The affect of bootstrap on Isolation Forest
This is well explained on the original paper Section 3.
As well as in the Supervised Random Forest, Isolation Forest makes use of sampling on both, features and instances, so the latter in this case helps alleviate 2 main problems:

Swamping

Swamping refers to wrongly identifying normal instances as anomalies. When normal instances are too close to anomalies, the number of partitions required to separate anomalies increases – which makes it harder to distinguish anomalies from normal in- stances.

Masking

Masking is the existence of too many anomalies concealing their own presence.

Contrary to existing methods where large sampling size is more desirable, isolation method works best when the sampling size is kept small. Large sampling size reduces iForest’s ability to isolate anomalies as normal instances can interfere with the isolation process and therefore reduces its ability to clearly isolate anomalies. Thus, sub-sampling provides a favourable environment for iForest to work well. Throughout this paper, sub-sampling is conducted by ran- dom selection of instances without replacement.