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Impact of climate change is expected to be especially noticeable at the edges of a species' distribution, where they meet suboptimal habitat conditions. In Mauritania and Iberia, two genetically differentiated populations of harbor porpoises (Phocoena phocoena) form an ecotype adapted to local upwelling conditions and distinct from other ecotypes further north on the NE Atlantic continental shelf and in the Black Sea. By analyzing the evolution of mitochondrial genetic variation in the Iberian population between two temporal cohorts (1990–2002 vs. 2012–2015), we report a substantial decrease in genetic diversity. Phylogenetic analyses including neighboring populations identified two porpoises in southern Iberia carrying a divergent haplotype closely related to those from the Mauritanian population, yet forming a distinct lineage. This suggests that Iberian porpoises may not be as isolated as previously thought, indicating possible dispersion from Mauritania or an unknown population in between, but none from the northern ecotype. Demo-genetic scenario testing by approximate Bayesian computation showed that the rapid decline in the Iberian mitochondrial diversity was not simply due to the genetic drift of a small population, but models support instead a substantial decline in effective population size, possibly resulting from environmental stochasticity, prey depletion, or acute fishery bycatches. These results illustrate the value of genetics time series to inform demographic trends and emphasize the urgent need for conservation measures to ensure the viability of this small harbor porpoise population in Iberian waters.

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, which opens opportunities to automatise counting tasks. In this study, we use machine learning to automate counting objects of interest without the need to label individual objects. By leveraging already existing image-level annotations, this approach can also give value to historical data that were collected and annotated over longer time series (typical for many ecological studies), without the aim of deep learning applications. We demonstrate deep learning regression on two fundamentally different counting tasks: (i) daily growth rings from microscopic images of fish otolith (i.e., hearing stone) and (ii) hauled out seals from highly variable aerial imagery. In the otolith images, our deep learning-based regressor yields an RMSE of 3.40 day-rings and an R2 of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and R2 of 0.72), which can be attributed to a lack of images with a high number of seals in the initial training set, compared to the test set. We then show how to improve performance substantially (RMSE of 19.03 seals and R2 of 0.77) by carefully selecting and relabelling just 100 additional training images based on initial model prediction discrepancy. The regression-based approach used here returns accurate counts (R2 of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research.

© The Authors, 2019. This article is distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License. The definitive version was published in Molecular Phylogenetics and Evolution (2019), doi:10.1016/j.ympev.2019.02.003., The advent of massive parallel sequencing technologies has resulted in an increase of studies based upon complete mitochondrial genome DNA sequences that revisit the taxonomic status within and among species. Spatially distinct monophyly in such mitogenomic genealogies, i.e., the sharing of a recent common ancestor among con-specific samples collected in the same region has been viewed as evidence for subspecies. Several recent studies in cetaceans have employed this criterion to suggest subsequent intraspecific taxonomic revisions. We reason that employing intra-specific, spatially distinct monophyly at non-recombining, clonally inherited genomes is an unsatisfactory criterion for defining subspecies based upon theoretical (genetic drift) and practical (sampling effort) arguments. This point was illustrated by a re-analysis of a global mitogenomic assessment of fin whales, Balaenoptera physalus spp., published by Archer et al. (2013), which proposed to further subdivide the Northern Hemisphere fin whale subspecies, B. p. physalus. The proposed revision was based upon the detection of spatially distinct monophyly among North Atlantic and North Pacific fin whales in a genealogy based upon complete mitochondrial genome DNA sequences. The extended analysis conducted in this study (1,676 mitochondrial control region, 162 complete mitochondrial genome DNA sequences and 20 microsatellite loci genotyped in 358 samples) revealed that the apparent monophyly among North Atlantic fin whales reported by Archer et al. (2013) to be due to low sample sizes. In conclusion, defining sub-species from monophyly (i.e., the absence of para- or polyphyly) can lead to erroneous conclusions due to relatively “trivial” aspects, such as sampling. Basic population genetic processes (i.e., genetic drift and migration) also affect the time to the most recent common ancestor and hence the probability that individuals in a sample are monophyletic., We are grateful to Hanne Jørgensen, Anna Sellas, Mary Beth Rew and Christina Færch-Jensen for technical assistance. We thank Drs. P. E. Rosel and K. D. Mullin (U.S. National Marine Fisheries Service Southeast Fisheries Science Center) and members of the U.S. Northeast and Southeast Region Marine Mammal Stranding Network and its response teams, including the International Fund for Animal Welfare, the Marine Mammal Stranding Center, Mystic Aquarium, the Riverhead Foundation for Marine Research and Preservation (K. Durham) and the Marine Mammal Stranding Program of the University of North Carolina Wilmington for access to fin whale samples from the western North Atlantic. We thank Gisli Vikingsson for providing samples. We are indebted to Dr. Eduardo Secchi for facilitating data sharing. Data collection in the Southern Ocean was conducted under research projects Baleias (CNPq grants 557064/2009-0 and 408096/2013-6), INTERBIOTA (CNPq 407889/2013-2) and INCT-APA (CNPq 574018/2008-5), of the Brazilian Antarctic Program and a contribution by the research consortium ‘Ecology and Conservation of Marine Megafauna – EcoMega-CNPq’. MAS was supported through a FCT Investigator contract funded by POPH, QREN European Social Fund, and Portuguese Ministry for Science and Education. Data collection in the Azores was funded by TRACE-PTDC/MAR/74071/2006 and MAPCET-M2.1.2/F/012/2011 [FEDER, COMPETE, QREN European Social Fund, and Proconvergencia Açores/EU Program]. Fin whale illustration herein is used with the permission of Frédérique Lucas. We acknowledge the Center for Information Technology of the University of Groningen for IT support and access to the Peregrine high performance-computing cluster.