Quantifying reliability and data deficiency in global vertebrate population trends using the Living Planet Index.

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. However, 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 partially sampled to fully sampled 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, whereas 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.

Habitat change and biased sampling influence estimation of diversity trends.

Recent studies have drawn contrasting conclusions about the extent to which local-scale measures of biodiversity are declining and whether such patterns conflict with the global-scale declines that have attracted much attention.1 A key source of high-quality data for such analyses comes from longitudinal biodiversity studies, which sample a given taxon repeatedly over time at a specific location.2 There has been relatively little consideration of how habitat change might lead to biases in the sampling and continuity of biodiversity time series data, and the consequent potential for bias in the biodiversity trends that result. Here, based on analysis of standardized routes from the North American Breeding Bird Survey (3,014 routes sampled over 18 years),3 we demonstrate that major local habitat change is associated with an increase in the rate of survey cessations. We further show that routes that were continued despite major habitat changes show reduced diversity. By simulating potential rates of loss, we show that the underlying real trends in taxonomic, functional, and phylogenetic diversity can even reverse in sign if more than a quarter of diversity is lost from routes that ceased and are thus no longer included in surveys. Our analyses imply that biodiversity loss can be underestimated by biases introduced if continued sampling in longitudinal studies is influenced by local change. We argue that researchers and conservation practitioners should be aware of the potential for bias in such data and seek to use more robust methods to evaluate biodiversity trends and make conservation decisions.