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
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
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 Balaenoptera 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
Due to highly complex social structures and habitat separation between populations of sperm whales, investigations on distribution of sperm whales is integral to management and conservation of the species. Pace and colleagues used a maximum entropy modeling technique (MaxEnt) to estimate the distribution of sperm whales over a raster layer of the study area. This modeling technique used presence and absence data of sperm whales in the Tyrrhenian Sea in Italy obtained from acoustic monitoring over 9 year time period spanning from 2007 to 2015.
Environmental variables were obtained from GIS raster layers, and spatial prediction maps were generated using ArcGIS. MaxEnt outputs were mapped which enabled the visualization of habitat suitability ranging from 0 to 1, unsuitable habitat to very suitable habitat.
This study showed the use of GIS to visualize habitat suitability based on models informed from acoustically derived presence/absence data of sperm whales. This type of technique could be used to inform management reform and to specifically target areas of highest risk.
Pace DS, Arcangeli A, Mussi B, et al (2018) Habitat suitability modeling in different sperm whale social groups: Sperm Whale Habitat Suitability Modeling. Jour Wild Mgmt 82:1062–1073. https://doi.org/10.1002/jwmg.21453
An important part of whale conservation is monitoring population recovery. This includes investigations on habitat use and abundance of specific populations and subpopulations. Humpback whales that winter off the coast of Brazil are of key interest due to contact with fisheries, marine traffic, and ocean noise pollution. Bortollotto and colleagues investigated the distribution and density of humpback whales in this region in relation to environmental features, specifically geographical features, unique to this area.
Density surface models were used investigate habitat use and measure abundance of humpback whales. Tracklines from surveys were analyzed using QGIS software. QGIS was used to subdivide these tracklines and several geographic variables were considered: depth, distance to coastline, slope and position in addition to time. The package ETOPO1 was used to extract bathymetry data as well of the depth of the ocean at the place of humpback sightings, and distance to shore. Several habitat models were used, including a habitat use model and an abundance estimation model. Results from these models indicate differences in habitat preferences that are driven by different biological variables such as age, sex, and social group composition. It was found that geographic positioning was highly correlated with sea surface temperature which explained the presence and absence of mothers and calves in this region.
This paper is an example of using GIS to extract pertinent geographic data related to biological trends. Several of the key variables from this study were obtained through GIS packages and software.
Bortolotto G, Danilewicz D, Hammond P, et al (2017) Whale distribution in a breeding area: spatial models of habitat use and abundance of western South Atlantic humpback whales. Mar Ecol Prog Ser 585:213–227. https://doi.org/10.3354/meps12393
Sperm whale presence within the Kaikōura Canyon in New Zealand has decreased by nearly 50% within the past 30 years. To investigate this shift in habitat use, Guerra and colleagues measured the presence and absence of whales within this region within generalized additive models (GAM) to investigate presence in relation to the geographic characteristics of the region (depth, slope, aspect).
The probability of sperm whale occurrence was modeled by the best GAM, which was evaluated using multi-fold validation. These models were used to predict the probability of occurrence of whales at any given location within Kaikōura Canyon. Maps were then generated within ArcGIS using the inverse distance weighting to the first power. This was accomplished using the IDW tool in ArcGIS. Bathymetry of the canyon was overlaid on this raster layer to help assess relationships between space and sperm whale occurrence.
The results suggest that sperm whales are highly selective about where they forage. Although foraging areas are highly complex to predict, they can be partially explained by depth, temperature, and other geographic features. This study shows the use of geographic variables and models to predict habitat use which can be used to conserve protected species such as the sperm whale.
Guerra, Marta, et al. “Fine-Scale Habitat Use of Foraging Sperm Whales Is Driven by Seafloor Topography and Water Column Structure.” Marine Mammal Science, vol. 38, no. 2, 2022, pp. 626–52. Wiley Online Library, https://doi.org/10.1111/mms.12881.
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.
Much of the world’s cetacean species rely on calls, clicks, and song to communicate and hunt. A species most susceptible to the growing amount of ocean noise are killer whales. Drackett and Dragićević aimed to increase the ability for GIS and spatial multicriterion evaluation (MCE) methods to represent the complex relationships between ocean noise in relation to the detection of acoustic refugia alongside other habitat criteria.
The GIS based Logistic Scoring of Preference (LSP)-MCE analysis is composed of input values, such as industrial sites, ports, aquaculture, kelp beds, and shipping traffic, that are weighed according to suitability which are represented within a raster layer within GIS. This suitability raster layer is superimposed on the area of interest (the Salish Sea), where this endangered population frequents. This study represents the use of spatial analysis and scoring to identify areas of most concern in regard to habitat suitability based on several criteria, including ocean noise. These maps can then be used to inform management decisions surrounding this population and area of concern.
Such use of spatial analysis can be expanded to include a host of new variables that can inform management decisions of other cetacean species.
Drackett, Logan, and Suzana Dragićević. “Suitability Analysis of Acoustic Refugia for Endangered Killer Whales (Orcinus Orca) Using the GIS-Based Logic Scoring of Preference Method.” Environmental Management, vol. 68, no. 2, Aug. 2021, pp. 262–78. DOI.org (Crossref), https://doi.org/10.1007/s00267-021-01481-y.
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.
Smith uses a predictive spatial habitat model using aerial surveys of humpback whales to identify and describe the wintering areas for humpback whales in the Great Barrier Reef Marine Park (GBRMP). Sighting data in addition to environmental variables derived from GIS (sea surface temperature, distance from coast, seafloor slope) was incorporated into this predictive model. Predictive modeling of humpback distribution was performed using Maxent. The output values, a suitability value, was incorporated into GIS as a 4.8×4.8km cell within a GBRMP base map. A “frequency distribution of environmental suitability values” was constructed and the area of these intervals was calculated using the Spatial Analyst Tool Zonal statistics within ArcGIS 9.3. Habitat model validation was performed by overlaying satellite tagged whale tracks and underlying habitat suitability values were assessed.
This study exemplified the ability of GIS to help to predict and identify potential habitat of a whale species. This type of modeling can be performed on numerous other whale species and subspecies to help inform management decisions. For my own purposes, factoring in data such as oil spills into habitat suitability could help to explain changes in species distribution over time.
Smith J, Grantham H, Gales N, et al (2012) Identification of humpback whale breeding and calving habitat in the Great Barrier Reef. Mar Ecol Prog Ser 447:259–272. https://doi.org/10.3354/meps09462