title: An overview of the utility of population simulation software in molecular ecology.
abstract: Stochastic simulation software that simultaneously model genetic, population and environmental processes can inform many topics in molecular ecology. These include forecasting species and community response to environmental change, inferring dispersal ecology, revealing cryptic mating, quantifying past population dynamics, assessing in situ management options and monitoring neutral and adaptive biodiversity change. Advances in population demographic-genetic simulation software, especially with respect to individual life history, landscapes and genetic processes, are transforming and expanding the ways that molecular data can be used. The aim of this review is to explain the roles that such software can play in molecular ecology studies (whether as a principal component or a supporting function) so that researchers can decide whether, when and precisely how simulations can be incorporated into their work. First, I use seven case studies to demonstrate how simulations are employed, their specific advantage/necessity and what alternative or complementary (nonsimulation) approaches are available. I also explain how simulations can be integrated with existing spatial, environmental, historical and genetic data sets. I next describe simulation features that may be of interest to molecular ecologists, such as spatial and behavioural considerations and species' interactions, to provide guidance on how particular simulation capabilities can serve particular needs. Lastly, I discuss the prospect of simulation software in emerging challenges (climate change, biodiversity monitoring, population exploitation) and opportunities (genomics, ancient DNA), in order to emphasize that the scope of simulation-based work is expanding. I also suggest practical considerations, priorities and elements of best practice. This should accelerate the uptake of simulation approaches and firmly embed them as a versatile tool in the molecular ecologist's toolbox.
authors.affiliation: National Institute for Mathematical and Biological Synthesis, University of Tennessee, 1122 Volunteer Blvd., Suite 110A Knoxville, TN, 37996-3410, USA.
authors.forename: Sean
authors.initials: S
authors.lastname: Hoban
mesh_terms: D003198:Computer Simulation; D004463:Ecology / Q000379:methods*; D005828:Genetics, Population / Q000379:methods*; D011157:Population Dynamics; D012984:Software; D013269:Stochastic Processes
keywords: Approximate Bayesian Computation; ex situ management; in situ; landscape; molecular markers; natural history; population dynamics; prediction