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  • 1
    Online Resource
    Online Resource
    Cambridge :Cambridge University Press,
    Keywords: Spatial analysis (Statistics). ; Electronic books.
    Description / Table of Contents: This book gives an overview of the wide range of spatial statistics available to analyse ecological data, and provides advice and guidance for graduate students and practising researchers who are either about to embark on spatial analysis in ecological studies or who have started but are unsure how to proceed.
    Type of Medium: Online Resource
    Pages: 1 online resource (381 pages)
    Edition: 1st ed.
    ISBN: 9780511197932
    DDC: 577/.015/195
    Language: English
    Note: Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Preface -- 1 Introduction -- Introduction -- 1.1 Process and pattern -- 1.2 Spatial pattern: spatial dependence versus spatial autocorrelation -- 1.3 The concept of stationarity -- 1.4 Sampling -- 1.4.1 Ecological data -- 1.4.2 Sampling design -- 1.5 Spatial statistics -- 1.5.1 Significance testing of ecological data -- 1.6 Concluding remarks -- 2 Spatial analysis of population data -- Introduction -- 2.1 Mapped point data in two dimensions -- 2.1.1 Distance to neighbours methods -- 2.1.2 Refined nearest neighbour analysis -- 2.1.3 Second-order point pattern analysis -- 2.1.4 Bivariate data -- 2.1.5 Multivariate point pattern analysis -- 2.2 Mark correlation function -- 2.3 Networks of events -- 2.4 Network analysis of areal units -- 2.5 Point patterns in other dimensions -- 2.5.1 One dimension -- Lacunarity -- 2.5.2 Three or more dimensions -- 2.6 Contiguous units analysis -- 2.6.1 Quadrat variance methods -- 2.6.2 Significance tests for quadrat variance methods -- 2.6.3 Adaptations for two or more species -- 2.6.4 Two or more dimensions -- SADIE -- 2.6.5 Spectral analysis and related techniques -- 2.6.6 Wavelets -- 2.7 Circumcircle methods -- 2.7.1 Univariate analysis -- 2.7.2 Bivariate analysis -- 2.7.3 Multivariate analysis -- 2.8 Concluding remarks -- 3 Spatial analysis of sample data -- Introduction -- 3.1 How to determine 'nearby' relationships among sampling units -- 3.2 Join count statistics -- 3.2.1 Considerations and other join count statistics -- 3.3 Global spatial statistics -- 3.3.1 Spatial autocorrelation coefficients for one variable -- 3.3.2 Variography -- 3.3.3 Fractal dimension -- 3.3.4 Sampling design effects on the estimation of spatial pattern -- 3.3.5 Spatial relationship between two variables -- 3.3.6 Spatial relationships among several variables. , 3.4 Local spatial statistics -- 3.5 Interpolation and spatial models -- 3.5.1 Proximity polygons -- 3.5.2 Trend surface analysis -- 3.5.3 Inverse distance weighting -- 3.5.4 Kriging -- 3.6 Concluding remarks -- 4 Spatial partitioning of regions: patch and boundary -- Introduction -- 4.1 Patch identification -- 4.1.1 Patch properties -- 4.1.2 Spatial clustering -- 4.1.3 Fuzzy classification -- 4.2 Boundary delineation -- 4.2.1 Ecological boundaries -- 4.2.2 Boundary properties -- 4.2.3 Boundary detection based on several variables -- 4.2.3.1 Multivariate methods -- 4.2.3.2 One-dimensional transect data -- 4.2.3.3 Two-dimensional area data -- 4.2.4 Boundary statistics -- 4.2.5 Overlap statistics -- 4.2.6 Boundary detection based on one variable -- 4.2.6.1 Hierarchical global partitioning using wavelets -- 4.2.6.2 Edge enhancement with kernel filters -- 4.3 Concluding remarks -- 5 Dealing with spatial autocorrelation -- Introduction -- 5.1 Solutions -- 5.1.1 Quick fixes -- 5.1.2 Adjusting the effective sample size -- 5.1.3 Other kinds of models -- 5.1.4 Particular examples -- Tests for proportions -- Correlation and linear regression -- 5.1.5 Restricted randomization and bootstrap -- 5.1.6 Model and Monte Carlo -- 5.2 More on induced autocorrelation and the relationships between variables -- 5.3 Models and reality -- 5.4 Considerations for sampling and experimental design -- 5.4.1 Sampling -- 5.4.2 Experimental design -- 5.5 Concluding remarks -- 6 Spatio-temporal analysis -- Introduction -- 6.1 Change in spatial statistics -- 6.2 Spatio-temporal join count -- 6.3 Spatio-temporal analysis of clusters and contagion -- 6.4 Polygon change analysis -- 6.5 Analysis of movement -- 6.6 Process and pattern -- 6.6.1 Tree regeneration, growth and mortality -- 6.6.2 Plant mobility -- 6.6.3 Lichen boundaries -- 6.7 Spatio-temporal orderliness and spatial synchrony. , 6.8 Chaos -- 6.9 Concluding remarks -- 6.9.1 Recommendations -- 7 Closing comments and future directions -- Back to basics -- 7.1 Programming skills -- 7.2 Stationarity -- 7.3 Null hypotheses -- 7.4 Numerical solutions -- 7.5 Statistical difficulties -- 7.6 Randomization and restricted randomization tests -- 7.7 Complementarity of methods -- 7.8 Future work -- Appendices -- References -- Index.
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  • 2
    Keywords: Biology Data processing ; Biology Data processing ; Landscape ecology ; Landscape ecology ; Statistics ; Ecology ; Conservation biology ; Landscape Ecology ; Statistics ; Ecology ; Conservation biology ; Environmental geography. ; Bioinformatics . ; Computational biology . ; Ökologie ; Raumordnung ; Mathematische Modellierung ; R
    Description / Table of Contents: This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (XVIII, 523 p. 131 illus., 44 illus. in color, online resource)
    Edition: Springer eBook Collection. Biomedical and Life Sciences
    ISBN: 9783030019891
    Series Statement: SpringerLink
    RVK:
    RVK:
    Language: English
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  • 3
    ISSN: 1432-1939
    Keywords: Key words Coastal dune ecosystems ; Ion exchange membrane spikes ; Soil nitrogen availability ; Soil resource heterogeneity ; Spatial statistics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract There are few studies in natural ecosystems on how spatial maps of soil attributes change within a growing season. In part, this is due to methodological difficulties associated with sampling the same spatial locations repeatedly over time. We describe the use of ion exchange membrane spikes, a relatively nondestructive way to measure how soil resources at a given point in space fluctuate over time. We used this method to examine spatial patterns of soil ammonium (NH+ 4) and nitrate (NO− 3) availability in a mid-successional coastal dune for four periods of time during the growing season. For a single point in time, we also measured soil NH+ 4 and NO− 3 concentrations from soil cores collected from the mid-successional dune and from an early and a late successional dune. Soil nitrogen concentrations were low and highly variable in dunes of all ages. Mean NH+ 4 and NO− 3 concentrations increased with the age of the dune, whereas coefficients of variation for NH+ 4 and NO− 3 concentrations decreased with the age of the dune. Soil NO− 3 concentration showed strong spatial structure, but soil NH+ 4 concentration was not spatially structured. Plant-available NH+ 4 and NO− 3 showed relatively little spatial structure: only NO− 3 availability in the second sampling period had significant patch structure. Spatial maps of NH+ 4 and NO− 3 availability changed greatly over time, and there were few significant correlations among soil nitrogen availability at different points in time. NO− 3 availability in the second sampling period was highly correlated (r = 0.90) with the initial soil NO− 3 concentrations, providing some evidence that patches of plant-available NO− 3 may reappear at the same spatial locations at irregular points in time.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Hydrobiologia 300-301 (1995), S. 1-16 
    ISSN: 1573-5117
    Keywords: Experiments ; spatial patterns ; limnology ; oceanography ; philosophy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Description / Table of Contents: Résumé L'auteur passe en revue quelques-unes des pratiques habituelles en limnologie et en océanographie et discute des possibilités d'amélioration dans ces domaines. L'examen de 253 articles parus dans le périodique Limnology and Oceanography en 1980, 1985 et 1990 montre que la majorité de ceux-ci (〉60%) est à dominante descriptive, et que l'approche expérimentale n'est utilisée que dans 30% des cas. Parmi les 27% d'articles présentant des modèles, seuls 3% valident ces modèles en utilisant des données de terrain. Un seul parmi les 253 articles présente des critères biologiques de rejet des hypothèses. La discussion porte sur l'importance des études descriptives en limnologie et en océanographie, l'emploi des techniques numériques pour détecter des phénomènes spatio-temporels dans les données, la signification du réductionnisme dans les sciences aquatiques, l'introduction d'hypothèses ad hoc, les critères de choix des sites d'études, des stations et des échantillonnages dans les études littorales et pélagiques, et les stratégies valables lorsqu'une approche expérimentale ne peut être employée en raison de facteurs environnementaux non contrôlables.
    Notes: Abstract This paper reviews some of the current practices in limnology and oceanography and discusses ways to improve our habits in these fields. A survey of all 253 articles published in the journal Limnology and Oceanography in 1980, 1985, and 1990 indicates that the majority of papers (〉60%) were predominantly descriptive, only about 30% used an experimental approach. Of the 27% articles presenting models, only 3% validated these models using field data. Only one out of 253 papers presented biological criteria to reject hypotheses. We discuss the significance of descriptive studies in the fields of limnology and oceanography, the use of numerical techniques to detect spatio-temporal patterns in the data, the significance of reductionism in aquatic sciences, the introduction of ad hoc hypotheses, the problem of selecting study sites, stations, and sample locations in shore and pelagic studies, and strategies available when an experimental approach cannot be used because environmental factors cannot be controlled.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Plant ecology 83 (1989), S. 209-222 
    ISSN: 1573-5052
    Keywords: Kriging ; Pattern analysis ; Reliability ; Sampling theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Using spatial analysis methods such as spatial autocorrelation coefficients (Moran's I and Geary's c) and kriging, we compare the capacity of different sampling designs and sample sizes to detect the spatial structure of a sugar-maple (Acer saccharum L.) tree density data set gathered from a secondary growth forest of southwestern Québec. Three different types of subsampling designs (random, systematic and systematic-cluster) with small sample sizes (50 and 64 points), obtained from this larger data set (200 points), are evaluated. The sensitivity of the spatial methods in the detection and the reconstruction of spatial patterns following the application of the various subsampling designs is discussed. We find that the type of sampling design plays an important role in the capacity of autocorrelation coefficients to detect significant spatial autocorrelation, and in the ability to accurately reconstruct spatial patterns by kriging. Sampling designs that contain varying sampling steps, like random and systematic-cluster designs, seem more capable of detecting spatial structures than a systematic design.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Plant ecology 80 (1989), S. 107-138 
    ISSN: 1573-5052
    Keywords: Ecological theory ; Mantel test ; Mapping ; Model ; Spatial analysis ; Spatial autocorrelation ; Vegetation map
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.
    Type of Medium: Electronic Resource
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  • 7
    Publication Date: 2022-05-25
    Description: Author Posting. © American Institute of Biological Sciences, 2005. This article is posted here by permission of American Institute of Biological Sciences for personal use, not for redistribution. The definitive version was published in BioScience 55 (2005): 501–510, doi:10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2.
    Description: Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application.
    Description: This paper is the result of a National Science Foundation (NSF) workshop on quantitative environmental and integrative biology (DEB-0092081). J. L. G. would like to acknowledge financial support from the NSF (DEB-0107555).
    Keywords: Ecological complexity ; Quantitative conservation biology ; Cyberinfrastructure ; Metadata ; Semantic Web
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: 577104 bytes
    Format: application/pdf
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