Search

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.

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).

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.

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.