IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v430y2020ics0304380020301800.html
   My bibliography  Save this article

Making quantitative predictions on the yield of a species immersed in a multispecies community: The focal species method

Author

Listed:
  • Fort, Hugo

Abstract

The abundance or yield of a species is an ecological quantity of paramount importance when making management and conservation decisions. The linear generalized Lotka–Volterra equations (LGLVE) constitute the simplest theoretical framework for predicting such yields. However, in many ecological communities (e.g. tropical forests), the number of coexisting species S can be very large and estimating the entire interaction matrix A from experiments is practically unfeasible.

Suggested Citation

  • Fort, Hugo, 2020. "Making quantitative predictions on the yield of a species immersed in a multispecies community: The focal species method," Ecological Modelling, Elsevier, vol. 430(C).
  • Handle: RePEc:eee:ecomod:v:430:y:2020:i:c:s0304380020301800
    DOI: 10.1016/j.ecolmodel.2020.109108
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020301800
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109108?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. Jin Li, 2017. "Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    2. Fort, Hugo, 2018. "Quantitative predictions from competition theory with an incomplete knowledge of model parameters tested against experiments across diverse taxa," Ecological Modelling, Elsevier, vol. 368(C), pages 104-110.
    3. Fort, Hugo, 2018. "On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model," Ecological Modelling, Elsevier, vol. 387(C), pages 154-162.
    4. Nathaniel D. Mueller & James S. Gerber & Matt Johnston & Deepak K. Ray & Navin Ramankutty & Jonathan A. Foley, 2012. "Closing yield gaps through nutrient and water management," Nature, Nature, vol. 490(7419), pages 254-257, October.
    Full references (including those not matched with items on IDEAS)

    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. Fort, Hugo, 2018. "On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model," Ecological Modelling, Elsevier, vol. 387(C), pages 154-162.
    2. Cao, Juan & Zhang, Zhao & Tao, Fulu & Chen, Yi & Luo, Xiangzhong & Xie, Jun, 2023. "Forecasting global crop yields based on El Nino Southern Oscillation early signals," Agricultural Systems, Elsevier, vol. 205(C).
    3. Westhoek, Henk & Ingram, John & van Berkum, Siemen & Hajer, Maarten, 2015. "The European food system and natural resources: Impacts and Options," 148th Seminar, November 30-December 1, 2015, The Hague, The Netherlands 229279, European Association of Agricultural Economists.
    4. Giacomo Falchetta & Nicolò Stevanato & Magda Moner-Girona & Davide Mazzoni & Emanuela Colombo & Manfred Hafner, 2020. "M-LED: Multi-sectoral Latent Electricity Demand Assessment for Energy Access Planning," Working Papers 2020.09, Fondazione Eni Enrico Mattei.
    5. Kathrin Stenchly & Marc Victor Hansen & Katharina Stein & Andreas Buerkert & Wilhelm Loewenstein, 2018. "Income Vulnerability of West African Farming Households to Losses in Pollination Services: A Case Study from Ouagadougou, Burkina Faso," Sustainability, MDPI, vol. 10(11), pages 1-12, November.
    6. Singh, Kuntal & McClean, Colin J. & Büker, Patrick & Hartley, Sue E. & Hill, Jane K., 2017. "Mapping regional risks from climate change for rainfed rice cultivation in India," Agricultural Systems, Elsevier, vol. 156(C), pages 76-84.
    7. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    8. Fort, Hugo & Grigera, Tomás S., 2021. "A method for predicting species trajectories tested with trees in barro colorado tropical forest," Ecological Modelling, Elsevier, vol. 446(C).
    9. Mr. Emmanuel Momolu Pope & Prof. Wilson Opile & Dr. Lucas Ngode & Dr. Chepkoech Emmy, 2023. "Assessment of Upland Rice Production Constraints and Farmers’ Preferred Varieties in Liberia," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(2), pages 1307-1322, February.
    10. Samuthirapandi Subburaj & Thiyagarajan Thulasinathan & Viswabharathy Sakthivel & Bharathi Ayyenar & Rohit Kambale & Veera Ranjani Rajagopalan & Sudha Manickam & Raghu Rajasekaran & Gopalakrishnan Chel, 2024. "Genetic Enhancement of Blast and Bacterial Leaf Blight Resistance in Rice Variety CO 51 through Marker-Assisted Selection," Agriculture, MDPI, vol. 14(5), pages 1-20, April.
    11. F. Jorge Bornemann & David P. Rowell & Barbara Evans & Dan J. Lapworth & Kamazima Lwiza & David M.J. Macdonald & John H. Marsham & Kindie Tesfaye & Matthew J. Ascott & Celia Way, 2019. "Future changes and uncertainty in decision-relevant measures of East African climate," Climatic Change, Springer, vol. 156(3), pages 365-384, October.
    12. Purola, Tuomo & Lehtonen, Heikki, 2020. "Evaluating profitability of soil-renovation investments under crop rotation constraints in Finland," Agricultural Systems, Elsevier, vol. 180(C).
    13. Yibo Luan & Wenquan Zhu & Xuefeng Cui & Günther Fischer & Terence P. Dawson & Peijun Shi & Zhenke Zhang, 2019. "Cropland yield divergence over Africa and its implication for mitigating food insecurity," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(5), pages 707-734, June.
    14. Zheng, Huifang & Mei, Peipei & Wang, Wending & Yin, Yulong & Li, Haojie & Zheng, Mengyao & Ou, Xingqi & Cui, Zhenling, 2023. "Effects of super absorbent polymer on crop yield, water productivity and soil properties: A global meta-analysis," Agricultural Water Management, Elsevier, vol. 282(C).
    15. Minghao Bai & Shenbei Zhou & Ting Tang, 2022. "A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets," Land, MDPI, vol. 11(10), pages 1-27, September.
    16. Nina Repar & Pierrick Jan & Thomas Nemecek & Dunja Dux & Martina Alig Ceesay & Reiner Doluschitz, 2016. "Local versus Global Environmental Performance of Dairying and Their Link to Economic Performance: A Case Study of Swiss Mountain Farms," Sustainability, MDPI, vol. 8(12), pages 1-19, December.
    17. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    18. Hampf, Anna C. & Carauta, Marcelo & Latynskiy, Evgeny & Libera, Affonso A.D. & Monteiro, Leonardo & Sentelhas, Paulo & Troost, Christian & Berger, Thomas & Nendel, Claas, 2018. "The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach," Agricultural Systems, Elsevier, vol. 165(C), pages 1-13.
    19. Dapeng WANG & Liang ZHENG & Songdong GU & Yuefeng SHI & Long LIANG & Fanqiao MENG & Yanbin GUO & Xiaotang JU & Wenliang WU, 2018. "Soil nitrate accumulation and leaching in conventional, optimized and organic cropping systems," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 64(4), pages 156-163.
    20. Xiong, Wei & Balkovič, Juraj & van der Velde, Marijn & Zhang, Xuesong & Izaurralde, R. César & Skalský, Rastislav & Lin, Erda & Mueller, Nathan & Obersteiner, Michael, 2014. "A calibration procedure to improve global rice yield simulations with EPIC," Ecological Modelling, Elsevier, vol. 273(C), pages 128-139.

    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:430:y:2020:i:c:s0304380020301800. 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.