IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v218y2008i1p95-109.html
   My bibliography  Save this article

Reproduction of olive tree habitat suitability for global change impact assessment

Author

Listed:
  • Moriondo, M.
  • Stefanini, F.M.
  • Bindi, M.

Abstract

The olive tree is so typical of the Mediterranean climate that its presence in a territory qualifies the climate of this as Mediterranean. Many clues indicated that in the past olive cultivation limits moved northward or southward in the Northern Hemisphere according to warmer or cooler climate, respectively. This makes the olive tree cultivation area a possible biological indicator of changes in climate and the identification of the climatological parameters that limit its cultivation plays an important role for climate change impact assessment. In this work, three different approaches were compared, with the aim to compare methodologies suited to predict olive tree distribution over the Mediterranean basin: two classifiers (Random Forest, RF and an Artificial Neural Network, ANN) and a spatial model to infer climatic limiters of plant distribution (CLPD). These methodologies were applied within a framework including a geographical information system (GIS), which spatially defined olive tree cultivated area, and climatological informative layers (average temperature and cumulated rainfall, 50km×50km), which were used as predictor variables. The results indicated that RF achieved on the whole, the lowest classification error (113 misclassified cases on 1906 test cases) followed by ANN (128 cases) and CLPD (153 cases). A validation test, performed over areas out of the Mediterranean basin where olive tree is cultivated (i.e. California and Southern Australia), confirmed the goodness of the RF fitted model in predicting olive tree suitable areas. In general, climatic predictor variables of the coldest and warmest periods of the year were the most significant in determining the limits of suitable olive cultivation area for these methodologies. In particular, temperature of January and July and rainfall of October and July were the climatic predictor variables having highest significance for both RF and ANN. Temperature of January >2°C, of July >20°C and cumulated annual rainfall >240mm were the bounds found in the spatial model. The fitted RF model, coupled with the results of both Regional and General Circulation Model, was finally proposed to assess climate change impact on olive tree cultivated area in the Mediterranean basin.

Suggested Citation

  • Moriondo, M. & Stefanini, F.M. & Bindi, M., 2008. "Reproduction of olive tree habitat suitability for global change impact assessment," Ecological Modelling, Elsevier, vol. 218(1), pages 95-109.
    • Handle: RePEc:eee:ecomod:v:218:y:2008:i:1:p:95-109
    DOI: 10.1016/j.ecolmodel.2008.06.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380008003050
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2008.06.024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gian-Reto Walther & Eric Post & Peter Convey & Annette Menzel & Camille Parmesan & Trevor J. C. Beebee & Jean-Marc Fromentin & Ove Hoegh-Guldberg & Franz Bairlein, 2002. "Ecological responses to recent climate change," Nature, Nature, vol. 416(6879), pages 389-395, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammed Mustafa Özdel & Beyza Ustaoğlu & İsa Cürebal, 2024. "Modeling of the Potential Distribution Areas Suitable for Olive ( Olea europaea L.) in Türkiye from a Climate Change Perspective," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    2. S. M. Alfieri & M. Riccardi & M. Menenti & A. Basile & A. Bonfante & F. Lorenzi, 2019. "Adaptability of global olive cultivars to water availability under future Mediterranean climate," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(3), pages 435-466, March.
    3. Maselli, Fabio & Chiesi, Marta & Brilli, Lorenzo & Moriondo, Marco, 2012. "Simulation of olive fruit yield in Tuscany through the integration of remote sensing and ground data," Ecological Modelling, Elsevier, vol. 244(C), pages 1-12.
    4. Ayse Yavuz Ozalp & Halil Akinci, 2023. "Evaluation of Land Suitability for Olive ( Olea europaea L.) Cultivation Using the Random Forest Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-22, June.
    5. Filippo Gambella & Leonardo Bianchini & Massimo Cecchini & Gianluca Egidi & Agostino Ferrara & Luca Salvati & Andrea Colantoni & Donato Morea, 2021. "Moving toward the north? The spatial shift of olive groves in Italy," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 129-135.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mayeul Dalleau & Stéphane Ciccione & Jeanne A Mortimer & Julie Garnier & Simon Benhamou & Jérôme Bourjea, 2012. "Nesting Phenology of Marine Turtles: Insights from a Regional Comparative Analysis on Green Turtle (Chelonia mydas)," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-13, October.
    2. Bu, Lingduo & Chen, Xinping & Li, Shiqing & Liu, Jianliang & Zhu, Lin & Luo, Shasha & Lee Hill, Robert & Zhao, Ying, 2015. "The effect of adapting cultivars on the water use efficiency of dryland maize (Zea mays L.) in northwestern China," Agricultural Water Management, Elsevier, vol. 148(C), pages 1-9.
    3. Anne Goodenough & Adam Hart, 2013. "Correlates of vulnerability to climate-induced distribution changes in European avifauna: habitat, migration and endemism," Climatic Change, Springer, vol. 118(3), pages 659-669, June.
    4. Monika Punia & Suman Nain & Amit Kumar & Bhupendra Singh & Amit Prakash & Krishan Kumar & V. Jain, 2015. "Analysis of temperature variability over north-west part of India for the period 1970–2000," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 935-952, January.
    5. Wesley R. Brooks & Stephen C. Newbold, 2013. "Ecosystem damages in integrated assessment models of climate change," NCEE Working Paper Series 201302, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2013.
    6. Nicoletta Cannone & M. Guglielmin & P. Convey & M. R. Worland & S. E. Favero Longo, 2016. "Vascular plant changes in extreme environments: effects of multiple drivers," Climatic Change, Springer, vol. 134(4), pages 651-665, February.
    7. Groeneveld, Jürgen & Johst, Karin & Kawaguchi, So & Meyer, Bettina & Teschke, Mathias & Grimm, Volker, 2015. "How biological clocks and changing environmental conditions determine local population growth and species distribution in Antarctic krill (Euphausia superba): a conceptual model," Ecological Modelling, Elsevier, vol. 303(C), pages 78-86.
    8. Norman Myers, 2003. "Conservation of Biodiversity: How Are We Doing?," Environment Systems and Decisions, Springer, vol. 23(1), pages 9-15, March.
    9. Donohue, John G. & Piiroinen, Petri T., 2015. "Mathematical modelling of seasonal migration with applications to climate change," Ecological Modelling, Elsevier, vol. 299(C), pages 79-94.
    10. John H Matthews & Bart AJ Wickel & Sarah Freeman, 2011. "Converging Currents in Climate-Relevant Conservation: Water, Infrastructure, and Institutions," PLOS Biology, Public Library of Science, vol. 9(9), pages 1-4, September.
    11. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.
    12. Feng Dong & Chih-Ming Hung & Shou-Hsien Li & Xiao-Jun Yang, 2021. "Potential Himalayan community turnover through the Late Pleistocene," Climatic Change, Springer, vol. 164(1), pages 1-10, January.
    13. Ding, Helen & Nunes, Paulo A.L.D., 2014. "Modeling the links between biodiversity, ecosystem services and human wellbeing in the context of climate change: Results from an econometric analysis of the European forest ecosystems," Ecological Economics, Elsevier, vol. 97(C), pages 60-73.
    14. Chan, Nathan & Wichman, Casey, 2017. "The Effects of Climate on Leisure Demand: Evidence from North America," RFF Working Paper Series 17-20, Resources for the Future.
    15. Zhang, Jiarui & Jørgensen, Sven E. & Lu, Jianjian & Nielsen, Søren N. & Wang, Qiang, 2014. "A model for the contribution of macrophyte-derived organic carbon in harvested tidal freshwater marshes to surrounding estuarine and oceanic ecosystems and its response to global warming," Ecological Modelling, Elsevier, vol. 294(C), pages 105-116.
    16. Richter, Andries & Grasman, Johan, 2013. "The transmission of sustainable harvesting norms when agents are conditionally cooperative," Ecological Economics, Elsevier, vol. 93(C), pages 202-209.
    17. A. Kosanic & S. Harrison & K. Anderson & I. Kavcic, 2014. "Present and historical climate variability in South West England," Climatic Change, Springer, vol. 124(1), pages 221-237, May.
    18. Rougier, Thibaud & Drouineau, Hilaire & Dumoulin, Nicolas & Faure, Thierry & Deffuant, Guillaume & Rochard, Eric & Lambert, Patrick, 2014. "The GR3D model, a tool to explore the Global Repositioning Dynamics of Diadromous fish Distribution," Ecological Modelling, Elsevier, vol. 283(C), pages 31-44.
    19. Andrew J Allyn & Michael A Alexander & Bradley S Franklin & Felix Massiot-Granier & Andrew J Pershing & James D Scott & Katherine E Mills, 2020. "Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-28, April.
    20. Nye, Janet A. & Gamble, Robert J. & Link, Jason S., 2013. "The relative impact of warming and removing top predators on the Northeast US large marine biotic community," Ecological Modelling, Elsevier, vol. 264(C), pages 157-168.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:218:y:2008:i:1:p:95-109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.