A Critical Analysis of Bumblefoot: Care and Preventative Measures in Captive Penguins

Bumblefoot is a progressive and sometimes deadly infection that afflicts penguins living in human care. The most prominent cause of the disease is the extended amount of time that captive penguins spend standing in comparison to their pelagic and wild counterparts. For years, facilities have treated bumblefoot with surgery and antibiotics. However, this approach is palliative rather than preventative and has become problematic as bacteria develop stronger resistance to antibiotics. To address the behavioral abnormalities underlying the onset of bumblefoot, zoos and aquariums should utilize environmental enrichment. Many forms of environmental enrichment, including the relationship penguins have with their keepers and colored balls or rings, may prove effective at encouraging these aquatic birds to spend more time in the water. However, few studies have worked to determine the effectiveness of environmental enrichment as a preventative measure for bumblefoot. When used in addition to behavioral husbandry, or the training associated with achieving the voluntarily participation of penguins in their daily care, environmental enrichment may be the key to eradicating bumblefoot from the lives of captive penguins while also providing them with a more stimulating and healthier environment

Radiative transfer modeling through terrestrial atmosphere and ocean accounting for inelastic scattering processes: Software package SCIATRAN.

SCIATRAN is a comprehensive software package which is designed to model radiative transfer processes in the terrestrial atmosphere and ocean in the spectral range from the ultraviolet to the thermal infrared (0.18–40 μm). It accounts for multiple scattering processes, polarization, thermal emission and ocean–atmosphere coupling. The main goal of this paper is to present a recently developed version of SCIATRAN which takes into account accurately inelastic radiative processes in both the atmosphere and the ocean. In the scalar version of the coupled ocean–atmosphere radiative transfer solver presented by Rozanov et al. [61] we have implemented the simulation of the rotational Raman scattering, vibrational Raman scattering, chlorophyll and colored dissolved organic matter fluorescence. In this paper we discuss and explain the numerical methods used in SCIATRAN to solve the scalar radiative transfer equation including trans-spectral processes, and demonstrate how some selected radiative transfer problems are solved using the SCIATRAN package. In addition we present selected comparisons of SCIATRAN simulations with those published benchmark results, independent radiative transfer models, and various measurements from satellite, ground-based, and ship-borne instruments. The extended SCIATRAN software package along with a detailed User's Guide is made available for scientists and students, who are undertaking their own research typically at universities, via the web page of the Institute of Environmental Physics (IUP), University of Bremen: http://www.iup.physik.uni-bremen.de

Investigation of spectral band requirements for improving retrievals of phytoplankton functional types.

Studying phytoplankton functional types (PFTs) from space is possible due to recent advances in remote sensing. Though a variety of products are available, the limited number of wavelengths available compared to the number of model parameters needed to be retrieved is still a major problem in using ocean-color data for PFT retrievals. Here, we investigated which band placement could improve retrievals of three particular PFTs (diatoms, coccolithophores and cyanobacteria). In addition to analyzing dominant spectral features in the absorption spectra of the target PFTs, two previously-developed methods using measured spectra were applied to simulated data. Such a synthetic dataset allowed for significantly increasing the number of scenarios and enabled a full control over parameters causing spectral changes. We evaluated the chosen band placement by applying an adapted ocean reflectance inversion, as utilized in the generalized inherent optical properties (GIOP) retrieval. Results show that the optimal band settings depend on the method applied to determine the bands placement, as well as on the internal variability of the dataset investigated. Therefore, continuous hyperspectral instruments would be most beneficial for discriminating multiple PFTs, though a small improvement in spectral sampling and resolution does not significantly modify the results. Bands, which could be added to future instruments (e.g., Ocean and Land Colour Instrument (OLCI) instrument on the upcoming Sentinel-3B,-3C,-3D, etc., and further satellites) in order to enhance PFT retrieval capabilities, were also determined

Synergistic exploitation of hyper- and multispectral Sentinel measurements to determine Phytoplankton Functional Types at best spatial and temporal resolution (SynSenPFT)

We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) – diatoms, coccolithophores and cyanobacteria – by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted

Corrigendum: Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT) [Front. Mar. Sci,(203),4] DOI: 10.3389/fmars.2017.00203

This is the final version. Available on open access from Frontiers Media via the DOI in this recordThe article to which this is the corrigendum is in ORE at http://hdl.handle.net/10871/38250In the original article, we neglected, but would like to acknowledge the North-German Supercomputing Alliance (HLRN) for providing HPC resources that have contributed to the research results reported in this paper. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way

Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT)

This is the final version. Available from Frontiers Media via the DOI in this record.The corrigendum to this article is in ORE at http://hdl.handle.net/10871/38256We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) - diatoms, coccolithophores and cyanobacteria - by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.ESA SEOM SY-4Sci Synergy projectSFB/TR 172 (AC)3 “Arctic Amplification” subproject C03DFG-Priority Program SPP 1158 “Antarktis” PhySyn BU2913/3-1Helmholtz Climate Initiative REKLIMHelmholtz Association of German Research Centres (HGF

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis

High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR&thinsp;≦&thinsp;GPP &lt; SIF &lt; VIs∕VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models.</p

Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development

This is the final version. Available from Frontiers Media via the DOI in this record.To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.ESA SEOM SY-4Sci Synergy projectNAS