In its initial development, the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter was formulated with a target motion model that accounts for survival and birth. The filter is capable of addressing a wide variety of multi-target tracking challenges with birth as the only means of new target track instantiation, still it is not readily equipped to address scenarios in which the appearance of new targets is conditional on the behavior of preexisting ones. In this paper, we extend development of the δ-GLMB filter to incorporate target spawning. Derivation yields a predicted multi-target density in labeled random finite set form, then exploiting the versatility of the family of GLMB distributions, the density is approximated as δ-GLMB and processed in a joint prediction and update, facilitating efficient implementation while preserving its cardinality and probability hypothesis density (PHD). A key development is the inclusion of a spawn track’s ancestry in its label, which is jointly estimated along with its state, an outcome with applications across multiple fields. Results are verified through simulation.