-occurrence of gray whales and vessel traffic in the North Pacific Ocean

With increasing vessel traffic, whales are becoming more susceptible to vessel strikes. In order to measure the extent and risk of these vessel strikes on gray whales of the North Pacific, Silber and colleagues used published range data from gray whales (in polygonal data format) to help inform management reform. ArcGIS was used to convert Keyhole Markup Language (KML) data into vector format and the Buffer tool was used to transform linear features to vectors. The Clip Tool was used to extract vessel traffic data that was derived from Automatic Identification Systems (AIS).

Silber identified the risk of not only large tankers to fatal vessel strikes, but also the potential risk of strikes from fishing vessels due to the sheer amount of commercial fishery activity in this region. Maps generated within GIS using both vessel and gray whale range data were used to identify areas of high vessel-strike risk. This type of data can be applied to a number of whale species to help manage vessel traffic in a way that can reduce the risk for whale fatality.

Silber G, Weller D, Reeves R, et al (2021) Co-occurrence of gray whales and vessel traffic in the North Pacific Ocean. Endang Species Res 44:177–201. https://doi.org/10.3354/esr01093

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Global distribution of fin whales Balaenoptera physalus in the post-whaling era (1980-2012): Post-whaling era fin whale distribution

Previously mapped global distributions of fin whales have conflicting data around the presence these whales in equatorial regions of the globe. Edwards and colleagues used published data, including line-transect surveys, photo-ID mark recapture estimates, and other extraneous observations to study the patterns of distribution of fin whales around the world.

Edwards used ArcGIS to visualize published survey boundaries, including those that had never previously been digitized. GIS was used to define vertices and polygons which encompasses survey tracks which were overlayed on the World Cylindrical Equal Area projection. Each grid cell represented the estimated distance that a fin whale could travel each day. Maps were generated for each data collection method: density estimates, line-transect surveys, individual sightings.

This study showed the capability of GIS to incorporate several data sources into a single visual representation of fin whale distribution around the globe. Our best predictions and estimates of animal presence and distribution are supported by multiple lines of evidence over vast time frames. Edwards showed the power of ArcGIS to not only act as a data repository, but also as an effective visualization tool.  

Edwards EF, Hall C, Moore TJ, et al (2015) Global distribution of fin whales B alaenoptera physalus in the post-whaling era (1980-2012): Post-whaling era fin whale distribution. Mammal Review 45:197–214. https://doi.org/10.1111/mam.12048

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Whale Trails – a Smart Phone Application for Whale Tracking

Whale watching is a substantial industry that takes place across the globe. With the abundant use of smartphones and smart technology, Maynecke proposed to implement an application that can record location in lat/long in addition to bearing. This data along with corresponding photographs can then be sent to GIS systems such as ArcGIS and Quantum GIS to be processed. Multiple submissions of a single encounter can be used within ArcGIS to calculate a highly accurate track using intersection points.

Such applications that utilize GIS will be integral to collecting consistent and accurate data regarding marine species. A focus of this project were humpback whales, which have highly identifiable fluke edges and patterns that can be used for identification. Given the increasing effects of climate change, tracking shifts in migratory patterns using GIS will be integral in assessing climate impacts on cetaceans.

Maynecke, Jan-Olaf. “Whale Trails – a Smart Phone Application for Whale Tracking.” International Congress on Environmental Modelling and Software. June, 2014. p. 8.

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A Customization of the Arc Marine Data Model to Support Whale Tracking via Satellite Telemetry

Here, Lord-Castillo tailors a multi-dimensional ocean data model to be compatible with satellite telemetry tagging program through Oregon State University’s Marine Mammal Institute. This paper exemplifies the development of data sharing within ArcGIS with the intention that its associated analytical tools can be utilized by different shareholders. Here, authors developed non-spatial object classes and feature classes specific to Arc Marine, which was a data model originally written using Microsoft Visio software. Within this customization study, they found two concepts to help improve future uses of Arc Marine for animal tracking: increased multidimensionality and creating an expandable platform.

This concept is integral to improving spatial data storage, management, and analysis with the goal of investigating animal movements and implementing conservation strategies given migratory patterns and behaviors. This is similar to the data I am working with which incorporates point locations, genotypes, sex, telemetry tracks, and a host of other environmental variables that can be extracted using GIS.

Lord-Castillo, Brett K., et al. “A Customization of the Arc Marine Data Model to Support Whale Tracking via Satellite Telemetry.” Transactions in GIS, vol. 13, no. s1, 2009, pp. 63–83. Wiley Online Library, https://doi.org/10.1111/j.1467-9671.2009.01159.x.

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Autumn movements of fin whales (Balaenoptera physalus) from Svalbard, Norway, revealed by satellite tracking

Lydersen and colleagues set out to investigate fin whales and their migratory paths using satellite tracking. Tags were deployed on 25 fin whales and stayed attached for a range of 6 to 95 days with an average duration of 33. It was found that 10 of the 25 individuals stayed within the study region, and 15 individuals travelled out of the immediate study area and tracks varied among individuals. Environmental variables such as ocean depth, sea surface temperature, and distance to the nearest coast were extracted from geodatabases given the location of the whales. In this case, Lydersen extracted bathymetry data from the ETOPO 1 Arc-Minute global relief data set from the National Geophysical Data Center, NOAA, sea surface temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) v5, and finally distance to the nearest coast from the land file (1:10 m) from www.naturalearthdata.com.

Track maps of whales are an incredible tool in both visualizing and analyzing whale movements. These tracks provide important information on foraging behavior, activity level, and migratory destinations or lack thereof. The locations that satellite tags transmit allow for extraction of environmental variables from a variety of geodatabases, such as those listed above, that can provide further insight into whale behavior and foraging strategies.

Lydersen, C., Vacquié-Garcia, J., Heide-Jørgensen, M.P., Øien, N., Guinet, C. & Kovacs, K.M. (2020) Autumn movements of fin whales (Balaenoptera physalus) from Svalbard, Norway, revealed by satellite tracking. Scientific Reports, 10, 16966.

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Statistical modeling of North Atlantic right whale migration along the mid-Atlantic region of the eastern seaboard of the United States

Many whale species exhibit long migrations during which data collection events become limited, but statistic modeling can help to fill in the gaps of sighting data. Firestone and colleagues created a model to predict the time and location that right whales occur within the eastern US waters. Firestone makes use of the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) which is a publicly available georeference to map traffic intensity and port volume statistics. This data is important in predicting the probability of vessel strikes and entanglements within this region which could pose conservation concerns to this species. This study was able to use this model and subsequent predictions to inform further survey efforts and management regulations to help reduce the likelihood of vessel strikes and entanglements along the right whale migratory corridor.

This study implemented GIS by mapping the ship traffic intensity, as measured by a heat map, overlayed with the right whale migratory corridor. Here we can visually assess the impact that certain shipping corridors may have on whales during certain times of the year. Mapping anthropogenic impacts with wildlife occurrences can help manage global populations, including my own study species in the Gulf of Mexico (GoM). I could use techniques such as this to measure the overlap of the Deepwater Horizon oil spill on the sperm whale population in the GoM.

Firestone, J., Lyons, S.B., Wang, C. & Corbett, J.J. (2008) Statistical modeling of North Atlantic right whale migration along the mid-Atlantic region of the eastern seaboard of the United States. Biological Conservation, 141, 221–232.