Revealing uncertainty in the status of biodiversity change

Abstract

Biodiversity faces unprecedented threats from rapid global change¹. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets^2-11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

Fig. 1. Impact of correlative non-independence on collective abundance trends.

The text and images show the objective, implicit and key features of large-scale abundance datasets, current approaches for analysis, the problem, its implications and the solution.

Fig. 2. Widely used statistical models misrepresent biodiversity abundance trends.

Abundance trend projections across ten high-profile datasets under three different models. Circles represent the collective trend (the coefficient describing the change in abundance over time averaged across all species and locations) for each dataset in our three models (from left to right): random intercept, random slope and the correlated effect model that simultaneously accounts for temporal, spatial and phylogenetic correlative non-independence. We specify four categories of trend: significant increase—coefficient is positive and significant; non-significant increase—coefficient is positive but not significant (that is, no detectable change); non-significant decrease—coefficient is negative but not significant (that is, no detectable change); significant decrease—coefficient is negative and significant. Significance indicates that the coefficient does not overlap zero at a 50% credible interval. Coefficients and 95% credible intervals are available in Supplementary Table 4. We use the collective trend coefficient and 50% credible intervals (represented by shading) to produce abundance projections for each model in each dataset from an arbitrary baseline abundance of 100. Abundance projections cover the time span of the observed data and are presented on the log₁₀ scale. Source Data

Fig. 3. More complex models better represent population dynamics and improve the validity of conclusions across ecological scales.

a–c, Example of how the three models (random intercept (a), random slope (b) and correlated effect (c)) describe abundance patterns at different ecological scales (finer ecological scales on the left). The population-level column showcases how each of the three models produce different estimates of abundance trends (lines are the median values with 95% credible interval shading) for all three bat species (genus Myotis) with data in a given location, with data points representing the observed abundance values. The site-level column depicts how the species’ trends, under different models, influence the site-level trend (that is, a trend for a given location; black), in which the line and 95% credible intervals describe the median trend and variability in trend (respectively) across all species in the given location. At the collective level, the median trend for each unique site is represented by a faded grey line, and the median collective trend coefficient and 95% credible intervals are depicted by the coloured line and shading. At the site and collective levels, credible intervals solely describe uncertainty in the main parameter of interest, the rate of change coefficient, not the intercept. The final column describes how a hypothetical population would change under the median collective trend coefficient and 50% credible intervals projected from a relative baseline abundance of 100. This example is based on data in the Living Planet. In each plot, we restrict the time frame to the temporal extent of the population-level trends (1987–2015), instead of the total temporal extent of our Living Planet sample. Source Data

Fig. 4. Abundance change varies over phylogenetic and spatial extents.

Evidence of abundance change at different significance thresholds (for example, at an 80% CI threshold, dark red indicates evidence of declines whereas dark blue indicates evidence of increases). a, For the phylogeny, the species-level trends were derived by summing across hierarchical taxonomic random effects and phylogenetic correlation terms. Asymptotic species-level confidence thresholds were derived using uncertainty in phylogenetic predictions at multiple z-scores. To improve visualization, phylogenetic branch lengths are log transformed. b, For space, we take taxonomic and phylogenetic information from a for one iconic and abundant North American species, the American robin Turdus migratorius, and combine this with hierarchical and correlative spatial terms to make population-level predictions across terrestrial space. Asymptotic confidence thresholds were derived at the population scale (for example, species in a given site) using multiple z-scores. These predictions are drawn from the correlated effect model and BioTIME data (Supplementary Table 1). Source Data

Extended Data Fig. 1. Impact of phylogenetic and spatial signal on inference.

Fold change in the standard deviation of the abundance-time coefficient between the correlated effect and random slopes model, plotted against the mean signal. Mean signal is calculated by finding the mean of the phylogenetic signal (variance captured by the phylogeny divided by the sum of the phylogenetic and non-phylogenetic variance) and spatial signal (as in the phylogeny). n = 10 with each point representing a dataset. Shading represents 95% confidence intervals from a linear model.

Extended Data Fig. 2. Contribution of spatial, temporal and phylogenetic components to collective trend uncertainty.

Relative fold change in the standard deviation of the collective trend as additional components (space, time or phylogeny) are added to the random slope model. Models are compared to the standard deviation of the collective trend in the random slope model. In each model comparison (y-axis), n = 10 with each point representing a dataset. The larger point and error bar represents the mean change and associated standard deviation around this mean.