Biology subjects

Johnston, A.

Publications and source records attributed to Johnston, A..

26 records · Page 2Linked to original sources

bioRxiv2020

Visual predictions, neural oscillations and Newtonian physics.

Prediction is considered a core function of the human visual brain, but relating this suggestion to real life is problematic, as findings regarding the neural correlates of prediction rely on abstracted experiments, not reminiscent of a typical visual diet. We addressed this by having people view videos of basketball, and asking them to predict jump shot outcomes while we recorded eye movements and brain activity. We used the brains understanding of physics to manipulate predictive success, by inverting footage. People had enhanced alpha-band activity in occipital brain regions when watching upright videos, and this predicted both an increase in predictive success and enhanced ball tracking. Alpha-band activity in visual brain regions has been linked to inhibition, so we regard our results as evidence that inhibition of task irrelevant information is a core function of predictive processes in the visual brain, enacted as people complete visual tasks typical of daily life.

neuroscience↗

bioRxiv2019

The challenges of estimating the distribution of flight heights from telemetry or altimetry data

BackgroundGlobal positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their niche that we need to know to understand their ecology and conservation needs, and to manage our own use of the airspace. However, there are various sources of error in flight height data (\"height\" above ground, as opposed to \"altitude\" above a reference like the sea level): vertical error from the devices themselves, error in the ground elevation below the tracked animals, and error in the horizontal position of the animals and thus the predicted ground elevation below them.\n\nMethodsWe used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management.\n\nResultsErrors of a magnitude of 20 meters appear in benign conditions (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative recorded flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences.\n\nConclusionThe state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to raw flight height records, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.

ecology↗

bioRxiv2019

Best practices for making reliable inferences from citizen science data: case study using eBird to estimate species distributions

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort. To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes. Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes. Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

ecology↗

bioRxiv2018

Effects of sampling duration and intervals on confidence in population trend estimates

Species population trends are fundamental to conservation, underpinning lUCN red-list classifications, many national lists of threatened species and are also used globally to convey to policy makers the state of nature. Clearly, its crucial to quantify how much we can trust population trend data. Yet many studies analyzing large numbers of population time series lack a straightforward way to estimate confidence in each trend. Here we artificially degrade 27,930 waterbird population time series to see how often subsets of the data correctly estimate the direction and magnitude of each populations true trend. We find you need to sample many years to be confident that there is no significant trend in a population. Conversely, if a significant trend is detected, even from only a small subset of years, this is likely to be representative of the populations true trend. This means that if a significant decline is detected in a population, it is likely to be correct and conservation action should be taken immediately, but if the trend is insignificant, confidence in this can only be high with many samples. Our full results provide a clear and quantitative way to assign confidence to species trends, and lays the foundation for similar studies of other taxa that can help to add rigor to large-scale population analyses.

ecology↗

bioRxiv2018

Finding the signal in the noise of Citizen Science Observations

While many observations of species are being collected by citizen science projects worldwide, it can be challenging to identify projects collecting data that effectively monitor biodiversity. Over the past several years the allure of taking a \"Big Data\" approach has provided the opportunity to gather massive quantities of observations via the Internet, too often with insufficient information to describe how the observations were made. Information about species populations -- where and when they occur and how many of them are there -- (i.e., the signal) can be lost because insufficient information is gathered to account for the inherent biases in data collection (i.e., the noise). Here we suggest that citizen science projects that have succeeded in motivating large numbers of participants, must consider factors that influence the ecological process that affect species populations as well as the observation process that determines how observations are made. Those citizen science projects that collect sufficient contextual information describing the observation process can be used to generate increasingly accurate information about the distribution and abundance of organisms. We illustrate this using eBird as a case study, describing how this citizen science platform is able to collect vital contextual information on the observation process while maintaining a broad global constituency of participants. We highlight how eBird provides information with which to generate biodiversity indicators -- specifically distribution, abundance, and habitat associations -- across the entire annual cycle, even for populations of long distance migratory birds, a highly challenging taxon.

ecology↗

bioRxiv2018

Modeling Avian Full Annual Cycle Distribution and Population Trends with Citizen Science Data

Information on species distributions and abundances, and how these change over time are central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at relevant scales across range-wide extents for two main reasons. First, local and regional processes that affect populations vary throughout the year and across species ranges, requiring fine-scale, year-round information across broad -- sometimes hemispheric -- spatial extents. Second, while citizen science projects can collect data at these scales, using these data requires appropriate analysis to address known sources of bias. Here we present an analytical framework to address these challenges and generate year-round, range-wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long-distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and relative abundance with enough spatiotemporal resolution to support inference across a range of spatial scales throughout the annual cycle. Additionally, we generate intra-annual estimates of the range, intra-annual estimates of the associations between species and the local environment, and inter-annual trends in relative abundance. This is the first example of an analysis to capture intra- and inter-annual distributional dynamics across the entire range of a broadly distributed, highly mobile species.

ecology↗

bioRxiv2017

Endosymbiont diversity and evolution across the weevil tree of life

As early as the time of Paul Buchner, a pioneer of endosymbionts research, it was shown that weevils host diverse bacterial endosymbionts, probably only second to the hemipteran insects. To date, there is no taxonomically broad survey of endosymbionts in weevils, which preclude any systematic understanding of the diversity and evolution of endosymbionts in this large group of insects, which comprise nearly 7% of described diversity of all insects. We gathered the largest known taxonomic sample of weevils representing four families and 17 subfamilies to perform a study of weevil endosymbionts. We found that the diversity of endosymbionts is exceedingly high, with as many as 44 distinct kinds of endosymbionts detected. We recovered an ancient origin of association of Nardonella with weevils, dating back to 124 MYA. We found repeated losses of this endosymbionts, but also cophylogeny with weevils. We also investigated patterns of coexistence and coexclusion.

evolutionary biology↗

bioRxiv2016

A sensitized mutagenesis screen in Factor V Leiden mice identifies novel thrombosis suppressor loci

Factor V Leiden (F5L) is a common genetic risk factor for venous thromboembolism in humans. We conducted a sensitized ENU mutagenesis screen for dominant thrombosuppressor genes based on perinatal lethal thrombosis in mice homozygous for F5L (F5L/L) and haploinsufficient for tissue factor pathway inhibitor (Tfpi+/-). F8 deficiency enhanced survival of F5L/L Tfpi+/- mice, demonstrating that F5L/L Tfpi+/- lethality is genetically suppressible. ENU-mutagenized F5L/L males and F5L/+ Tfpi+/- females were crossed to generate 6,729 progeny, with 98 F5L/L Tfpi+/- offspring surviving until weaning. Sixteen lines exhibited transmission of a putative thrombosuppressor to subsequent generations, with these lines referred to as MF5L (Modifier of Factor 5 Leiden) 1-16. Linkage analysis in MF5L6 identified a chromosome 3 locus containing the tissue factor gene (F3). Though no ENU-induced F3 mutation was identified, haploinsufficiency for F3 (F3+/-) suppressed F5L/L Tfpi+/- lethality. Whole exome sequencing in MF5L12 identified an Actr2 gene point mutation (p.R258G) as the sole candidate. Inheritance of this variant is associated with suppression of F5L/L Tfpi+/- lethality (p=1.7x10-6), suggesting that Actr2p.R258G is thrombosuppressive. CRISPR/Cas9 experiments to generate an independent Actr2 knockin/knockout demonstrated that Actr2 haploinsufficiency is lethal, supporting a hypomorphic or gain of function mechanism of action for Actr2p.R258G. Our findings identify F8 and the Tfpi/F3 axis as key regulators in determining thrombosis balance in the setting of F5L and also suggest a novel role for Actr2 in this process.\n\nSignificance StatementVenous thromboembolism (VTE) is a common disease characterized by the formation of inappropriate blood clots. Inheritance of specific genetic variants, such as the Factor V Leiden polymorphism, increases VTE susceptibility. However, only ~10% of people inheriting Factor V Leiden develop VTE, suggesting the involvement of other genes that are currently unknown. By inducing random genetic mutations into mice with a genetic predisposition to VTE, we identified two genomic regions that reduce VTE susceptibility. The first includes the gene for blood coagulation Factor 3 and its role was confirmed by analyzing mice with an independent mutation in this gene. The second contains a mutation in the Actr2 gene. These findings identify critical genes for the regulation of blood clotting risk.

genetics↗