By Courtney Hann (M.S. Marine Resource Management)
A week ago, I successfully defended my Masters of Science thesis on “Citizen Science Research: A Focus on Historical Whaling Data and a Current Citizen Science Project, Whale mAPP”, which included a 60 minute presentation to my committee, colleagues, friends, and family. Although a bit nervous at the start, my two weeks of revisions and practice prepared me to enjoy the experience once it started, and be thankful for all of the guidance and knowledge I have gained while at Oregon State University and with the Geospatial Ecology of Marine Megafauna Lab.
My thesis focused on the value of collaboration and creativity in developing new methods for gathering and analyzing marine mammal data; and was driven by the overall question of
How do we study marine mammals over vast spatial and temporal scales without breaking the bank, while still being scientifically rigorous?
This is important because marine mammal data collected over large spatial and temporal scales is relatively rare, and requires extensive collaboration and funding (Calambokidis et al. 2008; Dahlheim et al. 2009). A majority of marine mammal research is conducted over limited time frames (weeks to months) and on local spatial scales, requiring the data to be extrapolated out in order to understand regional patterns (Baker et al. 1985; Rosa et al. 2012). As a result, ecological modeling and other analyses are limited by geographic and temporal scale (Hamazaki 2002; Redfern et al. 2006).
I presented two potential approaches to the use of citizen science data to cost-effectively study marine mammal distributions across vast spatial and temporal scales. The first method is described below:
(1) Use the oldest form of large cetacean citizen science data, historical whaling records, to analyze species trends across extensive spatial and temporal scales. Amazingly, these 200-year-old records provide some of the most informative data for highlighting regional and global marine mammal distributions and abundance estimates (Gregr and Trites 2001; Torres et al. 2013). This information is vital for adapting management strategies as populations recover, change their distribution due to climate changes, or undergo various interactions with humans (net entanglements, ship strikes, competition for commercially important fish and invertebrate species, etc.).
Replicating such datasets today is not fiscally feasible with traditional research methods, but distribution data is still vital for understanding how populations have changed over time and how they are responding to large-scale climate and anthropogenic changes. Modern day citizen science research may be the solution to collecting such baseline data. Therefore, the following second method was evaluated:
(2) Data collected by 39 volunteers using the marine mammal citizen science app, Whale mAPP (www.whalemapp.org), over the summer of 2014 was examined to interpret various spatial, users, and species biases present in the dataset. In addition, the educational benefits, user motivations, and suggestions for revisions to the citizen science project were investigated with two user surveys. Results were used to revise Whale mAPP and highlight both the potential and limitations of citizen science data collected with Whale mAPP.
While I believe in the power of citizen science research for expanding our knowledge of large-scale marine mammal distributions, it is important to continue to interpret the biases in the dataset and truly examine how we can use the results for research. For, although collecting an abundance of data may be fun and exciting, careful examination of the methods and analyses techniques are vital if we hope to one day use the data to inform management and conservation decisions. I hope that my research contributes not only to this knowledge, but also to opening our eyes to the value of embracing a new method of data collection. Such a method relies on collaboration across various disciplines including biologists, managers, educators, app developers, volunteers, and statisticians. Maybe someday a current citizen science project, such as Whale mAPP, will provide a dataset as vast, abundant, and valuable as historical whaling records. Even the possibility of accomplishing such a goal is worth fighting for.
Literature Cited
Baker, C. S., L.M. Herman, A. Perry, et al. 1985. Population characteristics and migration of summer and late-season humpback whales (Megaptera novaengliae) in Southeastern Alaska. Marine Mammal Science 1:304–323.
Calambokidis, J., E.A. Falcone, T.J. Quinn, et al. 2008. SPLASH: Structure of Populations, Levels of Abundance and Status of Hump- back Whales in the North Pacific. Final Report for Contract AB133F-03-RP- 00078 prepared by Cascadia Research for U.S. Department of Commerce.
Dahlheim, M. E., P.A. White and J.M. Waite. 2009. Cetaceans of Southeast Alaska: distribution and seasonal occurrence. J. Biogeogr 36:410–426
Gregr, E.J., A.W. Trites. 2001. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58:1265–1285
Hamazaki, T. 2002. Spatiotemporal prediction models of cetacean habitats in the mid-western North Atlantic Ocean (from Cape Hatteras, North Carolina, USA to Nova Scotia, Canada). Marine Mammal Science 18:920–939.
Redfern, J.V., M.C. Ferguson, E.A. Becker, et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series 310: 271–295.
Rosa, L. D., J.K. Ford and A.W. Trites. 2012. Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Cont. Shelf Res. 36:89–104.
Torres, L. G., T. D. Smith, P. Sutton, A. MacDiarmid, J. Bannister, and T. Miyashita. 2013. From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale. Diversity and Distributions 19:1138-1152.
