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Identifying hotspots for rare species under climate change scenarios: improving saproxylic beetle conservation in Italy

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Abstract

Our aim is to model rare species (with few occurrences) but modelling the distribution of species with few occurrence data and many predictor variables leads to model overfitting. Thus, we use the recently developed ensemble of small models, which showed high predictive accuracy in modelling the distribution of rare species to estimate the current and future distribution of 56 rare (and endangered) saproxylic beetle species. Thus, we stacked predictions from individual species distribution models to derive rare species richness. We used current and five future general circulation models and three representative concentration pathways to test whether the distribution of hotspots for rare species shifts due to climate change under different future scenarios. Moreover, we verified the representativeness of existing protected area systems under future climate conditions in Italy. Specifically, we identified potential hotspots for rare species richness through a cumulative relative frequency distribution function. The current surface covered by hotspots is 50.4% of the study area corresponding to 151,223 km2 (mainly from central to northern Italy). Currently, only 35,124 km2 of rare saproxylic hotspots are covered by protected areas (PAs) and they will decrease by about 2–72% in 2070 depending on the future scenarios considered. Our results confirmed that the shift of the distribution of hotspots for rare species might occur due to climate change under different future scenarios and that the existing PAs system would be inadequate for assuring the conservation of rare saproxylic beetles in Italy under current and future climate conditions.

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References

  • Araújo MB (2004) Matching species with reserves—uncertainties from using data at different resolutions. Biol Conserv 118:533–538. https://doi.org/10.1016/j.biocon.2003.10.006

    Article  Google Scholar 

  • Araújo MB, Alagador D, Cabeza M, Nogués-Bravo D, Thuiller W (2011) Climate change threatens European conservation areas. Ecol Lett 14:484–492. https://doi.org/10.1111/j.1461-0248.2011.01610.x

    Article  PubMed  PubMed Central  Google Scholar 

  • Audisio P, Baviera C, Carpaneto GM, Biscaccianti AB, Battistoni A, Teofili C, Rondinini C (2014) Lista rossa IUCN dei coleotteri saproxilici italiani. Comitato Italiano IUCN e Ministero dell’Ambiente e della Tutela del Territorio e del Mare, Roma:132

  • Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423. https://doi.org/10.1111/j.1365-2664.2006.01136.x

    Article  Google Scholar 

  • Bartolino V, Maiorano L, Colloca F (2011a) A frequency distribution approach to hotspot identification. Popul Ecol 53:351–359

    Article  Google Scholar 

  • Bartolino V, Maiorano L, Colloca F (2011b) Frequency distribution curves and the identification of hotspots: response to comments. Popul Ecol 53:603–604

    Article  Google Scholar 

  • Benito BM, Svenning J-C, Kellberg-Nielsen T, Riede F, Gil-Romera G, Mailund T, Kjaergaard PC, Sandel BS (2017) The ecological niche and distribution of Neanderthals during the last interglacial. J Biogeogr 44:51–61. https://doi.org/10.1111/jbi.12845

    Article  Google Scholar 

  • Böhm R, Auer I, Brunetti M, Maugeri M, Nanni T, Schöner W (2001) Regional temperature variability in the European Alps: 1760-1998 from homogenized instrumental time series. Int J Climatol 21:1779–1801. https://doi.org/10.1002/joc.689

    Article  Google Scholar 

  • Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FK (2002) Evaluating resource selection functions. Ecol Model 157:281–300. https://doi.org/10.1016/S0304-3800(02)00200-4

    Article  Google Scholar 

  • Braunisch V, Coppes J, Arlettaz R, Suchant R, Schmid H, Bollmann K (2013) Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36:971–983. https://doi.org/10.1111/j.1600-0587.2013.00138.x

    Article  Google Scholar 

  • Breiner FT, Guisan A, Bergamini A, Nobis MP, Anderson B (2015) Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol Evol 6:1210–1218. https://doi.org/10.1111/2041-210X.12403

    Article  Google Scholar 

  • Broennimann O, Petitpierre B, Randin C, Engler R, Breiner F, D’Amen M, Pellissier L, Pottier J, Pio D, Mateo RG (2017) ecospat: spatial ecology miscellaneous methods. R package version 2

  • Bunnell FL, Houde I, Johnston B, Wind E (2002) How dead trees sustain live organisms in western forests. Laudenslayer, William F., Jr.; Shea, Patrick J, 2–4

  • Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer, Berlin

    Google Scholar 

  • Calabrese JM, Certain G, Kraan C, Dormann CF (2014) Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob Ecol Biogeogr 23:99–112. https://doi.org/10.1111/geb.12102

    Article  Google Scholar 

  • Carpaneto G (2006) Aspetti faunistici. Le Faggete Appenniniche. Avanguardie e Relitti di Foresta Continentale. Quaderni Habitat 71–126

  • Carpaneto GM, Baviera C, Biscaccianti AB, Brandmayr P, Mazzei A, Mason F, Battistoni A, Teofili C, Rondinini C, Fattorini S, Audisio P (2015) A red list of Italian Saproxylic Beetles: taxonomic overview, ecological features and conservation issues (Coleoptera). Fragm Entomol 47:53. https://doi.org/10.4081/fe.2015.138

    Article  Google Scholar 

  • Cavalli R, Mason F (2003) Techniques for re-establishment of dead wood for saproxylic fauna conservation. LIFE nature project NAT/IT/99/6245 Bosco della Fontana (Mantova, Italy). Gianluigi Arcari Editore, Mantova

  • Cayuela L, Galvez-Bravo L, Carrascal LM, Fábio S, Comments on Bartolino et al (2011) Limits of cumulative relative frequency distribution curves for hotspot identification. Popul Ecol 53:597–601

    Article  Google Scholar 

  • Chiari S, Carpaneto GM, Zauli A, Zirpoli GM, Audisio P, Ranius T, Leather SR, Quicke D (2013) Dispersal patterns of a saproxylic beetle, Osmoderma eremita, in Mediterranean woodlands. Insect Conserv Divers 6:309–318. https://doi.org/10.1111/j.1752-4598.2012.00215.x

    Article  Google Scholar 

  • Conrad KF, Warren MS, Fox R, Parsons MS, Woiwod IP (2006) Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol Conserv 132:279–291. https://doi.org/10.1016/j.biocon.2006.04.020

    Article  Google Scholar 

  • Czwienczek E (2012) Responses of forest insects to climate change. Herbivory and plant quality along, Eur Elev Gradients

    Google Scholar 

  • D’Amen M, Bombi P, Campanaro A, Zapponi L, Bologna MA, Mason F (2013) Protected areas and insect conservation: questioning the effectiveness of Natura 2000 network for saproxylic beetles in Italy. Anim Conserv 16:370–378. https://doi.org/10.1111/acv.12016

    Article  Google Scholar 

  • D’Amen M, Pradervand J-N, Guisan A (2015a) Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework. Glob Ecol Biogeogr 24:1443–1453. https://doi.org/10.1111/geb.12357

    Article  Google Scholar 

  • D’Amen M, Dubuis A, Fernandes RF, Pottier J, Pellissier L, Guisan A (2015b) Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. J Biogeogr 42:1255–1266. https://doi.org/10.1111/jbi.12485

    Article  Google Scholar 

  • Della Rocca F, Stefanelli S, Pasquaretta C, Campanaro A, Bogliani G (2014) Effect of deadwood management on saproxylic beetle richness in the floodplain forests of northern Italy: some measures for deadwood sustainable use. J Insect Conserv 18:121–136. https://doi.org/10.1007/s10841-014-9620-1

    Article  Google Scholar 

  • Devictor V, Julliard R, Couvet D, Jiguet F (2008) Birds are tracking climate warming, but not fast enough. Proc R Soc Lond B 275:2743–2748

    Article  Google Scholar 

  • Di Cola V, Broennimann O, Petitpierre B, Breiner FT, D’Amen M, Randin C, Engler R, Pottier J, Pio D, Dubuis A, Pellissier L, Mateo RG, Hordijk W, Salamin N, Guisan A (2017) ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40:774–787. https://doi.org/10.1111/ecog.02671

    Article  Google Scholar 

  • Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling WD (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628

    Article  Google Scholar 

  • Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342. https://doi.org/10.1111/j.2041-210X.2010.00036.x

    Article  Google Scholar 

  • Eyre MD, Rushton SP, Luff ML, Telfer MG (2005) Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude. J Biogeogr 32:973–983. https://doi.org/10.1111/j.1365-2699.2005.01258.x

    Article  Google Scholar 

  • Fawcett T (2004) ROC graphs: notes and practical considerations for researchers. Mach Learn 31:1–38

    Google Scholar 

  • Feldhaar H, Schauer, B (2018) Dispersal of saproxylic insects. In: Ulyshen M (eds) Saproxylic insects. Zoological monographs, vol 1. Springer, Cham, pp 515–546. https://doi.org/10.1007/978-3-319-75937-1_15

  • Fischer J, Lindenmayer DB (2007) Landscape modification and habitat fragmentation: a synthesis. Global Ecol Biogeogr 16:265–280. https://doi.org/10.1111/j.1466-8238.2007.00287.x

    Article  Google Scholar 

  • Fischlin A, Midgley GF, Hughs L, Price J, Leemans R, Gopal B, Turley C, Rounsevell M, Dube P, Tarazona J (2007) Ecosystems, their properties, goods and services

  • Forister ML, McCall AC, Sanders NJ, Fordyce JA, Thorne JH, O’Brien J, Waetjen DP, Shapiro AM (2010) Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc Natl Acad Sci USA 107:2088–2092. https://doi.org/10.1073/pnas.0909686107

    Article  PubMed  PubMed Central  Google Scholar 

  • Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122. https://doi.org/10.1371/journal.pone.0097122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Franco A, Hill JK, Kitschke C, Collingham YC, Roy DB, Fox R, Huntley B, Thomas CD (2006) Impacts of climate warming and habitat loss on extinctions at species’ low-latitude range boundaries. Global Chang Biol 12:1545–1553. https://doi.org/10.1111/j.1365-2486.2006.01180.x

    Article  Google Scholar 

  • Gasner MR, Jankowski JE, Ciecka AL, Kyle KO, Rabenold KN (2010) Projecting the local impacts of climate change on a Central American montane avian community. Biol Conserv 143:1250–1258. https://doi.org/10.1016/j.biocon.2010.02.034

    Article  Google Scholar 

  • Gough LA, Sverdrup-Thygeson A, Milberg P, Pilskog HE, Jansson N, Jonsell M, Birkemoe T (2015) Specialists in ancient trees are more affected by than generalists. Ecol Evolut 5:5632–5641

    Article  Google Scholar 

  • Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, Regan TJ, Brotons L, McDonald-Madden E, Mantyka-Pringle C, Martin TG, Rhodes JR, Maggini R, Setterfield SA, Elith J, Schwartz MW, Wintle BA, Broennimann O, Austin M, Ferrier S, Kearney MR, Possingham HP, Buckley YM (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. https://doi.org/10.1111/ele.12189

    Article  PubMed  PubMed Central  Google Scholar 

  • Hannah L, Midgley G, Andelman S, Araújo M, Hughes G, Martinez-Meyer E, Pearson R, Williams P (2007) Protected area needs in a changing climate. Front Ecol Environ 5:131–138. https://doi.org/10.1890/1540-9295(2007)5%5b131:PANIAC%5d2.0.CO;2

    Article  Google Scholar 

  • Hanula JL, Engstrom RT (2000) Comparison of Red-cockaded Woodpecker (Picoides borealis) Nestling Diet in Old-growth and Old-field during climatic fluctuations. J Insect Conserv 14:297–303

    Google Scholar 

  • Hardy PB, Kinder PM, Sparks TH, Dennis RLH (2010) Elevation and habitats: the potential of sites at different altitudes to provide refuges for phytophagous insects distribution models come at the expense of transferability? Ecography 35:276–288. https://doi.org/10.1111/j.1600-0587.2011.06999.x

    Article  Google Scholar 

  • Harrison X (2017) A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6:e4794

    Article  Google Scholar 

  • Hedin J, Ranius T, Nilsson SG, Smith HG (2008) Restricted dispersal in a flying beetle assessed by telemetry. Biodivers Conserv 17:675–684. https://doi.org/10.1007/s10531-007-9299-7

    Article  Google Scholar 

  • Heikkala O, Suominen M, Junninen K, Hämäläinen A, Kouki J (2014) Effects of retention level and fire on retention tree dynamics in boreal forests. For Ecol Manag 328:193–201

    Article  Google Scholar 

  • Heikkinen RK, Marmion M, Luoto M (2012) Does the interpolation accuracy of species Longleaf Pine (Pinus palustris) Habitats. Am Midl Nat 144:370–376. https://doi.org/10.1674/0003-0031(2000)144%5b0370:CORCWP%5d2.0.CO;2

    Article  Google Scholar 

  • Heller NE, Zavaleta ES (2009) Biodiversity management in the face of climate change: a review of 22 years of recommendations. Biol Conserv 142:14–32. https://doi.org/10.1016/j.biocon.2008.10.006

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276

    Article  Google Scholar 

  • Hitch AT, Leberg PL (2007) Breeding distributions of North American bird species moving north as a result of climate change. Conserv Biol 21:534–539. https://doi.org/10.1111/j.1523-1739.2006.00609.x

    Article  PubMed  Google Scholar 

  • Hof C, Levinsky I, AraÚjo MB, Rahbek C (2011) Rethinking species’ ability to cope with rapid climate change. Global Change Biol 17:2987–2990

    Article  Google Scholar 

  • Hoyle M, James M (2005) Global warming, human population pressure, and viability of the world’s smallest butterfly. Conserv Biol 19:1113–1124. https://doi.org/10.1111/j.1523-1739.2005.00166.x

    Article  Google Scholar 

  • Ko CY, Root TL, Lee PF (2011) Movement distances enhance validity of predictive models. Ecol Model 222:947–954

    Article  Google Scholar 

  • Lachat T, Wermelinger B, Gossner MM, Bussler H, Isacsson G, Müller J (2012) Saproxylic beetles as indicator species for dead-wood amount and temperature in European beech forests. Ecol Ind 23:323–331. https://doi.org/10.1016/j.ecolind.2012.04.013

    Article  Google Scholar 

  • Li R, Xu M, Wong MHG, Qiu S, Sheng Q, Li X, Song Z (2015) Climate change-induced decline in bamboo habitats and species diversity: implications for giant panda conservation. Divers Distrib 21:379–391

    Article  Google Scholar 

  • Lomba A, Pellissier L, Randin C, Vicente J, Moreira F, Honrado J, Guisan A (2010) Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biol Conserv 143:2647–2657. https://doi.org/10.1016/j.biocon.2010.07.007

    Article  Google Scholar 

  • Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15:59–69. https://doi.org/10.1111/j.1472-4642.2008.00491.x

    Article  Google Scholar 

  • McCullagh P, Nelder JA (1989) Generalized linear models, vol 37. CRC Press

  • McGeoch MA, Schroeder M, Ekbom B, Larsson S (2007) Saproxylic beetle diversity in a managed boreal forest: importance of stand characteristics and forestry conservation measures. Divers Distrib 13:418–429. https://doi.org/10.1111/j.1472-4642.2007.00350.x

    Article  Google Scholar 

  • Motta R, Nola P (2001) Growth trends and dynamics in sub-alpine forest stands in the Varaita Valley (Piedmont, Italy) and their relationships with human activities and global change. J Veg Sci 12:219–230

    Article  Google Scholar 

  • Naughton-Treves L, Buck Holland M, Brandon KE (2005) The role of protected areas in conserving biodiversity and sustaining local livelihoods. Annu Rev Environ Resour 30:219–252

    Article  Google Scholar 

  • Pacifici M, Foden WB, Visconti P, Watson JEM, Butchart SH, Kovacs KM, Scheffers BR, Hole DG, Martin TG, Akçakaya HR, Corlett RT, Huntley B, Bickford D, Carr JA, Hoffmann AA, Midgley GF, Pearce-Kelly P, Pearson RG, Williams SE, Willis SG, Young B, Rondinini C (2015) Assessing species vulnerability to climate change. Nature climate change 5:215–224. https://doi.org/10.1038/nclimate2448

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pascual LL, Luigi M, Alessandra F, Emilio B, Luigi B (2011) Hotspots of species richness, threat and endemism for terrestrial vertebrates in SW Europe. Acta Oecol 37:399–412

    Article  Google Scholar 

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643. https://doi.org/10.1111/jbi.12227

    Article  Google Scholar 

  • Ranius T (2002) Influence of stand size and quality of tree hollows on saproxylic beetles in Sweden. Biol Conserv 103:85–91

    Article  Google Scholar 

  • Ranius T (2006) Measuring the dispersal of saproxylic insects: a key characteristic for their conservation. Popul Ecol 48:177–188. https://doi.org/10.1007/s10144-006-0262-3

    Article  Google Scholar 

  • Rebelo H, Jones G (2010) Ground validation of presence-only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: vespertilionidae). J Appl Ecol 47:410–420. https://doi.org/10.1111/j.1365-2664.2009.01765.x

    Article  Google Scholar 

  • Rocchini D, Garzon-Lopez CX, Marcantonio M, Amici V, Bacaro G, Bastin L, Brummitt N, Chiarucci A, Foody GM, Hauffe HC, He KS, Ricotta C, Rizzoli A, Rosà R (2017) Anticipating species distributions: handling sampling effort bias under a Bayesian framework. Sci Total Environ 584–585:282–290. https://doi.org/10.1016/j.scitotenv.2016.12.038

    Article  CAS  PubMed  Google Scholar 

  • Ruffo S, Stoch F (2005) Checklist e distribuzione della fauna italiana: 10.000 specie terrestri e delle acque interne. Museo civico di storia naturale di. Scienze della Vita, Verona

    Google Scholar 

  • Scoppola A, Blasi C, Abbate G, Cutini M, Di MARZIOP, Fabozzi C, Fortini P (1995) Analisi critica e considerazioni fitogeografiche sugli ordini e le alleanze dei querceti e boschi misti a caducifoglie dell’Italia peninsulare. Ann Bot 51:81–112

    Google Scholar 

  • Sedgeley JA (2001) Quality of cavity microclimate as a factor influencing selection of maternity roosts by a tree-dwelling bat, Chalinolobus tuberculatus, in New Zealand. J Appl Ecol 38:425–438

    Article  Google Scholar 

  • Seibold S, Bässler C, Brandl R, Büche B, Szallies A, Thorn S, Ulyshen MD, Müller J (2016) Microclimate and habitat heterogeneity as the major drivers of beetle diversity in dead wood. J Appl Ecol 53:934–943

    Article  Google Scholar 

  • Southwood TRE, Henderson PA (2000) Ecological methods, 3rd edn. Blackwell, Oxford

    Google Scholar 

  • Stoch F (2000) CKmap 5.3. Ministero dell’Ambiente e della Tutela del Territorio, Dir. Prot. Nat

  • Stokland JN, Siitonen J, Jonsson BG (2012) Biodiversity of dead wood. Cambridge University Press, pp 248–274

  • Stolar J, Nielsen SE, Franklin J (2015) Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers Distrib 21:595–608. https://doi.org/10.1111/ddi.12279

    Article  Google Scholar 

  • Thomas JA, Telfer MG, Roy DB, Preston CD, Greenwood JJD, Asher J, Fox R, Clarke RT, Lawton JH (2004) Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303:1879–1881. https://doi.org/10.1126/science.1095046

    Article  CAS  PubMed  Google Scholar 

  • Tulloch AI, Sutcliffe P, Naujokaitis-Lewis I, Tingley R, Brotons L, Ferraz KMP, Possingham H, Guisan A, Rhodes JR (2016) Conservation planners tend to ignore improved accuracy of modelled species distributions to focus on multiple threats and ecological processes. Biol Conserv 199:157–171. https://doi.org/10.1016/j.biocon.2016.04.023

    Article  Google Scholar 

  • van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. https://doi.org/10.1007/s10584-011-0148-z

    Article  Google Scholar 

  • Williams JW, Kharouba HM, Veloz S, Vellend M, McLachlan J, Liu Z, Bette Otto-Bliesner B, He F (2013) The ice age ecologist: testing methods for reserve prioritization during the last global warming. Glob Ecol Biogeogr 22:289–301. https://doi.org/10.1111/j.1466-8238.2012.00760.x

    Article  Google Scholar 

  • Wilson RJ, Gutiérrez D, Gutiérrez J, Monserrat VJ (2007) An elevational shift in butterfly species richness and composition accompanying recent climate change. Global Change Biol 13:1873–1887

    Google Scholar 

  • Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x

    Article  Google Scholar 

  • Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14

    Article  Google Scholar 

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Acknowledgements

We thank Prof. Francesco Sartori and Prof. Francesco Bracco, managers of the Riserva Naturale Integrale Bosco Siro Negri, who supported part of this research through funds from the Italian Ministry of the Environment and Protection of Land and Sea. We thank Kelsey Horvath for improving the English language. We are grateful to the Linda Xavier and three anonymous reviewers for their useful comments and suggestions on the previous version of this manuscript.

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Correspondence to Francesca Della Rocca.

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Communicated by Frank Chambers.

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Della Rocca, F., Bogliani, G., Breiner, F.T. et al. Identifying hotspots for rare species under climate change scenarios: improving saproxylic beetle conservation in Italy. Biodivers Conserv 28, 433–449 (2019). https://doi.org/10.1007/s10531-018-1670-3

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