Microservice architectures aim to localize change, yet feature requests may require implementation across service boundaries. We investigate how well such downstream scope can be anticipated before implementation begins. Using issue--pull-request--commit traceability and implementation evidence corrected through targeted human adjudication, we reconstruct 866 repository-traceable feature requests from 13 open-source microservice projects and map their implementations to validated service boundaries. Cross-service changes occur in 101 requests (11.7%) and exhibit broader implementation footprints, particularly higher code churn, but no clear difference in lead time. Pre-implementation evidence provides limited cross-project predictive signal: the strongest leave-one-system-out model reaches a project-macro Macro-F1 of 0.484, but its advantage over the majority baseline is not significant after Holm correction and is sensitive to small evaluation folds. A local LLM offers no reliable improvement over classical models. Downstream workload is substantially harder to estimate, as learned models do not consistently outperform training-fold median baselines. A simple service-mention heuristic also performs comparably to learned scope triage. These findings show that early feature-request evidence is more useful for lightweight cross-service scope triage than for automated scope determination or precise workload estimation.

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