Information on Biotic Interactions Improves Transferability of Distribution Models

Godsoe, William; Murray, Rua; Plank, Michael J.

doi:10.1086/679440

Cited by 44 publications

(52 citation statements)

References 48 publications

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“…This finding is consistent with theoretical work showing that competition can have more effect in extreme environments [13,22]. More work is needed to conclude on this last point however, as species' responses to deteriorating environmental conditions might also be asymmetric.…”

Section: Resultssupporting

confidence: 88%

Predicting biotic interactions and their variability in a changing environment

et al. 2016

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Global environmental change is altering the patterns of biodiversity worldwide. Observation and theory suggest that species' distributions and abundances depend on a suite of processes, notably abiotic filtering and biotic interactions, both of which are constrained by species' phylogenetic history. Models predicting species distribution have historically mostly considered abiotic filtering and are only starting to integrate biotic interaction. However, using information on present interactions to forecast the future of biodiversity supposes that biotic interactions will not change when species are confronted with new environments. Using bacterial microcosms, we illustrate how biotic interactions can vary along an environmental gradient and how this variability can depend on the phylogenetic distance between interacting species.

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Section: Resultssupporting

confidence: 88%

Predicting biotic interactions and their variability in a changing environment

et al. 2016

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“…Given their demonstrated effects on local adaptation and population dynamics, the potential for biotic interactions to influence large-scale distributions is increasingly discussed in the literature (e.g., Araújo Godsoe et al 2015;O'Brien et al 2017;Bridle et al 2019). Given their demonstrated effects on local adaptation and population dynamics, the potential for biotic interactions to influence large-scale distributions is increasingly discussed in the literature (e.g., Araújo Godsoe et al 2015;O'Brien et al 2017;Bridle et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Maladaptation beyond a geographic range limit driven by antagonistic and mutualistic biotic interactions across an abiotic gradient

Benning

Moeller

2019

Evolution

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Species’ geographic range limits often result from maladaptation to the novel environments beyond the range margin. However, we rarely know which aspects of the n‐dimensional environment are driving this maladaptation. Especially of interest is the influence of abiotic versus biotic factors in delimiting species’ distributions. We conducted a 2‐year reciprocal transplant experiment involving manipulations of the biotic environment to explore how spatiotemporal gradients in precipitation, fatal mammalian herbivory, and pollination affected lifetime fitness within and beyond the range of the California annual plant, Clarkia xantiana ssp. xantiana. In the first, drier year of the experiment, fitness outside the range edge was limited mainly by low precipitation, and there was some evidence for local adaptation within the range. In the second, wetter year, we did not observe abiotic limitations to plant fitness outside the range; instead biotic interactions, especially herbivory, limited fitness outside the range. Together, protection from herbivory and supplementation of pollen resulted in three‐ to sevenfold increases in lifetime fitness outside the range margin in the abiotically benign year. Overall, our work demonstrates the importance of biotic interactions, particularly as they interact with the abiotic environment, in determining fitness beyond geographic range boundaries.

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“…Disentangling their effects from environmentally driven covariance is difficult, especially when histories of human exposure are unknown, or the magnitude of impacts unobservable. Recent studies have also reconciled transferability with strong evidence for the role of biotic interactions in shaping species' ranges at large spatial scales [52], offering a blueprint for determining when biotic information can support predictions under unobserved conditions. Methods that incorporate functional responses have now progressed to combine data from different regions and use nonstationary model coefficients, enabling enhanced transferability [8,53].…”

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confidence: 97%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

466

456

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Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

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Information on Biotic Interactions Improves Transferability of Distribution Models

Cited by 44 publications

References 48 publications

Predicting biotic interactions and their variability in a changing environment

Predicting biotic interactions and their variability in a changing environment

Maladaptation beyond a geographic range limit driven by antagonistic and mutualistic biotic interactions across an abiotic gradient

Outstanding Challenges in the Transferability of Ecological Models

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