Localisation of gamma-ray interaction points in thick monolithic CeBr3 and LaBr3:Ce scintillators

Localisation of gamma-ray interaction points in monolithic scintillator crystals can simplify the design and improve the performance of a future Compton telescope for gamma-ray astronomy. In this paper we compare the position resolution of three monolithic scintillators: a 28x28x20 mm3 (length x breadth x thickness) LaBr3:Ce crystal, a 25x25x20 mm3 CeBr3 crystal and a 25x25x10 mm3 CeBr3 crystal. Each crystal was encapsulated and coupled to an array of 4x4 silicon photomultipliers through an optical window. The measurements were conducted using 81 keV and 356 keV gamma-rays from a collimated 133Ba source. The 3D position reconstruction of interaction points was performed using artificial neural networks trained with experimental data. Although the position resolution was significantly better for the thinner crystal, the 20 mm thick CeBr3 crystal showed an acceptable resolution of about 5.4 mm FWHM for the x and y coordinates, and 7.8 mm FWHM for the z-coordinate (crystal depth) at 356 keV. These values were obtained from the full position scans of the crystal sides. The position resolution of the LaBr3:Ce crystal was found to be considerably worse, presumably due to the highly diffusive optical in- terface between the crystal and the optical window of the enclosure. The energy resolution (FWHM) measured for 662 keV gamma-rays was 4.0% for LaBr3:Ce and 5.5% for CeBr3. The same crystals equipped with a PMT (Hamamatsu R6322-100) gave an energy resolution of 3.0% and 4.7%, respectively

Handling missing values in trait data

Aim: Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets. Location: Any. Time period: Any. Major taxa studied: Any. Methods: We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete-case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response). Results: Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete-case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions: Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors.Fil: Johnson, Thomas F.. University of Reading; Reino UnidoFil: Isaac, Nick J. B.. Centre For Ecology And Hydrology; Reino UnidoFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Centro de Investigaciones del Bosque Atlántico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: González Suárez, Manuela. University of Reading; Reino Unid

Butterfly abundance is determined by food availability and is mediated by species traits

1. Understanding the drivers of population abundance across species and sites is crucial for effective conservation management. At present, we lack a framework for predicting which sites are likely to support abundant butterfly communities. 2. We address this problem by exploring the determinants of abundance among 1111 populations of butterflies in the UK, spanning 27 species on 54 sites. Our general hypothesis is that the availability of food resources is a strong predictor of population abundance both within and between species, but that the relationship varies systematically with species’ traits. 3. We found strong positive correlations between butterfly abundance and the availability of food resources. Our indices of host plant and nectar are both significant predictors of butterfly population density, but the relationship is strongest for host plants, which explain up to 36% of the inter-site variance in abundance for some species. 4. Among species, the host plant–abundance relationship is mediated by butterfly species traits. It is strongest among those species with narrow diet breadths, low mobility and habitat specialists. Abundance for species with generalist diet and habitat associations is uncorrelated with our host plant index. 5. The host plant–abundance relationship is more pronounced on sites with predominantly north-facing slopes, suggesting a role for microclimate in mediating resource availability. 6. Synthesis and applications. We have shown that simple measures can be used to help understand patterns in abundance at large spatial scales. For some butterfly species, population carrying capacity on occupied sites is predictable from information about the vegetation composition. These results suggest that targeted management to increase host plant availability will translate into higher carrying capacity. Among UK butterflies, the species that would benefit most from such intervention have recently experienced steep declines in both abundance and distribution. The host plant–abundance relationship we have identified is likely to be transferrable to other systems characterized by strong interspecific interactions across trophic levels. This raises the possibility that the quality of habitat patches for specialist species is estimable from rapid assessment of the host plant resource

Comparing life histories across taxonomic groups in multiple dimensions: how mammal-like are insects?

Explaining variation in life histories remains a major challenge because they are multi-dimensional and there are many competing explanatory theories and paradigms. An influential concept in life history theory is the ’fast-slow continuum’, exemplified by mammals. Determining the utility of such concepts across taxonomic groups requires comparison of the groups’ life histories in multidimensional space. Insects display enormous species richness and phenotypic diversity, but testing hypotheses like the ’fast-slow continuum’ has been inhibited by incomplete trait data. We use phylogenetic imputation to generate complete datasets of seven life history traits in orthopterans (grasshoppers and crickets) and examine the robustness of these imputations for our findings. Three phylogenetic principal components explain 83-96% of variation in these data. We find consistent evidence of an axis mostly following expectations of a ’fast-slow continuum’, except that ’slow’ species produce larger, not smaller, clutches of eggs. We show that the principal axes of variation in orthopterans and reptiles are mutually explanatory, as are those of mammals and birds. Essentially, trait covariation in Orthoptera, with ’slow’ species producing larger clutches, is more reptile-like than mammal-or-bird-like. We conclude that the ’fast-slow continuum’ is less pronounced in Orthoptera than in birds and mammals, reducing the universal relevance of this pattern, and the theories that predict it

Recent trends in UK insects that inhabit early successional stages of ecosystems

Improved recording of less popular groups, combined with new statistical approaches that compensate for datasets that were hitherto too patchy for quantitative analysis, now make it possible to compare recent trends in the status of UK invertebrates other than butterflies. Using BRC datasets, we analysed changes in status between 1992 and 2012 for those invertebrates whose young stages exploit early seral stages within woodland, lowland heath and semi-natural grassland ecosystems, a habitat type that had declined during the 3 decades previous to 1990 alongside a disproportionally high number of Red Data Book species that were dependent on it. Two clear patterns emerged from a meta-analysis involving 299 classifiable species belonging to ten invertebrate taxa: (i) during the past 2 decades, most early seral species that are living near their northern climatic limits in the UK have increased relative to the more widespread members of these guilds whose distributions were not governed by a need for a warm micro-climate; and (ii) independent of climatic constraints, species that are restricted to the early stages of woodland regeneration have fared considerably less well than those breeding in the early seral stages of grasslands or, especially, heathland. The first trend is consistent with predicted benefits for northern edge-of-range species as a result of climate warming in recent decades. The second is consistent with our new assessment of the availability of early successional stages in these three ecosystems since c. 1990. Whereas the proportion and continuity of early seral patches has greatly increased within most semi-natural grasslands and lowland heaths, thanks respectively to agri-environmental schemes and conservation management, the representation of fresh clearings has continued to dwindle within UK woodlands, whose floors are increasingly shaded and ill-suited for this important guild of invertebrates

The use of opportunistic data for IUCN Red List assessments

IUCN Red Lists are recognized worldwide as powerful instruments for the conservation of species. Quantitative criteria to standardize approaches for estimating population trends, geographic ranges and population sizes have been developed at global and sub-global levels. Little attention has been given to the data needed to estimate species trends and range sizes for IUCN Red List assessments. Few regions collect monitoring data in a structured way and usually only for a limited number of taxa. Therefore, opportunistic data are increasingly used for estimating trends and geographic range sizes. Trend calculations use a range of proxies: (i) monitoring sentinel populations, (ii) estimating changes in available habitat, or (iii) statistical models of change based on opportunistic records. Geographic ranges have been determined using: (i) marginal occurrences, (ii) habitat distributions, (iii) range-wide occurrences, (iv) species distribution modelling (including site-occupancy models), and (v) process-based modelling. Red List assessments differ strongly among regions (Europe, Britain and Flanders, north Belgium). Across different taxonomic groups, in European Red Lists IUCN criteria B and D resulted in the highest level of threat. In Britain, this was the case for criterion D and criterion A, while in Flanders criterion B and criterion A resulted in the highest threat level. Among taxonomic groups, however, large differences in the use of IUCN criteria were revealed. We give examples from Europe, Britain and Flemish Red List assessments using opportunistic data and give recommendations for a more uniform use of IUCN criteria among regions and among taxonomic groups

Statistics for citizen science: extracting signals of change from noisy ecological data

1. Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Long-term monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant timescales. 2. We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends. 3. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful. 4. We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and Occupancy-Detection models. 5. The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection

Morphological and geographical traits of the British Odonata

Trait data are fundamental for many aspects of ecological research, particularly for modeling species response to environmental change. We synthesised information from the literature (mainly field guides) and direct measurements from museum specimens, providing a comprehensive dataset of 26 attributes, covering the 43 resident species of Odonata in Britain. Traits included in this database range from morphological traits (e.g. body length) to attributes based on the distribution of the species (e.g. climatic restriction). We measured 11 morphometric traits from five adult males and five adult females per species. Using digital callipers, these measurements were taken from dry museum specimens, all of which were wild caught individuals. Repeated measures were also taken to estimate measurement error. The trait data are stored in an online repository (https://github.com/BiologicalRecordsCentre/Odonata_traits), alongside R code designed to give an overview of the morphometric data, and to combine the morphometric data to the single value per trait per species data

Microclimate affects landscape level persistence in the British Lepidoptera

Microclimate has been known to drive variation in the distribution and abundance of insects for some time. Until recently however, quantification of microclimatic effects has been limited by computing constraints and the availability of fine-scale biological data. Here, we tested fine-scale patterns of persistence/extinction in butterflies and moths against two computed indices of microclimate derived from Digital Elevation Models: a summer solar index, representing fine-scale variation in temperature, and a topographic wetness index, representing fine-scale variation in moisture availability. We found evidence of microclimate effects on persistence in each of four 20 × 20 km British landscapes selected for study (the Brecks, the Broads, Dartmoor, and Exmoor). Broadly, local extinctions occurred more frequently in areas with higher minimum or maximum solar radiation input, while responses to wetness varied with landscape context. This negative response to solar radiation is consistent with a response to climatic warming, wherein grid squares with particularly high minimum or maximum insolation values provided an increasingly adverse microclimate as the climate warmed. The variable response to wetness in different landscapes may have reflected spatially variable trends in precipitation. We suggest that locations in the landscape featuring cooler minimum and/or maximum temperatures could act as refugia from climatic warming, and may therefore have a valuable role in adapting conservation to climatic change

Handling missing values in trait data

Aim Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Researchers traditionally have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, yet trait data are often not missing at random (e.g. more data for bigger species). Here we evaluate the performance of approaches for handling missing values considering biased datasets. Location Any Time period Any Major taxa studied Any Methods We simulated continuous traits and separate response variables to test performance of nine imputation methods and complete-case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating error in imputed trait values (deviation from the true value) and inferred trait-response relationships (deviation from the true relationship between a trait and response). Results Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response-trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete-case analysis frequently outperforming Mice imputation, and to a lesser degree BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions Imputation can effectively handle missing data under some conditions, but is not always the best solution. None of the methods we tested could effectively deal with severe biases, which may be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimise errors