Predicting the suitability area of heath alliances over France using open-source data

References

• Agrillo E, Alessi N, Jiménez-Alfaro B, Casella L, Angelini P, Argagnon O, Crespo G, Fernández-González F, Monteiro-Henriques T, Neto CS. 2018. The use of large databases to characterize habitat types: the case of Quercus suber woodlands in Europe. Rendiconti Lincei Scienze Fisiche e Naturali. 29:283–293.
   Google Scholar
• Agrillo E, Filipponi F, Pezzarossa A, Casella L, Smiraglia D, Orasi A, Attorre F, Taramelli A. 2021. Earth observation and biodiversity big data for forest habitat types classification and mapping. Remote Sens. 13(7):1231.
   Google Scholar
• Álvarez-Martínez JM, Jiménez-Alfaro B, Barquín J, Ondiviela B, Recio M, Silió-Calzada A, Juanes JA. 2018. Modelling the area of occupancy of habitat types with remote sensing. Methods Ecol Evol. 9(3):580–593.
   Web of Science ®Google Scholar
• Angiolini C, Foggi B, Viciani D, Gabellini A. 2007. Acidophytic shrublands in the north-west of the Italian peninsula: ecology, chorology and syntaxonomy. Plant Biosyst. 141(2):134–163.
   Web of Science ®Google Scholar
• Angiolini C, Viciani D, Bonari G, Lastrucci L. 2017. Habitat conservation prioritization: a floristic approach applied to a Mediterranean wetland network. Plant Biosyst. 151(4):598–612.
   Web of Science ®Google Scholar
• Baatar U-O, Dirnböck T, Essl F, Moser D, Wessely J, Willner W, Jiménez-Alfaro B, Agrillo E, Csiky J, Indreica A, et al. 2019. Evaluating climatic threats to habitat types based on co-occurrence patterns of characteristic species. Basic Appl Ecol. 38:23–35.
   Web of Science ®Google Scholar
• Balakrishnama S, Ganapathiraju A. 1998. Linear discriminant analysis - a brief tutorial. Simral (USA): Institute for Signal and Information Processing.
   Google Scholar
• Bensettiti F, Boullet V, Chavaudret-Laborie C, Deniaud J, editors. 2005. “Cahiers d’habitats” Natura 2000. Connaissance et gestion des habitats et des espèces d’intérêt communautaire. Paris: la Documentation française.
   Google Scholar
• Bensettiti F, Gaudillat V, Haury J, editors. 2002. “Cahiers d’habitats” Natura 2000. Connaissance et gestion des habitats et des espèces d’intérêt communautaire. Paris: la Documentation française.
   Google Scholar
• Besnard AG, Davranche A, Maugenest S, Bouzillé JB, Vian A, Secondi J. 2015. Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient. Ecol Indic. 58:47–54.
   Web of Science ®Google Scholar
• Biondi E, Burrascano S, Casavecchia S, Copiz R, Del Vico E, Galdenzi D, Gigante D, Lasen C, Spampinato G, Venanzoni R. 2012. Diagnosis and syntaxonomic interpretation of Annex I Habitats (Dir. 92/43/EEC) in Italy at the alliance level. Plant Sociol. 49(1):5–37.
   Google Scholar
• Bioret F, Gaudillat V, Royer JM. 2013. The Prodrome of French vegetation: a national synsystem for phytosociological knowledge and management issues. Plant Sociol. 50(1):17–21.
   Google Scholar
• Bivand R, Keitt T, Rowlingson B. 2015. rgdal: Bindings for the Geospatial Data Abstraction Library [Internet]. http://CRAN.R-project.org/package=rgdal.
   Google Scholar
• Bohl CL, Kass JM, Anderson RP. 2019. A new null model approach to quantify performance and significance for ecological niche models of species distributions. J Biogeogr. 46(6):1101–1111.
   Web of Science ®Google Scholar
• Bonhomme M. 2008. Synthèse des données recueillies sur les landes mésophiles à xérophiles du Limousin. Acta Bot Gallica. 155(1):69–78.
   Web of Science ®Google Scholar
• Borre JV, Paelinckx D, Mücher CA, Kooistra L, Haest B, De Blust G, Schmidt AM. 2011. Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. J Nature Conserv. 19(2):116–125.
   Web of Science ®Google Scholar
• Brenning A, Bangs D, Becker M, Schratz P, Polakowski F. 2018. Package ‘RSAGA’. The Comprehensive R Archive Network. https://CRANR-project.org/package=RSAGA.
   Google Scholar
• Canellas C, Gibelin A-L, Lassègues P, Kerdoncuff M, Dandin P, Simon P. 2014. Les normales climatiques spatialisées Aurelhy 1981–2010 : températures et précipitations. Météorologie. 8(85):47–55.
   Google Scholar
• Čarni A, Ćuk M, Zelnik I, Franjić J, Igić R, Ilić M, Krstonošić D, Vukov D, Škvorc Ž. 2021. Wet meadow plant communities of the alliance Trifolion pallidi on the southeastern margin of the Pannonian Plain. Water. 13(3):381.
   Web of Science ®Google Scholar
• Choisnet G, Perrin G, Boullet V, Thébaud G. in press. Contribution au prodrome des végétations de France: les Genisto pilosae-Callunetea vulgaris class. nov. Doc Phytosoc.
   Google Scholar
• Chytrý M, Hennekens SM, Jiménez-Alfaro B, Knollová I, Dengler J, Jansen F, Landucci F, Schaminée JH, Aćić S, Agrillo E, et al. 2016. European Vegetation Archive (EVA): an integrated database of European vegetation plots. Appl Veg Sci. 19(1):173–180.
   Web of Science ®Google Scholar
• Chytrý M, Tichý L, Hennekens SM, Knollová I, Janssen JA, Rodwell JS, Peterka T, Marcenò C, Landucci F, Danihelka J. 2020. EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science.
   Google Scholar
• Clair M, Gaudillat V, Michez N, Poncet L, Poncet L. 2017. HABREF v3. 1, référentiel des typologies d’habitats et de végétation pour la France. Guide méthodologique. [place unknown]: service du patrimoine naturel, Muséum national d’histoire naturelle, Paris.
   Google Scholar
• Davies CE, Moss D, Hill MO. 2004. EUNIS habitat classification revised 2004. Report to: European Environment Agency-European Topic Centre on Nature Protection and Biodiversity. p. 127–143.
   Google Scholar
• De Cáceres M, Chytrý M, Agrillo E, Attorre F, Botta-Dukát Z, Capelo J, Czúcz B, Dengler J, Ewald J, Faber-Langendoen D, et al. 2015. A comparative framework for broad-scale plot-based vegetation classification. Appl Veg Sci. 18(4):543–560.
   Web of Science ®Google Scholar
• Devillers P, Devillers-Terschuren J, Ledant J. 1991. CORINE biotopes manual. Habitats of the European Community Office for Official Publications of the European Communities, Luxembourg.
   Google Scholar
• Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 17(1):43–57.
   Web of Science ®Google Scholar
• European Commission. 2013. Interpretation manual of European Union Habitats, version EUR 28. Bruxelles: European Commission.
   Google Scholar
• European Environment Agency. 2016. Biogeographical regions [Internet]. https://www.eea.europa.eu/data-and-maps/data/biogeographical-regions-europe-3#tab-data-visualisations.
   Google Scholar
• Evans D, Arvela M. 2011. Assessment and reporting under Article 17 of the Habitats Directive. Explanatory notes & guidelines for the period 2007–2012. Final Draft Paris: European Topic Centre on Biological Diversity.
   Google Scholar
• Fenske K, Feilhauer H, Förster M, Stellmes M, Waske B. 2020. Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series. Int J Appl Earth Obs Geoinf. 87:102036.
   Web of Science ®Google Scholar
• Freeman TG. 1991. Calculating catchment area with divergent flow based on a regular grid. Comput Geosci. 17(3):413–422.
   Web of Science ®Google Scholar
• García González JC, Redondo JA, Garzón A. 2015. EU-DEM upgrade documentation EEA user manual [Internet]. Indra Sistemas. https://land.copernicus.eu/user-corner/technical-library/eu-dem-v1-1-user-guide.
   Google Scholar
• Géhu J-M. 2011. On the opportunity to celebrate the centenary of modern phytosociology in 2010. Plant Biosyst. 145(sup1):4–8.
   Google Scholar
• Glemarec E, Delassus L, Boullet V. in press. Contribution au prodrome des végétations de France: les Calluno vulgaris-Ulicetea minoris Braun-Blanq. & Tüxen ex Klika & Hadač. Doc Phytosoc.
   Google Scholar
• Gonçalves J, Henriques R, Alves P, Sousa-Silva R, Monteiro AT, Lomba Â, Marcos B, Honrado J. 2016. Evaluating an unmanned aerial vehicle-based approach for assessing habitat extent and condition in fine-scale early successional mountain mosaics. Appl Veg Sci. 19(1):132–146.
   Web of Science ®Google Scholar
• Guillera-Arroita G, Lahoz-Monfort JJ, Elith J. 2014. Maxent is not a presence–absence method: a comment on Thibaud et al. Methods Ecol Evol. 5(11):1192–1197.
   Web of Science ®Google Scholar
• Guisan A, Weiss SB, Weiss AD. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 143(1):107–122.
   Web of Science ®Google Scholar
• Haest B, Vanden Borre J, Spanhove T, Thoonen G, Delalieux S, Kooistra L, Mücher C, Paelinckx D, Scheunders P, Kempeneers P. 2017. Habitat mapping and quality assessment of NATURA 2000 heathland using airborne imaging spectroscopy. Remote Sens. 9(3):266.
   Google Scholar
• Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, et al. 2017. SoilGrids250m: global gridded soil information based on machine learning. Bond-Lamberty B, editor. PLoS One. 12(2):e0169748.
   PubMed Web of Science ®Google Scholar
• Hijmans RJ. 2015. raster: geographic data analysis and modeling [Internet]. http://CRAN.R-project.org/package=raster.
   Google Scholar
• Hubert-Moy L, Rozo C, Perrin G, Bioret F, Rapinel S. 2022. Large scale and fine-grained mapping of heathland habitats using open satellite remote sensing data. Remote Sens Ecol Conserv. 8(4):448–463.
   Web of Science ®Google Scholar
• IGN. 2018. RGE ALTI V2 - Descriptif de contenu [Internet]. Saint Mandé: IGN. https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/MODELES_3D/RGE_ALTI/DC_RGEALTI_2-0.pdf.
   Google Scholar
• Iwahashi J, Pike RJ. 2007. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology (Amst). 86(3–4):409–440.
   Web of Science ®Google Scholar
• Jeanmougin M, Dehais C, Meinard Y. 2017. Mismatch between Habitat Science and Habitat Directive: lessons from the French (Counter) Example. Conserv Lett. 10(5):634–644.
   Web of Science ®Google Scholar
• Jiménez-Alfaro B, Suárez-Seoane S, Chytrý M, Hennekens SM, Willner W, Hájek M, Agrillo E, Álvarez-Martínez JM, Bergamini A, Brisse H, et al. 2018. Modelling the distribution and compositional variation of plant communities at the continental scale. Divers Distrib. 24(7):978–990.
   Web of Science ®Google Scholar
• Karasiak N, Dejoux J-F, Monteil C, Sheeren D. 2022. Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing. Mach Learn. 111(7):2715–2740.
   Web of Science ®Google Scholar
• Keith DA, Rodríguez JP, Rodríguez-Clark KM, Nicholson E, Aapala K, Alonso A, Asmussen M, Bachman S, Basset A, Barrow EG, et al. 2013. Scientific foundations for an IUCN Red List of ecosystems. PLoS One. 8(5):e62111.
   PubMed Web of Science ®Google Scholar
• Kopecký M, Čížková Š. 2010. Using topographic wetness index in vegetation ecology: does the algorithm matter: topographic wetness index in vegetation ecology? Appl Veg Sci. 13(4):450–459.
   Web of Science ®Google Scholar
• Kuhn M. 2008. Caret package. J Stat Softw. 28(5):1–26.
   PubMed Web of Science ®Google Scholar
• Kuhn M, Johnson K. 2013. Applied predictive modeling. New York (NY): Springer-Verlag.
   Google Scholar
• Liu C, White M, Newell G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr. 40(4):778–789.
   Web of Science ®Google Scholar
• Loidi J, Biurrun I, Campos JA, García-Mijangos I, Herrera M. 2010. A biogeographical analysis of the European Atlantic lowland heathlands. J Veg Sci. 21(5):832–842.
   Web of Science ®Google Scholar
• Mack B. 2014. oneClass: an R package for one-class classification [Internet]. https://github.com/benmack/oneClass.
   Google Scholar
• Merow C, Smith MJ, Silander JA, Jr. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography. 36(10):1058–1069.
   Web of Science ®Google Scholar
• Messant A, Lehmann S, Moulin J, Lagacherie P, Jalabert S, Noraz A, Lemercier B, Chafchafi A, Mure J-P, Laroche B. 2021. Diffusion des référentiels régionaux pédologiques sous la forme d’une carte des sols dominants (France métropolitaine-Hors Corse) accessible sur le Géoportail. Etude et Gestion Des Sols. 28:57–69.
   Google Scholar
• Miller J, Franklin J. 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol Modell. 157(2–3):227–247.
   Web of Science ®Google Scholar
• Mucina L, Bültmann H, Dierßen K, Theurillat J-P, Raus T, Čarni A, Šumberová K, Willner W, Dengler J, García RG, et al. 2016. Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. Appl Veg Sci. 19(S1):3–264.
   Web of Science ®Google Scholar
• Perrin G, Rapinel S, Hubert-Moy L, Bioret F. 2020. Bioclimatic dataset of Metropolitan France under current conditions derived from the WorldClim model. Data Brief. 31:105815.
   PubMed Web of Science ®Google Scholar
• Peterka T, Hájek M, Jiroušek M, Jiménez-Alfaro B, Aunina L, Bergamini A, Dítě D, Felbaba-Klushyna L, Graf U, Hájková P, et al. 2017. Formalized classification of European fen vegetation at the alliance level. Appl Veg Sci. 20(1):124–142.
   Web of Science ®Google Scholar
• Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME. 2017. Opening the black box: an open-source release of Maxent. Ecography. 40(7):887–893.
   Web of Science ®Google Scholar
• Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecol Modell. 190(3–4):231–259.
   Web of Science ®Google Scholar
• Phillips SJ, Dudík M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 31(2):161–175.
   Web of Science ®Google Scholar
• Preislerová Z, Jiménez-Alfaro B, Mucina L, Berg C, Bonari G, Kuzemko A, Landucci F, Marcenò C, Monteiro-Henriques T, Novák P, et al. 2022. Distribution maps of vegetation alliances in Europe. Appl Veg Sci. 25(1):e12642.
   Web of Science ®Google Scholar
• Core Team R. 2019. A language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. p. 2012. https://www.R-project.org.
   Google Scholar
• Ramil Rego P, Rodríguez Guitián MA, López Castro H, Ferreiro da Costa J, Muñoz Sobrino C. 2013. Loss of European dry heaths in NW Spain: a case study. Diversity. 5(3):557–580.
   Google Scholar
• Rapinel S, Hubert-Moy L. 2021. One-class classification of natural vegetation using remote sensing: a review. Remote Sens. 13(10):1892.
   Google Scholar
• Rivas-Martínez S, Rivas-Saenz S, Penas A. 2011. Worldwide bioclimatic classification system. Global Geobot. 1(1):1–634.
   Google Scholar
• Rocchini D, Petras V, Petrasova A, Horning N, Furtkevicova L, Neteler M, Leutner B, Wegmann M. 2017. Open data and open source for remote sensing training in ecology. Ecol Inf. 40:57–61.
   Web of Science ®Google Scholar
• Royer J-M. 2008. Les landes atlantiques à leur limite orientale en Bourgogne et en Champagne méridionale. Acta Bot Gallica. 155(1):49–62.
   Web of Science ®Google Scholar
• Schaminée JHJ, Chytrý M, Hennekens SM, Janssen JAM, Knollová I, Rodwell JS, Tichý L. 2018. Updated crosswalk of the revised EUNIS Habitat Classification with the European vegetation classification and indicator species for the EUNIS grassland, shrubland and forest types. Wageningen (Netherlands): Wageningen Environmental Research.
   Google Scholar
• Schmidtlein S, Sassin J. 2004. Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sens Environ. 92(1):126–138.
   Web of Science ®Google Scholar
• Shom IGN. 2009. Trait de côte HISTOLITT® [Internet]; [accessed 2022 Feb 22]. https://carmenv2.carmencarto.fr/IHM/metadata/MEDITER/Publication/Specifications_techniques_TdCH_v2.pdf.
   Google Scholar
• Škvorc Ž, Ćuk M, Zelnik I, Franjić J, Igić R, Ilić M, Krstonošić D, Vukov D, Čarni A. 2020. Diversity of wet and mesic grasslands along a climatic gradient on the southern margin of the Pannonian Basin. Appl Veg Sci. 23(4):676–697.
   Web of Science ®Google Scholar
• SOLARGIS Solargis. 2019. Global Solar Atlas 2.0. A free web-based application developed and operated by the company s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP) [Internet]. https://globalsolaratlas.info/map.
   Google Scholar
• Suarez-Seoane S, Jimenez-Alfaro B, Obeso JR. 2020. Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry. Biodivers Conserv. 29(3):987–1008.
   Web of Science ®Google Scholar
• Technical University of Denmark. 2019. Global Wind Atlas 30 [Internet]. https://globalwindatlas.info.
   Google Scholar
• Thébaud G. 2011. Contribution au prodrome des végétations de France : les Oxycocco-Sphagnetea Braun-Blanq. & Tüxen ex V. Westh., Dijk, Paschier & Sissingh 1946 (tourbières acides eurosibériennes). J Bot Soc Bot Fr. 56:69–97.
   Google Scholar
• Thébaud G, Choisnet G, Roux C. 2021. Contribution to the survey of the heathlands of the French Massif central (habitats 4030 and 4060): analysis of phytosociological data. BIOM. 2(1):62–109.
   Google Scholar
• Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G. 2019. blockCV: an r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol Evol. 10(2):225–232.
   Web of Science ®Google Scholar
• Zevenbergen LW, Thorne CR. 1987. Quantitative analysis of land surface topography. Earth Surf Process Landforms. 12(1):47–56.
   Web of Science ®Google Scholar