Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Lancaster Environment Centre, Lancaster University, LA1 4YQ, Lancaster, UK; Department of Life Sciences, Imperial College London, SL5 7PY, Ascot, UK; School of Geography and the Environment, Environmental Change Institute, University of Oxford, OX1 3QY, Oxford, UK; School of Geography, University of Exeter, Amory Building, EX4 4RJ, Exeter, UK; School of Geography, University of Leeds, LS2 9JT, Leeds, UK; School of Earth and Environment, University of Leeds, LS2 9JT, Leeds, UK; Department of Social Policy, London School of Economics, WC2A 2AE, London, UK; Royal Botanic Gardens, TW9 3AB, Kew, Richmond, Surrey, UK; Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, WC2R 2LS, Strand, London, UK; Tropical Diversity Section, Royal Botanic Garden Edinburgh, EH3 5LR, Edinburgh, UK; School of Environmental Sciences, University of East Anglia, NR4 7TJ, Norwich, UK; Centre of Ecological Research and Forestry Applications (CREAF), Facultat de Ciencies, Universitat Autonoma de Barcelona, 08193, Bellaterra (Barcelona), Spain; Department of Zoology, Edward Grey Institute, Oxford University, OX1 3PS, UK; Department of Zoology, University of Cambridge, CB2 3EJ, Cambridge, UK
Biological Reviews (Impact Factor: 10.26). 01/2011; 86(86):457-474. DOI: 10.1111/j.1469-185X.2010.00155.x
Source: PubMed

ABSTRACT Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and 458 Jos Barlow and others improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Land use and land cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: 1) a lack of openness with regard to describing and making available the model inputs and model code, 2) the difficulties of conducting appropriate model validations and 3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land use policies based on plausible future scenarios, but to do that reliably may require further improvements in the discipline. This article is protected by copyright. All rights reserved.
    Global Change Biology 01/2014; · 8.22 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Effective management of natural resources requires understanding both the dynamics of the natural systems being subjected to management and the decision-making behaviour of stakeholders who are involved in the management process. We suggest that simulation modelling techniques can provide a powerful method platform for the transdisciplinary integration of ecological, economic and sociological aspects that is needed for exploring the likely outcomes of different management approaches and options. A concise review of existing literature on ecological and socio-economic modelling and approaches at the interface of these fields is presented followed by a framework coupling an individual-based ecological model with an agent-based socio-economic model. In this framework, each individual of the species of interest is represented on a spatially-explicit landscape, allowing the incorporation of individual variability. The socio-economic model also simulates inter-agent variability through the assignment of different attitudes and decision-making options for different agents; these may represent farmers, estate managers, policy-makers, the general public and/or other stakeholders. This structure enables variation in attitudes and circumstances of individual stakeholders, together with interactions between stakeholders, to be simulated. We discuss strengths and limitations of such an approach, and the information requirements for building a robust model to inform a real management situation.
    iEMSs 2012 - Managing Resources of a Limited Planet; 07/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
    Plant Ecology & Diversity 01/2013; · 0.92 Impact Factor

Full-text (5 Sources)

Available from
May 29, 2014