Analysis Of Ecological Data Research Articles

Environmental stratifications as the basis for national, European and global ecological monitoring

There is growing urgency for integration and coordination of global environmental and ecological data and indicators required to respond to the ‘grand challenges’ the planet is facing, including climate change and biodiversity decline. A consistent stratification of land into relatively homogenous strata provides a valuable spatial framework for comparison and analysis of ecological and environmental data across large heterogeneous areas. We discuss how statistical stratification can be used to design national, European and global biodiversity observation networks. The value of strategic ecological survey based on stratified samples is first illustrated using the United Kingdom (UK) Countryside Survey, a national monitoring programme that has measured ecological change in the UK countryside for the last 35 years. We then present a design for a European-wide sampling design for monitoring common habitats, and discuss ways of extending these approaches globally, supported by the recently developed Global Environmental Stratification. The latter provides a robust spatial analytical framework for the identification of gaps in current monitoring efforts, and systematic design of new complementary monitoring and research. Examples from Portugal and the transboundary Kailash Sacred Landscape in the Himalayas illustrate the potential use of this stratification, which has been identified as a focal geospatial dataset within the Group on Earth Observation Biodiversity Observation Network (GEO BON).

Setting the stage for marine spatial planning: Ecological and social data collation and analyses in Canada's Pacific waters

Canada's Pacific coast is one region where there is a renewed commitment to pursue marine spatial planning (MSP). The British Columbia Marine Conservation Analysis (BCMCA) project aimed to set the stage for MSP, and was designed to provide resource managers, scientists, decision-makers, and stakeholders with a new set of resources to inform coast-wide integrated marine planning and management initiatives. Geographic Information Systems and the decision support tool Marxan were used to develop two main products: (1) an atlas of known marine ecological values and human uses; and (2) analyses of areas of conservation value and human use value. 110 biophysical datasets and 78 human use datasets were collated and refined where applicable, as identified through five ecological expert workshops, one expert review of physical marine classification and representation, and guidance from the human use data working group. Ecological data richness maps and Marxan results show the importance of nearshore and continental shelf regions. Data richness maps for the six categories of human uses show that all, except shipping and transport, are also closely linked to the shoreline and continental shelf. An example ecological Marxan solution identifying areas of conservation value overlapped human use sector footprints by percentages ranging from 92% (i.e., 92% of planning units selected by Marxan also contain commercial fisheries) to 3%. The experience of the BCMCA project has the potential to provide valuable guidance to regions seeking to jump-start planning processes by collating spatial information and carrying out exploratory analyses.

Tempo and mode in the influence of global climatic changes on mammalian evolution

Due to their extraordinary fossil record during the Cenozoic, mammals are a key factor in order to understand how global climatic changes have shaped biotic evolution, especially in continental environments. During the last decades, climatic influence on the evolution of mammal faunas has been reflected mainly in two sets of studies. First, there are works focused in the use of observed variations in the structure of mammalian palaeocommunities through extensive time sequences in order to detect significant palaeoclimatic changes. These studies are based in the concepts of community convergence and uniformitarism, and make use of modern ecological analogues to infer the climatic conditions in which the extinct faunas lived. On the other hand, when data on faunal change through time are confronted with independent data on palaeoclimatic changes it may be statistically inferred whether there has been simultaneous occurrence of climatic changes, faunal turnover and modifications in the ecological structure of the palaeofaunas. The second set of studies is based on the statistical analysis of ecological data from modern faunas. Extant assemblages reflect in their ecological characteristics the past influence of global climate changes on the evolution of mammalian lineages living today, both in geographical and cladal basis. Finally, there are new works based on the study of the phylogenetic signal that can be found in modern clades, which are able to identify periods of enhanced or reduced cladogenesis. Although all these approaches cannot be used simultaneously because of latent problems derived of circular reasoning, the development of methodological protocols for the combined analysis of their results has the potential to shed new light on the question of how and when climate changes affect evolution of biotic communities. Session on “Linked effects of climate change, orbital forcing, tectonics and biotic interaction: Which model is required to explain faunal evolution during the Quaternary?”

Richard E. Plant: Spatial Data Analysis in Ecology and Agriculture Using R

Richard E. Plant: Spatial Data Analysis in Ecology and Agriculture Using R

Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation

Summary1. Spatial analysis of ecological data is central to many interesting questions in ecology. Bayesian implementation of spatially explicit models has received increasing attention from ecologists as Monte Carlo Markov Chain (MCMC) methods have become freely accessible. MCMC simulations offer a flexible framework for modelling extensive ecological data, but they also come with a wide range of problems regarding convergence, processing time and implementation.2. We introduce to ecologists an alternative procedure for fitting Bayesian hierarchical spatial models (BHSM) with quite general spatial covariance structures. This procedure uses integrated nested Laplace approximations (INLA) as an alternative to MCMC.3. We show, using a case study of species distribution model with binary areal data, that implementing BHSM with INLA does not require advanced programming skills, yields accurate results compared with MCMC and is rapid (e.g. a few seconds with small to moderate data sets). BHSMs efficiently removed spatial autocorrelation in the residuals and fairly evaluated uncertainty in parameter estimates and predictions.4. The rapidity of INLA significantly decreased the processing time and allowed both sensitivity analyses on priors and cross‐validation tests to be performed within a reasonable amount of time, which ultimately increased model transparency.

Cascade multivariate regression tree: a novel approach for modelling nested explanatory sets

Summary1. Ecological data analysis frequently calls for the assessment of the relationship between species composition and a set of explanatory variables of interest. The assessment may have to be pursued while taking into account the influence of another set of explanatory variables. The hypothetical nature and structure of the influence of an explanatory set on the effect of a distinct explanatory set guides the proper choice of modelling methodology for a combined explanatory assessment.2. Here, we describe a framework where the relationship between the response data and a main set of explanatory variables is not linear. It may, for example, take the form of abrupt changes in the response following thresholds of the explanatory variables, or any other nonlinearizable relationship. The influence of a second set of explanatory variables is determined a posteriori, after the influence of the main explanatory set has been taken into account. This is useful when one of the sets is thought to have an effect that varies as a function of the other.3. To achieve this type of assessment, we propose a cascade of multivariate regression trees (CMRT). We decompose the total dispersion of a response matrix between two explanatory data sets in a nested manner. By handling each leaf (group) resulting from the first‐level multivariate regression tree (MRT) analysis as separate independent data sets in following analyses, we can separate the explanatory power of the first partition from those of the subordinate partitions computed using a second explanatory set. A preliminary biological hypothesis will guide the choice of which set of explanatory variables should be used to compute the main partition. The method could be extended to more than two explanatory data sets whose effects on the response data are hierarchical.4. Cascade of multivariate regression trees allows the users to impose a nested structure to their causal hypotheses in MRT analysis. To illustrate this new procedure, we use the well‐known and readily available Doubs fish and oribatid mite data sets and provide the necessary R functions in a package available on CRAN (http://cran.r‐project.org).

Bioinformatics tools in predictive ecology: applications to fisheries.

There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.

Continuous-Time Proportional Hazards Regression for Ecological Monitoring Data

We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for inference and the computation can be carried out using standard statistical software packages. This approach has significant computational advantages over some of the existing methods that rely on Monte Carlo simulations. Simulation experiments are conducted and demonstrate that our method has sound finite-sample properties. A real dataset from an ecological study that monitored bark beetle colonization of red pines in Wisconsin is analyzed using the proposed models and inference. Supplementary materials that contain technical details are available online.

A brief introduction of structural equation model and its application in ecology

Natural systems are essentially complex. In most cases, fully understanding natural systems requires the capacity to examine simultaneous influences and responses among multiple interacting factors. Compared with traditional multivariate methods, structural equation model (SEM) could specify the causal or dependent relationships among variables using the prior knowledge of researchers before conducting relevant experiments, i.e. initial models. SEM could not only identify the individual path coefficient for each relationship, but also estimate the whole model fit to determine whether to revise the initial models. We attempt to introduce SEM from the following as- pects: definition and types of variables in SEM, detailed procedures for how to analyze data through SEM, some applications of SEM in ecology and recommended software. We encourage more researchers to apply SEM in ecological data analyses in order to improve understanding of natural systems and advance the field of ecology.

Point process analyses of variations in smoking rate by setting, mood, gender, and dependence.

The immediate emotional and situational antecedents of ad-libitum smoking are still not well understood. We reanalyzed data from ecological momentary assessment using novel point process analyses to assess how craving, mood, and social setting influence smoking rate, as well as to assess the moderating effects of gender and nicotine dependence. Smokers (N = 304) recorded craving, mood, and social setting using electronic diaries when smoking and at random nonsmoking times over 16 days of smoking. Point process analysis, which makes use of the known random sampling scheme for momentary variables, examined main effects of setting and interactions with gender and dependence. Increased craving was associated with higher rates of smoking, particularly among women. Negative affect was not associated with smoking rate, even in interaction with arousal, but restlessness was associated with substantially higher smoking rates. Women's smoking tended to be less affected by negative affect. Nicotine dependence had little moderating effect on situational influences. Smoking rates were higher when smokers were alone or with others who were smoking, and smoking restrictions reduced smoking rates. However, the presence of others who are smoking undermined the effects of restrictions. The more sensitive point process analyses confirmed earlier findings, including the surprising conclusion that negative affect by itself was not related to smoking rates. Contrary to hypothesis, men's and not women's smoking was influenced by negative affect. Both smoking restrictions and the presence of others who are not smoking suppress smoking, but the presence of others who are not smoking undermines the effects of restrictions. Point process analyses of ecological momentary assessment data can bring out even small influences on smoking rate.

Ecoinformatics and global change – an overdue liaison

AbstractThe field of ecoinformatics provides concepts, methods and standards to guide management and analysis of ecological data with particular emphasis on exploration of co‐occurrences of organisms and their linkage to environmental conditions and taxon attributes. In this editorial, introducing the Special Feature ‘Ecoinformatics and global change’, we reflect on the development of ecoinformatics and explore its importance for future global change research with special focus on vegetation‐plot data. We show how papers in this Special Feature illustrate important directions and approaches in this emerging field. We suggest that ecoinformatics has the potential to make profound contributions to pure and applied sciences, and that the analyses, databases, meta‐databases, data exchange formats and analytical tools presented in this Special Feature advance this approach to vegetation science and illustrate and address important open questions. We conclude by describing important future directions for the development of the field including incentives for data sharing, creation of tools for more robust statistical analysis, utilities for integration of data that conform to divergent taxonomic standards, and databases that provide detailed plot‐specific data so as to allow users to find and access data appropriate to their research needs.

Fast, Easy, and Publication‐Quality Ecological Analyses with PC‐ORD

The recent version 6 release of the software PC‐ORD Multivariate Analysis of Ecological Data (MjM Software 〈www.pcord.com〉) earns a solid A grade for ease of use, comprehensiveness of analysis tools, and excellent graphing capabilities. New features in version 6 include the addition of several analysis tools (PCoA, RDA, SumF), the enhancement of others (partial Mantel test, orthogonal rotation in NMS), additional graphing features (boxplots, convex hulls) and new options that finally make importing data from Excel easy.

On thinning of chains in MCMC

Summary1. Markov chain Monte Carlo (MCMC) is a simulation technique that has revolutionised the analysis of ecological data, allowing the fitting of complex models in a Bayesian framework. Since 2001, there have been nearly 200 papers using MCMC in publications of the Ecological Society of America and the British Ecological Society, including more than 75 in the journal Ecology and 35 in the Journal of Applied Ecology.2. We have noted that many authors routinely ‘thin’ their simulations, discarding all but every kth sampled value; of the studies we surveyed with details on MCMC implementation, 40% reported thinning.3. Thinning is often unnecessary and always inefficient, reducing the precision with which features of the Markov chain are summarised. The inefficiency of thinning MCMC output has been known since the early 1990’s, long before MCMC appeared in ecological publications.4. We discuss the background and prevalence of thinning, illustrate its consequences, discuss circumstances when it might be regarded as a reasonable option and recommend against routine thinning of chains unless necessitated by computer memory limitations.

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

Stability in Ecosystem Functioning across a Climatic Threshold and Contrasting Forest Regimes

Classical ecological theory predicts that changes in the availability of essential resources such as nitrogen should lead to changes in plant community composition due to differences in species-specific nutrient requirements. What remains unknown, however, is the extent to which climate change will alter the relationship between plant communities and the nitrogen cycle. During intervals of climate change, do changes in nitrogen cycling lead to vegetation change or do changes in community composition alter the nitrogen dynamics? We used long-term ecological data to determine the role of nitrogen availability in changes of forest species composition under a rapidly changing climate during the early Holocene (16k to 8k cal. yrs. BP). A statistical computational analysis of ecological data spanning 8,000 years showed that secondary succession from a coniferous to deciduous forest occurred independently of changes in the nitrogen cycle. As oak replaced pine under a warming climate, nitrogen cycling rates increased. Interestingly, the mechanism by which the species interacted with nitrogen remained stable across this threshold change in climate and in the dominant tree species. This suggests that changes in tree population density over successional time scales are not driven by nitrogen availability. Thus, current models of forest succession that incorporate the effects of available nitrogen may be over-estimating tree population responses to changes in this resource, which may result in biased predictions of future forest dynamics under climate warming.

A method for under-sampled ecological network data analysis: plant-pollination as case study

In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.

Bridging gaps between statistical and mathematical modeling in ecology

Congratulations to Heisey and colleagues (2010) for an analysis of complex ecological data within a process-oriented framework and an equally thoughtful discussion of the ongoing challenges of linking process and pattern. Their work joins a growing literature linking modern statistical methods for describing patterns in data with expanded sets of ecologically-motivated mathematical models of population dynamics. My comments build on the authors’ framework and discussion to describe a continuum that (I feel) encompasses a conceptual path between process and pattern and illustrates how hierarchical models provide a convenient area within which to explore this continuum.

Comparing spatially‐varying coefficients models for analysis of ecological data with non‐stationary and anisotropic residual dependence

Summary1. When exploring spatially complex ecological phenomena using regression models it is often unreasonable to assume a single set of regression coefficients can capture space‐varying and scale‐dependent relationships between covariates and the outcome variable. This is especially true when conducting analysis across large spatial domains, where there is an increased propensity for anisotropic dependence structures and non‐stationarity in the underlying spatial processes.2. Geographically weighted regression (GWR) and Bayesian spatially‐varying coefficients (SVC) are the most common methods for modelling such data. This paper compares these methods for modelling data generated from non‐stationary processes. The comparison highlights some strengths and limitations of each method and aims to assist those who seek appropriate methods to better understand spatially complex ecological systems. Both synthetic and ecological data sets are used to facilitate the comparison.3. Results underscored the need for the postulated model to approximate the underlying mechanism generating the data. Further, results show GWR and SVC can produce very different regression coefficient surfaces and hence dramatically different conclusions can be drawn regarding the impact of covariates. The trade‐off between the richer inferential framework of SVC models and computational demands is also discussed.

Spatial analyses of ecological count data: A density map comparison approach

Analysing spatial patterns of population distributions may help to infer the decisive underlying ecological processes. Here we propose a method adapted to the spatial analysis of count data. Named MAPCOMP (MAP COMParison), it is based on the calculation of a formal distance, the Hellinger distance, between the density map of counts and the density map of sampling effort. Statistical tests of spatial homogeneity are based on count permutations across sampling sites and on valuable properties of the Hellinger distance. We assessed the efficiency of MAPCOMP by simulating different types and locations of clusters of individuals and compared its performance to the classical red–blue SADIE method, used as a reference. The two methods were also compared with respect to counts of codling moth larvae in orchards. Thanks to its better theoretical properties than SADIE, MAPCOMP was efficient in detecting spatial inhomogeneity when clusters were located on square or elongated spatial domains and more or less close to the edges, even for small sample sizes. It also appeared not very sensitive to edge effects. Another advantage of MAPCOMP is a bandwidth parameter that allows assessing the spatial extent of heterogeneity, if any.

Beyond the income inequality hypothesis and human health: a worldwide exploration

OBJECTIVE: To analyze whether the relationship between income inequality and human health is mediated through social capital, and whether political regime determines differences in income inequality and social capital among countries. METHODS: Path analysis of cross sectional ecological data from 110 countries. Life expectancy at birth was the outcome variable, and income inequality (measured by the Gini coefficient), social capital (measured by the Corruption Perceptions Index or generalized trust), and political regime (measured by the Index of Freedom) were the predictor variables. Corruption Perceptions Index (an indirect indicator of social capital) was used to include more developing countries in the analysis. The correlation between Gini coefficient and predictor variables was calculated using Spearman's coefficients. The path analysis was designed to assess the effect of income inequality, social capital proxies and political regime on life expectancy. RESULTS: The path coefficients suggest that income inequality has a greater direct effect on life expectancy at birth than through social capital. Political regime acts on life expectancy at birth through income inequality. CONCLUSIONS: Income inequality and social capital have direct effects on life expectancy at birth. The "class/welfare regime model" can be useful for understanding social and health inequalities between countries, whereas the "income inequality hypothesis" which is only a partial approach is especially useful for analyzing differences within countries.