Philippa Kaur

1.Urban, M. C. & Skelly, D. K. Evolving metacommunities: Toward an evolutionary perspective on metacommunities. Ecology 87, 1616–1626 (2006).Article 

Google Scholar 
2.Cortez, M. H. & Ellner, S. P. Understanding rapid evolution in predator-prey interactions using the theory of fast-slow dynamical systems. Am. Nat. 176, E109–E127. https://doi.org/10.1086/656485 (2010).Article 
PubMed 

Google Scholar 
3.Ellner, S. P., Geber, M. A. & Hairston, N. G. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614. https://doi.org/10.1111/j.1461-0248.2011.01616.x (2011).Article 
PubMed 

Google Scholar 
4.Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596. https://doi.org/10.1111/j.1420-9101.2010.02029.x (2010).CAS 
Article 
PubMed 

Google Scholar 
5.Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639. https://doi.org/10.1086/652433 (2010).Article 
PubMed 

Google Scholar 
6.Meyer, J. R. & Kassen, R. The effects of competition and predation on diversification in a model adaptive radiation. Nature 446, 432–435. https://doi.org/10.1038/nature05599 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47(47), 507–532. https://doi.org/10.1146/annurev-ecolsys-121415-032254 (2016).Article 

Google Scholar 
8.Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799. https://doi.org/10.1111/j.1365-294X.2011.05397.x (2012).CAS 
Article 
PubMed 

Google Scholar 
9.Schluter, D., Price, T. D. & Grant, P. R. ecological character displacement in Darwin Finches. Science 227, 1056–1059. https://doi.org/10.1126/science.227.4690.1056 (1985).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Munkemuller, T. & Gallien, L. VirtualCom: A simulation model for eco-evolutionary community assembly and invasion. Methods Ecol. Evol. 6, 735–743. https://doi.org/10.1111/2041-210x.12364 (2015).Article 

Google Scholar 
11.Munoz, F. et al. ecolottery: Simulating and assessing community assembly with environmental filtering and neutral dynamics in R. Methods Ecol. Evol. 9, 693–703. https://doi.org/10.1111/2041-210x.12918 (2018).Article 

Google Scholar 
12.Ruffley, M., Peterson, K., Week, B., Tank, D. C. & Harmon, L. J. Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation. Ecol. Evol. 9, 13218–13230. https://doi.org/10.1002/ece3.5773 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
13.van der Plas, F. et al. A new modeling approach estimates the relative importance of different community assembly processes. Ecology 96, 1502–1515. https://doi.org/10.1890/14-0454.1 (2015).Article 

Google Scholar 
14.Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 7, 2069–2079. https://doi.org/10.1038/ismej.2013.93 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Dieckmann, U. & Doebeli, M. On the origin of species by sympatric speciation. Nature 400, 354–357 (1999).ADS 
CAS 
Article 

Google Scholar 
16.Geritz, S. A. H., Kisdi, E., Meszena, G. & Metz, J. A. J. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12, 35–57 (1998).Article 

Google Scholar 
17.Vellend, M. Conceptual synthesis in community ecology. Q. R. Biol. 85, 183–206 (2010).Article 

Google Scholar 
18.Urban, M. C. et al. The evolutionary ecology of metacommunities. Trends Ecol. Evol. 23, 311–317 (2008).Article 

Google Scholar 
19.Pausas, J. G. & Verdu, M. The jungle of methods for evaluating phenotypic and phylogenetic structure of communities. Bioscience 60, 614–625. https://doi.org/10.1525/bio.2010.60.8.7 (2010).Article 

Google Scholar 
20.Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785. https://doi.org/10.1111/j.1469-185X.2012.00224.x (2012).Article 
PubMed 

Google Scholar 
21.Kraft, N. J. B., Cornwell, W. K., Webb, C. O. & Ackerly, D. D. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. Am. Nat. 170, 271–283. https://doi.org/10.1086/519400 (2007).Article 
PubMed 

Google Scholar 
22.Wilson, J. B., Weiher, E. & Keddy, P. Assembly Rules in Plant Communities (Cambridge University Press, 1999).Book 

Google Scholar 
23.MacArthur, R. H. & Levins, R. Limiting similarity convergence and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).Article 

Google Scholar 
24.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505. https://doi.org/10.1146/annurev.ecolysis.33.010802.150448 (2002).Article 

Google Scholar 
25.Pontarp, M., Brännström, A. & Petchey, O. L. Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC). Methods Ecol. Evol. 10, 450–460. https://doi.org/10.1111/2041-210x.13129 (2019).Article 

Google Scholar 
26.Mittelbach, G. G. & Schemske, D. W. Ecological and evolutionary perspectives on community assembly. Trends Ecol. Evol. 30, 241–247. https://doi.org/10.1016/j.tree.2015.02.008 (2015).Article 
PubMed 

Google Scholar 
27.Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715. https://doi.org/10.1111/j.1461-0248.2009.01314.x (2009).Article 
PubMed 

Google Scholar 
28.Pontarp, M. & Petchey, O. L. Ecological opportunity and predator–prey interactions: Linking eco-evolutionary processes and diversification in adaptive radiations. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2017.2550 (2018).Article 

Google Scholar 
29.Seehausen, O. African cichlid fish: A model system in adaptive radiation research. Proc. R. Soc. B Biol. Sci. 273, 1987–1998. https://doi.org/10.1098/rspb.2006.3539 (2006).Article 

Google Scholar 
30.Schluter, D. The Ecology of Adaptive Radiation (Columbia University Press, 2000).
Google Scholar 
31.Nosil, P. Ecological Speciation (Oxford University Press, 2012).Book 

Google Scholar 
32.Christiansen, F. B. & Loeschcke, V. Evolution and intraspecific exploitative competition I. One-locus theory for small additive gene effects. Theor. Popul. Biol. 18, 297–313 (1980).MathSciNet 
Article 

Google Scholar 
33.Brown, J. S. & Vincent, T. L. A theory for the evolutionary game. Theor. Popul. Biol. 31, 140–166 (1987).MathSciNet 
Article 

Google Scholar 
34.Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264. https://doi.org/10.1038/Nature01274 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
35.Brose, U., Williams, R. J. & Martinez, N. D. Allometric scaling enhances stability in complex food webs. Ecol. Lett. 9, 1228–1236. https://doi.org/10.1111/j.1461-0248.2006.00978.x (2006).Article 
PubMed 

Google Scholar 
36.Leyequien, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741. https://doi.org/10.1007/s11284-006-0311-6 (2007).Article 

Google Scholar 
37.Yvon-Durocher, G. et al. Across ecosystem comparisons of size structure: Methods, approaches and prospects. Oikos 120, 550–563. https://doi.org/10.1111/j.1600-0706.2010.18863.x (2011).Article 

Google Scholar 
38.Rudolf, V. H. W. Seasonal shifts in predator body size diversity and trophic interactions in size-structured predator-prey systems. J. Anim. Ecol. 81, 524–532. https://doi.org/10.1111/j.1365-2656.2011.01935.x (2012).Article 
PubMed 

Google Scholar 
39.DeLong, J. P. & Vasseur, D. A. A dynamic explanation of size-density scaling in carnivores. Ecology 93, 470–476 (2012).Article 

Google Scholar 
40.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer-resource interaction parameters. J. Anim. Ecol. 81, 1193–1201. https://doi.org/10.1111/j.1365-2656.2012.02013.x (2012).Article 
PubMed 

Google Scholar 
41.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892. https://doi.org/10.1111/j.2007.0030-1299.15559.x (2007).Article 

Google Scholar 
42.Pontarp, M., Ripa, J. & Lundberg, P. On the origin of phylogenetic structure in competitive metacommunities. Evol. Ecol. Res. 14, 269–284 (2012).
Google Scholar 
43.Pontarp, M., Ripa, J. & Lundberg, P. The biogeography of adaptive radiations and the geographic overlap of sister species. Am. Nat. 186, 565–581 (2015).Article 

Google Scholar 
44.Barabás, G., Pigolotti, S., Gyllenberg, M., Dieckmann, U. & Meszéna, G. Continuous coexistence or discrete species? A new review of an old question. (2012).45.Brännström, A. et al. Modelling the ecology and evolution of communities: A review of past achievements, current efforts, and future promises. Evol. Ecol. Res. 14, 601–625 (2012).
Google Scholar 
46.Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).Article 

Google Scholar 
47.Vamosi, S. M., Heard, S. B., Vamosi, J. C. & Webb, C. O. Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18, 572–592. https://doi.org/10.1111/j.1365-294X.2008.04001.x (2009).CAS 
Article 
PubMed 

Google Scholar 
48.Sjödin, H., Ripa, J. & Lundberg, P. Principles of niche expansion. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2603 (2018).Article 

Google Scholar 
49.Ackermann, M. & Doebeli, M. Evolution of niche width and adaptive diversification. Evolution 58, 2599–2612 (2004).Article 

Google Scholar 
50.Urban, M. C. & De Meester, L. Community monopolization: Local adaptation enhances priority effects in an evolving metacommunity. Proc. R. Soc. B. Biol. Sci. 276, 4129–4138 (2009).Article 

Google Scholar 
51.Urban, M. C., De Meester, L., Vellend, M., Stoks, R. & Vanoverbeke, J. A crucial step toward realism: responses to climate change from an evolving metacommunity perspective. Evol. Appl. 5, 154–167. https://doi.org/10.1111/j.1752-4571.2011.00208.x (2012).Article 
PubMed 

Google Scholar 
52.Pontarp, M. & Wiens, J. J. The origin of species richness patterns along environmental gradients: Uniting explanations based on time, diversification rate and carrying capacity. J. Biogeogr. 44, 722–735. https://doi.org/10.1111/jbi.12896 (2017).Article 

Google Scholar 
53.Pontarp, M. & Petchey, O. L. Community trait overdispersion due to trophic interactions: Concerns for assembly process inference. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2016.1602 (2016).Article 

Google Scholar 
54.Pontarp, M. et al. The latitudinal diversity gradient: Novel understanding through mechanistic eco-evolutionary. Trends Ecol. Evol. 34, 211–223. https://doi.org/10.1016/j.tree.2018.11.009 (2019).Article 
PubMed 

Google Scholar 
55.Case, T. J. An Illustrated Guide to Theoretical Ecology (Oxford University Press, Inc, 2000).
Google Scholar 
56.Barabas, G., Michalska-Smith, M. J. & Allesina, S. The effect of intra- and interspecific competition on coexistence in multispecies communities. Am. Nat. 188, E1–E12. https://doi.org/10.1086/686901 (2016).Article 
PubMed 

Google Scholar 
57.Heinz, S. K., Mazzucco, R. & Dieckmann, U. Speciation and the evolution of dispersal along environmental gradients. Evol. Ecol. 23, 53–70. https://doi.org/10.1007/s10682-008-9251-7 (2009).Article 

Google Scholar 
58.Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define fitness for general ecolgical scenarios. Trends Ecol. Evol. 7, 198–202. https://doi.org/10.1016/0169-5347(92)90073-k (1992).CAS 
Article 
PubMed 

Google Scholar 
59.Doebeli, M. & Dieckmann, U. Evolutionary branching and sympatric speciation caused by different types of ecological interactions. Am. Nat. 156, S77–S101. https://doi.org/10.1086/303417 (2000).Article 
PubMed 

Google Scholar 
60.Ito, H. C. & Dieckmann, U. A new mechanism for recurrent adaptive Radiations. Am. Nat. 170, E96–E111. https://doi.org/10.1086/521229 (2007).Article 
PubMed 

Google Scholar 
61.Cressman, R. et al. Unlimited niche packing in a Lotka-Volterra competition game. Theor. Popul. Biol. 116, 1–17 (2017).Article 

Google Scholar 
62.Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100. https://doi.org/10.1093/bioinformatics/btn358 (2008).CAS 
Article 
PubMed 

Google Scholar 
63.Harmon-Threatt, A. N. & Ackerly, D. D. Filtering across spatial scales: Phylogeny, biogeography and community structure in bumble bees. PLoS ONE https://doi.org/10.1371/journal.pone.0060446 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Pybus, O. G. & Harvey, P. H. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. B Biol. Sci. 267, 2267–2272. https://doi.org/10.1098/rspb.2000.1278 (2000).CAS 
Article 

Google Scholar  More

Figure 1 presents the track of the eight-month cruise, and Table 1 provides the detail of the legs and dates. On a routine basis R/V Hesperides sailed at an average speed of 11 knots from around 3 pm to 4 am (local time). The vessel arrived on station at around 4 am daily to carry out sampling operations at a fixed point for about 11 hours.Fig. 1Cruise track and integrated backscatter at different stations (NASC, daytime 200 to 1000 m).Full size imageTable 1 Dates and starting points of the 7 legs of the Malaspina cruise.Full size tableAcoustic measurements were carried out continuously using a Simrad EK60 echosounder), operating at 38 and 120 kHz (7° beamwidth transducers) with a ping rate of 0.5 Hz. Unfortunately, the 120 kHz failed during the first leg of the cruise and only 38 kHz data were collected. Echosounder observations were recorded down to 1000 m depth. The echosounder files are in the proprietary Simrad raw format and can be read by various softwares (e.g., LSSS, Echoview, Sonar5, MATECHO, ESP3, echopype, pyEcholab). GPS locations and calibration constants are imbedded in each file.Additionally, daytime data integrated over 2 m vertical bins from 200 to 1000 m depth are provided as Nautical Area Scattering Coefficient (NASC). Each “voxel” is the average of all cleaned and validated data recorded over that depth range, in a time period starting 8 hours before the start of the station (defined as start of the CTD cast) and ending 8 hours after the start of the station, with only data recorded in the period between 1 hour after local sunrise and 1 hour prior to local sunset accepted (i.e., during local daytime hours, but removing crepuscular periods when vertical migration of biota is strong). The relatively long interval over which data were accepted around each station was chosen since the station sampling resulted in noisy acoustic data,, a long interval was therefore chosen to ensure valid data on all stations.Finally, summaries of per station daytime and nighttime acoustic data (omitting data recorded within 1 hour of sunrise and sunset) are provided. The data fields in this file are station date, latitude and longitude, and per day and night average NASC 200–1000 m, average NASC 0–1000, weighted mean depth (WMD) of NASC 200–1000 m, migration amplitude, NASC day-to-night ratio and migration ratio. More

We analyse weekly reported counts of suspected and confirmed human cases and deaths attributed to LF (as defined in Supplementary Table 1), between 1 January 2012 and 30 December 2019, from across the entire of Nigeria. The weekly counts were reported from 774 LGAs in 36 Federal states and the Federal Capital Territory, under Integrated Disease Surveillance and Response (IDSR) protocols, and collated by the NCDC. All suspected cases, confirmed cases and deaths from notifiable infectious diseases (including viral haemorrhagic fevers; VHFs) are reported weekly to the LGA Disease Surveillance and Notification Officer (DSNO) and State Epidemiologist (SE). IDSR routine data on priority diseases are collected from inpatient and outpatient registers in health facilities, and forwarded to each LGA’s DSNO using SMS or paper form. Subsequently, individual LGA DSNOs collate and forward the data to their respective SE, also by SMS and paper form, for weekly and monthly reporting respectively to NCDC. From mid-2017 onwards, data entry in 18 states has been conducted using a mobile phone-based electronic reporting system called mSERS, with the data entered using a customised Excel spreadsheet that is used to manually key into NCDC-compatible spreadsheets. Data from this surveillance regime (WERs) were collated by epidemiologists at NCDC throughout the period 2012 to March 2018 (Supplementary Fig. 1).Throughout the study period, within-country LF surveillance and response has been strengthened under NCDC coordination2,20,33. LGAs are now required to notify immediately any suspected case to the state-level, which in turn reports to NCDC within 24 h, and also sends a cumulative weekly report of all reported cases. A dedicated, multi-sectoral NCDC LF TWG was set up in 2016 with the responsibility of coordinating all LF preparedness and response activities across states. Further capacity building occurred in 2017 to 2019, with the opening of three additional LF diagnostic laboratories in Abuja (Federal Capital Territory), Abakaliki (Ebonyi state) and Owo (Ondo state) (to a total of five; Fig. 2) and the rollout of intensive country-wide training on surveillance, clinical case management and diagnosis. We note that, due to the rapid expansion in a test capacity, the definition of a suspected case in our data has subtly changed over the surveillance period: from 2012 to 2016, suspected cases include probable cases that were not lab-tested, whereas from 2017 to 2019, all suspected cases were tested and confirmed to be negative.In addition to the WERs data, since 2017 LF case reporting data has also been collated by the LF TWG and used to inform the weekly NCDC LF Situation Reports (SitRep data; https://ncdc.gov.ng/diseases/sitreps). This regime includes post hoc follow-ups to ensure more accurate case counts, so our analyses use WER-derived case data from 2012 to 2016, and SitRep-derived case data from 2017 to 2019 (see Fig. 1 for full time series). A visual comparison of the data from each separate time series, including the overlap period (2017 to March 2018) is provided in Supplementary Fig. 1, and all statistical models considered random intercepts for the different surveillance regimes. Where other studies of recent Nigeria LF incidence have been more spatially and temporally restricted34,35, the extended monitoring period and fine spatial granularity of these data provide the opportunity for a detailed empirical perspective on the local drivers of LF at a country-wide scale and their relationship to changes in reporting effort.Recent trends in LF surveillance in NigeriaWe visualised temporal and seasonal trends in suspected and confirmed LF cases within and between years, for both surveillance datasets. Weekly case counts were aggregated to country-level and visualised as both annual case accumulation curves, and aggregated weekly case totals (Fig. 1 and Supplementary Fig. 1). We also mapped annual counts of suspected and confirmed cases across Nigeria at the LGA-level to examine spatial changes in reporting over the surveillance period (Fig. 2). State and LGA shapefiles used for modelling and mapping were obtained from Humanitarian Data Exchange under a CC-BY-IGO license (https://data.humdata.org/dataset/nga-administrative-boundaries).Analyses of aggregated district data are sensitive to differences in scale and shape of aggregation (the modifiable areal unit problem; MAUP36), and LGA geographical areas in Nigeria are highly skewed and vary over >3 orders of magnitude (median 713 km2, mean 1175 km2, range 4–11,255 km2). We therefore also aggregated all LGAs across Nigeria into 130 composite districts with a more even distribution of geographical areas, using distance-based hierarchical clustering on LGA centroids (implemented using hclust in R), with the constraint that each new cluster must contain only LGAs from within the same state (to preserve potentially important state-level differences in surveillance regime). Weekly and annual suspected and confirmed LF case totals were then calculated for each aggregated district. We used these spatially aggregated districts to test for the effects of scale on spatial drivers of LF occurrence and incidence.Statistical analysisWe analysed the full case time series (Fig. 1) to characterise the spatiotemporal incidence and drivers of LF in Nigeria, while controlling for year-on-year increases and expansions of surveillance effort. We firstly modelled annual LF occurrence and incidence at a country-wide scale, to identify the spatial, climatic and socio-ecological correlates of disease risk across Nigeria. Secondly, we modelled seasonal and temporal trends in weekly LF incidence within hyperendemic areas in the north and south of Nigeria, to identify the seasonal climatic conditions associated with LF risk dynamics and evaluate the scope for forecasting. All data processing and modelling was conducted in R v.3.4.1 with the packages R-INLA v.20.03.1737, raster v.3.4.1338 and velox v0.2.039. Statistical modelling was conducted using hierarchical regression in a Bayesian inference framework (integrated nested Laplace approximation (INLA)), which provides fast, stable and accurate posterior approximation for complex, spatially and temporally-structured regression models37,40, and has been shown to outperform alternative methods for modelling environmental phenomena with evidence of spatially biased reporting41.Processing climatic and socio-ecological covariatesWe collated geospatial data on socio-ecological and climatic factors that are hypothesised to influence either M. natalensis distribution and population ecology (rainfall, temperature and vegetation patterns), frequency and mode of human–rodent contact (poverty and improved housing prevalence), both of the above (agricultural and urban land cover) or likelihood of LF reporting (travel time to nearest laboratory with LF diagnostic capacity and travel time to nearest hospital). For each LGA we extracted the mean value for each covariate across the LGA polygon. The full suite of covariates tested across all analyses, data sources and associated hypotheses are described in Supplementary Table 5.We collated climate data spanning the full monitoring period and up until the date of analysis (July 2011 to January 2021). We obtained daily precipitation rasters for Africa42 from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) project; this dataset is based on combining sparse weather station data with satellite observations and interpolation techniques, and is designed to support hydrologic forecasts in areas with poor weather station coverage (such as tropical West Africa)42. A recent study ground-truthing against weather station data showed that CHIRPS provides greater overall accuracy than other gridded precipitation products in Nigeria43. Air temperature daily minimum and maximum rasters were obtained from NOAA and were also averaged to calculate daily mean temperature. EVI, a measure of vegetation quality, was obtained from processing 16-day composite layers from NASA (National Aeronautics and Space Administration) (excluding all grid cells with unreliable observations due to cloud cover and linearly interpolating between observations to give daily values; Supplementary Table 5).We derived several spatial bioclimatic variables to capture conditions across the full monitoring period (Jan 2012 to Dec 2019): mean precipitation of the driest annual month, mean precipitation of the wettest annual month, precipitation seasonality (coefficient of variation), annual mean air temperature, air temperature seasonality, annual mean EVI and EVI seasonality. We also calculated monthly total precipitation, 3-month SPI44, average daily mean (Tmean), minimum (Tmin) and maximum (Tmax) temperature and EVI variables at sequential time lags prior to reporting week for seasonal modelling (described below in Temporal drivers). SPI is a standardised measure of drought or wetness conditions relative to the historical average conditions for a given period of the year. SPI was calculated within a rolling 3-month window across the full 40-year historical CHIRPS rainfall time series (1981–2020) using the R package SPEI v.1.744.We accessed annual human population rasters at 100 m resolution from WorldPop. We accessed the proportion of the population living in poverty in 2010 ( More

Phototactic behaviourThe optimization results of the proposed behavioural setup allowed the phototactic behaviour of the studied D. magna clone and the effects of FK treatment on this behaviour to be monitored and quantified. Furthermore, the effects of the FKs were evident only upon light exposure, were more apparent after a short (5 min) acclimation to light and were tightly regulated by the light intensity. The above-mentioned factors agree with previous studies, which found a marked positive phototactism of clone P132,8528 and that the effects of the FKs become consistent after 5 min of light exposure28. Moreover, it has also been reported that light intensity controls anti-predatory defences in Daphnia29.The effects of the pharmacological treatments were consistent for GABAergic and muscarinic cholinergic compounds across two or three identical non-consecutive experiments performed over more than one year. Consistency of the toxicological results and, in particular, of the behavioural responses should be compulsory in toxicological studies to increase the credibility and robustness of the findings30,31. Agonists of these two neurotransmitter receptors (DZP, PILO) and the antagonist of the GABA receptor (PICRO) affected the induction of the phototactic behavioural changes (i.e., interfered with fish recognition). The receptor agonists DZP and PILO counteracted the negative phototactism evoked by the FKs, whereas PICRO enhanced the effect of the FKs, increasing the negative phototactism. None of the three applied substances when applied alone induced anti-predatory fish phototactic behaviour, indicating that these compounds interfered with the FK sensorial pathway. Alternatively, the muscarinic cholinergic antagonist SCOP interfered with phototaxis itself, almost completely abolishing the positive phototactic behaviour of the studied clone under both control and FK conditions. This indicates that the muscarinic cholinergic signalling pathway could potentially be a major regulator of anti-predatory fish phototactic behaviour. In D. pulex and D. galeata, the formation of neck teeth or helmets in response to predatory kairomones released by invertebrate predators has been related to a series of biological reactions that involve kairomone perception and neuronal signals, which are converted into endocrine signals and subsequently induce changes in the expression of morphogenetic factors32,33. We previously showed that DZP, PILO, PICRO and SCOP were neuroactive in D. magna, affecting sensitization and/or habituation motile responses to repetitive light stimuli34; thus, it is likely that these compounds disrupted neurological signalling pathways related to the phototactism shifts caused by FK perception or to the phototaxis itself.Little is known about how phototaxis is neuronally coded. In D. pulex, both in silico and experimental works have shown that histaminergic neurons may mediate phototactic responses to UV irradiation12. By using histamine immunohistochemistry, the previous authors labelled putative photoreceptors in the compound eye and neuronal projections from these cells to the brain. The D. pulex genome also has a putative Drosophila orthologue of histidine decarboxylase (the rate-limiting biosynthetic enzyme for histamine), as well as two putative histamine-gated chloride channels (hclA and hclB orthologues). Exposure of D. magna to cimetidine, an H2 receptor antagonist known to block both hclA and hclB in D. melanogaster, inhibited the negative phototactic responses of these orthologues to UV irradiation. In another study, it was found that short-day photoperiods induced a significant increase in light-avoidance behaviours relative to controls and increased glutamate signalling, which is a critical pathway in arthropod light-avoidance behaviour35. It has also been reported that a group of serotonergic cells located in the protocerebrum probably control phototactic behaviour16. Notably, the perception of predatory kairomones and neuronal and cellular wiring is largely unknown in Daphnia2. For example, the receptors that detect invertebrate cues from Notonecta in D. longicephala were shown to be located on the first antennae, from which neurites extend into the deutocerebrum of the brain. However, key olfactory neuronal structures, such as olfactory glomeruli in the deutocerebrum, were not found2.Our results obtained for DZP, an agonist of the GABAA receptor, agree with those of Weiss et al.11, who found that co-exposure to FKs and exogenous GABA ameliorated life history changes to FKs in a D. pulex clone, whereas co-exposure with the GABAA antagonist PICRO did not have any effect. The ineffectiveness of PICRO on the modulation of FK effects in D. pulex found by Weiss et al.11 might indicate species differences resulting from different receptor amino acid sequences. For example, GABAA receptor subtypes with a single amino acid replacement make the Drosophila GABAA receptor PICRO-insensitive36. Indeed, in crustaceans, lobster GABAA receptors were also found to be insensitive to PICRO37. There is also the possibility that FK-mediated changes in phototactic behaviour and life history traits may be controlled by different mechanisms6.Reported information on the modulatory effects of cholinergic compounds on anti-predatory defences in Daphnia is limited to invertebrate predatory cues, which, according to previous studies, should be regulated by neurological mechanisms distinct from those of fish2,11. Our results showed that the neurological cholinergic mechanisms that modulate induced defence responses against invertebrate predators or that mimic these responses are also able to do the same for fish predation but in the opposite way. Physostigmine and carbaryl, which are acetylcholinesterase inhibitors that increase acetylcholine receptor activity, enhanced and mimicked, respectively, the morphogenetic effects of invertebrate kairomones in several Daphnia species11,21,23. Conversely, atropine, which is a muscarinic acetylcholine receptor (AChR) inhibitor like SCOP, diminished neck tooth formation in D. pulex11,21. In our study, SCOP alone abolished the positive phototactism of the studied clone, which mimicked the effects of the FKs. Conversely, PILO, which is a muscarine AChR agonist, ameliorates the phototactic responses to FKs.The nicotinic AChR agonists (NICO, IMI) and antagonist (MEC) only marginally affected the phototactic responses to the FKs. This indicates that muscarinic cholinergic signalling but not nicotinic signalling is involved in phototaxis/phototactic behaviour. It is therefore possible that both FK and SCOP treatment, through inhibition of muscarinic cholinesterase receptor activity, diminished the positive phototaxis of the studied clone, and PILO activation of these receptors ameliorated the effects of the FKs. In insects, neurons that connect olfactory inputs to higher-order brain areas that coordinate behavioural responses are thought to be under cholinergic control38.In general, GABA is known to have inhibitory functions. It has been proposed that the continuous activation of the GABAergic neuronal pathway by endogenous GABA without predatory cues prevents life history shifts11, which in our case would be the transition from positive to negative phototaxis. FKs and PICRO relieve inhibition, which can be re-established by the experimental application of GABAA receptor agonists such as DZP or GABA itself. Our results and those of Weiss et al.11 agree with the previous argument.Equi-effective mixtures of the tested agonists and antagonists had similar effects on D. magna responses to FKs as the single mixture compound treatments did, indicating that the joint effects of agonists and antagonists of the GABAergic and cholinergic signalling pathways can act cooperatively and probably independently, modulating the effects of FKs. This is in line with other findings that showed that key ecophysiological responses in Daphnia are regulated by several signalling receptor pathways, which likely ensures more robust control. This is the case for the storage lipid dynamics associated with moulting and reproduction39.The involvement of additional neurotransmitter signalling pathways, such as the serotonergic pathway, can also be taken into consideration despite being less consistent. Agonists of the serotonin receptor (such as serotonin) or treatments that increase serotonin levels (such as fluoxetine) ameliorated the effects of the FKs in only one experiment, but treatments that decreased serotonin, such as PCPA, increased the effects of the FKs in two out of the three experiments. Previously, we reported that serotonin activity in the brains of D. magna increased with algae food levels, and thus, the effects of fluoxetine on the enhancement of brain serotonin levels could only be observed under limited food conditions24. This indicates that the high levels of food used in our experiments probably prevented fluoxetine from increasing the already high serotonin levels in the central nervous system. Interestingly, inducible fish kairomone changes in phototactic behaviour in Daphnia increased with food level40, which is probably related to high levels of serotonin. On the other hand, the effects of PCPA, which decreases serotonin concentrations26, are unlikely to be modulated by food since this drug inhibits tryptophan hydrolase, the serotonin synthesis rate-limiting enzyme in D. magna41. This is apparently the case in our study.Neurophysiological stimulation experiments with dopaminergic/adrenergic agonists and antagonists were inconclusive since in only one out of two experiments the dopaminergic agonist APO diminish negative phototaxis after FK exposure. We also did not find any effects from the glutamatergic agonists and antagonists on phototactism. This could be related to the low stability of dopaminergic compounds in water and the reported small effects of glutaminergic compounds on the Daphnia motile response to light34.Consistent failure of the tested antihistaminergic drugs to modulate phototactism to visible light disagrees with previous findings that discovered that these drugs affected phototactism but at much higher doses12.Metabolomic changesThe study of metabolomic changes across the treatments that modulated FK-mediated phototactic changes or altered phototaxis provided further experimental evidence of the involvement of key neurological signalling metabolic pathways. Caution must be exercised, however, since the studied receptor agonist and antagonist drugs do not change the neurotransmitters or their related metabolites. Nevertheless, little is known about how these drugs may affect the Daphnia neuronal metabolome. The cholinergic neurotransmitter system is one of the most important systems that plays a pivotal role in learning and memory in animal species, including D. magna34,42. Whole-body concentrations of acetylcholine decreased in females exposed to FKs and those exposed to SCOP and increased in those exposed to the agonists PILO and DZP. Thus, it is possible to establish a direct link between the decreased levels of acetylcholine and decreased positive phototactism in the studied clone. The results obtained for the GABAergic and serotonergic signalling pathways were less convincing, as FKs alone did not consistently affect the levels of GABA and serotonin. However, co-exposure to FK and the GABAA receptor agonist DZP increased endogenous GABA levels, which is in line with the results reported by Weiss et al.11, who also found that the addition of exogenous GABA ameliorated FK effects. Interestingly, the summarized results depicted in Fig. 4 showed that serotonin levels dereased upon exposure to SCOP, PICRO and PILO but PILO also increase the levels of the serotonin degradation metabolite 5-HIAA. This may indicate that PILO may affect the turnover rather than the levls of serotionin.Previous findings have reported altered responses to light in D. magna individuals lacking serotonin16. Therefore, it is possible to establish a link between the observed marked negative phototactism of females exposed to SCOP and low levels of serotonin.Dopaminergic- and adrenergic-related metabolites deserve special attention, although there is only evidence that dopamine is involved in the proliferation and structural formation of morphological defences in Daphnia for invertebrate kairomones22. In some invertebrates, adrenergic signalling is considered to be absent, and the analogous functions are performed by octopamine43. In our study, fish kairomones and SCOP decreased the levels of dopamine and octopamine, whereas females co-treated with the agonists DZP and PILO and FKs showed relatively high levels of dopamine. In the insect Drosophila melanogaster, which shares many gene signalling pathways with Daphnia44, individuals deficient in dopamine show reduced positive phototactism45. Unfortunately, it is not possible to know whether the observed changes in DA in the whole bodies of D. magna indicate that DA is less used or used in excess. Figure 4 indicates that FK and SCOP reduced both DA and its intermediary metabolite L-DOPA. SCOP also increased the DA degradation metabolite 3-MT and two norepinephrine metabolites/neurotransmitters (NOEM, EPPY) that ultimately depend on DA. This means that FK decreased DA probably decreasing its intermediary metabolite L-DOPA, whereas SCOP decreased DA to a greater extent decreasing its intermediary L-DOPA but also increasing its turnover rate. Our neurophysiological stimulation experiments with dopaminergic active compounds are also not conclusive. This suggests that further research is needed to study the involvement of dopaminergic signalling in the response to fish. Existing studies on adrenergic signalling in daphnids indicated that β-blockers such as propranolol diminish the heart rate46 and motile responses to light27, which are related to the known role of adrenergic signalling that regulates blood pressure47 and other fight-or-flight responses to stress48. Future research is needed to elucidate the involvement of OCT, EPPY and NORM in the phototactic response of D. magna to FKs.In summary, this study provides consistent results that muscarinic cholinergic and GABAergic receptor agonists and antagonists are able to ameliorate or enhance, respectively, the phototactic response of adult females from the studied D. magna clone to FKs. Furthermore, inhibition of the muscarinic acetylcholine receptor by SCOP induced the phototactic response to fish kairomones. This may indicate that muscarinic cholinergic antagonists changed phototaxis, whereas muscarinic cholinergic agonists and GABAergic agonists and antagonists changed the perception of FKs. Serotonergic agonists and antagonists were also able to diminish and increase FK effects, respectively, but only in half of the trials performed. The fact that we could not observe effects from the remaining neuroactive agents (i.e., dopaminergic, histaminergic, glutamatergic) could simply be because they are not relevant for predator-induced anti-phototaxis. The study of neurotransmitters and their related metabolite changes allowed us to identify acetylcholine and GABA as putative key metabolites associated with the observed phototactic modulatory effects of FK and cholinergic and GABAergic compounds. Increased and decreased levels of dopamine in the whole bodies of D. magna were related to positive and negative phototactic behaviours, respectively, but could not be related to neurophysiological studies with the tested dopaminergic drugs. More

1.Mcguire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009). Details Arctic changes under RCP scenarios using a multi-model approach forecasting vegetation offsets of some carbon emissions.
Google Scholar 
2.Brandt, J. P. The extent of the North American boreal zone. Environ. Rev. 17, 101–161 (2009).
Google Scholar 
3.Chadburn, S. et al. Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models. Biogeosciences 14, 5143–5169 (2017).CAS 

Google Scholar 
4.Karjalainen, O. et al. Data descriptor: circumpolar permafrost maps and geohazard indices for near-future infrastructure risk assessments. Sci. Data 6, 190037 (2019).
Google Scholar 
5.Hjort, J. et al. Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nat. Commun. 9, 5147 (2018).CAS 

Google Scholar 
6.Abramov, A., Vishnivetskaya, T. & Rivkina, E. Are permafrost microorganisms as old as permafrost? FEMS Microbiol. Ecol. 97, fiaa260 (2021).CAS 

Google Scholar 
7.Ricketts, M. P. et al. The effects of warming and soil chemistry on bacterial community structure in Arctic tundra soils. Soil Biol. Biochem. 148, 107882 (2020).CAS 

Google Scholar 
8.Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).CAS 

Google Scholar 
9.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020). Seminal paper that identifies abrupt permafrost thaw as an important mechanism in rapid Arctic change.CAS 

Google Scholar 
10.Nikrad, M. P., Kerkhof, L. J. & Aggblom, M. M. The subzero microbiome: microbial activity in frozen and thawing soils. FEMS Microbiol. Ecol. 92, fiw081 (2016).
Google Scholar 
11.Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–24 (2019).CAS 

Google Scholar 
12.Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).CAS 

Google Scholar 
13.Anthony, K. W. et al. 21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes. Nat. Commun. 9, 3262 (2018).
Google Scholar 
14.Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).CAS 

Google Scholar 
15.Hong, E., Perkins, R. & Trainor, S. Thaw settlement hazard of permafrost related to climate warming in Alaska. Arctic 67, 93–103 (2014).
Google Scholar 
16.Trofimenko, Y. V., Evgenev, G. I. & Shashina, E. V. Functional loss risks of highways in permafrost areas due to climate change. Procedia Eng. 189, 258–264 (2017).
Google Scholar 
17.Wurzbacher, C., Nilsson, R. H., Rautio, M. & Peura, S. Poorly known microbial taxa dominate the microbiome of permafrost thaw ponds. ISME J. 11, 1938–1941 (2017).
Google Scholar 
18.Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).CAS 

Google Scholar 
19.Gross, M. Permafrost thaw releases problems. Curr. Biol. 29, R39–R41 (2019).CAS 

Google Scholar 
20.Walsh, M. G., De Smalen, A. W. & Mor, S. M. Climatic influence on anthrax suitability in warming northern latitudes. Sci. Rep. 8, 9269 (2018).
Google Scholar 
21.Zolkos, S. et al. Mercury export from Arctic great rivers. Environ. Sci. Technol. 54, 4140–4148 (2020).CAS 

Google Scholar 
22.Ewing, S. A. et al. Uranium isotopes and dissolved organic carbon in loess permafrost: modeling the age of ancient ice. Geochim. Cosmochim. Acta 152, 143–165 (2015).CAS 

Google Scholar 
23.Eriksson, M. On Weapons Plutonium in the Arctic Environment (Thule, Greenland). PhD thesis, Lund Univ. (2002).24.Colgan, W. et al. The abandoned ice sheet base at Camp Century, Greenland, in a warming climate. Geophys. Res. Lett. 43, 8091–8096 (2016).
Google Scholar 
25.Anisimov, O., Kokorev, V. & Zhiltcova, Y. Arctic ecosystems and their services under changing climate: predictive-modeling assessment. Geogr. Rev. 107, 108–124 (2017).
Google Scholar 
26.Pelletier, M., Allard, M. & Levesque, E. Ecosystem changes across a gradient of permafrost degradation in subarctic Québec (Tasiapik Valley, Nunavik, Canada). Arct. Sci. 5, 1–26 (2019).
Google Scholar 
27.Perryman, C. R. et al. Heavy metals in the Arctic: distribution and enrichment of five metals in Alaskan soils. PLoS ONE 15, e0233297 (2020).CAS 

Google Scholar 
28.Gilichinsky, D. A. & Rivkina, E. M. Permafrost microbiology. Encycl. Earth Sci. Ser. 6, 726–732 (1995). Details the (at the time) emergent field of permafrost microbiology, extremophilic species and future prospects for emergent microbes.
Google Scholar 
29.Steven, B., Léveillé, R., Pollard, W. H. & Whyte, L. G. Microbial ecology and biodiversity in permafrost. Extremophiles 10, 259–267 (2006).
Google Scholar 
30.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases—carbon dioxide, methane, and nitrous oxide. Glob. Change Biol. 23, 3121–3138 (2017).
Google Scholar 
31.Mackelprang, R., Saleska, S. R., Jacobsen, C. S., Jansson, J. K. & Taş, N. Permafrost meta-omics and climate change. Annu. Rev. Earth Planet. Sci. 44, 439–462 (2016).CAS 

Google Scholar 
32.Graham, D. E. et al. Microbes in thawing permafrost: the unknown variable in the climate change equation. ISME J. 6, 709–712 (2012).CAS 

Google Scholar 
33.Abbott, B. W. et al. Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment. Environ. Res. Lett. 11, 034014 (2016).
Google Scholar 
34.Ren, J. et al. Biomagnification of persistent organic pollutants along a high-altitude aquatic food chain in the Tibetan Plateau: processes and mechanisms. Environ. Pollut. https://doi.org/10.1016/j.envpol.2016.10.019 (2016).35.Dean, J. F. et al. Abundant pre-industrial carbon detected in Canadian Arctic headwaters: implications for the permafrost carbon feedback. Environ. Res. Lett. 13, 34024 (2018).
Google Scholar 
36.Jeffries, M. O., Overland, J. E. & Perovich, D. K. The Arctic shifts to a new normal. Phys. Today 66, 35–40 (2013).
Google Scholar 
37.El-Sayed, A. & Kamel, M. Future threat from the past. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-020-11234-9 (2020).38.Houwenhuyse, S., Macke, E., Reyserhove, L., Bulteel, L. & Decaestecker, E. Back to the future in a petri dish: origin and impact of resurrected microbes in natural populations. Evol. Appl. 11, 29–41 (2018).
Google Scholar 
39.Miner, K. R. et al. Organochlorine pollutants within a polythermal glacier in the Interior Eastern Alaska Range. Water 10, 1157 (2018).
Google Scholar 
40.Li, F. et al. Arctic sea-ice loss intensifies aerosol transport to the Tibetan Plateau. Nat. Clim. Change 10, 1037–1044 (2020).CAS 

Google Scholar 
41.Eriksson, M., Lindahl, P., Roos, P., Dahlgaard, H. & Holm, E. U, Pu, and Am nuclear signatures of the thule hydrogen bomb debris. Environ. Sci. Technol. 42, 4717–4722 (2008).CAS 

Google Scholar 
42.Lind, O. C. et al. Characterization of U/Pu particles originating from the nuclear weapon accidents at Palomares, Spain, 1966 and Thule, Greenland, 1968. Sci. Total Environ. 376, 294–305 (2007).CAS 

Google Scholar 
43.Slemmons, K. E. H., Saros, J. E. & Simon, K. The influence of glacial meltwater on alpine aquatic ecosystems: a review. Environ. Sci. Process. Impacts 15, 1794 (2013).CAS 

Google Scholar 
44.Bidleman, T. F., Jantunen, L. M., Kurt-Karakus, P. B. & Wong, F. Chiral persistent organic pollutants as tracers of atmospheric sources and fate: review and prospects for investigating climate change influences. Atmos. Pollut. Res. 3, 371–382 (2012).CAS 

Google Scholar 
45.Chen, M. et al. Release of perfluoroalkyl substances from melting glacier of the Tibetan Plateau: insights into the impact of global warming on the cycling of emerging pollutants. J. Geophys. Res. Atmos. 124, 7442–7456 (2019).
Google Scholar 
46.Goodman, S. & Kertysova, K. The Nuclearisation of the Russian Arctic: New Reactors, New Risks (European Leadership Network, 2020); https://www.europeanleadershipnetwork.org/wp-content/uploads/2020/06/The-nuclearisation-of-the-Russian-Arctic-2.pdf47.Byrne, S. et al. Persistent organochlorine pesticide exposure related to a formerly used defense site on St. Lawrence Island, Alaska: data from sentinel fish and human sera. Toxicol. Environ. Health 78, 37–54 (2015).
Google Scholar 
48.The National Academies of Sciences Understanding and Responding to Global Health Security Risks from Microbial Threats in the Arctic (National Academies Press, 2020); https://doi.org/10.17226/2588749.Edwards, A. et al. Microbial genomics amidst the Arctic crisis. Microb. Genom. 6, e000375 (2020). Catalogues known genomic diversity, evolution dynamics and environment of Arctic microbes.
Google Scholar 
50.Botnen, S. S., Mundra, S., Kauserud, H. & Eidesen, P. B. Glacier retreat in the high Arctic: opportunity or threat for ectomycorrhizal diversity? FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiaa171 (2020).51.Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).CAS 

Google Scholar 
52.Ward, C. P., Nalven, S. G., Crump, B. C., Kling, G. W. & Cory, R. M. Photochemical alteration of organic carbon draining permafrost soils shifts microbial metabolic pathways and stimulates respiration. Nat. Commun. 8, 772 (2017).
Google Scholar 
53.Taş, N. et al. Landscape topography structures the soil microbiome in Arctic polygonal tundra. Nat. Commun. 9, 777 (2018).
Google Scholar 
54.Price, P. B. Microbial genesis, life and death in glacial ice. Can. J. Microbiol. 55, 1–11 (2009).CAS 

Google Scholar 
55.Niederberger, T. D. et al. Microbial characterization of a subzero, hypersaline methane seep in the Canadian high Arctic. ISME J. 4, 1326–1339 (2010).CAS 

Google Scholar 
56.Malavin, S., Shmakova, L., Claverie, J. M. & Rivkina, E. Frozen Zoo: a collection of permafrost samples containing viable protists and their viruses. Biodivers. Data J. 8, e51586 (2020).
Google Scholar 
57.Gilichinsky, D., Rivkina, E., Shcherbakova, V., Laurinavichuis, K. & Tiedje, J. Supercooled water brines within permafrost—an unknown ecological niche for microorganisms: a model for astrobiology. Astrobiology 3, 331–341 (2003).CAS 

Google Scholar 
58.Legendre, M. et al. Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology. Proc. Natl Acad. Sci. USA 111, 4274–4279 (2014).CAS 

Google Scholar 
59.Legendre, M. et al. In-depth study of Mollivirus sibericum, a new 30,000-yold giant virus infecting Acanthamoeba. Proc. Natl Acad. Sci. USA 112, E5327–E5335 (2015).CAS 

Google Scholar 
60.MacKelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011). Uses deep metagenomic sequencing to map the impacts of thaw on the Arctic microbial community structure and genomics.CAS 

Google Scholar 
61.Mühlemann, B. et al. Diverse variola virus (smallpox) strains were widespread in northern Europe in the Viking age. Science 369, eaaw8977 (2020).
Google Scholar 
62.Ng, T. F. F. et al. Preservation of viral genomes in 700-y-old caribou feces from a subarctic ice patch. Proc. Natl Acad. Sci. USA 111, 16842–16847 (2014).
Google Scholar 
63.Shmakova, L. et al. A living bdelloid rotifer from 24,000-year-old Arctic permafrost. Curr. Biol. 31, PR712–R713 (2021).
Google Scholar 
64.Siliakus, M. F., van der Oost, J. & Kengen, S. W. M. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles 21, 651–670 (2017).CAS 

Google Scholar 
65.Edwards, A. Coming in from the cold: potential microbial threats from the terrestrial cryosphere. Front. Earth Sci. 3, 12 (2015).
Google Scholar 
66.Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 11, 2305–2318 (2017).CAS 

Google Scholar 
67.Bale, N. J. et al. Fatty acid and hopanoid adaption to cold in the methanotroph Methylovulum psychrotolerans. Front. Microbiol. 10, 589 (2019).
Google Scholar 
68.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).CAS 

Google Scholar 
69.Ji, M. et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature 552, 400–403 (2017).CAS 

Google Scholar 
70.Burkert, A., Douglas, T. A., Waldrop, M. P. & Mackelprang, R. Changes in the active, dead, and dormant microbial community structure across a Pleistocene permafrost chronosequence. Appl. Environ. Microbiol. 85, e02646–18 (2019).CAS 

Google Scholar 
71.Colangelo-Lillis, J., Eicken, H., Carpenter, S. D. & Deming, J. W. Evidence for marine origin and microbial-viral habitability of subzero hypersaline aqueous inclusions within permafrost near Barrow, Alaska. FEMS Microbiol. Ecol. 92, fiw053 (2016).CAS 

Google Scholar 
72.Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).CAS 

Google Scholar 
73.Zhong, Z.-P. et al. Viral ecogenomics of Arctic cryopeg brine and sea ice. mSystems https://doi.org/10.1128/mSystems.00246-20 (2020).74.Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat. Microbiol. 6, 246–256 (2021).CAS 

Google Scholar 
75.Aslam, S. N., Huber, C., Asimakopoulos, A. G., Steinnes, E. & Mikkelsen, Ø. Trace elements and polychlorinated biphenyls (PCBs) in terrestrial compartments of Svalbard, Norwegian Arctic. Sci. Total Environ. 685, 1127–1138 (2019).CAS 

Google Scholar 
76.Winiger, P. et al. Source apportionment of circum-Arctic atmospheric black carbon from isotopes and modeling. Sci. Adv. 5, eaau8052 (2019).CAS 

Google Scholar 
77.Villa, S., Migliorati, S., Monti, G. S., Holoubek, I. & Vighi, M. Risk of POP mixtures on the Arctic food chain. Environ. Toxicol. Chem. 36, 1181–1192 (2017).CAS 

Google Scholar 
78.Ma, J., Hung, H., Tian, C. & Kallenborn, R. Revolatilization of persistent organic pollutants in the Arctic induced by climate change. Nat. Clim. Change 1, 255–260 (2011).CAS 

Google Scholar 
79.Ji, X., Abakumov, E. & Polyakov, V. Assessments of pollution status and human health risk of heavy metals in permafrost-affected soils and lichens: a case-study in Yamal Peninsula, Russia Arctic. Hum. Ecol. Risk Assess. 25, 2142–2159 (2019).CAS 

Google Scholar 
80.Mu, C. et al. Carbon and mercury export from the Arctic rivers and response to permafrost degradation. Water Res. 161, 54–60 (2019).CAS 

Google Scholar 
81.Brown, T. M., Macdonald, R. W., Muir, D. C. G. & Letcher, R. J. The distribution and trends of persistent organic pollutants and mercury in marine mammals from Canada’s eastern Arctic. Sci. Total Environ. 618, 500–517 (2018).CAS 

Google Scholar 
82.Ferrario, C., Finizio, A. & Villa, S. Legacy and emerging contaminants in meltwater of three alpine glaciers. Sci. Total Environ. 574, 350–357 (2017).CAS 

Google Scholar 
83.Miner, K. R., Bogdal, C., Pavlova, P. A., Steinlin, C. & Kreutz, K. J. Quantitative screening level assessment of human risk from PCB in glacial meltwater: Silvretta Glacier, Swiss Alps. Ecotoxicol. Environ. Saf. 166, 251–258 (2018).CAS 

Google Scholar 
84.Octaviani, M., Stemmler, I., Lammel, G. & Graf, H. F. Atmospheric transport of persistent organic pollutants to and from the Arctic under present-day and future climate. Environ. Sci. Technol. 49, 3593–3602 (2015).CAS 

Google Scholar 
85.Nielsen, S. P., Iosjpe, M. & Strand, P. Collective doses to man from dumping of radioactive waste in the Arctic seas. Sci. Total Environ. 202, 135–146 (1997).CAS 

Google Scholar 
86.Eickmeyer, D. C. et al. Interactions of polychlorinated biphenyls and organochlorine pesticides with sedimentary organic matter of retrogressive thaw slump-affected lakes in the tundra uplands adjacent to the Mackenzie Delta, NT, Canada. J. Geophys. Res. G Biogeosci. 121, 411–421 (2016).CAS 

Google Scholar 
87.St Pierre, K. A. et al. Unprecedented increases in total and methyl mercury concentrations downstream of retrogressive thaw slumps in the western Canadian Arctic. Environ. Sci. Technol. 52, 14099–14109 (2018).
Google Scholar 
88.Birnbaum, L. S. When environmental chemicals act like uncontrolled medicine. Trends Endocrinol. Metab. 24, 321–323 (2013).CAS 

Google Scholar 
89.Potapowicz, J., Szumińska, D., Szopińska, M. & Polkowska, Ż. The influence of global climate change on the environmental fate of anthropogenic pollution released from the permafrost: part I. Case study of Antarctica. Sci. Total Environ. 651, 1534–1548 (2019).CAS 

Google Scholar 
90.Kim, K.-S. et al. Associations of organochlorine pesticides and polychlorinated biphenyls in visceral vs. subcutaneous adipose tissue with type 2 diabetes and insulin resistance. Chemosphere 94, 151–157 (2014).CAS 

Google Scholar 
91.Knutsen, H. K. et al. Risk to human health related to the presence of perfluorooctane sulfonic acid and perfluorooctanoic acid in food. EFSA J. 16, e05194 (2018).
Google Scholar 
92.Iszatt, N. et al. Prenatal and postnatal exposure to persistent organic pollutants and infant growth: a pooled analysis of seven European birth cohorts. Environ. Health Perspect. 123, 730–736 (2015).CAS 

Google Scholar 
93.Nadal, M., Marquès, M., Mari, M. & Domingo, J. L. Climate change and environmental concentrations of POPs: a review. Environ. Res. 143, 177–185 (2015).CAS 

Google Scholar 
94.Toxicological Profile for Lead (Agency for Toxic Substances and Disease Registry, 2020); https://www.atsdr.cdc.gov/toxprofiles/tp13.pdf95.Toxicological Profile for Mercury (Agency for Toxic Substances and Disease Registry, 1999); https://www.atsdr.cdc.gov/ToxProfiles/tp46.pdf96.Toxicological Profile for Cadmium (Agency for Toxic Substances and Disease Registry, 2012); https://www.atsdr.cdc.gov/toxprofiles/tp5.pdf97.Halbach, K., Mikkelsen, Ø., Berg, T. & Steinnes, E. The presence of mercury and other trace metals in surface soils in the Norwegian Arctic. Chemosphere 188, 567–574 (2017).CAS 

Google Scholar 
98.Miner, K. R. et al. Legacy organochlorine pollutants in glacial watersheds: a review. Environ. Sci. Process. Impacts 19, 1474–1483 (2017).CAS 

Google Scholar 
99.Jamieson, H. E. The legacy of arsenic contamination from mining and processing refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Rev. Mineral. Geochem. 79, 533–551 (2014).
Google Scholar 
100.Tolvanen, A. et al. Mining in the Arctic environment—a review from ecological, socioeconomic and legal perspectives. J. Environ. Manag. 233, 832–844 (2019).
Google Scholar 
101.Liu, X., Jiang, S., Zhang, P. & Xu, L. Effect of recent climate change on Arctic Pb pollution: a comparative study of historical records in lake and peat sediments. Environ. Pollut. 160, 161–168 (2012).CAS 

Google Scholar 
102.Antcibor, I. et al. Trace metal distribution in pristine permafrost-affected soils of the Lena River delta and its hinterland, northern Siberia, Russia. Biogeosciences 11, 1–15 (2014).
Google Scholar 
103.Lim, A. G. et al. A revised pan-Arctic permafrost soil Hg pool based on western Siberian peat Hg and carbon observations. Biogeosciences 17, 3083–3097 (2020).CAS 

Google Scholar 
104.Schuster, P. F. et al. Permafrost stores a globally significant amount of mercury. Geophys. Res. Lett. 45, 1463–1471 (2018).CAS 

Google Scholar 
105.Schaefer, K. et al. Potential impacts of mercury released from thawing permafrost. Nat. Commun. 11, 4650 (2020). Estimates future releases of mercury from the permafrost from present to 2300, under RCP scenarios.CAS 

Google Scholar 
106.Jiskra, M. E., Sonke, J., Agnan, Y., Helmig, D. & Obrist, D. Insights from mercury stable isotopes on terrestrial-atmosphere exchange of Hg(0) in the Arctic tundra. Biogeosciences 16, 4051–4064 (2019).CAS 

Google Scholar 
107.Blais, J. M. et al. Arctic seabirds transport marine-derived contaminants. Science 309, 445 (2005).CAS 

Google Scholar 
108.Brimble, S. K. et al. High Arctic ponds receiving biotransported nutrients from a nearby seabird colony are also subject to potentially toxic loadings of arsenic, cadmium, and zinc. Environ. Toxicol. Chem. 28, 2426–2433 (2009).CAS 

Google Scholar 
109.Michelutti, N. et al. Trophic position influences the efficacy of seabirds as metal biovectors. Proc. Natl Acad. Sci. USA 107, 10543–10548 (2010).CAS 

Google Scholar 
110.Mallory, M. L. & Braune, B. M. Tracking contaminants in seabirds of Arctic Canada: temporal and spatial insights. Mar. Pollut. Bull. 64, 1475–1484 (2012).CAS 

Google Scholar 
111.Lehnherr, I. Methylmercury biogeochemistry: a review with special reference to Arctic aquatic ecosystems. Environ. Rev. 22, 229–243 (2014).CAS 

Google Scholar 
112.Steinlin, C. et al. A temperate alpine glacier as a reservoir of polychlorinated biphenyls: model results of incorporation, transport, and release. Environ. Sci. Technol. 50, 5572–5579 (2016).CAS 

Google Scholar 
113.Pavlova, P. A., Schmid, P., Zennegg, M., Bogdal, C. & Schwikowski, M. Trace analysis of hydrophobic micropollutants in aqueous samples using capillary traps. Chemosphere 106, 51–56 (2014).CAS 

Google Scholar 
114.Blais, J. M. et al. Melting glaciers: a major source of persistent organochlorines to subalpine Bow Lake in Banff National Park, Canada. Ambio 30, 410–415 (2001).CAS 

Google Scholar 
115.Lafrenière, M. J., Blais, J. M., Sharp, M. J. & Schindler, D. W. Organochlorine pesticide and polychlorinated biphenyl concentrations in snow, snowmelt, and runoff at Bow Lake, Alberta. Environ. Sci. Technol. 40, 4909–4915 (2006).
Google Scholar 
116.Elliott, J. E. et al. Factors influencing legacy pollutant accumulation in alpine osprey: biology, topography, or melting glaciers? Environ. Sci. Technol. 46, 9681–9689 (2012).CAS 

Google Scholar 
117.Walters, D. M. et al. Trophic magnification of organic chemicals: a global synthesis. Environ. Sci. Technol. 50, 4650–4658 (2016).CAS 

Google Scholar 
118.Miner, K. R., Wayant, N. & Ward, H. Preventing chemical release in hurricanes. Science 362, 166 (2018).CAS 

Google Scholar 
119.Quadroni, S. & Bettinetti, R. Health risk assessment for the consumption of fresh and preserved fish (Alosa agone) from Lago di Como (northern Italy). Environ. Res. 156, 571–578 (2017).CAS 

Google Scholar 
120.Mangano, M. C., Sarà, G. & Corsolini, S. Monitoring of persistent organic pollutants in the polar regions: knowledge gaps & gluts through evidence mapping. Chemosphere 172, 37–45 (2017).CAS 

Google Scholar 
121.Villa, S., Vighi, M., Maggi, V., Finizio, A. & Bolzacchini, E. Historical trends of organochlorine pesticides in an alpine glacier. J. Atmos. Chem. 46, 295–311 (2003).CAS 

Google Scholar 
122.Garmash, O. et al. Deposition history of polychlorinated biphenyls to the Lomonosovfonna glacier, Svalbard: a 209 congener analysis. Environ. Sci. Technol. 47, 12064–12072 (2013).CAS 

Google Scholar 
123.Bizzotto, E. C., Villa, S., Vaj, C. & Vighi, M. Comparison of glacial and non-glacial-fed streams to evaluate the loading of persistent organic pollutants through seasonal snow/ice melt. Chemosphere 74, 924–930 (2009).CAS 

Google Scholar 
124.Villa, S., Negrelli, C., Finizio, A., Flora, O. & Vighi, M. Organochlorine compounds in ice melt water from Italian alpine rivers. Ecotoxicol. Environ. Saf. 63, 84–90 (2006).CAS 

Google Scholar 
125.Miner, K. R. et al. A screening-level approach to quantifying risk from glacial release of organochlorine pollutants in the Alaskan Arctic. J. Expo. Sci. Environ. Epidemiol. 29, 293–301 (2018). Develops the first human risk assessment of glacially stored pollutants in the Arctic.
Google Scholar 
126.Czub, G. & McLachlan, M. S. A food chain model to predict the levels of lipophilic organic contaminants in humans. Environ. Toxicol. Chem. 23, 2356–2366 (2004).CAS 

Google Scholar 
127.Wang, X., Gong, P., Wang, C., Ren, J. & Yao, T. A review of current knowledge and future prospects regarding persistent organic pollutants over the Tibetan Plateau. Sci. Total Environ. 573, 139–154 (2016).CAS 

Google Scholar 
128.Desforges, J. P. et al. Predicting global killer whale population collapse from PCB pollution. Science 361, 1373–1376 (2018).CAS 

Google Scholar 
129.Macdonald, R. W. et al. Contaminants in the Canadian Arctic: 5 years of progress in understanding sources, occurrence and pathways. Sci. Total Environ. 254, 93–234 (2000).CAS 

Google Scholar 
130.Pavlova, P. A. et al. Polychlorinated biphenyls in a temperate alpine glacier: 1. Effect of percolating meltwater on their distribution in glacier ice. Environ. Sci. Technol. 49, 14085–14091 (2015).CAS 

Google Scholar 
131.Wania, F., Westgate, J. N., Technol, E. S. & Asap, A. On the mechanism of mountain cold-trapping of organic chemicals. Environ. Sci. Technol. 42, 9092–9098 (2008).CAS 

Google Scholar 
132.Strand, P. & Cooke, A. Environmental Radioactivity in the Arctic (Scientific Committee of the Environmental Radioactivity in the Arctic, 1995).133.Wright, S. M. et al. Spatial variation in the vulnerability of Norwegian Arctic counties to radiocaesium deposition. Sci. Total Environ. 202, 173–184 (1997).CAS 

Google Scholar 
134.Mitchell, P. I., León Vintró, L., Dahlgaard, H., Gascó, C. & Sánchez-Cabeza, J. A. Perturbation in the 240Pu/239Pu global fallout ratio in local sediments following the nuclear accidents at Thule (Greenland) and Palomares (Spain). Sci. Total Environ. 202, 147–153 (1997).CAS 

Google Scholar 
135.Khalturin, V. I., Rautian, T. G., Richards, P. G. & Leith, W. S. A review of nuclear testing by the Soviet Union at Novaya Zemlya, 1955–1990. Sci. Glob. Secur. 13, 1–42 (2005). Reviews the Novaya Zemlya nuclear testing site history, nuclear releases and posits environmental distribution.
Google Scholar 
136.Travkina, A. V. et al. Monitoring the environmental contamination of Kara Sea and shallow bays of Novaya Zemlya. J. Radioanal. Nucl. Chem. 311, 1673–1680 (2017).CAS 

Google Scholar 
137.Skorve, J. The environment of the nuclear test sites on Novaya Zemlya. Sci. Total Environ. 202, 167–172 (1997).CAS 

Google Scholar 
138.Sarkisov, A. A. The question of clean-up of radioactive contamination in the Arctic region. Her. Russ. Acad. Sci. 89, 7–22 (2019).
Google Scholar 
139.Pogrebov, V. B., Fokin, S. I., Galtsova, V. V. & Ivanov, G. I. Benthic communities as influenced by nuclear testing and radioactive waste disposal off Novaya Zemlya in the Russian Arctic. Mar. Pollut. Bull. 35, 333–339 (1997).CAS 

Google Scholar 
140.Miroshnikov, A. Y. et al. Radioecological investigations on the northern Novaya Zemlya Archipelago. Oceanology 57, 204–214 (2017).
Google Scholar 
141.Salbu, B. et al. Radioactive contamination from dumped nuclear waste in the Kara Sea—results from the joint Russian-Norwegian expeditions in 1992-1994. Sci. Total Environ. 202, 185–198 (1997).CAS 

Google Scholar 
142.Oughton, D. H., Børretzen, P., Salbu, B. & Tronstad, E. Mobilisation of 137Cs and 90Sr from sediments: potential sources to Arctic waters. Sci. Total Environ. 202, 155–165 (1997).CAS 

Google Scholar 
143.Faria, S. H., Weikusat, I. & Azuma, N. The microstructure of polar ice. Part I: highlights from ice core research. J. Struct. Geol. 61, 2–20 (2014).
Google Scholar 
144.Karlsson, N. B. et al. Ice-penetrating radar survey of the subsurface debris field at Camp Century, Greenland. Cold Reg. Sci. Technol. 165, 102788 (2019). The most recent ice-penetrating radar survey of Camp Century, Greenland, characterizing the location and concentration of wastes.
Google Scholar 
145.Vandecrux, B., Colgan, W. T., Solgaard, A., Steffensen, J. P. & Karlsson, N. B. Firn evolution at Camp Century, Greenland: 1966-2100. Front. Earth Sci. 9, 578978 (2021).
Google Scholar 
146.Vila, E., Hornero-Méndez, D., Azziz, G., Lareo, C. & Saravia, V. Carotenoids from heterotrophic bacteria isolated from Fildes Peninsula, King George Island, Antarctica. Biotechnol. Rep. 21, e00306 (2019).
Google Scholar 
147.Chaudhary, D. K., Kim, D. U., Kim, D. & Kim, J. Flavobacterium petrolei sp. nov., a novel psychrophilic, diesel-degrading bacterium isolated from oil-contaminated Arctic soil. Sci. Rep. 9, 4134 (2019).
Google Scholar 
148.de Gouw, J. A. et al. Daily satellite observations of methane from oil and gas production regions in the United States. Sci. Rep. 10, 1379 (2020).
Google Scholar 
149.Girardot, F. et al. Bacterial diversity on an abandoned, industrial wasteland contaminated by polychlorinated biphenyls, dioxins, furans and trace metals. Sci. Total Environ. 748, 141242 (2020).CAS 

Google Scholar 
150.Price, P. B. Microbial life in glacial ice and implications for a cold origin of life. FEMS Microbiol. Ecol. 59, 217–231 (2007).CAS 

Google Scholar 
151.Schütte, U. M. E. et al. Effect of permafrost thaw on plant and soil fungal community in a boreal forest: does fungal community change mediate plant productivity response? J. Ecol. 107, 1737–1752 (2019).
Google Scholar 
152.Jensen, P. E., Hennessy, T. W. & Kallenborn, R. Water, sanitation, pollution, and health in the Arctic. Environ. Sci. Pollut. Res. 25, 32827–32830 (2018).
Google Scholar 
153.Ewing, S. A. et al. Long-term anoxia and release of ancient, labile carbon upon thaw of Pleistocene permafrost. Geophys. Res. Lett. 42, 10730–10738 (2015).CAS 

Google Scholar 
154.Elder, C. D. et al. Seasonal sources of whole-lake CH4 and CO2 emissions from interior Alaskan thermokarst lakes. J. Geophys. Res. Biogeosci. 124, 1209–1229 (2019).CAS 

Google Scholar 
155.Jansen, E. et al. Past perspectives on the present era of abrupt Arctic climate change. Nat. Clim. Change 10, 714–721 (2020).
Google Scholar 
156.Nellier, Y.-M. et al. Mass budget in two high altitude lakes reveals their role as atmospheric PCB sinks. Sci. Total Environ. 511, 203–213 (2015).CAS 

Google Scholar 
157.Garnett, J. et al. Mechanistic insight into the uptake and fate of persistent organic pollutants in sea ice. Environ. Sci. Technol. 53, 6757–6764 (2019).CAS 

Google Scholar 
158.Kortenkamp, A. & Faust, M. Regulate to reduce chemical mixture risk. Science 361, 224–226 (2018).CAS 

Google Scholar 
159.Kirchgeorg, T. et al. Seasonal accumulation of persistent organic pollutants on a high altitude glacier in the eastern Alps. Environ. Pollut. 218, 804–812 (2016).CAS 

Google Scholar 
160.Weil, T. et al. Legal immigrants: invasion of alien microbial communities during winter occurring desert dust storms. Microbiome 5, 32 (2017).
Google Scholar 
161.Li, J. et al. Evidence for persistent organic pollutants released from melting glacier in the central Tibetan Plateau, China. Environ. Pollut. 220, 178–185 (2017).CAS 

Google Scholar 
162.Walvoord, M. A., Voss, C. I., Ebel, B. A. & Minsley, B. J. Development of perennial thaw zones in boreal hillslopes enhances potential mobilization of permafrost carbon. Environ. Res. Lett. 14, 015003 (2019).CAS 

Google Scholar 
163.Mogrovejo, D. C. et al. Prevalence of antimicrobial resistance and hemolytic phenotypes in culturable Arctic bacteria. Front. Microbiol. 11, 570 (2020).
Google Scholar 
164.Friedman, C. L. & Selin, N. E. Long-range atmospheric transport of polycyclic aromatic hydrocarbons: a global 3-D model analysis including evaluation of Arctic sources. Environ. Sci. Technol. 46, 9501–9510 (2012).CAS 

Google Scholar 
165.Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).
Google Scholar 
166.Vonk, J. E. et al. Reviews and syntheses: effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences 12, 7129–7167 (2015).CAS 

Google Scholar 
167.MacInnis, J. J. et al. Fate and transport of perfluoroalkyl substances from snowpacks into a lake in the high Arctic of Canada. Environ. Sci. Technol. 53, 10753–10762 (2019).CAS 

Google Scholar 
168.Yeung, L. W. Y. et al. Vertical profiles, sources, and transport of PFASs in the Arctic Ocean. Environ. Sci. Technol. 51, 6735–6744 (2017).CAS 

Google Scholar 
169.Colatriano, D. et al. Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria. Commun. Biol. 1, 90 (2018).
Google Scholar 
170.Commane, R. et al. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl Acad. Sci. USA 114, 5361–5366 (2017).CAS 

Google Scholar 
171.Hartmann, M. et al. Variation of ice nucleating particles in the European Arctic over the last centuries. Geophys. Res. Lett. https://doi.org/10.1029/2019GL082311 (2019).172.Murray, B. J., Carslaw, K. S. & Field, P. R. Opinion: cloud-phase climate feedback and the importance of ice-nucleating particles. Atmos. Chem. Phys. 21, 665–679 (2021).CAS 

Google Scholar 
173.Joyce, R. E. et al. Biological ice-nucleating particles deposited year-round in subtropical precipitation. Appl. Environ. Microbiol. 85, e01567-19 (2019).
Google Scholar 
174.Yumashev, D., van Hussen, K., Gille, J. & Whiteman, G. Towards a balanced view of Arctic shipping: estimating economic impacts of emissions from increased traffic on the Northern Sea Route. Clim. Change 143, 143–155 (2017).CAS 

Google Scholar 
175.Ramage, J. et al. Population living on permafrost in the Arctic. Popul. Environ. https://doi.org/10.1007/s11111-020-00370-6 (2021).176.Bartsch, A., Pointner, G., Ingeman-Nielsen, T. & Lu, W. Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2. Remote Sens. 12, 2368 (2020).
Google Scholar 
177.Dewailly, E. Canadian Inuit and the Arctic dilemma. Oceanography 19, 88–89 (2006).
Google Scholar 
178.Plaza, C. et al. Direct observation of permafrost degradation and rapid soil carbon loss in tundra. Nat. Geosci. 12, 627–631 (2019).CAS 

Google Scholar 
179.Kashuba, E. et al. Ancient permafrost staphylococci carry antibiotic resistance genes. Microb. Ecol. Health Dis. https://doi.org/10.1080/16512235.2017.1345574 (2017).180.Dcosta, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).CAS 

Google Scholar 
181.Perron, G. G. et al. Functional characterization of bacteria isolated from ancient Arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS ONE 10, e0069533 (2015).
Google Scholar 
182.Gilichinsky, D. et al. in Psychrophiles: From Biodiversity to Biotechnology (eds Margesin, R. et al.) 83–102 (Springer-Verlag, 2008).183.Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).CAS 

Google Scholar 
184.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).CAS 

Google Scholar 
185.Taubenberger, J. K. et al. Reconstruction of the 1918 influenza virus: unexpected rewards from the past. mBio 3, e00201–12 (2012).
Google Scholar 
186.Jordan, D., Tumpey, T. & Jester, B. The Deadliest Flu: The Complete Story of the Discovery and Reconstruction of the 1918 Pandemic Virus (US Center for Disease Control, 2019).187.Tumpey, T. M. et al. Characterization of the reconstructed 1918 Spanish influenza pandemic virus. Science 310, 77–80 (2005).CAS 

Google Scholar 
188.Revich, B., Tokarevich, N. & Parkinson, A. J. Climate change and zoonotic infections in the Russian Arctic. Int. J. Circumpolar Health 71, 18792 (2012).
Google Scholar 
189.Waits, A., Emelyanova, A., Oksanen, A., Abass, K. & Rautio, A. Human infectious diseases and the changing climate in the Arctic. Environ. Int. 121, 703–713 (2018).
Google Scholar 
190.Hueffer, K., Drown, D., Romanovsky, V. & Hennessy, T. Factors contributing to anthrax outbreaks in the circumpolar north. Ecohealth 17, 174–180 (2020).
Google Scholar 
191.Springer, Y. P. et al. Novel Orthopoxvirus infection in an Alaska resident. Clin. Infect. Dis. 64, 1737–1741 (2017).
Google Scholar 
192.Mackay, D. Multimedia Environmental Models (CRC Press, 2001).193.Mackay, D., Celsie, A. K. D., Powell, D. E. & Parnis, J. M. Bioconcentration, bioaccumulation, biomagnification and trophic magnification: a modelling perspective. Environ. Sci. Process. Impacts 20, 72–85 (2018).CAS 

Google Scholar 
194.Vizcaino, E., Grimalt, J. O., Fernandez-Somoano, A. & Tardon, A. Transport of persistent organic pollutants across the human placenta. Environ. Int. 65, 107–115 (2014).CAS 

Google Scholar 
195.Costa, O. et al. First-trimester maternal concentrations of polyfluoroalkyl substances and fetal growth throughout pregnancy. Environ. Int. https://doi.org/10.1016/j.envint.2019.05.024 (2019).196.Adetona, O. et al. Concentrations of select persistent organic pollutants across pregnancy trimesters in maternal and in cord serum in Trujillo, Peru. Chemosphere 91, 1426–1433 (2013).CAS 

Google Scholar 
197.Toxicological Profile for Plutonium (Agency for Toxic Substances and Disease Registry, 2010); https://www.atsdr.cdc.gov/toxprofiles/tp143.pdf198.Toxicological Profile for Cesium (Agency for Toxic Substances and Disease Registry, 2004); https://www.atsdr.cdc.gov/toxprofiles/tp157.pdf199.Serikova, S. et al. High carbon emissions from thermokarst lakes of western Siberia. Nat. Commun. 10, 1552 (2019).CAS 

Google Scholar 
200.Swingedouw, D. et al. Early warning from space for a few key tipping points in physical, biological, and social-ecological systems. Surv. Geophys. https://doi.org/10.1007/s10712-020-09604-6 (2020).201.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a high Arctic environment. Nat. Commun. 10, 1329 (2019).
Google Scholar 
202.Tank, S. E. et al. Landscape matters: predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach. Permafr. Periglac. Process. https://doi.org/10.1002/ppp.2057 (2020).203.Feng, J. et al. Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community. Microbiome 8, 3 (2020).
Google Scholar 
204.Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).
Google Scholar 
205.Donald, D. B. et al. Delayed deposition of organochlorine pesticides at a temperate glacier. Environ. Sci. Technol. 33, 1794–1798 (1999).CAS 

Google Scholar 
206.Hermanson, M. H. et al. Current-use and legacy pesticide history in the Austfonna ice cap, Svalbard, Norway. Environ. Sci. Technol. 39, 8163–8169 (2005).CAS 

Google Scholar 
207.Salvadó, J. A., Sobek, A., Carrizo, D. & Gustafsson, Ö. Observation-based assessment of PBDE loads in Arctic ocean waters. Environ. Sci. Technol. 50, 2236–2245 (2016).
Google Scholar 
208.Vecchiato, M. et al. The great acceleration of fragrances and PAHs archived in an ice core from Elbrus, Caucasus. Sci. Rep. 10, 10661 (2020).CAS 

Google Scholar 
209.Miteva, V., Teacher, C., Sowers, T. & Brenchley, J. Comparison of the microbial diversity at different depths of the GISP2 Greenland ice core in relationship to deposition climates. Environ. Microbiol. 11, 640–656 (2009).CAS 

Google Scholar  More

1.Aluja, M. Fruit fly (Diptera: Tephritidae) research in Latin America: myths, realities and dreams. Soc. Entomol. Bras. 28, 565–594 (1999).Article 

Google Scholar 
2.Weldon, C. W., Yap, S. & Taylor, P. W. Desiccation resistance of wild and mass-reared Bactrocera tryoni (Diptera: Tephritidae). Bull. Entomol. Res. 103, 690–699 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Weldon, C. W., Boardman, L., Marlin, D. & Terblanche, J. S. Physiological mechanisms of dehydration tolerance contribute to the invasion potential of Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) relative to its less widely distributed congeners. Front. Zool. 13, 15 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Weldon, C. W., Díaz-Fleischer, F. & Pérez-Staples, D. in Area-Wide Management of Fruit Fly Pests (eds. Pérez-Staples, D. et al.) 27–43 (CRC Press, 2020).5.Malacrida, A. R. et al. Globalization and fruit fly invasion and expansion: the medfly paradigm. Genetica 131, 1–9 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Diamantidis, A. D., Carey, J. R., Nakas, C. T. & Papadopoulos, N. T. Ancestral populations perform better in a novel environment: domestication of Mediterranean fruit fly populations from five global regions. Biol. J. Linn. Soc. 102, 334–345 (2011).Article 

Google Scholar 
7.Diamantidis, A. D. et al. Life history evolution in a globally invading tephritid: patterns of survival and reproduction in medflies from six world regions. Biol. J. Linn. Soc. 97, 106–117 (2009).Article 

Google Scholar 
8.Papadopoulos, N. T., Plant, R. E. & Carey, J. R. From trickle to flood: the large-scale, cryptic invasion of California by tropical fruit flies. Proc. R. Soc. Biol. Sci. Ser. B 280, 20131466 (2013).Article 

Google Scholar 
9.EUPHRESCO, project FLY_DETECT. Development and implementation of early detection tools and effective management strategies for invasive non-European and other selected fruit fly species of economic importance (FLY DETECT). Final report. https://doi.org/10.5281/zenodo.3732297. (2020)10.FSA PLH Panel, (EFSA Panel on Plant Health). Pest categorisation of non-EU Tephritidae. EFSA J. 18, e05931 (2020).
Google Scholar 
11.Carey, J. R. The Mediterranean fruit fly (Ceratitis capitata). Am. Entomol. 56, 158–163 (2010).Article 

Google Scholar 
12.Gutierrez, A. P. Applied Population Ecology: A Supply-Demand Approach. (Wiley, 1996).13.Sinclair, T. R. & Seligman, N. G. Crop modeling: from infancy to maturity. Agron. J. 88, 698–704 (1996).Article 

Google Scholar 
14.Gutierrez, A. P. & Ponti, L. Eradication of invasive species: why the biology matters. Environ. Entomol. 42, 395–411 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Asplen, M. K. et al. Invasion biology of spotted wing Drosophila (Drosophila suzukii): a global perspective and future priorities. J. Pest Sci. 88, 469–494 (2015).Article 

Google Scholar 
16.Neteler, M., Bowman, M. H., Landa, M. & Metz, M. GRASS GIS: a multi-purpose Open Source GIS. Environ. Model. Softw. 31, 124–130 (2012).Article 

Google Scholar 
17.Ekesi, S., Mohamed, S. & Meyer, M. D. Fruit Fly Research and Development in Africa—Towards a Sustainable Management Strategy to Improve Horticulture. (Springer, 2016).18.Vera, M. T., Rodriguez, R., Segura, D. F., Cladera, J. L. & Sutherst, R. W. Potential geographical distribution of the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), with emphasis on Argentina and Australia. Environ. Entomol. 31, 1009–1022 (2002).Article 

Google Scholar 
19.De Meyer, M., Robertson, M. P., Peterson, A. T. & Mansell, M. W. Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). J. Biogeogr. 35, 270–281 (2008).Article 

Google Scholar 
20.Tuel, A. & Eltahir, E. A. B. Why is the Mediterranean a climate change hot spot? J. Clim. 33, 5829–5843 (2020).Article 

Google Scholar 
21.Gaston, K. J. Geographic range limits: achieving synthesis. Proc. R. Soc. Biol. Sci. Ser. B 276, 1395–1406 (2009).Article 

Google Scholar 
22.IPCC, Intergovernmental Panel on Climate Change. Climate change 2014: Impacts, Adaptation, and Vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2014).23.Godefroid, M., Cruaud, A., Rossi, J. P. & Rasplus, J. Y. Assessing the risk of invasion by Tephritid fruit flies: intraspecific divergence matters. PLoS ONE 10, e0135209 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Ponti, L. et al. Biological invasion risk assessment of Tuta absoluta: mechanistic versus correlative methods. Biol. Invasions (in press).25.Carey, J. R., Papadopoulos, N. & Plant, R. The 30‐year debate on a multi‐billion‐dollar threat: tephritid fruit fly establishment in California. Am. Entomol. 63, 100–113 (2017).Article 

Google Scholar 
26.Gutierrez, A. P., Ponti, L. & Gilioli, G. Comments on the concept of ultra-low, cryptic tropical fruit fly populations. Proc. R. Soc. B Biol. Sci. 281, 20132825 (2014).Article 

Google Scholar 
27.McInnis, D. O. et al. Can polyphagous invasive tephritid pest populations escape detection for years under favorable climatic and host conditions? Am. Entomol. 63, 89–99 (2017).Article 

Google Scholar 
28.Barr, N. B. et al. Genetic diversity of Bactrocera dorsalis (Diptera: Tephritidae) on the Hawaiian islands: implications for an introduction pathway into California. J. Econ. Entomol. 107, 1946–1958 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Davies, N., Villablanca, F. X. & Roderick, G. K. Bioinvasions of the medfly Ceratitis capitata: source estimation using DNA sequences at multiple intron loci. Genetics 153, 351–360 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Meixner, M. D., McPheron, B. A., Silva, J. G., Gasparich, G. E. & Sheppard, W. S. The Mediterranean fruit fly in California: evidence for multiple introductions and persistent populations based on microsatellite and mitochondrial DNA variability. Mol. Ecol. Notes 11, 891–899 (2002).CAS 
Article 

Google Scholar 
31.Gutierrez, A. P., Ponti, L. & Cossu, Q. A. Effects of climate warming on olive and olive fly (Bactrocera oleae (Gmelin)) in California and Italy. Clim. Change 95, 195–217 (2009).Article 

Google Scholar 
32.Johnson, M. W. et al. High temperature affects olive fruit fly populations in California’s Central Valley. Calif. Agric. 65, 29–33 (2011).Article 

Google Scholar 
33.Gutierrez, A. P., Ponti, L. & Dalton, D. T. Analysis of the invasiveness of spotted wing Drosophila (Drosophila suzukii) in North America, Europe, and the Mediterranean Basin. Biol. Invasions 18, 3647–3663 (2016).Article 

Google Scholar 
34.Ponti, L., Gutierrez, A. P., Ruti, P. M. & Dell’Aquila, A. Fine-scale ecological and economic assessment of climate change on olive in the Mediterranean Basin reveals winners and losers. Proc. Natl Acad. Sci. USA 111, 5598–5603 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Andrewartha, H. G. & Birch, L. C. The Distribution and Abundance of Animals. (The University of Chicago Press, 1954).36.Huffaker, C. B. & Messenger, P. S. Theory and Practice of Biological Control. (Academic Press, 1976).37.Palladino, P. Defining ecology: ecological theories, mathematical models, and applied biology in the 1960s and 1970s. J. Hist. Biol. 24, 223–243 (1991).Article 

Google Scholar 
38.Dormann, C. F., Fründ, J. & Schaefer, H. M. Identifying causes of patterns in ecological networks: opportunities and limitations. Annu. Rev. Ecol. Evol. Syst. 48, 559–584 (2017).Article 

Google Scholar 
39.Evans, M. R. Modelling ecological systems in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 367, 181–190 (2012).Article 

Google Scholar 
40.Jørgensen, S. E., Nielsen, S. N. & Fath, B. D. Recent progress in systems ecology. Ecol. Model. 319, 112–118 (2016).Article 

Google Scholar 
41.FSA PLH Panel, (EFSA Panel on Plant Health). Pest categorisation of non-EU Tephritidae. EFSA J. 18, e05931 (2020).
Google Scholar 
42.Messenger, P. S. & van den Bosch, R. in Biological Control (ed. Huffaker, C. B.) 511 (Plenum/Rosetta Press, 1969).43.Grout, T. G. & Stoltz, K. C. Developmental rates at constant temperatures of three economically important Ceratitis spp. (Diptera: Tephritidae) from southern Africa. Environ. Entomol. 36, 1310–1317 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Papanastasiou, S. A., Nestel, D., Diamantidis, A. D., Nakas, C. T. & Papadopoulos, N. T. Physiological and biological patterns of a highland and a coastal population of the European cherry fruit fly during diapause. J. Insect Physiol. 57, 83–93 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Müller, H. G., Wu, S., Diamantidis, A. D., Papadopoulos, N. T. & Carey, J. R. Reproduction is adapted to survival characteristics across geographically isolated medfly populations. Proc. R. Soc. Biol. Sci. Ser. B 276, 4409–4416 (2009).Article 

Google Scholar 
46.Wang, J., Zeng, L. & Han, Z. An assessment of cold hardiness and biochemical adaptations for cold tolerance among different geographic populations of the Bactrocera dorsalis (Diptera: Tephritidae) in China. J. Insect Sci. Ludhiana 14, 292 (2014).47.Aluja, M. et al. Nonhost status of Citrus sinensis cultivar Valencia and C. paradisi cultivar Ruby Red to Mexican Anastrepha fraterculus (Diptera: Tephritidae). J. Econ. Entomol. 96, 1693–1703 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Dupuis, J. R., Ruiz‐Arce, R., Barr, N. B., Thomas, D. B. & Geib, S. M. Range‐wide population genomics of the Mexican fruit fly: toward development of pathway analysis tools. Evol. Appl. 12, 1641–1660 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Ricalde, M. P., Nava, D. E., Loeck, A. E. & Donatti, M. G. Temperature-dependent development and survival of Brazilian populations of the Mediterranean fruit fly, Ceratitis capitata, from tropical, subtropical and temperate regions. J. Insect Sci. 12, 33 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Duyck, P. F. & Quilici, S. Survival and development of different life stages of three Ceratitis spp. (Diptera: Tephritidae) reared at five constant temperatures. Bull. Entomol. Res. 92, 461–469 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Gutierrez, A. P. & Regev, U. The bioeconomics of tritrophic systems: applications to invasive species. Ecol. Econ. 52, 383–396 (2005).Article 

Google Scholar 
53.Gutierrez, A. P. & Ponti, L. The new world screwworm: prospective distribution and role of weather in eradication. Agric. Entomol. 16, 158–173 (2014).Article 

Google Scholar 
54.Gutierrez, A. P., Ponti, L. & Arias, P. A. Deconstructing the eradication of new world screwworm in North America: retrospective analysis and climate warming effects. Med. Vet. Entomol. 33, 282–295 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Egartner, A. & Lethmayer, C. Invasive fruit flies of economic importance in Austria – monitoring activities 2016. IOBCWPRS Bull. 123, 45–49 (2017).
Google Scholar 
56.Nugnes, F., Russo, E., Viggiani, G. & Bernardo, U. First record of an invasive fruit fly belonging to Bactrocera dorsalis complex (Diptera: Tephritidae) in Europe. Insects 9, 182 (2018).PubMed Central 
Article 

Google Scholar 
57.Liebhold, A. M. et al. Eradication of invading insect populations: from concepts to applications. Annu. Rev. Entomol. 61, 335–352 (2016).58.Tobin, P. C. et al. Determinants of successful arthropod eradication programs. Biol. Invasions 16, 401–414 (2014).Article 

Google Scholar 
59.Gilbert, N., Gutierrez, A. P., Frazer, B. D. & Jones, R. E. Ecological Relationships. (W.H. Freeman and Co., 1976).60.Gutierrez, A. P. Applied Population Ecology: A Supply-Demand Approach (Wiley, 1996).61.Gutierrez, A. P. The physiological basis of ratio-dependent predator-prey theory: the metabolic pool model as a paradigm. Ecology 73, 1552–1563 (1992).Article 

Google Scholar 
62.Gutierrez, A. P., Mills, N. J., Schreiber, S. J. & Ellis, C. K. A physiologically based tritrophic perspective on bottom-up-top-down regulation of populations. Ecology 75, 2227–2242 (1994).Article 

Google Scholar 
63.Mills, N. J. & Gutierrez, A. P. in Theoretical Approaches to Biological Control (eds. Hawkins, B. A. & Cornell, V. H.) (Cambridge University Press, 1999).64.Barlow, N. D. in Theoretical Approaches to Biological Control (eds. Hawkins, B. A. & Cornell, H. V.) 43–70 (Cambridge University Press, 1999).65.Manetsch, T. J. Time-varying distributed delays and their use in aggregative models of large systems. IEEE Trans. Syst. Man Cybern. 6, 547–553 (1976).Article 

Google Scholar 
66.Buffoni, G. & Pasquali, S. Structured population dynamics: continuous size and discontinuous stage structures. J. Math. Biol. 54, 555–595 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Di Cola, G., Gilioli, G. & Baumgärtner, J. in Ecological Entomology (eds. Huffaker, C. B. & Gutierrez, A. P.) (Wiley, 1999).68.Severini, M., Alilla, R., Pesolillo, S. & Baumgärtner, J. Fenologia della vite e della Lobesia botrana (Lep. Tortricidae) nella zona dei Castelli Romani. Riv. Ital. Agrometeorol. 3, 34–39 (2005).
Google Scholar 
69.Vansickle, J. Attrition in distributed delay models. IEEE Trans. Syst. Man Cybern. 7, 635–638 (1977).Article 

Google Scholar 
70.Wang, Y. H. & Gutierrez, A. P. An assessment of the use of stability analyses in population ecology. J. Anim. Ecol. 49, 435–452 (1980).Article 

Google Scholar 
71.Briére, J. F., Pracros, P., Le Roux, A. Y. & Pierre, J. S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).Article 

Google Scholar 
72.Frazer, B. D. & Gilbert, N. Coccinellids and aphids: a quantitative study of the impact of adult ladybirds (Coleoptera: Coccinellidae) preying on field populations of pea aphids (Homoptera: Aphididae). J. Entomol. Soc. Br. Columbia 73, 33–56 (1976).
Google Scholar 
73.Gutierrez, A. P. & Baumgärtner, J. U. Multitrophic level models of predator-prey energetics: I. Age-specific energetics models—pea aphid Acyrthosiphon pisum (Homoptera: Aphididae) as an example. Can. Entomol. 116, 924–932 (1984).
Google Scholar 
74.Bieri, M., Baumgärtner, J., Bianchi, G., Delucchi, V. & von Arx, R. Development and fecundity of pea aphid (Acyrthosiphon pisum Harris) as affected by constant temperatures and by pea varieties. Mitteilungen Schweiz. Entomol. Ges. 56, 163–171 (1983).
Google Scholar 
75.Messenger, P. S. & Flitters, N. E. Effect of constant temperature environments on the egg stage of three species of Hawaiian fruit flies. Ann. Entomol. Soc. Am. 51, 109–119 (1958).Article 

Google Scholar 
76.Carey, J. R. Demography and population dynamics of the Mediterranean fruit fly. Ecol. Model. 16, 125–150 (1982).Article 

Google Scholar 
77.Muñiz, M. & Gil, A. Laboratory studies on isolated pairs of Ceratitis capitata—results obtained during the last three years in Spain. In: Cavalloro R (ed), Fruit flies of economic importance; Joint Ad-Hoc Meeting of the Commission of the European Communities and the International Organization for Biological and Integrated Control, Hamburg, West Germany, A.A. Balkema, Rotterdam, Netherlands; Boston, MA, USA, 125–128 (1984).78.Vargas, R. I., Walsh, W. A., Jang, E. B., Armstrong, J. W. & Kanehisa, D. T. Survival and development of immature stages of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures. Ann. Entomol. Soc. Am. 89, 64–69 (1996).Article 

Google Scholar 
79.Vargas, R. I., Walsh, W. A., Kanehisa, D., Jang, E. B. & Armstrong, J. W. Demography of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures. Ann. Entomol. Soc. Am. 90, 162–168 (1997).Article 

Google Scholar 
80.Vargas, R. I., Walsh, W. A., Kanehisa, D., Stark, J. D. & Nishida, T. Comparative demography of three Hawaiian fruit flies (Diptera:Tephritidae) at alternating temperatures. Ann. Entomol. Soc. Am. 93, 75–81 (2000).Article 

Google Scholar 
81.Delrio, G., Conti, B. & Corvetti, A. Effects of abiotic factors on Ceratitis capitata (Wied.) (Diptera: Tephritidae)—I. Egg development under constant temperatures. In Fruit Flies of Economic Importance 84. Proceedings of the CEC/IOBC “Ad-hoc Meeting” (ed. Cavalloro, R.) 133–139 (A.A. Balkema, 1984).82.Duyck, P. F., Sterlin, J. F. & Quilici, S. Survival and development of different life stages of Bactrocera zonata (Diptera: Tephritidae) reared at five constant temperatures compared to other fruit fly species. Bull. Entomol. Res. 94, 89–93 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Powell, M. R. Modeling the response of the Mediterranean fruit fly (Diptera:Tephritidae) to cold treatment. J. Econ. Entomol. 96, 300–310 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Shoukry, A. & Hafez, M. The biology of the Mediterranean fruit fly Ceratitis capitata. Entomol. Exp. Appl. 26, 33–39 (1979).Article 

Google Scholar 
85.Duyck, P. F., David, P. & Quilici, S. Climatic niche partitioning following successive invasions by fruit flies in La Réunion. J. Anim. Ecol. 75, 518–526 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
86.Dhillon, M. K., Singh, R., Naresh, J. S. & Sharma, H. C. The melon fruit fly, Bactrocera cucurbitae: a review of its biology and management. J. Insect Sci. Ludhiana 5, 40 (2005).CAS 

Google Scholar 
87.Messenger, P. S. & Flitters, N. E. Bioclimatic studies of three species of fruit flies in Hawaii. J. Econ. Entomol. 47, 756–765 (1954).Article 

Google Scholar 
88.Keck, C. B. Effect of temperature on development and activity of the melon fly. J. Econ. Entomol. 44, 1001–1002 (1951).Article 

Google Scholar 
89.Yang, P., Carey, J. R. & Dowell, R. V. Comparative demography of two cucurbit-attacking fruit flies, Bactrocera tau and B. cucurbitae (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 87, 538–545 (1994).Article 

Google Scholar 
90.Vayssières, J. F., Carel, Y., Coubes, M. & Duyck, P. F. Development of immature stages and comparative demography of two cucurbit-attacking fruit flies in Reunion Island: Bactrocera cucurbitae and Dacus ciliatus (Diptera Tephritidae). Environ. Entomol. 37, 307–314 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
91.Huang, Y. B. & Chi, H. Age-stage, two-sex life tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) with a discussion on the problem of applying female age-specific life tables to insect populations. Insect Sci. 19, 263–273 (2012).Article 

Google Scholar 
92.Kandakoor, S. B., Chakravarthy, A. K., Rashmi, M. A. & Verghese, A. Effect of elevated carbon dioxide and temperature on biology of melon fruit fly, Bactrocera cucurbitae Coquillett (Tephritidae: Diptera). Afr. Entomol. 27, 36–42 (2019).Article 

Google Scholar 
93.Teruya, T. Effects of relative humidity during pupal development on subsequent eclosion and flight capability of the melon fly, Dacus cucurbitae Coquillett (Diptera:Tephiritidae). Appl. Entomol. Zool. 25, 521–523 (1990).Article 

Google Scholar 
94.Laskar, N. & Chatterjee, H. The effect of meteorological factors on the population dynamics of melon fly, Bactrocera cucurbitae (Coq.) (Diptera: Tephritidae) in the foot hills of Himalaya. J. Appl. Sci. Environ. Manag. 14, 53–58 (2010).95.Myers, S. W., Cancio-Martinez, E., Hallman, G. J., Fontenot, E. A. & Vreysen, M. J. B. Relative tolerance of six Bactrocera (Diptera: Tephritidae) species to phytosanitary cold treatment. J. Econ. Entomol. 109, 2341–2347 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
96.Zhou, S. H., Li, L., Zeng, B. & Fu, Y. G. Effects of short-term high-temperature conditions on oviposition and differential gene expression of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae. Int. J. Pest Manag. 66, 332–340 (2020).Article 
CAS 

Google Scholar 
97.Vargas, R. I. et al. Area-wide suppression of the Mediterranean fruit fly, Ceratitis capitata, and the Oriental fruit fly, Bactrocera dorsalis, in Kamuela, Hawaii. J. Insect Sci. 10, 135 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
98.Vargas, R. I. & Carey, J. R. Comparative survival and demographic statistics for wild Oriental fruit fly, Mediterranean fruit fly, and melon fly (Diptera: Tephritidae) on papaya. J. Econ. Entomol. 83, 1344–1349 (1990).Article 

Google Scholar 
99.Jang, E. B., Nagata, J. T., Chan, H. T. & Laidlaw, W. G. Thermal death kinetics in eggs and larvae of Bactrocera latifrons (Diptera: Tephritidae) and comparative thermotolerance to three other tephritid fruit fly species in Hawaii. J. Econ. Entomol. 92, 684–690 (1999).Article 

Google Scholar 
100.Xie, Q., Hou, B. & Zhang, R. Thermal responses of oriental fruit fly (diptera: tephritidae) late third instars: mortality, puparial morphology, and adult emerge. J. Econ. Entomol. 101, 736–741 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Armstrong, J. W., Tang, J. & Wang, S. Thermal death kinetics of Mediterranean, Malaysian, melon, and oriental fruit fly (Diptera: Tephritidae) eggs and third instars. J. Econ. Entomol. 102, 522–532 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
102.Choi, K. S., Samayoa, A. C., Hwang, S.-Y., Huang, Y.-B. & Ahn, J. J. Thermal effect on the fecundity and longevity of Bactrocera dorsalis adults and their improved oviposition model. PLOS ONE 15, e0235910 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Shukla, R. P. & Prasad, V. G. Population fluctuations of the oriental fruit fly, Dacus dorsalis Hendel in relation to hosts and abiotic factors. Trop. Pest Manag. 31, 273–275 (1985).Article 

Google Scholar 
104.Hurtado, H. et al. Demography of three Mexican tephritids: Anastrepha ludens, A. obliqua and A. serpentina. Fla. Entomol. 71, 110–120 (1988).
Google Scholar 
105.Liedo, P., Carey, J. R., Celedonio, H. & Guillen, J. Size specific demography of three species of Anastrepha fruit flies. Entomol. Exp. Appl. 63, 135–142 (1992).Article 

Google Scholar 
106.Carey, J. R. et al. Biodemography of a long-lived tephritid: Reproduction and longevity in a large cohort of female Mexican fruit flies, Anastrepha ludens. Exp. Gerontol. 40, 793–800 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
107.Berrigan, D. A., Carey, J. R., Guillen, J. & Celedonio, H. Age and host effects on clutch size in the Mexican fruit fly, Anastrepha ludens. Entomol. Exp. Appl. 47, 73–80 (1988).Article 

Google Scholar 
108.Quintero‐Fong, L. et al. Demography of a genetic sexing strain of Anastrepha ludens (Diptera: Tephritidae): effects of selection based on mating performance. Agric. Entomol. 20, 1–8 (2018).Article 

Google Scholar 
109.Tejeda, M. T. et al. Reasons for success: rapid evolution for desiccation resistance and life-history changes in the polyphagous fly Anastrepha ludens. Evolution 70, 2583–2594 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
110.Darby, H. H. & Kapp, E. M. Observations on the thermal death points of Anatrepha ludens (Loew). US Dep. Agric. Tech. Bull. 400, 12445 (1933).111.Flitters, N. E. & Messenger, P. S. Effect of temperature and humidity on development and potential distribution of the Mexican fruit fly in the United States. U. S. Dep. Agric. Tech. Bull. 1330, 1–36 (1965).112.Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric. Meteorol. 200, 233–248 (2015).Article 

Google Scholar 
113.Rienecker, M. M. et al. MERRA: NASA’s Modern-Era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).Article 

Google Scholar 
114.Dell’Aquila, A. et al. Effects of seasonal cycle fluctuations in an A1B scenario over the Euro-Mediterranean region. Clim. Res. 52, 135–157 (2012).Article 

Google Scholar 
115.Artale, V. et al. An atmosphere-ocean regional climate model for the Mediterranean area: assessment of a present climate simulation. Clim. Dyn. 35, 721–740 (2010).Article 

Google Scholar 
116.Giorgi, F. & Bi, X. Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys. Res. Lett. 32, L21715 (2005).Article 

Google Scholar 
117.Gualdi, S. et al. The CIRCE simulations: regional climate change projections with realistic representation of the Mediterranean sea. Bull. Am. Meteorol. Soc. 94, 65–81 (2013).Article 

Google Scholar 
118.Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).Article 

Google Scholar 
119.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

Google Scholar 
120.Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).CAS 
Article 

Google Scholar 
121.Rogelj, J., Meinshausen, M. & Knutti, R. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Change 2, 248–253 (2012).Article 

Google Scholar 
122.GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software, Version 7.9.dev. (Open Source Geospatial Foundation. http://grass.osgeo.org, (2021).123.Gutierrez, A. P. & Ponti, L. in Invasive Species and Global Climate Change (eds. Ziska, L. H. & Dukes, J. S.) 271–288 (CABI Publishing, 2014).124.Ponti, L. et al. Bioeconomic analogies as a unifying paradigm for modeling agricultural systems under global change in the context of geographic information systems. Geophys. Res. Abstr. 21, 13677 (2019). EGU2019.
Google Scholar  More

Study site, field survey, and in situ filtration
The field survey was performed in Tateyama Bay (34° 60′ N, 139° 48′ E), central Japan, in the proximity of the Kuroshio warm current facing the Pacific Ocean (Fig. 1). This area has many artificial reefs (ARs) created to improve fishing efficiency for fishers. Among the ARs, we focused on one high-rise steel AR (AR1), with a height of 30 m, where fish tended to aggregate (Fig. 1 and S1). Sampling stations were set up at the AR1 and at six linear distant points extending northeast and southwest. These stations were named E150, E500, E750, W150, W500, and W750, where “W” or “E” and the number of each station name represented northeast or southwest and distance in meters from the AR1, respectively (Table S1 and Fig. 1). Another station was set up at a second AR (AR2: 25 m height) 220 m from AR1 because we found AR2 by chance during the survey (Table S1 and Fig. 1), and it might affect the eDNA concentration at other stations.Figure 1(a) Location of sampling stations, cruise track, and a set net in Tateyama Bay. Gray areas indicate landmasses, a gray bold line indicates cruise track, and gray thin lines indicate depth contours with an interval of 20 m. The maps were created using ArcGIS Software 10.6.0.8321 by ESRI (https://www.esri.com/) based on the municipal boundary data of Japan (Esri Japan) and Global Map Japan (Geospatial information Authority of Japan) as well as the M7000-series isobath data set (Japan Hydrographic Association). A picture of the artificial reef (AR1) (b) taken one year after this survey (June 2019) and pictures of the dominant species, (c) splendid alfonsino (Beryx splendens), (d) chicken grunt (Parapristipoma trilineatum), (e) chub mackerel (Scomber japonicus), (f) red seabream (Pagrus major), and (g) jack mackerel (Trachurus japonicus). Photograph credits: (b) Nariaki Inoue, (c) Fumie Yamaguchi, (d, e, g) Yutaro Kawano, and (f) Masaaki Sato.Full size imageWe conducted water sampling at eight stations for eDNA analysis and performed an acoustic survey for estimating relative fish density using research vessel Takamaru (Japan Fisheries Research and Education Agency: FRA) on May 23, 2018. We started the echo sounder survey at the eastern part of the bay and continued it during the water sampling (Fig. 1). Although the echo sounder survey could not differentiate between fish species, we collected this data to assess the association between the estimated concentration of fish eDNA and the echo intensity measured by the echo sounder. Water sampling began at E750, then continued along the transect line to E150, AR1, W150, W500, W750, before going back to AR2. At each sampling station, we collected 10 L of seawater from both the middle and bottom layers by one cast of two Niskin water samplers (5L × 2 samples) and measured vertical profiles of water temperature and salinity with a conductivity-temperature-depth sensor (RINKO profiler, JFE Advantech Co., Ltd.). We subsampled 2L seawater from the 5 L seawater of Niskin sampler using measuring bottle and remaining 3 L seawater was used for pre-wash of measuring bottle and filtration devices. Two 2L samples were collected from two Niskin water samples, and then immediately filtered using a combination of Sterivex filter cartridges (nominal pore size = 0.45 μm; Merck Millipore) through an aspirator (i.e., the two filters were subsets of a single water collection) in a laboratory on the research vessel. After filtration (average time of 15 min), an outlet port of the filter cartridge was sealed with an outlet luer cap, 1.5 ml RNAlater (Thermo Fisher Scientific Inc., Waltham, MA) was injected into the cartridge using a filtered pipette tip to prevent eDNA degradation, and an inlet port was also sealed with an inlet luer cap14. The Niskin water samplers were bleached before each water collection using a commercial bleach solution while filtering devices (i.e., filter funnels and measuring cups used for filtration) were bleached after every filtration. We filtered 2L MilliQ water with a filter funnel and measuring cup as a field negative control to test for possible contamination. The filter cartridges were placed in a freezer immediately after filtration until eDNA extraction. In total we collected and filtered 32 eDNA samples (eight stations × two depth layers × two replicates). Disposable latex or nitrile gloves were worn during sampling and replaced between each sampling station.DNA extraction and purificationWorkspaces were sterilized prior to DNA extraction using 10% commercial bleach, and filter tip pipettes were used to safeguard against cross-contamination. Following the method developed by Miya et al.15, the eDNA was extracted and purified. Briefly, after removing RNAlater inside the cartridge using a centrifuge, proteinase-K solution was injected into the cartridge from the inlet port, and the port was re-capped with the inlet lure cap. The eDNA captured on the filter membrane was extracted by constant stirring of the cartridge at a speed of 20 rpm using a roller shaker placed in an incubator heated at 56 °C for 20 min. The eDNA extracts were transferred to a 2-ml tube from the inlet of the filter cartridges by centrifugation. The collected DNA was purified using a DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s protocol. After the purification, DNA was eluted using 100 μl of the elution buffer (buffer AE). All DNA extracts were frozen at − 20 °C until paired-end library preparation.Preparation of internal standard DNAsFive artificially designed and synthetic internal standard DNAs, which were similar but not identical to the region of any existing fish mitochondrial 12S rRNA, were included in the library preparation process to estimate the number of fish DNA copies [i.e., quantitative MiSeq sequencing (qMiseq)]7,16. They were designed to have the MiFish primer‐binding regions as those of known existing fishes and to have the conserved regions in the insert region. Variable regions in the insert region were replaced with random bases so that no known existing fish sequences had the same sequences as the standard sequences. The standard DNA size distribution of the library was estimated using an Agilent 2100 BioAnalyzer (Agilent, Santa Clara, CA, USA), and the concentration of double-stranded DNA of the library was quantified using a Qubit dsDNA HS assay kit and a Qubit fluorometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). Based on the quantification values obtained using the Qubit fluorometer, the copy number of the standard DNAs was adjusted as follows: Std. A (100 copies/µl), Std. B (50 copies/µl), Std. C (25 copies/µl), Std. D (12.5 copies/µl) and Std. E (2.5 copies/µl). Then, these standard DNAs were mixed.Paired-end library preparationTwo PCR‐level negative controls (i.e., each with and without internal standard DNAs) were employed for MiSeq run to monitor contamination during the experiments. The first-round PCR (1st PCR) was carried out with a 12-µl reaction volume containing 6.0 µl of 2 × KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland), 0.7 µl of each primer (5 µM), 2.6 µl of sterilized distilled H2O, 1.0 µl of standard DNA mix and 1.0 µl of template. Note that the standard DNA mix was included for each sample. The final concentration of each primer was 0.3 µM. We used a mixture of the following four PCR primers modified from original MiFish primers16: MiFish-U-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TCG GTA AAA CTC GTG CCA GC-3′) and MiFish-U-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCC AGT TTG-3′), MiFish-E-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TTG GTA AAT CTC GTG CCA GC-3′), and MiFish-E-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCT AGT TTG-3′). These primer pairs co-amplify a hypervariable region of the fish mitochondrial 12S rRNA gene (around 172 bp) and append primer-binding sites (5′ ends of the sequences before five Ns) for sequencing at both ends of the amplicon. The five random bases were used to enhance cluster separation on the flow cells during initial base call calibrations on the MiSeq platform. The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (35 cycles): denaturation at 98 (^circ)C for 20 s; annealing at 65 (^circ)C for 15 s; and extension at 72 (^circ)C for 15 s, with a final extension at the same temperature for 5 min. Eight replications were performed for the 1st PCR, and the replicates were pooled to minimize the PCR dropouts. The 1st PCR products from the eight tubes were pooled in a single 1.5-ml tube. Then, we sent the 1st PCR products to IDEA consultants, Inc. to outsource the following MiSeq sequencing processes. The pooled products were purified and size-selected for 200–400 bp using a SPRIselect (Beckman Coulter, Inc.) to remove dimers and monomers following the manufacturer’s protocol.The second-round PCR (2nd PCR) was carried out with a 24 µl reaction volume containing 12 µl of 2 × KAPA HiFi HotStart ReadyMix, 2.8 µl of each primer (5 µM), 4.4 µl of sterilized distilled H2O, and 2.0 µl of template. We used the following two primers to append the dual-index sequences (8 nucleotides indicated by Xs) and flowcell-binding sites for the MiSeq platform (5′ ends of the sequences before eight Xs): 2nd-PCR-forward (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACX XXX XXX XAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′); and 2nd- PCR-reverse (5′-CAA GCA GAA GAC GGC ATA CGA GAT XXX XXX XXG TGA CTG GAG TTC AGA CGT GTG CTC TTC CGA TCT-3′). The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (12 cycles): denaturation at 98 (^circ)C for 20 s; combined annealing and extension at 72 (^circ)C for 15 s, with a final extension at 72 (^circ)C for 5 min. The concentration of each second PCR product was measured by quantitative PCR using TB Green Fast qPCR Mix (Takara inc.). Each sample was diluted to a fixed concentration and combined (i.e., one pooled 2nd PCR product that included all samples). The pooled 2nd PCR product was size-selected to approximately 370 bp using BluePippin (Sage Science). The size-selected library was purified using the Agencourt AMPure XP beads, adjusted to 4 nM by quantitative PCR using TB Green Fast qPCR Mix (Takara Bio Inc.), and sequenced on the MiSeq platform using a MiSeq v2 Reagent Kit (2 × 150 bp) (Illumina, Inc.).Data preprocessing and taxonomic assignmentThe raw MiSeq data were converted into FASTQ files using the bcl2fastq program provided by Illumina (bcl2fastq v2.18). The FASTQ files were then demultiplexed using the command implemented in Claident17. We adopted this process rather than using FASTQ files demultiplexed by the Illumina MiSeq default program in order to remove sequences with low-quality scores and PCR artifacts (chimeras).The processed reads were subjected to a BLASTN search against the full NCBI database. We excluded unique sequences of the following settings: the sequence belonged to organisms other than bony fishes, sharks, and rays; the sequence similarity between queries and the top BLASTN hit was  More

1.Kang CS, Liang X, Dershwitz P, Gu W, Schepers A, Flatley A, et al. Evidence for methanobactin “theft” and novel chalkophore production in methanotrophs: impact on methanotrophic-mediated methylmercury degradation. ISME J. 2021;https://doi.org/10.1038/s41396-021-01062-1.2.Semrau JD, DiSpirito AA, Obulisamy PK, Kang-Yun CS. Methanobactin from methanotrophs: genetics, structure, function and potential applications. FEMS Microbiol Lett. 2020;367:fnaa045.CAS 
Article 

Google Scholar 
3.Kim HJ, Graham DW, DiSpiito AA, Alterman MA, Galeva N, Larive CK, et al. Methanobactin: a copper-acquisition compound from methane-oxidizing bacteria. Science. 2004;305:1612–5.CAS 
Article 

Google Scholar 
4.Lu X, Gu W, Zhao L, Farhan Ul Haque M, DiSpirito AA, Semrau JD, et al. Methylmercury uptake and degradation by methanotrophs. Sci Adv. 2017;3:e1700041.Article 

Google Scholar 
5.Ve T, Mathisen K, Helland R, Karlsen OA, Fjellbirkeland A, Røhr ÅK, et al. The Methylococcus capsulatus (Bath) secreted protein, MopE*, binds both reduced and oxidized copper. PLoS ONE. 2012;7:e43146.CAS 
Article 

Google Scholar 
6.DiSpirito AA, Semrau JD, Murrell JC, Gallagher WH, Dennison C, Vuilleumier S. Methanobactin and the link between copper and bacterial methane oxidation. Microbiol Mol Biol Rev. 2016;80:387–409.CAS 
Article 

Google Scholar 
7.Kenney GE, Rosenzweig AC. Genome mining for methanobactins. BMC Biol. 2013;11:17.CAS 
Article 

Google Scholar 
8.Yu Z, Zheng Y, Huang J, Chistoserdova L. Systems biology meets enzymology: recent insights into communal metabolism of methane and the role of lanthanides. Curr Issues Mol Biol. 2019;33:183–96.Article 

Google Scholar 
9.Gwak J-H, Jung M-Y, Hong HY, Kim J-G, Quan Z-X, Reinfelder JR, et al. Archaeal nitrification is constrained by copper complexation with organic matter in municipal wastewater treatment plants. ISME J. 2020;14:335–46.CAS 
Article 

Google Scholar 
10.Chang J, Kim DD, Semrau JD, Lee J, Heo H, Gu W, et al. Enhancement of nitrous oxide emissions in soil microbial consortia via copper competition between proteobacterial methanotrophs and denitrifiers. Appl Environ Microbiol. 2020;87:e02301–20.
Google Scholar  More