A biogeographical regionalization of Australian <i>Acacia</i> species

González‐Orozco, Carlos E.; Laffan, Shawn W.; Knerr, Nunzio; Miller, Joe

doi:10.1111/jbi.12153

Cited by 54 publications

(63 citation statements)

References 36 publications

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“…Our network bioregionalisation analysis of Acacia and eucalypts have confirmed the general outcomes of previous studies (Crisp et al 2004, Gonzalez-Orozco et al 2013, 2014b, but more importantly, it has generated new insights about Australian biodiversity patterns. Our network bioregionalisation analysis of Acacia and eucalypts have confirmed the general outcomes of previous studies (Crisp et al 2004, Gonzalez-Orozco et al 2013, 2014b, but more importantly, it has generated new insights about Australian biodiversity patterns.…”

Section: Discussionsupporting

confidence: 86%

“…To guarantee a reproducible solution, CWE was chosen to be maximized in the event of a tiebreaker (Gonzalez-Orozco et al 2013). A low value of b sim (low turnover) means a large number of taxa are shared between the cells.…”

Section: Clustering Methodsmentioning

confidence: 99%

“…Transition zones between different biomes correspond to changes in climatic and soil conditions (Bui et al 2014, Gonzalez-Orozco et al 2013, 2014a. 4) seems to be highly driven by changes in annual mean temperature and precipitation seasonality (Gonzalez-Orozco et al 2013). 4) seems to be highly driven by changes in annual mean temperature and precipitation seasonality (Gonzalez-Orozco et al 2013).…”

Section: Bioregionalisation Of Acacia and The Eucalyptsmentioning

confidence: 99%

“…This dendrogram can then be split at the first n number of branches to produce bioregions. To avoid this a tiebreaker can be used, which selects one of the cells that maximizes or minimizes a second score, such as CWE (Gonzalez-Orozco et al 2013). When two cells are tied in their similarity to a growing cluster according to a chosen metric, the algorithm selects one of the cells at random and the clustering will be different each time it is run.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of network and clustering methods to detect biogeographical regions

2017

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Bioregions are an important concept in biogeography, and are key to our understanding of biodiversity patterns across the world. The use of networks in biogeography to produce bioregions is a relatively novel approach that has been proposed to improve upon current methods. However, it remains unclear if they may be used in place of current methods and/or offer additional biogeographic insights. We compared two network methods to detect bioregions (modularity and map equation) with the conventional distance-based clustering method. We also explored the relationship between network and biodiversity metrics. For the analysis we used two datasets of iconic Australian plant groups at a continental scale, Acacia and eucalypts, as example groups. The modularity method detected fewer large bioregions produced the most succinct bioregionalisation for both plant groups corresponding to Australian biomes, while map equation detected many small bioregions including interzones at a natural scale of one. The clustering method was less sensitive than network methods in detecting bioregions. The network metric called participation coefficient from both network partition methods identified interzones or transition zones between bioregions. Furthermore, another network metric (betweenness) was highly correlated to richness and endemism. We conclude that the application of networks to biogeography offers a number of advantages and provides novel insights. More specifically, our study showed that these network partition methods were more efficient than the clustering method for bioregionalisation of continental-scale data in: 1) the identification of bioregions and 2) the quantification of biogeographic transition zones using the participation coefficient metric. The use of network methods and especially the participation coefficient metric adds to bioregionalisation by identifying transition zones which could be useful for conservation purposes and identifying biodiversity hotspots.

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Section: Discussionsupporting

confidence: 86%

Section: Clustering Methodsmentioning

confidence: 99%

Section: Bioregionalisation Of Acacia and The Eucalyptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparison of network and clustering methods to detect biogeographical regions

2017

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“…However, by the time of the Systematic Period, particularly in the early 1990s, there was a resurgence of biotic area classification [14], [15], [16], [17]. Of these new classifications, very few were in agreement, leaving phytogeography with more areas and area names than ever before [18]. In summary, there has to date been an accumulation of, often conflicting, area classifications, none of which were quantitatively produced or assessed.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Phytogeographical Regions of Australia Using Geospatial Turnover in Species Composition

et al. 2014

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The largest digitized dataset of land plant distributions in Australia assembled to date (750,741 georeferenced herbarium records; 6,043 species) was used to partition the Australian continent into phytogeographical regions. We used a set of six widely distributed vascular plant groups and three non-vascular plant groups which together occur in a variety of landscapes/habitats across Australia. Phytogeographical regions were identified using quantitative analyses of species turnover, the rate of change in species composition between sites, calculated as Simpson's beta. We propose six major phytogeographical regions for Australia: Northern, Northern Desert, Eremaean, Eastern Queensland, Euronotian and South-Western. Our new phytogeographical regions show a spatial agreement of 65% with respect to previously defined phytogeographical regions of Australia. We also confirm that these new regions are in general agreement with the biomes of Australia and other contemporary biogeographical classifications. To assess the meaningfulness of the proposed phytogeographical regions, we evaluated how they relate to broad scale environmental gradients. Physiographic factors such as geology do not have a strong correspondence with our proposed regions. Instead, we identified climate as the main environmental driver. The use of an unprecedentedly large dataset of multiple plant groups, coupled with an explicit quantitative analysis, makes this study novel and allows an improved historical bioregionalization scheme for Australian plants. Our analyses show that: (1) there is considerable overlap between our results and older biogeographic classifications; (2) phytogeographical regions based on species turnover can be a powerful tool to further partition the landscape into meaningful units; (3) further studies using phylogenetic turnover metrics are needed to test the taxonomic areas.

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The Australasian Virtual Herbarium: Tracking data usage and benefits for biological collections

Cantrill

2018

Appl Plant Sci

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Premise of the StudyGlobally, natural history collections are focused on digitizing specimens and information and making these data accessible. Usage information on National Herbarium of Victoria data made available through the Atlas of Living Australia and The Australasian Virtual Herbarium (AVH) is analyzed to understand how and by whom herbarium data are being used.MethodsSince 2010, AVH data usage information has been gathered from users and supplied to data custodians as a spreadsheet that includes number of download events, number of records downloaded, and user reasons for downloading data in predefined categories.ResultsSince 2010, in excess of 268,000 download events of 194 million records (excluding testing events) have been recorded for the National Herbarium of Victoria data set. This means, on average, every record has been downloaded 220 times in the past nine years. Data use grew continuously from 2010 to 2015 but decreased in 2016 due to fewer ecological projects.DiscussionData have primarily been used for ecological research, but there is an emerging trend for use in education including citizen science projects. Information about data use demonstrates relevance to funding agencies and helps inform the development of collections and prioritization of resources when digitizing material.

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A biogeographical regionalization of Australian Acacia species

Cited by 54 publications

References 36 publications

A comparison of network and clustering methods to detect biogeographical regions

A comparison of network and clustering methods to detect biogeographical regions

Quantifying Phytogeographical Regions of Australia Using Geospatial Turnover in Species Composition

The Australasian Virtual Herbarium: Tracking data usage and benefits for biological collections

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