Cristian Cruz Rodriguez

The urgency to take action to stop biodiversity loss has increased the need to provide tools to track changes in biodiversity over time. Among these tools, the Living Planet Index (LPI) is one of the most widely used indices for tracking changes in biodiversity. The LPI uses population data from documented studies and synthesizes them in an index to provide an overview of population change over time. By compiling and integrating existing data, the LPI offers a general picture of biodiversity trends. However, the variability in data across regions and taxa used needs to be tested to see if the data used impacts results. In our latest paper, we did this.

Through simulations, we studied how missing data and zeros impact the LPI’s performance, data gaps, and trends. The results show that, when datasets are complete, the LPI is informative and preserves the underlying population tendency. Although missing data increases variability, it generally results in only minor deviations from baseline trends. In contrast, the presence and distribution of zeros within the underlying data substantially influence the results and produce significantly lower trends, depending on their temporal position within the time series.

These results indicate that zeros—representing real absences in some populations—have a disproportionate impact on the index and can alter trend interpretation. Furthermore, the progressively increasing number of zeros highlights the importance of strengthening the robustness of biodiversity monitoring programmes. These findings highlight the need to develop complementary approaches for evaluating how data heterogeneity influences LPI trends, towards a comprehensive understanding and better-informed decisions on biodiversity change.

Data availability continues to limit the capacity to monitor biodiversity comprehensively, reinforcing the importance of making effective use of existing information. In this context, the implementation of regular monitoring programmes based on structured protocols can help reduce data gaps and improve the continuity of data streams.

These findings underscore the importance of testing indicators with a baseline to help identify limitations in the data used to calculate them. Our results also call for increased effort in identifying the cause and temporal position of zero values in time series that support the LPI calculation. Moreover, the progressively increasing number of zeros observed across datasets emphasizes the importance of strengthening monitoring protocols through existing sampling designs and long-term commitment.