Controlled comparison of species- and community-level models across novel climates and communities

Maguire, Kaitlin C.; Nieto‐Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.; Ferrier, Simon; Lorenz, David J.

doi:10.1098/rspb.2015.2817

Cited by 60 publications

(121 citation statements)

References 51 publications

(111 reference statements)

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“…Most studies have also applied model transfers to single species. Community-and ecosystem-level models that fit shared environmental responses for multiple species simultaneously could achieve higher transferability [23], but this potential has been inconsistently demonstrated. Integrated models that unite presence-only and presenceabsence data [24], and those that combine occupancy probabilities (e.g., derived from regional monitoring) with density-given-occupancy (e.g., derived from telemetry), offer further promise [25].…”

Section: Why Transfer Models In the First Place?mentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

460

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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Section: Why Transfer Models In the First Place?mentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

460

456

View full text Add to dashboard Cite

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“…

One of the six climate variables used to fit the models was listed incorrectly in the Environmental variables section under Material and methods [1]. Mean yearly evapotranspiration ratio was used, not mean yearly potential evapotranspiration.

…”

mentioning

confidence: 99%

Correction to ‘Controlled comparison of species- and community-level models across novel climates and communities’

Maguire¹,

Nieto‐Lugilde²,

Blois³

et al. 2016

Proc. R. Soc. B.

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“…3). ENMs are also limited in their ability to predict across time periods experiencing dramatic environmental change because current distributions (and thus the model) may not encompass all environments inhabitable by a species (Jackson and Overpeck 2000, Barve et al 2011, Maguire et al 2016. The spatial grain of the model depends on the spatial resolution of the environmental data; the finest grain obtainable for contemporary continental-scale climate data is usually ~1 km (Fick andHijmans 2017, Karger et al 2017), although spatial uncertainty in occurrence data coordinates may require using coarser environmental data.…”

Section: Environmental and Specimen Occurrence Datamentioning

confidence: 99%

“…One approach to integrating fossil data is to combine it simultaneously with contemporary occurrences as inputs in an ecological niche model that leverages the temporal distribution of samples by calibrating the ENM using data from multiple time intervals simultaneously (Nogués-Bravo 2009, Maguire et al 2016. However, to the best of our knowledge fossil occurrences have yet to be formally used in a quantitative approach that integrates genetic and fossil data for inferring detailed biogeographic history.…”

Section: Advance 2 Formal Integration Of Fossil Data Into the Inferementioning

confidence: 99%

Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil

Hoban

Dawson

Robinson³

et al. 2019

Ecography

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A primary focus of historical biogeography is to understand changes in species ranges, abundance and genetic connectivity, and changes in community composition. Traditionally, biogeographic inference has relied on distinct lines of evidence, including DNA sequences, fossils and hindcasted ecological niche models. In this review we propose that the development of integrative modeling approaches that leverage multiple distinct data types from diverse disciplines has the potential to revolutionize the field of biogeography. Although each data type contains information on a distinct aspect of species' biogeographic histories, few studies formally integrate multiple types in analysis. For example, post hoc congruence among analyses based on different data types (e.g. fossils and genetics) is commonly assumed to indicate likely biogeographic histories. Unfortunately, analyses of different data often reach discordant conclusions. Thus, fundamental and unresolved debates continue regarding speed and timing of postglacial migration, location and size of glacial refugia, and degree of long distance dispersal. Formal statistical integration can help address these issues. More specifically, formal integration can leverage all available evidence, account for inherent biases associated with different data types, and quantify data and process uncertainty. Novel, quantitative integration of data and models across fields is now possible due to recent advances in cyberinfrastructure, spatial modeling, online and aggregated ecological databases, data processing and quantitative methods. Our purpose is to make the case for and give examples of rigorous integration of genetic, fossil and environmental/ occurrence data for inferring biogeographic history. In particular, we 1) review the need for such a framework; 2) explain common data types and approaches used to infer biogeographic history (and the challenges with each); 3) review state-of-the-art examples of data integration in biogeography; 4) lay out a series of novel, suggested improvements on current methods; and 5) provide an outlook on technical feasibility and future opportunities. Ecography E4 awardGlossary ABC (approximate Bayesian computation; also see Box 1) -a simulation-based statistical method that approximates the likelihood of a model through comparisons of summary statistics calculated from simulated and observed data. Biogeographic history -the study of processes and patterns related to: geographic range shifts, contractions and expansions; demographic fluctuations; locations of refugia; and migration patterns. Bottleneck -a drastic reduction in population size that often also results in a loss of genetic diversity; colonization at a continental scale often involves serial bottlenecks. Coalescent theory -a body of theory that uses probabilistic models to reconstruct the genealogy of a sample backward in time from the sampled lineages (present) to the most recent common ancestor (past). DRM (dynamic range model) -a hierarchical Bayesian approach to estimate paramet...

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Controlled comparison of species- and community-level models across novel climates and communities

Cited by 60 publications

References 51 publications

Outstanding Challenges in the Transferability of Ecological Models

Outstanding Challenges in the Transferability of Ecological Models

Correction to ‘Controlled comparison of species- and community-level models across novel climates and communities’

Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil

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