[
Abolmaali, S.M.R., Tarkesh, M. & Bashari H. (2018). MaxEntmodeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecological Informatics, 43, 116–123. DOI: 10.1016/j.ecoinf.2017.10.002.
]Open DOISearch in Google Scholar
[
Abrha, H., Birhane, E., Hagos, H. & Manaye A. (2018). Predicting suitable habitats of endangeredJuniperusprocera tree under climate change in Northern Ethiopia. Journal of Sustainable Forestry, 37(8), 842–853. DOI: 10.1080/10549811.2018.1494000.
]Open DOISearch in Google Scholar
[
Achhal, A., Akabli, O., Barbero, M., Benabid, A., M’hirit, A., Peyre, C., Quezel, P. & Rivas-Martinez S. (1980). A propos de la valeurbioclimatiqueetdynamique de quelques essences forestières au Maroc. Ecol. Mediterr., 5, 211–249.10.3406/ecmed.1979.960
]Search in Google Scholar
[
Agnew, M.D. & Palutikof J.P. (2000). GIS-based construction of baseline climatologies for the Mediterranean using terrain variables. Clim. Res., 14(2), 115–127. DOI: 10.3354/cr014115.
]Open DOISearch in Google Scholar
[
Alcaraz, C. (1982). La végétation de l’ouest Algérien. PhD Thesis, University of Perpignan.
]Search in Google Scholar
[
Ayache, F., Santana, V.M. & Baeza M.J. (2020). Environmental and anthropogenic drivers of coniferous species distribution in Mediterranean drylands from North West Algeria. Folia Geobot., 55(1), 15–27. DOI: 10.1007/s12224-020-09362-8.
]Open DOISearch in Google Scholar
[
Benhassaini, H., Mehdadi, Z., Hamel, L. & Belkhodja M. (2007). Phytoécologie de Pistacia atlantica Desf. subsp. Atlantica dans le Nord-Ouestalgérien. Science et Changements Planétaires/Sécheresse, 18(3), 199‒205. DOI : 10.1684/sec.2007.0086.
]Open DOISearch in Google Scholar
[
Boudy, P. (1952). Guide du forestieren Afrique du Nord. Paris: La Maison Rustique.
]Search in Google Scholar
[
Brague-Bouragba, N., Brague, A., Dellouli, S. & Lieutier F. (2007). Comparaison des peuplements de Coléoptères et d’Araignéesen zone reboisée et en zone steppique dans une région présaharienne d’Algérie. C.R.Biol., 330(12), 923–939. DOI: 10.1016/j.crvi.2007.09.004.18068651
]Open DOISearch in Google Scholar
[
Das, A., Nagendra, H., Anand, M. & Bunyan M. (2015). Topographic and bioclimatic determinants of the occurrence of forest and grassland in tropical montane forest-grassland mosaics of the Western Ghats, India. PloS One, 10(6), e0130566. DOI: 10.1371/journal.pone.0130566.448830126121353
]Open DOISearch in Google Scholar
[
Djebbouri, M. & Terras M. (2019). Floristic diversity with particular reference to endemic, rare or endangered flora in forest formations of Saïda (Algeria). International Journal of Environmental Studies, 76(6), 990–1003. DOI: 10.1080/00207233.2019.1620541.
]Open DOISearch in Google Scholar
[
Dou, J., Yunus, A.P., Merghadi, A., Wang, X. & Yamagishi H. (2021). A Comparative Study of Deep Learning and Conventional Neural Network for Evaluating Landslide Susceptibility Using Landslide Initiation Zones. In F. Guzzetti et al. (Eds.), Understanding and Reducing Landslide Disaste Risk (pp. 215–223). Springer. DOI: 10.1007/978-3-030-60227-7_23.
]Open DOISearch in Google Scholar
[
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.M., Townsend Peterson, A., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. & Zimmermann N.E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151. DOI: 10.1111/j.2006.0906-7590.04596.x.
]Open DOISearch in Google Scholar
[
Fourcade, Y., Engler, J.O., 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(5), e97122. DOI: 10.1371/journal. pone.0097122.
]Open DOISearch in Google Scholar
[
Giles, J.R., Peterson, A.T., Busch, J.D., Olafson, P.U., Scoles, G.A., Davey, R.B., Mathews Pound, J., Kammlah, D.M., Lohmezer, K.H. & Wagner D.M. (2014). Invasive potential of cattle fever ticks in the southern United States. Parasites & Vectors, 7(1), 189–189. DOI: 10.1186/1756-3305-7-189.
]Open DOISearch in Google Scholar
[
Guisan, A., Edwards, J. & Hastie T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model., 157(2–3), 89–100. DOI: 10.1016/S0304-3800(02)00204-1.
]Open DOISearch in Google Scholar
[
Guisan, A. & Thuiller W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8(9), 993–1009. DOI: 10.1111/j.1461-0248.2005.00792.x.34517687
]Open DOISearch in Google Scholar
[
Hadjadj-Aoul, S. (1995). Les peuplements du thuya de berbérieen Algérie: phytoécologieSyntaxonomie, potentialités sylvicoles. PhD Thesis, University Aix-Marseille III. http://www.theses.fr/1995AIX30045.
]Search in Google Scholar
[
Hamdi, L., Defaflia, N., Fehdi, C. & Merghadi A. (2020). InSAR Investigation on DRAA-Douamis Sinkholes in Cheria Northeastern of Algeria. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 1034–1037). IEEE. DOI: 10.1109/IGARSS39084.2020.9323835.
]Open DOISearch in Google Scholar
[
Hardlife, M., Henry, N., Paradzayi, T., Mpakairi, K.S. & Eliah G. (2020). Predicting the invasion of a southern African savannah by the black wattle (Acacia mearnsii). J. For. Res., 31(5), 1995–2003. DOI: 10.1007/s11676-019-00975-0.
]Open DOISearch in Google Scholar
[
Hengl, T.& Reuter H.I. (2008). Geomorphometry: concepts, software, applications. Elsevier.
]Search in Google Scholar
[
Khosravi, R., Hemami, M.R., Malekian, M., Flint, A. & Flint L. (2016). Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model. Tur. J. Zool., 40(4), 574–585. DOI: 10.3906/zoo-1505-38.
]Open DOISearch in Google Scholar
[
Kumar, S. & Stohlgren T.J. (2009). Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and the Natural Environment, 1(4), 94–98. DOI: 10.5897/JENE.9000071.
]Open DOISearch in Google Scholar
[
Laala, A., Alatou, D. & Adimi A. (2021). Predicting potential habitat suitability of Quercus suber L. in Algeria under climate change scenarios. Afr. J. Ecol., 59(4), 976–987. DOI: 10.1111/aje.12906.
]Open DOISearch in Google Scholar
[
Laborde, J.P. (1993). Carte pluviométrique de l’Algérie du Nord à l’échelle du 1/500000. Agence Nationale des Ressources Hydrauliques, projet PNUD/ALG/88/021, une carte avec notice explicative.
]Search in Google Scholar
[
Lassueur, T., Joost, S. & Randin C.F. (2006). Very high resolution digital elevation models: Do they improve models of plant species distribution?. Ecol. Model., 198(1–2), 139–153. DOI: 10.1016/j.ecolmodel.2006.04.004.
]Open DOISearch in Google Scholar
[
Lecours, V., Dolan, M.F., Micallef, A. & Lucieer V.L. (2016). A review of marine ggeomorphometry, the quantitative study of the seafloor. Hydrology and Earth System Sciences, 20(8), 3207–3244. DOI: 10.5194/hess-20-3207-2016.
]Open DOISearch in Google Scholar
[
Leempoel, K., Parisod, C., Geiser, C., Daprà, L., Vittoz, P. & Joost S. (2015). Very high-resolution digital elevation models: are multi-scale derived variables ecologically relevant?. Methods in Ecology and Evolution, 6(12), 1373–1383. DOI: 10.1111/2041-210X.12427.
]Open DOISearch in Google Scholar
[
Mansour, Z., Abderahmane, H. & Soraya R. (2020). Using Topographic and Geographic Terrain Characteristics for Mapping Annual Rainfall in the Tafna Watershed, Western Algeria. International Journal of Water Resources and Arid Environments, 9(1), 80–86. https://www.psipw.org/attachments/article/2054/7e.pdf
]Search in Google Scholar
[
Meddi, M. & Hubert P. (2003). Impact de la modification du régime pluviométrique sur les ressources en eau du Nord-Ouest de l’Algérie. In IAHS Publication (pp. 229–235). Montpellier.
]Search in Google Scholar
[
Meddi, M., Meddi, H., Toumi, S. & Mehaiguen M. (2013). Regionalization of rainfall in North-Western Algeria. Geographia Technica, 17(1), 56–69. http://www.technicalgeography.org/pdf/1_2013/07_1_2013.pdf
]Search in Google Scholar
[
Merghadi, A., Yunus, A.P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D.T., Avtar, R. & Abderrahmane B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. DOI: 10.1016/j.ear-scirev.2020.103225.
]Open DOISearch in Google Scholar
[
Mod, H.K., Scherrer, D., Luoto, M. & Guisan A. (2016). What we use is not what we know: environmental predictors in plant distribution models. J. Veg. Sci., 27(6), 1308–1322. DOI: 10.1111/jvs.12444.
]Open DOISearch in Google Scholar
[
Nicolaci, A., Travaglini, D., Menguzzato, G., Nocentini, S., Veltri, A. & Iovino F. (2014). Ecological and anthropogenic drivers of Calabrian pine (Pinus nigra JF Arn. ssp. laricio (Poiret) Maire) distribution in the Sila mountain range. iForest-Biogeosciences and Forestry, 8(4), 497–508. DOI: 10.3832/ifor1041-007.
]Open DOISearch in Google Scholar
[
Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Townsend Peterson A. (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J.Biogeogr., 34(1), 102–117. DOI: 10.1111/j.1365-2699.2006.01594.x.
]Open DOISearch in Google Scholar
[
Phillips, S.J., Anderson, R.P. & Schapire R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecol. Model., 190(3–4), 231–259. DOI: 10.1016/j.ecolmodel.2005.03.026.
]Open DOISearch in Google Scholar
[
Phillips, S.J. & Dudík M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. DOI: 10.1111/j.0906-7590.2008.5203.x.
]Open DOISearch in Google Scholar
[
Quézel, P. (2002). Réflexions sur l’évolution de la flore et de la végétation au Maghreb Méditerranéen.Vol. 1. Paris: Ibis Press.
]Search in Google Scholar
[
Quézel, P. & Médail F. (2003). Ecologie et biogéographie des forêts du bassinMéditerranéen. Vol. 572. Paris: Elsevier.
]Search in Google Scholar
[
Sanders, T.G., Pitman, R. & Broadmeadow M.S.(2014). Species-specific climate response of oaks (Quercus spp.) under identical environmental conditions. iForest-Biogeosciences and Forestry, 7(2), 61–69. DOI: 10.3832/ifor0911-007.
]Open DOISearch in Google Scholar
[
Sharma, S., Arunachalam, K., Bhavsar, D. & Kala R. (2018). Modeling habitat suitability of Perillafrutescens with MaxEnt in Uttarakhand—A conservation approach. Journal of Applied Research on Medicinal and Aromatic Plants, 10, 99–105. DOI: 10.1016/J.JARMAP.2018.02.003.
]Open DOISearch in Google Scholar
[
Society for Ecological Restoration International Science & Policy Working Group (2004). The SER International Primer on Ecological Restoration.https://cdn.ymaws.com/www.ser.org/resource/resmgr/custom-pages/publications/ser_publications/ser_primer.pdf
]Search in Google Scholar
[
Terras, M. (2011). Typologie, cartographie des stations forestières et modélisations des Peuplements forestiers. Cas des massifs forestiers de la wilaya de Saida (Algérie). PhD Thesis, University of Tlemcen. http://dspace.univ-tlemcen.dz/handle/112/8446.
]Search in Google Scholar
[
Zhang, L., Liu, S., Sun, P., Wang, T., Wang, G., Wang, L. & Zhang X. (2016). Using DEM to predict Abiesfaxoniana and Quercusaquifolioides distributions in the upstream catchment basin of the Min River in southwest China. Ecological Indicators, 69, 91–99. DOI: 10.1016/j. ecolind.2016.04.008.
]Open DOISearch in Google Scholar
[
Zouidi, M., Borsali, A.H., Allam, A. & Gros R. (2019). Microbial activities and physicochemical properties of coniferous forest soils in two forest areas (arid and semi arid) of western Algeria. Bosque (Valdivia), 40, 163–171. DOI: 10.4067/S0717-92002019000200163.
]Open DOISearch in Google Scholar
