Complex long-term biodiversity change among invertebrates, bryophytes and lichens.

Large-scale biodiversity changes are measured mainly through the responses of a few taxonomic groups. Much less is known about the trends affecting most invertebrates and other neglected taxa, and it is unclear whether well-studied taxa, such as vertebrates, reflect changes in wider biodiversity. Here, we present and analyse trends in the UK distributions of over 5,000 species of invertebrates, bryophytes and lichens, measured as changes in occupancy. Our results reveal substantial variation in the magnitude, direction and timing of changes over the last 45 years. Just one of the four major groups analysed, terrestrial non-insect invertebrates, exhibits the declining trend reported among vertebrates and butterflies. Both terrestrial insects and the bryophytes and lichens group increased in average occupancy. A striking pattern is found among freshwater species, which have undergone a strong recovery since the mid-1990s after two decades of decline. We show that, while average occupancy among most groups appears to have been stable or increasing, there has been substantial change in the relative commonness and rarity of individual species, indicating considerable turnover in community composition. Additionally, large numbers of species have experienced substantial declines. Our results suggest a more complex pattern of biodiversity change in the United Kingdom than previously reported.

Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970-2015.

Here, we determine annual estimates of occupancy and species trends for 5,293 UK bryophytes, lichens, and invertebrates, providing national scale information on UK biodiversity change for 31 taxonomic groups for the time period 1970 to 2015. The dataset was produced through the application of a Bayesian occupancy modelling framework to species occurrence records supplied by 29 national recording schemes or societies (n = 24,118,549 records). In the UK, annual measures of species status from fine scale data (e.g. 1 × 1 km) had previously been limited to a few taxa for which structured monitoring data are available, mainly birds, butterflies, bats and a subset of moth species. By using an occupancy modelling framework designed for use with relatively low recording intensity data, we have been able to estimate species trends and generate annual estimates of occupancy for taxa where annual trend estimates and status were previously limited or unknown at this scale. These data broaden our knowledge of UK biodiversity and can be used to investigate variation in and drivers of biodiversity change.

A generalized regression model of arsenic variations in the shallow groundwater of Bangladesh.

Localized studies of arsenic (As) in Bangladesh have reached disparate conclusions regarding the impact of irrigation-induced recharge on As concentrations in shallow (≤50 m below ground level) groundwater. We construct generalized regression models (GRMs) to describe observed spatial variations in As concentrations in shallow groundwater both (i) nationally, and (ii) regionally within Holocene deposits where As concentrations in groundwater are generally high (>10 µg L-1). At these scales, the GRMs reveal statistically significant inverse associations between observed As concentrations and two covariates: (1) hydraulic conductivity of the shallow aquifer and (2) net increase in mean recharge between predeveloped and developed groundwater-fed irrigation periods. Further, the GRMs show that the spatial variation of groundwater As concentrations is well explained by not only surface geology but also statistical interactions (i.e., combined effects) between surface geology and mean groundwater recharge, thickness of surficial silt and clay, and well depth. Net increases in recharge result from intensive groundwater abstraction for irrigation, which induces additional recharge where it is enabled by a permeable surface geology. Collectively, these statistical associations indicate that irrigation-induced recharge serves to flush mobile As from shallow groundwater.

Exploiting strength, discounting weakness: combining information from multiple climate simulators.

This paper presents and analyses a statistical framework for combining projections of future climate from different climate simulators. The framework recognizes explicitly that all currently available simulators are imperfect; that they do not span the full range of possible decisions on the part of the climate modelling community; and that individual simulators have strengths and weaknesses. Information from individual simulators is automatically weighted, alongside that from historical observations and from prior knowledge. The weights for a simulator depend on its internal variability, its expected consensus with other simulators, the internal variability of the real climate and the propensity of simulators collectively to deviate from reality. The framework demonstrates, moreover, that some subjective judgements are inevitable when interpreting multiple climate change projections: by clarifying precisely what those judgements are, it provides increased transparency in the ensuing analyses. Although the framework is straightforward to apply in practice by a user with some understanding of Bayesian methods, the emphasis here is on conceptual aspects illustrated with a simplified artificial example. A 'poor man's version' is also presented, which can be implemented straightforwardly in simple situations.

Estimating functions and the generalized method of moments.

Estimating functions provide a very general framework for statistical inference, and are particularly useful when one is either unable or unwilling to specify a likelihood function. This paper aims to provide an accessible review of estimating function theory that has potential for application to the analysis and modelling of a wide range of complex systems. Assumptions are given in terms that can be checked relatively easily in practice, and some of the more technical derivations are relegated to an online supplement for clarity of exposition. The special case of the generalized method of moments is considered in some detail. The main points are illustrated by considering the problem of inference for a class of stochastic rainfall models based on point processes, with simulations used to demonstrate the performance of the methods.