Philippa Kaur

More stories

  • 113 Shares119 Views

    in Ecology

    A three-dimensional climate-smart conservation approach in the high seas

    Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Brito-Morales, I. et al. Towards climate-smart, three-dimensional protected areas for biodiversity conservation in the high seas. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01323-7 (2022). More

  • 50 Shares129 Views

    in Ecology

    A colourful tropical world

    von Humboldt, A. Views of Nature: Or Contemplations on the Sublime Phenomena of Creation (transl. Otté, E. C. & Bohn, H. G.) (Henry G. Bohn, 1850).Cooney, C. R. et al. Nat. Ecol. Evol., https://doi.org/10.1038/s41559-022-01714-1 (2022).Article 

    Google Scholar 
    Hawkins, B. A. et al. J. Biogeogr. 39, 825–841 (2012).Article 

    Google Scholar 
    Pulido-Santacruz, P. & Weir, J. T. Evolution 70, 860–872 (2016).Article 

    Google Scholar 
    Fine, P. V. A. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

    Google Scholar 
    Storch, D., Bohdalková, E. & Okie, J. Ecol. Lett. 21, 920–937 (2018).Article 

    Google Scholar 
    Jablonski, D., Roy, K. & Valentine, J. W. Science 314, 102–106 (2006).CAS 
    Article 

    Google Scholar 
    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. Nature 491, 444–448 (2012).CAS 
    Article 

    Google Scholar 
    Kennedy, J. D. et al. J. Biogeogr. 41, 1746–1757 (2014).Article 

    Google Scholar 
    Pontarp, M. et al. Trends Ecol. Evol. 34, 211–223 (2019).Article 

    Google Scholar  More

  • 150 Shares189 Views

    in Ecology

    Restructuring of plankton genomic biogeography in the surface ocean under climate change

    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science https://doi.org/10.1126/science.281.5374.237 (1998).Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature https://doi.org/10.1038/nature16942 (2016).Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Glob. Biogeochem. Cycles https://doi.org/10.1029/2011GB004099 (2012).Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps010257 (1983).Saab, M. A. Day-to-day variation in phytoplankton assemblages during spring blooming in a fixed station along the Lebanese coastline. J. Plankton Res. https://doi.org/10.1093/plankt/14.8.1099 (1992).Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. https://doi.org/10.1038/s41467-019-14105-1 (2020).Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsw086 (2016).Longhurst, A. R. Ecological Geography of the Sea (Elsevier, 2007).Fay, A. R. & McKinley, G. A. Global open-ocean biomes: mean and temporal variability. Earth Syst. Sci. Data https://doi.org/10.5194/essd-6-273-2014 (2014).Reygondeau, G. et al. Dynamic biogeochemical provinces in the global ocean. Glob. Biogeochem. Cycles https://doi.org/10.1002/gbc.20089 (2013).Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2020).Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences https://doi.org/10.5194/bg-17-609-2020 (2020).Hellweger, F. L., Van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science https://doi.org/10.1126/science.1254421 (2014).Laso-Jadart, R. et al. Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866). Ecol. Evol. https://doi.org/10.1002/ece3.6588 (2020).Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife https://doi.org/10.7554/eLife.46497 (2019).Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. https://doi.org/10.1038/s41467-017-02342-1 (2018).Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell https://doi.org/10.1016/j.cell.2019.10.014 (2019).Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data https://doi.org/10.1038/sdata.2017.93 (2017).Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data https://doi.org/10.1038/sdata.2015.23 (2015).Karsenti, E. et al. A holistic approach to marine eco-systems biology. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001177 (2011).Duarte, C. M. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol. Oceanogr. Bull. https://doi.org/10.1002/lob.10008 (2015).Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1519080113 (2016).Benedetti, F., Guilhaumon, F., Adloff, F. & Ayata, S. D. Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea. Ecography https://doi.org/10.1111/ecog.02434 (2018).Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).Article 

    Google Scholar 
    Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science https://doi.org/10.1126/science.1239352 (2013).Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences https://doi.org/10.5194/bg-10-6225-2013 (2013).Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science https://doi.org/10.1126/science.1224836 (2012).Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell https://doi.org/10.1016/j.cell.2019.10.008 (2019).Busseni, G. et al. Large scale patterns of marine diatom richness: drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13161 (2020).Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

    Google Scholar 
    Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics. Preprint at bioRxiv https://doi.org/10.1101/2020.10.15.341214 (2020).Delmont, T. O. et al. Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean. ISME J. https://doi.org/10.1038/s41396-021-01135-1 (2021).Boyer, et al. World Ocean Database 2013, NOAA Atlas NESDIS 72 (National Oceanic and Atmospheric Administration, 2013); https://doi.org/10.7289/V5NZ85MTSunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0364-5 (2020).Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0336-3 (2019).van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change https://doi.org/10.1007/s10584-011-0148-z (2011).Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling biomes in the North Pacific under global warming. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsq198 (2011).Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. https://doi.org/10.1038/s41561-019-0524-2 (2020).Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).Article 

    Google Scholar 
    Wrightson, L. & Tagliabue, A. Quantifying the impact of climate change on marine diazotrophy: insights from Earth system models. Front. Mar. Sci. 7, 635 (2020).Article 

    Google Scholar 
    Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 368, eaay9514 (2020).CAS 
    Article 

    Google Scholar 
    Luo, Y.-W. et al. Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth Syst. Sci. Data 4, 47–73 (2012).Article 

    Google Scholar 
    Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).Article 

    Google Scholar 
    Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).CAS 
    Article 

    Google Scholar 
    Agrawal, R. & Srikant, R. in Proceedings of the 20th International Conference on Very Large Data Bases (eds Bocca, J. B. et al.) 487–499 (Morgan Kaufmann, 1994).Laufkötter, C. et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences 13, 4023–4047 (2016).Article 

    Google Scholar 
    Iudicone, D. Some may like it hot. Nat. Geosci. https://doi.org/10.1038/s41561-020-0535-z (2020).Gorsky, G. et al. Expanding Tara Oceans protocols for underway, ecosystemic sampling of the ocean–atmosphere interface during Tara Pacific expedition (2016–2018). Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00750 (2019).Istace, B. et al. de novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer. Gigascience https://doi.org/10.1093/gigascience/giw018 (2017).Grand, M. M. et al. Developing autonomous observing systems for micronutrient trace metals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00035 (2019).Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: Draw geographical maps. R version 3.5.0 https://cran.r-project.org/web/packages/maps/index.html (2021).Jaccard, P. Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales. Bull. Murith. 31, 81–92 (1902).
    Google Scholar 
    Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data https://doi.org/10.1038/sdata.2017.39 (2017).Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11 (Flanders Marine Institute, 2019); https://doi.org/10.14284/386Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model Dev. https://doi.org/10.5194/gmd-8-2465-2015 (2015).Bibby, T. S. & Moore, C. M. Silicate:nitrate ratios of upwelled waters control the phytoplankton community sustained by mesoscale eddies in sub-tropical North Atlantic and Pacific. Biogeosciences https://doi.org/10.5194/bg-8-657-2011 (2011).Brun, P., Kiørboe, T., Licandro, P. & Payne, M. R. The predictive skill of species distribution models for plankton in a changing climate. Glob. Change Biol. https://doi.org/10.1111/gcb.13274 (2016).Redfield, A. C. in James Johnstone Memorial Volume (ed. Daniel, R. J.) 176–192 (Liverpool Univ. Press, 1934).Michelangeli, P. A., Vrac, M. & Loukos, H. Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys. Res. Lett. https://doi.org/10.1029/2009GL038401 (2009).Ridgeway, G. gbm: Generalized boosted regression models. R version 1.6–3.1 https://cran.r-project.org/web/packages/gbm/gbm.pdf (2010).Breiman, L. & Cutler, A. randomForest: Breiman and Cutler’s random forests for classification and regression. R package 4.1.0 https://www.stat.berkeley.edu/~breiman/RandomForests/ (2012).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn (Springer, 2002).Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. https://doi.org/10.1198/016214504000000980 (2004).Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. https://doi.org/10.1016/j.patrec.2005.10.010 (2006).Biecek, P. DALEX: explainers for complex predictive models. J. Mach. Learn. Res. 19, 1–5 (2018).
    Google Scholar 
    Jones, M. C. & Cheung, W. W. L. Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsu172 (2015).Vallejos, C. A. Exploring a world of a thousand dimensions. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0330-9 (2019).Kaufman, L. and Rousseeuw, P.J. in Statistical Data Analysis Based on the L1 Norm and Related Methods (ed. Dodge, Y.) 405–416 (North-Holland, 1987).Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. https://doi.org/10.1016/0377-0427(87)90125-7 (1987).Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I https://doi.org/10.1016/0967-0637(95)00021-W (1995).Hubert, L. & Arabie, P. Comparing partitions. J. Classif. https://doi.org/10.1007/BF01908075 (1985).Somerfield, P. J. Identification of the Bray–Curtis similarity index: comment on Yoshioka (2008). Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps07841 (2008).Bloom, S. Similarity indices in community studies: potential pitfalls. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps005125 (1981).Welch, B. L. The generalisation of student’s problems when several different population variances are involved. Biometrika 34, 28–35 (1947).CAS 

    Google Scholar 
    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
    Google Scholar 
    Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).Article 

    Google Scholar 
    Sthle, L. & Wold, S. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272 (1989).Article 

    Google Scholar 
    Bozdogan, H. Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).Article 

    Google Scholar 
    Frémont, P. et al. Biogeographies of genomic provinces from ‘Restructuring of plankton genomic biogeography in the surface ocean under climate change’. figshare. https://figshare.com/articles/dataset/Biogeographies_genomic_provinces/19071620 (2022). More

  • 63 Shares119 Views

    in Ecology

    Latitudinal gradients in avian colourfulness

    Darwin, C. R. On the Origin of Species, or the Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).Wallace, A. R. Natural Selection and Tropical Nature: Essays on Descriptive and Theoretical Biology 2nd edn (Macmillan, 1895).Darwin, C. R. A Naturalist’s Voyage Round the World (John Murray, 1913).Wallace, A. R. Colour in nature. Nature 19, 580–581 (1879).
    Google Scholar 
    Dalrymple, R. L. et al. Abiotic and biotic predictors of macroecological patterns in bird and butterfly coloration. Ecol. Monogr. 88, 204–224 (2018).
    Google Scholar 
    Adams, J. M., Kang, C. & June-Wells, M. Are tropical butterflies more colorful? Ecol. Res. 29, 685–691 (2014).
    Google Scholar 
    Bailey, S. F. Latitudinal gradients in colors and patterns of passerine birds. Condor 80, 372–381 (1978).
    Google Scholar 
    Wilson, M. F. & Von Neaumann, R. A. Why are neotropical birds more colourful than North American birds? Avicultural Mag. 78, 141–147 (1972).
    Google Scholar 
    Dalrymple, R. L. et al. Birds, butterflies and flowers in the tropics are not more colourful than those at higher latitudes. Glob. Ecol. Biogeogr. 24, 1424–1432 (2015).
    Google Scholar 
    Friedman, N. R. & Remeš, V. Ecogeographical gradients in plumage coloration among Australasian songbird clades. Glob. Ecol. Biogeogr. 26, 261–274 (2017).
    Google Scholar 
    Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).CAS 

    Google Scholar 
    Dunn, P. O., Armenta, J. K. & Whittingham, L. A. Natural and sexual selection act on different axes of variation in avian plumage color. Sci. Adv. 1, e1400155 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Stoddard, M. C. & Prum, R. O. How colorful are birds? Evolution of the avian plumage color gamut. Behav. Ecol. 22, 1042–1052 (2011).
    Google Scholar 
    Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).
    Google Scholar 
    Stoddard, M. C. & Prum, R. O. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of New World buntings. Am. Nat. 171, 755–776 (2008).
    Google Scholar 
    Delhey, K. The colour of an avifauna: a quantitative analysis of the colour of Australian birds. Sci. Rep. 5, 18514 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
    Google Scholar 
    Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 

    Google Scholar 
    Lynch, M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45, 1065–1080 (1991).PubMed 
    PubMed Central 

    Google Scholar 
    Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biol. Rev. Camb. Phil. Soc. 94, 1294–1316 (2019).
    Google Scholar 
    Marchetti, K. Dark habitats and bright birds illustrate the role of the environment in species divergence. Nature 362, 149–152 (1993).
    Google Scholar 
    Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).
    Google Scholar 
    Schemske, D. W. in Speciation and Patterns of Diversity Vol. 12 (eds Butlin, R. et al.) 219–239 (Cambridge Univ. Press, 2009).Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
    Google Scholar 
    MacArthur, R. H. Patterns of communities in the tropics. Biol. J. Linn. Soc. 1, 19–30 (1969).
    Google Scholar 
    Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 

    Google Scholar 
    Cooney, C. R. et al. Sexual selection predicts the rate and direction of colour divergence in a large avian radiation. Nat. Commun. 10, 1773 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Cooney, C. R., MacGregor, H. E. A., Seddon, N. & Tobias, J. A. Multi-modal signal evolution in birds: re-assessing a standard proxy for sexual selection. Proc. R. Soc. B 285, 20181557 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    van der Bijl, W. et al. Butterfly dichromatism primarily evolved via Darwin’s, not Wallace’s, model. Evol. Lett. 4, 545–555 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Darwin, C. R. The Descent of Man, and Selection in Relation to Sex (John Murray, 1871).Tobias, J. A., Montgomerie, R. & Lyon, B. E. The evolution of female ornaments and weaponry: social selection, sexual selection and ecological competition. Phil. Trans. R. Soc. B 367, 2274–2293 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Galván, I., Negro, J. J., Rodríguez, A. & Carrascal, L. M. On showy dwarfs and sober giants: body size as a constraint for the evolution of bird plumage colouration. Acta Ornithol. 48, 65–80 (2013).
    Google Scholar 
    Kiltie, R. A. Scaling of visual acuity with body size in mammals and birds. Funct. Ecol. 14, 226–234 (2000).
    Google Scholar 
    Zahavi, A. & Zahavi, A. The Handicap Principle (Oxford Univ. Press, 1997).Badyaev, A. V. & Hill, G. E. Avian sexual dichromatism in relation to phylogeny and ecology. Annu. Rev. Ecol. Evol. Syst. 34, 27–49 (2003).
    Google Scholar 
    Simpson, R. K., Johnson, M. A. & Murphy, T. G. Migration and the evolution of sexual dichromatism: evolutionary loss of female coloration with migration among wood-warblers. Proc. R. Soc. B 282, 20150375 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Helferich, G. Humboldt’s Cosmos (Tantor eBooks, 2011).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    He, Y. et al. Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds. Preprint at bioRxiv https://doi.org/10.1101/2021.07.22.453339 (2021).Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder–decoder with atrous separable convolution for semantic image segmentation. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.02611 (2018).Hussein, B. R., Malik, O. A., Ong, W.-H. & Slik, J. W. F. in Computational Science and Technology Lecture Notes in Electrical Engineering (eds Alfred, R. et al.) 321–330 (Springer Singapore, 2020).Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.5-15 https://CRAN.R-project.org/package=raster (2022).Maia, R., Gruson, H., Endler, J. A., White, T. E. & O’Hara, R. B. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods Ecol. Evol. 10, 1097–1107 (2019).
    Google Scholar 
    Stoddard, M. C. et al. Wild hummingbirds discriminate nonspectral colors. Proc. Natl Acad. Sci. USA 117, 15112–15122 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gomez, D. & Théry, M. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a neotropical rainforest bird community. Am. Nat. 169, S42–S61 (2007).
    Google Scholar 
    Blonder, B. Do hypervolumes have holes? Am. Nat. 187, E93–E105 (2016).
    Google Scholar 
    Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    Beckmann, M. et al. glUV: a global UV-B radiation data set for macroecological studies. Methods Ecol. Evol. 5, 372–383 (2014).
    Google Scholar 
    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
    Google Scholar 
    Tobias, J. A. & Pigot, A. L. Integrating behaviour and ecology into global biodiversity conservation strategies. Phil. Trans. R. Soc. B 374, 20190012 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Dunn, P. O., Whittingham, L. A. & Pitcher, T. E. Mating systems, sperm competition, and the evolution of sexual dimorphism in birds. Evolution 55, 161–175 (2001).CAS 

    Google Scholar 
    Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
    Google Scholar 
    Hawkins, B. A. et al. Structural bias in aggregated species-level variables driven by repeated species co-occurrences: a pervasive problem in community and assemblage data. J. Biogeogr. 44, 1199–1211 (2017).
    Google Scholar 
    Hadfield, J. D. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
    Google Scholar 
    Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/ More

  • 238 Shares129 Views

    in Ecology

    Development and validation of an eDNA protocol for monitoring endemic Asian spiny frogs in the Himalayan region of Pakistan

    Lindenmayer, D. et al. A checklist of attributes for effective monitoring of threatened species and threatened ecosystems. J. Environ. Manage. 262, 110312 (2020).PubMed 

    Google Scholar 
    Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 

    Google Scholar 
    IUCN. The IUCN Red List of Threatened Species. Version 2019-3. http://www.iucnredlist.org (2021).Adams, M. J. et al. Trends in amphibian occupancy in the United States. PLoS ONE 8, e64347 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Corn, P. S. Climate change and amphibians. Anim. Biodivers. Conserv. 28, 59–67 (2005).
    Google Scholar 
    Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS 
    CAS 

    Google Scholar 
    Baldwin, R. F. & deMaynadier, P. G. Assessing threats to pool-breeding amphibian habitat in an urbanizing landscape. Biol. Conserv. 142, 1628–1638 (2009).Borzée, A., Kyong, C. N., Kil, H. K. & Jang, Y. Impact of water quality on the occurrence of two endangered Korean anurans: Dryophytes suweonensis and Pelophylax chosenicus. Herpetologica 74, 1–7 (2018).
    Google Scholar 
    Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).ADS 
    CAS 

    Google Scholar 
    Caro, T., Rowe, Z., Berger, J., Wholey, P. & Dobson, A. An inconvenient misconception: climate change is not the principal driver of biodiversity loss. Conserv. Lett. e12868 (2022).Daszak, P. et al. Emerging infectious diseases and amphibian population declines. Emerg. Infect. 5, 735–748 (1999).CAS 

    Google Scholar 
    Fellers, G., Green, D. E. & Longcore, J. Oral chytridiomycosis in the mountain yellow-legged frog (Rana muscosa). Copeia 2001, 945–953Blaustein, A. R. et al. Effects of ultraviolet radiation on amphibians: field experiments. Am. Zool. 38, 799–812 (1999).
    Google Scholar 
    Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
    Google Scholar 
    Beebee, T. J. C. Amphibians breeding and climate. Nature 374, 219–220 (1995).ADS 
    CAS 

    Google Scholar 
    Donnelly, M. A. & Crump, M. L. Potential effects of climate change on two neotropical amphibian assemblages. In Potential Impacts of Climate Change on Tropical Forest Ecosystems (ed. Markham, A.) 401–421 (Springer Netherlands, 1998).Carey, C. & Alexander, M. A. Climate change and amphibian declines: is there a link? Divers. Distrib. 9, 111–121 (2003).
    Google Scholar 
    Fisher, R. N. & Shaffer, H. B. The decline of amphibians in California’s Great Central Valley. Conserv. Biol. 10, 1387–1397 (1996).
    Google Scholar 
    Sparling, D. W., Donald, W., Linder, G. & Bishop, C. A. Ecotoxicology of Amphibians and Reptiles. (SETAC Press, 2000).Rouse, M. J. & Daellenbach, U. S. Rethinking research methods for the resource-based perspective: isolating sources of sustainable competitive advantage. Strat. Manag. J. 20, 487–494 (1999).
    Google Scholar 
    Bridges, C. M. & Boone, M. D. The interactive effects of UV-B and insecticide exposure on tadpole survival, growth and development. Biol. Conserv. 113, 49–54 (2003).
    Google Scholar 
    Schmeller, D. S. et al. National responsibilities in European species conservation: a methodological review. Conserv. Biol. 22, 593–601 (2008).PubMed 

    Google Scholar 
    Anderson, S. Area and endemism. Q. Rev. Biol. 69, 451–471 (1994).
    Google Scholar 
    Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. N. Am. Benthol. Soc. 29, 344–358 (2010).
    Google Scholar 
    Gorman, C. E., Potts, B. M., Schweitzer, J. A. & Bailey, J. K. Shifts in species interactions due to the evolution of functional differences between endemics and non-endemics: an endemic syndrome hypothesis. PLoS ONE 9, e111190 (2014).Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).PubMed 

    Google Scholar 
    Fontaine, B. et al. The European Union’s 2010 target: putting rare species in focus. Biol. Conserv. 139, 167–185 (2007).
    Google Scholar 
    Saeed, M. et al. Rise in temperature causes decreased fitness and higher extinction risks in endemic frogs at high altitude forested wetlands in northern Pakistan. J. Therm. Biol. 95, 102809 (2021).McDonald, L. L. Sampling rare populations. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson W. L.) 11–42 (Island Press, 2004).Dodd Jr. K. Monitoring Amphibians in Great Smoky Mountains National Park (USGS Survey Circular, 2003).Qu, C. & Stewart, K. A. Evaluating monitoring options for conservation : traditional and environmental DNA tools for a critically endangered mammal. Sci. Nat. 106, 9 (2019).
    Google Scholar 
    Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

    Google Scholar 
    Schmidt, B. R., Kery, M., Ursenbacher, S., Hyman, O. J. & Collins, J. P. Site occupancy models in the analysis of environmental DNA presence/absence surveys: a case study of an emerging amphibian pathogen. Methods Ecol. Evol. 4, 646–653 (2013).
    Google Scholar 
    Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: emerging methods to estimate species diversity. Trends Ecol. Evol. 29, 97–106 (2014).PubMed 

    Google Scholar 
    Kéry, M. & Schmidt, B. R. Imperfect detection and its consequences for monitoring for conservation. Community Ecol. 9, 207–216 (2008).
    Google Scholar 
    Mackenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
    Google Scholar 
    Mackenzie, D. I. & Royle, J. A. Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42, 1105–1114 (2005).
    Google Scholar 
    Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Darling, J. A. & Mahon, A. R. From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 111, 978–988 (2011).CAS 
    PubMed 

    Google Scholar 
    Goldberg, C. S., Pilliod, D. S., Arkle, R. S. & Waits, L. P. Molecular detection of vertebrates in stream water: a demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PLoS ONE 6, e22746 (2011).Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, e0186462 (2017).Agersnap, S. et al. Monitoring of noble, signal and narrow-clawed crayfish using environmental DNA from freshwater samples. PLoS ONE 12, e0179261 (2017).Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 

    Google Scholar 
    Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

    Google Scholar 
    Sigsgaard, E. E., Carl, H., Møller, P. R. & Thomsen, P. F. Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples. Biol. Conserv. 183, 46–52 (2015).
    Google Scholar 
    Bedwell, M. E., Hopkins, K. V. S., Dillingham, C. & Goldberg, C. S. Evaluating Sierra Nevada yellow-legged frog distribution using environmental DNA. J. Wildl. Mangaement 85, 945–952 (2021).
    Google Scholar 
    Eiler, A., Löfgren, A., Hjerne, O., Nordén, S. & Saetre, P. Environmental DNA (eDNA) detects the pool frog (Pelophylax lessonae) at times when traditional monitoring methods are insensitive. Sci. Rep. 8, 5452 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brozio, S. et al. Development and application of an eDNA method to detect the critically endangered Trinidad golden tree frog (Phytotriades auratus) in bromeliad phytotelmata. PLoS ONE 12, e0170619 (2017).Pellet, J. & Schmidt, B. R. Monitoring distributions using call surveys: estimating site occupancy, detection probabilities and inferring absence. Biol. Conserv. 123, 27–35 (2005).
    Google Scholar 
    Weir, L. A., Royle, J. A., Nanjappa, P. & Jung, R. E. Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland. J. Herpetol. 39, 627–639 (2005).
    Google Scholar 
    Fiske, I. J. & Chandler, R. B. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23 (2011).
    Google Scholar 
    Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
    Google Scholar 
    Holland, M. M. & Parsons, T. J. Mitochondrial DNA sequence analysis – validation and use for forensic casework. Forensic Sci. Rev. 11, 21–50 (1999).CAS 
    PubMed 

    Google Scholar 
    Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Waits, L. P. & Paetkau, D. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J. Wildl. Manage. 69, 1419–1433 (2006).
    Google Scholar 
    Shokralla, S. et al. Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Mol. Ecol. Resour. 14, 892–901 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mills, L. S., Pilgrim, K. L., Schwartz, M. K. & McKelvey, K. Identifying lynx and other North American felids based on mtDNA analysis. Conserv. Genet. 1, 285–288 (2000).CAS 

    Google Scholar 
    Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).CAS 

    Google Scholar 
    Kim, P., Kim, D., Yoon, T. J. & Shin, S. Early detection of marine invasive species, Bugula neritina (Bryozoa: Cheilostomatida), using species-specific primers and environmental DNA analysis in Korea. Mar. Environ. Res. 139, 1–10 (2018).CAS 
    PubMed 

    Google Scholar 
    Dejean, T. et al. Persistence of environmental DNA in freshwater ecosystems. PLoS ONE 6, e23398 (2011).Xia, Z. et al. Early detection of a highly invasive bivalve based on environmental DNA (eDNA). Biol. Invasions 20, 437–447 (2018).
    Google Scholar 
    Torresdal, J. D., Farrell, A. D. & Goldberg, C. S. Environmental DNA detection of the golden tree frog (Phytotriades auratus) in bromeliads. PLoS ONE 12, e0168787 (2017).Biggs, J. et al. Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus). Biol. Conserv. 183, 19–28 (2015).
    Google Scholar 
    Pilliod, D. S., Goldberg, C. S., Arkle, R. S. & Waits, L. P. Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples. Can. J. Fish. Aquat. Sci. 70, 1123–1130 (2013).CAS 

    Google Scholar 
    Smith, D. H. V., Jones, B., Randall, L. & Prescott, D. R. C. Difference in detection and occupancy between two anurans: the importance of species-specific monitoring. Herpetol. Conserv. Biol. 9, 267–277 (2014).
    Google Scholar 
    Bayley, P. B. & Peterson, J. T. An approach to estimate probability of presence and richness of fish species. Trans. Am. Fish. Soc. 130, 620–633 (2004).
    Google Scholar 
    Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).
    Google Scholar 
    Scott, Jr., N. J. & Woodward, B. D. Surveys at breeding sites. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds. Heyer, W. R., Donnelly, M. A., McDiarmid, R. W., Hayek, L. C., & Foster, M. S.) 118–125 (Smithsonian Institution Press, 1994).Dejean, T. et al. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. J. Appl. Ecol. 49, 953–959 (2012).
    Google Scholar 
    Goldberg, C. S., Sepulveda, A., Ray, A., Baumgardt, J. & Waits, L. P. Environmental DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus antipodarum). Freshw. Sci. 32, 792–800 (2013).
    Google Scholar 
    Mahon, A. R. et al. Validation of eDNA surveillance sensitivity for detection of Asian carps in controlled and field experiments. PLoS ONE 8, e58316 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khan, M. S. Amphibians and Reptiles of Pakistan (Krieger Publishing Company, 2006).Ruppert, K. M., Davis, D. R., Rahman, M. S. & Kline, R. J. Development and assessment of an environmental DNA (eDNA) assay for a cryptic Siren (Amphibia: Sirenidae). Environ. Adv. 7, 100163 (2022).
    Google Scholar 
    Hobbs, J., Round, J. M., Allison, M. J. & Helbing, C. C. Expansion of the known distribution of the coastal tailed frog, Ascaphus truei, in British Columbia, Canada, using robust eDNA detection methods. PLoS ONE 14, e0213849 (2019).Barata, I. M., Griffiths, R. A., Fogell, D. J. & Buxton, A. S. Comparison of eDNA and visual surveys for rare and cryptic bromeliad-dwelling frogs. Herpetol. J. 31, 1–9 (2021).
    Google Scholar 
    Ahmed, W. et al. Site occupancy of two endemic stream frogs in different forest types in Pakistan. Herpetol. Conserv. Biol. 15, 506–511 (2020).
    Google Scholar 
    Richmond, O. M. W., Hines, J. E. & Beissinger, S. R. Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails. Ecol. Appl. 20, 2036–2046 (2010).PubMed 

    Google Scholar 
    Shea, C. P., Eaton, M. J. & MacKenzie, D. I. Implementation of an occupancy-based monitoring protocol for a widespread and cryptic species, the New England cottontail (Sylvilagus transitionalis). Wildl. Res. 46, 222–235 (2019).
    Google Scholar 
    Rota, C. T. et al. A multispecies occupancy model for two or more interacting species. Methods Ecol. Evol. 7, 1164–1173 (2016).
    Google Scholar 
    Ohler, A. & Dubois, A. Phylogenetic relationships and generic taxonomy of the tribe Paini (Amphibia, Anura, Ranidae, Dicroglossinae). Zoosystema 28, 769–784 (2006).
    Google Scholar 
    Jiang, J. et al. Phylogenetic relationships of the tribe Paini (Amphibia, Anura, Ranidae) based on partial sequences of mitochondrial 12s and 16s rRNA genes. Zool. Res. 362, 353–362 (2005).
    Google Scholar 
    Rais, M. et al. A note on recapture of Nanorana vicina (Anura: Amphibia) from Murree, Pakistan. J. Anim. Plant Sci. 24, 455–458 (2014).
    Google Scholar 
    Siddiqui, M. F., Ahmed, M., Khan, N. & Khan, I. A. A quantitative description of moist temperate conifer forests of Himalayan region of Pakistan and Azad Kashmir. Int. J. Biotechnol. 7, 175–185 (2010).
    Google Scholar 
    Beck, H. E. et al. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).Sheikh, M. I. & Hafeez, S. M. Forest and Forestry in Pakistan (A-one Publishers, 2001).Lodhi, A. Conservation of leopards in Ayubia National Park, Pakistan (MS Thesis) (University of Montana, 2007).Palumbi, S. R. Nucleic acids II: the polymerase chain reaction. In Molecular Systematics, 2nd Edition (eds. Hillis, D. M. et al.) 205–247 (Sinauer, 1996).Vences, M., Thomas, M., Van Der Meijden, A., Chiari, Y. & Vieites, D. R. Comparative performance of the 16S rRNA gene in DNA barcoding of amphibians. Front. Zool. 2, 5 (2005).Pounds, J. A. & Crump, M. L. Amphibian declines and climate disturbance: the case of the golden toad and the harlequin frog. Conserv. Biol. 8, 72–85 (1994).
    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/ (2021).Hutchinson, R. A., Valente, J. J., Emerson, S. C., Betts, M. G. & Dietterich, T. G. Penalized likelihood methods improve parameter estimates in occupancy models. Methods Ecol. Evol. 6, 949–959 (2015).
    Google Scholar 
    Clipp, H. L., Evans, A. L., Kessinger, B. E., Kellner, K., & Rota, C. T. A penalized likelihood for multispecies occupancy models improves predictions of species interactions. Ecology 102, e03520 (2021).PubMed 

    Google Scholar  More

  • 163 Shares129 Views

    in Ecology

    Population density, bottom-up and top-down control as an interactive triplet to trigger dispersal

    Nathan, R. The challenges of studying dispersal. Trends. Ecol. Evol. 16, 481–483. https://doi.org/10.1016/S0169-5347(01)02272-8 (2001).CAS 
    Article 

    Google Scholar 
    Bonte, D. et al. Costs of dispersal. Biol. Rev. Camb. Philos. Soc. 87, 290–312. https://doi.org/10.1111/j.1469-185X.2011.00201.x (2012).Article 
    PubMed 

    Google Scholar 
    Matthysen, E. Multicausality of dispersal: A review. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 3–18 (Oxford University Press, 2012).Chapter 

    Google Scholar 
    Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209. https://doi.org/10.1111/j.1461-0248.2008.01267.x (2009).Article 
    PubMed 

    Google Scholar 
    Poethke, H. J. & Hovestadt, T. Evolution of density- and patch-size-dependent dispersal rates. Proc. R. Soc. Lond. 269, 637–645. https://doi.org/10.1098/rspb.2001.1936 (2002).Article 

    Google Scholar 
    Benton, T. G. & Bowler, D. E. Dispersal in invertebrates: Influences on individual decisions. Ecol. Evol. 1, 41–49 (2012).
    Google Scholar 
    Legrand, D. et al. Ranking the ecological causes of dispersal in a butterfly. Ecography 38, 822–831. https://doi.org/10.1111/ecog.01283 (2015).Article 

    Google Scholar 
    Travis, J. M. J., Murrell, D. J. & Dytham, C. The evolution of density–dependent dispersal. Proc. R. Soc. Lond. B 266, 1837–1842. https://doi.org/10.1098/rspb.1999.0854 (1999).Article 

    Google Scholar 
    Matthysen, E. Density-dependent dispersal in birds and mammals. Ecography 28, 403–416. https://doi.org/10.1111/j.0906-7590.2005.04073.x (2005).Article 

    Google Scholar 
    de Meester, N., Derycke, S., Rigaux, A. & Moens, T. Active dispersal is differentially affected by inter- and intraspecific competition in closely related nematode species. Oikos 124, 561–570. https://doi.org/10.1111/oik.01779 (2015).Article 

    Google Scholar 
    Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225. https://doi.org/10.1017/S1464793104006645 (2005).Article 
    PubMed 

    Google Scholar 
    Bengtsson, G., Hedlund, K. & Rundgren, S. Food- and density-dependent dispersal: Evidence from a soil collembolan. J. Anim. Ecol. 63, 513. https://doi.org/10.2307/5218 (1994).Article 

    Google Scholar 
    Fellous, S., Duncan, A., Coulon, A. & Kaltz, O. Quorum sensing and density-dependent dispersal in an aquatic model system. PLoS ONE 7, e48436. https://doi.org/10.1371/journal.pone.0048436 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aguillon, S. M. & Duckworth, R. A. Kin aggression and resource availability influence phenotype-dependent dispersal in a passerine bird. Behav. Ecol. Sociobiol. 69, 625–633. https://doi.org/10.1007/s00265-015-1873-5 (2015).Article 

    Google Scholar 
    Byers, J. E. Effects of body size and resource availability on dispersal in a native and a non-native estuarine snail. J. Exp. Mar. Biol. Ecol. 248, 133–150. https://doi.org/10.1016/S0022-0981(00)00163-5 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    de Meester, N., Derycke, S. & Moens, T. Differences in time until dispersal between cryptic species of a marine nematode species complex. PLoS ONE 7, e42674. https://doi.org/10.1371/journal.pone.0042674 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sepulveda, A. J. & Marczak, L. B. Active dispersal of an aquatic invader determined by resource and flow conditions. Biol. Invasions 14, 1201–1209. https://doi.org/10.1007/s10530-011-0149-x (2012).Article 

    Google Scholar 
    Lobbia, P. A. & Mougabure-Cueto, G. Active dispersal in Triatoma infestans (Klug, 1834) (Hemiptera Reduviidae: Triatominae): Effects of nutritional status, the presence of a food source and the toxicological phenotype. Acta Trop. 204, 105345. https://doi.org/10.1016/j.actatropica.2020.105345 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Barbraud, C., Johnson, A. R. & Bertault, G. Phenotypic correlates of post-fledging dispersal in a population of greater flamingos: The importance of body condition. J. Anim. Ecol. 72, 246–257. https://doi.org/10.1046/j.1365-2656.2003.00695.x (2003).Article 

    Google Scholar 
    Bonte, D. & de La Peña, E. Evolution of body condition-dependent dispersal in metapopulations. J. Evol. Biol. 22, 1242–1251. https://doi.org/10.1111/j.1420-9101.2009.01737.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Moran, N. P., Sánchez-Tójar, A., Schielzeth, H. & Reinhold, K. Poor nutritional condition promotes high-risk behaviours: A systematic review and meta-analysis. Biol. Rev. Camb. Philos. Soc. 96, 269–288. https://doi.org/10.1111/brv.12655 (2021).Article 
    PubMed 

    Google Scholar 
    Altermatt, F. & Fronhofer, E. A. Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities. Freshw. Biol. 63, 22–32. https://doi.org/10.1111/fwb.12951 (2018).Article 

    Google Scholar 
    McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452. https://doi.org/10.1098/rsbl.2009.1082 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baines, C. B., McCauley, S. J. & Rowe, L. Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. Ecol. Evol. 5, 2307–2316. https://doi.org/10.1002/ece3.1508 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hammill, E., Fitzjohn, R. G. & Srivastava, D. S. Conspecific density modulates the effect of predation on dispersal rates. Oecologia 178, 1149–1158. https://doi.org/10.1007/s00442-015-3303-9 (2015).ADS 
    Article 
    PubMed 

    Google Scholar 
    Fronhofer, E. A. et al. Bottom-up and top-down control of dispersal across major organismal groups. Nat. Ecol. Evol. 2, 1859–1863. https://doi.org/10.1038/s41559-018-0686-0 (2018).Article 
    PubMed 

    Google Scholar 
    Delm, M. Vigilance for predators: Detection and dilution effects. Behav. Ecol. Sociobiol. https://doi.org/10.1007/BF00171099 (1990).Article 

    Google Scholar 
    Matthysen, E. Multicausality of dispersal: A review. Ecol. Evol. 1, 3–18 (2012).
    Google Scholar 
    Bowler, D. E. & Benton, T. G. Variation in dispersal mortality and dispersal propensity among individuals: The effects of age, sex and resource availability. J. Anim. Ecol. 78, 1234–1241. https://doi.org/10.1111/j.1365-2656.2009.01580.x (2009).Article 
    PubMed 

    Google Scholar 
    Giere, O. Meiobenthology. The microscopic motile fauna of aquatic sediments 2nd edn. (Springer, 2009).
    Google Scholar 
    Ptatscheck, C. & Traunspurger, W. The ability to get everywhere: Dispersal modes of free-living, aquatic nematodes. Hydrobiologia 22, 71. https://doi.org/10.1007/s10750-020-04373-0 (2020).Article 

    Google Scholar 
    Ptatscheck, C. & Gansfort, B. Dispersal of free-living nematodes. In Ecology of Freshwater Nematodes (ed. Traunspurger, W.) 151–184 (CABI, 2021).Chapter 

    Google Scholar 
    Traunspurger, W., Bergtold, M., Ettemeyer, A. & Goedkoop, W. Effects of copepods and chironomids on the abundance and vertical distribution of nematodes in a freshwater sediment. J. Freshw. Ecol. 21, 81–90. https://doi.org/10.1080/02705060.2006.9664100 (2006).Article 

    Google Scholar 
    Bargmann, C. I. Chemosensation in C. elegans. WormBook 1, 1–29. https://doi.org/10.1895/wormbook.1.123.1 (2006).Article 

    Google Scholar 
    Chasnov, J. R. & Chow, K. L. Why are there males in the hermaphroditic species Caenorhabditis elegans?. Genetics 160, 983–994 (2002).CAS 
    Article 

    Google Scholar 
    Ramot, D., Johnson, B. E., Berry, T. L., Carnell, L. & Goodman, M. B. The Parallel Worm Tracker: A platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3, e2208. https://doi.org/10.1371/journal.pone.0002208 (2008).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muschiol, D. & Traunspurger, W. Life cycle and calculation of the intrinsic rate of natural increase of two bacterivorous nematodes, Panagrolaimus sp. and Poikilolaimus sp. from chemoautotrophic Movile Cave, Romania. Nematology 9, 271–284. https://doi.org/10.1163/156854107780739117 (2007).Article 

    Google Scholar 
    Beier, S., Bolley, M. & Traunspurger, W. Predator-prey interactions between Dugesia gonocephala and free-living nematodes. Freshw. Biol. 49, 77–86. https://doi.org/10.1046/j.1365-2426.2003.01168.x (2004).Article 

    Google Scholar 
    Powers, E. M. & Sayre, R. M. A predacious soil turbellarian that feeds on free-living and plant-parasitic nematodes. Nematology 12, 619–629. https://doi.org/10.1163/187529266X00482 (1966).Article 

    Google Scholar 
    Kreuzinger-Janik, B., Kruscha, S., Majdi, N. & Traunspurger, W. Flatworms like it round: Nematode consumption by Planaria torva (Müller 1774) and Polycelis tenuis (Ijima 1884). Hydrobiologia 819, 231–242. https://doi.org/10.1007/s10750-018-3642-8 (2018).Article 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Practical use of the information-theoretic approach. In Model Selection and Inference (eds Burnham, K. P. & Anderson, D. R.) 75–117 (Springer, 1998).Chapter 

    Google Scholar 
    McCulloch, C. E., Searle, S. R. & Neuhaus, J. M. Generalized, Linear, and Mixed Models (Wiley, 2008).MATH 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c) (2020).Bonte, D., de Roissart, A., Wybouw, N. & van Leeuwen, T. Fitness maximization by dispersal: Evidence from an invasion experiment. Ecology 95, 3104–3111. https://doi.org/10.1890/13-2269.1 (2014).Article 

    Google Scholar 
    You, Y., Kim, J., Raizen, D. M. & Avery, L. Insulin, cGMP, and TGF-beta signals regulate food intake and quiescence in C. elegans: a model for satiety. Cell Metab. 7, 249–257. https://doi.org/10.1016/j.cmet.2008.01.005 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shtonda, B. B. & Avery, L. Dietary choice behavior in Caenorhabditis elegans. J. Exp. Biol. 209, 89–102. https://doi.org/10.1242/jeb.01955 (2006).Article 
    PubMed 

    Google Scholar 
    Mathieu, J. et al. Habitat quality, conspecific density, and habitat pre-use affect the dispersal behaviour of two earthworm species, Aporrectodea icterica and Dendrobaena veneta, in a mesocosm experiment. Soil Biol. Biochem. 42, 203–209. https://doi.org/10.1016/j.soilbio.2009.10.018 (2010).CAS 
    Article 

    Google Scholar 
    Oro, D., Cam, E., Pradel, R. & Martínez-Abraín, A. Influence of food availability on demography and local population dynamics in a long-lived seabird. Proc. R. Soc. Lond. B 271, 387–396. https://doi.org/10.1098/rspb.2003.2609 (2004).Article 

    Google Scholar 
    Harvey, S. C. Non-dauer larval dispersal in Caenorhabditis elegans. J. Exp. Zool. B Mol. Dev. Evol. 312B, 224–230. https://doi.org/10.1002/jez.b.21287 (2009).Article 
    PubMed 

    Google Scholar 
    Wilden, B., Majdi, N., Kuhlicke, U., Neu, T. R. & Traunspurger, W. Flatworm mucus as the base of a food web. BMC Ecol. 19, 15. https://doi.org/10.1186/s12898-019-0231-2 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gloria-Soria, A. & Azevedo, R. B. R. npr-1 Regulates foraging and dispersal strategies in Caenorhabditis elegans. Curr. Biol. 18, 1694–1699. https://doi.org/10.1016/j.cub.2008.09.043 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Harrison, R. G. Dispersal Polymorphisms in Insects. Annu. Rev. Ecol. Syst. 11, 95–118. https://doi.org/10.1146/annurev.es.11.110180.000523 (1980).Article 

    Google Scholar 
    Denno, R. F. & Peterson, M. A. Density-dependent dispersal and its consequences for population dynamics. Popul Dyn 1, 113–130 (2021).
    Google Scholar 
    Srinivasan, J. et al. A modular library of small molecule signals regulates social behaviors in Caenorhabditis elegans. PLoS Biol. 10, e1001237. https://doi.org/10.1371/journal.pbio.1001237 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bretscher, A. J. et al. Temperature, oxygen, and salt-sensing neurons in C. elegans are carbon dioxide sensors that control avoidance behavior. Neuron 69, 1099–1113. https://doi.org/10.1016/j.neuron.2011.02.023 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Freckman, D. W., Duncan, D. A. & Larson, J. R. Nematode density and biomass in an annual grassland ecosystem. J. Range Manag. 32, 418. https://doi.org/10.2307/3898550 (1979).Article 

    Google Scholar 
    Cote, J. et al. Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40, 56–73. https://doi.org/10.1111/ecog.02538 (2017).Article 

    Google Scholar  More

  • 150 Shares169 Views

    in Ecology

    Black Kites on a flyway between Western Siberia and the Indian Subcontinent

    Ferguson-Lees, J., Christie, D. A. Raptors of the World. Helm Identification Guides (Christopher Helm, London, 2001).
    Google Scholar 
    BirdLife International 2021 Species factsheet: Milvus migrans. Downloaded from http://www.birdlife.org on 10 May 2021.Sergio, F., Pedrini, P. & Marchesi, L. Adaptive selection of foraging and nesting habitat by black kites Milvus migrans and its implications for conservation: a multi-scale approach. Biol. Conserv. 112, 351–362 (2003).
    Google Scholar 
    Tanferna, A., López-Jiménez, L., Blas, J., Hiraldo, F. & Sergio, F. Habitat selection by Black kite breeders and floaters: implications for conservation management of raptor floaters. Biol. Conserv. 160, 1–9 (2013).
    Google Scholar 
    Cortés-Avizanda, A. et al. Spatial heterogeneity in resource distribution promotes facultative sociality in two Trans-Saharan migratory birds. PLoS ONE 6, e21016 (2011).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Panuccio, M., Agostini, N., Mellone. U. & Bogliani, G. Circannual variation in movement patterns of the Black Kite (Milvus migrans migrans): A review. Ethol. Ecol. Evol. 26, 1–18 (2013).Dickinson, E. C. & Remsen, J. V. The Howard and Moore Complete Checklist of the Birds of the World, 4th (Aves Press, 2013).
    Google Scholar 
    Clements, J. F. et al. The eBird/Clements Checklist of Birds of the World: v2019. (2019).Orta, J., Marks, J. S., Garcia, E. & Kirwan, G. M. Black Kite (Milvus migrans). In Birds of the World (eds. Billerman, S.M., Keeney, B.K., Rodewald, P.G. & Schulenberg T.S.) 168–172 (Cornell Lab of Ornithology, 2020).Gill, F., Donsker, D. & Rasmussen, P. IOC World Bird List – version 11.1 (worldbirdnames.org., 2021).Dementiev, G. P., Gladkov, N. A., Ptushenko, E. S., Spangenberg, E. P. & Sudilovskaya, A. M. Birds of the Soviet Union, Vol. 1 (Sovetskaya Nauka, Moscow, in Russian, 1951).
    Google Scholar 
    Stepanyan, L. S. Conspectus of the Ornithological Fauna of the USSR (Nauka, Moscow, in Russian, 1990).Karyakin, I. Problem of identification of Eurasian subspecies of the Black Kite and records of the Pariah Kite in Southern Siberia, Russia. Raptors Conserv. 34, 49–67 (2017).
    Google Scholar 
    Skyrpan, M. & Literák, I. A kite Milvus migrans migrans/lineatus in Ukraine. Biologia 74, 1669–1673 (2019).
    Google Scholar 
    Panter, C. T. et al. Kites (Milvus spp.) wintering on Crete. Eur. Zool. J. 87, 591–596 (2020).
    Google Scholar 
    Skyrpan, M. et al. Kites Milvus migrans lineatus (Milvus migrans migrans/lineatus) are spreading west across Europe. J. Ornithol. 162, 317–323 (2021).
    Google Scholar 
    Onrubia Baticón A. Patrones espacio-temporales de la migración de aves planeadoras en el Estrecho de Gibraltar (Spatial and temporal patterns of soaring birds migration through the straits of Gibraltar). Doctoral thesis (Universidad de León, 2015).Literák, I. et al. Weather-influenced water-crossing behaviour of black kites Milvus migrans during migration. Biologia 76, 1267–1273 (2021).
    Google Scholar 
    Ovčiariková, S. et al. Natal dispersal in Black Kites Milvus migrans migrans in Europe. J. Ornithol. 161, 935–951 (2020).
    Google Scholar 
    Sklyarenko, S., Gavrilov, E. & Gavrilov, A. Migratory flyways of raptors and owls in Kazakhstan according to ringing data. Vogelwarte 41, 263–268 (2002).
    Google Scholar 
    Probst, R. & Pavličev, M. Migration in the Novosibirsk region and the Kuznetsky Alatau, Russia. Sandgrouse 28, 114–118 (2006).
    Google Scholar 
    Harris, T. Migration Hotspots. The World’s Best Bird Migration Sites. (Bloomsbury, London, New Delhi, New York, Sydney, 2013).Hirano, T. & Ueda, M. Black Kite Milvus migrans in Japanese. Bird Res. News 810, 1–6 (2011).
    Google Scholar 
    Choudhuri, A. Migration of Black-eared or Large Indian Kite Milvus migrans lineatus Gray from Mongolia to North-Eastern India. J. Bombay Nat. Hist. Soc. 102, 229–230 (2005).
    Google Scholar 
    Davaasuren, B. Khurkh Bird Ringing Station Annual Report 2018. (Wildlife Science Conservation Center of Mongolia, Ulaanbaatar, 2019).Kumar, N. et al. GPS-telemetry unveils the regular high-elevation crossing of the Himalayan by a migratory raptor: Implications for definition of a “Central Asian Flyway”. Sci. Rep. 10, 15988 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Juhant, M. A. & Bildstein, K. L. Raptor migration across and around the Himalayas. In Bird Migration Across the Himalayas (eds. Prins, H. H. T. & Namgail, T.) 98–116 (Cambridge University Press, Cambridge, 2017).
    Google Scholar 
    Rotics, S. et al. The challenges of the first migration: Movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality. J. Anim. Ecol. 85, 938–947 (2016).PubMed 

    Google Scholar 
    Vidal-Mateo, J. et al. Wind effects on the migration routes of trans-Saharan soaring raptors: Geographical, seasonal and interspecific variation. Curr. Zool. 62, 89–97 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Safi, K. et al. Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1, 4 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Green, M., Alerstam, T., Clausen, P., Drent, R. & Ebbinge, B. S. Dark-bellied Brent Geese Branta bernicla bernicla, as recorded by satellite telemetry, do not minimize flight distance during spring migration. Ibis 144, 106–121 (2002).
    Google Scholar 
    Malmiga, G., Nilsson, C., Bäckman, J. & Alerstam, T. Interspecific comparison of the flight performance between sparrowhawks and common buzzards migrating at the Falsterbo peninsula: a radar study. Curr. Zool. 605, 670–679 (2014).
    Google Scholar 
    Vansteelant, W. M. G. et al. Regional and seasonal flight speeds of soaring migrants and the role of weather conditions at hourly and daily scales. J. Avian Biol. 46, 25–39 (2015).
    Google Scholar 
    Dodge, S., Bohrer, G. & Weinzierl, R. MoveBank track annotation project: linking animal movement data with the environment to discover the impact of environmental change in animal migration. In Workshop on GIScience in the Big Data Age in Conjunction with the Seventh International Conference on Geographic Information Science 2012 GIScience (eds. Janowicz, K., Kessler, C., Kauppinen, T. & Kolas, D.) 35–41 (Columbus, OH, 2012).Scott, G. R. et al. How bar-headed geese fly over the Himalayas. Physiology 30, 107–115 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Andreyenkova, N. G., Andreyenkov, O. V., Karyakin, I. V. & Zhimulev, I. F. New haplotypes of the mitochondrial gene cytB in the nesting population of the Siberian Black Kite Milvus migrans lineatus Gray, 1831 in the territory of the Republic of Tyva. Dokl. Biochem. Biophys. 482, 242–244 (2018).CAS 
    PubMed 

    Google Scholar 
    Mellone, U. et al. Interspecific comparison of the performance of soaring migrants in relation to morphology, meteorological conditions and migration strategies. PLoS ONE 7, e39833 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kemp, M. U., Emiel van Loon, E., Shamoun-Baranes, J. & Bouten, W. RNCEP: global weather and climate data at your fingertips. Methods Ecol. Evol. 3, 65–70 (2012).
    Google Scholar 
    Team, R.C. R: A Language and Environment for Statistical Computing. R 739 (Foundation for Statistical Computing [Internet], Vienna, Austria, 2018). https://www.R-project.org/Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    Andreyenkova, N. G. et al. Phylogeography and demographic history of the Black Kite Milvus migrans, raptor widespread in Eurasia, Australia and Africa. J. Avian Biol. 52, e02822 (2021).
    Google Scholar 
    Lindholm, A. & Forsten, A. Black Kites Milvus migrans in Russian Altai. Caluta 2, 1–6 (2011).
    Google Scholar 
    Vansteelant, W.M.G. An ontogenetic perspective on migration learning and critical life-history traits in raptors. In Abstracts of British Ornithologists’ Union 2019 Annual Conference Tracking Migration: Drivers, Challenges and Consequences of Seasonal Movements. 45–46. (University of Warwick, UK, 2019).Dixon, A., Rahman, L., Sokolov, A. & Sokolov, V. Peregrine Falcons crossing the „Roof of the World”. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.) 128–141 (Cambridge University Press, Cambridge, 2017).Parr, N. et al. High altitude flights by ruddy shelduck Tadorna ferruginea during trans-Himalayan migrations. J. Avian Biol. 48, 1310–1315 (2017).
    Google Scholar 
    Hawkes, L. A. et al. The paradox of extreme high-altitude migration in bar-headed geese Anser indicus. Proc. R. Soc. B 280, 1–8 (2013).
    Google Scholar 
    Bishop, C. M. et al. The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations. Science 347, 250–254 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Agostini, N., Pannucio, M. & Pasquaretta, C. Morphology, flight performace, and water crossing tendencies of Afro-Palearctic raptors during migration. Curr. Zool. 61, 951–958 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Altshuler, D. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

    Google Scholar 
    Santos, C. D. et al. Match between soaring modes of black kites and the fine-scale distribution of updrafts. Sci. Rep. 7, 6421 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ohlmann, K. The wind system in the Himalayas: From a Bird’s-Eye View. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 9–28 (Cambridge University Press, Cambridge, 2017).Heise, R. Birds, gliders and uplift systems over the Himalayas. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 229–40 (Cambridge University Press, Cambridge, 2017).Harel, R. et al. Decision-making by a soaring bird: time, energy and risk considerations at different spatiotemporal scales. Philos. T. R. Soc. B 371, 20150397 (2016).
    Google Scholar 
    Vansteelant, W. M. G., Shamoun-Baranes, J., McLaren, J., van Diermen, J. & Bouten, W. Soaring across continents: Decision-making of a soaring migrant under changing atmospheric conditions along an entire flyway. J. Avian Biol. 48, 887–896 (2017).
    Google Scholar 
    Nilsson, C., Klaassen, R. H. G. & Alerstam, T. Differences in speed and duration of bird migration between spring and autumn. Am. Nat. 181, 837–845 (2013).PubMed 

    Google Scholar 
    Kokko, H. Competition for early arrival in migratory birds. J. Anim. Ecol. 68, 940–150 (1999).
    Google Scholar 
    Moore, F.R., Smith, R.J. & Sandberg, R. Stopover ecology of intercontinental migrants: en route problems and consequences for reproductive performance. In Birds of Two Worlds: the Ecology and Evolution of Migration (eds. Greenberg, R. & Marra, P.P.), 251–261 (Johns Hopkins University Press, Baltimore, 2005).McNamara, J. M., Welham, R. K. & Houston, A. I. The timing of migration within the context of an annual routine. J. Avian Biol. 29, 416–423 (1998).
    Google Scholar 
    Köppen, U. et al. Seasonal migrations of four individual bar-headed geese Anser indicus from Kyrgyzstan followed by satellite telemetry. J. Ornithol. 151, 703–712 (2010).
    Google Scholar 
    Kölzsch, A. et al. Towards a new understanding of migration timing: slower spring than autumn migration in geese reflects different decision rules for stopover use and departure. Oikos 125, 1496–1507 (2016).
    Google Scholar 
    Butler, R. W., Williams, T. D., Warnock, N. & Bishop, M. A. Wind assistance: a requirement for migration of shorebirds? Auk 114, 456–466 (1997).
    Google Scholar 
    Santos, C. D., Silva, J. P., Muñoz, A. R., Onrubia, A. & Wikelski, M. The gateway to Africa: What determines sea crossing performance of a migratory soaring birds at the Strait of Gibraltar. J. Anim. Ecol. 89, 1317–1328 (2020).PubMed 

    Google Scholar 
    Kumerloeve, H. V. Überwintern des Schwarzmilans im vorderen Orient. Falke 14, 274–227 (1967).
    Google Scholar 
    Baumgart, W., Kasparek, M. & Stephan, B. Die Vögel Syrien: eine Übersicht (Max Kasparek Verlag, 1995).
    Google Scholar 
    Tsvelykh, A. N. & Panyushkin, V. E. Wintering of the Black Kite Milvus migrans in Ukraine. Vestn. Zool. 36, 81–83 (2002).
    Google Scholar 
    Sarà, M. The colonisation of Sicily by the Black Kite Milvus migrans. J. Raptor Res. 37, 167–172 (2003).
    Google Scholar 
    Domashevskii, S. V. First record of the Black Kite in winter in the northern part of Ukraine. Berkut 18, 212–213 (2009).
    Google Scholar 
    Ciach, M. & Kruszyk, R. Foraging of White Storks Ciconia ciconia on rubbish dumps on nonbreeding grounds. Waterbirds 33, 101–104 (2010).
    Google Scholar 
    Biricik, M. & Karakaş. R. Black Kites Milvus migrans winter in Southeastern Anatolia, Turkey. J. Raptor Res. 45, 370–373 (2011).Literák, I., Horal, D., Alivizatos, H. & Matušík, H. Common wintering of black kites Milvus migrans migrans in Greece, and new data on their wintering elsewhere in Europe. Slovak Raptor J. 11, 91–102 (2017).
    Google Scholar 
    Shirihai, H., Yosef, R., Alon, D., Kirwan, G.M. & Spaar, R. Raptor Migration in Israel and the Middle East (International Birdwatching Centre Eilat IBRCE, IOC, Israel, 2000).Forsman, D. Identification of Black-eared Kite. Bird. World 16, 156–216 (2003).
    Google Scholar 
    Abuladze, A. Birds of Prey of Georgia, Materials towards a Fauna of Georgia, Issue VI (Ilia State University, Tbilisi, 2013).
    Google Scholar 
    Brooke, R. K. The migratory Black Kite Milvus migrans migrans Aves: Accipitridae of the Palearctic in southern Africa. Durb. Mus. Novit. 10, 53–66 (1974).
    Google Scholar 
    Forsman, D. Flight Identifcation of Raptors of Europe (North Africa and the Middle East (Christopher Helm, 2016).
    Google Scholar 
    Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020). More

  • 250 Shares159 Views

    in Ecology

    Direct and latent effects of ocean acidification on the transition of a sea urchin from planktonic larva to benthic juvenile

    Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
    CAS 

    Google Scholar 
    Intergovernmental Panel on Climate Change. Climate Change 2013: 5th Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).
    Google Scholar 
    Torres, O., Kwiatkowski, L., Sutton, A. J., Dorey, N. & Orr, J. C. Characterizing mean and extreme diurnal variability of ocean CO2 system variables across marine environments. Geophys. Res. Lett. 48, 2 (2021).
    Google Scholar 
    Dorey, N., Lançon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point of sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
    Google Scholar 
    Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California current system. Biogeosci. Discuss. 9, 10371–10428 (2012).ADS 

    Google Scholar 
    Dupont, S. & Pörtner, H.-O. A snapshot into ocean acidification research. Mar. Biol. 160, 1765–1771 (2013).CAS 

    Google Scholar 
    Dupont, S. & Thorndyke, M. Chapter: Direct impacts of near-future ocean acidification on sea urchins. in Climate Change Perspective from the Atlantic: Past, Present and Future (eds. Fernández-Palacios, J. et al.) 461–485 (2013).Byrne, M. & Hernández, J. C. Chapter 16: Sea urchins in a high CO2 world: Impacts of climate warming and ocean acidification across life history stages. in Developments in Aquaculture and Fisheries Science vol. 43 281–297 (Elsevier, 2020).Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

    Google Scholar 
    L. Kelley, A., J. Lunden, J., 1 Ocean Acidification Research Center, College of Fisheries and Ocean Sciences, University of Alaska, Fairbanks, Fairbanks, AK, 99775, USA, & 2 Haverford College, Haverford, PA, 19041, USA. Meta-analysis identifies metabolic sensitivities to ocean acidification. AIMS Environ. Sci. 4, 709–729 (2017).Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl. Acad. Sci. U. S. A. 109, 18192–18197 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).ADS 
    CAS 

    Google Scholar 
    Runcie, D. E. et al. Genomic characterization of the evolutionary potential of the sea urchin Strongylocentrotus droebachiensis facing ocean acidification. Genome Biol. Evol. 8, 272 (2017).
    Google Scholar 
    Sewell, M. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).ADS 
    CAS 

    Google Scholar 
    Lucas, M. I., Walker, G., Holland, D. L. & Crisp, D. J. An energy budget for the free-swimming and metamorphosing larvae of Balanus balanoides (Crustacea: Cirripedia). Mar. Biol. 55, 221–229 (1979).
    Google Scholar 
    Shilling, F. M., Hoegh-Guldberg, O. & Manahan, D. T. Sources of energy for increased metabolic demand during metamorphosis of the abalone Haliotis rufescens (Mollusca). Biol. Bull. 191, 402–412 (1996).CAS 
    PubMed 

    Google Scholar 
    Meidel, S. K. & Scheibling, R. E. Effects of food type and ration on reproductive maturation and growth of the sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 134, 155–166 (1999).
    Google Scholar 
    Pearce, C. M. & Scheibling, R. E. Induction of metamorphosis of larvae of the green sea urchin, Strongylocentrotus droebachiensis by coralline red algae. Biol. Bull. 179, 304–311 (1990).CAS 
    PubMed 

    Google Scholar 
    Gosselin, P. & Jangoux, M. From competent larva to exotrophic juvenile: a morphofunctional study of the perimetamorphic period of Paracentrotus lividus (Echinodermata, Echinoida). Zoomorphology 118, 31–43 (1998).
    Google Scholar 
    Hinegardner, R. T. Growth and development of the laboratory cultured sea urchin. Biol. Bull. 137, 465–475 (1969).CAS 
    PubMed 

    Google Scholar 
    Strathmann, R. R. Length of pelagic period in echinoderms with feeding larvae from the Northeast Pacific. J. Exp. Biol. Ecol. 34, 23–27 (1978).
    Google Scholar 
    Byrne, M. et al. Unshelled abalone and corrupted urchins: Development of marine calcifiers in a changing ocean. Proc. Biol. Sci. 278, 2376–2383 (2011).PubMed 

    Google Scholar 
    Dupont, S., Dorey, N., Stumpp, M., Melzner, F. & Thorndyke, M. Long-term and trans-life-cycle effects of exposure to ocean acidification in the green sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 160, 1835–1843 (2013).CAS 

    Google Scholar 
    Uthicke, S. et al. Impacts of ocean acidification on early life-history stages and settlement of the coral-eating sea star Acanthaster planci. PLoS ONE 8, e82938 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dupont, S., Lundve, B. & Thorndyke, M. Near future ocean acidification increases growth rate of the lecithotrophic larvae and juveniles of the sea star Crossaster papposus. J. Exp. Zool. Mol. Dev. Evol. 314, 382–389 (2010).
    Google Scholar 
    Lim, Y.-K., Dang, X. & Thiyagarajan, V. Transgenerational responses to seawater pH in the edible oyster, with implications for the mariculture of the species under future ocean acidification. Sci. Total Environ. 782, 146704 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hettinger, A. et al. Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768 (2012).PubMed 

    Google Scholar 
    Hettinger, A. et al. Larval carry-over effects from ocean acidification persist in the natural environment. Glob. Change Biol. https://doi.org/10.1111/gcb.12307 (2013).Article 

    Google Scholar 
    Albright, R. & Langdon, C. Ocean acidification impacts multiple early life history processes of the Caribbean coral Porites astreoides. Glob. Change Biol. 17, 2478–2487 (2011).ADS 

    Google Scholar 
    Yuan, X. et al. Elevated CO2 delays the early development of scleractinian coral Acropora gemmifera. Sci. Rep. 8, 2787 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maboloc, E. A. & Chan, K. Y. K. Parental whole life cycle exposure modulates progeny responses to ocean acidification in slipper limpets. Glob. Change Biol. 2, 15647. https://doi.org/10.1111/gcb.15647 (2021).Article 

    Google Scholar 
    Mos, B., Byrne, M. & Dworjanyn, S. A. Effects of low and high pH on sea urchin settlement, implications for the use of alkali to counter the impacts of acidification. Aquaculture 528, 735618 (2020).CAS 

    Google Scholar 
    Harianto, J., Aldridge, J., Torres Gabarda, S. A., Grainger, R. J. & Byrne, M. Impacts of acclimation in warm-low pH conditions on the physiology of the sea urchin Heliocidaris erythrogramma and carryover effects for juvenile offspring. Front. Mar. Sci. 7, 588938 (2021).
    Google Scholar 
    Houlihan, E. P., Espinel-Velasco, N., Cornwall, C. E., Pilditch, C. A. & Lamare, M. D. Diffusive boundary layers and ocean acidification: Implications for sea urchin settlement and growth. Front. Mar. Sci. 7, 577562 (2020).
    Google Scholar 
    Norderhaug, K. M. & Christie, H. C. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Mar. Biol. Res. 5, 515–528 (2009).
    Google Scholar 
    Dickson, A., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements. (PICES Special Publication 3;191 pp, 2007).Lavigne, H. & Gattuso, J.-P. seacarb: seawater carbonate chemistry with R. R package version 2.4. http://CRAN.R-project.org/package=seacarb. (2011).R Core Team. R: A language and environment for statistical computing. R: A language and environment for statistical computing (2017).Guillard, R. R. L. & Ryther, J. H. Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt and Detonula confervacea (Cleve) Gran. Can. J. Microbiol. 8, 229–239 (1962).CAS 
    PubMed 

    Google Scholar 
    Stumpp, M., Wren, J., Melzner, F., Thorndyke, M. & Dupont, S. CO2 induced seawater acidification impacts sea urchin larval development I: Elevated metabolic rates decrease scope for growth and induce developmental delay. Comp. Biochem. Physiol. Mol. Integr. Physiol. 160, 331–340 (2011).CAS 

    Google Scholar 
    His, E., Heyvang, I., Geffard, O. & De Montaudouin, X. A comparison between oyster (Crassostrea gigas) and sea urchin (Paracentrotus lividus) larval bioassays for toxicological studies. Water Res. 33, 1706–1718 (1999).CAS 

    Google Scholar 
    U. S. National Institutes of Health, Bethesda, Maryland, U. ImageJ, Rasband, W.S., http://imagej.nih.gov/ij/.Smith, M. M., Cruz Smith, L., Cameron, R. A. & Urry, L. The larval stages of the sea urchin, Strongylocentrotus purpuratus. J. Morphol. 269, 713–733 (2008).PubMed 

    Google Scholar 
    Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J. & Kschischo, M. grofit: Fitting Biological Growth Curves with R. J. Stat. Softw., 33(7), 1–21. URL http://www.jstatsoft.org/v33/i07/. (2010).Pinheiro, J., Bates, D., & R-core. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models. Cran-R (2018).Pan, T.-C.F., Applebaum, S. L. & Manahan, D. T. Experimental ocean acidification alters the allocation of metabolic energy. Proc. Natl. Acad. Sci. U. S. A. 112, 4696–4701 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jager, T., Ravagnan, E. & Dupont, S. Near-future ocean acidification impacts maintenance costs in sea-urchin larvae: Identification of stress factors and tipping points using a DEB modelling approach. J. Exp. Mar. Biol. Ecol. 474, 11–17 (2016).
    Google Scholar 
    Hoegh-Guldberg, O. & Emlet, R. B. Energy use during the development of a lecithotrophic and a planktotrophic echinoid. Biol. Bull. 192, 27–40 (1997).CAS 
    PubMed 

    Google Scholar 
    Vaïtilingon, D. et al. Effects of delayed metamorphosis and food rations on the perimetamorphic events in the echinoid Paracentrotus lividus (Lamarck, 1816) (Echinodermata). J. Exp. Mar. Biol. Ecol. 262, 41–60 (2001).
    Google Scholar 
    García, E., Clemente, S. & Hernández, J. C. Ocean warming ameliorates the negative effects of ocean acidification on Paracentrotus lividus larval development and settlement. Mar. Environ. Res. 110, 61–68 (2015).PubMed 

    Google Scholar 
    Wangensteen, O. S., Dupont, S., Casties, I., Turon, X. & Palacín, C. Some like it hot: Temperature and pH modulate larval development and settlement of the sea urchin Arbacia lixula. J. Exp. Mar. Biol. Ecol. 449, 304–311 (2013).
    Google Scholar 
    García, E., Clemente, S. & Hernández, J. C. Effects of natural current pH variability on the sea urchin Paracentrotus lividus larvae development and settlement. Mar. Environ. Res. 139, 11–18 (2018).PubMed 

    Google Scholar 
    Marshall, D. J. & Keough, M. J. Variation in the dispersal potential of non-feeding invertebrate larvae: The desperate larva hypothesis and larval size. Mar. Ecol. Prog. Ser. 255, 145–153 (2003).ADS 

    Google Scholar 
    Huggett, M. J., Williamson, J. E., de Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).ADS 
    PubMed 

    Google Scholar 
    Espinel-Velasco, N., Agüera, A. & Lamare, M. Sea urchin larvae show resilience to ocean acidification at the time of settlement and metamorphosis. Mar. Environ. Res. 159, 104977 (2020).CAS 
    PubMed 

    Google Scholar 
    Lamare, M. & Barker, M. Settlement and recruitment of the New Zealand sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 218, 153–166 (2001).ADS 

    Google Scholar 
    Martin, S. et al. Early development and molecular plasticity in the Mediterranean sea urchin Paracentrotus lividus exposed to CO2-driven acidification. J. Exp. Biol. 214, 1357–1368 (2011).CAS 
    PubMed 

    Google Scholar 
    Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
    Google Scholar 
    Espinel-Velasco, N. et al. Effects of ocean acidification on the settlement and metamorphosis of marine invertebrate and fish larvae: a review. Mar. Ecol. Prog. Ser. 606, 237–257 (2018).ADS 

    Google Scholar 
    Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528 (2012).CAS 
    PubMed 

    Google Scholar 
    Gaylord, B. et al. Ocean acidification through the lens of ecological theory. Ecology 96, 3–15 (2015).PubMed 

    Google Scholar  More