Analysis Of Ecological Data Research Articles

Traditional forest protection and woodlots in the coastal savannah of Ghana

There is increased international interest in religiously based restrictions on land and forest stand use. However, the extent to which so-called sacred groves represent earlier forest ecosystems, and their possible role in biodiversity conservation, are interrelated and complex issues, and neglected in the context of Ghanaian savannahs, which are believed to be in transition from a forested past. Geographical information system (GIS) analysis of time-series images, aerial photographs, statistical analysis of ecological field data and social surveys were used to investigate the Ghanaian savannah. Sacred groves were found to be similar in plant species content and structure to the deciduous forest of southern Ghana. Between 1960 and 1996, these groves experienced far fewer forest losses than unprotected tree stands (<20%, as opposed to up to 100%). Non-sacred groves also contained lower plant species richness and higher numbers of invasive species such as neem (Azadirachta indica A. Juss). Sacred groves, while dependent on respect for religion, local cultural structures and individual peer pressure, offer a role that may support and also be supported by official conservation efforts.

Bias in evaluating the effects of marine protected areas: the importance of baseline data for the Galapagos Marine Reserve

Analysis of ecological baseline data collected for key resource species within the Galapagos Marine Reserve indicates that variation in animal density associated with the location of fully protected zones can be comparable to protected area effects. Even with a high level of interspersion between conservation, tourism and fishing management zones, major differences in densities of economically important species were evident between zone types prior to enforcement of fishing restrictions. Densities of the most valuable fishery resource, sea cucumbers, were three times higher in zones that remained open to fishing compared to ‘no-take’ conservation zones, and densities of sharks were five times higher in tourism zones than fishing or conservation zones. These results highlight bias in the socio-political processes that can accompany selection of marine protected areas, where fishers attempt to minimize perceived impacts on their livelihood by locating large protected zones in resource-poor areas, and tourism operators and sport divers argue for protection of areas containing atypically-interesting features. Bias in the location of fully protected zones can seriously confound ‘control-impact’ field investigations when data prior to prohibitions on fishing are lacking, including meta-analyses, which are dominated by such potentially systematically biased studies.

THE INCREASING IMPORTANCE OF 1/f-NOISES AS MODELS OF ECOLOGICAL VARIABILITY

The features of 1/f-noise processes offer important new insights into the field of population biology, greatly helping our quest for understanding and for prediction of ecological processes. 1/f-noises account quite satisfactorily for the observed nature of ecological fluctuations. This article reviews the application of 1/f-noise processes to ecology. After a discussion of the basic problems of population ecology that makes such an innovation necessary, we review the features of 1/f-noises concentrating especially on those aspects that make these processes attractive as a solution. We also present a discussion of the analysis of real ecological data, which confirms that there are good empirical as well as good theoretical reasons to establish a leading role for pink 1/f noise. We then discuss the consequences of such a model for our understanding of ecology. The article finishes with a number of observations about some aspects of ecological data and applications that are likely to drive research in a different direction from that associated with engineering and the physical sciences.

Understanding Environmental Complexity through a Distributed Knowledge Network

Understanding environmental complexity and other dimensions of ecological systems necessitates a holistic approach that can be achieved only by identifying, retrieving, and synthesizing diverse data from distributed sources; by collaborating with other scientists from a broad range of disciplines; and by investigating many different systems. Knowledge Network for Biocomplexity (KNB) is developing new software tools to advance ecological understanding through discovery, access, retrieval, and management of distributed and heterogeneous ecological and environmental data. To address the need for cultural change in ecologists and other environmental scientists and to promote collaborative and synthetic approaches, KNB and the National Center for Ecological Analysis and Synthesis are training a cadre of young investigators in techniques for the management and analysis of ecological data, with emphasis on multiscale integration and synthesis.

Spatial analysis of roadside Acacia populations on a road network using the network K-function

Spatial patterning of plant distributions has long been recognised as being important in understanding underlying ecological processes. Ripley’s K-function is a frequently used method for studying the spatial pattern of mapped point data in ecology. However, application of this method to point patterns on road networks is inappropriate, as the K-function assumes an infinite homogenous environment in calculating Euclidean distances. A new technique for analysing the distribution of points on a network has been developed, called the network K-function (for univariate analysis) and network cross K-function (for bivariate analysis). To investigate its applicability for ecological data-sets, this method was applied to point location data for roadside populations of three Acacia species in a fragmented agricultural landscape of south-eastern Australia. Kernel estimations of the observed density of spatial point patterns for each species showed strong spatial heterogeneity. Combined univariate and bivariate network K-function analyses confirmed significant clustering of populations at various scales, and spatial patterns of Acacia decora suggests that roadworks activities may have a stronger controlling influence than environmental determinants on population dynamics. The network K-function method will become a useful statistical tool for the analyses of ecological data along roads, field margins, streams and other networks.

A framework for the integration of ecosystem and human health in public policy: two case studies with infectious agents

Despite that a significant body of published literature exists in the complex area of interconnection among the environment, ecosystems, and human activity, relatively little attention has been paid to the integration and analysis of ecological and human health data in the form of a conceptual model. Human and ecological health protection generally have been treated as separate domains of policy, with significant differences in both the analytic methods used to characterize risks and the policies developed for risk reduction. Understanding the relationships among population growth, development, natural resource use, the environment, human health, and ecosystems is an important area of both scientific inquiry and environmental policy. The present paper focuses on the development of a conceptual model for understanding disease causation, particularly infectious disease, and the implications of such a model for public policy. The conceptual model incorporates ecological and human health risk assessment information applied to case studies of two infectious diseases. This article takes an initial step toward formalizing the conceptual model so that research and assessment procedures can be developed.

Biological indicators in marine and coastal waters: a statistical and modelling analysis of the MARS campaign

We perform a statistical analysis of ecological data collected in the MARS project from marine environments of the Red and Mediterranean Seas in Israel, and the North Sea in Germany. In the study of fish parasite data, we examine the basic premise that the complex host lifecycles of heteroxenous (h) parasites make them more sensitive to pollution than monoxenous (m) parasites which have only one host. Unlike results from analyses of earlier survey data, we find that the pooled h/m index for a community is not a clear indicator of environmental stress as originally hypothesized. This is due to the relatively large seasonal variability at the sampling sites over the course of the 3-year survey. Other possible indicators for water pollution gradients are checked, including species richness, diversity and prevalence of parasite populations. A theoretical model of host-parasite population dynamics that makes the presence of ecological thresholds transparent is offered to help explain the abrupt transitions prominent in the distributions of parasites at different sites under different pollution loadings. Heavy metal and chlorinated hydrocarbon data collected from fish tissue samples and molluscs of the three seas are assessed as bioindicators for pollution monitoring. The xenobiotic loads and indices of defence system activity and tissue damage are shown to have great potential as monitoring bioindicators.

GIVING MEANINGFUL INTERPRETATION TO ORDINATION AXES: ASSESSING LOADING SIGNIFICANCE IN PRINCIPAL COMPONENT ANALYSIS

Principal component analysis (PCA) is one of the most commonly used tools in the analysis of ecological data. This method reduces the effective dimensionality of a multivariate data set by producing linear combinations of the original variables (i.e., components) that summarize the predominant patterns in the data. In order to provide meaningful interpretations for principal components, it is important to determine which variables are associated with particular components. Some data analysts incorrectly test the statistical significance of the correlation between original variables and multivariate scores using standard statistical tables. Others interpret eigenvector coefficients larger than an arbitrary absolute value (e.g., 0.50). Resampling, randomization techniques, and parallel analysis have been applied in a few cases. In this study, we compared the performance of a variety of approaches for assessing the significance of eigenvector coefficients in terms of type I error rates and power. Two novel approaches based on the broken-stick model were also evaluated. We used a variety of simulated scenarios to examine the influence of the number of real dimensions in the data; unique versus complex variables; the magnitude of eigenvector coefficients; and the number of variables associated with a particular dimension. Our results revealed that bootstrap confidence intervals and a modified bootstrap confidence interval for the broken-stick model proved to be the most reliable techniques.

CANONICAL ANALYSIS OF PRINCIPAL COORDINATES: A USEFUL METHOD OF CONSTRAINED ORDINATION FOR ECOLOGY

A flexible method is needed for constrained ordination on the basis of any distance or dissimilarity measure, which will display a cloud of multivariate points by reference to a specific a priori hypothesis. We suggest the use of principal coordinate analysis (PCO, metric MDS), followed by either a canonical discriminant analysis (CDA, when the hypothesis concerns groups) or a canonical correlation analysis (CCorA, when the hypothesis concerns relationships with environmental or other variables), to provide a flexible and meaningful constrained ordination of ecological species abundance data. Called “CAP” for “Canonical Analysis of Principal coordinates,” this method will allow a constrained ordination to be done on the basis of any distance or dissimilarity measure. We describe CAP in detail, including how it can uncover patterns that are masked in an unconstrained MDS ordination. Canonical tests using permutations are also given, and we show how the method can be used (1) to place a new observation into the canonical space using only interpoint dissimilarities, (2) to classify observations and obtain misclassification or residual errors, and (3) to correlate the original variables with patterns on canonical plots. Misclassification error or residual error is used to obtain a non-arbitrary decision concerning the appropriate dimensionality of the response data cloud (number of PCO axes) for the ensuing canonical analysis. We suggest that a CAP ordination and an unconstrained ordination, such as MDS, together will provide important information for meaningful multivariate analyses of ecological data by reference to explicit a priori hypotheses. Corresponding Editor: A. M. Ellison

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy ( r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.

A general approach to modeling and analysis of species abundance data with extra zeros

A general method for the analysis of ecological count data with extra zeros is presented using a Markov birth process representation of discrete distributions. The method uses a nonparametric formulation of the birth process to model the residual variation and therefore allows the data to play a greater role in determining an appropriate distribution. This enables a more critical assessment of covariate effects and more accurate predictions to be made. The approach is also presented as a useful diagnostic tool for suggesting appropriate parametric models or verifying standard models. As an illustrative example, data describing abundance of a species of possum from the montane ash forests of the central highlands of Victoria, southeast Australia, is considered.

All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices

Spatial heterogeneity of ecological structures originates either from the physical forcing of environmental variables or from community processes. In both cases, spatial structuring plays a functional role in ecosystems. Ecological models should explicitly take into account the spatial structure of ecosystems. In previous work, we used a polynomial function of the geographic coordinates of the sampling sites to model broad-scale spatial variation in a canonical (regression-type) modelling context. In this paper, we propose a method for detecting and quantifying spatial patterns over a wide range of scales. This is obtained by eigenvalue decomposition of a truncated matrix of geographic distances among the sampling sites. The eigenvectors corresponding to positive eigenvalues are used as spatial descriptors in regression or canonical analysis. This method can be applied to any set of sites providing a good coverage of the geographic sampling area. This paper investigates the behaviour of the method using numerical simulations and an artificial pseudo-ecological data set of known properties.

Using Emergy Analysis in Ethnographic Field Research

A methodology derived from Howard T. Odum's theory is described for the collection and analysis of ecological and sociological data. This methodology allows for a detailed and systematic analysis of how human populations interact with their environmental settings via resource exchanges. Some research results are offered as examples.

Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure

Fuzzy clustering generates cluster membership weights that indicate how tightly each object is linked to its cluster relative to other clusters of a dendrogram. In a fuzzy clustering of the crustacean-zooplankton taxa of Lake Simcoe, a large (720 km2) hardwater lake in Ontario, Canada, we show how the membership weights can be used to rank all taxa for their contribution to the sampling unit (SU) classification, where the total number of SUs was 84 (7 years × 12 sampling sites). The validity of the results was confirmed by comparison with other more traditional methods of identifying variables important for object classifications and by permutation tests of matrix correlation before and after removal of low-ranked and highly ranked species. Fuzzy clustering of Lake Simcoe SUs also revealed (i) the likelihood of trends in zooplankton community composition over the 7-year period and (ii) differences in composition possibly related to sampling-station depth. In particular, the shallowest sampling station in southern Cook's Bay had a zooplankton community structure that differed significantly from other stations during all years of the study. As a preliminary screening or data exploration tool, fuzzy clustering is particularly useful for analysis of ecological data.

A Parallel Analysis of Individual and Ecological Data on Residential Radon and Lung Cancer in South-West England

Summary Parallel individual and ecological analyses of data on residential radon have been performed using information on cases of lung cancer and population controls from a recent study in south-west England. For the individual analysis the overall results indicated that the relative risk of lung cancer at 100 Bq m−3 compared with at 0 Bq m−3 was 1.12 (95% confidence interval (0.99, 1.27)) after adjusting for age, sex, smoking, county of residence and social class. In the ecological analysis substantial bias in the estimated effect of radon was present for one of the two counties involved unless an additional variable, urban–rural status, was included in the model, although this variable was not an important confounder in the individual level analysis. Most of the methods that have been recommended for overcoming the limitations of ecological studies would not in practice have proved useful in identifying this variable as an appreciable source of bias.

Fitting Multivariate Models to Community Data: A Comment on Distance-Based Redundancy Analysis

Nonparametric multivariate analysis of ecological data using permutation tests has two main challenges: (1) to partition the variability in the data according to a complex design or model, as is often required in ecological experiments, and (2) to base the analysis on a multivariate distance measure (such as the semimetric Bray-Curtis measure) that is reasonable for ecological data sets. Previous nonparametric methods have succeeded in one or other of these areas, but not in both. A recent contribution to Ecological Monographs by Legendre and Anderson, called distance-based redundancy analysis (db-RDA), does achieve both. It does this by calculating principal coordinates and subsequently correcting for negative eigenvalues, if they are present, by adding a constant to squared distances. We show here that such a correction is not necessary. Partitioning can be achieved directly from the distance matrix itself, with no corrections and no eigenanalysis, even if the distance measure used is semimetric. An ecological example is given to show the differences in these statistical methods. Empirical simulations, based on parameters estimated from real ecological species abundance data, showed that db-RDA done on multifactorial designs (using the correction) does not have type 1 error consistent with the significance level chosen for the analysis (i.e., does not provide an exact test), whereas the direct method described and advocated here does.

FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE-BASED REDUNDANCY ANALYSIS

Nonparametric multivariate analysis of ecological data using permutation tests has two main challenges: (1) to partition the variability in the data according to a complex design or model, as is often required in ecological experiments, and (2) to base the analysis on a multivariate distance measure (such as the semimetric Bray-Curtis measure) that is reasonable for ecological data sets. Previous nonparametric methods have succeeded in one or other of these areas, but not in both. A recent contribution to Ecological Monographs by Legendre and Anderson, called distance-based redundancy analysis (db-RDA), does achieve both. It does this by calculating principal coordinates and subsequently correcting for negative eigenvalues, if they are present, by adding a constant to squared distances. We show here that such a correction is not necessary. Partitioning can be achieved directly from the distance matrix itself, with no corrections and no eigenanalysis, even if the distance measure used is semimetric. An ecological example is given to show the differences in these statistical methods. Empirical simulations, based on parameters estimated from real ecological species abundance data, showed that db-RDA done on multifactorial designs (using the correction) does not have type 1 error consistent with the significance level chosen for the analysis (i.e., does not provide an exact test), whereas the direct method described and advocated here does.

Neighborhood characteristics of alcohol-related pedestrian injury collisions: a geostatistical analysis.

This study conducted a geostatistical analysis of ecological data to examine the relationships of neighborhood characteristics, including alcohol availability and alcohol consumption patterns to pedestrian injury collisions. The central research question asked whether it was possible to identify unique neighborhood characteristics related to alcohol- and non-alcohol-involved pedestrian injuries. It was hypothesized that greater numbers of alcohol-involved pedestrian injuries would be observed in areas with greater concentrations of alcohol outlets, even after adjusting for socioeconomic characteristics, environmental factors, and drinking patterns of neighborhood residents. It was also hypothesized that independent of drinking patterns and alcohol availability, greater numbers of pedestrian injuries would be observed in areas with higher unemployment, lesser income, greater population, and a predominance of younger or older age populations. Archival and individual-level data from a general population telephone survey were obtained from four California communities. The survey data included sociodemographic and drinking pattern measures. Archival data included environmental measures relevant to pedestrian travel and measures of alcohol availability. Units of analysis were geographic areas within each community defined by the spatial clustering of telephone survey respondents. The results showed that alcohol-involved pedestrian collisions occurred more often in areas with greater bar densities and greater population, and where the local population reported drinking more alcohol per drinking occasion. Pedestrian collisions not involving alcohol occurred more often in lower income areas with greater population and cross-street densities, and in areas having either younger or older age populations. The identification of neighborhood variables associated with pedestrian collisions has important implications for policy formation and targeted prevention efforts.

Kullback-Leibler information as a basis for strong inference in ecological studies

We describe an information-theoretic paradigm for analysis of ecological data, based on Kullback–Leibler information, that is an extension of likelihood theory and avoids the pitfalls of null hypothesis testing. Information-theoretic approaches emphasise a deliberate focus on the a priori science in developing a set of multiple working hypotheses or models. Simple methods then allow these hypotheses (models) to be ranked from best to worst and scaled to reflect a strength of evidence using the likelihood of each model (gi), given the data and the models in the set (i.e. L(gi | data)). In addition, a variance component due to model-selection uncertainty is included in estimates of precision. There are many cases where formal inference can be based on all the models in the a priori set and this multi-model inference represents a powerful, new approach to valid inference. Finally, we strongly recommend inferences based on a priori considerations be carefully separated from those resulting from some form of data dredging. An example is given for questions related to age- and sex-dependent rates of tag loss in elephant seals (Mirounga leonina).

Broken windows, crumpled fenders, and crime

This study uses state-level and city-level data from the U.S. to assess the relationship between the homicide rate and the motor-vehicle fatality rate. The analysis of ecological data for a multiyear period reveals that there is a mild to moderate relationship between homicide rates and traffic-fatality rates. The limitations of ecological data are discussed. The authors argue that law enforcement can play a minor but significant role in determining the level of each type of fatality and that public perception of police activity is important. Police are most visible in their patrol and traffic functions and research suggests that controlling traffic flow has an effect on crime. Alternatively, the traffic equivalent to “broken windows” might be “crumpled fenders.” Lack of enforcement may send a signal to residents that police are absent or uncaring, leading not only to a disregard of the traffic safety rules and to higher accident rates but also to higher rates of crime in the community generally, as reflected through the homicide statistics.