Prediction of coccidiosis prevalence in extensive backyard chickens in countries and regions of the Horn of Africa.

Coccidiosis is one of the leading morbidity causes in chickens, causing a reduction of body weight and egg production. Backyard chickens are at risk of developing clinical and subclinical coccidiosis due to outdoor housing and scavenging behaviour, jeopardizing food security in households. The objectives of this study were to estimate clinical prevalence of coccidiosis at country and regional levels in the Horn of Africa in extensive backyard chickens. A binomial random effects model was developed to impute prevalence of coccidiosis. Previously gathered prevalence data (n = 40) in backyard chickens was used to define the model. Precipitation (OR: 1.09 (95% CI: 1.05-1.13) and the presence of seasonal rainfall (OR: 1.85, 95% CI: 1.27-2.70) significantly increase prevalence. Results showed an overall prevalence of coccidiosis in the Horn of Africa of 0.21 (95% CI: 0.15-0.29). Ethiopia, the Republic of South Sudan and Kenya showed the highest prevalence and Djibouti the lowest. Significant differences between Djibouti and the countries with highest prevalence were found. However, no evidence of a significant difference between the rest of the countries. Kenya and Ethiopia showed larger prevalence differences between regions. Results could assist with the targeting of testing for coccidiosis, the observation for clinical disease of chickens living in specific regions and as a baseline for the evaluation of future control measures.

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors.

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient's sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.

Simultaneously estimating food web connectance and structure with uncertainty.

Food web models explain and predict the trophic interactions in a food web, and they can infer missing interactions among the organisms. The allometric diet breadth model (ADBM) is a food web model based on the foraging theory. In the ADBM, the foraging parameters are allometrically scaled to body sizes of predators and prey. In Petchey et al. (Proceedings of the National Academy of Sciences, 2008; 105: 4191), the parameterization of the ADBM had two limitations: (a) the model parameters were point estimates and (b) food web connectance was not estimated.The novelty of our current approach is: (a) We consider multiple predictions from the ADBM by parameterizing it with approximate Bayesian computation, to estimate parameter distributions and not point estimates. (b) Connectance emerges from the parameterization, by measuring model fit using the true skill statistic, which takes into account prediction of both the presences and absences of links.We fit the ADBM using approximate Bayesian computation to 12 observed food webs from a wide variety of ecosystems. Estimated connectance was consistently greater than previously found. In some of the food webs, considerable variation in estimated parameter distributions occurred and resulted in considerable variation (i.e., uncertainty) in predicted food web structure.These results lend weight to the possibility that the observed food web data is missing some trophic links that do actually occur. It also seems likely that the ADBM likely predicts some links that do not exist. The latter could be addressed by accounting in the ADBM for additional traits other than body size. Further work could also address the significance of uncertainty in parameter estimates for predicted food web responses to environmental change.

Twin study reveals non-heritable immune perturbations in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system underpinned by partially understood genetic risk factors and environmental triggers and their undefined interactions1,2. Here we investigated the peripheral immune signatures of 61 monozygotic twin pairs discordant for MS to dissect the influence of genetic predisposition and environmental factors. Using complementary multimodal high-throughput and high-dimensional single-cell technologies in conjunction with data-driven computational tools, we identified an inflammatory shift in a monocyte cluster of twins with MS, coupled with the emergence of a population of IL-2 hyper-responsive transitional naive helper T cells as MS-related immune alterations. By integrating data on the immune profiles of healthy monozygotic and dizygotic twin pairs, we estimated the variance in CD25 expression by helper T cells displaying a naive phenotype to be largely driven by genetic and shared early environmental influences. Nonetheless, the expanding helper T cells of twins with MS, which were also elevated in non-twin patients with MS, emerged independent of the individual genetic makeup. These cells expressed central nervous system-homing receptors, exhibited a dysregulated CD25-IL-2 axis, and their proliferative capacity positively correlated with MS severity. Together, our matched-pair analysis of the extended twin approach allowed us to discern genetically and environmentally determined features of an MS-associated immune signature.

Discussion on Competition for Spatial Statistics for Large Datasets.

We discuss the experiences and results of the AppStatUZH team's participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.

Remotely sensed between-individual functional trait variation in a temperate forest.

Trait-based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field-based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual-tree crowns within a temperate forest site and then assigning RS-derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between- and within-species variation across contiguous space.We used airborne imaging spectroscopy and laser scanning to collect individual-tree RS data from a mixed conifer-angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage-height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within-species trait variation into smaller-scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between-species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis.On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage-height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within-site environmental gradients potentially contributing to the coexistence of the eight abundant species.We conclude that with high-resolution RS data it is possible to delineate individual-tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field-based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual-based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests.

rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Association between environmental and climatic risk factors and the spatial distribution of cystic and alveolar echinococcosis in Kyrgyzstan.

BACKGROUND: Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE surgical incidence in 2014-2016 showed marked heterogeneity across communities, suggesting the presence of ecological determinants underlying CE and AE distributions. METHODOLOGY/PRINCIPAL FINDINGS: For this reason, in this study we assessed potential associations between community-level confirmed primary CE (no.=2359) or AE (no.=546) cases in 2014-2016 in Kyrgyzstan and environmental and climatic variables derived from satellite-remote sensing datasets using conditional autoregressive models. We also mapped CE and AE relative risk. The number of AE cases was negatively associated with 10-year lag mean annual temperature. Although this time lag should not be considered as an exact measurement but with associated uncertainty, it is consistent with the estimated 10-15-year latency following AE infection. No associations were detected for CE. We also identified several communities at risk for CE or AE where no disease cases were reported in the study period. CONCLUSIONS/SIGNIFICANCE: Our findings support the hypothesis that CE is linked to an anthropogenic cycle and is less affected by environmental risk factors compared to AE, which is believed to result from spillover from a wild life cycle. As CE was not affected by factors we investigated, hence control should not have a geographical focus. In contrast, AE risk areas identified in this study without reported AE cases should be targeted for active disease surveillance in humans. This active surveillance would confirm or exclude AE transmission which might not be reported with the present passive surveillance system. These areas should also be targeted for ecological investigations in the animal hosts.

Bayesian spatial modelling of terrestrial radiation in Switzerland.

The geographic variation of terrestrial radiation can be exploited in epidemiological studies of the health effects of protracted low-dose exposure. Various methods have been applied to derive maps of this variation. We aimed to construct a map of terrestrial radiation for Switzerland. We used airborne γ-spectrometry measurements to model the ambient dose rates from terrestrial radiation through a Bayesian mixed-effects model and conducted inference using Integrated Nested Laplace Approximation (INLA). We predicted higher levels of ambient dose rates in the alpine regions and Ticino compared with the western and northern parts of Switzerland. We provide a map that can be used for exposure assessment in epidemiological studies and as a baseline map for assessing potential contamination.

Generation and application of river network analogues for use in ecology and evolution.

Several key processes in freshwater ecology are governed by the connectivity inherent to dendritic river networks. These have extensively been analyzed from a geomorphological and hydrological viewpoint, yet structures classically used in ecological modeling have been poorly representative of the structure of real river basins, often failing to capture well-known scaling features of natural rivers. Pioneering work identified optimal channel networks (OCNs) as spanning trees reproducing all scaling features characteristic of natural stream networks worldwide. While OCNs have been used to create landscapes for studies on metapopulations, biodiversity, and epidemiology, their generation has not been generally accessible.Given the increasing interest in dendritic riverine networks by ecologists and evolutionary biologists, we here present a method to generate OCNs and, to facilitate its application, we provide the R-package OCNet. Owing to the stochastic process generating OCNs, multiple network replicas spanning the same surface can be built; this allows performing computational experiments whose results are irrespective of the particular shape of a single river network. The OCN construct also enables the generation of elevational gradients derived from the optimal network configuration, which can constitute three-dimensional landscapes for spatial studies in both terrestrial and freshwater realms. Moreover, the package provides functions that aggregate OCNs into an arbitrary number of nodes, calculate several descriptors of river networks, and draw relevant network features.We describe the main functionalities of the package and its integration with other R-packages commonly used in spatial ecology. Moreover, we exemplify the generation of OCNs and discuss an application to a metapopulation model for an invasive riverine species.In conclusion, OCNet provides a powerful tool to generate realistic river network analogues for various applications. It thereby allows the design of spatially realistic studies in increasingly impacted ecosystems and enhances our knowledge on spatial processes in freshwater ecology in general.

Epidemic cystic and alveolar echinococcosis in Kyrgyzstan: an analysis of national surveillance data.

BACKGROUND: Human cystic and alveolar echinococcosis are among the priority neglected zoonotic diseases for which WHO advocates control. The incidence of both cystic echinococcosis and alveolar echinococcosis has increased substantially in the past 30 years in Kyrgyzstan. Given the scarcity of adequate data on the local geographical variation of these focal diseases, we aimed to investigate within-country incidence and geographical variation of cystic echinococcosis and alveolar echinococcosis at a high spatial resolution in Kyrgyzstan. METHODS: We mapped all confirmed surgical cases of cystic echinococcosis and alveolar echinococcosis reported through the national echinococcosis surveillance system in Kyrgyzstan between Jan 1, 2014, and Dec 31, 2016, from nine regional databases. We then estimated crude surgical incidence, standardised incidence, and standardised incidence ratios (SIRs) of primary cases (ie, excluding relapses) based on age and sex at country, region, district, and local community levels. Finally, we tested the SIRs for global and local spatial autocorrelation to identify disease hotspots at the local community level. All incidence estimates were calculated per 100 000 population and averaged across the 3-year study period to obtain annual estimates. FINDINGS: The surveillance system reported 2359 primary surgical cases of cystic echinococcosis and 546 primary surgical cases of alveolar echinococcosis. Country-level crude surgical incidence was 13·1 per 100 000 population per year for cystic echinococcosis and 3·02 per 100 000 population per year for alveolar echinococcosis. At the local community level, we found annual crude surgical incidences up to 176 per 100 000 population in Sary-Kamysh (Jalal-Abad region) for cystic echinococcosis and 246 per 100 000 population in Uch-Dobo (Alay district, Osh region) for alveolar echinococcosis. Significant hotspots of cystic echinococcosis were found in four regions: Osh (five local communities in Uzgen district and four in Alay district), Naryn (three local communities in Jumgal district and one in Naryn district), Talas (three local communities in Talas district), and Chuy (one local community in Jayyl district). Significant alveolar echinococcosis hotspots were detected in the Osh region (11 communities in Alay district, including the local community of Sary Mogol, and one in Chong-Alay district) and in the Naryn region (five communities in Jumgal district and three in At-Bashy district), in the southwest and centre of the country. INTERPRETATION: Our analyses reveal remarkable within-country variation in the surgical incidence of cystic echinococcosis and alveolar echinococcosis in Kyrgyzstan. These high-resolution maps identify precise locations where interventions and epidemiological research should be targeted to reduce the burden of human cystic echinococcosis and alveolar echinococcosis. FUNDING: Swiss National Science Foundation.

Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland.

Bayesian network (BN) modeling is a rich and flexible analytical framework capable of elucidating complex veterinary epidemiological data. It is a graphical modeling technique that enables the visual presentation of multi-dimensional results while retaining statistical rigor in population-level inference. Using previously published case study data about feline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BN modeling analysis is presented. The analysis shows that reducing the group size and vaccinating animals are the two actionable factors directly associated with FCV status and are primary targets to control FCV infection. The presence of gingivostomatitis and Mycoplasma felis is also associated with FCV status, but signs of upper respiratory tract disease (URTD) are not. FCV data is particularly well-suited to a network modeling approach, as both multiple pathogens and multiple clinical signs per pathogen are involved, along with multiple potentially interrelated risk factors. BN modeling is a holistic approach-all variables of interest may be mutually interdependent-which may help to address issues, such as confounding and collinear factors, as well as to disentangle directly vs. indirectly related variables. We introduce the BN methodology as an alternative to the classical uni- and multivariable regression approaches commonly used for risk factor analyses. We advise and guide researchers about how to use BNs as an exploratory data tool and demonstrate the limitations and practical issues. We present a step-by-step case study using FCV data along with all code necessary to reproduce our analyses in the open-source R environment. We compare and contrast the findings of the current case study using BN modeling with previous results that used classical regression techniques, and we highlight new potential insights. Finally, we discuss advanced methods, such as Bayesian model averaging, a common way of accounting for model uncertainty in a Bayesian network context.

A Case Study Competition Among Methods for Analyzing Large Spatial Data.

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics. Supplementary materials regarding implementation details of the methods and code are available for this article online. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-018-00348-w.

Additive Bayesian networks for antimicrobial resistance and potential risk factors in non-typhoidal Salmonella isolates from layer hens in Uganda.

BACKGROUND: Multi-drug resistant bacteria are seen increasingly and there are gaps in our understanding of the complexity of antimicrobial resistance, partially due to a lack of appropriate statistical tools. This hampers efficient treatment, precludes determining appropriate intervention points and renders prevention very difficult. METHODS: We re-analysed data from a previous study using additive Bayesian networks. The data contained information on resistances against seven antimicrobials and seven potential risk factors from 86 non-typhoidal Salmonella isolates from laying hens in 46 farms in Uganda. RESULTS: The final graph contained 22 links between risk factors and antimicrobial resistances. Solely ampicillin resistance was linked to the vaccinating person and disposal of dead birds. Systematic associations between ampicillin and sulfamethoxazole/trimethoprim and chloramphenicol, which was also linked to sulfamethoxazole/trimethoprim were detected. Sulfamethoxazole/trimethoprim was also directly linked to ciprofloxacin and trimethoprim. Trimethoprim was linked to sulfonamide and ciprofloxacin, which was also linked to sulfonamide. Tetracycline was solely linked to ciprofloxacin. CONCLUSIONS: Although the results needs to be interpreted with caution due to a small data set, additive Bayesian network analysis allowed a description of a number of associations between the risk factors and antimicrobial resistances investigated.

Ecosystem service change caused by climatological and non-climatological drivers: a Swiss case study.

Understanding the drivers of ecosystem change and their effects on ecosystem services are essential for management decisions and verification of progress towards national and international sustainability policies (e.g., Aichi Biodiversity Targets, Sustainable Development Goals). We aim to disentangle spatially the effect of climatological and non-climatological drivers on ecosystem service supply and trends. Therefore, we explored time series of three ecosystem services in Switzerland between 2004 and 2014: carbon dioxide regulation, soil erosion prevention, and air quality regulation. We applied additive models to describe the spatial variation attributed to climatological (i.e., temperature, precipitation and relative sunshine duration) and non-climatological drivers (i.e., random effects representing other spatially structured processes) that may affect ecosystem service change. Obtained results indicated strong influences of climatological drivers on ecosystem service trends in Switzerland. We identified equal contributions of all three climatological drivers on trends of carbon dioxide regulation and soil erosion prevention, while air quality regulation was more strongly influenced by temperature. Additionally, our results showed that climatological and non-climatological drivers affected ecosystem services both negatively and positively, depending on the regions (in particular lower and higher altitudinal areas), drivers, and services assessed. Our findings highlight stronger effects of climatological compared to non-climatological drivers on ecosystem service change in Switzerland. Furthermore, drivers of ecosystem change display a spatial heterogeneity in their influence on ecosystem service trends. We propose an approach building on an additive model to disentangle the effect of climatological and non-climatological drivers on ecosystem service trends. Such analyses should be extended in the future to ecosystem service flow and demand to complete ecosystem service assessments and to demonstrate and communicate more clearly the benefits of ecosystem services for human well-being.

Progression and risk factors of pododermatitis in part-time group housed rabbit does in Switzerland.

In rabbits (Oryctolagus cuniculus L.), pododermatitis is a chronic multifactorial skin disease that appears mainly on the plantar surface of the hind legs. This presumably progressive disease can cause pain leading to poor welfare, yet the progression of this disease has not been thoroughly assessed on the level of individual animals. The aim of this longitudinal study thus was to investigate the possible risk factors and the progression of pododermatitis in group housed breeding does in Switzerland on litter and plastic slats. Three commercial rabbit farms with part-time group housing on litter and plastic slats were visited every four weeks throughout one year. During every visit, the same 201 adult female breeding rabbits (67 does per farm) were evaluated for the presence and severity of pododermatitis. Additionally, the does' age, parity, body weight, reproductive state, hybrid, claw length, cleanliness and moisture of the paws and the temperature and humidity inside the barns were recorded as potential risk factors. The risk factors were analysed through general linear models and additive Bayesian network (ABN) modelling using a directed acyclic graph (DAG) for visualising associations between potential risk factors. The progression of pododermatitis was analysed with a transition matrix. Relative humidity inside the barns, body weight, number of kindlings, age, and claw length were the most important risk factors, all being positively associated with pododermatitis. In contrast to expectations, the cleanliness of the left hind paw was negatively associated with the occurrence of pododermatitis, but the effect was small. In young does, the severity of pododermatitis quickly increased and in some rabbits proceeded to ulcerated spots. It was shown that 60.00%, 14.17% and 3.33% of ulcerated lesions recovered to a state without ulceration within 4, 8 or >12 weeks, respectively.

Modelling anthelmintic resistance by extending eggCounts package to allow individual efficacy.

The same anthelmintic treatment can have variable efficacy on individual animals even if the parasite population is homogenously susceptible. An extension of the R package eggCounts is proposed to take individual efficacy into account using a Bayesian hierarchical model. A simulation study is conducted to compare the performance of five different methods on estimating faecal egg count reduction and its uncertainty interval. Simulation results showed the individual efficacy model offered robust inference to two different data simulation procedures with low root mean squared error on the reduction estimate and appropriate uncertainty estimates. Different methods were used to evaluate the anthelmintic resistance in a dataset from USA with sheep and cattle faecal egg counts, where a strong anthelmintic resistance was detected. Open-source statistical tools were updated to include the proposed model.

The quantified self during travel: mapping health in a prospective cohort of travellers.

BACKGROUND: Travel medicine research has remained relatively unchanged in the face of rapid expansion of international travel and is unlikely to meet health challenges beyond infectious diseases. Our aim was to identify the range of health outcomes during travel using real-time monitoring and daily reporting of health behaviours and outcomes and identify traveller subgroups who may benefit from more targeted advice before and during travel. METHODS: We recruited a prospective cohort of travellers ≥ 18 years and planning travel to Thailand for <5 weeks from the travel clinics in Zurich and Basel (Switzerland). Participants answered demographic, clinical and risk behaviour questionnaires pre-travel and a daily health questionnaire each day during travel using a smartphone application. Environmental and location data were collected passively by GPS. Classification trees were used to identify predictors of health behaviour and outcomes during travel. RESULTS: Non-infectious disease events were relatively common, with 22.7% (17 out of 75 travellers) experiencing an accident, 40.0% ( n = 30) a wound or cut and 14.7% ( n = 11) a bite or lick from an animal. Mental health associated events were widely reported, with 80.0% ( n = 60) reporting lethargy, 34.7% ( n = 26) anxiety and 34.7% ( n = 26) feeling tense or irritable. Classification trees identified age, trip length, previous travel experience and having experienced a sports injury in the past year as the most important discriminatory variables for health threats. CONCLUSIONS: Our study offers a revolutionary look at an almost real-time timeline of health events and behaviours during travel using mHealth technology. Non-infectious disease related health issues were common in this cohort, despite being largely unaddressed in traditional travel medicine research and suggest a substantial potential for improving evidence-based travel medicine advice.

Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason.

The spring phenology of plants in temperate regions strongly responds to spring temperatures. Climate warming has caused substantial phenological advances in the past, but trends to be expected in the future are uncertain. A simple indicator is temperature sensitivity, the phenological advance statistically associated with a 1°C warmer mean temperature during the "preseason", defined as the most temperature-sensitive period preceding the phenological event. Recent analyses of phenological records have shown a decline in temperature sensitivity of leaf unfolding, but underlying mechanisms were not clear. Here, we propose that climate warming can reduce temperature sensitivity simply by reducing the length of the preseason due to faster bud development during this time period, unless the entire preseason shifts forward so that its temperature does not change. We derive these predictions theoretically from the widely used "thermal time model" for bud development and test them using data for 19 phenological events recorded in 1970-2012 at 108 stations spanning a 1600 m altitudinal range in Switzerland. We consider how temperature sensitivity, preseason start, preseason length and preseason temperature change (i) with altitude, (ii) between the periods 1970-1987 and 1995-2012, which differed mainly in spring temperatures, and (iii) between two non-consecutive sets of 18 years that differed mainly in winter temperatures. On average, temperature sensitivity increased with altitude (colder climate) and was reduced in years with warmer springs, but not in years with warmer winters. These trends also varied among species. Decreasing temperature sensitivity in warmer springs was associated with a limited forward shift of preseason start, higher temperatures during the preseason and reduced preseason length, but not with reduced winter chilling. Our results imply that declining temperature sensitivity can result directly from spring warming and does not necessarily indicate altered physiological responses or stronger constraints such as reduced winter chilling.

Zero-inflated hierarchical models for faecal egg counts to assess anthelmintic efficacy.

The prevalence of anthelmintic resistance has increased in recent years, as a result of the extensive use of anthelmintic drugs to reduce the infection of parasitic worms in livestock. In order to detect the resistance, the number of parasite eggs in animal faeces is counted. Typically a subsample of the diluted faeces is examined, and the mean egg counts from both untreated and treated animals are compared. However, the conventional method ignores the variabilities introduced by the counting process and by different infection levels across animals. In addition, there can be extra zero counts, which arise as a result of the unexposed animals in an infected population or animals. In this paper, we propose the zero-inflated Bayesian hierarchical models to estimate the reduction in faecal egg counts. The simulation study compares the Bayesian models with the conventional faecal egg count reduction test and other methods such as bootstrap and quasi-Poisson regression. The results show the Bayesian models are more robust and they perform well in terms of both the bias and the coverage. We further illustrate the advantages of our proposed model using a case study about the anthelmintic resistance in Swedish sheep flocks.