Long-term studies are essential to understand the impacts of global changes on the multiple facets of biological diversity. Here, we use distribution data for over 600 species of arthropods collected over 150 years from locations across Italy and test how multiple environmental stressors (climate, land use and human population density) influenced assemblage composition and functionality. By carefully reconstructing the temporal changes in these stressors, we explicitly tested how environmental changes can determine the observed changes in taxonomic and functional diversity. We found that rapid changes in precipitation destabilize the assemblages and maximize colonization and extinction rates, especially when coupled with changes in human population density (for taxonomy) or temperature (for functionality). Higher microclimatic heterogeneity increases the stability of biodiversity by reducing taxonomic and functional loss. Finally, changes in natural habitats increased colonization, influencing taxonomic nestedness and functional replacement. The integration of long-term datasets combining distributions, climate and traits may deepen our understanding of the processes underlying biodiversity responses to global-scale drivers.
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The R codes used to run the analyses are available at https://doi.org/10.6084/m9.figshare.14748057.
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).
Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).
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).
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
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).
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).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
Jarzyna, M. A. & Jetz, W. Detecting the multiple facets of biodiversity. Trends Ecol. Evol. 31, 527–538 (2016).
Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).
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).
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).
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).
Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565 (2018).
Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—a macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).
Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).
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).
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).
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).
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
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).
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2019).
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).
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).
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
Marta, S. et al. ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna. Sci. Data 6, 195 (2019).
Koleff, P., Gaston, K. J. & Lennon, J. T. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).
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).
Suggit, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).
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).
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).
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).
Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).
Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).
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).
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).
Beniston, M. et al. The European mountain cryosphere: a review of its current state, trends and future challenges. Cryosphere 12, 759–794 (2018).
Wang, J. et al. Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. 11, 528 (2020).
Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with nonstationary climate–fire models. Nat. Commun. 9, 3821 (2018).
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).
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).
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).
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).
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).
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).
Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).
Ranganathan, S., Swain, R. B. & Sumpter, D. J. T. The demographic transition and economic growth: implications for development policy. Palgrave Commun. 1, 15033 (2015).
Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience 53, 941–952 (2003).
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).
Ficetola, G. F. & Maiorano, L. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia 181, 683–693 (2016).
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).
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).
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).
Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).
Poff, N. L. et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change 6, 25–34 (2017).
Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).
Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).
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).
Lister, A. M. et al. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. B 345, 101–118 (1994).
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).
Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019).
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).
Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).
van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).
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).
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).
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).
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).
Crespi, A., Brunetti, M., Lentini, G. & Maugeri, M. 1961–1990 high-resolution monthly precipitation climatologies for Italy. Int. J. Climatol. 38, 878–895 (2018).
Peterson, T. C. et al. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517 (1998).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Burnham, K. & Anderson, D. Model Selection and Multi-model Inference (Springer, 2002).
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).
Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n‐dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).
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).
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Community Ecol. 19, 77–83 (2018).
Signorell, A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.40 (2021).
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).
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).
Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991).
Bivand, R. S. & Wong, D. W. Comparing implementations of global and local indicators of spatial association. Test 27, 716–748 (2018).
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).
Bivand, R. S., Gómez-Rubio, V. & Rue, H. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31 (2015).
Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).
R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
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).
We thank D. O’Brien for the revision of an early version of the manuscript. S.M. and G.F.F. are funded by the European Research Council under the European Community’s Horizon 2020 Programme, grant agreement no. 772284 (‘IceCommunities—Reconstructing community dynamics and ecosystem functioning after glacial retreat’).
The authors declare no competing interests.
Peer review information Nature Ecology & Evolution thanks Alistair Auffret and Marta Jarzyna for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended Data Fig. 1 Relationships between the rate of change of precipitation at slower, medium and faster changes of the human population density and taxonomic indices (βsim, βsne, Dgain, Dloss).
Relationships between taxonomic indices and the rate of change of precipitation (mm/year) at slower (−0.45), medium (0.72) and faster (1.82) changes of the human population density (cube-root transformed; (inhabitants/km2/year)1/3). In each plot, the thick red line represents the average predicted relationship on the link scale, while the grey lines represent 500 samples of the posterior distribution. a–c, turnover (βsim). d–f, nestedness (βsne). g–i, standardized gain (Dgain). j–l, standardized loss (Dloss).
Extended Data Fig. 2 Relationships between the rate of change of temperature with negative, stable and positive rate of changes of precipitation and functional indices (βsim, βsne, Dgain, Dloss).
Relationships between functional indices and the rate of change of temperature (°C/year) with negative (−10.06), stable (−1.96) and positive (5.4) rates of change in precipitation (mm/year). In each plot, the thick red line represents the average predicted relationship on the link scale, while the grey lines represent 500 samples of the posterior distribution. a–c, turnover (βsim). d–f, nestedness (βsne). g–i, standardized gain (Dgain). j–l, standardized loss (Dloss).
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Marta, S., Brunetti, M., Manenti, R. et al. Climate and land-use changes drive biodiversity turnover in arthropod assemblages over 150 years. Nat Ecol Evol 5, 1291–1300 (2021). https://doi.org/10.1038/s41559-021-01513-0
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