Introduction

This introduction to the volume argues for the central and integrating role of the subject matter of ethnobiological research in anthropology understood in its widest sense: in its biological, archaeo-historical, and socio-cultural dimensions. The background and current status of ethnobiology are assessed, and its contribution to anthropological issues considered under the following headings: the foundational paradigm of taxonomic orthodoxy; language and the translation of knowledge systems; cognition and culture; the social organization and transmission of knowledge; medical ethnobiology; the applied practice of ethnobiology; and – the meta-theory which binds all this together – the co-evolutionary paradigm as part of a wider 'biocultural synthesis'. The way in which the collected papers exemplify these themes is discussed.     

Theories and Major Hypotheses in Ethnobotany

Ethnobotany has evolved from a discipline that largely documented the diversity of plant use by local people to one focused on understanding how and why people select plants for a wide range of uses. This progress has been in response to a repeated call for theory-inspired and hypothesis-driven research to improve the rigor of the discipline. Despite improvements, recent ethnobotanical research has overemphasized the use of quantitative ethnobotany indices and statistical methods borrowed from ecology, yet underemphasized the development and integration of a strong theoretical foundation. To advance the field of ethnobotany as a hypothesis-driven, theoretically inspired discipline, it is important to first synthesize the existing theoretical lines of research. We review and discuss 17 major theories and hypotheses in ethnobotany that can be used as a starting point for developing research questions that advance our understanding of people–plant interactions. For each theory or major hypothesis, we identify its primary predictions and testable hypotheses and then discuss how these predictions have been tested. Developing research to test these predictions will make significant contributions to the field of ethnobotany and create the critical mass of primary literature necessary to develop meta-analyses and to advance new theories in ethnobotany.     

Ethnobiology and applied anthropology: rapprochement of the academic with the practical

Ethnobiology has long featured both academic and practical aspects, with its component disciplines tending to favour one or the other, such that anthropology focuses on classificatory and cognitive issues whereas botany concentrates on issues of resource use. Current trends within development, notably interest in indigenous knowledge that has emerged with participatory approaches, to which ethnobiology has contributed significantly, promise a new synthesis of the academic and practical. This paper describes five ways in which we can think of applied ethnobiology in this context, and illustrates each with examples drawn from natural resources management. The first application is assistance in the introduction of exogenous technology, facilitating technical interventions that are central to many development programmes, enabling a better match to cultural tradition, and promoting meaningful participation. The second application is the facilitating of local solutions to development, advocating the use of local knowledge to further development, asking what insider knowledge may have to recommend in advancing development in the face of outside influences. The third application is the furthering of cultural diffusion, seeking to establish if knowledge and practices in one place have relevance elsewhere, offering an innovative approach to development by drawing upon anthropology's cross-cultural comparative tradition in new ways. The fourth application is the advancing of the commercial use of knowledge, conducting research to find intelligence that may be marketable, even new to science, such bio-prospecting relating to the contentious issue of protecting intellectual property rights. Finally, ethnobiology may be used to support alternative development, promote the formulation of alternative views, and critique development as a capitalist imposition; indigenous activism with respect to international conventions indicates the shape of alternative agendas.     

Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses

Statistical phylogeographic studies contribute to our understanding of the factors that influence population divergence and speciation, and that ultimately generate biogeographical patterns. The use of coalescent modelling for analyses of genetic data provides a framework for statistically testing alternative hypotheses about the timing and pattern of divergence. However, the extent to which such approaches contribute to our understanding of biogeography depends on how well the alternative hypotheses chosen capture relevant aspects of species histories. New modelling techniques, which explicitly incorporate spatio-geographic data external to the gene trees themselves, provide a means for generating realistic phylogeographic hypotheses, even for taxa without a detailed fossil record. Here we illustrate how two such techniques – species distribution modelling and its historical extension, palaeodistribution modelling – in conjunction with coalescent simulations can be used to generate and test alternative hypotheses. In doing so, we highlight a few key studies that have creatively integrated both historical geographic and genetic data and argue for the wider incorporation of such explicit integrations in biogeographical studies.     

Assessing canopy health of native eucalypt forests

Summary   The assessment of forest health is an essential part of the monitoring of ecological sustainability in managed native forests. In Australia, unfortunately, very limited quantitative information on forest health is actually obtained for management and reporting purposes. In this article, we summarize current approaches used in Australia to assess native forest health and some recent developments in the application of remotely acquired digital imagery for classifying canopy health. In a recent study examining Bell miner associated dieback (BMAD), high-resolution airborne imagery was successfully manipulated to present severity categories for BMAD affected canopy. The potential of remotely sensed imagery lies not in map production but in the statistical modelling capacity of this spatial information, particularly when added to climatic and terrain-based spatial data sets. There are several statistical approaches to modelling these spatial datasets and in this article, we discuss our approach to producing a preliminary BMAD model. The importance of ground-based assessments is also emphasized and we recommend tree crown condition as a key health attribute for the spatial modelling of forests. Although significant progress has been made in the application of remote sensing technologies, the structural complexity of native forests means that there are still technical issues that require resolving before this approach becomes operationally routine.     

Prospects and challenges for parametric models in historical biogeographical inference

In historical biogeography, phylogenetic trees have long been used as tools for addressing a wide range of inference problems, from explaining common distribution patterns of species to reconstructing ancestral geographic ranges on branches of the tree of life. However, the potential utility of phylogenies for this purpose has yet to be fully realized, due in part to a lack of explicit conceptual links between processes underlying the evolution of geographic ranges and processes of phylogenetic tree growth. We suggest that statistical approaches that use parametric models to forge such links will stimulate integration and propel hypothesis-driven biogeographical inquiry in new directions. We highlight here two such approaches and describe how they represent early steps towards a more general framework for model-based historical biogeography that is based on likelihood as an optimality criterion, rather than having the traditional reliance on parsimony. The development of this framework will not be without significant challenges, particularly in balancing model complexity with statistical power, and these will be most apparent in studies of regions with many component areas and complex geological histories, such as the Mediterranean Basin.     

Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics

Functional genomics has been driven greatly by emerging experimental technologies. Its development as a scientific discipline will be enhanced by systems biology, which generates novel, quantitative hypotheses via modelling. However, in order to better assist crop improvement, the impact of developing functional genomics needs to be assessed at the crop level, given a projected diminishing effect of genetic alteration on phenotypes from the molecule to crop levels. This review illustrates a recently proposed research field, crop systems biology, which is located at the crossroads of crop physiology and functional genomics, and intends to promote communications between the two. Past experiences with modelling whole-crop physiology indicate that the layered structure of biological systems should be taken into account. Moreover, modelling not only plays a role in data synthesis and quantitative prediction, but certainly also in heuristics and system design. These roles of modelling can be applied to crop systems biology to enhance its contribution to our understanding of complex crop phenotypes and subsequently to crop improvement. The success of crop systems biology needs commitments from scientists along the entire knowledge chain of plant biology, from molecule or gene to crop and agro-ecosystem.     

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.     

Analysis of comparative data using generalized estimating equations

It is widely acknowledged that the analysis of comparative data from related species should be performed taking into account their phylogenetic relationships. We introduce a new method, based on the use of generalized estimating equations (GEE), for the analysis of comparative data. The principle is to incorporate, in the modelling process, a correlation matrix that specifies the dependence among observations. This matrix is obtained from the phylogenetic tree of the studied species. Using this approach, a variety of distributions (discrete or continuous) can be analysed using a generalized linear modelling framework, phylogenies with multichotomies can be analysed, and there is no need to estimate ancestral character state. A simulation study showed that the proposed approach has good statistical properties with a type-I error rate close to the nominal 5%, and statistical power to detect correlated evolution between two characters which increases with the strength of the correlation. The proposed approach performs well for the analysis of discrete characters. We illustrate our approach with some data on macro-ecological correlates in birds. Some extensions of the use of GEE are discussed.     

Wise use of statistical tools in ecological field studies

The currently dominating hypothetico-deductive research paradigm for ecology has statistical hypothesis testing as a basic element. Classic statistical hypothesis testing does, however, present the ecologist with two fundamental dilemmas when field data are to be analyzed: (1) that the statistically motivated demand for a random and representative sample and the ecologically motivated demand for representation of variation in the study area cannot be fully met at the same time; and (2) that the statistically motivated demand for independence of errors calls for sampling distances that exceed the scales of relevant pattern-generating processes, so that samples with statistically desirable properties will be ecologically irrelevant. Reasons for these dilemmas are explained by consideration of the classic statistical Neyman-Pearson test procedure, properties of ecological variables, properties of sampling designs, interactions between properties of the ecological variables and properties of sampling designs, and specific assumptions of the statistical methods. Analytic solutions to problems underlying the dilemmas are briefly reviewed. I conclude that several important research objectives cannot be approached without subjective elements in sampling designs. I argue that a research strategy entirely based on rigorous statistical testing of hypotheses is insufficient for field ecological data and that inductive and deductive approaches are complementary in the process of building ecological knowledge. I recommend that great care is taken when statistical tests are applied to ecological field data. Use of less formal modelling approaches is recommended for cases when formal testing is not strictly needed. Sets of recommendations, “Guidelines for wise use of statistical tools”, are proposed both for testing and for modelling. Important elements of wise-use guidelines are parallel use of methods that preferably belong to different methodologies, selection of methods with few and less rigorous assumptions, conservative interpretation of results, and abandonment of definitive decisions based a predefined significance level.     

A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH‐DIMENSIONAL MULTIVARIATE DATA

Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high‐dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high‐dimensional datasets.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.     

Infectious disease transmission and contact networks in wildlife and livestock

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.     

Children as ethnobotanists: methods and local impact of a participatory research project with children on wild plant gathering in the Grosses Walsertal Biosphere Reserve,Austria

Background

Ethically sound research in applied ethnobiology should benefit local communities by giving them full access to research processes and results. Participatory research may ensure such access, but there has been little discussion on methodological details of participatory approaches in ethnobiological research. This paper presents and discusses the research processes and methods developed in the course of a three-year research project on wild plant gathering, the involvement of children as co-researchers and the project’s indications for local impact.

Method

Research was conducted in the Grosses Walsertal Biosphere Reserve, Austria, between 2008 and 2010 in four research phases. In phase 1, 36 freelist interviews with local people and participant observation was conducted. In phase 2 school workshops were held in 14 primary school classes and their 189 children interviewed 506 family members with structured questionnaires. In phase 3, 27 children and two researchers co-produced participatory videos. In phase 4 indications for the impact of the project were investigated with questionnaires from ten children and with participant observation.

Results

Children participated in various ways in the research process and the scientific output and local impact of the project was linked to the phases, degrees and methods of children’s involvement. Children were increasingly involved in the project, from non-participation to decision-making. Scientific output was generated from participatory and non-participatory activities whereas local impact - on personal, familial, communal and institutional levels - was mainly generated through the participatory involvement of children as interviewers and as co-producers of videos. Creating scientific outputs from participatory video is little developed in ethnobiology, whereas bearing potential.

Conclusions

As ethnobotanists and ethnobiologists, if we are truly concerned about the impact and benefits of our research processes and results to local communities, the details of the research processes need to be deliberately planned and evaluated and then reported and discussed in academic publications.

     

Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland

This paper is an attempt, using statistical modelling techniques, to understand the patterns of vascular plant species richness at the poorly studied meso-scale within a relatively unexplored subarctic zone. Species richness is related to floristic-environmental composite variables, using occurrence data of vascular plants and environmental and spatial predictor variables in 362 1 km² grid squares in the Kevo Nature Reserve. Species richness is modelled in two different way. First, by detecting the major floristic-environmental gradients with the ordination procedure of canonical correspondence analysis, and subsequently relating these ordination axes to species richness by generalized linear modelling. Second, species richness is directly related to the composite environmental factors of explanatory variables, using partial least squares regression. The most important explanatory variables, as suggested by both approaches, are relatively similar, and largely reflect the influence of altitude or altitudinally related variables in the models. The most prominent floristic gradient in the data runs from alpine habitats to river valleys, and this gradient is the main source of variation in species richness. Some local environmental variables are also relatively important predictors; the grid squares rich in vascular plant taxa are mainly located in the lowlands of the reserve and are characterised by rivers and brooks, as well as by abundant cliff walls. The two statistical models account for approximately the same amount of variation in the species richness, with more than half of the variation unexplained. Potential reasons for the relatively modest fit are discussed, and the results are compared to the characteristics of the diversity-environment relationships at both broader- and finer-scales.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling

Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.