SimpleSDMLayers.jl and GBIF.jl: A Framework for Species Distribution Modeling in Julia

Dansereau, Gabriel; Poisot, Timothée

doi:10.21105/joss.02872

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…We used Julia v1.6.1 (Bezanson et al 2017) for most of the project and R v4.1.0 (R Core Team 2021) for some specific steps. We used the Julia package SimpleSDMLayers.jl (Dansereau and Poisot 2021) as the basic framework for our analyses, to download the WorldClim 2.1 data, and to map our results through the package's integration of Plots.jl. We also used StatsPlots.jl to produce the kernel density estimation plots in our rare species analysis.…”

Section: Softwarementioning

confidence: 99%

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Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Dansereau

Legendre

Poisot

2022

Oikos

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Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigated how LCBD indices can be predicted over broad spatial extents using species distribution modelling and citizen science data and examined the effect of scale changes on beta diversity quantification.Location: North America. Time period: 2000s.Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the sitewise difference using direct comparison, a spatial association test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship.Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided uniqueness estimates highly correlated with observed data with a statistically significant spatial association Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

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Section: Softwarementioning

confidence: 99%

“…We used the Julia package SimpleSDMLayers. jl (Dansereau and Poisot 2021) as the basic framework for our analyses, to download the WorldClim 2.1 data, and to map our results through the package's integration of Plots. jl.…”

Section: Softwarementioning

confidence: 99%

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Dansereau

Legendre

Poisot

2022

Oikos

Self Cite

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“…The entire pipeline is implemented in Julia 1.6 (Bezanson et al 2017) and is available under the permissive MIT License at https://osf.io/2zwqm/. The taxonomic cleanup steps are done using GBIF.jl (Dansereau and Poisot 2021). The network embedding and analysis is done using EcologicalNetworks.jl (Banville, Vissault, and Poisot 2021;Poisot et al 2019).…”

Section: Implementation and Code Availabilitymentioning

confidence: 99%

Food web reconstruction through phylogenetic transfer of low-rank network representation

Strydom¹,

Bouskila²,

Banville³

et al. 2021

Preprint

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Despite their importance in many ecological processes, collecting data and information on ecological interactions, and therefore species interaction networks, is an exceedingly challenging task. For this reason, large parts of the world have a deficit of data of which species interact, and what we can expect the network structure of these interactions to be. As data collection alone is unlikely to be sufficient at filling these global gaps, community ecologists must adopt predictive methods. In this contribution we develop such a method, relying on graph embedding (the extraction of explanatory latent variables from known graph structures) and transfer learning (the application of previous solution to novel problems with limited predictors overlap) in order to assemble a predicted list of trophic interactions between mammals of Canada. This interaction list is derived from extensive knowledge of the mammalian food web of Europe, despite the fact that there are fewer than 5% of common species between the two locations. We provide guidance on how this method can be adapted by substituting some approaches or predictors in order to make it more generally applicable to a broad family of ecological problems.

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“…We used the standard bioClim variables from WorldClim 2.1, which represent annual trends, ranges, and extremes of temperature and precipitation, but selected only 8 out of the 19 ones to avoid redundancy (bio1, bio2, bio5, bio6, bio12, bio13, bio14, bio15). We downloaded the data at a resolution of 10 arcminutes (around 18 km² at the equator), the coarsest resolution available, using the Julia package SimpleSDMLayers.jl (Dansereau and Poisot 2021). The coarse resolution should mitigate potential imprecision in the eBird data regarding the extent of the sampled areas in each observation checklist.…”

Section: Environmental Datamentioning

confidence: 99%

“…We used Julia v1.6.1 (Bezanson et al 2017) for most of the project and R v4.0.2 (R Core Team 2020) for some specific steps. We used the Julia package SimpleSDMLayers.jl (Dansereau and Poisot 2021) as the basic framework for our analyses, to download the WorldClim 2.1 data, and to map our results through the package's integration of Plots.jl. We also used StatsPlots.jl to produce the density plots in our rare species analysis.…”

Section: Softwarementioning

confidence: 99%

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Dansereau¹,

Legendre²,

Poisot³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigate how LCBD indices can be used to predict ecological uniqueness over broad spatial extents using species distribution modelling and citizen science data. Location: North America. Time period: 2000s. Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the site-wise difference using direct comparison, a spatial autocorrelation test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship. Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided highly correlated estimates with observed data, although spatially autocorrelated. Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

show abstract

SimpleSDMLayers.jl and GBIF.jl: A Framework for Species Distribution Modeling in Julia

Cited by 9 publications

References 4 publications

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Food web reconstruction through phylogenetic transfer of low-rank network representation

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

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