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Outstanding Challenges in the Transferability of Ecological Models

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Outstanding Challenges in the Transferability of Ecological Models UC Davis UC Davis Previously Published Works Title Outstanding Challenges in the Transferability of Ecological Models. Permalink https://escholarship.org/uc/item/4r97d4np Journal Trends in ecology & evolution, 33(10) ISSN 0169-5347 Authors Yates, Katherine L Bouchet, Phil J Caley, M Julian et al. Publication Date 2018-10-01 DOI 10.1016/j.tree.2018.08.001 Peer reviewed eScholarship.org Powered by the California Digital Library University of California TREE 2418 1–13 Review Outstanding Challenges in the Transferability of Ecological Models 1,2, ,y 3,y 4,5 4,5 Katherine L. Yates , * Phil J. Bouchet, M. Julian Caley, Kerrie Mengersen, 6 1 7 8 9 Christophe F. Randin, Stephen Parnell, Alan H. Fielding, Andrew J. Bamford, Stephen Ban, 10 11 12 13 14 A. Márcia Barbosa, Carsten F. Dormann, Jane Elith, Clare B. Embling, Gary N. Ervin, 15 16 17 18,19 20 Rebecca Fisher, Susan Gould, Roland F. Graf, Edward J. Gregr, Patrick N. Halpin, 21 22 23 24 Risto K. Heikkinen, Stefan Heinänen, Alice R. Jones, Periyadan K. Krishnakumar, 25 26 20,27 28,23 Valentina Lauria, Hector Lozano-Montes, Laura Mannocci, Camille Mellin, 29 30 31 32 Mohsen B. Mesgaran, Elena Moreno-Amat, Sophie Mormede, Emilie Novaczek, 33 20 34 35 Steffen Oppel, Guillermo Ortuño Crespo, A. Townsend Peterson, Giovanni Rapacciuolo, 20 13 36 36,37 38 Jason J. Roberts, Rebecca E. Ross, Kylie L. Scales, David Schoeman, Paul Snelgrove, 39 40 41 12 42,43 Göran Sundblad, Wilfried Thuiller, Leigh G. Torres, Heroen Verbruggen, Lifei Wang, 44 45 46 47,48 Seth Wenger, Mark J. Whittingham, Yuri Zharikov, Damaris Zurell, and 3,49 Ana M.M. Sequeira Predictive models are central to many scientific disciplines and vital for informing Highlights management in a rapidly changing world. However, limited understanding of the Models transferred to novel conditions could provide predictions in data-poor accuracy and precision of models transferred to novel conditions (their ‘trans- scenarios, contributing to more ’ fi fi ferability ) undermines con dence in their predictions. Here, 50 experts identi ed informed management decisions. priority knowledge gaps which, if filled, will most improve model transfers. These The determinants of ecological pre- are summarized into sixtechnical and six fundamental challenges, which underlie dictability are, however, still insuffi- the combined need to intensify research on the determinants of ecological ciently understood. predictability, including species traits and data quality, and develop best prac- Predictions from transferred ecological tices for transferring models. Of high importance is the identification of a widely models are affected by species’ traits, applicable set of transferability metrics, with appropriate tools to quantify the sampling biases, biotic interactions, nonstationarity, and the degree of sources and impacts of prediction uncertainty under novel conditions. environmental dissimilarity between reference and target systems. Predicting the Unknown We synthesize six technical and six Predictions facilitate the formulation of quantitative, testable hypotheses that can be refined and fundamental challenges that, if validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific resolved, will catalyze practical and disciplines, including ecology [2], where they provide means for mapping species distributions, conceptual advances in model explaining population trends, or quantifying the risks of biological invasions and disease outbreaks transfers. (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has We propose that the most immediate therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see obstacle to improving understanding Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them lies in the absence of a widely applic- [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in able set of metrics for assessing trans- ferability, and that encouraging the many situations, severe data deficiencies preclude the development of specific models, and the development of models grounded in collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that well-established mechanisms offers interest in transferable models (i.e., those that can be legitimately projected beyondthe spatial and the most immediate way of improving temporal bounds of their underlying data [7]) has grown. transferability. Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Trends in Ecology & Evolution, Month Year, Vol. xx, No. yy https://doi.org/10.1016/j.tree.2018.08.001 1 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). TREE 2418 1–13 1 Box 1. Why Transfer Models in the First Place? School of Environment and Life Sciences, University of Salford, Ecological models are extensively and increasingly used in support of environmental policy and decision making [77]. Manchester, UK The process of transferring models typically stems from the need to support resource management in the face of 2 Centre for Excellence in pervasive data deficiencies, limited research funding, and accelerating global change [5]. Spatial transfers have been Environmental Decisions, University of used to guide the design of protected areas, search for species on the brink of extinction, inform species relocations or Queensland, Brisbane, QLD, Australia reintroductions, outline hotspots of invasive pests, design field sampling campaigns, and assist the regulation of human 3 School of Biological Sciences, activities (e.g., [78,79]). For instance, cetacean density models developed off the east coast of the United States were University of Western Australia, 35 recently extrapolated throughout the western North Atlantic high seas to assist the management of potentially harmful Stirling Highway, Crawley, WA 6009, sonar exercises performed by the military [80]. Similarly, projections of Asian tiger mosquito (Aedes albopictus) Australia 4 distribution models onto all continents helped identify areas at greatest risk of invasion, with important implications ARC Centre for Excellence in ’ for human health [81]. Temporal transfers have largely been applied to forecast species responses to climate warming, Mathematical and Statistical Frontiers, retrospectively describe pristine population states, characterize evolutionary patterns of speciation, quantify the Queensland University of Technology, fi repercussions of land use changes, or estimate future ecosystem dynamics (e.g., [68,72,82]). Despite being dif cult Brisbane, QLD, Australia 5 to quantify, the societal and economic gains from transferring models can be substantial, and are most readily illustrated School of Mathematical Sciences, by the mitigation of costs associated with invasive species [83]. For instance, the establishment of the zebra mussel Queensland University of Technology, (Dreissena polymorpha) in the Great Lakes region of North America has led to $20–100 million in annual mitigation Brisbane, QLD, Australia 6 expenditure, with additional, unquantified nonmarket costs ensuing from the loss of biodiversity and ecosystem services Department of Ecology and Evolution, University of Lausanne, [5]. Transferred models accurately predicted the establishment of the zebra mussel 5 years before it was actually Lausanne, Switzerland discovered in the region, however model predictions were not used to take preventative action, illustrating that 7 Haworth Conservation Ltd, developing a transferable model is only the start of the road to informing decision makers (see Outstanding Questions). Bunessan, Isle of Mull, Scotland Ultimately, the widespread need to make proactive management decisions in data-poor situations drives the need to 8 Wildfowl & Wetlands Trust, improve our understanding of model transferability. This goal fundamentally requires better transferability metrics and Slimbridge, Gloucestershire, GL2 7BT, estimates of prediction uncertainty, which can assist in selecting the most consistent and effective management options UK while avoiding unanticipated outcomes [84]. 9 Canadian Parks and Wilderness Society, #410-698 Seymour Street, Vancouver, BC V6B 3K6, Canada simultaneously achieve high accuracy and precision, even when predicting into novel contexts, 10 CIBIO/InBIO-Centro de Investigação will provide maximum utility for decision making [9]. To date, however, tests of transferability em Biodiversidade e Recursos across taxa and geographic locations have failed to demonstrate consistent patterns (Figure 1), Genéticos, Universidade de Évora, 7004-516 Évora, Portugal and a general approach to developing transferable models remains elusive (but see [6,10]). 11 Biometry & Environmental System Here, we outline challenges that, if addressed, will improve the harmonization, uptake, and Analysis, University of Freiburg, application of model transfers in ecology. We argue that moving the field of model of transfer- Tennenbacher Str. 4, 79106 Freiburg, Germany ability forward requires a two-pronged approach focused on: (i) investing in fundamental 12 School of BioSciences, University of research aimed at enhancing predictability, and (ii) establishing technical standards for assess- Melbourne, VIC
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