Ecology

1.Revelle, R. & Suess, H. E. Carbon dioxide exchange between atmosphere and ocean and the question of an increase of atmospheric CO2 during the past decades. Tellus 9, 18–27 (1957).ADS 
CAS 

Google Scholar 
2.Frankignoulle, M. A complete set of buffer factors for acid/base CO2 system in seawater. J. Mar. Syst. 5, 111–118 (1994).
Google Scholar 
3.Egleston, E. S., Sabine, C. L. & Morel, F. M. M. Revelle revisited: Buffer factors that quantify the response of ocean chemistry to changes in DIC and alkalinity. Glob. Biogeochem. Cycles 24, GB1002 (2010).ADS 

Google Scholar 
4.Bates, N. et al. A time-series view of changing surface ocean chemistry due to ocean uptake of anthropogenic CO2 and ocean acidification. Oceanography 27(1), 126–141 (2014).MathSciNet 

Google Scholar 
5.Lauvset, S., Gruber, N., Landschützer, P., Olsen, A. & Tjiputra, J. Trends and drivers in global surface ocean pH over the past 3 decades. Biogeosciences 12(5), 1285–1298 (2015).ADS 

Google Scholar 
6.Ríos, A. F. et al. Decadal acidification in the Atlantic. Proc. Natl. Acad. Sci. 112(32), 9950–9955 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
7.Schulz, K. G. & Riebesell, U. Diurnal changes in seawater carbonate chemistry speciation at increasing atmospheric carbon dioxide. Mar. Biol. 160, 1889–1899 (2013).CAS 
PubMed 

Google Scholar 
8.Provoost, P., van Heuven, S., Soetaert, K., Laane, R. W. P. M. & Middelburg, J. J. Seasonal and long-term changes in pH in the Dutch coastal zone. Biogeosciences 7, 3869–3878 (2010).ADS 
CAS 

Google Scholar 
9.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: A multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Borges, A. V. & Gypens, N. Carbonate chemistry in the coastal zone responds more strongly to eutrophication than ocean acidification. Limnol. Oceanogr. 55, 346–353 (2010).ADS 
CAS 

Google Scholar 
11.Cai, W.-J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).ADS 
CAS 

Google Scholar 
12.Sunda, W. G. & Cai, W.-J. Eutrophication induced CO2-acidification of subsurface coastal waters: Interactive effects of temperature, salinity, and atmospheric pCO2. Environ. Sci. Technol. 46, 10651–10659 (2012).ADS 
CAS 
PubMed 

Google Scholar 
13.Jury, C. P., Thomas, F. I. M., Atkinson, M. J. & Toonen, R. J. Buffer capacity, ecosystem feedbacks, and seawater chemistry under global change. Water 5, 1303–1325 (2013).CAS 

Google Scholar 
14.Hagens, M. et al. Biogeochemical processes and buffering capacity concurrently affect acidification in a seasonally hypoxic coastal marine basin. Biogeosciences 12, 1561–1583 (2015).ADS 

Google Scholar 
15.Santschi, P., Höhener, P., Benoit, G. & Buchholtz-ten, B. M. Chemical processes at the sediment–water interface. Mar. Chem. 30, 269–315 (1990).CAS 

Google Scholar 
16.Pawlik, J. R. Chemical ecology of the settlement of benthic marine invertebrates. Oceangr. Mar. Biol. Annu. Rev. 30, 273–335 (1992).
Google Scholar 
17.Marinelli, R. L. & Woodin, S. A. Experimental evidence for linkages between infaunal recruitment, disturbance, and sediment surface chemistry. Limnol. Oceanogr. 47(1), 221–229 (2002).ADS 
CAS 

Google Scholar 
18.Clements, J. C. & Hunt, H. L. Marine animal behaviour in a high CO2 ocean. Mar. Ecol. Prog. Ser. 536, 259–279 (2015).ADS 
CAS 

Google Scholar 
19.Vopel, K., Laverock, B., Cary, C. & Pilditch, C. A. Effects of warming and CO2 enrichment on O2 consumption, porewater oxygenation and pH of subtidal silt sediment. Aquat. Sci. 83, 8 (2021).CAS 

Google Scholar 
20.Green, M. A., Jones, M. E., Boudreau, C. L., Moore, R. L. & Westman, B. A. Dissolution mortality of juvenile bivalves in coastal marine deposits. Limnol. Oceanogr. 49(3), 727–734 (2004).ADS 

Google Scholar 
21.Green, M. A., Waldbusser, G., Reilly, S., Emerson, K. & O’Donnell, S. Death by dissolution: Sediment saturation state as a mortality factor for juvenile bivalves. Limnol. Oceanogr. 54(4), 1037–1047 (2009).ADS 
CAS 

Google Scholar 
22.Green, M. A., Waldbusser, G. G., Hubazc, L., Cathcart, E. & Hall, J. Carbonate mineral saturation state as the recruitment cue for settling bivalves in marine muds. Estuaries Coasts 36, 18–27 (2013).CAS 

Google Scholar 
23.Clements, J. C., Woodard, K. D. & Hunt, H. L. Porewater acidification alters the burrowing behavior and post-settlement dispersal of juvenile soft-shell clams (Mya arenaria). J. Exp. Mar. Biol. Ecol. 477, 103–111 (2016).
Google Scholar 
24.Ries, J. B., Ghazaleh, M. N., Connolly, B., Westfield, I. & Castillo, K. D. Impacts of seawater saturation state (ΩA = 0.4–4.6) and temperature (10, 25 °C) on the dissolution kinetics of whole-shell biogenic carbonates. Geochim. Cosmochim. Acta 192, 318–337 (2016).ADS 
CAS 

Google Scholar 
25.Nimer, N. A., Brownlee, C. & Merrett, M. J. Extracellular carbonic anhydrase facilitates carbon dioxide availability for photosynthesis in the marine dinoflagellate Prorocentrum micans. Plant Physiol. 120, 105–112 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Hopkinson, B. M., Meile, C. & Shen, C. Quantification of extracellular carbonic anhydrase activity in two marine diatoms and investigation of its role. Plant Physiol. 162, 1142–1152 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Tachibana, M. et al. Localization of putative carbonic anhydrase in two marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana. Photosynth. Res. 109, 205–221 (2011).CAS 
PubMed 

Google Scholar 
28.Samukawa, M., Shen, C., Hopkinson, B. M. & Matsuda, Y. Localization of putative carbonic anhydrases in the marine diatom, Thalassiosira pseudonana. Photosynth. Res. 121, 235–249 (2014).CAS 
PubMed 

Google Scholar 
29.Matsuda, Y., Hopkinson, B. M., Nakajima, K., Dupont, C. L. & Tsuji, Y. Mechanisms of carbon dioxide acquisition and CO2 sensing in marine diatoms: A gateway to carbon metabolism. Philos. Trans. R. Soc. B 372, 20160403 (2017).
Google Scholar 
30.Milligan, A. J. & Morel, F. M. M. A proton buffering role for silica in diatoms. Science 297, 1848–1850 (2002).ADS 
CAS 
PubMed 

Google Scholar 
31.Subhas, A. V. et al. Catalysis and chemical mechanisms of calcite dissolution in seawater. Proc. Natl. Acad. Sci. 114, 8175–8180 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Middelburg, J. J., Soetaert, K. & Hagens, M. Ocean alkalinity, buffering and biogeochemical processes. Rev. Geophys. 58, e2019RG000681 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
33.Soetaert, K., Hofmann, A. F., Middelburg, J. J., Meysman, F. J. R. & Greenwood, J. The effect of biogeochemical processes on pH. Mar. Chem. 105, 30–51 (2007).CAS 

Google Scholar 
34.Zhu, Q., Aller, R. C. & Fan, Y. Two-dimensional pH distributions and dynamics in bioturbated marine sediments. Geochim. Cosmochim. Acta 70, 4933–4949 (2006).ADS 
CAS 

Google Scholar 
35.Vopel, K., Del-Río, C. & Pilditch, C. A. Effects of CO2 enrichment on benthic primary production and inorganic nitrogen fluxes in two coastal sediments. Sci. Rep. 8, 1035 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
36.Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanzen 167, 191–194 (1975).CAS 

Google Scholar 
37.Dickson, A. G., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements: PICES Special Publication 3. http://cdiac.ornl.gov/oceans/Handbook_2007.html (2007).38.Lewis, E. & Wallace, D. W. R. Program Developed for CO2 System Calculations. ORNL/CDIAC-105 (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, 1998).
Google Scholar 
39.Dickson, A. G. Standard potential of the reaction: AgCl(s) + 12H2(g) = Ag(s) + HCL(aq), and the standard acidity constant of the ion HSO4− in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22, 113–127 (1990).CAS 

Google Scholar 
40.Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicz, R. N. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).ADS 
CAS 

Google Scholar 
41.Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissolution of carbonic acid in seawater media. Deep Sea Res. 34(10), 1733–1743 (1987).ADS 
CAS 

Google Scholar 
42.Berg, P. N., Risgaard-Petersen, N. & Rysgaard, S. Interpretation of measured concentration profiles in sediment pore water. Limnol. Oceanogr. 43, 1500–1510 (1998).ADS 
CAS 

Google Scholar 
43.Revsbech, N. P., Nielsen, L. P. & Ramsing, N. B. A novel microsensor for determination of apparent diffusivity in sediments. Limnol. Oceanogr. 43, 986–992 (1998).ADS 
CAS 

Google Scholar 
44.Vopel, K., Pilditch, C. A., Wilson, P. & Ellwood, M. J. Oxidation of surface sediment: Effects of disturbance depth and seawater flow speed. Mar. Ecol. Prog. Ser. 392, 43–55 (2009).ADS 
CAS 

Google Scholar 
45.Broecker, W. S. & Peng, T.-H. Gas exchange rates between air and sea. Tellus 26(1–2), 21–35 (1974).ADS 
CAS 

Google Scholar 
46.Cussler, E. L. Diffusion: Mass Transfer in Fluid Systems (Cambridge University Press, 2009).
Google Scholar 
47.Li, Y.-H. & Gregory, S. Diffusion of ions in sea water and in deep-sea sediments. Geochim. Cosmochim. Acta 38(5), 703–714 (1974).ADS 
CAS 

Google Scholar 
48.Ullman, W. J. & Aller, R. C. Diffusion coefficients in nearshore marine sediments. Limnol. Oceanogr. 27(3), 552–556 (1982).ADS 
CAS 

Google Scholar 
49.Jørgensen, B. B. & Revsbech, N. P. Diffusive boundary layers and the oxygen uptake of sediments and detritus. Limnol. Oceanogr. 30(1), 111–122 (1985).ADS 

Google Scholar 
50.Rasmussen, H. & Jørgensen, B. B. Microelectrode studies of seasonal oxygen uptake in a coastal sediment: Role of molecular diffusion. Mar. Ecol. Prog. Ser. 81, 289–303 (1992).ADS 
CAS 

Google Scholar 
51.Nordstrom, D. K., Jenne, E. A. & Ball, J. W. Redox equilibria of iron in acid mine waters. In Chemical Modeling in Aqueous Systems. American Chemical Society Symposium Series Vol. 93 (ed. Jenne, E. A.) 57–79 (American Chemical Society, 1979).
Google Scholar 
52.Dushoff, J., Kain, M. P. & Bolker, B. M. I can see clearly now: Reinterpreting statistical significance. Methods Ecol. Evol. 10, 756–759 (2019).
Google Scholar  More

Case studyThe Zayandeh-Rud basin (Fig. 1), a arid region of Iran, was selected to evaluate the SPIs. The Zayandeh-Rud basin is located in the central part of Iran. It has an area of 26,972 km2 area, where there are multiple water stakeholders such as agriculture, industry, urban and the environment sectors, with agriculture being the main user of the basin. Water resources in the basin are divided into surface water and groundwater. Approximately 100,000 ha among 113,000 ha of the agricultural area is irrigated by Zayandeh-Rud dam, and 3100 mm3 of water resources are used in the agricultural sector. The main surface water source in the basin, Zayandeh-Rud River originates in the Zagros Mountains and is about 350 km long in a west to east direction passing by the city of Isfahan. The Zayandeh-Rud River is an important water source for the agricultural, industrial, health, and urban sectors in Central Iran and the Chaharmahal-Bakhtiari and Isfahan provinces.Figure 1The location of the Zayandeh-Rud basin in Iran.Full size imageMulti-criteria decision makingMulti-criteria decision making includes two categories of multi-objective decision making and multi-criteria decision making, which are implemented to select the best decision among several alternatives or to evaluate decisions. This work applies decision making as a multi-criteria decision to achieve a goal. Each decision includes objectives, alternatives, and criteria. A problem’s goal is first defined. Alternatives are different options for wastewater management in this instance that are assigned weights based on their contribution to achieving the goal. Criteria are also factors that are measured by the purpose of the alternatives23. The AHP method helps achieve a defined goal after completing the steps outlined below.The AHP methodThe Analytical Hierarchy Process (AHP), developed by Saaty24, is a multi-criteria decision-making method for solving complex problems. It combines objective and quantitative evaluation in an integrated manner based on multi-level comparisons, and helps organize the essential aspects of a problem into a hierarchical format. It regularly organizes tangible and intangible factors and offers a structured and a relatively simple solution to decision problems. The AHP method ranks alternatives propose to tackle a decision-making problem. The ranking is based through a sequence of pairwise comparisons of evaluation criteria and sub-criteria.The AHP structureIn a hierarchical structure the communication flow is top-down. First, indicators and evaluation criteria are defined from experts who are asked for their expert opinions. The criteria serve the purpose of determining the relative worth of alternatives entertained to solve a multi-criteria decision-making problem. Thereafter, the problem is divided into criteria and sub-criteria for the evaluation of alternatives. Figure 2 depicts a generic AHP structure depicting a goal to be met with (n) = 4 evaluation criteria, and (m=3) alternatives to cope with a problem (in our case SIPs).Figure 2Goal, criteria, and alternatives in a generic hierarchical structure.Full size imageThe pairwise comparison matrixThe pairwise comparison matrix ((A)), called the Saaty Hierarchy Matrix, measures the importance of each criterion (or sub-criterion) relative to other criteria based on a numeric scale ranging from 1 to 9. Criteria that are extremely preferred, very strongly preferred, strongly preferred, moderately preferred, and equally preferred are assigned the values 9, 7, 5, 3, and 1, respectively, in the scale of preference; intermediate values are assigned to adjacent scales of preference. Thus, the values 8, 6, 4, and 2 are assigned respectively to the adjacent scales (9,7), (7,5), (5,3), and (3,1)24. These numerical assignment of values is made based on the opinion of experts25. The pairwise comparison matrix ((A)), therefore, represents a set of relative weights assigned to the criteria23. The general form of a pairwise comparison matrix when there are (n) evaluation criteria is written in Eq. (1):$$A=left[{a}_{ij}right]=left[begin{array}{cccc}{1=w}_{1}/{w}_{1}& {w}_{1}/{w}_{2}& dots & {w}_{1}/{w}_{n}\ {w}_{2}/{w}_{1}& 1={w}_{2}/{w}_{2}& dots & {w}_{2}/{w}_{n}\ .& .& dots & .\ .& .& dots & .\ .& .& dots & .\ {w}_{n}/{w}_{1}& {w}_{n}/{w}_{2}& …& 1={w}_{n}/{w}_{n}end{array}right]$$
(1)

where ({w}_{i}/{w}_{j}) denotes the weight assigned to the (i)-th criterion relative to the (j)-th criterion24. Clearly, ({a}_{ji}=1/{a}_{ij}), with ({a}_{ji}={a}_{ij}=1) when (i=j).The ratio matrixThe ratio matrix ((R)) has elements ({r}_{ij}) is calculated by Eq. (2):$$R=left[{r}_{ij}right]=left[begin{array}{cccc}1& {a}_{12}& dots & {a}_{1n}\ 1/{a}_{12}& 1& dots & {a}_{2n}\ .& .& .& .\ .& .& .& .\ .& .& .& .\ 1/{a}_{1n}& 1/{a}_{2n}& dots & 1end{array}right]$$
(2)

clearly, ({r}_{ij}={a}_{ij}) when (jge i), and ({r}_{ij}=1/{a}_{ji}) when (j More

Study site and systemData were collected at Cape Crozier (77°27′S, 169°12′E), Ross Island, one of the largest Adélie penguin breeding colonies (~ 275 000 pairs at the time of the study32), during austral summer 2018–2019. Individuals arrive at Cape Crozier in late October/early November, lay (usually two) eggs in mid-November, and feed their chicks between mid-December and early February. They are one of the few penguin species that can fledge two chicks. During the brood/guard stage, one parent remains with the chick(s) while the other forages at sea. Nest reliefs at Crozier occur every 1–2 days during early chick-rearing and chicks are fed relatively small meals (0.43–0.58 kg) by the attending parent33. After about two weeks, chick demands are too great for adequate provisioning by one parent, so chicks are left on their own (“crèche” stage) while both parents forage simultaneously. Our study period included most of chick-rearing, i.e., all of the guard stage and half the crèche stage, from December 21, 2018 to January 15, 2019.Since 1997, every austral summer, the same subcolony of ~ 200 pairs (152 pairs in the year of study) was surrounded by a plastic fence, leaving only one opening as an access point, where the weighbridge was located30. The weighbridge consisted of an electronic scale, direction indicator, and radio frequency identification (RFID) reader34,35. In 2018–2019, it was installed on November 16 and removed on January 20. A subset of adult individuals were implanted with unique RFID tags beginning in 1997, with a few more added each year30,36. RFID code, date and time, direction, and weight were recorded automatically as the RFID-implanted birds crossed the weighbridge. Adults were captured on the nest during incubation, when they can be approached slowly and gently lifted off their nest. A warm hat was placed over the eggs or small chicks to avoid chilling, while the RFID tag was injected into the bird.All penguin survey, capture and handling methods used for data collection were performed following all relevant guidelines and regulations under the approval and oversight of the Institutional Animal Care and Use Committees of Oregon State University and Point Blue Conservation Science. Additionally, all work was approved and conducted under Antarctic Conservation Act permits issued by the US National Science Foundation and the U.S. Antarctic Program. The study is reported in accordance with ARRIVE guidelines.Diving parametersBetween November 2 and December 7, 2018, we equipped 32 RFID-implanted birds with geolocating dive recorders (“LUL” tags, 22 × 21 × 15 mm, weight = 4 g, from Atesys, Strasbourg, France, hereafter referred to as GDRs) that recorded light every minute, temperature (with a precision of ± 0.5 °C) every 30 s and pressure (with a precision of ± 0.3 m) every second for 12–15 months. Adults were captured using a hand net (2 m long handle) or on the nest during incubation (see above). The GDRs were encapsulated in flexible heat-shrink tubing shaped into a leg strap and attached to the tibio-fibula of each bird in the field using a polyester-coated stainless-steel zip tie to secure the ends of the strap together such that the tag could rotate freely around the leg but not slip over the tarsus joint. Tags were left in place for one year, with 21 recovered at the beginning of the 2019–2020 breeding season. Pressure data were processed in R (v. 3.6.0) with several processes modified from the diveMove package (v. 1.4.5)37. To correct for instrument drift, pressure data were zero offset corrected using the calibrateDepth function38. We used a depth threshold of 3 m to qualify as a dive. Following methods described in previous studies27,39,40, we computed a number of statistics about each dive including dive duration, maximum dive depth, post-dive interval duration, bottom time, the number of undulations (changes of any amplitude in underwater swimming duration from either ascent to descent, or descent to ascent—used for the purposes of categorizing dives) and the number of undulations  > 1 m (changes in underwater swimming direction from ascent to descent  > 1m39). The two undulation metrics are highly correlated (Pearson’s r = 0.92 in our data set). Bottom time was defined as the time spent at  > 60% of maximum depth of dive with  60 h (trip duration during chick-rearing takes 1–2 days on average36,39 but their frequency distribution showed a tail from 60 to 100 h in our data).Figure 2Conceptual visualization of the study design. (a) chick-rearing Adélie penguins breeding in a semi-enclosed subcolony are implanted with a RFID tag and equipped with a leg-mounted time-depth recorder (GDR). (b) Bird ID, departure mass and direction of travel are recorded by the weighbridge as penguins leave the colony to forage at sea. (c) During the foraging trip, the GDR tag records depth every second, enabling the calculation of several dive behavior metrics. (d) Bird ID, return mass and direction of travel are recorded by the weighbridge as penguins return to the colony to feed their chicks.Full size imageBody mass estimationFor each foraging trip, we calculated meal size and body mass change (see Supplementary Information for more details on the weight calculation). Meal size (in kg) is the difference between an individual’s out-mass (departing) and its most recent in-mass (returning from sea), i.e. this is a measure of how much food a parent left in the colony and includes both the food delivered to chicks and the food digested by the parent while attending the nest39. Body mass change (in kg) of individual birds over each foraging trip was calculated as the return mass (post-foraging trip at sea) minus the departure mass (pre-foraging trip at sea). Hence, body mass change measures the amount of food that was collected during the trip at sea (i.e. foraging success43), minus what could have been digested before returning to the colony at the end of this trip (Fig. 2). We further filtered trips based on these two variables, keeping only trips where meal size was  > 0 and  − 0.8 and  1 m per hour, as previous work indicated that undulations in the dive profile represent feeding and/or prey capture16,24,25, (2) dive (underwater) time per hour, (3) dive time per hour during foraging dives only, (4) bottom time per hour, (5) number of foraging dives per hour, (6) Attempts of Catch per Unit Effort (ACPUE, calculated as the number of undulations per trip divided by total bottom duration23,49). We also considered two variables calculated at the scale of dive bouts: (7) mean bout duration, thought to reflect the time spent within a prey patch50,51, (8) number of dives per bout, as an index of the size of the prey patch51,52,53. Dive bouts were defined as successive diving events interrupted by relatively longer surfacing periods. To separate post-dive intervals from inter-bout duration, we used a maximum likelihood approach54 using the diveMove package37 in R, which allowed us to determine a bout-ending-criterion (BEC). In this study, BEC = 47.6 s.Statistical analysesWe first calculated a Pearson correlation matrix using the corrplot package in R and removed highly correlated (r  > 0.7) behavioral covariates, keeping those that were the most correlated with body mass change. To test the hypothesis that some behavioral dive variables can be used to predict the amount of food collected while foraging at sea, we evaluated linear mixed models including body mass change as the dependent variable, each of the selected behavioral variables as independent variables and bird ID as a random effect, as well as a null model (intercept only) using the nlme package55 in R. Once we had determined the most competitive models, and as Adélie penguin’s foraging success can vary according to sex29,36 and chick needs39, and also be influenced by the trip duration56, we added sex, study day (day in the season as a Julian date with Dec 20 = 0) and trip duration (in hours) to the top intrinsic model(s) including potential interactions with the selected behavioral variable(s). A null model was also included in this second model set. Residuals were examined to verify normality, homogeneity of variances, and independence. To evaluate these models and determine the strength of evidence supporting specific effects, we used an information theoretic approach57. Models were ranked using the small-sample-size corrected version of Akaike Information Criterion (AICc), with the best model having the lowest AICc value. We calculated ΔAICc as the difference in AICc between each candidate model and the model with the lowest AICc value, and considered all models within 2 ΔAICc as competitive models57. We determined the strength of evidence supporting specific effects by examining the unstandardized effect sizes (slope coefficients and differences in means) and the associated 95% confidence intervals (CI). If the 95% CI for a parameter in a competitive model (ΔAICc  More

1.Jenkins, P. T. Free trade and exotic species introductions. Conserv. Biol. 10, 300–302 (1996).Article 

Google Scholar 
2.Sharov, A. A. Bioeconomics of managing the spread of exotic pest species with barrier zones. Risk Anal. 24, 879–892 (2004).Article 

Google Scholar 
3.Lodge, D. M. et al. Biological invasions: Recommendations for U.S. policy and management. Ecol. Appl. 16, 2035–2054 (2006).Article 

Google Scholar 
4.Yemshanov, D. et al. Optimizing surveillance strategies for early detection of invasive alien species. Ecol. Econ. 162, 87–99 (2019).Article 

Google Scholar 
5.Hauser, C. E. & Mccarthy, M. A. Streamlining “search and destroy”: Cost-effective surveillance for invasive species management. Ecol. Lett. 12, 683–692 (2009).Article 

Google Scholar 
6.Gottwald, T. R., da Graça, J. V. & Bassanezi, R. B. Citrus Huanglongbing: The pathogen and its impact. Plant Health Prog. https://doi.org/10.1094/PHP-2007-0906-01-RV (2007).Article 

Google Scholar 
7.Anderson, D. P. et al. Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases. Biol. Invasions 19, 2869–2884 (2017).Article 

Google Scholar 
8.Russell, J. C., Binnie, H. R., Oh, J., Anderson, D. P. & Samaniego-Herrera, A. Optimizing confirmation of invasive species eradication with rapid eradication assessment. J. Appl. Ecol. 54, 160–169 (2017).Article 

Google Scholar 
9.Moffitt, L. J., Stranlund, J. K. & Osteen, C. D. Robust detection protocols for uncertain introductions of invasive species. J. Environ. Manag. 89, 293–299 (2008).Article 

Google Scholar 
10.Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin Company, 1921).11.Ben-Haim, Y. Uncertainty, probability and information-gaps. Reliab. Eng. Syst. Saf. 85, 249–266 (2004).Article 

Google Scholar 
12.Johnson, D. R. & Geldner, N. B. Contemporary decision methods for agricultural, environmental, and resource management and policy. Annu. Rev. Resour. Econ. 11, 19–41 (2019).Article 

Google Scholar 
13.Baker, C. M. & Bode, M. Recent advances of quantitative modeling to support invasive species eradication on islands. Conserv. Sci. Pract. 3, e246. https://doi.org/10.1111/csp2.246 (2021).Article 

Google Scholar 
14.Bertsimas, D. & Sim, M. The price of robustness. Oper. Res. 52, 35–53 (2004).MathSciNet 
Article 

Google Scholar 
15.Ben-Haim, Y. & Demertzis, M. Decision making in times of knightian uncertainty: An info-gap perspective. Economics 10, 1–29 (2016).Article 

Google Scholar 
16.Ben-Haim, Y. Management of invasive species: Info-gap perspectives. in Invasive Species: Risk Assessment and Management (eds. Robinson, A., Walshe, T., Burgman, M. A., Nunn, M.) 266–286 (Cambridge University Press, 2017).17.Davidovitch, L. et al. Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island. J. Environ. Manag. 90, 2785–2793 (2009).Article 

Google Scholar 
18.Rout, T. M., Thompson, C. J. & McCarthy, M. A. Robust decisions for declaring eradication of invasive species. J. Appl. Ecol. 46, 782–786 (2009).Article 

Google Scholar 
19.Foxcroft, L. C. Developing thresholds of potential concern for invasive alien species: Hypotheses and concepts. Koedoe. https://doi.org/10.4102/koedoe.v51i1.157 (2009).Article 

Google Scholar 
20.Pitt, J. P. W. Modelling the Spread of Invasive Species Across Heterogeneous Landscapes. (Lincoln University, 2008).21.Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).Article 

Google Scholar 
22.Mcdonald-madden, E., Peter, W. J. B. & Possingham, H. P. Making robust decisions for conservation with restricted money and knowledge. J. Appl. Ecol. 45, 1630–1638 (2008).Article 

Google Scholar 
23.Rout, T. M., Moore, J. L. & Mccarthy, M. A. Prevent, search or destroy? A partially observable model for invasive species management. J. Appl. Ecol. 51, 804–813 (2014).Article 

Google Scholar 
24.Yemshanov, D. et al. Robust surveillance and control of invasive species using a scenario optimization approach. Ecol. Econ. 133, 86–98 (2017).Article 

Google Scholar 
25.Rödder, D., Solé, M. & Böhme, W. Predicting the potential distributions of two alien invasive Housegeckos (Gekkonidae: Hemidactylus frenatus, Hemidactylus mabouia). North-West. J. Zool. 4, 236–246 (2008).
Google Scholar 
26.Hoskin, C. J. The invasion and potential impact of the Asian House Gecko (Hemidactylus frenatus) in Australia. Austral. Ecol. 36, 240–251 (2011).Article 

Google Scholar 
27.García-Díaz, P., Ross, J. V., Vall-llosera, M. & Cassey, P. Low detectability of alien reptiles can lead to biosecurity management failure: A case study from Christmas Island (Australia). NeoBiota 45, 75–92 (2019).Article 

Google Scholar 
28.Scott, J. K. et al. Zero-tolerance biosecurity protects high-conservation-value island nature reserve. Sci. Rep. 7, 772–779 (2017).ADS 
Article 

Google Scholar 
29.Commonwealth Government of Australia. Approval—Gorgon Gas Development (EPBC Reference: 2008/4178). (2009).30.Jarrad, F. C. et al. Improved design method for biosecurity surveillance and early detection of non-indigenous rats. N. Z. J. Ecol. 35, 132–144 (2011).
Google Scholar 
31.Metlay, D. From tin roof to torn wet blanket: Predicting and observing ground water movement at a proposed nuclear waste site. in Prediction: Science, Decision Making, and the Future of Nature (eds. Sarewitz, D. R., Byerly, R., Pielke, R. A.). 276–319. (Island Press, 2000).32.Wintle, B. & Burgman, M. Expert Elicitation for Barrow Island Surveillance System Revision, Project Report. (2015).33.Vanderduys, E. & Kutt, A. Is the Asian house gecko, Hemidactylus frenatus, really a threat to Australia’s biodiversity?. Aust. J. Zool. 60, 361–367 (2013).Article 

Google Scholar 
34.McGinnis, S. M. & Stebbins, R. C. A Field Guide to Western Reptiles and Amphibians. 4th edn. (Houghton Mifflin Harcourt, 2018).35.Whittle, P., Jarrad, F., Edwards, K. & Mengersen, K. Design of the quarantine surveillance for non-indigenous species of invertebrates on Barrow Island. Rec. West. Aust. Mus. Suppl. 83, 113–130 (2013).Article 

Google Scholar 
36.Ben-Haim, Y. Info-gap Decision Theory: Decisions Under Severe Uncertainty. 2nd edn. (Academic Press, 2006).37.MathWorks. MATLAB R2018b. (MathWorks, 2018).38.Bogich, T. L., Liebhold, A. M. & Shea, K. To sample or eradicate? A cost minimization model for monitoring and managing an invasive species. J. Appl. Ecol. 45, 1134–1142 (2008).Article 

Google Scholar 
39.Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15, 803–812 (2012).Article 

Google Scholar 
40.Trebitz, A. S. et al. Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs. J. Environ. Manag. 202, 299–310 (2017).CAS 
Article 

Google Scholar 
41.Molina, R., Horton, T., Trappe, J. & Marcot, B. Addressing uncertainty: How to conserve and manage rare or little-known fungi. Fungal Ecol. 4, 134–146 (2011).Article 

Google Scholar  More

1.Paillard, D. The timing of Pleistocene glaciations from a simple multiple-state climate model. Nature 391, 378–381 (1998).ADS 

Google Scholar 
2.Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 183–195 (2004).CAS 

Google Scholar 
3.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Taberlet, P., Fumagalli, L., Wust-Saucy, A.-G. & Cosson, J.-F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 
PubMed 

Google Scholar 
5.Incagnone, G., Marrone, F., Barone, R., Robba, L. & Naselli-Flores, L. How do freshwater organisms cross the ‘dry ocean’? A review on passive dispersal and colonization processes with a special focus on temporary ponds. Hydrobiologia 750, 103–123 (2015).
Google Scholar 
6.Schmitt, T. & Varga, Z. Extra-Mediterranean refugia: The rule and not the exception?. Front Zool 9, 22 (2012).PubMed 
PubMed Central 

Google Scholar 
7.Hewitt, G. M. Speciation, hybrid zones and phylogeography—Or seeing genes in space and time. Mol. Ecol. 10, 537–549 (2001).CAS 
PubMed 

Google Scholar 
8.Habel, J. C., Drees, C., Schmitt, T. & Assmann, T. Review refugial areas and postglacial colonizations in the Western Palearctic. In Relict Species (eds Habel, J. C. & Assmann, T.) 189–197 (Springer, 2010).
Google Scholar 
9.Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Lin. Soc. 58, 247–276 (1996).
Google Scholar 
10.Marrone, F., Lo Brutto, S. & Arculeo, M. Molecular evidence for the presence of cryptic evolutionary lineages in the freshwater copepod genus Hemidiaptomus G.O. Sars, 1903 (Calanoida, Diaptomidae). Hydrobiologia 644, 115–125 (2010).CAS 

Google Scholar 
11.Husemann, M., Schmitt, T., Zachos, F. E., Ulrich, W. & Habel, J. C. Palaearctic biogeography revisited: Evidence for the existence of a North African refugium for Western Palaearctic biota. J. Biogeogr. 41, 81–94 (2014).
Google Scholar 
12.García-Vázquez, D., Bilton, D. T., Foster, G. N. & Ribera, I. Pleistocene range shifts, refugia and the origin of widespread species in western Palaearctic water beetles. Mol. Phylogenet. Evol. 114, 122–136 (2017).PubMed 

Google Scholar 
13.Perktas, U., Barrowclough, G. F. & Groth, J. G. Phylogeography and species limits in the green woodpecker complex (Aves: Picidae): Multiple Pleistocene refugia and range expansion across Europe and the Near East. Biol. J. Lin. Soc. 104, 710–723 (2011).
Google Scholar 
14.Stewart, J. R. & Lister, A. M. Cryptic northern refugia and the origins of the modern biota. Trends Ecol. Evol. 16, 608–613 (2001).
Google Scholar 
15.Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. B Biol. Sci. 277, 661–671 (2010).
Google Scholar 
16.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046 (2020).CAS 

Google Scholar 
17.Provan, J. & Bennett, K. D. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571 (2008).PubMed 

Google Scholar 
18.Antal, L. et al. Phylogenetic evidence for a new species of Barbus in the Danube River basin. Mol. Phylogenet. Evol. 96, 187–194 (2016).CAS 
PubMed 

Google Scholar 
19.Copilaş-Ciocianu, D., Fišer, C., Borza, P. & Petrusek, A. Is subterranean lifestyle reversible? Independent and recent large-scale dispersal into surface waters by two species of the groundwater amphipod genus Niphargus. Mol. Phylogenet. Evol. 119, 37–49 (2018).PubMed 

Google Scholar 
20.Říčanová, Š et al. Multilocus phylogeography of the European ground squirrel: Cryptic interglacial refugia of continental climate in Europe. Mol. Ecol. 22, 4256–4269 (2013).PubMed 

Google Scholar 
21.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northerly refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991 (2016).
Google Scholar 
22.Wielstra, B. et al. Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling. Front. Zool. 10, 13 (2013).PubMed 
PubMed Central 

Google Scholar 
23.Hutchison, D. W. & Templeton, A. R. Correlation of pairwise genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. Evolution 53, 1898–1914 (1999).PubMed 

Google Scholar 
24.Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).PubMed 
PubMed Central 

Google Scholar 
25.Ewart, K. M. et al. Phylogeography of the iconic Australian red-tailed black-cockatoo (Calyptorhynchus banksii) and implications for its conservation. Heredity 125, 85–100 (2020).PubMed 
PubMed Central 

Google Scholar 
26.Hutama, A. et al. Identifying spatially concordant evolutionary significant units across multiple species through DNA barcodes: Application to the conservation genetics of the freshwater fishes of Java and Bali. Glob. Ecol. Conserv. 12, 170–187 (2017).
Google Scholar 
27.Médail, F. & Baumel, A. Using phylogeography to define conservation priorities: The case of narrow endemic plants in the Mediterranean Basin hotspot. Biol. Cons. 224, 258–266 (2018).
Google Scholar 
28.Previšić, A., Walton, C., Kučinić, M., Mitrikeski, P. T. & Kerovec, M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan Peninsula. Mol. Ecol. 18, 634–647 (2009).PubMed 

Google Scholar 
29.Brendonck, L. & Riddoch, B. J. Wind-borne short-range egg dispersal in anostracans (Crustacea: Branchiopoda). Biol. J. Linn. Soc. 67, 87–95 (1999).
Google Scholar 
30.Horváth, Z., Vad, C. F. & Ptacnik, R. Wind dispersal results in a gradient of dispersal limitation and environmental match among discrete aquatic habitats. Ecography 39, 726–732 (2016).PubMed 

Google Scholar 
31.Brochet, A. L. et al. Field evidence of dispersal of branchiopods, ostracods and bryozoans by teal (Anas crecca) in the Camargue (southern France). Hydrobiologia 637, 255 (2009).
Google Scholar 
32.Figuerola, J. & Green, A. J. Dispersal of aquatic organisms by waterbirds: A review of past research and priorities for future studies. Freshw. Biol. 47, 483–494 (2002).
Google Scholar 
33.Vanschoenwinkel, B. et al. Dispersal of freshwater invertebrates by large terrestrial mammals: A case study with wild boar (Sus scrofa) in Mediterranean wetlands. Freshw. Biol. 53, 2264–2273 (2008).
Google Scholar 
34.Brendonck, L., Rogers, D. C., Olesen, J., Weeks, S. & Hoeh, W. R. Global diversity of large branchiopods (Crustacea : Branchiopoda) in freshwater. Hydrobiologia 595, 167–176 (2008).
Google Scholar 
35.Dumont, H. J. & Negrea, S. V. Introduction to the Class Branchiopoda. (Backhuys Publishers, 2002).36.Belk, D. Global status and trends in ephemeral pool invertebrate conservation: Implications for Californian fairy shrimp. In Ecology, Conservation, and Management of Vernal Pool Ecosystems—Proceedings from a 1996 conference 147–150 (California Native Plant Society, 1998).37.Jocque, M., Vanschoenwinkel, B. & Brendonck, L. Anostracan monopolisation of early successional phases in temporary waters?. Fundam. Appl. Limnol. 176, 127–132 (2010).
Google Scholar 
38.Lukić, D., Horváth, Z., Vad, C. F. & Ptacnik, R. Food spectrum of Branchinecta orientalis—Are anostracans omnivorous top consumers of plankton in temporary waters?. J. Plankton Res. 40, 436–445 (2018).
Google Scholar 
39.Lukić, D., Ptacnik, R., Vad, C. F., Pόda, C. & Horváth, Z. Environmental constraint of intraguild predation: Inorganic turbidity modulates omnivory in fairy shrimps. Freshw. Biol. 65, 226–239 (2020).
Google Scholar 
40.Waterkeyn, A., Grillas, P., Anton-Pardo, M., Vanschoenwinkel, B. & Brendonck, L. Can large branchiopods shape microcrustacean communities in Mediterranean temporary wetlands?. Mar. Freshw. Res. 62, 46–53 (2011).CAS 

Google Scholar 
41.Brendonck, L. & De Meester, L. Egg banks in freshwater zooplankton: Evolutionary and ecological archives in the sediment. Hydrobiologia 491, 65–84 (2003).
Google Scholar 
42.Hairston, N. G., Brunt, R. A. V., Kearns, C. M. & Engstrom, D. R. Age and survivorship of diapausing eggs in a sediment egg bank. Ecology 76, 1706–1711 (1995).
Google Scholar 
43.Lukić, D. et al. High genetic variation and phylogeographic relations among Palearctic fairy shrimp populations reflect persistence in multiple southern refugia during Pleistocene ice ages and postglacial colonisation. Freshw. Biol. 64, 1896–1907 (2019).
Google Scholar 
44.Marrone, F., Alfonso, G., Naselli-Flores, L. & Stoch, F. Diversity patterns and biogeography of Diaptomidae (Copepoda, Calanoida) in the Western Palearctic. Hydrobiologia 800, 45–60 (2017).CAS 

Google Scholar 
45.Vanschoenwinkel, B. et al. Toward a global phylogeny of the “living fossil’’ crustacean order of the Notostraca. PLos ONE 7, e34998 (2012).46.Boileau, M. & Hebert, P. Genetic consequences of passive dispersal in pond-dwelling Copepods. Evolution 45, 721–733 (1991).PubMed 

Google Scholar 
47.Deng, Z., Chen, Y., Ma, X., Hu, W. & Yin, M. Dancing on the top: Phylogeography and genetic diversity of high-altitude freshwater fairy shrimps (Branchiopoda, Anostraca) with a focus on the Tibetan Plateau. Hydrobiologia 848, 2611–2626 (2021).CAS 

Google Scholar 
48.Ketmaier, V. et al. Mitochondrial DNA regionalism and historical demography in the extant populations of Chirocephalus kerkyrensis (Branchiopoda: Anostraca). PLoS ONE 7, e30082 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Korn, M. et al. Phylogeny, molecular ecology and taxonomy of southern Iberian lineages of Triops mauritanicus (Crustacea: Notostraca). Org. Divers. Evol. 10, 409–440 (2010).
Google Scholar 
50.Stoch, F., Korn, M., Turki, S., Naselli-Flores, L. & Marrone, F. The role of spatial environmental factors as determinants of large branchiopod distribution in Tunisian temporary ponds. Hydrobiologia 782, 37–51 (2016).
Google Scholar 
51.Lindholm, M., d’Auriac, M. A., Thaulow, J. & Hobaek, A. Dancing around the pole: Holarctic phylogeography of the Arctic fairy shrimp Branchinecta paludosa (Anostraca, Branchiopoda). Hydrobiologia 772, 189–205 (2016).CAS 

Google Scholar 
52.Vörös, J., Alcobendas, M., Martínez-Solano, I. & García-París, M. Evolution of Bombina bombina and Bombina variegata (Anura: Discoglossidae) in the Carpathian Basin: A history of repeated mt-DNA introgression across species. Mol. Phylogenet. Evol. 38, 705–718 (2006).PubMed 

Google Scholar 
53.Zharov, A. A. et al. Pleistocene branchiopods (Cladocera, Anostraca) from Transbaikalian Siberia demonstrate morphological and ecological stasis. Water 12, 3063 (2020).
Google Scholar 
54.Velonà, A., Luchetti, A., Scanabissi, F. & Mantovani, B. Genetic variability and reproductive modalities in European populations of Triops cancriformis (Crustacea, Branchiopoda, Notostraca). Ital. J. Zool. 76, 366–375 (2009).
Google Scholar 
55.Vanschoenwinkel, B., Gielen, S., Vandewaerde, H., Seaman, M. & Brendonck, L. Relative importance of different dispersal vectors for small aquatic invertebrates in a rock pool metacommunity. Ecography 31, 567–577 (2008).
Google Scholar 
56.Hulsmans, A., Moreau, K., Meester, L. D., Riddoch, B. J. & Brendonck, L. Direct and indirect measures of dispersal in the fairy shrimp Branchipodopsis wolfi indicate a small scale isolation-by-distance pattern. Limnol. Oceanogr. 52, 676–684 (2007).ADS 

Google Scholar 
57.Vanschoenwinkel, B., Vries, C. D., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).
Google Scholar 
58.Sánchez, M. I., Green, A. J., Amat, F. & Castellanos, E. M. Transport of brine shrimps via the digestive system of migratory waders: Dispersal probabilities depend on diet and season. Mar. Biol. 151, 1407–1415 (2007).
Google Scholar 
59.Horváth, Z. et al. Eastern spread of the invasive Artemia franciscana in the Mediterranean Basin, with the first record from the Balkan Peninsula. Hydrobiologia 822, 229–235 (2018).
Google Scholar 
60.Muñoz, J., Amat, F., Green, A. J., Figuerola, J. & Gómez, A. Bird migratory flyways influence the phylogeography of the invasive brine shrimp Artemia franciscana in its native American range. PeerJ 1, e200 (2013).PubMed 
PubMed Central 

Google Scholar 
61.Muñoz, J. et al. Phylogeography and local endemism of the native Mediterranean brine shrimp Artemia salina (Branchiopoda: Anostraca). Mol. Ecol. 17, 3160–3177 (2008).PubMed 

Google Scholar 
62.Sánchez, M. I., Hortas, F., Figuerola, J. & Green, A. J. Comparing the potential for dispersal via waterbirds of a native and an invasive brine shrimp. Freshw. Biol. 57, 1896–1903 (2012).
Google Scholar 
63.Viana, D. S., Santamaría, L., Michot, T. C. & Figuerola, J. Migratory strategies of waterbirds shape the continental-scale dispersal of aquatic organisms. Ecography 36, 430–438 (2013).
Google Scholar 
64.Green, A. J. et al. Dispersal of invasive and native brine shrimps Artemia (Anostraca) via waterbirds. Limnol. Oceanogr. 50, 737–742 (2005).ADS 

Google Scholar 
65.Kappas, I. et al. Molecular and morphological data suggest weak phylogeographic structure in the fairy shrimp Streptocephalus torvicornis (Branchiopoda, Anostraca). Hydrobiologia 801, 21–32 (2017).CAS 

Google Scholar 
66.Rogers, D. C. Larger hatching fractions in avian dispersed anostracan eggs (Branchiopoda). J. Crustac. Biol. 34, 135–143 (2014).
Google Scholar 
67.Angeler, D. G., Viedma, O., Sánchez-Carrillo, S. & Alvarez-Cobelas, M. Conservation issues of temporary wetland Branchiopoda (Anostraca, Notostraca: Crustacea) in a semiarid agricultural landscape: What spatial scales are relevant?. Biol. Cons. 141, 1224–1234 (2008).
Google Scholar 
68.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. Distribution and conservation status of fairy shrimps (Crustacea: Anostraca) in the astatic soda pans of the Carpathian basin: the role of local and spatial factors. J. Limnol. 72, 103–116 (2013).
Google Scholar 
69.Svensson, L., Mullarney, K. & Zetterström, D. Collins Bird Guide 2nd edn. (HarperCollins Publishers Ltd., 2009).
Google Scholar 
70.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. The keystone role of anostracans and copepods in European soda pans during the spring migration of waterbirds. Freshw. Biol. 58, 430–440 (2013).
Google Scholar 
71.Gill, J. L. Ecological impacts of the late Quaternary megaherbivore extinctions. New Phytol. 201, 1163–1169 (2014).PubMed 

Google Scholar 
72.Neretina, A. N. et al. Crustacean remains from the Yuka mammoth raise questions about non-analogue freshwater communities in the Beringian region during the Pleistocene. Sci. Rep. 10, 859 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Chang, D. et al. The evolutionary and phylogeographic history of woolly mammoths: A comprehensive mitogenomic analysis. Sci. Rep. 7, 44585 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Lister, A. M., Sher, A. V., van Essen, H. & Wei, G. The pattern and process of mammoth evolution in Eurasia. Quatern. Int. 126–128, 49–64 (2005).
Google Scholar 
75.Vanschoenwinkel, B. et al. Passive external transport of freshwater invertebrates by elephant and other mud-wallowing mammals in an African savannah habitat. Freshw. Biol. 56, 1606–1619 (2011).
Google Scholar 
76.Waterkeyn, A., Pineau, O., Grillas, P. & Brendonck, L. Invertebrate dispersal by aquatic mammals: A case study with nutria Myocastor coypus (Rodentia, Mammalia) in Southern France. Hydrobiologia 654, 267–271 (2010).
Google Scholar 
77.Belk, D. & Brtek, J. Checklist of the Anostraca. Hydrobiologia 298, 315–353 (1995).
Google Scholar 
78.Marrone, F., Korn, M., Stoch, F., Naselli Flores, L. & Turki, S. Updated checklist and distribution of large branchiopods (Branchiopoda: Anostraca, Notostraca, Spinicaudata) in Tunisia. Biogeogr. J. Integr. Biogeogr. 31, 27–53 (2016).79.Mura, G. & Brtek, J. Revised key to families and genera of the Anostraca with notes on their geographical distribution. Crustaceana 73, 1037–1088 (2000).
Google Scholar 
80.Atashbar, B., Agh, N., Van Stappen, G., Mertens, J. & Beladjal, L. Combined effect of temperature and salinity on hatching characteristics of three fairy shrimp species (Crustacea: Anostraca). J. Limnol. 73, 574–583 (2014).
Google Scholar 
81.Eder, E., Hödl, W. & Gottwald, R. Distribution and phenology of large branchiopods in Austria. Hydrobiologia 359, 13–22 (1997).
Google Scholar 
82.Šćiban, M., Marković, A., Lukić, D. & Miličić, D. Autumn populations of Branchinecta orientalis G. O. Sars, 1903 and Chirocephalus diaphanus Prevost, 1803 (Crustacea, Branchiopoda) in the Central European Lowlands (Pannonian Plain, Serbia). North-West. J. Zool. 10, 435–437 (2014).
Google Scholar 
83.Alonso, M. A survey of the Spanish Euphyllopoda. Miscelania Zool. 9, 179–208 (1985).
Google Scholar 
84.Petkovski, S. On the presence of the genus Branchinecta Verrill, 1869 (Crustacea, Anostraca) in Yugoslavia. Hydrobiologia 226, 17–27 (1991).
Google Scholar 
85.Dimentman, C. The rainpool ecosystems of Israel: Geographical distribution of freshwater Anostraca (Crustacea). Israel J. Ecol. Evol. 30, 1–15 (1981).
Google Scholar 
86.Eid, E. K. New records of large branchiopods from northern Jordan (Crustacea: Branchiopoda). Zool. Middle East 46, 116–117 (2009).
Google Scholar 
87.Mura, G., Ozkutuk, S. R., Aygen, C. & Cottarelli, V. New data on the taxonomy and distribution of anostracan fauna from Turkey. J. Biol. Res. 15, 17–23 (2011).
Google Scholar 
88.Rogers, D. C., Quinney, D. L., Weaver, J. & Olesen, J. A new giant species of predatory fairy shrimp from Idaho, USA (Branchiopoda: Anostraca). J. Crustac. Biol. 26, 1–12 (2006).
Google Scholar 
89.Rodríguez-Flores, P. C., Jiménez-Ruiz, Y., Forró, L., Vörös, J. & García-París, M. Non-congruent geographic patterns of genetic divergence across European species of Branchinecta (Anostraca: Branchinectidae). Hydrobiologia 801, 47–57 (2017).
Google Scholar 
90.Atashbar, B., Agh, N., Van Stappen, G. & Beladjal, L. Diversity and distribution patterns of large branchiopods (Crustacea: Branchiopoda) in temporary pools (Iran). J. Arid. Environ. 111, 27–34 (2014).ADS 

Google Scholar 
91.Belk, D. & Esparza, C. E. Anostraca of the Indian Subcontinent. Hydrobiologia 298, 287–293 (1995).
Google Scholar 
92.Brtek, J. & Thiéry, A. The geographic distribution of the European Branchiopods (Anostraca, Notostraca, Spinicaudata, Laevicaudata). Hydrobiologia 298, 263–280 (1995).
Google Scholar 
93.Horn, W. & Paul, M. Occurrence and distribution of the Eurasian Branchinecta orientalis (Anostraca) in Central Asia (Northwest Mongolia, Uvs Nuur Basin) and in other holarctic areas. Lauterbornia 49, 81–91 (2004).
Google Scholar 
94.Marrone, F., Alonso, M., Pieri, V., Augugliaro, C. & Stoch, F. The crustacean fauna of Bayan Onjuul area (Tov Province, Mongolia) (Crustacea: Branchiopoda, Copepoda, Ostracoda). North West. J. Zool. 11, 288–295 (2015).
Google Scholar 
95.Mura, G. & Takami, G. A. A contribution to the knowledge of the anostracan fauna of Iran. Hydrobiologia 441, 117–121 (2000).
Google Scholar 
96.Naganawa, H. et al. Does the dispersal of fairy shrimps (Branchiopoda, Anostraca) reflect the shifting geographical distribution of freshwaters since the late Mesozoic?. Limnology https://doi.org/10.1007/s10201-019-00589-9 (2019).Article 

Google Scholar 
97.Padhye, S. M., Kulkarni, M. R. & Dumont, H. J. Diversity and zoogeography of the fairy shrimps (Branchiopoda: Anostraca) on the Indian subcontinent. Hydrobiologia 801, 117–128 (2017).
Google Scholar 
98.Petkovski, S. Taksonomsko-morfološka i zoogeografsko-ekološka studija Anostraca (Crustacea: Branchiopoda) jugoslovenskih zemalja. (Prirodno-matematički fakultet, Novi Sad, 1993).99.Pretus, J. L. A commented check-list of the Balearic Branchiopoda (Crustacea). Limnetica 6, 157–164 (1990).
Google Scholar 
100.van den Broeck, M., Waterkeyn, A., Rhazi, L. & Brendonck, L. Distribution, coexistence, and decline of Moroccan large branchiopods. J. Crustacean Biol. 35, 355–365 (2015).
Google Scholar 
101.Hijmans, R. J., Philips, S., Leathwick, J. & Elith, J. Package ‘dismo’. 9, 1–68 (2017).102.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. (2014).103.Hijmans, R. J., Cameron, S. E. & Parra, J. L. Climate Date from Worldclim (2004).104.Alfonso, G. & Marrone, F. Branchiopoda Anostraca, Notostraca, Spinicaudata. In Checklist of the Italian fauna (in press).105.Defaye, D., Rabet, N. & Thiéry, A. Atlas et bibliographie des crustaces branchiopodes (Anostraca, Notostraca, Spinicaudata) de France metropolitaine. Collection patrimoines naturels (1998).106.Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. PNAS 105, 13486–13491 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
108.Aguilar, A. et al. High intraspecific genetic divergence in the versatile fairy shrimp Branchinecta lindahli with a comment on cryptic species in the genus Branchinecta (Crustacea: Anostraca). Hydrobiologia 801, 59–69 (2017).
Google Scholar 
109.Jeffery, N. W., Elías-Gutiérrez, M. & Adamowicz, S. J. Species diversity and phylogeographical affinities of the Branchiopoda (Crustacea) of Churchill, Manitoba, Canada. PLoS ONE 6, e18364 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 

Google Scholar 
111.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).113.Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
114.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
115.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).116.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Leigh, J. W. & Bryant, D. popart: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
118.Xia, X. & Kumar, S. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
119.Xia, X. & Lemey, P. Assessing substitution saturation with DAMBE. In The phylogenetic Handbook 615–630 (Cambridge University Press, 2009).120.Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7 (2003).CAS 
PubMed 

Google Scholar 
121.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 

Google Scholar 
122.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 

Google Scholar 
123.Nychka, D. et al. fields: Tools for Spatial Data (2020).124.Oksanen, J. et al. vegan: Community ecology package. – R package ver. 2.0-4. http://CRAN.R-project.org/package=vegan. (2012). More

1.Falkowski PG. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature. 1997;387:272–5.CAS 

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

Google Scholar 
3.Capone DG, Zehr JP, Paerl HW, Bergman B, Carpenter EJ. Trichodesmium a globally significant marine cyanobacterium. Science (80-). 1997;276:1221–9.CAS 

Google Scholar 
4.Bergman B, Sandh G, Lin S, Larsson J, Carpenter EJ. Trichodesmium – a widespread marine cyanobacterium with unusual nitrogen fixation properties. FEMS Microbiol Rev. 2013;37:286–302. 37(3):286–302CAS 
PubMed 

Google Scholar 
5.Capone DG, Burns JA, Montoya JP, Subramaniam A, Mahaffey C, Gunderson T, et al. Nitrogen fixation by Trichodesmium spp.: An important source of new nitrogen to the tropical and subtropical North Atlantic Ocean. Glob Biogeochem Cycles. 2005;19:1–17.
Google Scholar 
6.Mahaffey C, Michaels AF, Capone DG. The conundrum of marine N2 fixation. Am J Sci. 2005;305:546–95.CAS 

Google Scholar 
7.Moore C, Mills MM, Achterberg EP, Geider RJ, Laroche J, Lucas MI, et al. Large-scale distribution of Atlantic nitrogen fixation controlled by iron availability. Nat Geosci. 2009;2:867–71.CAS 

Google Scholar 
8.Dyhrman ST, Webb EA, Anderson DM, Moffett JW, Waterbury JB. Cell-specific detection of phosphorus stress in Trichodesmium from the Western North Atlantic. Limnol Oceanogr. 2002;47:1832–6.
Google Scholar 
9.Snow JT, Schlosser C, Woodward EMS, Mills MM, Achterberg EP, Mahaffey C, et al. Environmental controls on the biogeography of diazotrophy and Trichodesmium in the Atlantic Ocean. Glob Biogeochem Cycles. 2015;29:865–84.CAS 

Google Scholar 
10.Jickells TD, An ZS, Andersen KK, Baker AR, Bergametti C, Brooks N, et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science. 2005;308:67–71.CAS 
PubMed 

Google Scholar 
11.Schlosser CA, Strzepek K, Gao X, Fant C, Blanc É, Paltsev S, et al. The future of global water stress: an integrated assessment. Earth’s Future. 2014;2:341–61.
Google Scholar 
12.Wu J, Sunda W, Boyle EA, Karl DM. Phosphate depletion in the Western North Atlantic. Ocean Sci. 2000;289:759–62.CAS 

Google Scholar 
13.Mather RL, Reynolds SE, Wolff GA, Williams RG, Torres-Valdes S, Woodward EMS, et al. Phosphorus cycling in the North and South Atlantic Ocean subtropical gyres. Nat Geosci. 2008;1:439–43.CAS 

Google Scholar 
14.Ward BA, Dutkiewicz S, Moore CM, Follows MJ. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol Oceanogr. 2013;58:2059–75.CAS 

Google Scholar 
15.Mills MM, Moore CM, Langlois R, Milne A, Achterberg E, Nachtigall K, et al. Nitrogen and phosphorus co-limitation of bacterial productivity and growth in the oligotrophic subtropical North Atlantic. Limnol Oceanogr. 2008;53:824–34.CAS 

Google Scholar 
16.Garcia NS, Fu F, Sedwick PN, Hutchins DA. Iron deficiency increases growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria. ISME J. 2015;9:238–45.CAS 
PubMed 

Google Scholar 
17.Walworth NG, Fu FX, Webb EA, Saito MA, Moran D, McLlvin MR, et al. Mechanisms of increased Trichodesmium fitness under iron and phosphorus co-limitation in the present and future ocean. Nat Commun. 2016;7:1–11.
Google Scholar 
18.Walworth NG, Fu FX, Lee MD, Cai X, Saito MA, Webb EA, et al. Nutrient-colimited Trichodesmium as a nitrogen source or sink in a future ocean. Appl Environ Microbiol. 2018;84:1–14.CAS 

Google Scholar 
19.Held NA, Webb EA, McIlvin MM, Hutchins DA, Cohen NR, Moran DM, et al. Co-occurrence of Fe and P stress in natural populations of the marine diazotroph Trichodesmium. Biogeosciences 2020;17:2537–51.
Google Scholar 
20.Polyviou D, Baylay AJ, Hitchcock A, Robidart J, Moore CM, Bibby TS. Desert dust as a source of iron to the globally important diazotroph Trichodesmium. Front Microbiol. 2018;8:2683.PubMed 
PubMed Central 

Google Scholar 
21.Snow JT, Polyviou D, Skipp P, Chrismas NA, Hitchcock A, Geider R, et al. Quantifying Integrated Proteomic Responses to Iron Stress in the Globally Important Marine Diazotroph Trichodesmium. PLOS ONE 2015;10:e0142626.22.Frischkorn KR, Haley ST, Dyhrman ST. Transcriptional and proteomic choreography under phosphorus deficiency and re-supply in the N2 fixing cyanobacterium Trichodesmium erythraeum. Front Microbiol. 2019;10:330. 2012;6:1728–39PubMed 
PubMed Central 

Google Scholar 
23.Rouco M, Frischkorn KR, Haley ST, Alexander H, Dyhrman ST. Transcriptional patterns identify resource controls on the diazotroph Trichodesmium in the Atlantic and Pacific oceans. ISME J. 2018;12:1486–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Shi T, Sun Y, Falkowski PG. Effects of iron limitation on the expression of metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101. Environ Microbiol. 2007;9:2945–56.CAS 
PubMed 

Google Scholar 
25.Saito MA, Bertrand EM, Dutkiewicz S, Bulygin VV, Moran DM, Monteiro FM, et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc Natl Acad Sci USA 2011;108:2184–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.La Roche J, Boyd PW, McKay RML, Geider RJ. Flavodoxin as an in situ marker for iron stress in phytoplankton. Nature. 1996;382:802–5.
Google Scholar 
27.De la Cerda B, Castielli O, Durán RV, Navarro JA, Hervás M, De la Rosa MA. A proteomic approach to iron and copper homeostasis in cyanobacteria. Brief Funct Genom Proteom. 2007;6:322–9.
Google Scholar 
28.Chappell PD, Webb EA. A molecular assessment of the iron stress response in the two phylogenetic clades of Trichodesmium. Environ Microbiol. 2010;12:13–27.CAS 
PubMed 

Google Scholar 
29.Polyviou D, Machelett MM, Hitchcock A, Baylay AJ, MacMillan F, Mark Moore C, et al. Structural and functional characterization of IdiA/FutA (Tery_3377), an iron-binding protein from the ocean diazotroph Trichodesmium erythraeum. J Biol Chem. 2018;293:18099–109.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Berman-Frank I, Lundgren P, Chen YB, Küpper H, Kolber Z, Bergman B, et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science. 2001;294:1534–7.CAS 
PubMed 

Google Scholar 
31.Berman-Frank I, Lundgren P, Falkowski P. Nitrogen fixation and photosynthetic oxygen evolution in cyanobacteria. Res Microbiol. 2003;154:157–64.CAS 
PubMed 

Google Scholar 
32.Sandh G, Ran L, Xu L, Sundqvist G, Bulone V, Bergman B. Comparative proteomic profiles of the marine cyanobacterium Trichodesmium erythraeum IMS101 under different nitrogen regimes. Proteomics. 2011;11:406–19.CAS 
PubMed 

Google Scholar 
33.Orchard ED, Webb EA, Dyhrman ST. Molecular analysis of the phosphorus starvation response in Trichodesmium spp. Environ Microbiol. 2009;11:2400–11.CAS 
PubMed 

Google Scholar 
34.Dyhrman ST, Ruttenberg KC. Presence and regulation of alkaline phosphatase activity in eukaryotic phytoplankton from the coastal ocean: Implications for dissolved organic phosphorus remineralization. Limnol Oceanogr. 2006;51:1381–90.CAS 

Google Scholar 
35.Karl DM. Nutrient dynamics in the deep blue sea. Trends Microbiol. 2002;10:410–8.CAS 
PubMed 

Google Scholar 
36.Polyviou D, Hitchcock A, Baylay AJ, Moore CM, Bibby TS. Phosphite utilization by the globally important marine diazotroph Trichodesmium. Environ Microbiol Rep. 2015;7:824–30.CAS 
PubMed 

Google Scholar 
37.Obata H, Karatani H, Matsui M, Nakayama E. Fundamental studies for chemical speciation of iron in seawater with an improved analytical method. Marine Chemistry. 1997;56:97–106.38.Kunde K, Wyatt NJ, González-Santana D, Tagliabue A, Mahaffey C, Lohan MC. Iron Distribution in the Subtropical North Atlantic: The Pivotal Role of Colloidal Iron. Glob Biogeochem Cycles. 2019;33:1532–47.CAS 

Google Scholar 
39.Woodward EMS, Rees AP. Nutrient distributions in an anticyclonic eddy in the northeast Atlantic Ocean, with reference to nanomolar ammonium concentrations. Deep Res Part II Top Stud Oceanogr. 2001;48:775–93.CAS 

Google Scholar 
40.Davis CE, Blackbird S, Wolff G, Woodward M, Mahaffey C. Seasonal organic matter dynamics in a temperate shelf sea. Prog Oceanogr. 2019;177:101925.
Google Scholar 
41.Lomas MW, Burke AL, Lomas DA, Bell DW, Shen C, Dyhrman ST, et al. Sargasso Sea phosphorus biogeochemistry: an important role for dissolved organic phosphorus (DOP). Biogeosci Discuss. 2009;6:10137–75.
Google Scholar 
42.Klawonn I, Lavik G, Böning P, et al. Simple approach for the preparation of 15−15N2-enriched water for nitrogen fixation assessments: evaluation, application and recommendations. Front Microbiol. 2015;6:769.PubMed 
PubMed Central 

Google Scholar 
43.Frischkorn KR, Haley ST, Dyhrman ST. Coordinated gene expression between Trichodesmium and its microbiome over day-night cycles in the North Pacific Subtropical Gyre. ISME J. 2018;12:997–1007.PubMed 
PubMed Central 

Google Scholar 
44.Tang W, Cerdán-García E, Berthelot H, Polyviou D, Wang S, Baylay A, et al. New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods. ISME J. 2020;14:2514–26.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Zehr JP, McReynolds LA. Use of degenerate oligonucleotides for amplification of the nifH gene from the marine cyanobacterium Trichodesmium thiebautii. Appl Environ Microbiol. 1989;55:2522–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Zani S, Mellon MT, Collier JL, Zehr JP. Expression of nifH genes in natural microbial assemblages in Lake George, New York, detected by reverse transcriptase PCR. Appl Environ Microbiol. 2000;66:3119–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Turk KA, Rees AP, Zehr JP, Pereira N, Swift P, Shelley R, et al. Nitrogen fixation and nitrogenase (nifH) expression in tropical waters of the eastern North Atlantic. ISME J. 2011;5:1201–12.CAS 
PubMed 

Google Scholar 
48.Hitchen J, Sooknanan R, Khanna A. ScriptSeq V2 Library Preparation Method: A Rapid and Efficient Method for Preparing Directional RNA-Seq Libraries. J Biomol Tech. 2012;23:S33–S34.PubMed Central 

Google Scholar 
49.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 2011;17:10–2.
Google Scholar 
50.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using. Microbiome. 2014;2:4904–9.
Google Scholar 
52.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Hyatt D, Chen GL, LoCascio PF, Land ML, Frank W, Larimer LJH. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:1–11.
Google Scholar 
55.Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30:1575–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42:D206–14.CAS 
PubMed 

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

Google Scholar 
58.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. correspondence QIIME allows analysis of high- throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Publ Gr. 2010;7:335–6.CAS 

Google Scholar 
59.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 

Google Scholar 
60.Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Westreich ST, Treiber ML, Mills DA, Korf I, Lemay DG. SAMSA2: a standalone metatranscriptome analysis pipeline. BMC Bioinforma. 2018;19:1–11.
Google Scholar 
62.Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Kopylova E, Noé L, Touzet H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012;28:3211–7.CAS 

Google Scholar 
64.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
PubMed 

Google Scholar 
65.Tatusova T, Ciufo S, Fedorov B, O’Neill K, Tolstoy I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 2014;42:D553–9.66.Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923–30.CAS 

Google Scholar 
68.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.
Google Scholar 
69.Wu S, Mi T, Zhen Y, Yu K, Wang F, Yu Z. A Rise in ROS and EPS Production: New Insights into the Trichodesmium erythraeum Response to Ocean Acidification. J Phycol. 2021;57:172–82.CAS 
PubMed 

Google Scholar 
70.Sedwick PN, Church TM, Bowie AR, Marsay CM, Ussher SJ, Achilles KM, et al. Iron in the Sargasso Sea (Bermuda Atlantic Time-series Study region) during summer: Eolian imprint, spatiotemporal variability, and ecological implications. Global Biogeochem Cycles. 2005;19:GB4006.71.Hatta M, Measures CI, Wu J, Roshan S, Fitzsimmons JN, Sedwick P, et al. An overview of dissolved Fe and Mn distributions during the 2010-2011 U.S. GEOTRACES north Atlantic cruises: GEOTRACES GA03. Deep Res Part II Top Stud Oceanogr. 2015;116:117–29.CAS 

Google Scholar 
72.Mahaffey C, Reynolds S, Davis CE, Lohan MC. Alkaline phosphatase activity in the subtropical ocean: insights from nutrient, dust and trace metal addition experiments. Front Mar Sci. 2014;1:73.
Google Scholar 
73.Church MJ, Mahaffey C, Letelier RM, Lukas R, Zehr JP, Karl DM. Physical forcing of nitrogen fixation and diazotroph community structure in the North Pacific subtropical gyre. Global Biogeochem Cycles. 2009;23:GB2020.74.Zehr JP, Capone DG. Changing perspectives in marine nitrogen fixation. Science. 2020;368:eaay9514.75.Benavides M, Moisander PH, Daley MC, Bode A, Arístegui J. Longitudinal variability of diazotroph abundances in the subtropical North Atlantic Ocean. J Plankton Res. 2016;38:662–72.CAS 

Google Scholar 
76.Luo YW, Doney SC, Anderson LA, Benavides M, Berman-Frank I, Bode A, et al. Database of diazotrophs in global ocean: Abundance, biomass and nitrogen fixation rates. Earth Syst Sci Data. 2012;4:47–73.
Google Scholar 
77.Moisander PH, Beinart RA, Voss M, Zehr JP. Diversity and abundance of diazotrophic microorganisms in the South China Sea during intermonsoon. ISME J. 2008;2:954–67.CAS 
PubMed 

Google Scholar 
78.Moisander PH, Serros T, Paerl RW, Beinart RA, Zehr JP. Gammaproteobacterial diazotrophs and nifH gene expression in surface waters of the South Pacific Ocean. ISME J 2014;8:1962–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Robidart JC, Church MJ, Ryan JP, et al. Ecogenomic sensor reveals controls on N2-fixing microorganisms in the North Pacific Ocean. ISME J. 2014;8:1175–85.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Stenegren M, Caputo A, Berg C, Bonnet S, Foster R. Distribution and drivers of symbiotic and free-living diazotrophic cyanobacteria in the western tropical South Pacific. Biogeosciences 2018;15:1559–78.CAS 

Google Scholar 
81.Langlois R, Großkopf T, Mills M, Takeda S, LaRoche J. Widespread Distribution and Expression of Gamma A (UMB), an Uncultured, Diazotrophic, γ-Proteobacterial nifH Phylotype. PLoS ONE. 2015;10:e0128912.PubMed 
PubMed Central 

Google Scholar 
82.Ratten J-M, LaRoche J, Desai DK, et al. Sources of iron and phosphate affect the distribution of diazotrophs in the North Atlantic. Deep Sea Res Part II: Topical Stud Oceanogr. 2015;116:332–41.CAS 

Google Scholar 
83.Voss, M, Croot, P, Lochte, K, Mills, M, Peeken, I. Patterns of nitrogen fixation along 10°N in the tropical Atlantic. Geophys Res Lett. 2004;31:L23S09.84.Bibby TS, Nield J, Barber J. Iron deficiency induces the formation of an antenna ring around trimeric photosystem I in cyanobacteria. Nature. 2001;412:743–5.85.Richier S, Macey AI, Pratt NJ, Honey DJ, Moore CM, Bibby TS. Abundances of iron-binding photosynthetic and nitrogen-fixing proteins of Trichodesmium both in culture and in situ from the North Atlantic. PLoS ONE. 2012;7:e35571.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Keren N, Aurora R, Pakrasi HB. Critical roles of bacterioferritins in iron storage and proliferation of cyanobacteria. Plant Physiol. 2004;135:1666–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.González A, Bes MT, Valladares A, Peleato ML, Fillat MF. FurA is the master regulator of iron homeostasis and modulates the expression of tetrapyrrole biosynthesis genes in Anabaena sp. PCC 7120. Environ Microbiol. 2012;14:3175–87.PubMed 

Google Scholar 
88.Sebastian M, Ammerman JW. The alkaline phosphatase PhoX is more widely distributed in marine bacteria than the classical PhoA. ISME J. 2009;3:563–72.CAS 
PubMed 

Google Scholar 
89.Browning TJ, Achterberg EP, Yong JC, Rapp I, Utermann C, Engel A, et al. Iron limitation of microbial phosphorus acquisition in the tropical North Atlantic. Nat Commun. 2017;8:1–7.
Google Scholar 
90.Proudfoot M, Kuznetsova E, Brown G, Rao NN, Kitagawa M, Mori H, et al. General enzymatic screens identify three new nucleotidases in Escherichia coli: Biochemical characterization of SurE, YfbR, and YjjG. J Biol Chem. 2004;279:54687–94.CAS 
PubMed 

Google Scholar 
91.Orchard ED, Benitez-Nelson CR, Pellechia PJ, Lomas MW, Dyhrman ST. Polyphosphate in Trichodesmium from the low-phosphorus Sargasso Sea. Limnol Oceanogr. 2010;55:2161–9.CAS 

Google Scholar 
92.Berman-Frank I, Cullen JT, Shaked Y, Sherrell RM, Falkowski PG. Iron availability, cellular iron quotas, and nitrogen fixation in Trichodesmium. Limnol Oceanogr. 2001;46:1249–60.CAS 

Google Scholar 
93.Schoffman H, Keren N. Function of the IsiA pigment–protein complex in vivo. Photosynth Res. 2019;141:343–53.CAS 
PubMed 

Google Scholar 
94.Küpper H, Ferimazova N, Šetlík I, Berman-Frank I. Traffic lights in Trichodesmium. Regulation of photosynthesis for nitrogen fixation studied by chlorophyll fluorescence kinetic microscopy. Plant Physiol. 2004;135:2120–33.PubMed 
PubMed Central 

Google Scholar 
95.Behrenfeld MJ, Milligan AJ. Photophysiological expressions of iron stress in phytoplankton. Ann Rev Mar Sci. 2013;5:217–46.PubMed 

Google Scholar 
96.Ho TY. Nickel limitation of nitrogen fixation in Trichodesmium. Limnol Oceanogr. 2013;58:112–20.CAS 

Google Scholar 
97.Tilman D. Resources: a graphical‐mechanistic approach to competition and predation. Am Nat. 1980;116:362–3.
Google Scholar 
98.Mills MM, Ridame C, Davey M, La Roche J, Geider RJ. Iron and phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature 2004;429:292–4.CAS 
PubMed 

Google Scholar 
99.Saito MA, McIlvin MR, Moran DM, Goepfert TJ, DiTullio GR, Post AF, et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science 2014;345:1173–7.CAS 
PubMed 

Google Scholar 
100.NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data. MODIS-Aqua Level 3 Mapped Chlorophyll Data Version R2018.0. NASA OB.DAAC, Greenbelt, MD, USA. Published online 2017. More

1.Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Woolhouse, M. E. et al. Temporal trends in the discovery of human viruses. Proc. R. Soc. B 275, 2111–2115 (2008).PubMed 
PubMed Central 

Google Scholar 
3.Smith, K. F. et al. Global rise in human infectious disease outbreaks. J. R. Soc. Interface 11, 20140950 (2014).PubMed 
PubMed Central 

Google Scholar 
4.Carlson, C. J. et al. Climate change will drive novel cross-species viral transmission. Preprint at bioRxiv https://doi.org/10.1101/2020.01.24.918755 (2020).5.Swei, A., Couper, L. I., Coffey, L. L., Kapan, D. & Bennett, S. Patterns, drivers, and challenges of vector-borne disease emergence. Vector Borne Zoonotic Dis. 20, 159–170 (2020).PubMed 
PubMed Central 

Google Scholar 
6.Belay, E. D. et al. Zoonotic disease programs for enhancing global health security. Emerg. Infect. Dis. 23, S65 (2017).PubMed Central 

Google Scholar 
7.Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).PubMed 
PubMed Central 

Google Scholar 
8.Carroll, D. et al. The global virome project. Science 359, 872–874 (2018).CAS 
PubMed 

Google Scholar 
9.Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).PubMed 

Google Scholar 
10.Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).PubMed 
PubMed Central 

Google Scholar 
12.Schmidt, J. P. et al. Spatiotemporal fluctuations and triggers of Ebola virus spillover. Emerg. Infect. Dis. 23, 415 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal–human interface. Phil. Trans. R. Soc. Biol. Sci. 374, 20190296 (2019).
Google Scholar 
14.Glennon, E. E. et al. Syndromic detectability of haemorrhagic fever outbreaks. Preprint at medRxiv https://doi.org/10.1101/2020.03.28.20019463 (2020).15.Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).PubMed 
PubMed Central 

Google Scholar 
16.Palmer, S., Brown, D. & Morgan, D. Early qualitative risk assessment of the emerging zoonotic potential of animal diseases. BMJ 331, 1256–1260 (2005).PubMed 
PubMed Central 

Google Scholar 
17.Grange, Z. L. et al. Ranking the risk of animal-to-human spillover for newly discovered viruses. Proc. Natl Acad. Sci. USA 118, e2002324118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Carlson, C. J. From PREDICT to prevention, one pandemic later. Lancet Microbe 1, e6–e7 (2020).PubMed 
PubMed Central 

Google Scholar 
19.Holmes, E., Rambaut, A. & Andersen, K. Pandemics: spend on surveillance, not prediction. Nature 558, 180–182 (2018).CAS 
PubMed 

Google Scholar 
20.Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).
Google Scholar 
21.Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).
Google Scholar 
22.Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Stephens, P. R. et al. Global mammal parasite database version 2.0. Ecology 98, 1476 (2017).PubMed 

Google Scholar 
24.Wardeh, M., Risley, C., McIntyre, M. K., Setzkorn, C. & Baylis, M. Database of host–pathogen and related species interactions, and their global distribution. Sci. Data 2, 150049 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Shaw, L. P. et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol. Ecol. 29, 3361–3379 (2020).PubMed 

Google Scholar 
26.Gibb, R. et al. Data proliferation, reconciliation, and synthesis in viral ecology. BioScience https://doi.org/10.1093/biosci/biab080 (2021).27.Dallas, T., Park, A. W. & Drake, J. M. Predicting cryptic links in host–parasite networks. PLoS Comput. Biol. 13, e1005557 (2017).PubMed 
PubMed Central 

Google Scholar 
28.Poisot, T. et al. Imputing the mammalian virome with linear filtering and singular value decomposition. Preprint at https://arxiv.org/abs/2105.14973 (2021).29.Carlson, C. J. et al. The Global Virome in One Network (VIRION): an atlas of vertebrate–virus associations. Preprint at bioRxiv https://doi.org/10.1101/2021.08.06.455442 (2021).30.Albery, G. F., Eskew, E. A., Ross, N. & Olival, K. J. Predicting the global mammalian viral sharing network using phylogeography. Nat. Commun. 11, 2260 (2020).31.Davies, T. J. & Pedersen, A. B. Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proc. R. Soc. B Biol. Sci. 275, 1695–1701 (2008).
Google Scholar 
32.Guy, C., Thiagavel, J., Mideo, N. & Ratcliffe, J. M. Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’. R. Soc. Open Sci. 6, 181182 (2019).PubMed 
PubMed Central 

Google Scholar 
33.Washburne, A. D. et al. Taxonomic patterns in the zoonotic potential of mammalian viruses. PeerJ 6, e5979 (2018).PubMed 
PubMed Central 

Google Scholar 
34.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Stephens, P. R. et al. The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts. Ecol. Lett. 19, 1159–1171 (2016).PubMed 

Google Scholar 
36.Longdon, B., Brockhurst, M. A., Russell, C. A., Welch, J. J. & Jiggins, F. M. The evolution and genetics of virus host shifts. PLoS Pathog. 10, e1004395 (2014).PubMed 
PubMed Central 

Google Scholar 
37.Farrell, M. J., Elmasri, M., Stephens, D. A. & Davies, T. J. Predicting missing links in global host–parasite networks. bioRxiv https://doi.org/10.1101/2020.02.25.965046 (2020).38.Gilbert, A. T. et al. Deciphering serology to understand the ecology of infectious diseases in wildlife. EcoHealth 10, 298–313 (2013).PubMed 

Google Scholar 
39.Becker, D. J., Seifert, S. N. & Carlson, C. J. Beyond infection: integrating competence into reservoir host prediction. Trends Ecol. Evol. 35, 1062–1065 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Walsh, M. G., Mor, S. M., Maity, H. & Hossain, S. A preliminary ecological profile of Kyasanur Forest disease virus hosts among the mammalian wildlife of the Western Ghats, India. Ticks Tick Borne Dis. 11, 101419 (2020).PubMed 

Google Scholar 
41.Plowright, R. K. et al. Prioritizing surveillance of Nipah virus in India. PLoS Negl. Trop. Dis. 13, e0007393 (2019).PubMed 
PubMed Central 

Google Scholar 
42.Schmidt, J. P. et al. Ecological indicators of mammal exposure to Ebolavirus. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180337 (2019).
Google Scholar 
43.Worsley-Tonks, K. E. et al. Using host traits to predict reservoir host species of rabies virus. PLoS Negl. Trop. Dis. 14, e0008940 (2020).PubMed 
PubMed Central 

Google Scholar 
44.Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. Infect. Dis. 11, 1842 (2005).PubMed 
PubMed Central 

Google Scholar 
45.Johnson, C. K. et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci. Rep. 5, 14830 (2015).
Google Scholar 
46.Elena, S. F. & Sanjuán, R. Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79, 11555–11558 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Duffy, S. Why are RNA virus mutation rates so damn high? PLoS Biol. 16, e3000003 (2018).PubMed 
PubMed Central 

Google Scholar 
48.Grewelle, R. E. Larger viral genome size facilitates emergence of zoonotic diseases. Preprint at bioRxiv https://doi.org/10.1101/2020.03.10.986109 (2020).49.Mollentze, N. & Streicker, D. G. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proc. Natl Acad. Sci. USA 117, 9423–9430 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Walker, J. W., Han, B. A., Ott, I. M. & Drake, J. M. Transmissibility of emerging viral zoonoses. PLoS ONE 13, e0206926 (2018).PubMed 
PubMed Central 

Google Scholar 
51.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010146117 (2020).52.Zhang, Z. et al. Rapid identification of human-infecting viruses. Transbound. Emerg. Dis. 66, 2517–2522 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Eng, C. L., Tong, J. C. & Tan, T. W. Predicting zoonotic risk of influenza A viruses from host tropism protein signature using random forest. Int. J. Mol. Sci. 18, 1135 (2017).PubMed Central 

Google Scholar 
54.Li, J. et al. Machine learning methods for predicting human-adaptive influenza A viruses based on viral nucleotide compositions. Mol. Biol. Evol. 37, 1224–1236 (2020).CAS 
PubMed 

Google Scholar 
55.Kim, B., Niu, X., Hunter, D. R. & Cao, X. A dynamic additive and multiplicative effects model with application to the United Nations voting behaviors. Preprint at https://arxiv.org/abs/1803.06711 (2018).56.Becker, D. et al. Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs. Lancet Microbe (in the press).57.Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Plourde, B. T. et al. Are disease reservoirs special? Taxonomic and life history characteristics. PLoS ONE 12, e0180716 (2017).PubMed 
PubMed Central 

Google Scholar 
59.Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Albery, G. F. & Becker, D. J. Fast-lived hosts and zoonotic risk. Trends Parasitol. 37, 117–129 (2021).CAS 
PubMed 

Google Scholar 
61.Young, C. C. & Olival, K. J. Optimizing viral discovery in bats. PLoS ONE 11, e0149237 (2016).PubMed 
PubMed Central 

Google Scholar 
62.Albery, G. F. et al. Urban-adapted mammal species have more known pathogens. Preprint at bioRxiv https://doi.org/10.1101/2021.01.02.425084 (2021).63.Wille, M., Geoghegan, J. L. & Holmes, E. C. How accurately can we assess zoonotic risk? PLoS Biol. 19, e3001135 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Gibb, R. et al. Mammal virus diversity estimates are unstable due to accelerating discovery effort. Preprint at bioRxiv https://doi.org/10.1101/2021.08.10.455791 (2021).65.Xu, G. J. et al. Comprehensive serological profiling of human populations using a synthetic human virome. Science 348, aaa0698 (2015).PubMed 
PubMed Central 

Google Scholar 
66.Geoghegan, J. L. & Holmes, E. C. Predicting virus emergence amid evolutionary noise. Open Biol. 7, 170189 (2017).PubMed 
PubMed Central 

Google Scholar 
67.Fischhoff, I. R., Castellanos, A. A., Rodrigues, J. P., Varsani, A. & Han, B. A. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2021.1651 (2021).68.Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Thompson, A. J., de Vries, R. P. & Paulson, J. C. Virus recognition of glycan receptors. Curr. Opin. Virol. 34, 117–129 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Kocher, J. F. et al. Bat caliciviruses and human noroviruses are antigenically similar and have overlapping histo-blood group antigen binding profiles. Mbio 9, e00869-18 (2018).PubMed 
PubMed Central 

Google Scholar 
71.Chiramel, A. I. et al. TRIM5α restricts flavivirus replication by targeting the viral protease for proteasomal degradation. Cell Rep. 27, 3269–3283 (2019).CAS 
PubMed 

Google Scholar 
72.Young, F., Rogers, S. & Robertson, D. L. Predicting host taxonomic information from viral genomes: a comparison of feature representations. PLoS Comput. Biol. 16, e1007894 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).CAS 
PubMed 

Google Scholar 
74.Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Truong, P., Garcia-Vallve, S. & Puigbo, P. An unsupervised algorithm for host identification in flaviviruses. Life https://doi.org/10.3390/life11050442 (2021).76.Mollentze, N., Babayan, S. & Streicker, D. Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biol. 19, e3001390 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Wang, W. et al. A network-based integrated framework for predicting virus–prokaryote interactions. NAR Genom. Bioinform. 2, lqaa044 (2020).PubMed 
PubMed Central 

Google Scholar 
78.Bartoszewicz, J. M., Seidel, A. & Renard, B. Y. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom. Bioinform. 3, lqab004 (2021).PubMed 
PubMed Central 

Google Scholar 
79.He, X. et al. Neural collaborative filtering. In Proc. 26th International Conference on World Wide Web 26, 173–182 (Republic and Canton of Geneva, Switzerland, 2017).80.Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. NIPS’17: Proc. 31st International Conference on Neural Information Processing Systems 31, 6533–6542 (2017).
Google Scholar 
81.Hamilton, W. L., Ying, R. & Leskovec, J. Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017).
Google Scholar 
82.Bergner, L. M. et al. Characterizing and evaluating the zoonotic potential of novel viruses discovered in vampire bats. Viruses 13, 252 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424–1432 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Schulz, J. E. et al. Serological evidence for henipa-like and filo-like viruses in Trinidad bats. J. Infect. Dis. 221, S375–S382 (2020).PubMed 
PubMed Central 

Google Scholar 
85.Brook, C. E. et al. Disentangling serology to elucidate henipa- and filovirus transmission in Madagascar fruit bats. J. Anim. Ecol. 88, 1001–1016 (2019).PubMed 
PubMed Central 

Google Scholar 
86.Seifert, S. N. et al. Rousettus aegyptiacus bats do not support productive Nipah virus replication. J. Infect. Dis. 221, S407–S413 (2020).CAS 
PubMed 

Google Scholar 
87.Carlson, C. J. et al. The future of zoonotic risk prediction. Phil. Trans. R. Soc. B Biol. Sci. 376, 20200358 (2021).
Google Scholar 
88.Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Menachery, V. D. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat. Med. 21, 1508–1513 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Guan, Y. et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).CAS 
PubMed 

Google Scholar 
91.Woo, P. C. Y. et al. Characterization and complete genome sequence of a novel coronavirus, coronavirus HKU1, from patients with pneumonia. J. Virol. 79, 884–895 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).CAS 
PubMed 

Google Scholar 
93.Wang, M. et al. SARS-CoV infection in a restaurant from palm civet. Emerg. Infect. Dis. 11, 1860–1865 (2005).PubMed 
PubMed Central 

Google Scholar 
94.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).PubMed 
PubMed Central 

Google Scholar 
95.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Xiao, K. et al. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature 583, 286–289 (2020).CAS 
PubMed 

Google Scholar 
97.Lam, T.-Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).CAS 
PubMed 

Google Scholar 
98.Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
99.Holmes, E. C. et al. The origins of SARS-CoV-2: a critical review. Cell 184, 4848–4856 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021).CAS 
PubMed 

Google Scholar 
101.Chandler, J. C. et al. SARS-CoV-2 exposure in wild white-tailed deer (Odocoileus virginianus). Proc. Natl Acad. Sci. USA 118, e2114828118 (2021).PubMed 

Google Scholar 
102.Jia, P., Dai, S., Wu, T. & Yang, S. New approaches to anticipate the risk of reverse zoonosis. Trends Ecol. Evol. 36, 580–590 (2021).PubMed 
PubMed Central 

Google Scholar 
103.Lednicky, J. A. et al. Isolation of a novel recombinant canine coronavirus from a visitor to Haiti: further evidence of transmission of coronaviruses of zoonotic origin to humans. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab924 (2021).104.Vlasova, A. N. et al. Novel canine coronavirus isolated from a hospitalized pneumonia patient, East Malaysia. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab456 (2021).105.Lednicky, J. A. et al. Emergence of porcine delta-coronavirus pathogenic infections among children in Haiti through independent zoonoses and convergent evolution. Preprint at medRxiv https://doi.org/10.1101/2021.03.19.21253391 (2021).106.Hay, A. J. & McCauley, J. W. The WHO global influenza surveillance and response system (GISRS)—a future perspective. Influenza Other Respir. Viruses 12, 551–557 (2018).PubMed 
PubMed Central 

Google Scholar 
107.Subbarao, K. et al. Characterization of an avian influenza A (H5N1) virus isolated from a child with a fatal respiratory illness. Science 279, 393–396 (1998).CAS 
PubMed 

Google Scholar 
108.Kandeel, A. et al. Zoonotic transmission of avian influenza virus (H5N1), Egypt, 2006–2009. Emerg. Infect. Dis. 16, 1101 (2010).PubMed 
PubMed Central 

Google Scholar 
109.Ke, C. et al. Human infection with highly pathogenic avian influenza A (H7N9) virus, China. Emerg. Infect. Dis. 23, 1332 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Gaidet, N. et al. Evidence of infection by H5N2 highly pathogenic avian influenza viruses in healthy wild waterfowl. PLoS Pathog. 4, e1000127 (2008).PubMed 
PubMed Central 

Google Scholar 
111.Webster, R. G., Bean, W. J., Gorman, O. T., Chambers, T. M. & Kawaoka, Y. Evolution and ecology of influenza A viruses. Microbiol. Mol. Biol. Rev. 56, 152–179 (1992).CAS 

Google Scholar 
112.Pawar, S. D. et al. Avian influenza surveillance reveals presence of low pathogenic avian influenza viruses in poultry during 2009–2011 in the West Bengal State, India. Virol. J. 9, 151 (2012).PubMed 
PubMed Central 

Google Scholar 
113.Parry, R., Wille, M., Turnbull, O. M., Geoghegan, J. L. & Holmes, E. C. Divergent influenza-like viruses of amphibians and fish support an ancient evolutionary association. Viruses 12, 1042 (2020).CAS 
PubMed Central 

Google Scholar 
114.Campbell, P. J. et al. The M segment of the 2009 pandemic influenza virus confers increased neuraminidase activity, filamentous morphology, and efficient contact transmissibility to A/Puerto Rico/8/1934-based reassortant viruses. J. Virol. 88, 3802–3814 (2014).PubMed 
PubMed Central 

Google Scholar 
115.Carlson, C. Evolutionary surprise, artificial intelligence, and H5N8. The Verena Blog https://www.viralemergence.org/blog/evolutionary-surprise-artificial-intelligence-and-h5n8 (2021).116.Wardeh, M., Baylis, M. & Blagrove, M. S. Predicting mammalian hosts in which novel coronaviruses can be generated. Nat. Commun. 12, 780 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Crossman, L. C. Leveraging deep learning to simulate coronavirus spike proteins has the potential to predict future zoonotic sequences. Preprint at bioRxiv https://doi.org/10.1101/2020.04.20.046920 (2020). More

1.Colwell, R. K. Spatial scale and the synchrony of ecological disruption. Nature https://doi.org/10.1038/s41586-021-03760-4 (2021).2.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
CAS 
Article 

Google Scholar 
3.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

Google Scholar 
4.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 
Article 

Google Scholar 
5.Ricklefs, R. E. Disintegration of the ecological community. Am. Nat. 172, 741–750 (2008).Article 

Google Scholar 
6.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).ADS 
CAS 
Article 

Google Scholar 
7.Nadeau, C. P., Urban, M. C. & Bridle, J. R. Coarse climate change projections for species living in a fine-scaled world. Glob. Change Biol. 23, 12–24 (2017).ADS 
Article 

Google Scholar 
8.Stewart, S. B. et al. Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species. Ecography 44, 626–639 (2021).Article 

Google Scholar 
9.Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).ADS 
Article 

Google Scholar 
10.McKechnie, A. E. & Wolf, B. O. The Physiology of Heat Tolerance in Small Endotherms. Physiology 34, 302–313 (2019).CAS 
Article 

Google Scholar 
11.Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
12.Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).ADS 
Article 

Google Scholar 
13.Molnár, P. K., Derocher, A. E., Thiemann, G. W. & Lewis, M. A. Predicting survival, reproduction and abundance of polar bears under climate change. Biol. Conserv. 143, 1612–1622 (2010).Article 

Google Scholar 
14.Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
Article 

Google Scholar 
15.Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl Acad. Sci. USA 113, 13791–13796 (2016).Article 

Google Scholar 
16.Genin, A., Levy, L., Sharon, G., Raitsos, D. E. & Diamant, A. Rapid onsets of warming events trigger mass mortality of coral reef fish. Proc. Natl Acad. Sci. USA 117, 25378–25385 (2020).ADS 
CAS 
Article 

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

Google Scholar 
18.Ruthrof, K. X. et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses. Sci. Rep. 8, 13094 (2018).ADS 
Article 

Google Scholar 
19.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
Article 

Google Scholar 
20.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
Article 

Google Scholar 
21.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).ADS 
Article 

Google Scholar 
22.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
23.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).ADS 
CAS 
Article 

Google Scholar 
24.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).ADS 
CAS 
Article 

Google Scholar 
25.Williams, J. W., Ordonez, A. & Svenning, J. C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).Article 

Google Scholar 
26.NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 10.05.2021. More