Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Dataset: phytoplankton climatologyProchlorococcus, Synechococcus and picoeukaryotic phytoplankton estimated mean cell abundance (cells/ml) in 1-degree grids for 25 layers from 0m to 200 m depth. Cell abundance was estimated with quantitative niche models for each lineage (Flombaum et al., 2013; Flombaum et al., 2020), inputs from the monthly mean of temperature and nitrate from the World Ocean Atlas, and PAR from MODIS-Aqua Level-3 Mapped Photosynthetically Available Radiation Data Version 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/811147NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Interactions among resource partitioning, sampling effect, and facilitation on the biodiversity effect: A modeling approach

Resource partitioning, facilitation, and sampling effect are the three mechanisms behind the biodiversity effect, which is depicted usually as the effect of plant-species richness on aboveground net primary production. These mechanisms operate simultaneously but their relative importance and interactions are difficult to unravel experimentally. Thus, niche differentiation and facilitation have been lumped together and separated from the sampling effect. Here, we propose three hypotheses about interactions among the three mechanisms and test them using a simulation model. The model simulated water movement through soil and vegetation, and net primary production mimicking the Patagonian steppe. Using the model, we created grass and shrub monocultures and mixtures, controlled root overlap and grass water-use efficiency (WUE) to simulate gradients of biodiversity, resource partitioning and facilitation. The presence of shrubs facilitated grass growth by increasing its WUE and in turn increased the sampling effect whereas root overlap (resource partitioning) had, on average, no effect on sampling effect. Interestingly, resource partitioning and facilitation interacted so the effect of facilitation on sampling effect decreased as resource partitioning increased. Sampling effect was enhanced by the difference between the two functional groups in their efficiency in using resources. Morphological and physiological differences make one group outperform the other, once those differences were established further differences did not enhance the sampling effect. In addition, grass WUE and root overlap positively influence the biodiversity effect but showed no interactions.Fil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Sala, Osvaldo Esteban. Arizona State University. School of Life Sciences and School of Sustainability; Estados UnidosFil: Rastetter, Edward B.. Marine Biological Laboratory. The Ecosystem Center; Estados Unido

Biomass rcp85 CMIP5 data - mean picophytoplankton surface biomass estimated for the climate models under the Representative Concentration Pathway 8.5

Dataset: Biomass rcp85 CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Representative Concentration Pathway 8.5 (RCP8.5) – equivalent to a radiative forcing of 8.5 W m-2 in 2100) scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783527NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Global cell abundance of picoeukaryotic phytoplankton, predicted by neural network models using average temperatures and nitrate from the World Ocean Atlas 2005

Dataset: Global picoeukaryotic phytoplankton distributionGlobal cell abundance of picoeukaryotic phytoplankton, predicted by our neural network models using average temperatures and nitrate from the World Ocean Atlas 2005 (1°x1° resolution), and 8 d average PAR and K490 values derived from satellite data (SeaWiFS 0.083°x0.083°) and obtained as an output cells/ml for each set of conditions in a 1°x1° resolution. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783537NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Biomass historic CMIP5 data - mean picophytoplankton surface biomass estimated for climate models under the Historical scenario

Dataset: Biomass historic CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Historical scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783516NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton abundances in the global ocean

Marine picophytoplankton is the most abundant photosynthetic group on Earth; however, it is still underrepresented in dynamic ecosystem models. Major constraints for understanding its role in the ecosystem at a global scale are sparse data and lack of a baseline description of its distribution. Here, we present three datasets to assess the global abundance of the principal groups of picophytoplankton, Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton: (1) a compilation of 109,045 field observations with ancillary environmental data, (2) a global monthly climatology of 1° grids from 0 to 200 m, and (3) four climate scenarios projections, from the Coupled Model Intercomparison Project 5, spanning years 1901 to 2100. Together this set of observational and modeled data can improve our understanding of the role of picophytoplankton in the global ecosystem.Fil: Visintini Adomaitis, Natalia Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Martiny, Adam Camilo. University of California at Irvine; Estados UnidosFil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentin

Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re‐examination of 16 grassland biodiversity studies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111813/1/fec12432.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111813/2/fec12432-sup-0002-Suppinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111813/3/fec12432-sup-0001-LaySummary.pd

Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities

Aim: Climate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes. Location: Andes. Taxon: Plants. Methods: We (i) conducted a literature review on Andean vegetation responses to past and contemporary climatic change, (ii) analysed future climate projections for different elevations and slope orientations at 19 Andean locations using an ensemble of model outputs from the Coupled Model Intercomparison Project 5, and (iii) calculated changes in the suitable climate envelope area of Andean biomes and compared these results to studies that used species distribution models. Results: Future climatic changes (2040–2070) are projected to be stronger at high-elevation areas in the tropical Andes (up to 4°C under RCP 8.5), while in the temperate Andes temperature increases are projected to be up to 2°C. Under this worst-case scenario, temperate deciduous forests and the grasslands/steppes from the Central and Southern Andes are predicted to show the greatest losses of suitable climatic space (30% and 17%–23%, respectively). The high vulnerability of these biomes contrasts with the low attention from researchers modelling Andean species distributions. Critical knowledge gaps include a lack of an Andean wide plant checklist, insufficient density of weather stations at high-elevation areas, a lack of high-resolution climatologies that accommodates the Andes' complex topography and climatic processes, insufficient data to model demographic and ecological processes, and low use of palaeo data for distribution modelling. Main conclusions: Climate change is likely to profoundly affect the extent and composition of Andean biomes. Temperate Andean biomes in particular are susceptible to substantial area contractions. There are, however, considerable challenges and uncertainties in modelling species and biome responses and a pressing need for a region-wide approach to address knowledge gaps and improve understanding and monitoring of climate change impacts in these globally important biomes.publishedVersio

Resource Supply Overrides Temperature as a Controlling Factor of Marine Phytoplankton Growth

The universal temperature dependence of metabolic rates has been used to predict how ocean biology will respond to ocean warming. Determining the temperature sensitivity of phytoplankton metabolism and growth is of special importance because this group of organisms is responsible for nearly half of global primary production, sustains most marine food webs, and contributes to regulate the exchange of CO2 between the ocean and the atmosphere. Phytoplankton growth rates increase with temperature under optimal growth conditions in the laboratory, but it is unclear whether the same degree of temperature dependence exists in nature, where resources are often limiting. Here we use concurrent measurements of phytoplankton biomass and carbon fixation rates in polar, temperate and tropical regions to determine the role of temperature and resource supply in controlling the large-scale variability of in situ metabolic rates. We identify a biogeographic pattern in phytoplankton metabolic rates, which increase from the oligotrophic subtropical gyres to temperate regions and then coastal waters. Variability in phytoplankton growth is driven by changes in resource supply and appears to be independent of seawater temperature. The lack of temperature sensitivity of realized phytoplankton growth is consistent with the limited applicability of Arrhenius enzymatic kinetics when substrate concentrations are low. Our results suggest that, due to widespread resource limitation in the ocean, the direct effect of sea surface warming upon phytoplankton growth and productivity may be smaller than anticipated