Future scenarios and conservation strategies for a rear-edge marginal population of Pinus nigra Arnold in Italian central Apennines
Abstract
Aim of study: To forecast the effects of climate change on the spatial distribution of Black pine of Villetta Barrea in its natural range and to define a possible conservation strategy for the species
Area of study: A rear-edge marginal population of Pinus nigra spp. nigra in Abruzzo region, central Italian Apennines
Matherials and Methods: For its adaptive and genetic traits this population is considered endemic of the Italian peninsula and represents a rear-edge marginal population of nigra subspecies. The spatial distribution of the tree in the administrative Region (Abruzzo) was used to define the ecological traits while three modelling techniques (GLM, GAM, Random Forest) were used to build a Species distribution model according to two climatic scenarios.
Main results: The marginal population's range was predicted to shift at higher elevations as consequence of climatic adaptation. Many zones, represented by the higher part of the mountains surrounding the study area (currently bare and inhospitable for trees), were identified as suitable in future for the species. However, in the case of a rapid climate change, this marginal population may not be able to move as fast as necessary. An in-situ adaptive management integrated with an assisted migration protocol might be considered to enforce the natural regeneration and improve the richness and variability of the genetic pool.
Research highlights: Most of the genetic richness is held in small populations at the borders of natural distribution of forest species. Monitoring this MAP could be useful to understand the adaptive processes of the species and could support the future management of many other within-core populations.
Keywords: Species Distribution Models; Mediterranean forests; Abruzzo; climate change; altitudinal shift.
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Allouche O, Tsoar A, Kadmon R, 2006. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223–1232. http://dx.doi.org/10.1111/j.1365-2664.2006.01214.x
Araújo MB, Whittaker RJ, Ladle RJ, Erhard M, 2005. Reducing uncertainty in projections of extinction risk from climate change. Global Ecol Biogeogr 14: 529–538. http://dx.doi.org/10.1111/j.1466-822X.2005.00182.x
Attorre F, Alfò M, De Sanctis M, Francesconi F, Valenti R, Vitale M, Bruno F, 2011. Evaluating the effects of climate change on tree species abundance and distribution in the Italian peninsula. Appl Veg Sci 14: 242–255. http://dx.doi.org/10.1111/j.1654-109X.2010.01114.x
Attorre F, Alfo M, De Sanctis M, Bruno F, 2007. Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. Int J Climatol 1843: 1825–1843. http://dx.doi.org/10.1002/joc.1495
Barbet-Massin M, Jiguet F, Albert CH, Thuiller W, 2012. Selecting pseudo-absences for species distribution models: How, where and how many? Methods Ecol Evol 3: 327–338. http://dx.doi.org/10.1111/j.2041-210X.2011.00172.x
Bedia J, Busqué J, Gutiérrez JM, 2011. Predicting plant species distribution across an alpine rangeland in northern Spain. A comparison of probabilistic methods. Appl Veg Sci 14: 415–432. http://dx.doi.org/10.1111/j.1654-109X.2011.01128.x
Bedia J, Herrera S, Gutiérrez JM, 2013. Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections. Glob Planet Change 107: 1–12. http://dx.doi.org/10.1016/j.gloplacha.2013.04.005
Benito-Garzón M, Fernández-Manjarrés JF, 2015. Testing scenarios for assisted migration of forest trees in Europe. New For 46: 979-994. http://dx.doi.org/10.1007/s11056-015-9481-9
Bernetti G, 1995. Selvicoltura speciale. Turin, UTET, 415 pp.
Brang P, Spathelf P, Larsen JB, Bauhus J, Boncina A, Chauvin C, Drossler L, Garcia-Guemes C, Heiri C, Kerr G, et al., 2014. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry 87: 492–503. http://dx.doi.org/10.1093/forestry/cpu018
Breiman L 2001. Random forests. Machine learning 5–32. doi: 10.1023/A:1010933404324 http://dx.doi.org/10.1023/A:1010933404324
Brunetti M, Maugeri M, Nanni T, Simolo C, Spinoni J, 2014. High-resolution temperature climatology for Italy: interpolation method intercomparison. Int J Climatol 34:1278–1296. http://dx.doi.org/10.1002/joc.3764
Bruschi P, Di Santo D, Grossoni P, Tani C, 2005. Caratterizzazione tassonomica del.Pino nero della Majella. Inf Bot Ital 38: 241–251.
Cantiani P, Chiavetta U, 2015. Estimating the mechanical stability of Pinus nigra Arn. using an alternative approach across several plantations in central Italy. iForest - Biogeosciences For 8: 846–852.
Cha G, 1997. The Impacts of Climate Change on Potential Natural Vegetation Distribution. J For Res 2: 147–152. http://dx.doi.org/10.1007/BF02348212
Ciancio O, Iovino F, Menguzzato G, Nicolaci A, Nocentini S, 2006. Structure and growth of a small group selection forest of calabrian pine in Southern Italy: A hypothesis for continuous cover forestry based on traditional silviculture. For Ecol Manag 224: 229–234.
Ciancio O, Nocentini S, 2011. Biodiversity conservation and systemic silviculture: Concepts and applications. Plant Biosyst 145: 411–418. http://dx.doi.org/10.1080/11263504.2011.558705
Corona P, Nocentini S, 2009. A parameter-based method for determining thinning intensity. Ital For e Mont 64: 359–365. http://dx.doi.org/10.4129/IFM.2009.6.03
Ducci F, 2015. Genetic resources and forestry in the Mediterranean region in relation to global change. Ann Silvic Res 39(2): 70–93.
Elith J, Leathwick JR, 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu Rev Ecol Evol Syst 40: 677–697. http://dx.doi.org/10.1146/annurev.ecolsys.110308.120159
European-Soil-Bureau, 1999. Soil map of Italy.
Flower A, Murdock TQ, Taylor SW, Zwiers FW, 2013. Using an ensemble of downscaled climate model projections to assess impacts of climate change on the potential distribution of spruce and Douglas-fir forests in British Columbia. Environ Sci Policy 26: 63–74. http://dx.doi.org/10.1016/j.envsci.2012.07.024
Forester BR, Dechaine EG, Bunn AG, 2013. Integrating ensemble species distribution modelling and statistical phylogeography to inform projections of climate change impacts on species distributions. Divers Distrib 19: 1480–1495. http://dx.doi.org/10.1111/ddi.12098
Friedman JH, 1991. Multivariate Adaptive Regression Splines. Ann Stat 19: 1–67. http://dx.doi.org/10.1214/aos/1176347963
Gellini R, Grossoni P, 2003. Botanica Forestale Vol. I - Gimnosperme.
Grivet D, Climent J, Zabal-Aguirre M, Neale DB, Vendramin GG, Gonzalez-Martinez SC, 2013. Adaptive evolution of Mediterranean pines. Mol Phylogenet Evol 68: 555–566. http://dx.doi.org/10.1016/j.ympev.2013.03.032
Guisan A, Thuiller W, 2005. Predicting species distribution: Offering more than simple habitat models. Ecol Lett 8: 993–1009. http://dx.doi.org/10.1111/j.1461-0248.2005.00792.x
Guisan A, Zimmermann NE, 2000. Predictive habitat distribution models in ecology. Ecol Modell 135: 147–186. http://dx.doi.org/10.1016/S0304-3800(00)00354-9
Hamann A, Aitken SN, 2013. Conservation planning under climate change : accounting for adaptive species distribution models. Biodivers Res 19: 268–280.
Hampe A, Petit RJ, 2005. Conserving biodiversity under climate change: The rear edge matters. Ecol Lett 8: 461–467 http://dx.doi.org/10.1111/j.1461-0248.2005.00739.x
Hastie T, Tibshirani R, Friedman J, 2008. The Elements of Statistical Learning. 745 pp.
Hijmans RJ, Cameron SE, Parra JL, Jones G, Jarvis A, 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 1965–1978. http://dx.doi.org/10.1002/joc.1276
IPCC, 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Isaac-Renton MG, Roberts DR, Hamann A, Spiecker H, 2014. Douglas-fir plantations in Europe: A retrospective test of assisted migration to address climate change. Glob Chang Biol 20: 2607–2617. http://dx.doi.org/10.1111/gcb.12604
Isajev V, Fady B, Semerci H, Andonovski V, 2004. EUFORGEN Technical Guidelines for genetic conservation and use for European black pine (Pinus nigra).
Jiménez-Valverde A, Lobo JM, 2007. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica 31: 361–369. http://dx.doi.org/10.1016/j.actao.2007.02.001
Kruskal W, Wallis W, 1952. Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47: 583–621. http://dx.doi.org/10.1080/01621459.1952.10483441
Liaw A, Wiener M, 2002. Classification and Regression by randomForest. R news 2: 18–22.
Lindner M, 2000. Developing adaptive forest management strategies to cope with climate change. Three Physiol 20: 299–307. http://dx.doi.org/10.1093/treephys/20.5-6.299
Liu C, White M, Newell G, 2011. Measuring and comparing the accuracy of species distribution models with presence-absence data. Ecography 34: 232–243. http://dx.doi.org/10.1111/j.1600-0587.2010.06354.x
Marchetti M, Chiavetta U, Santopuoli G, 2006. La cartografi a forestale su base tipologica della Regione Abruzzo : dai "prodromi" alla carta forestale dell' Italia centrale.
Marmion M, Parviainen M, Luoto M, Heikkien RK, Thuiller W, 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 59–69. http://dx.doi.org/10.1111/j.1472-4642.2008.00491.x
Mátýas C, Vendramin GG, Fady B, 2009. Forests at the limit: evolutionary - genetic consequences of environmental changes at the receding (xeric) edge of distribution. Report from a research workshop. Ann For Sci 66: 800–800. http://dx.doi.org/10.1051/forest/2009081
Mcinerny GJ, Etienne RS, 2013. "Niche" or "distribution" modelling ? A response to Warren. Trends Ecol Evol 28: 191–192. http://dx.doi.org/10.1016/j.tree.2013.01.007
Melini D, 2013. A spatial model for sporadic tree species distribution in support of tree oriented silviculture. Ann Silvic Res 37:64–68.
Merow C, Smith MJ, Edwards TC, Guisan A, Mc Mahon SM, Normand S, Thuiller W, Wuest RO, Zimmermann NE, Elith J, 2014. What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281. doi: 10.1111/ecog.00845 http://dx.doi.org/10.1111/ecog.00845
Montgomery DC, Peck EA, Vining GG, 2012. Introduction to Linear Regression Analysis. 672 pp.
Parmesan C, 2006. Ecological and Evolutionary Responses to Recent Climate Change. Annu Rev Ecol Evol Syst 37: 637–669. http://dx.doi.org/10.1146/annurev.ecolsys.37.091305.110100
Pearson RG, Dawson TP, 2003. Predicting the impacts of climate change on the distribution of speces: are bioclimate envelope models useful? Glob Ecol Biogeogr 12: 361–371. http://dx.doi.org/10.1046/j.1466-822X.2003.00042.x
Petit RJ, Aguinagalde I, De Beaulieu J-L, Bittaku C, Brewer S, Cheddadi R, Ennos R, Finsechi S,Grivet D, Lascoux M et al., 2003. Glacial refugia: hotspots but not melting pots of genetic diversity. Science (New York, NY) 300: 1563–5. http://dx.doi.org/10.1126/science.1083264
Provan J, Maggs CA, 2012. Unique genetic variation at a species' rear edge is under threat from global climate change. Proc Biol Sci 279: 39–47. http://dx.doi.org/10.1098/rspb.2011.0536
Quézel P, Médail F, 2003. Ecologie et biogéographie du bassin méditerranéen. 576 pp.
R CoreTeam, 2015. R: A language and environment for statistical computing.
Ramirez-Villegas J, Jarvis A, 2010. Downscaling Global Circulation Model Outputs: The Delta Method. Policy Anal, 18 pp.
Resco De Dios V, Fischer C, Colinas C, 2007. Climate change effects on mediterranean forests and preventive measures. New For 33: 29–40. http://dx.doi.org/10.1007/s11056-006-9011-x
Schueler S, Falk W, Koskela J, Lefèvre F, Bozzano M, Hubert J, Kraigher H, Longauer R, Olrik DC, 2014. Vulnerability of dynamic genetic conservation units of forest trees in Europe to climate change. Glob Chang Biol 20: 1498–1511. http://dx.doi.org/10.1111/gcb.12476
Thuiller W, Georges D, Engler R, 2014. biomod2: Ensemble platform for species distribution modeling.
Trivedi MR, Berry PM, Morecroft MD, Dawson TP, 2008. Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Glob Chang Biol 14:1089–1103. http://dx.doi.org/10.1111/j.1365-2486.2008.01553.x
Vacchiano G, Motta R, 2014. An improved species distribution model for Scots pine and downy oak under future climate change in the NW Italian Alps. Ann For Sci
Van Houwelingen J, Le Cressie S, 1990. Predictive value of statistical models. Stat Med 9: 1303–1325. http://dx.doi.org/10.1002/sim.4780091109
Vázquez A, Climent JM, Casais L, Quintana JR, 2015. Current and future estimates for the fire frequency and the fire rotation period in the main woodland types of peninsular Spain : a case-study approach. For Syst 24 (2): e031, 13 pages.
Vidakovic M, 1974. Genetics of European Black Pine (Pinus nigra Arn.). Academia Sci et Art Slavorum Meridionalum Zagreb Annales Forestales, Anali za Šumarstvo 6: 57–86.
Vitale M, Mancini M, Matteucci G, Francesconi F, Valenti R, Attorre F, 2012. Model-based assessment of ecological adaptations of three forest tree species growing in Italy and impact on carbon and water balance at national scale under current and future climate scenarios. iForest - Biogeosciences For 5: 235–246.
Wang T, Campbell EM, O'Neill GA., Aitken SN, 2012. Projecting future distributions of ecosystem climate niches: Uncertainties and management applications. For Ecol Manag 279: 128–140.
Warren DL, 2012. In defense of "niche modeling." Trends Ecol Evol 27: 497–500. http://dx.doi.org/10.1016/j.tree.2012.03.010
Warren DL, 2013. "Niche modeling": That uncomfortable sensation means it's working. A reply to McInerny and Etienne. Trends Ecol Evol 28: 193–194. http://dx.doi.org/10.1016/j.tree.2013.02.003
Zaniewski E, Lehmann A, Overton J McC, 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Modell 157: 261–280. http://dx.doi.org/10.1016/S0304-3800(02)00199-0
Zhang L, Liu S, Sun P, Wang T, Wang G, Zhang X, Wang L, 2015. Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties. PloS One 10:e0120056. http://dx.doi.org/10.1371/journal.pone.0120056
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