Example: Southern redback salamander, Plethodon serratus - PowerPoint PPT Presentation

Example southern redback salamander plethodon serratus l.jpg
Download
1 / 10

  • 298 Views
  • Updated On :
  • Presentation posted in: Pets / Animals

Example: Southern redback salamander, Plethodon serratus. Terrestrial salamander in southern Appalachians Abundance is difficult to estimate Highly variable counts from natural cover or coverboard sampling Likely that p < 1. 50m. 10m. 10m. 10m. “Site” Design. Natural Cover Transect.

Related searches for Example: Southern redback salamander, Plethodon serratus

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

Example: Southern redback salamander, Plethodon serratus

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Example southern redback salamander plethodon serratus l.jpg

Example: Southern redback salamander, Plethodon serratus

  • Terrestrial salamander in southern Appalachians

  • Abundance is difficult to estimate

  • Highly variable counts from natural cover or coverboard sampling

  • Likely that p < 1


Slide2 l.jpg

50m

10m

10m

10m

“Site” Design

Natural Cover Transect

3m

Cover Boards

Sampling Details

39 “Sites” sampled

4 Years (1998-2001)

Sampled 5 times/year


Plethodon serratus occupancy hypotheses l.jpg

Plethodon serratus Occupancy : Hypotheses

  • Expect probability of occupancy influenced by previous disturbance history, (dist).

  • Expect temporal variation in occupancy and colonization probabilities due to variations in annual rainfall (declined 1999-2001 for April-June months).

  • Expect temporal variation in detection probabilities due to yearly rainfall variations, p (year •), or consistent yearly patterns of seasonal availability, p (• t).


Plethodon serratus occupancy modeling l.jpg

Plethodon serratus Occupancy : Modeling

  • Model parameterization: (t, t )

  • Candidate Models include combinations of:

    • Occupancy (3 levels):

      - y(•) : Occupancy constant

      - y(dist): Occupancy varies by disturbance history

      - y(year): Occupancy varies by year

    • Colonization (2 levels):

      • γ(•): Colonization constant

      • γ(year): Colonization varies by year

    • Detection (3 levels):

      • p(•): Detection constant

      • p (year •): Detection varies due to yearly rainfall

      • p(• t): Detection varies due to seasonal availability within the sampling season


P t model results occupancy detection l.jpg

Compare Models with p(• t)

AIC

AIC

K

Ψ

1998

Ψ

1999

Ψ

2000

Ψ

2001

p(1)

p(5)

Ψ(dist)γ(•)p(• t)

752.5

0.00

8

0.94

(0.42)

0.94

(0.42)

0.94

(0.42)

0.94

(0.42)

0.85

0.23

Ψ(•)γ(•)p(• t)

766.2

13.7

7

0.79

0.79

0.79

0.79

0.85

0.23

Ψ(•)γ(year)p(• t)

769.4

16.9

9

0.78

0.78

0.78

0.78

0.85

0.23

Ψ(year)γ(•)p(• t)

771.6

19.4

8

0.82

0.81

0.79

0.76

0.86

0.23

Naïve Estimates

0.58

0.82

0.82

0.74

p(• t) Model Results: Occupancy & Detection

K = number of parameters. For models with Ψ(dist),the first occupancy estimate is for undisturbed sites, followed by disturbed site estimate in parentheses. Only detection probabilities for first & last sample reported.


P t model results colonization l.jpg

Compare Models

withp(• t)

w

∆ AIC

K

γ

1998

γ

1999

γ

2000

Ψ(dist)γ(•)p(• t)

0.99

0.00

8

0.21

0.21

0.21

Ψ(•)γ(•)p(• t)

0.01

13.7

7

0.22

0.22

0.22

Ψ(•)γ(year)p(• t)

0.00

16.9

9

0.32

0.25

0.14

Ψ(year)γ(•)p(• t)

0.00

19.4

8

0.17

0.17

0.17

Naïve Estimates

0.28

0.05

0.00

p(• t) Model Results: Colonization

K = number of parameters. w = Akaike weight, evidence (probability) the given model is the ‘best’


P year model results occupancy detection l.jpg

p(year •) Model Results: Occupancy & Detection

K = number of parameters. For models with Ψ(dist),the first occupancy estimate is for undisturbed sites, followed by disturbed site estimate in parentheses.


P year model results colonization l.jpg

p(year •) Model Results: Colonization

K = number of parameters. w = Akaike weight, evidence (probability) the given model is the ‘best’


P model results occupancy detection l.jpg

p(• •) Model Results: Occupancy & Detection

K = number of parameters. For models with Ψ(dist),the first occupancy estimate is for undisturbed sites, followed by disturbed site estimate in parentheses.


P model results colonization l.jpg

p(• •) Model Results: Colonization

K = number of parameters. w = Akaike weight, evidence (probability) the given model is the ‘best’


  • Login