Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators

Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator’s accuracy or reliability. Instead, a modelling approach can be used.
We developed a model of trend reliability, using simulated datasets as stand-ins for the \"real world\", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to u..., These data are entirely simulated. We used R code to generate simulated population time series. We added observation error to the simulated time series, degraded them by randomly removing observations, then sampled repeatedly and calculated both the partially and fully sampled trends using the method of the Living Planet Index. The partially sampled trends were then compared with the fully sampled trends using a distance metric.
We generated thousands of time series datasets with different underlying properties and tested to see which parameters affected the distance values. We then used the responsible parameters to build a model of trend accuracy and applied that model to regional taxonomic groups in the Living Planet Database. 
The simulated time series in both raw and degraded form as well as the trends and distance values are included here, divided into archives which are further described in the README file., The majority of the files are in the form of .RData, which can be opened in R. The code to do so is openly available at https://github.com/ShawnDove/DD_LPI. Distance values and other informative values are stored in .csv files, which do not require any specific software to view. Plots of the trends are included in .png format, which also do not require any specific software.,