1 / 10

Example: Southern redback salamander, Plethodon serratus

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.

Roberta
Télécharger la présentation

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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).

  4. 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

  5. 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.

  6. 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’

  7. 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.

  8. p(year •) Model Results: Colonization K = number of parameters. w = Akaike weight, evidence (probability) the given model is the ‘best’

  9. 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.

  10. p(• •) Model Results: Colonization K = number of parameters. w = Akaike weight, evidence (probability) the given model is the ‘best’

More Related