scholarly journals Comparison of the spatiotemporal distribution of three flatfish species in the Seine estuary nursery grounds

2021 ◽  
pp. 107471
Author(s):  
Thibault Cariou ◽  
Laurent Dubroca ◽  
Camille Vogel ◽  
Nicolas Bez
Keyword(s):  
Spatiotemporal Distribution ◽  
Seine Estuary ◽  
Nursery Grounds ◽  
Flatfish Species

Random isn't real: How the patchy distribution of ecological rewards may generate “incentive hope”

2019 ◽  
Vol 42 ◽  
Author(s):  
Laurel Symes ◽  
Thalia Wheatley
Keyword(s):  
Natural Environment ◽  
Random Distribution ◽  
Spatiotemporal Distribution ◽  
Patchy Distribution ◽  
Distribution Of Resources

AbstractAnselme & Güntürkün generate exciting new insights by integrating two disparate fields to explain why uncertain rewards produce strong motivational effects. Their conclusions are developed in a framework that assumes a random distribution of resources, uncommon in the natural environment. We argue that, by considering a realistically clumped spatiotemporal distribution of resources, their conclusions will be stronger and more complete.

Spatiotemporal distribution of foraging in a marine predator: behavioural drivers of hotspot formation

2020 ◽  
Vol 635 ◽  
pp. 187-202
Author(s):  
T Brough ◽  
W Rayment ◽  
E Slooten ◽  
S Dawson
Keyword(s):  
Time Of Day ◽  
Spatiotemporal Distribution ◽  
Temporal Effects ◽  
Marine Predator ◽  
Marine Predators ◽  
Additive Mixed Models ◽  
Density Analysis ◽  
The Times ◽  
Passive Acoustic ◽  
Spatial Locations

Many species of marine predators display defined hotspots in their distribution, although the reasons why this happens are not well understood in some species. Understanding whether hotspots are used for certain behaviours provides insights into the importance of these areas for the predators’ ecology and population viability. In this study, we investigated the spatiotemporal distribution of foraging behaviour in Hector’s dolphin Cephalorhynchus hectori, a small, endangered species from New Zealand. Passive acoustic monitoring of foraging ‘buzzes’ was carried out at 4 hotspots and 6 lower-use, ‘reference areas’, chosen randomly based on a previous density analysis of visual sightings. The distribution of buzzes was modelled among spatial locations and on 3 temporal scales (season, time of day, tidal state) with generalised additive mixed models using 82000 h of monitoring data. Foraging rates were significantly influenced by all 3 temporal effects, with substantial variation in the importance and nature of each effect among locations. The complexity of the temporal effects on foraging is likely due to the patchy nature of prey distributions and shows how foraging is highly variable at fine scales. Foraging rates were highest at the hotspots, suggesting that feeding opportunities shape fine-scale distribution in Hector’s dolphin. Foraging can be disrupted by anthropogenic influences. Thus, information from this study can be used to manage threats to this vital behaviour in the locations and at the times where it is most prevalent.

scholarly journals Large-scale sequencing of flatfish genomes provides insights into the polyphyletic origin of their specialized body plan

2021 ◽  
Author(s):  
Zhenming Lü ◽  
Li Gong ◽  
Yandong Ren ◽  
Yongjiu Chen ◽  
Zhongkai Wang ◽  
...  
Keyword(s):  
Retinoic Acid ◽  
Large Scale ◽  
Body Plan ◽  
Monophyletic Group ◽  
Transcriptomic Data ◽  
Flatfish Species ◽  
Genetic Origins

AbstractThe evolutionary and genetic origins of the specialized body plan of flatfish are largely unclear. We analyzed the genomes of 11 flatfish species representing 9 of the 14 Pleuronectiforme families and conclude that Pleuronectoidei and Psettodoidei do not form a monophyletic group, suggesting independent origins from different percoid ancestors. Genomic and transcriptomic data indicate that genes related to WNT and retinoic acid pathways, hampered musculature and reduced lipids might have functioned in the evolution of the specialized body plan of Pleuronectoidei. Evolution of Psettodoidei involved similar but not identical genes. Our work provides valuable resources and insights for understanding the genetic origins of the unusual body plan of flatfishes.

scholarly journals A Machine Learning Method for Predicting Vegetation Indices in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1147
Author(s):  
Xiangqian Li ◽  
Wenping Yuan ◽  
Wenjie Dong
Keyword(s):  
Machine Learning ◽  
Growing Season ◽  
Crop Growth ◽  
Spatiotemporal Distribution ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Severe Drought ◽  
Vegetation Growth ◽  
Extreme Gradient Boosting ◽  
Boosting Method

To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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