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A groundbreaking approach to scale and scaling in ecological theory and practiceScale is one of the most important concepts in ecology, yet researchers often find it difficult to find ecological systems that lend themselves to its study. Scaling in Ecology with a Model System synthesizes nearly three decades of research on the ecology of Sarracenia purpurea-the northern pitcher plant-showing how this carnivorous plant and its associated food web of microbes and macrobes can inform the challenging question of scaling in ecology.Drawing on a wealth of findings from their pioneering lab and field experiments, Aaron Ellison and Nicholas Gotelli reveal how the Sarracenia microecosystem has emerged as a model system for experimental ecology. Ellison and Gotelli examine Sarracenia at a hierarchy of spatial scales-individual pitchers within plants, plants within bogs, and bogs within landscapes-and demonstrate how pitcher plants can serve as replicate miniature ecosystems that can be studied in wetlands throughout the United States and Canada. They show how research on the Sarracenia microecosystem proceeds much more rapidly than studies of larger, more slowly changing ecosystems such as forests, grasslands, lakes, or streams, which are more difficult to replicate and experimentally manipulate.Scaling in Ecology with a Model System offers new insights into ecophysiology and stoichiometry, demography, extinction risk and species distribution models, food webs and trophic dynamics, and tipping points and regime shifts.

Biotic communities --- Botanical chemistry. --- Statistical methods. --- Alternative stable state. --- Aristolochia. --- Assembly rules. --- Author. --- Autocorrelation. --- Bacteria. --- Biodiversity. --- Biogeography. --- Biological interaction. --- Biomass (ecology). --- Bovine serum albumin. --- Bromeliaceae. --- Carnivorous plant. --- Conditional probability. --- Copyright. --- Density dependence. --- Detritus. --- Drosera. --- Ecological niche. --- Ecological succession. --- Ecology. --- Ecosystem. --- Environment variable. --- Estimation. --- Evolution. --- Field experiment. --- Food chain. --- Food web. --- Forecasting. --- Herbivore. --- Histogram. --- Hysteresis. --- INaturalist. --- Inference. --- Inquiline. --- Insect. --- Invertebrate. --- Keystone species. --- Larva. --- Markov chain. --- Measurement. --- Metabolism. --- Microecosystem. --- Micronutrient. --- Microorganism. --- Model organism. --- Negative feedback. --- Nepenthes. --- Nitrogen. --- Null hypothesis. --- Nutrient. --- Obligate. --- Observational error. --- Organism. --- Parameter space. --- Peptide. --- Perennial plant. --- Phenotype. --- Phenotypic plasticity. --- Photosynthesis. --- Pitcher plant. --- Plant morphology. --- Pollination. --- Population decline. --- Population dynamics. --- Population growth. --- Population size. --- Population vector. --- Predation. --- Prediction. --- Princeton University Press. --- Probability. --- Proportionality (mathematics). --- Protein. --- Protozoa. --- Regime shift. --- Rotifer. --- Sarracenia purpurea. --- Sarracenia. --- Sarraceniaceae. --- Shrub. --- Soil. --- Spatial scale. --- Species distribution. --- Sphagnum. --- Spreadsheet. --- State variable. --- Stochastic simulation. --- Stoichiometry. --- Taxon. --- Taxonomy (biology). --- Time series. --- Trade-off. --- Trophic level. --- Uncertainty. --- Vegetation. --- Weather forecasting. --- Weather station. --- Wetland. --- Wyeomyia smithii.

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This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.

Research & information: general --- Environmental economics --- hyperspectral LiDAR --- Red Edge --- AOTF --- vegetation parameters --- leaf chlorophyll content --- DLARI --- MDATT --- adaxial --- abaxial --- spectral reflectance --- peanut --- field spectroscopy --- hyperspectral --- heavy metals --- grapevine --- PLS --- SVM --- MLR --- multi-angle observation --- hyperspectral remote sensing --- BRDF --- vegetation classification --- object-oriented segmentation --- spectroscopy --- artificial intelligence --- proximal sensing data --- precision agriculture --- spectra --- vegetation --- plant --- classification --- discrimination --- feature selection --- waveband selection --- support vector machine --- random forest --- Natura 2000 --- invasive species --- expansive species --- biodiversity --- proximal sensor --- macronutrient --- micronutrient --- remote sensing --- hyperspectral imaging --- platforms and sensors --- analytical methods --- crop properties --- soil characteristics --- classification of agricultural features --- canopy spectra --- chlorophyll content --- continuous wavelet transform (CWT) --- correlation coefficient --- partial least square regression (PLSR) --- reproducibility --- replicability --- partial least squares --- Ethiopia --- Eragrostis tef --- hyperspectral remote sensing for soil and crops in agriculture --- hyperspectral imaging for vegetation --- plant traits --- high-resolution spectroscopy for agricultural soils and vegetation --- hyperspectral databases for agricultural soils and vegetation --- hyperspectral data as input for modelling soil, crop, and vegetation --- product validation --- new hyperspectral technologies --- future hyperspectral missions