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Agent-based simulations improve abundance estimation

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Abstract

Abundance is a fundamental characteristic of every biological population and is the focus of many research programs in ecology and conservation. In this paper I give an overview of the challenges of estimating abundance. I argue that truly understanding, validating, and refining the field techniques and quantitative methods used to estimate abundance can largely benefit from agent-based simulations. I illustrate this through the example of bird point counts and introduce the software bSims to test statistical and biological assumptions for estimating abundance and to aid survey design.

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References

  • Amundson CL, Royle JA, Handel CM (2014) A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts. The Auk: Ornithol Adv 131:476–494

    Article  Google Scholar 

  • Ball JR, Sólymos P, Schmiegelow FAK, Haché S, Schieck J, Bayne EM (2016) Regional habitat needs of a nationally listed species, Canada Warbler Cardellina canadensis, in Alberta Canada. Avian Conserv Ecol 11(2):10

    Article  Google Scholar 

  • Bayne EM, Lele SR, Sólymos P (2011) Bias in estimation of bird density and relative abundance when the closure assumption of multiple survey approaches is violated: a simulation study. Technical report, Boreal Avian Modelling Project. pp. 26

  • Blancher PJ, Rosenberg KV, Panjabi AO, Altman B, Couturier AR, Thogmartin WE, and the Partners in Flight Science Committee (2013) Handbook to the partners in flight population estimates database, Version 2.0. PIF Technical Series No 6. https://www.partnersinflight.org/wp-content/uploads/2018/11/PIF-Technical-Series-06-%E2%80%93-Handbook-to-PIF-Pop-Est-DB-2013.pdf

  • Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, Oxford

    Book  Google Scholar 

  • Cadieux P, Boulanger Y, Cyr D, Taylor AR, Price DT, Sólymos P, Stralberg D, Chen HYH, Brecka A, Tremblay JA (2020) Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada. Divers Distrib 26:668–682

    Article  Google Scholar 

  • Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2022) Shiny: web application framework for R_. R package version 1.7.4, https://CRAN.R-project.org/package=shiny

  • DeAngelis DL, Diaz SG (2019) Decision-making in agent-based modeling: a current review and future prospectus. Front Ecol Evol 6:237

    Article  Google Scholar 

  • Edwards BPM, Smith AC, Docherty TDS, Gahbauer MA, Gillespie CR, Grinde AR, Harmer T, Iles DT, Matsuoka SM, Michel NL, Murray A, Niemi GJ, Pasher J, Pavlacky DC Jr, Robinson BG, Ryder TB, Sólymos P, Stralberg D, Zlonis EJ (2022) Point count offsets for estimating population sizes of North American landbirds. Wiley, Hoboken

    Google Scholar 

  • Farnsworth GL, Pollock KH, Nichols JD, Simons TR, Hines JE, Sauer JR (2002) A removal model for estimating detection probabilities from point count surveys. Auk 119:414–425

    Article  Google Scholar 

  • Hanowski JM, Niemi GJ (1995) A comparison of on- and off-road bird counts: do you need to go off-road to count birds accurately? J Field Ornithol 66:469–483

    Google Scholar 

  • Johnson DH (2008) In defense of indices: the case of bird surveys. J Wildl Manage 72:857–868

    Google Scholar 

  • Kelling S, Hochachka WM, Fink D, Riedewald M, Caruana R, Ballard G, Hooker G (2009) Data-intensive science: a new paradigm for biodiversity studies. Bioscience 59:613–620

    Article  Google Scholar 

  • Kéry M, Royle JA (2016) Applied hierarchical modeling in Ecology. Academic Press, Cambridge

    Google Scholar 

  • Lele SR, Moreno M, Bayne EM (2012) Dealing with detection error in site occupancy surveys: what can we do with a single survey? J Plant Ecol 5(1):22–31. https://doi.org/10.1093/jpe/rtr042

    Article  Google Scholar 

  • MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–2255

    Article  Google Scholar 

  • Mahon CL, Bayne EM, Sólymos P, Matsuoka SM, Carlson M, Dzus E, Schmiegelow FKA, Cumming SG, Song SJ (2014) Does expected future landscape condition support proposed population objectives for boreal birds? For Ecol Manage 312:28–39

    Article  Google Scholar 

  • Marques TA, Buckland ST, Borchers DL, Tosh D, McDonald RA (2010) Point transect sampling along linear features. Biometrics 66:1247–1255

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  • Martin-Schwarze A, Niemi J, Dixon P (2021) Joint modeling of distances and times in point-count surveys. J Agric Biol Environ Stat 26:289–305. https://doi.org/10.1007/s13253-021-00437-3

    Article  MathSciNet  Google Scholar 

  • Matsuoka SM, Bayne EM, Sólymos P, Fontaine P, Cumming SG, Schmiegelow FKA, Song SA (2012) Using binomial distance-sampling models to estimate the effective detection radius of point-counts surveys across boreal Canada. Auk 129:268–282

    Article  Google Scholar 

  • Matsuoka SM, Mahon CL, Handel CM, Sólymos P, Bayne EM, Fontaine PC, Ralph CJ (2014) Reviving common standards in point-count surveys for broad inference across studies. Condor 116:599–608

    Article  Google Scholar 

  • R Core Team (2022) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

  • Railsback SF, Grimm V (2019) Agent-based and individual-based modeling: a practical introduction, 2nd edn. Princeton University Press, Princeton, p 360

    Google Scholar 

  • Rigby E (2016) Simulating effects of imperfect detectability in bird surveys. PhD Thesis, Univ. Minnesota. https://hdl.handle.net/11299/193439

  • Rosenberg KV, Dokter AM, Blancher PJ, Sauer JR, Smith AC, Smith PA, Stanton JC, Panjabi A, Helft L, Parr M, Marra PP (2019) Decline of the North American avifauna. Science. https://doi.org/10.1126/science.aaw1313

    Article  PubMed  Google Scholar 

  • Roy C, Michel N, Handel C, Van Wilgenburg S, Burkhalter J, Gurney K, Messmer D, Princé K, Rushing C, Saracco J, Schuster R, Smith AC, Smith PA, Sólymos P, Venier L, Zuckerberg B (2019) Monitoring boreal avian populations: how can we estimate trends and trajectories from noisy data? Avian Conserv Ecol 14(2):8

    Article  Google Scholar 

  • Royle A (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115. https://doi.org/10.1111/j.0006-341X.2004.00142.x

    Article  MathSciNet  PubMed  Google Scholar 

  • Shonfield J, Bayne EM (2017) Autonomous recording units in avian ecological research: current use and future applications. Avian Conserv Ecol 12(1):14. https://doi.org/10.5751/ACE-00974-120114

    Article  Google Scholar 

  • Sólymos P (2023) bSims: agent-based bird point count simulator. R package version 0.3–1, https://github.com/psolymos/bSims

  • Sólymos P, Lele SR (2016) Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods Ecol Evol 7:196–205

    Article  Google Scholar 

  • Sólymos P, Lele SR, Bayne EM (2012) Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error. Environmetrics 23:197–205

    Article  MathSciNet  Google Scholar 

  • Sólymos P, Matsuoka SM, Bayne EM, Lele SR, Fontaine P, Cumming SG, Stralberg D, Schmiegelow FKA, Song SJ (2013) Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods Ecol Evol 4:1047–1058

    Article  Google Scholar 

  • Sólymos P, Matsuoka SM, Cumming SG, Stralberg D, Fontaine P, Schmiegelow FKA, Song SJ, Bayne EM (2018a) Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity. Condor 120:765–786

    Article  Google Scholar 

  • Sólymos P, Matsuoka SM, Stralberg D, Barker NKS, Bayne EM (2018b) Phylogeny and species traits predict bird detectability. Ecography 41:1595–1603

    Article  ADS  Google Scholar 

  • Sólymos P, Toms JD, Matsuoka SM, Cumming SG, Barker NKS, Thogmartin WE, Stralberg D, Crosby AD, Dénes FV, Haché S, Mahon CL, Schmiegelow FKA, Bayne EM (2020) Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada. Condor 122:1–22

    Article  Google Scholar 

  • Somershoe SG, Twedt DJ, Reid B (2006) Combining breeding bird survey and distance sampling to estimate density of migrant and breeding birds. The Condor 108:691–699

    Article  Google Scholar 

  • Stanton JS, Blancher P, Rosenberg KV, Panjabi AO, Thogmartin WE (2019) Estimating uncertainty of North American landbird population sizes. Avian Conserv Ecol 14(1):4

    Article  Google Scholar 

  • U.S. Geological Survey and Canadian Wildlife Service (2020) Strategic Plan for the North American Breeding Bird Survey, 2020–30: U.S. Geological Survey Circular 1466, 10 p, https://doi.org/10.3133/cir1466

  • Van Wilgenburg SL, Beck EM, Obermayer B, Joyce T, Weddle B (2015) Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design. Avian Conserv Ecol 10(1):5

    Article  Google Scholar 

  • Van Wilgenburg SL, Mahon CL, Campbell G, McLeod L, Campbell M, Evans D, Easton W, Francis CM, Haché S, Machtans CS, Mader C, Pankratz RF, Russell R, Smith AC, Thomas P, Toms JD, Tremblay JA (2020) A cost efficient spatially balanced hierarchical sampling design for monitoring boreal birds incorporating access costs and habitat stratification. PLoS ONE 15(6):e0234494. https://doi.org/10.1371/journal.pone.0234494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yip DA, Bayne EM, Sólymos P, Campbell J, Proppe JD (2017) Sound attenuation in forested and roadside environments: implications for avian point count surveys. Condor 119:73–84

    Article  Google Scholar 

  • Yip DA, Knight EC, Haave-Audet E, Wilson SJ, Charchuk C, Scott CD, Sólymos P, Bayne EM (2020) Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations. Remote Sens Ecol Conserv 6:301–315

    Article  Google Scholar 

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Acknowledgements

I thank S. Lele for many discussions and feedback on an earlier draft of this paper. The development of the bSims R package and the ideas included in it was greatly influenced by collaborations and discussions with the Boreal Avian Modelling Project team, especially D. Iles, E. C. Knight, E. Bayne, D. Yip, S. Matsuoka, and S. Van Wilgenburg. Comments from two anonymous reviewers improved the clarity of the paper. The bSims package development was facilitated by the “Analysis of point count data in the presence of variable survey methodologies and detection error” training workshop delivered by the author as part of BIOS2 in 2021. I dedicate this paper to the loving memory of J. Solymos, Jr.

Funding

No funding was received for conducting this study. The author has no financial or proprietary interests in any material discussed in this article.

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Authors and Affiliations

  1. Department of Biological Sciences and Boreal Avian Modelling Project, University of Alberta, Edmonton, AB, Canada

    Péter Sólymos

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  1. Péter Sólymos
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Correspondence to Péter Sólymos.

Appendix

Appendix

R code to reproduce roadside results from the paper.

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Sólymos, P. Agent-based simulations improve abundance estimation. BIOLOGIA FUTURA 74, 377–392 (2023). https://doi.org/10.1007/s42977-023-00183-2

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