**An integrated open population distance sampling approach for modelling age-structured populations** This repository contain additional information related to the mansucript "An integrated open population distance sampling approach for modelling age-structured populations" by Erlend B. Nilsen & Chloé R. Nater. The paper is currently under review, and the link to the publicly available pre-print can be found in the registration metadata. The most recent release of the model code is located [here][1] **Contact** Erlend Nilsen: erlend.nilsen@nina.no Chloé Nater: chloe.nater@nina.no ----- **Paper summary** Estimation of abundance and demographic rates for populations of wild vertebrate species is a challenging but fundamental issue for both management and research into ecology and evolution. One approach that has been used extensively to estimate abundance of wild living populations is Distance Sampling (DS) methods based on line transect survey data. Historically, DS models were only available as open population models, and did not allow for the direct estimation of changes in abundance through time. The advent of open population formulations based on the DS framework greatly extended the scope of the models, but so far models that estimate both temporal dynamics in abundance as well as the underlying demographic rates has not been implemented. Here, we present an integrated distance sampling approach, that utilize age-structured survey data and auxiliary data from marked individuals to jointly estimate population dynamics and the temporal variation in the demographic rates (recruitment rate and survival probability) that determine the temporal transition. The underlying process model is based on a two-stage transition matrix ($A_t$), with matrix entries given by survival probabilities ($S$) and time-specific recruitment rates ($R_t$). This framework allow us to make full use of the available data, and to effectively integrate the two data sources in an integrated modelling framework. Moreover, demographic rates often respond to environmental variation, and our approach allow us to directly estimate the effect of such environmental covariates on demographic rate variation. We first fit the model to simulated data, to assess it's ability to recover the underlying population dynamics when the true values are known. Then, we use data from a study of willow ptarmigan (*Lagopus lagopus*) in Norway as a case study. [1]: https://zenodo.org/records/10462269