IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0213654.html
   My bibliography  Save this article

Survey design for broad-scale, territory-based occupancy monitoring of a raptor: Ferruginous hawk (Buteo regalis) as a case study

Author

Listed:
  • Tracey N Johnson
  • Kristen Nasman
  • Zachary P Wallace
  • Lucretia E Olson
  • John R Squires
  • Ryan M Nielson
  • Patricia L Kennedy

Abstract

Given the uncertain population status of low-density, widely-occurring raptors, monitoring changes in abundance and distribution is critical to conserving populations. Nest-based monitoring is a common, useful approach, but the difficulty and expense of monitoring raptor nests and importance of reliable trend data to conservation requires that limited resources are allocated efficiently. Power analyses offer a helpful tool to ensure that monitoring programs have the ability to detect trends and to optimize financial resources devoted to monitoring. We evaluated alternative monitoring designs for raptors to identify appropriate survey effort to detect population trends. We used data collected from a territory-occupancy study of ferruginous hawks throughout Wyoming to guide simulations and evaluate the ability to detect trends in occupancy rates. Results suggest that greater gains in precision of trend estimation may be achieved through the addition of more sites and not more visits; statistical power was ≥80% when monitoring lasted 20 years and population declines were 20%; and probability of detection affected statistical power less than rates of population decline. Monitoring at least 150 sites for 20 years would provide reasonable estimates of trend in occupancy given certain rates of detection and occupancy, but only for population declines of 20%. Removal sampling did not result in substantial changes of any metrics used to evaluate simulations, providing little justification for employing the standard design if territory occupancy is the variable of interest. Initial rates of territory occupancy may be biased high, a problem inherent to many studies that monitor territory occupancy. We explored the effects of lower rates of initial occupancy on the ability to detect trends. Although we present data from a study of ferruginous hawks, our simulations can be applied to other raptor species with similar life history and population dynamics to provide guidance for future trend estimation of territory occupancy.

Suggested Citation

  • Tracey N Johnson & Kristen Nasman & Zachary P Wallace & Lucretia E Olson & John R Squires & Ryan M Nielson & Patricia L Kennedy, 2019. "Survey design for broad-scale, territory-based occupancy monitoring of a raptor: Ferruginous hawk (Buteo regalis) as a case study," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
  • Handle: RePEc:plo:pone00:0213654
    DOI: 10.1371/journal.pone.0213654
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213654
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0213654&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0213654?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
    ---><---

    References listed on IDEAS

    as
    1. Fiske, Ian & Chandler, Richard, 2011. "unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i10).
    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. Linda M. Haines, 2016. "A Note on the Royle–Nichols Model for Repeated Detection–Nondetection Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 588-598, September.
    2. Johnston, Alison & Moran, Nick & Musgrove, Andy & Fink, Daniel & Baillie, Stephen R., 2020. "Estimating species distributions from spatially biased citizen science data," Ecological Modelling, Elsevier, vol. 422(C).
    3. Therin M Bradshaw & Abigail G Blake-Bradshaw & Auriel M V Fournier & Joseph D Lancaster & John O’Connell & Christopher N Jacques & Michael W Eichholz & Heath M Hagy, 2020. "Marsh bird occupancy of wetlands managed for waterfowl in the Midwestern USA," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    4. Zhiyuan Lv & Jun Yang & Ben Wielstra & Jie Wei & Fei Xu & Yali Si, 2019. "Prioritizing Green Spaces for Biodiversity Conservation in Beijing Based on Habitat Network Connectivity," Sustainability, MDPI, vol. 11(7), pages 1-20, April.
    5. Jha, Ashish & J, Praveen & Nameer, P.O., 2022. "Contrasting occupancy models with presence-only models: Does accounting for detection lead to better predictions?," Ecological Modelling, Elsevier, vol. 472(C).
    6. Karavarsamis, N. & Huggins, R.M., 2019. "Two-stage approaches to the analysis of occupancy data II. The heterogeneous model and conditional likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 195-207.
    7. Benjamin Juan Padilla & Chris Sutherland, 2021. "Defining dual-axis landscape gradients of human influence for studying ecological processes," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-17, November.
    8. Bryn E Evans & Cory E Mosby & Alessio Mortelliti, 2019. "Assessing arrays of multiple trail cameras to detect North American mammals," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.
    9. Ferreira, Guilherme Braga, 2018. "When the blanket is too short: Potential negative impacts of expanding indigenous land over a national park in a high priority area for conservation," Land Use Policy, Elsevier, vol. 76(C), pages 359-364.
    10. Mauriel Rodriguez Curras & Emiliano Donadío & Arthur D Middleton & Jonathan N Pauli, 2021. "Perceived risk structures the space use of competing carnivores," Behavioral Ecology, International Society for Behavioral Ecology, vol. 32(6), pages 1380-1390.
    11. Duarte, Adam & Adams, Michael J. & Peterson, James T., 2018. "Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches," Ecological Modelling, Elsevier, vol. 374(C), pages 51-59.
    12. Kowalewski, Lucas K. & Chizinski, Christopher J. & Powell, Larkin A. & Pope, Kevin L. & Pegg, Mark A., 2015. "Accuracy or precision: Implications of sample design and methodology on abundance estimation," Ecological Modelling, Elsevier, vol. 316(C), pages 185-190.
    13. Matthew R. P. Parker & Laura L. E. Cowen & Jiguo Cao & Lloyd T. Elliott, 2023. "Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 43-58, March.
    14. Linda M. Haines, 2020. "Multinomial N‐mixture models for removal sampling," Biometrics, The International Biometric Society, vol. 76(2), pages 540-548, June.
    15. Edgar Santos‐Fernandez & Julie Vercelloni & Aiden Price & Grace Heron & Bryce Christensen & Erin E. Peterson & Kerrie Mengersen, 2024. "Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology," International Statistical Review, International Statistical Institute, vol. 92(1), pages 43-61, April.
    16. Krista L. Noe & Christopher T. Rota & Mack W. Frantz & James T. Anderson, 2022. "Restored and Natural Wetland Small Mammal Communities in West Virginia, USA," Land, MDPI, vol. 11(9), pages 1-14, September.
    17. Alex Diana & Emily Beth Dennis & Eleni Matechou & Byron John Treharne Morgan, 2023. "Fast Bayesian inference for large occupancy datasets," Biometrics, The International Biometric Society, vol. 79(3), pages 2503-2515, September.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0213654. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.