Successfully a Master, or at Least a Bit More Enlightened

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.

PresentationM.S. pic

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 (, 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.

International Collaborations: What do the Oregon Coast and Maui’s dolphins have in common?

My name is Solène Derville and I am a master’s student in the Department of Biology at the Ecole Normale Supérieure of Lyon, France. As part of my master’s, I am spending a few months in Newport, where I am working under Dr Leigh Torres’s supervision in the GEMM Lab. Hopefully, this will be the starting point for a longer term collaboration, for a PhD project about the spatial ecology of humpback whales in New-Caledonia (South Western Pacific Ocean) which I am currently preparing.

Solene at Crater lake

On an early morning of February 2015, I am waiting at the airport for my flight to PORTLAND/PDX. I’ve had only one day to pack but I feel confident that I’ve made the right choices as my 23kg luggage contains mainly jumpers, sweatshirts, thick socks, and a brand new umbrella. I’ve got everything I need to face my four months internship in rainy Newport, Oregon.

A few disillusionments await me when I finally land: 1) my “saucisson” (fancy sausage) can’t pass customs and ends up in a bin despite my attempts to negotiate with the customs official, and 2) as soon as I am out of the airport, it starts raining. At first sight this looks like the harmless kind of drizzle I’ve experienced in England, until I realize it’s raining sideways! So much for buying a new umbrella…

Luckily, these small inconveniences don’t affect my spirits for long as I get to discover the richnesses Oregon has to offer.

My mouth drops open the first time someone tells me that I can see elk around Newport and that gray whales are commonly observed next to the jetty at this time of year. It’s difficult to describe to someone who’s always been living in this environment how exciting it is to me. I am not used to all this wilderness and certainly not to living so close to it. It’s a thrill to think that I only need to ride my bike for a few miles to meet the amazing local fauna.

Oregon Coast by Solene
Oregon Coast by Solene

Of course, the beauty of Oregon’s landscapes and the richness of its wildlife is not the only thing that catches my attention. I am immediately touched by the kindness of people, the sense of sharing and the deeply rooted sense of community. I feel welcomed at HMSC, and by my colleagues in the GEMM lab and I am eager to start my internship.

So what is my work here exactly?

Well, believe it or not, I’ve crossed the Atlantic Ocean and came to the US to actually work on a species of dolphins endemic to New-Zealand! Dr Leigh Torres, and I are investigating the fine-scale distribution and habitat selection patterns of Maui’s dolphin (Cephalorhyncus hectori maui). This subspecies of the more common Hector’s dolphin (Cephalorhyncus hectori, also endemic to New-Zealand) is the smallest dolphin in the world and unfortunately among the most endangered (listed as “critically endangered” by the IUCN). The Maui’s dolphin population is thought to have decreased to under 100 individuals in the past decades.

Maui's dolphin credit: Will Rayment
Maui’s dolphin credit: Will Rayment

In practice, this means I am doing data analysis so I spend my days in front of my computer. This may sound a bit dull, but computer work is actually a great part of research in ecology (apart from awesome field work stage, but this is only the tip of the iceberg). Speaking for myself, I’ve always found it very exciting to put together all this hard-won data to answer important questions, especially when the conservation of species as emblematic as the Maui’s dolphin is at stake. To tell the truth, the nerdy code writing work is also a lot of fun!

My data set consists of boat-based observations of Maui dolphin groups made during the 2010, 2011, 2013 and 2015 summer surveys. Overall about a hundred groups were observed. Based on these observations we would like to know: WHERE are the Maui dolphins (distribution pattern)? And WHY (habitat preferences)?

New Zealand
New Zealand

My job is first to describe the spatial distribution patterns of these observations given the year, composition of groups, or group behaviour (whether animals were feeding, resting etc.). This can be done using kernel density estimates: a very good method for “smoothing” a distribution in 2 dimensions and highlighting its main characteristics (extent, core areas etc.). This allows us to answer (or try to answer) the “WHERE” question.

Kernel density maps
Kernel density maps

The second stage of my analysis is to describe the environmental conditions at each of the dolphin group locations and compare them with the environmental conditions in surveyed areas where Maui dolphins where not observed. This allows us to better understand the environmental cues that Maui dolphins might be following to find “suitable” places for their every-day activities and therefore try answer the “WHY” question. In statistical jargon, we are exploring the relationship between probability of presence of Maui dolphins and environmental predictors such as: sea surface temperature, turbidity of the water, distance to closest river mouths, distance to the coast and depth.

The resulting models will be used to predict seasonal variations in Maui’s dolphin distribution, notably in winter when direct surveying is difficult because of weather conditions. Based on the resulting dynamic distribution models, we finally aim to predict how Maui’s dolphins might interact with anthropogenic activities or react to changes in their environment.

So far, preliminary results are very promising and I am hoping to share these soon!