Effects of environmental variability on abundance of commercial marine species in the northern Gulf of California

2019 ◽  
Vol 83 (3) ◽  
pp. 195
Author(s):  
T. Mónica Ruiz-Barreiro ◽  
Francisco Arreguín-Sánchez ◽  
Arturo González-Baheza ◽  
Juan C. Hernández-Padilla
Keyword(s):  
Environmental Variables ◽  
Gulf Of California ◽  
Environmental Changes ◽  
Environmental Variability ◽  
Species Abundance ◽  
Marine Species ◽  
Additive Models ◽  
Catch Per Unit Effort ◽  
Cross Correlation Analysis ◽  
Stock Abundance

Studies have shown that environmental variables significantly affect variation in stock abundance of marine populations. The northern Gulf of California (NGC) is a highly productive region of interest due to its fish resources and diversity. Conservation of the marine species inhabiting the region is of public interest. Our study analysed the influence of physical environmental factors on several commercial marine species, using catch per unit effort (CPUE) as a proxy for abundance. Generalized additive models were used to test the significance of selected environmental variables on stock abundance. Deseasonalized cross-correlation analysis was used to examine time-lagged correlations between CPUE and abiotic variables to identify response timings. The results suggest that for most commercial species the sea surface temperature and the long-term climate Pacific Decadal Oscillation index are the predominant predictors for species abundance, followed by the Colorado River discharge. The Multivariate ENSO Index and the Pacific-North American pattern indices also showed specific effects on certain species. The NGC is a highly dynamic region, where species respond to environmental changes according to the characteristics of their life histories.

scholarly journals Monitoring studies should consider temporal variability to reveal relations between cyanobacterial abundance and environmental variables

2015 ◽  
Vol 87 (3) ◽  
pp. 1717-1726 ◽  
Author(s):  
JULIANA WOJCIECHOWSKI ◽  
ANDRÉ A. PADIAL
Keyword(s):  
Temporal Variability ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Environmental Variability ◽  
Time Lag ◽  
Cyanobacteria Blooms ◽  
Environment Variables ◽  
Sampling Points ◽  
The Relationship

One of the main goals of monitoring cyanobacteria blooms in aquatic environments is to reveal the relationship between cyanobacterial abundance and environmental variables. Studies typically correlate data that were simultaneously sampled. However, samplings occur sparsely over time and may not reveal the short-term responses of cyanobacterial abundance to environmental changes. In this study, we tested the hypothesis that stronger cyanobacteria x environment relationships in monitoring are found when the temporal variability of sampling points is incorporated in the statistical analyses. To this end, we investigated relationships between cyanobacteria and seven environmental variables that were sampled twice yearly for three years across 11 reservoirs, and data from an intensive monitoring in one of these reservoirs. Poor correlations were obtained when correlating data simultaneously sampled. In fact, the 'highly recurrent' role of phosphorus in cyanobacteria blooms is not properly observed in all sampling periods. On the other hand, the strongest correlation values for the total phosphorus x cyanobacteria relationship were observed when we used the variation of sampling points. We have also shown that environment variables better explain cyanobacteria when a time lag is considered. We conclude that, in cyanobacteria monitoring, the best approach to reveal determinants of cyanobacteria blooms is to consider environmental variability.

Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia

2020 ◽  
Vol 71 (4) ◽  
pp. 542
Author(s):  
Karina L. Ryan ◽  
Denny Meyer
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Environmental Variability ◽  
Moving Average ◽  
Commercial Fishing ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Explanatory Variables ◽  
Unit Effort ◽  
Stock Abundance

Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.

scholarly journals Using extreme gradient boosting (XGBoost) to evaluate the importance of a suite of environmental variables and to predict recruitment of young-of-the-year spotted seatrout in Florida

2019 ◽  
Author(s):  
Elizabeth Herdter Smith
Keyword(s):  
Environmental Factors ◽  
Environmental Variables ◽  
Environmental Variability ◽  
Juvenile Fish ◽  
Species Abundance ◽  
Spotted Seatrout ◽  
Gradient Boosting ◽  
Future Research ◽  
Environmental Predictors ◽  
Extreme Gradient Boosting

AbstractEnvironmental factors strongly influence the success of juvenile fish recruitment and productivity, but species-specific environment-recruitment relationships have eluded researchers for decades. Most likely, this is because the environment-recruitment relationship is nonlinear, there are multi-level interactions between factors, and environmental variability may differentially affect recruitment among populations due to spatial heterogeneity. Identifying the most influential environmental variables may result in more accurate predictions of future recruitment and productivity of managed species. Here, gradient tree boosting was implemented using XGBoost to identify the most important predictors of recruitment for six estuary populations of spotted seatrout (Cynoscion nebulosus), an economically valuable marine resource in Florida. XGBoost, a machine learning method for regression and classification, was employed because it inherently models variable interactions and seamlessly deals with multi-collinearity, both of which are common features of ecological datasets. Additionally, XGBoost operates at a speed faster than many other gradient boosting algorithms due to a regularization factor and parallel computing functionality. In this application of XGBoost, the results indicate that the abundance of pre-recruit, juvenile spotted seatrout in spatially distinct estuaries is influenced by nearly the same set of environmental predictors. But perhaps of greater importance is that the results of this study show that this algorithm is highly effective at predicting species abundance and identifying important environmental factors (i.e. predictors of recruitment). It is strongly encouraged that future research explore the applicability of the XGBoost algorithm to other topics in marine and fisheries science and compare its performance to that of other statistical methods.

scholarly journals Collection of Environmental Variables and Bacterial Community Compositions in Marian Cove, Antarctica, during Summer 2018

Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 27
Author(s):  
Hyo-Ryeon Kim ◽  
Jae-Hyun Lim ◽  
Ju-Hyoung Kim ◽  
Il-Nam Kim
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Marine Bacteria ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Bacterial Community Composition ◽  
Composition Data ◽  
Decomposition Of Organic Matter ◽  
Class Level ◽  
Earth’S Climate

Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.

scholarly journals Spatio-temporal associations of albacore CPUEs in the Northeastern Pacific with regional SST and climate environmental variables

2014 ◽  
Vol 71 (7) ◽  
pp. 1717-1727 ◽  
Author(s):  
A. Jason Phillips ◽  
Lorenzo Ciannelli ◽  
Richard D. Brodeur ◽  
William G. Pearcy ◽  
John Childers
Keyword(s):  
North Pacific ◽  
Large Scale ◽  
Environmental Variability ◽  
Southern Oscillation ◽  
Spatial Dynamics ◽  
Catch Per Unit Effort ◽  
Variable Effect ◽  
Positive Effects ◽  
The North ◽  
Additive Mixed Models

Abstract This study investigated the spatial distribution of juvenile North Pacific albacore (Thunnus alalunga) in relation to local environmental variability [i.e. sea surface temperature (SST)], and two large-scale indices of climate variability, [the Pacific Decadal Oscillation (PDO) and the Multivariate El Niño/Southern Oscillation Index (MEI)]. Changes in local and climate variables were correlated with 48 years of albacore troll catch per unit effort (CPUE) in 1° latitude/longitude cells, using threshold Generalized Additive Mixed Models (tGAMMs). Model terms were included to account for non-stationary and spatially variable effects of the intervening covariates on albacore CPUE. Results indicate that SST had a positive and spatially variable effect on albacore CPUE, with increasingly positive effects to the North, while PDO had an overall negative effect. Although albacore CPUE increased with SST both before and after a threshold year of 1986, such effect geographically shifted north after 1986. This is the first study to demonstrate the non-stationary spatial dynamics of albacore tuna, linked with a major shift of the North Pacific. Results imply that if ocean temperatures continue to increase, US west coast fisher communities reliant on commercial albacore fisheries are likely to be negatively affected in the southern areas but positively affected in the northern areas, where current albacore landings are highest.

scholarly journals Conservation Status of Marine Biodiversity in Oceania: An Analysis of Marine Species on the IUCN Red List of Threatened Species

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Beth A. Polidoro ◽  
Cristiane T. Elfes ◽  
Jonnell C. Sanciangco ◽  
Helen Pippard ◽  
Kent E. Carpenter
Keyword(s):  
Threatened Species ◽  
Environmental Changes ◽  
Extinction Risk ◽  
Conservation Status ◽  
Marine Species ◽  
Iucn Red List ◽  
Red List ◽  
Marine Biodiversity ◽  
Conservation Priorities ◽  
Conservation Action

Given the economic and cultural dependence on the marine environment in Oceania and a rapidly expanding human population, many marine species populations are in decline and may be vulnerable to extinction from a number of local and regional threats. IUCN Red List assessments, a widely used system for quantifying threats to species and assessing species extinction risk, have been completed for 1190 marine species in Oceania to date, including all known species of corals, mangroves, seagrasses, sea snakes, marine mammals, sea birds, sea turtles, sharks, and rays present in Oceania, plus all species in five important perciform fish groups. Many of the species in these groups are threatened by the modification or destruction of coastal habitats, overfishing from direct or indirect exploitation, pollution, and other ecological or environmental changes associated with climate change. Spatial analyses of threatened species highlight priority areas for both site- and species-specific conservation action. Although increased knowledge and use of newly available IUCN Red List assessments for marine species can greatly improve conservation priorities for marine species in Oceania, many important fish groups are still in urgent need of assessment.

Climate and environmental context of the Mongol invasion of Syria and defeat at ‘Ayn Jālūt (1258–1260 CE)

Erdkunde ◽  
2021 ◽  
Vol 75 (2) ◽  
pp. 87-104
Author(s):  
Nicola Di Cosmo ◽  
Sebastian Wagner ◽  
Ulf Büntgen
Keyword(s):  
Strategic Alliances ◽  
Turning Point ◽  
Environmental Changes ◽  
Environmental Variability ◽  
State Of The Art ◽  
Eastern Mediterranean ◽  
The Caucasus ◽  
Mountainous Regions ◽  
The Past ◽  
Climate Reconstructions

After a successful conquest of large parts of Syria in 1258 and 1259 CE, the Mongol army lost the battle of 'Ayn Jālūt against Mamluks on September 3, 1260 CE. Recognized as a turning point in world history, their sudden defeat triggered the reconfiguration of strategic alliances and geopolitical power not only in the Middle East, but also across much of Eurasia. Despite decades of research, scholars have not yet reached consensus over the causes of the Mongol reverse. Here, we revisit previous arguments in light of climate and environmental changes in the aftermath of one the largest volcanic forcings in the past 2500 years, the Samalas eruption ~1257 CE. Regional tree ring-based climate reconstructions and state-of-the-art Earth System Model simulations reveal cooler and wetter conditions from spring 1258 to autumn 1259 CE for the eastern Mediterranean/Arabian region. We therefore hypothesize that the post-Samalas climate anomaly and associated environmental variability affected an estimated 120,000 Mongol soldiers and up to half a million of their horses during the conquest. More specifically, we argue that colder and wetter climates in 1258 and 1259 CE, while complicating and slowing the campaign in certain areas, such as the mountainous regions in the Caucasus and Anatolia, also facilitated the assault on Syria between January and March 1260. A return to warmer and dryer conditions in the summer of 1260 CE, however, likely reduced the regional carrying capacity and may therefore have forced a mass withdrawal of the Mongols from the region that contributed to the Mamluks’ victory. In pointing to a distinct environmental dependency of the Mongols, we offer a new explanation of their defeat at 'Ayn Jālūt, which effectively halted the further expansion of the largest ever land-based empire.

scholarly journals Distribution Patterns of Odonate Assemblages in Relation to Environmental Variables in Streams of South Korea

Insects ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 152 ◽  
Author(s):  
Da-Yeong Lee ◽  
Dae-Seong Lee ◽  
Mi-Jung Bae ◽  
Soon-Jin Hwang ◽  
Seong-Yu Noh ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Environmental Factors ◽  
South Korea ◽  
Habitat Heterogeneity ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Distribution Patterns ◽  
Self Organizing Map ◽  
Factors Affecting

Odonata species are sensitive to environmental changes, particularly those caused by humans, and provide valuable ecosystem services as intermediate predators in food webs. We aimed: (i) to investigate the distribution patterns of Odonata in streams on a nationwide scale across South Korea; (ii) to evaluate the relationships between the distribution patterns of odonates and their environmental conditions; and (iii) to identify indicator species and the most significant environmental factors affecting their distributions. Samples were collected from 965 sampling sites in streams across South Korea. We also measured 34 environmental variables grouped into six categories: geography, meteorology, land use, substrate composition, hydrology, and physicochemistry. A total of 83 taxa belonging to 10 families of Odonata were recorded in the dataset. Among them, eight species displayed high abundances and incidences. Self-organizing map (SOM) classified sampling sites into seven clusters (A–G) which could be divided into two distinct groups (A–C and D–G) according to the similarities of their odonate assemblages. Clusters A–C were characterized by members of the suborder Anisoptera, whereas clusters D–G were characterized by the suborder Zygoptera. Non-metric multidimensional scaling (NMDS) identified forest (%), altitude, and cobble (%) in substrata as the most influential environmental factors determining odonate assemblage compositions. Our results emphasize the importance of habitat heterogeneity by demonstrating its effect on odonate assemblages.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Spatiotemporal modelling for the annual egg production method of stock assessment using generalized additive models

1998 ◽  
Vol 55 (12) ◽  
pp. 2608-2621 ◽  
Author(s):  
N H Augustin ◽  
D L Borchers ◽  
E D Clarke ◽  
S T Buckland ◽  
M Walsh
Keyword(s):  
Environmental Variables ◽  
Egg Production ◽  
Survey Design ◽  
Stock Assessment ◽  
Generalized Additive Models ◽  
Production Method ◽  
Additive Models ◽  
Atlantic Mackerel ◽  
Egg Density ◽  
Survey Designs

Generalized additive models (GAMs) are used to model the spatiotemporal distribution of egg density as a function of locational and environmental variables. The main aim of using GAMs is to improve precision of egg abundance estimates needed for the annual egg production method. The application of GAMs requires a survey design with good coverage in space and time. If the only results available are from less optimal survey designs, they can be improved by using historical data for spawning boundaries. The method is applied to plankton egg survey data of Atlantic mackerel (Scomber scombrus) in 1995. The GAM-based method improves the precision of estimates substantially and is also useful in explaining complex space-time trends using environmental variables.

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