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Evaluating Multi-Level Models to Test Occupancy State Responses of Plethodontid Salamanders.

Kroll AJ, Garcia TS, Jones JE, Dugger K, Murden B, Johnson J, Peterman S, Peerman S, Brintz B, Rochelle M - PLoS ONE (2015)

Bottom Line: Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29).Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large.As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

View Article: PubMed Central - PubMed

Affiliation: Weyerhaeuser, Federal Way, Washington, United States of America.

ABSTRACT
Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Show MeSH
Average standard deviation, by combination, of all posterior mean estimates of the treatment effect estimator ‘Alpha3’ (see Eq 2) in the multi-scale model.Results are shown on the logit scale. Panels show the results for different combinations by simulated detection probability and post-treatment occupancy.
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pone.0142903.g004: Average standard deviation, by combination, of all posterior mean estimates of the treatment effect estimator ‘Alpha3’ (see Eq 2) in the multi-scale model.Results are shown on the logit scale. Panels show the results for different combinations by simulated detection probability and post-treatment occupancy.

Mentions: We observed improved posterior estimator precision with increasing numbers of harvest units, as expected (Figs 3 and 4). Number of plots per harvest unit, which represent subsamples, also played a role in estimator precision by affecting the amount of information available to estimate occupancy of the primary sampling unit. These trends also suggest improved precision with increasing detection probability, but very little impact due to the size of the treatment effect.


Evaluating Multi-Level Models to Test Occupancy State Responses of Plethodontid Salamanders.

Kroll AJ, Garcia TS, Jones JE, Dugger K, Murden B, Johnson J, Peterman S, Peerman S, Brintz B, Rochelle M - PLoS ONE (2015)

Average standard deviation, by combination, of all posterior mean estimates of the treatment effect estimator ‘Alpha3’ (see Eq 2) in the multi-scale model.Results are shown on the logit scale. Panels show the results for different combinations by simulated detection probability and post-treatment occupancy.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664280&req=5

pone.0142903.g004: Average standard deviation, by combination, of all posterior mean estimates of the treatment effect estimator ‘Alpha3’ (see Eq 2) in the multi-scale model.Results are shown on the logit scale. Panels show the results for different combinations by simulated detection probability and post-treatment occupancy.
Mentions: We observed improved posterior estimator precision with increasing numbers of harvest units, as expected (Figs 3 and 4). Number of plots per harvest unit, which represent subsamples, also played a role in estimator precision by affecting the amount of information available to estimate occupancy of the primary sampling unit. These trends also suggest improved precision with increasing detection probability, but very little impact due to the size of the treatment effect.

Bottom Line: Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29).Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large.As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

View Article: PubMed Central - PubMed

Affiliation: Weyerhaeuser, Federal Way, Washington, United States of America.

ABSTRACT
Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Show MeSH