in

Climate and land-use changes drive biodiversity turnover in arthropod assemblages over 150 years

  • 1.

    Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 2.

    Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 3.

    Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).

    Google Scholar 

  • 4.

    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 5.

    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Article 

    Google Scholar 

  • 6.

    Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 7.

    Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).

    Article 

    Google Scholar 

  • 8.

    Falaschi, M., Manenti, R., Thuiller, W. & Ficetola, G. F. Continental‐scale determinants of population trends in European amphibians and reptiles. Glob. Change Biol. 25, 3504–3515 (2019).

    Article 

    Google Scholar 

  • 9.

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 10.

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    PubMed 
    Article 
    CAS 

    Google Scholar 

  • 11.

    Jarzyna, M. A. & Jetz, W. Detecting the multiple facets of biodiversity. Trends Ecol. Evol. 31, 527–538 (2016).

    PubMed 
    Article 

    Google Scholar 

  • 12.

    Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 13.

    Bell, J. R. et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century. Glob. Change Biol. 25, 1982–1994 (2019).

    Article 

    Google Scholar 

  • 14.

    Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).

    PubMed 
    Article 

    Google Scholar 

  • 15.

    van Strien, A. J., van Swaay, C. A., van Strien-van Liempt, W. T., Poot, M. J. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234, 116–122 (2019).

    Article 

    Google Scholar 

  • 16.

    Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 17.

    Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—a macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).

    Article 

    Google Scholar 

  • 18.

    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 19.

    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).

    Article 

    Google Scholar 

  • 20.

    Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).

    Article 

    Google Scholar 

  • 21.

    Kondratyeva, A., Grandcolas, P. & Pavoine, S. Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution. Biol. Rev. 94, 1317–1337 (2019).

    PubMed 
    Article 

    Google Scholar 

  • 22.

    Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non‐native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).

    Article 

    Google Scholar 

  • 23.

    WallisDeVries, M. F. & van Swaay, C. A. A nitrogen index to track changes in butterfly species assemblages under nitrogen deposition. Biol. Conserv. 212, 448–453 (2017).

    Article 

    Google Scholar 

  • 24.

    Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 25.

    Sgardeli, V., Zografou, K. & Halley, J. M. Climate change versus ecological drift: assessing 13 years of turnover in a butterfly community. Basic Appl. Ecol. 17, 283–290 (2016).

    Article 

    Google Scholar 

  • 26.

    van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2019).

    Article 
    CAS 

    Google Scholar 

  • 27.

    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 28.

    Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 29.

    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 30.

    Marta, S. et al. ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna. Sci. Data 6, 195 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 31.

    Koleff, P., Gaston, K. J. & Lennon, J. T. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).

    Article 

    Google Scholar 

  • 32.

    Legendre, P. A temporal beta‐diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecol. Evol. 9, 3500–3514 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 33.

    Suggit, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).

    Article 

    Google Scholar 

  • 34.

    Baselga, A., Bonthoux, S. & Balent, G. Temporal beta diversity of bird assemblages in agricultural landscapes: land cover change vs. stochastic processes. PLoS ONE 10, e0127913 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 35.

    Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).

    Google Scholar 

  • 36.

    Mason, N. W., de Bello, F., Mouillot, D., Pavoine, S. & Dray, S. A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients. J. Veg. Sci. 24, 794–806 (2013).

    Article 

    Google Scholar 

  • 37.

    Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).

  • 38.

    Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).

    Article 

    Google Scholar 

  • 39.

    Brunetti, M., Maugeri, M., Monti, F. & Nanni, T. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345–381 (2006).

    Article 

    Google Scholar 

  • 40.

    Terzago, S., von Hardenberg, J., Palazzi, E. & Provenzale, A. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models. Cryosphere 11, 1625–1645 (2017).

    Article 

    Google Scholar 

  • 41.

    Beniston, M. et al. The European mountain cryosphere: a review of its current state, trends and future challenges. Cryosphere 12, 759–794 (2018).

    Article 

    Google Scholar 

  • 42.

    Wang, J. et al. Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. 11, 528 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 43.

    Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with nonstationary climate–fire models. Nat. Commun. 9, 3821 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 44.

    Jacobson, A. R., Provenzale, A., von Hardenberg, A., Bassano, B. & Festa-Bianchet, M. Climate forcing and density dependence in a mountain ungulate population. Ecology 85, 1598–1610 (2004).

    Article 

    Google Scholar 

  • 45.

    Imperio, S., Bionda, R., Viterbi, R. & Provenzale, A. Climate change and human disturbance can lead to local extinction of Alpine rock ptarmigan: new insight from the Western Italian Alps. PLoS ONE 8, e81598 (2013).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 46.

    Hoffmann, S., Beierkuhnlein, C., Field, R., Provenzale, A. & Chiarucci, A. Uniqueness of protected areas for conservation strategies in the European Union. Sci. Rep. 8, 6445 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 47.

    Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Article 

    Google Scholar 

  • 48.

    Queiroz, C., Beilin, R., Folke, C. & Lindborg, R. Farmland abandonment: threat or opportunity for biodiversity conservation? A global review. Front. Ecol. Environ. 12, 288–296 (2014).

    Article 

    Google Scholar 

  • 49.

    Falcucci, A., Maiorano, L. & Boitani, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).

    Article 

    Google Scholar 

  • 50.

    Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 51.

    Ranganathan, S., Swain, R. B. & Sumpter, D. J. T. The demographic transition and economic growth: implications for development policy. Palgrave Commun. 1, 15033 (2015).

    Article 

    Google Scholar 

  • 52.

    Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience 53, 941–952 (2003).

    Article 

    Google Scholar 

  • 53.

    Lacasella, F. et al. From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability. Ecol. Appl. 27, 575–588 (2017).

    PubMed 
    Article 

    Google Scholar 

  • 54.

    Ficetola, G. F. & Maiorano, L. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia 181, 683–693 (2016).

    PubMed 
    Article 

    Google Scholar 

  • 55.

    Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 56.

    Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 57.

    Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 58.

    Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).

    PubMed 
    Article 

    Google Scholar 

  • 59.

    Poff, N. L. et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change 6, 25–34 (2017).

    Article 

    Google Scholar 

  • 60.

    Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).

    PubMed 
    Article 

    Google Scholar 

  • 61.

    Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 62.

    Galland, T. et al. Colonization resistance and establishment success along gradients of functional and phylogenetic diversity in experimental plant communities. J. Ecol. 107, 2090–2104 (2019).

    Article 

    Google Scholar 

  • 63.

    Lister, A. M. et al. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).

    PubMed 
    Article 

    Google Scholar 

  • 64.

    Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. B 345, 101–118 (1994).

    CAS 
    Article 

    Google Scholar 

  • 65.

    Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).

    Article 

    Google Scholar 

  • 66.

    Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019).

  • 67.

    Chazdon, R. L., Colwell, R. K., Denslow, J. S. & Guariguata, M.R. in Forest Biodiversity Research, Monitoring and Modeling: Conceptual Background and Old World Case Studies (eds. Dallmeir, F. & Cominsky, J. A.) 285–309 (Parthenon, 1998).

  • 68.

    Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).

    Article 

    Google Scholar 

  • 69.

    van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).

    Article 

    Google Scholar 

  • 70.

    Osborn, T. J. & Jones, P. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst. Sci. Data 6, 61–68 (2014).

    Article 

    Google Scholar 

  • 71.

    New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000).

    Article 

    Google Scholar 

  • 72.

    Brunetti, M. et al. Projecting north eastern Italy temperature and precipitation secular records onto a high resolution grid. Phys. Chem. Earth. 40, 9–22 (2012).

    Article 

    Google Scholar 

  • 73.

    Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. High-resolution temperature climatology for Italy: interpolation method intercomparison. Int. J. Climatol. 34, 1278–1296 (2014).

    Article 

    Google Scholar 

  • 74.

    Crespi, A., Brunetti, M., Lentini, G. & Maugeri, M. 1961–1990 high-resolution monthly precipitation climatologies for Italy. Int. J. Climatol. 38, 878–895 (2018).

    Article 

    Google Scholar 

  • 75.

    Peterson, T. C. et al. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517 (1998).

    Article 

    Google Scholar 

  • 76.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article 

    Google Scholar 

  • 77.

    Burnham, K. & Anderson, D. Model Selection and Multi-model Inference (Springer, 2002).

  • 78.

    Blonder, B & Harris, D. J. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12 (2019).

  • 79.

    Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n‐dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).

    Article 

    Google Scholar 

  • 80.

    Barros, C., Thuiller, W., Georges, D., Boulangeat, I. & Münkemüller, T. N‐dimensional hypervolumes to study stability of complex ecosystems. Ecol. Lett. 19, 729–742 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 81.

    Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).

    PubMed 
    Article 

    Google Scholar 

  • 82.

    Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Community Ecol. 19, 77–83 (2018).

    Article 

    Google Scholar 

  • 83.

    Signorell, A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.40 (2021).

  • 84.

    Maclean, I. M. D., Suggitt, A. J., Wilson, R. J., Duffy, J. P. & Bennie, J. J. Fine-scale climate change: modelling fine-scale spatial variation in biologically meaningful rates of warming. Glob. Change Biol. 23, 256–268 (2017).

    Article 

    Google Scholar 

  • 85.

    Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article 

    Google Scholar 

  • 86.

    Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991).

    Article 

    Google Scholar 

  • 87.

    Bivand, R. S. & Wong, D. W. Comparing implementations of global and local indicators of spatial association. Test 27, 716–748 (2018).

    Article 

    Google Scholar 

  • 88.

    Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).

    Article 

    Google Scholar 

  • 89.

    Bivand, R. S., Gómez-Rubio, V. & Rue, H. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31 (2015).

    Google Scholar 

  • 90.

    Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).

    PubMed 
    Article 

    Google Scholar 

  • 91.

    R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

  • 92.

    Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Effects of wood ash and N fertilization on soil chemical properties and growth of Zelkova serrata across soil types

    Waging a two-pronged campaign against climate change