Revealing uncertainty in the status of biodiversity change.

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 datasets2-11 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.

Effect of time series length and resolution on abundance- and trait-based early warning signals of population declines.

Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal-to-noise ratio of ecological systems and the need for high quality time series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet, it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time series length and resolution on the forecasting ability of EWS. We show that EWS' performance decreases with decreasing time-series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time series length between 10 and five generations as a minimum requirement for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths one-half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

Revisiting the phylogeography and demography of European badgers (Meles meles) based on broad sampling, multiple markers and simulations.

Although the phylogeography of European mammals has been extensively investigated since the 1990s, many studies were limited in terms of sampling distribution, the number of molecular markers used and the analytical techniques employed, frequently leading to incomplete postglacial recolonisation scenarios. The broad-scale genetic structure of the European badger (Meles meles) is of interest as it may result from historic restriction to glacial refugia and/or recent anthropogenic impact. However, previous studies were based mostly on samples from western Europe, making it difficult to draw robust conclusions about the location of refugia, patterns of postglacial expansion and recent demography. In the present study, continent-wide sampling and analyses with multiple markers provided evidence for two glacial refugia (Iberia and southeast Europe) that contributed to the genetic variation observed in badgers in Europe today. Approximate Bayesian computation provided support for a colonisation of Scandinavia from both Iberian and southeastern refugia. In the whole of Europe, we observed a decline in genetic diversity with increasing latitude, suggesting that the reduced diversity in the peripheral populations resulted from a postglacial expansion processes. Although MSVAR v.1.3 also provided evidence for recent genetic bottlenecks in some of these peripheral populations, the simulations performed to estimate the method's power to correctly infer the past demography of our empirical populations suggested that the timing and severity of bottlenecks could not be established with certainty. We urge caution against trying to relate demographic declines inferred using MSVAR with particular historic or climatological events.