Philippa Kaur

Phylogenetic relations and systematics of AllactaginaeIntergeneric relationsOur data produced a robust phylogeny for Allactaginae above species level and thereby firmly proved that Allactaga s.l. (as recognised by Holden and Musser17) is paraphyletic to both Pygeretmus and Allactodipus. Both of the latter taxa are morphologically distinct from Allactaga by a number of unique apomorphies: a unique molar pattern and glans penis morphology in Allactodipus as well as high-crowned terraced molars, reduction of the premolar, and particular glans penis morphology in Pygeretmus. At the same time, the morphology of all other five-toed jerboas is relatively monotonous with variation only in terms of body size, relative molar crown height, size of auditory bullae, m1 morphotype frequency, and the rate of M3 reduction1,45. Such level of differences never allowed recognition of more than one genus.Thus, allactagines represent a case when descendant lineages with derived morphology are nested within a group with overall conserved morphology. This can be compared to paraphyly of white-toothed shrews Crocidura relative to Diplomesodon46, rorquals (Balaenoptera) relative to humpback whales (Megaptera)47, or tits (Parus s.l.) relative to morphologically aberrant ground tit (Pseudopodoces humilis)48. In such cases, the taxonomy should be changed in accordance with the monophyly principle, which is achieved by combining genera (as done in whales) or splitting the genus in question into new taxa (as done in tits). Unfortunately, any decision in this context is arbitrary as it is based on subjective weighting of morphological differences. For Allactaginae, the splitting approach was implemented18, which resulted in the elevation of Scarturus and Orientallactaga to the generic rank2, despite the fact that a synapomorphy-based morphological diagnosis of Scarturus can hardly be formulated.As an alternative to the morphology-based approach, temporal banding—a method which uses node age as a measure of rank49—was suggested as a standardised method for taxonomic ranking. In the present study, the age of divergence of major Allactaginae lineages was dated to the Pliocene. However, in other groups of Myodonta, Pliocene divergences were found both among genera (as in voles50 or hamsters51) and among congeneric species (as in Sicista52). Thus, the ambiguity remains unresolved; we see no better option than to retain the generic classification established by Michaux & Shenbrot2 (Table S10). However, it should be noted that the inferred age of divergence between S. tetradactylus + S. hotsoni and the VECE clades (3.9–4.1 Mya) is comparable or even larger than the divergence time of Allactodipus from Allactaga. If the temporal criterion (sensu Avise, Johns49) is accepted, one should consider elevating the VECE clade at least to subgeneric rank, with Scarturus proper including only two species. The diagnosis of the new taxon should be polythetic (medium to small jerboas with five-toes, bullae not enlarged, glans penis with longitudinal fold, molar low-to medium crowned, M3 not reduced). Although the name Paralactaga is traditionally used as a subgeneric for the S. euphraticus group and therefore may have been applied to the whole VECE clade, we believe that this is incorrect. The type species of Paralactaga—P. anderssoni Young, 1927—was described from the Late Miocene of China, which is inconsistent with the estimated time of origin of the VECE clade. Apparently all similarities between S. euphraticus group and Paralactaga proper are because of plesiomorphy. Therefore, we suggest that Paralactaga should be attributed to fossil taxa only.Species groups within ScarturusIn the present study, we analysed in detail the phylogenetic reconstructions and divergence times estimations for the species and species groups of the genus Scarturus. Our study is the first to examine the phylogenetic position of the enigmatic taxon described from Afghanistan and which is currently termed Scarturus williamsi caprimulga. The mitochondrial data provided clear evidence that this taxon is not closely related to any member of the S. euphraticus species group including S. williamsi. Instead, it belongs to a separate divergent lineage of Scarturus, which should be considered a separate species, Scarturus caprimulga. It also includes the jerboa from Kopet Dag provisionally classified by Hamidi et al25 as Paralactaga cf. williamsi. The mitochondrial difference between specimens from Afghanistan and those from Kopet Dag suggested a potential subspecies rank of the latter form, which is provisionally referred to as S. aff. caprimulga. More research on the distribution and genetic structure of this species is needed for further clarification. Our study has added more representative genetic data on the poorly known S. vinogradovi and confirmed it as a separate divergent branch within Scarturus s.l. and likely a distant sister group of S. caprimulga.Previous phylogenetic reconstructions of the S. euphraticus species group based on mtDNA data recovered a divergent branch within S. euphraticus53, which was subsequently classified as S. aulacotis2. With further addition of comprehensive nuclear data, the full species rank of this taxon is now completely supported. The relationships among the three species in the S. euphraticus group correspond to a hard trichotomy dated to the late Early Pleistocene.Nuclear data strongly support deep structuring within the S. elater species group, as previously demonstrated using mtDNA19,22,54, and confirmed the species status of S. indicus and S. heptneri. The divergence between S. elater and S. indicus estimated based on the nuclear loci was dated to approximately 1.5 Mya, which was slightly older than the 1.26 Mya inferred from mtDNA by Bannikova et al.22. Both S. indicus and S. elater included allopatric lineages that have separated 600–800 kya (i.e. dzungariae and strandi within elater, and aralychensis within indicus). Their formal taxonomic rank appears controversial: the level of divergence apparently conforms to species rank, whereas genetic data indicates potential gene flow between them. Thus, the mtDNA haplotypes of Scarturus specimens from the Zaisan depression (S. e. zaisanicus) form a subclade within S. elater s.str., whereas nuclear data suggest that S. e. zaisanicus is relatively close to S. e. dzungariae. This pattern suggests that the Zaisan population, while being a derivative of the Dzungar form, experienced mtDNA capture as a result of a past hybridisation event with S. elater. Gene flow between S. strandi and S. elater proper was indicated by the occurrence of elater mtDNA haplotypes in certain populations of strandi from north-western Kyzylkum22. All these taxa require additional research to produce a more accurate evaluation of gene flow intensity. Nevertheless, we suggest that dzungariae, strandi, and aralychensis should be considered semispecies or species in statu nascendi. Taxonomically, we regard them as parts of elater and indicus superspecies and refer to them as S. (elater) dzungariae, S. (elater) strandi, and S. (indicus) aralychensis, respectively.Phylogenetic relations within OrientallactagaWithin Orientallactaga, O. bullata and O. balikunica were supported as sister taxa based on nuclear data, which is consistent with their common morphology (enlarged bullae). However, mtDNA suggested that O. bullata is a sister taxon to O. sibirica, and the reason for this discrepancy is unclear, with ancient mtDNA introgression being the most obvious explanation. The crown age of Orientallactaga was dated to the early Early Pleistocene (Gelasian). Neither O. bullata nor O. balikunica show substantial intraspecific variation.In contrast, O. sibirica consists of several genetic lineages, which partly correspond to recognised subspecies. The mtDNA data tentatively supported subdivision of O. sibirica into western and eastern groups separated by the Tianshan–Altay zoogeographic boundary. The structure of variation in the eastern portion of the range (Mongolia, China) is well-studied23; however, the genetic data on the western portion are still fragmentary. Available mtDNA data provisionally support recognition of western subspecies such as O. s. ognevi (north-eastern to central Kazakhstan), O. s. dementjevi (Issyk-Kul region), and O. s. altorum (central Tianshan). The latter two forms are distributed in high-altitude areas of Tianshan, thus indicating that, in contrast to most other jerboa species, mountain areas might serve as foci of diversification in O. sibirica.The westernmost part of the range (western Kazakhstan, Qyzylkum) was assumed to be inhabited by a single O. s. suschkini subspecies after morphological revision1. However, three divergent mtDNA lineages were recovered based on the preliminary analysis of mtDNA data retrieved from museum specimens from the area, which suggests that the diversity of western populations is likely underestimated and in need of further examination.The crown age of O. sibirica was estimated at 500–600 kya, which was substantially younger than 2.2–3.2 Mya as inferred by Cheng et al.23; this discrepancy, however, can be explained by mtDNA saturation effects and usage of inaccurate secondary calibrations in their study.Variation within Allactaga and PygeretmusConsidering the phylogenetic position of Pygeretmus, our data firmly corroborated its separate phylogenetic position and rejected any affinity with Orientallactaga bullata as reconstructed by Wu et al.55. The latter result should be attributed to identification error. In our study, all three species of Pygeretmus were analysed to confirm phylogenetic proximity of P. shitkovi and P. platyurus relative to P. pumilio. Thus, the subgeneric status of Alactagulus containing the latter species was not contradicted; however, the split age between Pygeretmus s.str. and Alactagulus is relatively young, dated as Pliocene/Pleistocene boundary, indicating that morphological and life history traits of the former (e.g. slower locomotion) have evolved rather recently.A further taxon demonstrating a complex structure is Allactaga major. Our mtDNA data indicated that A. major consisted of several genetic lineages partly corresponding to morphological subspecies (A. m. spiculum, A. m. djetysuensis). A high level of divergence was observed between specimens from the northern Caucasus and Kazakhstan. A specimen of morphologically distinct A. m. spiculum (north-eastern Kazakhstan, western Siberia) was placed as a sister species to all other A. major with a divergence level compatible with species status.Several other species included unexpected genetic lineages that were apparently divergent at subspecies level (e.g. a southern Uzbekistan lineage of A. severtzovi and an Ili lineage of P. shitkovi). However, the resolving power of the employed set of 15 nuclear genes is insufficient for clarifying relationships within species. Therefore, these cases should be studied using larger samples and further nuclear loci.Divergence time estimates within AllactaginaeOur estimated divergence times were generally more recent than those produced by most previous studies. The root node of crown Allactaginae was dated to 7.7 (5.4–9.9) Mya by Wu et al.55, 8.1 (4.2–12.7) Mya by Zhang et al.56, or 8.87 (8.3–9.85) Mya by Pisano et al.4. The results by Wu et al.55 may be affected by a node density effect as their re-analysis with reduced taxon sampling of Allactaginae and Dipodinae produced younger dating at 5.8 (3.1–8.6) Mya. The latter two studies used only one to four nuclear loci and calibrated their analysis using non-Dipodidae calibration points. In both cases, the Early Miocene age of Sicista primus was used to calibrate crown Sicista, which lacks proper justification and may result in upward bias, as argued by Rusin et al.57.The earliest Allactaginae appeared in the Early Miocene and, in the Middle Miocene, the members of the primitive genus Protalactaga Young, 1927 became a common element of the Asian fauna3. During the Late Miocene, the diversity of allactagines persisted, and new genera emerged including Paralactaga Young, 1927 which is morphologically similar to Allactaga and is often considered its subgenus3,45. However, as can be derived from our results, all but one of the Middle and Late Miocene lineages went extinct without leaving any recent descendants, and all current diversity is a product of the Pliocene–Pleistocene evolution. This diversification pattern is unlike that observed in a different jerboa subfamily, Dipodinae, which includes lineages that had diverged in the Middle and early Late Miocene (Paradipus and Dipus, respectively)4,58.As estimated here, the onset of radiation among crown Allactaginae occurred in the latest Messinian and thus was nearly coincident with the Messinian crisis. However, it remains unclear how (or whether at all) climatic perturbations at the Miocene /Pliocene boundary affected the evolution of Allactaginae. The results of the diversification analysis suggested that, throughout the Pliocene and Pleistocene, the rate and mode of speciation in five-toed jerboas remained constant, indicating high tolerance of this group towards the climatic changes of this period.The minimum age of split observed between sympatric species was approximately 1 Mya as demonstrated by heptneri versus elater s.str. (and strandi). This was the estimate for the minimum time necessary for formation of effective reproductive barriers in allactagines (post- or pre-zygotic). Other phylogenetically close sympatric species pairs were S. elater/S. indicus (1.5 My), O. bullata/O. balikunica (1.5 My), and A. major/A. severtzovi (2.0 My).Geography of speciationOf 17 analysed episodes of speciation in Pliocene–Pleistocene, the patterns of range fragmentation in 10 episodes matched well to the classical vicariance scenario and those of six episodes matched to the founder-event speciation scenario; in one episode, both scenarios were equally probable. As the location of arising isolation barriers within the ancestor range seemed incidental, only in three cases the ancestors’ range was subdivided into two parts which were more or less equal in size: first, into East and West Central Asia; second, into Turan and Iran; third, into Anatolia with trans-Caucasus and northern Zagros and Levant with northern Mesopotamia and southern Zagros. In all other cases, the ancestors’ range was subdivided into the main part and relatively small peripheral isolates. As can be expected from the modern patterns of species diversity of Allactaginae, the discovered speciation events were unequally distributed: one episode in North Africa, one in the eastern part of Central Asia, three in the Middle East, four in the Iranian highland, four in Turan, and five in Kazakhstan. In most cases, range fragmentation coincided with extreme climate conditions within the analysed time periods: warmest and wettest (decrease of the area of arid lands: nodes 2–3, 5, 10, 12, and 14–15) or coldest and driest (closing narrow mountain passages due to mountain glaciation: nodes 4, 6–9, 13, and 16–18). In one case (node 11), fragmentation of the range coincided with moderate climate conditions.Successful modelling of fragmentation of geographic ranges as a base of speciation events seemed to agree with the hypothesis of Peterson et al.15, which states that ecological niches evolve little at or around the time of speciation events, whereas niche differences accumulate later. This hypothesis was supported by Peterson’s analysis59 of data published between 1999 and 2008 which demonstrated that niche conservatism was found in more than 70% of comparisons within species and between sister species, but in less than 50% of comparisons among closely-related (but not sister) species and across monophyletic lineages of species. Moreover, analysis of habitat niche evolution of arvicoline rodents16 demonstrated that closely related species with allopatric or parapatric distribution demonstrated small niche differences, whereas they were larger in species with sympatric distribution. This is a clear indication that interspecific competition forces natural selection to increase niche differences resulting in species co-occurrence. It was demonstrated that niche divergence/conservatism can be differently expressed between different niche/resource axes60. In voles, which have a highly specialised folivorous diet, habitat segregation seems to be the only type of niche differentiation. Closely related Allactaginae species are similar in diet and typically occur in allopatric or parapatric distribution patterns1, which may indicate their niche conservatism. The only exception to a pattern where species with similar diets show widely overlapping geographic distributions are Scatrurus elater and S. heptneri (these two species are similar in both, macro- and micro-habitat niches, and it is unclear which mechanisms allow them to co-occur22). Distantly related sympatric species typically show similarities regarding macro-habitat niches but marked differences in terms of micro-habitat niches (Allactaga major and Orientallactaga sibirica; O. sibirica and O. bullata; O. sibirica and O. balikunica; Pygerethmus pumilio and P. platyurus; P. pumilio and P. shitkovi; personal observations) and diet (Allactaga and Allactodipus; Allactaga and Scarturus; Allactaga and Pygeretmus; Orientallactaga and Pygeretmus; Scarturus and Pygeretmus1,61). Thus, macro-habitat niche conservatism may be expected even in sympatric species. More

1.Nehring, A. Über Tundren und Steppen der Jetzt- und Vorzeit, mit besonderer Berücksichtigung ihrer Fauna (F. Dummler, 1890).2.Chytrý, M. et al. A modern analogue of the Pleistocene steppe-tundra in southern Siberia. Boreas 48, 36–56 (2019).Article 

Google Scholar 
3.Graham, R. Late Wisconsin mammalian faunas and environmental gradients of the eastern United States. Paleobiology 2, 343–350 (1976).Article 

Google Scholar 
4.Webb, T. I. I. I. The appearance and disappearance of major vegetational assemblages: Long-term vegetational dynamics in eastern North America. Vegetatio 69, 177–187 (1987).Article 

Google Scholar 
5.Frest, T. J. & Dickson, J. R. Land snails (Pleistocene-recent) of the Loess Hills: A preliminary survey. Proc. Iowa Acad. Sci. 93, 130–157 (1986).
Google Scholar 
6.Nekola, J. C. Paleorefugia and neorefugia: The influence of colonization history on community pattern and process. Ecology 80, 2459–2473 (1999).Article 

Google Scholar 
7.Magri, D. et al. A new scenario for the Quaternary history of European beech populations: Palaeobotanical evidence and genetic consequences. New Phytol. 17, 199–221 (2006).Article 

Google Scholar 
8.Soltis, D. E., Morris, A. B., McLachlan, J. S., Manos, P. S. & Soltis, P. S. Comparative phylogeography of unglaciated eastern North America. Mol. Ecol. 15, 4261–4293 (2006).Article 

Google Scholar 
9.Graham, R. Quaternary mammal communities: Relevance of the individualistic response and non-analogue faunas. Paleontol. Soc. Papers 11, 141–158 (2005).Article 

Google Scholar 
10.Davis, M. B. Climatic instability, time lags, and community disequilibrium. In Community Ecology (eds Diamond, J. & Case, T. J.) 269–284 (Harper & Row, 1984).11.Baker, R. G. et al. A full-glacial biota from southeastern Iowa USA. J. Quat. Sci. 1, 91–107 (1986).Article 

Google Scholar 
12.Baker, R. G., Sullivan, A. E., Hallberg, G. R. & Horton, D. G. Vegetational changes in western Illinois during the onset of late Wisconsinan glaciation. Ecology 70, 1363–1376 (1989).Article 

Google Scholar 
13.Baker, R. G. et al. Mid-Wisconsinan environments on the eastern Great Plains. Quat. Sci. Rev. 28, 873–889 (2009).ADS 
Article 

Google Scholar 
14.Scott, G. H. Uniformitarianism, the uniformity of nature, and paleoecology. N. Zeal. J. Geol. Geophys. 6, 510–527 (1963).Article 

Google Scholar 
15.Horsák, M. et al. Snail faunas in the Southern Ural forests and their relations to vegetation: An analogue of the Early Holocene assemblages of Central Europe? J. Molluscan Stud. 76, 1–10 (2010).Article 

Google Scholar 
16.Ložek, V. Quartärmollusken der Tschechoslowakei (Nakladatelství Československé akademie věd, 1964).17.Horsák, M. et al. European glacial relict snails and plants: environmental context of their modern refugial occurrence in southern Siberia. Boreas 44, 638–657 (2015).Article 

Google Scholar 
18.Moine, O. Weichselian Upper Pleniglacial environmental variability in north-western Europe reconstructed from terrestrial mollusc faunas and its relationship with the presence/absence of human settlements. Quat. Int. 337, 90–113 (2014).Article 

Google Scholar 
19.Hošek, J. et al. Middle Pleniglacial pedogenesis on the northwestern edge of the Carpathian Basin: A multidisciplinary investigation of the Bíňa pedo-sedimentary section SW Slovakia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 321–339 (2017).Article 

Google Scholar 
20.Horsák, M., Škodová, J. & Cernohorsky, N. H. Ecological and historical determinants of Western Carpathian populations of Pupilla alpicola (Charpentier, 1837) in relation to its present range and conservation. J. Molluscan Stud. 77, 248–254 (2011).Article 

Google Scholar 
21.Nekola, J. C., Coles, F. B. & Horsák, M. Species assignment in Pupilla (Gastropoda: Pulmonata: Pupillidae): Integration of DNA-sequence data and conchology. J. Molluscan Stud. 81, 196–216 (2015).Article 

Google Scholar 
22.Horsák, M., Juřičková, L. & Picka, J. Měkkýši České a Slovenské republiky. Molluscs of the Czech and Slovak Republics (Kabourek, 2013).23.Welter-Schultes, F. W. European non-marine molluscs, a guide for species identification (Planet Poster Editions, 2012).24.von Proschwitz, T. Three land-snail species new to the Norwegian fauna: Pupilla pratensis (Clessin, 1871), Vertigo ultimathule von Proschwitz, 2007 and Balea sarsii Philippi, 1847 [= B. heydeni von Maltzan, 1881]. Fauna Norv. 30, 13–19 (2010).25.Kerney, M. P., Cameron, R. A. D. & Jungbluth, J. H. Die Landschnecken Nord- und Mitteleuropas (Parey Verlag, 1983).26.Horsáková, V., Nekola, J. C. & Horsák, M. When is a “cryptic” species not a cryptic species: A consideration from the Holarctic micro-landsnail genus Euconulus (Gastropoda: Stylommatophora). Mol. Phylogenet. Evol. 132, 307–320 (2019).Article 

Google Scholar 
27.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
28.Title, P. O. & Bemmels, J. B. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41, 291–307 (2018).Article 

Google Scholar 
29.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

Google Scholar 
30.Haase, M., Meng, S. & Horsák, M. Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae). Zool. J. Linnean. Soc. 191, 720–747 (2021).Article 

Google Scholar 
31.Ložek, V. Molluscan fauna from the loess series of Bohemia and Moravia. Quat. Int. 76–77, 141–156 (2001).Article 

Google Scholar 
32.Fordham, D. A. et al. PaleoView: A tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

Google Scholar 
33.Mysterud, A. The concept of overgrazing and its role in management of large herbivores. Wildlife Biol. 12, 129–141 (2006).Article 

Google Scholar 
34.Arnalds, Ó. The soils of Iceland. World Soils Book Series (Springer, 2015).35.Horsák, M. et al. Spring water table depth mediates within-site variation of soil temperature in groundwater-fed mires. Hydrol. Process. 35, e14293 (2021).36.Ložek, V. Zrcadlo minulosti. Česká a slovenská krajina v kvartéru (Dokořán, 2007).37.Ehlers, J., Gibbard, P. L. & Hughes, P. D., eds. Quaternary Glaciations—Extent and Chronology, Volume 15 (Elsevier, 2011). More

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

Google Scholar 
2.IPCC. AR5 Climate Change 2013: The Physical Science Basis (Intergovernmental Panel on Climate Change, 2013).
Google Scholar 
3.IPCC. AR5 Synthesis Report: Climate Change 2014 (Intergovernmental Panel on Climate Change, 2014).
Google Scholar 
4.Caldeira, K. & Wickett, M. E. Antropogenic carbon and ocean pH: The coming centuries may see more ocean acidification than the past 300 million years. Nature 425, 365 (2003).CAS 
PubMed 
ADS 

Google Scholar 
5.Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: The other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).PubMed 

Google Scholar 
6.Lowe, A. T., Bos, J. & Ruesink, J. Ecosystem metabolism drives pH variability and modulates long-term ocean acidification in the Northeast Pacific coastal ocean. Sci. Rep. 9, 963. https://doi.org/10.1038/s41598-018-37764-4 (2019).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
7.Justić, D., Rabalais, N. N. & Turner, R. E. Effects of climate change on hypoxia in coastal waters: A doubled CO2 scenario for the northern Gulf of Mexico. Limnol. Oceanogr. 41, 992–1003 (1996).ADS 

Google Scholar 
8.Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).CAS 
PubMed 
ADS 

Google Scholar 
9.del Giorgio, P. A. & Duarte, C. M. Respiration in the open ocean. Nature 420, 379–384 (2002).PubMed 
ADS 

Google Scholar 
10.Vaquer-Sunyer, R. & Duarte, C. M. Experimental evaluation of the response of coastal Mediterranean planktonic and benthic metabolism to warming. Estuaries Coast. 36, 697–707 (2013).CAS 

Google Scholar 
11.Fu, W., Randerson, J. T. & Moore, J. K. Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).ADS 

Google Scholar 
12.Gaarder, T. & Gran, H. H. Investigations of the production of plankton in the Oslo Fjord. Rapports Procès-Verbaux Réunions 42, 3–48 (1927).
Google Scholar 
13.Bender, M. et al. A comparison of four methods for determining planktonic community production. Limnol. Oceanogr. 32, 1085–1098 (1987).ADS 

Google Scholar 
14.Marra, J. Net and gross productivity: Weighing in with 14C. Aquat. Microb. Ecol. 56, 123–131 (2009).
Google Scholar 
15.Hitchcock, G. L., Kirkpatrick, G., Minnett, P. & Palubok, V. Net community production and dark community respiration in a Karenia brevis (Davis) bloom in West Florida coastal waters, USA. Harmful Algae 9, 351–358 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Stephenson, T. A., Zoond, A. & Eyre, J. The liberation and utilisation of oxygen by the population of rock-pools. J. Exp. Biol. 11, 162–172 (1934).
Google Scholar 
17.Beyers, R. J. Relationship between temperature and the metabolism of experimental ecosystems. Science 136, 980–982 (1962).CAS 
PubMed 
ADS 

Google Scholar 
18.Duarte, C. M. & Regaudie-de-Gioux, A. Thresholds of gross primary production for the metabolic balance of marine planktonic communities. Limnol. Oceanogr. 54, 1015–1022 (2009).CAS 
ADS 

Google Scholar 
19.Noël, L.M.-L. et al. Assessment of a field incubation method estimating primary productivity in rockpool communities. Estuar. Coast. Shelf Sci. 88, 153–159 (2010).ADS 

Google Scholar 
20.Hall, C. A. S. & Moll, R. Methods of assessing aquatic primary productivity. In Primary Productivity of the Biosphere (eds Lieth, H. & Whittaker, R. H.) 19–53 (Springer, 1975).
Google Scholar 
21.Platt, T. et al. Biological production of the oceans: The case for a consensus. Mar. Ecol. Prog. Ser. 52, 77–88 (1989).ADS 

Google Scholar 
22.Odum, H. T. Primary production in flowing waters. Limnol. Oceanogr. 1, 102–117 (1956).ADS 

Google Scholar 
23.Odum, H. T. & Hoskin, C. M. Comparative studies on the metabolism of marine waters. Publ. Inst. Mar. Sci. 5, 16–46 (1958).
Google Scholar 
24.Johnson, K. M., Burney, C. M. & Sieburth, J. M. Enigmatic marine ecosystem metabolism measured by direct diel ΣCO2 and O2 flux in conjunction with DOC release and uptake. Mar. Biol. 65, 49–60 (1981).CAS 

Google Scholar 
25.Volaric, M. P., Berg, P. & Reidenbach, M. A. Drivers of oyster reef ecosystem metabolism measured across multiple timescales. Estuaries Coast. 43, 2034–2045 (2020).CAS 

Google Scholar 
26.Collins, J. R. et al. An autonomous, in situ light-dark bottle device for determining community respiration and net community production. Limnol. Oceanogr. Method. 16, 323–338 (2018).
Google Scholar 
27.Steemann Nielsen, E. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).
Google Scholar 
28.Peterson, B. J. Aquatic primary productivity and the 14C-CO2 method: A history of the productivity problem. Ann. Rev. Ecol. Syst. 11, 359–385 (1980).
Google Scholar 
29.Jackson, D. F. & McFadden, J. Phytoplankton photosynthesis in Sanctuary Lake, Pymatuning Reservoir. Ecology 35, 2–4 (1954).
Google Scholar 
30.Van de Bogert, M. C., Carpenter, S. R. & Pace, M. L. Assessing pelagic and benthic metabolism using free water measurements. Limnol. Oceanogr. Methods 5, 145–155 (2007).
Google Scholar 
31.Barone, B., Nicholson, D., Ferrón, S., Firing, E. & Karl, D. The estimation of gross oxygen production and community respiration from autonomous time-series measurements in the oligotrophic ocean. Limnol. Oceanogr. Methods 17, 650–664 (2019).CAS 

Google Scholar 
32.Staehr, P. A. et al. Lake metabolism and the diel oxygen technique: State of the science. Limnol. Oceanogr. Methods 8, 628–644 (2010).CAS 

Google Scholar 
33.Nicholson, D. P., Wilson, S. T., Doney, S. C. & Karl, D. M. Quantifying subtropical North Pacific gyre mixed layer primary productivity from Seaglider observations of diel oxygen cycles. Geophys. Res. Lett. 42, 4032–4039 (2015).CAS 
ADS 

Google Scholar 
34.Mantikci, M., Hansen, J. L. S. & Markager, S. Photosynthesis enhanced dark respiration in three marine phytoplankton species. J. Exp. Mar. Biol. Ecol. 497, 188–196 (2017).CAS 

Google Scholar 
35.Truchot, J.-P. & Duhamel-Jouve, A. Oxygen and carbon dioxide in the marine intertidal environment: Diurnal and tidal changes in rockpools. Resp. Physiol. 39, 241–254 (1980).CAS 

Google Scholar 
36.Delille, B., Borges, A. V. & Delille, D. Influence of giant kelp beds (Macrocystis pyrifera) on diel cycles of pCO2 and DIC in the Sub-Antarctic coastal area. Estuar. Coast. Shelf Sci. 81, 114–122 (2009).ADS 

Google Scholar 
37.Woolway, R. I. et al. Diel surface temperature range scales with lake size. PLoS ONE 11, e0152466. https://doi.org/10.1371/journal.pone.0152466 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Andersen, M. R., Kragh, T. & Sand-Jensen, K. Extreme diel dissolved oxygen and carbon cycles in shallow vegetated lakes. Proc. R. Soc. B 284, 20171427. https://doi.org/10.1098/rspb.2017.1427 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Nielsen, K. J. Bottom-up and top-down forces in tide pools: Test of a food chain model in an intertidal community. Ecol. Monogr. 71, 187–217 (2001).
Google Scholar 
40.Altieri, A. H., Trussell, G. C., Ewanchuk, P. J., Bernatchez, G. & Bracken, M. E. S. Consumers control diversity and functioning of a natural marine ecosystem. PLoS ONE 4, e5291. https://doi.org/10.1371/journal.pone.0005291 (2009).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
41.O’Connor, N. E., Bracken, M. E. S., Crowe, T. P. & Donohue, I. Nutrient enrichment alters the consequences of species loss. J. Ecol. 103, 862–870 (2015).
Google Scholar 
42.Rheuban, J. E., Berg, P. & McGlathery, K. J. Multiple timescale processes drive ecosystem metabolism in eelgrass (Zostera marina) meadows. Mar. Ecol. Prog. Ser. 507, 1–13 (2014).ADS 

Google Scholar 
43.Barrón, C. et al. High organic carbon export precludes eutrophication responses in experimental rocky shore communities. Ecosystems 6, 144–153. https://doi.org/10.1007/s10021-002-0402-3 (2003).CAS 
Article 

Google Scholar 
44.Kraufvelin, P., Lindholm, A., Pedersen, M. F., Kirkerud, L. A. & Bonsdorff, E. Biomass, diversity and production of rocky shore macroalgae at two nutrient enrichment and wave action levels. Mar. Biol. 157, 29–47 (2010).
Google Scholar 
45.Epping, E. H. G. & Jørgensen, B. B. Light-enhanced oxygen respiration in benthic phototrophic communities. Mar. Ecol. Prog. Ser. 139, 193–203 (1996).ADS 

Google Scholar 
46.Graham, J. M., Kranzfelder, J. A. & Auer, M. T. Light and temperature as factors regulating seasonal growth and distribution of Ulothrix zonata (Ulvophyceae). J. Phycol. 21, 228–234. https://doi.org/10.1111/j.0022-3646.1985.00228.x (1985).Article 

Google Scholar 
47.Hotchkiss, E. R. & Hall, R. O. Jr. High rates of daytime respiration in three streams: Use of δ18OO2 and O2 to model diel ecosystem metabolism. Limnol. Oceanogr. 59, 798–810. https://doi.org/10.4319/lo.2014.59.3.0798 (2014).CAS 
Article 
ADS 

Google Scholar 
48.Song, C. et al. Continental-scale decrease in net primary productivity in streams due to climate warming. Nat. Geosci. 11, 415–420 (2018).CAS 
ADS 

Google Scholar 
49.Conley, D. J., Carstensen, J., Vaquer-Sunyer, R. & Duarte, C. M. Ecosystem thresholds with hypoxia. Hydrobiologia 629, 21–29 (2009).CAS 

Google Scholar 
50.Lefèvre, D., Bentley, T. L., Robinson, C., Blight, S. P. & Williams, P. J. L. The temperature response of gross and net community production and respiration in time-varying assemblages of temperate marine micro-plankton. J. Exp. Mar. Biol. Ecol. 184, 201–215 (1994).
Google Scholar 
51.López-Urrutia, Á., SanMartin, E., Harris, R. P. & Irigoien, X. Scaling the metabolic balance of the oceans. Proc. Natl Acad. Sci. USA 103, 8739–8744 (2006).PubMed 
PubMed Central 
ADS 

Google Scholar 
52.Grant, J. Sensitivity of benthic community respiration and primary production to changes in temperature and light. Mar. Biol. 90, 299–306 (1986).
Google Scholar 
53.Jankowski, K., Schindler, D. E. & Lisi, P. J. Temperature sensitivity of community respiration rates in streams is associated with watershed geomorphic features. Ecology 95, 2707–2714 (2014).
Google Scholar 
54.Yvon-Durocher, G., Jones, J. I., Trimmer, M., Woodward, G. & Montoya, J. M. Warming alters the metabolic balance of ecosystems. Phil. Trans. R. Soc. B. 365, 2117–2126 (2010).PubMed 
PubMed Central 

Google Scholar 
55.Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).CAS 
PubMed 
ADS 

Google Scholar 
56.Tyler, R. M., Brady, D. C. & Targett, T. E. Temporal and spatial dynamics of diel-cycling hypoxia in estuarine tributaries. Estuaries Coast. 32, 123–145 (2009).CAS 

Google Scholar 
57.Howard, E. M. et al. Oxygen and triple oxygen isotope measurements provide different insights into gross oxygen production in a shallow salt marsh pond. Estuaries Coast. 43, 1908–1922 (2020).CAS 

Google Scholar 
58.Luz, B. & Barkan, E. Assessment of oceanic productivity with the triple-isotope composition of dissolved oxygen. Science 288, 2028–2031 (2000).CAS 
PubMed 
ADS 

Google Scholar 
59.Winslow, L. A. et al. LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models. Inland Waters 6, 622–636 (2016).CAS 

Google Scholar 
60.Sorte, C. J. B. & Bracken, M. E. S. Warming and elevated CO2 interact to drive rapid shifts in marine community production. PLoS ONE 10, e0145191. https://doi.org/10.1371/journal.pone.0145191 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Hinode, K. et al. The phenology of gross ecosystem production in a macroalga and seagrass canopy is driven by seasonal temperature. Phycol. Res. 68, 298–312 (2020).CAS 

Google Scholar 
62.Bracken, M., Miller, L., Mastroni, S., Lira, S. & Sorte, C. Data from: Accounting for variation in temperature and oxygen availability when quantifying marine ecosystem metabolism. Dryad Dataset https://doi.org/10.7280/D1M39B (2021).Article 

Google Scholar 
63.Reiskind, J. B., Seamon, P. T. & Bowes, G. Alternative methods of photosynthetic carbon assimilation in marine macroalgae. Plant Physiol. 87, 686–692 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Williamson, M. & Griffiths, B. Biological invasions (Springer, New York, 1996).
Google Scholar 
2.Ricciardi, A. et al. Invasion science: a horizon scan of emerging challenges and opportunities. Trends Ecol. Evolut. 32, 464–474 (2017).
Google Scholar 
3.Van Saarloos, W. Front propagation into unstable states. Phys. Rep. 386, 29–222 (2003).MATH 
ADS 

Google Scholar 
4.OMalley, L., Korniss, G. & Caraco, T. Ecological invasion, roughened fronts, and a competitors extreme advance: integrating stochastic spatial-growth models. Bull. Math. Biol. 71, 1160–1188 (2009).MathSciNet 
MATH 

Google Scholar 
5.Lewis, M. A., Petrovskii, S. V. & Potts, J. R. The mathematics behind biological invasions Vol. 44 (Springer, New York, 2016).MATH 

Google Scholar 
6.Fisher, R. A. The wave of advance of advantageous genes. Ann. Eugenics 7, 355–369 (1937).MATH 

Google Scholar 
7.Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl. Acad. Sci. 104, 3883–3888 (2007).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
8.Korolev, K. S., Xavier, J. B. & Gore, J. Turning ecology and evolution against cancer. Nat. Rev. Cancer 14, 371–380 (2014).CAS 
PubMed 

Google Scholar 
9.Wolf, K. et al. Physical limits of cell migration: control by ecm space and nuclear deformation and tuning by proteolysis and traction force. J. Cell Biol. 201, 1069–1084 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Lu, P., Takai, K., Weaver, V. M. & Werb, Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harbor Persp. Biol. 3, a005058 (2011).
Google Scholar 
11.Wirtz, D., Konstantopoulos, K. & Searson, P. C. The physics of cancer: the role of physical interactions and mechanical forces in metastasis. Nat. Rev. Cancer 11, 512–522 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Spill, F., Reynolds, D. S., Kamm, R. D. & Zaman, M. H. Impact of the physical microenvironment on tumor progression and metastasis. Curr. Opin. Biotechnol. 40, 41–48 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).CAS 
PubMed 

Google Scholar 
14.Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).CAS 

Google Scholar 
15.Azimzade, Y. & Saberi, A. A. Short-range migration can alter evolutionary dynamics in solid tumors. J. Stat. Mech. Theory Exp. 2019, 103502 (2019).MathSciNet 
MATH 

Google Scholar 
16.West, J., Schenck, R., Gatenbee, C., Robertson-Tessi, M. & Anderson, A. R. Tissue structure accelerates evolution: premalignant sweeps precede neutral expansion. bioRxiv 542019 (2019).17.Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Cleary, A. S., Leonard, T. L., Gestl, S. A. & Gunther, E. J. Tumour cell heterogeneity maintained by cooperating subclones in wnt-driven mammary cancers. Nature 508, 113–117 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
19.Shahriari, K. et al. Cooperation among heterogeneous prostate cancer cells in the bone metastatic niche. Oncogene (2016).20.Calbo, J. et al. A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer. Cancer Cell 19, 244–256 (2011).CAS 
PubMed 

Google Scholar 
21.Martín-Pardillos, A. et al. The role of clonal communication and heterogeneity in breast cancer. BMC Cancer 19, 1–26 (2019).
Google Scholar 
22.Kim, T.-M. et al. Subclonal genomic architectures of primary and metastatic colorectal cancer based on intratumoral genetic heterogeneity. Clin. Cancer Res. 21, 4461–4472 (2015).CAS 
PubMed 

Google Scholar 
23.Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
24.Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
25.Capp, J.-P. et al. Group phenotypic composition in cancer. Elife 10, e63518 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Murray, J. D. Mathematical biology I: an introduction (2003).27.Mikhailov, A., Schimansky-Geier, L. & Ebeling, W. Stochastic motion of the propagating front in bistable media. Phys. Lett. A 96, 453–456 (1983).MathSciNet 
ADS 

Google Scholar 
28.Hatzikirou, H., Brusch, L., Schaller, C., Simon, M. & Deutsch, A. Prediction of traveling front behavior in a lattice-gas cellular automaton model for tumor invasion. Comput. Math. Appl. 59, 2326–2339 (2010).MathSciNet 
MATH 

Google Scholar 
29.Azimzade, Y., Sasar, M. & Maleki, I. Invasion front dynamics in disordered environments. Sci. Rep. 10, 1–10 (2020).
Google Scholar 
30.Azimzade, Y., Saberi, A. A. & Sahimi, M. Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments. Phys. Rev. E 100, 062409 (2019).PubMed 
ADS 

Google Scholar 
31.Rapin, G. et al. Roughness and dynamics of proliferating cell fronts as a probe of cell-cell interactions. Sci. Rep. 11, 1–9 (2021).ADS 

Google Scholar 
32.Pérez-Beteta, J. et al. Tumor surface regularity at mr imaging predicts survival and response to surgery in patients with glioblastoma. Radiology 171051 (2018).33.Pérez-Beteta, J. et al. Morphological mri-based features provide pretreatment survival prediction in glioblastoma. Eur. Radiol. 1–10 (2018).34.Brú, A. et al. Super-rough dynamics on tumor growth. Phys. Rev. Lett. 81, 4008 (1998).ADS 

Google Scholar 
35.Brú, A., Albertos, S., Subiza, J. L., García-Asenjo, J. L. & Brú, I. The universal dynamics of tumor growth. Biophys. J. 85, 2948–2961 (2003).PubMed 
PubMed Central 
ADS 

Google Scholar 
36.Huergo, M., Pasquale, M., González, P., Bolzán, A. & Arvia, A. Growth dynamics of cancer cell colonies and their comparison with noncancerous cells. Phys. Rev. E 85, 011918 (2012).CAS 
ADS 

Google Scholar 
37.Munn, L. L. Dynamics of tissue topology during cancer invasion and metastasis. Phys. Biol. 10, 065003 (2013).PubMed 
PubMed Central 
ADS 

Google Scholar 
38.Dey, B., Sekhar, G. R. & Mukhopadhyay, S. K. In vivo mimicking model for solid tumor towards hydromechanics of tissue deformation and creation of necrosis. J. Biol. Phys. 1–40 (2018).39.Block, M., Schöll, E. & Drasdo, D. Classifying the expansion kinetics and critical surface dynamics of growing cell populations. Phys. Rev. Lett. 99, 248101 (2007).CAS 
PubMed 
ADS 

Google Scholar 
40.Moglia, B., Guisoni, N. & Albano, E. V. Interfacial properties in a discrete model for tumor growth. Phys. Rev. E 87, 032713 (2013).ADS 

Google Scholar 
41.Moglia, B., Albano, E. V. & Guisoni, N. Pinning-depinning transition in a stochastic growth model for the evolution of cell colony fronts in a disordered medium. Phys. Rev. E 94, 052139 (2016).PubMed 
ADS 

Google Scholar 
42.Scianna, M. & Preziosi, L. A hybrid model describing different morphologies of tumor invasion fronts. Math. Model. Nat. Phenom. 7, 78–104 (2012).MathSciNet 
MATH 

Google Scholar 
43.Azimzade, Y., Saberi, A. A. & Sahimi, M. Role of the interplay between the internal and external conditions in invasive behavior of tumors. Sci. Rep. 8, 5968 (2018).PubMed 
PubMed Central 
ADS 

Google Scholar 
44.Ben-Jacob, E. et al. Generic modelling of cooperative growth patterns in bacterial colonies. Nature 368, 46 (1994).CAS 
PubMed 
ADS 

Google Scholar 
45.Family, F. & Vicsek, T. Dynamics of fractal surfaces (World Scientific, Singapore, 1991).MATH 

Google Scholar 
46.Vicsek, T. Fractal growth phenomena (World scientific, Singapore, 1992).MATH 

Google Scholar 
47.Swanson, K. R., Bridge, C., Murray, J. & Alvord, E. C. Jr. Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J. Neurol. Sci. 216, 1–10 (2003).PubMed 

Google Scholar 
48.Metzcar, J., Wang, Y., Heiland, R. & Macklin, P. A review of cell-based computational modeling in cancer biology. JCO Clin. Cancer Inform. 2, 1–13 (2019).
Google Scholar 
49.Azimzade, Y., Saberi, A. A. & Gatenby, R. A. Superlinear growth reveals the allee effect in tumors. Phys. Rev. E 103, 042405 (2021).CAS 
PubMed 
ADS 

Google Scholar 
50.Anderson, A. R., Weaver, A. M., Cummings, P. T. & Quaranta, V. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127, 905–915 (2006).CAS 
PubMed 

Google Scholar  More

Experimental designConsortia of auxotrophic E. coli genotypes, which previously evolved an obligate mutualistic cooperation26, were used to determine how this type of interaction affects the ability of the participating individuals to respond to environmental selection pressures. To this end, two main experimental treatment groups were established. First, each of the two cooperative auxotrophs was grown as amino acid-supplemented monoculture (i.e. tyrosine and tryptophan, 100 µM each). Second, both genotypes were cocultivated in the absence of amino acid supplementation. A treatment, in which monocultures were cultivated in the absence of amino acid supplementation was not included, because auxotrophic genotypes would not grow under these conditions. Also, an amino acid-supplemented coculture was not implemented in the experimental design, because competition between both auxotrophs was likely to result in a loss of one of the two genotypes (Supplementary Fig. 1). Moreover, previous experiments showed that amino acid supplementation does not completely abolish the mutualistic interaction. Hence, the experiment compared monocultures with externally provided amino acids (i.e. no mutualism) to cocultures, which could only grow when strains reciprocally exchanged amino acids (i.e. mutualism). Replicate populations of both treatment groups were serially propagated while being subject to a stepwise increasing concentration of one of four different antibiotics (i.e. ampicillin, kanamycin, chloramphenicol, and tetracycline) (Fig. 1). These four antibiotics differed in their mode of action. In this way, not just the effect of a single stressor was probed, but rather the ability of mutualistic consortia to adapt to environmental stress in general.Ancestral consortia differ in their growth levels and susceptibility to environmental stressBefore the actual evolution experiment was performed, both growth levels and susceptibility to environmental stress was determined in the ancestral consortia. Comparing the maximum growth rate and densities populations achieved after 72 h revealed that unsupplemented cocultures grew significantly slower (Benjamini–Hochberg correction: P  More

Significance tests of straw return methods, potassium fertilization levels and their interactionsAnalysis of variance (ANOVA) results showed that straw return methods and potassium fertilization levels had significant effects on maize photosynthesis, dry matter and yield from 2018 to 2020 (Table 3). Significant interactions between straw return methods and potassium fertilization levels were only found on Pn of 2018 and 2020, and Tr of 2018–2020. Through the comparison of three-year F-values, it could be found that the effect of potassium fertilization levels on maize photosynthesis, dry matter and yield was greater than that of straw return methods.Table 3 Significance of the effects of straw return methods, potassium fertilization levels and their interactions on maize growth and yield using ANOVA.Full size tableEffects of straw return and potassium fertilizer on photosynthesis of maizeThe straw return methods and potassium fertilization levels significantly influenced (p ≤ 0.05) the maize photosynthesis compared to control (CK), resulting in Pn, Gs and Tr values that were higher than those of CK, and Ci value that was lower than that of CK.Straw return and potassium supply increased Pn, Gs and Tr. From 2018 to 2020, compared with CK, Pn increased by 1.70–4.09 under SFK0, 2.65–5.77 under SFK30, 5.21–8.48 under SFK45, 7.31–11.44 under SFK60, 0.63–3.20 under FGK0, 2.50–5.11 under FGK30, 3.60–5.79 under FGK45, and 3.97–7.47 μmol·m-2·s-1 under FGK60 (Fig. 1a). Gs increased by 0.60–0.90 under SFK0, 0.10–0.13 under SFK30, 0.18,-0.19 under SFK45, 0.20–0.22 under SFK60, 0.02–0.06 under FGK0, 0.08–0.09 under FGK30, 0.13–0.17 under FGK45, and 0.15–0.19 mmol·m-2·s-1 under FGK60 (Fig. 1b). Tr increased by 0.55–0.87 under SFK0, 1.02–1.30 under SFK30, 1.51–1.67 under SFK45, 1.74–1.99 under SFK60, 0.49–0.71 under FGK0, 0.86–1.13 under FGK30, 1.12–1.38 under FGK45, and 1.27–1.47 mmol·m−2·s−1 under FGK60 (Fig. 1c).Figure 1Effects of straw return methods and potassium fertilization levels on maize photosynthesis.Full size imageStraw return and potassium supply decreased Ci. From 2018 to 2020, compared with CK, Ci decreased by 5.43–8.92 under SFK0, 10.59–14.05 under SFK30, 19.04–21.21 under SFK45, 21.77–23.81 under SFK60, 2.26–6.52 under FGK0, 8.59–12.07 under FGK30, 12.93–16.15 under FGK45, and 17.81–19.46 μmol·mol-−1 under FGK60 (Fig. 1d).Comprehensive analysis showed that Pn, Gs, Tr increased and Ci decreased significantly after the treatment of SF under the same potassium supply. Under the same straw return method, Pn, Gs and Tr values increased significantly with the potassium fertilization levels, while Ci decreased. The effects of straw return and potassium fertilizer on maize photosynthesis increased gradually from year to year.Effects of straw return and potassium fertilizer on dry matter of maizeWe can see from Fig. 2, the straw return methods and potassium fertilization levels significantly increased (p ≤ 0.05) the maize dry matter accumulation. Compared with CK, under the treatments of SFK0, SFK30, SFK45, SFK60, FGK0, FGK30, FGK45 and FGK60, the dry matter of R1 and R6 stage increased by 1454.45, 2288.75, 3982.85, 4961.45, 1042.96, 1744.54, 2890.65, 3408.39 and 2152.43, 4433.55, 6726.72, 8051.51, 1195.76, 3337.79, 5121.77, 6247.56 kg/ha in 2018; the dry matter increased by 1812.69, 2959.44, 4370.19, 5615.94, 1545.06, 2238.06, 3421.11, 4028.64 and 2588.52, 5319.60, 7500.74, 8912.64, 1649.67, 3832.46, 6065.90, 6864.33 kg/ha in 2019; the dry matter increased by 2535.39, 3612.35, 5544.00, 6720.12, 2474.18,2827.94, 4749.86, 4769.66 and 3235.18, 5798.75, 8577.48, 10,071.83, 2515.75, 4386.39, 7256.61, 7536.91 kg/ha in 2020.Figure 2Effects of straw return methods and potassium fertilization levels on maize dry matter. Values followed by different letters in the same year indicated indicate statistical significance at α = 0.05 under different treatments. The same below.Full size imageIn short, under the same straw return method, the increase of maize dry matter from R1 to R6 improved significantly with the potassium level, potassium fertilizer could improve the maize dry matter accumulation ability. The maize dry matter of R1 to R6 increased significantly after the treatment of SF compared to FG under the same potassium supply. The promotion effect of straw return and potassium fertilizer on maize dry matter increased from year to year.Effects of straw return and potassium fertilizer on maize yieldThe straw return methods and potassium fertilization levels significantly influenced (p ≤ 0.05) the maize yield compared to CK, resulting in maize yield values that were higher than those of CK. Straw return and potassium supply increased maize yield. From 2018 to 2020, compared with CK, maize yield increased by 9.73–10.32% under SFK0, 15.68–17.47% under SFK30, 24.02–25.58% under SFK45, 24.46–25.76% under SFK60, 5.79–7.83% under FGK0, 13.51–13.72% under FGK30, 18.64–19.01% under FGK45, and 21.19–21.69% under FGK60 (Fig. 3).Figure 3Effects of straw return methods and potassium fertilization levels on maize yield.Full size imageThe maize yield among treatments was as follows: SFK60  > SFK45  > FGK60  > FGK45  > SFK30  > FGK30  > SFK0  > FGK0  > CK. Compared to FG, the effect of SF on maize yield was more obvious. The maize yield increased significantly with the potassium fertilization levels under the potassium fertilization levels of 0–60 kg/ha in this test. The treatment of SFK60 recorded the highest average yield in the three-year test, which was 14,744.39 kg/ha. The maize yield in different planting years showed as follows: 2020  > 2019  > 2018, which indicated that the promotion effect of straw return and potassium fertilizer on maize yield increased from year to year.Correlation analysis of photosynthesis, dry matter accumulation and yield of maizePn, Gs, Tr and Ci were significantly correlated with dry matter accumulation. Pn, Gs and Tr were positively correlated with dry matter, while Ci was negatively correlated with the dry matter (Table 4). The results showed that the increase of Pn, Gs, Tr and the decrease of Ci could significantly improve maize dry matter. Dry matter was positively correlated with maize yield, indicating that the increase of dry matter accumulation could significantly improve maize yield. The increase of Pn, Gs, Tr and dry matter accumulation, as well as the decrease of Ci, could significantly increase maize yield.Table 4 Correlation analysis of photosynthesis, dry matter accumulation and yield of maize under two straw return methods.Full size tableUnder the method of SF, the correlation coefficients of Pn, Gs, Tr, dry matter at R1 stage, dry matter at R6 stage and Ci with yield were 0.862, 0.988, 0.962, 0.948, 0.971 and −0.978; the correlation coefficients were 0.838,0.975,0.970,0.930,0.979 and −0.973 under the method of FG. The results showed that, under the method of SF, the correlation coefficients between dry matter of R1 stage, Pn, Gs, Ci with yield were higher than that under the method of FG, which indicated that SF could promote the correlation between the dry matter of R1 stage, Pn, Gs, Ci with yield. Under the method of FG, the correlation coefficients between the dry matters of R6 stage, Tr with yield were higher than that under the method of SF, which indicated that FG could promote the correlation between the dry matter of R6 stage, Tr with yield.Effects of straw return and potassium fertilizer on the profit of maize plantingGross income is an important economic index that determines the profit or benefit that a farmer can obtain. On the other hand, net return reflects the actual income of the farmer. According to the average selling price of maize (1 yuan/kg) from 2018 to 2020, the net income of maize planting of different treatments was as follows: SFK45  > SFK60  > FGK60  > FGK45  > SFK30  > FGK30  > SFK0  > FGK0  > CK (Table 5). Compared to CK. the average net profit of maize planting in the three-year test increased by 421.26, 1049.07, 2014.82, 1980.44, 313.58, 1035.34, 1587.44, 1828.69 yuan/ha between the treatments of SFK0, SFK30, SFK45, SFK60, FGK0, FGK30, FGK45 and FGK60. Straw return and potassium supply increased the net profit of maize planting. The net profit of maize planting increased significantly after SF compared to FG under the same potassium supply. The treatment of SFK45 reached the maximum profit of maize planting, which was 2014.82 yuan/ha.Table 5 Effects of straw return methods and potassium fertilization levels on the profit of maize planting.Full size table More

1.Dudley, J. P. et al. Conserv. Biol. 16, 319–329 (2002).Article 

Google Scholar 
2.Hanson, T. et al. Conserv. Biol. 23, 578–587 (2009).Article 

Google Scholar 
3.Emadi, M. H. Int. J. Environ. Sci. 68, 267–279 (2011).
Google Scholar 
4.Dehgan, A. The Snow Leopard Project and Other Adventures in Warzone Conservation (Hachette Book Group, 2019).5.Maheshwari, A. Science 367, 1203 (2020).Article 

Google Scholar 
6.Smallwood, P. Bioscience 61, 506–511 (2011).Article 

Google Scholar 
7.Udvardy, M. D. F. A Classification of the Biogeographical Provinces of the World IUCN Occasional Paper 18 (IUCN, 1975).8.Polo, M., Marsden, W. & Komroff, M. The Travels of Marco Polo (The Modern library, 1953).9.Reinig, W. F. Z. Morphol. Ökol. Tiere. 17, 68–123 (1930).Article 

Google Scholar 
10.Hassinger, J. Fieldiana Zool. 53, 1–81 (1968).
Google Scholar 
11.Hassinger, J. Fieldiana Zool. 60, 1–195 (1973).
Google Scholar 
12.Afghanistan Post-Conflict Environmental Assessment (UNEP, 2003).13.Biodiversity Profile of Afghanistan (UNEP, 2008).14.Simms, A. et al. Int. J. Environ. Sci. 68, 299–312 (2011).
Google Scholar 
15.Moheb, Z. & Bradfield, D. Cat News 61, 15–16 (2014).
Google Scholar 
16.Ostrowski, S. et al. Oryx 50, 323–328 (2016).Article 

Google Scholar 
17.Moheb, Z., Jahed, N. & Noori, H. DSG Newsletter 28, 5–12 (2016).
Google Scholar 
18.Stevens, K. et al. Oryx 45, 265–271 (2011).Article 

Google Scholar 
19.Maheshwari, A. & Niraj, S. K. Glob. Ecol. Conserv. 14, 1–6 (2018).
Google Scholar 
20.Bhattacharjee, Y. Science 328, 1620–1620 (2010).Article 

Google Scholar 
21.Hunter, A., Luk, J. Esmen, Y. Afghanistan’s mighty copper reserves remain out of reach, even for China. Metal Bulletin (24 August 2021).22.Mallapaty, S. Nature 597, 15–16 (2021).CAS 
Article 

Google Scholar  More

1.Jessup CM, Forde SE, Bohannan BJM. Microbial experimental systems in ecology. In: Desharnais RA, editor. Advances in ecological research, Vol. 37. Elsevier, USA: Academic Press; 2005. p. 273–307.2.Brockmann D, Hufnagel L, Geisel T. The scaling laws of human travel. Nature. 2006;439:462–5.CAS 
Article 

Google Scholar 
3.Chan SY, Liu SY, Seng Z, Chua SL. Biofilm matrix disrupts nematode motility and predatory behavior. ISME J. 2021;15:260–9.CAS 
Article 

Google Scholar 
4.Thutupalli S, Uppaluri S, Constable GWA, Levin SA, Stone HA, Tarnita CE, et al. Farming and public goods production in Caenorhabditis elegans populations. Proc Natl Acad Sci USA. 2017;114:2289–94.CAS 
Article 

Google Scholar 
5.Otto G. Arresting predators. Nat Rev Microbiol. 2020;18:675.PubMed 

Google Scholar 
6.Worthy SE, Haynes L, Chambers M, Bethune D, Kan E, Chung K, et al. Identification of attractive odorants released by preferred bacterial food found in the natural habitats of C. elegans. PLoS ONE. 2018;13:e0201158.Article 

Google Scholar 
7.Choi JI, Yoon K-H, Subbammal Kalichamy S, Yoon S-S, Il Lee J. A natural odor attraction between lactic acid bacteria and the nematode Caenorhabditis elegans. ISME J. 2016;10:558–67.CAS 
Article 

Google Scholar 
8.Reilly DK, Srinivasan J. Caenorhabditis elegans olfaction. Oxford Research Encyclopedia of Neuroscience: Oxford University Press; 2017.9.Beale E, Li G, Tan M-W, Rumbaugh KP. Caenorhabditis elegans senses bacterial autoinducers. Appl Environ Microbiol. 2006;72:5135–7.CAS 
Article 

Google Scholar 
10.Werner KM, Perez LJ, Ghosh R, Semmelhack MF, Bassler BL. Caenorhabditis elegans recognizes a bacterial quorum-sensing signal molecule through the AWCON neuron. J Biol Chem. 2014;289:26566–73.CAS 
Article 

Google Scholar 
11.Wei Q, Ma LZ. Biofilm matrix and its regulation in Pseudomonas aeruginosa. Int J Mol Sci. 2013;14:20983–1005.Article 

Google Scholar 
12.Tal R, Wong HC, Calhoon R, Gelfand D, Fear AL, Volman G, et al. Three cdg operons control cellular turnover of cyclic di-GMP in Acetobacter xylinum: genetic organization and occurrence of conserved domains in isoenzymes. J Bacteriol. 1998;180:4416–25.CAS 
Article 

Google Scholar 
13.Chua SL, Liu Y, Li Y, Jun Ting H, Kohli GS, Cai Z, et al. Reduced Intracellular c-di-GMP content increases expression of quorum sensing-regulated genes in Pseudomonas aeruginosa. Front. Cell. Infect. Microbiol. 2017;7:451.Article 

Google Scholar 
14.Hengge R. Principles of c-di-GMP signalling in bacteria. Nat Rev Microbiol. 2009;7:263–73.CAS 
Article 

Google Scholar 
15.Hickman JW, Tifrea DF, Harwood CS. A chemosensory system that regulates biofilm formation through modulation of cyclic diguanylate levels. Proc Natl Acad Sci USA. 2005;102:14422–7.CAS 
Article 

Google Scholar 
16.Smith EE, Buckley DG, Wu Z, Saenphimmachak C, Hoffman LR, D’Argenio DA, et al. Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc Natl Acad Sci USA. 2006;103:8487–92.CAS 
Article 

Google Scholar 
17.Chua SL, Ding Y, Liu Y, Cai Z, Zhou J, Swarup S, et al. Reactive oxygen species drive evolution of pro-biofilm variants in pathogens by modulating cyclic-di-GMP levels. Open Biol. 2016;6:160162.Article 

Google Scholar 
18.Seviour T, Hansen SH, Yang L, Yau YH, Wang VB, Stenvang MR, et al. Functional amyloids keep quorum-sensing molecules in check. J Biol Chem. 2015;290:6457–69.CAS 
Article 

Google Scholar 
19.Ma L, Conover M, Lu H, Parsek MR, Bayles K, Wozniak DJ. Assembly and development of the Pseudomonas aeruginosa biofilm matrix. PLoS Pathog. 2009;5:e1000354.Article 

Google Scholar 
20.Whitehead NA, Barnard AML, Slater H, Simpson NJL, Salmond GPC. Quorum-sensing in Gram-negative bacteria. FEMS Microbiol Rev. 2001;25:365–404.CAS 
Article 

Google Scholar 
21.Zhang Y, Chou JH, Bradley J, Bargmann CI, Zinn K. The Caenorhabditis elegans seven-transmembrane protein ODR-10 functions as an odorant receptor in mammalian cells. Proc Natl Acad Sci USA. 1997;94:12162–7.CAS 
Article 

Google Scholar 
22.Sengupta P, Chou JH, Bargmann CI. odr-10 encodes a seven transmembrane domain olfactory receptor required for responses to the odorant diacetyl. Cell. 1996;84:899–909.CAS 
Article 

Google Scholar 
23.Cezairliyan B, Vinayavekhin N, Grenfell-Lee D, Yuen GJ, Saghatelian A, Ausubel FM. Identification of Pseudomonas aeruginosa phenazines that kill Caenorhabditis elegans. PLoS Pathog. 2013;9:e1003101.CAS 
Article 

Google Scholar 
24.Gallagher LA, Manoil C. Pseudomonas aeruginosa PAO1 kills Caenorhabditis elegans by cyanide poisoning. J Bacteriol. 2001;183:6207–14.CAS 
Article 

Google Scholar 
25.Lewenza S, Charron-Mazenod L, Giroux L, Zamponi AD. Feeding behaviour of Caenorhabditis elegans is an indicator of Pseudomonas aeruginosa PAO1 virulence. PeerJ. 2014;2:e521–e.Article 

Google Scholar 
26.Tan MW, Mahajan-Miklos S, Ausubel FM. Killing of Caenorhabditis elegans by Pseudomonas aeruginosa used to model mammalian bacterial pathogenesis. Proc Natl Acad Sci USA. 1999;96:715–20.CAS 
Article 

Google Scholar 
27.Tehseen M, Liao C, Dacres H, Dumancic M, Trowell S, Anderson A. Oligomerisation of C. elegans olfactory receptors, ODR-10 and STR-112, in yeast. PLoS ONE. 2014;9:e108680.Article 

Google Scholar 
28.Sooknanan J, Bhatt B, Comissiong DMG. A modified predator-prey model for the interaction of police and gangs. R Soc Open Sci. 2016;3:160083.CAS 
Article 

Google Scholar 
29.Arciola CR, Campoccia D, Montanaro L. Implant infections: adhesion, biofilm formation and immune evasion. Nat Rev Microbiol. 2018;16:397–409.CAS 
Article 

Google Scholar 
30.Deng Y, Liu SY, Chua SL, Khoo BL. The effects of biofilms on tumor progression in a 3D cancer-biofilm microfluidic model. Biosens Bioelectron. 2021;180:113113.CAS 
Article 

Google Scholar 
31.Kwok T-Y, Ma Y, Chua SL. Biofilm dispersal induced by mechanical cutting leads to heightened foodborne pathogen dissemination. Food Microbiol. 2022;102:103914.Article 

Google Scholar 
32.Yu M, Chua SL. Demolishing the great wall of biofilms in gram-negative bacteria: to disrupt or disperse? Medicinal Res Rev. 2020;40:1103–16.CAS 
Article 

Google Scholar 
33.Chua SL, Liu Y, Yam JKH, Chen Y, Vejborg RM, Tan BGC, et al. Dispersed cells represent a distinct stage in the transition from bacterial biofilm to planktonic lifestyles. Nat Commun. 2014;5:4462.CAS 
Article 

Google Scholar 
34.Liu SY, Leung MM-L, Fang JK-H, Chua SL. Engineering a microbial ‘trap and release’ mechanism for microplastics removal. Chem Eng J. 2021;404:127079.CAS 
Article 

Google Scholar  More