Biomathematics and Statistics Scotland

Introduction Wood borer pests represent a growing threat to forestry worldwide, with potential for an increase in climate change induced outbreaks that could significantly impact ecosystem functioning. Methods This study reviews the current state-of-the-art in bark beetle modeling as presented in peer-reviewed English language scientific papers and reviews cited in Clarivate Analytics Web of Science (WoS) Core Collection in the period from 2006 to 2023, but earlier influential papers cited in reviews from this period are also in scope. We categorize studies by modeling methodology, by host and by modeled processes. Results Ash, pine, and spruce hosts account for 88% of studies, the majority of which focus on the continental USA, British Columbia, Canada and Central Europe. In terms of methodology, statistical methods are the most commonly employed technique, and the majority of articles model just one or two key processes with pest demography and environmental factors being the most studied across different hosts. Commonalities in methods used across pest-host systems include: use of phenology-like models; application of static species distribution models (SDMs) to understand climate impacts; modeling of spread via local and long-range kernels; and use of economic cost-benefit analysis as a tool to guide management. Discussion We identify several gaps in current research including quantifying economic consequences of wood borer pests and the need for greater understanding of ecological impacts and resilience. Agent- and individual-based models may also provide useful tools for understanding the complexity of socio-ecological system dynamics. However, such developments should be in tandem with wider use of techniques for parameter estimation and uncertainty quantification, including Bayesian inference in particular of the dynamics of spatial spread. A related challenge is better quantification of pathways - including trade - for entry by invasive pests, coupled with a greater understanding of potential vulnerabilities of forest systems to environmental drivers like climate change, coupled with potentially multiple endemic, emerging and novel pests. Addressing these challenges would enable both better mitigation of risks associated with wood borer beetle infestations and better management of outbreaks when they do occur.

Different parasite challenge models have been used over many years in the development of novel vaccines against gastrointestinal nematodes of livestock. Typically, following vaccination with either native or recombinant proteins, the host animal is then given a bolus challenge of infective nematode larvae (iL3) or a trickle challenge where smaller numbers of iL3 are given at regular intervals over a period of time. The bolus method may be a robust test of whether high levels of vaccine-induced immunity translate to protection against a large pathogen insult, whereas the trickle infection method may be more appropriate to test the impacts of an exposure-induced anamnestic response. In the work presented here, three different models of testing gastrointestinal nematode vaccine efficacy were compared. Lambs (4-5months old) were vaccinated with the excretory/secretory products collected from ex-vivo Teladorsagia circumcincta fourth stage larvae (L4ESP) and then challenged with a bolus of 5,000 iL3 or with a trickle infection where 2,000 iL3 were administered 3 times per week for 4weeks (truncated trickle) or continuously challenged for 9weeks (trickle) until post-mortem. Monitoring of faecal egg counts following challenge demonstrated that there was no significant impact of vaccination on cumulative faecal egg count (cFEC) following bolus challenge but, during the period of regular ingestion of iL3 in both trickle challenge models, statistically-significant reductions in the cFEC [(40-46% reduction in mean cFEC (P<0.05); 68-71% reduction in median cFEC)] were observed. Shortly after cessation of the iL3 challenge in the truncated trickle challenge group, this protective effect of the vaccine on cFEC was lost whereas vaccine efficacy was maintained in the model with continued regular ingestion of iL3 (trickle challenge model, which most accurately reflect the parasite challenge in a field situation). In conclusion, the choice of challenge model had a profound impact on the ability to measure vaccine efficacy.

Climate-induced changes in precipitation will lead to greater frequency of high and low-flow events, causing further phosphorus losses due to increased mobilisation and delivery and decreased dilution. The uncertainty associated with climate-induced changes to water quality is rarely represented in water quality models. Bayesian Belief Networks (BBNs) are probabilistic graphical models incorporating uncertainty, making them useful frameworks for communicating risk. This study presents a set of catchment-specific BBNs to simulate total reactive phosphorus (TRP) concentrations in four agricultural catchments under projected climate change. Downscaled discharge time series from six climate models (five models plus their mean), for two Representative Concentration Pathways (RCP 4.5 and 8.5) and three time periods (the 2020s, the 2040s, and the 2080s), were used to create discharge scenarios for the catchment-specific BBNs. The BBN-simulated monthly mean TRP concentrations showed no obvious trends over time or differences between the RCP scenarios, with the ensemble-driven future TRP essentially replicating the results obtained for the baseline period. We found that in four small (7–12 km2) catchments farmed for livestock or arable crops with one or no wastewater treatment plants, the ensemble-projected effects of climate change alone were not a significant driver of monthly TRP concentrations. However, the single-model TRP concentration distributions simulated with the HadGEM2-ES model, showed differences from the baseline in the drier months. This difference occurred because the catchment-specific BBNs were sensitive to changes in the mean monthly discharge simulated using in the HadGEM2-ES projections but not by the other ensemble members.

Abstract Altea Lorenzo-Arribas looks at the work and legacy of a North American data visualisation pioneer

This study aimed to identify if sensor technology could be used to detect sickness-type signs (caused by a live vaccine) in laying hens compared to physiological and clinical sign scoring and behaviour observation. The experiment comprised 5 replicate batches (4 hens and 12 days per batch) using previously non-vaccinated hens (n = 20). Hens were moved on day 1 to a large experimental room with various designated zones (e.g., litter, perches, nest box), where they wore two sensors (FitBark, TrackLab). Saline was applied using ocular and nasal drops on day 3 as a control. A live vaccine (Infectious Laryngotracheitis, ILT, vaccine), applied using the same method on day 6, was used to induce mild respiratory and other responses. Physiological and clinical signs, and behaviour from videos were also recorded by a single observer. There were significant changes in body weight (p < 0.001), feed intake (p = 0.031), cloacal temperature (p < 0.001) and three out of five clinical signs (ocular discharge (p < 0.001), conjunctivitis (p < 0.001) and depression (p = 0.009)) over days. A significant decrease (p < 0.001) in activity level (FitBark) and distance travelled (both sensors) were identified over the study days, and activity and distance travelled were highly significantly associated (p < 0.001) with total clinical scores, with hens showing reduced activity and distance travelled with worsening total clinical scores. With behaviour observations from videos, the proportions of sitting, foraging and feeding behaviours (p = 0.044, 0.036 and 0.004, respectively), the proportion of total visit duration to the litter zone (p < 0.001) and perch (p = 0.037) with TrackLab and the proportions of visit counts of hens in the litter zone (p = 0.012) from video scanning changed significantly with days. This study suggests that the vaccine challenge caused associated changes in clinical/physiological signs and activity/distance travelled data from the sensors. Sensors may have a role in detecting changes in activity and movement in individual hens indicative of health or welfare problems.

Seabirds are important sentinels of climate and ecosystem change, but many breeding populations are difficult to monitor because of the remoteness and inaccessibility of their colonies, and the sometimes cryptic nature of their nests and burrows. Large‐scale monitoring of seabird populations at sea can also be used to estimate population trends and inform conservation efforts. However, although modern survey techniques can be used to estimate absolute abundance, many older survey methodologies have recorded only relative, and possibly biased, abundance. These approaches are exemplified in the western North Atlantic, where seabirds have been surveyed at sea using modern methods (Eastern Canada Seabirds at Sea, or ECSAS) since 2006, but under the simpler PIROP (Programme intégré de recherches sur les oiseaux pélagiques) protocol from 1965 to 1992. Methodological differences between these survey types limit our understanding of long‐term trends in seabird populations, both in the western North Atlantic and elsewhere. Hence, we conducted simultaneous surveys using both methods from 2014 to 2021 and used advances in model‐based distance sampling to allow comparison across these longer‐term datasets. We validated our methodology by comparing population trends of Northern Gannets Morus bassanus using the at‐sea data and breeding colony surveys. The trend in abundance at sea (2.69% increase annually) was similar to that at breeding colonies (2.91% increase annually), suggesting that our combined approach can be used to estimate seabird population changes robustly across the period spanned by the two survey programmes. We envision that analyses using similar combined survey methods could reveal decadal population trends and changes in conservation status of many seabird species that currently lack such information because of the absence of colony counts.

BackgroundPatients receiving chemotherapy require ongoing symptom monitoring and management to optimize their outcomes. In recent years, digital remote monitoring interventions have emerged to provide enhanced cancer care delivery experiences to patients and clinicians. However, patient and clinician experiential evaluations of these technologies are rare. Therefore, we explored user experiences and perceptions of one such intervention—Advanced Symptom Management System (ASyMS)—after its scaled deployment in the context of the Electronic Symptom Management System Remote Technology (eSMART) trial. The eSMART trial was a large, multicenter randomized controlled trial to evaluate the efficacy of ASyMS in 12 clinical sites in 5 European countries.ObjectiveIn this qualitative study, both patients’ and clinicians’ experiences of using ASyMS for up to 6 cycles of chemotherapy were explored to understand the impact of ASyMS on patients’ experiences, clinical practice, and supportive care delivery.MethodsFor this analysis, individual, semistructured, one-to-one interviews with 29 patients with breast, colorectal, and hematological cancers and 18 clinicians from Austria, Greece, Ireland, Norway, and the United Kingdom were conducted. Interviews focused on patients’ and clinicians’ experiences of using ASyMS, care organization and changes in practice following the introduction of ASyMS, perceived changes in care associated with the use of ASyMS, and its potential for future integration into routine chemotherapy care pathways.ResultsThematic analysis identified several themes that describe patients’ and clinicians’ experiences using ASyMS. One central orienting theme—ASyMS as a facilitator of change—was supported by 5 key themes associated with human and technology monitoring: reassurance, enhanced communications and relationships, knowing what is “normal” and what is to be expected, enhancing cancer care experiences, and informing future cancer care.ConclusionsThis study is the first to evaluate both patients’ and clinicians’ experiences of using a digital health intervention to remotely monitor chemotherapy symptoms across 5 countries. Experiences with ASyMS were positive from both patients’ and clinicians’ perspectives, although some improvements to support the wider-scale rollout and sustained implementation were identified. Overall, this study demonstrates that real-time remote monitoring systems can help patients feel more reassured during their chemotherapy treatments and can help clinicians provide the right care, at the right time, and in the right place.Trial RegistrationClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2016-015016

Experimental inversion of circadian and behavioral rhythms by 12h adversely affects markers of metabolic health. We investigated the effects of a more modest 5-h delay in behavioral cycles. Fourteen participants completed an 8-day in-patient laboratory protocol, with controlled sleep-wake opportunities, light-dark cycles, and diet. The 5-h delay in behavioral cycles was induced by delaying sleep opportunity. We measured melatonin to confirm central circadian phase, fasting markers and postprandial metabolism, energy expenditure, subjective sleepiness, and appetite, throughout the waking period. After the phase delay, there was slower gastric emptying at breakfast, lower fasting plasma glucose, higher postprandial plasma glucose and triglycerides, and lower thermic effect of feeding. Any changes were abolished or attenuated within 48-72 h. These data extend our previous findings, which showed no time-of-day effect in healthy adults on daytime energy expenditure or thermic effect of feeding when accounting for circadian variation in resting metabolic rate.

The incidence of vector-borne disease is on the rise globally, with burdens increasing in endemic countries and outbreaks occurring in new locations. Effective mitigation and intervention strategies require models that accurately predict both spatial and temporal changes in disease dynamics, but this remains challenging due to the complex and interactive relationships between environmental variation and the vector traits that govern the transmission of vector-borne diseases. Predictions of disease risk in the literature typically assume that vector traits vary instantaneously and independently of population density, and therefore do not capture the delayed response of these same traits to past biotic and abiotic environments. We argue here that to produce accurate predictions of disease risk it is necessary to account for environmentally driven and delayed instances of phenotypic plasticity. To show this, we develop a stage and phenotypically structured model for the invasive mosquito vector, Aedes albopictus, and dengue, the second most prevalent human vector-borne disease worldwide. We find that environmental variation drives a dynamic phenotypic structure in the mosquito population, which accurately predicts global patterns of mosquito trait-abundance dynamics. In turn, this interacts with disease transmission to capture historic dengue outbreaks. By comparing the model to a suite of simpler models, we reveal that it is the delayed phenotypic structure that is critical for accurate prediction. Consequently, the incorporation of vector trait relationships into transmission models is critical to improvement of early warning systems that inform mitigation and control strategies.