Emu Emu Society
Journal of BirdLife Australia
REVIEW

Towards modelling persistence of woodland birds: the role of genetics

Paul Sunnucks
+ Author Affiliations
- Author Affiliations

School of Biological Sciences and Australian Centre for Biodiversity, Monash University, Clayton, VIC 3800, Australia. Email: paul.sunnucks@monash.edu

Emu 111(1) 19-39 https://doi.org/10.1071/MU10008
Submitted: 11 February 2010  Accepted: 17 May 2010   Published: 21 February 2011

Abstract

Assessing how environmental change affects the probability of persistence of organisms requires an understanding of dispersal through, and occupation of, landscapes, and the associated demographic outcomes. Projections of differences in persistence probability can then be made under different scenarios of land-use and global environmental change. Rates and distances of dispersal, and demographic change and trajectories, are difficult to measure accurately, but genetic approaches can make major contributions. For two decades the field of molecular ecology has been providing useful life-history information relevant to population management, including key ecological attributes such as disease-resistance and thermal biology, mobility, dispersal and gene flow, habitat connectivity, the spatial and temporal scales of population processes, and demography. Genetic estimators of these factors could be employed to a much greater extent than they are currently. To facilitate this increased use, genetic estimates of functional connectivity (mobility and gene flow of organisms) and demography need to be integrated directly into decision-making processes. Population genetics is well suited to Bayesian approaches, with associated benefits including the ability to consider many factors, and estimation of error and parameter sensitivities. Genetic estimators based on the mobility and reproductive success of individual organisms and their key ecological traits can make unique contributions alongside other types of data into agent-based, spatially explicit modelling approaches of real landscape scenarios at the range of scales needed by managers. Virtually all the tools to do this exist. It is imperative that genetic samples be collected for contemporary and future analyses.


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