Should I use co-occurrence analysis?

[MittyAdai]

Hello! I am a student and also a beginner in ecology field and R,
My goal is to check whether two species are spatially segregated.
Not sure if i should use co-occurrence analysis in EcoSimR,
Anyway, I did run the model but I had a hard time interpreting it.
Hope someone could give me a hand to check if I'm doing it right!
(p.s. I'm not a English native speaker, please don't mind if I made some grammatical mistakes)

My data contains two species in 64 sites, and I referred to the examples of following website:
https://cran.microsoft.com/snapshot/2017-04-21/web/packages/EcoSimR/vignettes/CoOccurrenceVignette.html#caveats

Here are my code and results:

Create a Null model
REAA <- cooc_null_model(speciesData=dataREAA, algo="sim10", suppressProg=TRUE, algoOpts=list(rowWeights=(1:2),colWeights=(1:64)))
summary(REAA)
plot(REAA, type="hist")

Summary
Time Stamp: Fri Dec 3 20:55:29 2021
Reproducible: FALSE
Number of Replications: 1000
Elapsed Time: 0.49 secs
Metric: c_score
Algorithm: sim10
Observed Index: 138
Mean Of Simulated Index: 144.8
Variance Of Simulated Index: 2330.1
Lower 95% (1-tail): 72
Upper 95% (1-tail): 230
Lower 95% (2-tail): 60
Upper 95% (2-tail): 250
Lower-tail P = 0.469
Upper-tail P = 0.547
Observed metric > 453 simulated metrics
Observed metric < 531 simulated metrics
Observed metric = 16 simulated metrics
Standardized Effect Size (SES): -0.14091

If the observed c-score has no significant difference with simulated c-score,
does it mean the two species is just randomized distributed (not segregated or aggregated)?

Also, the result is somehow a little bit weird if I used the sim9:

Time Stamp: Fri Dec 3 22:15:23 2021
Reproducible:
Number of Replications:
Elapsed Time: 1.3 secs
Metric: c_score
Algorithm: sim9
Observed Index: 138
Mean Of Simulated Index: 138
Variance Of Simulated Index: 0
Lower 95% (1-tail): 138
Upper 95% (1-tail): 138
Lower 95% (2-tail): 138
Upper 95% (2-tail): 138
Lower-tail P = 1
Upper-tail P = 1
Observed metric > 0 simulated metrics
Observed metric < 0 simulated metrics
Observed metric = 1000 simulated metrics
Standardized Effect Size (SES): NaN

How should I explain this situation?

Hope someone could give me some guides,
Thanks a lot!
Mitty