Table 3.

gSPIM simulation results for the detection model and abundance

Scenario	${\lambda }_0$	$\sigma$	N	$n^{cap}$	N Cov	n Cov	N Wid	n Wid	N MSE
True	0.570	1.320	166.0	115.8	—	—	—	—	—
SPIM3A	0.562	1.321	165.2	115.6	0.933	0.983	36.9	2.3	105.0
SPIM2A	0.566	1.321	164.0	115.0	0.958	0.950	37.6	4.8	119.1
SPIM1A	0.574	1.329	159.8	112.8	0.908	0.908	41.5	10.8	171.2
True	0.297	1.320	166.0	88.4	—	—	—	—	—
SPIM3B	0.296	1.311	163.8	88.3	0.950	0.992	64.0	0.01	279.7
SPIM2B	0.296	1.310	163.9	88.3	0.958	0.975	64.3	0.07	277.6
SPIM1B	0.297	1.313	162.5	88.2	0.967	0.950	64.0	1.62	281.1
SCR	0.295	1.311	163.9	—	0.983	—	64.1	—	282.7

Scenarios indicate the model used (SPIM or SCR), the number of replicated assignments (1 to 3), and whether the low-quality samples were included (A) or not (B). The low-quality samples were not included in the SCR analysis by default. ${\lambda }_0$ is the baseline detection rate, σ is the detection function spatial-scale parameter, N is abundance, and $n^{cap}$ is the number of individuals captured (fewer when excluding the low-quality samples). The values listed here are the mean point estimates across 120 simulated datasets. “Cov” indicates the coverage of the 95% credible intervals, “Wid” indicates the mean width of the 95% credible intervals, and “MSE” indicates the mean-squared error of the point estimates.