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Gestation length is a key reproductive parameter influencing fecundity, population growth rates, and the recovery potential of baleen whales. However, direct knowledge of the gestation length in these large mammals remains limited, primarily inferred from whaling and observational data. Over the past decade, southern right whales have experienced a decline in reproductive success, likely linked to climate-change-induced shifts in foraging conditions. Understanding the population-level consequences of these changes requires detailed longitudinal reproductive data. This study analyzes multiyear steroid hormone profiles in the baleen of adult female southern right whales stranded along the South African coast. Results show an extended hormonal pattern characterized by two peaks in progestogens between 20 and 25 months—suggesting putative pregnancies lasting substantially longer than previous estimates. Sharp estrogen peaks during periods of elevated progestogen phases may indicate hormonal regulation of myometrial contractions at birth. A positive correlation between progestogens and glucocorticoids suggests a role for glucocorticoids in pregnancy maintenance, while androgens provide limited insight into female reproduction in this species. These findings imply a longer-than-expected gestation period for southern right whales and potentially across the balaenid family. This has important implications for understanding the timing and location of conception, relevant for conservation management strategies. Multipopulation studies alongside individual sighting histories are recommended to refine our understanding of southern right whale reproduction further. 

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.