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

AmericanBadger

model metadata:

---

survey	n_sites	detections	rep_period	model_aggragate_reps	state_buffer	iterations	burnin	thin	sigma.mean	sigma.sd
Humphreys Basin	31	11	7 days	300	2.5	350000	20000	20	1.15	0.37

global models:

---

model	covs	bayes_p_val	bayes_factor	revMCMC_constrained	revMCMC_unconstrained	revMCMC_noInteractions
sc.fm5.0	lure	0.62159	0.271	0.00109	0.0008485	0.0020000
sc.fm5.0	snow	0.67267	0.151	0.00109	0.0004848	0.0007879
sc.fm5.0	temp	0.96448	0.141	0.00048	0.0004848	0.0008485
sc.fm5.0	pass	0.41529	0.971	0	0.0046667	0.0000000
sc.fm5.0	temp*snow	0.98906	0.169	0	0.0009697	NA
sc.fm5.1	lure	0.46551	0.415	0.00109	0.0008485	0.0020000
sc.fm5.1	snow	0.80797	0.129	0.00109	0.0004848	0.0007879
sc.fm5.1	temp	0.96044	0.133	0.00048	0.0004848	0.0008485
sc.fm5.1	pass	0.44032	0.982	0	0.0046667	0.0000000
sc.fm5.1	lure*snow	0.9609	0.146	0	0.0007273	NA

single covariate models:

---

model	covs	bayes_p_val	bayes_factor	revMCMC_constrained	revMCMC_unconstrained	revMCMC_noInteractions
sc.fm2.0	lure	0.7182	0.190	0.00109	0.0008485	0.0020000
sc.fm2.1	snow	0.65094	0.145	0.00109	0.0004848	0.0007879
sc.fm2.2	temp	0.99546	0.107	0.00048	0.0004848	0.0008485
sc.fm2.3	pass	0.43578	1.019	0	0.0046667	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	104.0561

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:

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species	model	covs	n_effective	mode	hdi_89pct_lower	hdi_89pct_upper
AmericanBadger	sc.fm1	D	474	1.311	0.6	25.99

notes:

---

no covariate support

---

AmericanBlackBear

notes:

---

lure seems to be significant, but the top model (with lure) did not converge, more difficulty

with black bear and the sc model

---

AmericanMarten

model metadata:

---

survey	n_sites	detections	rep_period	model_aggragate_reps	state_buffer	iterations	burnin	thin	sigma.mean	sigma.sd
Humphreys Basin	31	42	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	0.02628	6.890	0.02764	0.0269697	0.0406061
sc.fm5.0	snow	0.32365	0.172	0.00327	0.0029091	0.0028485
sc.fm5.0	temp	0.65479	0.098	0.00018	0.0001818	0.0006667
sc.fm5.0	pass	0.86411	0.196	0	0.0011515	0.0000000
sc.fm5.0	temp*snow	0.65996	0.135	0	0.0004242	NA
sc.fm5.1	lure	0	234.752	0.02764	0.0269697	0.0406061
sc.fm5.1	snow	0.01174	5.292	0.00327	0.0029091	0.0028485
sc.fm5.1	temp	0.50219	0.165	0.00018	0.0001818	0.0006667
sc.fm5.1	pass	0.86027	0.182	0	0.0011515	0.0000000
sc.fm5.1	lure*snow	0.04792	2.987	0	0.0004848	NA

single covariate models:

---

model	covs	bayes_p_val	bayes_factor	revMCMC_constrained	revMCMC_unconstrained	revMCMC_noInteractions
sc.fm2.0	lure	0.02407	8.337	0.02764	0.0269697	0.0406061
sc.fm2.1	snow	0.09465	0.596	0.00327	0.0029091	0.0028485
sc.fm2.2	temp	0.92543	0.060	0.00018	0.0001818	0.0006667
sc.fm2.3	pass	0.9093	0.189	0	0.0011515	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	234.752	0.02764	0.0269697	0.0406061
sc.fm5.1	snow	0.01174	5.292	0.00327	0.0029091	0.0028485
sc.fm3.1	lure	0.02146	8.388	0.02764	0.0269697	0.0406061
sc.fm2.0	lure	0.02407	8.337	0.02764	0.0269697	0.0406061
sc.fm5.0	lure	0.02628	6.890	0.02764	0.0269697	0.0406061
sc.fm4.2	lure	0.03823	4.547	0.02764	0.0269697	0.0406061
sc.fm4.0	lure	0.03893	4.717	0.02764	0.0269697	0.0406061
sc.fm3.0	lure	0.04094	3.674	0.02764	0.0269697	0.0406061
sc.fm5.1	lure*snow	0.04792	2.987	0	0.0004848	NA
sc.fm2.1	snow	0.09465	0.596	0.00327	0.0029091	0.0028485

parameter support:

---

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

models with significant covariates:

---

waic:

---

model	description	WAIC
sc.fm2.0	lure	297.824
sc.fm1	dot model	298.259

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	900	17.299	6.04	62.85
AmericanMarten	sc.fm2.0	lure	15611	-0.818	-1.53	-0.31

notes:

---

moderate support for lure, too much to ignore

better estimate with the dot model.. but cant really justify ignoring waic

---

Bobcat

model metadata:

---

survey	n_sites	detections	rep_period	model_aggragate_reps	state_buffer	iterations	burnin	thin	sigma.mean	sigma.sd
Humphreys Basin	31	10	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	0.99579	0.129	0.00079	0.0015152	0.0015758
sc.fm5.0	snow	0.59088	0.462	0.00824	0.0101818	0.0122424
sc.fm5.0	temp	0.21206	1.188	0.54261	0.5366667	0.6448485
sc.fm5.0	pass	0.96952	0.251	0	0.0024242	0.0000606
sc.fm5.0	temp*snow	0.93075	0.307	0	0.0194545	NA
sc.fm5.1	lure	0.98502	0.172	0.00079	0.0015152	0.0015758
sc.fm5.1	snow	0.43507	0.609	0.00824	0.0101818	0.0122424
sc.fm5.1	temp	0.00297	46.505	0.54261	0.5366667	0.6448485
sc.fm5.1	pass	0.99335	0.273	0	0.0024242	0.0000606
sc.fm5.1	lure*snow	0.6408	0.264	0	0.0011515	NA

single covariate models:

---

model	covs	bayes_p_val	bayes_factor	revMCMC_constrained	revMCMC_unconstrained	revMCMC_noInteractions
sc.fm2.0	lure	0.96054	0.134	0.00079	0.0015152	0.0015758
sc.fm2.1	snow	0.09121	3.082	0.00824	0.0101818	0.0122424
sc.fm2.2	temp	0.00096	177.180	0.54261	0.5366667	0.6448485
sc.fm2.3	pass	0.91793	0.289	0	0.0024242	0.0000606

---

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	0.00096	177.180	0.54261	0.5366667	0.6448485
sc.fm5.1	temp	0.00297	46.505	0.54261	0.5366667	0.6448485
sc.fm4.2	temp	0.00367	34.741	0.54261	0.5366667	0.6448485
sc.fm4.0	temp	0.00551	47.815	0.54261	0.5366667	0.6448485
sc.fm2.1	snow	0.09121	3.082	0.00824	0.0101818	0.0122424
sc.fm3.0	snow	0.10523	2.593	0.00824	0.0101818	0.0122424

parameter support:

---

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

models with significant covariates:

---

waic:

---

model	description	WAIC
sc.fm2.2	temp	96.27115
sc.fm1	dot model	106.35869

Density per 100km, significant models:

---

top models density per 100sq km, significant covariates:

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species	model	covs	n_effective	mode	hdi_89pct_lower	hdi_89pct_upper
Bobcat	sc.fm2.2	D	595	71.639	7.25	161.95
Bobcat	sc.fm2.2	temp	15479	1.376	0.76	2.23

notes:

---

---

Coyote

model metadata:

---

survey	n_sites	detections	rep_period	model_aggragate_reps	state_buffer	iterations	burnin	thin	sigma.mean	sigma.sd
Humphreys Basin	31	188	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	0.01823	3.205	0.05776	0.0603636	0.0868485
sc.fm5.0	snow	0.02881	1.004	0.00236	0.0018788	0.0032121
sc.fm5.0	temp	0.96754	0.032	6e-05	0.0003030	0.0002424
sc.fm5.0	pass	0.10739	0.944	0	0.0041818	0.0000000
sc.fm5.0	temp*snow	0.02378	2.188	0	0.0550303	NA
sc.fm5.1	lure	0	170.277	0.05776	0.0603636	0.0868485
sc.fm5.1	snow	0.01763	1.536	0.00236	0.0018788	0.0032121
sc.fm5.1	temp	0.24908	0.124	6e-05	0.0003030	0.0002424
sc.fm5.1	pass	0.15406	0.634	0	0.0041818	0.0000000
sc.fm5.1	lure*snow	0.14597	0.283	0	0.0001212	NA

single covariate models:

---

model	covs	bayes_p_val	bayes_factor	revMCMC_constrained	revMCMC_unconstrained	revMCMC_noInteractions
sc.fm2.0	lure	0.00286	10.946	0.05776	0.0603636	0.0868485
sc.fm2.1	snow	0.09541	0.234	0.00236	0.0018788	0.0032121
sc.fm2.2	temp	0.72843	0.035	6e-05	0.0003030	0.0002424
sc.fm2.3	pass	0.12428	0.825	0	0.0041818	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.fm4.0	lure	0	44.457	0.05776	0.0603636	0.0868485
sc.fm5.1	lure	0	170.277	0.05776	0.0603636	0.0868485
sc.fm4.2	lure	0.00043	44.842	0.05776	0.0603636	0.0868485
sc.fm3.0	lure	0.00125	20.976	0.05776	0.0603636	0.0868485
sc.fm2.0	lure	0.00286	10.946	0.05776	0.0603636	0.0868485
sc.fm3.1	lure	0.00372	13.576	0.05776	0.0603636	0.0868485
sc.fm5.1	snow	0.01763	1.536	0.00236	0.0018788	0.0032121
sc.fm5.0	lure	0.01823	3.205	0.05776	0.0603636	0.0868485
sc.fm5.0	temp*snow	0.02378	2.188	0	0.0550303	NA
sc.fm3.0	snow	0.02633	0.943	0.00236	0.0018788	0.0032121
sc.fm5.0	snow	0.02881	1.004	0.00236	0.0018788	0.0032121
sc.fm4.0	snow	0.04314	0.612	0.00236	0.0018788	0.0032121
sc.fm4.2	snow	0.07909	0.397	0.00236	0.0018788	0.0032121
sc.fm2.1	snow	0.09541	0.234	0.00236	0.0018788	0.0032121
sc.fm5.0	pass	0.10739	0.944	0	0.0041818	0.0000000

parameter support:

---

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

models with significant covariates:

---

waic:

---

model	description	WAIC
sc.fm2.0	lure	939.6044
sc.fm1	dot model	947.8136

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.fm2.0	D	760	63.394	23.57	148.05
Coyote	sc.fm2.0	lure	16500	-0.346	-0.54	-0.18

notes:

---

lure has p support, and slight rev mcmc support, other vars have no support from rev mcmc

poor model convergenc, maybe try with higher M

estimate is questionable..

---

LongTailedWeasel

notes:

---

model did not converge

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WhiteTailedJackrabbit

notes:

---

poor convergence

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