Introduction | 1. Prepare data | 1.1 The dat data frame must . . . | 1.1.1 Get your data into the sf data format | 1.2 The inv list must . . . | 1.3 Check the inv and dat arguments using checkDat() | 2 Track individuals through time using trackSpp() | 2.1 Function arguments | 2.2 Function output | 3 Calculate local neighborhood density using getNeighbors() | 3.1 Function options and arguments | 3.2 Function outputs | 4 Next steps

Introduction | 1 Input data | 3.3 Assign age and recruitment data to first year | 3.4 Compare sequential years of data to track individuals through time | 3.4.1 Is there a gap between year i-1 and year i? | 3.4.2 Get data for year i | 3.4.3 Are there any observations in the "previous" year (inv[i-1])? | 3.4.4 Add a buffer around the "previous" year data | 3.4.5 Are there actually any observations in the "current" year (inv[i])? | 3.4.6 Store observations as "ghosts" to compare to data from the next year (inv[i+1]) during the next iteration of the loop. | 3.4.7 Are there any overlaps between polygons in tempPreviousYear and tempCurrentYear? | 3.4.8 Compare the overlap between tempPreviousYear and tempCurrentYear to assign trackIDs. | 3.4.9 Flag any suspect observations | 3.4.10 Separate the "ghosts" and the new recruits from the "parent" and "child" data frames | 3.4.11 Store the resulting demographic data | 3.4.12 Get ready for the next iteration of the loop | 4 Prepare the trackSpp() results to be returned! | 4.1 Aggregate the results by genet | 4.2 Informative messages | 5 Examples