IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v408y2019ic3.html
   My bibliography  Save this article

Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds

Author

Listed:
  • Fern, Rachel R.
  • Morrison, Michael L.
  • Wang, Hsiao-Hsuan
  • Grant, William E.
  • Campbell, Tyler A.

Abstract

Complex, biotic interactions are notably excluded from species distribution models (SDMs) as they are often difficult to quantify and accommodate in a traditional modeling framework, especially those with a temporal component. The territorial nature of breeding Cactus wren is well-documented and typically involves nest usurping (i.e., destruction) of conspecifics. Due to their similar nesting ecology, breeding Verdin are frequently the target of such behavior and are often forced to move or abandon nests. Using the Verdin/Cactus wren system as a case study, our goal was to evaluate the performance of SDMs that include only environmental predictors with SDMs that also include biotic relationships as predictors.

Suggested Citation

  • Fern, Rachel R. & Morrison, Michael L. & Wang, Hsiao-Hsuan & Grant, William E. & Campbell, Tyler A., 2019. "Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
  • Handle: RePEc:eee:ecomod:v:408:y:2019:i:c:3
    DOI: 10.1016/j.ecolmodel.2019.108743
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019302510
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.108743?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. Gill Ward & Trevor Hastie & Simon Barry & Jane Elith & John R. Leathwick, 2009. "Presence-Only Data and the EM Algorithm," Biometrics, The International Biometric Society, vol. 65(2), pages 554-563, June.
    2. Ian W. Renner & David I. Warton, 2013. "Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology," Biometrics, The International Biometric Society, vol. 69(1), pages 274-281, 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. Haider, Saira M. & Benscoter, Allison M. & Pearlstine, Leonard & D'Acunto, Laura E. & Romañach, Stephanie S., 2021. "Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach," Ecological Modelling, Elsevier, vol. 461(C).
    2. Coppée, Thomas & Paquet, Jean-Yves & Titeux, Nicolas & Dufrêne, Marc, 2022. "Temporal transferability of species abundance models to study the changes of breeding bird species based on land cover changes," Ecological Modelling, Elsevier, vol. 473(C).
    3. Szewczyk, Tim M. & Lee, Tom & Ducey, Mark J. & Aiello-Lammens, Matthew E. & Bibaud, Hayley & Allen, Jenica M., 2019. "Local management in a regional context: Simulations with process-based species distribution models," Ecological Modelling, Elsevier, vol. 413(C).

    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. Wiltshire, Kathryn H & Tanner, Jason E, 2020. "Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species," Ecological Modelling, Elsevier, vol. 429(C).
    2. Leandro, Camila & Jay-Robert, Pierre & Mériguet, Bruno & Houard, Xavier & Renner, Ian W., 2020. "Is my sdm good enough? insights from a citizen science dataset in a point process modeling framework," Ecological Modelling, Elsevier, vol. 438(C).
    3. Masahiro Kato & Shota Yasui, 2020. "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers 2009.13092, arXiv.org, revised Jun 2022.
    4. Schwemmer, Philipp & Güpner, Franziska & Adler, Sven & Klingbeil, Knut & Garthe, Stefan, 2016. "Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors," Ecological Modelling, Elsevier, vol. 320(C), pages 322-333.
    5. Abdollah Jalilian, 2017. "Modelling and classification of species abundance: a case study in the Barro Colorado Island plot," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2401-2409, October.
    6. Christophe Botella & Alexis Joly & Pascal Monestiez & Pierre Bonnet & François Munoz, 2020. "Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    7. Holder, Anna M. & Markarian, Arev & Doyle, Jessie M. & Olson, John R., 2020. "Predicting geographic distributions of fishes in remote stream networks using maximum entropy modeling and landscape characterizations," Ecological Modelling, Elsevier, vol. 433(C).
    8. Martín, Gerardo & Yáñez-Arenas, Carlos & Chiappa-Carrara, Xavier, 2022. "Discrepancies between point process models and environmental envelopes identify the niche centroid – geography configuration," Ecological Modelling, Elsevier, vol. 469(C).
    9. Moreno-Amat, Elena & Mateo, Rubén G. & Nieto-Lugilde, Diego & Morueta-Holme, Naia & Svenning, Jens-Christian & García-Amorena, Ignacio, 2015. "Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data," Ecological Modelling, Elsevier, vol. 312(C), pages 308-317.
    10. Saupe, E.E. & Barve, V. & Myers, C.E. & Soberón, J. & Barve, N. & Hensz, C.M. & Peterson, A.T. & Owens, H.L. & Lira-Noriega, A., 2012. "Variation in niche and distribution model performance: The need for a priori assessment of key causal factors," Ecological Modelling, Elsevier, vol. 237, pages 11-22.
    11. Halvorsen, Rune & Mazzoni, Sabrina & Dirksen, John Wirkola & Næsset, Erik & Gobakken, Terje & Ohlson, Mikael, 2016. "How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?," Ecological Modelling, Elsevier, vol. 328(C), pages 108-118.
    12. Herkt, K. Matthias B. & Barnikel, Günter & Skidmore, Andrew K. & Fahr, Jakob, 2016. "A high-resolution model of bat diversity and endemism for continental Africa," Ecological Modelling, Elsevier, vol. 320(C), pages 9-28.
    13. Chih-Wei Lin & Yu Hong & Weihao Tu & Jinfu Liu, 2022. "Multiperiod Dynamic Programming Algorithm for Optimizing a Nature Reserve," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    14. Daniel, Jeffrey & Horrocks, Julie & Umphrey, Gary J., 2018. "Penalized composite likelihoods for inhomogeneous Gibbs point process models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 104-116.
    15. Masahiro Kato, 2019. "Identifying Different Definitions of Future in the Assessment of Future Economic Conditions: Application of PU Learning and Text Mining," Papers 1909.03348, arXiv.org, revised Apr 2020.
    16. Ortner, Olivia & Wallentin, Gudrun, 2020. "Integration of landscape metric surfaces derived from vector data improves species distribution models," Ecological Modelling, Elsevier, vol. 431(C).
    17. Jeffrey Daniel & Julie Horrocks & Gary J. Umphrey, 2020. "Efficient Modelling of Presence-Only Species Data via Local Background Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 90-111, March.
    18. Li, Shuai & Huang, Chengdai & Song, Xinyu, 2023. "Detection of Hopf bifurcations induced by pregnancy and maturation delays in a spatial predator–prey model via crossing curves method," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    19. Lucas Kruger, 2018. "Population Estimates of Trindade Petrel (Pterodroma arminjoniana) by Ensemble Nesting Habitat Modelling," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 10(4), pages 145-157, April.
    20. Wang, Junhui & Fang, Yixin, 2013. "Analysis of presence-only data via semi-supervised learning approaches," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 134-143.

    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:408:y:2019:i:c:3. 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.