Online solutions and the ‘<scp>W</scp>allacean shortfall’: what does <scp>GBIF</scp> contribute to our knowledge of species' ranges?

Beck, Jan; Ballesteros‐Mejia, Liliana; Nagel, Peter; Kitching, Ian J.

doi:10.1111/ddi.12083

Cited by 138 publications

(129 citation statements)

References 21 publications

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“…With the development of computer hardware and Internet speed, the current availability of species and climate information sharing systems has greatly enhanced the field of SDM [19][20][21][22]. These have greatly inspired the use of a variety of algorithms for predicting the potential distribution of species.…”

Section: Figurementioning

confidence: 99%

“…A total of 32,674 specimens (32,434 from GBIF and 240 from CVH) were identified by the coordinates recorded in the database or by coordinates derived from a place name included in the database. The main reason for collating records with a coarse geographic resolution (0.5° × 0.5°) was that there may have been a sampling bias or error at a fine resolution in the GBIF and CVH occurrence records, which would produce models of lower rather than higher quality [21]. Another consideration was the calculation speed of the computer, identified in many previous studies, which was based on a spatial resolution between 50 km × 50 km and 200 km × 200 km [20,21,36].…”

Section: Species Occurrence Datamentioning

confidence: 99%

“…The main reason for collating records with a coarse geographic resolution (0.5° × 0.5°) was that there may have been a sampling bias or error at a fine resolution in the GBIF and CVH occurrence records, which would produce models of lower rather than higher quality [21]. Another consideration was the calculation speed of the computer, identified in many previous studies, which was based on a spatial resolution between 50 km × 50 km and 200 km × 200 km [20,21,36]. A total of 1174 grid cells (0.5° × 0.5°) were identified globally as containing black locust (details of the calculation process are given in Section 2.4.).…”

Section: Species Occurrence Datamentioning

confidence: 99%

“…The current availability of species-sharing information systems around the world makes it possible to easily study the geographical distribution of plants worldwide [20][21][22]. GBIF is a free and open access biodiversity database that integrates existing worldwide biodiversity data to form a user-oriented global biodiversity service network.…”

Section: Species Record Database and Species Modeling Toolsmentioning

confidence: 99%

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Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Guo

et al. 2014

Forests

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Black locust (Robinia pseudoacacia L.) is a tree species of high economic and ecological value, but is also considered to be highly invasive. Understanding the global potential distribution and ecological characteristics of this species is a prerequisite for its practical exploitation as a resource. Here, a maximum entropy modeling (MaxEnt) was used to simulate the potential distribution of this species around the world, and the dominant climatic factors affecting its distribution were selected by using a jackknife test and the regularized gain change during each iteration of the training algorithm. The results show that the MaxEnt model performs better than random, with an average test AUC value of 0.9165 (±0.0088). The coldness index, annual mean temperature and warmth index were the most important climatic factors affecting the species distribution, explaining 65.79% of the variability in the geographical distribution. Species response curves showed unimodal relationships with the annual mean temperature and warmth index, whereas there was a linear relationship with the coldness index. The dominant climatic conditions in the core of OPEN ACCESSForests 2014, 5 2774 the black locust distribution are a coldness index of −9.8 °C-0 °C, an annual mean temperature of 5.8 °C-14.5 °C, a warmth index of 66 °C-168 °C and an annual precipitation of 508-1867 mm. The potential distribution of black locust is located mainly in the United States, the United Kingdom, Germany, France, the Netherlands, Belgium, Italy, Switzerland, Australia, New Zealand, China, Japan, South Korea, South Africa, Chile and Argentina. The predictive map of black locust, climatic thresholds and species response curves can provide globally applicable guidelines and valuable information for policymakers and planners involved in the introduction, planting and invasion control of this species around the world.

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Section: Figurementioning

confidence: 99%

Section: Species Occurrence Datamentioning

confidence: 99%

Section: Species Occurrence Datamentioning

confidence: 99%

Section: Species Record Database and Species Modeling Toolsmentioning

confidence: 99%

See 2 more Smart Citations

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Guo

et al. 2014

Forests

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“…Recently, various datasets have been developed or collated in an attempt to provide baseline information on the current distributional range of invasive or exotic species at a regional or continental scale (Beck et al 2013). An inevitable downside of such datasets is that their accuracy is biased towards areas and time periods with a high collection intensity (Kalwij et al 2014a).…”

Section: Introductionmentioning

confidence: 99%

Annual monitoring reveals rapid upward movement of exotic plants in a montane ecosystem

2015

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There is increasing evidence that invasive species are threating montane ecosystems globally.However, trends in species distribution are difficult to observe directly due to a lack of data with suitable spatio-temporal resolution. Here, we aimed to detect spatio-temporal trends in exotic 1 Kalwij, J.M. et al.plants in a montane ecosystem, and to determine which drivers had a role affecting these trends.Each year, for a period of seven years, we recorded the upper elevational range limit of exotic plant species in road verges along an elevational gradient of 1,500-2,874 m a.s.l. in southern Africa. We fitted repeated-measures ANOVA models to test if upper elevational range limits changed over time. Generalized least squares models showed that exotic richness of annuals increased by 3.9 species per year. Also, the upper elevational range limits of established exotics ascended by 24.5 m/y for annuals (N = 17 species), and by 9.7 m/y for perennials (N = 26).These upward trends were too rapid to be explained by slow-acting drivers such as climatic

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Potential breeding distributions of U.S. birds predicted with both short‐term variability and long‐term average climate data

Bateman

Pidgeon

Radeloff

et al. 2016

Ecological Applications

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Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs.

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Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species' ranges?

Cited by 138 publications

References 21 publications

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Annual monitoring reveals rapid upward movement of exotic plants in a montane ecosystem

Potential breeding distributions of U.S. birds predicted with both short‐term variability and long‐term average climate data

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