Evaluating release alternatives for a long-lived bird species under uncertainty about long-term demographic rates

Abstract

The release of animals to reestablish an extirpated population is a decision problem that is often attended by considerable uncertainty about the probability of success. Annual releases of captive-reared juvenile Whooping Cranes (Grus americana) were begun in 1993 in central Florida, USA, to establish a breeding, non-migratory population. Over a 12-year period, 286 birds were released, but by 2004, the introduced flock had produced only four wild-fledged birds. Consequently, releases were halted over managers’ concerns about the performance of the released flock and uncertainty about the efficacy of further releases. We used data on marked, released birds to develop predictive models for addressing whether releases should be resumed, and if so, under what schedule. To examine the outcome of different release scenarios, we simulated the survival and productivity of individual female birds under a baseline model that recognized age and breeding-class structure and which incorporated empirically estimated stochastic elements. As data on wild-fledged birds from captive-reared parents were sparse, a key uncertainty that confronts release decision-making is whether captive-reared birds and their offspring share the same vital rates. Therefore, we used data on the only population of wild Whooping Cranes in existence to construct two alternatives to the baseline model. The probability of population persistence was highly sensitive to the choice of these three models. Under the baseline model, extirpation of the population was nearly certain under any scenario of resumed releases. In contrast, the model based on estimates from wild birds projected a high probability of persistence under any release scenario, including cessation of releases. Therefore, belief in either of these models suggests that further releases are an ineffective use of resources. In the third model, which simulated a population Allee effect, population persistence was sensitive to the release decision: high persistence probability was achieved only through the release of more birds, whereas extirpation was highly probable with cessation of releases. Despite substantial investment of time and effort in the release program, evidence collected to date does not favor one model over another; therefore, any decision about further releases must be made under considerable biological uncertainty. However, given an assignment of credibility weight to each model, a best, informed decision about releases can be made under uncertainty. Furthermore, if managers can periodically revisit the release decision and collect monitoring data to further inform the models, then managers have a basis for confronting uncertainty and adaptively managing releases through time.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. Akçakaya HR, Sjögren-Gulve P (2000) Population viability analyses in conservation planning: an overview. Ecol Bull 48:9–21

    Google Scholar 

  2. Akçakaya HR, McCarthy MA, Pearce JL (1995) Linking landscape data with population viability analysis: management options for the helmeted honeyeater (Lichenostomus melanopsis cassidix). Biol Conserv 73:169–176

    Google Scholar 

  3. Armstrong DP, Ewen JG (2001) Assessing the value of follow-up translocations: a case study using New Zealand robins. Biol Conserv 101:239–247

    Google Scholar 

  4. Beissinger SR, Westphal MI (1998) On the use of demographic models of population viability in endangered species management. J Wildl Manag 62:821–841

    Google Scholar 

  5. Bell TJ, Bowles ML, McEachern AK (2003) Projecting the success of plant population restoration with viability analysis. In: Brigham CA, Schwartz MW (eds) Population viability in plants: conservation, management, and modeling of rare plants. Springer, Berlin, pp 313–348

    Google Scholar 

  6. Binkley CS, Miller RS (1980) Survivorship of the Whooping Crane, Grus americana. Ecology 61:434–437

    Google Scholar 

  7. Buner F, Schaub M (2008) How do different releasing techniques affect the survival of reintroduced grey partidges Perdix perdix? Wildl Biol 14:26–35

    Google Scholar 

  8. Clark JS, Bjørnstad ON (2004) Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology 85:3140–3150

    Google Scholar 

  9. Courchamp F, Clutton-Brock T, Grenfell B (1999) Inverse density dependence and the Allee effect. Trends Ecol Evol 14:405–410

    CAS  PubMed  Google Scholar 

  10. Deredec A, Courchamp F (2007) Importance of the Allee effect for reintroductions. Ecoscience 14:440–451

    Google Scholar 

  11. Ewen JG, Armstrong DP (2007) Strategic monitoring of reintroductions in ecological restoration programmes. Ecoscience 14:401–409

    Google Scholar 

  12. Falk DA (1992) From conservation biology to conservation practice: strategies for protecting plant diversity. In: Fiedler PL, Jain SK (eds) Conservation biology: the theory and practice of nature conservation, preservation, and management. Chapman and Hall, New York, pp 398–431

    Google Scholar 

  13. Fischer J, Lindenmayer DB (2000) An assessment of the published results of animal relocations. Biol Conserv 96:1–11

    Google Scholar 

  14. Folk MJ, Nesbitt SA, Parker JM, Spalding MG, Baynes SB, Candelora KL (2008) Current status of nonmigratory whooping cranes in Florida. Proc N Am Crane Workshop 10:7–12

    Google Scholar 

  15. Gilks WR, Thomas A, Spiegelhalter DJ (1994) A language and program for complex Bayesian modelling. Stat 43:169–177

    Google Scholar 

  16. Gilpin ME, Soulé ME (1986) Minimum viable populations: processes of species extinction. In: Soulé ME (ed) Conservation biology: the science of scarcity and diversity. Sinauer, Sunderland, pp 19–34

    Google Scholar 

  17. Heath SR, Kershner EL, Cooper DM, Lynn S, Turner JM, Warnock N, Farabaugh S, Brock K, Garcelon DK (2008) Rodent control and food supplementation increase productivity of endangered San Clemente Loggerhead Shrikes (Lanius ludovicianus mearnsi). Biol Conserv 141:2506–2515

    Google Scholar 

  18. Kleiman DV (1989) Reintroduction of captive mammals for conservation. Bioscience 39:152–161

    Google Scholar 

  19. Le Gouar P, Robert A, Choisy JP, Henriquet S, Lecuyer P, Tessier C, Sarrazin F (2008) Roles of survival and dispersal in reintroduction success of Griffon Vulture (Gyps fulvus). Ecol Appl 18:859–872

    PubMed  Google Scholar 

  20. Link WA, Royle JA, Hatfield JS (2003) Demographic analysis from summaries of an age-structured population. Biometrics 59:778–785

    PubMed  Google Scholar 

  21. McCarthy MA, Andelman SJ, Possingham HP (2003) Reliability of relative predictions in population viability analysis. Conserv Biol 17:982–989

    Google Scholar 

  22. Melbourne BA, Hastings A (2008) Extinction risk depends strongly on factors contributing to stochasticity. Nature 454:100–103

    CAS  PubMed  Google Scholar 

  23. Menges ES (1991) The application of minimum viable population theory to plants. In: Falk DA, Holsinger KE (eds) Genetics and conservation of rare plants. Oxford University Press, New York, pp 45–61

    Google Scholar 

  24. Menges ES (1992) Stochastic modeling of extinction in plant populations. In: Fiedler PL, Jain SK (eds) Conservation biology: the theory and practice of nature conservation, preservation, and management. Chapman and Hall, New York, pp 253–275

    Google Scholar 

  25. Palmer MA, Falk DA, Zedler JB (2006) Ecological theory and restoration ecology. In: Falk DA, Palmer MA, Zedler JB (eds) Foundations of restoration ecology. Island Press, Washington D.C., pp 1–10

    Google Scholar 

  26. Pascual MA, Kareiva P, Hilborn R (1997) The influence of model structure on conclusions about the viability and harvesting of Serengeti wildebeest. Conserv Biol 11:966–976

    Google Scholar 

  27. Pavlik BM (1994) Demographic monitoring and the recovery of endangered plants. In: Bowles ML, Whelan CJ (eds) Restoration of endangered species: conceptual issues, planning, and implementation. Cambridge University Press, Cambridge, pp 322–350

    Google Scholar 

  28. Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628

    Google Scholar 

  29. Roche EA, Cuthbert FJ, Arnold TW (2008) Relative fitness of wild and captive-reared piping plovers: does egg salvage contribute to recovery of the endangered Great Lakes population? Biol Conserv 141:3079–3088

    Google Scholar 

  30. Runge MC, Johnson FA (2002) The importance of functional form in optimal control solutions of problems in population dynamics. Ecology 83:1357–1371

    Google Scholar 

  31. Sarrazin F, Barbault R (1996) Reintroduction: challenges and lessons for basic ecology. Trends Ecol Evol 11:474–478

    CAS  PubMed  Google Scholar 

  32. Sarrazin F, Legendre S (2000) Demographic approach to releasing adults versus young in reintroductions. Conserv Biol 14:488–500

    Google Scholar 

  33. SAS Institute Inc (2004) SAS/IML 9.1 user’s guide. SAS Institute, Cary

  34. Schaub M, Zink R, Beissmann H, Sarrazin F, Arlettaz R (2009) When to end releases in reintroduction programmes: demographic rates and population viability analysis of bearded vultures in the Alps. J Appl Ecol 46:92–100

    Google Scholar 

  35. Slotta-Bachmayr L, Boegel R, Kaczsensky P, Stauffer C, Walzer C (2004) Use of population viability analysis to identify management priorities and success in reintroducing Przewalski’s horses to southwestern Mongolia. J Wildl Manag 68:790–798

    Google Scholar 

  36. Stephens PA, Sutherland WJ (1999) Consequences of the Allee effect for behaviour, ecology and conservation. Trends Ecol Evol 14:401–405

    CAS  PubMed  Google Scholar 

  37. Stephens PA, Sutherland WJ, Freckleton RP (1999) What is the Allee effect? Oikos 87:185–190

    Google Scholar 

  38. Taylor BL, Wade PR, Ramakrishnan U, Gilpin M, Akçakaya HR (2002) Incorporating uncertainty in population viability analyses for the purpose of classifying species by risk. In: Beissinger SR, McCullough DR (eds) Population viability analysis. University of Chicago Press, Chicago, pp 239–252

    Google Scholar 

  39. Wade PR (2002) Bayesian population viability analysis. In: Beissinger SR, McCullough DR (eds) Population viability analysis. University of Chicago Press, Chicago, pp 213–238

    Google Scholar 

  40. Wakamiya SM, Roy CL (2009) Use of monitoring data and population viability analysis to inform reintroduction decisions: peregrine falcons in the Midwestern United States. Biol Conserv 142:1767–1776

    Google Scholar 

  41. Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic Press, San Diego

    Google Scholar 

  42. Wilson AC, Stanley Price MR (1994) Reintroduction as a reason for captive breeding. In: Olney PJS, Mace GM, Feistner ATC (eds) Creative conservation: interactive management of wild and captive animals. Chapman and Hall, London, pp 243–264

    Google Scholar 

  43. Wolf CM, Griffith B, Reed C, Temple SA (1996) Avian and mammalian translocations: update and reanalysis of 1987 survey data. Conserv Biol 10:1142–1154

    Google Scholar 

Download references

Acknowledgments

We thank the Florida Fish and Wildlife Conservation Commission and the International Whooping Crane Recovery Team for logistical support and guidance throughout our work. We also thank W.A. Link and J.S. Hatfield for helpful discussions early in our work, and we appreciate thoughtful manuscript reviews provided by S.K. Jacobi, L.A. Powell, and two anonymous reviewers. R. Boughton, B. Brooks, J.B. French, T.E. O’Meara, M. Putnam, J. Rodgers, and M. Spalding were members of a decision structuring team who provided input on selection of decision alternatives for simulation. Funding for our work was provided by the Wildlife: Terrestrial and Endangered Resources Program of USGS and by Patuxent Wildlife Research Center. Funding for the work by the FWC was supported in part by the U.S. Fish and Wildlife Service via Cooperative Agreement No. 401814-J-035. Finally, we thank the many employees and volunteers of the FWC who spent thousands of hours collecting the data that made our work possible. The use of trade, product, or company names does not imply endorsement by the U.S. Government. This work complied with applicable, current laws of the governments of the United States and the State of Florida.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Clinton T. Moore.

Additional information

Communicated by M. Schaub.

Appendix

Appendix

The statistical estimation models for survival and breeding class transition are described in the text. The accompanying mathematical descriptions are provided below and are more fully developed in Moore et al. (in preparation). Estimates from the models (Table 3) were used to construct the baseline PVA model.

Survival estimation

Each female bird j released into the wild as part of cohort c was a member of age or breeding class k (k ∈ {0, 1, 2, 3, 4 + , P, N, F}) in period i (3-month divisions of the year, starting from January 1993). The number of days that bird j survived within period i was assumed to be binomially distributed with probability \({ \text{p}_{ij}}(k,c)^{{1/d}_{i}} \) over x ij exposure days, where period i is d i days in length and 0 ≤ x ij ≤ d i.

Quarterly survival probability was modeled as a linear function of a mean (an intercept plus a female fixed effect) and random effects of age/breeding class, time, cohort, and individual:

$$ \begin{aligned}\log\!{\text{it}}\left({p_{ij} \left({k,c} \right)} \right)& = {{\upalpha}}_{k}^{G} + {{\updelta}}_{i}^{T} + {{\updelta}}_{cj}^{*} \\ & = \left({{{\upmu}}_{\text{female}} + {{\updelta}}_{k}^{G}} \right) + {{\updelta}}_{i}^{T} + \left({{{\updelta}}_{c}^{C0} + {{\updelta}}_{j}^{B0}} \right)I\left({\text{age = 0}} \right) \\ & + \left({{{\updelta}}_{c}^{C1} + {{\updelta}}_{j}^{B1}} \right)I\left({\text{age = 1}} \right) \\& + \left({{{\updelta}}_{c}^{{{\text{C}}2 +}} + {{\updelta}}_{j}^{{{\text{B}}2 +}}} \right)I\left({{\text{age}} \ge 2} \right)\\ \end{aligned} $$

where δ Gk is a random effect due to membership in age or breeding class k, δ Ti is a random effect due to period i, δ C*c are age-class specific random effects due to membership in release cohort c, δ B*j are age-class specific random effects due to bird j, and I(z) is the indicator function for expression z. Random effects were modeled as deviates from zero-centered normal distributions with corresponding variance parameters σ 2G , σ 2T , σ 2C0 , σ 2C1 , σ 2C2+ , σ 2B0 , σ 2B1 , and σ 2B2+ .

Posterior distributions of annual age and breeding-class specific survival rates (φk) were estimated by summing appropriate terms of the model above, transforming the sum to the probability scale, and multiplying four of the resulting terms together:

$$ {\varphi_{\it k}}= \prod\limits_{i = 1}^{4} {\log\!{\text{it}}^{- 1} \left({{{\upalpha}}_{k}^{G} + {{\updelta}}_{i}^{T} + {{\updelta}}_{cj}^{*}} \right)} .$$

Estimation of productivity and breeding class transition probabilities

Transition into breeding class P occurred when a female bird of age ≥2 years first exhibited pairing behavior. For never-paired bird j that was age k (k = 1, 2, 3, 4+) in the year previous to i, we assumed that first pairing occurred as a Bernoulli outcome with probability θ UPij (k). Transition probability was thus modeled as a linear function of fixed intercept (μUP) and age-specific (βk) effects and of random effects of time (δ PTi ) and bird (δ PBj ):

$$ \log\!{\text{it}}\left({{{\uptheta}}_{ij}^{UP} \left(k \right)} \right) = {{\upmu}}_{UP} + {{\upbeta}}_{k} + {{\updelta}}_{i}^{PT} + {{\updelta}}_{j}^{PB} $$

Random effects were modeled as deviates from zero-centered normal distributions with corresponding variance parameters σ 2PT and σ 2PB . We obtained posterior distributions of annual age-specific transition probability into class P (ψ (k)UP ) by transformation of the above sum.

Transition from class P into class N occurred when a female produced her first nestling but failed to produce a fledgling. Transition from class P into class F occurred when a female’s first nestling developed into a fledgling (male or female). For bird j belonging to class P, both events were assumed to occur as Bernoulli outcomes with probabilities θ PNj and θ PFj , respectively. Linear models relating each transition probability to a fixed intercept and a random effect due to bird were:

$$ \log\!{\text{it}}\left({{{\uptheta}}_{j}^{PN}} \right) = {{\upmu}}_{PN} + {{\updelta}}_{j}^{PNB} \quad{\text{and}}\quad\log\!{\text{it}}\left({{{\uptheta}}_{j}^{PF}} \right) = {{\upmu}}_{PF} + {{\updelta}}_{j}^{PFB} $$

Random effects were modeled as deviates from zero-centered normal distributions with corresponding variance parameters σ 2PNB and σ 2PFB . We obtained posterior distributions of annual transition probability from class P into classes N (ψPN) or F (ψPF). As no Florida bird has ever produced more than one fledgling in a year, ψPF serves as the estimate of productivity for birds in class P.

Transition from class N into class F occurred when a female who had only ever produced a nestling in prior attempts produced her first fledgling (male or female). We assumed that this event was a Bernoulli outcome with probability ψNF. Similarly, a bird already in class F produces another fledgling with probability ψFF. We modeled each of these probabilities as a simple mean with no random effects:

$$ \log\!{\text{it}}\left({{{\uppsi}}_{NF}} \right) = {{\upmu}}_{NF}\quad{\text{and}}\quad\log\!{\text{it}}\left({{{\uppsi}}_{FF}} \right) = {{\upmu}}_{FF} $$

These probabilities effectively serve as estimates of productivity for birds in class N and F, respectively.

Table 3 Estimates of age and breeding-class specific mean annual survival rate (φi), mean breeding transition rate (ψij), and associated random effects (σi) of 286 captive-reared female Whooping Cranes reintroduced in Florida, USA, 1993–2004 (Moore et al. in preparation)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Moore, C.T., Converse, S.J., Folk, M.J. et al. Evaluating release alternatives for a long-lived bird species under uncertainty about long-term demographic rates. J Ornithol 152, 339–353 (2012). https://doi.org/10.1007/s10336-010-0592-y

Download citation

Keywords

  • Decision-making
  • Endangered species
  • Florida
  • Grus americana
  • Population reintroduction
  • Population viability analysis
  • Uncertainty
  • Whooping Crane