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The blue-blindness (tritanopia) of the human foveola normally goes unnoticed but can be directly visualized by having observers view a flickering, monochromatic, short-wavelength field. The blue scotoma appears as a tiny dark spot in central vision, the visibility of which depends upon the wavelength of the field and the temporal frequency of modulation. Comparisons of fading times as a function of flicker frequency for the blue scotoma, foveal afterimages and optically stabilized images indicate a common time course, consistent with the hypothesis that perceptual filling-in of the foveal blue scotoma reflects the operation of neural processes similar to those involved in fading and regeneration of stabilized images.

Recognition memory and hemispheric specialization were assessed for abstract colour/black and white pictures of sport situations in painters and visually naive subjects using a forced choice yes/no tachistoscopic procedure. Reaction times showed a significant three-way interaction of picture type, expertise, and visual field, indicating that painters processed the abstract pictures in the right hemisphere and sport pictures leftwards relative to the novices. The novices showed an overall LVF/RH advantage, strongest for sport pictures. The opposing gradients in the painters indicate a preferential change of processing strategy by which descriptive systems appear to have developed for figurative, but not abstract pictures.

A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg x [ha.sup.-1]) in three forest strata in the Valer forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 [m.sup.2] circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR- based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and 'permanent' plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors. Key words: standard error, model error, aboveground biomass, nearest-neighbour thinning, model assisted, model dependent. Un seul modele de regression lineaire choisi a priori avec deux structures d'erreur alternatives est propose pour l'estimation assistee d'un modele (AM) et dependante d'un modele (DM) de l'etat et des changements de la biomasse aerienne des arbres (BA, Mg x [ha.sup.-1]) de trois strates forestieres de la foret de Valer dans le sud-est de la Norvege. Des donnees de terrain sur la hauteur des arbres et le diametre des tiges ont ete recoltees dans 145 placettes circulaires permanentes de 200 [m.sup.2]. Des donnees lidar ont ete recoltees pendant la meme periode sur toute la foret. Le modele de regression inclut deux variables explicatives associees au lidar: la hauteur moyenne du couvert elevee a la puissance 1,5 et l'ecart-type de la hauteur du couvert. Une eclaircie des plus proches voisins a ete appliquee aux donnees lidar de 2010 pour correspondre a la densite des donnees de 1999 de facon a contrer les effets de densite dans les variables explicatives. Les estimations du changement fondees sur un seul modele de regression etaient plus precises que celles tirees des modeles specifiques a l'annee (et sans eclaircie des donnees). Une structure d'erreur correlee et dependante de la hauteur du couvert a ete preferee a une repartition de l'erreur entre un effet de placette temporaire et de placette permanente. Dans le cas des estimations ponctuelles de la BA en 1999 et 2010, les estimations AM et DM des erreurs etaient numeriquement comparables, mais les erreurs DM de changement etaient beaucoup plus petites que les erreurs AM correspondantes. [Traduit par la Redaction] Mots-cles: erreur-type, erreur du modele, biomasse aerienne, eclaircie des plus proches voisins, assistance d'un modele, dependance d'un modele., Introduction Airborne laser scanner data (LiDAR) are now widely used in the form of auxiliary variable metrics (X) in support of forest inventories (Vauhkonen et al. 2014). Practical applications are [...]

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 x [10.sup.6] [km.sup.2] of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 x 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 [km.sup.2] and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org). Key words: nonparametric method, remote sensing, boreal forest, stand attribute, biomass, composition, national baseline inventory. L'Inventaire forestier national (IFN) du Canada est un programme d'echantillonnage concu pour appuyer la production de rapports sur les forets a l'echelle nationale. D'autre part, les cartes continues d'attributs forestiers sont necessaires pour appuyer les analyses strategiques des problematiques regionales de politique et d'amenagement. Nous avons donc produit des cartes couvrant 4,03 x [10.sup.6] [km.sup.2] de surface forestiere inventoriee en 2001 a partir d'observations normalisees provenant des placettes photos de l'IFN utilisees comme donnees de reference. Nous avons utilise la methode des k plus proches voisins (kNN) avec 26 couches de donnees geospatiales, y compris les donnees spectrales MODIS et les variables climatiques et topographiques, pour produire les cartes de 127 attributs forestiers a une resolution de 250 x 250 m. Les attributs a l'echelle du peuplement comprennent le type de couverture terrestre, la structure et l'abondance relative des especes d'arbres. Dans cet article, nous rapportons seulement la biomasse totale aerienne et vivante des arbres. Tous les autres attributs abordes sont presentes dans la section materiel supplementaire (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr- 2013-0401). En general, les ecarts des valeurs predites a l'echelle du pixel par rapport a celles des placettes photos de validation, sont plus importants dans les regions montagneuses et dans les zones ou, soit la biomasse, soit le taux d'echantillonnage de placettes photos est faible. Les valeurs predites a l'echelle du pixel sont surestimees lorsque les valeurs observees sont petites et sous-estimees lorsqu'elles sont grandes. Les mesures de precision sont ameliorees grace a l'agregation spatiale des pixels sur 1 [km.sup.2] et plus. Dans l'ensemble, ces nouveaux produits fournissent des informations de base uniques pour les analyses strategiques des forets (https://nfi.nfis.org). [Traduit par la Redaction] Mots-cles: methode non parametrique, teledetection, foret boreale, attribut de peuplement, biomasse, composition, inventaire national de reference., Introduction Canada's forests and wooded lands cover 40% of its territory and constitute one-tenth of the world's forest area (National Forest Inventory 2010). Provincial and territorial governments are responsible for [...]

Current estimators of variance for the k nearest neighbours (kNN) technique are designed for estimates of population totals. Their efficiency in small-area estimation problems can be poor. In this study, we propose a modified balanced repeated replication estimator of variance (BRR) of a kNN total that performs well in small-area estimation problems and under both simple random and cluster sampling. The BRR estimate of variance is the sum of variances and covariances of unit-level kNN estimates in the area of interest. In Monte Carlo simulations of simple random and cluster sampling from seven artificial populations with real and simulated forest inventory data, the agreement between averages of BRR estimates of variance and Monte Carlo sampling variances was good both for population and for small-area totals. The modified BRR estimator is currently limited to sample sizes no larger than 1984. An accurate approximation to the proposed BRR estimator allows significant savings in computing time. Resume: Les estimateurs courants de la variance pour la technique des k plus proches voisins (kPPV) ont ete concus pour estimer des totaux de population. Leur efficacite peut etre faible dans le cas de probleme d'estimation sur de petites superficies. Dans cette etude, nous proposons un estimateur modifie a replication repetee et equilibree (RRE) de la variance du total des kPPV qui est efficace pour des problemes d'estimation sur de petites superficies et pour des echantillonnages aleatoires simples et en grappes. L'estimation RRE de la variance est la somme des variances et des covariances des estimations unitaires des kPPV dans la superficie visee. Dans des simulations de Monte Carlo d'echantillonnages aleatoires simples et en grappes a partir de sept populations artificielles comportant des donnees d'inventaire forestier reelles et simulees, les moyennes des estimations de la variance RRE correspondaient bien aux variances d'echantillonnage Monte Carlo pour les totaux de population et de petite superficie. La taille maximale de l'echantillon de l'estimateur modifie RRE est presentement de 1 984. Une approximation juste de l'estimateur RRE propose permet de reduire substantiellement le temps de calcul. [Traduit par la Redaction], Introduction Forest inventories are in a good position to take advantage of the nonparametric (distribution-free) k nearest neighbours (kNN) technique for the estimation of quantitative or categorical attribute values for [...]

Research was conducted on chloroplast DNA inheritance in Pinus contorta and Pinus banksiana seedlings to determine if the mode of inheritance is consistent in the species' matings. DNA analyses showed that most seedlings inherited chloroplast DNA from the paternal parent. However, one maternal and eight nonparental genotypes were observed in the progeny arrays. They may have been caused by contamination, maternal leakage, parental chimerism or recombination. Results also indicated that chloroplast DNA's parental dominance is independent of genotype, species, source or mating direction.