Towards a collaborative, global infrastructure for biodiversity assessment

Guralnick, Robert; Hill, Andrew; Lane, Meredith A.

doi:10.1111/j.1461-0248.2007.01063.x

Cited by 146 publications

(113 citation statements)

References 34 publications

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“…Georeferencing of specimens.-Georeferencing of the ma− terial, the process of converting text−based locality descrip− tions into geographic co−ordinates, was performed using BioGeomancer (Guralnick et al 2007). BioGeomancer re− turns present−day geographic co−ordinates for named locali− ties.…”

Section: Methodsmentioning

confidence: 99%

A quantitative null model of additive diversity partitioning: examining the response of beta diversity to extinction

Layou¹

2007

Paleobiology

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Paleobiological diversity is often expressed as α (within-sample), β (among-sample), and γ (total) diversities. However, when studying the effects of extinction on diversity patterns, only variations in α and γ diversities are typically addressed. A null model that examines changes in β diversity as a function of percent extinction is presented here.The model examines diversity in the context of a hierarchical sampling strategy that allows for the additive partitioning of γ diversity into mean α and β diversities at varying scales. Here, the sampling hierarchy has four levels: samples, beds, facies, and region; thus, there are four levels of α diversity (α1, α2, α3, α4) and three levels of β diversity (β1, β2, and β3). Taxa are randomly assigned to samples within the hierarchy according to probability of occurrence, and initial mean α and β values are calculated. A regional extinction is imposed, and the hierarchy is resampled from the remaining extant taxa. Post-extinction mean α and β values are then calculated.Both non-selective and selective extinctions with respect to taxon abundance yield decreases in α, β, and γ diversities. Non-selective extinction with respect to taxon abundance shows little effect on diversity partitioning except at the highest extinction magnitudes (above 75% extinction), where the contribution of α1 to total γ increases at the expense of β3, with β1 and β2 varying little with increasing extinction magnitude. The pre-extinction contribution of α1 to total diversity increases with increased probabilities of taxon occurrence and the number of shared taxa between facies. Both β1 and β2 contribute equally to total diversity at low occurrence probabilities, but β2 is negligible at high probabilities, because individual samples preserve all the taxonomic variation present within a facies. Selective extinction with respect to rare taxa indicates a constant increase in α1 and constant decrease in β3 with increasing extinction magnitudes, whereas selective extinction with respect to abundant taxa yields the opposite pattern of an initial decrease in α1 and increase in β3. Both β1 and β2 remain constant with increasing extinction for both cases of selectivity. By comparing diversity partitioning before and after an extinction event, it may be possible to determine whether the extinction was selective with respect to taxon abundances, and if so, whether that selectivity was against rare or abundant taxa.Field data were collected across a Late Ordovician regional extinction in the Nashville Dome of Tennessee, with sampling hierarchy similar to that of the model. These data agree with the abundant-selective model, showing declines in α, β, and γ diversities, and a decrease in α1 and increase in β3, which suggests this extinction may have targeted abundant taxa.

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Section: Methodsmentioning

confidence: 99%

A quantitative null model of additive diversity partitioning: examining the response of beta diversity to extinction

Layou¹

2007

Paleobiology

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“…The more flexible the model, the more the data can remain in their original form, and thus the less work is required. The most rigid data models may require the mapping of taxa and other domain lists to a standard list, and retrospective georeferencing (Guralnick et al, 2007). Overall, assuming that userfriendly tools are available and that the provider knows how to use them, there does not seem to be much difference in terms of effort between metadata, semantic annotation, and data conversion.…”

Section: Perspectives On Data Conversionmentioning

confidence: 99%

A semantically integrated, user-friendly data model for species observation data

Veen

Reenen

Sluiter

et al. 2012

Ecological Informatics

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Recent decades have seen an increasing importance of large-scale ecological research, driven by increased awareness of the global influence of human activities on the biosphere. Such research requires species observation data covering many years, large areas and a broad range of taxonomic groups. As such data sets often cover small areas, and have been collected using varying methods, they can only be combined in a single analysis if they are made available at the same location and translated into a single format. Over the past decade, catalysed by the growth of the Internet, various technologies for data dissemination and data integration have been developed and applied in projects such as the Global Biodiversity Information Facility, the Knowledge Network for Biocomplexity, BioCASE and the British National Biodiversity Network (NBN). In the Netherlands, data are now made available from the National Database of Flora and Fauna (NDFF), which currently contains approximately 40 million observation records covering a broad variety of species. The NDFF uses a standardised, semantically integrated data model to combine effectively species observation data of various kinds. In this paper, we evaluate this approach and the NDFF data model, by comparison with Darwin Core, Access to Biological Collections Data (ABCD) and the Recorder 2000 model used by the NBN. We conclude that the high degree of standardisation in the NDFF data model has led to somewhat increased cost in data conversion, but also to improved semantic integration and ease-ofuse of species observation data. Together with the relative simplicity, completeness and flexibility of the model, this enables effective reuse of species observations in a user-friendly manner.

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“…Localities that have been retrospectively georeferenced have uncertainty associated with the lack of geographic details in the textual descriptions (Beaman et al 2004;Rowe 2005;Chapman and Wieczorek 2006). More standardized techniques have been developed that allow a better quantification of the positional error of occurrence data (Murphy et al 2004;Wieczorek et al 2004;Guralnick et al 2007;Guo et al 2008). The effect of the positional error on resulting ENM output using different methodologies has been underexplored.…”

Section: Comparing E M Performance Against Uncertaintymentioning

confidence: 99%

Locality Uncertainty and the Differential Performance of Four Common Niche-Based Modeling Techniques

Fernández¹,

Blum

Reichle³

et al. 2009

Biodiv. Inf.

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Abstract. We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections.

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Towards a collaborative, global infrastructure for biodiversity assessment

Cited by 146 publications

References 34 publications

A quantitative null model of additive diversity partitioning: examining the response of beta diversity to extinction

A quantitative null model of additive diversity partitioning: examining the response of beta diversity to extinction

A semantically integrated, user-friendly data model for species observation data

Locality Uncertainty and the Differential Performance of Four Common Niche-Based Modeling Techniques

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