in

Time-for-space substitution in N-mixture models for estimating population trends: a simulation-based evaluation

  • 1.

    Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations (Academic Press, New York, 2002).

    Google Scholar 

  • 2.

    Lindenmayer, D. B. & Likens, G. E. Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends Ecol. Evol. 24, 482–486 (2009).

    Article  Google Scholar 

  • 3.

    MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).

    Article  Google Scholar 

  • 4.

    Buckland, S. T., Rexstad, E. A., Marques, T. A. & Oedekoven, C. S. Distance Sampling: Methods and Applications (Springer, Berlin, 2015).

    Google Scholar 

  • 5.

    Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115 (2004).

    MathSciNet  Article  Google Scholar 

  • 6.

    Kéry, M. & Royle, J. A. Applied Hierarchical Modelling in Ecology (Academic Press, New York, 2016).

    Google Scholar 

  • 7.

    Ariefiandy, A. et al. Evaluation of three field monitoring-density estimation protocols and their relevance to Komodo dragon conservation. Biodivers. Conserv. 23, 2473–2490 (2014).

    Article  Google Scholar 

  • 8.

    Romano, A. et al. Conservation of salamanders in managed forests: methods and costs of monitoring abundance and habitat selection. For. Ecol. Manag. 400, 12–18 (2017).

    Article  Google Scholar 

  • 9.

    Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435 (2011).

    Article  Google Scholar 

  • 10.

    Dail, D. & Madsen, L. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics 67, 577–587 (2011).

    MathSciNet  CAS  Article  Google Scholar 

  • 11.

    Augustynczik, L. D. et al. Diversification of forest management regimes secures tree microhabitats and bird abundance under climate change. Sci. Total Environ. 650, 2717–2730 (2019).

    ADS  CAS  Article  Google Scholar 

  • 12.

    Peterman, W. E. & Semlitsch, R. D. Fine-scale habitat associations of a terrestrial salamander: the role of environmental gradients and implications for population dynamics. PLoS ONE 8, e62184. https://doi.org/10.1371/journal.pone.0062184 (2013).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 13.

    Balestrieri, R. et al. A guild-based approach to assessing the influence of beech forest structure on bird communities. For. Ecol. Manage. 356, 216–223 (2015).

    Article  Google Scholar 

  • 14.

    Barker, R. J., Schofield, M. R., Link, W. A. & Sauer, J. R. On the reliability of N-mixture models for count data. Biometrics 74, 369–377 (2017).

    MathSciNet  Article  Google Scholar 

  • 15.

    Link, W. A., Schofield, M. R., Barker, R. J. & Sauer, J. R. On the robustness of N-mixture models. Ecology 99, 1547–1551 (2018).

    Article  Google Scholar 

  • 16.

    Kéry, M. Identifiability in N-mixture models: a large-scale screening test with bird data. Ecology 99, 281–288 (2018).

    Article  Google Scholar 

  • 17.

    Priol, P. M. Using dynamic N-mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation. Ecol. Evol. 4, 2165–2177 (2014).

    Article  Google Scholar 

  • 18.

    Basile, M. et al. Measuring bird abundance—a comparison of methodologies between capture/recapture and audio-visual surveys. Avocetta 40, 55–61 (2016).

    Google Scholar 

  • 19.

    Ficetola, G. F. et al. N-mixture models reliably estimate the abundance of small vertebrates. Sci. Rep. 8, 10357. https://doi.org/10.1038/s41598-018-28432-8 (2018).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 20.

    Costa, A., Oneto, F. & Salvidio, S. Time-for-space substitution in N-mixture modeling and population monitoring. J. Wildl. Manag. 83, 737–741 (2019).

    Article  Google Scholar 

  • 21.

    Yamaura, Y. et al. Modelling community dynamics based on species-level abundance models from detection/nondetection data. J. Appl. Ecol. 48, 67–75 (2011).

    Article  Google Scholar 

  • 22.

    Dennis, E. B., Morgan, B. J. & Ridout, M. S. Computational aspects of N-mixture models. Biometrics 71, 237–246 (2015).

    MathSciNet  Article  Google Scholar 

  • 23.

    Duarte, A., Adams, M. J. & Peterson, J. T. Fitting N-mixture models to count data with unmodeled heterogeneity: bias, diagnostics, and alternative approaches. Ecol. Model. 374, 51–59 (2018).

    Article  Google Scholar 

  • 24.

    Costa, A., Romano, A. & Salvidio, S. Reliability of multinomial N-mixture models for estimating abundance of small terrestrial vertebrates. Biodiv. Conserv. 29, 2951–2965 (2020).

    Article  Google Scholar 

  • 25.

    Ficetola, G. F., Romano, A., Salvidio, S. & Sindaco, R. Optimizing monitoring schemes to detect trends in abundance over broad scales. Anim. Conserv. 21, 221–231 (2018).

    Article  Google Scholar 

  • 26.

    Fiske, I. & Chandler, R. unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Soft. 43, 1–23 (2011).

    Article  Google Scholar 

  • 27.

    Kéry, M., Royle, J.A., & Meredith, M. Package AHMbook version 0.1.4 (2016).

  • 28.

    MacKenzie, D. I. & Bailey, L. L. Assessing the fit of site-occupancy models. J. Agric. Biol. Environ. Stat. 9, 300–318 (2004).

    Article  Google Scholar 

  • 29.

    Knape, J. et al. Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit. Met. Ecol. Evol. 9, 2102–2114 (2018).

    Article  Google Scholar 

  • 30.

    Mazerolle, M. J. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.1-1 (2017).

  • 31.

    McIntyre, A. P. Empirical and simulation evaluations of an abundance estimator using unmarked individuals of cryptic forest-dwelling taxa. For. Ecol. Manage. 286, 129–136 (2012).

    Article  Google Scholar 

  • 32.

    Veech, J. A., Ott, J. R. & Troy, J. R. Intrinsic heterogeneity in detection probability and its effect on N-mixture models. Met. Ecol. Evol. 7, 1019–1028 (2016).

    Article  Google Scholar 

  • 33.

    Gervasi, V. A preliminary estimate of the apennine brown bear population size based on hair-snag sampling and multiple data source mark–recapture huggins models. Ursus 19, 105–121 (2008).

    Article  Google Scholar 

  • 34.

    Welsh, H. N. & Conroy, M. J. A Case for using plethodontid salamanders for monitoring biodiversity and ecosystem integrity of North American forests. Conserv. Biol. 15, 558–569 (2001).

    Article  Google Scholar 

  • 35.

    Warton, D. I., Stoklosa, J., Guillera-Arroita, G., MacKenzie, D. I. & Welsh, A. H. Graphical diagnostics for occupancy models with imperfect detection. Met. Ecol. Evol. 8, 408–419 (2017).

    Article  Google Scholar 

  • 36.

    Lunghi, E. Environmental suitability models predict population density, performance and body condition for microendemic salamanders. Sci. Rep. 8, 7527. https://doi.org/10.1038/s41598-018-25704-1 (2018).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 37.

    Kopler, I. & Malkinson, D. Differential response of mammals to agricultural fences: the effects of species vagility and body size. Basic Appl. Ecol. 33, 79–88 (2018).

    Article  Google Scholar 


  • Source: Ecology - nature.com

    An aggressive market-driven model for US fusion power development

    King Climate Action Initiative announces new research to test and scale climate solutions