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Critical studies of data visualization often highlight how the reductive nature of visualization methods excludes data limitations and qualities that are crucial to understanding those data. This case study explores how a data visualization could express contingent, situated, and contextual facets of data. We examine how such data limitations might be surfaced and represented within visualizations through an interplay between the critique of an existing data visualization and the development of alternative designs. Based on a case study of urban tree data, we interrogate data limitations in relation to four different types of missingness: Incompleteness, Emptiness, Absence, and Nothingness. Our study enables reflections on how data limitations can be investigated using visualizations and considers the development of a critical visualization practice.

Aim: Software use is ubiquitous in the species distribution modelling (SDM) domain; nearly every scientist working on SDM either uses or develops specialist SDM software; however, little is formally known about the prevalence or preference of one software over another. We seek to provide, for the first time, a 'snapshot' of SDM users, the methods they use and the questions they answer. Location: Global. Methods: We conducted a survey of over 300 SDM scientists to capture a snapshot of the community and used an extensive literature search of SDM papers in order to investigate the characteristics of the SDM community and its interactions with software developers in terms of co-authoring research publications. Results: Our results show that those members of the community who develop software and who are directly connected with developers are among the most highly connected and published authors in the field. We further show that the two most popular softwares for SDM lie at opposite ends of the 'use-complexity' continuum. Main conclusion: Given the importance of SDM research in a changing environment, with its increasing use in the policy domain, it is vital to be aware of what software and methodologies are being implemented. Here, we present a snapshot of the SDM community, the software and the methods being used.

Aim Human impacts on the biosphere are a matter of urgent and growing concern, with ecologists increasingly being asked to project biodiversity futures. The Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) is likely to comprehensively assess such projections, yet despite being widely used and potentially critical tools for analysing socio-environmental futures, integrated assessment models (IAMs) have received little attention from ecological modellers. We aim to raise awareness and understanding of IAMs among ecologists by describing the structure and composition of IAMs, assessing their utility for biodiversity projections and identifying limitations that hamper greater interaction between scientists using IAMs and those using ecological models. We also hope to inspire more accessible and applicable models by suggesting development needs for IAMs. Methods We conduct a systematic review of four state-of-the-art IAMs, which describes and contrasts key model features and analyses six aspects of IAMs that are of fundamental interest to ecologists. Conclusions IAMs could be valuable for modelling biodiversity futures; however, current IAMs were not developed for this application and challenges remain for ecologists looking to use their outputs. Separating and understanding the differences resulting from IAM formulation and those resulting from specific scenario assumptions is currently problematic, and current IAMs may be unable to accurately represent environmental conditions for both Earth-system projections and for building robust models of biodiversity because key ecological processes are absent. We suggest that model intercomparisons would identify differences in model dynamics, and detailed studies of how dynamical interactions between components influence behaviour would address why such differences arise. Bio-economic fisheries models and agriculture pollination models provide starting points for integrating key ecological feedbacks within IAMs. Ultimately, making IAMs more accessible within the multidisciplinary study of global change, drawing on user-centred research, would enable more resolved, reliable and accurate assessment of how Earth's socio-ecological system is approaching planetary boundaries.

By over-focusing on precise definitions, ecology has produced a confused idea of the niche concept. This, our second paper, develops a practical philosophy for the niche that approaches the concept at the correct level of abstraction. We deconstruct the niche into effect and response components and then reconstruct those parts into a general, visual schematic of the niche and ecology. Using this schematic we examine the relationship of particular niche ideas to ecology, and examine the relationship between niche and ecological sub-disciplines, particularly species distribution modelling (SDM). This general description clarifies the duality of the niche concept, as both a facet of species and a facet of environments. Unclear use of these dual concepts can confuse the scientific approach, and our ideas about uncertainty and error. By misclassifying models as concepts, ecology has confused the niche. Our practical philosophy uses the current wealth of ecological and niche ideas as a panoramic view of a general niche concept. We argue that stitching the niche produces a concept that underpins straightforward ecological thinking.

In order to attempt to understand the complexity inherent in nature, mathematical, statistical and computational techniques are increasingly being employed in the life sciences. In particular, the use and development of software tools is becoming vital for investigating scientific hypotheses, and a wide range of scientists are finding software development playing a more central role in their day-to-day research. In fields such as biology and ecology, there has been a noticeable trend towards the use of quantitative methods for both making sense of ever-increasing amounts of data [1] and building or selecting models [2]. As Research Fellows of the “2020 Science” project (http://www.2020science.net), funded jointly by the EPSRC (Engineering and Physical Sciences Research Council) and Microsoft Research, we have firsthand experience of the challenges associated with carrying out multidisciplinary computation-based science [3]–[5]. In this paper we offer a jargon-free guide to best practice when developing and using software for scientific research. While many guides to software development exist, they are often aimed at computer scientists [6] or concentrate on large open-source projects [7]; the present guide is aimed specifically at the vast majority of scientific researchers: those without formal training in computer science. We present our ten simple rules with the aim of enabling scientists to be more effective in undertaking research and therefore maximise the impact of this research within the scientific community. While these rules are described individually, collectively they form a single vision for how to approach the practical side of computational science. Our rules are presented in roughly the chronological order in which they should be undertaken, beginning with things that, as a computational scientist, you should do before you even think about writing any code. For each rule, guides on getting started, links to relevant tutorials, and further reading are provided in the supplementary material (Text S1).

We are now entering a time of immense environ- mental upheaval were, increasingly, experts are required to provide conservation assessments.

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.

Summary 1. Developing the next-generation of species distribution modelling (SDM) requires solutions to a number of widely recognised problems. Here, we address the problem of uncertainty in predictor variables arising from fine-scale environmental variation. 2. We explain how this uncertainty may cause scale-dependent ‘regression dilution’, elsewhere a well-understood statistical issue, and explain its consequences for SDM. We then demonstrate a simple, general correction for regression dilution based on Bayesian methods using latent variables. With this correction in place, unbiased estimates of species occupancy vs. the true environment can be retrieved from data on occupancy vs. measured environment, where measured environment is correlated with the true environment, but subject to substantial measurement error. 3. We then show how applying our correction to multiple co-occurring species simultaneously increases the accuracy of parameter estimates for each species, as well as estimates for the true environment at each survey plot – a phenomenon we call ‘neighbourly advice’. With a sufficient number of species, the estimates of the true environment at each plot can become extremely accurate. 4. Our correction for regression dilution could be integrated with models addressing other issues in SDM, e.g. biotic interactions and/or spatial dynamics. We suggest that Bayesian analysis, as employed here to address uncertainty in predictor variables, might offer a flexible toolbox for developing such next-generation species distribution models.

We investigate neutral evolution during range shifts in a strategic model of a metapopulation occupying a climate gradient. Using heritable, neutral markers, we track the spatio-temporal fate of lineages. Owing to iterated founder effects (‘mutation surfing’), survival of lineages derived from the leading range limit is enhanced. At trailing limits, where habitat suitability decreases, survival is reduced (mutations ‘wipe out’). These processes alter (i) the spatial spread of mutations, (ii) origins of persisting mutations and (iii) the generation of diversity. We show that large changes in neutral evolution can be a direct consequence of range shifting.

Projected responses of species' to climate change have so far included few of the factors that are important determinants of species' distributions within its range. In this paper we utilise a spatially explicit cellular lattice, colonisation–extinction model to investigate the effect of habitat loss, fragmentation and species characteristics on range shifting in response to climate change. Contrary to the predictions of patch occupancy in static climate models we show that fragmentation can have a positive effect on species survival when species have high colonisation rates. For species with low colonisation rates aggregative behaviours prevent success on fragmented landscapes at high levels of habitat loss, and range shifting is more successfully achieved where habitat is correlated. At levels of habitat loss near the extinction threshold, less fragmented landscapes can facilitate range shifting even for the best colonisers. We discuss how imposing a climate window may reduce percolation routes and have implications for the area of usable habitat at any given level of habitat availability. We demonstrate the importance of landscape structure for range shifting dynamics and argue that management of reserve networks needs to consider the requirements of species with different life history characteristics.

We are now entering a time of immense environmental upheaval in which, increasingly, experts are required to provide conservation assessments. Quantitative assessment of trends in species' range and abundance is costly, requiring extensive field studies over a long period of time. Unfortunately, many species are only known through a few "chance" sightings or a handful of specimens, and therefore extinction may be even harder to ascertain. Several methods have been proposed for estimating the probability of extinction. However, comparison within and between species is difficult because of variations in sighting rates. We applied a probabilistic method that incorporates sighting rate to the sighting record of Vietnamese slipper orchids (Paphiopedilum). The method generates a probability that another sighting will occur given the previous sighting rate and the time since last observation. This allows greater comparability between species discovered at different times. Its predictions were more highly correlated with the World Conservation Union criteria than previous methods. Trends in data collection and the political climate of a country, which affects access to material, are important potential sources of variation that affect sighting rates. A lack of understanding of the process by which data are generated makes inferring extinction from sighting records difficult because extinction status depends on how the sighting rate varies. However, such methods allow rapid conservation prioritization of taxa that are poorly known and would otherwise go unassessed. © 2006 Society for Conservation Biology.

Visualisations and graphics are fundamental to studying complex subject matter. However, beyond acknowledging this value, scientists and science-policy programmes rarely consider how visualisations can enable discovery, create engaging and robust reporting, or support online resources. Producing accessible and unbiased visualisations from complicated, uncertain data requires expertise and knowledge from science, policy, computing, and design. However, visualisation is rarely found in our scientific training, organisations, or collaborations. As new policy programmes develop [e.g., the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES)], we need information visualisation to permeate increasingly both the work of scientists and science policy. The alternative is increased potential for missed discoveries, miscommunications, and, at worst, creating a bias towards the research that is easiest to display.

International audience; Evolutionary adaptation is a key driver of species’ range dynamics. Understanding the factors that affect rates of adaptation at range margins is thus crucial for interpreting and predicting changes in species’ ranges. The spatial structure of environmental conditions is one of the determinants of whether and how quickly adaptations occur. However, while landscape structures at range edges are typically complex, most theoretical work has so far focused on relatively simple environmental geometries. Using an individual-based allelic model, we explore the effects of different landscape structures on the rate of adaptation to novel environments and investigate how these structures interact with the genetic architecture of the trait governing adaptation and the dispersal capacity of the considered species. Generally, we find that rapid adaptation is favored by a good match between the coarseness of the trait's genetic architecture (many loci of small effects versus few loci of large effects) and the coarseness of the landscape (abruptness of transitions in environmental conditions). For example, in rugged landscapes, adaptation is quicker for genetic architectures with few loci of large effects, while for shallow gradients the opposite is true. Moreover, dispersal capacities affect the rate of adaptation by modulating the ‘apparent coarseness’ of the landscape: a gradient perceived as smooth by species with limited dispersal capacities appears rather steep for highly dispersive ones. We also find that the distribution of evolving phenotypes strongly depends on the interplay of landscape structure and dispersal capacities, ranging from two distinct phenotypes for most rugged landscapes, over the co-occurrence of an additional third phenotype for highly dispersive species, to the whole range of phenotypes on smooth gradients. By identifying basic factors that drive the fixation probability of newly arising beneficial mutations, we hope to further broaden the understanding of evolutionary adaptation at range margins and, hence, species’ range dynamics.

Recently, Warren [1] argued that correlative modelling of species distribution and environmental data should be described as Ecological Niche Modelling (ENM) rather than Species Distribution Modelling (SDM). Although discussions of terminology could be posed as semantics and nit-picking, they are symptomatic of deeper challenges to robustly define the philosophy, methodology, and theory of this science. Terminology should standardise definitions and facilitate understanding and communication, while enabling synthesis and innovation.

In a discussion it is often easier to staunchly reject or offer resolute support for an idea. This third paper on the niche concept aims to develop a balanced argument by exploring general principles for determining an appropriate level for pitching the niche concept that will guide better use and less abuse of niche concepts. To do this we first have to accept that niche concepts are not necessarily essential for ecology. Rather than to improve niche concepts, our aim should then be to pitch the niche in terms of ecology. This aim helps us develop an ultimate goal of the niche by which we can evaluate the concepts we use. For species distribution modelling, there has been a focus on the niche as an equilibrium outcome that perhaps has less relevance for disequilibrium situations (e.g. climate change projections). As is the case for much of ecology, more causal explanations of species' distributions use alternative terminologies and less frequently use the word niche. We suggest that niche concepts that are better aligned with the rest of ecology could arise from taking more responsibility for our own implementations, and by explaining our models with terms other than niche. A general, holistic niche concept promotes this view and promotes practical thinking about what we are modelling and how we interpret those models, which in turn should help inspire and support innovative modelling approaches in species distribution modelling.

Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as key if we are to find solutions to pressing, global-scale societal challenges, including green technologies, sustainable food production, and drug development. Regulators and policy-makers have realized the power of such collaborations, for example, in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis on “breaking down barriers to create a genuine single market for knowledge, research and innovation” (http://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020). Cross-disciplinary collaborations are key to all partners in computational biology. On the one hand, for scientists working in theoretical fields such as computer science, mathematics, or statistics, validation of predictions against experimental data is of the utmost importance. On the other hand, experimentalists, such as molecular biologists, geneticists, or clinicians, often want to reduce the number of experiments needed to achieve a certain scientific aim, to obtain insight into processes that are inaccessible using current experimental techniques, or to handle large volumes of data, which are far beyond any human analysis skills. The synergistic and skilfulcombining ofdifferent disciplines can achieve insight beyond current borders and thereby generate novel solutions to complex problems. The combination of methods and data from different fields can achieve more than the sum of the individual parts could do alone. This applies not only to computational biology but also tomany other academic disciplines. Initiating and successfully maintaining cross-disciplinary collaborations can be challenging but highly rewarding. In a previous publication in this series, ten simple rules for a successful collaboration were proposed [1]. In the present guide, we go one step further and focus on the specific challenges associated with cross-disciplinary research, from the perspective of the theoretician in particular. As research fellows of the 2020 Science project (http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations. We intend this guide to be for early career computational researchers as well as more senior scientists who are entering a cross-disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with very different backgrounds. Rule 1: Enjoy Entering a Completely New Field of Research Collaborating with scientists from other disciplines is an opportunity to learn about cutting-edge science directly from experts. Make the most of being the novice. No one expects you to know everything about the new field. In particular, there is no pressure to understand everything immediately, so ask the “stupid” questions. Demonstrating your interest and enthusiasm is of much higher value than pretending to know everything already. An interested audience makes information sharing much easier for all partners in a collaboration. You should prepare for a deluge of new ideas and approaches. It is a good practice to read relevant textbooks and review papers, which your collaborators should be able to recommend, in order to quickly grasp the vocabulary (see Rule 3) and key ideas of the new field. This will make it easier for you to establish a common parlance between you and your collaborators, and allow you to build from there. You should try to discuss your work with a range of scientists from complementary fields. As well as getting feedback, this can help you identify new collaborative opportunities. Remember that contacts that do not lead directly to collaborations can still prove useful later in your career.

Software pervades every domain of science ( 1 – 3 ), perhaps nowhere more decisively than in modeling. In key scientific areas of great societal importance, models and the software that implement them define both how science is done and what science is done ( 4 , 5 ). Across all science, this dependence has led to concerns around the need for open access to software ( 6 , 7 ), centered on the reproducibility of research ( 1 , 8 – 10 ). From fields such as high-performance computing, we learn key insights and best practices for how to develop, standardize, and implement software ( 11 ). Open and systematic approaches to the development of software are essential for all sciences. But for many scientists this is not sufficient. We describe problems with the adoption and use of scientific software.

The ability to communicate is indispensable to a scientist. Given this, it is hugely perplexing that visual communication channels are so infrequently and poorly used. During undergraduate and postgraduate training, individuals will be given considerable feedback on designing, organising, and constructing spoken and written presentations; but not so for visuals and graphics.