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    Obligate mutualistic cooperation limits evolvability

    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

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    Potentials of straw return and potassium supply on maize (Zea mays L.) photosynthesis, dry matter accumulation and yield

    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

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    Biodiversity conservation in Afghanistan under the returned Taliban

    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

  • in

    Biofilm matrix cloaks bacterial quorum sensing chemoattractants from predator detection

    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

  • in

    Implications of H2/CO2 disequilibrium for life on Enceladus

    1.Cable, M. L. et al. Planet. Sci. J. 2, 132 (2021).Article 

    Google Scholar 
    2.Waite, J. H. et al. Science 356, 155–159 (2017).ADS 
    Article 

    Google Scholar 
    3.Hoehler, T. M., Alperin, M. J., Albert, D. B. & Martens, C. S. FEMS Microbiol. Ecol. 38, 33–41 (2001).Article 

    Google Scholar 
    4.Seewald, J. S. Science 356, 132–133 (2017).ADS 
    Article 

    Google Scholar 
    5.Amend, J. P., Aronson, H. S., Macalady, J. & Larowe, D. E. Environ. Microbiol. 22, 1971–1976 (2020).Article 

    Google Scholar 
    6.Schönheit, P., Moll, J. & Thauer, R. K. Arch. Microbiol. 127, 59–65 (1980).Article 

    Google Scholar 
    7.Hoehler, T. M., Albert, D. B., Alperin, M. J. & Martens, C. S. Limnol. Oceanogr. 44, 662–667 (1999).ADS 
    Article 

    Google Scholar 
    8.Wang, M. et al. Front. Microbiol. 7, 850 (2016).
    Google Scholar 
    9.Conrad, R., Schink, B. & Phelps, T. J. FEMS Microbiol. Ecol. 2, 353–360 (1986).Article 

    Google Scholar 
    10.Jabłoński, S., Rodowicz, P. & Łukaszewicz, M. Int. J. Syst. Evol. Biol. 65, 1360–1368 (2015).Article 

    Google Scholar  More

  • in

    Experience-dependent learning of behavioral laterality in the scale-eating cichlid Perissodus microlepis occurs during the early developmental stage

    1.Rogers, L. J. & Andrew, R. J. Comparative Vertebrate Lateralization (Cambridge University Press, 2002).
    Google Scholar 
    2.Bisazza, A. & Brown, C. Lateralization of cognitive functions in fish. In Fish Cognition and Behavior 2nd edn (eds Brown, C. et al.) 298–324 (Wiley-Blackwell, 2011).
    Google Scholar 
    3.Rogers, L. J., Vallortigara, G. & Andrew, R. J. Divided Brains: The Biology and Behaviour of Brain Asymmetries (Cambridge University Press, 2013).
    Google Scholar 
    4.Versace, E. & Vallortigara, G. Forelimb preferences in human beings and other species: multiple models for testing hypotheses on lateralization. Front. Psychol. 6, 233 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    5.Vallortigara, G. & Versace, E. Laterality at the neural, cognitive, and behavioral levels. In APA Handbook of Comparative Psychology: Vol. 1. Basic Concepts, Methods, Neural Substrate, and Behavior (eds. Call, J., Burghardt, G.M., Pepperberg, I.M., Snowdon, C.T. & Zentall, T.) 557–577 (2017).6.Frasnellis, E., Vallortigara, G. & Rogers, L. J. Left-right asymmetries of behaviour and nervous system in invertebrates. Neurosci. Biobehav. Rev. 36, 1273–1291 (2012).
    Google Scholar 
    7.Byrne, R. A., Kuba, M. J. & Meisel, D. V. Lateralized eye use in Octopus vulgaris shows antisymmetrical distribution. Anim. Behav. 68, 1107–1114 (2004).
    Google Scholar 
    8.Byrne, R. A., Kuba, M. J., Meisel, D. V., Griebel, U. & Mather, J. A. Octopus arm choice is strongly influenced by eye use. Behav. Brain Res. 172, 195–201 (2006).PubMed 

    Google Scholar 
    9.Lucky, N. S., Ihara, R., Yamaoka, K. & Hori, M. Behavioral laterality and morphological asymmetry in the Cuttlefish, Sepia lycidas. Zoolog. Sci. 29, 286–292 (2012).PubMed 

    Google Scholar 
    10.Stancher, G., Sovrano, V. A. & Vallortigara, G. Chapter 2-Motor asymmetries in fishes, amphibians, and reptiles. In Progress in Brain Research (eds Forrester, G. S. et al.) 33–56 (Elsevier, 2018).
    Google Scholar 
    11.Miletto Petrazzini, M. E., Sovrano, V. A., Vallortigara, G. & Messina, A. Brain and behavioral asymmetry: A lesson from fish. Front. Neuroanat. 14, 11 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    12.Roy, E. A., Bryden, P. & Cavill, S. Hand differences in pegboard performance through development. Brain Cogn. 53, 315–317 (2003).PubMed 

    Google Scholar 
    13.Michel, G. F., Tyler, A. N., Ferre, C. & Sheu, C. F. The manifestation of infant hand-use preferences when reaching for objects during the seven- to thirteen-month age period. Dev. Psychobiol. 48, 436–443 (2006).PubMed 

    Google Scholar 
    14.Porac, C. & Searleman, A. The effects of hand preference side and hand preference switch history on measures of psychological and physical well-being and cognitive performance in a sample of older adult right-and left-handers. Neuropsychologia 40, 2074–2083 (2002).PubMed 

    Google Scholar 
    15.Rogers, L. J. Light experience and asymmetry of brain function in chickens. Nature 297, 223–225 (1982).ADS 
    CAS 
    PubMed 

    Google Scholar 
    16.Rogers, L. J. Development and function of lateralization in the avian brain. Brain Res. Bull. 76, 235–244 (2008).ADS 
    PubMed 

    Google Scholar 
    17.Rogers, L. J. Asymmetry of motor behavior and sensory perception: Which comes first?. Symmetry 12, 690 (2020).
    Google Scholar 
    18.Tang, A. C. & Verstynen, T. Early life environment modulates ‘handedness’ in rats. Behav. Brain Res. 131, 1–7 (2002).PubMed 

    Google Scholar 
    19.Bisazza, A., Cantalupo, C. & Vallortigara, G. Lateral asymmetries during escape behavior in a species of teleost fish (Jenynsia lineata). Physiol. Behav. 61, 31–35 (1997).CAS 
    PubMed 

    Google Scholar 
    20.Bisazza, A., Dadda, M. & Cantalupo, C. Further evidence for mirror-reversed laterality in lines of fish selected for leftward or rightward turning when facing a predator model. Behav. Brain Res. 156, 165–171 (2005).PubMed 

    Google Scholar 
    21.Izvekov, E. I. & Nepomnyashchikh, V. A. Laterality of the initial stage of escape response in roach (Rutilus rutilus) upon impact of alternating electric current. Biol. Bull. 35, 30–36 (2008).
    Google Scholar 
    22.Hata, H. & Hori, M. Inheritance patterns of morphological laterality in mouth opening of zebrafish, Danio rerio. Laterality 17, 741–754 (2012).PubMed 

    Google Scholar 
    23.Lee, H. J., Kusche, H. & Meyer, A. Handed foraging behavior in scale-eating Cichlid Fish: Its potential role in shaping morphological asymmetry. PLoS ONE 7, e44670 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Yasugi, M. & Hori, M. Lateralized behavior in the attacks of largemouth bass on Rhinogobius gobies corresponding to their morphological antisymmetry. J. Exp. Biol. 215, 2390–2398 (2012).PubMed 

    Google Scholar 
    25.Matsui, S., Takeuchi, Y. & Hori, M. Relation between morphological antisymmetry and behavioral laterality in a Poeciliid Fish. Zoolog. Sci. 30, 613–618 (2013).PubMed 

    Google Scholar 
    26.Takeuchi, Y. et al. Specialized movement and laterality of fin-biting behaviour in Genyochromis mento in Lake Malawi. J. Exp. Biol. 222, 191676 (2019).
    Google Scholar 
    27.Sorvano, V. A., Rainoldi, C., Bisazza, A. & Vallortigara, G. Roots of brain specializations: Preferential left-eye use during mirror-image inspection in six species of teleost fish. Behav. Brain Res. 106, 175–180 (1999).CAS 
    PubMed 

    Google Scholar 
    28.Sovrano, V. A., Bisazza, A. & Vallortigara, G. Lateralization of response to social stimuli in fishes: A comparison between different methods and species. Physiol. Behav. 74, 237–244 (2001).CAS 
    PubMed 

    Google Scholar 
    29.Raffini, F. & Meyer, A. A comprehensive overview of the developmental basis and adaptive significance of a textbook polymorphism: head asymmetry in the cichlid fish Perissodus microlepis. Hydrobiologia 832, 65–84 (2019).
    Google Scholar 
    30.Berlinghieri, F., Panizzon, P., Penry-Williams, I. L. & Brown, C. Laterality and fish welfare-a review. Appl. Anim. Behav. Sci. 236, 105239 (2021).
    Google Scholar 
    31.Koblmüller, S., Egger, B., Sturmbauer, C. & Sefc, K. M. Evolutionary history of Lake Tanganyika’s scale-eating cichlid fishes. Mol. Phylogenet. Evol. 44, 1295–1305 (2007).PubMed 

    Google Scholar 
    32.Takeuchi, Y., Ochi, H., Kohda, M., Sinyinza, D. & Hori, M. A 20-year census of a rocky littoral fish community in Lake Tanganyika. Ecol. Freshw. Fish 19, 239–248 (2010).
    Google Scholar 
    33.Poll, M. Poissons Cichlidae. Resultats scientifiques, Exploration hydrobiologique du Lac Tanganyika. Inst. R. Sci. Nat. Belg. 3, 1–619 (1956).
    Google Scholar 
    34.Liem, K. & Stewart, D. Evolution of scale-eating cichlid fishes of Lake Tanganyika: a generic revision with a description of a new species. Bull. Mus. Comp. Zool. 147, 319–350 (1976).
    Google Scholar 
    35.Hori, M. Frequency-dependent natural-selection in the handedness of scale-eating cichlid fish. Science 260, 216–219 (1993).ADS 
    CAS 
    PubMed 

    Google Scholar 
    36.Takeuchi, Y., Hori, M. & Oda, Y. Lateralized kinematics of predation behavior in a Lake Tanganyika scale-eating cichlid fish. PLoS ONE 7, e29272 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Hori, M., Ochi, H. & Kohda, M. Inheritance pattern of lateral dimorphism in two cichlids (a scale eater, Perissodus microlepis, and an herbivore, Neolamprologus moorii) in Lake Tanganyika. Zoolog. Sci. 24, 486–492 (2007).PubMed 

    Google Scholar 
    38.Raffini, F., Fruciano, C., Franchini, P. & Meyer, A. Towards understanding the genetic basis of mouth asymmetry in the scale-eating cichlid Perissodus microlepis. Mol. Ecol. 26, 77–91 (2017).CAS 
    PubMed 

    Google Scholar 
    39.Takeuchi, Y., Hori, M., Tada, S. & Oda, Y. Acquisition of lateralized predation behavior associated with development of mouth asymmetry in a Lake Tanganyika scale-eating cichlid fish. PLoS ONE 11, e0147476 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    40.Takeuchi, Y. & Oda, Y. Lateralized scale-eating behaviour of cichlid is acquired by learning to use the naturally stronger side. Sci. Rep. 7, 8984 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Brainard, M. S. & Doupe, A. J. What songbirds teach us about learning. Nature 417, 351–358 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    42.Nelson, D. A., Marler, P. & Palleroni, A. A comparative approach to vocal learning: Intraspecific variation in the learning process. Anim. Behav. 50, 83–97 (1995).
    Google Scholar 
    43.Chaiken, M., Böhner, J. & Marler, P. Song acquisition in European starlings, Sturnus vulgaris: a comparison of the songs of live-tutored, tape-tutored, untutored, and wild-caught males. Anim. Behav. 46, 1079–1090 (1993).
    Google Scholar 
    44.Todt, D. & Böhner, J. Former experience can modify social selectivity during song learning in the nightingale (Luscinia megarhynchos). Ethology 97, 169–176 (1994).
    Google Scholar 
    45.Schneirla, T.C. The concept of development in comparative psychology. Concept Dev. 78–108 (1957).46.Alcock, J. Animal Behavior: An Evolutionary Approach (Sinauer Associates, 2001).
    Google Scholar 
    47.Nshombo, M., Yanagisawa, Y. & Nagoshi, M. Scale-eating in Perissodus microlepis (Cichlidae) and change of its food-habits with growth. Jpn. J. Ichthyol. 32, 66–73 (1985).
    Google Scholar 
    48.Zar, J. H. Biostatistical Analysis (Pearson Education, 1999).
    Google Scholar 
    49.Morishita, H. & Hensch, T. K. Critical period revisited: impact on vision. Curr. Opin. Neurobiol. 18, 101–107 (2008).CAS 
    PubMed 

    Google Scholar 
    50.Hess, E. H. Imprinting: Early Experience and the Developmental Psychobiology of Attachment (Van Norstrand, 1973).
    Google Scholar 
    51.Scott, J. P. Critical periods (Dowden, Hutchinson & Ross, 1978).
    Google Scholar 
    52.Kroodsma, D. Ontogeny of bird song. In Behavioral Development, 518–532 (Cambridge University Press, 1981).53.Rosa-Salva, O. et al. Sensitive periods for social development: Interactions between predisposed and learned mechanisms. Cognition 213, 104552 (2021).PubMed 

    Google Scholar 
    54.Vallortigara, G. Born Knowing: Imprinting and the Origins of Knowledge (MIT Press, 2021).
    Google Scholar 
    55.Hensch, T. K. Critical period plasticity in local cortical circuits. Nat. Rev. Neurosci. 6, 877–888 (2005).CAS 
    PubMed 

    Google Scholar 
    56.Penfield, W. & Roberts, L. Speech and Brain Mechanisms (Princeton University Press, 2014).
    Google Scholar 
    57.Rauschecker, J. P. & Singer, W. The effects of early visual experience on the cat’s visual cortex and their possible explanation by Hebb synapses. J. Physiol. 310, 215–239 (1981).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Pasternak, T. & Leinen, L. Pattern and motion vision in cats with selective loss of cortical directional selectivity. J. Neurosci. 6, 938–945 (1986).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Rauschecker, J. P. & Schrader, W. Effects of monocular strobe rearing on kitten striate cortex. Exp. Brain Res. 68, 525–532 (1987).CAS 
    PubMed 

    Google Scholar 
    60.Sengpiel, F., Stawinski, P. & Bonhoeffer, T. Influence of experience on orientation maps in cat visual cortex. Nat. Neurosci. 2, 727–732 (1999).CAS 
    PubMed 

    Google Scholar 
    61.Marler, P. R. & Slabbekoorn, H. Nature’s Music: The Science of Birdsong (Elsevier, 2004).
    Google Scholar 
    62.Zann, R. Vocal learning in wild and domesticated zebra finches: signature cues for kin recognition or epiphenomena? In Social Influences on Vocal Development (eds Snowdon, C. T. & Hausberger, M.) 85–97 (Cambridge University Press, 1997).
    Google Scholar 
    63.Curtiss, S. The Case of Genie, A Modern Day ‘Wild Child’ (Academic Press, 1977).
    Google Scholar 
    64.Pinker, S. The Language Instinct: The New Science of Language and Mind Vol. 7529 (Penguin, 1995).
    Google Scholar 
    65.Lenneberg, E. H. The biological foundations of language. Hosp. Pract. 2, 59–67 (1967).
    Google Scholar 
    66.Patkowski, M. S. The sensitive period for the acquisition of syntax in a second language 1. Lang Learn. 30, 449–468 (1980).
    Google Scholar 
    67.Johnson, J. S. & Newport, E. L. Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cogn. Psychol. 21, 60–99 (1989).CAS 
    PubMed 

    Google Scholar 
    68.Carroll, S. B., Greinier, J. K. & Weatherbee, S. D. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design (Blackwell Science, 2001).
    Google Scholar 
    69.Evidence from genes to behavior. Wullimann, MF. & Mueller T. Teleostean and mammalian forebrains contrasted. J. Comp. Neurol. 475, 143–162 (2004).
    Google Scholar 
    70.Salas, C. et al. Neuropsychology of learning and memory in teleost fish. Zebrafish 3, 157–171 (2006).PubMed 

    Google Scholar 
    71.Mills, E. L., Widzowski, D. V. & Jones, S. R. Food conditioning and prey selection by young yellow perch (Perca flavescens). Can. J. Fish. Aquat. Sci. 44, 549–555 (1987).
    Google Scholar 
    72.Warburton, K. Learning of foraging skills by fish. Fish Fish. 4, 203–215 (2003).
    Google Scholar 
    73.Lee, H. J. et al. Lateralized feeding behavior is associated with asymmetrical neuroanatomy and lateralized gene expressions in the brain in scale-eating cichlid fish. Genome Biol. Evol. 9, 3122–3136 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Takeuchi, Y., Ishikawa, A., Oda, Y. & Kitano, J. Lateralized expression of left-right axis formation genes is shared by adult brains of lefty and righty scale-eating cichlids. Comp. Biochem. Physiol. D 28, 99–106 (2018).CAS 

    Google Scholar 
    75.Raffini, F., Fruciano, C. & Meyer, A. Morphological and genetic correlates in the left–right asymmetric scale-eating cichlid fish of Lake Tanganyika. Biol. J. Linn. Soc. 124, 67–84 (2018).
    Google Scholar 
    76.Brawand, D. et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature 513, 375–381 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Cartner, S. C. et al. The Zebrafish in Biomedical Research: Biology, Husbandry, Diseases, and Research Applications (Academic Press, 2020).
    Google Scholar 
    78.Takahashi, R., Moriwaki, T. & Hori, M. Foraging behaviour and functional morphology of two scale-eating cichlids from Lake Tanganyika. J. Fish Biol. 70, 1458–1469 (2007).
    Google Scholar 
    79.Sazima, I. Scale-eating in characoids and other fishes. Environ. Biol. Fish. 9, 87–101 (1983).
    Google Scholar 
    80.Webb, P. W. Acceleration performance of rainbow trout Salmo gairdneri and green sunfish Lepomis cyanellus. J. Exp. Biol. 63, 451–465 (1975).
    Google Scholar 
    81.Wöhl, S. & Schuster, S. The predictive start of hunting archer fish: a flexible and precise motor pattern performed with the kinematics of an escape C-start. J. Exp. Biol. 210, 311–324 (2007).PubMed 

    Google Scholar 
    82.Vallortigara, G. & Rogers, L. J. Survival with an asymmetrical brain: advantages and disadvantages of cerebral lateralization. Behav. Brain Sci. 28, 575–589 (2005) (discussion 589-633).PubMed 

    Google Scholar  More

  • in

    Desertification risk fuels spatial polarization in ‘affected’ and ‘unaffected’ landscapes in Italy

    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

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    Recreating the lost sounds of spring

    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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