The application of a general time series model to floodplain fisheries in the Amazon

Vallejos, Ronny; Fabré, Nidia Noemí; Batista, Vandick da Silva; Acosta, Jonathan

doi:10.1016/j.envsoft.2013.07.004

Cited by 8 publications

(7 citation statements)

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“…Although the river level may be an important variable to assess catch productivity forecasts [ 45 ], there are other environmental and socio-economic factors that may be equally important. The period and the intensity to which the floodplains stay wet have an influence on the fishery activity.…”

Section: Discussionmentioning

confidence: 99%

“…When some of our variables are highly correlated we can opt on despise one or some of them aiming to not repeat our interpretations. Following [ 45 ], we used here the Pearson correlation method and used dispersion diagrams to analize the collinearity among our data.…”

Section: Methodsmentioning

confidence: 99%

“…The Redundancy Analysis (RDA) is also a model largely employed in ecological studies to analyse data time series. This method when applyed to fisheries studies empirically assums that the several environmental variables regulate the fishery resources' dinamics as much as the fishery effort do [ 45 ]. This kind of analysis assumes a linear behavior for the variables studied allowing that the unkown variables fit into a linear regression model.…”

Section: Methodsmentioning

confidence: 99%

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Multispecies Fisheries in the Lower Amazon River and Its Relationship with the Regional and Global Climate Variability

et al. 2016

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This paper aims to describe the spatial-temporal variability in catch of the main fishery resources of the Amazon River and floodplain lakes of the Lower Amazon, as well as relating the Catch per Unit of Effort with anomalies of some of the Amazon River, atmosphere and Atlantic Ocean system variables, determining the influence of the environment on the Amazonian fishery resources. Finfish landings data from the towns and villages of the Lower Amazon for the fisheries of three sites (Óbidos, Santarém and Monte Alegre), were obtained for the period between January 1993 and December 2004. Analysis of variance, detrended correspondence analysis, redundancy analysis and multiple regression techniques were used for the statistical analysis of the distinct time series. Fisheries production in the Lower Amazon presents differences between the Amazon River and the floodplain lakes. Production in the Amazon River is approximately half of the one of the floodplain lakes. This variability occurs both along the Lower Amazon River region (longitudinal gradient) and laterally (latitudinal gradient) for every fishing ground studied here. The distinct environmental variables alone or in association act differently on the fishery stocks and the success of catches in each fishery group studied here. Important variables are the flooding events; the soil the sea surface temperatures; the humidity; the wind and the occurence of El Niño-Southern Oscillation events. Fishery productivity presents a large difference in quantity and distribution patterns between the river and floodplain lakes. This variability occurs in the region of the Lower Amazon as well as laterally for each fishery group studied, being dependent on the ecological characteristics and life strategies of each fish group considered here.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multispecies Fisheries in the Lower Amazon River and Its Relationship with the Regional and Global Climate Variability

et al. 2016

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“…Benefits of this procedure embrace simplicity and speed of calculation, strength of results, and flexibility in the period of the seasonal component (Cleveland et al 1990). This decomposition practice is widely used in the natural sciences (e.g., Currie et al ; Vallejos et al ; Bourjea et al ).…”

Section: Methodsmentioning

confidence: 99%

The current response of soil thermal regime and carbon exchange of a paraglacial coastal land system in maritime Antarctica

et al. 2019

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Ice‐free areas of maritime Antarctica (MA) are undergoing rapid environmental adjustment due to climate change because glaciers retreated. In recently deglaciated areas, sensitive indicators related to soil can be used as proxies of the transition phase from glacial to nonglacial conditions at paraglacial coastal systems. This work aims at comprehending how paraglacial coastal land systems respond to adjustment processes in highly dynamic deglaciated areas, based on net ecosystem exchange, soil temperature (ST), and soil moisture (SM) temporal series in two different vegetation communities on a paraglacial coastal land system in MA. We selected a moss community (MC) and a mixed community with mosses and lichens (MLC). MC is located on a hydromorphic area with buffered ST regime, compared with MLC, where greater SM variation range and freezing conditions were observed. MC and MLC are currently acting as an atmospheric CO2 sink. In this work, MC showed a greater capacity of CO2 uptake during measurements (711.20 g CO2 m−2). In this part of Antarctica, where a recent trend of decreasing ST occurs, less permafrost and surface land degradation, combined with enhanced carbon storage, are expected. However, in the long term, following the readjustment of the paraglacial period, thawing is expected, coupled with permafrost degradation and carbon release to the atmosphere, under the predicted warming scenario in MA.

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“…In geostatistics, the term ''trend analysis'' is a simplified description of the task to track and isolate systematic surface (low-frequency) components that overlay spatial and temporal residuals. In that sense, trend estimation might be as simple an analysis as merely scanning across the spatial and temporal domains for a smoothed component; alternatively, it might be as sophisticated a task as tracking specific features like repetitive patterns in spatial domain or temporal seasonality in the time domain (Cleveland et al 1990;Diggle 1990;Vallejos et al 2013).…”

Section: Bme For a Data-driven Spatiotemporal Predictionmentioning

confidence: 99%

A GIS tool for spatiotemporal modeling under a knowledge synthesis framework

Kolovos³

2015

Stoch Environ Res Risk Assess

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In recent years, there has been a fast growing interest in the space-time data processing capacity of Geographic Information Systems (GIS). In this paper we present a new GIS-based tool for advanced geostatistical analysis of space-time data; it combines stochastic analysis, prediction, and GIS visualization technology. The proposed toolbox is based on the Bayesian Maximum Entropy theory that formulates its approach under a mature knowledge synthesis framework. We exhibit the toolbox features and use it for particulate matter spatiotemporal mapping in Taipei, in a proof-of-concept study where the serious preferential sampling issue is present. The proposed toolbox enables tight coupling of advanced spatiotemporal analysis functions with a GIS environment, i.e. QGIS. As a result, our contribution leads to a more seamless interaction between spatiotemporal analysis tools and GIS built-in functions; and utterly enhances the functionality of GIS software as a comprehensive knowledge processing and dissemination platform.

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The application of a general time series model to floodplain fisheries in the Amazon

Cited by 8 publications

References 50 publications

Multispecies Fisheries in the Lower Amazon River and Its Relationship with the Regional and Global Climate Variability

Multispecies Fisheries in the Lower Amazon River and Its Relationship with the Regional and Global Climate Variability

The current response of soil thermal regime and carbon exchange of a paraglacial coastal land system in maritime Antarctica

A GIS tool for spatiotemporal modeling under a knowledge synthesis framework

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