A modelling framework for integrating reproduction, survival and count data when projecting the fates of threatened populations
Tóm tắt
A key goal of ecological research is to obtain reliable estimates of population demographic rates, abundance and trends. However, a common challenge when studying wildlife populations is imperfect detection or breeding observation, which results in unknown survival status and reproductive output for some individuals. It is important to account for undetected individuals in population models because they contribute to population abundance and dynamics, and can have implications for population management. Promisingly, recent methodological advances provide us with the tools to integrate data from multiple independent sources to gain insights into the unobserved component of populations. We use data from five reintroduced populations of a threatened New Zealand bird, the hihi (Notiomystis cincta), to develop an integrated population modelling framework that allows missing values for survival status, sex and reproductive output to be modelled. Our approach combines parallel matrices of encounter and reproduction histories from marked individuals, as well as counts of unmarked recruits detected at the start of each breeding season. Integrating these multiple data types enabled us to simultaneously model survival and reproduction of detected individuals, undetected individuals and unknown (never detected) individuals to derive parameter estimates and projections based on all available data, thereby improving our understanding of population dynamics and enabling full propagation of uncertainty. The methods presented will be especially useful for management programmes for populations that are intensively monitored but where individuals are still imperfectly detected, as will be the case for most threatened wild populations.
Tài liệu tham khảo
Addison PFE, Cook CN, de Bie K (2016) Conservation practitioners’ perspectives on decision triggers for evidence-based management. J Appl Ecol 53:1351–1357. https://doi.org/10.1111/1365-2664.12734
Armstrong DP, Ewen JG (2013) Consistency, continuity and creativity: long-term studies of population dynamics on Tiritiri Matangi Island. N Z J Ecol 37:288–297
Armstrong DP et al (2002) Population dynamics of reintroduced forest birds on New Zealand islands. J Biogeogr 29:609–621. https://doi.org/10.1046/j.1365-2699.2002.00710.x
Armstrong DP, Castro I, Griffiths R (2007) Using adaptive management to determine requirements of re-introduced populations: the case of the New Zealand hihi. J Appl Ecol 44:953–962. https://doi.org/10.1111/j.1365-2664.2007.01320.x
Armstrong DP et al (2017) Using Bayesian mark-recapture modelling to quantify the strength and duration of post-release effects in reintroduced populations. Biol Cons 215:39–45. https://doi.org/10.1016/j.biocon.2017.08.033
Arnold TW, Clark RG, Koons DN, Schaub M (2018) Integrated population models facilitate ecological understanding and improved management decisions. J Wildl Manag 82:266–274. https://doi.org/10.1002/jwmg.21404
Besbeas P, Freeman SN, Morgan BJT, Catchpole EA (2002) Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540–547. https://doi.org/10.1111/j.0006-341X.2002.00540.x
Bolam FC et al (2019) Using the value of information to improve conservation decision making. Biol Rev Camb Philos Soc 94:629–647. https://doi.org/10.1111/brv.12471
Canessa S et al (2015) When do we need more data? A primer on calculating the value of information for applied ecologists. Methods Ecol Evol 6:1219–1228. https://doi.org/10.1111/2041-210X.12423
Conn PB, Cooch EG (2009) Multistate capture-recapture analysis under imperfect state observation: an application to disease models. J Appl Ecol 46:486–492. https://doi.org/10.1111/j.1365-2664.2008.01597.x
Converse SJ, Moore CT, Armstrong DP (2013) Demographics of reintroduced populations: estimation, modeling, and decision analysis. J Wildl Manag 77:1081–1093. https://doi.org/10.1002/jwmg.590
Cook CN, de Bie K, Keith DA, Addison PFE (2016) Decision triggers are a critical part of evidence-based conservation. Biol Cons 195:46–51. https://doi.org/10.1016/j.biocon.2015.12.024
Elphick CS (2008) How you count counts: the importance of methods research in applied ecology. J Appl Ecol 45:1313–1320. https://doi.org/10.1111/j.1365-2664.2008.01545.x
Ewen JG, Renwick R, Adams L, Armstrong DP, Parker KA (2013) 1980–2012: 32 years of re-introduction efforts of the hihi (stitchbird) in New Zealand. In: Soorae PS (ed) Global re-introduction perspectives: 2013. Further case studies from around the globe. IUCN/SSC Re-introduction Specialist Group and Environment Agency, Abu Dhabi, pp 68–73
Ewen JG, Soorae PS, Canessa S (2014) Reintroduction objectives, decisions and outcomes: global perspectives from the herpetofauna. Anim Conserv 17:74–81. https://doi.org/10.1111/acv.12146
Ewen JG, Walker L, Canessa S, Groombridge JJ (2015) Improving supplementary feeding in species conservation. Conserv Biol 29:341–349. https://doi.org/10.1111/cobi.12410
Genovart M, Oro D, Tenan S (2018) Immature survival, fertility, and density dependence drive global population dynamics in a long-lived species. Ecology 99:2823–2832. https://doi.org/10.1002/ecy.2515
Kendall WL, Stapleton S, White GC, Richardson JI, Pearson KN, Mason P (2019) A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data. Ecol Monogr. https://doi.org/10.1002/ecm.1329
Kéry M, Schaub M (2012) Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Waltham, Massachusetts, USA
King R, Morgan BJT, Gimenez O, Brooks SP (2010) Bayesian analysis for population ecology. CRC Press, Florida, USA
Koons DN, Arnold TW, Schaub M (2017) Understanding the demographic drivers of realized population growth rates. Ecol Appl 27:2102–2115. https://doi.org/10.1002/eap.1594
Lee AM et al (2015) An integrated population model for a long-lived ungulate: more efficient data use with Bayesian methods. Oikos 124:806–816. https://doi.org/10.1111/oik.01924
Lunn D, Spiegelhalter D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions. Stat Med 28:3049–3067. https://doi.org/10.1002/sim.3680
Ma Z, Chen G (2018) Bayesian methods for dealing with missing data problems. J Korean Stat Soc 47:297–313. https://doi.org/10.1016/j.jkss.2018.03.002
McCarthy MA, Possingham HP (2007) Active adaptive management for conservation. Conserv Biol 21:956–963. https://doi.org/10.1111/j.1523-1739.2007.00677.x
Nichols JD (2014) The role of abundance estimates in conservation decision-making. In: Verdade L, Lyra-Jorge M, Piña C (eds) Applied ecology and human dimensions in biological conservation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54751-5_8
Nichols JD, Williams BK (2006) Monitoring for conservation. Trends Ecol Evol 21:668–673. https://doi.org/10.1016/j.tree.2006.08.007
Panfylova J, Bemelmans E, Devine C, Frost P, Armstrong D (2016) Post-release effects on reintroduced populations of hihi. J Wildl Manag 80:970–977. https://doi.org/10.1002/jwmg.21090
Panfylova J, Ewen JG, Armstrong DP (2019) Making structured decisions for reintroduced populations in the face of uncertainty. Conserv Sci Pract. https://doi.org/10.1111/csp2.90
Penteriani V, Ferrer M, Delgado MM (2011) Floater strategies and dynamics in birds, and their importance in conservation biology: towards an understanding of nonbreeders in avian populations. Anim Conserv 14:233–241. https://doi.org/10.1111/j.1469-1795.2010.00433.x
Péron G, Crochét PA, Doherty PF Jr, Lebreton JD (2010) Studying dispersal at the landscape scale: efficient combination of population surveys and capture-recapture data. Ecology 91:3365–3375. https://doi.org/10.1890/09-1525.1
Pradel R (2005) Multievent: an extension of multistate capture-recapture models to uncertain states. Biometrics 61:442–447. https://doi.org/10.1111/j.1541-0420.2005.00318.x
Pradel R, Choquet R, Béchet A (2012) Breeding experience might be a major determinant of breeding probability in long-lived species: the case of the Greater flamingo. PLoS ONE. https://doi.org/10.1371/journal.pone.0051016
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Riecke TV et al (2019) Integrated population models: model assumptions and inference. Methods Ecol Evol 10:1072–1082. https://doi.org/10.1111/2041-210X.13195
Sanz-Aguilar A, Igual JM, Oro D, Genovart M, Tavecchia G (2016) Estimating recruitment and survival in partially monitored populations. J Appl Ecol 53:73–82. https://doi.org/10.1111/1365-2664.12580
Saunders SP et al (2019) Disentangling data discrepancies with integrated population models. Ecology. https://doi.org/10.1002/ecy.2714
Schaub M, Abadi F (2011) Integrated population models: a novel analysis framework for deeper insights into population dynamics. J Ornithol 152:S227–S237. https://doi.org/10.1007/s10336-010-0632-7
Schofield MR, Barker RJ, MacKenzie DI (2009) Flexible hierarchical mark-recapture modeling for open populations using WinBUGS. Environ Ecol Stat 16:369–387. https://doi.org/10.1007/s10651-007-0069-1
Tavecchia G, Sanz-Aguilar A, Cannell B (2016) Modelling survival and breeding dispersal to unobservable nest sites. Wildl Res 43:411–417. https://doi.org/10.1071/WR15187
Thomson DL, Conroy MJ, Cooch EG (2009) Modeling demographic processes in marked populations. Springer, New York
Wilson S, Gil-Weir KC, Clark RG, Robertson GJ, Bidwell MT (2016) Integrated population modeling to assess demographic variation and contributions to population growth for endangered whooping cranes. Biol Cons 197:1–7. https://doi.org/10.1016/j.biocon.2016.02.022
Zipkin EF, Saunders SP (2018) Synthesizing multiple data types for biological conservation using integrated population models. Biol Cons 217:240–250. https://doi.org/10.1016/j.biocon.2017.10.017
Zipkin EF, Rossman S, Yackulic CB, Wiens JD, Thorson JT, Davis RJ, Grant EHC (2017) Integrating count and detection–nondetection data to model population dynamics. Ecology 98:1640–1650. https://doi.org/10.1002/ecy.1831