Ecology

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

1.Fernandez, R. J. Do humans create deserts?. Trends Ecol. Evol. 17, 6–7 (2002).
Google Scholar 
2.Geist, H. J. & Lambin, E. F. Dynamic causal patterns of desertification. Bioscience 54(9), 817–829 (2004).
Google Scholar 
3.Imeson, A. Desertification, Land Degradation and Sustainability (Routledge, 2012).
Google Scholar 
4.Romm, J. Desertification: The next dust bowl. Nature 478, 450–451 (2011).ADS 
CAS 
PubMed 

Google Scholar 
5.Portnov, B. A. & Safriel, U. N. Combating desertification in the Negev: Dryland agriculture vs. dryland urbanization. J. Arid Environ. 56, 659–680 (2004).ADS 

Google Scholar 
6.Salvati, L., Bajocco, S., Ceccarelli, T., Zitti, M. & Perini, L. Towards a process-based evaluation of land vulnerability to soil degradation in Italy. Ecol. Ind. 11(5), 1216–1227 (2011).CAS 

Google Scholar 
7.Santini, M., Caccamo, G., Laurenti, A., Noce, S. & Valentini, R. A multi-model GIS framework for desertification risk assessment. Appl. Geogr. 30(3), 394–415 (2010).
Google Scholar 
8.Bajocco, S., Salvati, L. & Ricotta, C. Land degradation vs. Fire: A spiral process?. Prog. Phys. Geogr. 35(1), 3–18 (2011).
Google Scholar 
9.Incerti, G., Feoli, E., Salvati, L., Brunetti, A. & Giovacchini, A. Analysis of bioclimatic time series and their neural network-based classification to characterise drought risk patterns in South Italy. Int. J. Biometeorol. 51(4), 253–263 (2007).ADS 
CAS 
PubMed 

Google Scholar 
10.Salvati, L., Perini, L., Sabbi, A. & Bajocco, S. Climate Aridity and land use changes: A regional-scale analysis. Geogr. Res. 50(2), 193–203 (2012).
Google Scholar 
11.Coluzzi, R. et al. Investigating climate variability and long-term vegetation activity across heterogeneous Basilicata agroecosystems. Geomat. Nat. Haz. Risk 10(1), 168–180 (2019).
Google Scholar 
12.Imbrenda, V. et al. Analysis of landscape evolution in a vulnerable coastal area under natural and human pressure. Geomat. Nat. Haz. Risk 9(1), 1249–1279 (2018).
Google Scholar 
13.Imbrenda, V. et al. Land degradation and metropolitan expansion in a peri-urban environment. Geomat. Nat. Haz. Risk 12(1), 1797–1818 (2021).
Google Scholar 
14.Kairis, O., Karavitis, C., Kounalaki, A., Salvati, L. & Kosmas, C. The effect of land management practices on soil erosion and land desertification in an olive grove. Soil Use Manag. 29(4), 597–606 (2013).
Google Scholar 
15.Kairis, O., Karavitis, C., Salvati, L., Kounalaki, A. & Kosmas, K. Exploring the impact of overgrazing on soil erosion and land degradation in a dry Mediterranean agro-forest landscape (Crete, Greece). Arid Land Res. Manag. 29(3), 360–374 (2015).
Google Scholar 
16.Karamesouti, M. et al. Land-use and land degradation processes affecting soil resources: Evidence from a traditional Mediterranean cropland (Greece). CATENA 132, 45–55 (2015).
Google Scholar 
17.Kosmas, C. et al. Land degradation and long-term changes in agro-pastoral systems: An empirical analysis of ecological resilience in Asteroussia-Crete (Greece). CATENA 147, 196–204 (2016).
Google Scholar 
18.Jongman, R. H. G. Homogenisation and fragmentation of the European landscape: Ecological consequences and solutions. Landsc. Urban Plan. 58(2), 211–221 (2002).
Google Scholar 
19.Lavado Contador, J. F., Schnabel, S., Gomez Gutierrez, A. & Pulido Fernandez, M. Mapping sensitivity to land degradation in Extremadura, SW Spain. Land Degrad. Dev. 20(2), 129–144 (2009).
Google Scholar 
20.Otto, R., Krusi, B. O. & Kienast, F. Degradation of an arid coastal landscape in relation to land use changes in southern Tenerife (Canary Islands). J. Arid Environ. 70, 527–539 (2007).ADS 

Google Scholar 
21.Braje, T. J., Leppard, T. P., Fitzpatrick, S. M. & Erlandson, J. M. Archaeology, historical ecology and anthropogenic island ecosystems. Environ. Conserv. 44(3), 286–297 (2017).
Google Scholar 
22.Rick, T., Ontiveros, M. Á. C., Jerardino, A., Mariotti, A., Méndez, C. & Williams, A. N. Human-environmental interactions in Mediterranean climate regions from the Pleistocene to the Anthropocene. Anthropocene, 100253 (2020).23.Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).
Google Scholar 
24.Antrop, M. Landscape change and the urbanization process in Europe. Landsc. Urban Plan. 67(1), 9–26 (2004).
Google Scholar 
25.Bakra, N., Weindorf, D. C., Bahnassy, M. H. & El-Badawi, M. M. Multi-temporal assessment of land sensitivity to desertification in a fragile agro-ecosystem: Environmental indicators. Ecol. Ind. 15(1), 271–280 (2012).
Google Scholar 
26.Pacheco, F. A. L., Fernandes, L. F. S., Junior, R. F. V., Valera, C. A. & Pissarra, T. C. T. Land degradation: Multiple environmental consequences and routes to neutrality. Curr. Opin. Environ. Sci. Health 5, 79–86 (2018).
Google Scholar 
27.Gomes, E. et al. Agricultural land fragmentation analysis in a peri-urban context: From the past into the future. Ecol. Ind. 97, 380–388 (2019).
Google Scholar 
28.Gulcin, D. & Yilmaz, K. T. The assessment of landscape fragmentation in an agricultural environment: Degradation or contribution to ecosystem services?. Fresenius Environ. Bull. 25(12), 7941–7950 (2017).
Google Scholar 
29.Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).
Google Scholar 
30.Haddad, N. M., Brudvig, L. A. & Clobert, J. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1(2), 1–9 (2015).
Google Scholar 
31.Kouba, Y., Gartzia, M., El Aich, A. & Alados, C. L. Deserts do not advance, they are created: Land degradation and desertification in semiarid environments in the Middle Atlas, Morocco. J. Arid Environ. 158, 1–8 (2018).ADS 

Google Scholar 
32.Nagendra, H., Munroe, D. K. & Southworth, J. From pattern to process: Landscape fragmentation and the analysis of land use/land cover change. Agric. Ecosyst. Environ. 101(2), 111–115 (2004).
Google Scholar 
33.Lin, Y., Han, G., Zhao, M. & Chang, S. X. Spatial vegetation patterns as early signs of desertification: A case study of a desert steppe in Inner Mongolia, China. Landsc. Ecol. 25(10), 1519–1527 (2010).
Google Scholar 
34.Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449(7159), 213–217 (2007).ADS 
PubMed 

Google Scholar 
35.Girvetz, E. H., Thorne, J. H., Berry, A. M. & Jaeger, J. A. Integration of landscape fragmentation analysis into regional planning: A statewide multi-scale case study from California, USA. Landsc. Urban Plan. 86(3), 205–218 (2008).
Google Scholar 
36.Hargis, C. D., Bissonette, J. A. & David, J. L. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landsc. Ecol. 13(3), 167–186 (1998).
Google Scholar 
37.Llausàs, A. & Nogué, J. Indicators of landscape fragmentation: The case for combining ecological indices and the perceptive approach. Ecol. Ind. 15(1), 85–91 (2012).
Google Scholar 
38.Salvati, L. & Zitti, M. The environmental “risky” region: Identifying land degradation processes through integration of socio-economic and ecological indicators in a multivariate regionalization model. Environ. Manage. 44(5), 888 (2009).ADS 
PubMed 

Google Scholar 
39.Ferrara, A. et al. Updating the MEDALUS-ESA framework for worldwide land degradation and desertification assessment. Land Degrad. Dev. 31(12), 1593–1607 (2020).
Google Scholar 
40.Delfanti, L. et al. Solar plants, environmental degradation and local socioeconomic contexts: A case study in a Mediterranean country. Environ. Impact Assess. Rev. 61, 88–93 (2016).
Google Scholar 
41.Cowie, A. L. et al. Land in balance: The scientific conceptual framework for Land Degradation Neutrality. Environ. Sci. Policy 79, 25–35 (2018).
Google Scholar 
42.Lanfredi, M. et al. A geostatistics-assisted approach to the deterministic approximation of climate data. Environ. Model. Softw. 66, 69–77 (2015).
Google Scholar 
43.Coluzzi, R. et al. Density matters? Settlement expansion and land degradation in Peri-urban and rural districts of Italy. Environ. Impact Assess. Rev. 92, 106703 (2022).
Google Scholar 
44.Xie, H., Zhang, Y., Wu, Z. & Lv, T. A bibliometric analysis on land degradation: Current status, development, and future directions. Land 9(1), 28 (2020).
Google Scholar 
45.Ferrara, A., Salvati, L., Sateriano, A. & Nolè, A. Performance evaluation and costs assessment of a key indicator system to monitor desertification vulnerability. Ecol. Ind. 23, 123–129 (2012).
Google Scholar 
46.Salvati, L. From simplicity to complexity: The changing geography of land vulnerability to degradation in Italy. Geogr. Res. 51(3), 318–328 (2013).
Google Scholar 
47.Salvati, L. et al. Italy’s renewable water resources as estimated on the basis of the monthly water balance. Irrig. Drain. J. Int. Commiss. Irrig. Drain. 57(5), 507–515 (2008).
Google Scholar 
48.Salvati, L. et al. Assessing the effectiveness of sustainable land management policies for combating desertification: A data mining approach. J. Environ. Manage. 183, 754–762 (2016).CAS 
PubMed 

Google Scholar 
49.Recanatesi, F. et al. A fifty-year sustainability assessment of Italian agro-forest districts. Sustainability 8(1), 32 (2016).
Google Scholar 
50.Bajocco, S., De Angelis, A. & Salvati, L. A satellite-based green index as a proxy for vegetation cover quality in a Mediterranean region. Ecol. Ind. 23, 578–587 (2012).
Google Scholar 
51.Smiraglia, D. et al. The latent relationship between soil vulnerability to degradation and land fragmentation: A statistical analysis of landscape metrics in Italy, 1960–2010. Environ. Manage. 64(2), 154–165 (2019).ADS 
PubMed 

Google Scholar 
52.Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).CAS 
PubMed 

Google Scholar 
53.Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).
Google Scholar 
54.Zambon, I. et al. Land quality, sustainable development and environmental degradation in agricultural districts: A computational approach based on entropy indexes. Environ. Impact Assess. Rev. 64, 37–46 (2017).
Google Scholar 
55.Basso, B. et al. Evaluating responses to land degradation mitigation measures in Southern Italy. Int. J. Environ. Res. 6(2), 367–380 (2012).
Google Scholar 
56.Salvati, L. & Zitti, M. Land degradation in the Mediterranean Basin: Linking bio-physical and economic factors into an ecological perspective. Biota 6, 67–77 (2005).
Google Scholar 
57.Qi, Y. et al. Temporal-spatial variability of desertification in an agro-pastoral transitional zone of northern Shaanxi Province, China. CATENA 88(1), 37–45 (2012).
Google Scholar 
58.Sklenicka, P. Classification of farmland ownership fragmentation as a cause of land degradation: A review on typology, consequences, and remedies. Land Use Policy 57, 694–701 (2016).
Google Scholar 
59.Vos, W. & Meekes, H. Trends in European cultural landscape development: Perspectives for a sustainable future. Landsc. Urban Plan. 46(1), 3–14 (1999).
Google Scholar 
60.Mao, D. et al. Land degradation and restoration in the arid and semiarid zones of China: Quantified evidence and implications from satellites. Land Degrad. Dev. 29(11), 3841–3851 (2018).
Google Scholar 
61.Symeonakis, E., Calvo-Cases, A. & Arnau-Rosalen, E. Land use change and land degradation in southeastern Mediterranean Spain. Environ. Manage. 40(1), 80–94 (2007).ADS 
PubMed 

Google Scholar 
62.Ibanez, J., Martinez Valderrama, J. & Puigdefabregas, J. Assessing desertification risk using system stability condition analysis. Ecol. Model. 213, 180–190 (2008).
Google Scholar 
63.Hill, J., Stellmes, M., Udelhoven, T., Röder, A. & Sommer, S. Mediterranean desertification and land degradation: Mapping related land use change syndromes based on satellite observations. Global Planet. Change 64(3), 146–157 (2008).ADS 

Google Scholar 
64.Sommer, S. et al. Application of indicator systems for monitoring and assessment of desertification from national to global scales. Land Degrad. Dev. 22(2), 184–197 (2011).
Google Scholar 
65.Vogt, J. V. et al. Monitoring and Assessment of Land Degradation and Desertification: Towards new conceptual and integrated approaches. Land Degrad. Dev. 22(2), 150–165 (2011).
Google Scholar 
66.Scarascia, M. V., Battista, F. D. & Salvati, L. Water resources in Italy: Availability and agricultural uses. Irrig. Drain. J. Int. Commiss. Irrig. Drain. 55(2), 115–127 (2006).
Google Scholar 
67.Wang, H., Yuan, H., Xu, X. & Liu, S. Landscape structure of desertification grassland in source region of Yellow River. J. Appl. Ecol. 17(9), 1665–1670 (2006).
Google Scholar 
68.Wang, J. et al. Spatio-temporal pattern of land degradation from 1990 to 2015 in Mongolia. Environ. Dev. 34, 100497 (2020).
Google Scholar 
69.Briassoulis, H. Governing desertification in Mediterranean Europe: The challenge of environmental policy integration in multi-level governance contexts. Land Degrad. Dev. 22(3), 313–325 (2011).
Google Scholar 
70.Juntti, M. & Wilson, G. A. Conceptualising desertification in Southern Europe: Stakeholder interpretations and multiple policy agendas. Eur. Environ. 15, 228–249 (2005).
Google Scholar 
71.Gisladottir, G. & Stocking, M. Land degradation control and its global environmental benefits. Land Degrad. Dev. 16, 99–112 (2005).
Google Scholar  More

NATURE PODCAST
14 January 2022

Recreating the lost sounds of spring

How citizen science is helping us hear lost soundscapes.

Geoff Marsh

Geoff Marsh

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The researcher resurrecting our declining soundscapes.

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As our environments change, so too do the sounds they make — and this change in soundscape can effect us in a whole host of ways, from our wellbeing to the way we think about conservation. In this Podcast Extra we hear from one researcher, Simon Butler, who is combining citizen science data with technology to recreate soundscapes lost to the past. Butler hopes to better understand how soundscapes change in response to changes in the environment, and use this to look forward to the soundscapes of the future.Nature Communications: Bird population declines and species turnover are changing the acoustic properties of spring soundscapesNever miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed

doi: https://doi.org/10.1038/d41586-022-00023-8

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