IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v353y2017icp37-46.html
   My bibliography  Save this article

Correlating habitat suitability with landscape connectivity: A case study of Sichuan golden monkey in China

Author

Listed:
  • Liu, Fang
  • McShea, William J.
  • Li, Diqiang

Abstract

We examined the landscape suitability of the region currently occupied by the Sichuan golden money (Rhinopithecus roxellana) using occupancy models constructed in Maxent with presence-only data and environmental variables. The aim of the study was to estimate potential dispersal corridors between presently disjunct populations. Least-cost path analysis was used to estimate its dispersal paths across the fragmented landscape. The results indicate that core areas of suitable habitat are located in the Qinling, Dabashan, and Minshan Mountains, as well as small patches in the Qionglai, Daxiangling and Liangshan Mountains; the most suitable habitats are in nature reserves of the Minshan Mountain. Elevation and density of the human settlements were the most important factors for identifying suitable habitat; and we identified location of less populated areas where some suitable forest patches offer the potential for dispersal corridors for this species. The study implies that there is potential for expansion of the species distribution, if steps are taken to preserve current forest patches that maybe too small for residency but suitable for dispersal.

Suggested Citation

  • Liu, Fang & McShea, William J. & Li, Diqiang, 2017. "Correlating habitat suitability with landscape connectivity: A case study of Sichuan golden monkey in China," Ecological Modelling, Elsevier, vol. 353(C), pages 37-46.
    • Handle: RePEc:eee:ecomod:v:353:y:2017:i:c:p:37-46
    DOI: 10.1016/j.ecolmodel.2016.09.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016303805
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2016.09.004?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. Fernández, Daniel & Nakamura, Miguel, 2015. "Estimation of spatial sampling effort based on presence-only data and accessibility," Ecological Modelling, Elsevier, vol. 299(C), pages 147-155.
    2. Costa, Hugo & Ponte, Nuno B. & Azevedo, Eduardo B. & Gil, Artur, 2015. "Fuzzy set theory for predicting the potential distribution and cost-effective monitoring of invasive species," Ecological Modelling, Elsevier, vol. 316(C), pages 122-132.
    3. Freeman, Elizabeth A. & Moisen, Gretchen G., 2008. "A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa," Ecological Modelling, Elsevier, vol. 217(1), pages 48-58.
    4. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    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. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.

    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. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    2. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    3. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    4. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    5. Akpoti, Komlavi & Groen, Thomas & Dossou-Yovo, Elliott & Kabo-bah, Amos T. & Zwart, Sander J., 2022. "Climate change-induced reduction in agricultural land suitability of West-Africa's inland valley landscapes," Agricultural Systems, Elsevier, vol. 200(C).
    6. Adrien Guetté & Sébastien Caillault & Joséphine Pithon & Guillaume Pain & Hervé Daniel & Benoit Marchadour & Véronique Beaujouan, 2022. "Who and Where Are the Observers behind Biodiversity Citizen Science Data? Effect of Landscape Naturalness on the Spatial Distribution of French Birdwatching Records," Land, MDPI, vol. 11(11), pages 1-25, November.
    7. Duque-Lazo, J. & van Gils, H. & Groen, T.A. & Navarro-Cerrillo, R.M., 2016. "Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia," Ecological Modelling, Elsevier, vol. 320(C), pages 62-70.
    8. Sillero, Neftalí & Arenas-Castro, Salvador & Enriquez‐Urzelai, Urtzi & Vale, Cândida Gomes & Sousa-Guedes, Diana & Martínez-Freiría, Fernando & Real, Raimundo & Barbosa, A.Márcia, 2021. "Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling," Ecological Modelling, Elsevier, vol. 456(C).
    9. Vu, Khoa & Vuong, Nguyen Dinh Tuan & Vu-Thanh, Tu-Anh & Nguyen, Anh Ngoc, 2022. "Income shock and food insecurity prediction Vietnam under the pandemic," World Development, Elsevier, vol. 153(C).
    10. Jeong Soo Park & Donghui Choi & Youngha Kim, 2020. "Potential Distribution of Goldenrod ( Solidago altissima L.) during Climate Change in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    11. Kelly Jane Easterday & Patrick J McIntyre & James H Thorne & Maria J Santos & Maggi Kelly, 2016. "Assessing Threats and Conservation Status of Historical Centers of Oak Richness in California," Urban Planning, Cogitatio Press, vol. 1(4), pages 65-78.
    12. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    13. Watling, James I. & Romañach, Stephanie S. & Bucklin, David N. & Speroterra, Carolina & Brandt, Laura A. & Pearlstine, Leonard G. & Mazzotti, Frank J., 2012. "Do bioclimate variables improve performance of climate envelope models?," Ecological Modelling, Elsevier, vol. 246(C), pages 79-85.
    14. Salvador Arenas-Castro & João Gonçalves & Paulo Alves & Domingo Alcaraz-Segura & João P Honrado, 2018. "Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-31, June.
    15. Freeman, Elizabeth A. & Moisen, Gretchen G. & Frescino, Tracey S., 2012. "Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada," Ecological Modelling, Elsevier, vol. 233(C), pages 1-10.
    16. Robinson, Todd P. & van Klinken, Rieks D. & Metternicht, Graciela, 2010. "Comparison of alternative strategies for invasive species distribution modeling," Ecological Modelling, Elsevier, vol. 221(19), pages 2261-2269.
    17. Dean Fantazzini & Yufeng Xiao, 2023. "Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases," Econometrics, MDPI, vol. 11(3), pages 1-73, August.
    18. Buse, Jörn & Griebeler, Eva Maria, 2011. "Incorporating classified dispersal assumptions in predictive distribution models – A case study with grasshoppers and bush-crickets," Ecological Modelling, Elsevier, vol. 222(13), pages 2130-2141.
    19. Toshiya Matsuura & Ken Sugimura & Asako Miyamoto & Nobuhiko Tanaka, 2013. "Knowledge-Based Estimation of Edible Fern Harvesting Sites in Mountainous Communities of Northeastern Japan," Sustainability, MDPI, vol. 6(1), pages 1-18, December.
    20. Pecchi, Matteo & Marchi, Maurizio & Burton, Vanessa & Giannetti, Francesca & Moriondo, Marco & Bernetti, Iacopo & Bindi, Marco & Chirici, Gherardo, 2019. "Species distribution modelling to support forest management. A literature review," Ecological Modelling, Elsevier, vol. 411(C).

    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:353:y:2017:i:c:p:37-46. 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.