Problems,successes and challenges for the application of dispersion-corrected density-functional theory combined with dispersion-based implicit solvent models to large-scale hydrophobic self-assembly and polymorphism

The recent advent of dispersion-corrected density-functional theory (DFT) methods allows for quantitative modelling of molecular self-assembly processes, and we consider what is required to develop applications to the formation of large self-assembled monolayers (SAMs) on hydrophobic surfaces from organic solution. Focus is on application of the D3 dispersion correction of Grimme combined with the solvent dispersion model of Floris, Tomasi and Pascual–Ahuir to simulate observed scanning-tunnelling microscopy (STM) images of various polymorphs of tetraalkylporphyrin SAMs on highly oriented pyrolytic graphite surfaces. The most significant problem is identified as the need to treat SAM structures that are incommensurate with those of the substrate, providing a challenge to the use of traditional periodic-imaging boundary techniques. Using nearby commensurate lattices introduces non-systematic errors into calculated lattice constants and free energies of SAM formation that are larger than experimental uncertainties and polymorph differences. Developing non-periodic methods for polymorph interface simulation also remains a challenge. Despite these problems, existing methods can be used to interpret STM images and SAM atomic structures, distinguishing between multiple feasible polymorph types. They also provide critical insight into the factors controlling polymorphism. All this stems from a delicate balance that the intermolecular D3 and solvent Floris, Tomasi and Pascual–Ahuir corrections provide. Combined optimised treatments should yield fully quantitative approaches in the future.     

Dynamic models and large scale field experiments in environmental impact assessment and management

Abstract Dynamic numerical models and field experiments play important roles in impact assessment and management. Unfortunately, extreme and simplistic views have developed about whether and how to use these tools, so their complementary values to the manager are often not recognized. We often hear the outrageous claim (or hope) that numerical models can synthesize ‘all’ relevant information for predicting the impact of policy choice, hence making experimental experience unnecessary. From experimentalists, we hear the equally naive criticism that ecological systems are so complex that nothing is predictable without experimental experience. What we usually get from the proponents of these extremes are either models that are dangerously unreliable, or experiments that provide nice scientific answers to the wrong questions. Wise use of modelling begins with the following points: (i) explicit modelling is an excellent way to clarify policy concerns and identify processes that are most likely to be important in making predictions about policy effects; (ii) we can do a very good job of modelling some processes and relationships, particularly those having to do with basic spatial and temporal scales of impact as related to physical transport, chemical transformations, and life history characteristics of indicator populations (longevity, delays and response times due to age structured rates of reproduction and mortality); and (iii) there are some important dynamic processes, such as long-term accumulation of toxic materials in the environment, that unfold over such large space and time scales as to preclude direct experimental study (leaving only the issue of which models to use in making predictions, not whether to model - unless the processes are simply ignored). But points (i) and (ii) represent steps that a good experimentalist will take anyway: be clear about what practical results an experiment is intended to produce, and do not waste effort on experiments to measure things that can be predicted reliably from existing knowledge. The key to successful use of modelling and experimentation in management is in making good judgements about the interface between points (ii) and (iii); that is, in making good judgements about both what variables cannot be reliably predicted, and of these, which to treat experimentally and which to gamble on predicting from models.     

The science of visitor management in parks and protected areas: from verbal reports to simulation models

Visitor management in parks, wilderness and other protected areas requires information about visitor-environment interactions and, particularly, the distribution and flow of visitors in space and time. Such information is usually sketchy and based largely on the verbal reports of visitors. A review of recent psychological and neurological research and theory suggests that traditional verbal survey methods cannot in principle provide an adequate basis for models of human landscape navigation. We argue for more use of direct observation of visitor movements and the utility of travel simulation models. Simulation modelling of visitor flow can be helpful in making monitoring programs more efficient, in fine-tuning existing management programs, and in assessing the likely consequences of alternative future scenarios We review early efforts to simulate the flow of recreation use, describe several current modelling efforts and conclude with suggestions for a research and development effort.     

Genetic basis of yield as viewed from a crop physiologist's perspective

The final yield of a crop is the product of growth during the growing season and a number of developmental processes occurring throughout the life cycle of a crop, with most genes influencing the final outcome to a degree. However, recent advances in molecular biology have developed the potential to identify and map many genes or QTLs related to various important traits, including yield, plant adaptation and tolerance to stresses. Significant G×E interactions for yield have been identified, as have interactions associated with QTLs for yield. However, there is little evidence available to confirm that a QTL for yield from a parental line in one mapping population may improve yield when transferred into an adapted, high‐yielding line of another population. In order to narrow the apparent gap between the genotype and the phenotype with regard to yield, it is important to identify key traits related to yield and then attempt to identify and locate the genes controlling them. The partitioning of the developmental time to anthesis into different phases: from sowing to the onset of stem elongation and from then to anthesis, as a relatively simple physiological attribute putatively related to yield, is discussed. If the relationship holds in a wider range of conditions and the genetic factors responsible are located then the genetic basis of yield should be identified. There has also been significant progress in crop simulation modelling. Using knowledge of crop physiology and empirical relationships these models can simulate the performance of crops, including the G×E interactions. Such models require information regarding the genetic basis of yield, which are included in the form of genetic coefficients. Essentially models are constructed as decision‐making tools for management but may be of use in detecting prospective traits for selection within a breeding programme. Problems associated with this approach are discussed. This review discusses the need to use crop physiology approaches to analyse components of yield in order to reliably identify the genetic basis of yield.     

Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?

Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.     

Functional–structural plant models: a growing paradigm for plant studies

A number of research groups in various areas of plant biology as well as computer science and applied mathematics have addressed modelling the spatiotemporal dynamics of growth and development of plants. This has resulted in development of functional–structural plant models (FSPMs). In FSPMs, the plant structure is always explicitly represented in terms of a network of elementary units. In this respect, FSPMs are different from more abstract models in which a simplified representation of the plant structure is frequently used (e.g. spatial density of leaves, total biomass, etc.). This key feature makes it possible to build modular models and creates avenues for efficient exchange of model components and experimental data. They are being used to deal with the complex 3-D structure of plants and to simulate growth and development occurring at spatial scales from cells to forest areas, and temporal scales from seconds to decades and many plant generations. The plant types studied also cover a broad spectrum, from algae to trees. This special issue of Annals of Botany features selected papers on FSPM topics such as models of morphological development, models of physical and biological processes, integrated models predicting dynamics of plants and plant communities, modelling platforms, methods for acquiring the 3-D structures of plants using automated measurements, and practical applications for agronomic purposes.     

Interactions between temperature and nutrients across levels of ecological organization

Temperature and nutrient availability play key roles in controlling the pathways and rates at which energy and materials move through ecosystems. These factors have also changed dramatically on Earth over the past century as human activities have intensified. Although significant effort has been devoted to understanding the role of temperature and nutrients in isolation, less is known about how these two factors interact to influence ecological processes. Recent advances in ecological stoichiometry and metabolic ecology provide a useful framework for making progress in this area, but conceptual synthesis and review are needed to help catalyze additional research. Here, we examine known and potential interactions between temperature and nutrients from a variety of physiological, community, and ecosystem perspectives. We first review patterns at the level of the individual, focusing on four traits – growth, respiration, body size, and elemental content – that should theoretically govern how temperature and nutrients interact to influence higher levels of biological organization. We next explore the interactive effects of temperature and nutrients on populations, communities, and food webs by synthesizing information related to community size spectra, biomass distributions, and elemental composition. We use metabolic theory to make predictions about how population‐level secondary production should respond to interactions between temperature and resource supply, setting up qualitative predictions about the flows of energy and materials through metazoan food webs. Last, we examine how temperature–nutrient interactions influence processes at the whole‐ecosystem level, focusing on apparent vs. intrinsic activation energies of ecosystem processes, how to represent temperature–nutrient interactions in ecosystem models, and patterns with respect to nutrient uptake and organic matter decomposition. We conclude that a better understanding of interactions between temperature and nutrients will be critical for developing realistic predictions about ecological responses to multiple, simultaneous drivers of global change, including climate warming and elevated nutrient supply.     

Tools for integrating range change,extinction risk and climate change information into conservation management

Ecological niche models (ENMs) are the primary tool used to describe and forecast the potential influence of climate change on biodiversity. However, ENMs do not directly account for important biological and landscape processes likely to affect range dynamics at a variety of spatial scales. Recent advances to link ENMs with population models have focused on the fundamental step of integrating dispersal and metapopulation dynamics into forecasts of species geographic ranges. Here we use a combination of novel analyses and a synthesis of findings from published plant and animal case studies to highlight three seldom recognised, yet important, advantages of linking ENMs with demographic modelling approaches: 1) they provide direct measures of extinction risk in addition to measures of vulnerability based on change in the potential range area or total habitat suitability. 2) They capture life‐history traits that permit population density to vary in different ways in response to key spatial drivers, conditioned by the processes of global change. 3) They can be used to explore and rank the cost effectiveness of regional conservation alternatives and demographically oriented management interventions. Given these advantages, we argue that coupled methods should be used preferentially where data permits and when conservation management decisions require intervention, prioritization, or direct estimates of extinction risk.     

Study of an infant brain subjected to periodic motion via a custom experimental apparatus design and finite element modelling

This paper presents a rig that was specifically designed to simulate the shaking of mechanical models of biological systems, especially those related to shaken baby syndrome (SBS). The scope of this paper includes the testing of an anthropomorphic model that simulates an infant head and provides validation data for complex finite element (FE) modelling using three numerical methods (Lagrangian, Arbitrary-Lagrangian–Eulerian (ALE) and Eulerian method) for fluid structure coupling.The experiments for this study aim to provide an understanding of the influence of two factors on intracranial brain movement of the infant head during violent shaking: (1) the specific paediatric head structure: the anterior fontanelle and (2) the brain–skull interface.The results show that the Eulerian analysis method has significant advantages for the FSI modelling of brain–CSF–skull interactions over the more commonly used methods, e.g. the Lagrangian method. To the knowledge of the authors, this methodology has not been discussed in previous publication.The results indicate that the biomechanical investigation of SBS can provide more accurate results only if the skull with paediatric features and the brain–skull interface are correctly represented, which were overlooked in previous SBS studies.     

Setting Fish Quotas Based on Holistic Ecosystem Modelling Including Environmental Factors and Foodweb Interactions – A New Approach

We present a new approach to set fish quotas from holistic aquatic foodweb modelling (the LakeWeb-approach). This modelling includes changes in environmental conditions (nutrients, salinity, temperature, oxygen), process-based mass-balance calculations of nutrient concentrations from inflow, internal processes and outflow, calculations of how changes in nutrient concentrations affect primary production, how such changes influence secondary production and how this influence fish production and biomass. This approach gives dynamic, quantitative responses to alterations in driving variables and abiotic/biotic feedbacks. We have applied this approach for preliminary simulations of the cod biomass in the Baltic. We also show that this approach adds a new dimension in setting fish quotas, which in the future could complement, rather than compete with, the more established methods used today based on fish catch statistics and models based on other presuppositions. Our preliminary results indicate that under present environmental conditions (2003), the cod is likely to be extinct if the annual catch is between 95 and 100 kt. The present fish quota is 75 kt/yr in the Baltic, but the overfishing may be 35 kt/yr. We discuss cause–effect relationships regulating fish production, key factors influencing thresholds and points of no return connected to overfishing and changes in environmental conditions, factors regulating recovery and methods for setting optimal fish quotas using this modelling approach.     

OMNIITOX concept model supports characterisation modelling for life cycle impact assessment

Goal Scope Background  The main focus in OMNIITOX is on characterisation models for toxicological impacts in a life cycle assessment (LCA) context. The OMNIITOX information system (OMNIITOX IS) is being developed primarily to facilitate characterisation modelling and calculation of characterisation factors to provide users with information necessary for environmental management and control of industrial systems. The modelling and implementation of operational characterisation models on eco and human toxic impacts requires the use of data and modelling approaches often originating from regulatory chemical risk assessment (RA) related disciplines. Hence, there is a need for a concept model for the data and modelling approaches that can be interchanged between these different contexts of natural system model approaches. Methods. The concept modelling methodology applied in the OMNIITOX project is built on database design principles and ontological principles in a consensus based and iterative process by participants from the LCA, RA and environmental informatics disciplines. Results. The developed OMNIITOX concept model focuses on the core concepts of substance, nature framework, load, indicator, and mechanism, with supplementary concepts to support these core concepts. They refer to the modelled cause, effect, and the relation between them, which are aspects inherent in all models used in the disciplines within the scope of OMNIITOX. This structure provides a possibility to compare the models on a fundamental level and a language to communicate information between the disciplines and to assess the possibility of transparently reusing data and modelling approaches of various levels of detail and complexity. Conclusions  The current experiences from applying the concept model show that the OMNIITOX concept model increases the structuring of all information needed to describe characterisation models transparently. From a user perspective the OMNIITOX concept model aids in understanding the applicability, use of a characterisation model and how to interpret model outputs. Recommendations and Outlook  The concept model provides a tool for structured characterisation modelling, model comparison, model implementation, model quality management, and model usage. Moreover, it could be used for the structuring of any natural environment cause-effect model concerning other impact categories than toxicity.     

Quantification of sensory information transmission using timeseries decorrelation techniques

To estimate the information transmitted across a neuronal sensory system one has to deal with serial dependence among consecutive samples of the stimulus and the response signal. Common methods usually require a huge amount of data, or are restricted to Gaussian stimuli. Here, we describe stimulus and response as stochastic processes, i.e. as sequences of random variables, in the same coordinate system. Stimulus-response pairs of these random variables must not be considered independently because otherwise the transinformation is overestimated. To account for the linear fraction of the serial dependence, we present two decorrelation techniques based on coordinate transformation. They provide a representation of the processes with uncorrelated random variables and yield a more precise estimate of the transinformation.     

“Slash and burn” or “weed and manure”? A modelling approach to explore hypotheses of late Neolithic crop cultivation in pre-alpine wetland sites

The record of prehistoric crop cultivation in central Europe dates as far back as 5500 bc. In the pile-dwellings of the north-western pre-alpine forelands, dating roughly from 4300 to 800 bc, favourable taphonomic conditions provide evidence for the ways of cereal cultivation and consumption in unmatched detail. Based on different sets of (bio-) archaeological and palynological evidence, different hypotheses of crop husbandry methods have been developed for the wetland settlements. During the late Neolithic, two partly antithetic ideas are discussed: On one hand Shifting Cultivation assumes frequently shifted crop fields and the use of fire to provide nitrogen for plant uptake; On the other hand Permanent Cultivation reconstructs longer-ranging use of the fields, to which nitrogen may have been provided by various means. From the Bronze Age onwards, most probably some form of extensive ard cultivation was applied. In this article, we explore the implications of the different hypotheses for the socio-ecological system of the wetland sites. We combine the capability of agent-based modelling to simulate dynamic processes with the benefits provided by geographical information systems and the possibilities provided by the use of modern agro-ecosystem modelling tools. First, we used a mechanistic crop growth model, MONICA, to evaluate the influence of important factors of prehistoric crop yield formation: the climatic conditions, the soil texture and the degree of nitrogen availability. Second, we applied an agent-based model (WELASSIMO_crops) to simulate the spatial and economic implications related to the different crop husbandry methods. Our results provide quantitative information on the extent of crop husbandry activities in the wetland sites and on the effect of natural and anthropogenic factors on prehistoric crop yields. Without manure application, initial average yields of 1.0 t ha^?1 a^?1 are shown to decrease rapidly to only 50 % after 10 years. A manuring rate of 10 t ha^?1 a^?1 allows for higher yields of 1.7 t ha^?1 a^?1 and a slower rate of fertility decrease, but requires high numbers of livestock per capita. In shifting cultivation, high yields of 2.7 t ha^?1 a^?1 are reasonable, while necessitating a very large area and high labour input. Using the model results and a case study, we argue that permanent cultivation is more likely to have been the standard method, while burning of the landscape may have had different objectives than crop husbandry. We find that the combination of agent-based social and process-based biophysical modelling is a powerful tool to study the complex interdependencies in human-environment systems in the past.     

Response of milk fat concentration and yield to nutrient supply in dairy cows

Dietary changes alter dairy cow milk fat concentration (MFC) and yield (MFY) through modifications in the supply of nutrients, which act as precursors or inhibitors of mammary fat synthesis. The current models used to formulate dairy cow diets cannot predict changes in milk fat. The knowledge of the effects of the nutrients on milk fat would help to progress toward this prediction. To this end, we quantified and compared the milk fat responses to variations in the supply of seven nutrients derived from digestion: volatile fatty acids, glucose, proteins, long-chain fatty acids (LCFA) and t10,c12-conjugated linoleic acid (CLA). A database was compiled from studies involving digestive infusions of these nutrients in dairy cows. It included 147 comparisons between a nutrient infusion and a control treatment. The nutrient infusions were limited to the range of physiological variations to mimic nutrient changes after dietary modifications. We established models for the response of MFC, MFY and milk fatty acid (FA) composition to the supply of each nutrient. MFC and MFY responses to the nutrients were significant and linear, except for the MFC response to glucose that was curvilinear. The nutrients differed in their effects on MFC and MFY: acetate, butyrate and LCFA increased MFC and MFY, whereas propionate, glucose and t10,c12-CLA decreased them. Protein infusions increased MFY and decreased MFC because of an increase in milk yield. The effects of numerous interfering factors related to animals, diets or experimental conditions were tested on the residuals of the response models. The responses of milk FA percentages are also provided. When adjusted to the in vivo variations in the nutrients observed after dietary changes, the effects of the different nutrients were moderate. Finally, this study showed that several of these nutrients could contribute to the changes in milk fat production and composition observed after dietary changes. This is a first step toward predicting milk fat response to changes in nutrient supply.     

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi‐objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process‐based crop model of sunflower and a functional–structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modelling approach, which identified paths for desirable trait modification, including direction and intensity.     

Linking hyphal growth to colony dynamics: Spatially explicit models of mycelia

The processes responsible for the growth of fungal mycelia act over a vast range of spatial scales; while nutrient uptake occurs at the molecular level, the fungal colony can expand by the order of centimetres each day. Although experiments can provide exceptionally detailed information on processes at specific scales, it can be difficult to relate those processes between different spatial scales. In this article a series of mathematical models are described that link the different spatial scales found within a mycelium. The models are all closely related to each other and are applied to a range of growth environments, and the advantages and limitations of each modelling approach are discussed.     

Modelling as a tool to redesign livestock farming systems: a literature review

Livestock farming has recently come under close scrutiny, in response especially to environmental issues. Farmers are encouraged to redesign their livestock farming systems in depth to improve their sustainability. Assuming that modelling can be a relevant tool to address such systemic changes, we sought to answer the following question: ‘How can livestock farming systems be modelled to help farmers redesign their whole farming systems?’ To this end, we made a literature review of the models of livestock farming systems published from 2000 to mid-2009 (n = 79). We used an analysis grid based on three considerations: (i) system definition, (ii) the intended use of the model and (iii) the way in which farmers’ decision-making processes were represented and how agricultural experts and farmers were involved in the modelling processes. Consistent rationales in approaches to supporting changes in livestock farming were identified in three different groups of models, covering 83% of the whole set. These could be defined according to (i) the way in which farmers’ decisions were represented and (ii) the model's type of contribution to supporting changes. The first type gathered models that dynamically simulated the system according to different management options; the farmers’ decision-making processes are assumed to consist in choosing certain values for management factors. Such models allow long-term simulations and endorse different disciplinary viewpoints, but the farmers are weakly involved in their design. Models of the second type can indicate the best combination of farm activities under given constraints, provided the farmers’ objectives are profit maximisation. However, when used to support redesigning processes, they address neither how to implement the optimal solution nor its long-term consequences. Models of the third type enable users to dynamically simulate different options for the farming system, the management of which is assumed to be planned according to the farmers’ general objectives. Although more comprehensive, these models do not easily integrate different disciplinary viewpoints and different subsystems, which limits their usefulness as support tools for redesigning processes. Finally, we concluded about what specific requirements should be for modelling approaches if farmers were to be supported in redesigning their whole livestock farming systems using models.     

Relative Values of Physiological Parameters of P Response of Different Genotypes can be Measured in Experiments with Only Two P Treatments

Improved methods for selecting cultivars for their ability to grow well on low P soils will improve productivity and minimise pollution. Estimates are required of the relative values for different genotypes of physiologically meaningful parameters that define yield response to increasing levels of plant available soil P. The object of this study was to devise ways of obtaining these estimates from conventional trials that include numerous genotypes but only two P treatments. The parameters were estimated as coefficients of a modified Michaelis–Menten equation, namely A, the maximum yield that can be obtained with ample P, Km, the concentration of plant available P in the rooting medium at which yield is half the maximum, and B, the gradient of yield against available P as it approaches the origin. Two novel methods of estimation were devised. They both require inputs of yield and its variance at each of two concentrations of available P, but these must be within certain limits. Estimates of parameter values for 12 Brassica oleracea genotypes made by these methods from two P-levels in each of two experiments with six P levels were compared with the values obtained by conventional fitting to the data from all six P levels. According to regression and ranking analyses, the relative values of A obtained for the different genotypes by both two P-level methods were similar to those from the six P-level method and both were reproducible. None of the methods however detected any reproducible differences in Km. Application of one of the two P-level methods to published data for genotypes of other species indicated that there was little intra-species variation in Km within some data sets on maize, beans and leguminous cover crops but much within other data sets. Sensitivity and algebraic analyses are presented to define the experimental conditions required for successful use of two P-level methods. It is concluded that, provided these experimental conditions are met, measurements at two P levels can give almost as much information about intra-species differences in P response parameters as measurements at six P levels. The procedure may also be useful for interpreting other responses of a “diminishing returns” type such as those to other nutrients or plant spacing.     

Statistical phylogeography

While studies of phylogeography and speciation in the past have largely focused on the documentation or detection of significant patterns of population genetic structure, the emerging field of statistical phylogeography aims to infer the history and processes underlying that structure, and to provide objective, rather than ad hoc explanations. Methods for parameter estimation are now commonly used to make inferences about demographic past. Although these approaches are well developed statistically, they typically pay little attention to geographical history. In contrast, methods that seek to reconstruct phylogeographic history are able to consider many alternative geographical scenarios, but are primarily nonstatistical, making inferences about particular biological processes without explicit reference to stochastically derived expectations. We advocate the merging of these two traditions so that statistical phylogeographic methods can provide an accurate representation of the past, consider a diverse array of processes, and yet yield a statistical estimate of that history. We discuss various conceptual issues associated with statistical phylogeographic inferences, considering especially the stochasticity of population genetic processes and assessing the confidence of phylogeographic conclusions. To this end, we present some empirical examples that utilize a statistical phylogeographic approach, and then by contrasting results from a coalescent-based approach to those from Templeton's nested cladistic analysis (NCA), we illustrate the importance of assessing error. Because NCA does not assess error in its inferences about historical processes or contemporary gene flow, we performed a small-scale study using simulated data to examine how our conclusions might be affected by such unconsidered errors. NCA did not identify the processes used to simulate the data, confusing among deterministic processes and the stochastic sorting of gene lineages. There is as yet insufficient justification of NCA's ability to accurately infer or distinguish among alternative processes. We close with a discussion of some unresolved problems of current statistical phylogeographic methods to propose areas in need of future development.