Ecology

Ethical statementThe present study was discussed and approved by the ethics and animals’ welfare committee of the University of Veterinary Medicine, Vienna, Austria, in accordance with good scientific practice and national legislation (GZ: BMWFW-68.205/0151-WF/V/3b/2016 and GZ: BMWFW-68.205/0224-WF/V/3b/2016). All methods were carried out in accordance with relevant guidelines and regulations. We confirm that the study was carried out in compliance with the ARRIVE guidelines. No plants or plant parts were used in this study.Animals and study areaThe study animals were kept in an outdoor enclosure (~ 55 ha, for details see “Supplementary Material”). The study enclosure was covered with a deciduous forest, mainly Turkey oak (Quercus cerris) and pubescent oak (Quercus pubescens) and included only few meadow patches. For the present study ten adult females, were used. We concentrated on females only because the live capture and handling of males are hampered by the large size and ferocity of boars. Also, due to competition and high levels of aggression between males during rut, the stocking of the enclosure was strongly female biased. During the study period (12/2016–01/2019), the animal density was ~ 1 adult female/ha plus up to 20 males (total) of different ages. Due to this relatively high density, animals were supplemented with 1–1.5 kg corn/individual once a day (at 2:00–14:00 h) at two feeding areas, each ~ 40 × 20 m. The enclosure was part of a game reserve, which was enclosed by 2.5 m high, solid, non-transparent fencing and was closed for the public. Thus, the study site provided an environment without disturbances due to hikers, bikers or straying dogs. There were no battue hunts or other disturbances due to hunting or forest management activities during the study period in the enclosure.Animals were trapped once a year in autumn within the feeding sites to collect data on reproductive success and body condition of females and to separate some of them for implantation/explantation of loggers. While feeding, we closed the access gates and released the boars one by one trough a wooden corridor back into the enclosure. While in the wooden corridor we recorded the body mass of each individual (Gallagher SmartScale® 500, Groningen, Netherlands). Due to management reasons the juveniles (born in spring) were removed from the enclosure during this procedure.Implantation of temperature and heart rate loggersWe implanted a heart rate logger (DST centi-HRT, Star-Oddi, Gardabaer, Iceland) and two custom-built temperature loggers in each of ten female wild boars in October/November 2016 and 2017 (age 5 and 6 years). All details about surgery techniques and anaesthesia protocols are provided in the “Supplementary Material”. Explantations were carried out approximately one year after implantations. The last explanation was carried out in January 2019. One female was implanted in two consecutive years. Mean body mass at date of implantation for all females was 71.8 ± 15.5 kg.The heart rate logger was adjusted to record data at a time interval of 12 min to cover one year of data recording. To remove outliers, all initial data from these recorders were subjected to a running median over five consecutive values. The HR recorder was positioned subcutaneously, in proximity to the heart on the lateral rib cage, behind the moving area of the elbow, to avoid rubbing, or inserted and tethered into the ventral subperitoneal space caudal of the xiphoid process of the sternum.The self-built temperature loggers were covered with inert surgical wax and had a weight of ~ 8 g. Time interval of recording was 4 min, the accuracy 0.01 °C. One of the two temperature loggers had an especially flat shape (3.4 × 1.9 × 0.5 cm) to fit smoothly into the subcutaneous neck region. The second temperature logger was placed into the intraperitoneal cavity, tethered at the Linea alba (diameter = 2.1 cm, height = 1.2 cm). For details on surgery, see “Supplement”.We collected and evaluated a mean of 227.45 ± 160.69 days of heart rate recording per individual (SD, n = 11: 33 days, 58 days, 79 days, 89 days, 143 days, 189 days, 272 days, 345 days, 412 days, 421 days, 461 days), and a mean of 382.00 ± 100.17 days (SD), of subcutaneous logger recording per individual (n = 8: 143 days, 363 days, 411 days, 414 days, 419 days, 421 days, 424 days, 461 days). From the loggers implanted in the abdominal cavity we collected 338.71 ± 117.01 days (SD) per individual (n = 10: 140 days, 143 days, 363 days, 364 days, 411 days, 419 days, 421 days, 421 days, 424 days, 461 days). The hourly means of monitored heart rates of each animal over the course of the year are shown in Supplementary Fig. S1.Activity dataTo record the activity of animals, a telemetry system (Smartbow System, Zoetis, New Jersey, USA) was installed around the two neighbouring feeding areas and two close water ponds in the enclosure. The system consisted of a central solar power and computing station and ten receivers located at the height of 2–3 m. Part of the system were ear-tags (34 g; 52 mm × 36 mm × 17 mm, for details see “Supplementary Material”). The accelerometer (located inside ear-tags) measured triaxial acceleration (x, y, z). As an estimate of locomotor activity (ACT), we computed the total acceleration vector from sqrt (x2 + y2 + z2).Climate and mastThe study site in Eastern Austria (altitude 130 m) is generally characterised by a Pannonian climate. According to long-term climate records, the mean annual temperature is 10 °C in combination with a mean precipitation of 600–700 mm and 1898 h of sunshine per year (ZAMG, 1971–2000).We recorded ambient temperature (Ta) and black bulb temperature (Tab) at 2 m height directly at the study site (Vantage Pro 2 with black bulb extension, Davis Instruments, Hayward, USA).To assess the extent of the acorn mast, each autumn seven nets, 4 × 4 m, were set up to collect acorns at random locations. The nets were regularly emptied between Sept. and Nov. each year, and the collected acorns were dried and weighed. In the autumns prior to the study (2016) and during both full study years (2017/2018) there was seeding of at least part of the oaks. Over ~ 90 days in each autumn we collected 52.4 g/m2, 134.8 g/m2, and 37.5 g/m2 acorn in 2016, 2017, and 2018, respectively. Thus, 2017 was a full mast year but there were acorns available in autumn throughout the study period.Data analysisTo facilitate handling of data and to reduce autocorrelation we compiled and evaluated hourly means for all data, i.e., heart rates (HR; see Suppl. Fig. S1), intraperitoneal and subcutaneous body temperature (Tbip and Tbsc, respectively) and activity (ACT), as well as ambient air temperature (Ta) and black-bulb temperature (Tab). We further tested for effects of day of year (DOY) and hour of day (HOUR). We did not assess the influence of environmental conditions in different years, because due to logger-failures and thus scarcity of heart rates, all data were pooled for different years (with similarly warm conditions and food available year-round). Also, we did not further evaluate daily rhythms, because animals were always fed in the early afternoon, which may have influenced their timing.We investigated the effects of season (DOY), hour of day (HOUR), and Ta on the response variables HR, Tbip, Tbsc, and ACT. We additionally used Tbip, Tbsc, and ACT as predictors for HR. As many of the relationships between these were non-linear, we used general additive mixed models (GAMMs), as implemented in package mgcv60 in R61. This function fits non-linear splines to the data, which are penalized for their “wiggliness”, i.e., the number of turning points in the fit. Because the data were repeated measurements, we calculated for all response variables mixed models with an intercept for each animal ID as a random factor (using s (ID, bs = ”re”)). Hence, these mixed models allowed for differences in the mean level of heart rates, temperatures and activities, between individuals. All residuals of models were approximately normally distributed, as inspected by normal quantile–quantile plots. Hourly means of the response variables contained various degrees of autocorrelation. This was corrected by including autoregressive order 1 (AR1) error models in GAMM-functions, which successfully reduced the autocorrelation at lag 1 to nonsignificant levels. This was confirmed by comparing the autocorrelation function of model residuals (ACF) before and after their correction. To illustrate the effects of independent variables, we show population-level predictions from GAMMs. These graphs contain rug plots to illustrate the distribution of independent variables. Because these plots were too dense for all original data (resulting in black bars), we show uniform random samples (n = 1000) from each independent predictor variable.Because hourly mean data consisted of ~ 117,000 observations we used the mgcv function “bam”, which uses numerical methods designed for large datasets. To fit non-linear functions to predictors, we used the default thin plate splines. Only the cyclic variables DOY and HOUR were modelled using cubic cyclic splines, which are guaranteed to have identical start- and endpoints (e.g., at Jan 1 and Dec 31). GAMMs were always fitted using method REML. As Tbip and Tbsc were only moderately correlated (r = 0.30), both were entered simultaneously as independent variables in the model on heart rate.We did not use partial regression plots from multiple regressions that included activity. This is because activity could only be recorded partly, in the vicinity of telemetry receivers. Thus, models that include ACT as well as all other predictors simultaneously, were restricted to ~ 7% of the data. However, we still used a full multiple regression model HR for the purpose of assessing relative variable importance (of DOY, HOUR, Ta, Tbip, Tbsc, and ACT). F-values from this model provide an indication of the importance of different predictors.To model a possible role of solar radiation and basking we computed the difference between Tab and Ta, called Tdiff, which represents an index of radiation. We used again GAMMs to test if Tdiff would affect Tbip, Tbsc and HR after adjusting for effects of Ta, hour of day, and the random factor animal ID.For a comparison of species we also computed monthly means and SEMs of HR in wild boars, and created a graph of seasonal time courses in other ungulates as published in Arnold2 that were kindly provided by the author. If not stated otherwise we provide means ± SEM. More

1.Pradhan, S., Saha, G. K. & Khan, J. A. Food habits of the red panda, Ailurus fulgens, in the Singalila National Park, Darjeeling, India. J. Bombay Nat. Hist. Soc. 98, 224–230 (2001).
Google Scholar 
2.Bista, D. et al. Distribution and habitat use of red panda in the Chitwan–Annapurna Landscape of Nepal. PLoS ONE 12, e0178797 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Martin, P. S. The discovery of America. Science 179, 969–974 (1973).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Miller, G. H. et al. Pleistocene extinction of Genyornis newtoni: human impact on Australian megafauna. Science 283, 205–208 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Grayson, D. K. & Meltzer, D. J. A requiem for North America overkill. J. Archaeol. Sci. 30, 585–593 (2003).Article 

Google Scholar 
6.van der Kaars, S. et al. Humans rather than climate the primary cause of Pleistocene megafaunal extinction in Australia. Nat. Commun. https://doi.org/10.1038/ncomms14142 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Louys, J. & Roberts, P. Environmental drivers of megafaunal and hominin extinction in Southeast Asia. Nature 586, 402–406 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Ripple, W. J. et al. Tertiary fossil fungi from Kiandra, New South Wales. Proc. Linn. Soc. NSW. 97, 141–149 (1975).
Google Scholar 
9.Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Brook, S. M. et al. Lessons learned from the loss of a flagship: the extinction of the Javan rhinoceros Rhinoceros sondaicus annamiticus from Vietnam. Biol. Conserv. 174, 21–29 (2014).Article 

Google Scholar 
11.Prasad, V., Stromberg, C. A. E., Alimohammadian, H. & Sahni, A. Dinosaur coprolites and the early evolution of grasses and grazers. Science 310, 1177–1180 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Shillito, L. M., Blong, J. C., Green, E. J. & VanAsperen, E. N. The what, how and why of archaeological human coprolite analysis. Earth Sci. Rev. 207, 103196 (2020).CAS 
Article 

Google Scholar 
13.van Geel, B. et al. The ecological implications of a Yakutian mammoth’s last meal. Quat. Res. 69, 361–376 (2008).Article 
CAS 

Google Scholar 
14.Rawlence, N. J., Wood, J. R., Bocherens, H. & Rogers, K. M. Dietary interpretations for extinct megafauna using coprolites, intestinal contents and stable isotopes: Complimentary or contradictory?. Quat. Sci. Rev. 142, 173–178 (2016).ADS 
Article 

Google Scholar 
15.Carrion, J. S. Pleistocene landscape in central Iberia inferred from pollen analysis of hyena coprolite. J. Quat. Sci. 22(2), 191–202 (2007).Article 

Google Scholar 
16.Wood, J. R. et al. Coprolite deposits reveal the diet and ecology of the extinct New Zealand megaherbivore moa (Aves, Dinornithiformes). Quat. Sci. Rev. 27, 2593–2602 (2008).ADS 
Article 

Google Scholar 
17.Gravendeel, B. et al. Multiproxy study of the last meal of a mid-Holocene Oyogos Yar horse, Sakha Republic, Russia. The Holocene 24(10), 1288–1296 (2014).ADS 
Article 

Google Scholar 
18.Akeret, O., Haas, J. N., Leuzinger, U. & Jacomet, S. Plant macrofossils and pollen in goat/sheep faeces from the Neolithic lake-shore settlement Arbon Bleiche 3, Switzerland. The Holocene 9(2), 175–182 (1999).ADS 
Article 

Google Scholar 
19.Birks, H. H. et al. Evidence for the diet and habitat of two late Pleistocene mastodons from the Midwest, USA. Quat. Res. 79, 1–21 (2018).ADS 

Google Scholar 
20.van der Waal, C. et al. Large herbivores may alter vegetation structure of semi-arid savannas through soil nutrient mediation. Oecologia 165, 1095–1107 (2011).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Velazquez, N. J. & Burry, L. S. Palynological analysis of Lama guanicoe modern feces and its importance for the study of coprolites from Patagonia, Argentina. Rev. Palaeob. Palynol. 184, 14–23 (2012).Article 

Google Scholar 
22.Basumatary, S. K., McDonald, H. G. & Gogoi, R. Pollen and non-pollen palynomorph preservation in the dung of the Greater one –horned rhino (Rhinoceros unicornis), and its implication to palaeoecology and palaeodietary analysis: a case study from India. Rev. Palaeo. Palynol. 244, 153–162 (2017).Article 

Google Scholar 
23.Basumatary, S. K., Singh, H., McDonald, H. G., Tripathi, S. & Pokharia, A. K. Modern botanical analogue of endangered Yak (Bos mutus) dung from India: Plausible linkage with living and extinct megaherbivores. PLoS ONE 14(3), e0202723 (2019).24.Roberts, M. S. & Gittleman, J. L. Ailurus fulgens. Mammalian species. Am. Soc. Mammal. 222, 1–8 (1984).
Google Scholar 
25.Johnson, K. G., Schaller, G. B. & Hu, J. C. Comparative behavior of red and giant pandas in the Wolong Reserve, China. J. Mammal. 69, 552–564 (1988).Article 

Google Scholar 
26.Yonzon, P. B. & Hunter, M. L. Ecological study of the red panda in Nepal-Himalaya. red panda Biology 1, 7 (1989).
Google Scholar 
27.Wei, F. W., Wang, W., Zhou, A., Hu, J. & Wei, Y. Preliminary study on food selection and feeding strategy of red pandas. Acta Theriol. Sin. 15, 259–266 (1995).
Google Scholar 
28.Zhang, Z. J., Hu, J. C., Yang, J. D., Li, M. & Wei, F. W. Food habits and space-use of red panda, Ailurus fulgens in the Fengtongzhai Nature Reserve, China: Food effects and behavioural response. Acta Theriol. 54, 225–234 (2009).Article 

Google Scholar 
29.Dorji, S., Vernes, K. & Rajaratnam, R. Habitat correlates of the red panda in the temperate forests of Bhutan. PLoS ONE 6, e26483 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Panthi, S., Aryal, A., Raubenheimer, D., Lord, J. & Adhikari, B. Summer diet and distribution of the Red Panda (Ailurus fulgens fulgens) in Dhorpatan Hunting Reserve, Nepal. Zool. Stud. 51(5), 701–709 (2012).
Google Scholar 
31.Sharma, H. P., Swenson, J. E. & Belant, J. L. Seasonal food habits of the red panda (Ailurus fulgens) in Rara National Park, Nepal. Hystrix 25(1), 47–50 (2014).
Google Scholar 
32.Panthi, S., Coogan, S. C. P., Aryal, A. & Raubenheimer, D. Diet and nutrient balance of red panda in Nepal. Sci. Nat. 102, 54 (2015).Article 
CAS 

Google Scholar 
33.Thapa, A. & Basnet, K. Seasonal diet of wild red panda (Ailurus fulgens) in Langtang national park, Nepal Himalaya. Inter. J. Conser. Sci. 6(2), 261–270 (2015).CAS 

Google Scholar 
34.Thapa, A. et al. The endangered red panda in Himalayas: potential distribution and ecological habitat associates. Glob. Ecol. Conser. 21, e00890 (2020).35.Hu, Y. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6, eaax5751 (2020).36.IUCN. IUCN red list of threatened species. Version 2018.1. [Online] Available: www.iucnredlist.org (August 14, 2018).37.Salesa, M. J., Peigne, S., Antón, M. & Morales, J. Evolution of the Family Ailuridae: Origins and Old- World Fossil Record. In Red Panda: Biology and Conservation of the First Panda (ed. Glatston, A. R.) 27–41 (Elsevier, 2011).Chapter 

Google Scholar 
38.Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Chaudhury, A. An overview of the status and conservation of the red panda (Ailurus fulgens) in India, with reference to its global status. Oryx 35(3), 250–259 (2001).Article 

Google Scholar 
40.Eizirik, E. et al. Pattern and timing of diversification of the mammalian order carnivora inferred from multiple nuclear gene sequences. Mol. Phylogenet. Evol. 56(1), 49–63 (2015).Article 
CAS 

Google Scholar 
41.Hu, Y. et al. Comparative genomics reveals convergent evolution between bamboo-eating giant and red pandas. Proc. Natl. Acad. Sci. 114(5), 1081–1086 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jha, A. K. Release and reintroduction of captive-bred red pandas into Singalila National Park, Darjeeling, India. In Red panda: biology and conservation of the first panda (ed. Glatson, A. R.) 435–446 (Academic Press, 2011).Chapter 

Google Scholar 
43.Wikramanayake, E., E. Terrestrial Ecoregions of the Indo-Pacific: A Conservation Assessment. Washington, D.C.: Island Press. ISBN 1-55963-923-7 (2002).44.Janzen, D. H. Why bamboos wait so long to flower. Ann. Rev. Eco. Syst. 7, 347–391 (1976).Article 

Google Scholar 
45.van Geel, B. et al. Giant deer (Megaloceros giganteus) diet from Mid-Weichselian deposits under the present North Sea inferred from molar-embedded botanical remains. J. Quat. Sci. 33, 924–933 (2018).Article 

Google Scholar 
46.Basumatary, S. K. & McDonald, H. G. Coprophilous fungi from dung of the greater one-horned Rhino in Kaziranga National Park, India and its implication to palaeoherbivory and palaeoecology. Quat. Res. 88, 14–22 (2017).Article 

Google Scholar 
47.Swati, T. et al. Multiproxy studies on dung of endangered sangai (Rucervus eldii eldii) and Hog deer (Axis porcinus) from Manipur, India: Implication for paleoherbivory and paleoecology. Rev. Palaeob. Palyn. 263, 85–103 (2019).Article 

Google Scholar 
48.Goh, T. K., Ho, W. H., Hyde, K. D., Whitton, S. R. & Umali, T. E. New records and species of Canalisporium (Hyphomycetes), with a revision of the genus. Canadian J. Bot. 76, 142–152 (1998).
Google Scholar 
49.Heudre, D., Wetzel, C. E., Moreau, L. & Ector, L. Sellaphora davoutiana sp. Nov.: a new freshwater diatom species (Sellaphoraceae, Bacillariophyta) in lakes of Northeastern France. Phytotaxa 346(3), 269–279 (2018).Article 

Google Scholar 
50.Biswas, O. et al. Can grass phytoliths and indices be relied on during vegetation and climate interpretations in the eastern Himalayas? Studies from Darjeeling and Arunachal Pradesh, India. Quat. Sci. Rev. 134, 114–132 (2016).ADS 
Article 

Google Scholar 
51.Biswas, O. et al. A comprehensive calibrated phytolith based climatic index from the Himalaya and its application in palaeotemperature reconstruction. Sci. Total Environ. 750, 142 (2021).Article 
CAS 

Google Scholar 
52.Chaudhuri, A. B. Common grasses and sedges of Kurseong, Kalimpong and Darjeeling forest divisions, West Bengal. Indian For. 86(6), 336–348 (1960).
Google Scholar 
53.Hajra, P. K. & Verma, D. M. Flora of Sikkim, Vol. II. Botanical Survey of India, (1996).54.Neto, M. A. M. & Guerra, M. P. A new method for determination of the photosynthetic pathway in grasses. Photosyn. Res. 142, 51–56 (2019).CAS 
Article 

Google Scholar 
55.Frank, K., Bruckner, A., Hilpert, A., Heethoft, M. & Bluthgen, N. Nutrient quality of vertebrate dung as a diet for dung beetles. Sci. Rep. 17, 12141 (2017).ADS 
Article 
CAS 

Google Scholar 
56.Tieszen, L. L. Natural variations in the carbon isotope values of plants: implications for archaeology, ecology, and palaeoecology. J. Archaeol. Sci. 78, 227–248 (1991).Article 

Google Scholar 
57.Heaton, T. Spatial, species, and yemporal variations in the 13C/12C ratios of C3 plants: Implications for palaeodiet studies. J. Archaeol. Sci. 26, 637–649 (1999).Article 

Google Scholar 
58.Arens, N. C., Jahren, A. H. & Amundson, R. Can C3 plants faithfully record the carbon isotopic composition of atmospheric carbon dioxide?. Paleobiology 26(1), 137–164 (2000).Article 

Google Scholar 
59.Cerling, T. E., Harris, J. M. & Leakey, M. G. Browsing and grazing in modern and fossil proboscideans. Oecologia 120, 364–374 (1999).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Mac Fadden, B. J., Cerling, T. E., Harries, J. M. & Prado, J. L. Ancient latitudinal gradients of C3/C4 grasses interpreted from stable isotopes of New World Pleistocene horse (Equus) teeth. Global Ecol. Biog. 8, 137–149 (1999).
Google Scholar 
61.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late Holocene extinctions in Madagascar. Proc. Natl Acad. Sci. U.S.A. 100(19), 10800–10805 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Davis, O. K. & Shafer, D. S. Sporormiella fungal spores, a palynological means of detecting herbivore density. Palaeog. Palaeoclim. Palaeo. 237, 40–50 (2006).ADS 
Article 

Google Scholar 
63.Raper, D. & Bush, M. A test of Sporormiella representation as a predictor of megaherbivore presence and abundance. Quat. Res. 71, 490–496 (2009).Article 

Google Scholar 
64.Perrotti, A. G. & Van Asperen, E. N. 2019: Dung fungi as a proxy for megaherbivores: opportunities and limitations for archaeological applications. Veget. Hist. Archaeobot. 28, 93–104 (2019).Article 

Google Scholar 
65.Ingold, C. T. Ballistics in certain ascomycetes. New Phytol. 60, 143–149 (1961).Article 

Google Scholar 
66.Trail, F. Fungal cannons: explosive spore discharge in the Ascomycota. FEMS Microbio. Letters 276, 12–18 (2007).CAS 
Article 

Google Scholar 
67.Yafetto, L. The fastest flights in nature: high-speed spore discharge mechanisms among fungi. PLoS ONE 3, e3237 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Erdtman, G. An introduction to Pollen Analysis (Waltham, 1953).
Google Scholar 
69.Gupta, H.P. & Sharma, C. Pollen flora of North-west Himalaya. Indian Association of Palynostratigraphers, Lucknow, India, (1986).70.Van Geel, B. Environmental reconstruction of a Roman Period settlement site in Uitgeest (The Netherlands), with special reference to coprophilous fungi. J. Archaeo. Sci. 30, 873–883 (2003).Article 

Google Scholar 
71.Van Asperen, E. N., Kirby, J. R. & Hunt, C. O. The effect of preparation methods on dung fungal spores: Implications for recognition of megafaunal populations. Rev. Palaeobot. Palynol. 229, 1–8 (2016).Article 

Google Scholar 
72.Neumann, K. International code for phytolith nomenclature ICPN 2.0. Ann. Bot. 124, 189–199 (2019).Article 

Google Scholar 
73.Hill, M. O. & Gauch, H. G. Detrended correspondence analysis, an improved ordination technique. Vegetatio 42(1), 47–58 (1980).Article 

Google Scholar 
74.Ter Braak, C. J. F. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
75.Ter Braak, C. J. F. Canoco-a FORTRAN program for canonical community ordination by (partial) (detrended) (canonical) correspondence analysis, principal components analysis and redundancy analysis (version 2.1).Technical Rep. LWA-88-02. GLW, Wageningen, 95 pp. (1988).76.Ter Braak, C. J. F. & Smilauer, P. CANOCO 4.5. Biometris. Wageningen University and Research Center, Wageningen, 500 pp. (2002).77.Agnihotri, R. et al. Radiocarbon measurements using new automated graphite preparation laboratory coupled with stable isotope mass-spectrometry at Birbal Sahni Institute of Palaeosciences, Lucknow (India). J. Environ. Radioact. 213, 106156 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

NEWS AND VIEWS
15 September 2021

A spotlight on seafood for global human nutrition

What role might seafood have in boosting human health in diets of the future? A modelling study that assesses how a rise in seafood intake by 2030 might affect human populations worldwide offers a way to begin to answer this.

Lotte Lauritzen

 ORCID: http://orcid.org/0000-0001-7184-5949

0

Lotte Lauritzen

Lotte Lauritzen is in the Department of Nutrition, Exercise and Sports, University of Copenhagen, 1958 Frederiksberg C, Denmark.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

An adequate and sustainable supply and intake of nutritious food is essential to tackle major global health issues such as dietary deficiencies. Seafood, which in this context includes fish, shellfish and marine mammals, is rich in micronutrients (such as vitamin A, iron, vitamin B12 and calcium) needed to combat the most common such deficiencies. Seafood is also the dominant source of marine omega-3 fatty acids, which have many health-promoting effects. Writing in Nature, Golden et al.1 present ambitious research that puts seafood centre stage.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-021-02436-3

References1.Golden, C. et al. Nature https://doi.org/10.1038/s41586-021-03917-1 (2021).Article 

Google Scholar 
2.Food and Agriculture Organization of the United Nations. The State of World Fisheries and Aquaculture 2020. Sustainability in Action (FAO, 2020).3.FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All (FAO, 2021).4.Kumssa, D. B. et al. Sci. Rep. 5, 10974 (2015).PubMed 
Article 

Google Scholar 
5.Mithal, A. et al. Osteoporosis Int. 20, 1807–1820 (2009).Article 

Google Scholar 
6.Vuholm, S. et al. Eur. J. Nutr. 59, 1205–1218 (2020).PubMed 
Article 

Google Scholar 
7.Gebauer, S. K., Psota, T. L., Harris, W. S. & Kris-Etherton, P. M. Am. J. Clin. Nutr. 83, 1526S–1535S (2006).PubMed 
Article 

Google Scholar 
8.Djuricic, I. & Calder, P. C. Nutrients 13, 2421 (2021).PubMed 
Article 

Google Scholar 
Download references

Competing Interests
The author declares no competing interests.

Related Articles

Read the paper: Aquatic foods to nourish nations

Transforming the global food system

How to buffer against an urban food shortage

See all News & Views

Subjects

Ecology

Environmental sciences

Latest on:

Ecology

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Preventing spillover as a key strategy against pandemics
Correspondence 14 SEP 21

Puffins and friends suffer in washing-machine waves
Research Highlight 13 SEP 21

Environmental sciences

Anthropocene: event or epoch?
Correspondence 14 SEP 21

Spacefarers, protect our planet from falling debris
Correspondence 07 SEP 21

Australian bush fires and fuel loads
Correspondence 31 AUG 21

Jobs

Tenure-Track Faculty Position

Yale School of Medicine (YSM)
New Haven, CT, United States

Postdoctoral Associate – Mucosal Immunology

The Scripps Research Institute (TSRI) – Scripps Florida
Jupiter, FL, United States

Assitant Editor, Genes & Development

Cold Spring Harbor Laboratory (CSHL)
Cold Spring Harbor, United States

Open Rank Professor in Virology

American University
Washington, DC, United States More

We first identify the location of organic crop fields in Kern County and then estimate whether status as organic versus conventional fields determines pesticide use (Fig. 5).Fig. 5: Methodology overview.Figure outlines the main method steps from identifying organic fields to creating the analysis data to performing the statistical analyses. All images shown are simplified, visual representations of the datasets. CDFA refers to the California Department of Food and Agriculture, while APN is the Assessor’s Parcel Number and TRS is the Township-Range-Section. Identifying organic fields combines the created CDFA organic APN, CDFA organic TRS, and organic pesticides data layers together to create the final organic versus conventional fields layer used in the analysis data section. All analysis data layers are then inputted into the various statistical analyses.Full size imageIdentifying organic fieldsWe identified organic fields using a combination of California Department of Food and Agriculture (CDFA) records and Kern County Agricultural Commissioner’s Office spatial data (“fields shapefiles”) and pesticide use records. No single source was complete, and as such, we evaluated several different approaches to identifying organic fields.California Department of Food and Agriculture (CDFA) recordsData on the location of organic fields, per the California State Organic Program, for 2013–2019 was obtained by request from the California Department of Food and Agriculture (CDFA). The CDFA, through the State Organic Program, requires annual registration of certified organic producers who have an expected gross sale of over $5000. We were specifically interested in the pesticide aspects of organic production, which is governed in our study region by the USDA’s National List of Allowed and Prohibited Substances. The National List of Allowed and Prohibited Substances delineates which synthetic substances can be used and which natural substances cannot be used for pest control in US organic production. Besides substances specifically (dis)allowed on the National List, allowed substances include non-synthetic biological, botanical, and mineral inputs. Field location data were in the form of either Assessor’s Parcel Number (APN) or PLS System Township-Range-Section (TRS) values, though data were reported without systematic formatting. We harmonized the CDFA APN values to merge with the Kern County Assessor’s parcel shapefile (2017), which we then spatially joined with the Kern fields shapefiles. We followed a similar process with PLSS TRS values, which were then merged with the Kern County PLS Section shapefile, and joined to Kern field shapefiles. We refer to our final organic designation as “CDFA Organic”. Details of the data cleaning process are described in the Ancillary Data Processing Methods section below.Using pesticide use reports to refine organic field identificationAfter spot-checking pesticide use on CDFA Organic fields, it became clear we had not entirely eliminated conventional fields. This was likely due to variation in polygon geometries between PLSS Sections, Kern County Assessor parcels, and Kern agricultural fields data. To further refine our classification, we used field-level pesticide use, again from the Kern County Agricultural Commissioner’s Office. As thousands of pesticide products (active ingredients + adjuvants) are in use in Kern County, we took an iterative approach to eliminate fields using conventional pesticides. We first limited the universe of pesticides to those applied to fields that were CDFA Organic. We then identified the 50 most commonly used pesticide products by a number of applications, and manually classified each as organic or conventional. Having identified these products as described below, we matched them back in, eliminating fields that used conventional products and identifying as “PUR Organic” those that used only organic products. We repeated this process, hand identifying the most commonly used products and eliminating fields using conventional products until we had isolated fields using only organic products.To classify a product as organic or conventional, we first searched for each product’s U.S. EPA-registered product label, using the exact product name and EPA product registration number. If there was any indication on the label that the product was certified as organic by the Organic Materials Review Institute (OMRI), or said “for use in organic production” or “organic”, then the pesticide was identified as organic (n = 132). If there was no organic indication on the product label, we searched the OMRI certification database for products with identical names and manufacturers, and identified products as organic if such certifications existed (n = 39). If all ingredients were defined (i.e., no inert or undefined ingredients) and were known organic active ingredients, then the pesticide was identified as organic (n = 1) (Supplementary Data 1). We failed to find EPA-registered labels for three products and confirmed on the California Department of Pesticide Regulation website that they are either inactive or out of production (EPA registration numbers: 52467-50008-AA-5905, 36208-50020-AA, 2935-48-AA-120). Each of the three was rarely used (n  0) to be the same as the mechanisms determining the amount sprayed when some pesticides are used (pesticides when pesticides  > 0). Double-hurdle models64 are an alternative to the Tobit model that allows for the separation of these two decisions.The mechanisms underlying the two decisions (to spray, how much to spray if spraying) can differ such that different covariates can describe each process, and the same covariates are allowed to influence the two processes in different ways (i.e., sign and magnitude can differ). The first, binary decision is usually modeled with a probit model.$${{{{{rm{P}}}}}}left(y=0|{{{{{bf{x}}}}}}right)=1-Phi left({{{{{bf{x}}}}}}gammaright)$$
(1)
Then, the second decision is modeled as a linear model with pesticide use following a lognormal distribution, conditional on positive pesticide use64$$log (y)|{{{{{bf{x}}}}}},y , > , 0 sim {{{{{rm{Normal}}}}}}({{{{{bf{x}}}}}}{{{{{mathbf{upbeta }}}}}},{sigma }^{2})$$
(2)
where Φ is the standard normal cdf, x is a vector of explanatory variables including organic status, y is pesticide use, and ({{{{{mathbf{upbeta }}}}}}) is a vector of coefficients. We use a lognormal hurdle model rather than a truncated normal hurdle model since pesticide use is highly non-normal, and Q-Q plots suggested substantial model improvement using a lognormal rather than normal distribution. In contrast to the panel data models described in the Ancillary Statistical Methods below, our estimation equation used natural log-transformed variables for pesticides (and field and farm size) rather than inverse hyperbolic sine (IHS) transformation since only positive observations are included in the second hurdle model. Following insights derived from our panel data models (Supplementary Notes), we build on the basic hurdle model concept using the farm-by-crop family interaction as a random intercept in both the first and second hurdle. We chose the farm-by-crop family interaction rather than a crossed random effect due to computational feasibility with thousands of permits and hundreds of crops, due to similarity of results to the within estimator model (i.e., fixed effects in causal inference terminology; Supplementary Notes, Supplementary Table 2), and due to AIC/BIC (Supplementary Table 3). Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. Thus, we proceed with the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping. In doing so, observations, where the taxonomic family of the crop was unclear, were dropped. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.Our data are effectively repeated cross-sections rather than a true panel since fields are defined by the farm-site-year combination and thus generally change year-to-year or when crops rotate. We model it as such. This implies we do not require observations to have no spray in all time periods, as would be the case in a double hurdle panel model. Linking field IDs over time through spatial processing is complicated by crop rotations of different size areas. Since farmers may farm multiple fields under different management systems, as we illustrate here, and have different contractual obligations at a sub-farm level, requiring farms to never use pesticides on all fields is not reflective of on-the-ground decisions.We repeated the lognormal hurdle models individually for carrots, grapes, oranges, potatoes, and onions, which were the only widely-grown crops with more than 100 organic fields. This allowed for a different slope and intercept by crop type.We conduct several robustness checks. First, we do not have data on crop yields. However, to assess the potential implications of a yield gap on our results, we modify our per hectare rates following Ponisio et al.15 as a robustness check. We group commodities into cereals, roots and tubers, oilseeds, legumes/pulses, fruits, and vegetables and assign them the Ponisio et al.15 yield gap estimates for that group. Crops that did not fall into any of the above groups (e.g., cannabis) were provided the all-crop average from Ponisio et al.15. Second, we analyze how conventional and organic differ with respect to soil quality using a within estimator approach to account for crop-specific differences in soil quality. Third, binary toxicity metrics, while valuable given the number of chemicals and endpoints of interest here, nevertheless fail to distinguish gradations of toxicity for chemicals above (or below) the regulatory threshold. As mentioned above, the data needed to calculate many aggregate indices (e.g., Pesticide Load57 and Environmental Impact Quotient58) are not readily available for all of the chemicals in our study. For completeness, we attempted to calculate the Pesticide Toxicity Index for one well-studied endpoint, fish. We supplemented data provided in Nowell et al.41 with data from Standartox42. However, only about 70% of the chemicals used in our study matched, and pesticide products used on organic fields were more likely to lack toxicity information for one or more chemicals. We briefly discuss the highly preliminary investigation, given the non-random missing toxicity data.All spatial analyses were performed in R Statistical Software v 3.5.3 and all statistical analyses were performed Stata 16 MP. For all tests, statistical significance was based on two-tailed tests with (alpha =0.05.)Ancillary data processing methodsCleaning parcel dataTo spatially locate organic fields, we needed to match the Assessor’s parcel numbers (APNs) provided in the CDFA tabular data to APNs in the Kern County Parcel shapefile (from 2017). Over 90% of the APN entries in the CDFA data were in the format [xxx-xxx-xx], though multiple APNs were often provided in the same cell separated by line breaks, semi-colons, commas, and/or spaces. We made initial edits separating values into individual cells in Microsoft Excel since formatting was highly inconsistent. Observations whose APNs were not in the [xxx-xxx-xx] were modified so that their format matched. In the R environment, dashes were inserted after the third, sixth, and eighth characters (1234567895 became 123-456-78-95) for APNs that did not already contain them. Occasionally, APN numbers were provided with dashes, but with segments of incorrect length (e.g., 12-34-567). In these instances, APN segments were either trimmed from the right or padded with a zero on the left so they matched the [xxx-xxx-xx] format. This approach yielded the greatest number of matches and was checked for accuracy as described below. Additional segments (from APNs with more than two dashes and eight numeric characters) were dropped. A handful of APNs with fewer than eight numeric characters and no dashes were dropped entirely.The edited CDFA APNs were then joined with the Kern County Assessor’s parcel shapefile, creating the “CDFA organic shapefile”. In total, 1637 of 1829 individual CDFA records joined successfully. To evaluate the accuracy of joins between CDFA tabular data, Kern County parcel, and Kern County agricultural spatial data, we spot-checked ownership information using “Company” (CDFA) and “PERMITTEE” (Kern County agricultural data) values.To then identify the crop fields within the organic parcels, we performed a spatial join between the CDFA organic shapefile and the Kern County fields shapefiles. Prior to performing the join, the CDFA parcels’ dimensions were reduced with a 50-m buffer to eliminate spatial joins between CDFA parcels and crop fields that were only touching the parcel margins. Of five different buffer widths evaluated, 50 m reduced the number of false positives and negatives, as determined by comparing the “Company” and “PERMITTEE” values. We refer to the fields that match as “APN Organic”.Cleaning PLSS Township-Range-Section valuesEach year several producers reported Township, Section, and Range (TRS) values, consistent with the PLS System (PLSS), rather than APN values. We used these TRS values to identify PLSS Sections that contained organic fields.We separated any cell containing multiple TRS values and removed any prefixes such as “S”, “Section”, “Sec.”, “T”, and “R” that would prevent joining to Kern County PLSS spatial data in Excel. In the R environment, we padded the left side of the “S” value with a 0 if it was a single digit, then concatenated the three columns into a “TRS” column. We joined TRS from the CDFA tabular data to PLSS spatial data, which identified 563 Sections as containing organic fields, from 2013 to 2019, out of a total of 664 unique TRS codes in the CDFA dataset. We then performed a spatial join between PLSS Sections that contain organic fields and Kern County fields shapefiles, to identify all agriculture fields that overlap with those Sections. Additional processing using the Pesticide Use Reports is described above.Ancillary statistical methodsWe began with a pooled ordinary least squares (OLS) model that, as the name suggests, pools observations over farms, years, and crop types. However, there may be attributes of crops or farms that may be systematically different between organic and conventional, and this systematic difference could bias our pooled OLS results. To address this, we first considered propensity score approaches but were unable to find a sufficient balance of our covariate distribution between organic and conventional fields. As an alternative, we limited our sample to fields with overlapping farmers and crop types. In other words, we focused on the subset of fields that are grown by farmers producing both organic and conventional fields and to crops that are produced both conventionally and organically. However, this shrunk our dataset by two-thirds.To leverage more of our data, we next considered panel data models as a means to address unobserved variables. We consider both within-estimator models (also known as “fixed effects” in causal inference terminology, but different from the biostatistical use of the term) and random effects models (with random intercepts), seeking to capture characteristics of the crop, grower, and year. The advantage of a within-estimator approach is that the omitted variables are removed (through differencing) and thus, they can be correlated with covariates without biasing the estimation. In other words, pesticide use and all covariates are differenced from their crop-specific mean (or crop family, farmer, etc. specific mean, depending on the model). In doing so, the propensity for certain crops (crop family, farmer) to be grown organic or to be fast or slow adopters of new technologies is removed. The disadvantage is that characteristics shared by all fields of a crop (e.g., value) are lost in the differencing, and more importantly, that the differencing is not easily translated to nonlinear models that we employ later in the analysis. Random effects are more easily translated to nonlinear models. The disadvantage of random effects is the strong assumption that the unobserved variables are uncorrelated with the covariates18,65, which is required for random effects coefficient estimates to be unbiased. Here, we see the difference in coefficient estimates between the within-estimator and random effects models are quite small (Supplementary Table 2).Random effects particularly crossed random effects with thousands of permits and hundreds of crops, introduce computational challenges due to large, sparse matrices. Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. We proceed using the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping based on AIC/BIC (Supplementary Table 3), computational feasibility, and similarity to the within-estimator results (Supplementary Table 2). Observations, where the taxonomic family of the crop was unclear, were dropped in any models including family in either the random effects or the cluster robust standard errors. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.In the panel data models, we used IHS transformations to accommodate highly non-normal pesticide (and field and farm size) data. IHS is very similar to natural log transformation66 but is defined at zero, which is important given a sizable fraction of our observations have zero pesticide use. As with log–log transformations, IHS–IHS transformation can be interpreted as elasticities. We pre-multiply pesticide use by 100 to improve estimation66, though this does not affect interpretation. As described above, we leverage insights on model specification from the panel data models, but rely on the double hurdle models to parse apart the decision to spray from the decision of how much to spray.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More

1.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to conservation genetics (Cambridge university press, 2002).2.Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Martínez-Freiría, F., Velo-Antón, G. & Brito, J. C. Trapped by climate: Interglacial refuge and recent population expansion in the endemic Iberian adder Vipera seoanei. Divers. Distrib. 21, 331–344 (2015).Article 

Google Scholar 
4.Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).Article 

Google Scholar 
5.Veríssimo, J. et al. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biol. J. Linn. Soc. 119, 943–959 (2016).Article 

Google Scholar 
6.Chattopadhyay, B., Garg, K. M., Gwee, C. Y., Edwards, S. V. & Rheindt, F. E. Gene flow during glacial habitat shifts facilitates character displacement in a Neotropical flycatcher radiation. BMC Evol. Biol. 17, 1–15 (2017).Article 

Google Scholar 
7.Garg, K. M., Chattopadhyay, B., Koane, B., Sam, K. & Rheindt, F. E. Last Glacial Maximum led to community-wide population expansion in a montane songbird radiation in highland Papua New Guinea. BMC Evol. Biol. 20, 82 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Goodman, S. M., Raherilalao, M. J. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description and Biota (Association Vahatra in Antananarivo, The University of Chicago Press, 2018).10.Douglass, K. The diversity of late holocene shellfish exploitation in Velondriake, Southwest Madagascar. J. Island Coast. Archaeol. 12, 333–359 (2016).11.Yoder, A. D., Campbell, C. R., Blanco, M. B., Ganzhorn, J. U. & Goodman, S. M. Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar’s forests past. PNAS 113, 8049–8056 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L., Climate change. and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Townsend, T. M., Vieites, D. R., Glaw, F. & Vences, M. Testing species-level diversification hypotheses in Madagascar: the case of microendemic Brookesia leaf Chameleons. Syst. Biol. 58, 641–656 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Brown, J. L., Cameron, A., Yoder, A. D. & Vences, M. A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar. Nat. Commun. 5, 5046 (2014).15.Schüßler, D. et al. Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in northeastern Madagascar, with the description of a new species. Am. J. Primatol. 82, e23180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chikhi, L. & Bruford, M. Mammalian population genetics and genomics. Mamm. Genome https://doi.org/10.1079/9780851999104.0539 (2005).17.Olivieri, G. L., Sousa, V., Chikhi, L. & Radespiel, U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol. Conserv. 141, 1257–1271 (2008).Article 

Google Scholar 
18.Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).19.Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).21.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 47, 555–559 (2015).22.Salmona, J., Heller, R., Lascoux, M. & Shafer, A. Inferring demographic history using genomic data. in Population Genomics 511–537 (Springer, 2017).23.Beichman, A. C., Huerta-Sanchez, E. & Lohmueller, K. E. Using genomic data to infer historic population dynamics of nonmodel organisms. Annu. Rev. Ecol. Evol. Syst. 49, 433–456 (2018).Article 

Google Scholar 
24.Sgarlata, G. M. et al. Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern Madagascar: The discovery of a putative new species? Am. J. Primatol. 81, e23070 (2019).25.Demenocal, P. et al. Abrupt onset and termination of the African humid period:: rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361 (2000).Article 

Google Scholar 
26.Tierney, J. E. & DeMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Los, S. O. et al. Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya. Quat. Sci. Rev. 218, 34–48 (2019).Article 

Google Scholar 
28.Ivory, S. J. & Russell, J. Climate, herbivory, and fire controls on tropical African forest for the last 60ka. Quat. Sci. Rev. 148, 101–114 (2016).Article 

Google Scholar 
29.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, M. Holocene changes in eastern tropical Pacific climate inferred from a Galápagos lake sediment record. Quat. Sci. Rev. 27, 1166–1180 (2008).Article 

Google Scholar 
30.Martin-Puertas, C., Tjallingii, R., Bloemsma, M. & Brauer, A. Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data. J. Quat. Sci. 32, 427–436 (2017).Article 

Google Scholar 
31.Flenley, J. R. Tropical forests under the climates of the last 30,000 years. in Potential Impacts of Climate Change on Tropical Forest Ecosystems, 37–57 (Springer, 1998).32.Burrough, S. L. & Thomas, D. S. G. Central southern Africa at the time of the African humid period: a new analysis of Holocene palaeoenvironmental and palaeoclimate data. Quat. Sci. Rev. 80, 29–46 (2013).Article 

Google Scholar 
33.Ivory, S. J. & Russell, J. Lowland forest collapse and early human impacts at the end of the African humid period at Lake Edward, equatorial East. Afr. Quat. Res. 89, 7–20 (2018).Article 

Google Scholar 
34.Anderson, A. et al. New evidence of megafaunal bone damage indicates late colonization of Madagascar. PLoS ONE 13, 1–14 (2018).
Google Scholar 
35.Hansford, J. et al. Early Holocene human presence in Madagascar evidenced by exploitation of avian megafauna. Sci. Adv. 4, eaat6925 (2018).36.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late holocene extinctions in Madagascar. Proc. Natl Acad. Sci. USA 100, 10800–10805 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Railsback, L. B. et al. Relationships between climate change, human environmental impact, and megafaunal extinction inferred from a 4000-year multi-proxy record from a stalagmite from northwestern Madagascar. Quat. Sci. Rev. 234, 106244 (2020).Article 

Google Scholar 
38.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. PNAS 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Radimilahy, C. Mahilaka: an Archaeological Investigation of an Early Town in Northwestern Madagascar. Acta Universitatis Upsaliensis (University of Uppsala, 1998).40.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet 47, 555–559 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Lapierre, M., Lambert, A. & Achaz, G. Accuracy of demographic inferences from the site frequency spectrum: the case of the yoruba population. Genetics 206, 139–449 (2017).Article 

Google Scholar 
42.Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
Article 

Google Scholar 
43.Patton, A. H. et al. Contemporary demographic reconstruction methods are robust to genome assembly quality: a case study in Tasmanian devils. Mol. Biol. Evol. 36, 2906–2921 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mazet, O., Rodríguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity 116, 362–371 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Orozco-terWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).46.Mazet, O., Rodríguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Chikhi, L. et al. The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: Insights into demographic inference and model choice. Heredity 120, 13–24 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Simons, E. L., Godfrey, L. R., Vuillaume-Randriamanantena, M., Chatrath, P. S. & Gagnon, M. Discovery of new giant subfossil lemurs in the Ankarana Mountains of Northern Madagascar. J. Hum. Evol. 19, 311–319 (1990).Article 

Google Scholar 
49.Jungers, W. L., Godfrey, L. R., Simons, E. L. & Chatrath, P. S. Subfossil Indri indri from the Ankarana Massif of northern Madagascar. Am. J. Phys. Anthropol. 97, 357–366 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Wilson, J. M., Stewart, P. D. & Fowler, S. V. Ankarana — a rediscovered nature reserve in northern Madagascar. Oryx 22, 163–171 (1988).Article 

Google Scholar 
51.Everson, K. M., Jansa, S. A., Goodman, S. M. & Olson, L. E. Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest. J. Biogeogr. 47, 2059–2072 (2020).Article 

Google Scholar 
52.Douglass, K., Hixon, S., Wright, H. T., Godfrey, L. R. & Crowley, B. E. A critical review of radiocarbon dates clarifies the human settlement of Madagascar. Quat. Sci. Rev. 221, 105878 (2019).53.Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus. Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Johnson, J. A. et al. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol. Ecol. 18, 54–63 (2009).PubMed 
PubMed Central 

Google Scholar 
55.Sommer, S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol. Ecol. 12, 2845–2851 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Craul, M. et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol. Conserv. 142, 2862–2871 (2009).Article 

Google Scholar 
57.Parga, J. A., Sauther, M. L., Cuozzo, F. P., Jacky, I. A. Y. & Lawler, R. R. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am. J. Phys. Anthropol. 147, 21–29 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. Proc. Natl Acad. Sci. USA 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Schüler, L. & Hemp, A. Atlas of pollen and spores and their parent taxa of Mt. Kilimanjaro and tropical East Africa. Quat. Int. 425, 301–386 (2016).Article 

Google Scholar 
60.Du Puy, D. J. & Moat, J. Vegetation mapping and classification in Madagascar (using GIS): implications and recommendations for the conservation of biodiversity. in Chorology, Taxonomy and Ecology of the floras of Africa and Madagascar, 97–117 (1998, in press).61.Guillaumet, J.-L., Betsch, J.-M. & Callmander, M. W. Renaud Paulian et le programme du CNRS sur les hautes montagnes à Madagascar: étage vs domaine. Zoosystema 30, 723 (2008).PubMed 
PubMed Central 

Google Scholar 
62.Weisrock, D. W. et al. Delimiting species without nuclear monophyly in Madagascar’s mouse lemurs. PLoS ONE 5, e9883 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Publ. 267, 51–63 (2006).CAS 
Article 

Google Scholar 
64.Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landforms 26, 1237–1248 (2001).Article 

Google Scholar 
65.Hogg, A. G. et al. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55, 1889–1903 (2013).CAS 
Article 

Google Scholar 
66.Rina Evasoa, M. et al. Sources of variation in social tolerance in mouse lemurs (Microcebus spp.). BMC Ecol. 19, 1–16 (2019).CAS 
Article 

Google Scholar 
67.Aleixo-Pais, I. et al. The genetic structure of a mouse lemur living in a fragmented habitat in Northern Madagascar. Conserv. Genet. 20, 229–243 (2019).Article 

Google Scholar 
68.Radespiel, U., Jurić, M. & Zimmermann, E. Sociogenetic structures, dispersal and the risk of inbreeding in a small nocturnal lemur, the golden-brown mouse lemur (Microcebus ravelobensis). Behaviour 146, 607–628 (2009).Article 

Google Scholar 
69.Radespiel, U., Ehresmann, P. & Zimmermann, E. Species-specific usage of sleeping sites in two sympatric mouse lemur species (Microcebus murinus and M. ravelobensis) in northwestern Madagascar. Am. J. Primatol. 59, 139–151 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Larsen, P. A. et al. Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus). BMC Biol. 15, 1–17 (2017).Article 
CAS 

Google Scholar 
71.Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing. Data 195, 693–702 (2013).CAS 

Google Scholar 
73.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 1–13 (2014).Article 

Google Scholar 
74.Korneliussen, T. S. & Moltke, I. Sequence analysis NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-Statistic on low-coverage whole-genome data. G3 8, 551–566 (2017).PubMed Central 
Article 

Google Scholar 
76.Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L. Climate change and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Schneider, N., Chikhi, L., Currat, M. & Radespiel, U. Signals of recent spatial expansions in the grey mouse lemur (Microcebus murinus). BMC Evol. Biol. 10, 105 (2010).81.Radespiel, U., Lutermann, H., Schmelting, B. & Zimmermann, E. An empirical estimate of the generation time of mouse lemurs. Am. J. Primatol. 81, 1–8 (2019).Article 

Google Scholar 
82.Hawkins, M. T. R. et al. Genome sequence and population declines in the critically endangered greater bamboo lemur (Prolemur simus) and implications for conservation. BMC Genomics 19, 1–15 (2018).Article 
CAS 

Google Scholar 
83.Poelstra, J. et al. Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent. Syst. Biol. https://doi.org/10.1093/sysbio/syaa053 (2020).84.Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127.2, 233–244 (2021).Article 

Google Scholar 
85.Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Fredsted, T., Pertoldi, C., Schierup, M. H. & Kappeler, P. M. Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Mol. Ecol. 14, 2363–2372 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Radespiel, U., Schulte, J., Burke, R. J. & Lehman, S. M. Molecular edge effects in the endangered golden-brown mouse lemur Microcebus ravelobensis. Oryx 53, 716–726 (2019).Article 

Google Scholar 
88.Radespiel, U., Lutermann, H., Schmelting, B., Bruford, M. W. & Zimmermann, E. Patterns and dynamics of sex-biased dispersal in a nocturnal primate, the grey mouse lemur, Microcebus murinus. Anim. Behav. 65, 709–719 (2003).Article 

Google Scholar 
89.Radespiel, U., Rakotondravony, R. & Chikhi, L. Natural and anthropogenic determinants of genetic structure in the largest remaining population of the endangered golden-brown mouse lemur, Microcebus ravelobensis. Am. J. Primatol. 70, 860–870 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Schliehe-Diecks, S., Eberle, M. & Kappeler, P. M. Walk the line-dispersal movements of gray mouse lemurs (Microcebus murinus). Behav. Ecol. Sociobiol. 66, 1175–1185 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).92.Beerli, P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13, 827–836 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).Article 

Google Scholar 
94.Bagley, R. K., Sousa, V. C., Niemiller, M. L. & Linnen, C. R. History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei). Mol. Ecol. 26, 1022–1044 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Milner AM, Khamis K, Battin TJ, Brittain JE, Barrand NE, Füreder L, et al. Glacier shrinkage driving global changes in downstream systems. Proc Nat Acad Sci USA. 2017;114:9770.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Battin TJ, Wille A, Sattler B, Psenner R. Phylogenetic and functional heterogeneity of sediment biofilms along environmental gradients in a glacial stream. Appl Environ Microbiol. 2001;67:799–807.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Wilhelm L, Singer GA, Fasching C, Battin TJ, Besemer K. Microbial biodiversity in glacier-fed streams. ISME J. 2013;7:1651.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Ren Z, Gao H, Elser JJ, Zhao Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci Rep. 2017;7:12668.PubMed 
PubMed Central 

Google Scholar 
5.Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Nat Acad Sci USA. 2015;112:1326.
Google Scholar 
6.Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
PubMed Central 

Google Scholar 
7.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.8.Allen R, Hoffmann LJ, Larcombe MJ, Louisson Z, Summerfield TC. Homogeneous environmental selection dominates microbial community assembly in the oligotrophic South Pacific Gyre. Mol Ecol. 2020;29:4680–91.CAS 
PubMed 

Google Scholar 
9.Li Y, Gao Y, Zhang W, Wang C, Wang P, Niu L, et al. Homogeneous selection dominates the microbial community assembly in the sediment of the Three Gorges Reservoir. Sci Tot Environ. 2019;690:50–60.CAS 

Google Scholar 
10.Zhang K, Shi Y, Cui X, Yue P, Li K, Liu X, et al. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems. 2019;4:e00225–18.11.Thrash CJ, Temperton B, Swan BK, Landry ZC, Woyke T, DeLong EF, et al. Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. 2014;8:1440–51.PubMed 

Google Scholar 
12.Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science. 2008;320:1081.CAS 
PubMed 

Google Scholar 
13.Kent AG, Baer SE, Mouginot C, Huang JS, Larkin AA, Lomas MW, et al. Parallel phylogeography of Prochlorococcus and Synechococcus. ISME J. 2019;13:430–41.PubMed 

Google Scholar 
14.Brown MV, Furham JA. Marine bacterial microdiversity as revealed by internal transcribed spacer analysis. Aquat Microb Ecol. 2005;41:15–23.
Google Scholar 
15.Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Yung C-M, Vereen MK, Herbert A, Davis KM, Yang J, Kantorowska A, et al. Thermally adaptive tradeoffs in closely related marine bacterial strains. Environ Microbiol. 2015;17:2421–9.PubMed 

Google Scholar 
17.Props R, Denef VJ. Temperature and nutrient levels correspond with lineage-specific microdiversification in the ubiquitous and abundant freshwater genus. Limnohabitans Appl Environ Microbiol. 2020;86:e00140–00120.CAS 
PubMed 

Google Scholar 
18.Chase AB, Karaoz U, Brodie EL, Gomez-Lunar Z, Martiny AC, Martiny JBH. Microdiversity of an abundant terrestrial bacterium encompasses extensive variation in ecologically relevant traits. mBio. 2017;8:e01809–17.19.Choudoir MJ, Buckley DH. Phylogenetic conservatism of thermal traits explains dispersal limitation and genomic differentiation of Streptomyces sister-taxa. ISME J. 2018;12:2176–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.CAS 
PubMed 

Google Scholar 
21.Cohan FM, Koeppel AF. The origins of ecological diversity in prokaryotes. Curr Biol. 2008;18:R1024–34.CAS 
PubMed 

Google Scholar 
22.Larkin AA, Martiny AC. Microdiversity shapes the traits, niche space, and biogeography of microbial taxa. Environ Microbiol Rep. 2017;9:55–70.CAS 
PubMed 

Google Scholar 
23.Fodelianakis S, Lorz A, Valenzuela-Cuevas A, Barozzi A, Booth JM, Daffonchio D. Dispersal homogenizes communities via immigration even at low rates in a simplified synthetic bacterial metacommunity. Nat Commun. 2019;10:1314.PubMed 
PubMed Central 

Google Scholar 
24.Duarte CM, Røstad A, Michoud G, Barozzi A, Merlino G, Delgado-Huertas A, et al. Discovery of Afifi, the shallowest and southernmost brine pool reported in the Red Sea. Sci Rep. 2020;10:910.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Kohler TJ, Peter H, Fodelianakis S, Pramateftaki P, Styllas M, Tolosano M, et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front Microbiol. 2020;11:2922.
Google Scholar 
26.Amalfitano S, Fazi S. Recovery and quantification of bacterial cells associated with streambed sediments. J Microbiol Methods. 2008;75:237–43.CAS 
PubMed 

Google Scholar 
27.Hammes F, Berney M, Wang Y, Vital M, Köster O, Egli T. Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 2008;42:269–77.CAS 
PubMed 

Google Scholar 
28.Busi SB, Pramateftaki P, Brandani J, Fodelianakis S, Peter H, Halder R, et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ. 2020;8:e9973.PubMed 
PubMed Central 

Google Scholar 
29.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

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

Google Scholar 
31.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotech. 2019;37:852–7.CAS 

Google Scholar 
32.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Meth. 2016;13:581–3.CAS 

Google Scholar 
33.Props R, Kerckhof F-M, Rubbens P, De Vrieze J, Hernandez-Sanabria E, Waegeman W, et al. Absolute quantification of microbial taxon abundances. ISME J. 2017;11:584–7.PubMed 

Google Scholar 
34.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 

Google Scholar 
35.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Singer E, Bushnell B, Coleman-Derr D, Bowman B, Bowers RM, Levy A, et al. High-resolution phylogenetic microbial community profiling. ISME J. 2016;10:2020–32.PubMed 
PubMed Central 

Google Scholar 
37.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539–9.PubMed 
PubMed Central 

Google Scholar 
40.Foster ZSL, Sharpton TJ, Grünwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLOS Comput Biol. 2017;13:e1005404.PubMed 
PubMed Central 

Google Scholar 
41.R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.42.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan.43.Fodelianakis S, Moustakas A, Papageorgiou N, Manoli O, Tsikopoulou I, Michoud G, et al. Modified niche optima and breadths explain the historical contingency of bacterial community responses to eutrophication in coastal sediments. Mol Ecol. 2017;26:2006–18.CAS 
PubMed 

Google Scholar 
44.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 

Google Scholar 
45.Washburne AD, Silverman JD, Leff JW, Bennett DJ, Darcy JL, Mukherjee S, et al. Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets. PeerJ. 2017;5:e2969.PubMed 
PubMed Central 

Google Scholar 
46.Washburne AD, Silverman JD, Morton JT, Becker DJ, Crowley D, Mukherjee S, et al. Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data. Ecol Monogr. 2019;89:e01353.
Google Scholar 
47.Gawor J, Grzesiak J, Sasin-Kurowska J, Borsuk P, Gromadka R, Górniak D, et al. Evidence of adaptation, niche separation and microevolution within the genus Polaromonas on Arctic and Antarctic glacial surfaces. Extremophiles. 2016;20:403–13.PubMed 
PubMed Central 

Google Scholar 
48.Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10:333–45.CAS 
PubMed 

Google Scholar 
49.Tromas N, Taranu ZE, Castelli M, Pimentel JSM, Pereira DA, Marcoz R, et al. The evolution of realized niches within freshwater. Synechococcus Environ Microbiol. 2020;22:1238–50.PubMed 

Google Scholar 
50.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 

Google Scholar 
51.Cerqueira T, Barroso C, Froufe H, Egas C, Bettencourt R. Metagenomic signatures of microbial communities in deep-sea hydrothermal sediments of Azores Vent Fields. Microb Ecol. 2018;76:387–403.CAS 
PubMed 

Google Scholar 
52.Osburn MR, LaRowe DE, Momper LM, Amend JP. Chemolithotrophy in the continental deep subsurface: Sanford underground research facility (SURF), USA. Front Microbiol. 2014;5:610.53.Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice-covered Lakes. Environ Microbiol. 2018;20:2568–84.CAS 
PubMed 

Google Scholar 
54.Vick-Majors TJ, Priscu JC, Amaral-Zettler LA. Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered Antarctic lakes. ISME J. 2014;8:778–89.CAS 
PubMed 

Google Scholar 
55.Darcy JL, Lynch RC, King AJ, Robeson MS, Schmidt SK. Global distribution of Polaromonas phylotypes – evidence for a highly successful dispersal capacity. PloS ONE. 2011;6:e23742.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Smith HJ, Foreman CM, Ramaraj T. Draft genome sequence of a metabolically diverse Antarctic supraglacial stream organism, Polaromonas sp. strain CG9_12, determined using Pacific Biosciences single-molecule real-time sequencing technology. Genome Announc. 2014;2:e01242–01214.PubMed 
PubMed Central 

Google Scholar 
57.Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Liu Q, Zhou Y-G, Xin Y-H. High diversity and distinctive community structure of bacteria on glaciers in China revealed by 454 pyrosequencing. Syst Appl Microbiol. 2015;38:578–85.PubMed 

Google Scholar 
59.Kalyuzhnaya MG, Bowerman S, Lara JC, Lidstrom ME, Chistoserdova L. Methylotenera mobilis gen. nov., sp. nov., an obligately methylamine-utilizing bacterium within the family Methylophilaceae. Int J Syst Evol Microbiol. 2006;56:2819–23.CAS 
PubMed 

Google Scholar 
60.Kane SR, Chakicherla AY, Chain PSG, Schmidt R, Shin MW, Legler TC, et al. Whole-genome analysis of the methyl tert-butyl ether-degrading Beta-Proteobacterium Methylibium petroleiphilum PM1. J Bacteriol. 2007;189:1931.CAS 
PubMed 

Google Scholar 
61.Martineau C, Mauffrey F, Villemur R, Müller V. Comparative analysis of denitrifying activities of Hyphomicrobium nitrativorans, Hyphomicrobium denitrificans, and Hyphomicrobium zavarzinii. Appl Environ Microbiol. 2015;81:5003–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Dieser M, Broemsen ELJE, Cameron KA, King GM, Achberger A, Choquette K, et al. Molecular and biogeochemical evidence for methane cycling beneath the western margin of the Greenland Ice Sheet. ISME J. 2014;8:2305–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Michaud AB, Dore JE, Achberger AM, Christner BC, Mitchell AC, Skidmore ML, et al. Microbial oxidation as a methane sink beneath the West Antarctic Ice Sheet. Nat Geosci. 2017;10:582–6.CAS 

Google Scholar 
64.Bendall ML, Stevens SLR, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.PubMed 
PubMed Central 

Google Scholar 
65.Baker JM, Riester CJ, Skinner BM, Newell AW, Swingley WD, Madigan MT, et al. Genome sequence of Rhodoferax antarcticus ANT.BRT; a psychrophilic purple nonsulfur bacterium from an Antarctic microbial mat. Microorganisms. 2017;5:8.66.Crisafi F, Giuliano L, Yakimov MM, Azzaro M, Denaro R. Isolation and degradation potential of a cold-adapted oil/PAH-degrading marine bacterial consortium from Kongsfjorden (Arctic region). Rendiconti Lincei. 2016;27:261–70.
Google Scholar 
67.Zhong Z-P, Solonenko NE, Gazitúa MC, Kenny DV, Mosley-Thompson E, Rich VI, et al. Clean low-biomass procedures and their application to ancient ice core microorganisms. Front Microbiol. 2018;9:1094.68.Bai Y, Huang X, Zhou X, Xiang Q, Zhao K, Yu X, et al. Variation in denitrifying bacterial communities along a primary succession in the Hailuogou Glacier retreat area, China. PeerJ. 2019;7:e7356.PubMed 
PubMed Central 

Google Scholar 
69.Garcia-Lopez E, Rodriguez-Lorente I, Alcazar P, Cid C. Microbial communities in coastal glaciers and tidewater tongues of Svalbard archipelago, Norway. Front Mar Sci. 2019;5:512.70.Liu S, Wang H, Chen L, Wang J, Zheng M, Liu S, et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 2020;14:2488–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Harrold ZR, Skidmore ML, Hamilton TL, Desch L, Amada K, van Gelder W, et al. Aerobic and anaerobic thiosulfate oxidation by a cold-adapted, subglacial chemoautotroph. Appl Environ Microbiol. 2016;82:1486–95.CAS 
PubMed Central 

Google Scholar 
72.Franzetti A, Pittino F, Gandolfi I, Azzoni RS, Diolaiuti G, Smiraglia C, et al. Early ecological succession patterns of bacterial, fungal and plant communities along a chronosequence in a recently deglaciated area of the Italian Alps. FEMS Microbiol Ecol. 2020;96:10.73.Kohler TJ, Van Horn DJ, Darling JP, Takacs-Vesbach CD, McKnight DM. Nutrient treatments alter microbial mat colonization in two glacial meltwater streams from the McMurdo Dry Valleys, Antarctica. FEMS Microbiol Ecol. 2016;92:4.
Google Scholar 
74.Sawayama M, Suzuki T, Hashimoto H, Kasai T, Furutani M, Miyata N, et al. Isolation of a Leptothrix strain, OUMS1, from ocherous deposits in groundwater. Cur Microbiol. 2011;63:173–80.CAS 

Google Scholar 
75.Li Y, Cha Q-Q, Dang Y-R, Chen X-L, Wang M, McMinn A, et al. Reconstruction of the functional ecosystem in the high light, low temperature union glacier region, Antarctica. Front Microbiol. 2019;10.76.Cauvy-Fraunié S, Dangles O. A global synthesis of biodiversity responses to glacier retreat. Nat Ecol Evol. 2019;3:1675–85.PubMed 

Google Scholar 
77.Jorquera MA, Graether SP, Maruyama F. Editorial: bioprospecting and biotechnology of extremophiles. Front Bioeng Biotech. 2019;7:204.
Google Scholar 
78.Thompson JR, Pacocha S, Pharino C, Klepac-Ceraj V, Hunt DE, Benoit J, et al. Genotypic diversity within a natural coastal bacterioplankton population. Science. 2005;307:1311.CAS 
PubMed 

Google Scholar 
79.Chase AB, Gomez-Lunar Z, Lopez AE, Li J, Allison SD, Martiny AC, et al. Emergence of soil bacterial ecotypes along a climate gradient. Environ Microbiol. 2018;11:4112–26.
Google Scholar 
80.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 

Google Scholar 
81.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
PubMed Central 

Google Scholar 
82.Garcia-Garcia N, Tamames J, Linz AM, Pedros-Alio C, Puente-Sanchez F. Microdiversity ensures the maintenance of functional microbial communities under changing environmental conditions. ISME J. 2019;13:2969–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Becraft ED, Wood JM, Rusch DB, Kühl M, Jensen SI, Bryant DA, et al. The molecular dimension of microbial species: 1. Ecological distinctions among, and homogeneity within, putative ecotypes of Synechococcus inhabiting the cyanobacterial mat of Mushroom Spring, Yellowstone National Park. Front Microbiol. 2015;6:590.PubMed 
PubMed Central 

Google Scholar 
84.Becraft ED, Cohan FM, Kühl M, Jensen SI, Ward DM. Fine-scale distribution patterns of Synechococcus ecological diversity in microbial mats of Mushroom Spring, Yellowstone National Park. Appl Environ Microbiol. 2011;77:7689–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Koeppel A, Perry EB, Sikorski J, Krizanc D, Warner A, Ward DM, et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc Nat Acad Sci USA. 2008;105:2504.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.88.Ning D, Deng Y, Tiedje JM, Zhou J. A general framework for quantitatively assessing ecological stochasticity. Proc Nat Acad Sci USA. 2019;116:16892–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Zhou J, Deng Y, Zhang P, Xue K, Liang Y, Van Nostrand JD, et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc Nat Acad Sci USA. 2014;111:E836–45.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 

Google Scholar 
91.Ning D, Yuan M, Wu L, Zhang Y, Guo X, Zhou X, et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat Commun. 2020;11:4717.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Cohan FM. Systematics: the cohesive nature of bacterial species taxa. Curr Biol. 2019;29:169–72.
Google Scholar 
93.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

Google Scholar 
94.Callahan BJ, Grinevich D, Thakur S, Balamotis MA, Yehezkel TB. Ultra-accurate microbial amplicon sequencing with synthetic long reads. Microbiome. 2021;9:130.PubMed 
PubMed Central 

Google Scholar 
95.Matsuo Y, Komiya S, Yasumizu Y, Yasuoka Y, Mizushima K, Takagi T, et al. Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION™ nanopore sequencing confers species-level resolution. BMC Microbiol. 2021;21:35.CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Sci Rep. 2020;10:3209.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Suttle, C. A. Marine viruses — major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Rohwer, F. & Thurber, R. V. Viruses manipulate the marine environment. Nature 459, 207–212 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Simmonds, P. et al. Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Fuhrman, J. A. Marine viruses and their biogeochemical and ecological effects. Nature 399, 541–548 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Suttle, C. A. Viruses in the sea. Nature 437, 356–361 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Jiang, S., Steward, G., Jellison, R., Chu, W. & Choi, S. Abundance, distribution, and diversity of viruses in alkaline, hypersaline Mono Lake, California. Microb. Ecol. 47, 9–17 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Cai, L. et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 13, 1857–1864 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Wei, M. & Xu, K. New insights into the virus-to-prokaryote ratio (VPR) in marine sediments. Front. Microbiol. 11, 1102 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Wilhelm, S. W. & Suttle, C. A. Viruses and nutrient cycles in the sea. BioScience 49, 781–788 (1999).Article 

Google Scholar 
11.Brussaard, C. P. D. et al. Global-scale processes with a nanoscale drive: the role of marine viruses. ISME J. 2, 575–578 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Nee, S. & Maynard Smith, J. The evolutionary biology of molecular parasites. Parasitology 100, S5–S18 (1990).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hambly, E. & Suttle, C. A. The viriosphere, diversity, and genetic exchange within phage communities. Curr. Opin. Microbiol. 8, 444–450 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, M. B. et al. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 4, e234 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Holmes, E. C. What does virus evolution tell us about virus origins? J. Virol. 85, 5247–5251 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Wolf, Y. I. et al. Origins and evolution of the global RNA virome. mBio 9, e02329-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Kuhn, J. H. et al. Classify viruses-the gain is worth the pain. Nature 566, 318–320 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Record, N. R., Talmy, D. & Våge, S. Quantifying tradeoffs for marine viruses. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00251 (2016). Investigates trade-offs in phenotypes of marine viruses that may influence virus population dynamics and biogeography.Article 

Google Scholar 
20.Domingo, E. et al. Basic concepts in RNA virus evolution. FASEB J. 10, 859–864 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Solé, R. V., Ferrer, R., González-García, I., Quer, J. & Domingo, E. Red queen dynamics, competition and critical points in a model of RNA virus quasispecies. J. Theor. Biol. 198, 47–59 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Stern, A. & Sorek, R. The phage-host arms race: shaping the evolution of microbes. Bioessays 33, 43–51 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Daugherty, M. D. & Malik, H. S. Rules of engagement: molecular insights from host-virus arms races. Annu. Rev. Genet. 46, 677–700 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Tegally, H. et al. Sixteen novel lineages of SARS-CoV-2 in South Africa. Nat. Med. 27, 440–446 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Lederberg, J. in Emerging Viruses (ed. Morse, S. S.) 3–9 (Oxford University Press, 1993).26.Baltimore, D. Expression of animal virus genomes. Microbiol. Mol. Biol. Rev. 35, 235–241 (1971).CAS 

Google Scholar 
27.Coutinho, F. H., Edwards, R. A. & Rodríguez-Valera, F. Charting the diversity of uncultured viruses of archaea and bacteria. BMC Biol. 17, 109 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.King, A. M. Q., Adams, M. J., Carstens, E. B. & Lefkowitz, E. J. (eds) Virus Taxonomy. 163–173 (Elsevier, 2012).29.Forterre, P. The virocell concept and environmental microbiology. ISME J. 7, 233–236 (2013). Among the first reports articulating the viewpoint that infected cells undergoing active virus replication should be recognized as the ‘living form’ of a virus known as a virocell.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Lowen, A. C. Constraints, drivers, and implications of influenza A virus reassortment. Annu. Rev. Virol. 4, 105–121 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Mahner, M. & Kary, M. What exactly are genomes, genotypes and phenotypes? And what about phenomes? J. Theor. Biol. 186, 55–63 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Edwards, K. F. & Steward, G. F. Host traits drive viral life histories across phytoplankton viruses. Am. Nat. 191, 566–581 (2018). Examines the inter-relationships between virus traits and their consequences for population dynamics and the evolution of burst size.PubMed 
Article 
PubMed Central 

Google Scholar 
33.Flint, S. J., Racaniello, V. R., Rall, G. F., Skalka, A. M. & Enquist, L. W. Principles of Virology 4th Edn (Wiley, 2015).34.Ghabrial, S. A., Castón, J. R., Jiang, D., Nibert, M. L. & Suzuki, N. 50-plus years of fungal viruses. Virology 479–480, 356–368 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Dunigan, D. D. et al. Chloroviruses lure hosts through long-distance chemical signaling. J. Virol. 93, e01688-18 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Anantharaman, K. et al. Sulfur oxidation genes in diverse deep-sea viruses. Science 344, 757–760 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Mann, N. H., Cook, A., Millard, A., Bailey, S. & Clokie, M. Bacterial photosynthesis genes in a virus. Nature 424, 741 (2003). Shows how the virus genome interacts with the host to facilitate virus reproduction.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Mavrich, T. N. & Hatfull, G. F. Evolution of superinfection immunity in cluster A mycobacteriophages. mBio 10, e00971-19 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Marine, R. L., Nasko, D. J., Wray, J., Polson, S. W. & Wommack, K. E. Novel chaperonins are prevalent in the virioplankton and demonstrate links to viral biology and ecology. ISME J. 11, 2479–2491 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
40.ICTV. Virus Taxonomy: The ICTV Report on Virus Classification and Taxon Nomenclature. https://talk.ictvonline.org/ictv-reports/ictv_9th_report/ (2019).41.Ojosnegros, S. et al. Viral genome segmentation can result from a trade-off between genetic content and particle stability. PLoS Genet 7, e1001344 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Belshaw, R., Pybus, O. G. & Rambaut, A. The evolution of genome compression and genomic novelty in RNA viruses. Genome Res. 17, 1496–1504 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Van Etten, J. L., Agarkova, I. V. & Dunigan, D. D. Chloroviruses. Viruses 12, 20 (2020).Article 
CAS 

Google Scholar 
44.Iranzo, J. & Manrubia, S. C. Evolutionary dynamics of genome segmentation in multipartite viruses. Proc. Biol. Sci. 279, 3812–3819 (2012).PubMed 
PubMed Central 

Google Scholar 
45.Kellogg, C. A. & Paul, J. H. Degree of ultraviolet radiation damage and repair capabilities are related to G+C content in marine vibriophages. Aquat. Microb. Ecol. 27, 13–20 (2002).Article 

Google Scholar 
46.Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
47.Edwards, K. F., Steward, G. F. & Schvarcz, C. R. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol. Lett. 24, 363–373 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Bonachela, J. A. & Levin, S. A. Evolutionary comparison between viral lysis rate and latent period. J. Theor. Biol. 345, 32–42 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Yashchenko, V. V., Gavrilova, O. V., Rautian, M. S. & Jakobsen, K. S. Association of Paramecium bursaria Chlorella viruses with Paramecium bursaria cells: ultrastructural studies. Eur. J. Protistol. 48, 149–159 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
50.DeLong, J. P., Al-Ameeli, Z., Duncan, G., Van Etten, J. L. & Dunigan, D. D. Predators catalyze an increase in chloroviruses by foraging on the symbiotic hosts of zoochlorellae. Proc. Natl Acad. Sci. USA 113, 13780–13784 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Wang, I.-N. Lysis timing and bacteriophage fitness. Genetics 172, 17–26 (2006).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
53.You, L., Suthers, P. F. & Yin, J. Effects of Escherichia coli physiology on growth of phage T7 In vivo and in silico. J. Bacteriol. 184, 1888–1894 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA 110, 11463–11468 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Hellweger, F. L. Carrying photosynthesis genes increases ecological fitness of cyanophage in silico. Environ. Microbiol. 11, 1386–1394 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Schenk, H. & Sieber, M. Bacteriophage can promote the emergence of physiologically sub-optimal host phenotypes. bioRxiv https://doi.org/10.1101/621524 (2019).Article 

Google Scholar 
57.Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Zimmerman, A. E. et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat. Rev. Microbiol. 18, 21–34 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Grove, J. & Marsh, M. The cell biology of receptor-mediated virus entry. J. Cell Biol. 195, 1071–1082 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.McFadden, G., Mohamed, M. R., Rahman, M. M. & Bartee, E. Cytokine determinants of viral tropism. Nat. Rev. Immunol. 9, 645–655 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Bernheim, A. & Sorek, R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat. Rev. Microbiol. 18, 113–119 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Nussenzweig, P. M. & Marraffini, L. A. Molecular mechanisms of CRISPR-Cas immunity in bacteria. Annu. Rev. Genet. 54, 93–120 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Hampton, H. G., Watson, B. N. J. & Fineran, P. C. The arms race between bacteria and their phage foes. Nature 577, 327–336 (2020). An overview of the mechanisms and phenotypes related to phage infection and host defence mechanisms.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Samson, J. E., Magadán, A. H., Sabri, M. & Moineau, S. Revenge of the phages: defeating bacterial defences. Nat. Rev. Microbiol. 11, 675–687 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011). Demonstrates the role of virus host range in generating community-wide patterns of host–phage interactions.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Regoes, R. R. & Bonhoeffer, S. The HIV coreceptor switch: a population dynamical perspective. Trends Microbiol. 13, 269–277 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Atkinson, D., Ciotti, B. J. & Montagnes, D. J. Protists decrease in size linearly with temperature: ca. 2.5% C-1. Proc. R. Soc. Lond. B 270, 2605–2611 (2003).Article 

Google Scholar 
68.Falkowski, P. G. in Primary Productivity in the Sea (ed. Falkowski, P. G.) 99–119 (Springer, 1980).69.Salsbery, M. E. & DeLong, J. P. The benefit of algae endosymbionts in Paramecium bursariais temperature dependent. Evol. Ecol. Res. 19, 669–678 (2018).
Google Scholar 
70.Kimmance, S. A., Atkinson, D. & Montagnes, D. J. S. Do temperature–food interactions matter? Responses of production and its components in the model heterotrophic flagellate Oxyrrhis marina. Aquat. Microb. Ecol. 42, 63–73 (2006).Article 

Google Scholar 
71.Maat, D. S., van Bleijswijk, J. D. L., Witte, H. J. & Brussaard, C. P. D. Virus production in phosphorus-limited Micromonas pusilla stimulated by a supply of naturally low concentrations of different phosphorus sources, far into the lytic cycle. FEMS Microbiol. Ecol. 92, fiw136 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
72.Amla, D. V., Rowell, P. & Stewart, W. D. P. Metabolic changes associated with cyanophage N-1 infection of the cyanobacterium Nostoc muscorum. Arch. Microbiol. 148, 321–327 (1987).CAS 
Article 

Google Scholar 
73.Hadas, H., Einav, M., Fishov, I. & Zaritsky, A. Bacteriophage T4 development depends on the physiology of its host Escherichia coli. Microbiology 143, 179–185 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Demory, D. et al. Temperature is a key factor in Micromonas–virus interactions. ISME J. 11, 601–612 (2017). Shows the effect of temperature on the kinetics, phenotypes and life history strategies of prasinoviruses.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Schachtele, C. F., Oman, R. W. & Anderson, D. L. Effect of elevated temperature on deoxyribonucleic acid synthesis in bacteriophage φ29-infected Bacillus amyloliquefaciens. J. Virol. 6, 430–437 (1970).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Choua, M., Heath, M. R., Speirs, D. C. & Bonachela, J. A. The effect of viral plasticity on the persistence of host-virus systems. J. Theor. Biol. 498, 110263 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Ni, T. & Zeng, Q. Diel infection of cyanobacteria by cyanophages. Front. Mar. Sci. https://doi.org/10.3389/fmars.2015.00123 (2016).Article 

Google Scholar 
78.Sakowski, E. G. et al. Ribonucleotide reductases reveal novel viral diversity and predict biological and ecological features of unknown marine viruses. Proc. Natl Acad. Sci. USA 111, 15786–15791 (2014). Demonstrates that genomic features in the viral replicon (that is, module of genes responsible for viral genome replication) may predict the biogeographical distribution of viruses.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Reeson, A. F. et al. Effects of phenotypic plasticity on pathogen transmission in the field in a Lepidoptera-NPV system. Oecologia 124, 373–380 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Stearns, S. C. The evolutionary significance of phenotypic plasticity. BioScience 39, 436–445 (1989).Article 

Google Scholar 
81.Leggett, H. C., Benmayor, R., Hodgson, D. J. & Buckling, A. Experimental evolution of adaptive phenotypic plasticity in a parasite. Curr. Biol. 23, 139–142 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Oppenheim, A. B., Kobiler, O., Stavans, J., Court, D. L. & Adhya, S. Switches in bacteriophage lambda development. Annu. Rev. Genet. 39, 409–429 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Erez, Z. et al. Communication between viruses guides lysis–lysogeny decisions. Nature 541, 488–493 (2017). Demonstrates the use of communication peptides that determine lysogeny in temperate phages.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Weitz, J. S., Li, G., Gulbudak, H., Cortez, M. H. & Whitaker, R. J. Viral invasion fitness across a continuum from lysis to latency. Virus Evol. 5, vez006 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Labonté, J. M. et al. Single cell genomics indicates horizontal gene transfer and viral infections in a deep subsurface Firmicutes population. Front. Microbiol. 6, 349 (2015).PubMed 
PubMed Central 

Google Scholar 
86.Koskella, B. & Brockhurst, M. A. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916–931 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Marston, M. F. et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc. Natl Acad. Sci. USA 109, 4544–4549 (2012). Demonstrates the rapid co-evolution of virus and host but highlights the challenge of identifying the critical phenotypes mediating the interaction.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Frickel, J., Feulner, P. G. D., Karakoc, E. & Becks, L. Population size changes and selection drive patterns of parallel evolution in a host–virus system. Nat. Commun. 9, 1706 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
90.Knowles, B. et al. Temperate infection in a virus–host system previously known for virulent dynamics. Nat. Commun. 11, 4626 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Wang, I.-N., Dykhuizen, D. E. & Slobodkin, L. B. The evolution of phage lysis timing. Evol. Ecol. 10, 545–558 (1996).Article 

Google Scholar 
92.Abedon, S. T., Hyman, P. & Thomas, C. Experimental examination of bacteriophage latent-period evolution as a response to bacterial availability. Appl. Environ. Microbiol. 69, 7499–7506 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Palkovacs, E. P. & Hendry, A. P. Eco-evolutionary dynamics: intertwining ecological and evolutionary processes in contemporary time. F1000 Biol. Rep. 2, 1 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
94.Brown, C. M., Lawrence, J. E. & Campbell, D. A. Are phytoplankton population density maxima predictable through analysis of host and viral genomic DNA content? J. Mar. Biol. Assoc. UK 86, 491–498 (2006).CAS 
Article 

Google Scholar 
95.Wommack, K. E. & Colwell, R. R. Virioplankton: viruses in aquatic ecosystems. Microbiol. Mol. Biol. Rev. 64, 69–114 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Weitz, J. S. et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 9, 1352–1364 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
97.Poorvin, L., Rinta-Kanto, J. M., Hutchins, D. A. & Wilhelm, S. W. Viral release of iron and its bioavailability to marine plankton. Limnol. Oceanogr. 49, 1734–1741 (2004).CAS 
Article 

Google Scholar 
98.Shelford, E. J., Middelboe, M., Møller, E. F. & Suttle, C. A. Virus-driven nitrogen cycling enhances phytoplankton growth. Aquat. Microb. Ecol. 66, 41–46 (2012).Article 

Google Scholar 
99.Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
101.Dawkins, R. The Extended Phenotype: The Long Reach of the Gene (Oxford University Press, 1999).102.Dawkins, R. Extended phenotype–but not too extended. A reply to Laland, Turner and Jablonka. Biol. Philosophy 19, 377–396 (2004).Article 

Google Scholar 
103.Ogata, H. Habitat alterations by viruses: strategies by Tupanviruses and others. Microbes Environ. 33, 117–119 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
104.Abrahão, J. et al. Tailed giant Tupanvirus possesses the most complete translational apparatus of the known virosphere. Nat. Commun. 9, 749 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
105.Clark, H. F. & Wiktor, T. J. Plasticity of phenotypic characters of rabies-related viroses: spontaneous variation in the plaque morphology, virulence, and temperature-sensitivity characters of serially propagated Lagos bat and Mokola viruses. J. Infect. Dis. 130, 608–618 (1974).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Abedon, S. T. & Culler, R. R. Optimizing bacteriophage plaque fecundity. J. Theor. Biol. 249, 582–592 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Luo, E., Eppley, J. M., Romano, A. E., Mende, D. R. & DeLong, E. F. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 14, 1304–1315 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
108.Bidle, K. D. Elucidating marine virus ecology through a unified heartbeat. Proc. Natl Acad. Sci. USA 111, 15606–15607 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Schmidt, H. F., Sakowski, E. G., Williamson, S. J., Polson, S. W. & Wommack, K. E. Shotgun metagenomics indicates novel family A DNA polymerases predominate within marine virioplankton. ISME J. 8, 103–114 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
110.Nasko, D. J. et al. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front. Microbiol. 9, 3053 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
111.Harrison, A. O., Moore, R. M., Polson, S. W. & Wommack, K. E. Reannotation of the ribonucleotide reductase in a cyanophage reveals life history strategies within the virioplankton. Front. Microbiol. 10, 134 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
112.Breitbart, M. Marine viruses: truth or dare. Annu. Rev. Mar. Sci. 4, 425–448 (2012).Article 

Google Scholar 
113.Hurwitz, B. L. & U’Ren, J. M. Viral metabolic reprogramming in marine ecosystems. Curr. Opin. Microbiol. 31, 161–168 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
114.Lindell, D., Jaffe, J. D., Johnson, Z. I., Church, G. M. & Chisholm, S. W. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 438, 86–89 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
115.Rusconi, R., Garren, M. & Stocker, R. Microfluidics expanding the frontiers of microbial ecology. Annu. Rev. Biophys. 43, 65–91 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
116.Walker, G. M., Ozers, M. S. & Beebe, D. J. Cell infection within a microfluidic device using virus gradients. Sens. Actuators B Chem. 98, 347–355 (2004).CAS 
Article 

Google Scholar 
117.Cimetta, E. et al. Microfluidic-driven viral infection on cell cultures: theoretical and experimental study. Biomicrofluidics 6, 024127 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
118.Xu, N. et al. A microfluidic platform for real-time and in situ monitoring of virus infection process. Biomicrofluidics 6, 034122 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
119.Akin, D., Li, H. & Bashir, R. Real-time virus trapping and fluorescent imaging in microfluidic devices. Nano Lett. 4, 257–259 (2004).CAS 
Article 

Google Scholar 
120.Yu, J. Q. et al. Droplet optofluidic imaging for λ-bacteriophage detection via co-culture with host cell Escherichia coli. Lab. Chip 14, 3519–3524 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
121.Mashaghi, S. & van Oijen, A. M. Droplet microfluidics for kinetic studies of viral fusion. Biomicrofluidics 10, 024102 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
122.Fischer, A. E. et al. A high-throughput drop microfluidic system for virus culture and analysis. J. Virol. Methods 213, 111–117 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More