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
Ritter Range 32 19 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 0.08331 1.791 0.00236 0.0019394 0.0037576
sc.fm5.0 snow 0.07285 1.429 0.00182 0.0019394 0.0029697
sc.fm5.0 temp 0.00406 45.306 0.00297 0.0045455 0.0064848
sc.fm5.0 pass 0.51039 0.385 0 0.0010909 0.0000000
sc.fm5.0 temp*snow 0.93501 0.126 0 0.0007273 NA
sc.fm5.1 lure 0.0402 4.417 0.00236 0.0019394 0.0037576
sc.fm5.1 snow 0.27191 0.457 0.00182 0.0019394 0.0029697
sc.fm5.1 temp 0.00461 39.705 0.00297 0.0045455 0.0064848
sc.fm5.1 pass 0.49187 0.415 0 0.0010909 0.0000000
sc.fm5.1 lure*snow 0.88691 0.141 0 0.0016364 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.24904 0.465 0.00236 0.0019394 0.0037576
sc.fm2.1 snow 0.1976 err 0.00182 0.0019394 0.0029697
sc.fm2.2 temp 0.1196 err 0.00297 0.0045455 0.0064848
sc.fm2.3 pass 0.99709 0.177 0 0.0010909 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.fm4.2 temp 0.00248 52.294 0.00297 0.0045455 0.0064848
sc.fm4.0 temp 0.00291 err 0.00297 0.0045455 0.0064848
sc.fm5.0 temp 0.00406 45.306 0.00297 0.0045455 0.0064848
sc.fm5.1 temp 0.00461 39.705 0.00297 0.0045455 0.0064848
sc.fm4.2 snow 0.02103 5.107 0.00182 0.0019394 0.0029697
sc.fm5.1 lure 0.0402 4.417 0.00236 0.0019394 0.0037576
sc.fm4.0 snow 0.04719 err 0.00182 0.0019394 0.0029697
sc.fm4.2 lure 0.06244 2.404 0.00236 0.0019394 0.0037576
sc.fm5.0 snow 0.07285 1.429 0.00182 0.0019394 0.0029697
sc.fm4.0 lure 0.08283 err 0.00236 0.0019394 0.0037576
sc.fm5.0 lure 0.08331 1.791 0.00236 0.0019394 0.0037576



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 172.1906



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.



top models density per 100sq km, significant covariates:

species model covs n_effective mode hdi_89pct_lower hdi_89pct_upper
AmericanBlackBear sc.fm1 D 11201 1.123 1.12 2.25



notes:

lure and snow are on the borderline of significant, but seem to have overall thin support,

suspisious behavior of output distribution of n and density, multiple declining

peaks, not a smooth curve. could be poor convergence, maybe due to hibernation, seasonal migration..?

AmericanMarten



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Ritter Range 32 88 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 0.6698 0.072 0.00042 0.0008485 0.0007273
sc.fm5.0 snow 0.42675 0.096 0.00024 0.0002424 0.0006061
sc.fm5.0 temp 0.00566 11.730 0.00624 0.0075152 0.0115758
sc.fm5.0 pass 0.93308 0.137 0 0.0006667 0.0000000
sc.fm5.0 temp*snow 0.32976 0.166 0 0.0006667 NA
sc.fm5.1 lure 0.02554 2.474 0.00042 0.0008485 0.0007273
sc.fm5.1 snow 0.08009 0.557 0.00024 0.0002424 0.0006061
sc.fm5.1 temp 0.00433 10.601 0.00624 0.0075152 0.0115758
sc.fm5.1 pass 0.9898 0.125 0 0.0006667 0.0000000
sc.fm5.1 lure*snow 0.00721 5.798 0 0.0006667 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.44083 0.104 0.00042 0.0008485 0.0007273
sc.fm2.1 snow 0.8944 0.040 0.00024 0.0002424 0.0006061
sc.fm2.2 temp 0.02981 1.682 0.00624 0.0075152 0.0115758
sc.fm2.3 pass 0.86377 0.119 0 0.0006667 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.fm5.1 temp 0.00433 10.601 0.00624 0.0075152 0.0115758
sc.fm5.0 temp 0.00566 11.730 0.00624 0.0075152 0.0115758
sc.fm5.1 lure*snow 0.00721 5.798 0 0.0006667 NA
sc.fm4.2 temp 0.01068 3.595 0.00624 0.0075152 0.0115758
sc.fm4.0 temp 0.01207 4.700 0.00624 0.0075152 0.0115758
sc.fm5.1 lure 0.02554 2.474 0.00042 0.0008485 0.0007273
sc.fm2.2 temp 0.02981 1.682 0.00624 0.0075152 0.0115758
sc.fm5.1 snow 0.08009 0.557 0.00024 0.0002424 0.0006061



parameter support:

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



models with significant covariates:



waic:

model description WAIC
sc.fm2.2 temp 539.9674
sc.fm1 dot model 546.5134



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.fm2.2 D 1043 19.136 8.42 60.08
AmericanMarten sc.fm2.2 temp 16500 0.291 0.12 0.48



notes:

moderate to small amount of support for temp

Bobcat



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Ritter Range 32 6 7 days 300 2.5 350000 20000 20 1.175 0.14



global models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm5.0 lure 0.11103 4.064 0.01315 0.0135758 0.0201212
sc.fm5.0 snow 0.99543 0.144 0.00097 0.0009091 0.0014545
sc.fm5.0 temp 0.83334 0.266 0.00121 0.0007273 0.0013939
sc.fm5.0 pass 0.0978 5.444 0 0.0209697 0.0000000
sc.fm5.0 temp*snow 0.55237 0.472 0 0.0021212 NA
sc.fm5.1 lure 0.03254 23.447 0.01315 0.0135758 0.0201212
sc.fm5.1 snow 0.52128 0.595 0.00097 0.0009091 0.0014545
sc.fm5.1 temp 0.87765 0.288 0.00121 0.0007273 0.0013939
sc.fm5.1 pass 0.12582 4.177 0 0.0209697 0.0000000
sc.fm5.1 lure*snow 0.48466 0.713 6e-05 0.0008485 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.12673 3.318 0.01315 0.0135758 0.0201212
sc.fm2.1 snow 0.79594 0.178 0.00097 0.0009091 0.0014545
sc.fm2.2 temp 0.95035 0.137 0.00121 0.0007273 0.0013939
sc.fm2.3 pass 0.10391 6.885 0 0.0209697 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.fm5.1 lure 0.03254 23.447 0.01315 0.0135758 0.0201212
sc.fm5.0 pass 0.0978 5.444 0 0.0209697 0.0000000
sc.fm3.1 pass 0.10035 4.363 0 0.0209697 0.0000000
sc.fm2.3 pass 0.10391 6.885 0 0.0209697 0.0000000
sc.fm4.0 lure 0.10419 3.568 0.01315 0.0135758 0.0201212



parameter support:

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



models with significant covariates:



waic:

model description WAIC
sc.fm1 dot model 78.02406
sc.fm2.3 pass 86.57164



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
Bobcat sc.fm1 D 323 2.203 1.12 65.7



notes:

Dot model wins, probably further support against pass as a cov

moderate to small amount of support for pass, lure has tiny amount of support lean toward rejecting, very

few detections for multiple covs,

Coyote



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Ritter Range 32 43 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 0 141.695 0.76733 0.7723030 0.8312121
sc.fm5.0 snow 0.00532 9.415 0.11473 0.1120606 0.1793333
sc.fm5.0 temp 0.03859 2.442 0.01479 0.0133939 0.0370303
sc.fm5.0 pass 0.99975 0.156 0 0.0027879 0.0002424
sc.fm5.0 temp*snow 0.65036 0.125 0 0.0023636 NA
sc.fm5.1 lure 0 280.278 0.76733 0.7723030 0.8312121
sc.fm5.1 snow 0.00133 30.657 0.11473 0.1120606 0.1793333
sc.fm5.1 temp 0.00987 7.596 0.01479 0.0133939 0.0370303
sc.fm5.1 pass 0.99699 0.145 0 0.0027879 0.0002424
sc.fm5.1 lure*snow 0.23531 0.519 0.00248 0.0033333 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.00035 173.841 0.76733 0.7723030 0.8312121
sc.fm2.1 snow 0.00194 24.084 0.11473 0.1120606 0.1793333
sc.fm2.2 temp 0.33178 0.189 0.01479 0.0133939 0.0370303
sc.fm2.3 pass 0.58872 0.214 0 0.0027879 0.0002424



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.fm4.0 lure 0 198.074 0.76733 0.7723030 0.8312121
sc.fm5.0 lure 0 141.695 0.76733 0.7723030 0.8312121
sc.fm5.1 lure 0 280.278 0.76733 0.7723030 0.8312121
sc.fm2.0 lure 0.00035 173.841 0.76733 0.7723030 0.8312121
sc.fm3.1 lure 0.00037 204.154 0.76733 0.7723030 0.8312121
sc.fm4.2 lure 0.00108 78.070 0.76733 0.7723030 0.8312121
sc.fm4.2 snow 0.00126 32.861 0.11473 0.1120606 0.1793333
sc.fm5.1 snow 0.00133 30.657 0.11473 0.1120606 0.1793333
sc.fm4.0 snow 0.0015 24.163 0.11473 0.1120606 0.1793333
sc.fm3.0 lure 0.0019 61.870 0.76733 0.7723030 0.8312121
sc.fm2.1 snow 0.00194 24.084 0.11473 0.1120606 0.1793333
sc.fm5.0 snow 0.00532 9.415 0.11473 0.1120606 0.1793333
sc.fm3.0 snow 0.00623 7.616 0.11473 0.1120606 0.1793333
sc.fm5.1 temp 0.00987 7.596 0.01479 0.0133939 0.0370303
sc.fm4.2 temp 0.01978 5.396 0.01479 0.0133939 0.0370303
sc.fm4.0 temp 0.0205 4.228 0.01479 0.0133939 0.0370303
sc.fm5.0 temp 0.03859 2.442 0.01479 0.0133939 0.0370303



parameter support:

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



models with significant covariates:



waic:

model description WAIC
sc.fm3.0 lure + snow 292.9536
sc.fm2.0 lure 299.8385
sc.fm2.1 snow 302.0749
sc.fm1 dot model 312.3275



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.fm3.0 D 1100 7.790 3.37 26.39
Coyote sc.fm3.0 lure 16500 -0.977 -1.55 -0.54
Coyote sc.fm3.0 snow 16500 0.470 0.23 0.72



notes:

LongTailedWeasel



notes:

model did not converge

maybe a lack of spatial dependance

modeled sigma around .12 km

RedFox



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Ritter Range 32 3 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 0.96052 0.288 0.00164 0.0022424 0.0041818
sc.fm5.0 snow 0.51931 1.958 0.00976 0.0298182 0.1090909
sc.fm5.0 temp 0.39187 2.799 0.00903 0.0252121 0.1070909
sc.fm5.0 pass 0.33493 105.59 0.00073 0.3466061 0.0858788
sc.fm5.0 temp*snow 0.70796 1.394 0.00024 0.1620000 NA
sc.fm5.1 lure 0.72262 1.21 0.00164 0.0022424 0.0041818
sc.fm5.1 snow 0.00539 139.926 0.00976 0.0298182 0.1090909
sc.fm5.1 temp 0.01073 159.438 0.00903 0.0252121 0.1070909
sc.fm5.1 pass 0.36614 49.485 0.00073 0.3466061 0.0858788
sc.fm5.1 lure*snow 0.54308 1.25 0.00073 0.0027879 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.95165 0.21 0.00164 0.0022424 0.0041818
sc.fm2.1 snow 0.22818 1.157 0.00976 0.0298182 0.1090909
sc.fm2.2 temp 0.33029 0.766 0.00903 0.0252121 0.1070909
sc.fm2.3 pass 0.34389 94.176 0.00073 0.3466061 0.0858788



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.fm5.1 snow 0.00539 139.926 0.00976 0.0298182 0.1090909
sc.fm4.0 snow 0.00908 66.354 0.00976 0.0298182 0.1090909
sc.fm4.2 snow 0.01071 67.149 0.00976 0.0298182 0.1090909
sc.fm5.1 temp 0.01073 159.438 0.00903 0.0252121 0.1070909
sc.fm4.0 temp 0.01583 81.04 0.00903 0.0252121 0.1070909
sc.fm4.2 temp 0.01694 105.938 0.00903 0.0252121 0.1070909



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 32.60312



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 466 0.561 0.56 2.81



notes:

3 detecions, not much power

WhiteTailedJackrabbit



model metadata:

survey n_sites detections rep_period model_aggragate_reps state_buffer iterations burnin thin sigma.mean sigma.sd
Ritter Range 32 104 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 0.85773 0.066 0.00242 0.0015152 0.0021212
sc.fm5.0 snow 0.01017 4.552 1 0.0968485 0.0010303
sc.fm5.0 temp 0 9132.349 1 0.9878182 0.6446061
sc.fm5.0 pass 0.95576 0.224 0 0.0054545 0.0000000
sc.fm5.0 temp*snow 0 483981.916 1 1.0000000 NA
sc.fm5.1 lure 0.70985 0.061 0.00242 0.0015152 0.0021212
sc.fm5.1 snow 0.92357 0.039 1 0.0968485 0.0010303
sc.fm5.1 temp 0 115.292 1 0.9878182 0.6446061
sc.fm5.1 pass 0.82014 0.229 0 0.0054545 0.0000000
sc.fm5.1 lure*snow 0.05739 0.716 0 0.0025455 NA



single covariate models:

model covs bayes_p_val bayes_factor revMCMC_constrained revMCMC_unconstrained revMCMC_noInteractions
sc.fm2.0 lure 0.9542 0.041 0.00242 0.0015152 0.0021212
sc.fm2.1 snow 0.93151 0.035 1 0.0968485 0.0010303
sc.fm2.2 temp 0 72.455 1 0.9878182 0.6446061
sc.fm2.3 pass 0.74913 0.250 0 0.0054545 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.fm2.2 temp 0 72.455 1 0.9878182 0.6446061
sc.fm4.2 temp 0 437.260 1 0.9878182 0.6446061
sc.fm5.0 temp 0 9132.349 1 0.9878182 0.6446061
sc.fm5.0 temp*snow 0 483981.916 1 1.0000000 NA
sc.fm5.1 temp 0 115.292 1 0.9878182 0.6446061
sc.fm4.0 temp 0.00039 247.435 1 0.9878182 0.6446061
sc.fm5.0 snow 0.01017 4.552 1 0.0968485 0.0010303
sc.fm5.1 lure*snow 0.05739 0.716 0 0.0025455 NA



parameter support:

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



models with significant covariates:



waic:

model description WAIC
sc.fm5.0 lure + snow + temp + pass + temp * snow 502.8138
sc.fm2.2 temp 543.4455
sc.fm1 dot model 557.3899
sc.fm2.1 snow 561.0120



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.fm5.0 D 256 13.374 5.61 79.17
WhiteTailedJackrabbit sc.fm5.0 snow 16500 0.467 0.21 0.70
WhiteTailedJackrabbit sc.fm5.0 temp 16500 -0.813 -1.05 -0.55
WhiteTailedJackrabbit sc.fm5.0 temp*snow 16500 1.103 0.85 1.41



notes:

need to run snow + temp + temp*snow

vals are temp from the global model, including lure and pass..

n effective is not great, could be not converging well,

possibly poor trap dependence, modeled sigma is around .35 km