Raymond D. Dueser

Bio: Raymond D. Dueser is an academic researcher from Utah State University. The author has contributed to research in topics: Peromyscus & Barrier island. The author has an hindex of 20, co-authored 39 publications receiving 1595 citations. Previous affiliations of Raymond D. Dueser include Virginia Museum of Natural History & University of Virginia.

Microhabitats in a Forest-Floor Small Mammal Fauna

Abstract: This study examines microhabitat configurations for 3 small mammal species occupying second—growth mesic forest on Walker Branch Watershed in eastern Tennessee. Small mammal populations were sampled in oak—hickory, chestnut oak and pine forest types, with three 0.36—ha live—trapping grids per forest type. There were 255 total captures for 4 forest—floor species during 9,696 trap nights between 1 May and 16 August 1973. These captures included 2% Blarina brevicauda, 66% Peromyscus leucopus, 5% Ochrotomys nuttalli and 27% Tamias striatus. Blarina was captured most frequently in the pine forest type, Peromyscus and Tamias most frequently in the oak—hickory, and Ochrotomys exclusively in the pine. Twenty—nine variables describing proximate habitat structure were measured at each sample mammal capture site. There were significant overall species differences on 18 of these variables. A subset of variables was identified for each of the 3 most frequently observed species, consisting of only the variables of particular significance for distinguishing the capture sites of that species from those of the other species collectively. Discriminant analysis of these few variables both described the average microhabitat configuration, for a species and indicated the relative importance of each variable for characterizing or describing that configuration, subject to the assumptions of the analysis. The observed differences between species microhabitat configuration are consistent with both the composition by forest type of each species sample and the literature information for each species. Microhabitat segregation among these species is discussed with reference to recent theories of habitat selection, ecological, specialization and resource partitioning in equilibrium faunas.

Niche Pattern in a Forest-Floor Small-Mammal Fauna

Abstract: This study examines the relationships among 4 small mammal species in 2nd-growth mesic forest on Walker Branch Watershed in eastern Tennessee, USA. Populations of Bltarinna brev- icauda, Peromyscus leucopus, Ochrotomys nuttalli and Tamias striatus were live-trapped on 9 0.36- ha grids during the summer of 1973. Eight measures of physical habitat structure were used in dis- criminant analysis of the microhabitats occupied by these 4 species. Three statistically significant discriminant functions were calculated. Each discriminant function is represented as an axis in a 3- dimensional discriminant space. These 3 axes are interpreted as vegetation type, vegetation structure and litter-soil surface characteristics. The positions of the species in the discriminant space charac- terize the microhabitat configurations of the species relative to the ecological properties attributed to the axes. Each species differs significantly from every other species on at least 1 axis. The observed species differences are conservative estimates of microhabitat (structural niche) segregation. We propose measures of niche position (i.e., exploitation specialty) and niche breadth based on the discriminant analysis. Species i is represented by a cloud of ni sample points in the discriminant space. Sample point j for species i lies the distance dij from the origin of the space. The average of these distances (di) represents the average "position" for species i relative to the origin. Because this origin represents the average of the microhabitats sampled on the watershed, and because the microhabitats actually occurring on the watershed are assumed to vary continuously, the likelihood of a species encountering favored microhabitat on Walker Branch decreases as d increases. This d is, thus, interpreted as an index of niche position relative to the average of the microhabitats sampled. Variability among the dij values for species i (vi) measures degree of specialization, a direct measure of niche breadth for species i. Our data indicate that d and v are inversely related: breadth decreases as position becomes increasingly specialized. We incorporate measures of niche position, niche breadth and population abundance into an analysis of community "niche pattern." This niche pattern characterizes the 4 species as follows: Peromyscus is an abundant generalist, well-adapted to the watershed as a whole. Ochrotomys, the only other mouse, is a relatively rare specialist, poorly adapted to the watershed. Tamias occupies an intermediate position between Peromyscus and Ochrotomys, and exhibits intermediate abundance. Although we have little data for Bl/arinna, this rare species appears to be poorly adapted to the watershed. Species differences in niche breadth appear to be determined more by the relative fre- quencies and carrying capacities of the species exploitation specialties than by the relative efficiencies with which the species exploit some critical limiting factor(s). Although we have no experimental evidence, the niche pattern observed for this community is consistent with an inference of competitive coexistence. The niche parameters of transient species which are infrequently encountered on Walker Branch are briefly discussed.

Interspecific territoriality in two sympatric species of Peromyscus (Rodentia: Cricetidae)

Abstract: Aggressive behavior of two morphologically and ecologically similar sympatric congeners, Peromyscus leucopus noveboracensis and P. maniculatus nubiterrae, was studied in the field to determine whether coexistence between these two species could be mediated by interspecific territoriality. In intra- and interspecific paired behavioral trials conducted in the home range of one of the animals, resident animals won between 53 and 93% of paried encounters against opponents of either species. Thus, dominance was site-specific and not species-specific. Strong defense in centers of home ranges and lower levels of aggression on the periphery suggest that both species have defended core areas (territories) with peripheral areas of home range overlapping with neighbors of either species. Social organization of these two species is apparently based on mutual recognition of neighbors and intoleance and aggression toward strangers.

ARM! For the Future: Adaptive Resource Management in the Wildlife Profession

Abstract: The wildlife profession has a long-established tradition of examining and debating the quality and direction of wildlife research (Scheffer 1976, Romesburg 1981, Bailey 1982, McCabe 1985, Capen 1989, Nudds and Morrison 1991, Lancia et al 1993) This introspection is good, for it encourages the profession to improve and mature In this essay, we provide what we hope will be a significant milepost in that process by advocating a general philosophy and protocol for wildlife research and management Rather than articulating a list of specific research priorities and reiterating the need for additional research money, we encourage an encompassing, fundamental shift that will promote more efficient use of current research and management dollars Over the last several years, various groups and many individuals interested in the management of natural resources have recognized a need for reform in natural resources-related research These include the Ecological Society of America's Committee for a Research Agenda for the 1990's (Lubchenco et al 1991), the National Research Council's Committee on Forestry Research (Comm For Res 1990), the Society of American Forester's Task Force on Sustaining Long-term Forest Health and Productivity (Soc Am For 1993) and many others (Brussard 1991; Brussard and Ehrlich 1992; Levin 1992a,b; Levin 1993) There appears to be a general consensus that change is due Furthermore, intensifying political debates about management of natural resources (eg, timber harvests and ancient forests, sustainable development, and the preservation-conservation of biodiversity) call for integrated research and management to address uncertainty in wildlife and ecosystem management and thereby ameliorate controversy in the future (Clark 1992, Ludwig et al 1993, Ludwig 1994) Research and management can no longer afford to be "two solitudes"; distinctions between basic and applied research have blurred (Nudds 1979, Moffatt 1994) The central issue is the application of sound scientific principles to solve problems

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

Multivariate statistics for wildlife and ecology research

Abstract: 1 Introduction and Overview.- 1.1 Objectives.- 1.2 Multivariate Statistics: An Ecological Perspective.- 1.3 Multivariate Description and Inference.- 1.4 Multivariate Confusion!.- 1.5 Types of Multivariate Techniques.- 1.5.1 Ordination.- 1.5.2 Cluster Analysis.- 1.5.3 Discriminant Analysis.- 1.5.4 Canonical Correlation Analysis.- 2 Ordination: Principal Components Analysis.- 2.1 Objectives.- 2.2 Conceptual Overview.- 2.2.1 Ordination.- 2.2.2 Principal Components Analysis (PCA).- 2.3 Geometric Overview.- 2.4 The Data Set.- 2.5 Assumptions.- 2.5.1 Multivariate Normality.- 2.5.2 Independent Random Sample and the Effects of Outliers.- 2.5.3 Linearity.- 2.6 Sample Size Requirements.- 2.6.1 General Rules.- 2.6.2 Specific Rules.- 2.7 Deriving the Principal Components.- 2.7.1 The Use of Correlation and Covariance Matrices.- 2.7.2 Eigenvalues and Associated Statistics.- 2.7.3 Eigenvectors and Scoring Coefficients.- 2.8 Assessing the Importance of the Principal Components.- 2.8.1 Latent Root Criterion.- 2.8.2 Scree Plot Criterion.- 2.8.3 Broken Stick Criterion.- 2.8.4 Relative Percent Variance Criterion.- 2.8.5 Significance Tests.- 2.9 Interpreting the Principal Components.- 2.9.1 Principal Component Structure.- 2.9.2 Significance of Principal Component Loadings.- 2.9.3 Interpreting the Principal Component Structure.- 2.9.4 Communality.- 2.9.5 Principal Component Scores and Associated Plots.- 2.10 Rotating the Principal Components.- 2.11 Limitations of Principal Components Analysis.- 2.12 R-Factor Versus Q-Factor Ordination.- 2.13 Other Ordination Techniques.- 2.13.1 Polar Ordination.- 2.13.2 Factor Analysis.- 2.13.3 Nonmetric Multidimensional Scaling.- 2.13.4 Reciprocal Averaging.- 2.13.5 Detrended Correspondence Analysis.- 2.13.6 Canonical Correspondence Analysis.- Appendix 2.1.- 3 Cluster Analysis.- 3.1 Objectives.- 3.2 Conceptual Overview.- 3.3 The Definition of Cluster.- 3.4 The Data Set.- 3.5 Clustering Techniques.- 3.6 Nonhierarchical Clustering.- 3.6.1 Polythetic Agglomerative Nonhierarchical Clustering.- 3.6.2 Polythetic Divisive Nonhierarchical Clustering.- 3.7 Hierarchical Clustering.- 3.7.1 Polythetic Agglomerative Hierarchical Clustering.- 3.7.2 Polythetic Divisive Hierarchical Clustering.- 3.8 Evaluating the Stability of the Cluster Solution.- 3.9 Complementary Use of Ordination and Cluster Analysis.- 3.10 Limitations of Cluster Analysis.- Appendix 3.1.- 4 Discriminant Analysis.- 4.1 Objectives.- 4.2 Conceptual Overview.- 4.2.1 Overview of Canonical Analysis of Discriminance.- 4.2.2 Overview of Classification.- 4.2.3 Analogy with Multiple Regression Analysis and Multivariate Analysis of Variance.- 4.3 Geometric Overview.- 4.4 The Data Set.- 4.5 Assumptions.- 4.5.1 Equality of Variance-Covariance Matrices.- 4.5.2 Multivariate Normality.- 4.5.3 Singularities and Multicollinearity.- 4.5.4 Independent Random Sample and the Effects of Outliers.- 4.5.5 Prior Probabilities Are Identifiable.- 4.5.6 Linearity 153.- 4.6 Sample Size Requirements.- 4.6.1 General Rules.- 4.6.2 Specific Rules.- 4.7 Deriving the Canonical Functions.- 4.7.1 Stepwise Selection of Variables.- 4.7.2 Eigenvalues and Associated Statistics.- 4.7.3 Eigenvectors and Canonical Coefficients.- 4.8 Assessing the Importance of the Canonical Functions.- 4.8.1 Relative Percent Variance Criterion.- 4.8.2 Canonical Correlation Criterion.- 4.8.3 Classification Accuracy.- 4.8.4 Significance Tests.- 4.8.5 Canonical Scores and Associated Plots.- 4.9 Interpreting the Canonical Functions.- 4.9.1 Standardized Canonical Coefficients.- 4.9.2 Total Structure Coefficients.- 4.9.3 Covariance-Controlled Partial F-Ratios.- 4.9.4 Significance Tests Based on Resampling Procedures.- 4.9.5 Potency Index.- 4.10 Validating the Canonical Functions.- 4.10.1 Split-Sample Validation.- 4.10.2 Validation Using Resampling Procedures.- 4.11 Limitations of Discriminant Analysis.- Appendix 4.1.- 5 Canonical Correlation Analysis.- 5.1 Objectives.- 5.2 Conceptual Overview.- 5.3 Geometric Overview.- 5.4 The Data Set.- 5.5 Assumptions.- 5.5.1 Multivariate Normality.- 5.5.2 Singularities and Multicollinearity.- 5.5.3 Independent Random Sample and the Effects of Outliers.- 5.5.4 Linearity.- 5.6 Sample Size Requirements.- 5.6.1 General Rules.- 5.6.2 Specific Rules.- 5.7 Deriving the Canonical Variates.- 5.7.1 The Use of Covariance and Correlation Matrices.- 5.7.2 Eigenvalues and Associated Statistics.- 5.7.3 Eigenvectors and Canonical Coefficients.- 5.8 Assessing the Importance of the Canonical Variates.- 5.8.1 Canonical Correlation Criterion.- 5.8.2 Canonical Redundancy Criterion.- 5.8.3 Significance Tests.- 5.8.4 Canonical Scores and Associated Plots.- 5.9 Interpreting the Canonical Variates.- 5.9.1 Standardized Canonical Coefficients.- 5.9.2 Structure Coefficients.- 5.9.3 Canonical Cross-Loadings.- 5.9.4 Significance Tests Based on Resampling Procedures.- 5.10 Validating the Canonical Variates.- 5.10.1 Split-Sample Validation.- 5.10.2 Validation Using Resampling Procedures.- 5.11 Limitations of Canonical Correlation Analysis.- Appendix 5.1.- 6 Summary and Comparison.- 6.1 Objectives.- 6.2 Relationship Among Techniques.- 6.2.1 Purpose and Source of Variation Emphasized.- 6.2.2 Statistical Procedure.- 6.2.3 Type of Statistical Technique and Variable Set Characteristics.- 6.2.4 Data Structure.- 6.2.5 Sampling Design.- 6.3 Complementary Use of Techniques.- Appendix: Acronyms Used in This Book.

Evolution of Dispersal: Theoretical Models and Empirical Tests Using Birds and Mammals

Abstract: A fundamental question related to ecological and genetic aspects of dispersal is: What are the mechanisms for the evolution of dispersal? In recent years a prodigious number of mathematical models have described the evolution of dispersal. We believe that it is a propitious time to examine these models with respect to their underlying assumptions (Table 1), critical parameters, and predictions. Our objective is to elucidate common themes as well as differences in these models, and to present them in a manner comprehensible to individuals who lack extensive mathematical training, so that others might be encouraged to perform critical experiments in the field. We conclude the review with a summary of empirical work that uses data on birds

Bray-curtis ordination: an effective strategy for analysis of multivariate ecological data

Abstract: Publisher Summary Ordination implies an abstract space in which the entities form a constellation. In the Bray–Curtis ordination, the entities are samples and the attributes are species values in those samples. The aim of this method is to (1) calculate a distance matrix, (2) select two reference points (either real or synthetic samples) for determining direction of each axis, and (3) project all samples onto each such axis by their relationship to the two reference points. There are two major problems common to all ordination techniques, which include a function of the β-diversity or heterogeneity of the data set—that is, how different the samples are from one another. All ordinations distort the original multivariate data set and information is inevitably lost. Distortion in ordination has two kinds of consequences. The first is compressing and stretching distances in the ordination, compared with the original distance measures and relative to one another. The second consequence is the curvature of environmental axes, and this relates to Orloci's types A and C. Some of the alternatives to Bray–Curtis ordination are principal component analysis, reciprocal averaging, and iterative-stress minimization techniques.