Philippa Kaur

1.Gallo Marin, B. et al. Predictors of COVID-19 severity: A literature review. Rev. Med. Virol. 31, 1–10 (2021).CAS 
PubMed 
Article 

Google Scholar 
2.Worldometers. COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/ (Accessed 5 May 2021) (2021).3.The New York Times. Coronavirus World Map: Tracking the Global Outbreak. https://www.nytimes.com/interactive/2020/world/coronavirus-maps.html (Accessed 5 May) (2021).4.Sartorius, B., Lawson, A. & Pullan, R. Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England. Sci. Rep. 11, 5378 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Gamio, L. & Symonds, A. Global Virus Cases Reach New Peak, Driven by India and South America. https://nyti.ms/3xYVO94 (Accessed on 5 May 2021) (2021).6.Samuel, J. et al. COVID-19 public sentiment insights and machine learning for tweets classification. Information 11, 314 (2020).Article 

Google Scholar 
7.Borriello, A., Master, D., Pellegrini, A. & Rose, J. M. Preferences for a COVID-19 vaccine in Australia. Vaccine 39, 473–479 (2021).CAS 
PubMed 
Article 

Google Scholar 
8.Samuel, J. et al. Feeling positive about reopening? New normal scenarios from COVID-19 US reopen sentiment analytics. IEEE Access 8, 142173–142190 (2020).Article 

Google Scholar 
9.Max Roser, E. O.-O., Ritchie, H. & Hasell, J. Coronavirus pandemic (COVID-19). https://ourworldindata.org/coronavirus (Accessed on 5 June 2021) (2020).10.Centers for Disease Control and Prevention. About variants of the virus that causes COVID-19. https://www.cdc.gov/coronavirus/2019-ncov/transmission/variant.html (Accessed on 5 June 2021) (2021).11.Ali, G. G. M. N. et al. Public perceptions of COVID-19 vaccines: Policy implications from US spatiotemporal sentiment analytics. Healthcare 9, 1110 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Al Zobbi, M., Alsinglawi, B., Mubin, O. & Alnajjar, F. Measurement method for evaluating the lockdown policies during the COVID-19 pandemic. Int. J. Environ. Res. Public Health 17, 5574 (2020).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
13.Lu, X., Yuan, D., Chen, W. & Fung, J. A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China (2020). Preprint on webpage at www.researchsquare.com/article/rs-73671/v1.14.Vinceti, M. et al. Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking. EClinicalMedicine 25, 100457 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Rahman, M. et al. Machine learning on the COVID-19 pandemic, human mobility and air quality: A review. IEEE Access 9, 72420–72450 (2021).Article 

Google Scholar 
16.Rahman, M. et al. COVID-19 pandemic severity, lockdown regimes, and people’s mobility: Early evidence from 88 countries. Sustainability 12, 9101 (2020).Article 

Google Scholar 
17.Silva, P. C. et al. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos Solitons Fractals 139, 110088 (2020).MathSciNet 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Rahman, M. M. et al. Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon 7, e06200 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
19.USAFacts. US COVID-19 cases and deaths by state. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/ (Accessed 5 June 2021) (2020).20.Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ. 369(8242), m1328 (2020).21.Swapnarekha, H., Behera, H. S., Nayak, J. & Naik, B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos Solitons Fractals 138, 109947 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Xiang, Y. et al. COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models. Infect. Dis. Model. 6, 324–342 (2021).23.Roy, A. & Kar, B. Characterizing the spread of COVID-19 from human mobility patterns and SocioDemographic indicators. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, 39–48 (2020).24.Scarpone, C. et al. A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: A cross-sectional case study of COVID-19 incidence in Germany. Int. J. Health Geogr. 19, 32 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Polyzos, S., Samitas, A. & Spyridou, A. E. Tourism demand and the COVID-19 pandemic: An LSTM approach. Tour. Recreat. Res. 46(2), 1777053 (2020).26.Iwendi, C. et al. COVID-19 patient health prediction using boosted random forest algorithm. Front. Public Health 8, 357 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Hou, X. et al. Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race (2020). Preprint on webpage at https://doi.org/10.1101/2020.10.04.20206763v1.28.Tang, B. et al. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect. Dis. Model. 5, 248–255 (2020).PubMed 
PubMed Central 

Google Scholar 
29.Zhao, S. et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int. J. Infect. Dis. 92, 214–217 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Fanelli, D. & Piazza, F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals 134, 109761 (2020).MathSciNet 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Choi, S. & Ki, M. Estimating the reproductive number and the outbreak size of COVID-19 in Korea. Epidemiol. Health 42, e2020011 (2020).32.Tolles, J. & Luong, T. Modeling epidemics with compartmental models. JAMA 323, 2515–2516 (2020).PubMed 
Article 

Google Scholar 
33.Soures, N. et al. SIRNet: Understanding social distancing measures with hybrid neural network model for COVID-19 infectious spread. arXiv (2020). preprint on webpage at arXiv:2004.10376.34.Chimmula, V. K. R. & Zhang, L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 135, 109864 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Wang, D. et al. Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19. arXiv (2020). Preprint on webpage at arXiv:2010.09648.36.Kai, D., Goldstein, G.-P., Morgunov, A., Nangalia, V. & Rotkirch, A. Universal masking is urgent in the covid-19 pandemic: Seir and agent based models, empirical validation, policy recommendations. arXiv (2020). Preprint on webpage at arXiv:2004.13553.37.Panovska-Griffiths, J. et al. Modelling the potential impact of mask use in schools and society on COVID-19 control in the UK. Sci. Rep. 11, 8747 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Szczepanek, R. Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from cracow and machine learning. PeerJ 8, e10132 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Ahmed, I., Ahmad, M., Rodrigues, J. J., Jeon, G. & Din, S. A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65, 102571 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Spada, A. et al. Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2. Sci. Rep. 11, 8358 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Jiang, F. et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2, e000101 (2017).Article 

Google Scholar 
42.Khalifa, N. E. M., Taha, M. H. N., Ali, D. E., Slowik, A. & Hassanien, A. E. Artificial intelligence technique for gene expression by tumor RNA-Seq data: A novel optimized deep learning approach. IEEE Access 8, 22874–22883 (2020).Article 

Google Scholar 
43.Dexter, G. P., Grannis, S. J., Dixon, B. E. & Kasthurirathne, S. N. Generalization of machine learning approaches to identify notifiable conditions from a statewide health information exchange. AMIA Summits Transl. Sci. Proc. 2020, 152–161 (2020).PubMed 
PubMed Central 

Google Scholar 
44.Wang, Y., Liao, Z., Mathieu, S., Bin, F. & Tu, X. Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model. J. Hazard. Mater. 404, 123965 (2021).CAS 
PubMed 
Article 

Google Scholar 
45.Shao, Y. E., Hou, C.-D. & Chiu, C.-C. Hybrid intelligent modeling schemes for heart disease classification. Appl. Soft Comput. 14, 47–52 (2014).Article 

Google Scholar 
46.Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).Article 

Google Scholar 
47.Sun, J. et al. Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Sci. Rep. 10, 1–10 (2020).ADS 
CAS 
Article 

Google Scholar 
48.Chen, Y., Qin, R., Zhang, G. & Albanwan, H. Spatial temporal analysis of traffic patterns during the COVID-19 epidemic by vehicle detection using planet remote-sensing satellite images. Remote Sens. 13, 208 (2021).ADS 
Article 

Google Scholar 
49.Briz-Redón, Á. & Serrano-Aroca, Á. A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Sci. Total Environ. 728, 138811 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Liu, Q. et al. Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sens. 12, 1576 (2020).ADS 
Article 

Google Scholar 
51.Jarvis, K. F. & Kelley, J. B. Temporal dynamics of viral load and false negative rate influence the levels of testing necessary to combat COVID-19 spread. Sci. Rep. 11, 1–12 (2021).Article 
CAS 

Google Scholar 
52.Ugarte, M. D., Adin, A., Goicoa, T. & Militino, A. F. On fitting spatio-temporal disease mapping models using approximate Bayesian inference. Stat. Methods Med. Res. 23, 507–530 (2014).MathSciNet 
PubMed 
Article 

Google Scholar 
53.Zhang, C. H. & Schwartz, G. G. Spatial disparities in coronavirus incidence and mortality in the United States: An ecological analysis as of May 2020. J. Rural Health 36, 433–445 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Fitzpatrick, K. M., Harris, C. & Drawve, G. Fear of COVID-19 and the mental health consequences in America. Psychol. Trauma Theory Res. Pract. Policy 12, S17–S21 (2020).Article 

Google Scholar 
55.Lalmuanawma, S., Hussain, J. & Chhakchhuak, L. Applications of machine learning and artificial intelligence for covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 139, 110059 (2020).MathSciNet 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Adhikari, B., Xu, X., Ramakrishnan, N. & Prakash, B. A. Epideep: Exploiting embeddings for epidemic forecasting. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 577–586 (2019).57.Gautam, Y. Transfer learning for COVID-19 cases and deaths forecast using LSTM network. ISA Transactions (2021).58.Chen, S. et al. Exploring feasibility of multivariate deep learning models in predicting covid-19 epidemic. Front. Public Health 9, 661615 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Venna, S. R. et al. A novel data-driven model for real-time influenza forecasting. IEEE Access 7, 7691–7701 (2018).Article 

Google Scholar 
60.Wu, D. et al. Deepgleam: a hybrid mechanistic and deep learning model for covid-19 forecasting. arXiv (2021). Preprint on webpage at arXiv:2102.06684.61.Zhu, X. et al. Attention-based recurrent neural network for influenza epidemic prediction. BMC Bioinform. 20, 575 (2019).Article 

Google Scholar 
62.Ben Said, A., Erradi, A., Aly, H. & Mohamed, A. Predicting covid-19 cases using bidirectional lstm on multivariate time series. arXiv (2020). Preprint on webpage at arXiv:2009.12325.63.Aktay A., A. et al. Google COVID-19 Community Mobility Reports: anonymization process description (version 1.1). arXiv (2020). Preprint on webpage at arXiv:2004.04145.64.SafeGraph. SafeGraph Places Schema. https://docs.safegraph.com/docs (Accessed 15 Sept 2020) (2020).65.Johns Hopkins University. COVID-19 dashboard by the center for systems science and engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/us-map (Accessed 15 Sept 2020) (2020).66.Ray, E. L. et al. Ensemble forecasts of coronavirus disease 2019 (covid-19) in the us. MedRXiv (2020). Preprint on webpage at https://www.medrxiv.org/content/10.1101/2020.08.19.20177493v1.67.Cartenì, A., Di Francesco, L. & Martino, M. How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study. Sci. Total Environ. 741, 140489 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Reich, N. et al. har96, x. Zhang, jinghuichen, G. Espana, X. Xinyue, H. Biegel, L. Castro, Y. Wang, qjhong, E. Lee, A. Baxter, S. Bhatia, E. Ray, and abrennen, and ERDC CV19 Modeling Team (2020). Preprint on webpage at https://github.com/reichlab/covid19-forecast-hub/tree/master/data-processed/COVIDhub-ensemble.69.Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).CAS 
PubMed 
Article 

Google Scholar 
70.Yao, W., Huang, P. & Jia, Z. Multidimensional lstm networks to predict wind speed. In 2018 37th Chinese Control Conference (CCC), 7493–7497 (IEEE, 2018).71.Cromley, E. K. & McLafferty, S. L. GIS and Public Health (Guilford Press, XXX, 2011).
Google Scholar 
72.Veličković, P. et al. Cross-modal recurrent models for weight objective prediction from multimodal time-series data. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 178–186 (2018).73.Shi, X. & Yeung, D.-Y. Machine learning for spatiotemporal sequence forecasting: A survey. arXiv (2018). Preprint on webpage at arXiv:1808.06865.74.Bengio, S., Vinyals, O., Jaitly, N. & Shazeer, N. Scheduled sampling for sequence prediction with recurrent neural networks. arXiv (2015). Preprint on webpage at arXiv:1506.03099.75.Taieb, S. B. & Hyndman, R. Boosting multi-step autoregressive forecasts. In International Conference on Machine Learning, Vol. 32, 109–117 (PMLR, 2014). http://proceedings.mlr.press/v32/taieb14.html.76.Veličković, P. et al. Graph attention networks. arXiv (2017). Preprint on webpage at arXiv:1710.10903.77.Hamilton, W. L., Ying, R. & Leskovec, J. Inductive representation learning on large graphs. arXiv (2017). Preprint on webpage at arXiv:1706.02216. More

NEWS
05 November 2021

Whales’ gigantic appetites, climate fears — the week in infographics

Nature highlights three key infographics from the week in science and research.

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Climate scientists are scepticalThe momentous COP26 climate summit now under way in Glasgow, UK, represents one final opportunity for the governments of the world to craft a plan to meet their most ambitious goals for curbing climate change. Pledges are already flowing in, but the meeting has another week to run and much is still to be decided. Ahead of the summit, Nature conducted an anonymous survey of the 233 living authors of a climate-science report published in August by the Intergovernmental Panel on Climate Change, and received responses from 92 scientists — about 40% of the group. Their answers suggest strong scepticism that governments will markedly slow the pace of global warming, despite political promises made by international leaders as part of the landmark 2015 Paris climate agreement. Six in ten of the respondents, for example, said that they expect the world to warm by at least 3 °C by the end of the century, compared with conditions before the Industrial Revolution. That is far beyond the Paris agreement’s goal to limit warming to 1.5–2 °C.

Source: Nature analysis

Africa’s clinical trialsA shocking lack of COVID-19 vaccines in Africa, and the cost of existing treatments, means the continent really needs affordable, readily available COVID-19 drugs. These could reduce COVID-19 symptoms, lower the burden of disease on health-care systems and reduce deaths. The pandemic has given clinical research in Africa a boost: the Pan African Clinical Trials Registry recorded more clinical trials in 2020 than in 2019, and the number for 2021 is also on track to exceed 2019. But trials of COVID-19 drugs are still lacking in Africa, where they face infrastructure and recruitment challenges. One solution could be to establish a body to coordinate treatment trials on the continent.

Source: https://pactr.samrc.ac.za

The gluttony of whalesHow much do baleen whales, the largest known animals that have ever lived, eat? Three times as much as previously thought, report researchers who used cameras to study seven species of baleen whale. Writing in Nature, the researchers also suggest a feeding cycle involving iron and whale poo that could explain how such gluttony is possible. When whales eat iron-rich prey such as krill, they use the prey’s protein to make blubber — and defecate the iron-rich remains. Whale faeces might then provide a source of iron for microscopic marine algae called phytoplankton, and drive blooms of a type of plankton called diatoms. Diatoms, in turn, can move iron along the food chain when they are eaten by krill, which also excrete iron. Whales can further aid iron availability by mixing ocean waters through their vigorous tail movements.

doi: https://doi.org/10.1038/d41586-021-03066-5

Related Articles

African scientists race to test COVID drugs — but face major hurdles

Top climate scientists are sceptical that nations will rein in global warming

A whale of an appetite revealed by analysis of prey consumption

Subjects

Ecology

Climate change

Public health

Latest on:

Ecology

Baleen whale prey consumption based on high-resolution foraging measurements
Article 03 NOV 21

A whale of an appetite revealed by analysis of prey consumption
News & Views 03 NOV 21

A seagrass harbours a nitrogen-fixing bacterial partner
News & Views 03 NOV 21

Climate change

Scientists cheer India’s ambitious carbon-zero climate pledge
News 05 NOV 21

Carbon emissions rapidly rebounded following COVID pandemic dip
News 04 NOV 21

Glass is the hidden gem in a carbon-neutral future
Editorial 03 NOV 21

Public health

When are masks most useful? COVID cases offer hints
News 04 NOV 21

Why scientists worldwide are watching UK COVID infections
News Explainer 02 NOV 21

A reconstruction of early cryptic COVID spread
News & Views 01 NOV 21

Jobs

Associate or Senior Editor (Genome engineering), Nature Communications

Springer Nature
London, Greater London, United Kingdom

Senior Research Associate / Senior Scientist / Clinical Epidemiologist /Epidemiologist

Blind Veterans UK
London, United Kingdom

Department Manager in the area of Microbial Physiology/Microbial Systems Biology

Chr. Hansen A/S
Hørsholm, Denmark

Senior Scientist / Discovery project Leader, infectious diseases

Vaccibody AS
Oslo, Norway More

Identification and characterization of biotic and abiotic components in a penguin colony using RGB and multispectral camerasFigure 1 shows two image mosaics of a Chinstrap penguin (Pygoscelis antarcticus) colony, composed of 3800 pictures taken during a 29-min flight at 100 m altitude with a multispectral camera (MicaSense RedEdge-MX) using RGB bands (i.e., Red-668, Green-560 and Blue-475) (Fig. 1A) and the 10 wavelength bands covering the spectrum from visible to near-infrared light (Fig. 1B). With a resolution of 6 cm/pixel, penguin nests are clearly visible in the RGB mosaic, which are characterized by the absence of vegetation and with a predominant pink/brown color due to the abundance of guano deposition. The RGB mosaic also shows snow patches (white color), moss beds (green color) and one small lagoon with a bloom of red-pigmented greenalgae (Chlorophyceae) (Fig. 1A, upper right corner). Red algae (Chlamydomonas nivalis) patches on snow and ice are visible by zooming into a region of ice (Fig. 1A). More detailed information is obtained when the light spectrum from visible to near-infrared is used. Using the 10 wavelength bands, a thematic map was generated with the QGIS software and using a non-supervised classification method (Fig. 1B). Here it is possible to distinguish up to four species of mosses and three types of penguin guano that was verified with field observations.Figure 1Photomosaics of Vapour Col Chinstrap penguin colony on Deception Island composed of 3800 pictures taken at 100 m altitude with a 10 bands multispectral camera onboard a hexacopter, achieving 6 cm/pixel size. Panel (A): visible RGB mosaic (Red-668, Green-560 and Blue-475) with a zoom capture showing red snow patch; Panel (B): thematic map generated through non-supervised classification method.Full size imageDeception Island harbors up to 54 species of mosses, of which 13 species (including two endemics) have not been recorded elsewhere in the Antarctic. This, together with eight species of liverwort and 75 species of lichen, makes Deception Island an exceptional and unique place in Antarctica with legal protection under the Antarctic Treaty3. The use of a multispectral sensor onboard the UAV provides unique information to detect, classify and monitor moss beds without anthropogenic impacts. Antarctic moss bed health has already been assessed using multispectral sensors onboard UAVs12. Taxonomic identification would be feasible by indentifying previously each species in the field and later assigning the spectral signature using the UAV, as recently suggested by Miranda et al. (2020), who monitored lichens and mosses in the Antarctic using a combination of satellite imagery and UAVs13.Penguin guano has been suggested to be an important source of bioactive metals (e.g. Cu, Fe, Mn, Zn) for the sea surface waters, potentially fueling primary production of the Southern Ocean14. It has been suggested that the penguin species that feed mainly on Antarctic krill (Euphausia superba) (i.e., Chinstrap: Pygoscelis antarcticus, Adélie: Pygoscelis adeliae and Gentoo: Pygoscelis papua) excrete the highest concentrations of these bioactive metals15. Guano from these three congeneric penguin species has revealed the presence of microplastics across the Antarctic5. However, in order to estimate the magnitude of penguin fecal products that reach the sea, it is necessary to quantify the amount of guano excreted by the penguin colonies on land. This is possible with the multispectral reflectance data obtained from the UAV, which not only identify the guano coverage but also distinguishes different types of guano. Guano color is the result of diet, which, in turn, is related to the phase of the breeding cycle; therefore, a diet rich in krill is characterized by an excretion of pink guano, while a diet predominantly based on fish implies white guano16. Dark guano is the result of the mixture of guano with the soils that produce mud during wet precipitation.It is increasingly common in the Arctic and Antarctic to find well-developed algae blooms as highly visible red patches on the snow surface caused by red-pigmented green algae (Chlorophyceae), and that produce the phenomenon commonly-known as red snow17. These algal blooms play a crucial role in decreasing the snow-surface albedo and, consequently, accelerating the melt rate, as well as in nutrient and carbon cycling18,19. Mapping and monitoring the extent of snow algal blooms have so far been focused on satellite remote sensing; however, the spectral, temporal and spatial resolution of multi-spectral satellite imagery limits the study of most snow and ice algae18. Images taken from our UAV can enable the detection of patches of red snow on the surface snow with centimetric resolution (Fig. 1A). In addition, the image mosaic reveals the existence of a red snow bloom in a small pond located in a valley inside the colony (Supplementary Fig. S1). To the best of our knowledge, the existence of this bloom has not been previously reported and its monitoring could provide relevant information about the formation and proliferation of this bloom and its impact on cryospheric environments.As a whole, the image mosaic of the Chinstrap penguin colony in Vapour Col (the second largest breeding colony in the island with about 12,000 pairs of penguins20) may provide unique information about the different ecological niches linked to a penguin colony and their interactions. For example, the distribution and type of guano as nutrient and metal sources could be influencing the distribution and speciation of the flora in the area.3D geological formation using RGB cameraDeception Island is a complex volcanic system formed as a result of the explosive eruption of basaltic-to-andesitic magmas21. Among its multiple structures and stratigraphy, we surveyed the Murature formation, a consolidated andesitic lapilli tuff22. Using the quadcopter with a RGB camera and the software Pix4D we created a 3D photogrammetry of the Murature formation (Fig. 2; Supplementary Movie S1). The software uses a Structure from Motion photogrammetry algorithm, where obtained 3D points are interpolated to form a triangulated irregular network in order to obtain digital Surface model (DSM). This DSM is then used to project every image pixel and to calculate the georeferenced orthomosaic. For the Murature formation, the photogrammetry was generated with 843 pictures obtained from three 20-min flights at an altitude of 40 meters, taking pictures from two different angles to obtain the heights of the features (60° and 90°). With 1.4 cm/pixel resolution the resulting mosaic provides a unique view of the geological formation that will support the study of how the rocks were formed and its evolution in relation to the various geological processes that occurred on the island. 3D photogrammetry is also useful in geomorphological research. Specifically, in Deception Island morphometrics studies of landform (e.g. Crater and cone diameters, depths, slopes, heights, etc.) are useful to estimate the eruptive recurrence of the island, and in turn, for advising volcanic hazards23.Figure 23D photogrammetry of the Murature formation built with 843 RGB pictures taken from the RGB Hasselblad camera quadcopter DJI Mavic 2 Zoom at 40-m altitude, achieving 1.4 cm/pixel size.Full size imageThermal imagery to estimate animal abundance and to detect thermal anomaliesThe combination of UAV technology with a thermal-imaging camera is very useful for studying and monitoring wildlife and thermal anomalies on Deception Island. Chinstrap penguin and fur seal (Arctocephalus gazella) heat signatures were detected at Vapour Col and Baily Head, respectively (Fig. 3A, E). Figure 3A shows a mosaic from a Vapour Col section composed of 336 images taken with a thermal camera (FLIR Vue Pro R) onboard the hexacopter during a 29-min flight at 100 m altitude, whereas Fig. 3C shows one thermal picture of fur seals at Baily Head. Penguins and fur seals, with a thermal signature of 15 °C and 26 °C, respectively, are clearly identified. Penguins are highly sensitive to climate change and are considered “marine sentinels” for quantifying environmental change in the Southern Ocean24. However, the distribution and population dynamics of species such as the Chinstrap penguin are not well understood, mainly because they nest in remote and rugged areas, on-the-ground census work is difficult and sporadic25. As demonstrated for Adelia penguins26 the use of thermal imagery would allow reliable population estimates of Chinstrap penguins. Even, the use of RGB aerial images for animal counting would be far more accurate than from land-based surveys. Nevertheless, the scientific challenge is to develop a machine learning algorithm that can distinguish between animal species, based on their morphology and unique thermal fingerprint, which is only feasible using the high resolution provided by UAVs.Figure 3Thermal imagery. Panel (A): thermal mosaic of a section of Vapour Col (8.5 cm/pixel). Penguins are distinguished throughout the colony as small dots around 15 °C; Panel (B) and (D): RGB (Red-668, Green-560 and Blue-475 bands) and thermal picture of fumarole at Fumarole Bay (5.4 cm/pixel), respectively; Panel (C) and (E): RGB and Thermal image of Fur seals at Baily Head (5.4 cm/pixel), respectively.Full size imageOther useful application of thermal cameras onboard UAVs on Deception Island is the easy and precise detection and monitoring of thermal anomalies. Figure 3B–D shows a thermal picture of one of the multiple fumaroles on the island, reaching temperatures above 90 °C. Seismic monitoring of volcanos on Deception Island has being ongoing since 1986, including many recorded volcano-tectonic earthquakes, long-period events and volcanic tremor27. There have been six documented volcanic eruptions on the island between 1841 and 197128, nowadays volcanic and geothermal activities are limited to fumaroles and hot sands. Monitoring of these fumaroles using UAVs can provide a key in surveillance for early warming systems alerting of volcano activity on the island. UAVs not only accurately detect changes in temperature but also allow the increase in monitoring frequency when required.Surface water samplingUAVs provide unique opportunities for remote sample collection from surface waters, particularly in harsh or dangerous environments. Using a surface water sampling device described in the sampling and method sections we collected filtered fresh and saline surface waters at: (1) Three locations in Crater Lake (Fig. 4A). Crater Lake is part of the Antarctic Specially Protected Area (ASPA 140) due to its exceptional botanic and ecological value3. The use of drones for water sampling avoids human disturbance through the transportation and use of infrastructure, such as inflatable boats, and the risk that they pose to the natural ecological system. (2) One and six coastal locations in the Vapour Col and Baily Head penguin colonies, respectively (Fig. 4B, C). Access to the coastal zone inhabited by penguins requires approaches by boat (often assisted by an oceanographic vessel). The approaches do not only disturb the penguins that enter and exit the colony but, due to the coastal orography and waves, also dangerously hinders such an operation. The surface water sampling device onboard the UAV allowed in-situ water collection, minimizing the risk of impact on flora and fauna, limiting water disturbance and preventing contamination in the trace metal analysis. Attached to the sampling system we included a small multiparametric instrument referenced with time and GPS position to measure ancillary parameters, such as conductivity, temperature and depth (CastAway-CTD®) (Fig. 4D). The aerial water sampling has been validated for trace metal analysis using ICP-MS by comparing metal concentrations of samples collected in a saline pond with the surface water sampling device onboard the UAV (i.e. average ± SD, n = 3; Ti: 0.20 ± 0.09; V: 1.92 ± 0.07; Cr: 1.5 ± 0.1; Mn: 19.4 ± 0.4; Fe: 11.6 ± 0.5; Cu: 1.9 ± 0.2; Zn: 0.5 ± 0.3; all values in ppb) and the traditional peristaltic pump system used from land or on boats29 (i.e. average ± SD, n = 3; Ti: 0.20 ± 0.06; V: 1.93 ± 0.09; Cr: 1.3 ± 0.1; Mn: 19.1 ± 0.3; Fe: 11.8 ± 0.3; Cu: 2.1 ± 0.4; Zn: 0.4 ± 0.3; all values in ppb).Figure 4Locations of surface water samples collected in Crater lake (A), Vapour Col (B), and Baily Head (C) using aerial water sampling device, and picture of the UAV (hexacopter) carrying, at 100 m altitude, the water sampling device and the multiparametric instrument (D). Stations at Crater lake are plotted on a mosaic composed of 3096 pictures taken during three flights of 14 min each at 120 m altitude using a quadcopter with an integrated RGB camera and a multispectral camera array with 5 bands, achieving 6.5 cm/pixel size.Full size imageDeception Island is an example of the complexity of Antarctic environments, where environmental research studies need to deal with the inter- and multi-disciplinary analysis of processes, such as volcanic and geothermal activities, limnological process from its multiple lakes and ponds, sparse and exceptional flora and diverse fauna, among other. UAV surveys on Deception Island have demonstrated that this technology may substantially contribute to the progress in environmental biological, geological and chemical studies. UAVs permit researchers to study environmental processes at smaller spatial and temporal scales compared to other remote platforms (e.g. satellites), in a more cost-effective and safer way than on foot studies. Furthermore, they are less invasive and less disturbing to wildlife and the ecosystem. The simultaneous use of multi-sensors for multiple applications and the development of algorithms based on images obtained from the drone to detect, classify and count animals in real time are the new challenges that would significantly contribute to the study of the functioning of the Antarctic ecosystem and its ongoing environmental processes. More

Climatic conditions and phenologyThe growth and development of T. durum plants was moderately differentiated by weather conditions in the analyzed years (Table 1).Table 1 The duration of growing seasons (Days), sum of temperatures (Temp.) and sum of precipitation (Prec.) during the growth and development of T. durum Desf. in the analyzed years.Full size tableThe growing seasons of 2015, 2016 and 2017 lasted 136, 132 and 145 days, respectively; the sum of temperatures was determined at 2011.3, 1895.6 and 2069.9 °C, respectively, and the sum of precipitation was determined at 366.7, 360.1 and 350.5 mm, respectively. However, a comparison of cumulative temperatures and precipitation in the phenological phases of T. durum in each year of the study indicates that temperature and precipitation could have influenced the duration of the examined phases and plant growth indicators (Fig. 1). Weather conditions were generally favorable for the growth and development of T. durum in 2015 and 2016. Cumulative temperatures and precipitation were quite similar in 2015 and 2016 up to the booting stage, but precipitation levels in successive stages were higher in 2016 than in 2015. The growing season was shortest in 2016 and longest in 2017, mainly due to low temperatures during sowing and seed germination, and high precipitation during tillering, grain formation and ripening.Figure 1Cumulative temperatures and precipitation in the phenological phases of T. durum in 2015–2017.Full size imageBiophysical parameters: LAI and SPADThe LAI denotes the area of photosynthetic tissue per unit ground surface area (m2 m−2). The LAI is directly associated with plant canopy, and it is an indicator of net primary production, water and nutrient use, and the carbon balance. SPAD is a measure of leaf greenness that is directly associated with chlorophyll content and nitrogen sufficiency.The main effects of LAI and SPAD were analyzed separately in the framework of the Zadoks scale to reveal the significant effects of years, nitrogen rates and sowing density, and an absence of significant effects associated with the application of the growth regulator (see Tables 1.1–1.6 in the Supplementary Information). In the analyzed years, LAI and SPAD were similar in the 2nd node detectable stage (Z32), but they differed in the stem elongation stage (Z45) and the ear emergence and heading stage (Z59), when LAI values were higher and leaf greenness values were lower in 2016 and 2017 than in 2015. These findings can be attributed to moderate temperatures and precipitation in 2015, and high precipitation in the critical growth stages in the remaining years. The general trend associated with the nitrogen rate was similar across the examined growth stages, i.e. a significant increase in LAI and SPAD values with nearly identical effects were noted in treatments with nitrogen rates of 80 and 120 kg ha−1. A similar trend was observed in sowing density. In treatments with a sowing density of 450 and 550 germinating seeds per m2, LAI values continued to increase, whereas SPAD values were below those noted in the treatment with a sowing density of 350 germinating seeds per m2. The only significant interaction was observed between years and nitrogen rates.The average values of LAI continued to increase in successive growth stages and were determined at 1.30 at Z32, 1.75 at Z45, and 1.99 at Z59. In turn, leaf greenness was significantly lower in the stem elongation stage (Z45) than in the preceding (Z32) and subsequent (Z59) stages.The significant effect of the years × growth stages interaction for LAI and SPAD values resulted from similar means in stage Z32 in all years, as well as higher LAI values and lower SPAD values in subsequent growth stages in 2016 and 2017 than in 2015. In 2015, the increase in the nitrogen rate induced only a rising trend in LAI and SPAD values, whereas significant differences were observed in 2016 and 2017. To summarize, it should be noted that in successive Zadoks growth stages, the interactions between years, nitrogen rates and sowing density exerted significant effects on LAI and SPAD values, whereas the effects of year × nitrogen rate interactions were significant only in selected growth stages.Contribution of different sources of variation to physiological and biophysical parameters of plant growthThe calculated eta-squares η2 provide information about the contribution of different sources of variation to physiological variables (Table 2). The experimental years and agronomic factors (33.1% and 38.6%), growth stages, and interactions with other factors (32.5% and 39.3%) and random factors (34.4% and 22.1%) made similar contributions to the variation in the LAI and chlorophyll content. The variation in the net photosynthetic rate was related mostly to variations across years (32.6%) and the interactions between growth stages and other factors (24.3%). The variation in the transpiration rate was attributed mostly to the main effects of growth stages (45.8%) and the year × growth stage interaction (16.1%). Instantaneous WUE was strongly determined by variation in agronomic factors and growth stages (22.3% and 21.1%, respectively).Table 2 Eta-square (η2) values for the sources of variation in the leaf area index (LAI), chlorophyll content (SPAD), net photosynthetic ratio (Pn), transpiration rate (E) and instantaneous water use efficiency (WUE).Full size tableIt is worth noting that the variation in agronomic factors made a considerable contribution to the total variation in LAI (22.3%) and SPAD (11.8%), but only a marginal contribution to the net photosynthetic rate (0.4%) and transpiration (2.0%).Photosynthetic indicators— net photosynthetic rate, transpiration rate, and instantaneous water use efficiencyThe effects of the net photosynthetic rate (Pn), transpiration rate (E) and instantaneous WUE were highly differentiated in successive growth stages, and relatively small differences were noted for agronomic factors (see Tables 2.1–2.9 in the Supplementary Information). At the same time, the analyzed photosynthetic indicators differed in successive stages of growth. The net photosynthetic rate was similar in the 2nd node detectable stage (Z32) and the stem elongation stage (Z45) at 29.7 μmol CO2 m–2 s–1, and it was 15% higher at the end of the heading stage (Z59) than in the preceding stages. The transpiration rate continued to increase by 60% on average in successive stages of growth and development, from 1.59 H2O m–2 s–1 in stage Z32, to 2.52 mmol H2O m–2 s–1 in stage Z45, and 4.06 mmol H2O m–2 s–1 in stage Z59.An analysis of the results noted in different growth stages across years revealed significant year × growth stage and growth regulator × growth stage interactions (Fig. 2).Figure 2Mean values and standard error of photosynthesis indicators for year × growth stage (upper) and growth regulator × growth stage interactions (GR 0—without growth regulator, GR 1—with growth regulator).Full size imageThe year × growth stage interaction resulted from differences in the rates of photosynthesis and transpiration in the analyzed growth stages across years. In 2015, the net photosynthetic rate was similar in the first two growth stages, and it increased by around 30% at the end of the heading stage (Z59). In 2016, the photosynthetic rate continued to increase in successive growth stages. In 2017, the net photosynthetic rate was around 10% higher in the 2nd node detectable stage (Z32) than in the stem elongation stage (Z45) and at the end of the heading stage (Z59). The transpiration rate increased significantly in successive stages of plant growth and development, and the only exception was noted in 2015, when the analyzed parameter was similar in stages Z32 and Z45. The WUE index was highest in stage Z32, and a significant interaction was noted due to the correlation between the net photosynthetic rate and the transpiration rate in the remaining stages. Water use efficiency was similar in stages Z32 and Z45 in 2015, and in stages Z45 and Z59 in 2016, whereas significantly lower values in successive stages of plant growth were noted in 2017.The growth regulator was the only agronomic factor that induced significant differences in the net photosynthetic rate across the examined growth stages. Photosynthesis indicators were similar regardless of the application of the growth regulator, and significant interactions resulted mainly from varied disproportions between the end of heading and the stem elongation stage in treatments with and without the application of the growth regulator.It should be noted that the interactions between growth stages and nitrogen rates and sowing density were not significant, which implies that the effects of the interactions between increasing nitrogen rates and sowing density on photosynthetic indicators in successive growth stages were similar to the average values of photosynthetic indicators in the corresponding growth stages (Supplementary Information).Agronomic traitsThe means for yield components and yield are presented in Tables 3.1–3.8 of the Supplementary Information. Stem length was differentiated by the nitrogen rate and nitrogen rate × year interaction. Nitrogen rates of 80 and 120 kg N per ha increased stem length by 11% and 13%, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction resulted from the fact that the nitrogen-induced increase in stem length was smaller in 2015 (0.07 cm per 1 kg of nitrogen) than in 2016 and 2017 (0.09 cm per 1 kg of nitrogen). In 2015, ear length was similar to that noted in the remaining years, and only in 2017, ear length was 7% higher than in 2016. Ear length and the number of kernels per ear increased with a rise in nitrogen rate and decreased with a rise in sowing density.Grain weight per ear and 1000 kernel weight were highest in 2015 and significantly lower in the following years. Grain weight per ear increased only in response to the nitrogen rate of 120 kg ha−1, but 1000 kernel weight was not affected. Both traits decreased with a rise in sowing density. The significant year × nitrogen rate and year × sowing density interactions for both traits can be largely attributed to the magnitude of differences between years, rather than an increase or a decrease in this trend.The biological yield (grain and straw) differed across years and nitrogen rates. In 2016, the biological yield was similar to that noted in 2015 and significantly higher (by 30%) than that noted in 2017. The biological yield increased by 28% and 35% in response to nitrogen rates of 80 and 120 kg ha−1, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction was associated with variations in nitrogen use efficiency, and the difference between maximal biological yield was determined at 0.5 t ha−1 in 2015, 2.3 t ha−1 in 2016, and 2.8 t ha−1 in 2017.Grain yield was similar in 2015 (4.94 t ha−1) and 2016 (5.38 t ha−1), and it was significantly lowest in 2017 (3.87 t ha−1). Straw yield was highest in 2016 (2.86 t ha−1), and it exceeded the values noted in the remaining years by 16%. The harvest index was similar in 2015 and 2016, and it was 9% lower in 2017. Grain yield increased by 30% and 36%, whereas straw yield increased by 20% and 35% in response to the nitrogen rates of 80 and 120 kg ha−1, respectively. A minor increase in grain yield (3%) was observed in treatments with a sowing density of 550 seeds m−2 relative to the remaining sowing densities.Path modellingA simple correlation analysis of manifest variables in all phenological stages revealed significant correlations between the LAI and leaf greenness (SPAD) only in stage Z32, as well as a very strong correlation between the net photosynthetic rate and the transpiration rate, which was positive in stages Z32 and Z45 and negative in stage Z59. No simple correlations were noted between the indicators of physiological processes (Pn, E, WUE) and biophysical parameters (LAI, SPAD). AAll correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary information).All correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary Information). The outer and inner PLS-PM models well fit the data, and their goodness of fit was determined at 0.973 and 0.786, respectively. The outer weights provide information about the relative importance of a manifest variable for the corresponding latent variable (for details please see the Supplementary Information). Outer weights that exceed 0.3 are considered meaningful. By the same token, loading estimates represent the correlations between a latent variable and the corresponding manifest variables. Loadings higher than 0.7 capture more than 50% of the variability contributed by a latent variable to the corresponding manifest variable. In general, both indicators in the outer model, i.e. outer weights and loadings, exceeded the thresholds, which indicates that manifest variables were strongly related with latent variables. Growth regulators (({w}_{GR}) = − 0.007) and the length of the growing season (({w}_{DAYS})=0.197) provided the only evidence for the low explanatory value of latent variable A (agronomic factors).In the inner model, all equations that regressed latent variables well fit the data and were statistically significant (Table 3). The latent variables expressed by the value of R2 increased in successive stages of T. durum growth and development, from 0.218 in physiological processes in stage Z32 (Table 3, Eq. 1) to 0.698 and 0.708 in yield components and Biological Yield, respectively (Table 3, Eqs. 7 and 8). It is worth noting that in successive stages of growth, the value of physiological processes was relatively lower in comparison with biophysical parameters.Table 3 Parameters of regression models for latent variables.Full size tableThe analysis of path coefficients (βi) revealed that agronomic factors (A) and climate conditions (CC) in stages Z32, Z45 and Z59 exerted a specific influence on physiological processes (PP) and biophysical parameters (BP) of T. durum plants. Agronomic factors directly determined physiological processes in all stages and biophysical parameters in stages Z32 and Z59. At the same time, climate conditions did not exert a direct influence on physiological processes in any stage, but directly affected biophysical parameters in all stages. All of the modeled parameters, i.e. agronomic factors, climate conditions and physiological processes, significantly influenced biophysical parameters in stages Z32 and Z59, but not Z45. Consequently, it can be stated that agronomic factors were the main determinant of variability in physiological processes (photosynthesis, transpiration) in a model evaluating the impact of agricultural practices on yield and the manifest variables associated with T. durum growth and development. At the same time, physiological processes made a significant but negative contribution to biophysical parameters. A one unit increase in photosynthesis processes with constant values of agronomic factors and climate conditions implies a decrease of − 0.382, − 0.065 and − 0.395 in biophysical parameters in stages Z32, Z45 and Z59, respectively.The performance of every preceding latent variable in terms of its total impact on the target latent variable, i.e. the biological yield of T. durum (IPMA – Importance-Performance Map Analysis), was analyzed to highlight latent variables associated with agricultural practices that improve biological yield. The total effect (importance) of preceding latent variables (A, CC32, PP32, BP32, CC45, PP45, BP45, CC59, PP59, BP59, YC and CC) on the anticipated performance of the specific target (Biological Yield) is presented in Fig. 3.Figure 3Importance-Performance Map Analysis presenting the impact of latent variables on biological yield (A—agronomic factors, YC—yield components, CC32, CC45, CC59—climate conditions in growth stages, PP32, PP45, PP59—physiological processes, BP32, BP45, BP59—biophysical parameters in the phenological stages of plant growth and development Z32, Z45 and Z59, CC—climate conditions for the entire growing season).Full size imageThe importance and performance of latent variables that influenced the biological yield of T. durum varied. The biological yield of T. durum was affected mostly by agronomic factors (A), followed by yield components (YC) and biophysical parameters (BP) in growth stages Z59 (BP59) and Z32 (BP32), climate conditions in stage Z59 (CC59), and climate conditions in stage Z32 (CC32). A one unit increase in the above latent variables led to an increase of 0.575, 0.422, 0.234, 0.203 and 0.109 units in biological yield, respectively. At the same time, the performance scores of these latent variables were determined at 53.1, 53.5, 67.1, 59.6 and 61.8, respectively (scores closer to 100 denote higher performance). The remaining latent variables, in particular climate conditions for the entire growing season and physiological processes in stage Z32, were characterized by low importance and exerted a relatively small effect on biological yield performance.The results of the importance-performance analysis clearly indicate that latent variables have considerable potential to optimize the agricultural conditions for the growth and development of T. durum plants. More

Direct nucleotide sequencingReadable electropherograms were obtained from both direction in COI fragments of all three individuals of the Japanese spiny lobster. COI sequences determined by direct nucleotide sequencing ranged from 807 to 864 bp and have been deposited in International Nucleotide Sequence Database Collection (INSDC) under accession numbers of LC571524‒LC571526. No stop codon was observed in these sequences (designated by PJK1-direct, PJK2-direct, and PJK3-direct). No indel was observed between these sequences. All nucleotide substitutions at 19 variable sites observed between these sequences were transition at the 3rd position of a codon, and all substitutions were synonymous. The mean Kimura two parameter (K2P) distance between these three haplotypes was 1.510 ± 0.352% SE and that between these sequences and a reference sequence of P. japonicus (NC_004251) was 1.087 ± 0.270%, which were all well within the range reported for Japanese spiny lobster samples collected in Japan and Taiwan9,10.Electropherograms obtained by forward primer for 12S fragments were not readable, while those by reverse primer were readable in all individuals. 12S sequences determined by direct nucleotide sequencing using reverse primer alone ranged from 551 to 570 bp and have been deposited in INSDC under accession numbers of LC605705‒LC605707. Of nine variable sites, eight were transition and one was indel. The mean K2P distance between these three haplotypes (designated by PJK1-12Sdirect, PJK2-12Sdirect, and PJK3-12Sdirect) was 0.970 ± 0.338%, and that between these sequences and a reference sequence of P. japonicus was 0.835 ± 0.282%.Electropherograms obtained by both primers for Dloop fragments were readable only in one individual (PJK2). This Dloop sequence determined by direct nucleotide sequencing was 762 bp and deposited in INSDC under accession number of LC605749. K2P distance between this haplotype (designated by PJK2-Dloopdirect) and a reference sequence of P. japonicus was 3.666%. No indel was observed between the two sequences, and 25 of 27 variable sites were transition.Phylogenetic analysis of clones, heteroplasmy and NUMTsAmong the 36–42 positive COI clones examined per individual, sequences (809–892 bp) of 22–31 clones per individual (75 clones in total) were successfully determined. After alignment, both ends of all sequences were trimmed to fit the shortest sequence obtained by direct nucleotide sequencing, yielding 774–810 bp sequences. Eleven clones of PJK1 were identical to PJK1-direct, as well as seven of PJK2 to PJK2-direct and three of PJK3 to PJK3-direct. These dominant haplotypes (807 bp) were determined to be genuine COI haplotypes of each individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC 571527, LC571533 and LC571538). Nucleotide sequences of the remaining 54 clones were all different one another, in which 20 haplotypes were observed in PJK1, 14 in PJK2, and 20 in PJK3 (LC571541–LC571577, OK429332–OK429343, LC654683-LC654687).Phylogenetic tree constructed using three genuine COI haplotypes, 57 unique haplotypes and eight sequences of reference lobster species is shown in Fig. 1. Haplotypes detected from P. japonicus were segregated into four groups (designated by A, B, C and D). Among the outgroup species used, Australian rock lobster (P. cygnus) that morphologically and genetically belongs to the P. japonicus group11,12, appeared to be the closest kin to all haplotypes detected from P. japonicus. All haplotypes in group A were of the same length (807 bp), and no indel was observed. Three distinct clades (designated by c-I to c-III) were observed in group A, in which 14 haplotypes from PJK1, 11 from PJK2 and 11 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). PJK1-C25 was outlier, having 10 nucleotide differences from the genuine COI sequence. The numbers of variable nucleotide sites between haplotypes within c-I, c-II and c-III were 20, 15 and 26, respectively, of which nonsynonymous nucleotide substitutions were observed at 11, 13 and 10 sites. Stop codon was observed only in one haplotype (PJK3-C1). The mean K2P distance between different haplotypes within these clades ranged from 0.320 ± 0.075 to 0.561 ± 0.103%. The mean K2P distances between three clades ranged from 1.343 ± 0.339 to 2.178 ± 0.464%. Although group A must be composed of sequences containing those caused by Taq polymerase error or true heteroplasmic sequences as well as genuine haplotypes, it is difficult to determine the former two categories. All of the non-genuine haplotypes in group A had singleton difference one another, supporting the occurrence of Taq polymerase error. We determined haplotypes (marked with dagger in Fig. 1) differed by less than two substitutions from the genuine haplotype to be due to Taq polymerase error. This criterion may be reasonable, since Taq polymerase-mediated errors were estimated to occur approximately at a frequency of 7.2 × 10−5 per bp per cycle13 to one mutation per 10,000 nucleotides per cycle14. When Taq polymerase error is taken into account, these K2P distances within and between clades and number of haplotypes are likely to be somewhat overestimated. PJK1-C25, two (PJK1-C5 and PJK1-C60) in c-I clade, one (PJK2-C26) in c-II, and five (PJK3-C1, PJK3-C5, PJK3-C26, PJK3-C31, PJK3-C34) in c-III differed by 3 to 10 nucleotides from their genuine haplotypes, which were determined to be heteroplasmic haplotypes.Figure 1Neighbor-joining phylogenetic (NJ) tree showing relationships among 57 different haplotypes of cytochrome oxidase subunit I (COI) or COI-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and COI sequences of eight congeneric species derived from the GenBank database. Haplotypes detected from the same individual of the Japanese spiny lobster share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Stop codons were observed in haplotypes carrying asterisk. Haplotypes carrying dagger differ from the corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D ranged from 774 to 810 bp. K2P distance between haplotypes of groups A and B ranged from 7.169 to 8.177% with a mean of 7.754 ± 0.973%, that between A and C ranged from 12.073 to 17.392% with a mean of 14.521 ± 1.151%, and that between A and D ranged from 17.472 to 23.880% with a mean of 21.042 ± 1.600%. Multiple stop codons were observed in a haplotype of group B, in five of eight haplotypes of group C, and all haplotypes of group D. Three haplotypes in group C had no stop codon but differed in four to 10 deduced amino acids from the genuine haplotypes. BLAST homology search revealed no identical sequence for haplotypes in groups B to D but indicated that the closest species were P. japonicus or P. cygnus with moderate similarity (83–89% homology). Therefore, all haplotypes of groups B to D (LC571565–LC571570, LC571572–LC571577, LC654683-LC654687) were determined to be NUMTs.Among the 30–35 positive 12S clones examined per individual, sequences (772–806 bp) of 25–27 clones per individual (77 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 731–765 bp sequences. Thirteen clones of PJK1 were identical one another, as well as 12 of PJK2 and three of PJK3, and these were identical to PJK1-12Sdirect, PJK2-12Sdirect and PJK3-12Sdirect, respectively. These dominant haplotypes ranging from 761 to 762 bp in size were determined to be genuine 12S haplotypes of the individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC605708‒LC605710). Nucleotide sequences of the remaining 49 clones were all different one another, in which 12 haplotypes were observed in PJK1, 23 in PJK2, and 14 in PJK3 (LC605711‒LC605748, OK429126–OK429131, LC654678-LC654682).Since incorporation of all eight Panulirus species sequences made sequence alignment ambiguous because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 2). Haplotypes detected from P. japonicus were segregated into three groups (designated by A to C). Sequence size of haplotypes in group A ranged from 760 to 762 bp. Three distinct clades (s-I to s-III) were observed in group A, in which 12 haplotypes each from PJK1, PJK2 and PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within s-I, s-II and s-III were 24, 17 and 16, respectively. Of these variable sites, transversion was observed at five, one and three sites, and indel was observed at one, zero and one sites, respectively. The mean K2P distances between different haplotypes within these clades ranged from 0.345 ± 0.081 to 0.519 ± 0.101%. The mean K2P distances between three clades ranged from 0.936 ± 0.275 to 1.371 ± 0.359%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Five haplotypes in s-I clade and two haplotypes in s-III clade differed by three to six nucleotides from their genuine haplotypes, which were determined to be heteroplasmic copies.Figure 2Neighbor-joining phylogenetic (NJ) tree showing relationships among 52 different haplotypes of clones of 12S rDNA (12S) or 12S-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and 12S rDNA sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in group B varied from 731 to 762 bp. K2P distance between groups A and B ranged from 1.336 to 7.445% with a mean of 3.449 ± 0.398%, and those between a reference sequence of P. japonicus and groups A and B were 0.864 ± 0.236% and 3.189 ± 0.410%, respectively. Sequence size of haplotypes in group C varied from 744 to 765 bp. K2P distance between groups A and C ranged from 3.104 to 22.434% with a mean of 12.049 ± 0.901%, and those between a reference sequence of P. japonicus and group C ranged from 3.951 to 21.287% with a mean of 11.764 ± 0.901%. BLAST homology search indicated that the closest species for haplotypes in groups B and C was P. japonicus or P. cygnus with moderate to high similarity (84–98% homology). Therefore, all 13 haplotypes (LC605741‒LC605748, LC654678-LC654682) in groups B and C were determined to be NUMTs.Among the 36–49 positive Dloop clones examined per individual, sequences (777–893 bp) of 26–38 clones per individual (92 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 736–853 bp sequences. Three clones (821 bp) of PJK1 were identical one another and determined to be genuine haplotype of this individual. Nine clones (813 bp) of PJK2 were identical to PJK2-Dloopdirect and determined to be genuine haplotype of this individual. Three clones (821 bp) of PJK3 were identical one another and determined to be genuine haplotype of this individual. Representative sequences of these three genuine haplotypes were deposited in INSDC (LC605750‒LC605752). Nucleotide sequences of the remaining 78 clones were all different one another, in which 25 haplotypes were observed in PJK1, 17 in PJK2, and 36 in PJK3 (LC605753‒LC605815, LC654419-LC654430, LC654675-LC654677).Incorporation of all eight Panulirus species sequences made sequence alignment considerably unreliable because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 3). Haplotypes detected from P. japonicus were segregated into four groups (designated by A to D). Sequence size of haplotypes in group A ranged from 812 to 822 bp. Three distinct clades (d-I to d-III) were observed in group A, in which 17 haplotypes from PJK1, 13 from PJK2 and 15 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within d-I, d-II and d-III were 27, 61 and 28, respectively, of which indels were observed at five, two and four sites and transversion was observed at 0, six and six sites. The mean K2P distance between different haplotypes within these clades ranged from 0.340 ± 0.067 to 1.097 ± 0.139%. The mean K2P distance between these three clades ranged from 7.577 ± 0.951 to 8.770 ± 0.984%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Eight haplotypes in d-I clade, three in d-II clade, and four in d-III clade differed by three to five nucleotides from the genuine haplotype were determined to be heteroplasmic copies.Figure 3Neighbor-joining phylogenetic (NJ) tree showing relationships among 80 different haplotypes of control region (Dloop) or Dloop-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and control region sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D largely varied from 736 to 853 bp. K2P distances between group A and others ranged from 14.748 ± 1.030% (A vs B) to 61.619 ± 3.045% (A vs D), whereas that between haplotypes of group A and a reference sequence of P. japonicus was much smaller (6.333 ± 0.663%). BLAST homology search revealed no identical sequence for haplotypes in groups B to D and indicated that the closest species for haplotypes in groups B and C was P. japonicus with low to moderate similarity (74–88% homology). On the other hand, no significantly similar sequence was found for haplotypes in group D. Therefore, all 31 haplotypes (LC605788‒LC605815, LC654675-LC654677) in groups B to D were determined to be NUMTs.Impact of heteroplasmy and NUMTs for direct nucleotide sequencingPartial electropherogram obtained by direct nucleotide sequencing for COI amplicon of PJK3 is shown in Fig. 4 (top). Peak signals of this electropherogram are readable, but there are a number of sites where two (asterisk) or three (dagger) signals overlap. Alignment of a genuin haplotype (PJK-C7) and nine NUMTs sequences, corresponding to this partial electropherogram, is shown in Fig. 4 (bottom). At the sites where plural peaks overlap, different NUMT haplotypes were observed to share the same nucleotide different from the PJK3-direct. Heteroplasmic copies in COI determined in this study may have little negative impact on direct nucleotide sequencing, since nucleotides different from the genuine haplotypes were all unique to each heteroplasmic haplotype. Thus, the plural peaks at a site were composed of signals from genuine plus NUMT haplotypes, and the intensity of each peak was positively related to the copy numbers of these haplotypes. Frequent failure to obtain readable electropherograms in 12S and Dloop regions by direct sequencing may be due to extensive indels observed in the NUMT haplotypes.Figure 4A part of electropherogram obtained by direct nucleotide sequencing for COI region of PJK3 (top), and corresponding sequences from genuine haplotype (PJK3-C7) and nine NUMT haplotypes (see Fig. 1) are aligned (bottom). Apparent double (asterisk) and triple (dagger) peaks are observed at seven and five sites, respectively, which are comprised of signals from genuine and NUMT haplotypes.Full size image More

1.Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and cceanic components. Science 281, 237–240 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Falkowski, P. G., Barber, R. T. & Smetacek, V. Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200–206 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Chang. 7, 718–722 (2017).ADS 
Article 

Google Scholar 
5.Dutkiewicz, S., Scott, J. R. & Follows, M. J. Winners and losers: ecological and biogeochemical changes in a warming ocean. Glob. Biogeochem. Cycles 27, 463–477 (2013).ADS 
CAS 
Article 

Google Scholar 
6.Sarmiento, J. L. et al. Response of ocean ecosystems to climate warming. Glob. Biogeochem. Cycles 18, GB3003 (2004).ADS 
Article 
CAS 

Google Scholar 
7.Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming? Geophys. Res. Lett. 38, 1–6 (2011).Article 
CAS 

Google Scholar 
8.Vallina, S. M., Cermeno, P., Dutkiewicz, S., Loreau, M. & Montoya, J. M. Phytoplankton functional diversity increases ecosystem productivity and stability. Ecol. Modell. 361, 184–196 (2017).Article 

Google Scholar 
9.Dutkiewicz, S. et al. Impact of ocean acidification on the structure of future phytoplankton communities. Nat. Clim. Chang. 5, 1002–1006 (2015).ADS 
CAS 
Article 

Google Scholar 
10.Laufkotter, C. et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences 12, 6955–6984 (2015).ADS 
Article 

Google Scholar 
11.Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Glob. Biogeochem. Cycles 19, 1–14 (2005).Article 
CAS 

Google Scholar 
12.Anderson, S. I. & Rynearson, T. A. Variability approaching the thermal limits can drive diatom community dynamics. Limnol. Oceanogr. 65, 1961–1973 (2020).ADS 
CAS 
Article 

Google Scholar 
13.Boyd, P. W. Physiology and iron modulate diverse responses of diatoms to a warming Southern Ocean. Nat. Clim. Chang. 9, 148–152 (2019).ADS 
CAS 
Article 

Google Scholar 
14.Thomas, M. K. & Litchman, E. Effects of temperature and nitrogen availability on the growth of invasive and native cyanobacteria. Hydrobiologia 763, 357–369 (2016).Article 

Google Scholar 
15.Kremer, C. T., Thomas, M. K. & Litchman, E. Temperature- and size-scaling of phytoplankton population growth rates: Reconciling the Eppley curve and the metabolic theory of ecology. Limnol. Oceanogr. 62, 1658–1670 (2017).ADS 
Article 

Google Scholar 
16.Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57, 554–566 (2012).ADS 
Article 

Google Scholar 
17.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, 919–925 (2013).ADS 
Article 

Google Scholar 
18.Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, 1–11 (2019).Article 

Google Scholar 
20.Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA 113, 2964–2969 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Chang. 6, 4–11 (2015).
Google Scholar 
22.Uitz, J., Claustre, H., Gentili, B. & Stramski, D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Glob. Biogeochem. Cycles 24, 1–19 (2010).Article 
CAS 

Google Scholar 
23.Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Chang. 3, 979–984 (2013).ADS 
CAS 
Article 

Google Scholar 
24.Boyd, P. W. & Hutchins, D. A. Understanding the responses of ocean biota to a complex matrix of cumulative anthropogenic change. Mar. Ecol. Prog. Ser. 470, 125–135 (2012).ADS 
Article 

Google Scholar 
25.Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
26.Thomas, M. K., Kremer, C. T. & Litchman, E. Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Glob. Ecol. Biogeogr. 25, 75–86 (2016).Article 

Google Scholar 
27.Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).28.Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
Google Scholar 
29.Bissinger, J. E., Montagnes, D. J. S., Sharples, J. & Atkinson, D. Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression. Limnol. Oceanogr. 53, 487–493 (2008).ADS 
Article 

Google Scholar 
30.Prowe, A. E. F., Pahlow, M., Dutkiewicz, S. & Oschlies, A. How important is diversity for capturing environmental-change responses in ecosystem models? Biogeosciences 11, 3397–3407 (2014).ADS 
Article 

Google Scholar 
31.Chen, B. & Liu, H. Relationships between phytoplankton growth and cell size in surface oceans: Interactive effects of temperature, nutrients, and grazing. Limnol. Oceanogr. 55, 965–972 (2010).ADS 
CAS 
Article 

Google Scholar 
32.Barton, S. & Yvon‐Durocher, G. Quantifying the temperature dependence of growth rate in marine phytoplankton within and across species. Limnol. Oceanogr. 64, 2081–2091 (2019).ADS 
Article 

Google Scholar 
33.Sherman, E., Moore, J. K., Primeau, F. & Tanouye, D. Temperature influence on phytoplankton community growth rates. Glob. Biogeochem. Cycles 30, 550–559 (2016).ADS 
CAS 
Article 

Google Scholar 
34.Alexander, H. et al. Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean. Proc. Natl Acad. Sci. USA 112, E5972–E5979 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Cermeño, P. et al. The role of nutricline depth in regulating the ocean carbon cycle. Proc. Natl Acad. Sci. USA 105, 20344–20349 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Calvo, E., Pelejero, C., Pena, L. D., Cacho, I. & Logan, G. A. Eastern Equatorial Pacific productivity and related-CO2 changes since the last glacial period. Proc. Natl Acad. Sci. USA 108, 5537–5541 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.McCabe, R. M. et al. An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. 43, 10,366–10,376 (2016).Article 

Google Scholar 
38.Roberts, S. D., Van Ruth, P. D., Wilkinson, C., Bastianello, S. S. & Bansemer, M. S. Marine heatwave, harmful algae blooms and an extensive fish kill event during 2013 in South Australia. Front. Mar. Sci. 6, 1–20 (2019).CAS 
Article 

Google Scholar 
39.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
40.Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 1–12 (2019).MathSciNet 
Article 

Google Scholar 
41.Keeling, P. J. The endosymbiotic origin, diversification and fate of plastids. Philos. Trans. R. Soc. B Biol. Sci. 365, 729–748 (2010).CAS 
Article 

Google Scholar 
42.Yoon, H. S., Hackett, J. D., Pinto, G. & Bhattacharya, D. The single, ancient origin of chromist plastids. Proc. Natl Acad. Sci. USA 99, 15507–15512 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Jönsson, B. F. & Watson, J. R. The timescales of global surface-ocean connectivity. Nat. Commun. 7, 1–6 (2016).Article 
CAS 

Google Scholar 
46.Doblin, M. A. & van Sebille, E. Drift in ocean currents impacts intergenerational microbial exposure to temperature. Proc. Natl Acad. Sci. USA 113, 5700–5705 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Whittaker, K. & Rynearson, T. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc. Natl Acad. Sci. USA 114, 2651–2656 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Ward, B. A., Cael, B. B., Collins, S. & Robert Young, C. Selective constraints on global plankton dispersal. Proc. Natl Acad. Sci. USA 118, 1–7 (2021).
Google Scholar 
49.Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. Integr. Comp. Biol. 19, 357–366 (1979).
Google Scholar 
50.Collins, M. et al. in Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change (eds. Stocker, T. F. et al.) 1029–1136 (Cambridge University Press, 2013).51.Bopp, L., Aumont, O., Cadule, P., Alvain, S. & Gehlen, M. Response of diatoms distribution to global warming and potential implications: A global model study. Geophys. Res. Lett. 32, L19606 (2005).ADS 
Article 
CAS 

Google Scholar 
52.Ward, B. A. Temperature-correlated changes in phytoplankton community structure are restricted to polar waters. PLoS ONE 10, 1–15 (2015).
Google Scholar 
53.Winter, A., Henderiks, J., Beaufort, L., Rickaby, R. E. M. & Brown, C. W. Poleward expansion of the coccolithophore Emiliania huxleyi. J. Plankton Res. 36, 316–325 (2014).CAS 
Article 

Google Scholar 
54.Rivero-Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M. & Guikema, S. D. Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO2. Science 350, 1533–1537 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Steinacher, M. et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences Discuss. 7, 979–1005 (2010).ADS 
CAS 
Article 

Google Scholar 
56.Arrigo, K. R., van Dijken, G. L. & Strong, A. L. Environmental controls of marine productivity hot spots around Antarctica. J. Geophys. Res. Ocean. 120, 2813–2825 (2015).Article 

Google Scholar 
57.Aranguren-Gassis, M., Kremer, C. T., Klausmeier, C. A. & Litchman, E. Nitrogen limitation inhibits marine diatom adaptation to high temperatures. Ecol. Lett. 22, 1860–1869 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol. Oceanogr. 61, 1232–1244 (2016).ADS 
Article 

Google Scholar 
59.Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Allen, A. P., Gillooly, J. F., Savage, V. M. & Brown, J. H. Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Padfield, D., Yvon-Durocher, G., Buckling, A., Jennings, S. & Yvon-Durocher, G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol. Lett. 19, 133–142 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Baker, K. G. et al. Thermal niche evolution of functional traits in a tropical marine phototroph. J. Phycol. 54, 799–810 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.O’Donnell, D. R. et al. Rapid thermal adaptation in a marine diatom reveals constraints and trade-offs. Glob. Chang. Biol. 24, 4554–4565 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Seong, K. A., Jeong, H. J., Kim, S., Kim, G. H. & Kang, J. H. Bacterivory by co-occurring red-tide algae, heterotrophic nanoflagellates, and ciliates. Mar. Ecol. Prog. Ser. 322, 85–97 (2006).ADS 
Article 

Google Scholar 
65.Arizona Software Inc. GraphClick 3.0.2. http://www.arizona-software.ch/graphclick/ (2010).66.Norberg, J. Biodiversity and ecosystem functioning: a complex adaptive systems approach. Limnol. Oceanogr. 49, 1269–1277 (2004).ADS 
Article 

Google Scholar 
67.Bolker, B. & Team, R. D. C. bbmle: Tools for general maximum likelihood estimation. https://github.com/bbolker/bbmle (2017).68.R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).69.Riahi, K. et al. RCP 8.5-A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).ADS 
CAS 
Article 

Google Scholar 
70.Koenker, R. quantreg: Quantile regression. https://cran.r-project.org/package=quantreg (2019).71.Chen, B. & Laws, E. A. Is there a difference of temperature sensitivity between marine phytoplankton and heterotrophs? Limnol. Oceanogr. 62, 806–817 (2017).ADS 
Article 

Google Scholar 
72.Sal, S., Alonso-Saez, L., Bueno, J., Garcıa, F. C. & Lopez-Urrutia, A. Thermal adaptation, phylogeny, and the unimodal size scaling of marine phytoplankton growth. Limnol. Oceanogr. 60, 1212–1221 (2015).ADS 
Article 

Google Scholar 
73.Koenker, R. Quantile Regression, https://doi.org/10.1017/CBO9780511754098 (Cambridge University Press, 2005).74.Tomas, C. R. et al. Identifying Marine Phytoplankton. (Academic Press, 1997).75.He, X. & Hu, F. Markov chain marginal bootstrap. J. Am. Stat. Assoc. 97, 783–795 (2002).MathSciNet 
MATH 
Article 

Google Scholar 
76.Rynearson, T. A. Literature compilation of thermal growth rates from four phytoplankton functional types. Biological and Chemical Oceanography Data Management Office (BCO-DMO), (2021). https://doi.org/10.26008/1912/bco-dmo.839696.177.Rynearson, T. A. Estimated thermal capacities for phytoplankton strains. Biological and Chemical Oceanography Data Management Office (BCO-DMO), https://doi.org/10.26008/1912/bco-dmo.839713.1 (2021).78.Rynearson, T. A. Estimated thermal traits for phytoplankton. Biological and Chemical Oceanography Data Management Office (BCO-DMO), https://doi.org/10.26008/1912/bco-dmo.839689.1 (2021).79.Anderson, S. I. sianderson/PFT_thermal_response: Marine Phytoplankton Functional Types Exhibit Diverse Responses to Thermal Change. zenodo. https://doi.org/10.5281/zenodo.5507532 (2021).80.Buitenhuis, E. T., Pangerc, T., Franklin, D. J., Le Quéré, C. & Malin, G. Growth rates of six coccolithophorid strains as a function of temperature. Limnol. Oceanogr. 53, 1181–1185 (2008).ADS 
Article 

Google Scholar 
81.Stawiarski, B., Buitenhuis, E. T. & Le Quéré, C. The physiological response of picophytoplankton to temperature and its model representation. Front. Mar. Sci. 3, 1–13 (2016).Article 

Google Scholar  More

Field trialsField trials were conducted in three raised beds (1 × 2 × 0.6 m) on the Penn State University campus from July to August 2020. The raised beds were separated by at least 8 m to avoid treatment cross-contamination. Faba bean (Vicia faba L.) seeds were planted at a density of 20 seeds/ m2 (50 plants per bed), and each bed was caged using a metal-framed tent. “Noseeum” nylon mesh (Outdoor Wilderness Fabric s, Inc., Caldwell, ID) was draped over the frame and the edges buried in the soil of the bed. The sides of the cages were fastened closed with zippers to allow access.InsectsAphid and predator beetle colonies were raised separately on faba bean plants in cages (BugDorm 20 cm × 40 cm × 20 cm, BioQuip Products, Inc., Rancho Dominguez, CA) in the field. Larvae and adults of predator beetles were fed with a combination of A. pisum and Rhopalosiphum padi every other day (Supplementary information Fig. S1). Trials involving plants, insects, and entomopathogenic fungi were conducted according to institutional, national, and international guidelines and legislation.Fungal inoculations (Beavueria bassiana)We released first instar aphid nymphs on each faba bean plant on the raised beds (~ 1100 aphids) by gently shaking plastic containers with groups of 20 nymphs and placing them on the plants using a paintbrush. They were allowed to grow and reproduce for fifteen days. During the night, we sprayed spore suspension of the Beauveria strain GHA (BotaniGard ®, MT, USA) at 1.4 × 106 and 1.4 × 1012 spore ha−1, low and high load respectively. Two days after inoculation, we collected adult aphids (~ 4–5 days old) from the experimental plots and measured physiological parameters (see details below). Next, we released 300 adult beetles inside each aphid–fungal inoculated cage, allowed them to feed for 2–3 days in our experimental cages, and then collected beetles for physiological measurement.Identification of critical thermal limits (CTMax and CTMin) of healthy and infected insectsTo determine critical thermal maximum for locomotion (CTMax) of healthy and infected individuals of each species, we employed a protocol modified from Ribeiro et al.25, using a hotplate with a programmable heating rate controlled by a computer interface (Sable Systems, LV, USA). The temperature was monitored by independent thermocouple channels connected to a Hobo 4-channel data logger. One thermocouple was attached to the surface of the hotplate, and the other sensor was attached inside the glass tube plugged by a cotton ball in which we placed an individual insect. This equipment was located inside an automated thermal chamber (interior dimensions: width 40.5 cm × 35 cm length × 40 cm height). We transferred an adult aphid (4-day-old) into the glass tube and exposed it to increasing temperatures at a rate of 0.3 °C min−1 until its locomotion stopped. CTMax was recorded when the insect turned upside down and could no longer return to the upright position within 5 s. The insect was returned to a faba bean leaf for recovery (n = 10 individuals per treatment).To measure the critical thermal minimum for locomotion (CTMin) of healthy and infected individuals of each species (n = 10 individuals per treatment), we used an insulated incubator where the temperature was monitored by independent thermocouple channels connected to a Hobo 4-channel data logger. The sensors were attached inside three glass tubes, each tube with an adult (3 to 4-day-old), and plugged by a cotton ball. The glass tube was exposed to decreasing temperature at a rate of 0.3 °C min−1 until its locomotion stopped. CTMin was recorded when no movement was recorded within 5 s. The insect was returned to an aphid-infested faba bean leaf for recovery. Data were only considered valid if the insect displayed normal activity 2 h after a CTMax or CTMin test.Impacts of infection on voluntary exposure aphids and predator beetles to extreme thermal zonesTo examine how voluntary exposure to ETZ was affected by fungal infection, we collected aphids and predator beetles (3 to 5 day-old) from our field plots and transferred them to a dark plastic bottle. Next, a bottle containing the insects was attached to a choice test arena following a modified protocol from Navas et al.24. This experimental arena allows insects to freely move across extreme temperatures to access food in containers located at each end of the device. To reach food, individuals had to cross an ETZ, either warm or cold. The location of each insect was recorded after 60 min, and it was classified as: exploration for individuals that left the initial black bottle, warm or cold ETZ crossings. The experiment was replicated ten times for each species and treatment condition [aphid: healthy, infected (low and high spore load); predator beetle: healthy, infected (low and high spore load)].Effects of fungal infection and thermal conditions (critical thermal limits and voluntary exposure to ETZs) on longevity of aphids and predator beetlesTo examine whether fungal infection and thermal conditions alter longevity in aphids and beetles, we isolated three individuals from each factor combination (low, high fungal load, CTMin, CTMax, behavior: crosses to ETZ cold, warm, and no cross) from previous experiments, and counted the number of days the adults survived after the exposure to the thermal condition (n = 3 factor combination).Energetic cost associated with fungal infection of aphid and predator beetles under critical thermal limits and voluntary exposure to ETIntracellular ATP content was determined in neutralized perchloric acid extracts and by a spectrophotometric coupled enzyme assay, based on modified protocol from Churchill and Storey26 content (n = 3 per treatment condition). An insect was ground to powder using a mortar and pestle cooled in liquid nitrogen, and then weighed into 1.5 mL microcentrifuge tubes (Eppendorf). Powder was dissolved with 0.1 mL ice-cold TE buffer (50 mM Tris–HCl, pH 7.5 plus 1 mM EGTA) and homogenized by sonication (15 s, three times), using a Q500 Sonicator system (QSonica, Newtown, CT, USA). An aliquot (10 µL) of the well-mixed homogenate was removed for protein determination. Cells were lysed by adding 6% (v/v) ice-cold perchloric acid, strongly vortexed for 2 min and incubated at 4 °C for 10 min. Next, the cell homogenate was centrifuged at 14,462 rpm and 4 °C for 5 min. The resulting supernatant was neutralized by adding KOH/Tris (3 M/0.1 M) and centrifuged again to discard the perchlorate salts. Extracts were kept at 4 °C for their immediate utilization. ATP content was determined spectrophotometrically by following the production of NADPH at 340 nm (ε = 6.22 mM−1 cm−1) and using CARY WinUV-Vis Spectrophotometer (Agilent, Santa Clara, CA, USA). The following reagents were used for the spectrophotometric coupled enzyme assay: 5 U Hexokinase, 10 U Glucose 6-phosphate dehydrogenase, 1 mM NADP + , 5 mM MgCl2 and 10 mM Glucose in HE buffer (100 mM Hepes-HCl plus 1 mM EGTA, pH 7.0) at 25 °C. Chemicals were purchased from Roche (Manheim, Germany) and Sigma (St Louis, MO, USA).Infection statusWe used two different protocols to confirm fungal infection: (1) placing each individual in wet towel paper inside a Ziploc bag to observe hyphal growth27. (2) For insects used in ATP measurements, we followed a modified protocol from Wraight and Ramos28 and Castrillo et al.29. Insect were washed using a serial dilution technique, vortexed for 10 s, and mounted in a drop of lactophenol blue, diluted with distilled water. We then preserved insect body parts (i.e., legs and abdomen terga) at − 80 °C for 12 months and placed in Petri dishes containing potato dextrose agar (PDA HiMedia-GM096) medium (pH 6.8), and incubated for ten days. To confirm infection by B. bassiana, we observed plates every 3 days, identified fungal growth (dense white mycelia), then randomly chose three samples, collected mycelia, and DNA was extracted using PureLink Genomic DNA Kit (Invitrogen by Thermo Fisher Scientific, Waltham, MA, USA), according to manufacturer’s protocol. Next, we used PCR essays (25 µL) contained 1 × Q5 Hot Start High-Fidelity Master Mix (New England BioLabs), following a protocol modified from Castrillo et al.29 using primers GHTqF1 (5′-TTTTCATCGAAAGGTTGTTTCTCG) and GHTq R1 (5′-CTGTGCTGGGTACTGACGTG) amplified a 96-bp region of the SCAR fragment. The PCR protocol was initial denaturation at 98 °C, followed by 30 cycles at 98 °C for 1 min, annealing at 58 °C for 1 min; and extension at 72 °C for 1 min. PCR products were visualized in a 1.0% (wt/vol) agarose gel stained with ethidium bromide.Data analysisAll data were tested for statistical test assumptions using a qqplot, Levene’s homogeneity test and the Shapiro–Wilk normality test at alpha = 0.05 significance level. For critical thermal limits (CTMax and CTMin) experiments, the data sets were non-normal and transformation did not normalize the residuals, so we used nonparametric ANOVAs (Kruskal–Wallis) followed by post-hoc nonparametric multiple comparisons. For voluntary exposure to ETZs, we used a generalized linear model with treatment (healthy, low and high spore load) with Poisson distribution, followed by comparisons within each treatment group. For healthy insects, we used a t-test to compare crosses between warm or cold ETZs; for infected insects, we conducted ANOVAS for comparisons among 23 °C, warm or cold ETZs.ATP data: Data for CTMax of A. pisum were non-normal, and transformation did not normalize the residuals, nonparametric ANOVAs (Kruskal–Wallis) were then used and followed by post-hoc nonparametric pairwise comparisons with Wilcoxon tests. ATP data sets from voluntary exposure to ETZs were analyzed following the same protocol as described previously for in crosses analysis of ETZ experiment. Longevity was analyzed using a two-way ANOVA with fungal load and thermal condition (critical temperature and behavior) as factors. Analyses were performed in the R programming environment (v. 3.4.3., CRAN project)30 and JMP-Pro version 15 (SAS Institute 2020). More

1.Mayr, E. The species concept: Semantics versus semantics. Evolution 3, 371–372 (1949).Article 

Google Scholar 
2.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

Google Scholar 
3.Mace, G. M. The role of taxonomy in species conservation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 711–719 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Gustafson, K. D., Kensinger, B. J., Bolek, M. G. & Luttbeg, B. Distinct snail (Physa) morphotypes from different habitats converge in shell shape and size under common garden conditions. Evol. Ecol. Res. 16, 77–89 (2014).
Google Scholar 
5.Aksenova, O. V. et al. Species richness, molecular taxonomy and biogeography of the radicine pond snails (Gastropoda: Lymnaeidae) in the Old World. Sci. Rep. 8, 1–7 (2018).ADS 
CAS 
Article 

Google Scholar 
6.Liu, H. P. & Hershler, R. A new species and range extensions for three other species of pebblesnails (Lithoglyphidae, Fluminicola) from the upper Klamath basin, California-Oregon. ZooKeys 812, 47–67 (2019).Article 

Google Scholar 
7.Alda, P. et al. Systematics and geographical distribution of Galba species, a group of cryptic and worldwide freshwater snails. Mol. Phylogenet. Evol. 157, 107035 (2021).PubMed 
Article 

Google Scholar 
8.Taylor, D. W. Introduction to Physidae (Gastropoda: Hygrophila); biogeography, classification, morphology. Rev. Biol. Trop. 51(Supplement 1), 1–287 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Wethington, A. R. & Lydeard, C. A molecular phylogeny of Physidae (Gastropoda: Basommatophora) based on mitochondrial DNA sequences. J. Molluscan Stud. 73, 241–257 (2007).Article 

Google Scholar 
10.Ng, T. H. et al. Molluscs for sale: assessment of freshwater gastropods and bivalves in the ornamental pet trade. PLoS ONE 11, e0161130 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Saito, T., Prozorova, L., Hirano, T., Fukuda, H. & Chiba, S. Endangered freshwater limpets in Japan are actually alien invasive species. Conserv. Genet. 19, 947–958 (2018).Article 

Google Scholar 
12.Lydeard, C., Campbell, D. & Golz, M. Physa acuta Draparnaud, 1805 should be treated as a native of North America, not Europe. Malacologia 59, 347–350 (2016).Article 

Google Scholar 
13.Albrecht, C., Kroll, O., Terrazas, E. M. & Wilke, T. Invasion of ancient Lake Titicaca by the globally invasive Physa acuta (Gastropoda: Pulmonata: Hygrophila). Biol. Invasions 11, 1821–1826 (2009).Article 

Google Scholar 
14.Ng, T. H., Tan, S. K. & Yeo, D. C. Clarifying the identity of the long-established, globally-invasive Physa acuta Draparnaud, 1805 (Gastropoda: Physidae) in Singapore. BioInvasions Rec. 4, 189–194 (2015).Article 

Google Scholar 
15.Collado, G. A. Unraveling cryptic invasion of a freshwater snail in Chile based on molecular and morphological data. Biodivers. Conserv. 26, 567–578 (2017).Article 

Google Scholar 
16.Johnson, P. D. et al. Conservation status of freshwater gastropods of Canada and the United States. Fisheries 38, 247–282 (2013).Article 

Google Scholar 
17.Strong, E. E. & Whelan, N. V. Assessing the diversity of western North American Juga (Semisulcospiridae, Gastropoda). Mol. Phylogenet. Evol. 136, 87–103 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hebert, P. D., Ratnasingham, S. & De Waard, J. R. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B 270(supplement 1), S96-99 (2003).CAS 

Google Scholar 
19.Stöger, I. & Schrödl, M. Mitogenomics does not resolve deep molluscan relationships (yet?). Mol. Phylogenet. Evol. 69, 376–392 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Cunha, T. J. & Giribet, G. A congruent topology for deep gastropod relationships. Proc. R. Soc. B 286, 20182776 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Varney, R. M. et al. Assessment of mitochondrial genomes for heterobranch gastropod phylogenetics. BMC Ecol. Evol. 21, 6 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Remigio, E. A. & Hebert, P. D. Testing the utility of partial COI sequences for phylogenetic estimates of gastropod relationships. Mol. Phylogenet. Evol. 29, 641–647 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Collins, R. A. & Cruickshank, R. H. The seven deadly sins of DNA barcoding. Mol. Ecol. Resour. 13, 969–975 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Ratnasingham, S. & Hebert, P. D. A DNA-based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8, e66213 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Whelan, N. V. & Strong, E. E. Morphology, molecules and taxonomy: Extreme incongruence in pleurocerids (Gastropoda, Cerithioidea, Pleuroceridae). Zoolog. Scr. 45, 62–87 (2016).Article 

Google Scholar 
26.Razkin, O., Gómez-Moliner, B. J., Vardinoyannis, K., Martínez-Ortí, A. & Madeira, M. J. Species delimitation for cryptic species complexes: Case study of Pyramidula (Gastropoda, Pulmonata). Zool. Scr. 46, 55–72 (2017).Article 

Google Scholar 
27.Liu, H. P., Hershler, R. & Hovingh, P. Molecular evidence enables further resolution of the western North American Pyrgulopsis kolobensis complex (Caenogastropoda: Hydrobiidae). J. Molluscan Stud. 84, 103–107 (2018).Article 

Google Scholar 
28.Ward, R. D. DNA barcode divergence among species and genera of birds and fishes. Mol. Ecol. Resour. 9, 1077–1085 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Saadi, A. J., Davison, A. & Wade, C. M. Molecular phylogeny of freshwater snails and limpets (Panpulmonata: Hygrophila). Zool. J. Linn. Soc. 190, 518–531 (2020).Article 

Google Scholar 
30.Frest, T. J. & Johannes, E. J. An annotated checklist of Idaho land and freshwater mollusks. J. Idaho Acad. Sci. 36(2), 1–51 (2000).
Google Scholar 
31.Pip, E. & Franck, J. P. Molecular phylogenetics of central Canadian Physidae (Pulmonata: Basommatophora). Can. J. Zool. 86, 10–16 (2008).CAS 
Article 

Google Scholar 
32.Tariel, J., Plénet, S. & Luquet, É. Transgenerational plasticity of inducible defences: Combined effects of grand-parental, parental and current environments. Ecol. Evol. 10, 2367–2376 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Perrin, N. The life history parameters of Physa acuta (Gastropoda, Mollusca) in experimental conditions. Revue Suisse Zoologique 93, 725–736 (1986).Article 

Google Scholar 
34.Taylor, D. W. New species of Physa (Gastropoda: Hygrophila) from the western United States. Malacol. Rev. 21, 43–79 (1988).
Google Scholar 
35.U.S. Fish and Wildlife Service. Determination of endangered or threatened status for five aquatic snails in south central Idaho. Fed. Reg. 57, 59242–59257 (1992).
Google Scholar 
36.Rogers, D. C. & Wethington, A. R. Physa natricina Taylor 1988, junior synonym of Physa acuta Draparnaud, 1805 (Pulmonata: Physidae). Zootaxa 1662, 45–51 (2007).
Google Scholar 
37.Gates, K. K., Kerans, B. L., Keebaugh, J. L., Kalinowski, S. & Vu, N. Taxonomic identity of the endangered Snake River physa, Physa natricina (Pulmonata: Physidae) combining traditional and molecular techniques. Conserv. Genet. 14, 159–169 (2013).Article 

Google Scholar 
38.Moore, A. C., Burch, J. B. & Duda, T. F. Recognition of a highly restricted freshwater snail lineage (Physidae: Physella) in southeastern Oregon: Convergent evolution, historical context, and conservation considerations. Conserv. Genet. 16, 113–123 (2015).Article 

Google Scholar 
39.Dillon, R. T., Robinson, J. D. & Wethington, A. R. Empirical estimates of reproductive isolation among the freshwater pulmonate snails Physa acuta, P. pomilia, and P. hendersoni. Malacologia 49, 283–292 (2007).Article 

Google Scholar 
40.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Dyke, A. S., Moore, A. & Robertson, L. Deglaciation of North America. Geological Survey of Canada Open File 1574 (2003).42.Wethington, A. R., Wise, J. & Dillon, R. T. Jr. Genetic and morphological characterization of the Physidae of South Carolina (Gastropoda: Pulmonata: Basommatophora), with description of a new species. Nautilus 123, 282–292 (2009).
Google Scholar 
43.Ebbs, E. T., Loker, E. S. & Brant, S. V. Phylogeography and genetics of the globally invasive snail Physa acuta Draparnaud 1805, and its potential to serve as an intermediate host to larval digenetic trematodes. BMC Evol. Biol. 18, 1–7 (2018).Article 

Google Scholar 
44.Duggan, I. C. The freshwater aquarium trade as a vector for incidental invertebrate fauna. Biol. Invasions 12, 3757–3770 (2010).Article 

Google Scholar 
45.Van Leeuwen, C. H. et al. How did this snail get here? Several dispersal vectors inferred for an aquatic invasive species. Freshw. Biol. 58, 88–99 (2013).Article 

Google Scholar 
46.Coughlan, N. E., Kelly, T. C., Davenport, J. & Jansen, M. A. Up, up and away: Bird-mediated ectozoochorous dispersal between aquatic environments. Freshw. Biol. 62, 631–648 (2017).Article 

Google Scholar 
47.Bony, Y. K. et al. Ecological conditions for spread of the invasive snail Physa marmorata (Pulmonata: Physidae) in the Ivory Coast. Afr. Zool. 43, 53–60 (2008).Article 

Google Scholar 
48.Pierce, K. L. & Morgan, L. A. Is the track of the Yellowstone hotspot driven by a deep mantle plume?—Review of volcanism, faulting, and uplift in light of new data. J. Volcanol. Geotherm. Res. 188, 1–25 (2009).ADS 
CAS 
Article 

Google Scholar 
49.Smith, G. R. et al. Biogeography and timing of evolutionary events among Great Basin fishes. In Great Basin Aquatic Systems History. Smithsonian Contributions to the Earth Sciences Vol. 33 (eds Hershler, R. et al.) 175–234 (Smithsonian Institution Press, 2002).
Google Scholar 
50.Oviatt, C. G. Chronology of Lake Bonneville, 30,000 to 10,000 yr BP. Quatern. Sci. Rev. 110, 166–171 (2015).Article 

Google Scholar 
51.Safran, E. B. et al. Plugs or flood-makers? The unstable landslide dams of eastern Oregon. Geomorphology 248, 237–251 (2015).ADS 
Article 

Google Scholar 
52.Ely, L. L. et al. Owyhee River intracanyon lava flows: Does the river give a dam?. GSA Bull. 124, 1667–1687 (2012).Article 

Google Scholar 
53.Matthews, J. et al. Rapid range expansion of the invasive quagga mussel in relation to zebra mussel presence in the Netherlands and western Europe. Biol. Invasions 16, 23–42 (2014).Article 

Google Scholar 
54.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. Biotechol. 3, 294–299 (1994).CAS 

Google Scholar 
55.Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Uit de Weerd, D. R. & Gittenberger, E. Phylogeny of the land snail family Clausiliidae (Gastropoda: Pulmonata). Mol. Phylogenet. Evol. 67, 201–216 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Nixon, K. C. & Wheeler, Q. D. An amplification of the phylogenetic species concept. Cladistics 6, 211–223 (1990).Article 

Google Scholar 
59.Galtier, N. Delineating species in the speciation continuum: A proposal. Evol. Appl. 12, 657–663 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
60.DeSalle, R., Egan, M. G. & Siddall, M. The unholy trinity: Taxonomy, species delimitation and DNA barcoding. Philos. Trans. R. Soc. B Biol. Sci. 360, 1905–1916 (2005).CAS 
Article 

Google Scholar 
61.Bouchet, P. et al. Revised classification, nomenclator and typification of gastropod and monoplacophoran families. Malacologia 61, 1–526 (2017).Article 

Google Scholar 
62.Wethington, A. R. & Guralnick, R. Are populations of physids from different hot springs distinctive lineages?. Am. Malacol. Bull. 19, 135–144 (2004).
Google Scholar 
63.Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics 26, 680–682 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
Article 

Google Scholar 
65.Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21, 609–620 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Delicado, D., Arconada, B., Aguado, A. & Ramos, M. A. Multilocus phylogeny, species delimitation and biogeography of Iberian valvatiform springsnails (Caenogastropoda: Hydrobiidae), with the description of a new genus. Zool. J. Linn. Soc. 186, 892–914 (2019).Article 

Google Scholar 
68.Kapli, T. et al. Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo. Bioinformatics 33, 1630–1638 (2016).
Google Scholar 
69.Clement, M., Posada, D. C. & Crandall, K. A. TCS: A computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Hart, M. W. & Sunday, J. Things fall apart: Biological species form unconnected parsimony networks. Biol. Lett. 3, 509–512 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Minh, B. Q., Nguyen, M. A. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Meier, R., Zhang, G. & Ali, F. The use of mean instead of smallest interspecific distances exaggerates the size of the “barcoding gap” and leads to misidentification. Syst. Biol. 57, 809–813 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
74.Dellicour, S. & Flot, J. F. The hitchhiker’s guide to single-locus species delimitation. Mol. Ecol. Resour. 18, 1234–1246 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
75.Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, 2000).Book 

Google Scholar 
76.Dinapoli, A., Tamer, C., Franssen, S., Naduvilezhath, L. & Klussmann-Kolb, A. Utility of H3-gene sequences for phylogenetic reconstruction—a case study of heterobranch Gastropoda. Bonner Zoologische Beiträge 55(3/4), 191–202 (2006).
Google Scholar 
77.Ayyagari, V. S. & Sreerama, K. Molecular phylogenetic analysis of Pulmonata (Mollusca: Gastropoda) on the basis of histone-3 gene. Beni-Suef Univ. J. Basic Appl. Sci. 8, 1–8 (2019).Article 

Google Scholar  More