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Accurate identification of units for conservation is particularly challenging for marine species as obvious barriers to gene flow are generally lacking. Bryde’s whales (Balaenoptera spp.) are subject to multiple human-mediated stressors, including fisheries bycatch, ship strikes, and scientific whaling by Japan. For effective management, a clear understanding of how populations of each Bryde’s whale species/subspecies are genetically structured across their range is required. We conducted a population-level analysis of mtDNA control region sequences with 56 new samples from Oman, Maldives, and Bangladesh, plus published sequences from off Java and the Northwest Pacific. Nine diagnostic characters in the mitochondrial control region and a maximum parsimony phylogenetic analysis identified 2 genetically recognized subspecies of Bryde’s whale: the larger, offshore form, Balaenoptera edeni brydei, and the smaller, coastal form, Balaenoptera edeni edeni. Genetic diversity and differentiation indices, combined with a reconstructed maximum parsimony haplotype network, indicate strong differences in the genetic diversity and population structure within each subspecies. Discrete population units are identified for B. e. brydei in the Maldives, Java, and the Northwest Pacific and for B. e. edeni between the Northern Indian Ocean (Oman and Bangladesh) and the coastal waters of Japan. [ABSTRACT FROM PUBLISHER]

The conservation of species is one of the foundations of conservation biology. Successful species conservation has often been defined as simply the avoidance of extinction. We argue that this focus, although important, amounts to practicing conservation at the "emergency room door," and will never be a sufficient approach to conserving species. Instead, we elaborate a positive definition of species conservation on the basis of six attributes and propose a categorization of different states of species conservation using the extent of human management and the degree to which each of the attributes is conserved. These states can be used to develop a taxonomy of species "recovery" that acknowledges there are multiple stable points defined by ecological and social factors. With this approach, we hope to contribute to a new, optimistic conservation biology that is not based on underambitious goals and that seeks to create the conditions under which Earth's biological systems can thrive. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Humpback whales (Megaptera novaeangliae) annually undertake the longest migrations between seasonal feeding and breeding grounds of any mammal. Despite this dispersal potential, discontinuous seasonal distributions and migratory patterns suggest that humpbacks form discrete regional populations within each ocean. To better understand the worldwide population history of humpbacks, and the interplay of this species with the oceanic environment through geological time, we assembled mitochondrial DNA control region sequences representing approximately 2700 individuals (465 bp, 219 haplotypes) and eight nuclear intronic sequences representing approximately 70 individuals (3700 bp, 140 alleles) from the North Pacific, North Atlantic and Southern Hemisphere. Bayesian divergence time reconstructions date the origin of humpback mtDNA lineages to the Pleistocene (880 ka, 95% posterior intervals 550-1320 ka) and estimate radiation of current Northern Hemisphere lineages between 50 and 200 ka, indicating colonization of the northern oceans prior to the Last Glacial Maximum. Coalescent analyses reveal restricted gene flow between ocean basins, with long-term migration rates (individual migrants per generation) of less than 3.3 for mtDNA and less than 2 for nuclear genomic DNA. Genetic evidence suggests that humpbacks in the North Pacific, North Atlantic and Southern Hemisphere are on independent evolutionary trajectories, supporting taxonomic revision of M. novaeangliae to three subspecies. [ABSTRACT FROM AUTHOR]