Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan

Murase, Hiroto; Nagashima, Hiroshi; Yonezaki, Shiroh; Matsukura, Ryuichi; Kitakado, Toshihide

doi:10.1093/icesjms/fsp105

Cited by 124 publications

(68 citation statements)

References 23 publications

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“…An interesting feature of GAM is that it allows determination of the shape of the response curves from the data instead of fitting an a priori parametric model, making it data-driven instead of model driven [57]. GAMs have been widely applied to investigate nonlinear relationships between dependent and non-explanatory variables in various fields of study such as plant and aquatic ecology (e.g., [58]) and water quality (e.g., [59]), as well as for nonlinear relationships among biotic and bon-biotic variables in environmental settings due to complex interactions between environmental factors. Many studies have identified nonlinear relationships among components of forest ecosystems (e.g., [3,50,[60][61][62][63][64][65]).…”

Section: Linear and Nonlinear Model Estimationmentioning

confidence: 99%

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

2018

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Forest fires and burn severity mosaics have profound impacts on the post-fire dynamics and complexity of forest ecosystems. Numerous studies have investigated the relationship between topographic variables and susceptible tree covers with regard to burn severity. However, these relationships have not been fully elucidated, because most studies have assumed linearity in these relationships. Therefore, we examined the linearity and the nonlinearity in the relationships between topographic variables and susceptible tree covers with burn severity by comparing linear and nonlinear models. The site of the Samcheok fire, the largest recorded forest fire in Korea, was used as the study area. We generated 802 grid cells with a 500-m resolution that encompassed the entire study area and collected a dataset that included the topographic variables and percentage of red pine trees, which are the most susceptible tree cover types in Korea. We used conventional linear models and generalized additive models to estimate the linear and the nonlinear models based on topographic variables and Japanese red pine trees. The results revealed that the percentage of red pine trees had linear effects on burn severity, reinforcing the importance of silviculture and forest management to lower burn severity. Meanwhile, the topographic variables had nonlinear effects on burn severity. Among the topographic variables, elevation had the strongest nonlinear effect on burn severity, possibly by overriding the effects of susceptible fuels over elevation effects or due to the nonlinear effects of topographic characteristics on pre-fire fuel conditions, including the spatial distribution and availability of susceptible tree cover. To validate and generalize the nonlinear effects of elevation and other topographic variables, additional research is required at different fire sites with different tree cover types in different geographic locations.

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Section: Linear and Nonlinear Model Estimationmentioning

confidence: 99%

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

2018

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“…Compared with an LM, GAMs are data-driven rather than model-driven, and GAMs allow determination of the shape of the response curves from the data instead of fitting an a priori parametric model, which is limited in its available shape of response [27]. GAMs have been widely used in various fields, such as species distribution [28][29][30][31][32][33], plant ecology [34][35][36], and water quality dynamics [21,37,38]. For example, Murase et al [29] applied GAMs to fishery-survey data to reveal the influences of environmental factors, including surface water temperature, salinity, chlorophyll, near-seabed water temperature, salinity, and depth, on the distribution patterns of Japanese anchovy, sand lance, and krill.…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Hwang

Park

et al. 2016

Water

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Abstract:Although close relationships between the water quality of streams and the types of land use within their watersheds have been well-documented in previous studies, many aspects of these relationships remain unclear. We examined the relationships between urban land use and water quality using data collected from 527 sample points in five major rivers in Korea-the Han, Geum, Nakdong, Younsan, and Seomjin Rivers. Water quality data were derived from samples collected and analyzed under the guidelines of the Korean National Aquatic Ecological Monitoring Program, and land use was quantified using products provided by the Korean Ministry of the Environment, which were used to create a Geographic Information System. Linear models (LMs) and generalized additive models were developed to describe the relationships between urban land use and stream water quality, including biological oxygen demand (BOD), total nitrogen (TN), and total phosphorous (TP). A comparison between LMs and non-linear models (in terms of R 2 and Akaike's information criterion values) indicated that the general additive models had a better fit and suggested a non-linear relationship between urban land use and water quality. Non-linear models for BOD, TN, and TP showed that each parameter had a similar relationship with urban land use, which had two breakpoints. The non-linear models suggested that the relationships between urban land use and water quality could be categorized into three regions, based on the proportion of urban land use. In moderate urban land use conditions, negative impacts of urban land use on water quality were observed, which confirmed the findings of previous studies. However, the relationships were different in very low urbanization or very high urbanization conditions. Our results could be used to develop strategies for more efficient stream restoration and management, which would enhance water quality based on the degree of urbanization in watersheds. In particular, land use management for enhancing stream water quality might be more effective when urban land use is in the range of 1.1%-31.5% of a watershed. If urban land use exceeds 31.5% in a watershed, a more comprehensive approach would be required because water quality would not respond as rapidly as expected.

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“…These are generally statistical models that are constructed using observed habitat-organism relationships. Various statistical algorithms, including generalized linear models, generalized additive models, and maximum entropy models, are used to predict the distribution of marine organisms (Jones et al 2012;Murase et al 2009;Pittman and Brown 2011;Reiss et al 2011). Although SD models are powerful tools for evaluating the spatial distribution of organisms in diverse habitats, they are usually empirical and most of them cannot provide mechanistic predictions.…”

Section: Species Distribution Modelsmentioning

confidence: 99%

Modeling the coastal ecosystem complex: present situation and challenges

et al. 2018

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To enhance numerical modeling of the coastal ecosystem complex (CEC), we reviewed the CEC and related concepts along with the current coastal ecosystem model framework in this study. We identified two model implementation paths from the initial objectives to numerical models: specific model building, and the use of existing model frameworks. As the CEC is still at the conceptual stage, both paths are possible. Four important ecological features of CEC modeling (population connectivity, habitat heterogeneity, ontogeny of organisms, and trophic interactions) were also identified. Models for population connectivity, species distributions, life histories, and food webs were categorized using these features. We found that some previously established concepts (between-habitat interactions, coastal ecosystem mosaic, and seascape nursery) overlap with the CEC concept. Several existing integrated model frameworks were reviewed, focusing on their potential to simulate CEC processes. Building specific models for the CEC at the current conceptual stage will be challenging, and modification of existing models will be needed if they are to be used for CEC modeling. Habitat function, ontogenetic development in early life stages, and recruitment variability are important factors when modifying existing models for the development of CEC models. Although model complexity should become high to reproduce observed ecoclogical processes, an intermediate level of model ccomplexity is feasible to decrease parameter uncertainty in models for fisheries management.

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Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan

Cited by 124 publications

References 23 publications

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Modeling the coastal ecosystem complex: present situation and challenges

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