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Geospatial analysis to assess distribution patterns and predictive models for endangered plant species to support management decisions: a case study in the Balearic Islands

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Abstract

Species distribution modelling (SDM) has been used to support biodiversity management in recent years. However, the use of SDM at small scales with geolocation systems to obtain high-accuracy location data remains unexplored. In this study, we focused on Euphorbia fontqueriana, an endangered species to Mallorca (western Mediterranean basin), and we aimed to assess the spatial distribution patterns, and to generate a distribution map of the habitat suitability in a wider area. A differential GPS was used to geolocate all the ramets. We used the global Moran’s I index and local Getis Ord-Gi* method to assess the spatial patterns. We pre-selected derived topographic variables that were generated from LiDAR data (elevation, slope, northness and eastness), the connectivity index, normalised difference vegetation index, and soil type, as the environmental (predictor) variables. In addition, we ran the Maxent model using 1603 occurrence locations and seven environmental variables at a resolution of 2 × 2 m in grid size. The population consisted of 1625 ramets that were clustered (global Moran’s I index = 0.161, z score = 16.599, p value < 0.001) in several hotspots (i.e. areas with high plant density that were surrounded by areas with high plant densities). The Maxent model, which showed a good performance (AUC training score = 0.977), generated a habitat suitability map that displayed zones of high suitability in other areas away from the natural geographical area. Finally, we discuss the usefulness of this study to guide management purposes.

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References

  • Albert MJ, Iriondo JM (2009) Euphorbia fontqueriana Greuter. In: Iriondo JM, Albert MJ, Giménez Benavides L, Domínguez Lozano F, Escudero A (eds) Poblaciones en peligro: viabilidad demográfica de la flora vascular amenazada de España, Dirección General de Medio Natural y Política Forestal. Ministerio de Medio Ambiente, y Medio Rural y Marino), Madrid, pp 89–92

    Google Scholar 

  • Anselin L, Syabri I, Kho Y (2006) GeoDa: An Introduction to Spatial Data Analysis. Geogr Anal 38:5–22

    Google Scholar 

  • Bayly MJ, Angert AL (2019) Niche models do not predict experimental demography but both suggest dispersal limitation across the northern range limit of the scarlet monkeyflower (Erythranthe cardinalis). J Biogeogr 46(7):1316–1328

    Google Scholar 

  • Booth TH (2018) Species distribution modelling tools and databases to assist managing forests under climate change. For Ecol Manag 430:196–203

    Google Scholar 

  • Borthakur SK, Sharma Baruah P, Deka K, Das P, Sarma B, Adhikari D, Tanti B (2018) Habitat distribution modelling for improving conservation status of Brucea mollis Wall. ex Kurz.—An endangered potential medicinal plant of Northeast India. J Nat Conserv 43:104–110

    Google Scholar 

  • Braun-Blanquet J (1979) Fitosociología. Bases para el estudio de las comunidades vegetales. H. Blume Ediciones, Madrid

    Google Scholar 

  • Cavalli M, Trevisani S, Comiti F, Marchi L (2013) Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. Geomorphology 188:31–41

    Google Scholar 

  • Cook RE (1985) Growth and development in clonal plant populations. In: Jackson JBC, Buss LW, Cook RE (eds) Population biology and evolution of clonal organisms. Yale University Press, New Haven, pp 259–296

    Google Scholar 

  • Draper D, Marques I, Iriondo J (2019) Species Distribution models with field validation, a kew approach for successful selection of receptor sites in conservation translocations. Global Ecol Conserv. https://doi.org/10.1016/j.gecco.2019.e00653

    Article  Google Scholar 

  • Elith J, Graham C, Anderson R, Dudík M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Peterson A, Zimmermann N (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    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

    Google Scholar 

  • Environmental Systems Research Institute (ESRI) (2017) ArcGIS Release 10.6

  • Far JA (2019) Reproductive biology and habitat characterization of Euphorbia fontqueriana Greuter, an endangered endemism from Mallorca. Master Thesis dissertation, University of the Balearic Islands

  • Fenu G, Bacchetta G, Christodoulou C, Fournaraki C, Pietro G, Gotsiou P, Kyratzis A, Carole P, Vicens M, Pinna M, Montmollin B (2019) An early evaluation of translocation actions for endangered plant species on Mediterranean islands. Plant Divers 41(2):94–104

    PubMed  PubMed Central  Google Scholar 

  • Fois M, Fenu G, Cuena Lombraña A, Cogoni D, Bacchetta G (2015) A practical method to speed up the discovery of unknown populations using Species Distribution Models. J Nat Conserv 24(C):42–48

    Google Scholar 

  • Fox J (1997) Applied regression analysis, linear models, and related methods. SAGE Publications, Thousand Oaks

    Google Scholar 

  • Getis A, Ord JK (1992) The Analysis of Spatial Association by Use of Distance Statistics. Geogr Anal 24:189–206

    Google Scholar 

  • Gianinetto M, Aiello M, Vezzoli R, Polinelli F, Rulli MC, Chiarelli D, Bocchiola D, Ravazzani G, Soncini A (2020) Future scenarios of soil erosion in the Alps under climate change and land cover transformations simulated with automatic machine learning. Climate 8:28

    Google Scholar 

  • Gogol-Prokurat M (2011) Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol Appl 21(1):33–47

    PubMed  Google Scholar 

  • Greuter W (1968) Une espèce méconnue edémique des Illes Baléares: Euphorbia fontqueriana. Coll Bot (Barcelona) 7(1):531–537

    Google Scholar 

  • Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2–3):147–186

    Google Scholar 

  • Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch A, Regan T, Brotons L, McDonald-Madden E, Mantyka-Pringle C, Martin T, Rhodes J, Maggini R, Setterfield S, Elith J, Schwartz M, Wintle B, Broennimann O, Austin M, Buckley Y (2013) Predicting species distributions for conservation decisions. Ecol Lett 16(12):1424–1435

    PubMed  PubMed Central  Google Scholar 

  • IBM Corp (2016) IBM SPSS Statistics for Windows, Version 25.0. Armonk. IBM Corp, New York. Released 2016

    Google Scholar 

  • Lassueur T, Joost S, Randin C (2006) Very high resolution digital elevation models: do they improve models of plant species distribution? Ecol Model 198:139–153

    Google Scholar 

  • Leica Geosystems (2013) Leica RX1200 user manual. http://www.geoaxxis.de/manuals/tps1200/rx1220_user_en.pdf

  • Llorens L, Gil L, Tébar FJ (2007) La vegetació de l’Illa de Mallorca: bases per a la interpretació i gestió d’hàbitats. Associació Jardí Botànic de Palma, Palma

    Google Scholar 

  • Mulder VL, van Eck CM, Friedlingstein P, Arrouays D, Regnier P (2019) Controlling factors for land productivity under extreme climatic events in continental Europe and the Mediterranean Basin. Catena 182 [104124]

  • Nearing MA, Jetten V, Baffaut C, Cerdan O, Couturier A, Hernandez M, Le Bissonnais Y, Nichols MH, Nunes JP, Renscheler CS et al (2019) Modeling response of soil erosion and runoff to changes in precipitation and cover. Catena 61:131–154

    Google Scholar 

  • Ord JK, Getis A (1995) Local spatial autocorrelations statistics: distributional issues and an application. Geogr Anal 27(4):286–306

    Google Scholar 

  • Papeş M, Havel JE, Vander Zanden MJ (2016) Using maximum entropy to predict the potential distribution of an invasive freshwater snail. Freshwater Biol 61(4):457–471

    Google Scholar 

  • Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117

    Google Scholar 

  • Peterson AT (2003) Predicting the geography of species’ invasions via ecological niche modeling. Q Rev Biol 78:419–433

    PubMed  Google Scholar 

  • Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20(9):503–510

    PubMed  Google Scholar 

  • Phillips SJ, Dudík M (2008) Modeling of species distribution with Maxent: new extensions and a comprehensive evalutation. Ecograpy 31:161–175

    Google Scholar 

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3–4):231–259

    Google Scholar 

  • Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME (2017) Opening the black box: an open-source release of Maxent. Ecography 40:887–893

    Google Scholar 

  • Roberts DW (1986) Ordination on the basis of fuzzy set theory. Vegetatio 66:123–131

    Google Scholar 

  • Romo AM (1990) Observacions fitocenològiques a l’illa de Mallorca. Collect Bot 18:162–165

    Google Scholar 

  • Sáez L, Roselló JA, Fraga P (2017) Llibre vermell de la flora vascular de les Illes Balears, 2n edn. Conselleria de Medi Ambient, Agricultura i Pesca, Palma

    Google Scholar 

  • Salinas-Rodríguez MM, Sajama MJ, Gutiérrez-Ortega JS, Ortega-Baes P, Estrada-Castillón AE (2018) Identification of endemic vascular plant species hotspots and the effectiveness of the protected areas for their conservation in Sierra Madre Oriental, Mexico. J Nat Conserv 46:6–27

    Google Scholar 

  • Schipper A, Posthuma L, Zwart D, Huijbregts M (2014) Deriving Field-Based Species Sensitivity Distributions (f-SSDs) from Stacked Species Distribution Models (S-SDMs). Environ Sci Technol 48(24):14464–14471

    CAS  PubMed  Google Scholar 

  • Sellers PJ (1985) Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens 6(8):1335–1372

    Google Scholar 

  • Spiers JA, Oatham MP, Rostant LV, Farrell AD (2018) Applying species distribution modelling to improving conservation based decisions: a gap analysis of trinidad and tobago’s endemic vascular plants. Biodivers Conserv 27(11):2931–2949

    Google Scholar 

  • Swart C, Donaldson J, Barker N (2018) Predicting the distribution of Encephalartos latifrons, a critically endangered cycad in South Africa. Biodivers Conserv 27(8):1961–1980

    Google Scholar 

  • UNESCO (2011) UNESCO World Heritage Centre. http://whc.unesco.org/en/list/1371. Accessed 13 Dec 2013

  • Vadell J (2011) Classificació dels sòls a les Balears. Cartographic web viewer. https://ssigt.uib.cat/serveis/IDE/Visors/

  • Xie Y, Zhao X, Li L, Wang H (2010) Calculating NDVI for landsat7-etm data after atmospheric Correction using 6 s model: a case study in Zhangye City, China. In: 18th International Conference on Geoinformatics, Geoinformatics, pp 1–4

  • Xing-zhuang Y, Zhao G, Zhang M, Cui X, Fan H, Liu B (2020) Distribution pattern of endangered plant Semiliquidambar cathayensis (Hamamelidaceae) in response to climate change after the last interglacial period. Forests 11(4):434

    Google Scholar 

  • West AM, Kumar S, Brown CS, Stohlgren TJ, Bromberg J (2016) Field validation of an invasive species Maxent model. Ecol Inform 36:126–134

    Google Scholar 

  • Williams JN, Seo C, Thorne J, Nelson JK, Erwin S, O’Brien JM, Schwartz MW (2009) Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15(4):565–576

    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

    Google Scholar 

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Acknowledgements

We thank Miquel Capó, Joshua Borràs, Sebastià Perelló, Carles Cardona and Maite Bover for their collaboration in the fieldwork. We also thank the three anonymous reviewers for their valuable contributions that have allowed to improve an earlier version of the manuscript. This research was supported by Fundación Biodiversidad of the Ministerio para la Transición Ecológica (call 2018).

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Authors and Affiliations

  1. Department of Biology, Laboratory of Botany, Research Group on Plant Biology under Mediterranean Conditions, University of the Balearic Islands, Cra. Valldemossa, km 7.5, 07122, Palma, Balearic Islands, Spain

    Joana Cursach & Antoni Josep Far

  2. Department of Geography/Geographical Information Systems and Remote Sensing Service, University of the Balearic Islands, Cra. Valldemossa, km 7.5, 07112, Palma, Spain

    Maurici Ruiz

Authors
  1. Joana Cursach
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  2. Antoni Josep Far
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  3. Maurici Ruiz
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Contributions

JC and MR designed the study; JC and AJF collected field data; JC and MR analysed the data; JC wrote a first draft of the manuscript; all the authors contributed to the final version.

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Correspondence to Joana Cursach.

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Communicated by Daniel Sanchez Mata.

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Cursach, J., Far, A.J. & Ruiz, M. Geospatial analysis to assess distribution patterns and predictive models for endangered plant species to support management decisions: a case study in the Balearic Islands. Biodivers Conserv 29, 3393–3410 (2020). https://doi.org/10.1007/s10531-020-02029-y

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