Report on the “Trait-based approaches to ocean life” scoping workshop, October 5-8, 2015

"Trait-based Approaches to Ocean Life” Scoping Workshop, October 5-8, 2015, Waterville Valley, NH, USAFrom the introduction: Marine ecosystems are rich and biodiverse, often populated by thousands of competing and interacting species with a vast range of behaviors, forms, and life histories. This great ecological complexity presents a formidable challenge to understanding how marine ecosystems are structured and controlled, but also how they respond to natural and anthropogenic changes. The trait-based approach to ocean life is emerging as a novel framework for understanding the complexity, structure, and dynamics of marine ecosystems, but also their broader significance. Rather than considering species individually, organisms are characterized by essential traits that capture key aspects of diversity. Trait distributions in the ocean emerge through evolution and natural selection, and are mediated by the environment, biological interactions, anthropogenic drivers, and organism behavior. Because trait variations within and across communities lead to variation in the rates of crucial ecosystem functions such as carbon export, this mechanistic approach sheds light on how variability in the environment, including climate change, impacts marine ecosystems, biogeochemical cycles, and associated feedbacks to climate and society.Funding from the National Science Foundation and National Aeronautics and Space Administration), the Simons Foundation, and the Gordon and Betty Moore Foundation

A modeling study of seasonal variations of sea ice and plankton in the Bering and Chukchi Seas during 2007–2008

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97464/1/jgrc20061.pd

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Toward bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135 μg/L across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443 nm \u3e 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications

Phytoplankton functional types from Space.

The concept of phytoplankton functional types has emerged as a useful approach to classifying phytoplankton. It finds many applications in addressing some serious contemporary issues facing science and society. Its use is not without challenges, however. As noted earlier, there is no universally-accepted set of functional types, and the types used have to be carefully selected to suit the particular problem being addressed. It is important that the sum total of all functional types matches all phytoplankton under consideration. For example, if in a biogeochemical study, we classify phytoplankton as silicifiers, calcifiers, DMS-producers and nitrogen fix- ers, then there is danger that the study may neglect phytoplankton that do not contribute in any significant way to those functions, but may nevertheless be a significant contributor to, say primary production. Such considerations often lead to the adoption of a category of “other phytoplankton” in models, with no clear defining traits assigned them, but that are nevertheless necessary to close budgets on phytoplankton processes. Since this group is a collection of all phytoplankton that defy classification according to a set of traits, it is difficult to model their physi- ological processes. Our understanding of the diverse functions of phytoplankton is still growing, and as we recognize more functions, there will be a need to balance the desire to incorporate the increasing number of functional types in models against observational challenges of identifying and mapping them adequately. Modelling approaches to dealing with increasing functional diversity have been proposed, for example, using the complex adaptive systems theory and system of infinite diversity, as in the work of Bruggemann and Kooijman (2007). But it is unlikely that remote-sensing approaches might be able to deal with anything but a few prominent functional types. As long as these challenges are explicitly addressed, the functional- type concept should continue to fill a real need to capture, in an economic fashion, the diversity in phytoplankton, and remote sensing should continue to be a useful tool to map them. Remote sensing of phytoplankton functional types is an emerging field, whose potential is not fully realised, nor its limitations clearly established. In this report, we provide an overview of progress to date, examine the advantages and limitations of various methods, and outline suggestions for further development. The overview provided in this chapter is intended to set the stage for detailed considerations of remote-sensing applications in later chapters. In the next chapter, we examine various in situ methods that exist for observing phytoplankton functional types, and how they relate to remote-sensing techniques. In the subsequent chapters, we review the theoretical and empirical bases for the existing and emerging remote-sensing approaches; assess knowledge about the limitations, assumptions, and likely accuracy or predictive skill of the approaches; provide some preliminary comparative analyses; and look towards future prospects with respect to algorithm development, validation studies, and new satellite mis- sions

Determinants of non- response to a second assessment of lifestyle factors and body weight in the EPIC-PANACEA study.

BACKGROUND: This paper discusses whether baseline demographic, socio-economic, health variables, length of follow-up and method of contacting the participants predict non-response to the invitation for a second assessment of lifestyle factors and body weight in the European multi-center EPIC-PANACEA study. METHODS: Over 500.000 participants from several centers in ten European countries recruited between 1992 and 2000 were contacted 2-11 years later to update data on lifestyle and body weight. Length of follow-up as well as the method of approaching differed between the collaborating study centers. Non-responders were compared with responders using multivariate logistic regression analyses. RESULTS: Overall response for the second assessment was high (81.6%). Compared to postal surveys, centers where the participants completed the questionnaire by phone attained a higher response. Response was also high in centers with a short follow-up period. Non-response was higher in participants who were male (odds ratio 1.09 (confidence interval 1.07; 1.11), aged under 40 years (1.96 (1.90; 2.02), living alone (1.40 (1.37; 1.43), less educated (1.35 (1.12; 1.19), of poorer health (1.33 (1.27; 1.39), reporting an unhealthy lifestyle and who had either a low (25, 1.08 (1.06; 1.10); especially ≥30 kg/m2, 1.26 (1.23; 1.29)). CONCLUSIONS: Cohort studies may enhance cohort maintenance by paying particular attention to the subgroups that are most unlikely to respond and by an active recruitment strategy using telephone interviews.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Fourier Transform Infrared Spectroscopic Imaging and Multivariate Regression for Prediction of Proteoglycan Content of Articular Cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O –stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used

Mediterranean dietary pattern and cancer risk in the EPIC cohort

Background: Although several studies have investigated the association of the Mediterranean diet with overall mortality or risk of specific cancers, data on overall cancer risk are sparse. Methods: We examined the association between adherence to Mediterranean dietary pattern and overall cancer risk using data from the European Prospective Investigation Into Cancer and nutrition, a multi-centre prospective cohort study including 142 605 men and 335 873. Adherence to Mediterranean diet was examined using a score (range: 0–9) considering the combined intake of fruits and nuts, vegetables, legumes, cereals, lipids, fish, dairy products, meat products, and alcohol. Association with cancer incidence was assessed through Cox regression modelling, controlling for potential confounders. Results: In all, 9669 incident cancers in men and 21 062 in women were identified. A lower overall cancer risk was found among individuals with greater adherence to Mediterranean diet (hazard ratio=0.96, 95% CI 0.95–0.98) for a two-point increment of the Mediterranean diet score. The apparent inverse association was stronger for smoking-related cancers than for cancers not known to be related to tobacco (P (heterogeneity)=0.008). In all, 4.7% of cancers among men and 2.4% in women would be avoided in this population if study subjects had a greater adherence to Mediterranean dietary pattern. Conclusion: Greater adherence to a Mediterranean dietary pattern could reduce overall cancer risk

Missing Links: Referrer Behavior and Job Segregation

How does referral recruitment contribute to job segregation, and what can organizations do about it? Current theory on network effects in the labor market emphasizes the job-seeker perspective, focusing on the segregated nature of job-seekers’ information and contact networks, and leaves little role for organizational influence. But employee referrals are necessarily initiated from within a firm by referrers. We argue that referrer behavior is the missing link that can help organizations manage the segregating effects of referring. Adopting the referrer’s perspective of the process, we develop a computational model which integrates a set of empirically documented referrer behavior mechanisms gleaned from extant organizational case studies. Using this model, we compare the segregating effects of referring when these behaviors are inactive to the effects when the behaviors are active. We show that referrer behaviors substantially boost the segregating effects of referring. This impact of referrer behavior presents an opportunity for organizations. Contrary to popular wisdom, we show that organizational policies designed to influence referrer behaviors can mitigate most if not all of the segregating effects of referring