Indirect Methods:When complete data are lacking, demographers often turn to indirect methods. These techniques allow estimation of demographic measures even with incomplete information. Example: Suppose you have data on seed bank counts and reproductive adult counts but lack information on intermediate stages (seedlings, juveniles, vegetative adults). Indirect Estimation:Survival Rates: Estimate survival rates between adjacent stages. For instance, the survival rate from seedlings to juveniles. Fertility Rates: If you have reproductive adult data, estimate the average number of offspring produced per reproductive adult. Projection Matrix: Construct a projection matrix that describes transitions between stages. Use this matrix to project population dynamics. Iterative Approaches: Iteratively update population estimates based on available data and projected transitions. Challenges: Indirect methods rely on assumptions about transitions and may be sensitive to errors in these assumptions.

In summary, handling incomplete demographic data involves creativity, statistical methods, and acknowledging uncertainties. Researchers must adapt to the available information while making informed assumptions. Whether studying plants, animals, or human populations, these approaches help estimate population status and trends despite data limitations.

Hi Rich

State space models or integrated population models that include process and observation model components allow for models with gaps in data.

Here's one paper (of many) that discusses this approach.

https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.2714

The standard approach is to restrict the analysis to subjects with complete data on the variables involved in the analysis. The second approach is based on multiple imputation where missing values are replaced by two or more plausible values. The final approach is based on constructing the likelihood based on the incomplete observed data :)