Incorporating plant fossil data into species distribution models is not straightforward: Pitfalls and possible solutions

Amat, Elena Moreno; Rubiales, Juan Manuel; Morales‐Molino, César; García‐Amorena, Ignacio

doi:10.1016/j.quascirev.2017.06.022

Cited by 10 publications

(5 citation statements)

References 86 publications

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“…One solution is to evaluate predictions of past events (i.e., hindcasting) based on independent historical (e.g., harvest and museum records) or paleoecological datasets, although spatio-temporal, collector's, and taphonomic biases will complicate model calibration and validation [70]. However, for many species of management interest, such records remain unavailable or undermined by issues of spatial or temporal bias, mismatching resolutions between past and present data, and error propagation [71]. Sampling the response variable across its range of habitat variability offers an alternative.…”

Section: How Can We Best Transfer Models Through Time and Evaluate Thmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

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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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Section: How Can We Best Transfer Models Through Time and Evaluate Thmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

Self Cite

466

456

View full text Add to dashboard Cite

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“…Fossil data can be used to either calibrate models under past conditions or to assess the accuracy of projected models calibrated under current conditions [100]. Given that only two LGM fossil records of T. standishii are known, which cannot provide a complete picture of the species past LGM distribution [101] and is below the minimum sample size required for construction of accurate species distribution models [102,103], the latter approach was taken.…”

Section: Species Distribution Modellingmentioning

confidence: 99%

“…Posterior modes (95% HPDs) of divergence time (T 3 ) for northeast and southwest Japan regions were 176,000 (150,000-2,730,000) and 250,000 (153,000-11,412,000) years ago, respectively (Table S8). Posterior modes (95% of ancestral effective population size before divergence (N ANC_ALL ) were 1050 100) and 15,400 (40-159,000), respectively. The average numbers of migrants per generation (NM) ranged between 2.91-29.84 and 0.47-3.73 in northeast and southwest Japan, respectively (Figure S7).…”

Section: Past Demographic Change and Estimates Of Migrationmentioning

confidence: 99%

Genetic Distinctiveness but Low Diversity Characterizes Rear-Edge Thuja standishii (Gordon) Carr. (Cupressaceae) Populations in Southwest Japan

et al. 2021

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Rear-edge populations are of significant scientific interest because they can contain allelic variation not found in core-range populations. However, such populations can differ in their level of genetic diversity and divergence reflecting variation in life-history traits, demographic histories and human impacts. Using 13 EST-microsatellites, we investigated the genetic diversity and differentiation of rear-edge populations of the Japanese endemic conifer Thuja standishii (Gordon) Carr. in southwest Japan from the core-range in northeast Japan. Range-wide genetic differentiation was moderate (Fst = 0.087), with northeast populations weakly differentiated (Fst = 0.047), but harboring high genetic diversity (average population-level Ar = 4.76 and Ho = 0.59). In contrast, rear-edge populations were genetically diverged (Fst = 0.168), but contained few unique alleles with lower genetic diversity (Ar = 3.73, Ho = 0.49). The divergence between rear-edge populations exceeding levels observed in the core-range and results from ABC analysis and species distribution modelling suggest that these populations are most likely relicts of the Last Glacial Maximum. However, despite long term persistence, low effective population size, low migration between populations and genetic drift have worked to promote the genetic differentiation of southwest Japan populations of T. standishii without the accumulation of unique alleles.

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“…Projecting ENMs forward and backward in time has now received considerable attention, as models typically do not perform well under new conditions, which are known as non-analogue climate scenarios (Fitzpatrick and Hargrove 2009; McGuire and Davis 2013; Davis et al 2014; Moreno-Amat et al 2017). This problem is particularly relevant when projecting models to the past, when there was quite a bit of non-analogous climate compared with modern climates (Fitzpatrick and Hargrove 2009).…”

Section: Under the Hoodmentioning

confidence: 99%

The geography of phylogenetic paleoecology: integrating data and methods to better understand biotic response to climate change

Lamsdell

Congreve²,

Lawing

2021

Paleobiology

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Deeper knowledge about how species and communities respond to climate change and environmental gradients should be supported by evidence from the past, especially as modern responses are influenced by anthropogenic pressures, including human population growth, habitat destruction and fragmentation, and intensifying land use. There have been great advances in modeling species’ geographic distributions over shallow time, where consideration of evolutionary change is likely less important due to shorter time for evolution and speciation to occur. Over these shallow time periods, we have more resources for paleoclimate interpretation across large geographic landscapes. We can also gain insight into species and community changes by studying deep records of temporal changes. However, modeling species geographic distributions in deep time remains challenging, because for many species there is sparse coverage of spatial and temporal occurrences and there are fewer paleoclimate general circulation models (GCMs) to help interpret the geographic distribution of climate availability. In addition, at deeper time periods, it is essential to consider evolutionary change within lineages of species. I will discuss a framework that integrates evolutionary information in the form of phylogenetic relatedness from clades of extant closely related species, where and when there are associated fossil occurrences, and the geographic distribution of paleoclimate in deep time to infer species past geographic response to climate change and to estimate where and when there were hotspots of ancient diversification. More work is needed to better understand the evolution of physiological tolerances and how physiological tolerances relate to the climate space in which species occur.

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Incorporating plant fossil data into species distribution models is not straightforward: Pitfalls and possible solutions

Cited by 10 publications

References 86 publications

Outstanding Challenges in the Transferability of Ecological Models

Outstanding Challenges in the Transferability of Ecological Models

Genetic Distinctiveness but Low Diversity Characterizes Rear-Edge Thuja standishii (Gordon) Carr. (Cupressaceae) Populations in Southwest Japan

The geography of phylogenetic paleoecology: integrating data and methods to better understand biotic response to climate change

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