scholarly journals Vertical niche definition of test-bearing protists (Rhizaria) into the twilight zone revealed by in situ imaging

2019 ◽  
Author(s):  
Tristan Biard ◽  
Mark D. Ohman
Keyword(s):  
Generalized Additive Models ◽  
Biotic Interactions ◽  
Euphotic Zone ◽  
Important Variable ◽  
Additive Models ◽  
Ecological Niches ◽  
Twilight Zone ◽  
Biogeochemical Models ◽  
In Situ Imaging

AbstractThe Rhizaria is a super-group of ameoboid protists with ubiquitous distributions, from the euphotic zone to the twilight zone and beyond. While rhizarians have been recently described as important contributors to both silica and carbon fluxes, we lack the most basic information about their ecological preferences. Here, using the in situ imaging (Underwater Vision Profiler 5), we characterize the vertical ecological niches of different test-bearing rhizarian taxa in the southernCalifornia Current Ecosystem. We define three vertical layers between 0-500 m occupied, respectively, by 1) surface dwelling and mostly symbiont-bearing rhizarians (Acantharia and Collodaria), 2) flux-feeding phaeodarians in the lower epipelagic (100-200 m), and 3) Foraminifera and Phaeodaria populations adjacent to the Oxygen Minimum Zone. We then use Generalized Additive Models to analyze the response of each rhizarian category to a suite of environmental variables. The models explain between 13 and 93% of the total variance observed for the different groups. While temperature and the depth of the deep chlorophyll maximum, appear as the main factors influencing populations in the upper 200 m, silicic acid concentration is the most important variable related to the abundance of mesopelagic phaeodarians. The relative importance of biotic interactions (e.g., predation, parasitism) is still to be considered, in order to fully incorporate the dynamics of test-bearing pelagic rhizarians in ecological and biogeochemical models.

scholarly journals Widespread nitrous oxide undersaturation in farm waterbodies creates an unexpected greenhouse gas sink

2019 ◽  
Vol 116 (20) ◽  
pp. 9814-9819 ◽  
Author(s):  
Jackie R. Webb ◽  
Nicole M. Hayes ◽  
Gavin L. Simpson ◽  
Peter R. Leavitt ◽  
Helen M. Baulch ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Inorganic Nitrogen ◽  
Generalized Additive Models ◽  
Regional Analysis ◽  
Freshwater Ecosystems ◽  
Agricultural Landscapes ◽  
Additive Models ◽  
Water Column Stability ◽  
Flowing Waters

Nitrogen pollution and global eutrophication are predicted to increase nitrous oxide (N2O) emissions from freshwater ecosystems. Surface waters within agricultural landscapes experience the full impact of these pressures and can contribute substantially to total landscape N2O emissions. However, N2O measurements to date have focused on flowing waters. Small artificial waterbodies remain greatly understudied in the context of agricultural N2O emissions. This study provides a regional analysis of N2O measurements in small (<0.01 km2) artificial reservoirs, of which an estimated 16 million exist globally. We show that 67% of reservoirs were N2O sinks (−12 to −2 μmol N2O⋅m−2⋅d−1) in Canada’s largest agricultural area, despite their highly eutrophic status [99 ± 289 µg⋅L−1 chlorophyll-a (Chl-a)]. Generalized additive models indicated that in situ N2O concentrations were strongly and nonlinearly related to stratification strength and dissolved inorganic nitrogen content, with the lowest N2O levels under conditions of strong water column stability and high algal biomass. Predicted fluxes from previously published models based on lakes, reservoirs, and agricultural waters overestimated measured fluxes on average by 7- to 33-fold, challenging the widely held view that eutrophic N-enriched waters are sources of N2O.

Fine-scale horizontal distributions of multiple species of larval tuna off the Nansei Islands, Japan

2020 ◽  
Vol 636 ◽  
pp. 123-137 ◽  
Author(s):  
A Tawa ◽  
T Kodama ◽  
K Sakuma ◽  
T Ishihara ◽  
S Ohshimo
Keyword(s):  
Feeding Habits ◽  
Generalized Additive Models ◽  
Kuroshio Current ◽  
Additive Models ◽  
Sea Surface ◽  
Height Anomaly ◽  
Ecological Niches ◽  
Thunnus Orientalis ◽  
Nansei Islands ◽  
The Kuroshio

To quantitatively evaluate the distribution of tuna larvae relative to oceanographic conditions, we conducted investigations off the Nansei Islands in the western North Pacific in June from 2015 to 2017. Five species, namely Pacific bluefin tuna Thunnus orientalis (PBF), yellowfin tuna T. albacares (YFT), skipjack tuna Katsuwonus pelamis (SKJ), frigate tuna Auxis thazard, and bullet tuna A. rochei (BT), were collected in each year. The most dominant species was BT throughout the 3 yr period, followed by SKJ in 2015 and YFT in 2016 and 2017. The horizontal larval distributions of the 5 species were largely influenced by the Kuroshio Current: larvae of the 2 Auxis species were distributed in the Kuroshio and the Kuroshio inshore waters, whereas those of the other species were found in the Kuroshio offshore waters. These differences are consistent with the differences in spawner distributions among the tunas. Generalized additive models (GAMs) indicated that the larval densities were affected by the sea surface height anomaly and that the larvae were not always amassed by horizontal transport. Sea surface temperature (SST) and salinity possibly influenced the larval physiology and survival, thereby determining their densities. In the GAMs, PBF and YFT showed similar responses to SST, and YFT and SKJ similarly responded to salinity. To avoid overlapping their ecological niches, the larvae of 3 species (PBF, YFT, and SKJ) are expected to differ in other ways, including their vertical distributions and feeding habits.

Development of in situ Imaging Probe for Surgical Operation of Deep Brain Stimulation

2011 ◽  
Vol 131 (12) ◽  
pp. 427-428
Author(s):  
Toshihiko Noda ◽  
Pan Yi-Li ◽  
Ayato Tagawa ◽  
Takuma Kobayashi ◽  
Kiyotaka Sasagawa ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Surgical Operation ◽  
Imaging Probe ◽  
Deep Brain ◽  
In Situ Imaging

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

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