Philippa Kaur

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

Wirtz, K., Smith, S. L., Mathis, M. & Taucher, J. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01430-5 (2022).Article 

Google Scholar 
Watanabe, M., Kohata, K. & Kimura, T. Limnol. Oceanogr. 36, 593–602 (1991).Article 

Google Scholar 
Villareal, T. A. et al. Nature 397, 423–425 (1999).CAS 
Article 

Google Scholar 
Krumhardt, K. M., Lovenduski, N. S., Iglesias-Rodriguez, M. D. & Kleypas, J. A. Prog. Oceanogr. 159, 276–295 (2017).Article 

Google Scholar 
Alldredge, A. L. & Silver, M. W. Prog. Oceanogr. 20, 41–82 (1988).Article 

Google Scholar 
White, A. E., Spitz, Y. H. & Letelier, R. M. Mar. Ecol. Prog. Ser. 323, 35–45 (2006).Article 

Google Scholar 
Kwiatkowski, L. et al. Biogeosciences 17, 3439–3470 (2020).CAS 
Article 

Google Scholar 
Tittensor, D. P. et al. Nat. Clim. Change 11, 973–981 (2021).Article 

Google Scholar 
Giorgetta, M. A. et al. J. Adv. Model. Earth Syst. 5, 572–597 (2013).Article 

Google Scholar 
McGillicuddy, D. J. et al. Nature 394, 263–266 (1998).CAS 
Article 

Google Scholar 
Lévy, M., Franks, P. J. & Smith, K. S. Nat. Commun. 9, 4758 (2018).Article 

Google Scholar 
Durham, W. M. & Stocker, R. Annu. Rev. Mar. Sci. 4, 177–207 (2012).Article 

Google Scholar 
Cullen, J. J. Annu. Rev. Mar. Sci. 7, 207–239 (2015).Article 

Google Scholar 
Moeller, H. V., Laufkötter, C., Sweeney, E. M. & Johnson, M. D. Nat. Commun. 10, 1978 (2019).Article 

Google Scholar 
Fawcett, S. E., Johnson, K. S., Riser, S. C., Van Oostende, N. & Sigman, D. M. Mar. Chem. 207, 108–123 (2018).CAS 
Article 

Google Scholar  More

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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

Westberry, T., Behrenfeld, M., Siegel, D. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Glob. Biogeochem. Cycles 22 (2008).Richardson, K. & Bendtsen, J. Vertical distribution of phytoplankton and primary production in relation to nutricline depth in the open ocean. Mar. Ecol. Prog. Ser. 620, 33–46 (2019).CAS 

Google Scholar 
Oschlies, A. in Ocean Modeling in an Eddying Regime (eds Hecht, M. W. & Hasumi, H.) 115–130 (AGU, 2008).Letscher, R. T., Primeau, F. & Moore, J. K. Nutrient budgets in the subtropical ocean gyres dominated by lateral transport. Nat. Geosci. 9, 815–819 (2016).CAS 

Google Scholar 
Johnson, K. S., Riser, S. C. & Karl, D. M. Nitrate supply from deep to near-surface waters of the North Pacific subtropical gyre. Nature 465, 1062–1065 (2010).CAS 

Google Scholar 
Fawcett, S. E., Lomas, M. W., Casey, J. R., Ward, B. B. & Sigman, D. M. Assimilation of upwelled nitrate by small eukaryotes in the Sargasso Sea. Nat. Geosci. 4, 717–722 (2011).CAS 

Google Scholar 
Knapp, A. N., Casciotti, K. L., Berelson, W. M., Prokopenko, M. G. & Capone, D. G. Low rates of nitrogen fixation in eastern tropical South Pacific surface waters. Proc. Natl Acad. Sci. USA 113, 4398–4403 (2016).CAS 

Google Scholar 
Böttjer, D. et al. Temporal variability of nitrogen fixation and particulate nitrogen export at station ALOHA. Limnol. Oceanogr. 62, 200–216 (2017).
Google Scholar 
Gruber, N., Keeling, C. D. & Stocker, T. F. Carbon-13 constraints on the seasonal inorganic carbon budget at the BATS site in the northwestern Sargasso Sea. Deep Sea Res. 1 45, 673–717 (1998).CAS 

Google Scholar 
Doney, S. C., Glover, D. M. & Najjar, R. G. A new coupled, one-dimensional biological–physical model for the upper ocean: applications to the JGOFS Bermuda Atlantic Time-series Study (BATS) site. Deep Sea Res. 2 43, 591–624 (1996).CAS 

Google Scholar 
Ascani, F. et al. Physical and biological controls of nitrate concentrations in the upper subtropical North Pacific Ocean. Deep Sea Res 2 93, 119–134 (2013).CAS 

Google Scholar 
Gran, H. H. in Rapport Vol. 56, 1–112 (Bureau du Conseil permanent international pour l’exploration de la mer, 1929).Hasle, G. R. Phototactic vertical migration in marine dinoflagellates. Oikos 2, 162–175 (1950).
Google Scholar 
Villareal, T. A. et al. Upward transport of oceanic nitrate by migrating diatom mats. Nature 397, 423–425 (1999).CAS 

Google Scholar 
Villareal, T. & Carpenter, E. Buoyancy regulation and the potential for vertical migration in the oceanic cyanobacterium Trichodesmium. Microb. Ecol. 45, 1–10 (2003).CAS 

Google Scholar 
Wirtz, K. & Smith, S. L. Vertical migration by bulk phytoplankton sustains biodiversity and nutrient input to the surface ocean. Sci. Rep. 10, 1142 (2020).CAS 

Google Scholar 
Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Glob. Biogeochem. Cycles 30, 1756–1777 (2016).CAS 

Google Scholar 
Wang, W.-L., Moore, J. K., Martiny, A. C. & Primeau, F. W. Convergent estimates of marine nitrogen fixation. Nature 566, 205–211 (2019).CAS 

Google Scholar 
Karl, D. M., Letelier, R., Hebel, D. V., Bird, D. F. & Winn, C. D. in Marine Pelagic Cyanobacteria: Trichodesmium and Other Diazotrophs (eds Carpenter, E. J. et al.) 219–237 (Springer, 1992).Cullen, J. J. Subsurface chlorophyll maximum layers: enduring enigma or mystery solved? Ann. Rev. Mar. Sci. 7, 207–239 (2015).
Google Scholar 
Masuda, Y. et al. Photoacclimation by phytoplankton determines the distribution of global subsurface chlorophyll maxima in the ocean. Commun. Earth Environ. 2, 1–8 (2021).
Google Scholar 
Anugerahanti, P., Kerimoglu, O. & Smith, S. L. Enhancing ocean biogeochemical models with phytoplankton variable composition. Front. Mar. Sci. 8, 675428 (2021).
Google Scholar 
Pérez, V., Fernández, E., Marañón, E., Morán, X. A. G. & Zubkov, M. V. Vertical distribution of phytoplankton biomass, production and growth in the Atlantic subtropical gyres. Deep Sea Res. 1 53, 1616–1634 (2006).
Google Scholar 
Cornec, M. et al. Deep chlorophyll maxima in the global ocean: occurrences, drivers and characteristics. Glob. Biogeochem. Cycles 35, e2020GB006759 (2021).CAS 

Google Scholar 
Li, Q. P., Wang, Y., Dong, Y. & Gan, J. Modeling long-term change of planktonic ecosystems in the northern South China Sea and the upstream Kuroshio Current. J. Geophys. Res. 120, 3913–3936 (2015).
Google Scholar 
Latif, S., Ayub, Z. & Siddiqui, G. Seasonal variability of phytoplankton in a coastal lagoon and adjacent open sea in Pakistan. Turk. J. Botany 37, 398–410 (2013).CAS 

Google Scholar 
Liang, Y. et al. Nutrient-limitation induced diatom–dinoflagellate shift of spring phytoplankton community in an offshore shellfish farming area. Mar. Pollut. Bull. 141, 1–8 (2019).CAS 

Google Scholar 
Rahlff, J. et al. Short-term responses to ocean acidification: effects on relative abundance of eukaryotic plankton from the tropical Timor Sea. Mar. Ecol. Prog. Ser. 658, 59–74 (2021).CAS 

Google Scholar 
Kahru, M., Savchuk, O. & Elmgren, R. Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability. Mar. Ecol. Prog. Ser. 343, 15–23 (2007).
Google Scholar 
Klais, R., Tamminen, T., Kremp, A., Spilling, K. & Olli, K. Decadal-scale changes of dinoflagellates and diatoms in the anomalous Baltic Sea spring bloom. PLoS ONE 6, e21567 (2011).CAS 

Google Scholar 
Klais, R., Norros, V., Lehtinen, S., Tamminen, T. & Olli, K. Community assembly and drivers of phytoplankton functional structure. Funct. Ecol. 31, 760–767 (2017).
Google Scholar 
Villareal, T. A., Pilskaln, C. H., Montoya, J. P. & Dennett, M. Upward nitrate transport by phytoplankton in oceanic waters: balancing nutrient budgets in oligotrophic seas. PeerJ 2, e302 (2014).
Google Scholar 
Mignot, A. et al. Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: a bio-argo float investigation. Glob. Biogeochem. Cycles 28, 856–876 (2014).CAS 

Google Scholar 
Chen, B., Smith, S. L. & Wirtz, K. W. Effect of phytoplankton size diversity on primary productivity in the North Pacific: trait distributions under environmental variability. Ecol. Lett. 22, 56–66 (2019).
Google Scholar 
Cabré, A., Marinov, I. & Leung, S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 Earth system models. Clim. Dyn. 45, 1253–1280 (2015).
Google Scholar 
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Mod. Earth Sys. 5, 572–597 (2013).
Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Google Scholar 
Fu, W., Randerson, J. T. & Moore, J. K. Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).
Google Scholar 
Gliwicz, M. Z. Predation and the evolution of vertical migration in zooplankton. Nature 320, 746–748 (1986).
Google Scholar 
Huettel, M., Forster, S., Kloser, S. & Fossing, H. Vertical migration in the sediment-dwelling sulfur bacteria Thioploca spp. in overcoming diffusion limitations. Appl. Environ. Microbiol. 62, 1863–1872 (1996).CAS 

Google Scholar 
Waterbury, J. B., Willey, J. M., Franks, D. G., Valois, F. W. & Watson, S. W. A cyanobacterium capable of swimming motility. Science 230, 74–76 (1985).CAS 

Google Scholar 
McCarren, J. et al. Inactivation of swmA results in the loss of an outer cell layer in a swimming Synechococcus strain. J. Bacteriol. 187, 224–230 (2005).CAS 

Google Scholar 
Eppley, R. W., Holm-Hansen, O. & Strickland, J. D. Some observations on the vertical migration of dinoflagellates. J. Phycol. 4, 333–340 (1968).CAS 

Google Scholar 
Sengupta, A., Carrara, F. & Stocker, R. Phytoplankton can actively diversify their migration strategy in response to turbulent cues. Nature 543, 555–558 (2017).CAS 

Google Scholar 
Waite, A., Fisher, A., Thompson, P. & Harrison, P. Sinking rate verses cell volume relationships illuminate sinking rate control mechanisms in marine diatoms. Mar. Ecol. Prog. Ser. 157, 97–108 (1997).
Google Scholar 
Throndsen, J. Motility in some marine nanoplankton flagellates. Nor. J. Zool. 21, 193–200 (1973).
Google Scholar 
Gittleson, S. M., Hotchkiss, S. K. & Valencia, F. G. Locomotion in the marine dinoflagellate Amphidinium carterae (Hulburt). Trans. Am. Microsc. Soc. 93, 101–105 (1974).Barsanti, L. et al. Swimming patterns of the quadriflagellate Tetraflagellochloris mauritanica (Chlamydomonadales, Chlorophyceae). J. Phycol. 52, 209–218 (2016).
Google Scholar 
Schuech, R. & Menden-Deuer, S. Going ballistic in the plankton: anisotropic swimming behavior of marine protists. Limnol. Oceanogr. Fluids Environ. 4, 1–16 (2014).
Google Scholar 
Eppley, R. W., Holmes, R. W. & Strickland, J. D. Sinking rates of marine phytoplankton measured with a fluorometer. J. Exp. Mar. Biol. Ecol. 1, 191–208 (1967).
Google Scholar 
Bienfang, P. Phytoplankton sinking rates in oligotrophic waters off Hawaii, USA. Mar. Biol. 61, 69–77 (1980).
Google Scholar 
Lisicki, M., Rodrigues, M. F. V., Goldstein, R. E. & Lauga, E. Swimming eukaryotic microorganisms exhibit a universal speed distribution. Elife 8, e44907 (2019).CAS 

Google Scholar 
Moore, J. & Villareal, T. Buoyancy and growth characteristics of three positively buoyant marine diatoms. Mar. Ecol. Prog. Ser. 132 (1996).Hawaii Ocean Time-series (HOT) (School of Ocean and Earth Science and Technology at the University of Hawai’i, 2020); http://hahana.soest.hawaii.edu/hot/hot-dogsBermuda Atlantic Time-Series (BATS) (Bermuda Institure of Ocean Sciences, 2020); http://bats.bios.eduThe Japanese 55-Year Reanalysis (JRA-55) (Japan Meteorological Agency, 2020); http://jra.kishou.go.jp/JRA-55Ridgway, K., Dunn, J. & Wilkin, J. Ocean interpolation by four-dimensional weighted least squares—application to the waters around Australasia. J. Atmos. Ocean. Technol. 19, 1357–1375 (2002).
Google Scholar 
CSIRO Atlas of Regional Seas (CARS) (CSIRO, 2009); http://www.marine.csiro.au/~dunn/cars2009Ocean Colour (ESA-CCI, 2020); http://www.esa-oceancolour-cci.orgCloud (ESA-CCI, 2020); http://www.esa-cloud-cci.orgSea Surface Temperature (ESA-CCI, 2020); http://www.esa-sst-cci.orgRosati, A. & Miyakoda, K. A general circulation model for upper ocean simulation. J. Phys. Oceanogr. 18, 1601–1626 (1988).
Google Scholar 
Ralston, D. K., McGillicuddy, D. J. & Townsend, D. W. Asynchronous vertical migration and bimodal distribution of motile phytoplankton. J. Plankton Res. 29, 803–821 (2007).
Google Scholar 
Kamykowski, D. & Yamazaki, H. A study of metabolism-influenced orientation in the diel vertical migration of marine dinoflagellates. Limnol. Oceanogr. 42, 1189–1202 (1997).
Google Scholar 
Richardson, T. L., Cullen, J. J., Kelley, D. E. & Lewis, M. R. Potential contributions of vertically migrating Rhizosolenia to nutrient cycling and new production in the open ocean. J. Plankton Res. 20, 219–241 (1998).
Google Scholar 
Ross, O. N. & Sharples, J. Phytoplankton motility and the competition for nutrients in the thermocline. Mar. Ecol. Prog. Ser. 347, 21–38 (2007).CAS 

Google Scholar 
Chavez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Ann. Rev. Mar. Sci. 3, 227–260 (2011).
Google Scholar 
Saba, V. et al. An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences 8, 489–503 (2011).CAS 

Google Scholar 
Bhattathiri, P., Devassy, V. & Radhakrishna, K. Primary production in the Bay of Bengal during southwest monsoon of 1978. Mahasagar Bull. Natl Inst. Oceanogr. 13, 315–323 (1980).
Google Scholar 
Sarupria, J. & Bhargava, R. Seasonal primary production in different sectors of the EEZ of India. Mahasagar Bull. Natl Inst. Oceanogr. 26, 139–147 (1993).
Google Scholar 
Jyothibabu, R. et al. Differential response of winter cooling on biological production in the northeastern Arabian Sea and northwestern Bay of Bengal. Curr. Sci. 87, 783–791 (2004).
Google Scholar 
Kumar, S. P. et al. Is the biological productivity in the Bay of Bengal light limited? Curr. Sci. 98, 1331–1339 (2010).CAS 

Google Scholar 
Kumar, S. P. et al. Seasonal cycle of physical forcing and biological response in the Bay of Bengal. Ind. J. Mar. Sci. 39, 388–405 (2010).CAS 

Google Scholar 
Buitenhuis, E. T., Hashioka, T. & Quéré, C. L. Combined constraints on global ocean primary production using observations and models. Glob. Biogeochem. Cycles 27, 847–858 (2013).CAS 

Google Scholar  More

Sunday, J. M., Crim, R. N., Harley, C. D. G. & Hart, M. W. Quantifying rates of evolutionary adaptation in response to ocean acidification. PLoS ONE 6, e22881 (2011).CAS 
Article 

Google Scholar 
Kelly, M. W. & Hofmann, G. E. Adaptation and the physiology of ocean acidification. Funct. Ecol. 27, 980–990 (2013).Article 

Google Scholar 
Munday, P. L., Warner, R. R., Monro, K., Pandolfi, J. M. & Marshall, D. J. Predicting evolutionary responses to climate change in the sea. Ecol. Lett. 16, 1488–1500 (2013).Article 

Google Scholar 
Reusch, T. B. H. Climate change in the oceans: evolutionary versus phenotypically plastic responses of marine animals and plants. Evol. Appl. 7, 104–122 (2014).Article 

Google Scholar 
Sunday, J. M. et al. Evolution in an acidifying ocean. Trends Ecol. Evol. 29, 117–125 (2014).Article 

Google Scholar 
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).Article 

Google Scholar 
Przeslawski, R., Byrne, M. & Mellin, C. A review and meta-analysis of the effects of multiple abiotic stressors on marine embryos and larvae. Glob. Change Biol. 21, 2122–2140 (2015).Article 

Google Scholar 
Cattano, C., Claudet, J., Domenici, P. & Milazzo, M. Living in a high CO2 world: a global meta-analysis shows multiple trait-mediated fish responses to ocean acidification. Ecol. Monogr. 88, 320–335 (2018).Article 

Google Scholar 
Lohbeck, K., Riebesell, U. & Reusch, T. Adaptive evolution of a key phytoplankton species to ocean acidification. Nat. Geosci. 5, 346–351 (2012).CAS 
Article 

Google Scholar 
Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).Article 

Google Scholar 
Kelly, M. W., Padilla-Gamiño, J. L. & Hofmann, G. E. Natural variation and the capacity to adapt to ocean acidification in the keystone sea urchin Strongylocentrotus purpuratus. Glob. Change Biol. 19, 2536–2546 (2013).Article 

Google Scholar 
Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013).CAS 
Article 

Google Scholar 
Foo, S. A., Dworjanyn, S. A., Poore, A. G. B., Harianto, J. & Byrne, M. Adaptive capacity of the sea urchin Heliocidaris erythrogramma to ocean change stressors: responses from gamete performance to the juvenile. Mar. Ecol. Prog. Ser. 556, 161–172 (2016).CAS 
Article 

Google Scholar 
Malvezzi, A. J. et al. A quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification. Evol. Appl. 8, 352–362 (2015).CAS 
Article 

Google Scholar 
Bitter, M. C., Kapsenberg, L., Gattuso, J.-P. & Pfister, C. A. Standing genetic variation fuels rapid adaptation to ocean acidification. Nat. Commun. 10, 5821 (2019).CAS 
Article 

Google Scholar 
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn (Pearson Prentice Hall, 1996).Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Oxford Univ. Press, 1998).Ishimatsu, A., Hayashi, M. & Kikkawa, T. Fishes in high-CO2, acidified oceans. Mar. Ecol. Prog. Ser. 373, 295–302 (2008).CAS 
Article 

Google Scholar 
Melzner, F. et al. Physiological basis for high CO2 tolerance in marine ectothermic animals: pre-adaptation through lifestyle and ontogeny? Biogeosciences 6, 2313–2331 (2009).CAS 
Article 

Google Scholar 
Timothy A. Mousseau and Charles W. Fox. Maternal Effects as Adaptations 178–201 (Oxford Univ. Press, 1998).Marshall, D., Allen, R. & Crean, A. The ecological and evolutionary importance of maternal effects in the sea. Oceanogr. Mar. Biol. 46, 203–250 (2008).
Google Scholar 
Tasoff, A. J. & Johnson, D. W. Can larvae of a marine fish adapt to ocean acidification? Evaluating the evolutionary potential of California grunion (Leuresthes tenuis). Evol. Appl. 12, 560–571 (2019).CAS 
Article 

Google Scholar 
Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).Article 

Google Scholar 
Shimada, Y., Shikano, T., Murakami, N., Tsuzaki, T. & Seikai, T. Maternal and genetic effects on individual variation during early development in Japanese flounder Paralichthys olivaceus. Fish. Sci. 73, 244–249 (2007).CAS 
Article 

Google Scholar 
Johnson, D. W., Christie, M. R. & Moye, J. Quantifying evolutionary potential of marine fish larvae: heritability, selection, and evolutionary constraints. Evolution 64, 2614–2628 (2010).Article 

Google Scholar 
Miles, C. M., Hadfield, M. G. & Wayne, M. L. Heritability for egg size in the serpulid polychaete Hydroides elegans. Mar. Ecol. Prog. Ser. 340, 155–162 (2007).Article 

Google Scholar 
Iguchi, K. & Yamaguchi, M. Adaptive significance of inter- and intrapopulational egg size variation in ayu Plecoglossus altivelis (osmeridae). Copeia 1994, 184–190 (1994).Article 

Google Scholar 
Marshall, D. J. & Keough, M. J. Effects of settler size and density on early post-settlement survival of Ciona intestinalis in the field. Mar. Ecol. Prog. Ser. 259, 139–144 (2003).Article 

Google Scholar 
González-Ortegón, E. & Giménez, L. Environmentally mediated phenotypic links and performance in larvae of a marine invertebrate. Mar. Ecol. Prog. Ser. 502, 185–195 (2014).Article 

Google Scholar 
Pan, T.-C. F., Applebaum, S. L. & Manahan, D. T. Experimental ocean acidification alters the allocation of metabolic energy. Proc. Natl Acad. Sci. USA 112, 4696–4701 (2015).CAS 
Article 

Google Scholar 
Rollinson, N. & Hutchings, J. A. Environmental quality predicts optimal egg size in the wild. Am. Nat. 181, 76–90 (2013).Article 

Google Scholar 
Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Oxford Univ. Press, 1998).Munday, P. L. Transgenerational acclimation of fishes to climate change and ocean acidification. F1000Prime Rep. 6, 99 (2014).Article 

Google Scholar 
Murray, C. S., Malvezzi, A., Gobler, C. J. & Baumann, H. Offspring sensitivity to ocean acidification changes seasonally in a coastal marine fish. Mar. Ecol. Prog. Ser. 504, 1–11 (2014).Article 

Google Scholar 
Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).Article 

Google Scholar 
Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).Article 
CAS 

Google Scholar 
Bell, G. Evolutionary rescue and the limits of adaptation. Phil. Trans. R. Soc. B 368, p20120080 (2013).Article 

Google Scholar 
Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).Article 

Google Scholar 
Smyder, E. A., Martin, K. L. M. & Gatten, R. E. Jr Temperature effects on egg survival and hatching during the extended incubation period of California grunion, Leuresthes tenuis. Copeia 2002, 313–320 (2002).Article 

Google Scholar 
Barneche, D. R., Robertson, D. R., White, C. R. & Marshall, D. J. Fish reproductive-energy output increases disproportionately with body size. Science 360, 642–645 (2018).CAS 
Article 

Google Scholar 
Van Noordwijk, A. J. & de Jong, G. Acquisition and allocation of resources: their influence on variation in life history tactics. Am. Nat. 128, 137–142 (1986).Article 

Google Scholar 
Davidson, C. Spatial and Temporal Variability of Coastal Carbonate Chemistry in the Southern California Region. MSc thesis, Univ. California, San Diego (2015).Jones, J. M., Sweet, J., Brzezinski, M. A., McNair, H. M. & Passow, U. Evaluating carbonate system algorithms in a nearshore system: does total alkalinity matter? PLoS ONE 11, e0165191 (2016).Article 
CAS 

Google Scholar 
Gruber, N. et al. Rapid progression of ocean acidification in the California current system. Science 337, 220–223 (2012).CAS 
Article 

Google Scholar 
Turi, G., Lachkar, Z., Gruber, N. & Münnich, M. Climatic modulation of recent trends in ocean acidification in the California current system. Environ. Res. Lett. 11, 014007 (2016).Article 

Google Scholar 
Northcott, D. et al. Impacts of urban carbon dioxide emissions on sea-air flux and ocean acidification in nearshore waters. PLoS ONE 14, e0214403 (2019).CAS 
Article 

Google Scholar 
Rausher, M. D. The measurement of selection on quantitative traits: biases due to environmental covariances between traits and fitness. Evolution 46, 616–626 (1992).Article 

Google Scholar 
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Kruuk, L. E. B. Estimating genetic parameters in natural populations using the animal model. Phil. Trans. R. Soc. B 359, 873–890 (2004).Article 

Google Scholar 
Wilson, A. J. et al. An ecologist’s guide to the animal model. J. Anim. Ecol. 79, 13–26 (2010).Article 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, (2010).Heidelberger, P. & Welch, P. D. Simulation run length control in the presence of an initial transient. Oper. Res. 31, 1109–1144 (1983).Article 

Google Scholar 
Clark, F. N. The Life History of Leuresthes Tenuis, an Atherine Fish with Tide Controlled Spawning Habits Fish Bulletin No. 10 (California Department of Fish and Game, 1925).Johnson, D.W. Data from: Selection on offspring size and contemporary evolution under ocean acidification. Dryad https://doi.org/10.5061/dryad.0gb5mkm3w (2022) More

Zangoueinejad, R. & Alebrahim, M. T. Use of conventional and innovative organic materials as alternatives to black plastic mulch to suppress weeds in tomato production. Biol. Agric. Hortic. 37, 267–284. https://doi.org/10.1080/01448765.2021.1947377 (2021).Article 

Google Scholar 
Biswas, S. K., Akanda, A. R., Rahman, M. S. & Hossain, M. A. Effect of drip irrigation and mulching on yield, water-use efficiency and economics of tomato. Plant Soil Environ. 61, 97–102. https://doi.org/10.17221/804/2014-PSE (2015).Article 

Google Scholar 
Haapala, T., Palonen, P., Tamminen, A. & Ahokas, J. Effects of different paper mulches on soil temperature and yield of cucumber (Cucumis sativus L.) in the temperate zone. Agric. Food Sci. 24, 52–58. https://doi.org/10.23986/afsci.47220 (2015).CAS 
Article 

Google Scholar 
Shiukhy, S., Raeini-Sarjaz, M. & Chalavi, V. Colored plastic mulch microclimates affect strawberry fruit yield and quality. Int. J. Biometeorol. 59, 1061–1066. https://doi.org/10.1007/s00484-014-0919-0 (2015).ADS 
Article 
PubMed 

Google Scholar 
Sideman, R. G. Performance of sweetpotato cultivars grown using biodegradable black plastic mulch in New Hampshire. HortTechnology 25, 412–416. https://doi.org/10.21273/HORTTECH.25.3.412 (2015).Article 

Google Scholar 
Ferdous, Z., Datta, A. & Anwar, M. Plastic mulch and indigenous microorganism effects on yield and yield components of cauliflower and tomato in inland and coastal regions of Bangladesh. J. Crop Improv. 31, 261–279. https://doi.org/10.1080/15427528.2017.1293578 (2017).Article 

Google Scholar 
Lament, W. J. Jr. Plastic mulches for the production of vegetable crops. HortTechnology 3, 35–39. https://doi.org/10.21273/HORTTECH.3.1.35 (1993).Article 

Google Scholar 
Abdul-Baki, A. A., Teasdale, J. R., Goth, R. W. & Haynes, K. G. Marketable yields of fresh-market tomatoes grown in plastic and hairy vetch mulches. HortScience 37, 878–881. https://doi.org/10.21273/HORTSCI.37.6.878 (2002).Article 

Google Scholar 
Chalker-Scott, L. Impact of mulches on landscape plants and the environment—A review. J. Environ. Hortic. 25, 239–249. https://doi.org/10.24266/0738-2898-25.4.239 (2007).Article 

Google Scholar 
Kasirajan, S. & Ngouajio, M. Polyethylene and biodegradable mulches for agricultural applications: A review. Agron. Sustain. Dev. 32, 501–529. https://doi.org/10.1007/s13593-011-0068-3 (2012).CAS 
Article 

Google Scholar 
Iqbal, R. et al. Potential agricultural and environmental benefit of mulches—A review. Bull. Natl. Res. Cent. 44, 752020. https://doi.org/10.1186/s42269-020-00290-3 (2020).Article 

Google Scholar 
Travlos, I. et al. Efficacy of different herbicides on Echinocholoa colona (L.) Link control and the first case of its glyphosate resistance in Greece. Agronomy 10, 1056. https://doi.org/10.3390/agronomy10071056 (2000).CAS 
Article 

Google Scholar 
Travlos, I. S. & Chachalis, D. Glyphsate-resistant hairy fleabane (Conyza bonariensis) is reported in Greece. Weed Technol. 24, 569–573. https://doi.org/10.1614/WT-D-09-00080.1 (2010).CAS 
Article 

Google Scholar 
Tahmasebi, B. K. et al. Effectiveness of alternative herbicides on three Conyza species from Europe with and without glyphosate resistance. Crop Prot. 112, 350–355. https://doi.org/10.1016/j.cropro.2018.06.021 (2018).CAS 
Article 

Google Scholar 
Kanatas, P., Anthonopoulos, N., Gazoulis, I. & Travlos, I. S. Screening glyphosate-alternative weed control options in important perennial crops. Weed Sci. 69, 704–718. https://doi.org/10.1017/wsc.2021.55 (2021).Article 

Google Scholar 
Anthonopoulos, N. et al. Hot foam: Evaluation of a new, non-chemical weed control option in perennial crops. Smart Agric. Technol. 3, 1000063. https://doi.org/10.1016/j.atech.2022.100063 (2023).Article 

Google Scholar 
Espí, E., Salmerón, A., Fontecha, A., García, Y. & Real, A. I. Plastic films for agricultural applications. J. Plast. Film Sheeting 22, 85–102. https://doi.org/10.1177/8756087906064220 (2006).CAS 
Article 

Google Scholar 
Li, C. et al. Effects of biodegradable mulch on soil quality. Appl. Soil Ecol. 79, 59–69. https://doi.org/10.1016/j.apsoil.2014.02.012 (2014).ADS 
Article 

Google Scholar 
van Sebille, E. A global inventory of small floating plastic debris. Environ. Res. Lett. 10, 124006. https://doi.org/10.1088/1748-9326/10/12/124006 (2015).ADS 
Article 

Google Scholar 
Moreno, M. M., Cirujeda, A., Aibar, J. & Moreno, C. Soil thermal and productive responses of biodegradable mulch materials in a processing tomato (Lycopersicon esculentum Mill.). Crop. Soil Res. 54, 207–215. https://doi.org/10.1071/SR15065 (2016).Article 

Google Scholar 
Barnes, D. K. A., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos Trans. R. Soc. Lond. B Biol. Sci. 364, 1985–1998. https://doi.org/10.1098/rstb.2008.0205 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Moore, C. J. Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environ. Res. 108, 131–139. https://doi.org/10.1016/j.envres.2008.07.025 (2008).CAS 
Article 
PubMed 

Google Scholar 
Lim, X. Microplastics are everywhere—But are they harmful?. Nature 593, 22–25. https://doi.org/10.1038/d41586-021-01143-3 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cardinael, Ŕ, Cadisch, G., Gosme, M., Oelbermann, M. & van Noordwik, M. Climate change mitigation and adaptation agriculture: Why agroforestry should be part of the solution. Agric. Ecosyst. Environ. 319, 107555. https://doi.org/10.1016/j.agee.2021.107555 (2021).Article 

Google Scholar 
Ji, S. & Unger, P. W. Soil water accumulation under different precipitation, potential evaporation, and straw mulch conditions. Soil Sci. Soc. Am. J. 65, 442–448. https://doi.org/10.2136/sssaj2001.652442x (2001).ADS 
CAS 
Article 

Google Scholar 
Schmithals, A. & Kühn, N. To mulch or not to mulch? Effects of gravel mulch toppings on plant establishment and development in ornamental prairie plantings. PLoS ONE 12, e0171533. https://doi.org/10.1371/journal.pone.0171533 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pinamonti, F. Compost mulch effects on soil fertility, nutritional status and performance of grapevine. Nutr. Cycl. Agroecosyst. 51, 239–248. https://doi.org/10.1023/A:1009701323580 (1998).Article 

Google Scholar 
Cline, G. R. & Silvernail, A. F. Residual nitrogen and kill date effects on winter cover crop growth and nitrogen content in a vegetable production system. HortTechnology 11, 219–225. https://doi.org/10.21273/HORTTECH.11.2.219 (2001).CAS 
Article 

Google Scholar 
Cherr, C. M., Scholberg, J. M. S. & McSorley, R. Green manure approaches to crop production: A synthesis. Agron. J. 98, 302–319. https://doi.org/10.2134/agronj2005.0035 (2006).Article 

Google Scholar 
Nguyen, L. T. T., Ortner, K. A., Tiemann, L. K., Renner, K. A. & Kravchenko, A. N. Soil properties after one year of interseeded cover cropping in topographically diverse agricultural landscape. Agric. Ecosyst. Environ. 326, 107803. https://doi.org/10.1016/j.agee.2021.107803 (2021).CAS 
Article 

Google Scholar 
Breton, V., Crosaz, Y. & Rey, F. Effects of wood chip amendments on the revegetation performance of plant species on eroded marly terrains in a Mediterranean mountainous climate (Southern Alps, France). Solid Earth 7, 599–610. https://doi.org/10.5194/se-2016-11 (2016).ADS 
Article 

Google Scholar 
Wang, L., Gruber, S. & Claupein, W. Effects of woodchip mulch and barley intercropping on weeds in lentil crops. Weed Res. 52, 161–168. https://doi.org/10.1111/j.1365-3180.2012.00905.x (2012).Article 

Google Scholar 
Jabran, K. Use of mulches for managing field bindweed and purple nutsedge, and weed control in spinach. Int. J. Agric. Biol. 23, 1114–1120. https://doi.org/10.17957/IJAB/15.1394 (2020).CAS 
Article 

Google Scholar 
Keeley, J. E., Morton, B. A., Pedrosa, A. & Trotter, P. Role of allelopathy, heat and charred wood in the germination of chaparral herbs and suffrutescents. J. Ecol. 73, 445–458. https://doi.org/10.2307/2260486 (1985).Article 

Google Scholar 
Schumann, A. W., Little, K. M. & Eccles, N. S. Suppression of seed germination and early seedling growth by plantation harvest residues. S. Afr. J. Plant Soil 12, 170–172. https://doi.org/10.1080/02571862.1995.10634359 (1995).Article 

Google Scholar 
Rathinasabapathi, B., Ferguson, J. & Gal, M. Evaluation of allelopathic potential of wood chips for weed suppression in horticultural production systems. HortScience 40, 711–713. https://doi.org/10.21273/HORTSCI.40.3.711 (2005).Article 

Google Scholar 
Wezel, A. et al. Agroecological practices for sustainable agriculture. A review. Agron. Sustain. Dev. 34, 1–20. https://doi.org/10.1142/q0088 (2014).Article 

Google Scholar 
Rahmathulla, V. K. Management of climatic factors for successful silkworm (Bombyx mori L.) crop and higher silk production: A review. Psyche J. Entomol. 2012, 121234. https://doi.org/10.1155/2012/121234 (2012).Article 

Google Scholar 
Guttikunda, S. K. & Kopakka, R. V. Source emissions and health impacts of urban air pollution in Hyderadad, India. Air Qual. Atmos. Health 7, 195–207. https://doi.org/10.1007/s11869-013-0221-z (2014).CAS 
Article 

Google Scholar 
Dhaka, S. K. et al. PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: An interplay between the baseline pollution and meteorology. Sci. Rep. 10, 13442. https://doi.org/10.1038/s41598-020-70179-8 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ehret, D. L., Helmer, T. & Hall, J. W. Cuticle cracking in tomato fruit. J. Hortic. Sci. 68, 195–201. https://doi.org/10.1080/00221589.1993.11516343 (1993).Article 

Google Scholar 
Peet, M. M. & Willits, D. H. Role of excess water in tomato fruit cracking. Hortic. Sci. 30, 65–68. https://doi.org/10.21273/HORTSCI.30.1.65 (1995).Article 

Google Scholar 
Ikeda, T., Sakamoto, Y., Watanabe, S. & Okano, K. Water relations in fruit cracking of single-truss tomato plants. Environ. Control Biol. 37, 153–158. https://doi.org/10.2525/ecb1963.37.153 (1999).Article 

Google Scholar 
Uetani, M., Fujitani, S. & Kimura, M. Mitigation techniques on fruit cracking in tomato cultivation under rain shelter in summer and autumn. Bull. Oita Pref Agr. For. Fish. Res. Cent. 4, 11–25 (2014) (in Japanese with English summary).
Google Scholar 
Kuhns, L. J. Efficacy and phytotoxicity of three landscape herbicides with and without a light mulch. Proc. Northeast. Weed Sci. Soc. 46, 85–89 (1992).
Google Scholar 
Petrikovszki, R., Zalai, M., Bogdányi, F. T. & Tóth, F. The effect of organic mulching and irrigation on the weed species composition and the soil weed seed bank of tomato. Plants 9, 66. https://doi.org/10.3390/plants9010066 (2020).Article 
PubMed Central 

Google Scholar 
Egley, G. H. Weed seed and seedling reductions by soil solarization with transparent polyethylene sheets. Weed Sci. 31, 404–409. https://doi.org/10.1017/S0043174500069253 (1983).Article 

Google Scholar 
Ashworth, S. & Harrison, H. Evaluation of mulches for use in the home garden. HortScience 18, 180–182 (1983).
Google Scholar 
Chakrabory, R. C. & Sadhu, M. K. Effect of mulch type and colour on growth and yield of tomato (Lycopersicon esculentum). Indian J. Agric. Sci. 64, 608–612 (1994).
Google Scholar 
Bhella, H. S. Tomato response to trickle irrigation and black polyethylene mulch. J. Am. Soc. Hortic. Sci. 113, 543–546 (1988).
Google Scholar 
Garnaud, J. C. The Intensification of Horticultural Crop Production in the Mediterranean Basin by Protected Cultivation (FAO of the United Nations, 1974).
Google Scholar 
Ahmad, S. et al. Significance of partial root zone drying and mulches for water saving and weed suppression in wheat. J. Anim. Plant Sci. 30, 154–162. https://doi.org/10.36899/japs.2020.1.0018 (2020).Article 

Google Scholar 
Ahmad, S. et al. Mulching strategies for weeds control and water conservation in cotton. ARPN J. Agric. Biol. Sci. 10, 299–306 (2015).
Google Scholar 
Hartwing, N. L. & Ammon, H. U. Cover crops and living mulches. Weed Sci. 50, 688–699. https://doi.org/10.1614/0043-1745(2002)050[0688:AIACCA]2.0.CO;2 (2002).Article 

Google Scholar 
Samedani, B., Ranjbar, M., Rahimian, H. & Jahansoz, M. R. Utilization of rye and hairy vetch cover crops for weed control in transplanted tomato. Pak. J. Biol. Sci. 9, 2323–2327. https://doi.org/10.3923/pjbs.2006.2323.2327 (2006).Article 

Google Scholar 
Pickering, J. S. & Shepherd, A. Evaluation of organic landscape mulches: composition and nutrient releases characteristics. Arboric J. 24, 175–187. https://doi.org/10.1080/03071375.2000.9747271 (2000).Article 

Google Scholar 
Marí, A. I., Pardo, G., Aibar, J. & Cirujeda, A. Purple nutsedge (Cyperus rotundus L.) control with biodegradable mulches and its effect on fresh pepper production. Sci. Hortic. 263, 109111. https://doi.org/10.1016/j.scienta.2019.109111 (2020).CAS 
Article 

Google Scholar 
R Development Core Team. R: A Language and Environment of Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Kanda, Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 48, 452–458. https://doi.org/10.1038/bmt.2012.244 (2013).CAS 
Article 
PubMed 

Google Scholar  More

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