Common name search
Marine World Heritage Sites
16 harmful Algae experts from 13 countries are trained in data entry into the Ocean Biogeographic Information System (OBIS) and the Harmful Algae Event Database (HAEDAT). The data will be used for the Global HAB Status Report.
OBIS training HAB
OBIS training course in Senegal completed successfully. This is one of eight OBIS training courses on marine biodiversity data management that will be organized in 2017, making use of IOC's Ocean Teacher Global Academy learning platform.
OBIS training Senegal
The meeting report of the 6th session of the OBIS Steering Group is online. 38 decisions and recommendations were adopted.
steering group community
Dr Samuel Bosch recently joined the OBIS secretariat, as our Data Science Officer.
OBIS secretariat staff data science officer
The course provides an introduction to OBIS and includes best practices in marine biogeographic data management, data publication, data access, data analysis and data visualisation. The course aims to reinforce and expand the OBIS network in the South-East Asian Region and to increase the amount and quality of open access biodiversity data published through OBIS and its OBIS nodes to enhance research, species conservation and area-based management applications. The course will put extra focus on coral reef biodiversity. Applications should be submitted through the online form before 30 August 2017.
OBIS training course in Colombia completed successfully. This is one of several training courses on marine biodiversity data management that will be organized in Latin America in 2017, making use of OceanTeacher learning platform.
OBIS training Colombia
species distribution OBIS data
Costello & Chaudhary (2017) used data from OBIS to show that marine species richness is higher in the coastal tropics and decreases with depth. The paper reviews what factors have led to species diversification, and how this knowledge informs conservation priorities.
Two representations of species richness were compared to sea surface temperature and productivity. To minimise sampling effort bias, Estimated Species richness (ES50) was calculated as the number of species in 50 random samples from each 5 degree latitude-longitude cell derived from a dataset of 65,000 species distributions from OBIS in 2009 and equal area hexagons from 51,670 species from OBIS in 2015.
Four measures of species richness calculated from the above hexagons, and sea temperature, were plotted with depth. Species richness, calculated for 32,328 species with known depth of occurrence, for 50,000 km2 hexagrids in the depth range 0 – 500 m (interval of 100) and 500 – 9,000 m (interval of 500).
For environmental data see http://gmed.auckland.ac.nz/.
Details in: Costello MJ, Chaudhary C. 2017. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Current Biology 27, R511–R527. http://dx.doi.org/10.1016/j.cub.2017.04.060
species distribution OBIS data
A global analysis of the biogeography of species richness in razor clams (Solenidae) found the number of species was highest in the northern hemisphere, and dipped at the equator with a smaller peak in the southern hemisphere (Saeedi et al. 2016). Thus Chaudhary et al. (2016) reviewed previous studies and found that almost all latitudinal gradients in marine species richness peaked in the northern hemisphere, with a smaller southern hemisphere peak, and dip at the equator. This contradicted the prevailing paradigm that biodiversity peaks at the equator. A response to this paper suggested that the pattern could be affected by sampling bias (Fernandez and Marques 2016). Thus Chaudhary et al. (2017) used data from OBIS to show that indeed sampling bias influenced the gradient. However, this effect was reduced when using gamma (total species in a latitudinal band) over alpha (average species in latitude-longitude cells in a latitudinal band). Furthermore, when adjusted for sampling effort using ES50 index, the pattern was still bimodal with a dip at the equator, but the peaks in richness were equal in both hemispheres. The authors suggest that this may be because temperature is the main cause of the gradient and is getting too hot at the equator for some species.
These analyses would not have been possible without the integration of data across all taxa and geographic locations by OBIS (the full list of resources used is available in supplement info).
- Saeedi, H, Dennis TE, Costello MJ. 2016. Bimodal latitudinal species richness and high endemicity in razor clams (Mollusca). Journal of Biogeography, online. DOI: http://dx.doi.org/10.1111/jbi.12903
- Chaudhary C., Saeedi H., Costello MJ. 2016. Bimodality of latitudinal gradients in marine species richness. Trends in Ecology and Evolution, 31 (9), 670-676. DOI: http://dx.doi.org/10.1016/j.tree.2016.06.001
- Fernandez, M.O. and Marques, A.C. 2016. Diversity of diversities: a response to Chaudhary, Saeedi, and Costello. Trends Ecol. Evol. Published online November 26 2016. http://dx.doi.org/10.1016/j.tree.2016.10.013
- Chaudhary C., Saeedi H., Costello MJ. 2017. Marine Species Richness Is Bimodal with Latitude: A Reply to Fernandez and Marques. Trends in Ecology and Evolution, 31 DOI: http://dx.doi.org/10.1016/j.tree.2017.02.007
Some like it warm? Warm-dwelling species have increased in response to climate change in western/central Europe
Climate Change species abundance OBIS data
The effect of climate change on population abundances are less studied than those on species ranges. This is partly because population abundance data are harder to obtain. Nonetheless, abundance is an interesting variable to study. A species may change in abundance before there are changes in its range; therefore, we may detect climate change impacts on abundance that are not apparent if we just look at range edges. The aim of our study was to study the impacts of climate change on long-term abundance trends, using a broad range of species from all environmental realms. We included time-series data from 22 different communities since the 1980s, including 6 marine datasets collected from the North Sea (phytoplankton, benthic invertebrates and fish). Our test was based on the prediction that warm-adapted species should increase (or decrease less) than cold-adapted ones within each community under climate change. We used the population data to estimate species’ population trends and compiled distribution data (e.g., from GBIF and OBIS) to estimate species’ temperature preferences. We found a mixture of population trends in almost all datasets: many species have decreased, but many species have also increased. On average, temperature preference was positively related to population trends. Although some of the cold-adapted terrestrial species had decreased, more commonly warm-adapted terrestrial species had increased. We found weaker relationships in the marine and freshwater datasets although warm-dwelling marine fish have increased. Attributing changes in species’ abundance to particular drivers is tricky because populations are exposed to many drivers at the same time. By relating population trends to species characteristics (temperature preferences), we show how it is possible to detect the particular effects of climate change on species’ abundances, and how this is useful for comparative analysis of climate change impacts across environmental realms.
Full reference: Diana E. Bowler, Christian Hof, Peter Haase, Ingrid Kröncke, Oliver Schweiger, Rita Adrian, Léon Baert, Hans-Günther Bauer, Theo Blick, Rob W. Brooker, Wouter Dekoninck, Sami Domisch, Reiner Eckmann, Frederik Hendrickx, Thomas Hickler, Stefan Klotz, Alexandra Kraberg, Ingolf Kühn, Silvia Matesanz, Angelika Meschede, Hermann Neumann, Robert O’Hara, David J. Russell, Anne F. Sell, Moritz Sonnewald, Stefan Stoll, Andrea Sundermann, Oliver Tackenberg, Michael Türkay, Fernando Valladares, Kok van Herk, Roel van Klink, Rikjan Vermeulen, Karin Voigtländer, Rüdiger Wagner, Erik Welk, Martin Wiemers, Karen H. Wiltshire & Katrin Böhning-Gaese. 2017. Cross-realm assessment of climate change impacts on species’ abundance trends. Nature Ecology & Evolution 1: 0067 (doi:10.1038/s41559-016-0067)