Isolation and characterization of microsatellites in European alpine marmots (Marmota marmota)

For future investigations of the mating system of a highly social mammal (Marmota marmota), we identified 16 new microsatellites using an enrichment protocol. Five loci were revealed to be polymorphic. The polymorphism was rather low (two to six alleles among 24 individuals). However, these markers, added to the other six published microsatellites for M. marmota and Spermophilus citellus, will help to assess dispersal patterns and test for genetic monogamy in alpine marmots from the European Alps.     

Modelling the role of social behavior in the persistence of the alpine marmot Marmota marmota

A general rule of thumb for biological conservation obtained from simple models of hypothetical species is that for populations with strong environmental noise moderate increases in habitat size or quality do not substantially reduce extinction risk. However, whether this rule also holds for real species with complex behavior, such as social species with breeding units and reproductive suppression, is uncertain. Here we present a population viability analysis of the alpine marmot Marmota marmota, which displays marked social behavior, i.e. it lives in social groups of up to twenty individuals. Our analysis is based on a long‐term field study carried out in the Bavarian Alps since 1982. During the first fifteen years of this study, 687 marmots were individually marked and the movements and fate of 98 dispersing marmots were recorded with radio‐telemetry. Thus, in contrast to most other viability analyses of spatially structured populations, good data about dispersal exist. A model was constructed which is individual‐based, spatially explicit at the scale of clusters of neighbouring territories, and spatially implicit at larger scales. The decisive aspect of marmot life history, winter mortality, is described by logistic regression where mortality is increased by age and the severity of winter, and decreased by the number of subdominant individuals present in a group. Model predictions of group size distribution are in good agreement with the results of the field study. The model shows that the effect of sociality on winter mortality is very effective in buffering environmental harshness and fluctuations. This underpins theoretical results stating that the appropriate measure of the strength of environmental noise is the ratio between the variance of population growth rate and the intrinsic rate of increase. The lessons from our study for biological conservation are that simple, unstructured models may not be sufficient to assess the viability of species with complex behavioral traits, and that even moderate increases in habitat capacity may substantially reduce extinction risk even if environmental fluctuations seem high.     

Scent-deposition Behaviour in Alpine Marmots (Marmota marmota L.): its Role in Territorial Defence and Social Communication

Scent-deposition was investigated in Marmota marmota L. at Gran Paradiso National Park. 448 h of focal and scan sampling were performed on 22 marmots in four subsequent years. Results show that: (1) 97.4 % of the scent-deposition is done within 25 m of the edge of the territories or of the main burrows (X² = 100.4, df 9, p < 0.001); (2) being a reproducing adult is the most prominent factor in scent-deposition (X² = 32.4, df = 18, p < 0.002). The role of factors affecting scent-deposition in territorial defence and social communication is discussed.     

Rewarming rates of two large hibernators: Comparison of a monotreme and a eutherian

We measured body temperatures in two large hibernating mammals, the eutherian alpine marmot (Marmota marmota) and the egg-laying echidna (Tachyglossus aculeatus) from unrestrained animals in their natural environment. In both species hibernation is broken every 13 days on average by rewarming to euthermic temperatures. We found that the time course of a rewarming could be closely fitted with a sigmoid curve, allowing calculation of peak rewarming rate and corresponding body temperature. Maximum rewarming rates were twice as high in marmots as in echidnas (12.1±1.3 °C h⁻¹, n=10 cf. 6.2±1.2 °C h⁻¹, n=10). Peak rewarming rates were positively correlated with body temperature in echidnas, but negatively correlated in marmots.     

Socio-spatial Organization and Activity Distribution of the Alpine Marmot Marmota marmota: Preliminary Results

The behavioural ecology of the Alpine marmot Marmota marmota (Linné, 1758) was studied Jun.—Sep. 1990 in the Vanoise National Park (French Alps). We describe the socio-spatial structure of a high-altitude population, to give additional information on the formerly unclear marmot social organization. The social unit was the family group, with a common home range between 0.9 and 2.8 ha; a slight overlapping occurred (9 to 12.5%). Each home range had a central area of main burrows, where the hibernaculum was located, and peripheral areas. Space utilization and distribution of activities during the season were analysed. Some activities took place exclusively in the centre area while peripheral parts were used for foraging. This latter increased in Aug. and decreased in Sep., marmots tending to remain more and more at the centre area as the hibernation period approached.     

Isolation and characterization of microsatellite markers in hoary marmots (Marmota caligata)

Microsatellite loci were developed from hoary marmots (Marmota caligata) to aid in the investigation of the social structure and mating system of this species. Seven of the microsatellite loci developed were found to be moderately polymorphic with between two and seven alleles per locus. In addition to the microsatellites developed in hoary marmots we also tested markers developed for other scuirids, namely European alpine marmots (M. marmota), Columbian ground squirrels (Spermophilus columbianus) and European ground squirrels (S. citellus). Of these markers, 13 were polymorphic when amplified in hoary marmots with between two and nine alleles per locus.     

Multiparametric Analysis of Cell-Free DNA in Melanoma Patients

Cell-free DNA in blood (cfDNA) represents a promising biomarker for cancer diagnosis. Total cfDNA concentration showed a scarce discriminatory power between patients and controls. A higher specificity in cancer diagnosis can be achieved by detecting tumor specific alterations in cfDNA, such as DNA integrity, genetic and epigenetic modifications.The aim of the present study was to identify a sequential multi-marker panel in cfDNA able to increase the predictive capability in the diagnosis of cutaneous melanoma in comparison with each single marker alone. To this purpose, we tested total cfDNA concentration, cfDNA integrity, BRAF^V600E mutation and RASSF1A promoter methylation associated to cfDNA in a series of 76 melanoma patients and 63 healthy controls. The chosen biomarkers were assayed in cfDNA samples by qPCR. Comparison of biomarkers distribution in cases and controls was performed by a logistic regression model in both univariate and multivariate analysis. The predictive capability of each logistic model was investigated by means of the area under the ROC curve (AUC). To aid the reader to interpret the value of the AUC, values between 0.6 and 0.7, between 0.71 and 0.8 and greater than 0.8 were considered as indicating a weak predictive, satisfactory and good predictive capacity, respectively. The AUC value for each biomarker (univariate logistic model) was weak/satisfactory ranging between 0.64 (BRAF^V600E) to 0.85 (total cfDNA). A good overall predictive capability for the final logistic model was found with an AUC of 0.95. The highest predictive capability was given by total cfDNA (AUC:0.86) followed by integrity index 180/67 (AUC:0.90) and methylated RASSF1A (AUC:0.89).An approach based on the simultaneous determination of three biomarkers (total cfDNA, integrity index 180/67 and methylated RASSF1A) could improve the diagnostic performance in melanoma.     

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ₂, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.     

Social behaviour and individual differences in free-living Alpine marmots (Marmota marmota)

I studied the behaviour of free-living Alpine marmots, Marmota marmota, from 1 June to 19 August 1973 in Vanoise National Park, France. Comparing animals of the same sex and age class, individual differences in behaviour generally were greater between colonies than within colonies. Individual differences among adults exceeded those of yearlings, and greater individual differences occurred with regard to social behaviour than with relatively asocial behaviour. General patterns of social biology resembled those of the North American marmots, exceptions being a tendency for adult males to remain somewhat away from and at higher elevation than their colonies, and a distinctly lower frequency of greeting behaviour. Implications of these findings for the evolution of marmot social behaviour are discussed.     

Prediction model of random forest for the risk of hyperuricemia in a Chinese basic health checkup test

Objectives: The present study aimed to develop a random forest (RF) based prediction model for hyperuricemia (HUA) and compare its performance with the conventional logistic regression (LR) model. Methods: This cross-sectional study recruited 91,690 participants (14,032 with HUA, 77,658 without HUA). We constructed a RF-based prediction model in the training sets and evaluated it in the validation sets. Performance of the RF model was compared with the LR model by receiver operating characteristic (ROC) curve analysis. Results: The sensitivity and specificity of the RF models were 0.702 and 0.650 in males, 0.767 and 0.721 in females. The positive predictive value (PPV) and negative predictive value (NPV) were 0.372 and 0.881 in males, 0.159 and 0.978 in females. AUC of the RF models was 0.739 (0.728–0.750) in males and 0.818 (0.799–0.837) in females. AUC of the LR models were 0.730 (0.718–0.741) for males and 0.815 (0.795–0.835) for females. The predictive power of RF was slightly higher than that of LR, but was not statistically significant in females (Delong tests, P=0.0015 for males, P=0.5415 for females). Conclusion: Compared with LR, the good performance in HUA status prediction and the tolerance of features associations or interactions showed great potential of RF in further application. A prospective cohort is necessary for HUA developing prediction. People with high risk factors should be encouraged to actively control to reduce the probability of developing HUA.     

Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data

Aim The proportion of sampled sites where a species is present is known as prevalence. Empirical studies have shown that prevalence can affect the predictive performance of species distribution models. This paper uses simulated species data to examine how prevalence and the form of species environmental dependence affect the assessment of the predictive performance of models. Methods Simulated species data were based on various functions of simulated environmental data with differing degrees of spatial correlation. Seven model performance measures – sensitivity, specificity, class‐average (CA), overall prediction success, kappa (κ), normalized mutual information (NMI) and area under the receiver operating characteristic curve (AUC) – were applied to species models fitted by three regression methods. The response of the performance measures to prevalence was then assessed. Three probability threshold selection methods used to convert fitted logistic model values to presence or absence were also assessed. Results The study shows that the extent to which prevalence affects model performance depends on the modelling technique and its degree of success in capturing dominant environmental determinants. It also depends on the statistic used to measure model performance and the probability threshold method. The response based on κ generally preferred models with medium prevalence. All performance measures were least affected by prevalence when the probability threshold was chosen to maximize predictive performance or was based directly on prevalence. In these cases, the responses based on AUC, CA and NMI generally preferred models with small or large prevalence. Main conclusions The effect of prevalence on the predictive performance of species distribution models has a methodological basis. Relevant factors include the success of the fitted distribution model in capturing the dominant environmental determinant, the model performance measure and the probability threshold selection method. The fixed probability threshold method yields a marked response of model performance to prevalence and is therefore not recommended. The study explains previous empirical results obtained with real data.     

Relative importance of management, meteorological and environmental factors in the spatial distribution of Fasciola hepatica in dairy cattle in a temperate climate zone

Fasciola hepatica, a trematode parasite with a worldwide distribution, is the cause of important production losses in the dairy industry. Diagnosis is hampered by the fact that the infection is mostly subclinical. To increase awareness and develop regionally adapted control methods, knowledge on the spatial distribution of economically important infection levels is needed. Previous studies modelling the spatial distribution of F. hepatica are mostly based on single cross-sectional samplings and have focussed on climatic and environmental factors, often ignoring management factors. This study investigated the associations between management, climatic and environmental factors affecting the spatial distribution of infection with F. hepatica in dairy herds in a temperate climate zone (Flanders, Belgium) over three consecutive years. A bulk-tank milk antibody ELISA was used to measure F. hepatica infection levels in a random sample of 1762 dairy herds in the autumns of 2006, 2007 and 2008. The infection levels were included in a Geographic Information System together with meteorological, environmental and management parameters. Logistic regression models were used to determine associations between possible risk factors and infection levels. The prevalence and spatial distribution of F. hepatica was relatively stable, with small interannual differences in prevalence and location of clusters. The logistic regression model based on both management and climatic/environmental factors included the factors: annual rainfall, mowing of pastures, proportion of grazed grass in the diet and length of grazing season as significant predictors and described the spatial distribution of F. hepatica better than the model based on climatic/environmental factors only (annual rainfall, elevation and slope, soil type), with an Area Under the Curve of the Receiver Operating Characteristic of 0.68 compared with 0.62. The results indicate that in temperate climate zones without large climatic and environmental variation, management factors affect the spatial distribution of F. hepatica, and should be included in future spatial distribution models.     

Differentiation of Alpine marmot populations traced by DNA fingerprinting.

As revealed by allozyme studies, the genetic variation of the Alpine marmot (Marmota m. marmota) has been reduced by a species-wide bottleneck at the end of the last glaciation. Therefore the more variable microsatellite loci were used as a genetic marker system to investigate variablility and differentiation of four autochthonous and four allochthonous populations founded by the release of small numbers of individuals during the last 150 years. The microsatellite loci detected by the DNA-probe (ATCC)₄ were found to be polymorphic in all populations, but the amount of variation was lower than in comparable mammalian species. In spite of founder effects the variation in the allochthonous populations was not significanlty reduced compared to the autochthonous populations. The autochthonous populations from Austria and from the eastern part of Switzerland were genetically similar, only the population from western Switzerland was clearly differentiated from the others. In the allochthonous populations similarities in the microsatellite patterns reveal genetic affinities to putative autochthonous source populations of the founder individuals.     

Biotic structure indirectly affects associated prey in a predator-specific manner via changes in the sensory environment

Sex-specific senescence has been commonly reported in highly dimorphic and polygynous species. However, whether between-sex differences in senescence occur in monogamous and monomorphic species is poorly known. In this study, we used an extensive dataset of 20 years of mass measurements on free-ranging male and female Alpine marmots (Marmota marmota), a medium-sized, long-lived, social and hibernating mammal, to assess sex-specific patterns of senescence in body mass. We tested for the occurrence of both a decrease in body mass scaled to absolute age (called chronological senescence) and a decrease in body mass scaled to individual age at death (called terminal decline). Whereas males showed evidence of both chronological senescence and terminal decline in body mass, females did not show any detectable senescence in body mass. This unexpected between-sex difference of senescence in a species subject to weak sexual selection might be shaped either by costs of an asymmetric intra-sex competition for mates or by costs of social thermoregulation.     

Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea

Lycorma delicatula (Hemiptera: Fulgoridae) is an invasive insect in Korea which causes plant damages by sucking and sooty molds. Lycorma delicatula was first detected in South Korea in 2004, where its introduction and spreading possibly were affected by human activity-related factors. Here, we used MaxEnt to describe current distribution of L. delicatula in Korea and tried to find out the impact of human influences on distribution. We used 143 sites of occurrence data, 19 bioclimatic variables, duration of temperature below −11 °C, average daily minimum temperature in January, cumulative thermal unit variable, the distribution of grape orchard variable and human footprint to create models. These models were estimated by two sets of 24 candidates with feature combinations and regularization multipliers. In addition, these two sets were created as models with and without footprint to assess human influence on distribution. Model selection for optimal model was performed by selecting a model with a lowest sum of each rank in small sample-size corrected Akaike’s information criterion and difference between training and test AUC. Model of LQ10 parameter combinations was selected as optimal models for both model sets. Consequently, both of distribution maps from these models showed similar patterns of presence probability for L. delicatula. Both models expected that low altitude regions were relatively more suitable for L. delicatula than mountain areas in Korea. Footprint might be limited for the distribution and L. delicatula might already occupy most of available habitats. Human-related factors might contribute to spread of L. delicatula to uninfected areas.     

Inter-Model Comparison of the Landscape Determinants of Vector-Borne Disease: Implications for Epidemiological and Entomological Risk Modeling

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.     

Comparative assessment of habitat suitability and niche overlap of three medicinal and melliferous Satureja L. species (Lamiaceae) from the eastern Adriatic region: Exploring potential for cultivation

In Croatia, there are three Satureja species groups (tSsg) within the Satureja L. genus that are most widely used as medicinal and melliferous plants: the Satureja montana complex, S. subspicata complex and S. cuneifolia. Due to the high melliferous and medicinal potential of these taxa, our objective was to comparatively assess the habitat suitability of the tSsg to estimate their cultivation potential in the eastern Adriatic region. In addition, we assessed their overlaps in geographic and environmental space and investigated the occurrence of the putative hybrid taxon S. × karstiana in the areas of overlap between the two parental taxa (S. montana and S. subspicata). Finally, the differences in the environmental preferences of these three related species groups were investigated. The species distribution models were built using the maximum entropy method (MaxEnt) based on the occurrences of the tSsg, obtained from our field research in 2018–2021 and partly from the Flora Croatica Database. Six environmental variables (three bioclimatic and three topographic) were selected as predictors. The potential habitat suitability of the tSsg under current conditions showed high predictive model performance, with AUC values ranging from 0.890 to 0.910 ± 0.025. The environmental predictors with the highest mean percent contributions to the models were annual temperature range (Bio 7) for S. montana (74.3%) and S. subspicata (65.9%) and downwards surface solar radiation (Srad) for S. cuneifolia (40%). Majority (>90%) of the known S. × karstiana hybrid occurrences were found within the areas of habitat suitability overlap between the two parental taxa, confirming the model accuracy and efficiency in the discovery of putative hybrids. Finally, the niche overlap in environmental space between the investigated taxa pairs was the highest between S. montana and S. subspicata, enabling their hybridization, and lowest between S. cuneifolia and S. subspicata. Due to the widest potential distribution and broadest environmental niche, our results suggest that S. montana has the highest potential for cultivation for medicinal and melliferous purposes in the eastern Adriatic, particularly in light of ongoing climate change.     

A Predictive Model Combining Fecal Calgranulin B and Fecal Occult Blood Tests Can Improve the Diagnosis of Colorectal Cancer

Aim

Current fecal screening tools for colorectal cancer (CRC), such as fecal occult blood tests (FOBT), are limited by their low sensitivity. Calgranulin B (CALB) was previously reported as a candidate fecal marker for CRC. This study investigated whether a combination of the FOBT and fecal CALB has increased sensitivity and specificity for a diagnosis of CRC.

Materials and Methods

Patients with CRC (n = 175), and healthy individuals (controls; n = 151) were enrolled into the development (81 cases and 51 controls) and validation (94 cases and 100 controls) sets. Stool samples were collected before bowel preparation. CALB levels were determined by western blotting. FOBT and fecal CALB results were used to develop a predictive model based on logistic regression analysis. The benefit of adding CALB to a model with only FOBT was evaluated as an increased area under the receiver operating curve (AUC), partial AUC, and reclassification improvement (RI) in cases and controls, and net reclassification improvement (NRI).

Results

Mean CALB level was significantly higher in CRC patients than in controls (P<0.001). CALB was not associated with tumor stage or cancer site, but positivity on the FOBT was significantly higher in advanced than in earlier tumor stages. At a specificity of 90%, the cross-validated AUC and sensitivity were 89.81% and 82.72%, respectively, in the development set, and 92.74% and 79.79%, respectively, in the validation set. The incremental benefit of adding CALB to the model, as shown by the increase in AUC, had a p-value of 0.0499. RI in cases and controls and NRI all revealed that adding CALB significantly improved the prediction model.

Conclusion

A predictive model using a combination of FOBT and CALB may have greater sensitivity and specificity and AUC for predicting CRC than models using a single marker.