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Esa13 spatial and temporal synchrony in small mammal populations
1. Spatial and temporal synchrony in
small mammal populations: the role
of intrinsic and extrinsic factors
Aaron C. Greenville, Glenda M. Wardle and Chris R. Dickman
Desert Ecology Research Group
School of Biological Sciences
University of Sydney
@AarontheEcolog
4. Aims
1. Spatial structure of
small mammal
populations
– Moran effect?
2. Density dependence and extrinsic
factors
Introduction
Methods
Results
Conclusion
5. Study species
Rodents:
Sandy inland mouse, Ps.
hermannsburgensis, 12 g
Photo by Bobby Tamayo
Dasyurid marsupials:
Mulgara, Dasycercus
blythi, 100 g
Introduction
Ningaui, Ningaui ridei, 8 g
Lesser hairy-footed dunnart,
Sminthopsis youngsoni, 10 g
Methods
Results
Conclusion
14. Conclusion
• Moran effect present for rodents -synchronous
– Landscape-scale
– Density dependency
• Mulgara similar to their prey + wildfire
– Landscape-scale
– Density dependency
• Insectivorous dasyurids – asynchronous
– Local-scale
– Weak density dependency
• Management
Introduction
Methods
Results
Conclusion
15. Acknowledgements
•
•
•
•
•
Bobby Tamayo and the DERG team.
All our volunteers.
Bush Heritage Australia.
Bedourie Hotel.
ARC, APA and Paddy Pallin Science Grant.
@AarontheEcolog
Volunteer info: http://bit.ly/1fxVOhH
For more:
www.AarontheEcolog.wordpress.com
Editor's Notes
-Determining the factors that influence the spatial dynamics of species’ populations remains a key goal in ecology and is an imperative for managing species that are in decline. -Sub-populations across species’ ranges seldom share the same level of resources, and this may lead to different densities and growth rates among them.-Dispersal may dampen these differences, but decreases with distance-nonetheless, local populations can still behave synchronously across large spatial scales, suggesting that external drivers are operating
-The Moran effect/theorem provides a theoretical basis for population synchrony across large areas-and states that sub-populations with a common density dependent structure can be synchronized by a spatially correlated density independent factor, such as climate
-In the simpson desert
-Up to 22 years of live-trapping for 9 sites. Over 130 trips.-Captures converted to per 100 trap nights
-Used multi-variate AR state-space models – these models are based on gompertzpopulation growth model.-And can incorporate both process error (variation due to stochastic events) and observation error to yield better estimates of population size.-All these models were run with and without density dependency. We also used the best fitting model (AICc)
After the spatial structure of the sub-populations was found, we added covariates:Food e.g. Seed for rodents, rodents for predatorsVegetation cover, spinifexRainfallPredators or competitors.
-Best fitting model Density dependence found in rodents
-Best fitting model Density dependence found mulgara.
-Best fitting model asychrony/independentWeak density dependence
-Best fitting model asychrony/independentWeak density dependence
-number std co-efficents and CI
-Sub-populations were shown to be synchronous or to have two structures if driven by a large-scale driver (e.g. rainfall or wildfire),Or asynchronous if driven by local events, extrinsic and intrinsic drivers contributing at different levels. -Density dependence was detected in all species, but was weak for small insectivorous dasyurids, suggesting that environmental stochasticity and interactions with other species on a local scale are more important in driving their population dynamics than intrinsic factors. -In contrast, rodents and the carnivorous dasyurids were driven by both extrinsic and intrinsic factors that operate at the landscape scale, suggesting the moran’s effect is operating.
-Any questions for now or find me on twitter.-For a summary of this talk, who was Moran and more on MARSS models see my blog.