Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Limited climatic space for alternative ecosystem states in Africa.

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Recognizability bias in citizen science photographs.

Citizen science and automated collection methods increasingly depend on image recognition to provide the amounts of observational data research and management needs. Recognition models, meanwhile, also require large amounts of data from these sources, creating a feedback loop between the methods and tools. Species that are harder to recognize, both for humans and machine learning algorithms, are likely to be under-reported, and thus be less prevalent in the training data. As a result, the feedback loop may hamper training mostly for species that already pose the greatest challenge. In this study, we trained recognition models for various taxa, and found evidence for a 'recognizability bias', where species that are more readily identified by humans and recognition models alike are more prevalent in the available image data. This pattern is present across multiple taxa, and does not appear to relate to differences in picture quality, biological traits or data collection metrics other than recognizability. This has implications for the expected performance of future models trained with more data, including such challenging species.

Insights into the quantification and reporting of model-related uncertainty across different disciplines.

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research.

Evaluation of the artus® Prep&Amp UM RT-PCR for detection of SARS-CoV-2 from nasopharyngeal swabs without prior nucleic acid eluate extraction.

Here we describe a retrospective clinical evaluation of the QIAGEN artus® SARS-CoV-2 Prep&Amp UM RT-PCR assay that detects SARS-CoV-2 RNA without the need for a nucleic acid eluate extraction procedure. Using Roche SARS-CoV-2 RT-PCR on the cobas® 8800 platform as a reference standard, a total of 225 confirmed SARS-CoV-2 positive and 320 negative nasopharyngeal swabs in viral transport media, were used to evaluate the artus® assay. Using the RT-PCR cycle threshold as a semi-quantitative marker of viral load, an assessment of over 370,000 SARS-CoV-2 RT-PCR positive results was used in the design of the reference positive specimen cohort. The viral load of all reference positive specimens used in the evaluation was a unique and accurate representation of the range and levels of SARS-CoV-2 positivity observed over a 13-month period of the COVID-19 pandemic. The artus® RT-PCR detects the presence of SARS-CoV-2 RNA, an internal control, and the human RNase P gene to ensure specimen quality. The diagnostic sensitivity of artus® was 92.89% with a specificity of 100%. To assess the analytical sensitivity, a limit of detection was performed using the 1st WHO NIBSC SARS-CoV-2 international standard, recording a 95% LOD of 1.1 × 103 IU/ml. The total invalid rate of specimens was 7.34% due to a lack of detectable RNase P (Ct >35). The artus® SARS-CoV-2 Prep&Amp UM RT-PCR assay is a new rapid RT-PCR assay, which may be considered to produce acceptable levels of diagnostic sensitivity and specificity whilst potentially halving the laboratory processing time.

An Overview of SARS-CoV-2 Molecular Diagnostics in Europe.

The COVID-19 pandemic has led to the rapid development of a plethora of molecular diagnostic assays with real-time polymerase chain reaction (RT-PCR) at the forefront. In this review, we will discuss the history and utility of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) molecular diagnostics and the associated current and future regulatory process in Europe. We will assess the performance characteristics of a range of the most common SARS-CoV-2 molecular tests currently used in Europe with a focus on as rapid molecular platforms, stand-alone RT-PCR kits, the role of low-throughput and high-throughput end-to-end testing platforms, and the rapidly evolving field of SARS-CoV-2 variant of concern identification.

Evaluation of a novel direct RT-LAMP assay for the detection of SARS-CoV-2 from saliva samples in asymptomatic individuals.

Large scale screening of health care workers and the general population for asymptomatic COVID-19 infection requires modalities that are amenable to testing at scale while retaining acceptable levels of sensitivity and specificity. This study evaluated a novel COVID-19 Direct-RT LAMP assay using saliva samples in asymptomatic individuals by comparison to RT-PCR. Additional studies were performed using VTM collected from routine diagnostic testing. Analytical sensitivity was determined for Direct RT-LAMP assay using the WHO International Standard. Finally, quantified results from RT-PCR testing of 9177 nose and throat swabs obtained from routine diagnostic testing were used to estimate the sensitivity of Direct RT-LAMP using the limit of detection curve obtained from the analytical sensitivity data. Results from saliva testing demonstrated a sensitivity of 40.91% and a specificity of 100% for Direct RT-LAMP. The sensitivity and specificity for nose and throat swabs were 44.85% and 100% respectively. The 95% limit of detection (LOD) for Direct RT-LAMP was log 7.13 IU/ml (95% 6.9-7.5). The estimated sensitivity for Direct-RT LAMP based on the results of 9117 nose and throat swabs was 34% and 45% for saliva and VTM respectively. The overall diagnostic sensitivity of Direct RT-LAMP was low compared to RT-PCR. Testing of nose and throat swabs and estimating the sensitivity based on a large cohort of clinical samples demonstrated similar results. This study highlights the importance of utilising the prospective collection of samples from the intended target population in the assessment of diagnostic sensitivity.

Cognitive biases among those with frequent or chronic headaches or migraines: a systematic review and meta-analysis.

ABSTRACT: The aim of this systematic review and meta-analysis was to determine the pattern of cognitive processing biases (ie, attentional, interpretation, and memory bias) towards headache and pain information observed in individuals with frequent or chronic headaches or migraines, compared with individuals without. We identified 11 studies (total N = 841). Most studies (10 of 11) assessed attentional bias. For reaction time-based attentional bias (k = 8), significant group differences were observed, with small (500 ms; g = 0.200) to medium (≥ 1000 ms; g = 0.544) effects. Those with chronic headaches showed an absolute attentional bias towards headache and pain information. In 2 eye-tracking studies, both found significant medium-to-large group differences on initial orientation. Interpretation and memory bias research was also reviewed. A medium-to-large effect was found for interpretation bias (k = 2; g = 0.708), whereas the effect for recall memory bias (k = 3) was not significant. Overall, the findings confirm an attentional bias for headache-related stimuli among people with headache, with some evidence for interpretation bias but equivocal evidence for a memory bias. For attentional biases, eye-tracking studies found evidence for biases in initial orienting. We provide suggestions for how to extend the current research to better understand cognitive biases in chronic headache.

Rewriting results sections in the language of evidence.

Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields.

Real-world SARS CoV-2 testing in Northern England during the first wave of the COVID-19 pandemic.

OBJECTIVES: SARS-CoV-2 emerged in South Asia in 2019 and has resulted in a global pandemic. Public Health England (PHE) Manchester rapidly escalated testing for SARS-CoV-2 in the highest COVID-19 incidence location in England. The results of the PHE Manchester SARS-CoV-2 surveillance during the first wave are presented. METHODS: Retrospective data were collected for patients fitting the PHE SARS-CoV-2 case definition from 11th February to 31st August 2020. Respiratory tract, tissue, faecal, fluid and cerebrospinal (CSF) samples were tested for SARS-CoV-2 by a semi-quantitative real-time reverse-transcription PCR. RESULTS: Of the 204,083 tests for SARS-CoV-2, 18,011 were positive demonstrating a positivity of 8.90%. Highest positivity was in nasal swabs (20.99%) followed by broncheo-alveolar lavage samples (12.50%). None of the faecal, fluid or CSF samples received were positive for SARS-CoV-2. CONCLUSIONS: There was a high incidence of SARS-CoV-2 patients in the North-West of England during the first UK wave of the Covid-19 pandemic. Highest positivity rate was in nasal specimens suggesting this is the optimum sample type within this dataset for detecting SARS-CoV-2. Further studies are warranted to assess the utility of testing faecal, fluid and CSF samples. Rapid escalation of testing via multiple platforms was required to ensure prompt diagnosis and isolate infected cases to reduce transmission of the virus.

The Phylogenetic Structure of Reptile, Avian and Uropathogenic Escherichia coli with Particular Reference to Extraintestinal Pathotypes.

The impact of the Gram-negative bacterium Escherichia coli (E. coli) on the microbiomic and pathogenic phenomena occurring in humans and other warm-blooded animals is relatively well-recognized. At the same time, there are scant data concerning the role of E. coli strains in the health and disease of cold-blooded animals. It is presently known that reptiles are common asymptomatic carriers of another human pathogen, Salmonella, which, when transferred to humans, may cause a disease referred to as reptile-associated salmonellosis (RAS). We therefore hypothesized that reptiles may also be carriers of specific E. coli strains (reptilian Escherichia coli, RepEC) which may differ in their genetic composition from the human uropathogenic strain (UPEC) and avian pathogenic E. coli (APEC). Therefore, we isolated RepECs (n = 24) from reptile feces and compared isolated strains' pathogenic potentials and phylogenic relations with the aforementioned UPEC (n = 24) and APEC (n = 24) strains. To this end, we conducted an array of molecular analyses, including determination of the phylogenetic groups of E. coli, virulence genotyping, Pulsed-Field Gel Electrophoresis-Restriction Analysis (RA-PFGE) and genetic population structure analysis using Multi-Locus Sequence Typing (MLST). The majority of the tested RepEC strains belonged to nonpathogenic phylogroups, with an important exception of one strain, which belonged to the pathogenic group B2, typical of extraintestinal pathogenic E. coli. This strain was part of the globally disseminated ST131 lineage. Unlike RepEC strains and in line with previous studies, a high percentage of UPEC strains belonged to the phylogroup B2, and the percentage distribution of phylogroups among the tested APEC strains was relatively homogenous, with most coming from the following nonpathogenic groups: C, A and B1. The RA-PFGE displayed a high genetic diversity among all the tested E. coli groups. In the case of RepEC strains, the frequency of occurrence of virulence genes (VGs) was lower than in the UPEC and APEC strains. The presented study is one of the first attempting to compare the phylogenetic structures of E. coli populations isolated from three groups of vertebrates: reptiles, birds and mammals (humans).

Comment on "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate", author Coro.

In this letter we present comments on the article "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus" by Coro published in 2020.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld's deer.

Despite its value for conservation decision-making, we lack information on population abundances for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical methods that combine multiple data sources are promising approaches to maximize use of available information. We built a Bayesian hierarchical model that combined different survey data of the endangered Eld's deer in Shwesettaw Wildlife Sanctuary (SWS) in Myanmar and tested our approach in simulation experiments. We combined spatially-restricted line-transect abundance data with more spatially-extensive camera-trap occupancy data to enable estimation of the total deer abundance. The integrated model comprised an ecological model (common to both survey types, based on the equivalence between cloglog-transformed occurrence probability and log-transformed expected abundance) and separate observation models for each survey type. We estimated that the population size of Eld's deer in SWS is c. 1519 (1061-2114), suggesting it is the world's largest wild population. The simulations indicated that the potential benefits of combining data include increased precision and better sampling of the spatial variation in the environment, compared to separate analysis of each survey. Our analytical approach, which integrates the strengths of different survey methods, has widespread application for estimating species' abundances, especially in information-poor regions of the world.

Modelling seasonal dynamics, population stability, and pest control in Aedes japonicus japonicus (Diptera: Culicidae).

BACKGROUND: The invasive temperate mosquito Aedes japonicus japonicus is a potential vector for various infectious diseases and therefore a target of vector control measures. Even though established in Germany, it is unclear whether the species has already reached its full distribution potential. The possible range of the species, its annual population dynamics, the success of vector control measures and future expansions due to climate change still remain poorly understood. While numerous studies on occurrence have been conducted, they used mainly presence data from relatively few locations. In contrast, we used experimental life history data to model the dynamics of a continuous stage-structured population to infer potential seasonal densities and ask whether stable populations are likely to establish over a period of more than one year. In addition, we used climate change models to infer future ranges. Finally, we evaluated the effectiveness of various stage-specific vector control measures. RESULTS: Aedes j. japonicus has already established stable populations in the southwest and west of Germany. Our models predict a spread of Ae. j. japonicus beyond the currently observed range, but likely not much further eastwards under current climatic conditions. Climate change models, however, will expand this range substantially and higher annual densities can be expected. Applying vector control measures to oviposition, survival of eggs, larvae or adults showed that application of adulticides for 30 days between late spring and early autumn, while ambient temperatures are above 9 °C, can reduce population density by 75%. Continuous application of larvicide showed similar results in population reduction. Most importantly, we showed that with the consequent application of a mixed strategy, it should be possible to significantly reduce or even extinguish existing populations with reasonable effort. CONCLUSION: Our study provides valuable insights into the mechanisms concerning the establishment of stable populations in invasive species. In order to minimise the hazard to public health, we recommend vector control measures to be applied in 'high risk areas' which are predicted to allow establishment of stable populations to establish.

Standards for distribution models in biodiversity assessments.

Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines.