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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
Spatial dynamics
Abund

4

Time

2

Abund

3

Abund

Abund

1

Time
Time

Time
i

Introduction

Methods

= growth rate
Results

Conclusion
Moran effect

Introduction

Methods

Results

Conclusion
Aims
1. Spatial structure of
small mammal
populations
– Moran effect?

2. Density dependence and extrinsic
factors
Introduction

Methods

Results

Conclusion
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
1. Spatial structure
•
•
•
•
•

Asynchronous
Oasis
Wildfire
Productivity
Synchronous

3
2
1
3
1
2
4
1
2

1
3
2
5

• Multivariate
autoregressive statespace (MARSS)
models
Introduction

Methods

1
9
1
1

2
3
1
6
2
3
1
7

Results

1
8

Conclusion
2. Covariates

1
2
2

2

1
1

1
1

Introduction

1

Methods

Results

Conclusion
Sandy inland mouse, Ps.
hermannsburgensis, 12 g

Results:

Results:
Abundance (log)

0 1 2 3 4

Photo by Bobby Tamayo

1

1

1

40

60

80

100

120

20

40

60

80

100

120

0 1 2 3 4

1

1

20

0

1

0

1

1
1

Trips
Introduction

Methods

Results

Conclusion
2
1
2
1
1

20

40

60

80

100

120

0

20

40

60

80

100

120

2.0

2
1

0

1.0

1

0.0

Abundance (log)

1.0

2.0

Results:

1

0.0

1

1
1

1

Trips
Introduction

Methods

Results

Conclusion
120

3.0

40

80

120

40

80

120

40

80

120

Shitty Site

0.0

1.5

3.0
1.5
0.0

1.5

0

0

Kunnamuka Swamp East

3.0

South Site

0

3.0

80

0.0

1.5

3.0

Field River South

1.5

40

0.0

Abundance (log)

0

3

Carlo

0.0

0.0

1.5

3.0

Main Camp

0

40

80

120

0

40

80

120

4

7

0

8

40

80

120

3.0

Tobermorey West

1.5

3.0

0.0

1.5

1

Field River North

0.0

1.5

9
6

Tobermorey East
3.0

5

0.0

2

0

40

80

120

0

40

80

Trips
Introduction

Methods

Results

Conclusion

120
100

2.0

0 20

60

100

60

100

60

100

1.0

2.0

Shitty Site

0.0

1.0
0.0

1.0

0 20

0 20

Kunnamuka Swamp East
2.0

South Site
2.0

0.0

1.0

2.0

Field River South

1.0

60

0.0

Abundance (log)

0 20

3

Carlo

0.0

0.0

1.0

2.0

Main Camp

0 20

60

100

0 20

60

100

4

7

Introduction

0 20

8

Methods

60

100

2.0

Tobermorey West

1.0

2.0

0.0

1.0

1

Field River North

0.0

1.0

9
6

Tobermorey East
2.0

5

0.0

2

0 20

Results

60

100

Trips

0 20

60

Conclusion

100
Extrinsic factors:

Sandy inland mouse, Ps.
hermannsburgensis, 12 g

0.27 ( 0.44, 0.1)

Photo by Bobby Tamayo

Introduction

Methods

Results

Conclusion
Extrinsic factors:

1.79 (0.46, 3.12)

Introduction

Methods

Results

Conclusion
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
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

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Esa13 spatial and temporal synchrony in small mammal populations

Editor's Notes

  1. -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
  2. -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
  3. -In the simpson desert
  4. -Up to 22 years of live-trapping for 9 sites. Over 130 trips.-Captures converted to per 100 trap nights
  5. -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)
  6. 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.
  7. -Best fitting model Density dependence found in rodents
  8. -Best fitting model Density dependence found mulgara.
  9. -Best fitting model asychrony/independentWeak density dependence
  10. -Best fitting model asychrony/independentWeak density dependence
  11. -number std co-efficents and CI
  12. -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.
  13. -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.