Rethinking pathways to the dioecy-polyploidy association: Genera with many dioecious species have fewer polyploids.

PREMISE: Numerous studies have found a positive association between dioecy and polyploidy; however, this association presents a theoretical conflict: While polyploids are predicted to benefit from self-reproduction for successful establishment, dioecious species cannot self-reproduce. We propose a theoretical framework to resolve this apparent conflict. We hypothesize that the inability of dioecious species to self-reproduce hinders their establishment as polyploids. We therefore expect that genera with many dioecious species have fewer polyploids, leading to a negative association between polyploidy and dioecy across genera. METHODS: We used three publicly available databases to determine ploidy and sexual systems for 131 genera and 546 species. We quantified (1) the relationship between the frequency of polyploid species and the frequency of dioecious species across genera, and (2) the proportion of polyploids with hermaphroditism and dioecy across species, adjusting for phylogenetic history. RESULTS: Across genera, we found a negative relationship between the proportion of polyploids and the proportion of dioecious species, a consistent trend across clades. Across all species, we found that sexual system (dioecious or not) was not associated with polyploidy. CONCLUSIONS: Polyploids are rare in genera in which the majority of species are dioecious, consistent with the theory that self-reproduction favors polyploid establishment. The low frequency of polyploidy among dioecious species indicates the association is not as widespread as previously suggested. Our findings are consistent with previous studies identifying a positive relationship between the two traits, but only if polyploidy promotes a transition to dioecy, and not the reverse.

We should not necessarily expect positive relationships between biodiversity and ecosystem functioning in observational field data.

Our current, empirical understanding of the relationship between biodiversity and ecosystem function is based on two information sources. First, controlled experiments which show generally positive relationships. Second, observational field data which show variable relationships. This latter source coupled with a lack of observed declines in local biodiversity has led to the argument that biodiversity-ecosystem functioning relationships may be uninformative for conservation and management. We review ecological theory and re-analyse several biodiversity datasets to argue that ecosystem function correlations with local diversity in observational field data are often difficult to interpret in the context of biodiversity-ecosystem function research. This occurs because biotic interactions filter species during community assembly which means that there can be a high biodiversity effect on functioning even with low observed local diversity. Our review indicates that we should not necessarily expect any specific relationship between local biodiversity and ecosystem function in observational field data. Rather, linking predictions from biodiversity-ecosystem function theory and experiments to observational field data requires considering the pool of species available during colonisation: the local species pool. We suggest that, even without local biodiversity declines, biodiversity loss at regional scales-which determines local species pools-may still negatively affect ecosystem functioning.

Random population fluctuations bias the Living Planet Index.

The Living Planet Index (LPI) is a standardized indicator for tracking population trends through time. Due to its ability to aggregate many time series in a single metric, the LPI has been proposed as an indicator for the Convention on Biological Diversity's post-2020 Global Biodiversity Strategy. However, here we show that random population fluctuations introduce biases when calculating the LPI. By combining simulated and empirical data, we show how random fluctuations lead to a declining LPI even when overall population trends are stable and imprecise estimates of the LPI when populations increase or decrease nonlinearly. We applied randomization null models that demonstrate how random fluctuations exaggerate declines in the global LPI by 9.6%. Our results confirm substantial declines in the LPI but highlight sources of uncertainty in quantitative estimates. Randomization null models are useful for presenting uncertainty around indicators of progress towards international biodiversity targets.

Large seasonal and spatial variation in nano- and microphytoplankton diversity along a Baltic Sea-North Sea salinity gradient.

Aquatic phytoplankton experience large fluctuations in environmental conditions during seasonal succession and across salinity gradients, but the impact of this variation on their diversity is poorly understood. We examined spatio-temporal variation in nano- and microphytoplankton (> 2 µm) community structure using almost two decades of light-microscope based monitoring data. The dataset encompasses 19 stations that span a salinity gradient from 2.8 to 35 along the Swedish coastline. Spatially, both regional and local phytoplankton diversity increased with broad-scale salinity variation. Diatoms dominated at high salinity and the proportion of cyanobacteria increased with decreasing salinity. Temporally, cell abundance peaked in winter-spring at high salinity but in summer at low salinity. This was likely due to large filamentous cyanobacteria blooms that occur in summer in low salinity areas, but which are absent in higher salinities. In contrast, phytoplankton local diversity peaked in spring at low salinity but in fall and winter at high salinity. Whilst differences in seasonal variation in cell abundance were reasonably well-explained by variation in salinity and nutrient availability, variation in local-scale phytoplankton diversity was poorly predicted by environmental variables. Overall, we provide insights into the causes of spatio-temporal variation in coastal phytoplankton community structure while also identifying knowledge gaps.