Model of Habitat Characteristics of Sardinella lemuru in the Bali Strait, Indonesia

This article describes a new approach to the study of the environmental conditions that relate to the Sardinella lemuru habitat in the Bali Strait, through remote sensing data and fish catch data using the generalized additive model. Data that are acquired daily and then compiled into monthly data for sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and sea surface depth (SSD) were used for the years 2008–2010. The objectives of the study are to describe the variability of the environmental conditions in the Bali Strait, to analyze a combination model of environmental factors in estimating the Sardinella lemuru habitat, and to map potential Sardinella lemuru fishing areas. We illustrate the proposed method by constructing seven generalized additive models with catches of Sardinella lemuru as a variable response and use sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and SSD as covariant models for assessing the environmental characteristics of the abundance of Sardinella lemuru. Predicted values were validated using a linear model. Based on the three model parameters, habitat selection for Sardinella lemuru was significantly (P < 0.0001) influenced by photosynthetically available radiation (50–55 Einstein m-2 d-1), sea surface chlorophyll-a concentration (0.2–2.0 mgm-3), sea surface temperature (28.95–29.64 °C), and SSD (60–150 m). Catch predictions show a consistent trend toward environmental conditions and water depth. Our method allows for improvement with the validation of catch predictions that were observed and collected monthly, and the result was significant (P < 0.001, r2 = 0.816). Photosynthetically available radiation explains the highest deviation in continued generalized additive models; therefore, it was considered to be the best predictor of habitat, followed by sea surface chlorophyll-a concentration, sea surface temperature, and then SSD. New research results supplement several previous studies that relate to the analysis of environmental parameters in estimating the fish habitat and can be used in mapping the distribution of potential Sardinella lemuru fishing areas in spatial and temporal scales.

Seasonal and interannual variations in the propagation of photosynthetically available radiation through the Arctic atmosphere

The Arctic atmosphere–surface system transmits visible light from the Sun to the ocean, determining the annual cycle of light available to microalgae. This light is referred to as photosynthetically available radiation (PAR). A known consequence of Arctic warming is the change at the atmosphere–ocean interface (longer ice-free season, younger ice), implying an increase in the percentage of PAR being transferred to the water. However, much less is known about the recent changes in how much PAR is being transferred by the overlaying atmosphere. We studied the transfer of PAR through the atmosphere between May 21 and July 23 at a pan-Arctic scale for the period ranging from 2000 to 2016. By combining a large data set of atmospheric and surface conditions into a radiative transfer model, we computed the percentage of PAR transferred to the surface. We found that typical Arctic atmospheres convey between 60% and 70% of the incident PAR received from the Sun, meaning the Arctic atmosphere typically transmits more light than most sea ice surfaces, with the exception of mature melt ponds. We also found that the transfer of PAR through the atmosphere decreased at a rate of 2.3% per decade over the studied period, due to the increase in cloudiness and the weaker radiative interaction between the atmosphere and the surface. Further investigation is required to address how, in the warmer Arctic climate, this negative trend would compensate for the increased surface transmittance and its consequences on marine productivity.

Global distribution of photosynthetically available radiation on the seafloor

A 21 year (1998–2018) continuous monthly data set of the global distribution of light (photosynthetically available radiation, PAR, or irradiance) reaching the seabed is presented. This product uses ocean color and bathymetric data to estimate benthic irradiance, offering critical improvements on a previous data set. The time series is 4 times longer (21 versus 5 years), the spatial resolution is better (pixel size of 4.6 versus 9.3 km at the Equator), and the bathymetric resolution is also better (pixel size of 0.46 versus 3.7 km at the Equator). The paper describes the theoretical and methodological bases and data processing. This new product is used to estimate the surface area of the seafloor where (1) light does not limit the distribution of photosynthetic benthic organisms and (2) net community production is positive. The complete data set is provided as 14 netCDF files available on PANGAEA (Gentili and Gattuso, 2020a, https://doi.org/10.1594/PANGAEA.910898). The R package CoastalLight, available on GitHub (https://github.com/jpgattuso/CoastalLight.git, last access: 29 July 2020), allows us (1) to download geographical and optical data from PANGAEA and (2) to calculate the surface area that receives more than a given threshold of irradiance in three regions (nonpolar, Arctic, and Antarctic). Such surface areas can also be calculated for any subregion after downloading data from a remotely and freely accessible server.

Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats

The vertical distribution of irradiance in the ocean is a key input to quantify processes spanning from radiative warming, photosynthesis to photo-oxidation. Here we use a novel dataset of thousands local-noon downwelling irradiance at 490 nm (Ed(490)) and photosynthetically available radiation (PAR) profiles captured by 103 BGC-Argo floats spanning three years (from October 2012 to January 2016) in the world’s ocean, to evaluate several published algorithms and satellite products related to diffuse attenuation coefficient (Kd). Our results show: (1) MODIS-Aqua Kd(490) products derived from a blue-to-green algorithm and two semi-analytical algorithms show good consistency with the float-observed values, but the Chla-based one has overestimation in oligotrophic waters; (2) The Kd(PAR) model based on the Inherent Optical Properties (IOPs) performs well not only at sea-surface but also at depth, except for the oligotrophic waters where Kd(PAR) is underestimated below two penetration depth (2zpd), due to the model’s assumption of a homogeneous distribution of IOPs in the water column which is not true in most oligotrophic waters with deep chlorophyll-a maxima; (3) In addition, published algorithms for the 1% euphotic-layer depth and the depth of 0.415 mol photons m−2 d−1 isolume are evaluated. Algorithms based on Chla generally work well while IOPs-based ones exhibit an overestimation issue in stratified and oligotrophic waters, due to the underestimation of Kd(PAR) at depth.

Global distribution of photosynthetically available radiation on the seafloor

A 21-year (1998–2018) continuous monthly data set of the global distribution of light (photosynthetically available radiation; PAR) reaching the seabed is presented. It uses ocean colour and bathymetric data to estimate benthic irradiance, offering critical improvements on a previous data set (Gattuso et al., 2006). The time series is 4 times longer (21 vs 5 years), the spatial resolution is better (pixel size of 4.6 vs 9.3 km at the equator) and the bathymetric resolution is also better (pixel size of 0.46 vs 3.7 km at the equator). The paper describes the theoretical and methodological bases and data processing. This new product is used to estimate the surface area of the sea floor where (1) light does not limit the distribution of photosynthetic benthic organisms and (2) net community production is positive. The complete data set is provided as 14 netCDF files available on PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.910898). The R package CoastalLight, available on Github (https://github.com/jpgattuso/CoastalLight.git), allows (1) to download geographical and optical data from PANGAEA and (2) to calculate the surface area that receives more than a given threshold of irradiance in three regions (non polar, Arctic and Antarctic). Such surface areas can also be calculated for any sub-region after downloading data from a remotely and freely accessible server.

Primary production and associated environmental conditions in the East Siberian Sea in autumn

Spatial variability of primary production (PP) was study on vast area of East Siberian Sea in autumn 2017. Water column PP (IPP) value was equal to 28±13 mgC m-2 day-1 on average that testify ultraoligotrophic conditions. IPP was limited by low incident and underwater photosynthetically available radiation and nitrate concentration. Ammonium concentration partly compensates lack of dissolved nitrogen.

Comparison of Satellite, Model, and In Situ Values of Photosynthetically Available Radiation (PAR)

Photosynthetically available radiation (PAR) incident at the sea surface penetrates into the water column and drives oceanic primary production. Ecosystem models to estimate phytoplankton biomass and primary production require an estimate of sea surface PAR, which is available from satellite ocean color imagery and atmospheric model predictions. Because the PAR values could come from either source, it is important to understand the variability and accuracies of each. We performed spatial and temporal analyses covering multiple years and seasons, and clear/cloudy conditions. We compare values derived from the imagery to those from the models and to in situ measurements in the Gulf of Mexico to validate the imagery and models and to assess PAR variability based on source. Averaged over space or time, the relative errors in PAR between the six sources (two satellite, three model, and in situ) are generally less than 5%–7%, but they can vary up to 11%. However, the errors and biases on a daily or pixel-by-pixel basis are larger, and the averages can mask seasonal trends.