Spatio‐phylogenetic multispecies distribution models

Kaldhusdal, Arne; Brandl, Roland; Müller, Jörg; Möst, Lisa; Hothorn, Torsten

doi:10.1111/2041-210x.12318

Cited by 16 publications

(15 citation statements)

References 29 publications

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“…; Kaldhusdal et al. ) that were, unfortunately, not computationally tractable in our case, but nonetheless offer exciting possibilities for the future.…”

Section: Discussionmentioning

confidence: 89%

“…We emphasise here how the two approaches may provide complementary information, and encourage their joint use: our assemblage-based results suggest that factors such as NPP and species richness are likely affecting birdsong, but their lack of significance in the crossspecies analysis suggests that more work is needed to both confirm this and disentangle the influence of these factors. Recent efforts suggest potential for combined approaches (e.g., Freckleton and Jetz 2009;Morales-Castilla et al 2013;Kaldhusdal et al 2015) that were, unfortunately, not computationally tractable in our case, but nonetheless offer exciting possibilities for the future.…”

Section: Patterns and Approachesmentioning

confidence: 96%

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Global macroevolution and macroecology of passerine song

et al. 2018

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Studying the macroevolution of the songs of Passeriformes (perching birds) has proved challenging. The complexity of the task stems not just from the macroevolutionary and macroecological challenge of modeling so many species, but also from the difficulty in collecting and quantifying birdsong itself. Using machine learning techniques, we extracted songs from a large citizen science dataset, and then analyzed the evolution, and biotic and abiotic predictors of variation in birdsong across 578 passerine species. Contrary to expectations, we found few links between life-history traits (monogamy and sexual dimorphism) and the evolution of song pitch (peak frequency) or song complexity (standard deviation of frequency). However, we found significant support for morphological constraints on birdsong, as reflected in a negative correlation between bird size and song pitch. We also found that broad-scale biogeographical and climate factors such as net primary productivity, temperature, and regional species richness were significantly associated with both the evolution and present-day distribution of bird song features. Our analysis integrates comparative and spatial modeling with newly developed data cleaning and curation tools, and suggests that evolutionary history, morphology, and present-day ecological processes shape the distribution of song diversity in these charismatic and important birds.

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“…; Kaldhusdal et al. ) that were, unfortunately, not computationally tractable in our case, but nonetheless offer exciting possibilities for the future.…”

Section: Discussionmentioning

confidence: 89%

Section: Patterns and Approachesmentioning

confidence: 96%

Global macroevolution and macroecology of passerine song

et al. 2018

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“…; Harris ) or phylogenetic relationships (Kaldhusdal et al . ), and are estimated here as a Gaussian random field (GRF) (Thorson et al . In press).…”

Section: Methodsmentioning

confidence: 99%

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

et al. 2015

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Summary1 Predicting and explaining the distribution and density of species is one of the oldest concerns in ecology. Species distributions can be estimated using geostatistical methods, which estimate a latent spatial variable explaining observed variation in densities, but geostatistical methods may be imprecise for species with low densities or few observations. Additionally, simple geostatistical methods fail to account for correlations in distribution among species and generally estimate such cross-correlations as a post hoc exercise. 2 We therefore present spatial factor analysis (SFA), a spatial model for estimating a low-rank approximation to multivariate data, and use it to jointly estimate the distribution of multiple species simultaneously. We also derive an analytic estimate of cross-correlations among species from SFA parameters. 3 As a first example, we show that distributions for 10 bird species in the breeding bird survey in 2012 can be parsimoniously represented using only five spatial factors. As a second case study, we show that forward prediction of catches for 20 rockfishes (Sebastes spp.) off the U.S. West Coast is more accurate using SFA than analysing each species individually. Finally, we show that single-species models give a different picture of cross-correlations than joint estimation using SFA. 4 Spatial factor analysis complements a growing list of tools for jointly modelling the distribution of multiple species and provides a parsimonious summary of cross-correlation without requiring explicit declaration of habitat variables. We conclude by proposing future research that would model species cross-correlations using dissimilarity of species' traits, and the development of spatial dynamic factor analysis for a low-rank approximation to spatial time-series data.

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“…We use phylogenetic distances here to obviate the need for eigenvector selection and for greater transparency in the interpretation of P (i.e., higher values of P in a locality indicate a higher probability of finding the target species at that locality). Another alternative would be to explore phylogenetic generalized linear mixed models (PGLMMs; Ives & Helmus, ), which use random effects to account for phylogenetic covariation, but the size of macroecological datasets makes fitting such models challenging (if not impossible) using current implementations (Kaldhusdal, Brandl, Müller, Möst, & Hothorn, ).…”

Section: Methodsmentioning

confidence: 99%

Combining phylogeny and co‐occurrence to improve single species distribution models

Morales‐Castilla

Davies

Pearse

et al. 2017

Global Ecol. Biogeogr.

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Aim We present a novel quantitative framework that combines information on phylogeny and the spatial distributions of related species to enhance the single‐species distributional models commonly used in ecology. Innovation While species distribution models (SDMs) are becoming increasingly sophisticated, they rarely take into consideration the shared evolutionary histories of species. Species are not independent entities, and phylogenies may capture how species have configured their spatial distributions as a response to their ecological similarities and interactions in space and through evolutionary time. Our framework provides a flexible approach to include phylogenies as a surrogate for missing trait data within SDMs, and may be particularly valuable for disentangling current and historical drivers of species distributions and modelling data‐poor species when their close relatives are better sampled. Main conclusions Using both simulations and empirical examples, we demonstrate how the inclusion of phylogenetic information can significantly improve the fit of species distribution models by up to 30% for certain species. We show that the potential of phylogenies to improve model fit directly relates to the phylogenetic structure of species distributions and suggest that our framework has the potential to reconcile the apparent conflict between current and historical drivers of biodiversity patterns.

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Spatio‐phylogenetic multispecies distribution models

Cited by 16 publications

References 29 publications

Global macroevolution and macroecology of passerine song

Global macroevolution and macroecology of passerine song

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

Combining phylogeny and co‐occurrence to improve single species distribution models

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