scholarly journals Zooplankton abundance trends and patterns in Shelikof Strait, western Gulf of Alaska, USA, 1990–2017

2020 ◽  
Vol 42 (3) ◽  
pp. 334-354 ◽  
Author(s):  
David G Kimmel ◽  
Janet T Duffy-Anderson
Keyword(s):  
Time Series ◽  
Generalized Additive Models ◽  
Zooplankton Community ◽  
Strong Relationship ◽  
Gulf Of Alaska ◽  
Additive Models ◽  
Environmental Data ◽  
Dynamic Factor ◽  
Zooplankton Abundance ◽  
Underlying Trend

Abstract A multivariate approach was used to analyze spring zooplankton abundance in Shelikof Strait, western Gulf of Alaska. abundance of individual zooplankton taxa was related to environmental variables using generalized additive models. The most important variables that correlated with zooplankton abundance were water temperature, salinity and ordinal day (day of year when sample was collected). A long-term increase in abundance was found for the calanoid copepod Calanus pacificus, copepodite stage 5 (C5). A dynamic factor analysis (DFA) indicated one underlying trend in the multivariate environmental data that related to phases of the Pacific Decadal Oscillation. DFA of zooplankton time series also indicated one underlying trend where the positive phase was characterized by increases in the abundance of C. marshallae C5, C. pacificus C5, Eucalanus bungii C4, Pseudocalanus spp. C5 and Limacina helicina and declines in the abundance of Neocalanus cristatus C4 and Neocalanus spp. C4. The environmental and zooplankton DFA trends were not correlated over the length of the entire time period; however, the two time series were correlated post-2004. The strong relationship between environmental conditions, zooplankton abundance and time of sampling suggests that continued warming in the region may lead to changes in zooplankton community composition and timing of life history events during spring.

scholarly journals Cycles, trends, and residual variation in the Iberian sardine (Sardina pilchardus) recruitment series and their relationship with the environment

2011 ◽  
Vol 69 (5) ◽  
pp. 739-750 ◽  
Author(s):  
M. Begoña Santos ◽  
Rafael González-Quirós ◽  
Isabel Riveiro ◽  
José M. Cabanas ◽  
Carmela Porteiro ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Generalized Additive Models ◽  
Causal Link ◽  
Additive Models ◽  
Dynamic Factor ◽  
Sardina Pilchardus ◽  
Residual Variation ◽  
Stock Biomass ◽  
Spawning Stock ◽  
Short Time

Abstract Santos, M. B., González-Quirós, R., Riveiro, I., Cabanas, J. M., Porteiro, C., and Pierce, G. J. 2012. Cycles, trends, and residual variation in the Iberian sardine (Sardina pilchardus) recruitment series and their relationship with the environment. – ICES Journal of Marine Science, 69: 739–750. Recruitment variability is an important component of the dynamics of Iberian sardine (Sardine pilchardus). Since 2006, poor recruitment has led to a decrease in stock biomass, the latest in a series of such crises for sardine fisheries. Understanding the mechanisms behind recruitment fluctuations has been the objective of many previous studies, and various relationships between recruitment and environmental variables have been proposed. However, such studies face several analytical challenges, including short time-series and autocorrelated data. A new analysis of empirical relationships with environmental series is presented, using statistical methods designed to cope with these issues, including dynamic factor analysis, generalized additive models, and mixed models. Relationships are identified between recruitment and global (number of sunspots), regional (NAOAutumn), and local [winter wind strength, sea surface temperature (SST), and upwelling] environmental variables. Separating these series into trend and noise components permitted further investigation of the nature of the relationships. Whereas the other three environmental variables were related to the trend in recruitment, SST was related to residual variation around the trend, providing stronger evidence for a causal link, possible mechanisms for which are discussed. After the removal of trend and cyclic components, residual variation in recruitment is also weakly related to the previous year's spawning-stock biomass.

scholarly journals Food or physics: plankton communities structured across Gulf of Alaska eddies

2021 ◽  
Author(s):  
Caitlin Kroeger ◽  
Chelle Gentemann ◽  
Marisol García-Reyes ◽  
Sonia Batten ◽  
William Sydeman
Keyword(s):  
Generalized Additive Models ◽  
Life Stage ◽  
Mesoscale Eddies ◽  
Gulf Of Alaska ◽  
Additive Models ◽  
Phytoplankton Distribution ◽  
Center Edge ◽  
Eddy Dynamics ◽  
Anticyclonic Eddies ◽  
Plankton Communities

Oceanic features, such as mesoscale eddies that entrap and transport water masses, create heterogeneous seascapes to which biological communities may respond. To date, however, our understanding of how internal eddy dynamics influence plankton community structuring is limited by sparse sampling of eddies and their associated biotic communities. In this paper, we used 10 years of archived Continuous Plankton Recorder (CPR) data (2002-2013) associated with 9 mesoscale eddies in the Northeast Pacific/Gulf of Alaska to test the hypothesis that eddy origin and rotational direction determines the structure and dynamics of entrained plankton communities. Using generalized additive models and accounting for confounding factors (e.g., timing of sampling), we found peak diatom abundance within both cyclonic and anticyclonic eddies near the eddy edge. Zooplankton abundances, however, varied with distance to the eddy center/edge by rotational type and eddy life stage, and differed by taxonomic group. For example, the greatest abundance of small copepods was found near the center of anticyclonic eddies during eddy maturation and decay, but near the edge of cyclonic eddies during eddy formation and intensification. Distributions of copepod abundances across eddy surfaces were not mediated by phytoplankton distribution. Our results therefore suggest that physical mechanisms such as internal eddy dynamics exert a direct impact on the structure of zooplankton communities rather than indirect mechanisms involving potential food resources.

scholarly journals Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models

2020 ◽  
Vol 12 (10) ◽  
pp. 4006
Author(s):  
Fhumulani Mathivha ◽  
Caston Sigauke ◽  
Hector Chikoore ◽  
John Odiyo
Keyword(s):  
South Africa ◽  
Time Series ◽  
Water Resource Management ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Drought Risk ◽  
Drought Forecasting ◽  
Medium Term ◽  
Drought Conditions ◽  
Semi Arid

Forecasting extreme hydrological events is critical for drought risk and efficient water resource management in semi-arid environments that are prone to natural hazards. This study aimed at forecasting drought conditions in a semi-arid region in north-eastern South Africa. The Standardized Precipitation Evaporation Index (SPEI) was used as a drought-quantifying parameter. Data for SPEI formulation for eight weather stations were obtained from South Africa Weather Services. Forecasting of the SPEI was achieved by using Generalized Additive Models (GAMs) at 1, 6, and 12 month timescales. Time series decomposition was done to reduce time series complexities, and variable selection was done using Lasso. Mild drought conditions were found to be more prevalent in the study area compared to other drought categories. Four models were developed to forecast drought in the Luvuvhu River Catchment (i.e., GAM, Ensemble Empirical Mode Decomposition (EEMD)-GAM, EEMD-Autoregressive Integrated Moving Average (ARIMA)-GAM, and Forecast Quantile Regression Averaging (fQRA)). At the first two timescales, fQRA forecasted the test data better than the other models, while GAMs were best at the 12 month timescale. Root Mean Square Error values of 0.0599, 0.2609, and 0.1809 were shown by fQRA and GAM at the 1, 6, and 12 month timescales, respectively. The study findings demonstrated the strength of GAMs in short- and medium-term drought forecasting.

scholarly journals Testing the ability of species distribution models to infer variable importance

2019 ◽  
Author(s):  
Adam B. Smith ◽  
Maria J. Santos
Keyword(s):  
Predictive Accuracy ◽  
Permutation Test ◽  
Generalized Additive Models ◽  
Simulated Data ◽  
Variable Importance ◽  
Additive Models ◽  
Environmental Data ◽  
Boosted Regression Trees ◽  
Distribution Models ◽  
Model Algorithm

AbstractModels of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an “omniscient” model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent, and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the Continuous Boyce Index. In scenarios with one influential and one uninfluential variable, models were unable to discriminate reliably between variables in conditions that are normally challenging for generating accurate predictions: training occurrences <8-64; prevalence >0.5; small spatial extent; environmental data with coarse resolution when spatial autocorrelation is low; and correlation between environmental variables where |r| >0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent’s change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.

scholarly journals Changes in Planktivory and Herbivory Regimes in a Shallow South American Lake (Lake Blanca Chica, Argentina) Over the Last 250 Years

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 597
Author(s):  
David Carrozzo ◽  
Simona Musazzi ◽  
Andrea Lami ◽  
Francisco E. Córdoba ◽  
María de los Ángeles González Sagrario
Keyword(s):  
Size Structure ◽  
Generalized Additive Models ◽  
Management Strategies ◽  
Zooplankton Community ◽  
Fish Predation ◽  
Additive Models ◽  
Fish Scales ◽  
Pheophorbide A ◽  
Food Web Structure ◽  
Species Dominance

Shallow lakes are vulnerable ecosystems impacted by human activities and climate change. The Cladocera occupy a central role in food webs and are an excellent paleoecological indicator of food web structure and trophic status. We conducted a paleolimnological study in Lake Blanca Chica (Argentina) to detect changes on the planktivory and herbivory regimes over the last 250 years. Generalized additive models were fitted to the time series of fish predation indicators (ephippial abundance and size, mucrone size, fish scales, and the planktivory index) and pheophorbide a concentration. The cladoceran assemblage changed from littoral-benthic to pelagic species dominance and zooplankton switched from large-bodied (Daphnia) to small-bodied grazers (Bosmina) ca. 1900 due to increased predation. The shift in planktivory regime (ca. 1920–1930), indicated by fish scales and the planktivory index, as well as herbivory (ca. 1920–1950), was triggered by eutrophication. Changes in planktivory affected the size structure of Bosmina, reducing its body size. This study describes the baseline for the lake as well as the profound changes in the composition and size structure of the zooplankton community due to increased predation and the shift in the planktivory regime. These findings will provide a reference status for future management strategies of this ecosystem.

scholarly journals Distribution and Catch Rate Characteristics of Narrow-Barred Spanish Mackerel (Scomberomorus commerson) in Relation to Oceanographic Factors in the Waters Around Taiwan

2021 ◽  
Vol 8 ◽  
Author(s):  
Lu-Chi Chen ◽  
Jinn-Shing Weng ◽  
Muhamad Naimullah ◽  
Po-Yuan Hsiao ◽  
Chen-Te Tseng ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Environmental Data ◽  
La Niña ◽  
Northeast Monsoon ◽  
Spatial And Temporal Patterns ◽  
Spanish Mackerel ◽  
La Nina ◽  
Scomberomorus Commerson

This study investigated the relationship of the catch rates (CRs) of Spanish mackerel (Scomberomorus commerson) with oceanographic factors in the waters around Taiwan by using high-resolution fishery and environmental data for the period 2011–2016. The investigation results revealed that trammel nets accounted for 69.79% of the total catch of S. commerson and were operated mostly in the Taiwan Strait (TS). We noted seasonal variations in the distribution of high CRs. These CRs were observed in the southwestern TS, including the waters along the southwestern coast of Taiwan and around the Penghu Islands, and extended to the Taiwan Bank during autumn; they increased in winter. To predict the spatial and temporal patterns of Spanish mackerel density and their relationship with oceanographic and spatiotemporal variables, generalized additive models were used. These models explained 48.4% of the total deviance, which was consistent with the assumed Gaussian distribution. Moreover, all variables examined were significant CR predictors (p &lt; 0.05). Latitude and longitude were the key factors influencing the spatiotemporal distribution of S. commerson, and sea surface chlorophyll a concentration was a key oceanographic factor. Observing projected changes in El Niño/Southern Oscillation events for S. commerson revealed that CRs were higher and distributed further southward during La Niña events than during other events. We inferred that the S. commerson distribution gradually moved toward the southwest with the northeast monsoon, which was enhanced during La Niña in winter.

scholarly journals Generalized Additive Models Analysis of the Atmospheric Pollutants Response to the Emission Reduction and Meteorology During the COVID-19 Lockdown in the North of Africa (Morocco)

2021 ◽  
Author(s):  
salah eddine sbai ◽  
farida Bentayeb ◽  
Hao Yin
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Emission Reduction ◽  
Meteorological Factors ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Environmental Data ◽  
Atmospheric Pollutants ◽  
Photochemical Oxidation ◽  
The North

Abstract Climate and air quality change due to COVID 19 lockdown (LCD) are extremely concerned subjects of several research recently. The contribution of meteorological factors and emission reduction to air pollution change over the north of Morocco has been investigated in this study using the framework generalized additive models (GAM), that have been proved to be a robust technique for the environmental data sets, focusing on main atmospheric pollutants in the region including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), secondary inorganic aerosols (SIA), nom-methane volatile organic compounds (NMVOC) and carbon monoxide (CO) from the regional air pollution dataset of the Copernicus Atmosphere Monitoring Service (CAMS). Our results indicate that secondary air pollutants (PM2.5, PM10 and O3) are more influenced by metrological factors and the other air pollutants reported by this study in comparison with primary air pollutants (NO2 and SO2). We found that meteorological factors contribute to O3, PM2.5, PM10 and SIA average mass concentration by 22%, 5%, 3% and 34% before LCD and by 28%, 19%, 5% and 42% during LCD respectively. The increase in meteorological factors effect during LCD shows the contribution of photochemical oxidation to air pollution due to increase in atmospheric oxidant (O3 and OH radical) during LCD, which can explain the response of PM to emission reduction. Our study indicates that PM (PM2.5, PM10) has more controlled by SO2 due to the formation of sulfate particles especially under high oxidants level. The positive correlation between westward wind at 10m (WW10M), Northward Wind at 10m (NW10M) and PM indicates the implication of sea salt particles transported from Mediterranean Sea and Atlantic Ocean. This study shows the contribution of atmospheric oxidation capacity to air pollution change.

scholarly journals On Generalized Additive Models with Dependent Time Series Covariates

2018 ◽  
pp. 289-308
Author(s):  
Márton Ispány ◽  
Valdério A. Reisen ◽  
Glaura C. Franco ◽  
Pascal Bondon ◽  
Higor H. A. Cotta ◽  
...  
Keyword(s):  
Time Series ◽  
Generalized Additive Models ◽  
Additive Models
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