While continual pretraining (CPT) is a practical way to extend large language models to new languages, naïve finetuning often erodes existing capabilities through catastrophic forgetting. We investigate which model layers drive this trade-off, and whether interventions at these layers can guide knowledge preservation during adaptation. We interpolate gemma-3-4b model states before and after CPT on five language families to localize forgetting on reading comprehension and translation, finding that middle-layer reversion yields the largest comprehension recovery, while translation effects vary by language family and direction. Guided by these findings, we evaluate CPT strategies that leverage this layer information to mitigate forgetting: layer freezing, layer-range L2 regularization, post-hoc layer reversion, and model souping, comparing all strategies against joint multilingual and family-specific vanilla CPT baselines. We find that preserving the layer weights identified via model interpolation substantially reduces comprehension loss relative to joint CPT, with layer freezing exceeding base model performance on average. However, these strategies yield mixed translation results: dense training or post-hoc reversion often outperforms both training-time constraints and family-specific specialization, complicating prior assumptions about how models should be aligned when extended to new tasks. Instead, we argue that multilingual adaptation strategy should be informed by target language, base model knowledge, and downstream task, and propose interpolation-based localization as a diagnostic for identifying candidate layers before committing to a training-time intervention in a new setting.