More stories

  • 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

    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

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    Subscribe
    Subscribe

    iTunes
    Google Podcast
    acast
    RSS

    The researcher resurrecting our declining soundscapes.

    Your browser does not support the audio element.

    Download MP3

    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

    Related Articles

    Using sound to explore events of the Universe

    Pioneers of sound

    Healing sound: the use of ultrasound in drug delivery and other therapeutic applications

    Subjects

    Ecology

    Conservation biology

    Latest on:

    Ecology

    Wind power versus wildlife: root mitigation in evidence
    Correspondence 11 JAN 22

    Two million species catalogued by 500 experts
    Correspondence 11 JAN 22

    EU Nature Restoration Law needs ambitious and binding targets
    Correspondence 11 JAN 22

    Jobs

    Postdoctoral Research Assistant

    Cancer Research UK Beatson Institute
    glasgow, United Kingdom

    Postdoctoral Training Fellow in Data Science (ID:53)

    Institute of Cancer Research (ICR)
    London, United Kingdom

    Post Doctoral Research Fellow in Bioinformatics (ID:54)

    Institute of Cancer Research (ICR)
    London, United Kingdom

    Postdoctoral Training Fellow – Systems and Precision Medicine team (ID:87)

    Institute of Cancer Research (ICR)
    London, United Kingdom More

  • in

    Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

    Comparison of spectral water index methodsFigure 3 shows the results of different spectral water index methods. Through overlay analysis with the original image and detailed visual analysis, it was found that AWEIsh had the best extraction performance and could accurately identify the boundary of the water body. NDWI, MNDWI, and EWI had different degrees of leakage extraction; NDWI and EWI had an evident leakage extraction in the northwest corner of the image, and the water leakage extraction of MNDWI was mainly concentrated in the middle of the image. There was a lot of water body misidentified in WI, especially in the southeast corner of the image.Figure 3Results of water extraction from different spectral water index methods. (a) The original image. The threshold values and extracted water bodies from (b) NDWI, (c) MNDWI, (d) EWI, and (f) AWEIsh methods determined by the OTSU algorithm. (e) The extraction result of WI.Full size imageBased on the visual interpretation of the water boundary, 100 test samples were selected and the confusion matrix32 was calculated to obtain the extraction accuracy of the water body from three aspects: commission error, omission error, and overall accuracy (Table 1). As seen from the table, the overall accuracy of AWEIsh was the highest, attaining a value of 99.14%, with extremely low commission and omission errors. WI had the lowest overall accuracy and the highest commission error, and could not distinguish water bodies and low reflectivity features effectively. The overall accuracies of NDWI, MNDWI, and EWI were similar. Comparing the results of the visual interpretation and quantitative analysis, the rapid extraction model of surface water based on the Google Earth Engine utilizing AWEIsh index was used for assessing the spatiotemporal changes of water bodies.Table 1 Accuracy comparison of different spectral water index methods.Full size tableTime series analysis of typical reservoir areaTo understand the inter-annual variation trend and intra-annual variation of the reservoir area in the dry zone of Sri Lanka, time series analysis was conducted with the Maduru Oya Reservoir as the case study area. The Maduru Oya Reservoir is the second largest reservoir in Sri Lanka, located in the east-central region, which is the main water source for irrigation and drinking, and has a high incidence of chronic kidney disease of unknown aetiology (CKDu). Figure 4 shows the inter-annual and intra-annual variations of Maduru Oya Reservoir area.Figure 4Observed area change in the Maduru Oya Reservoir. (a) Inter-annual variation of the Maduru Oya Reservoir area; (b) Intra-annual variation of the Maduru Oya Reservoir area in 2017.Full size imageFigure 4 shows that the inter-annual fluctuation of Maduru Oya Reservoir area is slight, while the intra-annual fluctuation is significant. From 1988 to 2018, the reservoir area showed an overall increasing trend with slight float; the smallest area was recorded in 1992 (27.43 km2) and the largest area in 2013 (42.97 km2) (Fig. 4a). The rainy season in the dry zone of Sri Lanka occurs from October to February, and the dry season occurs from March to September. In 2017, the maximum area of the Maduru Oya Reservoir was noted in February, and the minimum area was noted in September. The area in February was 2.24 times bigger than that of September, with a difference of 31.58 km2. The maximum area of reservoirs or lakes generally occurs at the end of the wet season (February), and the minimum area occurs at the end of the dry season (September)2, which is consistent with the occurrence of maximum and minimum area in the Maduru Oya Reservoir in 2017(Fig. 4b). The area of the reservoir increased significantly in May during the dry season. According to meteorological data33, there were persistent strong winds and torrential rains in Sri Lanka in May 2017, resulting in an abnormal increase in the reservoir area. Generally, the period in which the area increased was from October to February (rainy season), while March to September (dry season) was the period in which the area decreased regardless of the influence of abnormal weather factors. The intra-annual fluctuation of the reservoir was severe, and there was a risk of drought and flooding at the same time. This observation implied that the seasonal regulation of water resources must be focussed in the future.Analysis of spatiotemporal change of inland lakes and reservoirsTo systematically analyze the spatiotemporal variation characteristics of inland water in Sri Lanka in recent years, and considering the cloud cover of Landsat-5/8 images, 1995, 2005 and 2015 were selected as the study year with an interval of 10 years. The distribution information of surface water in three stages was obtained by running the rapid extraction model of surface water in the Google Earth Engine. According to statistics, the surface water areas of Sri Lanka in 1995, 2005, and 2015 were 1654.18 km2, 1964.86 km2, and 2136.81 km2, respectively. In the past 20 years, the water area of Sri Lanka has increased significantly. To further analyse the spatiotemporal changes of inland lakes and reservoirs, a 5-m buffer data of rivers in 2015 were produced in ArcGIS10.3 software; further, the area corresponding to the river channels were removed from the three images and only the lagoon areas were preserved. Lagoons are ubiquitous in the coastal areas of Sri Lanka, with flood discharge, aquaculture, coastal protection, and other functions34. The results consisting of the extracted lakes, reservoirs, and lagoons are shown in Fig. 5.Figure 5Water extraction results for Sri Lanka in 1995, 2005, and 2015. The administrative boundary data of Sri Lanka comes from the Humanitarian Data Exchange (HDX) open platform (https://data.humdata.org). The maps were generated by geospatial analysis of ArcGIS software (version ArcGIS 10.3; http://www.esri.com/software/arcgis/arcgis-for-desktop).Full size imageThe overall water area of lakes and reservoirs in Sri Lanka showed an increasing trend from 1995 to 2015, and the lagoon area increased over these 20 years (Fig. 5). Because the lagoon does not belong to inland freshwater sensu stricto, the corresponding statistical analysis was not included in the following step. According to statistics, the total area covered of lakes and reservoirs in Sri Lanka were 1020.41 km2, 1270.53 km2, and 1417.68 km2 in 1995, 2005, and 2015 respectively. In the past 20 years, the area of lakes and reservoirs in Sri Lanka has increased by a considerable margin, attaining a value of 397.27 km2. To further analyse the spatiotemporal variation of inland lakes and reservoirs, they were divided into four grades according to their area: I ( More

  • in

    Enhancement of diatom growth and phytoplankton productivity with reduced O2 availability is moderated by rising CO2

    Field studiesPhotosynthetic carbon fixation was investigated at eight different stations in the Pearl River estuary of the South China Sea (Fig. 1a and Supplementary Table 1), where the phytoplankton assemblages were dominated by diatoms45 during the time of our investigation (June 2015). Samples were collected from 10 to 20 m depths and transferred immediately into 50 mL quartz tubes and sealed to prevent gas exchange. The samples were inoculated with 100 μL of 5 μCi (0.185 MBq) NaH14CO3 solution for 2.15 h. All the incubations were carried out under incident solar radiation, attenuated with neutral density filters to simulate light intensities at the sampling depths, and the temperature was controlled with flow-through surface seawater.After incubation, the cells were filtered onto glass-fiber filters (25 mm, Whatman GF/F, USA) and stored at −20 ° C until measurement, during which the filters were exposed to HCl fumes overnight and dried (20 °C, 6 h) to remove unincorporated NaH14CO3 as CO2. The incorporated radioactivity was measured by liquid scintillation counting (LS 6500, Beckman Coulter, USA), and photosynthetic carbon fixation rates were estimated as previously reported46. Since the measurements were carried out under varying and low light levels similar to in situ levels at depths of 10 and 20 m, we normalized the photosynthetic rates to light intensity (μmol C (μg Chl a)−1 h−1 (μmol photons m−2 s−1)−1) to obtain the light use efficiency of photosynthesis (PLUE). This was done to allow for a meaningful comparison among different stations according to the linear relationship of photosynthetic carbon fixation under low solar irradiance levels46, which lies within the range of sunlight levels used in the present fieldwork ( More

  • in

    Drivers of variation in occurrence, abundance, and behaviour of sharks on coral reefs

    1.Bird, C. S. et al. A global perspective on the trophic geography of sharks. Nat. Ecol. Evol. 2(2), 299–305. https://doi.org/10.1038/s41559-017-0432-z (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Ferretti, F., Worm, B., Britten, G. L., Heithaus, M. R. & Lotze, H. K. Patterns and ecosystem consequences of shark declines in the ocean. Ecol. Lett. 13(8), 1055–1071. https://doi.org/10.1111/j.1461-0248.2010.01489.x (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Hammerschlag, N., Schmitz, O. J., Flecker, A. S., Lafferty, K. D., Sih, A., Atwood, T. B., Gallagher, A. J., Irschick, D. J., Skubel, R., & Cooke, S. J. Ecosystem function and services of aquatic predators in the anthropocene. In Trends in Ecology and Evolution Vol. 34, Issue 4, 369–383. (Elsevier Ltd, 2019). https://doi.org/10.1016/j.tree.2019.01.0054.Heithaus, M. R., Frid, A., Wirsing, A. J. & Worm, B. Predicting ecological consequences of marine top predator declines. Trends Ecol. Evol. 23(4), 202–210. https://doi.org/10.1016/j.tree.2008.01.003 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Williams, J. J., Papastamatiou, Y. P., Caselle, J. E., Bradley, D. & Jacoby, D. M. P. Mobile marine predators: An understudied source of nutrients to coral reefs in an unfished atoll. Proc. R. Soc. B Biol. Sci. 285(1875), 20172456. https://doi.org/10.1098/rspb.2017.2456 (2018).Article 

    Google Scholar 
    6.Dulvy, N. K., Simpfendorfer, C. A., Davidson, L. N. K., Fordham, S. V., Bräutigam, A., Sant, G., & Welch, D. J. Challenges and priorities in shark and ray conservation. In Current Biology, Vol. 27, Issue 11, R565–R572. (Cell Press, 2017). https://doi.org/10.1016/j.cub.2017.04.038.7.MacNeil, M. A. et al. Global status and conservation potential of reef sharks. Nature 583(7818), 801–806. https://doi.org/10.1038/s41586-020-2519-y (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.MacKeracher, T., Diedrich, A. & Simpfendorfer, C. A. Sharks, rays and marine protected areas: A critical evaluation of current perspectives. Fish Fish. 20(2), 255–267. https://doi.org/10.1111/faf.12337 (2019).Article 

    Google Scholar 
    9.Albano, P. S. et al. Successful parks for sharks: No-take marine reserve provides conservation benefits to endemic and threatened sharks off South Africa. Biol. Conserv. 261, 109302 (2021).Article 

    Google Scholar 
    10.Bond, M. E. et al. Reef sharks exhibit site-fidelity and higher relative abundance in marine reserves on the Mesoamerican Barrier reef. PLOS ONE 7(3), e32983. https://doi.org/10.1371/journal.pone.0032983 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Ruppert, J. L. W. et al. Human activities as a driver of spatial variation in the trophic structure of fish communities on Pacific coral reefs. Glob. Change Biol. 24(1), e67–e79. https://doi.org/10.1111/gcb.13882 (2018).Article 

    Google Scholar 
    12.Valdivia, A., Cox, C. E. & Bruno, J. F. Predatory fish depletion and recovery potential on Caribbean reefs. Sci. Adv. 3, e1601303 (2017).ADS 
    Article 

    Google Scholar 
    13.Dwyer, R. G. et al. Individual and population benefits of marine reserves for reef sharks. Curr. Biol. 30(3), 480–489. https://doi.org/10.1016/j.cub.2019.12.005 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. (2021).15.Wickham, H, ggplot2: Elegant Graphics for Data Analysis. Springer, New York. ISBN 978-3-319-24277-4 (2016).16.Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R J. 5(1), 144–161 (2013).Article 

    Google Scholar 
    17.Desbiens, A. A. et al. Revisiting the paradigm of shark-driven trophic cascades in coral reef ecosystems. Ecology 102(4), e03303. https://doi.org/10.1002/ecy.3303 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Morrissey, J. E. & Gruber, S. H. Habitat selection by juvenile lemon sharks, Negaprion brevirostris. Environ. Biol. Fishes 38, 311–319 (1993).Article 

    Google Scholar 
    19.Clementi, G. et al. Anthropogenic pressures on reef-associated sharks in jurisdictions with and without directed shark fishing. Mar. Ecol. Prog. Ser. 661, 175–186. https://doi.org/10.3354/meps13607 (2021).ADS 
    Article 

    Google Scholar 
    20.Juhel, J. B. et al. Isolation and no-entry marine reserves mitigate anthropogenic impacts on grey reef shark behavior. Sci. Rep. 9(1), 1–11. https://doi.org/10.1038/s41598-018-37145-x (2019).CAS 
    Article 

    Google Scholar 
    21.Goetze, J. S. et al. Fish wariness is a more sensitive indicator to changes in fishing pressure than abundance, length or biomass. Ecol. Appl. 27, 1178–1189 (2017).Article 

    Google Scholar 
    22.Mitchell, J. D. et al. Quantifying shark depredation in a recreational fishery in the Ningaloo Marine Park and Exmouth Gulf, Western Australia. Mar. Ecol. Prog. Ser. 587, 141–157. https://doi.org/10.3354/meps12412 (2018).ADS 
    Article 

    Google Scholar 
    23.Mitchell, J. D. et al. A novel experimental approach to investigate the potential for behavioural change in sharks in the context of depredation. J. Exp. Mar. Biol. Ecol. 530–531, 151440. https://doi.org/10.1016/j.jembe.2020.151440 (2020).Article 

    Google Scholar 
    24.Speed, C. W., Cappo, M. & Meekan, M. G. Evidence for rapid recovery of shark populations within a coral reef marine protected area. Biol. Cons. 220, 308–319. https://doi.org/10.1016/j.biocon.2018.01.010 (2018).Article 

    Google Scholar 
    25.Bond, M. E., Albanese, J. V., Heithaus, E. A. B. M. R. & Cerrato, R. D. G. R. Top predators induce habitat shifts in prey within marine protected areas. Oecologia 190(2), 375–385. https://doi.org/10.1007/s00442-019-04421-0 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Lester, E. K. et al. Relative influence of predators, competitors and seascape heterogeneity on behaviour and abundance of coral reef mesopredators. Oikos 130, 2239–2249. https://doi.org/10.1111/oik.08463 (2021).Article 

    Google Scholar 
    27.Phenix, L. et al. Evaluating the effects of large marine predators on mobile prey behavior across subtropical reef systems. Ecol. Evol. 9, 13740–13751 (2019).Article 

    Google Scholar 
    28.Shea, B. D. et al. Effects of exposure to large sharks on the abundance and behavior of mobile prey fishes along a temperate coastal gradient. PLOS ONE 15(3), e0230308. https://doi.org/10.1371/journal.pone.0230308 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Sherman, C. S., Heupel, M. R., Moore, S. K., Chin, A. & Simpfendorfer, C. A. When sharks are away, rays will play: Effects of top predator removal in coral reef ecosystems. Mar. Ecol. Prog. Ser. 641, 145–157. https://doi.org/10.3354/meps13307 (2020).ADS 
    Article 

    Google Scholar 
    30.Ryan, K. L., Hall, N. G., Lai, E. K., Smallwood, C. B., Tate, A., Taylor, S. M., & Wise, B. S. Statewide Survey of Boat-Based Recreational Fishing in Western Australia 2017/18, 8. Fisheries Research Report No. 297 (2019).31.Cresswell, A. K. et al. Disentangling the response of fishes to recreational fishing over 30 years within a fringing coral reef reserve network. Biol. Cons. 237, 514–524. https://doi.org/10.1016/j.biocon.2019.06.023 (2019).Article 

    Google Scholar 
    32.Strydom, S. et al. Too hot to handle: Unprecedented seagrass death driven by marine heatwave in a World Heritage Area. Glob. Change Biol. 26(6), 3525–3538. https://doi.org/10.1111/gcb.15065 (2020).ADS 
    Article 

    Google Scholar 
    33.Goetze, J. S., & Fullwood, L. A. F. Fiji’s largest marine reserve benefits reef sharks. In Coral Reefs Vol. 32, Issue 1, 121–125. (Springer, 2013). https://doi.org/10.1007/s00338-012-0970-4.34.Juhel, J. B. et al. Reef accessibility impairs the protection of sharks. J. Appl. Ecol. 55(2), 673–683. https://doi.org/10.1111/1365-2664.13007 (2018).Article 

    Google Scholar 
    35.Birt, M. J. et al. Isolated reefs support stable fish communities with high abundances of regionally fished species. Ecol. Evol. 11(9), 4701–4718. https://doi.org/10.1002/ece3.7370 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Fitzpatrick, R. et al. A comparison of the seasonal movements of tiger sharks and green turtles provides insight into their predator-prey relationship. PLOS ONE 7(12), e51927. https://doi.org/10.1371/journal.pone.0051927 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Mourier, J. et al. Extreme inverted trophic pyramid of reef sharks supported by spawning groupers. Curr. Biol. 26(15), 2011–2016. https://doi.org/10.1016/j.cub.2016.05.058 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Braccini, M., Molony, B. & Blay, N. Patterns in abundance and size of sharks in northwestern Australia: Cause for optimism. ICES J. Mar. Sci. 77(1), 72–82. https://doi.org/10.1093/icesjms/fsz187 (2020).Article 

    Google Scholar 
    39.Holmes, T., Rule, M., Bancroft, K., Shedrawi, G., Murray, K., Wilson, S., & Kendrick, A. Ecological Monitoring in the Ningaloo Marine Reserves 2017 (2017).40.Martín, G., Espinoza, M., Heupel, M. & Simpfendorfer, C. A. Estimating marine protected area network benefits for reef sharks. J. Appl. Ecol. 57(10), 1969–1980. https://doi.org/10.1111/1365-2664.13706 (2020).Article 

    Google Scholar 
    41.Ferreira, L. C. et al. Crossing latitudes-long-distance tracking of an apex predator. PLOS ONE 10(2), e0116916. https://doi.org/10.1371/journal.pone.0116916 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Priede, I. G., Bagley, P. M., Smith, A., Creasey, S. & Merrett, N. R. Scavenging deep demersal fishes of the Porcupine Seabight, north-east Atlantic: Observations by baited camera, trap and trawl. J. Mar. Biol. Assoc. 74(3), 481–498. https://doi.org/10.1017/S0025315400047615 (1994).Article 

    Google Scholar 
    43.Stobart, B. et al. Performance of baited underwater video: Does it underestimate abundance at high population densities?. PLOS ONE 10(5), e0127559. https://doi.org/10.1371/journal.pone.0127559 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Papastamatiou, Y. P., Lowe, C. G., Caselle, J. E. & Friedlander, A. M. Scale-dependent effects of habitat on movements and path structure of reef sharks at a predator-dominated atoll. Ecology 90(4), 996–1008 (2009).Article 

    Google Scholar 
    45.Rizzari, J. R., Frisch, A. J. & Magnenat, K. A. Diversity, abundance, and distribution of reef sharks on outer-shelf reefs of the Great Barrier Reef Australia. Mar. Biol. 161(12), 2847–2855. https://doi.org/10.1007/s00227-014-2550-3 (2014).Article 

    Google Scholar 
    46.Speed, C., Field, I., Meekan, M. & Bradshaw, C. Complexities of coastal shark movements and their implications for management. Mar. Ecol. Prog. Ser. 408, 275–293. https://doi.org/10.3354/meps08581 (2010).ADS 
    Article 

    Google Scholar 
    47.Espinoza, M., Cappo, M., Heupel, M. R., Tobin, A. J. & Simpfendorfer, C. A. Quantifying shark distribution patterns and species-habitat associations: Implications of marine park zoning. PLOS ONE 9(9), e106885. https://doi.org/10.1371/journal.pone.0106885 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Mourier, J., Planes, S. & Buray, N. Trophic interactions at the top of the coral reef food chain. Coral Reefs 32(1), 285. https://doi.org/10.1007/s00338-012-0976-y (2013).ADS 
    Article 

    Google Scholar 
    49.Raoult, V., Broadhurst, M. K., Peddemors, V. M., Williamson, J. E. & Gaston, T. F. Resource use of great hammerhead sharks (Sphyrna mokarran) off eastern Australia. J. Fish Biol. 95(6), 1430–1440. https://doi.org/10.1111/jfb.14160 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Andrzejaczek, S. et al. Biologging tags reveal links between fine-scale horizontal and vertical movement behaviors in tiger sharks (Galeocerdo cuvier). Front. Mar. Sci. 6(May), 1–13. https://doi.org/10.3389/fmars.2019.00229 (2019).ADS 
    Article 

    Google Scholar 
    51.Andrzejaczek, S. et al. Depth-dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks. R. Soc. Open Sci. 7(8), 200789. https://doi.org/10.1098/rsos.200789 (2020).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Brooks, E. J., Sloman, K. A., Sims, D. W. & Danylchuk, A. J. Validating the use of baited remote underwater video surveys for assessing the diversity, distribution and abundance of sharks in the Bahamas. Endang. Species Res. 13(3), 231–243. https://doi.org/10.3354/esr00331 (2011).Article 

    Google Scholar 
    53.Santana-Garcon, J. et al. Calibration of pelagic stereo-BRUVs and scientific longline surveys for sampling sharks. Methods Ecol. Evol. 5(8), 824–833. https://doi.org/10.1111/2041-210X.12216 (2014).Article 

    Google Scholar 
    54.Barnett, A., Abrantes, K. G., Seymour, J. & Fitzpatrick, R. Residency and spatial use by reef sharks of an isolated seamount and its implications for conservation. PLOS ONE 7(5), e36574. https://doi.org/10.1371/journal.pone.0036574 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Papastamatiou, Y. P. et al. Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming. Mov. Ecol. 6(1), 1–15. https://doi.org/10.1186/s40462-018-0127-3 (2018).Article 

    Google Scholar 
    56.Vianna, G. M. S., Meekan, M. G., Meeuwig, J. J. & Speed, C. W. Environmental influences on patterns of vertical movement and site fidelity of grey reef sharks (Carcharhinus amblyrhynchos) at aggregation sites. PLOS ONE 8(4), e60331. https://doi.org/10.1371/journal.pone.0060331 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Lear, K. O., Whitney, N. M., Morris, J. J. & Gleiss, A. C. Temporal niche partitioning as a novel mechanism promoting co-existence of sympatric predators in marine systems. Proc. R. Soc. B: Biol. Sci. 288(1954), 20210816. https://doi.org/10.1098/rspb.2021.0816 (2021).Article 

    Google Scholar 
    58.Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572(7770), 461–466. https://doi.org/10.1038/s41586-019-1444-4 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Langlois, T. et al. A field and video annotation guide for baited remote underwater stereo-video surveys of demersal fish assemblages. Methods Ecol. Evol. 11(11), 1401–1409. https://doi.org/10.1111/2041-210X.13470 (2020).Article 

    Google Scholar 
    60.Lin, X. & Zhang, D. Inference in generalized additive mixed models by using smoothing splines. J. R. Stat. Soc. 61(2), 381–400 (1999).MathSciNet 
    Article 

    Google Scholar 
    61.Fisher, R., Wilson, S. K., Sin, T. M., Lee, A. C. & Langlois, T. J. A simple function for full-subsets multiple regression in ecology with R. Ecol. Evol. 8(12), 6104–6113. https://doi.org/10.1002/ece3.4134 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Mullahy, J. Specification and testing of some modified count data models. J. Econom. 33, 341–365 (1986).MathSciNet 
    Article 

    Google Scholar 
    63.Tweedie, M. An index which distinguishes between some important exponential families. In Statistics: Applications and New Directions: Proceedings of the Indian Statistical Institute Golden Jubelee International Conference Vol. 604 (1984).64.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 

    Google Scholar 
    65.Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33(2), 261–304. https://doi.org/10.1177/0049124104268644 (2004).MathSciNet 
    Article 

    Google Scholar 
    66.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference; A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

    Google Scholar 
    67.Ward‐Paige, C. A., Keith, D. M., Worm, B. & Lotze, H. K. Recovery potential and conservation options for elasmobranchs. J. Fish Biol. 80(5), 1844–1869 (2012).68.Graham, F et al. Use of marine protected areas and exclusive economic zones in the subtropical western North Atlantic Ocean by large highly mobile sharks. Divers. Distrib. 22(5), 534–546 (2016).69.Morgan, A., Calich, H., Sulikowski, J. & Hammerschlag, N. Evaluating spatial management options for tiger shark (Galeocerdo cuvier) conservation in US Atlantic Waters. ICES J. Mar. Sci. 77(7–8), 3095–3109 (2020).70.Harvey, E. S. & Shortis, M. R. A system for stereo-video measurement of sub-tidal organisms. Mar. Technol. Soc. J. 29(4), 10–22 (1995).71.R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/72.McLean, D. L. et al. Distribution, abundance, diversity and habitat associations of fishes across a bioregion experiencing rapid coastal development. Estuar. Coast Shelf S. 178, 36–47 (2016).73.Althaus, F.et al. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: the CATAMI classification scheme. PloS one 10(10), e0141039 (2015).74.Wilson, S. K., Graham, N. A. J. & Polunin, N. V. C. Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs. Mar. Biol. 151(3), 1069–1076 (2007).75.Roff, G. et al. The ecological role of sharks on coral reefs. Trends Ecol. Evol. 31(5), 395–407 (2016). 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