Accuracy of a rapid enzyme-linked immunosorbent assay to measure progesterone in mares

The aim of this study was to validate an enzyme-linked immunosorbent assay (ELISA) for the measurement of progesterone (P4) in mares. Specifically, the objectives were as follows: (1) to determine the specificity and sensitivity of the ELISA test for determination of P4, (2) to measure the potential agreement between the 2 people performing the test, and 3) to evaluate the effect of time on the outcome. Ten mares were sampled on the day before ovulation (D-1), and on days 1 (D1), 3 (D3), and 5 (D5) following ovulation, during the reproductive season. While mares were cycling regularly, estrus was induced by the injection of 5 mg of prostaglandin (PGF2) and monitored starting on the 4th day by daily transrectal palpation and ultrasonography to determine the time of ovulation. Blood was collected and all samples (n=96) were assayed for P4 by a semiquantitative ELISA, by chemiluminescent immunoassay, and by radioimmunoassay (RIA). Based on the RIA, values of P4 on D-1, D1, D3, and D5 were significantly different (P < 0.0001) with mean and standard deviation(s) of 0.004, s = 0.52; 2.05, s = 2.58; 8.37, s = 4.17; and 12.76, s = 4.00 ng/mL respectively. The sensitivity and specificity of the semiquantitative assay were 94% and 95%, respectively for the lowest values of P4 (< 1.0 ng/mL). The value of kappa was 0.90 between 2 individuals performing the test. In conclusion, these results suggest that the semiquantitative test may be used reliably and economically to evaluate P4 levels in equine plasma in the clinical     

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.     

Sentinel-2 images reveal functional biophysical heterogeneities in crop mosaics

Landscape Ecology - Identifying landscape structure and understanding its functions are crucial for biological control. However, the relationship between the crop mosaic phenological heterogeneity...     

Effectiveness of Combined Use of Mineral Oil and Insecticide Spray in Reducing <Emphasis Type="Italic">Potato Virus Y</Emphasis> (PVY) Spread under Field Conditions in New Brunswick,Canada

In the 2014 and 2015 crop seasons, the efficacies of different types, rates and combinations of mineral oil and insecticide foliar sprays for reducing Potato virus Y (PVY) spread were tested in controlled field trials in New Brunswick (NB), Canada. Experimental plots were planted with certified PVY-free Goldrush, supplemented with known virus-infected seed to raise PVY inoculum to 2.3% and 3% at the beginning of the 2014 and 2015 seasons, respectively. Treatments consisted of mineral oil-only sprays at different application rates, insecticide-only sprays of differing numbers, and several combined mineral oil and insecticide spray regimes, all compared to a no-spray control treatment. PVY spread to 18% (2014) and 22% (2015) of initially virus-free plants in no-spray control plots, with significant reductions observed in PVY spread in several treatments. Greatest PVY reductions, as low as 4% (2014) and 12% (2015), were in combined mineral oil and insecticide spray treatments, followed by oil-only sprays; while insecticide-only sprays did not significantly reduce PVY spread. As well as measuring PVY spread to marked test plants and randomly collected post-harvest tuber sample from the plots, exhibited similar treatment pattern for PVY incidence. Multiple logistic regression modeling confirmed the relative efficacy of combined oil and insecticide sprays for reducing PVY spread, while accounting for variable inoculum and aphid factors. Modeling also highlighted the importance of planting low-PVY seed initially, and of early application of foliar sprays. Local best management practice recommendations for reduction of in-field PVY spread were discussed.     

Synthetic Aperture Radar (SAR) images improve habitat suitability models

Context

The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors.

Objectives

The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs).

Methods

We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model.

Results

We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species.

Conclusions

Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.