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    Author Correction: Recent expansion of oil palm plantations into carbon-rich forests

    In the version of this article initially published, there were mistakes in affiliations 1, 2 and 6. The corrected affiliations should read as follows: 1. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China; 2. Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Tsinghua University, Beijing, China; 6. Department of Geography, Department of Earth Sciences, and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China. The affiliations have been corrected in the HTML and PDF versions of the article. More

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    Same-sex competition and sexual conflict expressed through witchcraft accusations

    The data used here provides evidence that particular relationships may determine sex-specific patterns of witchcraft accusation. Cases where women were targeted frequently came from affinal kin, while those directed at men were often from unrelated individuals and blood relatives. Most previous research on factors that determine the sex of accused ‘witches’ has largely consisted of qualitative studies of a single society or a few societies48, or historical studies that have not tested for correlations49. Our findings, in support of the overarching hypothesis that accusations may be driven by various forms of competition, can be tentatively aligned with evolutionary literature on patterns of intrasexual and kin competition, intersexual conflict and polygamous mating30,31,50.Men were more often accused than women in our sample, although we did not have a prediction in relation to this. But the finding suggests how overall patterns of competition within relationships may contribute to societal ‘phenotypes’ of witches as male or female. The ethnography of the Ndembele perhaps indicates why women were less frequently targeted in Bantu societies: ‘in a case of witchcraft, the complainant is actuated by caprice, jealousy or pique; and the defendant is a person of wealth or popularity, and is always a man, for the women have neither wealth nor honor worth coveting’51.Our predictions about how the sex of accused ‘witches’ might be associated with particular relationship categories were supported. The majority of accusations targeting men came from unrelated individuals, which is unsurprising, as inclusive fitness52 would not mitigate the effects of competition between them. Blood relatives were the next most common relationship category directing accusations at men. This aligns with more recent studies indicating that witchcraft fears between family members are significant in parts of Africa, to the extent that they can be construed as ‘the dark side of kinship’53. In evolutionary terms, kin may compete with one another in environments where resources are limited30,31,50 and in societies with patrilineal inheritance related males, and particularly brothers, compete for resources in order to marry31. This aligns with an ethnographic observation that among the Banyoro witchcraft accusations often occurred between brothers over inheritance, but not between brothers and sisters, whose interests did not conflict21. The situations relating to accusations of men were also often connected to the acquisition of wealth and status, such as rivalry over village headmanships32, power struggles between a chief’s counsellors54 or disputes over inheritance55. These connections can be found in more recent contexts such as twentieth century Ghana, where notions of obtaining political power and wealth through occult means involving human sacrifice were pervasive56.Accusations of women were more likely to come from affines. Husbands were the largest category of affinal kin to accuse women (Supplementary Fig. 2). The higher rate of accusations from husbands to wives than wives to husbands aligns with evolutionary perspectives suggesting male coercion of females is a strategy to maximize male reproductive success39,41. Accusations of wives who were suspected of being unfaithful can be interpreted as a strategy for reducing investment in unrelated offspring35,41. In a case from the Shona a woman gave birth to a stillborn child. This was attributed to an affair before marriage, and was followed by divorce and the repayment of bridewealth to her husband, who commented she was ‘a witch, a woman who had killed her own child’48. Other ethnographic accounts suggest accusations of wives by husbands were an attempt to gain control within the marital relationship55.A significant number of accusations of women by affinal kin were from co-wives in polygynous marriages, and these were often notably associated with jealousy connected to a husband’s attention and investment32. Evolutionary models predict competition for reproductive resources would occur among co-resident breeding women57, as has been found to occur among the Mosuo of southwest China58. In the patrilocal social systems that are predominant in our sample, women disperse at marriage and are isolated from kin, so conflict may be more extreme30. This is consistent with ethnographic observations reporting that the relationship between co-wives in polygynous marriages was often (although not always) marked by conflict, and liable to produce witchcraft accusations38,59.There were accusations of women from other categories of their affinal kin (Supplementary Fig. 2). These again may result from competition for a husband’s time and resources between his kin and wife. New wives may be vulnerable in environments where they enter their husband’s families as unrelated strangers, and are potentially expendable, at least before the arrival of offspring. Some accounts of accusations indicate that accusations of wives by in-laws in patrilocal households are common29.Accusations directed at elderly individuals targeted women more often than men. This may form part of a broader pattern of geronticide: societies close to subsistence-level are documented as sometimes accepting the abandonment or killing of elderly people19,60. In modern Tanzania, ‘witches’ are mostly post-reproductive women, who are more likely to be murdered in periods of income shock19. This is also the case in contemporary Ghana, where accusations are frequently directed at middle-aged or elderly women, whose families may subsequently cease to provide them with financial or material assistance61. In our sample, elderly women may have been targeted more frequently as a result of longer female lifespans: in a polygynous society, men may marry younger women, so wives would be widowed at an earlier age than husbands. Among the Bantu, older men were accused, but some were possibly protected by their status.Accusers’ payoffs from accusations are not always explicit but they can be inferred. The most common outcome of accusations in our sample was that accused ‘witches’ were exiled from their communities or forced to move from where they were living. This would mean resources and cooperative assistance they would have used became available to their accusers or others nearby. Where the accused acquires a negative reputation, which was the second most common outcome, there may be a subtle removal of benefits, which may be preferred to direct ‘punishment’ as it is less costly62. Accusers’ gains need not be direct, as harming behaviours may reduce the overall pressure of competition in an environment28. 8% of accusations in the sample resulted in the acquisition of either resources or political positions from the accused, or in preventing the accused from acquiring them. Where the accused were penalised in other ways, such performing ceremonies to reconcile with accusers, this is perhaps akin to classic cooperation models involving the punishment of defectors (although the accused may not actually be uncooperative)11, providing accusers with subordinate partners who offer fitness benefits to avoid more serious allegations63. Where an accusation does not ‘stick’, ethnographic accounts sometimes indicate it was reversed through divination or ordeal54. In other cases, for various reasons accusations are short-lived and forgotten about4. Finally, although not tested in this dataset, accusers may gain informal prestige and dominance, an outcome analogous to competitive punishment63.Not all of the cases in our dataset support the hypothesis that witchcraft accusations are a mechanism for competition. There is a significant proportion where the accusation of a particular individual appears to be incidental, or dependent a on circumstantial association between the ‘witch’ and a negative event. Such accusations are unlikely to provide accusers with a competitive advantage. There are several possible explanations for such cases. They are in line with the hypothesis that witchcraft belief arises from attempts to identify the cause of an impactful misfortune3,4. Cultural evolutionary explanations of witchcraft beliefs suggest that they are a maladaptive attempt to explain misfortune. Although it is inaccurate, belief in witches is maintained through bias and selective inattention to evidence that would otherwise counter it64. Alternatively this could be viewed under the contention that superstitious beliefs (or errors in attributing cause and effect) are broadly adaptive if they occasionally lead individuals to acts which provide them with fitness benefits65.Although witchcraft accusations may be a mechanism for mitigating the damage to accusers’ reputations in harmful competitive acts, as with any behavioural strategy it is not without risks. Accusers may suffer costs in the form subsequent reputational damage or counter-accusations, as with punishment63, depending on factors such the level of support for an accusation by other members of the community.One limitation of our dataset is that it contains realized allegations of witchcraft, that cannot be tested against baseline population measures. We could not examine the risk that a particular individual, such as an elderly woman, would be accused. Instead, the analysis shows the odds, given an accusation occurred, that the ‘witch’ was male or female, given certain predictors. For example, if the accused was elderly, there are increased odds they were female rather than male.A dataset using historic witchcraft cases is almost certainly affected by selection bias. Cases with sensational outcomes are more likely to be reported, and cases that are dismissed or where the accused removes themselves from their accusers are liable to be overlooked19. Most incidents in our sample were reported anecdotally. Obtaining a random sample of witchcraft accusations within a population is challenging, if not impossible1,66. Attempts to systematically collect cases within a given location and timeframe cannot guarantee that all are brought to the attention of researchers19. Comparative studies of this kind usually use all the data that is available and control for confounding effects. Our sensitivity analyses suggest the large number of accusations of men in the dataset probably reflects patterns of accusations in these societies, rather than male-focused bias from ethnographers. There are many accounts of cultures where witches are predominantly male33,34,49. But the accuracy of historic ethnographic accounts cannot be verified, especially in relation to one-off events such as witchcraft accusations, just as it is unclear how much uncertainty there is in the ethnographic record overall67. Ethnographers may not always have noted the characteristics of the individuals involved, or there may be times where they were mistaken in reporting the circumstances surrounding an accusation. There are several explanations for cases where the identities of accusers or purported victims of witchcraft were not reported. Not all cases had identifiable ‘victims’, for example when the accused was thought to have used witchcraft to promote their own success, or ethnographers could not denote the relationship between the accused and their accusers when suspicions of witchcraft were communicated through general gossip. In a small number of cases, ethnographer perspectives on accusations (and possible inability to access further information) are salient, as they may ascribe more importance to one relationship over another in reporting a case, such as a witch’s envy of their victim, or a witch’s argument with an accuser.However, it is likely that ethnographers were for the most part accurate in documenting variables of interest such as the sex of an accused individual and their relationships with accusers. There is less certainty in relation to the situation connected to an accusation, especially taking into recent research that indicates the prevalence of phenomena such as the misperception of causation68,69. Our attempts to account for such possibilities with sensitivity analyses and meta-data on the production of ethnographies cannot conclusively provide reassurance that bias has not affected results, and so this section of the analysis should be treated with caution and regarded as exploratory. The situations documented in our study do however align with accounts of accusations from more contemporary observers and studies from different geographic locations, suggesting that similar causes of accusations arise convergently in different societies. For example in modern contexts accusations have led to accusers gaining land or property in India6 and cessation of the obligation to provide material and financial assistance to elderly relatives in Ghana61. One advantage of our cross-cultural data being drawn from numerous ethnographies is that it is not reliant on the perspective of one individual, meaning that random perceptual error or individual (as opposed to cultural) bias is more likely to be mitigated in the results than would be the case in the study of a single culture by one ethnographer.As a further limitation, we were reliant on accessible ethnographic records from the best-documented societies. Although selection bias in favour of better described societies is present in our sample, this should not impact the main aim of this research, which is to understand the determinants of witchcraft accusations being directed at male or female targets.Overall our findings may indicate allegations of witchcraft stem from diverse forms of competition between individuals. This aligns with evolutionary approaches to competition and conflict. Accusations may provide fitness benefits by allowing individuals to target competitors, but the exact form and direction of competition is determined by aspects of socio-ecology. This in turn influences which sex is most likely to be accused and the overall portrayal of witches in a society. Accusations may be more likely to occur in some relationships rather than others, when there is a gain for the accuser, as in disputes over inheritance and property, or where another individual may pose a threat, or by simply reducing numbers of competitors. The success of witchcraft accusations in removing competitors and their flexibility as an adaptive strategy may explain their widespread distribution. More

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    Machine learning-based global maps of ecological variables and the challenge of assessing them

    The quality of global maps can be assessed in different ways. One way is global assessment where a single statistic is chosen to summarize the quality of the entire map: the map accuracy. For a categorical variable, this can be the probability that for a randomly chosen location on the map, the map value corresponds to the true value. For a continuous variable, it can be the RMSE, describing for a randomly chosen location on the map the expected difference between the mapped value and the true value. When a probability sample, such as a completely spatially random sample, is available for the area for which a global assessment is needed, then map accuracy can be estimated model-free (also called design-based, e.g., by using the unweighted sample mean in case of a completely spatially random sample). This circumvents modeling of spatial correlation because observations are independent by design6,9. This approach is called model-free because no model needs to be assumed about the distribution or correlation of the data: the only source of randomness is the random selection of sample units from a target population. If a probability sample is not available this approach cannot be used, and automatically the accuracy assessment approach becomes model-based10, which involves modeling a spatial process by assuming distributions and taking spatial correlations into account, and choosing estimation methods accordingly.Using naive random n-fold or leave-one-out cross-validation methods (or a simple random train-test split) to assess global model quality (usually equated with map accuracy) makes sense when the data are independent and identically distributed. When this is not the case, dependencies between nearby samples, e.g., in a spatial cluster, are ignored and result in biased, overly optimistic model assessment, as shown in, e.g., Ploton et al.5. Alternative cross-validation approaches such as spatial cross-validation5,11 that control for such dependencies are the only way to overcome this bias. Different spatial cross-validation strategies have been developed in the past few years, all aiming at creating independence between cross-validation folds5,11,12,13. Cross-validation creates prediction situations artificially by leaving out data points and predicting their value from the remaining points. If the aim is to assess the accuracy of a global map, the prediction situations created need to resemble those encountered while predicting the global map from the reference data (see Fig. 1 and discussions in Milà et al.14). This occurs naturally when reference data were obtained by (completely spatially random) probability sampling, but in other cases, this has to be forced for instance by controlling spatial distances (spatial cross-validation). Such forcing, however, is only possible when the distances in space that need to be resembled are available in the reference data. In the extreme case where all reference data come from a single cluster, this is impossible. When all reference data come from a small number of clusters, larger distances are available between clusters but do not provide substantial independent information about variation associated with these distances. Lack of information about larger distances means that we cannot assess the quality of predictions associated with such distances and cannot properly estimate global quality measures. Alternative approaches such as experiments with synthetic data15 or a validation using independent data at a higher level of integration16 would then be options to support confidence in the predictions.Another way of accuracy assessment is local assessment: for every location, a quality measure is reported, again as probability or prediction error. Such a local assessment predicts how close the map value is to newly observed values at particular locations. If the measurement error is quantified explicitly, a smoother, measurement-error-free value may be predicted10. If the model accounts for change of support10,17, predictions errors may refer to average values over larger areas such as 1 × 1, 5 × 5, or 10 × 10 km grid cells. Examples of local assessment in the context of global ecological mapping are modeled prediction errors using Quantile Regression Forests18 or mapped variance of predictions made by ensembles1,2. Neither of these examples quantifies spatial correlation or measurement error, or addresses change of support, as it is known from other modeling frameworks19. By omitting to model the spatial process, the local accuracy estimates as presented in the global studies that motivated this comment are disputable.The difference between global and local assessment is striking, in particular for global maps. A global, single number averages out all variability in prediction errors, and obscures any differences, e.g., between continents or climate zones. It is of little value for interpreting the quality of the map for particular regions. More

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    The distribution of manta rays in the western North Atlantic Ocean off the eastern United States

    Couturier, L. et al. Biology, ecology and conservation of the Mobulidae. J. Fish Biol. 80, 1075–1119 (2012).CAS 

    Google Scholar 
    Herman, J., Hovestadt-Euler, M., Hovestadt, D. & Stehmann, M. Contributions to the study of the comparative morphology of teeth and other relevant ichthyodorulites in living supraspecific taxa of Chondrichthyan fishes. Part B: Batomorphii 4c: Order Rajiformes-Suborder Myliobatoidei-Superfamily Dasyatoidea-Family Dasyatidae-Subfamily Dasyatinae-Genus: Urobatis, Subfamily Potamotrygoninae-Genus: Paratrygon, Superfamily Plesiobatoidea-Family Plesiobatidae-Genus: Plesiobatis, Superfamily Myliobatoidea-Family Myliobatidae-Subfamily Myliobatinae-Genera: Aetobatus, Aetomylaeus, Myliobatis and Pteromylaeus, Subfamily Rhinopterinae-Genus: Rhinoptera and Subfamily Mobulinae-Genera: Manta and Mobula. Addendum 1 to 4a: erratum to Genus Pteroplatytrygon. Bull. Koninlijk Belgisch Inst Natuurwetenschappen-Biol. (2000).Adnet, S., Cappetta, H., Guinot, G. & NOTARBARTOLO DI SCIARA, G. Evolutionary history of the devilrays (Chondrichthyes: Myliobatiformes) from fossil and morphological inference. Zool. J. Linnean Soc. 166, 132–159 (2012).
    Google Scholar 
    Naylor, G. J. et al. A DNA sequence–based approach to the identification of shark and ray species and its implications for global elasmobranch diversity and parasitology. Bull. Am. Mus. Nat. Hist. 2012, 1–262 (2012).
    Google Scholar 
    Kitchen-Wheeler, A.-M. The Behaviour and Ecology of Alfred mantas (Manta alfredi) in the Maldives (Newcastle University, 2013).
    Google Scholar 
    Paig-Tran, E. M., Kleinteich, T. & Summers, A. P. The filter pads and filtration mechanisms of the devil rays: Variation at macro and microscopic scales. J. Morphol. 274, 1026–1043 (2013).
    Google Scholar 
    Aschliman, N. C., Claeson, K. M. & McEachran, J. D. Phylogeny of batoidea. Biol. Sharks Relat. 2, 57–96 (2012).
    Google Scholar 
    Poortvliet, M. et al. A dated molecular phylogeny of manta and devil rays (Mobulidae) based on mitogenome and nuclear sequences. Mol. Phylogenet. Evol. 83, 72–85 (2015).CAS 

    Google Scholar 
    Marshall, A. D., Compagno, L. J. & Bennett, M. B. Redescription of the genus Manta with resurrection of Manta alfredi (Krefft, 1868)(Chondrichthyes; Myliobatoidei; Mobulidae). Zootaxa 2301, 1–28 (2009).
    Google Scholar 
    White, W. T. et al. Phylogeny of the manta and devilrays (Chondrichthyes: Mobulidae), with an updated taxonomic arrangement for the family. Zool. J. Linn. Soc. 182, 50–75 (2018).
    Google Scholar 
    Service, N. O. a. A. A. F. Vol. 83 (ed U.S. Department of Commerce) 2916–2931 (U.S. Department of Commerce, Federal Register, 2018).Service, N. O. a. A. A. F. Vol. 84 (ed U.S. Department of Commerce) 66652–66664 (U.S. Department of Commerce, Federal Register, 2019).Clark, T. B. Abundance, home range, and movement patterns of manta rays (Manta alfredi, M. birostris) in Hawaiʻi, [Honolulu]:[University of Hawaii at Manoa],[December 2010], (2010).Burgess, K. Feeding ecology and habitat use of the giant manta ray Manta birostris at a key aggregation site off mainland Ecuador (2017).Beale, C. S., Stewart, J. D., Setyawan, E., Sianipar, A. B. & Erdmann, M. V. Population dynamics of oceanic manta rays (Mobula birostris) in the Raja Ampat Archipelago, West Papua, Indonesia, and the impacts of the El Niño-Southern Oscillation on their movement ecology. Divers. Distrib. 25, 1472–1487 (2019).
    Google Scholar 
    Bertolini, F. Dentatura dei Selaci in rapporto con la nutrizione. (editore non identificato, 1933).Bigelow, H. B. Sawfishes, guitarfishes, skates and rays. Sawfishes, guitarfishes, skates and rays, and chimaeroids, 1–514 (1953).Rohner, C. A. et al. Mobulid rays feed on euphausiids in the Bohol Sea. R. Soc. Open Sci. 4, 161060 (2017).ADS 

    Google Scholar 
    Stewart, J. D. et al. Trophic overlap in mobulid rays: insights from stable isotope analysis. Mar. Ecol. Prog. Ser. 580, 131–151 (2017).ADS 

    Google Scholar 
    De Boer, M., Saulino, J., Lewis, T. & Notarbartolo-Di-Sciara, G. New records of whale shark (Rhincodon typus), giant manta ray (Manta birostris) and Chilean devil ray (Mobula tarapacana) for Suriname. Mar. Biodivers. Rec. 8 (2015).Hacohen-Domené, A., Martínez-Rincón, R. O., Galván-Magaña, F., Cárdenas-Palomo, N. & Herrera-Silveira, J. Environmental factors influencing aggregation of manta rays (Manta birostris) off the northeastern coast of the Yucatan Peninsula. Mar. Ecol. 38, e12432 (2017).ADS 

    Google Scholar 
    Service, N. O. a. A. A. N. O. What is the Loop Current? https://oceanservice.noaa.gov/facts/loopcurrent.html (2021).Service, N. O. a. A. A. N. O. How fast is the Gulf Stream? https://oceanservice.noaa.gov/facts/gulfstreamspeed.html (2021).Childs, J. N. The Occurrence, Habitat Use and Behavior of Sharks and Rays Associating with Topographic Highs in the Gulf of Mexico. M.S. Thesis, Texas A&M University (2001).Stewart, J. D., Nuttall, M., Hickerson, E. L. & Johnston, M. A. Important juvenile manta ray habitat at Flower Garden Banks National Marine Sanctuary in the northwestern Gulf of Mexico. Mar. Biol. 165, 1–8 (2018).CAS 

    Google Scholar 
    Pate, J. H. & Marshall, A. D. Urban manta rays: Potential manta ray nursery habitat along a highly developed Florida coastline. Endanger. Spec. Res. 43, 51–64 (2020).
    Google Scholar 
    Hosegood, J. et al. Phylogenomics and species delimitation for effective conservation of manta and devil rays. Mol. Ecol. 29, 4783–4796 (2020).
    Google Scholar 
    Hinojosa-Alvarez, S., Walter, R. P., Diaz-Jaimes, P., Galván-Magaña, F. & Paig-Tran, E. M. A potential third manta ray species near the Yucatán Peninsula? Evidence for a recently diverged and novel genetic Manta group from the Gulf of Mexico. PeerJ 4, e2586 (2016).
    Google Scholar 
    Bucair, N., Venables, S. K., Balboni, A. P. & Marshall, A. D. Sightings trends and behaviour of manta rays in Fernando de Noronha Archipelago, Brazil. Mar. Biodivers. Rec. 14, 1–11 (2021).
    Google Scholar 
    Garzon, F., Graham, R., Witt, M. & Hawkes, L. Ecological niche modeling reveals manta ray distribution and conservation priority areas in the Western Central Atlantic. Anim. Conserv. 24, 322–334 (2021).
    Google Scholar 
    Stewart, J. D. et al. Research priorities to support effective manta and devil ray conservation. Front. Mar. Sci. 5, 314 (2018).
    Google Scholar 
    Garrison, L. P. Abundance of coastal and continental shelf stocks of bottlenose dolphins in the northern Gulf of Mexico: 2011–2012. (National Marine Fisheries Service, Southeast Fisheries Science Center, Miami, Florida, 2017).Garrison, L. P., Ortega-Ortiz, J. & Rappucci, G. Abundance of coastal and continental shelf stocks of bottlenose dolphins in the northern Gulf of Mexico: 2017–2018. (National Marine Fisheries Service, Southeast Fisheries Science Center, Miami, Florida, 2021).Palka, D. L. et al. Atlantic Marine Assessment Program for Protected Species: 2010–2014. (US Dept. of the Interior, Bureau of Ocean Energy Management, Atlantic OCS Region, Washington, DC, 2017).Palka, D. et al. Atlantic Marine Assessment Program for Protected Species: FY15 – FY19. (US Dept. of the Interior, Bureau of Ocean Energy Management, Atlantic OCS Region, Washington, DC, 2021).Laake, J. L. & Borchers, D. L. in Advanced distance sampling (eds S.T. Buckland et al.) 108–189 (Oxford University Press, 2004).mrds: Mark-Recapture Distance Sampling v. 2.2.2 (https://CRAN.R-project.org/package=mrds, 2020).Akaike, H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60, 255–265 (1973).MathSciNet 
    MATH 

    Google Scholar 
    Consortium, N. A. R. W. (2018).Miller, D. L., Rexstad, E., Thomas, L., Marshall, L. & Laake, J. L. Distance sampling in R. J. Stat. Softw. 89, 1–28 (2019).
    Google Scholar 
    Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE https://doi.org/10.1371/journal.pone.0073051 (2013).Article 

    Google Scholar 
    rerddap: General Purpose Client for ‘ERDDAP’ Servers v. 0.7.4 (https://cran.r-project.org/package=rerddap, 2021).Belkin, I. M. & O’Reilly, J. E. An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. J. Mar. Syst. 78, 319–326 (2009).
    Google Scholar 
    grec: GRadient-Based RECognition of Spatial Patterns in Environmental Data v. 1.3.1 (https://github.com/LuisLauM/grec, 2020).Del Castillo, C. E. et al. Multispectral in situ measurements of organic matter and chlorophyll fluorescence in seawater: Documenting the intrusion of the Mississippi River plume in the West Florida Shelf. Limnol. Oceanogr. 46, 1836–1843 (2001).ADS 

    Google Scholar 
    Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
    CAS 

    Google Scholar 
    Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 73, 3–36 (2011).MathSciNet 
    MATH 

    Google Scholar 
    Brodie, S. et al. Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. 5, 219 (2018).
    Google Scholar 
    Hazen, E. L. et al. WhaleWatch: A dynamic management tool for predicting blue whale density in the California Current. J. Appl. Ecol. 54, 1415–1428 (2017).
    Google Scholar 
    Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 1–8 (2011).
    Google Scholar 
    Farmer, N. A. et al. Timing and locations of reef fish spawning off the southeastern United States. PLoS ONE 12, e0172968 (2017).
    Google Scholar 
    Heyman, W. D. et al. Cooperative monitoring, assessment, and management of fish spawning aggregations and associated fisheries in the US Gulf of Mexico. Mar. Policy 109, 103689 (2019).
    Google Scholar 
    Shumway, R. H. & Stoffer, D. S. (Springer, 2017).astsa: Applied Statistical Time Series Analysis v. 1.12 (https://CRAN.R-project.org/package=astsa, 2020).Hosmer, D. W. Jr., Lemeshow, S. & Sturdivant, R. X. Applied logistic regression Vol. 398 (Wiley, New York, 2013).MATH 

    Google Scholar 
    Service, N. O. a. A. A. F. Giant manta ray recovery outline, https://www.fisheries.noaa.gov/resource/document/giant-manta-ray-recovery-outline (2020).Kashiwagi, T., Marshall, A. D., Bennett, M. B. & Ovenden, J. R. Habitat segregation and mosaic sympatry of the two species of manta ray in the Indian and Pacific Oceans: Manta alfredi and M. birostris. Mar. Biodivers. Rec. 4 (2011).Adams, D. H. & Amesbury, E. Occurrence of the manta ray, Manta birostris, in the Indian River Lagoon, Florida. Florida Sci., 7–9 (1998).Milessi, A. C. & Oddone, M. C. Primer registro de Manta birostris (Donndorff 1798)(Batoidea: Mobulidae) en el Rio de La Plata, Uruguay. Gayana (Concepción) 67, 126–129 (2003).
    Google Scholar 
    Medeiros, A., Luiz, O. & Domit, C. Occurrence and use of an estuarine habitat by giant manta ray Manta birostris. J. Fish Biol. 86, 1830–1838 (2015).CAS 

    Google Scholar 
    Shropshire, T. A. et al. Quantifying spatiotemporal variability in zooplankton dynamics in the Gulf of Mexico with a physical–biogeochemical model. Biogeosciences 17, 3385–3407 (2020).ADS 

    Google Scholar 
    Strömberg, K. P., Smyth, T. J., Allen, J. I., Pitois, S. & O’Brien, T. D. Estimation of global zooplankton biomass from satellite ocean colour. J. Mar. Syst. 78, 18–27 (2009).
    Google Scholar 
    Yoder, J. Environmental control of phytoplankton production on the southeastern US continental shelf. Oceanogr. Southeast. US Cont. Shelf 2, 93–103 (1985).
    Google Scholar 
    Yoder, J. A., Atkinson, L. P., Lee, T. N., Kim, H. H. & McClain, C. R. Role of gulf stream frontal eddies in forming phytoplankton patches on the outer southeastern shelf 1. Limnol. Oceanogr. 26, 1103–1110 (1981).ADS 

    Google Scholar 
    Cloern, J. E. Tidal stirring and phytoplankton bloom dynamics in an estuary. J. Mar. Res. 49, 203–221 (1991).
    Google Scholar 
    Blauw, A. N., Beninca, E., Laane, R. W., Greenwood, N. & Huisman, J. Dancing with the tides: fluctuations of coastal phytoplankton orchestrated by different oscillatory modes of the tidal cycle. PLoS ONE 7, e49319 (2012).ADS 
    CAS 

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

    Google Scholar 
    Huey, R. B. & Kingsolver, J. G. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4, 131–135 (1989).CAS 

    Google Scholar 
    Schulte, P. M., Healy, T. M. & Fangue, N. A. Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure. Integr. Comp. Biol. 51, 691–702 (2011).
    Google Scholar 
    Huey, R. B. & Stevenson, R. Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. Am. Zool. 19, 357–366 (1979).
    Google Scholar 
    Angilletta, M. J. Jr. Estimating and comparing thermal performance curves. J. Therm. Biol 31, 541–545 (2006).
    Google Scholar 
    Angilletta, M. J. Jr., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol 27, 249–268 (2002).
    Google Scholar 
    Lear, K. O. et al. Thermal performance responses in free-ranging elasmobranchs depend on habitat use and body size. Oecologia 191, 829–842 (2019).ADS 

    Google Scholar 
    Thorrold, S. R. et al. Extreme diving behaviour in devil rays links surface waters and the deep ocean. Nat. Commun. 5, 1–7 (2014).
    Google Scholar 
    Freedman, R. & Roy, S. S. Spatial patterning of Manta birostris in United States east coast offshore habitat. Appl. Geogr. 32, 652–659 (2012).
    Google Scholar 
    Graham, R. T. et al. Satellite tracking of manta rays highlights challenges to their conservation. PLoS ONE 7, e36834 (2012).ADS 
    CAS 

    Google Scholar 
    Duffy, C. & Abbott, D. Sightings of mobulid rays from northern New Zealand, with confirmation of the occurrence of Manta birostris in New Zealand waters. (2003).Dewar, H. et al. Movements and site fidelity of the giant manta ray, Manta birostris, in the Komodo Marine Park, Indonesia. Mar. Biol. 155, 121–133 (2008).
    Google Scholar 
    Johnston, M. A. et al. Long-term monitoring at east and west Flower Garden Banks: 2017 annual report. (Flower Garden Banks National Marine Sanctuary, Galveston, Texas, 2018).Morita, K., Fukuwaka, M. A., Tanimata, N. & Yamamura, O. Size-dependent thermal preferences in a pelagic fish. Oikos 119, 1265–1272 (2010).
    Google Scholar 
    Gilchrist, G. W. Specialists and generalists in changing environments. I. Fitness landscapes of thermal sensitivity. Am. Nat. 146, 252–270 (1995).
    Google Scholar 
    Kingsolver, J. G. The Well-temperatured biologist: (American Society of Naturalists Presidential Address). Am. Nat. 174, 755–768 (2009).
    Google Scholar 
    Stevenson, R. Body size and limits to the daily range of body temperature in terrestrial ectotherms. Am. Nat. 125, 102–117 (1985).
    Google Scholar 
    Blanton, J., Atkinson, L., Pietrafesa, L. & Lee, T. The intrusion of Gulf Stream water across the continental shelf due to topographically-induced upwelling. Deep Sea Res. Part A Oceanogr. Res. Pap. 28, 393–405 (1981).ADS 

    Google Scholar 
    Savidge, G. A preliminary study of the distribution of chlorophyll a in the vicinity of fronts in the Celtic and western Irish Seas. Estuar. Coast. Mar. Sci. 4, 617–625 (1976).ADS 
    CAS 

    Google Scholar 
    Pingree, R. & Griffiths, D. Tidal fronts on the shelf seas around the British Isles. J. Geophys. Res. Oceans 83, 4615–4622 (1978).ADS 

    Google Scholar 
    Tett, P. Modelling phytoplankton production at shelf-sea fronts. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Sci. 302, 605–615 (1981).ADS 

    Google Scholar 
    Bumpus, D. F. & Wehe, T. Hydrography of the Western Atlantic: coastal water circulation off the east coast of the United States between Cape Hatteras and Florida. (Woods Hole Oceanographic Institution, 1949).Clark, T. B. Population structure of Manta birostris (Chondrichthyes: Mobulidae) from the Pacific and Atlantic Oceans. Texas A&M University (2002).Kashiwagi, T. et al. in The Joint Meeting of Ichthyologists & Herpetologist. Austin: American Elasmobranch Society Conference. 254–255.Notarbartolo-di-Sciara, G. Natural history of the rays of the genus Mobula in the Gulf of California. Fish. Bull. 86, 45–66 (1988).
    Google Scholar 
    Notarbartolo-di-Sciara, G. A revisionary study of the genus Mobula Rafinesque, 1810 (Chondrichthyes: Mobulidae) with the description of a new species. Zool. J. Linn. Soc. 91, 1–91 (1987).
    Google Scholar 
    Canese, S. et al. Diving behavior of the giant devil ray in the Mediterranean Sea. Endangered Species Research 14, 171–176 (2011).
    Google Scholar 
    Stewart, J. D. et al. Spatial ecology and conservation of Manta birostris in the Indo-Pacific. Biol. Cons. 200, 178–183 (2016).
    Google Scholar 
    Farmer, N. A. et al. Population consequences of disturbance by offshore oil and gas activity for endangered sperm whales (Physeter macrocephalus). Biol. Cons. 227, 189–204 (2018).
    Google Scholar 
    Farmer, N. A., Gowan, T. A., Powell, J. R. & Zoodsma, B. J. Evaluation of alternatives to winter closure of black sea bass pot gear: Projected impacts on catch and risk of entanglement with North Atlantic right whales Eubalaena glacialis. Mar. Coast. Fish. 8, 202–221 (2016).
    Google Scholar 
    Miller, M. & Klimovich, C. Endangered Species Act status review report: Giant manta ray (Manta birostris) and reef manta ray (Manta alfredi). Report to National Marine Fisheries Service, Office of Protected Resources. Silver Spring, MD (2016).Croll, D. A. et al. Vulnerabilities and fisheries impacts: the uncertain future of manta and devil rays. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 562–575 (2016).
    Google Scholar 
    Carlson, J. K. Estimated incidental take of smalltooth sawfish (Pristis pectinata) and giant manta ray (Manta birostris) in the South Atlantic and Gulf of Mexico shrimp trawl fishery. 16 (National Marine Fisheries Service, Southeast Fisheries Science Center, Panama City Laboratory, Panama City, Florida, 2020).Essumang, D. First determination of the levels of platinum group metals in Manta birostris (Manta Ray) caught along the Ghanaian coastline. Bull. Environ. Contam. Toxicol. 84, 720–725 (2010).CAS 

    Google Scholar 
    Hajbane, S. & Pattiaratchi, C. B. Plastic pollution patterns in offshore, nearshore and estuarine waters: A case study from Perth Western Australia. Front. Mar. Sci. 4, 63 (2017).
    Google Scholar 
    Germanov, E. S. et al. Microplastics on the menu: Plastics pollute Indonesian manta ray and whale shark feeding grounds. Front. Mar. Sci. 6, 679 (2019).
    Google Scholar 
    McCauley, D. J. et al. Reliance of mobile species on sensitive habitats: A case study of manta rays (Manta alfredi) and lagoons. Mar. Biol. 161, 1987–1998 (2014).
    Google Scholar 
    Guard, U. S. C. 2019 recreational boating statistics. 83 (U.S. Department of Homeland Security, U.S. Coast Guard, Office of Auxiliary and Boating Safety, Washington, DC, 2019).Roberts, B. in Florida Sportsman (2020).Pate, J. H., Macdonald, C. & Wester, J. Surveys of recreational anglers reveal knowledge gaps and positive attitudes towards manta ray conservation in Florida. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 1410–1419 (2021).
    Google Scholar 
    Currier, R., Kirkpatrick, B., Simoniello, C., Lowerre-Barbieri, S. & Bickford, J. in OCEANS 2015-MTS/IEEE Washington. 1–3 (IEEE).Young, J. M. et al. The FACT Network: Philosophy, evolution, and management of a collaborative coastal tracking network. Mar. Coast. Fish. 12, 258–271 (2020).
    Google Scholar  More

  • in

    Microscale carbon distribution around pores and particulate organic matter varies with soil moisture regime

    Minasny, B. et al. Soil carbon 4 per mille. Geoderma 292, 59–86 (2017).ADS 
    Article 

    Google Scholar 
    Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehmann, J., Bossio, D. A., Kögel-Knabner, I. & Rillig, M. C. The concept and future prospects of soil health. Nat. Rev. Earth Environ. 1, 544–553 (2020).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Lavallee, J. M., Soong, J. L. & Cotrufo, M. F. Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century. Glob. Change Biol. 26, 261–273 (2020).ADS 
    Article 

    Google Scholar 
    Kravchenko, A. N. et al. Microbial spatial footprint as a driver of soil carbon stabilization. Nat. Commun. 10, 3121 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Dungait, J. A. J., Hopkins, D. W., Gregory, A. S. & Whitmore, A. P. Soil organic matter turnover is governed by accessibility not recalcitrance. Glob. Change Biol. 18, 1781–1796 (2012).ADS 
    Article 

    Google Scholar 
    Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Keiluweit, M., Nico, P. S., Kleber, M. & Fendorf, S. Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils? Biogeochemistry 127, 157–171 (2016).CAS 
    Article 

    Google Scholar 
    Rohe, L. et al. Denitrification in soil as a function of oxygen availability at the microscale. Biogeosciences 18, 1185–1201 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Hall, S. J. & Silver, W. L. Reducing conditions, reactive metals, and their interactions can explain spatial patterns of surface soil carbon in a humid tropical forest. Biogeochemistry 125, 149–165 (2015).CAS 
    Article 

    Google Scholar 
    Hagedorn, F., Bruderhofer, N., Ferrari, A. & Niklaus, P. A. Tracking litter-derived dissolved organic matter along a soil chronosequence using 14C imaging: Biodegradation, physico-chemical retention or preferential flow? Soil Biol. Biochem. 88, 333–343 (2015).CAS 
    Article 

    Google Scholar 
    Védère, C., Vieublé Gonod, L., Pouteau, V., Girardin, C. & Chenu, C. Spatial and temporal evolution of detritusphere hotspots at different soil moistures. Soil Biol. Biochem. 150, 107975 (2020).Article 
    CAS 

    Google Scholar 
    Silver, W. L., Lugo, A. E. & Keller, M. Soil oxygen availability and biogeochemistry along rainfall and topographic gradients in upland wet tropical forest soils. Biogeochemistry 44, 301–328 (1999).
    Google Scholar 
    Schuur, E. A. G., Chadwick, O. A. & Matson, P. A. Carbon cycling and soil carbon storage in mesic to wet hawaiian montane forests. Ecology 82, 3182–3196 (2001).Article 

    Google Scholar 
    Tiemeyer, B. et al. High emissions of greenhouse gases from grasslands on peat and other organic soils. Glob. Change Biol. 22, 4134–4149 (2016).ADS 
    Article 

    Google Scholar 
    Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Cleveland, C. C., Wieder, W. R., Reed, S. C. & Townsend, A. R. Experimental drought in a tropical rain forest increases soil carbon dioxide losses to the atmosphere. Ecology 91, 2313–2323 (2010).PubMed 
    Article 

    Google Scholar 
    Moyano, F. E., Manzoni, S. & Chenu, C. Responses of soil heterotrophic respiration to moisture availability: an exploration of processes and models. Soil Biol. Biochem. 59, 72–85 (2013).CAS 
    Article 

    Google Scholar 
    Franzluebbers, A. J. Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils. Appl. Soil Ecol. 11, 91–101 (1999).Article 

    Google Scholar 
    Thomsen, I. K., Schjønning, P., Jensen, B., Kristensen, K. & Christensen, B. T. Turnover of organic matter in differently textured soils: II. Microbial activity as influenced by soil water regimes. Geoderma 89, 199–218 (1999).ADS 
    Article 

    Google Scholar 
    Nunan, N., Leloup, J., Ruamps, L. S., Pouteau, V. & Chenu, C. Effects of habitat constraints on soil microbial community function. Sci. Rep. 7, 4280 (2017).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Ruamps, L. S., Nunan, N. & Chenu, C. Microbial biogeography at the soil pore scale. Soil Biol. Biochem. 43, 280–286 (2011).CAS 
    Article 

    Google Scholar 
    Strong, D. T., Wever, H. D., Merckx, R. & Recous, S. Spatial location of carbon decomposition in the soil pore system. Eur. J. Soil Sci. 55, 739–750 (2004).Article 

    Google Scholar 
    Vogel, H.-J. et al. A holistic perspective on soil architecture is needed as a key to soil functions. Eur. J. Soil Sci. 73, e13152 (2022).Article 

    Google Scholar 
    Lehmann, J. et al. Spatial complexity of soil organic matter forms at nanometre scales. Nat. Geosci. 1, 238–242 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Steffens, M. et al. Identification of distinct functional microstructural domains controlling C storage in soil. Environ. Sci. Technol. 51, 12182–12189 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Elyeznasni, N. et al. Exploration of soil micromorphology to identify coarse-sized OM assemblages in X-ray CT images of undisturbed cultivated soil cores. Geoderma 179-180, 38–45 (2012).ADS 
    Article 

    Google Scholar 
    Hayes, T. L., Lindgren, F. T. & Gofman, J. W. A quantitative determination of the Osmium tetroxide-lipoprotein interaction. J. Cell Biol. 19, 251–255 (1963).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Belazi, D., Solé-Domènech, S., Johansson, B., Schalling, M. & Sjövall, P. Chemical analysis of osmium tetroxide staining in adipose tissue using imaging ToF-SIMS. Histochem. Cell Biol. 132, 105–115 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schulz M., et al. Structured heterogeneity in a marine terrace chronosequence: upland mottling. Vadose Zone J. 15, vzj2015.07.0102 (2016).Fimmen et al. Fe–C redox cycling: a hypothetical biogeochemical mechanism that drives crustal weathering in upland soils. Biogeochemistry 87, 127–141 (2008).CAS 
    Article 

    Google Scholar 
    Zheng, H., Kim, K., Kravchenko, A., Rivers, M. & Guber, A. Testing Os staining approach for visualizing soil organic matter patterns in intact samples via X-ray dual-energy tomography scanning. Environ. Sci. Technol. 54, 8980–8989 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Périé, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315–325 (2008).Article 

    Google Scholar 
    Rawls, W. J., Pachepsky, Y. A., Ritchie, J. C., Sobecki, T. M. & Bloodworth, H. Effect of soil organic carbon on soil water retention. Geoderma 116, 61–76 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    Quigley M. Y., Rivers M. L. & Kravchenko A. N. Patterns and sources of spatial heterogeneity in soil matrix from contrasting long term management practices. Front. Environ. Sci. 6 (2018).Arai, M. et al. An improved method to identify osmium-stained organic matter within soil aggregate structure by electron microscopy and synchrotron X-ray micro-computed tomography. Soil Tillage Res. 191, 275–281 (2019).Article 

    Google Scholar 
    Peth, S. et al. Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography. Soil Biol. Biochem. 78, 189–194 (2014).CAS 
    Article 

    Google Scholar 
    Rawlins, B. G. et al. Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography. Soil 2, 659–671 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Plattner H. & Zingsheim H. P. Electron Microscopic Methods in Cellular and Molecular Biology. In: Subcellular Biochemistry (ed. Roodyn D. B.). (Plenum Press, 1983).Litman, R. B. & Barrnett, R. J. The mechanism of the fixation of tissue components by osmium tetroxide via hydrogen bonding. J. Ultrastruct. Res. 38, 63–86 (1972).CAS 
    PubMed 
    Article 

    Google Scholar 
    Vepraskas M. & Lindbo D. Redoximorphic features as related to soil hydrology and hydric soils. In: Hydropedology: Synergistic Integration of Soil Science and Hydrology (ed. Lin H.). Academic Press (2012).See C. R., et al. Hyphae move matter and microbes to mineral microsites: integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. 28, 2527–2540 (2022).Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 
    Article 

    Google Scholar 
    Hagedorn, F., Kaiser, K., Feyen, H. & Schleppi, P. Effects of redox conditions and flow processes on the mobility of dissolved organic carbon and nitrogen in a forest soil. J. Environ. Qual. 29, 288–297 (2000).CAS 
    Article 

    Google Scholar 
    Grybos, M., Davranche, M., Gruau, G., Petitjean, P. & Pédrot, M. Increasing pH drives organic matter solubilization from wetland soils under reducing conditions. Geoderma 154, 13–19 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Keiluweit, M., Wanzek, T., Kleber, M., Nico, P. & Fendorf, S. Anaerobic microsites have an unaccounted role in soil carbon stabilization. Nat. Commun. 8, 1771 (2017).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Eusterhues, K., Rumpel, C. & Kögel-Knabner, I. Stabilization of soil organic matter isolated via oxidative degradation. Org. Geochem. 36, 1567–1575 (2005).CAS 
    Article 

    Google Scholar 
    Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M. & Hendricks, D. M. Mineral control of soil organic carbon storage and turnover. Nature 389, 170–173 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Lucas, M., Schlüter, S., Vogel, H.-J. & Vetterlein, D. Soil structure formation along an agricultural chronosequence. Geoderma 350, 61–72 (2019).ADS 
    Article 

    Google Scholar 
    Sokol, N. W., Sanderman, J. & Bradford, M. A. Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry. Glob. Change Biol. 25, 12–24 (2019).ADS 
    Article 

    Google Scholar 
    Marschner, B. & Kalbitz, K. Controls of bioavailability and biodegradability of dissolved organic matter in soils. Geoderma 113, 211–235 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    Stirling, E., Smernik, R. J., Macdonald, L. M. & Cavagnaro, T. R. The effect of fire affected Pinus radiata litter and char addition on soil nitrogen cycling. Sci. Total Environ. 664, 276–282 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Kravchenko, A. N. et al. Hotspots of soil N2O emission enhanced through water absorption by plant residue. Nat. Geosci. 10, 496 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Kim, K., Guber, A., Rivers, M. & Kravchenko, A. Contribution of decomposing plant roots to N2O emissions by water absorption. Geoderma 375, 114506 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Goebel, M. O., Bachmann, J., Reichstein, M., Janssens, I. A. & Guggenberger, G. Soil water repellency and its implications for organic matter decomposition – is there a link to extreme climatic events? Glob. Change Biol. 17, 2640–2656 (2011).ADS 
    Article 

    Google Scholar 
    Brodowski, S., Amelung, W., Haumaier, L., Abetz, C. & Zech, W. Morphological and chemical properties of black carbon in physical soil fractions as revealed by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Geoderma 128, 116–129 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    Diel, J., Vogel, H.-J. & Schlüter, S. Impact of wetting and drying cycles on soil structure dynamics. Geoderma 345, 63–71 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Surey R., et al. Contribution of particulate and mineral-associated organic matter to potential denitrification of agricultural soils. Front. Environ. Sci. 9 (2021).Kaiser, M., Ellerbrock, R. H. & Sommer, M. Separation of coarse organic particles from bulk surface soil samples by electrostatic attraction. Soil Sci. Soc. Am. J. 73, 2118–2130 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Atkinson, R., Posner, A. & Quirk, J. P. Adsorption of potential-determining ions at the ferric oxide-aqueous electrolyte interface. J. Phys. Chem. 71, 550–558 (1967).CAS 
    Article 

    Google Scholar 
    Mueller, C. W. et al. Submicron scale imaging of soil organic matter dynamics using NanoSIMS – from single particles to intact aggregates. Org. Geochem. 42, 1476–1488 (2012).Article 
    CAS 

    Google Scholar 
    Herrmann, A. M. et al. Nano-scale secondary ion mass spectrometry—a new analytical tool in biogeochemistry and soil ecology: A review article. Soil Biol. Biochem. 39, 1835–1850 (2007).CAS 
    Article 

    Google Scholar 
    Schlüter, S., Eickhorst, T. & Mueller, C. W. Correlative imaging reveals holistic view of soil microenvironments. Environ. Sci. Technol. 53, 829–837 (2019).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. Med. Imaging, IEEE Trans. 29, 196–205 (2010).Article 

    Google Scholar 
    Otsu, N. A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975).
    Google Scholar 
    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. methods 9, 676–682 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schlüter, S., Leuther, F., Vogler, S. & Vogel, H.-J. X-ray microtomography analysis of soil structure deformation caused by centrifugation. Solid Earth 7, 129–140 (2016).ADS 
    Article 

    Google Scholar 
    Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schlüter, S., Sheppard, A., Brown, K. & Wildenschild, D. Image processing of multiphase images obtained via X-ray microtomography: a review. Water Resour. Res. 50, 3615–3639 (2014).ADS 
    Article 

    Google Scholar 
    Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).CAS 
    PubMed 

    Google Scholar 
    Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
    Google Scholar 
    Surey, R. et al. Differences in labile soil organic matter explain potential denitrification and denitrifying communities in a long-term fertilization experiment. Appl. Soil Ecol. 153, 103630 (2020).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2020). More

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    Greenhouse gas emissions rise due to tillage

    Globally, agriculture represents a substantial contributor to net greenhouse gas (GHG) emissions (c. 25%)1, and accounts for at least 10% of all GHG emissions in the United States2. To address the current climate emergency, agriculture remains a key player, with substantial potential to contribute to the solution. Reduced tillage as part of a ‘conservation agriculture’ approach is considered an important way of achieving this and is gaining popularity globally. Leaving the soil uncultivated, also referred to as zero or no tillage (that is, not ploughing), has been shown to offer considerable benefits for the ‘health’ of soil, including improved soil structure, a thriving soil faunal community (for example, earthworms) and, potentially, sequestration of carbon3. It has recently been shown, for temperate arable systems, that there is potential for a substantial (up to 30%) reduction in GHG emissions by simply moving to direct drilling, as the resulting changes in the soil structure help reduce GHG emissions4. Minimizing tillage also dramatically cuts the diesel consumption linked to crop production. However, there are negatives associated with this reductionist approach, most notably the proliferation of weed plant species that have traditionally been controlled via the implementation of tillage. More

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    Anthropogenic disruptions to longstanding patterns of trophic-size structure in vertebrates

    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    Price, S. A. & Hopkins, S. S. B. The macroevolutionary relationship between diet and body mass across mammals. Biol. J. Linn. Soc. Lond. 115, 173–184 (2015).Article 

    Google Scholar 
    Hiiemae, K. M. in Feeding: Form, Function, and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 411–448 (Academic Press, 2000).Pineda-Munoz, S., Evans, A. R. & Alroy, J. The relationship between diet and body mass in terrestrial mammals. Paleobiology 42, 659–669 (2016).Article 

    Google Scholar 
    Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS ONE 8, e68714 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jarman, P. J. The Effect of the Creation of Lake Kariba upon the Terrestrial Ecology of the Middle Zambezi Valley, with Particular References to the Large Mammals. PhD thesis, Univ. of Manchester (1968).Bell, R. H. V. A grazing ecosystem in the Serengeti. Sci. Am. 225, 86–93 (1971).Article 

    Google Scholar 
    Belovsky, G. E. Optimal foraging and community structure: the allometry of herbivore food selection and competition. Evol. Ecol. 11, 641–672 (1997).Article 

    Google Scholar 
    Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, e22 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).Burness, G. P., Diamond, J. & Flannery, T. Dinosaurs, dragons, and dwarfs: the evolution of maximal body size. Proc. Natl Acad. Sci. USA 98, 14518–14523 (2001).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bergmann, C. Über die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse (Vandenhoeck & Ruprecht Verlage, 1848).Gearty, W., McClain, C. R. & Payne, J. L. Energetic tradeoffs control the size distribution of aquatic mammals. Proc. Natl Acad. Sci. USA 115, 4194–4199 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74, 245–255 (2020).Article 
    PubMed 

    Google Scholar 
    Tucker, M. A. & Rogers, T. L. Examining predator–prey body size, trophic level and body mass across marine and terrestrial mammals. Proc. Biol. Sci. 281, 20142103 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Archibald, J. D. Extinction and Radiation: How the Fall of the Dinosaurs Led to the Rise of Mammals (The Johns Hopkins Univ. Press, 2011).Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alroy, J. Cope’s rule and the dynamics of body mass evolution in North American fossil mammals. Science 280, 731–734 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    Smith, F. A. et al. The evolution of maximum body size of terrestrial mammals. Science 330, 1216–1219 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Smith, F. A., Smith, R. E. E., Lyons, S. K. & Payne, J. L. Body size downgrading of mammals over the Late Quaternary. Science 360, 310–313 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Slater, G. J. Phylogenetic evidence for a shift in the mode of mammalian body size evolution at the Cretaceous-Palaeogene boundary. Methods Ecol. Evol. 4, 734–744 (2013).Article 

    Google Scholar 
    Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).Article 

    Google Scholar 
    Slater, G. J., Goldbogen, J. A. & Pyenson, N. D. Independent evolution of baleen whale gigantism linked to Plio-Pleistocene ocean dynamics. Proc. Biol. Sci. 284, 20170546 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Bojarska, K. & Selva, N. Spatial patterns in brown bear Ursus arctos diet: the role of geographical and environmental factors. Mamm. Rev. 42, 120–143 (2012).Article 

    Google Scholar 
    Virgós, E. et al. Body size clines in the European badger and the abundant centre hypothesis. J. Biogeogr. 38, 1546–1556 (2011).Article 

    Google Scholar 
    Lyons, S. K., Smith, F. A. & Brown, J. H. Of mice, mastodons and men: human-mediated extinctions on four continents. Evol. Ecol. Res. 6, 339–358 (2004).
    Google Scholar 
    Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Blois, J. L. & Hadly, E. A. Mammalian response to Cenozoic climatic change. Annu. Rev. Earth Planet. Sci. 37, 181–208 (2009).CAS 
    Article 

    Google Scholar 
    Tomašových, A. & Kidwell, S. M. Fidelity of variation in species composition and diversity partitioning by death assemblages: time-averaging transfers diversity from beta to alpha levels. Paleobiology 35, 94–118 (2009).Article 

    Google Scholar 
    Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl Acad. Sci. USA 113, 838–846 (2016).Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2018).Article 

    Google Scholar 
    Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 868–873 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Enquist, B. J., Abraham, A. J., Harfoot, M. B. J., Malhi, Y. & Doughty, C. E. The megabiota are disproportionately important for biosphere functioning. Nat. Commun. 11, 699 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116 (2016).Article 

    Google Scholar 
    Bellwood, D. R., Hoey, A. S. & Choat, J. H. Limited functional redundancy in high diversity systems: resilience and ecosystem function on coral reefs. Ecol. Lett. 6, 281–285 (2003).Article 

    Google Scholar 
    Leip, A. et al. Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004 (2015).Article 
    CAS 

    Google Scholar 
    Smith, D., King, R. & Allen, B. L. Impacts of exclusion fencing on target and non-target fauna: a global review. Biol. Rev. Camb. Philos. Soc. 95, 1590–1606 (2020).Article 
    PubMed 

    Google Scholar 
    Galetti, M. et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).Article 
    PubMed 

    Google Scholar 
    Sandom, C. J. et al. Learning from the past to prepare for the future: felids face continued threat from declining prey. Ecography 41, 140–152 (2018).Article 

    Google Scholar 
    Zavaleta, E. et al. Ecosystem responses to community disassembly. Ann. N. Y. Acad. Sci. 1162, 311–333 (2009).Article 
    PubMed 

    Google Scholar 
    Hoy, S. R., Peterson, R. O. & Vucetich, J. A. Climate warming is associated with smaller body size and shorter lifespans in moose near their southern range limit. Glob. Change Biol. 24, 2488–2497 (2018).Article 

    Google Scholar 
    Peralta-Maraver, I. & Rezende, E. L. Heat tolerance in ectotherms scales predictably with body size. Nat. Clim. Change 11, 58–63 (2020).Article 

    Google Scholar 
    Smith, F. A. et al. Unraveling the consequences of the terminal Pleistocene megafauna extinction on mammal community assembly. Ecography 39, 223–239 (2016).Article 

    Google Scholar 
    Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smith, F. A., Elliott Smith, R. E., Lyons, S. K., Payne, J. L. & Villaseñor, A. The accelerating influence of humans on mammalian macroecological patterns over the Late Quaternary. Quat. Sci. Rev. 211, 1–16 (2019).Article 

    Google Scholar 
    Middleton, O. S., Scharlemann, J. P. W. & Sandom, C. J. Homogenization of carnivorous mammal ensembles caused by global range reductions of large-bodied hypercarnivores during the Late Quaternary. Proc. Biol. Sci. 287, 20200804 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, eaay7650 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Schreiber, E. A. & Burger, J. Biology of Marine Birds (CRC Press, 2001).Cooke, R. S. C., Bates, A. E. & Eigenbrod, F. Global trade-offs of functional redundancy and functional dispersion for birds and mammals. Glob. Ecol. Biogeogr. 28, 484–495 (2019).Article 

    Google Scholar 
    Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648 (2009).Article 

    Google Scholar 
    Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89–94 (2013).Article 

    Google Scholar 
    Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

    Google Scholar 
    Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).Article 

    Google Scholar 
    Atwood, T. B. et al. Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Sci. Adv. 6, eabb8458 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pineda-Munoz, S. & Alroy, J. Dietary characterization of terrestrial mammals. Proc. Biol. Sci. 281, 20141173 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

    Google Scholar 
    Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).Article 

    Google Scholar 
    Kidwell, S. M. & Flessa, K. W. The quality of the fossil record: populations, species, and communities. Annu. Rev. Earth Planet. Sci. 24, 433–464 (1996).CAS 
    Article 

    Google Scholar 
    Miller, J. H. et al. Ecological fidelity of functional traits based on species presence–absence in a modern mammalian bone assemblage (Amboseli, Kenya). Paleobiology 40, 560–583 (2014).Article 

    Google Scholar 
    Smith, F. A. et al. Similarity of mammalian body size across the taxonomic hierarchy and across space and time. Am. Nat. 163, 672–691 (2004).Article 
    PubMed 

    Google Scholar 
    Andermann, T., Faurby, S., Cooke, R., Silvestro, D. & Antonelli, A. iucn_sim: a new program to simulate future extinctions based on IUCN threat status. Ecography 44, 162–176 (2021).Article 

    Google Scholar 
    Mooers, A., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for phylogenetic conservation prioritization. PLoS ONE 3, e3700 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koch, P. L. & Barnosky, A. D. Late Quaternary extinctions: state of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250 (2006).Article 

    Google Scholar 
    Clauset, A. & Erwin, D. H. The evolution and distribution of species body size. Science 321, 399–401 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Clauss, M. et al. The maximum attainable body size of herbivorous mammals: morphophysiological constraints on foregut, and adaptations of hindgut fermenters. Oecologia 136, 14–27 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Alexander, R. M. All-time giants: the largest animals and their problems. Palaeontology 41, 1231–1245 (1998).
    Google Scholar 
    Dobson, G. P. On being the right size: heart design, mitochondrial efficiency and lifespan potential. Clin. Exp. Pharmacol. Physiol. 30, 590–597 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Blackburn, T. M., Gaston, K. J. & Loder, N. Geographic gradients in body size: a clarification of Bergmann’s rule. Divers. Distrib. 5, 165–174 (1999).Article 

    Google Scholar  More

  • in

    Emerging weed resistance increases tillage intensity and greenhouse gas emissions in the US corn–soybean cropping system

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).US Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2018 (EPA, 2020).Lu, C. et al. Century‐long changes and drivers of soil nitrous oxide (N2O) emissions across the contiguous United States. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16061 (2022).Article 

    Google Scholar 
    Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).CAS 
    Article 

    Google Scholar 
    2004 National Crop Residue Management Survey (Conservation Technology Information Center, 2004); www.ctic.purdue.eduClaassen, R., Bowman, M., Wallander, J., David, M. & Steven, S. Tillage Intensity and Conservation Cropping in the United States, EIB-197 (United States Department of Agriculture, Economic Research Service, 2018).Grant, R. F. Changes in soil organic matter under different tillage and rotation: mathematical modeling in ecosystems. Soil Sci. Soc. Am. J. 61, 1159–1175 (1997).CAS 
    Article 

    Google Scholar 
    Claassen, R., Langpap, C. & Wu, J. Impacts of federal crop insurance on land use and environmental quality. Am. J. Agric. Econ. 99, 592–613 (2017).Article 

    Google Scholar 
    Davis, A. S. Cover-crop roller–crimper contributes to weed management in no-till soybean. Weed Sci. 58, 300–309 (2010).CAS 
    Article 

    Google Scholar 
    Pittelkow, C. M. et al. Nitrogen management and methane emissions in direct-seeded rice systems. Agron. J. 106, 968–980 (2014).CAS 
    Article 

    Google Scholar 
    Weber, J. F., Kunz, C., Peteinatos, G. G., Zikeli, S. & Gerhards, R. Weed control using conventional tillage, reduced tillage, no-tillage, and cover crops in organic soybean. Agric 7, 43 (2017).
    Google Scholar 
    Triplett, G. B. & Dick, W. A. No-tillage crop production: a revolution in agriculture!. Agron. J. 100, 153–165 (2008).Article 

    Google Scholar 
    Wade, T., Claassen, R. & Wallander, S. Conservation-Practice Adoption Rates Vary Widely by Crop and Region, EIB-147, 40 (United States Department of Agriculture, Economic Research Service, 2015).Perry, E. D., Ciliberto, F., Hennessy, D. A. & Moschini, G. Genetically engineered crops and pesticide use in US maize and soybeans. Sci. Adv. https://doi.org/10.1126/sciadv.1600850 (2016).Article 

    Google Scholar 
    Heap, I. & Duke, S. O. Overview of glyphosate-resistant weeds worldwide. Pest Manag. Science 74, 1040–1049 (2018).CAS 
    Article 

    Google Scholar 
    Owen, M. D. K. Diverse approaches to herbicide-resistant weed management. Weed Sci. 64, 570–584 (2016).Article 

    Google Scholar 
    Van Deynze, B., Swinton, S. M. & Hennessy, D. A. Are glyphosate-resistant weeds a threat to conservation agriculture? Evidence from tillage practices in soybeans. Am. J. Agric. Econ. https://doi.org/10.1111/ajae.12243 (2021).Eagle, A. et al. Greenhouse Gas Mitigation Potential of Agricultural Land Management in the United States. A Synthesis of the Literature (Technical Working Group on Agricultural Greenhouse Gases, 2010).Parton, W. J. et al. Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870–2000. Proc. Natl. Acad. Sci. USA 112, E4681–E4688 (2015).CAS 
    Article 

    Google Scholar 
    Stevanović, M. et al. Mitigation strategies for greenhouse gas emissions from agriculture and land-use change: consequences for food prices. Environ. Sci. Technol. 51, 365–374 (2017).Article 

    Google Scholar 
    Glenk, K., Eory, V., Colombo, S. & Barnes, A. Adoption of greenhouse gas mitigation in agriculture: an analysis of dairy farmers’ perceptions and adoption behaviour. Ecol. Econ. 108, 49–58 (2014).Article 

    Google Scholar 
    Galik, C., Murray, B. & Parish, M. Near-term pathways for achieving forest and agricultural greenhouse gas mitigation in the US Climate 5, 69 (2017).Article 

    Google Scholar 
    Pape, D. et al. Managing Agricultural Land for Greenhouse Gas Mitigation within the United States (ICF/USDA, 2016); https://www.usda.gov/sites/default/files/documents/White_Paper_WEB71816.pdfCooper, H. V., Sjögersten, S., Lark, R. M. & Mooney, S. J. To till or not to till in a temperate ecosystem? Implications for climate change mitigation. Environ. Res. Lett. 16, 054022 (2021).CAS 
    Article 

    Google Scholar 
    Baker, N. T. Tillage Practices in the Conterminous United States, 1989–2004—Datasets Aggregated by Watershed (No. 573), U.S. Geological Survey, 2011; https://pubs.usgs.gov/ds/ds573/pdf/dataseries573final.pdfPrice, A. et al. Glyphosate-resistant Palmer amaranth: a threat to conservation agriculture. J. Soil Water Conserv. 66, 265–275 (2011).Article 

    Google Scholar 
    Livingston, M., Fernandez-Cornejo, J. & Frisvold, G. B. Economic returns to herbicide resistance management in the short and long run: the role of neighbor effects. Weed Sci. 64, 595–608 (2016).Article 

    Google Scholar 
    Cao, P., Lu, C. & Yu, Z. Historical nitrogen fertilizer use in agricultural ecosystems of the contiguous United States during 1850–2015: application rate, timing, and fertilizer types. Earth Syst. Sci. Data 10, 969–984 (2018).Article 

    Google Scholar 
    US Greenhouse Gas Emissions and Sinks, 1990–2016, Epa 430-R-18-003 (EPA, 2018).Deng, Q. et al. Assessing the impacts of tillage and fertilization management on nitrous oxide emissions in a cornfield using the DNDC model. J. Geophys. Res. Biogeosciences https://doi.org/10.1002/2015JG003239 (2016).Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016).CAS 
    Article 

    Google Scholar 
    Yu, Z., Lu, C., Cao, P. & Tian, H. Long-term terrestrial carbon dynamics in the Midwestern United States during 1850–2015: roles of land use and cover change and agricultural management. Glob. Chang. Biol. 12, 3218–3221 (2018).
    Google Scholar 
    Lu, C. et al. Increasing carbon footprint of grain crop production in the US western Corn Belt. Environ. Res. Lett. 13, 124007 (2018).CAS 
    Article 

    Google Scholar 
    Wimberly, M. C. et al. Cropland expansion and grassland loss in the eastern Dakotas: new insights from a farm-level survey. Land Use Policy 63, 160–173 (2017).Article 

    Google Scholar 
    Adler, P. R., Del Grosso, S. J. & Parton, W. J. Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems. Ecol. Appl. 17, 675–691 (2007).Article 

    Google Scholar 
    Halvorson, A. D., Schweissing, F. C., Bartolo, M. E. & Reule, C. A. Corn response to nitrogen fertilization in a soil with high residual nitrogen. Agron. J. 97, 1222–1229 (2005).Article 

    Google Scholar 
    Al-Kaisi, M. M., Archontoulis, S. V., Kwaw-Mensah, D. & Miguez, F. Tillage and crop rotation effects on corn agronomic response and economic return at seven Iowa locations. Agron. J. 107, 1411–1424 (2015).Article 

    Google Scholar 
    Jarecki, M. et al. Long-term trends in corn yields and soil carbon under diversified crop rotations. J. Environ. Qual. 47, 635–643 (2018).CAS 
    Article 

    Google Scholar 
    Gelfand, I. et al. Carbon debt of Conservation Reserve Program (CRP) grasslands converted to bioenergy production. Proc. Natl Acad. Sci. USA 108, 13864–13869 (2011).CAS 
    Article 

    Google Scholar 
    West, T. O. & Post, W. M. Soil organic carbon sequestration rates by tillage and crop rotation. Soil Sci. Soc. Am. J. 66, 1930–1946 (2002).CAS 
    Article 

    Google Scholar 
    Ogle, S. M. et al. Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. Glob. Chang. Biol. 16, 810–822 (2010).Article 

    Google Scholar 
    Al-Kaisi, M. M., Yin, X. & Licht, M. A. Soil carbon and nitrogen changes as influenced by tillage and cropping systems in some Iowa soils. Agric. Ecosyst. Environ. 105, 635–647 (2005).CAS 
    Article 

    Google Scholar 
    Perry, E. D., Moschini, G. C. & Hennessy, D. A. Testing for complementarity: glyphosate tolerant soybeans and conservation tillage. Am. J. Agric. Econ. https://doi.org/10.1093/ajae/aaw001 (2016).Perry, E. D., Hennessy, D. A. & Moschini, G. C. Product concentration and usage: behavioral effects in the glyphosate market. J. Econ. Behav. Organ. 158, 543–559 (2019).Article 

    Google Scholar 
    Yu, Z. & Lu, C. Historical cropland expansion and abandonment in the continental US during 1850 to 2016. Glob. Ecol. Biogeogr. 27, 322–333 (2018).Article 

    Google Scholar 
    Yu, Z., Lu, C., Tian, H. & Canadell, J. G. Largely underestimated carbon emission from land use and land cover change in the conterminous US. Glob. Chang. Biol. https://doi.org/10.1111/gcb.14768 (2019).Yu, Z., Lu, C., Hennessy, D. A., Feng, H. & Tian, H. Impacts of tillage practices on soil carbon stocks in the US corn–soybean cropping system during 1998 to 2016. Environ. Res. Lett. 15, 014008 (2020).CAS 
    Article 

    Google Scholar 
    Liu, M. et al. Long-term trends in evapotranspiration and runoff over the drainage basins of the Gulf of Mexico during 1901–2008. Water Resour. Res. 49, 1988–2012 (2013).Article 

    Google Scholar 
    Lu, C. & Tian, H. Net greenhouse gas balance in response to nitrogen enrichment: perspectives from a coupled biogeochemical model. Glob. Chang. Biol. 19, 571–588 (2013).Article 

    Google Scholar 
    Tian, H. et al. The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature 531, 225–228 (2016).CAS 
    Article 

    Google Scholar 
    Chen, G. et al. Drought in the southern United States over the 20th century: variability and its impacts on terrestrial ecosystem productivity and carbon storage. Clim. Change 114, 379–397 (2012).CAS 
    Article 

    Google Scholar 
    Lu, C. et al. Effect of nitrogen deposition on China’s terrestrial carbon uptake in the context of multifactor environmental changes. Ecol. Appl. 22, 53–75 (2012).Article 

    Google Scholar 
    Ren, W. et al. Spatial and temporal patterns of CO2 and CH4 fluxes in China’s croplands in response to multifactor environmental changes. Tellus 63, 222–240 (2011).CAS 
    Article 

    Google Scholar 
    Tian, H. et al. Net exchanges of CO2, CH4, and N2O between China’s terrestrial ecosystems and the atmosphere and their contributions to global climate warming. J. Geophys. Res. Biogeosci. 116, 1–13 (2011).
    Google Scholar 
    Ren, W., Tian, H., Tao, B., Huang, Y. & Pan, S. China’s crop productivity and soil carbon storage as influenced by multifactor global change. Glob. Chang. Biol. 18, 2945–2957 (2012).Article 

    Google Scholar 
    Residue Management Choices: A Guide to Managing Crop Residues in Corn and Soybeans (USDA Natural Resources Conservation Service and University of Wisconsin, 2019). More