Parameter summaries and model selection

Models evaluated:

model description
sc.fm1 dot model
sc.fm2.0 lure
sc.fm2.1 snow
sc.fm2.2 temp
sc.fm2.3 pass
sc.fm3.0 lure + snow
sc.fm3.1 lure + pass
sc.fm4.0 lure + snow + temp
sc.fm4.2 lure + snow + temp + pass
sc.fm5.0 lure + snow + temp + pass + temp * snow
sc.fm5.1 lure + snow + temp + pass + lure * snow




AmericanBlackBear



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Mono creek 19 20 7 days 300 2.5 350000 20000 20 2.3 0.268



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0.06431 err 0.1397 0.1730303 0.3136970
sc.fm5.0 snow 0 err 0.84861 0.8772727 0.9225455
sc.fm5.0 temp 0.11162 err 0.17539 0.2658182 0.4783030
sc.fm5.0 pass 0.37755 err 0.01297 0.5502424 0.3532727
sc.fm5.0 temp*snow 0.27323 err 0.01352 0.1116970 NA
sc.fm5.1 lure 0.11902 err 0.1397 0.1730303 0.3136970
sc.fm5.1 snow 0.15608 err 0.84861 0.8772727 0.9225455
sc.fm5.1 temp 0.02731 err 0.17539 0.2658182 0.4783030
sc.fm5.1 pass 0.39045 err 0.01297 0.5502424 0.3532727
sc.fm5.1 lure*snow 0.78434 err 0.01673 0.0319394 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.00749 err 0.1397 0.1730303 0.3136970
sc.fm2.1 snow 0 err 0.84861 0.8772727 0.9225455
sc.fm2.2 temp 0 err 0.17539 0.2658182 0.4783030
sc.fm2.3 pass 0.37121 err 0.01297 0.5502424 0.3532727



All models: Covariates with p values below .11 (bayesian p, probability covariate is not null)

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.1 snow 0 err 0.84861 0.8772727 0.9225455
sc.fm2.2 temp 0 err 0.17539 0.2658182 0.4783030
sc.fm3.0 snow 0 err 0.84861 0.8772727 0.9225455
sc.fm5.0 snow 0 err 0.84861 0.8772727 0.9225455
sc.fm4.2 snow 0.00643 err 0.84861 0.8772727 0.9225455
sc.fm2.0 lure 0.00749 err 0.1397 0.1730303 0.3136970
sc.fm3.1 lure 0.00801 err 0.1397 0.1730303 0.3136970
sc.fm4.0 snow 0.00854 err 0.84861 0.8772727 0.9225455
sc.fm3.0 lure 0.01492 err 0.1397 0.1730303 0.3136970
sc.fm4.0 lure 0.01939 err 0.1397 0.1730303 0.3136970
sc.fm4.0 temp 0.02368 err 0.17539 0.2658182 0.4783030
sc.fm4.2 lure 0.02514 err 0.1397 0.1730303 0.3136970
sc.fm5.1 temp 0.02731 err 0.17539 0.2658182 0.4783030
sc.fm4.2 temp 0.03273 err 0.17539 0.2658182 0.4783030
sc.fm5.0 lure 0.06431 err 0.1397 0.1730303 0.3136970



parameter support:

level support covariates
good pval and rev MCMC snow
moderate pval or rev MCMC lure - temp
poor neither pass - snow*temp - lure*snow



models with significant covariates:



waic:

model description WAIC
sc.fm4.0 lure + snow + temp 116.2644
sc.fm3.0 lure + snow 122.2659
sc.fm2.1 snow 128.4427
sc.fm2.2 temp 131.5311
sc.fm2.0 lure 138.6216
sc.fm1 dot model 143.7446



Density per 100km, significant models:

## Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: NAs introduced by coercion
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
AmericanBlackBear sc.fm4.0 D 10856 err 0.62 0.62
AmericanBlackBear sc.fm4.0 lure 14828 -1.64 -2.80 -0.65
AmericanBlackBear sc.fm4.0 snow 12240 -1.291 -2.81 -0.53
AmericanBlackBear sc.fm4.0 temp 15342 0.822 0.35 1.40



notes:

err in mode due to flat posterior distribution, equal to hdi, flat distribution may indicate convergance problem, the model does not seem to like black bear detections, ?? hibernation, im/emigration.. ??

AmericanMarten



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Mono creek 19 34 7 days 300 2.5 350000 20000 20 0.765 0.13



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0 263.258 0.69939 0.7049697 0.7777576
sc.fm5.0 snow 0.01666 5.477 0.00497 0.0049697 0.0080000
sc.fm5.0 temp 0.92483 0.096 0.00073 0.0006061 0.0010909
sc.fm5.0 pass 0.14477 1.340 6e-05 0.0156364 0.0000000
sc.fm5.0 temp*snow 0.05006 2.854 0 0.0018182 NA
sc.fm5.1 lure 0 228.606 0.69939 0.7049697 0.7777576
sc.fm5.1 snow 0.18703 1.039 0.00497 0.0049697 0.0080000
sc.fm5.1 temp 0.84899 0.080 0.00073 0.0006061 0.0010909
sc.fm5.1 pass 0.15409 1.167 6e-05 0.0156364 0.0000000
sc.fm5.1 lure*snow 0.71824 0.290 0.00079 0.0013333 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0 135.886 0.69939 0.7049697 0.7777576
sc.fm2.1 snow 0.13377 0.701 0.00497 0.0049697 0.0080000
sc.fm2.2 temp 0.24714 0.218 0.00073 0.0006061 0.0010909
sc.fm2.3 pass 0.17929 1.101 6e-05 0.0156364 0.0000000



All models: Covariates with p values below .11 (bayesian p, probability covariate is not null)

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0 135.886 0.69939 0.7049697 0.7777576
sc.fm3.0 lure 0 194.155 0.69939 0.7049697 0.7777576
sc.fm3.1 lure 0 452.635 0.69939 0.7049697 0.7777576
sc.fm5.0 lure 0 263.258 0.69939 0.7049697 0.7777576
sc.fm5.1 lure 0 228.606 0.69939 0.7049697 0.7777576
sc.fm4.2 lure 0.00068 72.705 0.69939 0.7049697 0.7777576
sc.fm4.0 lure 0.00177 63.548 0.69939 0.7049697 0.7777576
sc.fm5.0 snow 0.01666 5.477 0.00497 0.0049697 0.0080000
sc.fm5.0 temp*snow 0.05006 2.854 0 0.0018182 NA



parameter support:

level support covariates
good pval and rev MCMC lure
moderate pval or rev MCMC
poor neither snow - temp - pass - snow*temp - lure*snow



models with significant covariates:



waic:

model description WAIC
sc.fm2.0 lure 242.0755
sc.fm1 dot model 255.6601



Density per 100km, significant models:



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
AmericanMarten sc.fm2.0 D 913 9.234 3.11 36.65
AmericanMarten sc.fm2.0 lure 16030 -1.035 -1.60 -0.60



notes:

temp, snow and snow*temp do not have consistent support

Coyote



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Mono creek 19 50 7 days 300 2.5 350000 20000 20 0.865 0.205



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0.99313 0.061 0.00061 0.0006061 0.0009091
sc.fm5.0 snow 0.75455 0.069 0.00042 0.0004848 0.0005455
sc.fm5.0 temp 0.16245 0.367 0.00139 0.0012121 0.0024242
sc.fm5.0 pass 0.9953 0.173 0 0.0006061 0.0000000
sc.fm5.0 temp*snow 0.31044 0.231 0 0.0004242 NA
sc.fm5.1 lure 0.62849 0.102 0.00061 0.0006061 0.0009091
sc.fm5.1 snow 0.9925 0.055 0.00042 0.0004848 0.0005455
sc.fm5.1 temp 0.41594 0.125 0.00139 0.0012121 0.0024242
sc.fm5.1 pass 0.9977 0.152 0 0.0006061 0.0000000
sc.fm5.1 lure*snow 0.22412 0.203 0 0.0003030 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.58882 0.087 0.00061 0.0006061 0.0009091
sc.fm2.1 snow 0.77924 0.059 0.00042 0.0004848 0.0005455
sc.fm2.2 temp 0.15513 0.322 0.00139 0.0012121 0.0024242
sc.fm2.3 pass 0.99625 0.176 0 0.0006061 0.0000000



All models: Covariates with p values below .11 (bayesian p, probability covariate is not null)

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions



parameter support:

level support covariates
good pval and rev MCMC
moderate pval or rev MCMC
poor neither lure - snow - temp - pass - snow*temp - lure*snow



models with significant covariates:



waic:

model description WAIC
sc.fm1 dot model 347.3106



Density per 100km, significant models:



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
Coyote sc.fm1 D 975 12.654 4.97 60.26



notes:

no covariate support..

LongTailedWeasel



notes:

model did not converge – poor estimates – see model selection for posterior distribution etc.. modeled sigma .16 km, maybe a lack of trap dependence

RedFox



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Mono creek 19 13 7 days 300 2.5 350000 20000 20 1 0.14



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0.99886 0.177 0.00067 0.0009091 0.0010303
sc.fm5.0 snow 0.77684 0.147 0.00115 0.0007879 0.0015758
sc.fm5.0 temp 0.49985 0.272 0.00091 0.0012727 0.0018788
sc.fm5.0 pass 0.16329 1.957 0 0.0081212 0.0000000
sc.fm5.0 temp*snow 0.10624 1.511 0 0.0177576 NA
sc.fm5.1 lure 0.98071 0.182 0.00067 0.0009091 0.0010303
sc.fm5.1 snow 0.63752 0.21 0.00115 0.0007879 0.0015758
sc.fm5.1 temp 0.33451 0.35 0.00091 0.0012727 0.0018788
sc.fm5.1 pass 0.2015 1.636 0 0.0081212 0.0000000
sc.fm5.1 lure*snow 0.62194 0.21 0 0.0021212 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.92847 err 0.00067 0.0009091 0.0010303
sc.fm2.1 snow 0.33877 err 0.00115 0.0007879 0.0015758
sc.fm2.2 temp 0.43043 err 0.00091 0.0012727 0.0018788
sc.fm2.3 pass 0.17444 1.823 0 0.0081212 0.0000000



All models: Covariates with p values below .11 (bayesian p, probability covariate is not null)

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 temp*snow 0.10624 1.511 0 0.0177576 NA



parameter support:

level support covariates
good pval and rev MCMC
moderate pval or rev MCMC
poor neither lure - snow - temp - pass - snow*temp - lure*snow



models with significant covariates:



waic:

model description WAIC
sc.fm1 dot model 104.9693



Density per 100km, significant models:



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
RedFox sc.fm1 D 3557 1.242 1.24 4.97



notes:

small amount of support for luresnow interaction, could maybe run snow + temp + luresnow and check waic

WhiteTailedJackrabbit



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Mono creek 19 66 7 days 300 2.5 350000 20000 20 0.322 0.094



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0.17699 0.386 0.00897 0.0086667 0.0121818
sc.fm5.0 snow 0.80924 0.064 0.00048 0.0004848 0.0005455
sc.fm5.0 temp 0.30448 0.156 0.00497 0.0056364 0.0063636
sc.fm5.0 pass 0.92061 0.249 0 0.0012121 0.0000000
sc.fm5.0 temp*snow 0.79313 0.068 0 0.0002424 NA
sc.fm5.1 lure 0.14569 0.563 0.00897 0.0086667 0.0121818
sc.fm5.1 snow 0.54805 0.142 0.00048 0.0004848 0.0005455
sc.fm5.1 temp 0.36511 0.142 0.00497 0.0056364 0.0063636
sc.fm5.1 pass 0.91253 0.284 0 0.0012121 0.0000000
sc.fm5.1 lure*snow 0.62168 0.149 0 0.0001818 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.03856 1.815 0.00897 0.0086667 0.0121818
sc.fm2.1 snow 0.75012 0.060 0.00048 0.0004848 0.0005455
sc.fm2.2 temp 0.04525 1.173 0.00497 0.0056364 0.0063636
sc.fm2.3 pass 0.9237 0.239 0 0.0012121 0.0000000



All models: Covariates with p values below .11 (bayesian p, probability covariate is not null)

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm3.0 lure 0.03294 2.393 0.00897 0.0086667 0.0121818
sc.fm2.0 lure 0.03856 1.815 0.00897 0.0086667 0.0121818
sc.fm2.2 temp 0.04525 1.173 0.00497 0.0056364 0.0063636
sc.fm3.1 lure 0.04563 1.589 0.00897 0.0086667 0.0121818



parameter support:

level support covariates
good pval and rev MCMC
moderate pval or rev MCMC
poor neither lure - snow - temp - pass - snow*temp - lure*snow



models with significant covariates:



waic:

model description WAIC
sc.fm1 dot model 405.143



Density per 100km, significant models:



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
WhiteTailedJackrabbit sc.fm1 D 561 36.4 9.94 134.19



notes:

poor connvergance.. no covariate support