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General introduction and methodological overview

María J. Serra Varela

edited by: Julián Gonzalo Jiménez, Delphine Grivet

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Forests ecosystems, climate change and conservation. [...] Despite their importance, we have lost approximately 1.3 % of the total forest area during the last decade, and although deforestation rates are decreasing, they are still high (data for the period 2000-2010 [...]). Nevertheless, fortunately, in some regions, such as Europe, we find an inverse trend with an increasing forest cover [...]. In Europe, 33 % of the total land area (215 million ha) are covered by forests from which more than half are coniferous, the rest being broadleaved and mixed [...]. Among these, Mediterranean forests located in the Mediterranean Basin, stand out due to their considerably high plant diversity as a result of a noteworthy variety of habitats - e.g. 290 wooden species vs only 135 for non-Mediterranean Europe -, and of the many historical and paleo-geographic episodes in the area, especially during the last glaciation period. Mediterranean forests are dominated by evergreen species – although deciduous species are also represented - and in particular Mediterranean conifers are characterized by higher within-species diversity than other conifers [...]. Accordingly, the Mediterranean Basin, which shelters the vast majority of Mediterranean forests in the world, has been identified as a biodiversity hotspot [...].
Anthropogenic climate change, majorly characterized by global warming [...], is becoming a major threat for natural forests [...] and biodiversity. In the face of climate change, species can migrate, adapt, or become extinct [...] and, in such context, forest ecosystems are especially vulnerable, due to their sessile nature that constrains migration and to their long life-span which does not allow for rapid adaptation to environmental changes [...]. In the leading edge of the distribution, migration constitutes the most important process, as trees become main sources of propagules for new available habitats. In contrast, in the trailing edge, adaptive responses of trees are particularly important [...], as it is where species truly face the need to persist in current sites while the environmental conditions are changing [...]. The extent to which populations will adapt, depends on genetic diversity, phenotypic variation (i.e. the ability of an individual to change its phenotype responding to environment), strength of selection, fecundity, interspecific competition and biotic interactions [...]. Although phenotypic plasticity plays a major role for survival in the short term, evolutionary adaptation becomes crucial in long periods [...]. Mediterranean regions are particularly vulnerable to climate change [...], due to their position at the rear edge of the distribution of species [...], and to the predicted increased frequency of extreme events such as droughts and fires [...]. This threat is particularly relevant not only because of their ecological importance, but also because these forests play an essential role for the society [...] – as such, the Mediterranean Basin is considered as an important priority for conservation. Nevertheless, despite their threatened situation, Mediterranean forests remain underrepresented in the current European conservation network [...] and in currently available conservation literature [...].
Conservation of biodiversity at broad scales is challenging and requires international collaboration to standardize concepts and procedures. Initially, the United Nations Environment Programme (UNEP) gathered experts on biological diversity in 1988, resulting in the development of the convention on biological diversity text [...]. This document highlights that conserving biodiversity requires maintaining diversity within species, between species, and between ecosystems. Thus, the CBD extended the goal of conservation from preserving species and their habitats to maintaining their capacity to evolve and adapt to new environmental conditions. In fact, the CBD explicitly highlights the importance of maintaining infra-specific differentiation, and particularly genetic variation as the basis of species divergence when aiming to conserve biodiversity.
Infra-specific differences within forest populations appear due to different processes. Plants rely on pollen and seeds to disperse, but their dispersal abilities are often limited. Thus, when a new factor, such as an environmental or topographic change, appears it may lead to population fragmentation, and consequently to the interruption of gene flow. Within this context, populations evolve independently through neutral and/or adaptive genetic processes resulting in different genetic lineages or clades, an effect that is increased by genetic drift in small populations. If this process continues through time it can ultimately lead to speciation [...].
These genetically differentiated clades, which may show (or not) morphological differences, are likely to diverge in their evolutionary potential and adaptive capacity in which genetic diversity plays a major role [...]. Higher genetic variation implies higher evolutionary potential [...] as selection acts on it, promoting best adapted genotypes and eradicating deleterious ones, ultimately leading to local adaptation. Thus, maintaining or increasing genetic diversity is a major challenge for scientists and managers in the current climatic change context leading to the development of conservation genetics. [...]

In Ph.D. Thesis: Integrating infra-specific variation of Mediterranean conifers in species distribution models - Applications for vulnerability assessment and conservation (2017), pp. 19-54 
Key: INRMM:14439295



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  1. Aitken, S.N., Yeaman, S., Holliday, J. a., Wang, T., Curtis-McLane, S., 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evolutionary Applications, 1, 95–111.
  2. Allen, C.D., Macalady, A.K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H. (Ted), Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.H., Allard, G., Running, S.W., Semerci, A., Cobb, N., 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management, 259, 660–684.
  3. Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223–1232.
  4. Araújo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743–753.
  5. Araújo, M.B., New, M., 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47.
  6. Austin, M.P., Belbin, L., Meyers, J.A., Doherty, M.D., Luoto, M., 2006. Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory. Ecological Modelling, 199, 197–216.
  7. Barbet-Massin, M., Jiguet, F., Albert, C.H., Thuiller, W., 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 327–338.
  8. Bedia, J., Herrera, S., Gutiérrez, J.M., Manuel, J., 2013. Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections. Global and Planetary Change, 1–12.
  9. Beierkuhnlein, C., Thiel, D., Jentsch, A., Willner, E., Kreyling, J., 2011. Ecotypes of European grass species respond differently to warming and extreme drought. Journal of Ecology, 99, 703–713.
  10. Benito-Garzón, M., Alía, R., Robson, T.M., Zavala, M.A., 2011. Intra-specific variability and plasticity influence potential tree species distributions under climate change. Global Ecology and Biogeography, 20, 766–778.
  11. Bower, A.D., Aitken, S.N., 2008. Ecological genetics and seed transfer guidelines for Pinus albicaulis (Pinaceae). American journal of botany, 95, 66–76.
  12. Breiman, L., 2001. Random forests. Machine Learning, 45, 5–32.
  13. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey.
  14. Broennimann, O., Treier, U. a, Müller-Schärer, H., Thuiller, W., Peterson, A.T., Guisan,A., 2007. Evidence of climatic niche shift during biological invasion. Ecology letters, 10, 701–709.
  15. Brotons, L., Thuiller, W., Araújo, M.B., Hirzel, A.H., 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437–448.
  16. Brown, J.L., Knowles, L.L., 2012. Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika. Molecular ecology, 21, 3757–3775.
  17. Bucci, G., González-Martínez, S.C., Le Provost, G., Plomion, C., Ribeiro, M.M., Sebastiani, F., Alía, R., Vendramin, G.G., 2007. Range-wide phylogeography and gene zones in Pinus pinaster Ait. revealed by chloroplast microsatellite markers. Molecular Ecology, 16, 2137–2153.
  18. Burban, C., Petit, R.J., 2003. Phylogeography of maritime pine inferred with organelle markers having contrasted inheritance. Molecular Ecology, 12, 1487–1495.
  19. Busby, J.R., 1991. BIOCLIM - a bioclimate analysis and prediction system. Nature Conservation Cost Effective Biological Surveys and Data Analysis (ed. by C.R. Margules) and M.P. Austin), pp. 64–68. CSIRO.
  20. Canadell, J.G., Raupach, M.R., 2008. Managing forests for climate change mitigation. Science, 320, 1456.
  21. Carstens, B.C., Richards, C.L., 2007. Integrating coalescent and ecological niche modeling in comparative phylogeography. Evolution; international journal of organic evolution, 61, 1439–54.
  22. CBD, 1992. Convention on Biological Diversity Text, United Nations Environment Programme, Rio de Janeiro.
  23. Coops, N.C., Waring, R.H., 2011. Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America. Ecological Modelling, 222, 2119–2129.
  24. Crozier, L., Dwyer, G., 2006. Combining population-dynamic and ecophysiological models to predict climate-induced insect range shifts. The American naturalist, 167, 853–866.
  25. D’Amen, M., Zimmermann, N.E., Pearman, P.B., 2013. Conservation of phylogeographic lineages under climate change. Global Ecology and Biogeography, 22, 93–104.
  26. Dawson, T.P., Jackson, S.T., House, J.I., Prentice, I.C., Mace, G.M., 2011. Beyond predictions: biodiversity conservation in a changing climate. Science, 332, 53–58.
  27. de Rigo, D., Bosco, C., San-Miguel-Ayanz, J., Houston Durrant, T., Barredo, J. I., Strona, G., Caudullo, G., Di Leo, M., Boca, R., 2016. Forest resources in Europe: an integrated perspective on ecosystem services, disturbances and threats. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e015b50+.
  28. de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+
  29. Dietterich, T., 1996. Machine learning. ACM Computing Surveys, 28.
  30. Dormann, C.F., Schymanski, S.J., Cabral, J., Chuine, I., Graham, C.H., Hartig, F., Kearney, M.R., Morin, X., Römermann, C., Schröder, B., Singer, A., 2012. Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119–2131.
  31. Dungan, J.L., Dale, M.R.T., Legendre, P., Citron-Pousty, S., 2002. A balanced view of scale in spatial statistical analysis. Ecography, 25, 626–640.
  32. Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methods improve prediction of species ’ distributions from occurrence data. Ecography, 29, 129–151.
  33. Elith, J., Leathwick, J., 2007. Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13, 265–275.
  34. Elith, J., Leathwick, J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 1–19.
  35. Elton, C., 1927. Animal Ecology, (ed. by Methuen) Sidgwick and Jackson.
  36. Fady, B., 2005. Is there really more biodiversity in Mediterranean forest ecosystems? Taxon, 54, 905–910.
  37. FAO, 2006. Better forestry, less poverty; A practitioner’s guide, Food and Agriculture Organization of the United Nations, Rome.
  38. FAO, 2010. Global Forest Resources Assessment 2010, Food and Agriculture Organization of the United Nations, Rome; Italy.
  39. FAO, 2014. State of the World’s Forests, Food and Agriculture Organization of the United Nations, Rome, Italy.
  40. Farber, O., Kadmon, R., 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160, 115–130.
  41. Ferrier, S., 1984. The Status of the Rufous Scrub-Bird Atrichornis rufescens: habitat geographical variation and abundance.
  42. Fielding, A.H., 2002. What are the appropriate characteristics of an accuracy measure? Predicting Species Occurrences Issues of Accuracy and Scale (ed. by J.M. Scott), P.J. Heglund), M.L. Morrison), J.B. Haufler), M.G. Raphael), W.A. Wall), and F.B. Samson), p. 868. Island Press.
  43. Fielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38– 49.
  44. Fleishman, E., Nally, R. Mac, Fay, J.P., 2003. Validation tests of predictive models of butterfly occurrence based on environmental variables. Conservation Biology, 17, 806– 817.
  45. Fordham, D.A., Wigley, T.M.L., Brook, B.W., 2011. Multi-model climate projections for biodiversity risk assessments. Ecological Applications, 21, 3317–3331.
  46. Frankham, R., 2010. Challenges and opportunities of genetic approaches to biological conservation. Biological Conservation, 143, 1919–1927.
  47. Fraser, D.., Bernatchez, L., 2001. Adaptive evolutionary conservation: towards a unified concept for defining coservation units. Molecular Ecology, 10, 2741–2752.
  48. Gauquelin, T., Michon, G., Joffre, R., Duponnois, R., Génin, D., Fady, B., Bou Dagher- Kharrat, M., Derridj, A., Slimani, S., Badri, W., Alifriqui, M., Auclair, L., Simenel, R., Aderghal, M., Baudoin, E., Galiana, A., Prin, Y., Sanguin, H., Fernandez, C., Baldy, V., 2016. Mediterranean forests, land use and climate change: a social-ecological perspective. Regional Environmental Change.
  49. Graham, C.H., Hijmans, R.J., 2006. A comparison of methods for mapping species ranges and species richness. Global Ecology and Biogeography, 15, 578–587.
  50. Grenouillet, G., Buisson, L., Casajus, N., Lek, S., 2011. Ensemble modelling of species distribution: The effects of geographical and environmental ranges. Ecography, 34, 9– 17.
  51. Grinnell, J., 1916. The niche-relationship of the California Thrasher. The Auk, 34, 427–433.
  52. Grinnell, J., 1904. The origin and distribution of the chestnut-backed chickadee. The Auk, 21, 364–382.
  53. Guisan, A., Broennimann, O., Engler, R., Vust, M., Yoccoz, N.G., Lehmann, A., Zimmermann, N.E., 2006. Using niche-based models to improve the sampling of rare species. Conservation Biology, 20, 501–511.
  54. Guisan, A., Thuiller, W., 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993–1009.
  55. Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., McDonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S. a, Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M.R., Possingham, H.P., Buckley, Y.M., 2013. Predicting species distributions for conservation decisions. Ecology letters, 16, 1424–1435.
  56. Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.
  57. Gummer, D.L., Schumaker, N.H., Bender, D.J., Heinrichs, J.A., 2010. Assessing critical habitat: Evaluating the relative contribution of habitats to population persistence. Biological Conservation, 143, 2229–2237.
  58. Hamann, A., Aitken, S.N., Yanchuk, A.D., 2004. Cataloguing in situ protection of genetic resources for major commercial forest trees in British Columbia. Forest Ecology and Management, 197, 295–305.
  59. Hampe, A., Petit, R.J., 2005. Conserving biodiversity under climate change: The rear edge matters. Ecology Letters, 8, 461–467.
  60. Hand, D.J., 2012. Assessing the performance of classification methods. International Statistical Review, 80, 400–414.
  61. Hand, D.J., 2010. Evaluating diagnostic tests: The area under the ROC curve and the balance of errors. Statistics in Medicine, 29, 1502–1510.
  62. Hanewinkel, M., Cullmann, D.A., Schelhaas, M.J., Nabuurs, G.J., Zimmermann, N.E., 2012. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change, 3, 203–207.
  63. Hastie, T.J., Tibshirani, R., 1990. Generalized additive models, Chapman & Hall/CRC, Boca Raton - London - New York - Washington D.C.
  64. Heikkinen, R.K., Luoto, M., Virkkala, R., Pearson, R.G., Körber, J.H., 2007. Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecology and Biogeography, 16, 754–763.
  65. Higgins, S.I., O’Hara, R.B., Bykova, O., Cramer, M.D., Chuine, I., Gerstner, E.-M., Hickler, T., Morin, X., Kearney, M.R., Midgley, G.F., Scheiter, S., 2012. A physiological analogy of the niche for projecting the potential distribution of plants. Journal of Biogeography, 39, 2132–2145.
  66. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978.
  67. Hijmans, R.J., Graham, C.H., 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology, 12, 2272– 2281.
  68. Holmes, K.W., Kyriakidis, P.C., Chadwick, O. a., Soares, J.V., Roberts, D. a., 2005. Multiscale variability in tropical soil nutrients following land-cover change. Biogeochemistry, 74, 173–203.
  69. Hutchinson, G.E., 1957. Concluding remarks- Cold Spring Harbor Symposia on Quantitative Biology. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415– 427.
  70. IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, (ed. by T.F. Stocker, D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,.
  71. IPCC, 2007. Climate Change 2007: impacts, adaptation and vulnerability: contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel, Cambridge.
  72. Jaramillo-Correa, J.P., Rodríguez-Quilón, I., Grivet, D., Lepoittevin, C., Sebastiani, F., Heuertz, M., Garnier-Géré, P., Alía, R., Plomion, C., Vendramin, G., González- Martínez, S., 2015. Molecular proxies of climate maladaptation in a long-lived tree. Genetics, 199, 1–15.
  73. Kearney, M.R., 2006. Habitat, environment and niche: what are we modelling? Oikos, 115, 186–191.
  74. Kearney, M.R., Porter, W., 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology letters, 12, 334–50.
  75. Kearney, M.R., Porter, W.P., 2004. Mapping the fundamental niche: Physiology, climate, and the distribution of a nocturnal lizard. Ecology, 85, 3119–3131.
  76. Knowles, L.L., Alvarado-Serrano, D.F., 2010. Exploring the population genetic consequences of the colonization process with spatio-temporally explicit models: insights from coupled ecological, demographic and genetic models in montane grasshoppers. Molecular ecology, 19, 3727–3745.
  77. Köble, R., Seufert, G., 2001. Novel maps for forest tree species in Europe. Proceedings of the 8th European Symposium on the Physico-Chemical Behaviour of Air Pollutants: “A Changing Atmosphere!,” pp. 17–20. Data set available online at: , Torino.
  78. Kramer, K., Degen, B., Buschbom, J., Hickler, T., Thuiller, W., Sykes, M.T., de Winter, W., 2010. Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—Range, abundance, genetic diversity and adaptive response. Forest Ecology and Management, 259, 2213–2222.
  79. Kremen, C., Cameron, A., Moilanen, A., Phillips, S.J., Thomas, C.D., Beentje, H., Dransfield, J., Fisher, B.L., Glaw, F., Good, T.C., Harper, G.J., Hijmans, R.J., Lees, D.C., Louis, E., Nussbaum, R.A., Raxworthy, C.J., Razafimpahanana, A., Schatz, G.E., Vences, M., Vieites, D.R., Wright, P.C., Zjhra, M.L., 2008. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science, 320, 222–226.
  80. Laikre, L., Allendorf, F.W., Aroner, L.C., Baker, C.S., Gregovich, D.P., Hansen, M.M., Jackson, J.A., Kendall, K.C., McKelvey, K., Neel, M.C., Olivieri, I., Ryman, N., Schwartz, M.K., Bull, R.S., Stetz, J.B., Tallmon, D.A., Taylor, B.L., Vojta, C.D., Waller, D.M., Waples, R.S., 2010. Neglect of genetic diversity in implementation of the convention on biological diversity. Conservation Biology, 24, 86–88.
  81. Ledig, E.T., Ledig, F.T., 1986. Conservation strategies for forest gene resources. Forest Ecology and Management, 14, 77–90.
  82. Leech, S.M., Almuedo, P.L., Neill, G.O., 2011. Assisted Migration: Adapting forest management to a changing climate. BC Journal of Ecosystems and Management, 12, 18–34.
  83. Lefèvre, F., Koskela, J., Hubert, J., Kraigher, H., Longauer, R., Olrik, D.C., Schueler, S., Bozzano, M., Alizoti, P., Bakys, R., Baldwin, C., Ballian, D., Black-Samuelsson, S., Bednarova, D., Bordács, S., Collin, E., de Cuyper, B., de Vries, S.M.G., Eysteinsson, T., Frýdl, J., Haverkamp, M., Ivankovic, M., Konrad, H., Koziol, C., Maaten, T., Notivol Paino, E., Oztürk, H., Pandeva, I.D., Parnuta, G., Pilipovič, A., Postolache, D., Ryan, C., Steffenrem, A., Varela, M.C., Vessella, F., Volosyanchuk, R.T., Westergren, M., Wolter, F., Yrjänä, L., Zariŋa, I., 2013. Dynamic conservation of forest genetic resources in 33 European countries. Conservation biology: the journal of the Society for Conservation Biology, 27, 373–84.
  84. Levin, S.A., 1992. The problem of pattern and scale in ecology. Ecology, 73, 1943–1967.
  85. Lindner, M., Maroschek, M., Netherer, S., Kremer, A., Barbati, A., Garcia-Gonzalo, J., Seidl, R., Delzon, S., Corona, P., Kolstr??m, M., Lexer, M.J., Marchetti, M., 2010. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management, 259, 698–709.
  86. Lobo, J.M., Jiménez-Valverde, A., Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145–151.
  87. López-Carrasco, C., López-Sánchez, A., San Miguel, A., Roig, S., 2015. The effect of tree cover on the biomass and diversity of the herbaceous layer in a Mediterranean dehesa. Grass and Forage Science, 70, 639–650.
  88. Lorena, A.C., Jacintho, L.F.O., Siqueira, M.F., Giovanni, R. De, Lohmann, L.G., de Carvalho, A.C.P.L.F., Yamamoto, M., 2011. Comparing machine learning classifiers in potential distribution modelling. Expert Systems with Applications, 38, 5268–5275.
  89. Lorenzen, E.D., Nogués-Bravo, D., Orlando, L., Weinstock, J., Binladen, J., Marske, K.A., Cooper, A., 2011. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature, 479, 359–364.
  90. Mackey, B.G., Lindenmayer, D.B., 2001. Towards a hierarchical framework for modelling the spatial distribution of animals. Journal of Biogeography, 28, 1147–1166.
  91. Marini, M.A., Barbet-Massin, M., Esteves Lopes, L., Jiguet, F., 2009. Major current and future gaps of Brazilian reserves to protect Neotropical savanna birds. Biological conservation, 142, 3039–3050.
  92. Matyas, C., Nagy, L., Jarmay, E.U., 2009. Genetic background of response of trees to aridification at the xeric forest limit and consequences for bioclimatic modelling. Bioclimatology and Natural Hazards, pp. 179–196. Springer, Netherlands.
  93. Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Mücher, C. a., Watkins, J.W., 2005. A climatic stratification of the environment of Europe. Global Ecology and Biogeography, 14, 549–563.
  94. Moore, P., Hawkins, S.J., Thompson, R.C., 2007. Role of biological habitat amelioration in altering the relative responses of congeneric species to climate change. Marine Ecology Progress Series, 334, 11–19.
  95. Moreno, A., Hasenauer, H., 2016. Spatial downscaling of European climate data. International Journal of Climatology, 36, 1444–1458.
  96. Morin, X., Viner, D., Chuine, I., 2008. Tree species range shifts at a continental scale: new predictive insights from a process-based model. Journal of Ecology, 96, 784–794.
  97. Moritz, C., 1994. Defining evolutionarily-significant-units for conservation. Trends in Ecology & Evolution, 9, 373–375.
  98. Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, A.B., Kent, J., 2000. Biodiversity hotspots for conservation priorities. Nature, 403, 72.
  99. Mac Nally, R., 2000. Regression and model-building in conservation biology, biogeography and ecology: The distinction between - and reconciliation of - “predictive” and “explanatory” models. Biodiversity and Conservation, 9, 655–671.
  100. Nelder, J.A., Wedderburn, R.W.M., 1972. Generalized Linear Models. Journal of the Royal Statistical Association, 135, 370–384.
  101. Nicotra, A.B., Beever, E.A., Robertson, A.L., Hofmann, G.E., O’Leary, J., 2015. Assessing the components of adaptive capacity to improve conservation and management efforts under global change. Conservation Biology, 29, 1268–1278.
  102. Nix, H.A., McMahon, J., MacKenzie, M.D., 1977. Potential areas of production and the future of Pigeon Pea and other grain legumes in Australia. The potential for Pigeon Pea in Australia Proceedings of Pigeon Pea Cajanus cajan L Millsp Field Day (ed. by E.S. Wallis) and P.C. Whitman), pp. 5/1–5/12. University of Queensland, Australia.
  103. Noce, S., Collalti, A., Valentini, R., Santini, M., 2016. Hot spot maps of forest presence in the Mediterranean basin. iForest - Biogeosciences and Forestry, 809.
  104. O’Neill, G.A., Hamann, A., Wang, T., 2008. Accounting for population variation improves estimates of the impact of climate change on species’ growth and distribution. Journal of Applied Ecology, 45, 1040–1049.
  105. Olden, J.D., Jackson, D.A., 2002. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology, 47, 1976–1995.
  106. Oney, B., Reineking, B., O’Neill, G.A., Kreyling, J., 2013. Intraspecific variation buffers projected climate change impacts on Pinus contorta. Ecology and Evolution, 3, 437– 449.
  107. Parra-Olea, G., Martinez-Meyer, E., Pérez-Ponce de León, G., 2005. Forecasting climate change effects on salamander distribution in the highlands of central Mexico. Biotropica, 37, 202–208.
  108. Pautasso, M., Dehnen-Schmutz, K., Holdenrieder, O., Pietravalle, S., Salama, N., Jeger, M.J., Lange, E., Hehl-Lange, S., 2010. Plant health and global change - Some implications for landscape management. Biological Reviews, 85, 729–755.
  109. Pearman, P.B., D’Amen, M., Graham, C.H., Thuiller, W., Zimmermann, N.E., 2010. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. Ecography, 33, 990–1003.
  110. Pearson, R.G., 2010. Species’ distribution modeling for conservation educators and practitioners. Lessons in Conservation, 3, 54–89.
  111. Pease, K.M., Freedman, A.H., Pollinger, J.P., McCormack, J.E., Buermann, W., Rodzen, J., Banks, J., Meredith, E., Bleich, V.C., Schaefer, R.J., Jones, K., Wayne, R.K., 2009. Landscape genetics of California mule deer (Odocoileus hemionus): the roles of ecological and historical factors in generating differentiation. Molecular Ecology, 18, 1848–1862.
  112. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.
  113. Preston, K.L., Rotenberry, J.T., Redak, R.A., Allen, M.F., 2008. Habitat shifts of endangered species under altered climate conditions: importance of biotic interactions. Global change biology., 14, 2501–2515.
  114. Richards, C.L., Carstens, B.C., Lacey Knowles, L., 2007. Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses. Journal of Biogeography, 34, 1833–1845.
  115. Rodríguez-Quilón, I., Santos-del-Blanco, L., Serra-Varela, M.J., Koskela, J., González- Martínez, S.C., Alía, R., 2016. Capturing neutral and adaptive genetic diversity for conservation: maritime pine as a case study. Ecological Applications, accepted.
  116. Rotach, P., 2005. In situ conservation. Conservation and Management of Forest Genetic Resources in Europe (ed. by T. Geburek) and J. Turok), pp. 535–565. Arbora Publishers, Zvolen.
  117. Sánchez de Dios, R., Benito-Garzón, M., Sainz-Ollero, H., 2009. Present and future extension of the Iberian submediterranean territories as determined from the distribution of marcescent oaks. Plant Ecology, 204, 189–205.
  118. Santini, A., Ghelardini, L., De Pace, C., Desprez-Loustau, M.L., Capretti, P., Chandelier, A., Cech, T., Chira, D., Diamandis, S., Gaitniekis, T., Hantula, J., Holdenrieder, O., Jankovsky, L., Jung, T., Jurc, D., Kirisits, T., Kunca, A., Lygis, V., Malecka, M., Marcais, B., Schmitz, S., Schumacher, J., Solheim, H., Solla, A., Szabò, I., Tsopelas, P., Vannini, A., Vettraino, a. M., Webber, J., Woodward, S., Stenlid, J., 2013. Biogeographical patterns and determinants of invasion by forest pathogens in Europe. New Phytologist, 197, 238–250.
  119. Schoville, S.D., Bonin, A., François, O., Lobreaux, S., Melodelima, C., Manel, S., 2012. Adaptive Genetic Variation on the Landscape: Methods and Cases. Annual Review of Ecology, Evolution, and Systematics, 43, 23–43.
  120. Schueler, S., Falk, W., Koskela, J., Lefèvre, F., Bozzano, M., Hubert, J., Kraigher, H., Longauer, R., Olrik, D.C., 2014. Vulnerability of dynamic genetic conservation units of forest trees in Europe to climate change. Global Change Biology, 20, 1498–1511.
  121. Schueler, S., Kapeller, S., Konrad, H., Geburek, T., Mengl, M., Bozzano, M., Koskela, J., Lefèvre, F., Hubert, J., Kraigher, H., Longauer, R., Olrik, D.C., 2013. Adaptive genetic diversity of trees for forest conservation in a future climate: a case study on Norway spruce in Austria. Biodiversity and Conservation, 22, 1151–1166.
  122. Segurado, P., Araújo, M.B., 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography, 31, 1555–1568.
  123. Serra-Varela, M.J., Alía, R., Pórtoles, J., Gonzalo-Jiménez, J., Soliño, M., Grivet, D. & Raposo, R. (submitted). Incorporating exposure to pitch canker disease to support management decisions of Pinus pinaster Ait. in the face of climate change. Forest Ecology and Management.
  124. Serra-Varela, M.J., Alía, R., Ruíz-Daniels, R., Zimmermann, N.E., Gonzalo-Jiménez, J. & Grivet, D. (in revision). Assessing vulnerability of two Mediterranean conifers to support genetic conservation management. Diversity and Distributions .
  125. Serra-Varela, M.J., Grivet, D., Vincenot, L., Broennimann, O., Gonzalo-Jiménez, J., Zimmermann, N.E., 2015. Does phylogeographic structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.). Global Ecology and Biogeography, 24, 1302–1313.
  126. Sgró, C.M., Lowe, A.J., Hoffmann, A.A., 2011. Building evolutionary resilience for conserving biodiversity under climate change. Evolutionary Applications, 4, 326–337.
  127. Shafer, A.B.A., Wolf, J.B.W., Alves, P.C., Bergström, L., Bruford, M.W., Brännström, I., Colling, G., Dalén, L., De Meester, L., Ekblom, R., Fawcett, K.D., Fior, S., Hajibabaei, M., Hill, J.A., Hoezel, A.R., Höglund, J., Jensen, E.L., Krause, J., Kristensen, T.N., Krützen, M., McKay, J.K., Norman, A.J., Ogden, R., Österling, E.M., Ouborg, N.J., Piccolo, J., Popovic, D., Primmer, C.R., Reed, F.A., Roumet, M., Salmona, J., Schenekar, T., Schwartz, M.K., Segelbacher, G., Senn, H., Thaulow, J., Valtonen, M., Veale, A., Vergeer, P., Vijay, N., Vilà, C., Weissensteiner, M., Wennerström, L., Wheat, C.W., Zielinski, P., 2015. Genomics and the challenging translation into conservation practice. Trends in Ecology and Evolution, 30, 78–87.
  128. Skov, F., Svenning, J., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography, 27, 366–380.
  129. Soberón, J., 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology letters, 10, 1115–23.
  130. Soberón, J., Peterson, A.T., 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics, 2, 1–10.
  131. Soulé, M.E., Wilcox, B.M., 1980. Conservation Biology: An Evolutionary-ecological Perspective., Sunderland, USA.
  132. Steinmann, K., Linder, H.P., Zimmermann, N.E., 2009. Modelling plant species richness using functional groups. Ecological Modelling, 220, 962–967.
  133. Sturrock, R.N., Frankel, S.J., Brown, A. V., Hennon, P.E., Kliejunas, J.T., Lewis, K.J., Worrall, J.J., Woods, A.J., 2011. Climate change and forest diseases. Plant Pathology, 60, 133–149.
  134. Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L. J. Collingham, Y.C., Hughes, L., 2004. Extinction risk from climate change. Nature, 427, 145–148.
  135. Thomassen, H.A., Cheviron, Z.A., Freedman, A.H., Harrigan, R.J., Wayne, R.K., Smith, T.B., 2010. Spatial modelling and landscape-level approaches for visualizing intraspecific variation. Molecular Ecology, 19, 3532–3548.
  136. Thuiller, W., Albert, C., Araújo, M.B., Berry, P.M., Cabeza, M., Guisan, A., Hickler, T., Midgley, G.F., Paterson, J., Schurr, F.M., Sykes, M.T., Zimmermann, N.E., 2008. Predicting global change impacts on plant species’ distributions: Future challenges. Perspectives in Plant Ecology, Evolution and Systematics, 9, 137–152.
  137. Thuiller, W., Lafourcade, B., Engler, R., Araújo, M.B., 2009. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography, 32, 369–373.
  138. Tylianakis, J.M., Didham, R.K., Bascompte, J., Wardle, D.A., 2008. Global change and species interactions in terrestrial ecosystems. Ecology Letters, 11, 1351–1363.
  139. Vandergast, A.G., Bohonak, A.J., Hathaway, S. a., Boys, J., Fisher, R.N., 2008. Are hotspots of evolutionary potential adequately protected in southern California? Biological Conservation, 141, 1648–1664.
  140. Vendramin, G.G., Anzidei, M., Madaghiele, A., Bucci, G., 1998. Distribution of genetic diversity in Pinus pinaster Ait. as revealed by chloroplast microsatellites. Theoretical and Applied Genetics, 97, 456–463.
  141. Verner, J., Morrison, M.L., Ralph, C.J., 1986. Wildlife 2000: modeling habitat relationships of terrestrial vertebrates, University of Wisconsin Press, Madison, WI.
  142. Wang, T., Hamann, A., Yanchuk, A.D., O’Neill, G.A., Aitken, S.N., 2006. Use of response functions in selecting lodgepole pine populations for future climates. Global Change Biology, 12, 2404–2416.
  143. Wiens, J.A., Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A., Samson, F.B., 2002. Predicting Species occurences: progress, Problems and prospects. Predicting species occurences issues of accuracy and scale (ed. by J.M. Scott, P.J. Heglund, M.L. Morrison, J.B. Haufler, M.G. Raphael, W.A. Wall and F.B. Samson), pp. 739–749. Island Press.
  144. Zonneveld, M. Van, Scheldeman, X., Escribano, P., Viruel, M. a, Van Damme, P., Garcia, W., Tapia, C., Romero, J., Sigueñas, M., Hormaza, J.I., 2012. Mapping genetic diversity of cherimoya (Annona cherimola Mill.): application of spatial analysis for conservation and use of plant genetic resources. PLoS ONE, 7, e29845.

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