Biodiversity & Conservation

, Volume 13, Issue 1, pp 275–284 | Cite as

Species Survival in Fragmented Landscapes: Where to From Here?

  • Brett A. MelbourneEmail author
  • Kendi F. Davies
  • Chris R. Margules
  • David B. Lindenmayer
  • Denis A. Saunders
  • Christian Wissel
  • Klaus Henle


We summarise the contributions of empiricists, modellers, and practitioners in this issue of Biodiversity and Conservation, and highlight the most important areas for future research on species survival in fragmented landscapes. Under the theme ‘uncertainty in research and management’, we highlight five areas for future research. First, we know little about the effects of density dependence on the viability of metapopulations, a requirement for fragmented landscapes. Second, successful early attempts suggest that it is worth developing more rigorous calibration methods for population viability analysis with spatially explicit, individual-based models. In particular, the balance between model complexity, ease of calibration, and precision, needs to be addressed. Third, we need to improve methods to discriminate between models, including alternatives to time-series approaches. Fourth, when our ability to reduce model uncertainty is weak, we need to incorporate this uncertainty in population viability analysis. Fifth, population viability analysis and decision analysis can be integrated to make uncertainty an explicit part of the decision process. An important future direction is extending the decision framework to adaptive management. Under the theme ‘tools for quantifying risk and predicting species sensitivity to fragmentation’, we highlight three areas for future research. First, we need to develop tools to support comparative approaches to population viability analysis. Second, population modelling can be used to find rules of thumb to support conservation decisions when very little is known about a species. Rules of thumb need to be extended to the problem of managing for multiple species. Third, species’ traits might be useful for predicting sensitivity but predictions could be further refined by considering the relative importance of population processes at different scales. Under the theme ‘tools for reassembling fragmented landscapes’, we consider the ‘focal species’ approach, and highlight aspects of the approach that require more rigorous testing. Finally, we highlight two important areas for future research not presented in the previous themes or papers in this volume. First, we need to incorporate the deterministic effects of habitat modification into the modelling framework of population viability analysis. Second, an avenue of research that remains largely unexplored is the combination of landscape-scale experiments and population modelling, especially using data from existing fragmentation experiments and from experiments designed to test the effects of defragmenting landscapes.

Extinction Focal species Habitat fragmentation Habitat modification Metapopulation Modelling Population viability analysis Rules of thumb Traits 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Beissinger S.R. and Westphal M.I. 1998. On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62: 821-841.Google Scholar
  2. Bolker B. and Pacala S.W. 1997. Using moment equations to understand stochastically driven spatial pattern formation in ecological systems. Theoretical Population Biology 52: 179-197.Google Scholar
  3. Brooker L. 2002. The application of focal species knowledge to landscape design in agricultural lands using the ecological neighbourhood as a template. Landscape and Urban Planning 60: 185-210.Google Scholar
  4. Burgman M.A. and Possingham H.P. 2000. Population viability analysis for conservation: the good, the bad and the undescribed. In: Young A.G. and Clarke G.M. (eds), Genetics, Demography and Viability of Fragmented Populations. Cambridge University Press, Cambridge, UK, pp. 97-112.Google Scholar
  5. Cappuccino N. and Harrison S. 1996. Density-perturbation experiments for understanding population regulation. In: Floyd R.B., Sheppard A.W. and De Barro P.J. (eds), Frontiers of Population Ecology. CSIRO Publishing, Melbourne, Australia, pp. 53-64.Google Scholar
  6. Chesson P. 1998. Making sense of spatial models in ecology. In: Bascompte J. and Solé R.V. (eds), Modeling Spatiotemporal Dynamics in Ecology. Landes Bioscience, Austin, Texas, pp. 151-166.Google Scholar
  7. Davies K.F., Margules C.R. and Lawrence K.F. 2000. Which traits of species predict population declines in experimental forest fragments? Ecology 81: 1450-1461.Google Scholar
  8. Davies K.F., Gascon C. and Margules C.R. 2001a. Habitat fragmentation: consequences, management, and future research priorities. In: Soulé M.E. and Orians G.H. (eds), Conservation Biology: Research Priorities for the Next Decade. Island Press, Washington, DC, pp. 81-97.Google Scholar
  9. Davies K.F., Melbourne B.A. and Margules C.R. 2001b. Effects of within-and between-patch processes on community dynamics in a fragmentation experiment. Ecology 82: 1830-1846.Google Scholar
  10. Didham R.K., Hammond P.M., Lawton J.H., Eggleton P. and Stork N.E. 1998. Beetle species responses to tropical forest fragmentation. Ecological Monographs 68: 295-323.Google Scholar
  11. Gaston K.J., Pressey R.L. and Margules C.R. 2002. Persistence and vulnerability: retaining biodiversity in the landscape and in protected areas. Journal of Biosciences 27: 361-384.Google Scholar
  12. Ginzburg L.R., Ferson S. and Akçakaya H.R. 1990. Reconstructibility of density dependence and the conservative assessment of extinction risks. Conservation Biology 4: 63-70.Google Scholar
  13. Harrison S. and Bruna E. 1999. Habitat fragmentation and large-scale conservation: what do we know for sure? Ecography 22: 225-232.Google Scholar
  14. Kendall B.E., Briggs C.J., Murdoch W.W., Turchin P., Ellner S.P., McCauley E. et al. 1999. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology 80: 1789-1805.Google Scholar
  15. Kintsch J.A. and Urban D.L. 2002. Focal species, community representation, and physical proxies as conservation strategies: a case study in the Amphibolite Mountains, North Carolina, USA. Conservation Biology 16: 936-947.Google Scholar
  16. Lambeck R.J. 1997. Focal species: a multi-species umbrella for nature conservation. Conservation Biology 11: 849-856.Google Scholar
  17. Lambeck R.J. 2002. Focal species and restoration ecology: response to Lindenmayer et al. Conservation Biology 16: 549-551.Google Scholar
  18. Laurance W.F., Ferreira L.V., Rankin-De Merona J.M. and Laurance S.G. 1998a. Rain forest fragmentation and the dynamics of Amazonian tree communities. Ecology 79: 2032-2040.Google Scholar
  19. Laurance W.F., Ferreira L.V., Rankin-De Merona J.M., Laurance S.G., Hutchings R.W. and Lovejoy T.E. 1998b. Effects of forest fragmentation on recruitment patterns in Amazonian tree communities. Conservation Biology 12: 460-464.Google Scholar
  20. Lindenmayer D.B., Lacy R.C. and Pope M.L. 2000. Testing a simulation model for population viability analysis. Ecological Applications 10: 580-597.Google Scholar
  21. Lindenmayer D.B., Manning A.D., Smith P.L., Possingham H.P., Fischer J., Oliver I. et al. 2002. The focal-species approach and landscape restoration: a critique. Conservation Biology 16: 338-345.Google Scholar
  22. Lindenmayer D.B., Possingham H.P., Lacy R.C., McCarthy M.A. and Pope M.L. 2003. Lessons from landscape-scale population modeling in a fragmented system. Ecology Letters 6: 41-47.Google Scholar
  23. Malcolm J.R. 1994. Edge effects in Central Amazonian forest fragments. Ecology 75: 2438-2445.Google Scholar
  24. Mac Nally R., Bennett A.F. and Horrocks G. 2000. Forecasting the impacts of habitat fragmentation. Evaluation of species-specific predictions of the impact of habitat fragmentation on birds in the box-ironbark forests of central Victoria, Australia. Biological Conservation 95: 7-29.Google Scholar
  25. Margules C.R. 1993. The Wog Wog habitat fragmentation experiment. Environmental Conservation 19: 316-325.Google Scholar
  26. McCarthy M.A., Lindenmayer D.B. and Possingham H.P. 2000. Testing spatial PVA models of Australian treecreepers (Aves: Climacteridae) in fragmented forest. Ecological Applications 10: 1722-1731.Google Scholar
  27. Noss R.F., Carroll C., Vance-Borland K. and Wuerthner G. 2002. A multicriteria assessment of the irreplaceability and vulnerability of sites in the Greater Yellowstone Ecosystem. Conservation Biology 16: 895-908.Google Scholar
  28. Pascual M.A., Kareiva P. and Hilborn R. 1997. The influence of model structure on conclusions about the viability and harvesting of Serengeti wildebeest. Conservation Biology 11: 966-976.Google Scholar
  29. Possingham H.P., Lindenmayer D.B. and Norton T.W. 1993. A framework for improved threatened species management using population viability analysis. Pacific Conservation Biology 1: 39-45.Google Scholar
  30. Ruckelshaus M., Hartway C. and Kareiva P. 1997. Assessing the data requirements of spatially explicit dispersal models. Conservation Biology 11: 1298-1306.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Brett A. Melbourne
    • 1
    Email author
  • Kendi F. Davies
    • 1
  • Chris R. Margules
    • 2
  • David B. Lindenmayer
    • 3
  • Denis A. Saunders
    • 4
  • Christian Wissel
    • 5
  • Klaus Henle
    • 6
  1. 1.CSIRO Sustainable EcosystemsWembleyAustralia
  2. 2.CSIRO Sustainable Ecosystems and the Rainforest Co-operative Research Centre, CSIRO Tropical Forest Research CentreAthertonAustralia
  3. 3.Centre for Resource and Environmental StudiesAustralian National UniversityCanberra
  4. 4.C/-CSIRO Sustainable EcosystemsCanberraAustralia
  5. 5.Department of Ecological Modelling, Centre for Environmental ResearchUFZ Leipzig-HalleLeipzigGermany
  6. 6.Department of Conservation Biology and Natural Resources, Centre for Environmental ResearchUFZ Leipzig-HalleLeipzigGermany

Personalised recommendations