(Almost) all the humpback whales of the North Pacific: A collaborative and comprehensive photo-ID dataset

doi:10.21203/rs.3.rs-2294878/v1

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for the majority of living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs supplemented with community science data. All available images were compared using a recently developed machine learning artificial intelligence image recognition algorithm able to rapidly and accurately detect matches between individuals. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,379 encounters, with each individual encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), and with an annual average of 87.1% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

Biological sciences/Biological techniques/Software

Earth and environmental sciences/Ocean sciences/Marine biology

Biological sciences/Systems biology/Population dynamics

Earth and environmental sciences/Ecology/Conservation

The humpback whale, Megaptera novaeangliae, is a globally distributed species with a complex population structure that varies among oceans^1–3. Individuals engage in extensive seasonal migrations between high-latitude feeding areas during the spring, summer, and fall, and low-latitude tropical waters to mate and calve in winter and spring^{1, 3–7}. Migratory timing is loosely staggered and governed by age class, sex, and reproductive condition⁸. In the North Pacific, humpback whales breed in waters off Central America, Mexico, Hawaiʻi, the Mariana Islands, the Philippines, and Japan. They primarily feed in coastal to continental shelf waters off Russia, the Bering Sea, Alaska, British Columbia, Washington, Oregon, and California. Humpback whale populations worldwide were severely depleted of extensive commercial whaling until late in the 20th century, listed on the U.S. Endangered Species List (ESA) in 1970 due to an estimated 31,785 humpback whales taken in the North Pacific from 1900 to 1979^9–11.

Following a ban on humpback whale catches by the International Whaling Commission in 1966, and the cessation of Soviet illegal whaling in the following decade¹⁰, there has been evidence of humpback whale population growth. Two studies have evaluated the status of humpback whales in the full North Pacific, first in the 1990s³, then a study entitled Structure of Populations, Levels of Abundance and Status of Humpback Whales (SPLASH) conducted from 2004 to 2006⁷, finding an annual population growth rate of 4.9–6.8% as of 2006. A major portion of SPLASH relied on the identification and resighting of individual humpback whales with a method of trained observers visually matching photographs of the undersides of each whale’s tail (flukes), based on unique white and black pigmentation pattern, together with its unique trailing edge contours^21,22. Using this method, SPLASH documented 7,640 individual humpback whales in 18,469 unique encounters across all known breeding and feeding areas of humpback whales in the North Pacific, 36.3% of the estimated 21,063 living individuals²³. SPLASH reinforced the value of broad-scale data sharing and collaboration and exposed gaps in knowledge of humpback whale status in the North Pacific.

In 2016, using photographic identifications (photo-ID) data and other lines of evidence, NOAA Fisheries, pursuant to the ESA, defined 14 humpback populations globally, termed Distinct Population Segments (DPSs)¹². DPS designations are based on breeding areas where many whales show long-term site fidelity¹⁴, yet in feeding areas, whales also show high site fidelity and arguably face greater biological and anthropogenic stressors¹⁵. Four DPSs occur in the North Pacific; based on varying rates of recovery, two remain listed as endangered (Central America and Western North Pacific DPSs), one is considered threatened (Mexico DPS), and one has been deemed to not warrant listing (Hawaiʻi DPS; ESA, 81 FR 62259, September 2016). The complex long-distance migrations undertaken by humpback whales expose populations to diverse management regimes, anthropogenic risks, and ecological conditions¹³. For example, a very large marine heatwave developed in the North Pacific from 2013 to 2016¹⁶, causing a major negative impact on humpback whale food resources and sharp declines in abundance, survival, and reproductive success of humpbacks in Hawaiʻi and Southeast Alaska; this event coincided with the removal of the Hawaiʻi DPS from the ESA^17–20.

Understanding of humpback whale behavior, ecology and conservation issues has advanced with many regional studies, often using individual photo-ID data^{14,17, 31–36,18, 24–30}. However, in the years since SPLASH, local and regional photo-ID datasets were seldom integrated with one another, in part due to prohibitively time-intensive visual matching of individual ID fluke photos in ever-growing catalogs. This study established the North Pacific Humpback Whale Photo-ID (NPPID) collaboration with an ultimate goal of advancing knowledge of humpback whale population structure and migratory movement in the North Pacific by establishing a high resight rate for individual whales across the full study region. A central objective of the effort was to implement a collaboration framework to facilitate data availability, access, and readiness. Due to the amount of data involved and the difficulty of obtaining long-term funding, to be successful the system needed to drive the incremental cost of acquisition of each successive datapoint to near zero. Such a system required effective technology and web-based data management to submit, quality control, identify, and curate encounter data for a growing set of known individual whales. The NPPID was built on newly established automated fluke photo-ID matching technology that is more accurate and orders of magnitude faster than visual matching³⁷, benefitting from results of previous matching algorithms³⁸. However, a system is not technology alone; with data for tens of thousands of whale encounters per year, the system needed to sustainably nurture positive collaboration practices. Therefore, the NPPID was developed as a shared effort utilizing the user-friendly and interactive Happywhale.com web-based platform. Here we describe the process of building an ocean-basin-wide ongoing photo-ID collaboration involving 43 research groups and thousands of public contributors (community scientists) that has enabled rapid feedback for population and longitudinal studies of humpback whales across the North Pacific, with practical implications for navigating the use of complex multi-contributor datasets.

The North Pacific Humpback Whale Photo-ID (NPPID) Collaboration

This effort began in 2018 as a data sharing initiative reviving the collaboration established with the 2004–2006 SPLASH study⁷, supplemented by photo-ID images from community scientists. We built upon the SPLASH dataset, study methodology, and collaboration, but did not have a budget for data acquisition or fieldwork. All SPLASH collaborators known to be active in North Pacific humpback whale studies were invited to join, along with all known newer regional researchers and organizations. Data collection relied on existing archives and ongoing field efforts by the individual collaborators. All dedicated data collection by study collaborators was carried out in accordance with permitting requirements of respective authorities (permit details in listed in acknowledgements). Data collection from community scientists was sourced primarily from whale watch companies operating under regulations and guidelines of respective national, regional and local authorities. A primary incentive for participation in the NPPID collaboration was the promise of novel and fully automated image recognition technology³⁷ that effectively eliminated the cumbersome, time-intensive visual matching process from photo-ID data management.

Definitions:

Photo-Identification (Photo-ID/IDs): An image of the ventral side of a humpback whale’s tail (flukes) that, through pigmentation pattern and trailing edge contour, identifies it as a unique individual, similar to a human face or fingerprint.

Photo-ID catalog: A curated collection of photo-IDs (fluke photos) of individual unique humpback whales known in a region where each individual is assigned a unique identification string.

Community science: Often referred to as citizen science, scientific work undertaken by members of the general public, especially whale watch operators and naturalist guides. This is often done in collaboration with scientists conducting specific research or for a specific project.

Encounter: A single sighting of a unique humpback whale supported by a referenced photo-ID image, recorded on a specific day at a specific location.

Matches: Two photo-ID encounters confirmed to be the same individual.

False negatives/duplicates: Cases of two or more photo-ID catalog individual records initially thought to be different individuals, later found to be the same individual and combined into one individual record.

NPPID: North Pacific Humpback Whale Photo-ID collaboration. The collaboration includes 43 research organizations across the North Pacific basin.

SPLASH: A 2004–2006 ocean-basin-wide study of humpback whales in the North Pacific entitled Structure of Populations, Levels of Abundance and Status of Humpbacks^7,23.

Season: A sampling window defined in time and space, to document and compare counts of individual whales.

Feeding season: Defined by calendar year encounters north of 32.55 °N

Breeding season: Defined by 1 August through 31 July encounters south of 32.55 °N

Through a memorandum of agreement (MOA, Supplementary material I), all research organizations in the NPPID committed to sharing photo-ID images and supporting associated data for every available encounter, defined as one sighting of a unique individual per day, within their respective study areas. The specific research aim was to further knowledge of population structure and migratory movement via photographic mark-recapture population model development ^{e.g. 23,39}. The broader conceptual aim was to create an asset that, if successful, could become an ongoing, living dataset for continued population monitoring. Under the terms of the MOA, each data contributor chose whether their data were publicly visible via Happywhale or only visible to collaborators who had signed the MOA. The MOA limited data use to a defined set of manuscripts about population status and migratory patterns; additional use required agreement from all collaborators. The infrastructure, compiled data, and collaborator connections will remain after the period of the current MOA and will therefore need to be addressed with further agreement among collaborators if the dataset is going to be an ongoing, living dataset.

Data Integration And Quality Control

Humpback whale encounter data were delivered to Happywhale data managers from collaborators in a wide range of states of reconciliation, from unmatched original scans and digital photos to fully edited sets of images (i.e., exposure adjusted as needed and cropped tightly around flukes) with IDs assigned to each individual whale. The minimum data required for each encounter was: date, location, and photo-ID image or confirmed individual ID. All supporting attribute data, where provided, was maintained with the encounter, including filename, date, time, location, individual ID from the collaborator’s naming/numbering system, observers, vessel name, observed whale sex, age class, behavior, group composition, etc. Because the state of every dataset varied at the time of delivery, all data were managed through a standard series of steps:

Image management and matching: Images were quality-controlled through cropping tightly around the flukes and, if necessary, exposure adjustment to facilitate algorithmic ID followed by visual ID confirmation. All images were quality scored on a 0–5 scale, where 0 represented photos in which no photo-ID features were visible, and 1–5 represented very poor to excellent quality photos, respectively³⁷. All photo-ID images were matched to a progressively growing set of known whales via an automated image recognition system³⁷. Every match proposed by the system was confirmed by a trained observer.
Supporting attribute data curation: Given the diversity of supporting data formats received, standardization was necessary for dataset management. In older data (i.e., digital scans of slides) and data collected opportunistically, date and location precision required categorization or exclusion. Locations were categorized as general (confident of location within 200 kilometers), approximate (confident of location within 20 kilometers), or precise (confident of location within 2 kilometers). Within the precise location category, data source was categorized as: (a) camera GPS embedded into the image, (b) sourced from a synchronous GPS track, (c) recorded from a GPS unit, (d) recorded via a mobile app, or (e) manually transcribed. For encounters without a known date, an approximate date to month, season or year was assigned as information allowed, with date precision noted in encounter attributes. Encounters without a date known at least to year and/or location known confidently within 200 kilometers were excluded. Descriptive observational data and contextual information such as whale sex, age class, behavior, mother/calf relationships or group composition, and entanglement or ship strike scarring were recorded with each encounter when available and without standardization. Data quality was reviewed on import, where errors were detected in date, location, and ID outliers, with an opportunity for review by both data managers and data contributors before entry into a relational database.
Efficiency with large datasets: To increase efficiency for collaborators with large, well-curated datasets, some encounters were delivered with an individual ID name/number and supporting date, location, and attribute data, without a photo-ID image linked to every encounter. These encounters were expected to have error rates at or below the 1–3% missed match rates found in good-to-high quality photo-ID datasets³⁷, and were linked to known individuals represented in one or more catalog photos.

Many-to-one reference catalogs. All images were automatically matched against all individual humpback whales known at the time of each respective dataset integration. Across the NPPID study area, 39 separate catalog systems were received that had collaborator-specific individual IDs (Table 1). These ID naming systems were accommodated into a many-to-one ID structure so that any individual could be tracked via any of the multiple catalog IDs assigned to them.

Table 1

Humpback whale photo-ID reference catalog naming systems integrated in this study. A single unified dataset allows cross-referencing of all known IDs for each individual. Some collaborating research groups share naming systems, however those IDs are accounted for only once in the table below.
Reference catalog (Associated References)	Code	Individuals	2001–2021 encounters	Regions
Alaska Whale Foundation	AWF	780	10508	Southeast Alaska
BALYENA.ORG⁶⁸	BALYENA	228	1193	Philippines
Bree Witteveen Alaska Catalog	BREE	1906	7959	Alaska Peninsula and Gulf of Alaska
MML Cetacean Assessment and Ecology Program	CAEP	412	3889	Pelagic North Pacific
Association ELI-S	CCN	124	1713	Nicaragua
Canadian Pacific Humpback Collaboration	CPHC	1197	14569	British Columbia
Cascadia Research Collective^{3, 69–72}	CRC	7017	66967	British Columbia to Central America, mostly US West Coast
Clayoquot and Barkley Sound	CS	530	6094	Central British Columbia
DFO Canada	DFO	1982	26494	British Columbia
Eye of the Whale	EOTW	172	1589	Gulf of Alaska and Hawai`i
ECOBAC FIBB Catalog	FIBB	2818	27130	Mexico
Glacier Bay National Park^22,29,30	GBNP	72	732	Glacier Bay and Icy Strait, Southeast Alaska
Gulf Watch Alaska	GWAK	361	2216	Kenai Fjords and Prince William Sound, Gulf of Alaska
Hawaiian Islands Humpback Whale National Marine Sanctuary	HIHWNMS	1051	7368	Hawai’i
Happywhale³⁷	HW	11851	78070	Pan-Pacific
Juneau Flukes	JUNEAU	19	1180	Juneau, Southeast Alaska
Kachemak Bay Whales	KBAY	391	2188	Kachemak Bay, Gulf of Alaska
Keiki Kohola Project	KKP	33	198	Hawai’i
Marine Education and Research Society	MERS	308	7288	Central and southern British Columbia
Humpbacks of the Salish Sea (HWSS)	HWSS	483	7853	Salish Sea
Marine Mammals of Oaxaca	MMO	65	781	Oaxaca, Mexico
North Coast Cetacean Society	NCCS	329	5169	Northern British Columbia
North Coast Cetacean Research Initiative	NCCRI	188	1860	Northern British Columbia
North Gulf Oceanic Society	NGOS	39	396	Gulf of Alaska
Okinawa Churashima Foundation⁶⁸	OCF	1732	6322	Okinawa, Japan
OSU Marine Mammal Institute WTG⁷³	OSUWTG	1242	14765	Central and eastern Pacific
Pacific Islands Fisheries Science Center, NOAA Fisheries²⁴	PIFSC	92	245	Mariana Islands and Northwestern Hawaiian
Pacific Whale Foundation	PWF	5022	33346	Hawai’i-focused, pan-Pacific
Prince William Sound Catalog	PWS	184	1570	Prince William Sound, Alaska
Russian Cetacean Habitat Project^68,74	RCHP	2028	6545	Russia
Sayulita Humpback Whale	SAYU	145	1376	Sayulita, Mexico
SE Alaska Humpback Whale Catalog	SEAK	2571	34284	Southeast Alaska
SPLASH Project^7,23	SPLASH	7838	64144	Pan-Pacific
The Dolphin Institute encounter code¹⁴	TDI	1055	5970	Hawai’i and Southeast Alaska
The Marine Mammal Center	TMMC	67	1827	San Francisco Bay, California
UABCS Programa de Investigación de Mamiferos Marinos	UABCS	601	3950	Baja California Sur and Revillagigedo Islands, Mexico
Universidad Nacional Autónoma de Mexico	UNAM	1053	9045	Mexico
Whales of Guerrero	WGRP	329	5242	Guerrero, Mexico
Whale Trust	WTM	1994	15708	Hawai’i

Reconciliation of duplicate IDs. Every image was matched within and between all collaborator catalogs. One individual ID per catalog was allowed such that where individuals were found with duplicate IDs due to false negatives (where a previously undetected match within a collaborator catalog was found), the contributor chose the persisting ID (typically the lowest of a sequential ID series). Each duplicate ID was noted in the attributes for the individual whale. Newly detected (i.e., unmatched) individuals were added to the continually growing reference set, with the collaborator ID, if available, or with a newly assigned Happywhale catalog ID. False positives (where two different whales were combined into one individual record) were minimized through trained observer review of every match.

Community science data contributions. Opportunistic images submitted through Happywhale were also matched against all known whales, supplementing the research collaboration with community science-sourced encounter data. The same image and data quality control standards described above were applied. All community science data contributors implicitly acknowledged their choice of data usage rights during the submission process and had the option of changing usage rights settings among established levels of Creative Commons usage rights (https://en.wikipedia.org/wiki/Creative_Commons_license). Unlike research collaborators participating under the terms of the MOA, public contributors did not have the option of restricting public visibility. Public contributors had access to an encounter comment system whereby suspected data errors and outliers could be brought to the attention of data managers, creating a feedback loop for review and error detection.

Information system structure and development. The NPPID data management system is an integrated workflow of image processing, individual identification, recording, and curating encounter and individual attribute information. Data are structured through units of contributors (i.e., “users”), images, encounters, individual humpback whales and surveys (i.e., “voyages”), linked by a series of workflow processes (Fig. 1). The cloud-based information architecture was composed of a dedicated server for the Java Spring application using a PostgreSQL database populated with Darwin Core compatible fields⁴⁰. Submitted binary media were stored in a Simple Storage Service (S3) system for global retrieval. The ID system uses a combination of a Node server and a Python Flask app to run the PyTorch-based ID algorithm.

During the collaboration, ongoing system development brought enhanced functionality and sophistication to data management processes within the Happywhale.com web platform. In 2021, the automated image recognition system was rebuilt to deliver results in under 0.1 seconds per image. This efficiency reduced server load, which has accommodated direct access by collaborators to batch process photo-ID images directly via web and mobile app interfaces in the lab or field. Near instantaneous access to image processing was adopted by many collaborators to facilitate more efficient and effective internal data management.

Collaborators were invited to directly manage their data import process and ongoing curation, with training, feedback, and quality control oversight by system managers. Some collaborators used the system as a principal repository of their data while others maintained their own separate data management systems during the study. As import and management tools developed in a constantly evolving system, collaborators were increasingly enabled and encouraged to manage their own data.

System use, outreach, and data accessibility

Public awareness of the opportunity to contribute to whale conservation science was spread through word of mouth, social media and documentary films, primarily seeking to reach naturalists, whale watch guides and enthusiasts already familiar with the concept of marine mammal photo-ID and equipped with camera gear sufficient to create quality images. Would-be contributors were directed to Happywhale.com with little guidance beyond a request for humpback whale photo-ID photos from any date and location so long as the contributor could confirm the date and location. The data upload process sought to balance ease of access with rigor for data quality, with data validation dependent on the image management process.

Data are searchable and accessible in ‘map view’ (Fig. 2) and ‘list view’ formats via Happywhale. Users can expand a search from a set of encounters (for example, all encounters contributed by one user or all encounters in a geographic area in a defined time-period) to all sightings globally of individuals within the found set. This allows quick visual exploration of migratory connections for any set of whales. For collaborators, data are available for export into a standard format translatable to downstream processes in GIS or statistical software. A notification system provides each contributor with alerts to novel developments regarding individuals they have encountered (e.g., initial identification typically within a few days of submission, discovery of duplicate individuals, ongoing resightings).

Analytics - Documenting detection probability

The SPLASH project actively developed collaborations and supported field efforts in all known (at the time) humpback whale breeding and feeding areas of the North Pacific in pursuit of comprehensively representative sample sizes⁷. The NPPID project relied on contributions from existing datasets, ongoing field efforts, and community science image contributions. With successive integration of datasets, detection probabilities progressively increased, leading to a dominance of resightings (documenting an individual multiple times) and fewer new whales added to the comprehensive catalog, causing a shift in methodology from predominantly cataloging new whales, to confirming matches of known whales. To understand the proportion of the populations sampled in our growing known dataset, we plotted a discovery curve of new versus total identified individuals (Fig. 3) and a modified discovery curve of individuals identified over time (Fig. 4) to describe effort through the course of the history of the dataset. For data analysis, feeding seasons are defined by calendar year encounters north of 32.55°N, while breeding seasons are defined by 1 August through 31 July encounters south of 32.55°N (i.e., a December 2015 encounter in Hawaiʻi is considered to be in the 2016 breeding season).

The NPPID collaboration involved 43 research organizations and included data from all nations around the North Pacific Rim where humpback whales are known to regularly occur (Tables 1–2, Fig. 5). The complete ocean-basin dataset totaled 30,100 individual whales (February 1977 through August 2022). During the 2001 through 2021 study period 27,956 unique individuals were documented in 157,379 encounters (Fig. 2, Table 2). Most encounters were represented by a single photo-ID image, while 30.7% contained additional supporting images such as multiple views of the flukes, dorsal fin to fluke series and/or behavioral and anatomical images of the same individual. Naming/numbering protocols for 39 reference catalogs were combined into one unified set, with an average of 1.96 IDs per individual (range: 1 to 10). Most encounters (66.3%, documenting 24,049 individuals) were sourced from research collaborators, with the remaining 32.7% submitted by community scientists (documenting 15,298 individuals; by region, Fig. 5). The community science-sourced component of the dataset was contributed by 3,413 Happywhale users (Supplementary material II), a subset of the August 2022 global total of 12,800 users registered in Happywhale. By volume, most community science sourced images were contributed by whale watch tour naturalists who consistently photographed and uploaded photo-ID images of every whale they were able to photograph. Most encounters (65.6%) are publicly visible, with the remainder visible only to collaboration members (Tables 1–2 by region and research group). 26.2% (41,170) of encounters were received from collaborators with individual IDs but without photos. An additional 6,318 humpback whale encounters (3.9% of total North Pacific encounters), primarily from public contributors, remained unidentified to individual due to image quality or no photo-ID.

Table 2

Dataset detail by collaborating research organization for all data contributed. The resighting ratio statistic reports the average number of encounters of each identified individual.
Collaborating Organization	Identified Encounters	Individuals	Resighting Ratio	% Publicly Visible
Alaska Whale Foundation	3887	1387	2.8	26
Association ELI-S	133	129	1.0	98
BALYENA.ORG	479	231	2.1	57
Cascadia Research Collective	44310	7117	6.2	99
Commander Islands National Park	579	564	1.0	100
Ecologia y Conservación de Ballenas, A.C. ECOBAC	6892	2998	2.3	48
Eye of the Whale Marine Mammal Research	247	229	1.1	100
Department of Fisheries and Oceans Canada	4680	1669	2.8	37
Glacier Bay National Park & Preserve	10753	649	16.6	6
Happywhale	5085	1762	2.9	100
Hawai`i Marine Mammal Consortium	2702	1894	1.4	18
Humpback Whales of the Salish Sea	1990	517	3.8	1
International Whaling Commission	168	156	1.1	100
Juneau Flukes	377	73	5.2	89
Marine Education and Research Society	17400	541	32.2	2
OSU Marine Mammal Institute WTG	1789	1281	1.4	100
Murdoch University	3985	2198	1.8	96
Pacific Islands Fisheries Science Center, NOAA Fisheries, Honolulu, HI, USA	142	99	1.4	65
NOAA Fisheries Southwest Science Center, La Jolla, CA, USA	1265	971	1.3	100
NOAA Fisheries Science Center, Alaska, USA	2052	1373	1.5	91
NOAA Hawaiʻian Islands Humpback Whale National Marine Sanctuary	2309	1940	1.2	39
North Coast Cetacean Research Initiative, Ocean Wise	1080	280	3.9	0
North Coast Cetacean Society	5562	453	12.3	9
North Gulf Oceanic Society	1315	639	2.1	100
Okinawa Churashima Foundation	5989	1735	3.5	10
Pacific Whale Foundation, Wailuku, HI, USA	9730	5065	1.9	100
Pacific Wildlife Foundation, Canada	755	494	1.5	100
Russian Cetacean Habitat Project	3692	2057	1.8	61
Simmons University/ Emmanuel College	108	99	1.1	0
The Dolphin Institute, Hilo, HI, USA	3706	2407	1.5	27
The Keiki Kohola Project	350	311	1.1	88
Universidad Autónoma de Baja California Sur	2518	2100	1.2	100
University of Alaska Fairbanks	4934	2186	2.3	98
University of Alaska Southeast	5200	1945	2.7	34
University of Hawaiʻi at Mānoa, Hawaiʻi Institute of Marine Biology	341	305	1.1	100
VE Enterprises	1257	699	1.8	98
Whale Trust	2984	2183	1.4	93
Whales of Guerrero	698	571	1.2	100
Winged Whale Research	477	218	2.2	100

An annual average of 87.1% of individuals (84.1%-91.8%) were documented in more than one season (Table 3, by region Fig. 5). During the three-year SPLASH study, the cumulative number of individuals documented increased annually by an average of 20.7%, while from 2017 forward, with a comparable or greater number of individuals identified per year, cumulative individuals increased by an average of 5%, due to the documentation of a higher proportion of living individuals (Fig. 3). Data collection temporarily surged during the 2004 to 2006 SPLASH study, then increased gradually from 2007 and 2014 and more strongly from 2015 (Fig. 4).

Approximately 2,300 duplicates (false negatives) were found within the 39 collaborator catalogs, where the same whale was given multiple IDs within one catalog due to an undetected match (7.6% of total individuals). The range of false negatives across catalogs received was 0.1–11% in catalogs of greater than 100 individuals. In the SPLASH dataset of 7,971 total individuals, 331 (4.2%) previously undetected false negatives were found. False positive error rates, were estimated to be below 0.1%, removed whenever detected with each review of individual records during data management processes. Over 5,700 encounter comments were received through Happywhale, in many cases alerting data managers to potential errors in date, location and/or whale identities.

The NPPID collaboration established a comprehensive, broad-scale, and rich dataset made possible by a rapid and rewarding feedback process connecting collaborator and community science data around the North Pacific Ocean-basin. Effort was variable over time, much higher in some areas than others and skewed to the central and eastern North Pacific. However, data collection occurred in all known humpback whale breeding and feeding areas, with high rates of resighting individuals throughout. From 2001 to 2021, an annual average of 87.1% of whales were encountered in more than one season, averaging 5.6 seasons of encounters per individual. From 2017 forward, annual number of individuals identified met or exceeded SPLASH study years, though with an average of only 5.0% new individuals documented each year (Fig. 3). This 5.0% includes recruitment – calves and juveniles new to the populations – and a progressively smaller proportion of previously undocumented adults, suggesting that the dataset contains most of the humpback whales of the North Pacific. The NPPID collaboration is the first of its kind to develop a long-term individual ID database on this scale, establishing a unique foundation of a dataset well suited for humpback whale population modeling, and for any research benefitting from individual identification.

This study began during the development of fast and accurate automated image recognition for humpback whale flukes and demonstrated the scalability for the algorithm used. We could not initially predict how comprehensively we might document the populations of humpback whales across the NPPID study area, but in a relatively short period the results exceeded expectations of what was possible. As of August 2022, 56 months after the creation of this study, 30,100 North Pacific individual whales had been documented. Some regions are now extremely well sampled. In Southeast Alaska and northern British Columbia, for example, between 2011 and 2019, fewer than 5.5% of individuals encountered each year were unique, encountered in only one season (Table 3, Fig. 5), suggesting that very few individuals in these waters remain unknown to the NPPID dataset. Substantial data gaps exist, particularly in the western North Pacific, in pelagic feeding areas such as the Aleutian Islands, and in the Mexican offshore breeding area of the Revillagigedo Islands. Yet even in these least sampled regions, greater than 50% of individuals were encountered in more than one season; by resampling populations in breeding, migratory corridor, and feeding areas, the impact of effort bias on population models can be reduced²³.

Accessibility And User Agreements

Data collection should not be an end unto itself and sharing is a core tenet of good data management⁴⁷. The Happywhale.com web platform was developed to make data accessible by design, aiming for a user experience that is both easy and rewarding. Users were motivated to contribute more and higher-quality data by a simple user interface to upload images, which then rewarded them with rapid results of information about “their” individual whales. Accessibility creates a public good as a resource for research, education, and science communication. In the existing database, 65.6% of all North Pacific humpback whale encounter data are publicly visible. Researchers and community scientists can explore migratory connections across the North Pacific, via the web platform (Fig. 2). For research collaborators, this has inspired studies that would not have been possible without the large collective investment in building a platform and populating it with a comprehensive and contemporary dataset^48,49. To date the NPPID collaboration has contributed data to seven other peer reviewed papers^{17,26,27, 50–53}. Accessible information about North Pacific humpback whale individuals has also proven very useful for resource managers, for example tracking fishing gear entanglement cases and individual identification and past sighting histories of dead or stranded whales⁵⁴.

We recognize that including many actors and an open science stance can add complexity to a collaboration⁵⁵ with concerns for misuse of shared or public data⁵⁶. Successful aspects of this collaboration bring opportunities but also pose two challenges that the collaboration must address: One, how do we encourage contributing researchers to allow public visibility of data to allow the widest possible benefit while ensuring data is used correctly in context and proper credit is preserved? Two, how do we simplify and clarify co-authorship policies to be effective, meaningful, and not so complex as to prevent publication?

An ideal collaboration builds datasets that directly answer present biological and management questions and simultaneously creates data sharing readiness. Data readiness for study of ecological change depends on both standardized repositories and aligned research interests^17,57,58. The NPPID dataset has been successfully applied in this context, contributing to challenging management issues such as, for example, the US West Coast Dungeness crab fishery. Here, researchers can readily determine the proportion of whales in the endangered Central American DPS^{49, 59–61}. The NPPID collaboration began with a MOA, offering co-authorship to contributors in a series of manuscripts investigating humpback whale migratory patterns and population status in the North Pacific. Collaborators wishing to address additional research questions must seek permission from all relevant data contributors. While the communication required is a cost imposed on prospective studies, community is built around mutually beneficial collaboration. The MOA created an effective working group and context for this study, but expires with completion of the specified series of manuscripts; future success will require clear use, sharing and management policy with oversight maintained into the future.

Data Quality Improved By Accessibility

Accessibility adds value as part of the FAIR Principles (Findable, Accessible, Interoperable, and Reusable) for scientific data⁶² that guided this study design. Accessibility also serves the immediate practical value of improving data quality, consistency, and repeatability. Active collaboration and public access to data make knowledge gaps more visible and encourages effort to fill them⁶³. With many eyes reviewing the dataset, from curious public enthusiasts exploring encounters of ‘their’ whales or an area of their personal interest to research collaborators pursuing diverse lines of inquiry, an ongoing collaborative quality control process frequently detects data discrepancies. Happywhale user comments – over 5,700 as of August 2022 – alerted NPPID data managers to enough errors that public accessibility to review might be considered as a systematic method of quality control, worthy of attention for its own value and efficiency.

All datasets will contain errors; more accurate image recognition, repeatedly applied, and review of data by diverse users will continually detect some, but not all errors. The SPLASH study estimated a 9%-10% rate of missed matches, the largest model error correction factor in the associated mark-recapture population estimate²³. This kind of accuracy assessment rarely appears in photo-ID based mark-recapture studies, yet missed matches existed in every dataset larger than 100 individuals involved in this study. Our finding of 331 false negatives in 7,971 (4.2%) total individuals in the SPLASH study, when added to algorithm error estimation rates for good-to-high quality images of 1%-3%³⁷, suggests the 9%-10% error estimation was 3%-4% high. In our most accurately matched large dataset, the 2004–2020 whales of Glacier Bay National Park and Preserve, Alaska, missed matches accounted for only 0.15% (1 of 633 individuals, a first-summer calf to adult match with substantial fluke pigment change). All other datasets of more than 100 individuals showed from 2%-11% detectable false negative missed match rates. Considering this range and other sources of error and bias, it is important to understand and account for limitations in any dataset, including ours.

Effort Bias And Appropriate Use

Ideally, a dataset should be created with its specific use in mind a priori, following a good data management plan⁴⁷ with an optimized data workflow⁶⁴. However, because we have been building a dataset gathered from post-SPLASH photo-ID archives and opportunistic efforts, standardization has had to stand in for a priori data management plans. Here the effort is heterogeneous, and any study design or interpretation of data must account for this to ensure appropriate use. It would be easy, for example, to falsely interpret the lower effort in the western Pacific as evidence of lower whale populations. Datasets cannot be assumed to provide an error-free (i.e., devoid of effort bias) documentation of humpback whale presence in the study area; no clear rule can be set a priori to identify the appropriate application of an evolving dataset of this nature. It is therefore recommended that any potential data user actively engage with collaborating researchers to understand data limitations and potential. Data contributors can also be the primary data users, a group that will benefit from increased knowledge of and aptitude with the data management system built through Happywhale.

Because there could not be a comprehensive data collection plan across this scale of a study area and time period, the full dataset might be considered opportunistic, a sum of collected efforts of dedicated research, research from platforms of opportunity, and community science contributions. Figure 4 demonstrates a large increase in data collection over time, elevated during the 2004–2006 SPLASH study, then building to similar levels from 2017 forward. Data collection rates have benefitted from improvement in digital cameras, growing popularity of whale watching, the 2015 establishment of Happywhale, increased effort by many NPPID collaborators to capture fluke photos within existing field efforts, and the 2020 establishment and NOAA Fisheries funding of the SPLASH-2 program, which helps fund data collection efforts in poorly sampled areas and infrastructure to support submissions to Happywhale. Our peak sample year was 2019, with 6,384 of 30,100 known North Pacific humpback whales documented (21.2%). The COVID-19 pandemic interrupted both field research efforts and tourism in 2020 and 2021, though we believe sampling will recover and continue to increase.

Building A Successful Collaboration

The NPPID study benefitted from the largely successful precedent of the SPLASH study both in providing a foundation of data (Fig. 4) and as a collaborative framework. This study formed in an environment with substantial demand for new methods to efficiently manage many thousands of images accumulated since the end of SPLASH, where separate research efforts were constrained by time-intensive visual matching of photo-ID datasets. Although the SPLASH study produced notable insight and remains frequently cited, and the catalog was made available online, it was only for a static period of time (2004–2006) and was not built for efficient matching. The role of the NPPID collaboration agreement was to establish clear expectations, creating an environment of openness, trust, transparency and consistency. This context was necessary for research collaborators to feel safe sharing images and data that were products of many thousands of person-hours in the field. Positive and useful feedback delivered by rapid results from image recognition efforts was also necessary. The ‘wow factor’ of instant gratification when most flukes in hundreds of images immediately matched to known individuals enticed researchers to join the collaboration, a welcome change from years of toil over visually matching isolated photo-ID datasets. Success was crafted by a combination of a high quality product supported by solid guiding principles of Transparency, Responsibility, User focus, Sustainability and Technology (TRUST) to promote digital repository trustworthiness⁶⁵. The idea behind these principles is that as a data repository, we must earn the trust of the community we serve and demonstrate that we are reliable and capable of appropriately managing the data we curate. Through this intentional framework comes empowerment with a feeling of collective ownership, rather than isolated possession, supporting sustainability for this collaboration by creating active participation of research users. As an ongoing, living dataset, the NPPID developed active, increasingly decentralized participation in ongoing data management with an intent to serve diverse needs in the research community. System development remains ongoing, with a focus on providing research collaborators with tools to become more directly involved with data management. This development reduces centralized data management costs, serves the real-time needs of collaborators, and benefits the dataset with local expertise, potentially detecting data issues that would not be recognized by remote data managers.

The NPPID effort has established a single unified repository reconciling all available research catalogs and ID nomenclature, aggregating all individual identities and encounter data into a state of data readiness unprecedented on a long-term and ocean-basin-scale. The potential that is now being realized is, first, cost savings and organizational effectiveness. Particularly in well-sampled areas, immediate access to fast and reliable photo-ID functionality revolutionized data processing. Collaborators reported that this “saves countless hours of visual matching, allowing us to get our data out in products, papers, and outreach more quickly” (JN) and “reduces lab time by 90%” (AS). However, collaborators face the challenge of how to maximize the present and future value of the NPPID dataset. A primary outstanding need is to create clarity for how researchers efficiently access, establish permission, and create sub-collaborations to develop further studies.

System functionality was developed in a constant cycle to be able to accommodate progressively larger datasets over the course of the study. This dataset appears to document the majority of living humpback whales across the ocean basin, inspiring a subsequent ambition to monitor populations in near-real time. With heterogeneous sampling effort over the study area, critical data gaps can be identified for understanding abundance and population structure, as well as establishing minimum sample sizes for reliable, robust population models. Given the low cost of data storage, and if the incremental cost of each additional data point is driven to near zero, there is no cost to overshooting a threshold of ‘enough’ data. Having now acquired sufficient baseline data for North Pacific populations in the face of a changing ocean, we aim for data readiness to understand the implications of ecosystem events on a timescale that benefits resource management. This study concentrates on humpback whales of the North Pacific, but the concept and methods can be ongoing and extended to many species. Baleen whales are recognized to influence marine ecosystems on a massive scale⁴¹. In recognition of the concept of essential biological variables^42,43, there is a need for marine observation and data at an ocean-basin-wide scale^44–46. This dataset, the collaboration agreement, and the system established to create and maintain it, can contribute to understanding essential ocean variables.

This study established extremely cost effective and utilitarian information architecture, delivering an essential service for ongoing studies. If investment in collaborator engagement, upkeep, development, and data management continue, the future of this collaborative system promises great contributions to the understanding of North Pacific humpback whales. Sustainability will require a transition from the centralized efforts of a several-year study to a project at a stable institution with community ownership and oversight. We see this effort not as collecting and possessing a dataset, but as curating a public good for the betterment of science, education, and marine conservation. The FAIR and TRUST principles are central to guiding development and recognize that accessibility requires more than just a data search feature via a web browser. To truly achieve full potential will require decentralizing data management to research collaborators, a shift that requires system development, user training, and commitment. The involvement of scientists in data management has evolved through time from a common disconnect⁶⁶ to a current trend of ecological “big data” where data management is a necessary skill for ecologists, as has already happened with statistics and GIS⁶⁷. We believe that the establishment of this scale-shifting dataset, if invested in going forward, will continue to improve understanding, awareness, stewardship, and respect for the North Pacific marine ecosystem.

Data Availability Statement

The publicly viewable 65.6% of the full dataset used in this study, with ongoing additions and updates is available for exploration at Happywhale.com. All data is available with collaborator agreement to explore at Happywhale.com and in spreadsheet format; please contact the corresponding author for discussion and permission. Approximately one third of the dataset is public domain, but the collaborators believe that providing this demised dataset for open access download would be a disservice to the integrity of the full dataset.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author contributions:

TC, JAC, PC and KS conceived of the study, TC, JAC, KA, JEM, JS and ST secured funding, TC and KS developed the Happywhale system and web platform, TC, JAC, KA, JS, ST, PC, JMA, LB, CB, AB, JKB, RC, JJC, AJGC, JC, JDW, ND, TDV, KD, OF, RF, KF, JF, AFJ, CG, BG, CH, JH, MC, JJ, MJ, NK, EL, MM, EM, PML, CM, CMM, JRM, JN, HRN, HN, HO, MO, AP, DP, HP, EQR, RFRB, NR, FS, TS, SS, IS, AS, OT, JU, MVA, OVZ, BW, JW, KY and DZ contributed data, TC, JC, ND, KF, PML, EJL, HN, MO, RFRB, DZ and AM managed data, TC, JJC, KF and DP crafted the manuscript including table and figure development, TC, KA, JB, AB, PC, JJC, JDW, TDV, KD, KF, JF, CG, CH, MC, EL, CMM, JEM, JRM, JN, AP, DP, HP, HRN, EQR, NR, JS, OT, and MVA edited the manuscript, and all authors reviewed the manuscript.

Acknowledgments

The authors would like to thank María González for creating manuscript figures, and Kaitlin Palmer for early review. The Happywhale dataset would not exist without the dedication, support, trust of each and every one of the thousands of image contributors who create the strong community of marine mammal enthusiasts who make Happywhale a resource for ocean conservation science. The authors most strongly wish to thank the whale watch guides, leaders, photographers and tour company owners who make it part of their mission to share and educate the world about whales and ocean life. A full list of contributors to the North Pacific dataset in Happywhale is in Supplemental Material II. Happywhale wishes to acknowledge funding support from Cheesemans’ Ecology Safaris, NOAA Fisheries, Pacific States Marine Fisheries Commission, Cascadia Research Collective, University of Alaska Southeast, The Marine Mammal Center, the Hurtigruten Foundation, Booking Cares, Viking Expeditions, Lindblad Expeditions, Amber Group, Defenders of Wildlife, and all individual supporters of Happywhale.

Daniel Palacios and Craig Hayslip (OSU Marine Mammal Institute WTG) acknowledge the financial support from the U.S. Navy’s Office of Naval Research; the Tagging of Pacific Pelagics program; the Steve Irwin Foundation; the Pacific Life Foundation; the Makana Aloha Foundation; the Navy’s Pacific Fleet Commander (via CESU Cooperative Agreement No. N62473-19-2-0002); and private donors to the Oregon State University Marine Mammal Institute Endowment. Photographs were collected under NMFS Marine Mammal Protection Act/Endangered Species Act Research Permit Nos. 841 (1995 to 1998), 369-1440 (1998 to 2004), 369-1757 (2005 to 2008), 14856 (2014 to 2018), and 21585 (2019) issued to Bruce Mate.

Okinawa Churashima Foundation thanks S. Ozawa Y. Miyamura H. Miyahira K. Tamura and K. Miyahira for the data collections in Okinawa.

Cascadia Research Collective would like to thank the many contributors and participants in the SPLASH study that was an important foundation and dataset to the current effort. We thank

NOAA for funding some of the data collection especially off the US West Coast, Central America, and southern Mexico in recent years as part of their support for SPLASH-2.

All data in the Mariana Islands were collected under NMFS MMPA permits 15240, 21482 and the CNMI DFW permits 03086-15, 03292-16, 03564-17, 03752-18. Funding was provided by the U.S. Navy (Commander, U.S. Pacific Fleet) and the Pacific Islands Fisheries Science Center. We would like to thank Allan D. Ligon and Adam C. Ü for their significant contribution to PIFSC fieldwork in the Marianas and NAVFAC/HDR for their contribution of images from their survey of FDM in 2020.

UNAM would like to thank M.J. Vázquez-Cuevas and the many students from UNAM that for years have worked on the sea and curating photographs.

Pacific Whale Foundation acknowledges that funding was provided by the members of Pacific Whale Foundation and a number of private donors. Photographs were collected under MMPA/NMFS permits 323, 399, 565, 812, 982, 468-1574, 13427, 16479 and 21321. We acknowledge the support of many team members, both staff and volunteers, that contributed to PWF’s long-term humpback whale research and in particular thank Abigail Machernis, Florence Sullivan, and Elizabeth Beato for their assistance curating the PWF catalog and uploading it to Happywhale.

University of Alaska Southeast lab of Heidi Pearson acknowledges photographs were collected under NMFS permits 14122, 18529, and 20688 and UAF IACUC protocols 464648 and 1604256. We thank Chris Pearson and the many UAS undergraduates who helped with data collection and analysis. Funding was provided by Alaska EPSCoR, the University of Alaska Foundation Angus Gavin Migratory Bird Research Grant, Alaska Sea Grant, BLaST, NMFS through a grant to the Pacific States Marine Fisheries Commission.

The North Coast Cetacean Society would like to thank the Save Our Seas Foundation, Willowgrove Foundation, the Department of Fisheries and Oceans, Donnor Canadian Foundation, all private donors, volunteers and staff and the Gitga’at and Kitasoo/Xai’xias First Nations. Photographs were collected under research permit MML-43.

Glacier Bay and UAS Sitka acknowledges all data were collected under NMFS MMPA permits 600, 845, 945-1499, 945-1776, 15844, 21059, P263, P317, 473-1700, 473-1700, 14122, and 18529. We acknowledge the many Park staff who helped collect and process the data over the decades of study.

Ester Quintana-Rizzo would like to thank the funding support of various institutions including Cascadia Research Collective; Fondo Nacional de Ciencia Tecnología, awarded by the Consejo Nacional de Ciencia y Tecnología, through the Secretaria Nacional de Ciencia y Tecnología (Fodecyt 85-2007 Project), Cetacean Society International, Sarasota Dolphin Research Institute; Idea Wild, Defensores de la Naturaleza Foundation, and SPLASH 2 coordination provided by NOAA Fisheries West Coast Region and NOAA Fisheries Office of Protected Resources

Whales of Guerrero would like to thank the communities of Barra de Potosí and Zihuatanejo in Mexico for their assistance and support, the dedicated scientists, educators, community members and expedition guests who collected the data with us (in particular, Arturo Mellin, Terra Hanks, Victoria Pouey Santalou, Claudia Auladell Quintana and Cristina Martin, Idea Wild, Cetacean Society International, USFWS/Semarnat Wildlife Without Borders, National Geographic Society, Lighthouse Foundation, Norcross Wildlife, Oceanic Society, SEE Turtles, NOAA, Cascadia Research Collective, Smultea Environmental Services, Mysticetus, Adobe, Luis Medrano Gonzalez at UNAM, and the private donors who made this work possible. Data was collected under research permits: SGPA/DGVS/12143/16, SGPA/DGVS/011899/17, SGPA/DGVS/010770/18

Ecología y Conservación de Ballenas, A.C. would like to thank FIBB Catalog contributors: Ecotours Vallarta, Vallarta Natours, Instituto Tecnológico de Bahía de Banderas, CRIP-INAPESCA, Vallarta Adventures, Orca de Sayulita, Oceanfriendly and Cielo Abierto who collected data. We also want to thank Fundación Ecológica BIOMAR, Stanley W. Ekstrom Foundation, Junghanns, Opequimar Centro Marino, PV Marine, Ecotours Vallarta, October Hill Foundation and many other private donors for their kind contributions to make this project possible.

NOAA Hawaiian Islands Humpback Whale National Marine Sanctuary would like to acknowledge the support of staff and volunteers that contributed to the sanctuaryʻs long-term humpback whale fluke catalog; and the funding support provided by Whale Trust (via Whale Tales awards), the Volgenau Foundation, Deborah and Michael Rybak, Whaleman Foundation, and many private donors that make the catalog possible. In particular, special thanks to Rachel Finn for currating the sanctuaryʻs catalog. Photographs were collected under MMPA/ NMFS permits 14682, 15240, 20311, 932-1489, 932-1905 and 18786.

Alaska Whale Foundation images were obtained under NOAA/NMFS Research Permits No. 0716-1456, 716-1705, 14599 and 19703.

Whale Trust would like to acknowledge the many supporters who have funded our research over the years, ultimately allowing our photo-id dataset to be shared. In particular, we would like to thank Cathy Maxwell and Haley Robb for their efforts in helping to manage our photo-id catalog with Happy Whale submissions. These images were collected under NMFS-NOAA permits: 753, 987, 13846, 19925.

For the Philippines, we thank WWF-Philippines, all the volunteers and researchers of BALYENA.ORG particularly Elson Q. Aca, Shotaro Nakagun, Timothy Gardner and Takeya Sakamoto. We are grateful for the funding support from Ocean Park Conservation Foundation – Hong Kong, The Rufford Foundation, Whale Trust and all our donors.

Marine Education and Research Society (MERS) acknowledges this work would not be possible without the contributions of the ecotourism, research, and conservation communities of northeastern Vancouver Island, and the staff, volunteers, and supporters of the Marine Education and Research Society (MERS). Funding was provided by Fisheries and Oceans Canada and the North Island Marine Mammal Stewardship Association. Data collected under research licenses MML-42 and MML-57.

Funding for the Fisheries and Oceans Canada (DFO) humpback catalog and database was provided by the DFO Species at Risk Program and the Parks Canada Agency. DFO would like to thank the many individuals and organizations that have contributed to this effort over the years.

Marine Megafauna and Fisheries Research Group (MMAPE) would like to thank Ricky Rebolledo and Vicotira Jansen for all their generosity in lending us a zodiac to be able to take pictures in the Revillagigedo archipelago. We want to say thank you so much to Sea Shepherd Conservation Society, and all the people who participated as part of the crew of R/V Martin Sheen and R/V White Holly, during the research campaigns to the Revillagigedo archipelago from 2019 to 2021, and especially we want to thank Eva Hidalgo, Andrea Bonilla, John Payne, Derek Kimball and JP. Also, we thank all the students who supported both the field work and the photo editing work. We also thank the staff of the Revillagigedo National Park, particularly Erendida Frías for all her support in organizing the visits to the park. We thank the SEP-CONACYT #204778 project that financed part of the expeditions to Revillagigedo from 2019 to 2021. Finally, to the academic department of fisheries engineering of the UABCS, to the Dirección General de Ecología del Municipio de Los Cabos and to the staff of Cabo Expeditions, particularly Diego Ruíz and Oscar Ortiz, who supported us during our Photo-ID work in Los Cabos.

Clapham, P. J. & Mead, J. G. Megaptera novaeangliae. Mamm. Species 3, 1–9 (1999).
Mackintosh, N. A. THE NATURAL HISTORY OF WHALEBONE WHALES. Biol. Rev. 21, 60–74 (1946).
Calambokidis, J. et al. MOVEMENTS AND POPULATION STRUCTURE OF HUMPBACK WHALES IN THE NORTH PACIFIC. Mar. Mammal Sci. 17, 769–794 (2001).
Chittleborough, R. G. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (Borowski). Mar. Freshw. Res. 16, 33–128 (1965).
Dawbin, W. H. 9. The Seasonal Migratory Cycle of Humpback Whales. Whales, Dolphins, and Porpoises 145–170 (2021). doi:10.1525/9780520321373-011/HTML
Baker, C. et al. Migratory movement and population structure of humpback whales (Megaptera novaeanglieae) in the central and eastern North Pacific. Mar. Ecol. Prog. Ser. 31, 105–119 (1986).
Calambokidis, J. et al. SPLASH: Structure of populations, levels of abundance and status of humpback whales in the North Pacific. Final Rep. Contract AB133F-03-RP-00078. U.S. Dept Commer. West. Adm. Cent. Seattle, Washingt. (2008).
Craig, A. S., Herman, L. M., Gabriele, C. M. & Pack, A. A. Migratory Timing of Humpback Whales (Megaptera novaeangliae) in the Central North Pacific Varies with Age, Sex and Reproductive Status. Behaviour 140, 981–1001 (2003).
Rocha, R. C., Clapham, P. J. & Ivashchenko, Y. V. Emptying the oceans: a summary of industrial whaling catches in the 20th century. Mar. Fish. Rev. 76, 37–48 (2014).
Ivashchenko, Y. V & Clapham, P. J. Too Much Is Never Enough : The Cautionary Tale of Soviet Illegal Whaling. Mar. Fish. Rev. 76, 1–21 (2014).
Johnson, J. H. & Wolman, A. A. The Humpback Whale, Megaptera novaeangliae. Mar. Fish. Rev. 46, 30–37 (1984).
Bettridge, S. et al. Status review of the humpback whale (Megaptera novaeangliae) under the endangered species act. NOAA-TM-NMFS-SWFSC-540. (2015).
Johnson, C. M. et al. Protecting Blue Corridors - Challenges and solutions for migratory whales navigating national and international seas. (2022). doi:10.5281/ZENODO.6196131
Herman, L. M. et al. Resightings of humpback whales in Hawaiian waters over spans of 10–32 years: Site fidelity, sex ratios, calving rates, female demographics, and the dynamics of social and behavioral roles of individuals. Mar. Mammal Sci. 27, 736–768 (2011).
Smith, T. D. et al. An Ocean-basin-wide Mark-Recapture Study of the North Atlantic Humpback Whale (Megaptera novaeangliae). Mar. Mammal Sci. 15, 1–32 (1999).
Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Chang. 2016 611 6, 1042–1047 (2016).
Gabriele, C. M. et al. Sharp decline in humpback whale (Megaptera novaeangliae) survival and reproductive success in southeastern Alaska during and after the 2014–2016 Northeast Pacific marine heatwave. Mamm. Biol. (2022). doi:10.1007/s42991-021-00187-2
Cartwright, R. et al. Fluctuating reproductive rates in Hawaii’s humpback whales, Megaptera novaeangliae, reflect recent climate anomalies in the North Pacific. R. Soc. Open Sci. 6, 181463 (2019).
Frankel, A. S., Gabriele, C. M., Yin, S. & Rickards, S. H. Humpback whale abundance in Hawai‘i: Temporal trends and response to climatic drivers. Mar. Mammal Sci. mms.12856 (2021). doi:10.1111/MMS.12856
Kügler, A., Lammers, M., Zang, E., Kaplan, M. & Mooney, T. Fluctuations in Hawaii’s humpback whale Megaptera novaeangliae population inferred from male song chorusing off Maui. Endanger. Species Res. 43, 421–434 (2020).
Katona, S. K. & Whitehead, H. P. Identifying Humpback Whales using their natural markings. Polar Rec. (Gr. Brit). 20, 439–444 (1981).
Jurasz, C. & Jurasz, V. Results of 1977 Studies on Humpback Whales in Glacier Bay National Monument. Final Rept. (1978).
Barlow, J. et al. Humpback whale abundance in the North Pacific estimated by photographic capture-recapture with bias correction from simulation studies. Mar. Mammal Sci. 27, 793–818 (2011).
Hill, M. et al. Found: a missing breeding ground for endangered western North Pacific humpback whales in the Mariana Archipelago. Endanger. Species Res. 41, 91–103 (2020).
Espinoza Rodríguez, I. J., Frisch Jordán, A. & Noriega Betancourt, F. Humpback whales in Banderas Bay, Mexico: relative abundance and temporal patterns between 2004 and 2017. Lat. Am. J. Aquat. Mamm. 16, 33–39 (2021).
Martien, K. K. et al. NOAA Technical Memorandum NMFS-SWFSC-658 EVALUATION OF MEXICO DISTINCT POPULATION SEGMENT OF HUMPBACK WHALES AS UNITS UNDER THE MARINE MAMMAL PROTECTION ACT. (2021). doi:10.25923/nvw1-mz45
Taylor, B. L. et al. Evaluation of Humpback Whales Wintering in Central America and Southern Mexico as a Demographically Independent Population. U.S. Dep. Commer. Natl. Ocean. Atmos. Adm. Natl. Mar. Fish. Serv. Southwest Fish. Sci. Cent. 3, 103–111 (2021).
Wade, P. R. et al. Estimates of abundance and migratory destination for North Pacific humpback whales in both summer feeding areas and winter mating and calving areas. in Paper SC/66b/IA21 submitted to the Scientific Committee of the International Whaling Commission, June 2016, Bled, Slovenia. Available at https://archive.iwc.int/. (2016).
Hendrix, A. N., Straley, J., Gabriele, C. M. & Gende, S. M. Bayesian estimation of humpback whale (megaptera novaeangliae) population abundance and movement patterns in southeastern Alaska. Can. J. Fish. Aquat. Sci. 69, 1783–1797 (2012).
Gabriele, C. M. et al. Natural history, population dynamics, and habitat use of humpback whales over 30 years on an Alaska feeding ground. Ecosphere 8, e01641 (2017).
Cates, K. A. et al. Corticosterone in central North Pacific male humpback whales (Megaptera novaeangliae): Pairing sighting histories with endocrine markers to assess stress. Gen. Comp. Endocrinol. 296, 113540 (2020).
Cates, K. A. et al. Testosterone trends within and across seasons in male humpback whales (Megaptera novaeangliae) from Hawaii and Alaska. Gen. Comp. Endocrinol. 279, 164–173 (2019).
Pack, A. A. et al. Comparing depth and seabed terrain preferences of individually identified female humpback whales (Megaptera novaeangliae), with and without calf, off Maui, Hawaii. Mar. Mammal Sci. 34, 1097–1110 (2018).
Pack, A. A. et al. Habitat preferences by individual humpback whale mothers in the Hawaiian breeding grounds vary with the age and size of their calves. Anim. Behav. 133, 131–144 (2017).
Pack, A. A. et al. Size-assortative pairing and discrimination of potential mates by humpback whales in the Hawaiian breeding grounds. Anim. Behav. 84, 983–993 (2012).
Herman, L. M. et al. Humpback whale song: who sings? Behav. Ecol. Sociobiol. 2013 6710 67, 1653–1663 (2013).
Cheeseman, T. et al. Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. 2021 1–15 (2021). doi:10.1007/S42991-021-00180-9
Weideman, H. J. et al. Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales. in ICCV 2017 Workshop on Visual Wildlife Monitoring (2017).
Stevick, P. et al. North Atlantic humpback whale abundance and rate of increase four decades after protection from whaling. Mar. Ecol. Prog. Ser. 258, 263–273 (2003).
Wieczorek, J. et al. Darwin Core: An Evolving Community-Developed Biodiversity Data Standard. PLoS One 7, e29715 (2012).
Savoca, M. S. et al. Baleen whale prey consumption based on high-resolution foraging measurements. Nat. 2021 5997883 599, 85–90 (2021).
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 2019 34 3, 539–551 (2019).
Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).
Bax, N. J. et al. A response to scientific and societal needs for marine biological observations. Front. Mar. Sci. 6, 395 (2019).
Muller-Karger, F. E. et al. Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) frameworks. Front. Mar. Sci. 5, 211 (2018).
Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Chang. Biol. 24, 2416–2433 (2018).
Michener, W. K. Ten Simple Rules for Creating a Good Data Management Plan. PLOS Comput. Biol. 11, e1004525 (2015).
Darling, J. D. et al. Humpback whales (Megaptera novaeangliae) attend both Mexico and Hawaii breeding grounds in the same winter: mixing in the northeast Pacific. Biol. Lett. 18, (2022).
Tackaberry, J., Dobson, E., Flynn, K., Cheeseman, T. & Calambokidis, J. Low Resighting Rate of Entangled Humpback Whales Within the California, Oregon, and Washington Region Based on Photo-Identification and Long-Term Life History Data. Front. Mar. Sci. 0, 2092 (2022).
Curtis, K. A. et al. NOAA Technical Memorandum NMFS ABUNDANCE OF HUMPBACK WHALES (MEGAPTERA NOVAEANGLIAE) WINTERING IN CENTRAL AMERICA AND SOUTHERN MEXICO FROM A ONE-DIMENSIONAL SPATIAL CAPTURE-RECAPTURE MODEL. (2022). doi:10.25923/9cq1-rx80
Patton, D. & Lawless, S. Surface and Underwater Observation of a Humpback Whale (Megaptera novaeangliae) Birth in Progress off Lahaina, Maui, and Subsequent Encounter of the Female with a Healthy Calf. Aquat. Mamm. 550–558 (2021). doi:10.1578/AM.47.6.2021.550
Henderson, E. E., Deakos, M. & Engelhaupt, D. Dive and movement behavior of a humpback whale competitive group and a multiday association between a primary escort and female in Hawaiʻi. Mar. Mammal Sci. (2021). doi:10.1111/MMS.12891
Lowe, C. L. et al. Patterns of cortisol and corticosterone concentrations in humpback whale (Megaptera novaeangliae) baleen are associated with different causes of death. Conserv. Physiol. 9, (2021).
Lowe, C. L. et al. Case studies on longitudinal mercury content in humpback whale (Megaptera novaeangliae) baleen. Heliyon 8, e08681 (2022).
Gewin, V. Data sharing: An open mind on open data. Nat. 2016 5297584 529, 117–119 (2016).
Mills, J. A. et al. Archiving Primary Data: Solutions for Long-Term Studies. Trends Ecol. Evol. 30, 581–589 (2015).
Urbano, F., Cagnacci, F. & Initiative, E. C. Data Management and Sharing for Collaborative Science: Lessons Learnt From the Euromammals Initiative. Front. Ecol. Evol. 0, 577 (2021).
Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Reports 2021 111 11, 1–17 (2021).
Samhouri, J. F. et al. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8, e01860 (2017).
Samhouri, J. F. et al. Marine heatwave challenges solutions to human–wildlife conflict. Proc. R. Soc. B 288, (2021).
Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 2020 111 11, 1–12 (2020).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016 31 3, 1–9 (2016).
Costello, M. J., Horton, T. & Kroh, A. Sustainable Biodiversity Databasing: International, Collaborative, Dynamic, Centralised. Trends Ecol. Evol. 33, 803–805 (2018).
Hackett, R. A. et al. A data management workflow of biodiversity data from the field to data users. Appl. Plant Sci. 7, e11310 (2019).
Lin, D. et al. The TRUST Principles for digital repositories. Sci. Data 2020 71 7, 1–5 (2020).
Lynch, C. How do your data grow? Nat. 2008 4557209 455, 28–29 (2008).
Urbano, F. et al. Wildlife tracking data management: a new vision. Philos. Trans. R. Soc. B Biol. Sci. 365, 2177–2185 (2010).
Titova, O. V. et al. Photo-identification matches of humpback whales (Megaptera novaeangliae) from feeding areas in Russian Far East seas and breeding grounds in the North Pacific. Mar. Mammal Sci. 34, 100–112 (2018).
Calambokidis, J. & Barlow, J. Abundance of blue and humpback whales in the Eastern North Pacific estimated by capture-recapture and line-transect methods. Mar. Mammal Sci. 20, 63–85 (2004).
Calambokidis, J. et al. Interchange and isolation of humpback whales off California and other North Pacific feeding grounds. Mar. Mammal Sci. 12, 215–226 (1996).
Calambokidis, J. & Barlow, J. Trends in the Abundance of Humpback Whales in the North Pacific Ocean from 1980 to 2006. in WC Report SC/A17/NP/10 for the Workshop on the Comprehensive Assessment of North Pacific Humpback Whales. 18–21 April 2017. Seattle, WA. 16 (2017).
Calambokidis, J. et al. Migratory destinations of humpback whales that feed off California, Oregon and Washington. Mar. Ecol. Prog. Ser. 192, 295–304 (2000).
Palacios, D. M. et al. Humpback Whale Tagging in Support of Marine Mammal Monitoring Across Multiple Navy Training Areas in the Pacific Ocean: Final Report for the Pacific Northwest Feeding Area in Summer/Fall 2019, Including Historical Data from Previous Tagging Efforts off th. (2020).
Burdin, A. M., Titova, O. & Hoyt, E. Humpback Whales of Russian Far East Seas. Photo-ID Catalog 2004–2014. Publisher: Russian Geographical Society (2014). Available at: https://www.researchgate.net/publication/272493253_Humpback_Whales_of_Russian_Far_East_Seas_Photo-ID_Catalog_2004-2014. (Accessed: 22nd August 2022)

Table 3 is available in the Supplementary Files section

No competing interests reported.

(Almost) all the humpback whales of the North Pacific: A collaborative and comprehensive photo-ID dataset

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

The North Pacific Humpback Whale Photo-ID (NPPID) Collaboration

Definitions:

Data Integration And Quality Control

Results

Discussion

Accessibility And User Agreements

Data Quality Improved By Accessibility

Effort Bias And Appropriate Use

Building A Successful Collaboration

Conclusion: Sustainability And Maximizing Future Value

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1