Literature DB >> 30585658

Lessons from 30 years of population viability analysis of wildlife populations.

Robert C Lacy1.   

Abstract

Population viability analysis (PVA) has been used for three decades to assess threats and evaluate conservation options for wildlife populations. What has been learned from PVA on in situ populations are valuable lessons also for assessing and managing viability and sustainability of ex situ populations. The dynamics of individual populations are unpredictable, due to limited knowledge about important factors, variability in the environment, and the probabilistic nature of demographic events. PVA considers such uncertainty within simulations that generate the distribution of likely fates for a population; management of ex situ populations should also take into consideration the uncertainty in our data and in the trajectories of populations. The processes affecting wildlife populations interact, with feedbacks often leading to amplified threats to viability; projections of ex situ populations should include such feedbacks to allow for management that foresees and responds to the cumulative and synergistic threats. PVA is useful for evaluating conservation options only if the goals for each population and measures of success are first clearly identified; similarly, for ex situ populations to contribute maximally to species conservation, the purposes for the population and definitions of sustainability in terms of acceptable risk must be documented. PVA requires a lot of data, knowledge of many processes affecting the populations, modeling expertize, and understanding of management goals and constraints. Therefore, to be useful in guiding conservation it must be a collaborative, trans-disciplinary, and social process. PVA can help integrate management of in situ and ex situ populations within comprehensive species conservation plans.
© 2018 Wiley Periodicals, Inc.

Keywords:  conservation planning; ex situ; extinction; in situ; simulation; threat assessment; uncertainty

Mesh:

Year:  2018        PMID: 30585658     DOI: 10.1002/zoo.21468

Source DB:  PubMed          Journal:  Zoo Biol        ISSN: 0733-3188            Impact factor:   1.421


  6 in total

1.  Endogenous Lifecycle Models for Chemical Risk Assessment.

Authors:  Matthew A Etterson; Gerald T Ankley
Journal:  Environ Sci Technol       Date:  2021-11-08       Impact factor: 11.357

2.  Alarm communication networks as a driver of community structure in African savannah herbivores.

Authors:  Kristine Meise; Daniel W Franks; Jakob Bro-Jørgensen
Journal:  Ecol Lett       Date:  2019-11-27       Impact factor: 9.492

3.  Multiple life-stage inbreeding depression impacts demography and extinction risk in an extinct-in-the-wild species.

Authors:  A E Trask; G M Ferrie; J Wang; S Newland; S Canessa; A Moehrenschlager; M Laut; L Barnhart Duenas; J G Ewen
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

4.  A stochastic model for estimating sustainable limits to wildlife mortality in a changing world.

Authors:  Oliver Manlik; Robert C Lacy; William B Sherwin; Hugh Finn; Neil R Loneragan; Simon J Allen
Journal:  Conserv Biol       Date:  2022-04-28       Impact factor: 7.563

Review 5.  An analysis of threats, strategies, and opportunities for African rhinoceros conservation.

Authors:  Admire Chanyandura; Victor K Muposhi; Edson Gandiwa; Never Muboko
Journal:  Ecol Evol       Date:  2021-05-01       Impact factor: 2.912

6.  eDNA sampled from stream networks correlates with camera trap detection rates of terrestrial mammals.

Authors:  Arnaud Lyet; Loïc Pellissier; Alice Valentini; Tony Dejean; Abigail Hehmeyer; Robin Naidoo
Journal:  Sci Rep       Date:  2021-06-15       Impact factor: 4.379

  6 in total

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