Analysis Of Ecological Data Research Articles

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Environmental classification addresses issues involving the representation and analysis of continuous and variable ecological data. This study creates a methodology to define topo-climatic landscapes (TCL) in the north-west of Catalonia, which is situated in the north-east of the Iberian Peninsula. TCL provide data regarding the ecological behavior of a landscape in terms of its topography, physiognomy, and climate, which are the main drivers of an ecosystem. The variables selected are derived from a variety of different sources, such as remote sensing and climatic atlases. The methodology employed combines unsupervised iterative cluster classification with supervised fuzzy classification. Twenty eight TCL, which can be differentiated in terms of their vegetation physiognomy and vegetation altitudinal range type, were selected for the study area. Furthermore, a hierarchy among the TCL is established which permits the merging of clusters and allows for changes in thematic resolution. By using the topo-climatic landscape map, managers can identify patches with similar environmental conditions and at the same time assess the uncertainty involved in classification.

Proboscis profiler: a tool for detecting acanthocephalan morphotypes

Molecular studies conducted over the past 25 years have revealed previously unrecognised diversity in the phylum Acanthocephala. Several nominal species have been shown to represent complexes of morphologically cryptic biological species, a situation potentially confounding the analysis of ecological data. A software tool, 'Proboscis profiler', was developed to detect morphological heterogeneity in collections of superficially similar acanthocephalan worms based on the multivariate statistical analysis of proboscis hook dimensions. Proboscis profiler identifies objective, natural groups in a collection of acanthocephalans which may correspond to distinct biological species or populations. Initial trials demonstrate that Proboscis profiler can discriminate biological acanthocephalan species of the Echinorhynchus gadi Zoega in Müller, 1776 complex and differentiate between dorsal and ventral hook rows from the proboscis of E. salmonis Müller, 1784. Proboscis profiler is free software and can be downloaded from http://acanthocephala.sourceforge.net.

A comparison of self-organizing feature map clustering with TWINSPAN and fuzzy C-means clustering in the analysis of woodland communities in the Guancen Mts, China

SOFM (self-organizing feature map) clustering is powerful in analyzing and solving complicated and non-linear problems. This method was used and compared with fuzzy C-means clustering and TWINSPAN, the most common classification methods, in analysis of plant communities in the Guancen Mts., China. The dataset consisted of importance values of 112 species in 53 quadrats of 10 m × 20 m. All the three methods classified the 53 quadrats into eight groups, representing eight associations of vegetation. They were all effective in the analysis of ecological data. The consistency of SOFM clustering with fuzzy C-means clustering (FCM) and TWINSPAN classification was 81.1% and 94.3%, respectively. SOFM clustering has some advantages and more potentiality in application to studies of ecology.

A generic framework for deriving process stoichiometry in environmental models

It has been recognized for many decades that the stoichiometry of biological reactions is important for linking ecological and biogeochemical processes. However, only during the past decade has the scientific community become aware that “biological stoichiometry” may also help bridge evolutionary biology and ecosystem ecology. This awareness led to increasing attention to biological process stoichiometry in ecology during the past decade. Despite this trend in ecological data analysis and interpretation, ecological models are still predominantly formulated without sufficient attention to process stoichiometry. To support scientists in formulating stoichiometry in process models based on elemental mass conservation, we transfer techniques from chemical process engineering to achieve the following objectives: (i) develop a generic mathematical framework to formulate and solve stoichiometric equations; (ii) facilitate the extension of currently used process stoichiometries to consider more elements (e.g. adding S and/or Si to C, H, O, N and P); (iii) identify the need for stoichiometric constraints of biogeochemical processes in addition to elemental mass balances; (iv) unify approaches to characterize organic matter by elemental mass fractions, organic carbon, or chemical oxygen demand; (v) provide a small package of functions for the statistics and graphics software R ( http://www.r-project.org) to support environmental model building, and (vi) show how to incorporate automatic stoichiometric calculations into next generation environmental simulation software. The small R package “stoichcalc” can be downloaded from http://www.eawag.ch/ ∼reichert or from the package repository of the R project ( http://www.r-project.org).

Regression Versus Causation, Revisited

I recently discussed pitfalls in attempted causal inference based on reduced‐form regression models. I used as motivation a real‐world example from a paper by Dr. Sneeringer, which interpreted a reduced‐form regression analysis as implying the startling causal conclusion that “doubling of [livestock] production leads to a 7.4% increase in infant mortality.” This conclusion is based on: (A) fitting a reduced‐form regression model to aggregate (e.g., county‐level) data; and (B) (mis)interpreting a regression coefficient in this model as a causal coefficient, without performing any formal statistical tests for potential causation (such as conditional independence, Granger‐Sims, or path analysis tests). Dr. Sneeringer now adds comments that confirm and augment these deficiencies, while advocating methodological errors that, I believe, risk analysts should avoid if they want to reach logically sound, empirically valid, conclusions about cause and effect. She explains that, in addition to (A) and (B) above, she also performed other steps such as (C) manually selecting specific models and variables and (D) assuming (again, without testing) that hand‐picked surrogate variables are valid (e.g., that log‐transformed income is an adequate surrogate for poverty). In her view, these added steps imply that “critiques of A and B are not applicable” to her analysis and that therefore “a causal argument can be made” for “such a strong, robust correlation” as she believes her regression coefficient indicates. However, multiple wrongs do not create a right. Steps (C) and (D) exacerbate the problem of unjustified causal interpretation of regression coefficients, without rendering irrelevant the fact that (A) and (B) do not provide evidence of causality. This reply focuses on whether any statistical techniques can produce the silk purse of a valid causal inference from the sow's ear of a reduced‐form regression analysis of ecological data. We conclude that Dr. Sneeringer's analysis provides no valid indication that air pollution from livestock operations causes any increase in infant mortality rates. More generally, reduced‐form regression modeling of aggregate population data—no matter how it is augmented by fitting multiple models and hand‐selecting variables and transformations—is not adequate for valid causal inference about health effects caused by specific, but unmeasured, exposures.

Cystobasidiopsis nirenbergiae, a new agaricostilbomycete ( Pucciniomycotina)

A new genus, Cystobasidiopsis, and a new species, Cystobasidiopsis nirenbergiae, are described for a fungus isolated from an arable loess soil in Ahlum near Braunschweig, Niedersachsen, Germany. An integrated analysis of morphological, ecological, ultrastructural and molecular data indicates that the new species belongs to the Chionosphaeraceae within the Agaricostilbales. Relevant characteristics of the new species are discussed and compared with those of related taxa.

Interdisciplinary assessment of the status quo of the marine fishery systems in the state of Espirito Santo, Brazil, using Rapfish

Summary The aim of the present study was to carry out a comparative analysis of fishery systems in the state of Espirito Santo, Brazil (western coast of South America, between 18 and 22! S) using the Rapfish method - a rapid, innovative assessment model of fishery sustainability consisting of a multivariate ordination analysis of ecological, technological, social and economic data. The fisheries were divided into 26 fishery systems according to type of fleet, gear, target resource, ecosystem and environmental characteristics. A series of variables categorized in numeric scales from one to five were determined for each system. The attributes were compared through multivariate techniques. The comparison between fisheries in the state reflect the characteristic small-scale fisheries of the region, with minimal state intervention in management. The analysis of the social dimension revealed that the correlations between systems were mainly associated to the degree of isolation of the communities. The analysis of the ecological dimension revealed a clear coast ⁄ open ocean gradient and urbanized centers ⁄ isolated locations gradient related to the degree of commitment to the environment with regard to anthropogenic impact and the availability of resources. The economic and technological dimensions were arranged by variations in product destination, cost of fishing gear and degree of technology employed. The management dimension exhibited little sensitivity to the multivariate method em- ployed. Few systems stood apart due to the few existing management measures that di! erentiated them. The pattern of sustainability found in the kite diagrams displays a balance between the dimensions in the perspective of maximum approximation to the optimum sustainability index. Either overfished and better socio-economic conditions, or margin- alized people with use a more more sound strategy of resource exploitation.

Inferring evolutionarily significant units of bacterial diversity from broad environmental surveys of single-locus data

By far the greatest challenge for diversity studies is to characterize the diversity of prokaryotes, which probably encompasses billions of species, most of which are unculturable. Recent advances in theory and analysis have focused on multi-locus approaches and on combined analysis of molecular and ecological data. However, broad environmental surveys of bacterial diversity still rely on single-locus data, notably 16S ribosomal DNA, and little other detailed information. Evolutionary methods of delimiting species from single-locus data alone need to consider population genetic and macroevolutionary theories for the expected levels of interspecific and intraspecific variation. We discuss the use of a recent evolutionary method, based on the theory of coalescence within independently evolving populations, compared with a traditional approach that uses a fixed threshold divergence to delimit species.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

How to Make Models Add Up — A Primer on GLMMs

Many problems in the analysis of ecological data have the format where there is an observed response that may be predicted by several covariates. Although the response can take several forms (e.g. measurements, counts, observations of presence/absence), and the covariates can also vary (e.g. be measurements themselves, or be grouped according to the treatment applied, the time or location of of sampling, etc.), most of these problems can be handled in a single framework, the Generalized Linear Mixed Model (GLMM). The framework encompasses regression, ANOVA, generalized linear models, and equivalent models with random as well as fixed effects. Here, the different parts of the GLMM are described, building from regression and ANOVA to show how the extra components — the wider range of distributions, and random effects — can be added into the same framework, and how the parameters of the fitted model can be estimated and interpreted. Being able to handle data with GLMMs helps ecologists to analyse the majority of their data.

The importance of accounting for spatial and temporal correlation in analyses of ecological data

Ecological ApplicationsVolume 19, Issue 3 p. 574-577 Forum–Hierarchical Statistical Models in Ecology The importance of accounting for spatial and temporal correlation in analyses of ecological data Jennifer A. Hoeting, Corresponding Author Jennifer A. Hoeting jah@lamar.colostate.edu Department of Statistics, Colorado State University, Fort Collins, Colorado 80523-1877 USA E-mail: jah@lamar.colostate.eduSearch for more papers by this author Jennifer A. Hoeting, Corresponding Author Jennifer A. Hoeting jah@lamar.colostate.edu Department of Statistics, Colorado State University, Fort Collins, Colorado 80523-1877 USA E-mail: jah@lamar.colostate.eduSearch for more papers by this author First published: 01 April 2009 https://doi.org/10.1890/08-0836.1Citations: 53 Corresponding Editor: N. T. Hobbs. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume19, Issue3April 2009Pages 574-577 RelatedInformation

Methods to quantify variable importance: implications for the analysis of noisy ecological data

Determining the importance of independent variables is of practical relevance to ecologists and managers concerned with allocating limited resources to the management of natural systems. Although techniques that identify explanatory variables having the largest influence on the response variable are needed to design management actions effectively, the use of various indices to evaluate variable importance is poorly understood. Using Monte Carlo simulations, we compared six different indices commonly used to evaluate variable importance; zero-order correlations, partial correlations, semipartial correlations, standardized regression coefficients, Akaike weights, and independent effects. We simulated four scenarios to evaluate the indices under progressively more complex circumstances that included correlation between explanatory variables, as well as a spurious variable that was correlated with other explanatory variables, but not with the dependent variable. No index performed perfectly under all circumstances, but partial correlations and Akaike weights performed poorly in all cases. Zero-order correlations was the only measure that detected the presence of a spurious variable, whereas only independent effects assigned overlap areas correctly once the spurious variable was removed. We therefore recommend using zero-order correlations to eliminate predictor variables with correlations near zero, followed by the use of independent effects to assign overlap areas and rank variable importance.

Ecology and web allometry of Clitaetra irenae, an arboricolous African orb-weaving spider (Araneae, Araneoidea, Nephilidae)

Abstract Analysis of ecological data of the arboricolous nephilid spider Clitaetra irenae Kuntner 2006, endemic to Maputaland forests, South Africa, indicates the species' dependence on this highly threatened habitat. We tested C. irenae habitat dependence via GIS analysis by plotting the known distribution against southern African ecoregions. In the southern part of its range, C. irenae inhabits almost exclusively one ecoregion, the Maputaland coastal plain forests; but further north, in tropical southern Africa, it continues inland into Malawi's woodlands. We test and refute the hypotheses that C. irenae inhabits exclusively mature trees, trees of a particular species, trees with a smooth bark, tree habitats at certain height above ground, and only closed canopy forest stands. The ecological niche of C. irenae is flexible as long as suitable trees under at least partially closed canopy are available. We quantify the C. irenae ontogenetic web changes from orb to ladder and the simultaneous hub displaceme...

Use of recurrence analysis to measure the dynamical stability of a multi-species community model

Quantifying the effects of species richness and environmental disturbance on the stability of communities is a long-standing challenge in ecology. In this study, multivariate recurrence analysis was used to assess the dynamical stability of modelled ecological communities subject to random, correlated environmental noise. Based on an analysis of biomass time series for each species, we show that two measures computed from the joint recurrence matrix, the Kolmogorov entropy and percent determinism, capture aspects of community stability that are not detected using the coefficient of variation for the whole community. In particular, when population fluctuations are correlated in time, recurrence analysis is a superior method for detecting the stabilizing effect of species richness on a community. We conclude that recurrence analysis is an appropriate tool for the analysis of ecological data, and that it may be particularly useful for detecting the relative importance of exogenous and endogenous drivers on the dynamics of ecological communities.

A standardized sampling method to estimate mosquito richness and abundance for research and public health surveillance programmes

Programmes involving mosquito research and surveillance are normally focused on the study of aquatic larval stages, but sampling methods are varied and not systematized, which hinders the comparative analysis of ecological data. A standardized method for assessing the richness and abundance of mosquito larval populations of value for the analysis of mosquito diversity is presented. Based on the study of all the aquatic sites in a one hectare sample area with a proportional number of dips according to the size of the aquatic habitat, comparative data can be obtained on species richness and relative abundance of species found between pairs of sites or in the same area at different times. This technique provides information on the total mosquito fauna at each site, helps recognize species of medical importance and estimates the abundance of each species; parameters that are not estimated by the current entomological indexes used in surveillance programmes. The quality of the inventory is obtained by estimation of the efficiency effort. Procedures for calculating alpha, beta and gamma diversity are presented. The technique was validated in a natural and an urban zone at La Mancha, Veracruz, Mexico, over two years of sampling made during different climatic seasons.

Unexpected failures of recommended tests in basic statistical analyses of ecological data

Abstract. Ecologists, when analyzing the output of simple experiments, often have to compare statistical samples that simultaneously are of uneven size, unequal variance and distribute non-normally. Although there are special tests designed to address each of these unsuitable characteristics, it is unclear how their combination affects the tests. Here we compare the performance of recommended tests using generated data sets that simulate statistical samples typical in ecological research. We measured rates of type I and II errors, and found that common parametric tests such as ANOVA are quite robust to non-normality, uneven sample size, unequal variance, and their effect combined. ANOVA and randomization tests produced very similar results. At the same time, the t-test for unequal variance unexpectedly lost power with samples of uneven size. Also, non-parametric tests were strongly affected by unequal variance in large samples, yet non-parametric tests could complement parametric tests when testing samples of uneven size. Thus, we demonstrate that the robustness of each kind of test strongly depends on the combination of parameters (distribution, sample size, equality of variances). We conclude that manuals should be revised to offer more elaborate instructions for applying specific statistical tests.

Sustainable shrimp fishery management recommendations for a coastal lagoon in Baja California Sur, Mexico

The Baja California Peninsula is considered México's most productive in terms of commercial fisheries. Very few quantitative assessments of the economic importance of this region exist, especially considering artisanal fisheries and their relationships with ecological data. Datasets from government records on shrimp capture in Magdalena Bay, an arid coastal lagoon of Baja California Sur were collected, analysed and correlated with ecological data. Over the 10-year period analysed, fisheries from Magdalena Bay made up 27.5% of the shrimp capture of the state, contributing over US$15.5 million to the economy of the country. The impressive value of this resource warrants considerable attention in the design of land-use plans for the future of the region. Analysis of ecological and economical data on shrimp fishery activities in the Magdalena Bay region enables us to propose recommendations to enhance the sustainable development of the local inhabitants. A thorough assessment of the potential value of alternative economic activities should be conducted to determine their potential to provide similar productive use of the natural resources of the region as the shrimp fishery. Also, from a more specific management viewpoint, harvest rates of small shrimp in the channels should be reduced.

Community coverage and HIV prevention: Assessing metrics for estimating HIV incidence through syringe exchange

Background Evaluations of syringe-exchange programme effectiveness that attempt to measure “coverage” by determining the percentage of the at-risk population reached by a programme are insufficient since programmes must provide syringes on a continual basis. Determining the relationship between the extent of programme coverage and its impact (i.e., reductions in disease risk or incidence) is complicated by the lack of controlled trials with cohorts of drug users and instead has to be estimated by imputation, mathematical modelling, or ecological data analysis. This report offers an approach to determine community-wide impact and discusses the limitations of that approach. Methods Easily created programme tracking data were maintained by exchanges in New Haven, CT, USA and Chicago, IL, USA. Data compiled by month quantified the number of unique participants visiting syringe-exchange programmes and the number of syringes distributed. “Coverage”, defined either as the percentage of individuals reached or percentage of community syringe need met, was estimated by incorporating measures of the size of the injector population or injection frequency. These measures of coverage are placed in the context of changing programme operations to estimate the effect of these changes on coverage. Finally, data on AIDS cases from New Haven and Chicago were used to estimate the community-wide impact of syringe exchange. Results Two mobile syringe-exchange programmes operated with very different exchange policies. Programme data revealed that coverage of individuals rarely exceeded 10% and was higher in New Haven than in Chicago. On the other hand, coverage measured as the percentage of syringe need met was higher at the Chicago exchange that employed the less restrictive policy. The impact of syringe exchange in the two cities was measured by comparing subsequent AIDS cases. The relative reduction in injection-related AIDS cases as a function of all new AIDS diagnoses was 21.7% in New Haven and 41.4% in Chicago. Conclusions A modest investment in the collection of programme data can yield reliable and interpretable information on the extent of programme reach and retention. Limitations to the approach result from the ecological nature of the data and from the need to use data from outside the programme that may be less reliable. For the cases presented here, coverage rates will vary as a function of the programme policies; however, even modest coverage rates – well below those recommended by UNAIDS – can have significant impacts on HIV epidemics. Restrictive policies appeared to increase the coverage if measured only by the proportion of monthly participants and not by the proportion of syringe need met by a programme. More generally, programmes can collect programmatic data and some rapid assessment data (estimates of IDU population and injection frequency) to estimate of the impact of their programmes.

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.

Cyberinfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment

The LTER Grid Pilot Study was conducted by the National Center for Supercomputing Applications, the University of New Mexico, and Michigan State University, to design and build a prototype grid for the ecological community. The featured grid application, the Biophony Grid Portal, manages acoustic data from field sensors and allows researchers to conduct real-time digital signal processing analysis on high-performance systems via a web-based portal. Important characteristics addressed during the study include the management, access, and analysis of a large set of field collected acoustic observations from microphone sensors, single signon, and data provenance. During the development phase of this project, new features were added to standard grid middleware software and have already been successfully leveraged by other, unrelated grid projects. This paper provides an overview of the Biophony Grid Portal application and requirements, discusses considerations regarding grid architecture and design, details the technical implementation, and summarizes key experiences and lessons learned that are generally applicable to all developers and administrators in a grid environment.