A comparison of paired- with blocked-quadrat variance methods for the analysis of spatial pattern

Temporal Progress and Spatial Patterns of Northern Corn Leaf Blight in Corn Fields in China

Northern corn leaf blight (NCLB), caused by Exserohilum turcicum, is a devastating disease of corn in China. To enhance our understanding of NCLB epidemiology, the temporal progress and spatial patterns of NCLB were investigated. A susceptible corn cultivar, Xianyu 335, was planted in a field in Beijing in 2016 and 2017. Leaf lesions of NCLB on each plant were counted twice a week during the growing seasons. In addition, temporal disease progress was monitored for 8 weeks in three commercial corn fields in each of Yanqing, Miyun, Daxing, and Haidian Districts of Beijing in 2017, and the spatial patterns of diseased plants and NCLB lesion counts per plant were assessed in three commercial corn fields with moderate to high NCLB incidence in Yanqing District. The results demonstrated that a logistic model was the most appropriate to describe the temporal progress of NCLB incidence. The initial disease incidence was the key factor affecting disease epidemics under various conditions in the four districts of Beijing during the study. The higher the initial incidence of NCLB, the higher the final incidence. Thus, the earlier in the season NCLB incidence attained 1%, the higher was the final disease incidence. Greater than 1.0 variance-to-mean ratios suggested that the leaf lesions of NCLB tended to be aggregated on a plant. According to results from join-counts, variance of moving window averages, and semivariogram analysis, diseased corn plants and lesion numbers on each plant were aggregated in the field. The clustered pattern of NCLB lesions and infected plants suggested that conidia produced locally on diseased plants were important for disease spread within the field. The aggregated pattern of diseased plants suggested that plants should be sampled from more sites in a field to accurately estimate incidence of NCLB.

SPECTRUM OF SELECTION: NEW APPROACHES TO DETECTING THE SCALE-DEPENDENT RESPONSE TO HABITAT

Detecting habitat selection depends on the spatial scale of analysis, but multi-scale studies have been limited by the use of a few, spatially variable, hierarchical levels. We developed spatially explicit approaches to quantify selection along a continuum of scales using spatial (coarse-graining) and geostatistical (variogram) pattern analyses at multiple levels of habitat use (seasonal range, travel routes, feeding areas, and microsites). We illustrate these continuum-based approaches by applying them to winter habitat selection by woodland caribou (Rangifer tarandus caribou) using two key habitat components, Cladina lichens and snow depth. We quantified selection as the reduction in variance in used relative to available sites, thus avoiding reliance on correlations between organism and habitat, for which interpretation can be impeded by cross-scale correlations. By consistently selecting favorable habitat features, caribou experienced reduced variance in these features. The degree to which selection was accounted for by the travel route, feeding area, or microsite levels varied across the scale continuum. Caribou selected for Cladina within a 13-km scale domain and selected shallower snow at all scales. Caribou responded most strongly at the dominant scales of patchiness, implicating habitat heterogeneity as an underlying cause of multi-scale habitat selection. These novel approaches enable a spatial understanding of resource selection behavior.

Pratique de l’analyse de l’autocorrélation spatiale en géomorphologie : définitions opératoires et tests

 L’autocorrélation spatiale peut être définie comme la ressemblance des valeurs prises par une variable, exprimée en fonction de leur localisation géographique. L’analyse de l’autocorrélation permet de quantifier la régularité spatiale d’un phénomène (une forme de complexité spatiale) et de déterminer la portée de la dépendance spatiale afin, notamment, de définir un dispositif d’échantillonnage garantissant l’indépendance des données, autorisant ainsi l’utilisation des tests statistiques usuels. L’article aborde trois points : i) définition opératoire de l’autocorrélation pour des variables quantitatives ou qualitatives ; ii) utilisation des tests de randomisation pour tester l’hypothèse nulle d’absence d’autocorrélation ; iii) illustration des deux points précédents par des exemples. Trois jeux de données simulés sont présentés pour illustrer les différences entre les statistiques : le premier ne présente aucune structure spatiale, le deuxième est caractérisé par une structure spatiale périodique, le troisième est un gradient linéaire. Deux jeux de données géomorphologiques sont également analysés : i) une série de segments fluviaux élémentaires, distribués longitudinalement, et sur lesquels ont été mesurées la largeur et l’incision d’un lit fluvial ; ii) la cartographie des formes d’érosion d’un bassin versant, traitée comme une image matricielle. Dans le premier cas, la structure emboîtée des tronçons géomorphologiquement homogènes est mise en lumière, et ce à différentes échelles spatiales. Le second exemple montre qu’une analyse omnidirectionnelle peut conduire à sous-estimer la portée de l’autocorrélation lorsque le phénomène étudié présente une orientation géographique privilégiée. Dans ce cas, il peut s’avérer impossible de définir un échantillon de données spatialement indépendantes, répondant aux exigences des tests statistiques classiques.

Ecological structures recognized by means of entropy analysis: assessment of differences between entropy values

A procedure is outlined for the assessment of differences between entropy values obtained from a data set. The problem posed is that of identifying the data matrix representing least uncertainty in the disposition of its elements from two or more given matrices. Monte Carlo simulations were used, through which the maximum and minimum entropy values that could be obtained from each data set were estimated. Such values could be obtained by randomly interchanging the elements of each matrix and may serve as references in estimating the degree of order or randomness that an original matrix possesses.