Ratio of Nutrient and Minerals to Defensive Compounds Indicative of Plant Quality and Tolerance to Herbivory in Pear Trees

The phenolic ester 3-0-trans-p-coumaroyltormentic acid and the volatile compounds (E,E)-α-farnesene and methyl salicylate have been identified previously in pear tree cultivars ‘Bartlett,’ ‘NY10355,’ and ‘Conference.’ Here, using the same dried extract of leaf samples, the contents of total protein, chlorophyll, and the minerals calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), sodium (Na), carbon (C), and nitrogen (N) were determined. Differences between pear cultivars were found with regard to their absolute content in Ca, K, chlorophyll, and total protein, and to carbon/nitrogen values in uninfested leaves compared with leaves infested by Cacopsylla pyricola (Foester) and C. pyri L. (Homoptera, Psyllidae) on partially and completely infested trees. The ratio of nutrients and minerals to the constitutive and Cacopsylla-induced phenolic and two volatiles reflects a diminution of the leaf quality as food for Cacopsylla nymphs in infested leaves of all cultivars. In uninfested leaves, the magnitude of this ratio in partially infested trees of cv. ‘Conference’ with local response, together with Ca ion content and chlorophyll loss, point to classification of this cultivar as tolerant to herbivory.     

Autologistic models with interpretable parameters

Ecologists are interested in characterizing succession processes, in particular monitoring the spread of invasive species and their effect on resident species. In situations for which binary response variables representing presence or absence of plants are observed over a spatial lattice, it may be desirable to use a model that accounts for the statistical dependence in the data, as well as the effect of potential covariates. One such model is the autologistic regression model. We show that the typical parameterization of the autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, and propose an alternative (centered) parameterization that overcomes this difficulty.We use the centered autologistic model to study the dynamics over time of two species, Rumex acetosella and Lonicera japonica, in an abandoned agricultural field in New Jersey, and compare the results to those obtained from using the traditional autologistic parameterization.     

Regeneration growth in different light environments of mixed species, multiaged, mountainous forests of Romania

Growth of regenerating trees in different light environments was studied for the mountainous, mixed-species forests in the Carpathian Mountains of Romania. The primary species in these mixtures were silver fir (Abies alba Mill.), European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) Karst). Seedlings/saplings of these species were selected and measured in different stands from two different geographical locations. Regenerating trees were measured for height and diameter growth during the summer of 2002. For each seedling/sapling, percentage of above canopy light (PACL) and stand basal area (BA) were used to assess available and occupied growing space respectively. Regeneration growth was compared against these two variables and regression relationships were developed. Using these models, we predicted the dynamics of regeneration as both growth and species composition. Our results showed that in low-light environments (PACL<20–35%; BA>30 m²/ha), shade tolerant fir and beech clearly outcompeted the spruce. Therefore, in dense stands, spruce could be eliminated by the shade tolerant species. For intermediate levels of cover (PACL=35–70%; BA=15–35 m²/ha) the spruce grew at comparable rates as the beech and fir. All three species showed similar growth rates in open conditions (PACL>80–90%; BA<15–20 m²/ha) with the spruce having a tendency to outgrow the others. However, in terms of establishment, such conditions favor spruce and inhibit fir and beech.     

Classification of soil types using geographic object-based image analysis and random forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.