Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology
Comparative analyses of oceanic ecosystems require an objective framework to define coherent study regions and scale the patterns and processes observed within them. We applied the hierarchical patch mosaic paradigm of landscape ecology to the study of the seasonal variability of the North Pacific t...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00796611_v120_n_p291_Kavanaugh |
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todo:paper_00796611_v120_n_p291_Kavanaugh |
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dspace |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Biogeochemistry Models North Pacific Pelagic environment Seascapes Seasonal variations Multiple linear regressions North Pacific Pelagic biogeochemistries Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Classification efficiency Multiple linear regressions North Pacific Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Analysis of variance (ANOVA) Ecosystems Linear regression Models Oceanography Plants (botany) Atmospheric temperature Biogeochemistry Ecology Ecosystems Linear regression Models Oceanography Plants (botany) Surface waters Time series analysis Biogeochemistry Analysis of variance (ANOVA) biogeochemistry biophysics carbon dioxide chlorophyll a hierarchical system hypothesis testing marine ecosystem net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature seasonal variation spatiotemporal analysis variance analysis biogeochemistry chlorophyll a comparative study error analysis Eulerian analysis hierarchical system landscape ecology multiple regression net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature spatiotemporal analysis variance analysis Pacific Ocean Pacific Ocean (North) Pacific Ocean Pacific Ocean (North) |
| spellingShingle |
Biogeochemistry Models North Pacific Pelagic environment Seascapes Seasonal variations Multiple linear regressions North Pacific Pelagic biogeochemistries Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Classification efficiency Multiple linear regressions North Pacific Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Analysis of variance (ANOVA) Ecosystems Linear regression Models Oceanography Plants (botany) Atmospheric temperature Biogeochemistry Ecology Ecosystems Linear regression Models Oceanography Plants (botany) Surface waters Time series analysis Biogeochemistry Analysis of variance (ANOVA) biogeochemistry biophysics carbon dioxide chlorophyll a hierarchical system hypothesis testing marine ecosystem net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature seasonal variation spatiotemporal analysis variance analysis biogeochemistry chlorophyll a comparative study error analysis Eulerian analysis hierarchical system landscape ecology multiple regression net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature spatiotemporal analysis variance analysis Pacific Ocean Pacific Ocean (North) Pacific Ocean Pacific Ocean (North) Kavanaugh, M.T. Hales, B. Saraceno, M. Spitz, Y.H. White, A.E. Letelier, R.M. Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| topic_facet |
Biogeochemistry Models North Pacific Pelagic environment Seascapes Seasonal variations Multiple linear regressions North Pacific Pelagic biogeochemistries Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Classification efficiency Multiple linear regressions North Pacific Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Analysis of variance (ANOVA) Ecosystems Linear regression Models Oceanography Plants (botany) Atmospheric temperature Biogeochemistry Ecology Ecosystems Linear regression Models Oceanography Plants (botany) Surface waters Time series analysis Biogeochemistry Analysis of variance (ANOVA) biogeochemistry biophysics carbon dioxide chlorophyll a hierarchical system hypothesis testing marine ecosystem net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature seasonal variation spatiotemporal analysis variance analysis biogeochemistry chlorophyll a comparative study error analysis Eulerian analysis hierarchical system landscape ecology multiple regression net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature spatiotemporal analysis variance analysis Pacific Ocean Pacific Ocean (North) Pacific Ocean Pacific Ocean (North) |
| description |
Comparative analyses of oceanic ecosystems require an objective framework to define coherent study regions and scale the patterns and processes observed within them. We applied the hierarchical patch mosaic paradigm of landscape ecology to the study of the seasonal variability of the North Pacific to facilitate comparative analysis between pelagic ecosystems and provide spatiotemporal context for Eulerian time-series studies. Using 13-year climatologies of sea surface temperature (SST), photosynthetically active radiation (PAR), and chlorophyll a (chl-a), we classified seascapes in environmental space that were monthly-resolved, dynamic and nested in space and time. To test the assumption that seascapes represent coherent regions with unique biogeochemical function and to determine the hierarchical scale that best characterized variance in biogeochemical parameters, independent data sets were analyzed across seascapes using analysis of variance (ANOVA), nested-ANOVA and multiple linear regression (MLR) analyses. We also compared the classification efficiency (as defined by the ANOVA F-statistic) of resultant dynamic seascapes to a commonly-used static classification system. Variance of nutrients and net primary productivity (NPP) were well characterized in the first two levels of hierarchy of eight seascapes nested within three superseascapes (R2=0.5-0.7). Dynamic boundaries at this level resulted in a nearly 2-fold increase in classification efficiency over static boundaries. MLR analyses revealed differential forcing on pCO2 across seascapes and hierarchical levels and a 33% reduction in mean model error with increased partitioning (from 18.5μatm to 12.0μatm pCO2). Importantly, the empirical influence of seasonality was minor across seascapes at all hierarchical levels, suggesting that seascape partitioning minimizes the effect of non-hydrographic variables. As part of the emerging field of pelagic seascape ecology, this effort provides an improved means of monitoring and comparing oceanographic biophysical dynamics and an objective, quantitative basis by which to scale data from local experiments and observations to regional and global biogeochemical cycles. © 2013. |
| format |
JOUR |
| author |
Kavanaugh, M.T. Hales, B. Saraceno, M. Spitz, Y.H. White, A.E. Letelier, R.M. |
| author_facet |
Kavanaugh, M.T. Hales, B. Saraceno, M. Spitz, Y.H. White, A.E. Letelier, R.M. |
| author_sort |
Kavanaugh, M.T. |
| title |
Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| title_short |
Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| title_full |
Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| title_fullStr |
Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| title_full_unstemmed |
Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology |
| title_sort |
hierarchical and dynamic seascapes: a quantitative framework for scaling pelagic biogeochemistry and ecology |
| url |
http://hdl.handle.net/20.500.12110/paper_00796611_v120_n_p291_Kavanaugh |
| work_keys_str_mv |
AT kavanaughmt hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology AT halesb hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology AT saracenom hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology AT spitzyh hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology AT whiteae hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology AT letelierrm hierarchicalanddynamicseascapesaquantitativeframeworkforscalingpelagicbiogeochemistryandecology |
| _version_ |
1807323762040766464 |
| spelling |
todo:paper_00796611_v120_n_p291_Kavanaugh2023-10-03T14:54:28Z Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology Kavanaugh, M.T. Hales, B. Saraceno, M. Spitz, Y.H. White, A.E. Letelier, R.M. Biogeochemistry Models North Pacific Pelagic environment Seascapes Seasonal variations Multiple linear regressions North Pacific Pelagic biogeochemistries Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Classification efficiency Multiple linear regressions North Pacific Pelagic environment Photosynthetically active radiation Sea surface temperature (SST) Seascapes Seasonal variation Analysis of variance (ANOVA) Ecosystems Linear regression Models Oceanography Plants (botany) Atmospheric temperature Biogeochemistry Ecology Ecosystems Linear regression Models Oceanography Plants (botany) Surface waters Time series analysis Biogeochemistry Analysis of variance (ANOVA) biogeochemistry biophysics carbon dioxide chlorophyll a hierarchical system hypothesis testing marine ecosystem net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature seasonal variation spatiotemporal analysis variance analysis biogeochemistry chlorophyll a comparative study error analysis Eulerian analysis hierarchical system landscape ecology multiple regression net primary production pelagic ecosystem photosynthetically active radiation quantitative analysis sea surface temperature spatiotemporal analysis variance analysis Pacific Ocean Pacific Ocean (North) Pacific Ocean Pacific Ocean (North) Comparative analyses of oceanic ecosystems require an objective framework to define coherent study regions and scale the patterns and processes observed within them. We applied the hierarchical patch mosaic paradigm of landscape ecology to the study of the seasonal variability of the North Pacific to facilitate comparative analysis between pelagic ecosystems and provide spatiotemporal context for Eulerian time-series studies. Using 13-year climatologies of sea surface temperature (SST), photosynthetically active radiation (PAR), and chlorophyll a (chl-a), we classified seascapes in environmental space that were monthly-resolved, dynamic and nested in space and time. To test the assumption that seascapes represent coherent regions with unique biogeochemical function and to determine the hierarchical scale that best characterized variance in biogeochemical parameters, independent data sets were analyzed across seascapes using analysis of variance (ANOVA), nested-ANOVA and multiple linear regression (MLR) analyses. We also compared the classification efficiency (as defined by the ANOVA F-statistic) of resultant dynamic seascapes to a commonly-used static classification system. Variance of nutrients and net primary productivity (NPP) were well characterized in the first two levels of hierarchy of eight seascapes nested within three superseascapes (R2=0.5-0.7). Dynamic boundaries at this level resulted in a nearly 2-fold increase in classification efficiency over static boundaries. MLR analyses revealed differential forcing on pCO2 across seascapes and hierarchical levels and a 33% reduction in mean model error with increased partitioning (from 18.5μatm to 12.0μatm pCO2). Importantly, the empirical influence of seasonality was minor across seascapes at all hierarchical levels, suggesting that seascape partitioning minimizes the effect of non-hydrographic variables. As part of the emerging field of pelagic seascape ecology, this effort provides an improved means of monitoring and comparing oceanographic biophysical dynamics and an objective, quantitative basis by which to scale data from local experiments and observations to regional and global biogeochemical cycles. © 2013. Fil:Saraceno, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00796611_v120_n_p291_Kavanaugh |