Ecology

Matthews, E. G., Lawrence, J. F., Bouchard, P., Steiner, W. E. Jr. & Ślipiński, S. A. Tenebrionidae Latreille, 1802. In Handbook of Zoology. A Natural History of the Phyla of the Animal Kingdom. Vol. IV—Arthropoda: Insecta. Part 38 Coleoptera, Beetles. Vol. 2: Systematics (Part 2) (eds Leschen, R. A. B. et al.) 574–659 (Walter de Gruyter GmbH & Co, 2010).
Google Scholar 
Kergoat, G. J. et al. Higher-level molecular phylogeny of darkling beetles (Coleoptera: Tenebrionidae). Syst. Entomol. 39, 486–499. https://doi.org/10.1111/syen.12065 (2014).Article 

Google Scholar 
Bouchard, P. et al. Review of genus-group names in the family Tenebrionidae (Insecta, Coleoptera). Zookeys 26, 1–633. https://doi.org/10.3897/zookeys.1050.64217 (2021).Article 

Google Scholar 
Matthews, E. G. & Bouchard, P. Tenebrionid Beetles of Australia 398 (Australian Biological Resources Study, 2008).
Google Scholar 
Thomas, D. B. J. R. Patterns in the abundance of some tenebrionid beetles in the Mojave Desert. Environ. Entomol. 8, 568–657 (1979).Article 

Google Scholar 
Seely, M. K. & Louw, G. N. First approximation of the effects of rainfall on the ecology and energetics of a Namib Desert dune ecosystem. J. Arid Environ. 3, 25–54 (1980).ADS 
Article 

Google Scholar 
Crawford, C. S. The community ecology of macroarthropod detritivores. In The Ecology of Desert Communities (ed. Polis, G. A.) 89–112 (The University of Arizona Press, 1991).
Google Scholar 
Mordkovich, V. G. Species richness, population structure and functional significance of black-beetles (Coleoptera: Tenebrionidae) in steppes of Northern Asia. Russ. Entomol. J. 11, 57–68 (2002).
Google Scholar 
Bartholomew, A. & El Moghrabi, J. Seasonal preference of darkling beetles (Tenebrionidae) for shrub vegetation due to high temperatures, not predation or food availability. J. Arid Environ. 156, 34–40 (2018).ADS 
Article 

Google Scholar 
Cheli, G. H., Bosco, T. & Flores, G. The role of Nyctelia dorsata Fairmaire, 1905 (Coleoptera: Tenebrionidae) on litter fragmentation processes and soil biogeochemical cycles in arid Patagonia. Ann. Zool. 72, 129–134. https://doi.org/10.3161/00034541ANZ2022.72.1.011 (2022).Article 

Google Scholar 
Nørgaard, T. & Dacke, M. Fog-basking behaviour and water collection efficiency in Namib Desert Darkling beetles. Front. Zool. 7, 23. https://doi.org/10.1186/1742-9994-7-23 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Comanns, P. Passive water collection with the integument: Mechanisms and their biomimetic potential. J. Exp. Biol. 221, jeb153130. https://doi.org/10.1242/jeb.153130 (2018).Article 
PubMed 

Google Scholar 
Doyen, J. T. Familial and subfamilial classification of the Tenebrionoidea (Coleoptera) and a revised generic classification of the Coniontini (Tentyriidae). Quest. Entomol. 8, 357–376 (1972).
Google Scholar 
Schulze, L. The Tenebrionidae of Southern Africa. XLII. Description of the early stages of Carchares macer Pascoe and Herpiscus sommeri Solier with a discussion of some phylogenetic aspects arising from the incongruities of adult and larval systematics. Sci. Pap. Namib Desert Res. Stn. 53, 139–149 (1969).
Google Scholar 
Kamiński, M. J. et al. Reevaluation of Blapimorpha and Opatrinae: Addressing a major phylogeny-classification gap in darkling beetles (Coleoptera: Tenebrionidae: Blaptinae). Syst. Entomol. 46, 140–156. https://doi.org/10.1111/syen.12453 (2021).Article 

Google Scholar 
Skopin, N. G. [Larvae of the subfamily Pimeliinae (Coleoptera, Tenebrionidae)]. Lichinki podsemeystva Pimeliinae (Coleoptera, Tenebrionidae). Trudy Nauchno-Issledovatelskogo Instituta Zashchity Rastenii Kazakhstanskoy Akademii Selskokhozyastvennykh Nauk 7, 191–298 (1962).
Google Scholar 
Skopin, N. G. Die Larven der Tenebrioniden des Tribus Pycnocerini (Coleoptera, Heteromera). Ann. Museé R. l’Afrique Centrale 127, 1–35 (1964).
Google Scholar 
Iwan, D. & Bečvář, S. Description of the early stages of Anomalipus plebejus plebejulus (Coleoptera: Tenebrionidae) from Zimbabwe with notes on the classifcation of the Opatrinae. Eur. J. Entomol. 97, 403–412 (2000).Article 

Google Scholar 
Koch, C. Monograph of the Tenebrionidae of southern Africa Vol I (Tentyriinae, Molurini Trachynotina: Somaticus Hope). Transvaal Mus. Mem. 7, 242 (1955).
Google Scholar 
Kergoat, G. J. Cretaceous environmental changes led to high extinction rates in a hyperdiverse beetle family. BMC Evol. Biol. 14, 220. https://doi.org/10.1186/s12862-014-0220-1 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Smith, A. D., Dornburg, R. & Wheeler, Q. D. Larvae of the genus Eleodes (Coleoptera, Tenebrionidae): Matrix-based descriptions, cladistic analysis, and key to late instars. Zookeys 415, 217–268 (2014).Article 

Google Scholar 
Kamiński, M. J. et al. Immature stages of beetles representing the ‘Opatrinoid’ clade (Coleoptera: Tenebrionidae): An overview of current knowledge of the larval morphology and some resulting taxonomic notes on Blapstinina. Zoomorphology 138, 349–370. https://doi.org/10.1007/s00435-019-00443-7 (2019).Article 

Google Scholar 
Rasa, O. A. E. Bechavioural adaptations to moisture as an environmental constraint in a nocturnal burrow-linhabiting Kalahari detritivore Parastizopus amraticpes Peringuey (Coleoptera: Tenebrionidae). Koedoe 37(1), 57–66 (1994).Article 

Google Scholar 
Rasa, O. A. E. Ecological factors influencing burrow location, group size and mortality in a nocturnal fossorial Kalahari detritivore, Parastizopus armaticeps Peringuey (Coleoptera: Tenebrionidae). J. Arid Environ. 29, 353–365 (1995).ADS 
Article 

Google Scholar 
Fabricius, J. C. Supplementum Entomologia Systematica. (Impensis CG Proft, 1978).Péringuey, L. Fourth contribution to the South African coleopterous fauna. Description of new Coleoptera in the South African Museum. Trans. S. Afr. Philos. Soc. 6, 95–136 (1892).Article 

Google Scholar 
Endrody-Younga, S. A revision of the subtribe Gonopina (Coleoptera: Tenebrionidae: Opatrinae: Platynotini). Ann. Transvaal Mus. 37, 1–54 (2000).
Google Scholar 
Kamiński, M. J. Notes on species diversity patterns in Stizopina (Coleoptera: Tenebrionidae), with description of a new genus from Nama Karoo. Ann. Zool. 65, 131–148. https://doi.org/10.3161/00034541ANZ2015.65.2.002 (2015).Article 

Google Scholar 
Schulze, L. The Tenebrionidae of Southern Africa. XXXVIII. On the morphology of the larvae of some Stizopina (Coleoptera: Opatrini). Sci. Pap. Namib Desert Res. Stn. 19, 1–23 (1963).
Google Scholar 
Schulze, L. A review of silk production and spinning activities in Arthropoda with special reference to spinning in Tenebrionid larvae (Coleoptera) and Brown, J. M. M.: A chromatographic analysis of Tenebrionid silk. Mem. Transvaal Mus. 51, 409–410 (1975).
Google Scholar 
Rasa, O. A. E. & Endrödy-Younga, S. Intergeneric associations of stizopinid tenebrionids relative to their geographical distribution (Coleoptera: Tenebrionidae: Opatrini: Stitzopina). Afr. Entomol. 5, 231–239 (1997).
Google Scholar 
Kamiński, M. J., Raś, M., Steiner, W. E. & Iwan, D. Immature stages of beetles representing the ‘Opatrinoid’ clade (Coleoptera: Tenebrionidae): An overview of current knowledge of the pupal morphology. Ann. Zool. 68, 825–836. https://doi.org/10.3161/00034541ANZ2018.68.4.006 (2018).Article 

Google Scholar 
Doyen, J. T. The skeletal anatomy of Tenebrio molitor (Coleoptera: Tenebrionidae). Ann. Entomol. Soc. Am. 5, 103–150 (1966).
Google Scholar 
Ohde, T., Yaginuma, T. & Niimi, T. Insect morphological diversification through the modification of wing serial homologs. Science 340, 495 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhu, J. Y., Yang, P., Zhang, Z., Wu, G. X. & Yang, B. Transcriptomic immune response of Tenebrio molitor pupae to parasitization by Scleroderma guani. PLoS ONE 8, e54411 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raś, M., Iwan, D. & Kamiński, M. J. Tracheal system in post-embryonic development of holometabolous insects: A case study using mealworm beetle. J. Anat. 232, 997–1015. https://doi.org/10.1111/joa.12808 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Kwon, G. T. et al. Mealworm larvae (Tenebrio molitor L.) exuviae as a novel prebiotic material for BALB/c mouse gut microbiota. Food Sci. Biotechnol. 29(4), 531–537. https://doi.org/10.1007/s10068-019-00699-1 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Machona, O., Chidzwondo, F. & Mangoyi, R. Tenebrio molitor: Possible source of polystyrene-degrading bacteria. BMC Biotechnol. 22, 2. https://doi.org/10.1186/s12896-021-00733-3 (2022).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jösting, E. A. Die Innervierung des Skelettmuskelsystems des Mehlwurms (Tenebrio molitor L., Larve). Zool. Jb. Anat. 67, 381–460 (1942).
Google Scholar 
Burakowski, B., Mroczkowski, M. & Stefańska, J. Chrząszcze: Coleoptera. Cucujoidea, Część 3. Katalog Fauny Polski, XXIII, 14 (1987).Schulze, L. The Tenebrionidae of southern Africa. XXXIII. Description of the larvae of Gonopus tibialis Fabricius and Gonopus agrestis Fahraeus (Gonopina, sensu Koch 1956). Cimbebasia 5, 1–12 (1962).
Google Scholar 
Lawrence, J. F., Pollock, D. A. & Ślipiński, A. Tenebrionoidea. In Handbook of Zoology. A Natural History of the Phyla of the Animal kingdom, Vol. IV. Arthropoda: Insecta (eds Leschen, R. A. B. et al.) 487–659 (Walter de Gruyter, 2010).
Google Scholar 
Lawrence, J. F. et al. Phylogeny of the Coleoptera based on morphological characters of adults and larvae. Ann. Zool. 61(1), 1–217 (2011).Article 

Google Scholar 
Beutel, R. G. & Friedrich, F. Comparative study of larvae of Tenebrionoidea (Coleoptera: Cucujiformia). Eur. J. Entomol. 102, 241–264 (2005).Article 

Google Scholar 
Fredrich, F. & Beutel, R. G. The thorax of Zorotypus (Hexapoda, Zoraptera) and a new nomenclature for the musculature of Neoptera. Arthropod Struct. Dev. 37, 29–54 (2008).Article 

Google Scholar 
Beutel, R. G., Friedrich, F., Yang, X.-K. & Ge, S.-Q. Insect Morphology and Phylogeny: A Textbook for Students of Entomology 515 (Walter de Gruyter, 2014).
Google Scholar 
Aibekova, L. et al. The skeletomuscular system of the mesosoma of Formica rufa workers (Hymenoptera: Formicidae). Insect Syst. Divers. 6(2), 1–26. https://doi.org/10.1093/isd/ixac002 (2022).Article 

Google Scholar 
Raś, M. Digging adaptations in psammophilous beetle larvae. Harvard Dataverse https://doi.org/10.7910/DVN/NNAETE (2022).SkyScan. Method Notes, Skyscan 1172 Desktop Micro-CT (Skyscan, 2008).
Google Scholar 
R Core Team. 2020. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020) https://www.R-project.org/.Sokal, R. R. & Rohlf, F. J. Biometry 937 (W.H. Freeman, 2011).
Google Scholar 
Cloudsley-Thompson, J. L. Terrestrial animals in dry heat: Arthropods. In Handbook of Physiology. Section 4: Adaptation to the Environment 414–436 (American Physiological Society, 1964).
Google Scholar 
Cloudsley-Thompson, J. L. Adaptations of Arthropoda to arid environments. Annu. Rev. Entomol. 20, 261–283. https://doi.org/10.1146/annurev.en.20.010175.001401 (1975).CAS 
Article 
PubMed 

Google Scholar 
Draney, M. L. The subelytral cavity of desert tenebrionids. Fla. Entomol. 76, 539–549 (1993).Article 

Google Scholar 
Duncan, F. D. The role of the subelytral cavity in water loss in the flightless dung beetle, Circellium bacchus (Coleoptera: Scarabaeinae). Eur. J. Entomol. 99(2), 253–258. https://doi.org/10.14411/eje.2002.034 (2002).Article 

Google Scholar 
Endrödy-Younga, S. & Tschinkel, W. Estimation of population size and dispersal in Anomalipus mastodon Fåhraeus, 1870 (Coleoptera: Tenebrionidae: Platynotini). Ann. Transvaal Mus. 36(4), 21–30 (1993).
Google Scholar 
Iwan, D. Insecta Coleoptera Tenebrionidae Pedinini Platynotina. Vol. 93 of Faune de Madagascar 178 (Editions Quae, 2010).
Google Scholar 
Wallwork, J. A. Desert Soil Fauna 296 (Praeger Publication, 1982).
Google Scholar 
Iwan, D. Oviviparity in tenebrionid beetles of the melanocratoid Platynotina (Coleoptera: Tenebrionidae: Platynotini) from Madagascar with notes on the viviparous beetles. Ann. Zool. 50, 15–25 (2000).
Google Scholar 
Kaufmann, T. Observations on some factors which influence aggregated by Blaps sulcata in Israel. Ann. Entomol. Soc. Am. 59, 660–664 (1966).Article 

Google Scholar 
Kiihnelt, G. On the biology and temperature accommodation of Lepidochora argentogrisea Koch. Sci. Pap. Namib Desert Res. Stn. 51, 121–122 (1969).
Google Scholar 
Hamilton, W. J. Competition and thermoregulatory behaviour of the Namib desert tenebrionid beetle genus Cardiosis. Ecology 52, 810–822 (1971).Article 

Google Scholar 
Watt, J. A revised subfamily classifcation of Tenebrionidae (Coleoptera). N. Z. J. Zool. 11, 381–452 (1974).Article 

Google Scholar 
Burakowski, B. Laboratory methods for rearing soil beetles (Coleoptera). Memorab. Zool. 46, 1–66 (1993).
Google Scholar 
De Block, M. & Stoks, R. Fitness effects from egg to reproduction: Bridging the life history transition. Ecology 86, 185–197 (2005).Article 

Google Scholar 
Pechenik, J. A. Larval experience and latent effects: Metamorphosis is not a new beginning. Integr. Comp. Biol. 46, 323–333 (2006).PubMed 
Article 

Google Scholar 
Doyen, J. T. Reconstitution of Coelometopini, Tenebrionini and related tribes of America north of Colombia (Coleoptera: Tenebrionidae). J. N. Y. Entomol. Soc. 97, 277–304 (1989).
Google Scholar 
St. George, R. A. Studies on the larvae on North American beetles of the subfamily Tenebrioninae with a description of the larva and pupa of Merinus laevis (Olivier). Proc. U.S. Natl. Mus. 65, 1–22. https://doi.org/10.5479/si.00963801.65-2514.1 (1924).Article 

Google Scholar 
Purchart, L. & Nabozhenko, M. V. First description of larva and pupa of the genus Deretus (Coleoptera: Tenebrionidae) with key to the larvae of the tribe Helopini. Acta Entomol. Musei Natl. Pragae 52, 295–302 (2012).
Google Scholar 
Steiner, W. Larvae and pupae of two North American darkling beetles (Coleoptera, Tenebrionidae, Stenochiinae), Glyptotus cribratus LeConte and Cibdelis blaschkei Mannerheim, with notes on ecological and behavioural similarities. ZooKeys 415, 311–327. https://doi.org/10.3897/zookeys.415.6891 (2014).Article 

Google Scholar 
Wagner, G. & Gosik, R. Comparative morphology of immature stages of two sympatric Tenebrionidae species, with comments on their biology. Zootaxa 4111, 201–222 (2017).Article 

Google Scholar  More

Hu, S., Niu, Z., Chen, Y., Li, L. & Zhang, H. Global wetlands: Potential distribution, wetland loss, and status. Sci. Total Environ. 586, 319–327 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Guo, M., Li, J., Sheng, C., Xu, J. & Wu, L. A review of wetland remote sensing. Sensors 17, 777 (2017).ADS 
PubMed Central 
Article 

Google Scholar 
Mingwu, Z., Haijiang, J., Desuo, C. & Chunbo, J. The comparative study on the ecological sensitivity analysis in Huixian karst wetland, China. Procedia Environ. Sci. 2, 386–398 (2010).Article 

Google Scholar 
Li, Z., Jin, Z. & Li, Q. Changes in Land Use and their Effectson Soil Properties in Huixian KarstWetland System. Pol. J. Environ. Stud. 26, 699–707 (2017).Article 

Google Scholar 
Jiang, X., Xiong, Z., Liu, H., Liu, G. & Liu, W. Distribution, source identification, and ecological risk assessment of heavy metals in wetland soils of a river–reservoir system. Environ. Sci. Pollut. Res. 24, 436–444 (2016).Article 
CAS 

Google Scholar 
Fu, B. et al. Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data. Int. J. Appl. Earth Obs. Geoinf. 104, 102553 (2021).
Google Scholar 
Xu, D. et al. Distribution, speciation, environmental risk, and source identification of heavy metals in surface sediments from the karst aquatic environment of the Lijiang River, Southwest China. Environ. Sci. Pollut. Res. 23, 9122–9133 (2016).CAS 
Article 

Google Scholar 
Gao, P. et al. Spatial and temporal changes of P and Ca distribution and fractionation in soil and sediment in a karst farmland-wetland system. Chemosphere 220, 644–650 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gil-Márquez, J. M., Barberá, J. A., Andreo, B. & Mudarra, M. Hydrological and geochemical processes constraining groundwater salinity in wetland areas related to evaporitic (karst) systems. A case study from Southern Spain. J. Hydrol. 544, 538–554 (2017).Chamberlin, C. A. et al. Mass balance implies Holocene development of a low-relief karst patterned landscape. Chem. Geol. 527, 118782 (2019).ADS 
CAS 
Article 

Google Scholar 
Watts, A. C. et al. Evidence of biogeomorphic patterning in a low-relief karst landscape. Earth Surf. Proc. Land. 39, 2027–2037 (2014).ADS 
Article 

Google Scholar 
Fan, Z., Li, J., Yue, T., Zhou, X. & Lan, A. Scenarios of land cover in Karst area of Southwestern China. Environ. Earth Sci. 74, 6407–6420 (2015).Article 

Google Scholar 
Wang, S., Zhang, L., Zhang, H., Han, X. & Zhang, L. Spatial-temporal wetland landcover changes of poyang lake derived from landsat and HJ-1A/B data in the dry season from 1973–2019. Remote Sens. 12, 1595 (2020).ADS 
Article 

Google Scholar 
Szabó, L., Deák, B., Bíró, T., Dyke, G. J. & Szabó, S. NDVI as a proxy for estimating sedimentation and vegetation spread in artificial lakes—monitoring of spatial and temporal changes by using satellite images overarching three decades. Remote Sens. 12, 1468 (2020).ADS 
Article 

Google Scholar 
Malekmohammadi, B. & Rahimi Blouchi, L. Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. Ecol. Indic. 41, 133–144 (2014).Article 

Google Scholar 
Tian, Y. et al. Monitoring invasion process of spartina alterniflora by seasonal sentinel-2 imagery and an object-based random forest classification. Remote Sens. 12, 1383 (2020).ADS 
Article 

Google Scholar 
Lane, C. et al. Improved wetland classification using eight-band high resolution satellite imagery and a hybrid approach. Remote Sens. 6, 12187–12216 (2014).ADS 
Article 

Google Scholar 
Betbeder, J., Rapinel, S., Corgne, S., Pottier, E. & Hubert-Moy, L. TerraSAR-X dual-pol time-series for mapping of wetland vegetation. ISPRS J. Photogramm. Remote. Sens. 107, 90–98 (2015).ADS 
Article 

Google Scholar 
Franklin, S. E., Skeries, E. M., Stefanuk, M. A. & Ahmed, O. S. Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: A case study in the Hudson Bay Lowlands Ecoregion. Int. J. Remote Sens. 39, 1615–1627 (2017).Article 

Google Scholar 
Cao, J. et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10, 89 (2018).ADS 
Article 

Google Scholar 
Liu, T. & Abd-Elrahman, A. Multi-view object-based classification of wetland land covers using unmanned aircraft system images. Remote Sens. Environ. 216, 122–138 (2018).ADS 
Article 

Google Scholar 
Churches, C. E., Wampler, P. J., Sun, W. & Smith, A. J. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data. Int. J. Appl. Earth Obs. Geoinf. 30, 203–216 (2014).ADS 

Google Scholar 
Gerke, M. & Xiao, J. Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification. ISPRS J. Photogramm. Remote. Sens. 87, 78–92 (2014).ADS 
Article 

Google Scholar 
Maulik, U. & Chakraborty, D. Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery. ISPRS J. Photogramm. Remote. Sens. 77, 66–78 (2013).ADS 
Article 

Google Scholar 
Crasto, N. et al. A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta. Remote Sens. Environ. 164, 90–102 (2015).ADS 
Article 

Google Scholar 
O’Neil, G. L., Goodall, J. L. & Watson, L. T. Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification. J. Hydrol. 559, 192–208 (2018).ADS 
Article 

Google Scholar 
Howard, A. G. Some improvements on deep convolutional neural network based image classification. arXiv.org https://doi.org/10.48550/arXiv.1805.07836 (2013).Yao, X. et al. Land use classification of the deep convolutional neural network method reducing the loss of spatial features. Sensors 19, 2792 (2019).ADS 
PubMed Central 
Article 

Google Scholar 
Chen, Y., Fan, R., Yang, X., Wang, J. & Latif, A. Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water 10, 585 (2018).Article 

Google Scholar 
Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018).ADS 
Article 

Google Scholar 
Srinivas, S., Subramanya, A. & Babu, R. V. Training Sparse Neural Networks. in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (IEEE, 2017).Liang, S., Lan, Y., Jiang, S., Li, Y. & Lu, Z. The activities of microbial communities in Huixian Wetland sediments under the interactive toxicity of Cu(II) and pentachloronitrobenzene. Acta Ecol. Sin. 37, 379–391 (2017).Article 

Google Scholar 
Feng, W. Fish diversity in huixian wetland in guangxi. Wetland Science 44, (2017).Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Mutanga, O., Adam, E. & Cho, M. A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf. 18, 399–406 (2012).ADS 

Google Scholar 
van Beijma, S., Comber, A. & Lamb, A. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens. Environ. 149, 118–129 (2014).ADS 
Article 

Google Scholar 
Badrinarayanan, V., Kendall, A. & Cipolla, R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495 (2017).PubMed 
Article 

Google Scholar 
Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Int. Conf. Mach. Learn. 37, 448–456 (2015).
Google Scholar 
Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3431–3440 (IEEE, 2015).Chen, L.-C., Barron, J. T., Papandreou, G., Murphy, K. & Yuille, A. L. semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 4545–4546 (IEEE, 2016).Eigen, D. & Fergus, R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. in 2015 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2015).Hu, Y. et al. Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland. Acta Oceanol. Sin. 38, 142–150 (2019).Article 

Google Scholar 
Liu, F. & Fang, M. Semantic segmentation of underwater images based on improved Deeplab. J. Marine Sci. Eng. 8, 188 (2020).Article 

Google Scholar 
Dronova, I. Object-based image analysis in wetland research: A review. Remote Sens. 7, 6380–6413 (2015).ADS 
Article 

Google Scholar 
Zhang, Z. & Sabuncu, M. R. Generalized cross entropy loss for training deep neural networks with noisy labels. arXiv.org https://arxiv.org/abs/1805.07836 (2018).Ruder, S. An overview of gradient descent optimization algorithms. arXiv.org https://arxiv.org/abs/1609.04747 (2016).Song, S. et al. Intelligent object recognition of urban water bodies based on deep learning for multi-source and multi-temporal high spatial resolution remote sensing imagery. Sensors 20, 397 (2020).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
Sun, G. et al. Fusion of multiscale convolutional neural networks for building extraction in very high-resolution images. Remote Sens. 11, 227 (2019).ADS 
Article 

Google Scholar 
Al-Najjar, H. A. H. et al. Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sens. 11, 1461 (2019).ADS 
Article 

Google Scholar 
Villoslada, M. et al. Fine scale plant community assessment in coastal meadows using UAV based multispectral data. Ecol. Ind. 111, 105979 (2020).Article 

Google Scholar 
Zhao, H. & Liu, H. Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granul. Comput. 5, 411–418 (2019).Article 

Google Scholar 
Hu, K., Zhang, S. & Zhao, X. Context-based conditional random fields as recurrent neural networks for image labeling. Multimedia Tools Appl. 79, 17135–17145 (2019).Article 

Google Scholar 
Wang, M. et al. Assessing texture features to classify coastal wetland vegetation from high spatial resolution imagery using completed local binary patterns (CLBP). Remote Sens. 10, 778 (2018).ADS 
Article 

Google Scholar 
Szantoi, Z., Escobedo, F., Abd-Elrahman, A., Smith, S. & Pearlstine, L. Analyzing fine-scale wetland composition using high resolution imagery and texture features. Int. J. Appl. Earth Obs. Geoinf. 23, 204–212 (2013).ADS 

Google Scholar 
Bhatnagar, S., Gill, L., Regan, S., Waldren, S. & Ghosh, B. A nested drone-satellite approach to monitoring the ecological conditions of wetlands. ISPRS J. Photogramm. Remote. Sens. 174, 151–165 (2021).ADS 
Article 

Google Scholar  More

Trivers, R. L. Parent-offspring conflict. Am. Zool. 14, 249–264 (1974).Article 

Google Scholar 
Gittleman, J. L. & Thompson, S. D. Energy allocation in mammalian reproduction. Am. Zool. 28, 863–875 (1988).Article 

Google Scholar 
Kerby, J. & Post, E. Capital and income breeding traits differentiate trophic match-mismatch dynamics in large herbivores. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120484 (2013).Article 

Google Scholar 
Costa, D. P. Reproductive and foraging energetics of pinnipeds: Implications for life history patterns. In The Behaviour of Pinnipeds (ed. D. Renouf) 300–344 (Springer, Netherlands, 1991).Costa, D. P., Boeuf, B. J. L., Huntley, A. C. & Ortiz, C. L. The energetics of lactation in the Northern elephant seal, Mirounga angustirostris. J. Zool. 209, 21–33 (1986).Article 

Google Scholar 
Crocker, D. E., Williams, J. D., Costa, D. P. & Le Boeuf, B. J. Maternal traits and reproductive effort in northern elephant seals. Ecology 82, 3541–3555 (2001).Article 

Google Scholar 
Shero, M. R., Krotz, R. T., Costa, D. P., Avery, J. P. & Burns, J. M. How do overwinter changes in body condition and hormone profiles influence Weddell seal reproductive success? Funct. Ecol. 29, 1278–1291 (2015).Article 

Google Scholar 
Lönnerdal, B. Bioactive proteins in human milk—potential benefits for preterm infants. Clin. Perinatol. 44, 179–191 (2017).PubMed 
Article 

Google Scholar 
Fields, D. A. et al. Associations between human breast milk hormones and adipocytokines and infant growth and body composition in the first 6 months of life. Pediatr. Obes. 12, 78–85 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Klein, L. D. et al. Concentrations of trace elements in human milk: comparisons among women in Argentina, Namibia, Poland, and the United States. PLoS ONE 12, e0183367 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Burns, J. M. & Hammill, M. O. Does iron availability limit oxygen store development in seal pups? In 4th CPB Meeting in Africa: Mara 2008. “Molecules to migration: The pressures of life” International Proceedings 417–428 (Medimond Publishing Co., 2008).Burns, J. M., Lestyk, K., Folkow, L. P., Hammill, M. O. & Blix, A. S. Size and distribution of oxygen stores in harp and hooded seals from birth to maturity. J. Comp. Physiol. B 177, 687–700 (2007).CAS 
PubMed 
Article 

Google Scholar 
Kooyman, G. L. Diverse divers: Physiology and behavior. (Springer-Verlag, 1989).Butler, P. J. & Jones, D. R. Physiology of diving of birds and mammals. Physiol. Rev. 77, 837–899 (1997).CAS 
PubMed 
Article 

Google Scholar 
Kanatous, S. B., DiMichele, L. V., Cowan, D. F. & Davis, R. W. High aerobic capacities in skeletal muscles of pinnipeds: adaptations to diving hypoxia. J. Appl. Physiol. 86, 1247–1256 (1999).CAS 
PubMed 
Article 

Google Scholar 
Shero, M. R., Andrews, R. D., Lestyk, K. C. & Burns, J. M. Development of the aerobic dive limit and muscular efficiency in northern fur seals (Callorhinus ursinus). J. Comp. Physiol. B 182, 425–436 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shero, M. R., Costa, D. P. & Burns, J. M. Scaling matters: Incorporating body composition into Weddell seal seasonal oxygen store comparisons reveals maintenance of aerobic capacities. J. Comp. Physiol. B 185, 811–824 (2015).CAS 
PubMed 
Article 

Google Scholar 
Shero, M. R., Reiser, P. J., Simonitis, L. & Burns, J. M. Links between muscle phenotype and life history: differentiation of myosin heavy chain composition and muscle biochemistry in precocial and altricial pinniped pups. J. Compar. Physiol. B, https://doi.org/10.1007/s00360-019-01240-w (2019).Burns, J. M., Lestyk, K., Freistroffer, D. & Hammill, M. O. Preparing muscles for diving: age-related changes in muscle metabolic profiles in Harp (Pagophilus groenlandicus) and hooded (Cystophora cristata) seals. Physiol. Biochem. Zool. 88, 167–182 (2015).CAS 
PubMed 
Article 

Google Scholar 
Kooyman, G. L., Wahrenbrock, E. A., Castellini, M. A., Davis, R. W. & Sinnett, E. E. Aerobic and anaerobic metabolism during voluntary diving in Weddell seals: evidence of preferred pathways from blood chemistry and behavior. J. Comp. Physiol. 138, 335–346 (1980).CAS 
Article 

Google Scholar 
Wallace, D. F. The regulation of iron absorption and homeostasis. Clin. biochemist. Rev. 37, 51–62 (2016).
Google Scholar 
Juan, S.-H. & Aust, S. D. Studies on the interaction between ferritin and ceruloplasmin. Arch. Biochem. Biophys. 355, 56–62 (1998).CAS 
PubMed 
Article 

Google Scholar 
Hagler, L. et al. Influence of dietary iron deficiency on hemoglobin, myoglobin, their respective reductases, and skeletal muscle mitochondrial respiration. Am. J. Clin. Nutr. 34, 2169–2177 (1981).CAS 
PubMed 
Article 

Google Scholar 
Kooyman, G. L. Weddell seal: Consummate Diver. (Cambridge University Press, 1981).Heerah, K. et al. Ecology of Weddell seals during winter: Influence of environmental parameters on their foraging behaviour. Deep Sea Res. Part II: Topical Stud. Oceanogr. 88–89, 23–33 (2013).ADS 
Article 

Google Scholar 
Hindell, M. A., Harcourt, R., Waas, J. R. & Thompson, D. Fine-scale three-dimensional spatial use by diving, lactating female Weddell seals Leptonychotes weddellii. Mar. Ecol. Prog. Ser. 242, 275–284 (2002).ADS 
Article 

Google Scholar 
Sato, K. et al. Deep foraging dives in relation to the energy depletion of Weddell seal (Leptonychotes weddellii) mothers during lactation. Polar Biol. 25, 696–702 (2002).Article 

Google Scholar 
Wheatley, K. E., Bradshaw, C. J., Davis, L. S., Harcourt, R. G. & Hindell, M. A. Influence of maternal mass and condition on energy transfer in Weddell seals. J. Anim. Ecol. 75, 724–733 (2006).PubMed 
Article 

Google Scholar 
Walcott, S. M. Evaluating the dynamics of physiological, environmental and behavioral parameters to the cost of the annual pelage molt in a polar pinniped: the Weddell seal (Leptonychotes weddellii) MSc thesis, University of Alaska Anchorage, (2019).Beltran, R. S. et al. Seasonal resource pulses and the foraging depth of a Southern Ocean top predator. Proc. R. Soc. B: Biol. Sci. 288, 20202817 (2021).CAS 
Article 

Google Scholar 
Shero, M. R., Goetz, K. T., Costa, D. P. & Burns, J. M. Temporal changes in Weddell seal dive behavior over winter: Are females increasing foraging effort to support gestation? Ecol. Evol. 8, 11857–11874 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Looker, A. C. & Johnson, C. L. Prevalence of elevated serum transferrin saturation in adults in the United States. Ann. Intern. Med. 129, 940–945 (1998).CAS 
PubMed 
Article 

Google Scholar 
Eleftheriadis, T., Liakopoulos, V., Antoniadi, G. & Stefanidis, I. Which is the best way for estimating transferrin saturation. Ren. Fail. 32, 1022–1023 (2010).PubMed 
Article 

Google Scholar 
McLaren, C. E. et al. Distribution of transferrin saturation in an Australian population: relevance to the early diagnosis of hemochromatosis. Gastroenterology 114, 543–549 (1998).CAS 
PubMed 
Article 

Google Scholar 
Emmett, B. & Hochachka, P. W. Scaling of oxidative and glycolytic enzymes in mammals. Respir. Physiol. 45, 261–272 (1981).CAS 
PubMed 
Article 

Google Scholar 
Clark, C. A., Burns, J. M., Schreer, J. F. & Hammill, M. O. Erythropoietin concentration in developing harbor seals (Phoca vitulina). Gen. Comp. Endocrinol. 147, 262–267 (2006).CAS 
PubMed 
Article 

Google Scholar 
Richmond, J. P., Burns, J. M., Rea, L. D. & Mashburn, K. L. Postnatal ontogeny of erythropoietin and hematology in free-ranging Steller sea lions (Eumetopias jubatus). Gen. Comp. Endocrinol. 141, 240–247 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hadley, G. L., Rotella, J. J. & Garrott, R. A. Influence of maternal characteristics and oceanographic conditions on survival and recruitment probabilities of Weddell seals. Oikos 116, 601–613 (2006).Article 

Google Scholar 
Hall, A. C., McConnell, B. J. & Barker, R. J. Factors affecting first-year survival in grey seals and their implications for life history strategies. J. Anim. Ecol. 70, 138–149 (2001).
Google Scholar 
Proffitt, K. M., Garrott, R. A. & Rotella, J. J. Long-term evaluation of body mass at weaning and postweaning survival rates of Weddell seals in Erebus Bay, Antarctica. Mar. Mamm. Sci. 24, 677–689 (2008).Article 

Google Scholar 
Burns, J. M. & Castellini, M. A. Physiological and behavioral determinants of the aerobic dive limit in Weddell seal (Leptonychotes weddellii) pups. J. Comp. Physiol. B 166, 473–483 (1996).Article 

Google Scholar 
Costa, D. P., Kuhn, C. E., Weise, M. J., Shaffer, S. A. & Arnould, J. P. Y. When does physiology limit the foraging behaviour of freely diving mammals? Int. Congr. Ser. 1275, 359–366 (2004).Article 

Google Scholar 
Hadley, G. L., Rotella, J. J. & Garrott, R. A. Evaluation of reproductive costs for Weddell seals in Erebus Bay, Antarctica. J. Anim. Ecol. 76, 448–458 (2007).PubMed 
Article 

Google Scholar 
Young, S. P., Fahmy, M. & Golding, S. Ceruloplasmin, transferrin and apotransferrin facilitate iron release from human liver cells. FEBS Lett. 411, 93–96 (1997).CAS 
PubMed 
Article 

Google Scholar 
Mazzaro, L. M., Dunn, J. L., St. Aubin, D. J., Andrews, G. A. & Chavey, P. S. Serum indices of body stores of iron in northern fur seals (Callorhinus ursinus) and their relationship to hemochromatosis. Zoo. Biol. 23, 205–218 (2004).Article 

Google Scholar 
Yalçn, S. S., Baykan, A., Yurdakök, K., Yalçn, S. & Gücüs, A. I. The factors that affect milk-to-serum ratio for iron during early lactation. J. Pediatr. Hematol. Oncol. 31, 85–90 (2009).Article 

Google Scholar 
Geiseler, S. J., Blix, A. S., Burns, J. M. & Folkow, L. P. Rapid postnatal development of myoglobin from large liver iron stores in hooded seals. J. Exp. Biol. 216, 1793–1798 (2013).CAS 
PubMed 

Google Scholar 
Samokyszyn, V. M., Miller, D. M., Reif, D. W. & Aust, S. D. Inhibition of superoxide and ferritin-dependent lipid peroxidation by ceruloplasmin. J. Biol. Chem. 264, 21–26 (1989).CAS 
PubMed 
Article 

Google Scholar 
Kohgo, Y., Ikuta, K., Ohtake, T., Torimoto, Y. & Kato, J. Body iron metabolism and pathophysiology of iron overload. Int. J. Hematol. 88, 7–15 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, P. et al. The effect of serum iron concentration on iron secretion into mouse milk. J. Physiol. 522(Pt 3), 479–491 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Erdogan, S., Celik, S. & Erdogan, Z. Seasonal and locational effects on serum, milk, liver and kidney chromium, manganese, copper, zinc, and iron concentrations of dairy cows. Biol. Trace Elem. Res. 98, 51–61 (2004).CAS 
PubMed 
Article 

Google Scholar 
Kaldor, I. & Morgan, E. H. Iron metabolism during lactation and suckling in a marsupial, the quokka (Setonix brachyurus). Comp. Biochem. Physiol. Part A: Physiol. 84, 691–694 (1986).CAS 
Article 

Google Scholar 
Tedman, R. A. & Green, B. Water and sodium fluxes in suckling pups of Weddell seals (Leptonychotes weddelli). J. Zool. 212, 29–42 (1987).Article 

Google Scholar 
National Institutes of Health, Supplements, O. o. D. Iron Fact Sheet for Consumers, https://ods.od.nih.gov/factsheets/Iron-Consumer/ (2021).Saarinen, U. M., Siimes, M. A. & Dallman, P. R. Iron absorption in infants: high bioavailability ofbreast milk iron as indicated by the extrinsic tag method of iron absorption and by the concentration of serum ferritin. J. Pediatrics 91, 36–39 (1977).CAS 
Article 

Google Scholar 
Loh, T.-T. Iron metabolism of the lactating mouse. Proc. Soc. Exp. Biol. Med. 137, 962–965 (1971).CAS 
PubMed 
Article 

Google Scholar 
Folkow, L. P., Nordoy, E. S. & Blix, A. S. Distribution and diving behavior of harp seals (Pagophilus groenlandica) from the Greenland Sea stock. Polar Biol. 27, 281–298 (2004).Article 

Google Scholar 
Beck, C. A., Bowen, W. D. & Iverson, S. J. Seasonal changes in buoyancy and diving behaviour of adult grey seals. J. Exp. Biol. 203, 2323–2330 (2000).CAS 
PubMed 
Article 

Google Scholar 
Gentry, R. L. & Kooyman, G. L. Fur seals: maternal strategies on land and at sea. (Princeton University Press, 1986).McDonald, B. I. & Ponganis, P. J. Insights from venous oxygen profiles: oxygen utilization and management in diving California sea lions. J. Exp. Biol. 216, 3332–3341 (2013).CAS 
PubMed 
Article 

Google Scholar 
Noren, S. R., Iverson, S. J. & Boness, D. J. Development of the blood and muscle oxygen stores in gray seals (Halichoerus grypus): Implications for juvenile diving capacity and the necessity of a terrestrial postweaning fast. Physiol. Biochem. Zool. 78, 482–490 (2005).PubMed 
Article 

Google Scholar 
Weise, M. J. & Costa, D. P. Total body oxygen stores and physiological diving capacity of California sea lions as a function of sex and age. J. Exp. Biol. 210, 278–289 (2007).PubMed 
Article 

Google Scholar 
Burns, J. M., Hindell, M. A., Bradshaw, C. J. A. & Costa, D. P. Fine-scale habitat selection by crabeater seals as determined by diving behavior. Deep Sea Res. II 55, 500–514 (2008).ADS 
Article 

Google Scholar 
Burns, J. Crabeater seal oxygen stores. U.S. Antarctic Program (USAP) Data Center. https://doi.org/10.15784/601583 (2022).Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish. Fish. 11, 203–209 (2010).Article 

Google Scholar 
Williams, T. M. The cost of foraging by a marine predator, the Weddell seal Leptonychotes weddellii: pricing by the stroke. J. Exp. Biol. 207, 973–982 (2004).PubMed 
Article 

Google Scholar 
Wheatley, K. E., Bradshaw, C. J. A., Harcourt, R. G. & Hindell, M. A. Feast or famine: evidence for mixed capital–income breeding strategies in Weddell seals. Oecologia 155, 11–20 (2008).ADS 
PubMed 
Article 

Google Scholar 
Honda, K., Sahrul, M., Hidaka, H. & Tatsukawa, R. Organ and tissue distribution of heavy metals, and their growth-related changes in Antarctic Fish, Pagothenia borchgrevinki. Agric. Biol. Chem. 47, 2521–2532 (1983).CAS 

Google Scholar 
Galbraith, E. D., Le Mézo, P., Solanes Hernandez, G., Bianchi, D. & Kroodsma, D. Growth limitation of marine fish by low iron availability in the open ocean. Front. Marine Sci. 6, https://doi.org/10.3389/fmars.2019.00509 (2019).Pollycove, M. & Mortimer, R. The quantitative determination of iron kinetics and hemoglobin synthesis in human subjects. J. Clin. Invest. 40, 753–782 (1961).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Åkeson, Å., Ehrenstein, G. V., Hevesy, G. & Theorell, H. Life span of myoglobin. Arch. Biochem. Biophys. 91, 310–318 (1960).PubMed 
Article 

Google Scholar 
Tift, M. S. et al. Adaptive potential of the heme oxygenase/carbon monoxide pathway during hypoxia. Front. Physiol. 11, https://doi.org/10.3389/fphys.2020.00886 (2020).Tift, M. S., Ponganis, P. J. & Crocker, D. E. Elevated carboxyhemoglobin in a marine mammal, the northern elephant seal. J. Exp. Biol. 217, 1752–1757 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ma, Y.-J. et al. A modified carbon monoxide breath test for measuring erythrocyte lifespan in small animals. BioMed. Res. Int. 2016, 7173156 (2016).PubMed 
PubMed Central 

Google Scholar 
Zhang, H.-D. et al. Human erythrocyte lifespan measured by Levitt’s CO breath test with newly developed automatic instrument. J. Breath. Res. 12, 036003 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hochachka, P. W. & Somero, G. N. Biochemical adaptation. (Oxford University Press, 2002).De Miranda, M. A., Schlater, A. E., Green, T. L. & Kanatous, S. B. In the face of hypoxia: myoglobin increases in response to hypoxic conditions and lipid supplementation in cultured Weddell seal skeletal muscle cells. J. Exp. Biol. 215, 806–813 (2012).PubMed 
Article 
CAS 

Google Scholar 
Kanatous, S. B. & Mammen, P. P. Regulation of myoglobin expression. J. Exp. Biol. 213, 2741–2747 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Halvorsen, S. & Bechensteen, A. G. Physiology of erythropoietin during mammalian development. Acta Paediatr. Suppl. 438, 17–26 (2002).Article 

Google Scholar 
Hochachka, P. W. Mechanism and evolution of hypoxia-tolerance in humans. J. Exp. Biol. 201, 1243–1254 (1998).CAS 
PubMed 
Article 

Google Scholar 
Klopfleisch, R. & Olias, P. The pathology of comparative animal models of human haemochromatosis. J. Comp. Pathol. 147, 460–478 (2012).CAS 
PubMed 
Article 

Google Scholar 
Henriksson, J. & Reitman, J. S. Time course of changes in human skeletal muscle succinate dehydrogenase and cytochrome oxidase activities and maximal oxygen uptake with physical activity and inactivity. Acta Physiol. Scand. 99, 91–97 (1977).CAS 
PubMed 
Article 

Google Scholar 
Goetz, K. T. Movement, habitat, and foraging behavior of Weddell seals (Leptonychotes weddellii) in the western Ross Sea, Antarctica, University of California Santa Cruz, (2015).Cisewski, B., Strass, V. H., Rhein, M. & Krägefsky, S. Seasonal variation of diel vertical migration of zooplankton from ADCP backscatter time series data in the Lazarev Sea, Antarctica. Deep Sea Res. Part I: Oceanographic Res. Pap. 57, 78–94 (2010).ADS 
CAS 
Article 

Google Scholar 
Jones, R. M. & Smith, W. O. The influence of short-term events on the hydrographic and biological structure of the southwestern Ross Sea. J. Mar. Syst. 166, 184–195 (2017).Article 

Google Scholar 
Smith, W. O. & Nelson, D. M. Importance of ice edge phytoplankton production in the Southern Ocean. Bioscience 36, 251–257 (1986).CAS 
Article 

Google Scholar 
Rivkin, R. B. Seasonal patterns of planktonic production in McMurdo Sound, Antarctica. Am. Zool. 31, 5–16 (2015).Article 

Google Scholar 
Proffitt, K. M., Rotella, J. J. & Garrott, R. A. Effects of pup age, maternal age, and birth date on pre-weaning survival rates of Weddell seals in Erebus Bay, Antarctica. Oikos 119, 1255–1264 (2010).Article 

Google Scholar 
Beltran, R. S., Kirkham, A. L., Breed, G. A., Testa, J. W. & Burns, J. M. Reproductive success delays moult phenology in a polar mammal. Sci. Rep. 9, 5221 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mellish, J.-A. E., Tuomi, P. A., Hindle, A. G. & Horning, M. Chemical immobilization of Weddell seals (Leptonychotes weddellii) by ketamine/midazolam combination. Vet. Anaesth. Analg. 37, 123–131 (2010).CAS 
PubMed 
Article 

Google Scholar 
Shero, M. R., Pearson, L. E., Costa, D. P. & Burns, J. M. Improving the precision of our ecosystem calipers: a modified morphometric technique for estimating marine mammal mass and body composition. PLoS ONE 9, e91233 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foldager, N. & Blomqvist, C. G. Repeated plasma volume determination with the Evans blue dye dilution technique: the method and the computer program. Comput. Biol. Med. 21, 35–41 (1991).CAS 
PubMed 
Article 

Google Scholar 
El-Sayed, H., Goodall, S. R. & Hainsworth, F. R. Re-evaluation of Evans blue dye dilution method of plasma volume measurement. Clin. Lab. Haem. 17, 189–194 (1995).CAS 

Google Scholar 
Reynafarje, B. Simplified method for the determination of myoglobin. J. Lab. Clin. Med. 61, 138–145 (1963).CAS 
PubMed 

Google Scholar 
Prewitt, J. S., Freistroffer, D. V., Schreer, J. F., Hammill, M. O. & Burns, J. M. Postnatal development of muscle biochemistry in nursing harbor seal (Phoca vitulina) pups: Limitations to diving behavior? J. Comp. Physiol. B 180, 757–766 (2010).CAS 
PubMed 
Article 

Google Scholar 
Polasek, L., Dickson, K. A. & Davis, R. W. Metabolic indicators in the skeletal muscles of harbor seals (Phoca vitulina). Am. J. Physiol. Regul. Integr. Comp. Physiol. 290, R1720–R1727 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kooyman, G. L., Castellini, M. A., Davis, R. W. & Maue, R. A. Aerobic diving limits of immature Weddell seals. J. Comp. Physiol. 151, 171–174 (1983).Article 

Google Scholar 
Davis, R. W. & Kanatous, S. B. Convective oxygen transport and tissue oxygen consumption in Weddell seals during aerobic dives. J. Exp. Biol. 202, 1091–1113 (1999).CAS 
PubMed 
Article 

Google Scholar 
Lenfant, C., Johansen, K. & Torrance, J. D. Gas transport and oxygen storage capacity in some pinnipeds and the sea otter. Respir. Physiol. 9, 277–286 (1970).CAS 
PubMed 
Article 

Google Scholar 
Kleiber, M. The fire of life: an introduction to animal energetics. (R.E. Krieger Pub. Co., 1975).Sato, K., Mitani, Y., Cameron, M. F., Siniff, D. B. & Naito, Y. Factors affecting stroking patterns and body angle in diving Weddell seals under natural conditions. J. Exp. Biol. 206, 1461–1470 (2003).PubMed 
Article 

Google Scholar 
Zuur, A. F., Hilbe, J. M. & Ieno, E. N. A Beginner’s Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists. (Highland Statistics Newburgh, 2013).Shero, M. Weddell seal iron dynamics and oxygen stores across lactation. U.S. Antarctic Program (USAP) Data Center. https://doi.org/10.15784/601575. (2022).Anderson, R. S. et al. Zinc, copper, iron and calcium concentrations in bitch milk. J. Nutr. 121, S81–S82 (1991).CAS 
PubMed 
Article 

Google Scholar 
Griffiths, M., Green, B., MC Leckie, R., Messer, M. & Newgrain, K. Constituents of platypus and echidna milk, with particular reference to the fatty acid complement of the triglycerides. Aust. J. Biol. Sci. 37, 323–330 (1984).CAS 
Article 

Google Scholar 
Peddemors, V. M., de Muelenaere, H. J. H. & Devchand, K. Comparative milk composition of the bottlenosed dolphin (Tursiops truncatus), humpback dolphin (Sousa plumbea) and common dolphin (Delphinus delphis) from southern African waters. Comp. Biochem. Physiol. Part A Physiol. 94, 639–641 (1989).CAS 
Article 

Google Scholar 
Ullrey, D. E. et al. Blue-green color and composition of Stejneger’s beaked whale (Mesoplodon stejnegeri) milk. Comp. Biochem. Physiol. B Comp. Biochem. 79, 349–352 (1984).CAS 
Article 

Google Scholar 
Dosako, S. I. et al. Milk of Northern fur seal: composition, especially carbohydrate and protein. J. Dairy Sci. 66, 2076–2083 (1983).CAS 
PubMed 
Article 

Google Scholar 
Oftedal, O. T., Boness, D. J. & Tedman, R. The Behavior, Physiology, and Anatomy of Lactation in the Pinnipedia. (Genoyways, H. H. eds) (Current Mammalogy. Springer, Boston, MA, 1987).Habran, S., Pomeroy, P. P., Debier, C. & Das, K. Changes in trace elements during lactation in a marine top predator, the grey seal. Aquat. Toxicol. 126, 455–466 (2013).CAS 
PubMed 
Article 

Google Scholar 
Seal, U. S., Erickson, A. W., Siniff, D. B. & Cline, D. R. Blood chemistry and protein polymorphisms in three species of Antarctic seals (Lobodon carcinophagus, Leptonychootes weddellii, and Mirounga leonina) In Antarctic Pinnipedia 181–192 (1971).Green, B., Fogerty, A., Libke, J., Newgrain, K. & Shaughnessy, P. Aspects of lactation in the crab-eater seal (Lobodon-Carcinophagus). Aust. J. Zool. 41, 203–213 (1993).Article 

Google Scholar 
Casey, C. E., Smith, A. & Zhang, P. Microminerals in human and animal milks, In Handbook of milk composition 622–674 (ed. R. G. Jensen) (Academic Press, 1995). More

Karl D, Michaels A, Bergman B, Capone D, Carpenter E, Letelier R, et al. Dinitrogen fixation in the world’s oceans. In: Boyer EW, Howarth RW, editors. The nitrogen cycle at regional to global scales. Dordrecht: Springer; 2002. p. 47–98.Berthelot H, Benavides M, Moisander PH, Grosso O, Bonnet S. High-nitrogen fixation rates in the particulate and dissolved pools in the Western Tropical Pacific (Solomon and Bismarck Seas): N2 fixation in the Western Pacific. Geophys Res Lett. 2017;44:8414–23.CAS 
Article 

Google Scholar 
Rahav E, Bar-Zeev E, Ohayion S, Elifantz H, Belkin N, Herut B, et al. Dinitrogen fixation in aphotic oxygenated marine environments. Front Microbiol. 2013;4:227.PubMed 
PubMed Central 
Article 

Google Scholar 
Bentzon-Tilia M, Traving SJ, Mantikci M, Knudsen-Leerbeck H, Hansen JL, Markager S, et al. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME J. 2015;9:273–85.CAS 
PubMed 
Article 

Google Scholar 
Messer LF, Doubell M, Jeffries TC, Brown MV, Seymour JR. Prokaryotic and diazotrophic population dynamics within a large oligotrophic inverse estuary. Aquat Micro Ecol. 2015;74:1–15.Article 

Google Scholar 
Sipler RE, Gong D, Baer SE, Sanderson MP, Roberts QN, Mulholland MR, et al. Preliminary estimates of the contribution of Arctic nitrogen fixation to the global nitrogen budget. Limnol Oceanogr Lett. 2017;2:159–66.Article 

Google Scholar 
Benavides M, Bonnet S, Berman-Frank I, Riemann L. Deep into oceanic N2 fixation. Front Mar Sci. 2018;5:1–4.Article 

Google Scholar 
Mulholland MR, Bernhardt PW, Widner BN, Selden CR, Chappell PD, Clayton S, et al. High rates of N2 fixation in temperate, Western North Atlantic coastal waters expand the realm of marine diazotrophy. Glob Biogeochem Cycles. 2019;33:826–40.CAS 
Article 

Google Scholar 
Zehr JP. Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 2011;19:162–73.CAS 
PubMed 
Article 

Google Scholar 
Riemann L, Farnelid H, Steward G. Nitrogenase genes in non-cyanobacterial plankton: prevalence, diversity and regulation in marine waters. Aquat Micro Ecol. 2010;61:235–47.Article 

Google Scholar 
Farnelid H, Andersson AF, Bertilsson S, Al-Soud WA, Hansen LH, Sørensen S, et al. Nitrogenase gene amplicons from global marine surface waters are dominated by genes of non-cyanobacteria. PLoS ONE. 2011;6:e19223.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Delmont TO, Quince C, Shaiber A, Esen ÖC, Lee ST, Rappé MS, et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat Microbiol. 2018;3:804–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh H-J, Cuenca M, et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell. 2019;179:1068–1083.e21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bombar D, Paerl RW, Riemann L. Marine non-cyanobacterial diazotrophs: moving beyond molecular detection. Trends Microbiol. 2016;24:916–27.CAS 
PubMed 
Article 

Google Scholar 
Moisander PH, Benavides M, Bonnet S, Berman-Frank I, White AE, Riemann L. Chasing after non-cyanobacterial nitrogen fixation in marine pelagic environments. Front Microbiol. 2017;8:1736.PubMed 
PubMed Central 
Article 

Google Scholar 
Eady RR, Postgate JR. Nitrogenase. Nature. 1974;249:805–10.CAS 
PubMed 
Article 

Google Scholar 
Wong PP, Burris RH. Nature of oxygen inhibition of nitrogenase from azotobacter vinelandii. Proc Natl Acad Sci USA 1972;69:672–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berman-Frank I, Quigg A, Finkel ZV, Irwin AJ, Haramaty L. Nitrogen-fixation strategies and Fe requirements in cyanobacteria. Limnol Oceanogr. 2007;52:2260–9.Article 

Google Scholar 
Inomura K, Bragg J, Follows MJ. A quantitative analysis of the direct and indirect costs of nitrogen fixation: a model based on Azotobacter vinelandii. ISME J. 2017;11:166–75.CAS 
PubMed 
Article 

Google Scholar 
Paerl HW. Microzone formation: its role in the enhancement of aquatic N2 fixation. Limnol Oceanogr. 1985;30:1246–52.CAS 
Article 

Google Scholar 
Paerl HW, Prufert LE. Oxygen-poor microzones as potential sites of microbial N2 fixation in nitrogen-depleted aerobic marine waters. Appl Env Microbiol. 1987;53:1078–87.CAS 
Article 

Google Scholar 
Riemann L, Rahav E, Passow U, Grossart H-P, de Beer D, Klawonn I, et al. Planktonic aggregates as hotspots for heterotrophic diazotrophy: the plot thickens. Front Microbiol. 2022;13:1092.Article 

Google Scholar 
Braun ST, Proctor LM, Zani S, Mellon MT, Zehr JP. Molecular evidence for zooplankton-associated nitrogen-fixing anaerobes based on amplification of the nifH gene. FEMS Microbiol Ecol. 1999;28:273–9.CAS 
Article 

Google Scholar 
Farnelid H, Tarangkoon W, Hansen G, Hansen PJ, Riemann L. Putative N2-fixing heterotrophic bacteria associated with dinoflagellate–Cyanobacteria consortia in the low-nitrogen Indian Ocean. Aquat Micro Ecol. 2010;61:105–17.Article 

Google Scholar 
Scavotto RE, Dziallas C, Bentzon-Tilia M, Riemann L, Moisander PH. Nitrogen-fixing bacteria associated with copepods in coastal waters of the North Atlantic Ocean: diazotroph community in association with copepods. Environ Microbiol. 2015;17:3754–65.CAS 
PubMed 
Article 

Google Scholar 
Farnelid H, Turk-Kubo K, Ploug H, Ossolinski JE, Collins JR, Van Mooy BAS, et al. Diverse diazotrophs are present on sinking particles in the North Pacific Subtropical Gyre. ISME J. 2019;13:170–82.PubMed 
Article 

Google Scholar 
Geisler E, Bogler A, Rahav E, Bar-Zeev E. Direct detection of heterotrophic diazotrophs associated with planktonic aggregates. Sci Rep. 2019;9:1–9.CAS 
Article 

Google Scholar 
Pedersen JN, Bombar D, Paerl RW, Riemann L. Diazotrophs and N2-fixation associated with particles in coastal estuarine waters. Front Microbiol. 2018;9:2759.PubMed 
PubMed Central 
Article 

Google Scholar 
Paerl RW, Hansen TNG, Henriksen NNSE, Olesen AK, Riemann L. N2-fixation and related O2 constraints on model marine diazotroph Pseudomonas stutzeri BAL361. Aquat Micro Ecol. 2018;81:125–36.Article 

Google Scholar 
Rahav E, Giannetto MJ, Bar-Zeev E. Contribution of mono and polysaccharides to heterotrophic N2 fixation at the eastern Mediterranean coastline. Sci Rep. 2016;6:27858.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chakraborty S, Andersen KH, Visser AW, Inomura K, Follows MJ, Riemann L. Quantifying nitrogen fixation by heterotrophic bacteria in sinking marine particles. Nat Commun. 2021;12:4085.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stocker R, Seymour JR, Samadani A, Hunt DE, Polz MF. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc Natl Acad Sci USA 2008;105:4209–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stocker R, Seymour JR. Ecology and physics of bacterial chemotaxis in the ocean. Microbiol Mol Biol Rev. 2012;76:792–812.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Garren M, Son K, Raina J-B, Rusconi R, Menolascina F, Shapiro OH, et al. A bacterial pathogen uses dimethylsulfoniopropionate as a cue to target heat-stressed corals. ISME J. 2014;8:999–1007.CAS 
PubMed 
Article 

Google Scholar 
Son K, Menolascina F, Stocker R. Speed-dependent chemotactic precision in marine bacteria. Proc Natl Acad Sci USA 2016;113:8624–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brumley DR, Carrara F, Hein AM, Yawata Y, Levin SA, Stocker R. Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients. Proc Natl Acad Sci USA 2019;116:10792–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Müller‐Niklas G, Stefan S, Kaltenböok E, Herndl GJ. Organic content and bacterial metabolism in amorphous aggregations of the northern Adriatic Sea. Limnol Oceanogr. 1994;39:58–68.Article 

Google Scholar 
Grossart H-P, Czub G, Simon M. Algae–bacteria interactions and their effects on aggregation and organic matter flux in the sea. Environ Microbiol. 2006;8:1074–84.PubMed 
Article 

Google Scholar 
Smith DC, Simon M, Alldredge AL, Azam F. Intense hydrolytic enzyme activity on marine aggregates and implications for rapid particle dissolution. Nature. 1992;359:139–42.CAS 
Article 

Google Scholar 
Kiørboe T, Ploug H, Thygesen UH. Fluid motion and solute distribution around sinking aggregates. I. Small-scale fluxes and heterogeneity of nutrients in the pelagic environment. Mar Ecol Prog Ser. 2001;211:1–13.Article 

Google Scholar 
Kiørboe T, Jackson GA. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol Oceanogr. 2001;46:1309–18.Article 

Google Scholar 
Raina J-B, Lambert BS, Parks DH, Rinke C, Siboni N, Bramucci A, et al. Chemotaxis shapes the microscale organisation of the ocean’s microbiome. Nature. 2022;605:132–8.CAS 
PubMed 
Article 

Google Scholar 
Lambert BS, Raina J-B, Fernandez VI, Rinke C, Siboni N, Rubino F, et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat Microbiol. 2017;2:1344–9.CAS 
PubMed 
Article 

Google Scholar 
Clerc EE, Raina J-B, Lambert BS, Seymour J, Stocker R. In situ chemotaxis assay to examine microbial behavior in aquatic ecosystems. J Vis Exp. 2020;159:e61062.
Google Scholar 
Boström KH, Riemann L, Kühl M, Hagström Å. Isolation and gene quantification of heterotrophic N2-fixing bacterioplankton in the Baltic Sea. Environ Microbiol. 2007;9:152–64.PubMed 
Article 
CAS 

Google Scholar 
Farnelid H, Harder J, Bentzon-Tilia M, Riemann L. Isolation of heterotrophic diazotrophic bacteria from estuarine surface waters: heterotrophic diazotrophs in the Baltic Sea. Environ Microbiol. 2014;16:3072–82.CAS 
PubMed 
Article 

Google Scholar 
ZoBell CE. Studies on Marine Bacteria I. The cultural requirements of heterotrophic aerobes. J Mar Res. 1941;4:41–75.Alldredge AL, Gotschalk C, Passow U, Riebesell U. Mass aggregation of diatom blooms: Insights from a mesocosm study. Deep Sea Res Part II Top Stud Oceanogr. 1995;42:9–27.CAS 
Article 

Google Scholar 
Thornton DCO. Diatom aggregation in the sea: mechanisms and ecological implications. Eur J Phycol. 2002;37:149–61.Article 

Google Scholar 
Turner J. Zooplankton fecal pellets, marine snow and sinking phytoplankton blooms. Aquat Micro Ecol. 2002;27:57–102.Article 

Google Scholar 
Schnetzer A, Lampe RH, Benitez-Nelson CR, Marchetti A, Osburn CL, Tatters AO. Marine snow formation by the toxin-producing diatom, Pseudo-nitzschia australis. Harmful Algae. 2017;61:23–30.CAS 
Article 

Google Scholar 
Dittmar T, Koch B, Hertkorn N, Kattner G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnol Oceanogr Methods. 2008;6:230–5.CAS 
Article 

Google Scholar 
Marie D, Partensky F, Jacquet S, Vaulot D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR green. Appl Environ Microbiol. 1997;63:186–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bramucci AR, Focardi A, Rinke C, Hugenholtz P, Tyson GW, Seymour JR, et al. Microvolume DNA extraction methods for microscale amplicon and metagenomic studies. ISME Commun. 2021;1:1–5.Article 

Google Scholar 
Rinke C, Low S, Woodcroft BJ, Raina J-B, Skarshewski A, Le XH, et al. Validation of picogram- and femtogram-input DNA libraries for microscale metagenomics. PeerJ. 2016;4:e2486.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio. 2013.Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics. 2007;23:1282–8.CAS 
PubMed 
Article 

Google Scholar 
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
PubMed 
Article 

Google Scholar 
Clarke KR, Gorley RN, Somerfield PJ, Warwick RM. Change in marine communities: an approach to statistical analysis and interpretation. 3rd ed. Plymouth: Primer-E Ltd; 2014.Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019;20:1160–6.CAS 
PubMed 
Article 

Google Scholar 
Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics. 2019;35:4453–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edler D, Klein J, Antonelli A, Silvestro D. raxmlGUI 2.0 beta: a graphical interface and toolkit for phylogenetic analyses using RAxML. bioRxiv. 2019. https://doi.org/10.1101/800912.Barbera P, Kozlov AM, Czech L, Morel B, Darriba D, Flouri T, et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst Biol. 2019;68:365–9.PubMed 
Article 

Google Scholar 
Czech L, Barbera P, Stamatakis A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics. 2020;36:3263–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

Google Scholar 
Bentzon-Tilia M, Severin I, Hansen LH, Riemann L. Genomics and ecophysiology of heterotrophic nitrogen-fixing bacteria isolated from estuarine surface water. mBio. 2015;6:e00929–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martínez-Pérez C, Mohr W, Schwedt A, Dürschlag J, Callbeck CM, Schunck H, et al. Metabolic versatility of a novel N2-fixing Alphaproteobacterium isolated from a marine oxygen minimum zone: novel N2-fixer from oxygen minimum zone off Peru. Environ Microbiol. 2018;20:755–68.PubMed 
Article 
CAS 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 
CAS 

Google Scholar 
Eschemann A, Kühl M, Cypionka H. Aerotaxis in Desulfovibrio. Environ Microbiol. 1999;1:489–94.CAS 
PubMed 
Article 

Google Scholar 
Zhu S, Kojima S, Homma M. Structure, gene regulation and environmental response of flagella in Vibrio. Front Microbiol. 2013;4:410.Silva MA, Salgueiro CA. Multistep signaling in nature: a close-up of Geobacter chemotaxis sensing. Int J Mol Sci. 2021;22:9034.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Taylor BL, Zhulin IB, Johnson MS. Aerotaxis and other energy-sensing behavior in bacteria. Annu Rev Microbiol. 1999;53:103–28.CAS 
PubMed 
Article 

Google Scholar 
Colin R, Sourjik V. Emergent properties of bacterial chemotaxis pathway. Curr Opin Microbiol. 2017;39:24–33.CAS 
PubMed 
Article 

Google Scholar 
Stocker R. Marine microbes see a sea of gradients. Science. 2012;338:628–33.CAS 
PubMed 
Article 

Google Scholar 
Turk‐Kubo KA, Karamchandani M, Capone DG, Zehr JP. The paradox of marine heterotrophic nitrogen fixation: abundances of heterotrophic diazotrophs do not account for nitrogen fixation rates in the Eastern Tropical South Pacific. Environ Microbiol. 2014;16:3095–114.PubMed 
Article 
CAS 

Google Scholar 
Bentzon-Tilia M, Farnelid H, Jürgens K, Riemann L. Cultivation and isolation of N2-fixing bacteria from suboxic waters in the Baltic Sea. FEMS Microbiol Ecol. 2014;88:358–71.CAS 
PubMed 
Article 

Google Scholar  More

Ceballos, G. et al. Accelerated modern human – induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253, https://doi.org/10.1126/sciadv.1400253 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509, https://doi.org/10.1126/science.1194442 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Whittaker, R. J. et al. Conservation biogeography: assessment and prospect. Divers. Distrib. 11, 3–23, https://doi.org/10.1111/j.1366-9516.2005.00143.x (2005).Article 

Google Scholar 
Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Env. Resour. 28, 137–167, https://doi.org/10.1146/annurev.energy.28.050302.105532 (2003).Article 

Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545, https://doi.org/10.1093/biosci/bix014 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Brito, J. C. et al. Unravelling biodiversity, evolution and threats to conservation in the Sahara-Sahel. Biol. Rev. 89, 215–231, https://doi.org/10.1111/brv.12049 (2014).Article 
PubMed 

Google Scholar 
Sampaio, M. et al. Beyond the comfort zone: amphibian diversity and distribution in the West Sahara-Sahel using mtDNA and nuDNA barcoding and spatial modelling. Conserv. Genet. 22(2), 233–248, https://doi.org/10.1007/s10592-021-01331-8 (2021).Article 

Google Scholar 
Velo-Antón, G. et al. DNA barcode reference library for the West Sahara-Sahel reptiles, figshare, https://doi.org/10.6084/m9.figshare.20338335 (2022).Carranza, S., Arnold, E. N., Geniez, P., Roca, J. & Mateo, J. A. Radiation, multiple dispersal and parallelism in the skinks, Chalcides and Sphenops (Squamata: Scincidae), with comments on Scincus and Scincopus and the age of the Sahara Desert. Mol. Phyl. Evol. 46, 1071–1094, https://doi.org/10.1016/j.ympev.2007.11.018 (2008).CAS 
Article 

Google Scholar 
Gonçalves, D. V. et al. Phylogeny of North African Agama lizards (Reptilia: Agamidae) and the role of the Sahara desert in vertebrate speciation. Mol. Phyl. Evol. 64, 582–591, https://doi.org/10.1016/j.ympev.2012.05.007 (2012).Article 

Google Scholar 
Gonçalves, D. V. et al. The role of climatic cycles and trans-Saharan migration corridors in species diversification: biogeography of Psammophis schokari group in North Africa. Mol. Phyl. Evol. 118, 64–74, https://doi.org/10.1016/j.ympev.2017.09.009 (2018).Article 

Google Scholar 
Gonçalves, D. V. et al. Assessing the role of aridity-induced vicariance and ecological divergence in species diversification in North-West Africa using Agama lizards. Biol. J. Linn. Soc. 124, 363–380, https://doi.org/10.1093/biolinnean/bly055 (2018).Article 

Google Scholar 
Metallinou, M. et al. Conquering the Sahara and Arabian deserts: Systematics and biogeography of Stenodactylus geckos (Reptilia: Gekkonidae). BMC Evol. Biol. 12, 258, https://doi.org/10.1186/1471-2148-12-258 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Metallinou, M. et al. Species on the rocks: Systematics and biogeography of the rock-dwelling Ptyodactylus geckos (Squamata: Phyllodactylidae) in North Africa and Arabia. Mol. Phyl. Evol. 85, 208–220, https://doi.org/10.1016/j.ympev.2015.02.010 (2015).Article 

Google Scholar 
Kapli, P. et al. Historical biogeography of the lacertid lizard Mesalina in North Africa and the Middle East. J. Biogeog. 42, 267–279, https://doi.org/10.1111/jbi.12420 (2015).Article 

Google Scholar 
Tamar, K., Geniez, P., Brito, J. C. & Crochet, P. A. Systematic revision of Acanthodactylus busacki (Squamata: Lacertidae) with a description of a new species from Morocco. Zootaxa 4276(3), 357–386, https://doi.org/10.11646/ZOOTAXA.4276.3.3 (2017).Article 

Google Scholar 
Velo-Antón, G., Martínez-Freiría, F., Pereira, P., Crochet, P.-A. & Brito, J. C. Living on the edge: ecological and genetic connectivity of the Spiny-footed lizard, Acanthodactylus aureus, confirms the Atlantic Sahara desert as biogeographic corridor and centre of lineage diversification. J. Biogeog. 45, 1031–1042, https://doi.org/10.1111/jbi.13176 (2018).Article 

Google Scholar 
Vale, C. G., Pimm, S. L. & Brito, J. C. Overlooked mountain rock pools in deserts are critical local hotspots of biodiversity. PLoS ONE 10, e0118367, https://doi.org/10.1371/journal.pone.0118367 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brito, J. C. et al. Conservation Biogeography of the Sahara-Sahel: additional protected areas are needed to secure unique biodiversity. Divers. Distrib. 22, 371–384, https://doi.org/10.1111/ddi.12416 (2016).Article 

Google Scholar 
Hawlitschek, O. et al. Comprehensive DNA barcoding of the herpetofauna of Germany. Mol. Ecol. Res. 16, 242–253, https://doi.org/10.1111/1755-0998.12416 (2016).CAS 
Article 

Google Scholar 
Hebert, P. D. N., Cywinska, A., Ball, S. L. & Jeremy, R. Biological Identifications through DNA Barcodes. P. Roy. Soc. Lond. B Bio. 270, 313–321, https://doi.org/10.1098/rspb.2002.2218 (2003).CAS 
Article 

Google Scholar 
Murphy, R. W. et al. Cold Code: the global initiative to DNA barcode amphibians and nonavian reptiles. Mol. Ecol. Res. 13, 161–167, https://doi.org/10.1111/1755-0998.12050 (2013).CAS 
Article 

Google Scholar 
Vasconcelos, R. et al. Unexpectedly high levels of cryptic diversity uncovered by a complete DNA barcoding of reptiles of the Socotra Archipelago. PLoS ONE 11, e0149985, https://doi.org/10.1371/journal.pone.0149985 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krishnamurthy, P. K. & Francis, R. A. A critical review on the utility of DNA barcoding in biodiversity conservation. Biodiv. Conserv. 21, 1901–1919, https://doi.org/10.1007/s10531-012-0306-2 (2012).Article 

Google Scholar 
DeSalle, R. & Amato, G. The expansion of conservation genetics. Nat. Rev. Genet. 5, 702–12, https://doi.org/10.7312/amat12832-006 (2004).CAS 
Article 
PubMed 

Google Scholar 
Campos, J. C. & Brito, J. C. Mapping underrepresented land cover heterogeneity in arid regions: the Sahara-Sahel example. ISPRS J. Photogramm 146, 211–220, https://doi.org/10.1016/j.isprsjprs.2018.09.012 (2018).Article 

Google Scholar 
Brito, J. C. et al. Armed conflicts and wildlife decline: Challenges and recommendations for effective conservation policy in the Sahara‐Sahel. Conserv. Lett. 11(5), e12446, https://doi.org/10.1111/conl.12446 (2018).Article 

Google Scholar 
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336, https://doi.org/10.1038/nature25181 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Geniez, P., Mateo, J. A. & Bons, J. A checklist of the amphibians and reptiles of Western Sahara (Amphibia, Reptilia). Herpetozoa 133, 149–63 (2000).
Google Scholar 
Geniez, P., Mateo, J. A., Geniez, M. & Pether, J. The Amphibians and Reptiles of the Western Sahara. An Atlas and Field Guide. Chimaira Editions (2004). Available at https://doi.org/10.1643/0045-8511(2007)2007[772:TAAROT]2.0.CO;2Trape, J. – F. & Mané, Y. Guide des Serpents d’Afrique Occidentale: Savane et Désert. IRD éditions (2006).Trape, J.-F., Trape, S. & Chirio, L. Lézards, Crocodiles et Tortues d’Afrique Occidentale et du Sahara. IRD éditions (2012).Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. USA 110, 15758–15763, https://doi.org/10.1073/pnas.1314445110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nagy, Z. T., Sonet, G., Glaw, F. & Vences, M. First large-scale DNA barcoding assessment of reptiles in the biodiversity hotspot of Madagascar, based on newly designed COI primers. PLoS ONE 7, e34506, https://doi.org/10.1371/journal.pone.0034506 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids. Res. 32, 1792–1797, https://doi.org/10.1093/nar/gkh340 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120, https://doi.org/10.1007/BF01731581 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Brown, S. D. et al. Spider: An R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Mol. Ecol. Res. 12, 562–565, https://doi.org/10.1111/j.1755-0998.2011.03108.x (2012).ADS 
Article 

Google Scholar 
Meier, R., Shiyang, K., Vaidya, G. & Ng, P. K. L. DNA barcoding and taxonomy in diptera: a tale of high intraspecific variability and low identification success. Syst. Biol. 55, 715–728, https://doi.org/10.1080/10635150600969864 (2006).Article 
PubMed 

Google Scholar 
Pizzigalli, C. et al. Phylogeographic diversification of the Mesalina olivieri species complex (Squamata: Lacertidae) with the description of a new species and a new subspecies endemic from North West Africa. J. Zool. Syst. Evol. Res. 59, 2321–2349, https://doi.org/10.1111/jzs.12516 (2021).Article 

Google Scholar 
Mediannikov, O., Trape, S. & Trape, J.-F. A molecular study of the genus Agama (Squamata: Agamidae) in West Africa, with description of two new species and a review of the taxonomy, geographic distribution, and ecology of currently recognized species. Russ. J. Herpetol. 19, 115–142, https://doi.org/10.30906/1026-2296-2012-19-2-115-142 (2012).Article 

Google Scholar 
Wagner, P., Wilms, T. M., Bauer, A. & Böhme, W. Studies on African Agama. V. On the origin of Lacerta agama Linnaeus, 1758 (Squamata: Agamidae). Bonn. zool. Beitr. 56, 215–223 (2009).
Google Scholar  More

In this picture, I’m face to face with an anaesthetized 250-kilogram male grizzly bear (Ursus arctos horribilis), which was caught near Sparwood and Elkford in Canada. With help from conservation inspector Joe Caravetta, who is sitting next to me, and my field technician Laura Smit, I’m putting a GPS-enabled collar on the bear so that we can track his movements.The first time I worked with a bear this size, it was absolutely exhilarating, a real adrenaline rush. I thought, “My whole head could fit inside this animal’s jaws.” Over time, it has become fairly routine. I learnt to trust the anaesthetic — a mix of drugs given using an air-powered dart gun — and we constantly monitor the bears’ vital signs.While I’m attaching the collar, Laura collects hair samples for genetic studies. We measure the bear’s temperature and oxygen levels, and take hair samples to get an idea of his diet. We weigh him, which is quite a challenge: we use a custom-made tarpaulin with handles to wrap him up like a bear taco. We attach the handles to a hanging scale and, with a rope over a tree branch, winch him up. This particular bear is eight years old and has 29% body fat, which is very healthy for spring.Ultimately, the collars will help us to reduce conflict between bears and the people who live in the area — I’ve seen bears rip shed doors off to get to livestock, and peel open an outdoor freezer like a can of sardines.At times, it’s chaos for both humans and bears, and people react by shooting the bear — the most common cause of death for younger ones. Tracking bears with collars will help us to find solutions.From tracking the bears, we’ve learnt that they are adapting their habits to avoid people, and they become more nocturnal as they get older. We’ve helped local communities to adapt, too: we’ve launched cost-share initiatives for electrical fencing, which is a really effective bear deterrent. More

Stanier, R. Y. & Van Niel, C. B. Arch. Mikrobiol. 42, 17–35 (1962).CAS 
Article 

Google Scholar 
Schavemaker, P. E. & Muñoz-Gómez, S. A. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01833-9 (2022).Article 

Google Scholar 
Lane, N. & Martin, W. F. Nature 467, 929–934 (2010).CAS 
Article 

Google Scholar 
Lynch, M. & Marinov, G. K. PNAS 112, 15690–15695 (2015).CAS 
Article 

Google Scholar 
Cavalier-Smith, T. & Chao, E. E. Protoplasma 257, 621–753 (2020).CAS 
Article 

Google Scholar 
Zachar, I. & Szathmáry, E. Biol. Direct 12, 19 (2017).Article 

Google Scholar 
Cavalier-Smith, T. Cold Spring Harb. Perspect. Biol. 6, 1–31 (2014).Article 

Google Scholar 
de Duve, C. Nat. Rev. Genet. 8, 395–403 (2007).Article 

Google Scholar 
Shiratori, T., Suzuki, S., Kakizawa, Y. & Ishida, K. Nat. Commun. 10, 5529 (2019).Article 

Google Scholar 
Martin, W. F., Tielens, A. G. M., Mentel, M., Garg, S. G. & Gould, S. B. Microbiol. Mol. Biol. Rev. 81, 8–17 (2017).Article 

Google Scholar 
Jékely, G. Biol. Direct 2, 3 (2007).Article 

Google Scholar 
Stanier, R. Y. Some aspects of the biology of cells and their possible evolutionary significance. Organization and Control in Prokaryotic and Eukaryotic Cells. In Proc. 20th Symposium of the Society for General Microbiology (eds Charles, H. P. & Knight, B. C. J. G) 20, 1–38 (Cambridge University Press, Cambridge, 1970).Zachar, I., Szilágyi, A., Számadó, S. & Szathmáry, E. PNAS USA 115, E1504–E1510 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Burns, J. A., Pittis, A. A. & Kim, E. Nat. Ecol. Evol. 2, 697–704 (2018).Article 

Google Scholar 
Bremer, N., Tria, F. D. K., Skejo, J., Garg, S. G. & Martin, W. F. Genome Biol. Evol. 14, evac079 (2022).Article 

Google Scholar 
Imachi, H. et al. Nature 577, 519–525 (2020).CAS 
Article 

Google Scholar 
Zachar, I. & Boza, G. Cell. Mol. Life Sci. 77, 3503–3523 (2020).CAS 
Article 

Google Scholar 
Devos, D. P. Mol. Biol. Evol. 38, 3531–3542 (2021).CAS 
Article 

Google Scholar  More

Effects of grid spacing on habitat hydraulic complexity metricsThe sensitivity of the habitat hydraulic complexity metrics to Δs was examined by calculating the metrics for Δs = 0.06, 0.12, 0.18, and 0.24 m (for M4, Δs = Δx = Δy). Figure 3 shows the variation of the metrics with grid spacing for scenarios with boulders. A preliminary assessment of no-boulder scenarios (S1-L and S1-H) showed that all the metrics decreased by increasing the grid spacing. However, because the metrics are mostly used in complex rather than non-obstructed and 1-D flows, the plots only include scenarios with boulder placement to highlight the effects of grid spacing on the metrics in complex flows. All the metrics generally decreased as Δs increased. At the low flow rate, by changing the Δs from the smallest to largest, i.e., 0.06 m to 0.024, the mean decreases in the M1, M2, and M4 metrics (averaged over all the scenarios with boulders) were 45.1, 9.9, and 74.7%, respectively. At the high flow rate, these reductions were 34.8, 14.7, and 82.5% for M1, M2, and M4, respectively. Table 2 shows the p-values associated with the changes in the metrics due to increasing Δs from 0.06 to 0.24 m for all scenarios. The table indicates that changes in M1 and M4 were statistically significant while for M2 they were not (p-values  > 0.05 for all scenarios except for S2-H). This result indicated the considerable influence of grid spacing on M1 and M4 metrics in the reaches with boulder placement. Additionally, the differences in the reported average reductions due to changing the flow rate were less than 10%, indicating an insubstantial effect of flow rate on the habitat hydraulic complexity metrics’ sensitivity to the grid spacing. The significant sensitivity of the metrics M1 and M4 to the grid spacing in this study is contrary to the findings of a previous study in which an insignificant correlation was found between the habitat hydraulic complexity metrics and Δs29. This difference can be attributed to different topographic features in the studied reaches. In the previous findings, measurements were mainly taken around the bends and reaches with no significant obstruction29, in which a more uniform flow with smaller velocity gradients is expected. However, in this study, the systematic boulder placement generated more complex flow patterns with noticeable velocity gradients. Therefore, due to the variations of flow velocities in the zone studied, substantially different values for the metrics are anticipated by computing the metrics over different spatial scales.Figure 3Variation of the habitat hydraulic complexity metrics with grid spacing (Δs) for scenarios with boulder placement. (a) kinetic energy gradient metric, M1, (b) normalized kinetic energy gradient metric, M2, (c) modified recirculation metric M4.Full size imageTable 2 p-values associated with changing the grid spacing from 0.06 to 0.24 m.Full size tableStatistical distribution of habitat hydraulic complexity metricsTable 3 lists the mean, minimum, maximum, and standard deviations of the habitat hydraulic complexity metrics (Δs = 0.06 m) for all the scenarios. To complement the results from Table 3 and assess whether the influences of solely changing the boulder concentration or flow rate on the metrics were statistically significant, Table 4 shows p-values associated with changing flow rate from low to high for a given boulder concentration, and Table 5 shows p-values associated with gradually increasing the boulder concentration for a given flow rate.Table 3 The statistical parameters of the habitat hydraulic complexity metrics in the detailed measurement zone.Full size tableTable 4 p-values from a t-test associated with changes in flow rate for a given boulder concentration.Full size tableTable 5 p-values from a t-test associated with changes in boulder concertation for a given flow rate.Full size tableFor metric M1, the mean M1 values for scenarios incorporating boulders showed the same order of magnitude as values from previous studies for reaches with single and multiple boulders27 but they were about one order of magnitude larger than calculated values in the confluence of two rivers11. Using a larger grid spacing in the study in the confluence of two rivers11 can be the reason for this difference. For a scenario at the higher flow rate, the mean M1 was on average (averaged for all the scenarios) 36% greater than its counterpart at the lower flow rate and this change in M1 values was statistically significant with p  More