Assessing the validity of autologistic regression

Combining bird tracking data with high-resolution thermal mapping to identify microclimate refugia

Elevated temperatures can have a range of fitness impacts, including high metabolic cost of thermoregulation, hence access to microclimate refugia may buffer individuals against exposure to high temperatures. However, studies examining the use of microclimate refugia, remain scarce. We combined high resolution microclimate modelling with GPS tracking data as a novel approach to identify the use and availability of cooler microclimate refugia (sites > 0.5 °C cooler than the surrounding landscape) at the scales experienced by individual animals. 77 little bustards (Tetrax tetrax) were tracked between 2009 and 2019. The 92,685 GPS locations obtained and their surrounding 500 m areas were characterised with hourly temperature and habitat information at 30 m × 30 m and used to determine microclimate refugia availability and use. We found that the semi-natural grassland landscapes used by little bustards have limited availability of cooler microclimate areas-fewer than 30% of the locations. The use of cooler microclimate sites by little bustards increased at higher ambient temperatures, suggesting that individuals actively utilise microclimate refugia in extreme heat conditions. Microclimate refugia availability and use were greater in areas with heterogeneous vegetation cover, and in coastal areas. This study identified the landscape characteristics that provide microclimate opportunities and shelter from extreme heat conditions. Little bustards made greater use of microclimate refugia with increasing temperatures, particularly during the breeding season, when individuals are highly site faithful. This information can help identify areas where populations might be particularly exposed to climate extremes due to a lack of microclimate refugia, and which habitat management measures may buffer populations from expected increased exposure to temperature extremes.

Differential environmental and genomic architectures shape the natural diversity for trichome patterning and morphology in different Arabidopsis organs

Despite the adaptive and taxonomic relevance of the natural diversity for trichome patterning and morphology, the molecular and evolutionary mechanisms underlying these traits remain mostly unknown, particularly in organs other than leaves. In this study, we address the ecological, genetic and molecular bases of the natural variation for trichome patterning and branching in multiple organs of Arabidopsis (Arabidopsis thaliana). To this end, we characterized a collection of 191 accessions and carried out environmental and genome-wide association (GWA) analyses. Trichome amount in different organs correlated negatively with precipitation in distinct seasons, thus suggesting a precise fit between trichome patterning and climate throughout the Arabidopsis life cycle. In addition, GWA analyses showed small overlapping between the genes associated with different organs, indicating partly independent genetic bases for vegetative and reproductive phases. These analyses identified a complex locus on chromosome 2, where two adjacent MYB genes (ETC2 and TCL1) displayed differential effects on trichome patterning in several organs. Furthermore, analyses of transgenic lines carrying different natural alleles demonstrated that TCL1 accounts for the variation for trichome patterning in all organs, and for stem trichome branching. By contrast, two other MYB genes (TRY and GL1), mainly showed effects on trichome patterning or branching, respectively.

RNA meets toxicology: efficacy indicators from the experimental design of RNAi studies for insect pest management

RNA interference (RNAi) selectively targets genes and silences their expression in vivo, causing developmental defects, mortality and altered behavior. Consequently, RNAi has emerged as a promising research area for insect pest management. However, it is not yet a viable alternative over conventional pesticides despite several theoretical advantages in safety and specificity. As a first step toward a more standardized approach, a machine learning algorithm was used to identify factors that predict trial efficacy. Current research on RNAi for pest management is highly variable and relatively unstandardized. The applied random forest model was able to reliably predict mortality ranges based on bioassay parameters with 72.6% accuracy. Response time and target gene were the most important variables in the model, followed by applied dose, double-stranded RNA (dsRNA) construct size and target species, further supported by generalized linear mixed effect modeling. Our results identified informative trends, supporting the idea that basic principles of toxicology apply to RNAi bioassays and provide initial guidelines standardizing future research similar to studies of traditional insecticides. We advocate for training that integrates genetic, organismal, and toxicological approaches to accelerate the development of RNAi as an effective tool for pest management. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models

BACKGROUND

Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables.

METHODOLOGY

Entomological data for the period 2008-2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic.

RESULTS

A total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season.

CONCLUSIONS

A methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0-5 FTD (48%), medium 5.1-35 FTD (18%) and high 35.1-60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies.

MYB transcription factors drive evolutionary innovations in Arabidopsis fruit trichome patterning

Both inter- and intra-specific diversity has been described for trichome patterning in fruits, which is presumably involved in plant adaptation. However, the mechanisms underlying this developmental trait have been hardly addressed. Here we examined natural populations of Arabidopsis (Arabidopsis thaliana) that develop trichomes in fruits and pedicels, phenotypes previously not reported in the Arabidopsis genus. Genetic analyses identified five loci, MALAMBRUNO 1-5 (MAU1-5), with MAU2, MAU3, and MAU5 showing strong epistatic interactions that are necessary and sufficient to display these traits. Functional characterization of these three loci revealed cis-regulatory mutations in TRICHOMELESS1 and TRIPTYCHON, as well as a structural mutation in GLABRA1. Therefore, the multiple mechanisms controlled by three MYB transcription factors of the core regulatory network for trichome patterning have jointly been modulated to trigger trichome development in fruits. Furthermore, analyses of worldwide accessions showed that these traits and mutations only occur in a highly differentiated relict lineage from the Iberian Peninsula. In addition, these traits and alleles were associated with low spring precipitation, which suggests that trichome development in fruits and pedicels might be involved in climatic adaptation. Thus, we show that the combination of synergistic mutations in a gene regulatory circuit has driven evolutionary innovations in fruit trichome patterning in Arabidopsis.

The functional composition of the neophytic flora changes in response to environmental conditions along a rural-urban gradient

Compared to rural environments, cities are known to be extraordinarily rich in plant species. In particular, the proportion of alien plant species is higher in urban areas. This is attributed to specific urban conditions, which provide a large variety of habitats due to high geological heterogeneity. It can also be attributed to the role of cities as centres for plant introductions and the consequential increased propagule pressure. Neophytes, alien plant species introduced after the discovery of the Americas, appear to contribute especially strongly to the increased proportion of alien plants in cities. To investigate whether the plant traits of neophytes can be explained by environmental variables, we modelled the composition of their pollination types and growth forms as well as their diaspore weight and the onset of flowering in response to a selection of climatic, geological, land cover and traffic network variables with data from Germany. To test for a specific urban effect, we included their interactions with the area of urban land use.

In general, we found that climatic variables were the most important predictors for all traits. However, when considering interactions with urbanisation, non-climatic variables, which often were not significant as the main effect, remained in the final models. This points to an existing ‘urban effect’. However, it is much smaller compared to the purely climatic effects. We conclude that interferences and alterations mainly related to urbanisation and human activity in general are responsible for the different ecological processes found in cities compared to rural areas. In addition, we argue that considering functional traits is an appropriate way to identify the ecological mechanisms related to urbanisation.

Remote reefs and seamounts are the last refuges for marine predators across the Indo-Pacific

Since the 1950s, industrial fisheries have expanded globally, as fishing vessels are required to travel further afield for fishing opportunities. Technological advancements and fishery subsidies have granted ever-increasing access to populations of sharks, tunas, billfishes, and other predators. Wilderness refuges, defined here as areas beyond the detectable range of human influence, are therefore increasingly rare. In order to achieve marine resources sustainability, large no-take marine protected areas (MPAs) with pelagic components are being implemented. However, such conservation efforts require knowledge of the critical habitats for predators, both across shallow reefs and the deeper ocean. Here, we fill this gap in knowledge across the Indo-Pacific by using 1,041 midwater baited videos to survey sharks and other pelagic predators such as rainbow runner (Elagatis bipinnulata), mahi-mahi (Coryphaena hippurus), and black marlin (Istiompax indica). We modeled three key predator community attributes: vertebrate species richness, mean maximum body size, and shark abundance as a function of geomorphology, environmental conditions, and human pressures. All attributes were primarily driven by geomorphology (35%-62% variance explained) and environmental conditions (14%-49%). While human pressures had no influence on species richness, both body size and shark abundance responded strongly to distance to human markets (12%-20%). Refuges were identified at more than 1,250 km from human markets for body size and for shark abundance. These refuges were identified as remote and shallow seabed features, such as seamounts, submerged banks, and reefs. Worryingly, hotpots of large individuals and of shark abundance are presently under-represented within no-take MPAs that aim to effectively protect marine predators, such as the British Indian Ocean Territory. Population recovery of predators is unlikely to occur without strategic placement and effective enforcement of large no-take MPAs in both coastal and remote locations.

Global forest loss disproportionately erodes biodiversity in intact landscapes

Global biodiversity loss is a critical environmental crisis, yet the lack of spatial data on biodiversity threats has hindered conservation strategies. Theory predicts that abrupt biodiversity declines are most likely to occur when habitat availability is reduced to very low levels in the landscape (10-30%). Alternatively, recent evidence indicates that biodiversity is best conserved by minimizing human intrusion into intact and relatively unfragmented landscapes. Here we use recently available forest loss data to test deforestation effects on International Union for Conservation of Nature Red List categories of extinction risk for 19,432 vertebrate species worldwide. As expected, deforestation substantially increased the odds of a species being listed as threatened, undergoing recent upgrading to a higher threat category and exhibiting declining populations. More importantly, we show that these risks were disproportionately high in relatively intact landscapes; even minimal deforestation has had severe consequences for vertebrate biodiversity. We found little support for the alternative hypothesis that forest loss is most detrimental in already fragmented landscapes. Spatial analysis revealed high-risk hot spots in Borneo, the central Amazon and the Congo Basin. In these regions, our model predicts that 121-219 species will become threatened under current rates of forest loss over the next 30 years. Given that only 17.9% of these high-risk areas are formally protected and only 8.9% have strict protection, new large-scale conservation efforts to protect intact forests are necessary to slow deforestation rates and to avert a new wave of global extinctions.

Lung and stomach cancer associations with groundwater radon in North Carolina, USA

Background

The risk of indoor air radon for lung cancer is well studied, but the risks of groundwater radon for both lung and stomach cancer are much less studied, and with mixed results.

Methods

Geomasked and geocoded stomach and lung cancer cases in North Carolina from 1999 to 2009 were obtained from the North Carolina Central Cancer Registry. Models for the association with groundwater radon and multiple confounders were implemented at two scales: (i) an ecological model estimating cancer incidence rates at the census tract level; and (ii) a case-only logistic model estimating the odds that individual cancer cases are members of local cancer clusters.

Results

For the lung cancer incidence rate model, groundwater radon is associated with an incidence rate ratio of 1.03 [95% confidence interval (CI) = 1.01, 1.06] for every 100 Bq/l increase in census tract averaged concentration. For the cluster membership models, groundwater radon exposure results in an odds ratio for lung cancer of 1.13 (95% CI = 1.04, 1.23) and for stomach cancer of 1.24 (95% CI = 1.03, 1.49), which means groundwater radon, after controlling for multiple confounders and spatial auto-correlation, increases the odds that lung and stomach cancer cases are members of their respective cancer clusters.

Conclusion

Our study provides epidemiological evidence of a positive association between groundwater radon exposure and lung cancer incidence rates. The cluster membership model results find groundwater radon increases the odds that both lung and stomach cancer cases occur within their respective cancer clusters. The results corroborate previous biokinetic and mortality studies that groundwater radon is associated with increased risk for lung and stomach cancer.

Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study

The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with 'ground truth' histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.

Highly Pathogenic Avian Influenza H5N1 in Mainland China

Highly pathogenic avian influenza (HPAI) H5N1 has posed a significant threat to both humans and birds, and it has spanned large geographic areas and various ecological systems throughout Asia, Europe and Africa, but especially in mainland China. Great efforts in control and prevention of the disease, including universal vaccination campaigns in poultry and active serological and virological surveillance, have been undertaken in mainland China since the beginning of 2006. In this study, we aim to characterize the spatial and temporal patterns of HPAI H5N1, and identify influencing factors favoring the occurrence of HPAI H5N1 outbreaks in poultry in mainland China. Our study shows that HPAI H5N1 outbreaks took place sporadically after vaccination campaigns in poultry, and mostly occurred in the cold season. The positive tests in routine virological surveillance of HPAI H5N1 virus in chicken, duck, goose as well as environmental samples were mapped to display the potential risk distribution of the virus. Southern China had a higher positive rate than northern China, and positive samples were mostly detected from chickens in the north, while the majority were from duck in the south, and a negative correlation with monthly vaccination rates in domestic poultry was found (R = -0.19, p value = 0.005). Multivariate panel logistic regression identified vaccination rate, interaction between distance to the nearest city and national highway, interaction between distance to the nearest lake and wetland, and density of human population, as well as the autoregressive term in space and time as independent risk factors in the occurrence of HPAI H5N1 outbreaks, based on which a predicted risk map of the disease was derived. Our findings could provide new understanding of the distribution and transmission of HPAI H5N1 in mainland China and could be used to inform targeted surveillance and control efforts in both human and poultry populations to reduce the risk of future infections.

Tsetse fly (G. f. fuscipes) distribution in the Lake Victoria basin of Uganda

Tsetse flies transmit trypanosomes, the causative agent of human and animal African trypanosomiasis. The tsetse vector is extensively distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. Given scarce resources to carry out regular full scale field tsetse surveys to up-date existing tsetse maps, there is a need to devise inexpensive means for regularly obtaining dependable area-wide tsetse data to guide control activities. In this study we used spatial epidemiological modelling techniques (logistic regression) involving 5000 field-based tsetse-data (G. f. fuscipes) points over an area of 40,000 km², with satellite-derived environmental surrogates composed of precipitation, temperature, land cover, normalised difference vegetation index (NDVI) and elevation at the sub-national level. We used these extensive tsetse data to analyse the relationships between presence of tsetse (G. f. fuscipes) and environmental variables. The strength of the results was enhanced through the application of a spatial autologistic regression model (SARM). Using the SARM we showed that the probability of tsetse presence increased with proportion of forest cover and riverine vegetation. The key outputs are a predictive tsetse distribution map for the Lake Victoria basin of Uganda and an improved understanding of the association between tsetse presence and environmental variables. The predicted spatial distribution of tsetse in the Lake Victoria basin of Uganda will provide significant new information to assist with the spatial targeting of tsetse and trypanosomiasis control.

Trypanosomiasis is a vector-borne disease transmitted to both humans and animals by the tsetse fly. The tsetse vector is distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. This study makes available dependable tsetse fly distribution data (maps) for use by decision makers. The approach makes use of modelling techniques involving limited field-sampled tsetse data points distributed across an area of approximately 40,000km² within the Lake Victoria basin of Uganda. Precipitation, temperature, landcover, normalised difference vegetation index (NDVI, a measure of the amount of green vegetation) and elevation data were used as environmental covariates. We used logistic regression to analyse the relationships between presence of tsetse and the environmental covariates. The results indicated that tsetse are more likely to be present in areas with a greater proportion of riverine vegetation and forest cover. The key outputs are a predicted tsetse distribution map for the Lake Victoria basin of Uganda and an increased understanding of the association between tsetse presence and environmental variables. This will provide a vital resource for the planning and spatial targeting of future tsetse control activities.

Linking student performance in Massachusetts elementary schools with the "greenness" of school surroundings using remote sensing

Various studies have reported the physical and mental health benefits from exposure to "green" neighborhoods, such as proximity to neighborhoods with trees and vegetation. However, no studies have explicitly assessed the association between exposure to "green" surroundings and cognitive function in terms of student academic performance. This study investigated the association between the "greenness" of the area surrounding a Massachusetts public elementary school and the academic achievement of the school's student body based on standardized tests with an ecological setting. Researchers used the composite school-based performance scores generated by the Massachusetts Comprehensive Assessment System (MCAS) to measure the percentage of 3rd-grade students (the first year of standardized testing for 8-9 years-old children in public school), who scored "Above Proficient" (AP) in English and Mathematics tests (Note: Individual student scores are not publically available). The MCAS results are comparable year to year thanks to an equating process. Researchers included test results from 2006 through 2012 in 905 public schools and adjusted for differences between schools in the final analysis according to race, gender, English as a second language (proxy for ethnicity and language facility), parent income, student-teacher ratio, and school attendance. Surrounding greenness of each school was measured using satellite images converted into the Normalized Difference Vegetation Index (NDVI) in March, July and October of each year according to a 250-meter, 500-meter, 1,000-meter, and 2000-meter circular buffer around each school. Spatial Generalized Linear Mixed Models (GLMMs) estimated the impacts of surrounding greenness on school-based performance. Overall the study results supported a relationship between the "greenness" of the school area and the school-wide academic performance. Interestingly, the results showed a consistently positive significant association between the greenness of the school in the Spring (when most Massachusetts students take the MCAS tests) and school-wide performance on both English and Math tests, even after adjustment for socio-economic factors and urban residency.

A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study

There has been an increasing interest in the analysis of spatially distributed multivariate binary data motivated by a wide range of research problems. Two types of correlations are usually involved: the correlation between the multiple outcomes at one location and the spatial correlation between the locations for one particular outcome. The commonly used regression models only consider one type of correlations while ignoring or modeling inappropriately the other one. To address this limitation, we adopt a Bayesian nonparametric approach to jointly modeling multivariate spatial binary data by integrating both types of correlations. A multivariate probit model is employed to link the binary outcomes to Gaussian latent variables; and Gaussian processes are applied to specify the spatially correlated random effects. We develop an efficient Markov chain Monte Carlo algorithm for the posterior computation. We illustrate the proposed model on simulation studies and a multidrug-resistant tuberculosis case study.

Mapping amyotrophic lateral sclerosis lake risk factors across northern New England

Background

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.

Methods

Our eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots.

Results

Several distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R² values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively.

Conclusions

The strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.

Predicting cycling accident risk in Brussels: a spatial case-control approach

This paper aims at predicting cycling accident risk for an entire network and identifying how road infrastructure influences cycling safety in the Brussels-Capital Region (Belgium). A spatial Bayesian modelling approach is proposed using a binary dependent variable (accident, no accident at location i) constructed from a case-control strategy. Control sites are sampled along the 'bikeable' road network in function of the potential bicycle traffic transiting in each ward. Risk factors are limited to infrastructure, traffic and environmental characteristics. Results suggest that a high risk is statistically associated with the presence of on-road tram tracks, bridges without cycling facility, complex intersections, proximity to shopping centres or garages, and busy van and truck traffic. Cycle facilities built at intersections and parked vehicles located next to separated cycle facilities are also associated with an increased risk, whereas contraflow cycling is associated with a reduced risk. The cycling accident risk is far from being negligible in points where there is actually no reported cycling accident but where they are yet expected to occur. Hence, mapping predicted accident risks provides planners and policy makers with a useful tool for accurately locating places with a high potential risk even before accidents actually happen. This also provides comprehensible information for orienting cyclists to the safest routes in Brussels.

A multilevel model for spatially correlated binary data in the presence of misclassification: an application in oral health research

Dental caries is a highly prevalent disease affecting the tooth's hard tissues by acid-forming bacteria. The past and present caries status of a tooth is characterized by a response called caries experience (CE). Several epidemiological studies have explored risk factors for CE. However, the detection of CE is prone to misclassification because some cases are neither clearly carious nor noncarious, and this needs to be incorporated into the epidemiological models for CE data. From a dentist's point of view, it is most appealing to analyze CE on the tooth's surface, implying that the multilevel structure of the data (surface-tooth-mouth) needs to be taken into account. In addition, CE data are spatially referenced, that is, an active lesion on one surface may impact the decay process of the neighboring surfaces, and that might also influence the process of scoring CE. In this paper, we investigate two hypotheses: that is, (i) CE outcomes recorded at surface level are spatially associated; and (ii) the dental examiners exhibit some spatial behavior while scoring CE at surface level, by using a spatially referenced multilevel autologistic model, corrected for misclassification. These hypotheses were tested on the well-known Signal Tandmobiel® study on dental caries, and simulation studies were conducted to assess the effect of misclassification and strength of spatial dependence on the autologistic model parameters. Our results indicate a substantial spatial dependency in the examiners' scoring behavior and also in the prevalence of CE at surface level.

Cultural macroevolution on neighbor graphs : vertical and horizontal transmission among Western North American Indian societies

What are the driving forces of cultural macroevolution, the evolution of cultural traits that characterize societies or populations? This question has engaged anthropologists for more than a century, with little consensus regarding the answer. We develop and fit autologistic models, built upon both spatial and linguistic neighbor graphs, for 44 cultural traits of 172 societies in the Western North American Indian (WNAI) database. For each trait, we compare models including or excluding one or both neighbor graphs, and for the majority of traits we find strong evidence in favor of a model which uses both spatial and linguistic neighbors to predict a trait's distribution. Our results run counter to the assertion that cultural trait distributions can be explained largely by the transmission of traits from parent to daughter populations and are thus best analyzed with phylogenies. In contrast, we show that vertical and horizontal transmission pathways can be incorporated in a single model, that both transmission modes may indeed operate on the same trait, and that for most traits in the WNAI database, accounting for only one mode of transmission would result in a loss of information.

Predicting the distribution of intensive poultry farming in Thailand

Intensification of animal production can be an important factor in the emergence of infectious diseases because changes in production structure influence disease transmission patterns. In 2004 and 2005, Thailand was subject to two highly pathogenic avian influenza epidemic waves and large surveys were conducted of the poultry sector, providing detailed spatial data on various poultry types. This study analysed these data with the aim of establishing the distributions of extensive and intensive poultry farms, based on the number of birds per holder. Once poultry data were disaggregated into these two production systems, they were analysed in relation to anthropogenic factors using simultaneous autoregressive models. Intensive chicken production was clustered around the capital city of Bangkok and close to the main consumption and export centres. Intensively-raised ducks, mainly free-grazing, showed a distinct pattern with the highest densities distributed in a large area located in the floodplain of the Chao Phraya River. Accessibility to Bangkok, the percentage of irrigated areas and human population density were the most important predictors explaining the geographical distribution of intensively-raised poultry. The distribution of extensive poultry showed a higher predictability. Extensive poultry farms were distributed more homogeneously across the country and their distribution was best predicted by human population density.

Guild composition and habitat use of voles in 2 forest landscapes in south-eastern Norway

It is widely believed that intensive forestry has influenced small mammal population dynamics, and thereby the entire mammalian community in Fennoscandian boreal forests. The nature of these impacts on the different species is subject to debate. We live-trapped voles between 2006 and 2009 in 2 commercially harvested forests in south-eastern Norway. We investigated the variation in vole abundance among habitat types (e.g. mature forest and clear-cut) and the hypothesis that graminivorous species such as field voles (Microtus agrestis L.) benefit from clear-cuts at the expense of forest dwellers (i.e. the bank vole, Myodes glareolus Schreb.), using fine-scale descriptors of the ground vegetation. We could not find support for the hypothesis that field voles show a preference for clear-cuts, and their overall abundance was low, while bank voles were the dominant species in all habitat types, including clear-cuts in the peak and pre-peak years. We found a positive association between bank vole abundance and bilberry (Vaccinium myrtillus L.) availability rather than a specific habitat type. Low field vole density in clear-cuts might be due to variation in local productivity and ground vegetation as well as to large variation in the species temporal dynamics. The latter is particularly associated with the widespread decline of field voles in Scandinavia. Logging has the potential to negatively affect bank vole population dynamics because of the negative effect on bilberry development.

Altitudinal and climatic adaptation is mediated by flowering traits and FRI, FLC, and PHYC genes in Arabidopsis

Extensive natural variation has been described for the timing of flowering initiation in many annual plants, including the model wild species Arabidopsis (Arabidopsis thaliana), which is presumed to be involved in adaptation to different climates. However, the environmental factors that might shape this genetic variation, as well as the molecular bases of climatic adaptation by modifications of flowering time, remain mostly unknown. To approach both goals, we characterized the flowering behavior in relation to vernalization of 182 Arabidopsis wild genotypes collected in a native region spanning a broad climatic range. Phenotype-environment association analyses identified strong altitudinal clines (0-2600 m) in seven out of nine flowering-related traits. Altitudinal clines were dissected in terms of minimum winter temperature and precipitation, indicating that these are the main climatic factors that might act as selective pressures on flowering traits. In addition, we used an association analysis approach with four candidate genes, FRIGIDA (FRI), FLOWERING LOCUS C (FLC), PHYTOCHROME C (PHYC), and CRYPTOCHROME2, to decipher the genetic bases of this variation. Eleven different loss-of-function FRI alleles of low frequency accounted for up to 16% of the variation for most traits. Furthermore, an FLC allelic series of six novel putative loss- and change-of-function alleles, with low to moderate frequency, revealed that a broader FLC functional diversification might contribute to flowering variation. Finally, environment-genotype association analyses showed that the spatial patterns of FRI, FLC, and PHYC polymorphisms are significantly associated with winter temperatures and spring and winter precipitations, respectively. These results support that allelic variation in these genes is involved in climatic adaptation.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning.

Thresholds in forest bird occurrence as a function of the amount of early-seral broadleaf forest at landscape scales

Recent declines in broadleaf-dominated, early-seral forest globally as a function of intensive forest management and/or fire suppression have raised concern about the viability of populations dependent on such forest types. However, quantitative information about the strength and direction of species associations with broadleaf cover at landscape scales are rare. Uncovering such habitat relationships is essential for understanding the demography of species and in developing sound conservation strategies. It is particularly important to detect points in habitat reduction where rates of population decline may accelerate or the likelihood of species occurrence drops rapidly (i.e., thresholds). Here, we use a large avian point-count data set (N = 4375) from southwestern and northwestern Oregon along with segmented logistic regression to test for thresholds in forest bird occurrence as a function of broadleaf forest and early-seral broadleaf forest at local (150-m radius) and landscape (500-2000-m radius) scales. All 12 bird species examined showed positive responses to either broadleaf forest in general, and/or early-seral broadleaf forest. However, regional variation in species response to these conditions was high. We found considerable evidence for landscape thresholds in bird species occurrence as a function of broadleaf cover; threshold models received substantially greater support than linear models for eight of 12 species. Landscape thresholds in broadleaf forest ranged broadly from 1.35% to 24.55% mean canopy cover. Early-seral broadleaf thresholds tended to be much lower (0.22-1.87%). We found a strong negative relationship between the strength of species association with early-seral broadleaf forest and 42-year bird population trends; species most associated with this forest type have declined at the greatest rates. Taken together, these results provide the first support for the hypothesis that reductions in broadleaf-dominated early-seral forest due to succession and intensive forest management have led to population declines of constituent species in the Pacific northwestern United States. Forest management treatments that maintain or restore even small amounts of broadleaf vegetation could mitigate further declines.

Modeling of spatially referenced environmental and meteorological factors influencing the probability of Listeria species isolation from natural environments

Many pathogens have the ability to survive and multiply in abiotic environments, representing a possible reservoir and source of human and animal exposure. Our objective was to develop a methodological framework to study spatially explicit environmental and meteorological factors affecting the probability of pathogen isolation from a location. Isolation of Listeria spp. from the natural environment was used as a model system. Logistic regression and classification tree methods were applied, and their predictive performances were compared. Analyses revealed that precipitation and occurrence of alternating freezing and thawing temperatures prior to sample collection, loam soil, water storage to a soil depth of 50 cm, slope gradient, and cardinal direction to the north are key predictors for isolation of Listeria spp. from a spatial location. Different combinations of factors affected the probability of isolation of Listeria spp. from the soil, vegetation, and water layers of a location, indicating that the three layers represent different ecological niches for Listeria spp. The predictive power of classification trees was comparable to that of logistic regression. However, the former were easier to interpret, making them more appealing for field applications. Our study demonstrates how the analysis of a pathogen's spatial distribution improves understanding of the predictors of the pathogen's presence in a particular location and could be used to propose novel control strategies to reduce human and animal environmental exposure.

Spatial analysis improves species distribution modelling during range expansion

Species distribution models (SDMs) assume equilibrium between species' distribution and the environment. However, this assumption can be violated under restricted dispersal and spatially autocorrelated environmental conditions. Here we used a model to simulate species' ranges expansion under two non-equilibrium scenarios, evaluating the performance of SDM coupled with spatial eigenvector mapping. The highest fit is for the models that include space, although the relative importance of spatial variables during the range expansion differs in the two scenarios. Incorporating space to the models was important only under colonization-lag non-equilibrium, under the expected scenario. Thus, mechanisms that generate range cohesion and determine species' distribution under climate changes can be captured by spatial modelling, with advantages compared with other techniques and in line with recent claims that SDMs have to account for more complex dynamic scenarios.