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- Author:
- Arhonditsis, George B.; Neumann, Alex; Shimoda, Yuko; Kim, Dong-Kyun; Dong, Feifei; Onandia, Gabriela; Yang, Cindy; Javed, Aisha; Brady, Meghan; Visha, Ariola; Ni, Felicity; Cheng, Vincent
- Source:
- Ecological informatics 2019 v.53 pp. 100968
- ISSN:
- 1574-9541
- Subject:
- Cyanobacteria; Soil and Water Assessment Tool model; adaptive management; basins; business enterprises; case studies; chlorophyll; diagenesis; dissolved oxygen; ecological models; ecosystems; fractionation; limnology; model validation; nutrient content; organic matter; philosophy; phosphorus; pollution load; prediction; rivers; sand; temperature; total phosphorus; water pollution; water quality; watersheds; zooplankton; Lake Erie
- Abstract:
- ... We present a technical analysis of all the recent modelling work that has been conducted to support the adaptive management process in Lake Erie; the most biologically productive system of the Great Lakes. With a wealth of models developed, Lake Erie represents a unique case study where an impressive variety of data-driven and process-based models have been developed to elucidate the major watersh ...
- DOI:
- 10.1016/j.ecoinf.2019.05.014
-
https://dx.doi.org/10.1016/j.ecoinf.2019.05.014
- Author:
- Arhonditsis, George B.; Neumann, Alex; Shimoda, Yuko; Kim, Dong-Kyun; Dong, Feifei; Onandia, Gabriela; Yang, Cindy; Javed, Aisha; Brady, Meghan; Visha, Ariola; Ni, Felicity; Cheng, Vincent
- Source:
- Ecological informatics 2019 v.53 pp. 100969
- ISSN:
- 1574-9541
- Subject:
- adaptive management; agricultural land; application rate; basins; business enterprises; decision making; diagenesis; ecosystem services; ecosystems; evolution; fertilizers; fractionation; groundwater; hydrologic cycle; land cover; land use planning; limnology; manure spreading; models; monitoring; nutrient content; organic matter; philosophy; phosphorus; phytoplankton; pollution load; prediction; runoff; sand; storms; surface water; tile drainage; urban areas; water quality; watersheds; Lake Erie
- Abstract:
- ... In Lake Erie, a wide variety of statistical and process-based models have significantly advanced our understanding of the major causal linkages/ecosystem processes underlying the local water quality problems. In this study, our aim is to identify knowledge gaps, monitoring assessment objectives, and management recommendations that should be critically reviewed through the iterative monitoring-mode ...
- DOI:
- 10.1016/j.ecoinf.2019.05.015
-
https://dx.doi.org/10.1016/j.ecoinf.2019.05.015
- Author:
- dos Santos, Anderson Aparecido; Gonçalves, Wesley Nunes
- Source:
- Ecological informatics 2019 v.53 pp. 100977
- ISSN:
- 1574-9541
- Subject:
- data collection; ecosystems; fish; humans; neural networks; tourism; wetlands; Pantanal
- Abstract:
- ... Fish species recognition is an important task to preserve ecosystems, feed humans, and tourism. In particular, the Pantanal is a wetland region that harbors hundreds of species and is considered one of the most important ecosystems in the world. In this paper, we present a new method based on convolutional neural networks (CNNs) for Pantanal fish species recognition. A new CNN composed of three br ...
- DOI:
- 10.1016/j.ecoinf.2019.100977
-
https://dx.doi.org/10.1016/j.ecoinf.2019.100977
- Author:
- Tong, Linjing; Liu, Yangyang; Wang, Qian; Zhang, Zhaoying; Li, Jianlong; Sun, Zhengguo; Khalifa, Muhammad
- Source:
- Ecological informatics 2019 v.53 pp. 100979
- ISSN:
- 1574-9541
- Subject:
- climatic factors; drought; ecosystems; environmental protection; grasslands; humans; land use; net primary productivity; overgrazing; regression analysis; savannas; shrublands; socioeconomic development; Africa
- Abstract:
- ... Grassland plays a key role in socioeconomic development and environmental protection in Africa. Climate variation and human activities are two main drivers of grassland dynamics. Quantitatively assessing the contributions of these two factors and understanding the driving mechanisms are important in ecosystem adaptation and management. In this research, the spatiotemporal patterns of grassland dyn ...
- DOI:
- 10.1016/j.ecoinf.2019.100979
-
https://dx.doi.org/10.1016/j.ecoinf.2019.100979