Philippa Kaur

Fisheries overexploitation is a problem in all oceans and seas globally. Authorities and administrations in charge of assigning quotas have very little fine-grained information on the fish captures, and instead use large-scale, coarse data to assess the health level of fisheries. Thus, being able to cross-match fish species and sizes, to the sea regions they were captured from, can be helpful in this regard, providing finer-grained information.Previous attempts at assembling datasets for fish detection and classification exist, ranging from fish detection or counting in underwater images and video streams1,2,3, to counting on belts on trawler ships4, to classification in laboratory conditions5,6, or in underwater preprocessed images of single fish7,8,9, or single fish in free-form pictures10, as well as simultaneous detection and classification of several fish11,12. However, none of the works found in the literature addresses the topic of simultaneous instance segmentation and species classification, along with fish size estimation, in a fish market environment, as is the aim of this paper. Instance segmentation refers to the extraction of pixel-level masks for each individual object (in this case fish specimens), rather than bounding boxes (object detection), or class label masks (e.g. a single mask for all fish specimens of the same species, also referred to as semantic segmentation). Moreover, works in the literature use pictures taken in laboratory conditions (with a single fish per image, shown from the side), or in underwater conditions. Only French et al.4 uses pictures of fish catches on a belt, for counting purposes. Table 1 shows a summary of the datasets identified in the literature, along with their characteristics, including how the proposed dataset compares.Table 1 Summary of previous datasets found in the literature, and comparison to proposed dataset.Full size tableThe DeepFish project (website: http://deepfish.dtic.ua.es/) is aimed at providing fish species classification and size estimation for fish specimens arriving at fish markets, both for the automation of fish sales, and the retrieval of fine-grained information about the health of fisheries. For a period of six months (April to September 2021), images have been captured at the fish market in El Campello (Alicante, Spain). Images of market trays show a variety of fish species, including targeted as well as accidental captures from the ‘Cabo de la Huerta’, an important site for protection and preservation of marine habitats and biodiversity as defined by the European Comission Habitats Directive (92/43/EEC). From the pictures, a total of 59 different species are identified with 12 species having more than 100 specimens and 25 with more than 10 specimens, as shown in Table 2. There is a high imbalance of species captured due to the natural variation in fish species populations according to seasonality and other ecological factors (rarity of the species, i.e. total population count, etc). Due to some species showing sexual dimorphism (i.e. Symphodus tinca), this species is split into two separate class labels, leading to a different number of species, and class labels (59 species, but 60 class labels). The dataset presents a high temporal imbalance too. As shown in Fig. 1, the capture of new fish tray images was not evenly distributed during the six month study period. Several factors contributed to this: wholesale fish market operating days (e.g. no weekend data, holidays and stop periods, etc.), fish species variability (one of the aims was to be able to capture at least 100 specimens from several species, and seasonality meant some could not be available for capture in later months), as well as the time availability of research group members to attend the fish arrival, tray preparation and auctioning in the evenings.Table 2 Distribution of fish species in the dataset.Full size tableFig. 1Temporal distribution of fish tray images captured. It can be observed that April (04) and May (05) were much more active than the rest of months. This is due to several contributing factors.Full size imageThe resulting DeepFish dataset introduced here contains annotated images from 1,291 fish market trays, with a total of 7,339 specimens (individual fish instances) which were labelled (species and mask) using a specially-adapted version of the Django labeller instance segmentation labelling tool13. Subsequently, another JSON file is generated, following the Microsoft Common Objects in Context (MS COCO) dataset format14, which can be directly fed to a neural network. This is done via a script that is also provided15. Figure 2 shows the distribution of individuals for the selected species within the dataset. Furthermore, Fig. 3 shows examples of the trays, with instance segmentation (ground truth silhouette, i.e. as an interpolation from human-provided points) along with species labelling (different colour shading).Fig. 2Graphical view of the distribution of fish species in the DeepFish dataset for species above 10 specimens. Note, Symphodus tinca is considered separately due to sexual dimorphism (211 male; 335 female samples).Full size imageFig. 3Examples of ground truth fish instance masks with class labelling, showing the 12 species (13 labels) with more than 100 specimens (in bold in Table 2).Full size imageFrom the point of view of research, this data is important for the classification of fish species, instance segmentation, as well as specimen size estimation (e.g. as a regression problem, or otherwise). From an end-results perspective, data automatically labelled with fish instance segmentation accompanied by species name and estimated size is useful to different stakeholders, namely: fishing authorities (to understand how much of each species is being caught per zone), maritime conservation (to calculate depletion of fisheries), but also managers of the markets themselves, as well as clients (digitized sales, e-commerce), etc.The usage of the provided data can be manifold, as it can be used for several problems, namely: object detection and classification, which involves finding objects (in this case fish specimens) providing a bounding box, and a class for each of these boxes; additionally, the data can also be used for semantic segmentation, which can provide a pixel-wise segmentation of the image providing labels (in this case species labels) to different pixel regions of the image; furthermore, also instance segmentation is possible, in which not just a single label for all instances of the same species is provided, but each specimen is provided with a mask (specimen segmentation), as well as a label (species). Furthermore, several measurements of each fish are provided, which can also be used to estimate their size, since they have been shown to be correlated with each other16. These are estimated from the calculated homography (given the tray size is known), given the burden of measuring each fish due to the large amount of specimens in the dataset. More

Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy. Quat. Sci. Rev. 106, 14–28 (2014).Article 

Google Scholar 
Heiri, O. et al. Validation of climate model-inferred regional temperature change for late-glacial Europe. Nat. Commun. 5, 1–7 (2014).Article 
CAS 

Google Scholar 
Muschitiello, F. et al. Fennoscandian freshwater control on Greenland hydroclimate shifts at the onset of the Younger Dryas. Nat. Commun. 6, 1–8 (2015).Article 
CAS 

Google Scholar 
Renssen, H. et al. Multiple causes of the Younger Dryas cold period. Nat. Geosci. 8, 946–949 (2015).CAS 
Article 

Google Scholar 
Mangerud, J. The discovery of the Younger Dryas, and comments on the current meaning and usage of the term. Boreas 50, 1–5 (2021).Article 

Google Scholar 
Cheng, H. et al. Timing and structure of the Younger Dryas event and its underlying climate dynamics. Proc. Natl. Acad. Sci. USA 117, 23408–23417 (2020).CAS 
Article 

Google Scholar 
van Hoesel, A., Hoek, W. Z., Pennock, G. M. & Drury, M. R. The Younger Dryas impact hypothesis: a critical review. Quat. Sci. Rev. 83, 95–114 (2014).Article 

Google Scholar 
Partin, J. W. et al. Gradual onset and recovery of the Younger Dryas abrupt climate event in the tropics. Nat. Commun. 6, 1–9 (2015).Article 

Google Scholar 
Reinig, F. et al. Precise date for the Laacher See eruption synchronizes the Younger Dryas. Nature 595, 66–69 (2021).CAS 
Article 

Google Scholar 
Ammann, B. et al. Quantification of biotic responses to rapid climatic changes around the Younger Dryas — a synthesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 313–347 (2000).Article 

Google Scholar 
Hoek, W. Z. Vegetation response to the ∼ 14.7 and ∼ 11.5 ka cal. BP climate transitions: is vegetation lagging climate? Glob. Planet. Change 30, 103–115 (2001).Article 

Google Scholar 
Litt, T. et al. Correlation and synchronisation of Lateglacial continental sequences in northern central Europe based on annually laminated lacustrine sediments. Quat. Sci. Rev. 20, 1233–1249 (2001).Article 

Google Scholar 
Muschitiello, F. & Wohlfarth, B. Time-transgressive environmental shifts across Northern Europe at the onset of the Younger Dryas. Quat. Sci. Rev. 109, 49–56 (2015).Article 

Google Scholar 
Nakagawa, T. et al. The spatio-temporal structure of the Lateglacial to early Holocene transition reconstructed from the pollen record of Lake Suigetsu and its precise correlation with other key global archives: implications for palaeoclimatology and archaeology. Glob. Planet. Change 202, 103493 (2021).Article 

Google Scholar 
Ammann, B. et al. Vegetation responses to rapid warming and to minor climatic fluctuations during the Late-Glacial Interstadial (GI-1) at Gerzensee (Switzerland). Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 40–59 (2013).Article 

Google Scholar 
Engels, S. et al. Subdecadal‐scale vegetation responses to a previously unknown late‐Allerød climate fluctuation and Younger Dryas cooling at Lake Meerfelder Maar (Germany). J. Quat. Sci 31, 741–752 (2016).Article 

Google Scholar 
Van Raden, U. J. et al. High-resolution late-glacial chronology for the Gerzensee lake record (Switzerland): δ18O correlation between a Gerzensee-stack and NGRIP. Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 13–24 (2013).Article 

Google Scholar 
Blaga, C. I., Reichart, G.-J., Lotter, A. F., Anselmetti, F. S. & Sinninghe Damsté, J. S. A TEX86 lake record suggests simultaneous shifts in temperature in Central Europe and Greenland during the last deglaciation. Geophys. Res. Lett. 40, 948–953 (2013).Article 

Google Scholar 
Rach, O., Brauer, A., Wilkes, H. & Sachse, D. Delayed hydrological response to Greenland cooling at the onset of the Younger Dryas in western Europe. Nat. Geosci. 7, 109 (2014).CAS 
Article 

Google Scholar 
Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001).CAS 
Article 

Google Scholar 
Doncaster, C. P. et al. Early warning of critical transitions in biodiversity from compositional disorder. Ecology 97, 3079–3090 (2016).Article 

Google Scholar 
Jones, G. et al. The Lateglacial to early Holocene tephrochronological record from Lake Hämelsee, Germany: a key site within the European tephra framework. Boreas 47, 28–40 (2018).Article 

Google Scholar 
Blaga, C. I., Reichart, G.-J., Heiri, O. & Damsté, J. S. S. Tetraether membrane lipid distributions in water-column particulate matter and sediments: a study of 47 European lakes along a north–south transect. J. Paleolimnol. 41, 523–540 (2009).Article 

Google Scholar 
Bechtel, A., Smittenberg, R. H., Bernasconi, S. M. & Schubert, C. J. Distribution of branched and isoprenoid tetraether lipids in an oligotrophic and a eutrophic Swiss lake: insights into sources and GDGT-based proxies. Org. Geochem. 41, 822–832 (2010).CAS 
Article 

Google Scholar 
Lowe, J. et al. On the timing of retreat of the Loch Lomond (‘Younger Dryas’) Readvance icefield in the SW Scottish Highlands and its wider significance. Quat. Sci. Rev. 219, 171–186 (2019).Article 

Google Scholar 
Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R news 8, 20–25 (2008).
Google Scholar 
Merkt, J. & Müller, H. Varve chronology and palynology of the Lateglacial in Northwest Germany from lacustrine sediments of Hämelsee in Lower Saxony. Quat. Int. 61, 41–59 (1999).Article 

Google Scholar 
Litt, T. & Stebich, M. Bio-and chronostratigraphy of the lateglacial in the Eifel region, Germany. Quat. Int. 61, 5–16 (1999).Article 

Google Scholar 
Reimer, P. J. et al. The IntCal20 Northern hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 
Article 

Google Scholar 
Giesecke, T. Holocene dynamics of the southern boreal forest in Sweden. The Holocene 15, 858–872 (2005).Article 

Google Scholar 
Müller, D. et al. New insights into lake responses to rapid climate change: the Younger Dryas in Lake Gościąż, central Poland. Boreas 50, 535–555 (2021).Article 

Google Scholar 
Davis, B. A. S. et al. The Eurasian Modern Pollen Database (EMPD), version 2. Earth Syst. Sci. data 12, 2423–2445 (2020).Article 

Google Scholar 
Neugebauer, I. et al. A Younger Dryas varve chronology from the Rehwiese palaeolake record in NE-Germany. Quat. Sci. Rev. 36, 91–102 (2012).Article 

Google Scholar 
Ralska-Jasiewiczowa, M. et al. Very fast environmental changes at the Pleistocene/Holocene boundary, recorded in laminated sediments of Lake Gościaż, Poland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 193, 225–247 (2003).Article 

Google Scholar 
Bonk, A. et al. Varve microfacies and chronology from a new sediment record of Lake Gościąż (Poland). Quat. Sci. Rev. 251, 106715 (2021).Article 

Google Scholar 
Brauer, A., Haug, G. H., Dulski, P., Sigman, D. M. & Negendank, J. F. W. An abrupt wind shift in western Europe at the onset of the Younger Dryas cold period. Nat. Geosci. 1, 520–523 (2008).CAS 
Article 

Google Scholar 
Mekhaldi, F. et al. Radionuclide wiggle matching reveals a nonsynchronous early Holocene climate oscillation in Greenland and western Europe around a grand solar minimum. Clim. Past 16, 1145–1157 (2020).Article 

Google Scholar 
Mayfield, R. J. et al. Metrics of structural change as indicators of chironomid community stability in high latitude lakes. Quat. Sci. Rev. 249, 106594 (2020).Article 

Google Scholar 
van der Knaap, W. O. & Van Leeuwen, J. F. N. Climate-pollen relationships AD 1901–1996 in two small mires near the forest limit in the northern and central Swiss Alps. The Holocene 13, 809–828 (2003).Article 

Google Scholar 
Bazelmans, J. et al. Environmental changes in the late Allerød and early Younger Dryas in the Netherlands: a multiproxy high-resolution record from a site with two Pinus sylvestris populations. Quat. Sci. Rev. 272, 107199 (2021).Article 

Google Scholar 
Birks, H. H., Battarbee, R. W. & Birks, H. J. B. The development of the aquatic ecosystem at Kråkenes Lake, western Norway, during the late glacial and early Holocene-a synthesis. J. Paleolimnol 23, 91–114 (2000).Article 

Google Scholar 
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).Article 

Google Scholar 
Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. IntCal13 calibrated ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from Kråkenes, western Norway. J. Quat. Sci 29, 506–507 (2014).Article 

Google Scholar 
Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. Precise 14 C ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from western Norway and their comparison with the Greenland Ice Core (GICC 05) chronology. J. Quat. Sci 28, 490–500 (2013).Article 

Google Scholar 
Wohlfarth, B. et al. Hässeldala–a key site for last termination climate events in northern Europe. Boreas 46, 143–161 (2017).Article 

Google Scholar 
Brauer, A. et al. High resolution sediment and vegetation responses to Younger Dryas climate change in varved lake sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 18, 321–329 (1999).Article 

Google Scholar 
Lane, C. S., Brauer, A., Blockley, S. P. E. & Dulski, P. Volcanic ash reveals time-transgressive abrupt climate change during the Younger Dryas. Geology 41, 1251–1254 (2013).Bronk Ramsey, C. et al. Improved age estimates for key Late Quaternary European tephra horizons in the RESET lattice. Quat. Sci. Rev. 118, 18–32 (2015).Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos. 111, https://doi.org/10.1029/2005JD006079 (2006).Brauer, A., Endres, C., Zolitschka, B. & Negendank, J. F. W. AMS radiocarbon and varve chronology from the annually laminated sediment record of Lake Meerfelder Maar, Germany. Radiocarbon 42, 355–368 (2000).CAS 
Article 

Google Scholar 
Wulf, S. et al. Tracing the Laacher See Tephra in the varved sediment record of the Trzechowskie palaeolake in central Northern Poland. Quat. Sci. Rev. 76, 129–139 (2013).Article 

Google Scholar 
Brauer, A. et al. The importance of independent chronology in integrating records of past climate change for the 60–8 ka INTIMATE time interval. Quat. Sci. Rev. 106, 47–66 (2014).Article 

Google Scholar 
Lane, C. S. et al. The Late Quaternary tephrostratigraphy of annually laminated sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 122 192–206 (2015).Article 

Google Scholar 
Adolphi, F. & Muscheler, R. Synchronizing the Greenland ice core and radiocarbon timescales over the Holocene–Bayesian wiggle-matching of cosmogenic radionuclide records. Clim. Past 12, 15–30 (2016).Article 

Google Scholar 
Muschitiello, F. et al. Deep-water circulation changes lead North Atlantic climate during deglaciation. Nat. Commun. 10, 1–10 (2019).CAS 
Article 

Google Scholar 
Adolphi, F. et al. Persistent link between solar activity and Greenland climate during the Last Glacial Maximum. Nat. Geosci. 7, 662–666 (2014).CAS 
Article 

Google Scholar 
Siegenthaler, U., Heimann, M. & Oeschger, H. 14C variations caused by changes in the global carbon cycle. Radiocarbon 22, 177–191 (1980).CAS 
Article 

Google Scholar 
Muscheler, R., Adolphi, F. & Svensson, A. Challenges in 14C dating towards the limit of the method inferred from anchoring a floating tree ring radiocarbon chronology to ice core records around the Laschamp geomagnetic field minimum. Earth Planet. Sci. Lett. 394, 209–215 (2014).CAS 
Article 

Google Scholar 
Muschitiello, F. An improved and continuous synchronization of the Greenland ice-core and Hulu Cave U-Th timescales using probabilistic inversion. Clim. Past Discuss. 1–39 https://doi.org/10.5194/cp-2021-116 (2021).Moore, P. D., Webb, J. A. & Collison, M. E. Pollen analysis. (Blackwell scientific publications, 1991).Engels, S. et al. Haemelsee: late-glacial pollen counts. PANGAEA, https://doi.org/10.1594/PANGAEA.939693 (2021).Weltje, G. J. & Tjallingii, R. Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: Theory and application. Earth Planet. Sci. Lett. 274, 423–438 (2008).CAS 
Article 

Google Scholar 
Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol 25, 101–110 (2001).Article 

Google Scholar 
Brooks, S. J., Langdon, P. G. & Heiri, O. The identification and use of Palaearctic Chironomidae larvae in palaeoecology. Quat. Res. Assoc. Tech. Guid. i–vi. Vol. 10, 1–276 (2007).Heiri, O., Brooks, S. J., Birks, H. J. B. & Lotter, A. F. A 274-lake calibration data-set and inference model for chironomid-based summer air temperature reconstruction in Europe. Quat. Sci. Rev. 30, 3445–3456 (2011).Article 

Google Scholar 
Heiri, O. & Lotter, A. F. Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids. J. Paleolimnol 26, 343–350 (2001).Article 

Google Scholar 
Rach, O., Hadeen, X. & Sachse, D. An automated solid phase extraction procedure for lipid biomarker purification and stable isotope analysis. Org. Geochem. 142, 103995 (2020).CAS 
Article 

Google Scholar 
Huguet, C. et al. An improved method to determine the absolute abundance of glycerol dibiphytanyl glycerol tetraether lipids. Org. Geochem. 37, 1036–1041 (2006).CAS 
Article 

Google Scholar 
Hopmans, E. C., Schouten, S. & Damsté, J. S. S. The effect of improved chromatography on GDGT-based palaeoproxies. Org. Geochem. 93, 1–6 (2016).CAS 
Article 

Google Scholar 
Birks, H. J. B. & Birks, H. H. Biological responses to rapid climate change at the Younger Dryas—Holocene transition at Kråkenes, western Norway. The Holocene 18, 19–30 (2008).Article 

Google Scholar 
R CORE TEAM, A. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2012. URL http://www.R-project.org (2020).Engels, S., van Geel, B., Buddelmeijer, N. & Brauer, A. High-resolution palynological evidence for vegetation response to the Laacher See eruption from the varved record of Meerfelder Maar (Germany) and other central European records. Rev. Palaeobot. Palynol. 221, 160–170 (2015).Article 

Google Scholar 
Hughes, A. L. C., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J. & Svendsen, J. I. The last Eurasian ice sheets–a chronological database and time‐slice reconstruction, DATED‐1. Boreas 45, 1–45 (2016).Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Qiao, L. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01376-8 (2022). More

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds.) Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782. https://doi.org/10.1016/j.scitotenv.2020.137782 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Turner, J. & Marshall, G. J. Climate Change in the Polar Regions (Cambridge University Press, 2011).Book 

Google Scholar 
Meredith, M. et al. Polar Regions. Chapter 3, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. https://www.ipcc.ch/srocc/chapter/chapter-3-2/ (2019).Rignot, E. et al. Four decades of Antarctic Ice Sheet mass balance from 1979–2017. Proc. Natl. Acad. Sci. USA 116, 1095–1103. https://doi.org/10.1073/pnas.1812883116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Siegert, M. et al. The Antarctic Peninsula under a 1.5°C global warming scenario. Front. Environ. Sci. 7, 102. https://doi.org/10.3389/fenvs.2019.00102 (2019).Article 

Google Scholar 
Iz, H. B. Is the global sea surface temperature rise accelerating?. Geod. Geodyn. 9, 432–438. https://doi.org/10.1016/j.geog.2018.04.002 (2018).Article 

Google Scholar 
Qiu, Z. et al. Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp. Proc. R. Soc. B. 286, 20181887. https://doi.org/10.1098/rspb.2018.1887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Burge, C. A., Kim, C. J., Lyles, J. M. & Harvell, C. D. Special issue Oceans and Humans Health: The ecology of marine opportunists. Microb. Ecol. 65, 869–879. https://doi.org/10.1007/s00248-013-0190-7 (2013).Article 
PubMed 

Google Scholar 
Cavicchioli, R. et al. Scientists’ warning to humanity: Microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586. https://doi.org/10.1038/s41579-019-0222-5 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Harvell, C. D. et al. Emerging marine diseases–climate links and anthropogenic factors. Science 285, 1505–1510. https://doi.org/10.1126/science.285.5433.1505 (1999).CAS 
Article 
PubMed 

Google Scholar 
Egan, S. & Gardiner, M. Microbial dysbiosis: Rethinking disease in marine ecosystems. Front. Microbiol. 7, 991. https://doi.org/10.3389/fmicb.2016.00991 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilkins, L. G. E. et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 17, e3000533. https://doi.org/10.1371/journal.pbio.3000533 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 17498. https://doi.org/10.1038/s41598-019-53580-w (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tsuchiya, M. Mass mortality in a population of the mussel Mytilus edulis L. caused by high temperature on rocky shores. J. Exp. Mar. Biol. Ecol. 66, 101–111. https://doi.org/10.1016/0022-0981(83)90032-1 (1983).Article 

Google Scholar 
Malham, S. K. et al. Summer mortality of the Pacific oyster, Crassostrea gigas, in the Irish Sea: The influence of temperature and nutrients on health and survival. Aquaculture 287, 128–138. https://doi.org/10.1016/j.aquaculture.2008.10.006 (2009).CAS 
Article 

Google Scholar 
Beyer, J. et al. Blue mussels (Mytilus edulis spp.) as sentinel organisms in coastal pollution monitoring: A review. Mar. Environ. Res. 130, 338–365. https://doi.org/10.1016/j.marenvres.2017.07.024 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ladeiro, M. P. et al. Mussel as a tool to define continental watershed quality. In Organismal and Molecular Malacology (ed Ray, S.), IntechOpen. https://doi.org/10.5772/67995 (2017).Bonacci, S. et al. Esterase activities in the bivalve mollusc Adamussium colbecki as a biomarker for pollution monitoring in the Antarctic marine environment. Mar. Pollut. Bull. 49, 445–455. https://doi.org/10.1016/j.marpolbul.2004.02.033 (2004).CAS 
Article 
PubMed 

Google Scholar 
Storhaug, E. et al. Seasonal and spatial variations in biomarker baseline levels within Arctic populations of mussels (Mytilus spp.). Sci. Total Environ. 656, 921–936. https://doi.org/10.1016/j.scitotenv.2018.11.397 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Caza, F. et al. Liquid biopsies for omics-based analysis in sentinel mussels. PLoS ONE 14, e0223525. https://doi.org/10.1371/journal.pone.0225359 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ignatiadis, M., Sledge, G. W. & Jeffrey, S. S. Liquid biopsy enters the clinic – implementation issues and future challenges. Nat. Rev. Clin. Oncol. 18, 297–312. https://doi.org/10.1038/s41571-020-00457-x (2021).Article 
PubMed 

Google Scholar 
Kowarsky, M. et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc. Natl. Acad. Sci. USA 114, 9623–9628. https://doi.org/10.1073/pnas.1707009114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, H. et al. Circulating microbiome DNA: An emerging paradigm for cancer liquid biopsy. Cancer Lett. 521, 82–87. https://doi.org/10.1016/j.canlet.2021.08.036 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367. https://doi.org/10.3389/fmicb.2016.01367 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Lokmer, A. & Wegner, M. K. Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection. ISME J. 9, 670–682. https://doi.org/10.1038/ismej.2014.160 (2015).CAS 
Article 
PubMed 

Google Scholar 
Auguste, M. et al. Exposure to TiO2 nanoparticles induces shifts in the microbiota composition of Mytilus galloprovincialis hemolymph. Sci. Total Environ. 670, 129–137. https://doi.org/10.1016/j.scitotenv.2019.03.133 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl. Acad. Sci. USA 113, E5062–E5071. https://doi.org/10.1073/pnas.1609157113 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Musella, M. et al. Tissue-scale microbiota of the Mediterranean mussel (Mytilus galloprovincialis) and its relationship with the environment. Sci. Total Environ. 717, 137209. https://doi.org/10.1016/j.scitotenv.2020.137209 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Féral, J.-P. et al. PROTEKER: Implementation of a submarine observatory at the Kerguelen islands (Southern Ocean). Underw. Technol. 34, 3–10. https://doi.org/10.3723/ut.34.003 (2016).Article 

Google Scholar 
Spain, E. A. et al. Shallow seafloor gas emissions near Heard and McDonald Islands on the Kerguelen Plateau, southern Indian Ocean. Earth Space Sci. 7, e2019EA000695. https://doi.org/10.1029/2019EA000695 (2020).ADS 
Article 

Google Scholar 
Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome. 8, 47. https://doi.org/10.1186/s40168-020-00826-9 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L.-Y. et al. Comparison of bacterial community in aqueous and oil phases of water-flooded petroleum reservoirs using pyrosequencing and clone library approaches. Appl. Microbiol. Biotechnol. 98, 4209–4221. https://doi.org/10.1007/s00253-013-5472-y (2014).CAS 
Article 
PubMed 

Google Scholar 
Gutierrez, T., Berry, D., Teske, A. & Aitken, M. D. Enrichment of Fusobacteria in sea surface oil slicks from the Deepwater Horizon oil spill. Microorganisms. 4, 24. https://doi.org/10.3390/microorganisms4030024 (2016).CAS 
Article 
PubMed Central 

Google Scholar 
Michelou, V. K., Caporaso, J. G., Knight, R. & Palumbi, S. R. The ecology of microbial communities associated with Macrocystis pyrifera. PLoS ONE 8, e67480. https://doi.org/10.1371/annotation/48e29578-a073-42e7-bca4-2f96a5998374 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Florez, J. Z. et al. Structure of the epiphytic bacterial communities of Macrocystis pyrifera in localities with contrasting nitrogen concentrations and temperature. Algal Res. 44, 101706. https://doi.org/10.1016/j.algal.2019.101706 (2019).Article 

Google Scholar 
Minich, J. J. et al. Elevated temperature drives kelp microbiome dysbiosis, while elevated carbon dioxide induces water microbiome disruption. PLoS ONE 13, e0192772. https://doi.org/10.1371/journal.pone.0192772 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, J. D., Lemay, M. A. & Parfrey, L. W. Diverse bacteria utilize alginate within the microbiome of the giant kelp Macrocystis pyrifera. Front. Microbiol. 9, 1914. https://doi.org/10.3389/fmicb.2018.01914 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pierce, M. L. & Ward, J. E. Microbial ecology of the Bivalvia, with an emphasis on the family Ostreidae. J. Shellfish Res. 37, 793–806. https://doi.org/10.2983/035.037.0410 (2018).Article 

Google Scholar 
Pierce, M. L. & Ward, J. E. Gut Microbiomes of the Eastern Oyster (Crassostrea virginica) and the Blue Mussel (Mytilus edulis): Temporal variation and the influence of marine aggregate-associated microbial communities. mSphere. 4, e00730-19. https://doi.org/10.1128/mSphere.00730-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Delille, D. & Gleizon, F. Distribution of enteric bacteria in Antarctic seawater surrounding the Port-aux-Francais permanent station (Kerguelen Island). Mar. Pollut. Bull. 46, 1179–1183. https://doi.org/10.1016/S0025-326X(03)00164-4 (2003).CAS 
Article 
PubMed 

Google Scholar 
Nguyen, T. V. & Alfaro, A. C. Metabolomics investigation of summer mortality in New Zealand Greenshell mussels (Perna canaliculus). Fish Shellfish Immunol. 106, 783–791. https://doi.org/10.1016/j.fsi.2020.08.022 (2020).CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Comparative 16SrDNA gene-based microbiota profiles of the Pacific oyster (Crassostrea gigas) and the Mediterranean Mussel (Mytilus galloprovincialis) from a shellfish farm (Ligurian Sea, Italy). Microb. Ecol. 75, 495–504. https://doi.org/10.1007/s00248-017-1051-6 (2018).CAS 
Article 
PubMed 

Google Scholar 
Romalde, J. L., Diéguez, A. L., Lasa, A. & Balboa, S. New Vibrio species associated to molluscan microbiota: A review. Front. Microbiol. 4, 413. https://doi.org/10.3389/fmicb.2013.00413 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Narayan, N. R. et al. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences. BMC Genom. 21, 56. https://doi.org/10.1186/s12864-019-6427-1 (2020).CAS 
Article 

Google Scholar 
Peng, W. et al. Integrated 16S rRNA sequencing, metagenomics, and metabolomics to characterize gut microbial composition, function, and fecal metabolic phenotype in non-obese type 2 diabetic Goto-Kakizaki rats. Front. Microbiol. 10, 3141. https://doi.org/10.3389/fmicb.2019.03141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Koner, S. et al. Assessment of carbon substrate catabolism pattern and functional metabolic pathway for microbiota of limestone caves. Microorganisms 9, 1789. https://doi.org/10.21203/rs.3.rs-549787/v1 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. F. et al. Temperature elevation and Vibrio cyclitrophicus infection reduce the diversity of haemolymph microbiome of the mussel Mytilus coruscus. Sci. Rep. 9, 16391. https://doi.org/10.1038/s41598-019-52752-y (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Scanes, E. et al. Climate change alters the haemolymph microbiome of oysters. Mar. Pollut. Bull. 164, 111991. https://doi.org/10.1016/j.marpolbul.2021.111991 (2021).CAS 
Article 
PubMed 

Google Scholar 
Hylander, B. L. & Repasky, E. A. Temperature as a modulator of the gut microbiome: What are the implications and opportunities for thermal medicine?. Int. J. Hyperth. 36, 83–89. https://doi.org/10.1080/02656736.2019.1647356 (2019).CAS 
Article 

Google Scholar 
Lo Giudice, A. et al. Marine bacterioplankton diversity and community composition in an antarctic coastal environment. Microb. Ecol. 63, 210–223. https://doi.org/10.1007/s00248-011-9904-x (2012).Article 
PubMed 

Google Scholar 
Yumoto, I. et al. Temperature and nutrient availability control growth rate and fatty acid composition of facultatively psychrophilic Cobetia marina strain L-2. Arch. Microbiol. 181, 345–351. https://doi.org/10.1007/s00203-004-0662-8 (2004).CAS 
Article 
PubMed 

Google Scholar 
Weingarten, E. A., Atkinson, C. L. & Jackson, C. R. The gut microbiome of freshwater Unionidae mussels is determined by host species and is selectively retained from filtered seston. PLoS ONE 14, e0224796. https://doi.org/10.1371/journal.pone.0224796 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa, M., Ward, J. E. & Shumway, S. E. Selective capture and ingestion of particles by suspension-feeding bivalve molluscs: A review. J. Shellfish Res. 37, 727–746. https://doi.org/10.2983/035.037.0405 (2018).Article 

Google Scholar 
Griffiths, C. L. & King, J. A. Some relationships between size, food availability and energy balance in the ribbed mussel Aulacomya ater. Mar. Biol. 51, 141–149. https://doi.org/10.1007/BF00555193 (1979).Article 

Google Scholar 
Riisgård, H. U. Filtration rate and growth in the blue mussel, Mytilus edulis Linneaus, 1758: Dependence on algal concentration. J. Shellfish Res. 10, 29–36 (1991).
Google Scholar 
Sonier, R. et al. Picophytoplankton contribution to Mytilus edulis growth in an intensive culture environment. Mar. Biol. 163, 73. https://doi.org/10.1007/s00227-016-2845-7 (2016).Article 

Google Scholar 
Jacobs, P., Troost, K., Riegman, R. & Van der Meer, J. Length-and weight-dependent clearance rates of juvenile mussels (Mytilus edulis) on various planktonic prey items. Helgol. Mar. Res. 69, 101–112. https://doi.org/10.1007/s10152-014-0419-y (2015).ADS 
Article 

Google Scholar 
Ward, J. E. & Shumway, S. E. Separating the grain from the chaff: Particle selection in suspension- and deposit-feeding bivalves. J. Exp. Mar. 300, 83–130. https://doi.org/10.1016/j.jembe.2004.03.002 (2004).Article 

Google Scholar 
Waite, A. M., Safi, K. A., Hall, J. A. & Nodder, S. D. Mass sedimentation of picoplankton embedded in organic aggregates. Limnol. Oceanogr. 45, 87–97. https://doi.org/10.4319/lo.2000.45.1.0087 (2000).ADS 
Article 

Google Scholar 
Ward, J. E. & Kach, D. J. Marine aggregates facilitate ingestion of nanoparticles by suspension-feeding bivalves. Mar. Environ. Res. 68, 137–142. https://doi.org/10.1016/j.marenvres.2009.05.002 (2009).CAS 
Article 
PubMed 

Google Scholar 
Ward, J. E. Biodynamics of suspension-feeding in adult bivalve molluscs: Particle capture, processing, and fate. Invertebr. Biol. 115, 218–231. https://doi.org/10.2307/3226932 (1996).Article 

Google Scholar 
Rosa, M. et al. Physicochemical surface properties of microalgae and their combined effects on particle selection by suspension-feeding bivalve molluscs. J. Exp. Mar. 486, 59–68. https://doi.org/10.1016/j.jembe.2016.09.007 (2017).CAS 
Article 

Google Scholar 
Allam, B. & Espinosa, E. P. Bivalve immunity and response to infections: Are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12. https://doi.org/10.1016/j.fsi.2016.03.037 (2016).CAS 
Article 
PubMed 

Google Scholar 
Barr, J. J. et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl. Acad. Sci. USA 110, 10771–10776. https://doi.org/10.1073/pnas.1305923110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Allam, B. & Espinosa, E. P. Mucosal immunity in mollusks. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 325–370 (Academic Press, 2015).Chapter 

Google Scholar 
Huang, J. et al. Hemocytes in the extrapallial space of Pinctada fucata are involved in immunity and biomineralization. Sci. Rep. 8, 4657. https://doi.org/10.1038/s41598-018-22961-y (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. J. et al. Isolation and characterization of two bacteriophages and their preventive effects against pathogenic Vibrio coralliilyticus causing mortality of Pacific oyster (Crassostrea gigas) larvae. Microorganisms. 8, 926. https://doi.org/10.3390/microorganisms8060926 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Ihara, H. et al. Sulfur-oxidizing bacteria mediate microbial community succession and element cycling in launched marine sediment. Front. Microbiol. 8, 152. https://doi.org/10.3389/fmicb.2017.00152 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jørgensen, B. B. & Nelson, D. C. Sulfide oxidation in marine sediments: Geochemistry meets microbiology. Geol. S. Am. S. 379, 63–81. https://doi.org/10.1130/0-8137-2379-5.63 (2004).Article 

Google Scholar 
Zhou, M. et al. Surface currents and upwelling in Kerguelen Plateau regions. Biogeosci. Discuss. 11, 6845–6876. https://doi.org/10.5194/bgd-11-6845-2014 (2014).ADS 
Article 

Google Scholar 
Gille, S. T., Carranza, M. M., Cambra, R. & Morrow, R. Wind-induced upwelling in the Kerguelen Plateau region. Biogeosciences 11, 6389–6400. https://doi.org/10.5194/bg-11-6389-2014 (2014).ADS 
Article 

Google Scholar 
Park, Y. H., Roquet, F., Durand, I. & Fuda, J. L. Large-scale circulation over and around the Northern Kerguelen Plateau. Deep Sea Res. II(55), 566–581. https://doi.org/10.1016/j.dsr2.2007.12.030 (2008).ADS 
Article 

Google Scholar 
Renac, C. et al. Hydrothermal fluid interaction in basaltic lava units, Kerguelen Archipelago (SW Indian Ocean). Eur. J. 22, 215–234. https://doi.org/10.1127/0935-1221/2009/0022-1993 (2010).CAS 
Article 

Google Scholar 
Vancanneyt, M. et al. Sphingomonas alaskensis sp. nov., a dominant bacterium from a marine oligotrophic environment. Int. J. Syst. Evol. 51, 73–79. https://doi.org/10.1099/00207713-51-1-73 (2001).CAS 
Article 

Google Scholar 
Helmuth, B. S. & Hofmann, G. E. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull. 201, 374–384. https://doi.org/10.2307/1543615 (2001).CAS 
Article 
PubMed 

Google Scholar 
Testut, L., Wöppelmann, G., Simon, B. & Téchiné, P. The sea level at Port-aux-Français, Kerguelen Island, from 1949 to the present. Ocean Dyn. 56, 464–472. https://doi.org/10.1007/s10236-005-0056-8 (2006).ADS 
Article 

Google Scholar 
Pohl, B. et al. Recent climate variability around the Kerguelen Islands (Southern Ocean) seen through weather regimes. J. Appl. Meteorol. Climatol. 60, 711–731. https://doi.org/10.1175/JAMC-D-20-0255.1 (2021).ADS 
Article 

Google Scholar 
PROTEKER. Ilôt Channer (Passe Royale)—Sea water temperature at 5 and 13 m depth (T°C) daily average 2014–2019. https://www.proteker.net/swt-ilot-channer-passe-royale/ (2021).Caza, F. et al. Comparative analysis of hemocyte properties from Mytilus edulis desolationis and Aulacomya ater in the Kerguelen Islands. Mar. Environ. Res. 110, 174–182. https://doi.org/10.1016/j.marenvres.2015.09.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
Caza, F., Cledon, M. & St-Pierre, Y. Biomonitoring climate change and pollution in marine ecosystems: A review on Aulacomya ater. J. Mar. Biol. 2016, 7183813. https://doi.org/10.1155/2016/7183813 (2016).Article 

Google Scholar 
Rey-Campos, M. et al. High individual variability in the transcriptomic response of Mediterranean mussels to Vibrio reveals the involvement of myticins in tissue injury. Sci. Rep. 9, 3569. https://doi.org/10.1038/s41598-019-39870-3 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caza, F. et al. Hemocytes released in seawater act as Trojan horses for spreading of bacterial infections in mussels. Sci. Rep. 10, 19696. https://doi.org/10.1038/s41598-020-76677-z (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, C. L. & Somero, G. N. Thermal stress and cellular signaling processes in hemocytes of native (Mytilus californianus) and invasive (M. galloprovincialis) mussels: Cell cycle regulation and DNA repair. Comp. Biochem. Physiol. 165, 159–168. https://doi.org/10.1016/j.cbpa.2013.02.024 (2013).CAS 
Article 

Google Scholar 
Lockwood, B. L., Sanders, J. G. & Somero, G. N. Transcriptomic responses to heat stress in invasive and native blue mussels (genus Mytilus): Molecular correlates of invasive success. J. Exp. Biol. 213, 3548–3558. https://doi.org/10.1242/jeb.046094 (2010).CAS 
Article 
PubMed 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1. https://doi.org/10.1093/nar/gks808 (2013).CAS 
Article 
PubMed 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. & Blanchet, F. G. Vegan: Community Ecology Package. 2. 3-0 (2015).Ssekagiri, A., Sloan, W. & Ijaz, U. Z. microbiomeSeq: an R package for analysis of microbial communities in an environmental context, In ISCB Africa ASBCB Conference (Kumasi, Ghana, 2017).Cao, Y. Microbiome marker: Microbiome Biomarker Analysis Toolkit. R package version 0.99.0 (2020). https://github.com/yiluheihei/microbiomeMarker. Accessed March 2022.Kanehisa, M. et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).CAS 
Article 
PubMed 

Google Scholar 
Iwai, S. et al. Piphillin: Improved prediction of metagenomic content by direct inference from human microbiomes. PLoS ONE 11, e0166104. https://doi.org/10.1371/journal.pone.0166104 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. https://doi.org/10.1093/nar/gkx295 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

cpDNA haplotype databaseDNA sequencing of the choloroplast (cp) markers produced sequences of the following lengths: 573 bp (atpB-rbcL); 487 bp (petG-trnP); 500 bp (trnL1-trnL2); and 593 bp (psbM-trnD). Alignment of the 352 individuals from the 44 populations yielded a total 28 variable sites: 11 in the atpB-rbcL spacer, seven in both the petG-trnP and psbM-trnD spacers, and three in the trnL1-trnL2 spacer (Supplementary Table S1). Based on these 28 variable sites (21 base substitutions and 7 deletions) across the combined intergenic regions, a total of 22 unique haplotypes were found (Fig. 1a).Figure 1(a) Chloroplast haplotype distribution in the Shorea leprosula populations. The pie chart colours indicate haplotype distributions; and sector areas are proportional to sample size (Map was generated by ArcGIS-ArcMap version 10.8). (b) STRUCTURE analysis identified two clusters (K = 2) corresponding to Region A and B.Full size imageSSR allele frequency databaseThe reproducibility of SSR genotyping was confirmed by achieving consistent genotypes from five independent PCR amplifications on a single individual for each of the ten SSR loci. Individual bar plots from STRUCTURE analysis are presented in Fig. 1b. At the highest Delta K likelihood scores, the best representation of the data was K = 2 suggesting that the 44 populations in Peninsular Malaysia can be divided into two main genetic clusters: Region A and Region B. The first cluster, ‘Region A’ consists of 12 populations, namely SBadak, BPerangin, BEnggang, GJerai, RTelui, GInas, GBongsu, Belum, Piah, BHijau, Korbu and Bubu. The second cluster, ‘Region B’ consists of 32 populations, namely Behrang, Ampang, HGombak, HLangat, SLalang, PPanjang, Berembun, Angsi, Kenaboi, Triang, Pasoh, BSenggeh, GLedang, Krau, TNegara, Terenggun, SBetis, USat, CTongkat, HTerengganu, Jengai, AGading, Tekam, Beserah, Jengka, Lentang, Lesong, ERompin, GArong, Labis, AHitam and Panti. Similarly, the UPGMA dendrogram analysis also divided the 44 populations into two genetic clusters (Fig. 2) corresponding to Region A and B of the STRUCTURE result.Figure 2Dendrogram showing the relationship between 44 populations of Shorea leprosula in Peninsular Malaysia based on the UPGMA cluster analysis of SSR markers.Full size imageSSR allele frequency databases were established according to Region A and B, and characterized to evaluate the relative usefulness of each SSR marker in forensic investigation. The distribution of allele frequencies for each locus is listed in Table S2 (Region A database) and Table S3 (Region B database). Forensic parameters are shown in Table 1, with a total of 143 alleles and 174 alleles detected in the Region A and B databases, respectively. The observed (Ho) and expected (He) heterozygosity ranged from 0.3570 to 0.8346 and 0.4375 to 0.8795, respectively for populations in the Region A database; and ranged from 0.3298 to 0.8356 and 0.3469 to 0.8793, respectively for populations in the Region B database. The power of discrimination (PD) for the SSR loci ranged from 0.601 to 0.972 and 0.554 to 0.975, in Region A and B databases, respectively. The most discriminating locus was Sle605 in both the Region A (PD = 0.972) and Region B (PD = 0.975) databases. Minimum allele frequency was adjusted for alleles falling below the thresholds of 0.0066 (Region A) and 0.0024 (Region B).Table 1 Genetic diversity and forensic variables (A: total number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HWE: Hardy–Weinberg equilibrium; MP: matching probability; PD: power of discrimination) for each the 10 SSR loci of Shorea leprosula in the Region A and B databases.Full size tableDeviations from HWE were detected in four of the SSR loci for Region A (SleT11, SleT15, SleT17 and Sle465) and six SSR loci in Region B (SleT01, SleT11, SleT15, SleT17, SleT29 and SleT31). We evaluated these loci in each population independently to rule out the possible presence of null alleles. There were four populations in Region A (GJerai, RTelui, GBongsu and Piah) where a single one locus deviated from HWE; whereas there were eight populations in Region B (Behrang, HGombak, SLalang, Angsi, Klau, USat, Jengka and Panti) with a single locus and a single population (GLedang) with two loci that deviated from HWE (Table S4). Observed deviation from HWE was substantially lower in each population (either absence or not more than two loci) and thus it might be due to Wahlund effect caused by population substructuring in both Region A and B. Linkage disequilibrium (LD) testing was used to evaluate the independence of frequencies for all the SSR genotypes. A total of 13.3% and 28.9% of the 45 pairwise loci were found significant evidence of LD for Region A and B, respectively. Some of the loci might be linked as a result of population substructuring and inbreeding (inbreeding coefficient = 0.0822 [Peninsular Malaysia]). These results are in line with observations in real populations, where the assumption of completely random mating and zero migration required for HWE and LD are unlikely to be met, either in humans, animals or plants 21,22,23.Mean self-assignment, the proportion of individuals correctly assigned back to their population, was 45.9% and ranged from 14.3% (Kenaboi) to 81.3% (CTongkat) between population (Table 2). At the regional level, correct assignment rate of individuals to their region of origin was higher, 87.4% for Region A and 90.0% for Region B, (average of 88.7%).Table 2 Self-assignment test outcomes for Shorea leprosula individuals at the population and regional levels.Full size tableConservativeness of the databaseThe coancestry coefficient (θ) for Peninsular Malaysia (0.0579) was higher than those of Region A (0.0454) and Region B (0.0500) (Table 3). A total of 4.54% and 5.00% of the genetic variability was distributed among populations within Region A and Region B, respectively. In terms of inbreeding coefficient (f), the value for the Region A database (f = 0.0892) was highest, followed by Peninsular Malaysia (f = 0.0822) and Region B (f = 0.0666). All the θ and f values were significantly greater than zero, demonstrated by the 95% confidence intervals not overlapping with zero. Both of the θ and f values were used to calculate the conservativeness of each database by testing the cognate database (Porigin) against the regional database (Pcombined). The databases were non-conservative at the calculated θ value. In order for both the Region databases (A and B) to be conservative, the value of θ was adjusted from 0.0454 to 0.1900 for Region A and from 0.0500 to 0.1500 for Region B. For the Region A database, the most common SSR profile frequency is 2.69 × 10–7 or 1 in 3.72 million and the rarest profile frequency is 1.84 × 10–14 or 1 in 54.3 trillion. For the Region B database, the most common SSR profile frequency is 1.06 × 10–7 or 1 in 9.43 million and the rarest profile frequency is 4.03 × 10–16 or 1 in 2.48 quadrillion.Table 3 Coancestry (θ) and inbreeding (f) coefficients for Shorea leprosula at each hierarchical level.Full size table More

Game, E. T. et al. Pelagic protected areas: The missing dimension in ocean conservation. Trends Ecol. Evol. 24, 360–369 (2009).PubMed 
Article 

Google Scholar 
McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641–1255647 (2015).PubMed 
Article 
CAS 

Google Scholar 
Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950–2010. PLoS ONE 10, e0129342 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Croxall, J. P. et al. Seabird conservation status and threats: A global assessment of priorities. Bird Conserv. Int. 22, 1–34 (2012).Article 

Google Scholar 
Dias, M. P. et al. Threats to seabirds: A global assessment. Biol. Conserv. 237, 525–537 (2019).Article 

Google Scholar 
Trathan, P. N. et al. Pollution, habitat loss, fishing, and climate change as critical threats to penguins. Conserv. Biol. 29, 31–41 (2014).PubMed 
Article 

Google Scholar 
Boersma, D. et al. Applying science to pressing conservation needs for penguins. Conserv. Biol. 34, 103–112 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ropert-Coudert, Y. et al. Happy feet in a hostile world? The future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).Article 

Google Scholar 
Maestro, M., Pérez-Cayeiro, M. L., Chica-Ruiz, J. A. & Reyes, H. Marine protected areas in the 21st century: Current situation and trends. Ocean Coast. Manag. 171, 28–36 (2019).Article 

Google Scholar 
Hays, G. C. et al. Key questions in marine megafauna movement ecology. Trends Ecol. Evol. 31, 463–475 (2016).PubMed 
Article 

Google Scholar 
Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).Article 

Google Scholar 
Marra, P. P., Cohen, E. B., Loss, S. R., Rutter, J. E. & Tonra, C. M. A call for full annual cycle research in animal ecology. Biol. Lett. 11, 20150552 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kürten, N. et al. High individual repeatability of the migratory behaviour of a long-distance migratory seabird. Mov. Ecol. 10, 5 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Weimerskirch, H. et al. Lifetime foraging patterns of the wandering albatross: Life on the move!. J. Exp. Mar. Biol. Ecol. 450, 68–78 (2014).Article 

Google Scholar 
Trebilco, R., Gales, R., Baker, G. B., Terauds, A. & Sumner, M. D. At sea movement of Macquarie Island giant petrels: Relationships with marine protected areas and Regional Fisheries Management Organisations. Biol. Conserv. 141, 2942–2958 (2008).Article 

Google Scholar 
Clay, T. A. et al. A comprehensive large-scale assessment of fisheries bycatch risk to threatened seabird populations. J. Appl. Ecol. 56, 1882–1893 (2019).Article 

Google Scholar 
Meier, R. E. et al. Tracking, feather moult and stable isotopes reveal foraging behaviour of a critically endangered seabird during the non-breeding season. Divers. Distrib. 23, 130–145 (2017).Article 

Google Scholar 
Frankish, C. K., Phillips, R. A., Clay, T. A., Somveille, M. & Manica, A. Environmental drivers of movement in a threatened seabird: Insights from a mechanistic model and implications for conservation. Divers. Distrib. 26, 1315–1329 (2020).Article 

Google Scholar 
Ratcliffe, N. et al. Changes in prey fields increase the potential for spatial overlap between gentoo penguins and a krill fishery within a marine protected area. Divers. Distrib. 27, 552–563 (2021).Article 

Google Scholar 
Grémillet, D. et al. Persisting worldwide seabird-fishery competition despite seabird community decline. Curr. Biol. 28, 4009–4013 (2018).PubMed 
Article 
CAS 

Google Scholar 
Bogdanova, M. I. et al. Multi-colony tracking reveals spatio-temporal variation in carry-over effects between breeding success and winter. Mar. Ecol. Prog. Ser. 578, 167–181 (2017).Article 
ADS 

Google Scholar 
van Bemmelen, R. et al. Flexibility in otherwise consistent non-breeding movements of a long-distance migratory seabird, the long-tailed skua. Mar. Ecol. Prog. Ser. 578, 197–211 (2017).Article 
ADS 

Google Scholar 
Robinson, W. M. L., Butterworth, D. S. & Plagányi, É. E. Quantifying the projected impact of the South African sardine fishery on the Robben Island penguin colony. ICES J. Mar. Sci. 72, 1882–1883 (2015).Article 

Google Scholar 
Sherley, R. B. et al. Bottom-up effects of a no-take zone on endangered penguin demographics. Biol. Lett. 11, 20150237 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Studholme, K. R., Hipfner, J. M., Domalik, A. D., Ivrson, S. J. & Crossin, G. T. Year-round tracking reveals multiple migratory tactics in a sentinel North Pacific seabird, Cassin’s auklet. Mar. Ecol. Prog. Ser. 619, 169–185 (2019).Article 
ADS 

Google Scholar 
Salton, M., Saraux, C., Dann, P. & Chiaradia, A. Carry-over body mass effect from winter to breeding in a resident seabird, the little penguin. R. Soc. Open Sci. 2, 140390 (2015).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Barbraud, C. et al. Density dependence, prey accessibility and prey depletion by fisheries drive Peruvian seabird population dynamics. Ecography 41, 1092–1102 (2018).Article 

Google Scholar 
Grémillet, D. et al. Starving seabirds: Unprofitable foraging and its fitness consequences in Cape gannets competing with fisheries in the Benguela upwelling ecosystem. Mar. Biol. 163, 1–11 (2016).Article 

Google Scholar 
Cook, A. S. C. P., Dadam, D., Mitchell, I., Ross-Smith, V. H. & Robinson, R. A. Indicators of seabird reproductive performance demonstrate the impact of commercial fisheries on seabird populations in the North Sea. Ecol. Indic. 38, 1–11 (2014).Article 

Google Scholar 
Thiebot, J.-B. et al. Adjustment of pre-moult foraging strategies in Macaroni Penguins Eudyptes chrysolophus according to locality, sex and breeding status. Ibis 156, 511–522 (2014).Article 

Google Scholar 
Brasso, R. L. et al. Unique pattern of molt leads to low intraindividual variation in feather mercury concentrations in penguins. Environ. Toxicol. Chem. 32, 2331–2334 (2013).CAS 
PubMed 
Article 

Google Scholar 
Cooper, J. Moult of the black-footed penguin. Int. Zoo Yearb. 18, 22–27 (1978).Article 

Google Scholar 
Cherel, Y., Charrassin, J. & Challet, E. Energy and protein requirements for molt in the king penguin Aptenodytes patagonicus. Am. J. Physiol. 266, R1182–R1188 (1994).CAS 
PubMed 
Article 

Google Scholar 
Brown, C. R. Energetic cost of moult in macaroni penguins (Eudyptes chrysolophus) and rockhopper penguins (E. chrysocome). J. Comp. Physiol. B 155, 515–520 (1985).Article 

Google Scholar 
Dehnhard, N. et al. Survival of rockhopper penguins in times of global climate change. Aquat. Conserv. Mar. Freshw. Ecosyst. 23, 777–789 (2013).
Google Scholar 
Rebstock, G. & Boersma, D. Oceanographic conditions in wintering grounds affect arrival date and body condition in breeding female Magellanic penguins. Mar. Ecol. Prog. Ser. 601, 253–267 (2018).Article 
ADS 

Google Scholar 
Green, J. A., Boyd, I. L., Woakes, A. J., Warren, N. L. & Butler, P. J. Evaluating the prudence of parents: Daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus. J. Avian Biol. 40, 529–538 (2009).Article 

Google Scholar 
Crawford, R. J. M., Makhado, A. B., Upfold, L. & Dyer, B. M. Mass on arrival of rockhopper penguins at Marion Island correlated with breeding success. Afr. J. Mar. Sci. 30, 185–188 (2008).Article 

Google Scholar 
Crawford, R. J. M. et al. Food habits of an endangered seabird indicate recent poor forage fish availability off western South Africa. ICES J. Mar. Sci. 76, 1344–1352 (2019).
Google Scholar 
Okes, N. C. et al. Competition for shifting resources in the southern Benguela upwelling: Seabirds versus purse-seine fisheries. Biol. Conserv. 142, 2361–2368 (2009).Article 

Google Scholar 
Campbell, K. J. et al. Local forage fish abundance influences foraging effort and offspring condition in an endangered marine predator. J. Appl. Ecol. 56, 1751–1760 (2019).Article 

Google Scholar 
Grémillet, D. et al. Spatial match-mismatch in the Benguela upwelling zone: Should we expect chlorophyll and sea-surface temperature to predict marine predator distributions?. J. Appl. Ecol. 45, 610–621 (2008).Article 
CAS 

Google Scholar 
Sherley, R. B. et al. Metapopulation tracking juvenile penguins reveals an ecosystem-wide ecological trap. Curr. Biol. 27, 1–6 (2017).Article 
CAS 

Google Scholar 
Sherley, R. B. et al. Influence of local and regional prey availability on breeding performance of African penguins Spheniscus demersus. Mar. Ecol. Prog. Ser. 473, 291–301 (2013).Article 
ADS 

Google Scholar 
Cury, P. M. et al. Global seabird response to forage fish depletion—One-third for the birds. Science 334, 1703–1706 (2011).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Crawford, R. J. M. et al. Collapse of South Africa’s penguins in the early 21st century. Afr. J. Mar. Sci. 33, 139–156 (2011).Article 

Google Scholar 
Sherley, R. B. et al. The conservation status and population decline of the African penguin deconstructed in space and time. Ecol. Evol. 10, 8506–8516 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Weller, F. et al. A system dynamics approach to modelling multiple drivers of the African penguin population on Robben Island, South Africa. Ecol. Model. 277, 38–56 (2014).Article 

Google Scholar 
Pichegru, L. Increasing breeding success of an Endangered penguin: Artificial nests or culling predatory gulls?. Bird Conserv. Int. 23, 296–308 (2013).Article 

Google Scholar 
Weller, F. et al. System dynamics modelling of the Endangered African penguin populations on Robben and Dyer islands, South Africa. Ecol. Model. 327, 44–56 (2016).Article 

Google Scholar 
Pichegru, L. et al. Overlap between vulnerable top predators and fisheries in the Benguela upwelling system: Implications for marine protected areas. Mar. Ecol. Prog. Ser. 391, 199–208 (2009).Article 
ADS 

Google Scholar 
Sherley, R. B. et al. Bayesian inference reveals positive but subtle effects of experimental fishery closures on marine predator demographics. Proc. R. Soc. B 285, 20172443 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Pichegru, L., Grémillet, D., Crawford, R. J. M. & Ryan, P. G. Marine no-take zone rapidly benefits endangered penguin. Biol. Lett. 6, 498–501 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weller, F. et al. Penguins’ perilous conservation status calls for complementary approach based on sound ecological principles: Reply to Butterworth et al. (2015). Ecol. Model. 337, 1–3 (2016).Article 

Google Scholar 
Butterworth, D. S., Plagányi, E. E., Robinson, W. M. L., Moosa, N. & de Moor, C. L. Penguin modelling approach queried. Ecol. Model. 316, 78–80 (2015).Article 

Google Scholar 
Pichegru, L. et al. Sex-specific foraging behaviour and a field sexing technique for Endangered African penguins. Endanger. Species Res. 19, 255–264 (2013).Article 

Google Scholar 
Roberts, J. African Penguin (Spheniscus demersus) Distribution During the Non-breeding Season: Preparation for, and Recovery from, a Moulting Fast (University of Cape Town, 2016).
Google Scholar 
Dias, M. P. et al. Identification of marine Important Bird and Biodiversity Areas for penguins around the South Shetland Islands and South Orkney Islands. Ecol. Evol. 8, 10520–10529 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lascelles, B. G. et al. Applying global criteria to tracking data to define important areas for marine conservation. Divers. Distrib. 22, 422–431 (2016).Article 

Google Scholar 
Department of Forestry, Fisheries and Environment, T. National data and information report for marine spatial planning: Knowledge baseline for marine spatial planning in South Africa. (2021).Kirkman, S. P. et al. Evaluating the evidence for ecological effectiveness of South Africa’s marine protected areas. Afr. J. Mar. Sci. 43, 389–412 (2021).Article 

Google Scholar 
Harris, L. R. et al. Practical marine spatial management of ecologically or biologically significant marine areas: Emerging lessons from evidence-based planning and implementation in a developing-world context. Front. Mar. Sci. 9, 831678 (2022).Article 

Google Scholar 
Whitehead, T. O., Kato, A., Ropert-Coudert, Y. & Ryan, P. G. Habitat use and diving behaviour of macaroni Eudyptes chrysolophus and eastern rockhopper E. chrysocome filholi penguins during the critical pre-moult period. Mar. Biol. 163, 19 (2016).Article 

Google Scholar 
Warwick-Evans, V., Downie, R., Santos, M. & Trathan, P. N. Habitat preferences of Adélie Pygoscelis adeliae and Chinstrap Penguins Pygoscelis antarctica during pre-moult in the Weddell Sea (Southern Ocean). Polar Biol. 42, 703–714 (2019).Article 

Google Scholar 
Green, C.-P. et al. The role of allochrony in influencing interspecific differences in foraging distribution during the non-breeding season between two congeneric crested penguin species. PLoS ONE 17, e0262901 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pütz, K., Ingham, R. J. & Smith, J. G. Satellite tracking of the winter migration of Magellanic Penguins Spheniscus magellanicus breeding in the Falkland Islands. Ibis 142, 614–622 (2000).Article 

Google Scholar 
Pütz, K. et al. Post-moult movements of sympatrically breeding Humboldt and Magellanic Penguins in south-central Chile. Glob. Ecol. Conserv. 7, 49–58 (2016).Article 

Google Scholar 
Pütz, K., Ingham, R. J., Smith, J. G. & Lüthi, B. H. Winter dispersal of rockhopper penguins Eudyptes chrysocome from the Falkland Islands and its implications for conservation. Mar. Ecol. Prog. Ser. 240, 273–284 (2002).Article 
ADS 

Google Scholar 
Thiebot, J.-B., Cherel, Y., Trathan, P. N. & Bost, C. A. Coexistence of oceanic predators on wintering areas explained by population-scale foraging segregation in space or time. Ecology 93, 122–130 (2012).PubMed 
Article 

Google Scholar 
Thiebot, J.-B., Bost, C.-A., Poupart, T. A., Filippi, D. & Waugh, S. M. Extensive use of the high seas by Vulnerable Fiordland Penguins across non-breeding stages. J. Ornithol. 161, 1033–1043 (2020).Article 

Google Scholar 
Mattern, T. et al. Marathon penguins—Reasons and consequences of long-range dispersal in Fiordland penguins/Tawaki during the pre-moult period. PLoS ONE 13, e0198688 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bost, C.-A., Thiebot, J.-B., Pinaud, D., Cherel, Y. & Trathan, P. N. Where do penguins go during the inter-breeding period? Using geolocation to track the winter dispersion of the macaroni penguin. Biol. Lett. 5, 473–476 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baylis, A. M. M., Tierney, M., Orben, R. A., González de la Peña, D. & Brickle, P. Non-breeding movements of gentoo penguins at the Falkland Islands. Ibis 163, 507–518 (2021).Article 

Google Scholar 
Orgeret, F. et al. Exploration during early life: Distribution, habitat and orientation preferences in juvenile king penguins. Mov. Ecol. 7, 29 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thiebot, J. B., Lescroël, A., Barbraud, C. & Bost, C. A. Three-dimensional use of marine habitats by juvenile emperor penguins Aptenodytes forsteri during post-natal dispersal. Antarct. Sci. 25, 536–544 (2013).Article 
ADS 

Google Scholar 
Pütz, K. et al. Post-fledging dispersal of king penguins (Aptenodytes patagonicus) from two breeding sites in the South Atlantic. PLoS ONE 9, e97164 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Birt, V., Birt, T., Goulet, D., Cairns, D. & Montevecchi, W. Ashmole’s halo: Direct evidence for prey depletion by a seabird. Mar. Ecol. Prog. Ser. 40, 205–208 (1987).Article 
ADS 

Google Scholar 
Furness, R. W. & Birkhead, T. R. Seabird colony distributions suggest competition for food supplies during the breeding season. Nature 311, 655–656 (1984).Article 
ADS 

Google Scholar 
Carpenter-Kling, T. et al. Foraging in a dynamic environment: Response of four sympatric sub-Antarctic albatross species to interannual environmental variability. Ecol. Evol. 10, 11277–11295 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kowalczyk, N. D., Reina, R. D., Preston, T. J. & Chiaradia, A. Environmental variability drives shifts in the foraging behaviour and reproductive success of an inshore seabird. Oecologia 178, 967–979 (2015).PubMed 
Article 
ADS 

Google Scholar 
Machovsky-Capuska, G. E. et al. The nutritional nexus: Linking niche, habitat variability and prey composition in a generalist marine predator. J. Anim. Ecol. 87, 1286–1298 (2018).PubMed 
Article 

Google Scholar 
Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).PubMed 
Article 

Google Scholar 
Kappes, M. A. et al. Hawaiian albatrosses track interannual variability of marine habitats in the North Pacific. Prog. Oceanogr. 86, 246–260 (2010).Article 
ADS 

Google Scholar 
Bost, C. A. et al. Large-scale climatic anomalies affect marine predator foraging behaviour and demography. Nat. Commun. 6, 8220 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Brown, C. J. et al. Effects of climate-driven primary production change on marine food webs: Implications for fisheries and conservation. Glob. Chang. Biol. 16, 1194–1212 (2010).Article 
ADS 

Google Scholar 
Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).Article 

Google Scholar 
McInnes, A. M., Ryan, P. G., Lacerda, M. & Pichegru, L. Targeted prey fields determine foraging effort thresholds of a marine diver: Important cues for the sustainable management of fisheries. J. Appl. Ecol. 56, 2206–2215 (2019).Article 

Google Scholar 
van Eeden, R., Reid, T., Ryan, P. G. & Pichegru, L. Fine-scale foraging cues for African penguins in a highly variable marine environment. Mar. Ecol. Prog. Ser. 543, 257–271 (2016).Article 
ADS 

Google Scholar 
Coetzee, J. C., van der Lingen, C. D., Hutchings, L. & Fairweather, T. P. Has the fishery contributed to a major shift in the distribution of South African sardine?. ICES J. Mar. Sci. 65, 1676–1688 (2008).Article 

Google Scholar 
Blamey, L. K. et al. Ecosystem change in the southern Benguela and the underlying processes. J. Mar. Syst. 144, 9–29 (2015).Article 

Google Scholar 
Roy, C., Van Der Lingen, C. D., Coetzee, J. C. & Lutjeharms, J. R. E. Abrupt environmental shift associated with changes in the distribution of Cape anchovy Engraulis encrasicolus spawners in the southern Benguela. Afr. J. Mar. Sci. 29, 309–319 (2007).Article 

Google Scholar 
McInnes, A. M. et al. Small pelagic fish responses to fine-scale oceanographic conditions: Implications for the endangered African penguin. Mar. Ecol. Prog. Ser. 569, 187–203 (2017).CAS 
Article 
ADS 

Google Scholar 
Barange, M., Hampton, I. & Roel, B. A. Trends in the abundance and distribution of anchovy and sardine on the South African continental shelf in the 1990s, deduced from acoustic surveys. S. Afr. J. Mar. Sci. 21, 367–391 (1999).Article 

Google Scholar 
Hutchings, L. et al. Spawning on the edge: Spawning grounds and nursery areas around the southern African coastline. Mar. Freshw. Res. 53, 307–318 (2002).Article 

Google Scholar 
Verheye, H. M., Hutchings, L., Huggett, J. A. & Painting, S. J. Mesozooplankton dynamics in the Benguela ecosystem, with emphasis on the herbivorous copepods. S. Afr. J. Mar. Sci. 12, 561–584 (1992).Article 

Google Scholar 
Hutchings, L., Jarre, A., Lamont, T., van den Berg, M. & Kirkman, S. P. St Helena Bay (southern Benguela) then and now: Muted climate signals, large human impact. Afr. J. Mar. Sci. 34, 559–583 (2012).Article 

Google Scholar 
Goschen, W. S. & Schumann, E. H. Upwelling and the occurrence of cold water around Cape Recife, Algoa Bay, South Africa. S. Afr. J. Mar. Sci. 16, 57–67 (1995).Article 

Google Scholar 
Hutchings, L. et al. The Benguela Current: An ecosystem of four components. Prog. Oceanogr. 83, 15–32 (2009).Article 
ADS 

Google Scholar 
Goschen, W. S., Schumann, E. H., Bernard, K. S., Bailey, S. E. & Deyzel, S. H. P. Upwelling and ocean structures off Algoa Bay and the south-east coast of South Africa. Afr. J. Mar. Sci. 34, 525–536 (2012).Article 

Google Scholar 
van der Lingen, C. D. Diet of sardine Sardinops sagax in the southern Benguela upwelling ecosystem. S. Afr. J. Mar. Sci. 24, 301–316 (2002).Article 

Google Scholar 
van der Lingen, C. D., Hutchings, L. & Field, J. G. Comparative trophodynamics of anchovy Engraulis encrasicolus and sardine Sardinops sagax in the southern Benguela: Are species alternations between small pelagic fish trophodynamically mediated?. Afr. J. Mar. Sci. 28, 465–477 (2006).Article 

Google Scholar 
Wright, K. L. B., Pichegru, L. & Ryan, P. G. Penguins are attracted to dimethyl sulphide at sea. J. Exp. Biol. 214, 2509–2511 (2011).PubMed 
Article 

Google Scholar 
Hagen, C. et al. Evaluating the state of knowledge on fishing exclusions around major African Penguin colonies. (2014).Fort, J. et al. Multicolony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea ice cover. Divers. Distrib. 19, 1322–1332 (2013).Article 

Google Scholar 
Reiertsen, T. K. et al. Prey density in non-breeding areas affects adult survival of black-legged kittiwakes Rissa tridactyla. Mar. Ecol. Prog. Ser. 509, 289–302 (2014).Article 
ADS 

Google Scholar 
Fayet, A. L. et al. Ocean-wide drivers of migration strategies and their influence on population breeding performance in a declining seabird. Curr. Biol. 27, 3871–3878 (2017).CAS 
PubMed 
Article 

Google Scholar 
Desprez, M., Jenouvrier, S., Barbraud, C., Delord, K. & Weimerskirch, H. Linking oceanographic conditions, migratory schedules and foraging behaviour during the non-breeding season to reproductive performance in a long-lived seabird. Funct. Ecol. 32, 2040–2053 (2018).Article 

Google Scholar 
Randall, R. M. & Randall, B. The annual cycle of the Jackass Penguin Spheniscus demersus at St Croix Island, South Africa. In Proc. Symp. Birds Sea Shore 427–450 (1981).Wolfaardt, A. C., Underhill, L. G. & Visagie, J. Breeding and moult phenology of African penguins Spheniscus demersus at Dassen Island. Afr. J. Mar. Sci. 31, 119–132 (2009).Article 

Google Scholar 
Crawford, R. J. M. et al. Molt of the African penguin, Spheniscus demersus, in relation to its breeding season and food availability. Acta Zool. Sin. 52, 444–447 (2006).
Google Scholar 
Randall, R. M. Biology of the Jackass Penguin Spheniscus demersus (L.) at St Croix, South Africa (Univeristy of Port Elizabeth, 1983).
Google Scholar 
Harding, C. T. Tracking African Penguins (Spheniscus demersus) Outside of the Breeding Season: Regional Effects and Fishing Pressure During the Pre-moult Period (University of Cape Town, 2013).
Google Scholar 
Wilson, R. P. The Jackass Penguin (Spheniscus demersus) as a pelagic predator. Mar. Ecol. Prog. Ser. 25, 219–227 (1985).Article 
ADS 

Google Scholar 
Freitas, C. argosfilter: Argos locations filter. (2012).Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Calenge, C. The package ‘adehabitat’ for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Vander Wal, E. & Rodgers, A. R. An individual-based quantitative approach for delineating core areas of animal space use. Ecol. Model. 224, 48–53 (2012).Article 

Google Scholar 
Dinno, A. dunn.test: Dunn’s Test of Multiple Comparisons Using Rank Sums (2017).Bhattacharyya, A. On a measure of divergence between two multinomial populations. Indian J. Stat. 7, 401–406 (1946).MathSciNet 
MATH 

Google Scholar 
Beal, M. et al. track2KBA: An R package for identifying important sites for biodiversity from tracking data. Methods Ecol. https://doi.org/10.1111/2041-210X.13713 (2021).Article 

Google Scholar 
Donald, P. F. et al. Important Bird and Biodiversity Areas (IBAs): The development and characteristics of a global inventory of key sites for biodiversity. Bird Conserv. 29, 177–198 (2019).Article 

Google Scholar 
Handley, J. M. et al. Evaluating the effectiveness of a large multi-use MPA in protecting Key Biodiversity Areas for marine predators. Divers. Distrib. 26, 715–729 (2020).Article 

Google Scholar 
Strimas-Mackey, M. smoothr: Smooth and tidy spatial features. R package version 0.2.2. https://CRAN.R-project.org/package=smoothr (2018).Department of Forestry Fisheries and the Environment, T. South Africa Marine Protected Area Zonations (SAMPAZ_OR_2021_Q3). https://egis.environment.gov.za/data_egis/data_dow (2021).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2021). More

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