This study sought to identify the effects of culture and sex on mate preferences using samples drawn world-wide. Thirty-seven samples were obtained from 33 countries located on six continents and five islands (N = 9,474). Hierarchical multiple regressions revealed strong effects of both culture and sex, moderated by specific mate characteristics. Chastity proved to be the mate characteristic on which cultures varied the most. The preference ordering of each sample was contrasted with an international complement. Each culture displayed a unique preference ordering, but there were some similarities among all cultures as reflected in a positive manifold of the cross-country correlation matrix. Multidimensional scaling of the cultures yielded a five dimensional solution, the first two of which were interpreted. The first dimension was interpreted as Traditional versus Modern, with China, India, Iran, and Nigeria anchoring one end and the Netherlands, Great Britain, Finland, and Sweden anchoring the other. The second dimension involved valuation of education, intelligence, and refinement. Consistent sex differences in value attached to eaming potential and physical attractiveness supported evolution-based hypotheses about the importance of resources and reproductive value in mates. Discussion emphasizes the importance of psychological mate preferences for scientific disciplines ranging from evolutionary biology to sociology.

The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. The aim of the work was to study the relevance of using NDVI-based sampling strategies to improve estimation of mean field yield. The study was conducted in nine non-irrigated vine fields located in southern France. For each field, NDVI was derived from multi-spectral airborne images. The variables which define the yield: [berry weight at harvest (BWh), bunch number per vine (BuN) and berry number per bunch (BN)] were measured on a regular grid. This database allowed for five different sampling schemes to be tested. These sampling methods were mainly based on a stratification of NDVI values, they differed in the way as to whether NDVI was used as ancillary information to design a sampling strategy for BuN, BN, BW or for all yield variables together. Results showed a significant linear relationship between NDVI and BW, indicating the interest of using NDVI information to optimize sampling for this parameter. However this result is mitigated by the low incidence of BW in the yield variance (4 %) within the field. Other yield components, BuN and BN explain a higher percentage of yield variance (60 and 11 % respectively) but did not show any clear relationship with NDVI. A large difference was observed between fields, which justifies testing the optimized sampling methods on all of them and for all yield variables. On average, sampling methods based on NDVI systematically improved vine field yield estimates by at least 5-7 % compared to the random method. Depending on the fields, error improvement ranged from -2 to 15 %. Based on these results, the practical & B. Tisseyre recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values.

Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).

<p style="text-align: justify;"><strong>Aims</strong>: The objective of this paper is to study the temporal stability of withinfield spatial variability of the Normalised Difference Vegetative Index (NDVI) at two time scales: intra-annual and inter-annual. This study aims to provide answers to the practical use of NDVI and, in particular, to determine whether it is possible (i) to advance the date of image acquisition in order to increase the time required for image analysis and interpretation before harvest and (ii) to verify if an image acquired in one year can be used to manage the vineyard in the following years.</p><p style="text-align: justify;"><strong>Methods and results</strong>: The study was conducted on 17 individual fields. The analysis of the intra-annual stability was performed with four images in 2007 and two images in 2006 that were acquired at different stages of vine development. The analysis of the inter-annual stability was performed with five images taken around veraison on five different years over a period of 10 years (from 1999 to 2009). For the 17 fields of the study, a sampling grid was defined to take into account the characteristics of image processing and the particular shape of each field. A rank coefficient of correlation (Spearman) was used to characterize the correlation between dates of acquisition (images). A Kendall test was implemented to individually characterize and identify the source of the observed temporal stability.</p><p style="text-align: justify;"><strong>Conclusion</strong>: In Mediterranean conditions, this study highlighted the temporal stability of within-field NDVI patterns both within a season or between seasons. Regarding the intra-annual scale, an image acquired from 15 to 20 days before veraison had a significant correlation (p &lt; 0.05) with an image acquired at harvest. For earlier images (i.e., taken around flowering), the strength of the correlation decreased as the time lag between two images increased. This decrease was probably linked to summer pruning operations, the presence of an inter-row cover crop or a spring vigour that differed from the final vigour in some fields. Regarding the inter-annual scale, images acquired at veraison were all significantly correlated (p &lt; 0.05) over the 10 year period regardless of the time lag between image acquisition. The degree of the correlation decreases continuously with time. For the 17 fields of the experiment, a decrease in the stability of NDVI between years was noticeable when significant changes in vine training (irrigation, replanting) occurred. For fields that did not undergo major changes, the spatial patterns of NDVI could be considered relatively stable over time for periods up to 3 to 5 years according to the age of the vineyard.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: This study showed that in Mediterranean conditions it is possible (i) to advance the date of image acquisition to at least 20 days before veraison if the objective is to highlight the spatial variability at harvest, (ii) to use information from an image acquired at veraison over several subsequent years if the field does not undergo major changes in management practices and (iii) to use earlyseason images (around flowering) as a potential source of information for managing other operations.</p>

Residential wood burning can be a significant wintertime source of ambient fine particles in urban and suburban areas. We developed a statistical model to predict minute (min) levels of particles with median diameter of o1 mm (PM1) from mobile monitoring on evenings of winter weekends at different residential locations in Quebec, Canada, considering wood burning emissions. The 6 s PM1 levels were concurrently measured on 10 preselected routes travelled 3 to 24 times during the winters of 2008--2009 and 2009--2010 by vehicles equipped with a GRIMM or a dataRAM sampler and a Global Positioning System device. Route-specific and global land-use regression (LUR) models were developed using the following spatial and temporal covariates to predict 1-min-averaged PM1 levels: chimney density from property assessment data at sampling locations, PM2.5 ''regional background'' levels of particles with median diameter of o2.5 mm (PM2.5) and temperature and wind speed at hour of sampling, elevation at sampling locations and day of the week. In the various routes travelled, between 49% and 94% of the variability in PM1 levels was explained by the selected covariates. The effect of chimney density was not negligible in ''cottage areas.'' The R 2 for the global model including all routes was 0.40. This LUR is the first to predict PM1 levels in both space and time with consideration of the effects of wood burning emissions. We show that the influence of chimney density, a proxy for wood burning emissions, varies by regions and that a global model cannot be used to predict PM in regions that were not measured. Future work should consider using both survey data on wood burning intensity and information from numerical air quality forecast models, in LUR models, to improve the generalisation of the prediction of fine particulate levels.