Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions

Edler, Daniel; Guedes, Thaís B.; Zizka, Alexander; Rosvall, Martin; Antonelli, Alexandre

doi:10.1093/sysbio/syw087

Cited by 111 publications

(181 citation statements)

References 27 publications

Supporting

Mentioning

177

Contrasting

Unclassified

Order By: Relevance

“…For this, we used a recent method based on network analysis 59 to identify biogeographical regions for tree species in Europe (Fig. 4, left panel), applying the online tool Infomap Bioregions 60 (http://bioregions.mapequation.org) at a spatial resolution of 0.5 degrees. This revealed a strong consistency with the biogeographical regions presented in Rueda et al 61 (Fig.…”

Section: Technical Validationmentioning

confidence: 99%

EU-Forest, a high-resolution tree occurrence dataset for Europe

2017

View full text Add to dashboard Cite

We present EU-Forest, a dataset that integrates and extends by almost one order of magnitude the publicly available information on European tree species distribution. The core of our dataset (~96% of the occurrence records) came from an unpublished, large database harmonising forest plot surveys from National Forest Inventories on an INSPIRE-compliant 1 km×1 km grid. These new data can potentially benefit several disciplines, including forestry, biodiversity conservation, palaeoecology, plant ecology, the bioeconomy, and pest management.

show abstract

Section: Technical Validationmentioning

confidence: 99%

EU-Forest, a high-resolution tree occurrence dataset for Europe

2017

View full text Add to dashboard Cite

show abstract

“…On the other hand, long-term occupancy of a geographic region provides more time for species to accumulate in a region (e.g. Biogeographical regions for sigmodontines in (A), constructed using Infomap Bioregions (Edler et al 2017). Biogeographical regions for sigmodontines in (A), constructed using Infomap Bioregions (Edler et al 2017).…”

Section: Sigmodontine Diversification Historymentioning

confidence: 99%

Tracing the diversification history of a Neogene rodent invasion into South America

2018

View full text Add to dashboard Cite

We investigated spatial patterns of evolutionary relatedness and diversification rates to test hypotheses about the historical biogeographic processes underlying the radiation of Neotropical rats and mice (Sigmodontinae, ~400 species). A negative correlation between mean phylogenetic distance and diversification rates of rodent assemblages reveals a pattern of species co-occurrence in which assemblages of closely related species are also the fastest diversifying ones. Subregions of the Neotropics occupied by distantly related species that are on average more slowly diversifying include Central America, northern South America, and the Atlantic forest. In southern South America, recent species turnover appears to have been higher. Ancestral locations for the main tribes of sigmodontines were also estimated, suggesting eastern South America and the Amazonian lowlands were colonized before some central Andean regions, even though the latter are now centers of species richness for these rodents. Moreover, a past connection between the tropical Andes and the Atlantic Forest is suggested by our results, highlighting a role for a hypothetical arc connecting the two biomes, which would have impacted many other groups of organisms. Whether rapid, recent speciation in some regions is related to Quaternary climatic fluctuations and the young age of sigmodontines (~12.7 Ma crown age) or instead to intrinsic traits of these rodents remains an open question. If the former is true, we hypothesize that contrasting trends will characterize older Neotropical clades.

show abstract

“…Infomap in turn works with respect to an information‐theoretic map equation which attempts to simplify the network structure by using an efficient random walk process, and to minimize the length of code of a modular description of pathways on a network by using minimum description length principle (Rosvall & Bergstrom, ; Rosvall, Axelsson, & Bergstrom, ; for further details see Bernardo‐Madrid et al, ). INFOMAP BIOREGIONS relies on an adaptive spatial resolution to reflect differences on data density, where sampling units vary in size according to record density (observations available) (Edler et al, ). Thus, we set the maximum cell size to 1° (which is convenient for the size of the study area; M. Rosvall, pers.…”

Section: Methodsmentioning

confidence: 99%

Network‐ and distance‐based methods in bioregionalization processes at regional scale: An application to the terrestrial mammals of Iran

Yusefi

Safi

Brito

2019

Journal of Biogeography

View full text Add to dashboard Cite

Aim In recent years, novel approaches have been proposed to improve current bioregionalization methods, but these have not been thoroughly compared. We assessed the applicability of the recently developed network‐based clustering method (Infomap algorithm) in bioregionalization analysis at regional spatial scales and compared the results with commonly used distance‐based methods (hierarchical clustering algorithm). We also identified climate regions by using a model‐based cluster analysis (Gaussian algorithm). Finally, we quantified the representation of climate regions and bioregions in current protected areas (PAs). Location Iran. Taxa Terrestrial mammals. Methods To define bioregions we used the Infomap algorithm and distance‐based clustering methods based on species distribution data (over 14,000 occurrence records for 188 species). The Infomap algorithm was applied using the interactive web application “INFOMAP BIOREGIONS” and the distance‐based clustering was based on unweighted pair‐group method using arithmetic averages (UPGMA). To identify climate regions we used principal components analysis and a model‐based cluster analysis both based on 15 climatic variables as well as a terrain ruggedness index. Results The Infomap algorithm detected nine biogeographical units: seven bioregions and two transition zones. The distance‐based method suggested five bioregions. The identified bioregions differed between methods with some consistent spatial patterns across methods. Temperature and precipitation explained 85.8% of the environmental variation. Eight climate regions were identified. In general, climate variation and bioregional patterns are currently poorly represented in PAs (<25% coverage). Main conclusions The network‐based method allowed identifying bioregions at regional scale and was apparently more sensitive than the conventional distance‐based method. The detection of transition zones by the Infomap algorithm was an advantage, and stressed the fact that the distribution of Iranian mammalian fauna is complex, especially in the south‐eastern part where contact areas between several bioregions are found. The identified bioregions (especially the distance‐based bioregions) and climate regions tended to match well with previous bioregionalization studies and the global terrestrial ecoregions. When thoroughly compared and understood, bioregions and climate regions provide a framework for regional biodiversity conservation planning.

show abstract

Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions

Cited by 111 publications

References 27 publications

EU-Forest, a high-resolution tree occurrence dataset for Europe

EU-Forest, a high-resolution tree occurrence dataset for Europe

Tracing the diversification history of a Neogene rodent invasion into South America

Network‐ and distance‐based methods in bioregionalization processes at regional scale: An application to the terrestrial mammals of Iran

Contact Info

Product

Resources

About