No abstract

Species richness has dominated our view of global biodiversity patterns for centuries. The dominance of this paradigm is reflected in the focus by ecologists and conservation managers on richness and associated occurrence-based measures for understanding drivers of broad-scale diversity patterns and as a biological basis for management. However, this is changing rapidly, as it is now recognized that not only the number of species but the species present, their phenotypes and the number of individuals of each species are critical in determining the nature and strength of the relationships between species diversity and a range of ecological functions (such as biomass production and nutrient cycling). Integrating these measures should provide a more relevant representation of global biodiversity patterns in terms of ecological functions than that provided by simple species counts. Here we provide comparisons of a traditional global biodiversity distribution measure based on richness with metrics that incorporate species abundances and functional traits. We use data from standardized quantitative surveys of 2,473 marine reef fish species at 1,844 sites, spanning 133 degrees of latitude from all ocean basins, to identify new diversity hotspots in some temperate regions and the tropical eastern Pacific Ocean. These relate to high diversity of functional traits amongst individuals in the community (calculated using Rao's Q), and differ from previously reported patterns in functional diversity and richness for terrestrial animals, which emphasize species-rich tropical regions only. There is a global trend for greater evenness in the number of individuals of each species, across the reef fish species observed at sites ('community evenness'), at higher latitudes. This contributes to the distribution of functional diversity hotspots and contrasts with well-known latitudinal gradients in richness. Our findings suggest that the contribution of species diversity to a range of ecosystem functions varies over large scales, and imply that in tropical regions, which have higher numbers of species, each species contributes proportionally less to community-level ecological processes on average than species in temperate regions. Metrics of ecological function usefully complement metrics of species diversity in conservation management, including when identifying planning priorities and when tracking changes to biodiversity values.

Networks of citizen scientists (CS) have the potential to observe biodiversity and species distributions at global scales. Yet the adoption of such datasets in conservation science may be hindered by a perception that the data are of low quality. This perception likely stems from the propensity of data generated by CS to contain greater levels of variability (e.g., measurement error) or bias (e.g., spatio-temporal clustering) in comparison to data collected by scientists or instruments. Modern analytical approaches can account for many types of error and bias typical of CS datasets. It is now possible to (1) describe how the sampling process influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools. Information from these modeling approaches can further be incorporated into predictions of species distributions and estimates of biodiversity. By detailing how CS data are generated, patterns can be discerned from complex datasets that are unevenly distributed and collected by many observers with varying skill levels. Even so, gaining the full potential from even the best designed CS projects requires meta-data describing the sampling process, reference data to allow for standardization, and insightful modeling suitable to the type of response data of interest.

General cognitive function is substantially heritable across the human life course from adolescence to old age. We investigated the genetic contribution to variation in this important, health- and well-being-related trait in middle-aged and older adults. We conducted a meta-analysis of genome-wide association studies of 31 cohorts (N=53 949) in which the participants had undertaken multiple, diverse cognitive tests. A general cognitive function phenotype was tested for, and created in each cohort by principal component analysis. We report 13 genome-wide significant single-nucleotide polymorphism (SNP) associations in three genomic regions, 6q16.1, 14q12 and 19q13.32 (best SNP and closest gene, respectively: rs10457441, P=3.93 × 10−9, MIR2113; rs17522122, P=2.55 × 10−8, AKAP6; rs10119, P=5.67 × 10−9, APOE/TOMM40). We report one gene-based significant association with the HMGN1 gene located on chromosome 21 (P=1 × 10−6). These genes have previously been associated with neuropsychiatric phenotypes. Meta-analysis results are consistent with a polygenic model of inheritance. To estimate SNP-based heritability, the genome-wide complex trait analysis procedure was applied to two large cohorts, the Atherosclerosis Risk in Communities Study (N=6617) and the Health and Retirement Study (N=5976). The proportion of phenotypic variation accounted for by all genotyped common SNPs was 29% (s.e.=5%) and 28% (s.e.=7%), respectively. Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predicted in the Generation Scotland cohort (N=5487; P=1.5 × 10−17). In hypothesis-driven tests, there was significant association between general cognitive function and four genes previously associated with Alzheimer's disease: TOMM40, APOE, ABCG1 and MEF2C.

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five novel loci for intracranial volume and confirmed two known signals. Four of the loci are also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic=0.748), which indicated a similar genetic background and allowed for the identification of four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, Parkinson’s disease, and enriched near genes involved in growth pathways including PI3K–AKT signaling. These findings identify biological underpinnings of intracranial volume and provide genetic support for theories on brain reserve and brain overgrowth.