In the vast and often inscrutable expanses of the North Atlantic Ocean, one of the planet’s most majestic yet enigmatic giants roams: the North Atlantic right whale. These colossal marine mammals, despite their impressive size, exist in alarmingly small numbers, and their extensive migratory patterns across broad territories have posed a considerable challenge to scientists striving to monitor and conserve them effectively. In recent years, breakthroughs in ecological modeling have brought new hope for improving our understanding of these rare whales’ habits. A pioneering study led by researchers at Bigelow Laboratory for Ocean Sciences has unveiled a novel approach that integrates detailed prey dynamics into species distribution models, thereby refining predictions of right whale movements and habitat preferences throughout different seasons.
The North Atlantic right whale’s survival hinges critically on its ability to opportunistically locate concentrated patches of zooplankton, primarily species of copepods that serve as the foundation of their diet. Previous modeling efforts largely relied on indirect measures, such as satellite-derived chlorophyll concentrations, to estimate zooplankton abundance. These proxies, while accessible and valuable for broad ecological assessments, introduce a layer of abstraction that obscures the nuanced feeding ecology of right whales. Chlorophyll measures, for instance, represent phytoplankton biomass rather than the actual zooplankton densities, and thus fail to capture the full complexity of prey availability influencing whale distribution. Recognizing these limitations, the Bigelow research team embarked on developing an advanced modeling framework that incorporates direct observations of key zooplankton species and their energetic contributions crucial to right whale foraging success.
Focusing on fine-scale prey dynamics, the study specifically accounts for the daily energy thresholds of right whales linked to their needs while foraging. By quantifying the abundance of preferred zooplankton species, including the prominent fatty copepod Calanus finmarchicus and lesser-studied secondary prey such as Pseudocalanus, the model better represents the actual foraging landscape from the whales’ perspective. Unlike indirect proxies, this prey-centric approach enables more precise predictions of where whales concentrate, reflecting their true biological requirements and spatial-temporal feeding behavior. This methodological leap was facilitated by the integration of extensive zooplankton abundance data collected during the comprehensive NOAA Fisheries Ecosystem Monitoring Survey, a resource critical for bridging the gap between oceanographic measurements and biological patterns.
One of the standout revelations from the research is the complex role of secondary prey species in the right whale diet, an aspect previously underestimated. While the presence of Calanus finmarchicus strongly correlated with right whale aggregation, the model showed an unexpected inverse relationship with higher densities of the smaller, less calorically dense copepod Pseudocalanus. This counterintuitive finding suggests that secondary prey may have either a more nuanced dietary utility or potentially different ecological significance — possibly acting as indicators of environmental conditions less favorable to large whale concentrations or representing competitive dynamics with preferred prey. These new questions underscore the intricate trophic interactions governing right whale foraging strategies and hint at broader ecosystem complexities yet to be fully unraveled.
The methodological innovation embodied in this modeling framework primarily involves improving the spatial and temporal resolution of prey fields by directly interpolating observed zooplankton distributions relative to the whales’ energy demands. Unlike static habitat proxies, this dynamic approach facilitates the generation of density surface models that better align with empirical sightings and movement patterns recorded by NOAA, advancing the precision of species-habitat predictions. Such predictive enhancement is a critical step forward in marine conservation, enabling stakeholders to anticipate whale occurrences more accurately, thereby informing management decisions about shipping routes, fishing regulations, and habitat protections to minimize human impacts on this endangered species.
Collaboration was key to the study’s success, bringing together a multidisciplinary team from prominent institutions including Bigelow Laboratory, the University of Maine’s Darling Marine Center, the Anderson Cabot Center for Ocean Life at the New England Aquarium, Duke University, and NOAA’s Northeast Fisheries Science Center. This synergy of expertise facilitated the combination of marine ecology, oceanography, physiological modeling, and advanced computational techniques to address the challenge of mapping elusive whale populations. The resulting publication in the peer-reviewed journal Endangered Species Research marks a significant advancement not only in species distribution modeling but also in marine ecosystem science at large.
Tracking North Atlantic right whales remains a daunting endeavor due to their highly migratory nature, low population density, and wide-ranging habitat use. Traditional monitoring relies heavily on visual surveys and acoustic detection, both resource-intensive and limited by weather and daylight conditions. The integration of refined ecological models that incorporate detailed prey fields offers a complementary tool with the potential to enhance real-time monitoring capabilities. By embedding biological realism into the computational frameworks, researchers can now generate habitat suitability maps that are more responsive to immediate ecological conditions, a critical attribute for anticipating shifts driven by climate variability or anthropogenic disturbances.
Moreover, the approach taken by the researchers addresses a critical shortcoming of proxy-based methods that often mask the heterogeneity of zooplankton communities. Since right whales exhibit selective foraging behavior, consuming a few key copepod species with distinct nutritional profiles, recognizing the species-specific distribution and abundance of prey is fundamental to understanding whale ecology. As lead author Camille Ross emphasized, tailoring prey information to the predator’s energetic needs paves the way for building models with enhanced ecological validity, which ultimately can translate to more effective conservation practices.
The study also highlights a potentially broader applicability of this methodology beyond right whales. Many marine organisms depend on zooplankton, and the energy-centric prey field estimation framework offers promising opportunities for modeling trophic interactions across various species, such as commercially important larval lobsters and other predators. This cross-taxa adaptability could revolutionize ecological modeling approaches, embedding bioenergetic constraints into species distribution predictions to generate more ecologically informed management tools.
Importantly, these modeling advances resonate strongly with the needs of conservation practitioners and industry stakeholders. As Nick Record, senior research scientist at Bigelow Laboratory, notes, co-development of predictive tools with end-users such as NOAA, state agencies, and maritime industries ensures that scientific innovations translate directly into actionable strategies. Enhanced forecasting of whale distribution equips managers with the foresight to mitigate collision risks, enforce seasonal protections, and balance ecological imperatives alongside economic activities, thereby fostering coexistence between human enterprise and vulnerable marine megafauna.
The research also sets the stage for future work to unravel the interplay between environmental variability, prey dynamics, and whale behavior in a rapidly changing ocean. Underlying oceanographic shifts, potentially driven by climate change, may reorganize zooplankton communities, altering prey availability and thus influencing whale foraging patterns and migration routes. Developing and refining models that seamlessly integrate biotic interactions and energy requirements equips scientists with tools to predict and possibly preempt detrimental impacts on right whale populations, crucial for orienting adaptive conservation in an uncertain future.
Ultimately, this study signifies a major stride in right whale conservation science, emphasizing the primacy of detailed, biologically relevant prey data in species distribution modeling. By moving beyond indirect proxies and embracing a more mechanistic understanding of predator-prey dynamics, the researchers provide a blueprint for the next generation of ecological models designed to meet the complexities of marine megafauna management. The survival of the North Atlantic right whale, one of the ocean’s most imperiled giants, may well depend on the scientific advancements and transdisciplinary efforts exemplified in this work.
Subject of Research: Animals
Article Title: Incorporating prey fields into North Atlantic right whale density surface models
News Publication Date: 11-Sep-2025
Web References:
https://doi.org/10.3354/esr01435
https://www.fisheries.noaa.gov/new-england-mid-atlantic/ecosystems/monitoring-ecosystem-northeast
https://science.nasa.gov/earth/nasa-data-helps-map-tiny-plankton-that-feed-giant-right-whales/
References:
Ross, C., Brady, D., Record, N., et al. (2025). Incorporating prey fields into North Atlantic right whale density surface models. Endangered Species Research. https://doi.org/10.3354/esr01435
Image Credits:
New England Aquarium (NMFS permit #25739)
Keywords:
Zooplankton, Endangered species, Ecological modeling, Whales, Predation
