Ideally, you will calculate status and trend scores with the same data layers: you would calculate status for the most recent 5 years with 5 years of data for each of the data layers used in that goal model. Then, you will report the most recent year as the status score, and calculate the trend of status scores for the most recent 5 years, and report that as the trend score.
However, sometimes there is not adequate data through time for all data layers involved in the status models. This has been the case in with some of the global habitat layers. In some cases we calculated status for the present year only, and calculated trend from data that could reasonably track habitat trend. We have used the inverse of coastal population in the past, under the assumption that more people mean less mangroves.
Ideally, you will calculate status and trend scores with the same data layers: you would calculate status for the most recent 5 years with 5 years of data for each of the data layers used in that goal model. Then, you will report the most recent year as the status score, and calculate the trend of status scores for the most recent 5 years, and report that as the trend score.
However, sometimes there is not adequate data through time for all data layers involved in the status models. This has been the case in with some of the global habitat layers. In some cases we calculated status for the present year only, and calculated trend from data that could reasonably track habitat trend. We have used the inverse of coastal population in the past, under the assumption that more people mean less mangroves.