Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e., palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services.     

Closing the gap between plant ecology and Quaternary palaeoecology

Ecology and Quaternary palaeoecology have largely developed as parallel disciplines. Although both pursue related questions, information exchange is often hampered by particularities of the palaeoecological data and a communication gap has been perceived between the disciplines. Based on selected topics and developments mainly in Quaternary palaeoecology, we show that both disciplines have converged somewhat during recent years, while we still see untapped potential for closer interactions. Macroecology is probably the discipline that most easily combines different time scales and where co‐operations between palaeoecologists, geneticists and vegetation modellers have been inspiring. Quantitative vegetation reconstructions provide robust estimates of tree composition and land cover at different spatial scales, suitable for testing hypotheses about long‐term vegetation changes or as quantitative background data in studies on contemporary vegetation patterns. Palaeo data also hold yet unexplored potential to study the drivers of long‐term diversity, and aspects of functional diversity may facilitate comparisons between continents and over glacial–interglacial cycles.     

Comparing the strength of modular signal,and evaluating alternative modular hypotheses,using covariance ratio effect sizes with morphometric data

The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure () derived from the covariance ratio, and develop hypothesis‐testing procedures for their comparison. Computer simulations demonstrate that displays appropriate statistical properties and low levels of mis‐specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi ) and goodness of fit (MINT ) suffer from high false positive rates and high model mis‐specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.     

Challenges in the presentation and analysis of plant-macrofossil stratigraphical data

Plant-macrofossil analysis is being increasingly used in Quaternary science, particularly palaeoecology and vegetation history. Although the techniques of macrofossil analysis are well-tried and relatively simple, the resulting data consisting of qualitative binary presences and absences, ordinal classes, and quantitative counts are not simple from the viewpoint of numerical data-analysis. This essay reviews the nature of macrofossil data and discusses the problem of zero and non-zero values. Problems in the presentation of macrofossil data are outlined and possible solutions are discussed. The handling of such data is discussed in terms of data summarisation, data analysis, and data interpretation. Newly developed numerical methods that take account of the mixed nature and the stratigraphical ordering of macrofossil data are outlined, such as (distance-based) multivariate regression trees, canonical analysis of principal coordinates, principal curves, cascade multivariate regression trees, and RLQ analysis. These and other techniques outlined have the potential to help exploit the full potential of macrofossil stratigraphical data in Quaternary palaeoecology.     

Ontogenetic variation in the predatory behavior of the orb-weaver spider <Emphasis Type="Italic">Azilia histrio</Emphasis>: detecting changes in behavioral organization

Spiders in different ontogenetic stages vary in their predatory behavior. The more advanced the development of an individual, the more similar is its behavior to that of the adult. Studies have shown that the behavior of subadult and adult spiders are similar, but the similarity may be superficial, since the analytical approach usually adopted cannot evaluate important features, such as the organization of the foraging system. Systems are characterized by a modular organization, which needs appropriate analytical approaches to be properly evaluated. We tested whether there is a difference between the predatory sequences of adult and subadult spiders (Azilia histrio) and identified the most appropriate methodological procedure for the characterization of this difference. We used multivariate analysis, contrasting an approach that focuses on frequency of behavioral categories, taken independently, with an approach that directly evaluates the sequential organization of behavioral categories, thus allowing the analysis of the structure of the foraging system. While the independent variables approach did not show differences between ontogenetic stages, the organizational analytical approach resulted in a number of important differences between the ontogenetic stages. We describe the foraging system for the first time in the genus Azilia, while discussing hypotheses for the observed organizational change.     

Testing separate families of segregation hypotheses: bootstrap methods.

Aspects of the statistical modeling and assessment of hypotheses concerning quantitative traits in genetics research are discussed. It is suggested that a traditional approach to such modeling and hypothesis testing, whereby competing models are "nested" in an effort to simplify their probabilistic assessment, can be complimented by an alternative statistical paradigm - the separate-families-of-hypotheses approach to segregation analysis. Two bootstrap-based methods are described that allow testing of any two, possibly non-nested, parametric genetic hypotheses. These procedures utilize a strategy in which the unknown distribution of a likelihood ratio-based test statistic is simulated, thereby allowing the estimation of critical values for the test statistic. Though the focus of this paper concerns quantitative traits, the strategies described can be applied to qualitative traits as well. The conceptual advantages and computational ease of these strategies are discussed, and their significance levels and power are examined through Monte Carlo experimentation. It is concluded that the separate-families-of-hypotheses approach, when carried out with the methods described in this paper, not only possesses some favorable statistical properties but also is well suited for genetic segregation analysis.     

Ecological memory at millennial time-scales: the importance of data constraints,species longevity and niche features

Ecological memory describes how antecedent conditions drive the dynamics of an ecological system. Palaeoecological records are paramount to understand ecological memory at millennial time-scales, but the concept is widely neglected in the literature, and a formal approach is lacking. Here, we fill such a gap by introducing a quantitative framework for ecological memory in palaeoecology, and assessing how data constraints and taxa traits shape ecological memory patterns. We simulate the population dynamics and pollen abundance of 16 virtual taxa with different life and niche traits as a response to an environmental driver. The data is processed to mimic a realistic sediment deposition and sampled at increasing depth intervals. We quantify ecological memory with Random Forests, and assess how data properties and taxa traits shape ecological memory. We find that life-span and niche features modulate the relative importance of the antecedent values of the driver and the pollen abundance over periods of 240 yr and longer. Additionally, we find that accumulation rate and decreasing pollen-sampling resolution inflate the importance of antecedent pollen abundance. Our results suggest that: 1) ecological memory patterns are sensitive to varying accumulation rates. A better understanding on the numerical basis of this effect may enable the assimilation of ecological memory concepts and methods in palaeoecology; 2) incorporating niche theory and models is essential to better understand the nature of ecological memory patterns at millennial time-scales. 3) Long-lived generalist taxa are highly decoupled from the environmental signal. This finding has implications on how we interpret the abundance-environment relationship of real taxa with similar traits, and how we use such knowledge to forecast their distribution or reconstruct past climate.     

The design and analysis of microarray experiments: applications in parasitology

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most "interesting" in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to "class comparison," where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve "class prediction," for which a range of supervised multivariate techniques are available, or "class discovery," for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.     

The synthesis of long-term ecological research in the context of concurrently developed ecological theory: design of a research strategy for the Upper Rhône River and its floodplain

• 1 Using sites from the Upper Rhône River, France, as an example, the objective of this paper is to identify the essential elements needed to test current ecological theories with previously collected data. Procedures developed may enable other groups to design comparable research strategies for syntheses of long-term studies of ecological systems.
• 2 Because of the high number (more than 200) and turnover of researchers, the long study period (about 17 years), the evolution of research methods and interests, and the diverse systematic groups that were considered (from micro-organisms to birds), the data available for a synthesis were quite heterogeneous. The application of a ‘fuzzy coding’ technique allowed such disparate information to be structured for analysis.
• 3 The habitat templet concept and the patch dynamics concept were selected for analysis with existing data on the Upper Rhône because theories, such as these, that link ecological responses to habitat templets are a focus of current ecological debate and potentially may serve as a general tool for ecologically orientated river management.
• 4 A preliminary trial to structure the existing knowledge, to identify (and manage) gaps in it, and to create and apply the analytical tools in a way that predictions from theory could be tested was an essential element in the design of this project.
• 5 Predictions derived from the theoretical concepts had to match the format of the available information on the Upper Rhône; potential bias was avoided by having a priori predictions developed by previously uninvolved colleagues.
• 6 Synthesis of the long-term study of the Upper Rhône in the context of concurrently developed ecological theory required, at times, an unconventional research strategy. Hence, the generation of hypotheses and methods, the presentation of results, and consequently the discussions in papers of this special issue of Freshwater Biology (Statzner, Resh & Dolédec, 1994) represent an innovative approach to testing ecological theory.

     

Revisiting Amazonian phylogeography: insights into diversification hypotheses and novel perspectives

The Amazon Basin harbors one of the richest biotas on Earth, such that a number of diversification hypotheses have been formulated to explain patterns of Amazonian biodiversity and biogeography. For nearly two decades, phylogeographic approaches have been applied to better understand the underlying causes of genetic differentiation and geographic structure among Amazonian organisms. Although this research program has made progress in elucidating several aspects of species diversification in the region, recent methodological and theoretical developments in the discipline of phylogeography will provide new perspectives through more robust hypothesis testing. Herein, we outline central aspects of Amazonian geology and landscape evolution as well as climate and vegetation dynamics through the Neogene and Quaternary to contextualize the historical settings considered by major hypotheses of diversification. We address each of these hypotheses by reviewing key phylogeographic papers and by expanding their respective predictions. We also propose future directions for devising and testing hypotheses. Specifically, combining the exploratory power of phylogeography with the statistical rigor of coalescent methods will greatly expand analytical inferences on the evolutionary history of Amazonian biota. Incorporation of non-genetic data from Earth science disciplines into the phylogeographic approach is key to a better understanding of the influence of climatic and geophysical events on patterns of Amazonian biodiversity and biogeography. In addition, achieving such an integrative enterprise must involve overcoming issues such as limited geographic and taxonomic sampling. These future challenges likely will be accomplished by a combination of extensive collaborative research and incentives for conducting basic inventories.     

Impacts of onshore wind energy production on birds and bats: recommendations for future life cycle impact assessment developments

Purpose

Models for quantifying impacts on biodiversity from renewable energy technologies are lacking within life cycle impact assessment (LCIA). We aim to provide an overview of the effects of wind energy on birds and bats, with a focus on quantitative methods. Furthermore, we investigate and provide the necessary background for how these can be integrated into new developments of LCIA models in future.

Methods

We reviewed available literature summarizing the effects of wind energy developments on birds and bats. We provide an overview of available quantitative assessment methods that have been employed outside of the LCIA framework to model the different impacts of wind energy developments on wildlife. Combining the acquired knowledge on impact pathways and associated quantitative methods, we propose possibilities for future approaches for a wind energy impact assessment methodology for LCIA.

Results and discussion

Wind energy production has impacts on terrestrial biodiversity through three main pathways: collision, disturbance, and habitat alterations. Birds and bats are consistently considered the most affected taxonomic groups, with different responses to the before-mentioned impact pathways. Outside of the LCIA framework, current quantitative impact assessment prediction models include collision risk models, species distribution models, individual-based models, and population modeling approaches. Developed indices allow scaling of species-specific vulnerability to mortality, disturbance, and/or habitat alterations.

Conclusions

Although insight into the causes behind collision risk, disturbance, and habitat alterations for bats and birds is still limited, the current knowledge base enables the development of a robust assessment tool. Modeling the impacts of habitat alterations, disturbance, and collisions within an LCIA framework is most appropriate using species distribution models as those enable the estimation of species’ occurrences across a region. Although local-scale developments may be more readily feasible, further up-scaling to global coverage is recommended to allow comparison across regions and technologies, and to assess cumulative impacts.

     

Error propagation methods for LCA—a comparison

Purpose

The analysis of uncertainty in life cycle assessment (LCA) studies has been a topic for more than 10 years, and many commercial LCA programs now feature a sampling approach called Monte Carlo analysis. Yet, a full Monte Carlo analysis of a large LCA system, for instance containing the 4,000 unit processes of ecoinvent v2.2, is rarely carried out by LCA practitioners. One reason for this is computation time. An alternative faster than Monte Carlo method is analytical error propagation by means of a Taylor series expansion; however, this approach suffers from being explained in the literature in conflicting ways, hampering implementation in most software packages for LCA. The purpose of this paper is to compare the two different approaches from a theoretical and practical perspective.

Methods

In this paper, we compare the analytical and sampling approaches in terms of their theoretical background and their mathematical formulation. Using three case studies—one stylized, one real-sized, and one input–output (IO)-based—we approach these techniques from a practical perspective and compare them in terms of speed and results.

Results

Depending on the precise question, a sampling or an analytical approach provides more useful information. Whenever they provide the same indicators, an analytical approach is much faster but less reliable when the uncertainties are large.

Conclusions

For a good analysis, analytical and sampling approaches are equally important, and we recommend practitioners to use both whenever available, and we recommend software suppliers to implement both.     

The need for biological assessment of water quality: Australian perspective

Abstract Biological assessment of water quality in Australia is entering a stage of rapid development largely because of the inclusion of biological indicators in water quality guidelines and growing concern for ecological values. Approaches to water quality assessment include toxicity testing, use of biomarkers and several methods using community structure. For assessment diverse organisms such as fish, algae and (the most commonly used) macro-invertebrates are used. Interaction of data analysis with methods of data collection requires co-ordinated research on both fronts. Recent developments in the use of multivariate statistics to produce models for predicting water quality are likely to be useful in Australia. Much innovative work is still needed in Australia on the use of algae and fish, defining tolerance categories and establishing monitoring programmes performed in time-frames equivalent to those in use for physical and chemical methods.     

Robustness of path analysis of family resemblance against deviations from multivariate normality

Path analysis is one of several methods available for quantitative genetic analysis, providing for both tests of hypotheses and estimates of relevant parameters. Central to the theory is the assumption that the observations follow a multivariate normal distribution within families. The purpose of the present investigation is to assess the effects of a certain type of departures from multivariate normality using quantitative family data on lipid and lipoprotein levels. The results show that even large departures produce reasonably unbiased parameter estimates. Whereas moderate departures lead to few inferential errors in hypothesis testing, gross departures from multivariate normality may have considerable effects on likelihood ratio tests.     

A functional generalized F-test for signal detection with applications to event-related potentials significance analysis

Motivated by the analysis of complex dependent functional data such as event-related brain potentials (ERP), this paper considers a time-varying coefficient multivariate regression model with fixed-time covariates for testing global hypotheses about population mean curves. Based on a reduced-rank modeling of the time correlation of the stochastic process of pointwise test statistics, a functional generalized F-test is proposed and its asymptotic null distribution is derived. Our analytical results show that the proposed test is more powerful than functional analysis of variance testing methods and competing signal detection procedures for dependent data. Simulation studies confirm such power gain for data with patterns of dependence similar to those observed in ERPs. The new testing procedure is illustrated with an analysis of the ERP data from a study of neural correlates of impulse control.     

Partitioning the variation among spatial,temporal and environmental components in a multivariate data set

Abstract We propose a method of partitioning the variation in a multivariate set of data according to (i) environmental variables, (ii) variables describing the spatial structure in the data and (iii) temporal variables. This method is an extension of an existing method for partialling out the spatial component of environmental variation, using canonical analysis. Our proposed method extends this approach by including temporal variables in the analysis. Thus, the partitioning of variation for a data matrix of species’abundances or biomass can include, by our methodology, the following components: (1) pure environmental, (2) pure spatial, (3) pure temporal, (4) pure spatial component of environmental, (5) pure temporal component of environmental, (6) pure combined spatial/temporal component, (7) combined spatial/temporal component of environmental and (8) unexplained. In addition, permutation testing accompanying the analyses allows tests of significance for the relationship between the different components and the species data. We illustrate the method with a set of survey data of penaeid species (prawns) obtained on the far northern Great Barrier Reef, Australia. This extension is a useful tool for multivariate analysis of ecological data from surveys, where space, time and environment commonly overlap and are important influences on observed variation.     

Coreferentiality: a new method for the hypothesis-based analysis of phenotypes characterized by multivariate data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.     

Integrating physiological, ecological and evolutionary change: a Price equation approach

We use a general quantitative framework – the Price equation – to partition phenotypic responses to environmental change into separate physiological, evolutionary and ecological components. We demonstrate how these responses, which potentially occur over different timescales and are usually studied in isolation, can be combined in an additive way; and we discuss the main advantages of doing this. We illustrate our approach using two worked examples, concerning the emergence of toxin resistance within microbial communities, and the estimation of carbon uptake by marine phytoplankton in high-CO₂ environments. We find that this approach allows us to exclude particular mechanistic hypotheses with regard to community-level transformations, and to identify specific instances where appropriate data are lacking. Thus Price's equation provides not only a powerful conceptual aid, but also a means for testing hypotheses and for directing empirical research programmes.