large c-hat and possible assumption violation

[SBB]

Hello,
I have a group of single-season single-species occupancy models that I'm trying to run GOF tests on. I used the most parameterized model and got the below results. The c-hat is >7, indicating overdispersion. I am wondering if this c-hat is due to violating the assumption of no false-positive detections, or is there another problem? My ultimate goal is actually to model the data set using a false-positive occupancy model but I don't want to start working with that model if there is another reason for lack of fit.

Assessing Model Fit for Single-season model:

History(cohort) Observed Expected Chi-square
-00000000( 0 0) 13.0000 12.548597880 0.02
000000000( 1 1) 8.0000 7.885554839 0.00
100000000( 1 3) 2.0000 1.913799593 0.00
100000100( 1 4) 2.0000 0.860937402 1.51
000000100( 1 7) 1.0000 1.030081746 0.00
000001100( 1 10) 1.0000 0.462470588 0.62
100011000( 1 14) 1.0000 0.072438668 11.88
000001000( 1 15) 1.0000 1.030081746 0.00
101110000( 1 17) 1.0000 0.000914045 1092.04
-00001000( 0 22) 1.0000 0.784478397 0.06
-10001100( 0 27) 1.0000 0.032881869 28.44
-00010000( 0 32) 1.0000 0.250042302 2.25
-00000100( 0 33) 1.0000 0.784478397 0.06
Test Statistic = 1143.2274 min(expect)=9.140450e-004

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Test Statistic (data) = 1143.2274

From 1000 parametric bootstraps...
Probability of test statistic >= observed = 0.0030
Lowest simulated Test Stat = 10.6198
Average simulated Test Stat = 149.2143
Median simulated Test Stat = 98.0387
Highest simulated Test Stat = 15795.1701
Estimate of c-hat = 7.6616 (=TestStat/AvgTestStat)
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- Distribution of simulated test statistics ----------
TestStat Prop.
1184.6378 0.9900:********************************************************************************
1974.3963 0.0070:*
2764.1548 0.0010:*
3553.9133 0.0010:*

Thanks,
Sarah