DOI: 10.5061/dryad.mpg4f4r52
title: Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators

description: 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 unsampled trends. The model revealed that the
 proportion of species represented in the database is not always indicative
 of trend reliability. Important factors are the number and length of time
 series, as well as their mean growth rates and variance in their growth
 rates, both within and between time series. We found that many trends in
 the Living Planet Index need more data to be considered reliable,
 particularly trends across the global south. In general, bird trends are
 the most reliable, while reptile and amphibian trends are most in need of
 additional data. We simulated three different solutions for reducing data
 deficiency, and found that collating existing data (where available) is
 the most efficient way to improve trend reliability, and that revisiting
 previously-studied populations is a quick and efficient way to improve
 trend reliability until new long-term studies can be completed and made
 available.
description: 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.
description: 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.