Explore the vast range of titles available.

Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data.

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author's extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the freely available statistical package R.     The addition of R in this new edition has allowed coverage of additional methods for classification and ordination, and also logistic regression, GLMs, GAMs, regression trees as well as multinomial regression to simulate vegetation types. A package of statistical functions, specifically written for the book, covers topics not found elsewhere, such as analysis and plot routines for handling synoptic tables. All data sets presented in the book are now also part of the R package 'dave', which is freely available online at the R Archive webpage.  The book and data analysis tools combined provide a complete and comprehensive guide to carrying out data analysis students, researchers and practitioners in vegetation science and plant ecology. Summary: * A completely revised and updated edition of this popular introduction to data analysis in vegetation ecology * Now includes practical examples using the freely available statistical package 'R' * Written by a world renowned expert in the field * Complex concepts and operations are explained using clear illustrations and case studies relating to real world phenomena * Highlights both the potential and limitations of the methods used, and the final interpretations * Gives suggestions on the use of the most widely used statistical software in vegetation ecology and how to start analysing data Praise for the first edition: \"This book will be a valuable addition to the shelves of early postgraduate candidates and postdoctoral researchers. Through the excellent background material and use of real world examples, Wildi has taken the fear out of trying to understand these much needed data analysis techniques in vegetation ecology.\" —Austral Ecology

Vegetation Description and Data Analysis: A Practical Approach, Second Edition is a fully revised and up-dated edition of this key text. The book takes account of recent advances in the field whilst retaining the original reader-friendly approach to the coverage of vegetation description and multivariate analysis in the context of vegetation data and plant ecology. Since the publication of the hugely popular first edition there have been significant developments in computer hardware and software, new key journals have been established in the field and scope and application of vegetation description and analysis has become a truly global field. This new edition includes full coverage of new developments and technologies. This contemporary and comprehensive edition of this well-known and respected textbook will prove invaluable to undergraduate and graduate students in biological sciences, environmental science, geography, botany, agriculture, forestry and biological conservation. Fully international approach Includes illustrative case studies throughout Now with new material on: the nature of plant communities; transitional areas between plant communities; induction and deduction of plant ecology; diversity indices and dominance diversity curves; multivariate analysis in ecology. Accessible, reader-friendly style Now with new and improved illustrations.

Vegetation Description and Data Analysis: A PracticalApproach, Second Edition is a fully revised and up-datededition of this key text. The book takes account of recent advancesin the field whilst retaining the original reader-friendly approachto the coverage of vegetation description and multivariate analysisin the context of vegetation data and plant ecology. Since the publication of the hugely popular first edition therehave been significant developments in computer hardware andsoftware, new key journals have been established in the field andscope and application of vegetation description and analysis hasbecome a truly global field. This new edition includes fullcoverage of new developments and technologies. This contemporary and comprehensive edition of this well-known andrespected textbook will prove invaluable to undergraduate andgraduate students in biological sciences, environmental science,geography, botany, agriculture, forestry and biologicalconservation. * Fully international approach * Includes illustrative case studies throughout * Now with new material on: the nature of plant communities;transitional areas between plant communities; induction anddeduction of plant ecology; diversity indices and dominancediversity curves; multivariate analysis in ecology. * Accessible, reader-friendly style * Now with new and improved illustrations

1. The study of plant-pollinator interactions in a network context is receiving increasing attention. This approach has helped to identify several emerging network patterns such as nestedness and modularity. However, most studies are based only on qualitative information, and some ecosystems, such as deserts and tropical forests, are underrepresented in these data sets. 2. We present an exhaustive analysis of the structure of a 4-year plant-pollinator network from the Monte desert in Argentina using qualitative and quantitative tools. We describe the structure of this network and evaluate sampling completeness using asymptotic species richness estimators. Our goal is to assess the extent to which the realized sampling effort allows for an accurate description of species interactions and to estimate the minimum number of additional censuses required to detect 90% of the interactions. We evaluated completeness of detection of the community-wide pollinator fauna, of the pollinator fauna associated with each plant species and of the plant-pollinator interactions. We also evaluated whether sampling completeness was influenced by plant characteristics, such as flower abundance, flower life span, number of interspecific links (degree) and selectiveness in the identity of their flower visitors, as well as sampling effort. 3. We found that this desert plant-pollinator network has a nested structure and that it exhibits modularity and high network-level generalization. 4. In spite of our high sampling effort, and although we sampled 80% of the pollinator fauna, we recorded only 55% of the interactions. Furthermore, although a 64% increase in sampling effort would suffice to detect 90% of the pollinator species, a fivefold increase in sampling effort would be necessary to detect 90% of the interactions. 5. Detection of interactions was incomplete for most plant species, particularly specialists with a long flowering season and high flower abundance, or generalists with short flowering span and scant flowers. Our results suggest that sampling of a network with the same effort for all plant species is inadequate to sample interactions. 6. Sampling the diversity of interactions is labour intensive, and most plant-pollinator networks published to date are likely to be undersampled. Our analysis allowed estimating the completeness of our sampling, the additional effort needed to detect most interactions and the plant traits that influence the detection of their interactions.

1. Understanding the structure of ecological networks is a crucial task for interpreting community and ecosystem responses to global change. 2. Despite the recent interest in this subject, almost all studies have focused exclusively on one specific network property. The question remains as to what extent different network properties are related and how understanding this relationship can advance our comprehension of the mechanisms behind these patterns. 3. Here, we analysed the relationship between nestedness and modularity, two frequently studied network properties, for a large data set of 95 ecological communities including both plant-animal mutualistic and host-parasite networks. 4. We found that the correlation between nestedness and modularity for a population of random matrices generated from the real communities decreases significantly in magnitude and sign with increasing connectance independent of the network type. At low connectivities, networks that are highly nested also tend to be highly modular; the reverse happens at high connectivities. 5. The above result is qualitatively robust when different null models are used to infer network structure, but, at a finer scale, quantitative differences exist. We observed an important interaction between the network structure pattern and the null model used to detect it. 6. A better understanding of the relationship between nestedness and modularity is important given their potential implications on the dynamics and stability of ecological communities.

Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

1. Feedback between plants and soil organisms has become widely recognized as a driving force of community composition and ecosystem functioning. However, there is little uniformity in quantification and analysis of plant-soil feedback effects. Meta-analysis suggested that the various experimental methods tend to result in different feedback values. Yet, a direct comparison of the different experimental approaches and their statistical analyses is lacking. 2. We used currently applied methods to calculate plant-soil feedback value ranges and compared their statistical analyses to those based on actual biomass data. Then, we re-analysed a case study to compare plant-soil feedback values obtained under the same environmental conditions, but using different experimental approaches: soil sterilization, addition of soil inoculum, and soil conditioning by 'own' vs. 'foreign' plant species. 3. Different measures to calculate plant-soil feedback values were more variable in positive than in negative feedback values. Analysis of calculated feedback values that are based on treatment averages can lead to a serious inflation of type I errors. 4. In our case study, both the strength and the direction of the feedback effects depended on the experimental approach that was chosen, leading to diverging conclusions on whether feedback to a certain soil was positive or negative. Soil sterilization and addition of soil organisms yielded larger feedback than comparison of own and foreign soil. 5. Synthesis. The ecological interpretation of plant-soil feedback effects strongly depends on the experimental procedure. When the research question focuses on the strength and the sign of plant-soil feedback, soil sterilization (presumed that the side effect of increased nutrient availability can be controlled) or addition of soil inoculum is to be preferred. When the research question concerns the specificity of soil feedback effects, plant performance can be better compared between own and foreign soil. We recommend that when using calculated feedback values, the original data need to be presented as well in order to trace the cause of the effect.

Background Because of diverse topographical habitats, the Chenab River wetland harbors a wealth of medicinal and food plant species. This paper presents first quantitative assessment on the ethnobotanical use of plants by the local peoples residing in the Chenab riverine area. Methods The ethnobotanical data were collected from six parts of the Chenab River wetland: Mandi Bahuddin, Gujranwala, Gujrat, Sargodha, and Sialkot during 2014 to 2015, using semi-structured interviews. Quantitative indices including informant consensus factor (FCI), relative frequency of citation (RFC), relative importance level (RIL), use value (UV), fidelity level (FL), and corrected fidelity level (CFL) were used to analyze the data. Results On the whole, 129 medicinal plant species belonging to 112 genera of 59 families were reported, with herbs as dominant life forms (51%). Poaceae was the leading family with 13 species, and leaves were the most frequently utilized plant parts (28%). Herbal medicines were mostly used in the form of powder or decoction, and were mainly taken orally. Withania somnifera , Solanum surattense , Solanum nigrum , Azadirachta indica , Ficus benghalensis , Morus nigra , Morus alba , Polygonum plebeium , and Tribulus terrestris were among the highly utilized plant species, with highest UV, RFC, RIL, FL, and CFL values. The reported ailments were grouped into 11 categories based on FCI values, whereas highest FIC was recorded for gastrointestinal diseases and glandular diseases (0.41 and 0.34, respectively). The use report (UR) and frequency of citation (FC) depicted strong positive correlation ( r  = 0.973; p =  0.01). The value of determination ( r 2  = 0.95) indicating 95% variation in UR can be explained in terms of the FC. Conclusion The significant traditional knowledge possessed by local communities depicts their strong relation with phytodiversity. Reported data could be helpful in sustainable use and protection of plant species in the Chenab wetland, with special emphasis on medicinal plants. Furthermore, screening of plant-borne active ingredients and in vivo / in vitro pharmacological activities could be of interest for novel drug synthesis.

OpenSimRoot is an open-source, functional–structural plant model and mathematical description of root growth and function. We describe OpenSimRoot and its functionality to broaden the benefits of root modeling to the plant science community. OpenSimRoot is an extended version of SimRoot, established to simulate root system architecture, nutrient acquisition and plant growth. OpenSimRoot has a plugin, modular infrastructure, coupling single plant and crop stands to soil nutrient and water transport models. It estimates the value of root traits for water and nutrient acquisition in environments and plant species. The flexible OpenSimRoot design allows upscaling from root anatomy to plant community to estimate the following: resource costs of developmental and anatomical traits; trait synergisms; and (interspecies) root competition. OpenSimRoot can model three-dimensional images from magnetic resonance imaging (MRI) and X-ray computed tomography (CT) of roots in soil. New modules include: soil water-dependent water uptake and xylem flow; tiller formation; evapotranspiration; simultaneous simulation of mobile solutes; mesh refinement; and root growth plasticity. OpenSimRoot integrates plant phenotypic data with environmental metadata to support experimental designs and to gain a mechanistic understanding at system scales.