Relevant bibliographies by topics / Ecological modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ecological modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 December 2024

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Journal articles on the topic "Ecological modeling"

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Kneese, Allan. "Economic–ecological modeling." Ecological Economics 4, no. 1 (October 1991): 76–78. http://dx.doi.org/10.1016/0921-8009(91)90010-c.

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Perrings, Charles. "Economic-ecological modeling." Futures 20, no. 6 (December 1988): 696–98. http://dx.doi.org/10.1016/0016-3287(88)90012-2.

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JØrgensen, S. E. "Ecosystem Management and Ecological Modeling." Scientific World JOURNAL 2 (2002): 107–21. http://dx.doi.org/10.1100/tsw.2002.80.

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It is the intention of this paper to demonstrate that environmental technology must be supplemented by other tools to be able to solve environmental problems properly. Five cases are used to illustrate the possibilities of ecological engineering, a new engineering field based on ecology, as chemical engineering is based on chemistry. It encompasses restoration of ecosystems, utilization of ecosystems to the benefit of both mankind and nature, construction of ecosystems, and ecologically sound planning of ecosystems from a holistic point of view. Ecological engineering requires a good knowledge of the system properties of ecosystems to be able to fully utilize the possibilities that ecosystem management offers. Models reflecting the ecosystem properties are furthermore needed to be able to quantify the effects of the ecological engineering solutions to the environmental problems. This is clearly demonstrated in two of the five case studies presented in the paper.
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Steverson, Brian K. "Ecocentrism and Ecological Modeling." Environmental Ethics 16, no. 1 (1994): 71–88. http://dx.doi.org/10.5840/enviroethics199416143.

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Lundmark, Cathy. "Ecological Modeling in the News." BioScience 54, no. 9 (2004): 880. http://dx.doi.org/10.1641/0006-3568(2004)054[0880:emitn]2.0.co;2.

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Costanza, Robert, Lisa Wainger, and Carl Folke. "Modeling Complex Ecological Economic Systems." BioScience 43, no. 8 (September 1993): 545–55. http://dx.doi.org/10.2307/1311949.

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Urban, Dean L. "MODELING ECOLOGICAL PROCESSES ACROSS SCALES." Ecology 86, no. 8 (August 2005): 1996–2006. http://dx.doi.org/10.1890/04-0918.

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Denoël, Mathieu, and Gentile Francesco Ficetola. "Kernels and Ecological Niche Modeling." Bulletin of the Ecological Society of America 96, no. 3 (July 2015): 496–99. http://dx.doi.org/10.1890/0012-9623-96.3.496.

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Boyd, I. L. "The Art of Ecological Modeling." Science 337, no. 6092 (July 19, 2012): 306–7. http://dx.doi.org/10.1126/science.1225049.

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Melton, Forrest, Brad Lobitz, Woody Turner, Edwin Sheffner, and John Haynes. "Ecological modeling for applied science." Eos, Transactions American Geophysical Union 86, no. 35 (2005): 319. http://dx.doi.org/10.1029/2005eo350004.

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Dissertations / Theses on the topic "Ecological modeling"

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Kuang, Yan. "Modeling and analysis of competing dynamic ecological systems." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/35555.

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Doctor of Philosophy
Department of Industrial & Manufacturing Systems Engineering
David H. Ben-Arieh
The dynamic relationship between competing ecological systems has long been and will continue to be one of vital topics in both ecology and mathematical ecology because of its importance and universal existence. Mathematical modeling has become an effective tool to model and simulate the dynamic system, providing decision makers with strategy recommendations. Although a great amount of previous work has attempted to model the biological mechanisms including dispersal, only rarely has there been a systematic investigation on different spatial effects. The author introduces spatial games as a modeling approach with different constructions towards different dynamic systems in order to benefit from the systematic research on spatial dynamics when studying the competing ecological systems. This research developed models of two systems: (1) two-spotted spider mite prey-predator system; (2) tomato spotted wilt virus (TSWV) and west flower thrips (WFT) vector-borne disease system. For two-spotted spider mite system, the author presented four spatial mathematical models as well as a novel spatial game model to describe the spatial movement of two competing species. For the TSWV-WFT system, a spatial game was introduced to describe the spatial dynamics of adult thrips and the novel model was validated with experimental data. The author also gave suggestions for efficiently controlling the vector-borne disease by performing sensitivity analysis towards parameters. The major contribution of this research is to introduce spatial games as a tool to describe the dynamic schemes in ecological systems. Compared to a traditional dynamic model, a spatial game model is more expressive and informative. This approach uses a payoff function and a movement probability function that can be adjusted based on habits, characteristics and mobility schemes of different competing entities, which has enriched its modeling power. The methodology and modeling approach used in this dissertation can be applied to other competing species dynamic systems, and have a broad impact on research areas related to mathematical ecology, biology modeling, epidemiology, pest control, vector-borne disease control, and ecological decision-making processes.
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Khondkaryan, Lusine. "Ecological niche modeling of rodent and flea species /." [Sedeh Boker, Israel] : Ben-Gurion University of the Negev, 2008. http://aranne5.lib.ad.bgu.ac.il/others/KhondkaryanLusine.pdf.

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Lin, Yi. "Bayesian spatial and ecological modeling of suicide rates." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/12568.

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Suicide and suicide attempts constitute major public and mental health problems in many countries. The risk factors of suicide include not only psychological and other individual features but also the characteristics of the community in which the people live. Therefore, in order to better understand the potential impacts of community characteristics on suicide, the regional level effects of suicide need to be thoroughly examined. For this thesis, an ecological analysis was incorporated into a Bayesian disease mapping study in order to estimate suicide rates, explore regional risk factors, and discern spatial patterns in suicide risks. Fully Bayesian disease mapping and ecological regression methods were used to estimate area-specific suicide risks, investigate spatial variations, and explore and quantify the associations between regional characteristics and suicide occurrences. The fact that spatially smoothed estimates of suicide rates highlight the high risk regions can act as stable health outcome indicators at the regional level. Furthermore, regional characteristics explored as potential risk factors of suicide rates can provide a better understanding of regional variations of suicide rates. Both can help in planning future public health prevention programs. In order to avoid multicollinearity among risk factors and reduce the dimensionality of the risk indicators, Principal Component Analysis and Empirical Bayes method (via Penalized Quasi-Likelihood) were applied in variable selection and highlighting risk patterns. Using 10-year aggregated data for all age groups and both genders, this study conducted a comprehensive analysis of suicide hospitalization and mortality rates in eighty-four Local Health Areas in British Columbia (Canada). A broad range of regional characteristics was investigated and different associations with suicide rates were observed in different demographic and gender groups. The major regional risk patterns related to suicide rates across age groups were social and economic characteristics, which include unemployment rates, income, education attainment, marital status, family structure, and dwellings. Some age groups also showed a relation to aboriginal population, immigrants, and language. The results of this study may inform policy initiatives and programs for suicide prevention.
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Mak, Sunny Y. "Ecological niche modeling of Cryptococcus gattii in British Columbia." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31989.

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Cryptococcus gattii unexpectedly emerged on Vancouver Island, British Columbia (BC), Canada in 1999 causing human and animal illness. Prior to its discovery on Vancouver Island, this microscopic fungal organism was limited to tropical and sub-tropical regions of the world with eucalyptus trees as the environmental reservoir. Environmental sampling for C. gattii in southwestern BC has isolated the organism from native vegetation, soil, air and water. Since it is not possible to sample every location for the presence or absence of C. gattii on Vancouver Island or the BC mainland, ecological niche modeling using the Genetic Algorithm for Rule-set Prediction (GARP) was performed to identify the optimal and potential ecological niche areas of C. gattii in BC. Human and animal surveillance and environmental sampling data were used as input data points to build and test the ecological niche models based on 15 predictor environmental data layers (topographic, climatic, biogeoclimatic, and soil). Training and testing accuracy of the C. gattii ecological niche models were 99.4% and 99.2% based on the distribution of human cases, 98.7% and 98.3% based on the distribution of animal cases, and 99.7% and 99.7% based on the distribution of positive environmental sampling locations (p-value <0.0001 for all models). Forecasted optimal C. gattii ecological niche areas in BC include the central and south eastern coast of Vancouver Island, Gulf Islands, Sunshine Coast and Vancouver Lower Mainland. They are characterized by areas of low lying elevations, daily January average temperatures above freezing, and presence within the Coastal Douglas-fir and Coastal Western Hemlock xeric maritime biogeoclimatic zones. The results of these analyses are visualized using Geographic Information Systems, and shared with public health to prioritize future C. gattii environmental sampling in previously unidentified areas and increase public and physician awareness of cryptococcal disease in BC.
Arts, Faculty of
Geography, Department of
Graduate
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Lindström, Tom. "Spatial Spread of Organisms : Modeling ecological and epidemiological processes." Doctoral thesis, Linköpings universitet, Teoretisk Biologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54839.

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This thesis focuses on the spread of organisms in both ecological and epidemiological contexts. In most of the studies presented, displacement is modeled with a spatial kernel function, which is characterized by scale and shape. These are measured by the net squared displacement (or kernel variance) and kurtosis, respectively. If organisms disperse by the assumptions of a random walk or correlated random walk, a Gaussian shaped kernel is expected. Empirical studies often report deviations from this, and commonly leptokurtic distributions are found, often as a result of heterogeneity in the dispersal process. In the studies presented in two of the included papers, the importance of the kernel shape is tested, by using a family of kernels where the shape and scale can be separated effectively. Both studies utilize spectral density approaches for modeling the spatial environment. It is concluded that the shape is not important when studying the population distribution in a habitat/matrix context. The shape is however important when looking at the invasion of organisms in a patchy environment, when the arrangement of patches deviates from randomly distributed. The introduced method for generating patch distribution is also compared to empirical distributions of patches (farms and old trees). Here it is concluded that the assumptions used for modeling of the spatial environment are consistent with the observed patterns. These assumptions include fractal properties such that the same aggregational patterns are found at different scales. In a series of papers, movements of animals are considered as vectors for between-herd disease spread. The studies are based on data found in databases held by the Swedish Board of Agricultural (SJV), consisting of reported movements, as well as farm location and characteristics. The first study focuses on the distance related probability of contacts between herds. In the following papers, the analysis is expanded to include production type and herd size. Movement data of pigs (and cattle in Paper I) are analyzed with Bayesian models, implemented with Markov Chain Monte Carlo (MCMC). This is a flexible approach that allows for parameter estimations of complex models, and at the same time includes parameter uncertainty. In Paper IV, the effects of the included factors are investigated. It is shown that all three factors (herd size, production type structure and distance related probability of contacts) are expected to influence disease spread dynamics, however the production type structure is found to be the most important factor. This emphasizes the value of keeping such information in central databases. The models presented can be used as support for risk analysis and disease tracing. However, data reliability is always a problem, and implementation may be improved with better quality data. The thesis also shows that utilizing spatial kernels for description of the spatial spread of organisms is an appropriate approach. However, these kernels must be flexible and flawed assumptions about the shape may lead to erroneous conclusions. Hence, the joint distribution of kernel shape and scale should be estimated. The flexibility of Bayesian analysis, implemented with MCMC techniques, is a good approach for this, and further allows for implementation of more complex models where other factors may be included.
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Кубатко, Олександр Васильович, Александр Васильевич Кубатко, and Oleksandr Vasylovych Kubatko. "Modeling the sustainable development with the ecological kuznets curve." Thesis, Видавництво СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/7908.

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Gilfillan, Dennis, Kimberlee Hall, Timothy Andrew Joyner, and Phillip R. Scheuerman. "Canonical Variable Selection for Ecological Modeling of Fecal Indicators." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/5479.

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More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.
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Gilfillan, Dennis, Kimberlee Hall, Timothy Andrew Joyner, and Phillip Scheuerman. "Canonical Variable Selection for Ecological Modeling of Fecal Indicators." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/5589.

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More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.
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Star, Bastiaan, and n/a. "Space matters : modeling selection in spatially heterogeneous environments." University of Otago. Department of Zoology, 2008. http://adt.otago.ac.nz./public/adt-NZDU20080507.151534.

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Selection in spatially heterogeneous environments is a convenient explanation for the high levels of genetic variation observed in natural populations. Indeed, theoretical studies predict that spatial heterogeneity leads to higher levels of variation in a variety of selection models. These models, however, have assumed quite restrictive parameters (e.g., two alleles, fixed gene flow and specific selection schemes). Therefore, the effect on spatial heterogeneity is still poorly understood for a wider range of parameters (e.g., multiple alleles, different levels of gene flow and more general selection schemes). We have relaxed some of the assumptions that have limited the previous models and studied the effect of spatial heterogeneity using simple single-locus viability selection models. First, we investigate the rarity of the parts of fitness space maintaining variation for multiple alleles and different levels of gene flow by randomly sampling that space using a "fitness space" approach. The volume of fitness space maintaining variation is always larger in a spatial model compared to a single-population model regardless of gene flow. Moreover, this volume is relatively larger for higher numbers of alleles, indicating that spatial heterogeneity is more efficient maintaining higher levels of variation. Second, we investigate the ease with which a more natural process of recurrent mutation and selection evolves to the particular area of fitness space maintaining variation using a "construction" approach. Depending on the amount of gene flow, the construction approach leads to both higher and lower levels of variation compared to a single-population model. Thus, spatial heterogeneity can both constrain and promote the ease with which a natural process of mutation and selection evolves to maintain variation. Also, the construction approach results in variation being maintained in a more stable subset of the volume of fitness space than the volume that resulted from the fitness space approach. Third, we investigate the effect of higher and lower levels of spatial environmental heterogeneity using the construction approach. The different levels of heterogeneity and gene flow interact to influence the amount of variation that is eventually maintained and this interaction effect is especially strong for intermediate levels of gene flow. More heterogeneous environments can maintain higher levels of variation, but selection in these environments also results in a higher level of migration load, lowering the final amount of adaptation that is achieved by the simulated evolutionary process. Finally, we investigate effect of genetic drift and finite populations using the construction approach. Interestingly, two different effects emerge for smaller and larger populations; in smaller populations genetic drift lowers the amount of variation as expected, whereas, more surprisingly, genetic drift increases the amount of variation in larger populations. Overall, spatial heterogeneity has profound effects on the outcome of selection, resulting in elevated levels of genetic variation for a wide variety of parameters.
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Zhang, Hongyan. "Ecological modeling of the lower trophic levels of Lake Erie." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1163785412.

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Books on the topic "Ecological modeling"

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Zhang, Wen-Jun. Ecological modeling. Hauppauge, N.Y: Nova Science Publishers, 2011.

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C, Braat Leon, and Lierop, Wal F. J. van., eds. Economic-ecological modeling. Amsterdam: North-Holland, 1987.

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Dale, Virginia H., ed. Ecological Modeling for Resource Management. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97276.

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H, Dale Virginia, ed. Ecological modeling for resource management. New York: Springer, 2003.

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Dale, Virginia H. Ecological modeling for resource management. New York: Springer, 2003.

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Malkinson, Dan, Danny Czamanski, and Itzhak Benenson, eds. Modeling of Land-Use and Ecological Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40199-2.

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Voinov, Alexey. Systems science and modeling for ecological economics. Amsterdam: Elsevier Academic Press, 2008.

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M, Nisbet R., ed. Ecological dynamics. New York: Oxford University Press, 1998.

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Butusov, Oleg, and Valeriy Meshalkin. Fundamentals of informatization and mathematical modeling of ecological systems. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1477254.

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The concept, theory and methodological foundations of environmental informatics are presented, the tools of informatization and digitalization of technogenic-natural systems are described, methods of mathematical modeling of ecological systems in industrial areas are considered. The main methods of ecological informatics, methods of mathematical and computer modeling of quasi-static (long-term) dynamics of ecosystems are described. The theoretical foundations of calculating the "dose-effect" dependencies as the main indicators of the degree of impact of industrial emissions on the environment are presented. The results of practical application of mathematical models of woodlands are given. The purpose and architecture of decision support systems for environmental protection are described; the principles of automated organizational and managerial decision-making to reduce emissions of hazardous chemicals into the atmosphere are outlined. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduates and undergraduates studying in the field of training "Energy- and resource-saving processes of chemical technology, petrochemistry and biotechnology". It can also be used by undergraduates and undergraduates studying in the areas of training "Technosphere safety" and "Organization and management of high-tech industries".
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1951-, Pugesek Bruce H., Tomer Adrian, and Eye Alexander von, eds. Structural equation modeling: Applications in ecological and evolutionary biology. Cambridge, UK: Cambridge University Press, 2003.

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Book chapters on the topic "Ecological modeling"

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Brush, Mark J., and Lora A. Harris. "Ecological Modeling." In Encyclopedia of Estuaries, 214–23. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-8801-4_17.

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Hauhs, Michael. "Ecological Modeling." In Encyclopedia of Systems Biology, 645–47. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1482.

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Turner, Monica G., and Virginia H. Dale. "Modeling Landscape Disturbance." In Ecological Studies, 323–51. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4757-4244-2_13.

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Reyes, Enrique, Kenneth Rose, and Dubravko Justić. "Estuarine Ecological Modeling." In Estuarine Ecology, 519–36. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118412787.ch21.

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Park, Hotaek, and Takeshi Yamazaki. "Carbon-Water Cycle Modeling." In Ecological Studies, 279–97. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6317-7_12.

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Zucchetto, James, and Ann-Mari Jansson. "Modeling Approaches and Results." In Ecological Studies, 95–168. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5124-8_3.

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Mauchamp, Andre, Serge Rambal, John A. Ludwig, and David J. Tongway. "Multiscale Modeling of Vegetation Bands." In Ecological Studies, 146–66. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0207-0_8.

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Cushman, Samuel A., Tzeidle N. Wasserman, and Kevin McGarigal. "Modeling Landscape Fire and Wildlife Habitat." In Ecological Studies, 223–45. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-94-007-0301-8_9.

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Huang, Yuanyuan. "Ecological Platform for Assimilating Data (EcoPAD) for Ecological Forecasting." In Land Carbon Cycle Modeling, 293–300. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429155659-43.

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Huang, Yuanyuan. "Ecological Platform for Assimilating Data (EcoPAD) for Ecological Forecasting." In Land Carbon Cycle Modeling, 192–98. 2nd ed. New York: CRC Press, 2024. http://dx.doi.org/10.1201/9781032711126-38.

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Conference papers on the topic "Ecological modeling"

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Williams, Claire, Rowan Martindale, and Corinne Myers. "ECOLOGICAL NICHE MODELING OF ECOLOGICALLY IMPORTANT CARIBBEAN CORAL SPECIES." In GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania. Geological Society of America, 2023. http://dx.doi.org/10.1130/abs/2023am-392860.

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Lawrence, Ian, and Michael Harper. "Ecological Modelling Strategies." In Specialty Symposium on Urban Drainage Modeling at the World Water and Environmental Resources Congress 2001. Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40583(275)55.

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"FUZZY APPROACHES FOR MODELING DYNAMICAL ECOLOGICAL SYSTEMS." In 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003614603740379.

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Lisitsyn, V. I., N. S. Kamalova, N. Yu Evsikova, and N. N. Matveev. "Thermodynamic substantiation of ecological and physiological modeling." In Лесные экосистемы как глобальный ресурс биосферы: вызовы, угрозы, решения в контексте изменения климата. Воронеж: Воронежский государственный лесотехнический университет им. Г.Ф. Морозова, 2022. http://dx.doi.org/10.58168/iff2022_52-63.

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Menshutkin, V. V., and T. R. Minina. "MATHEMATICAL MODELING AND REGIONAL SOCIO-ECOLOGICAL SYSTEMS." In Problems of transformation and regulation of regional socio-economic systems. Petersburg State University of Economics, 2023. http://dx.doi.org/10.52897/978-5-7310-6226-8-2023-51-102-109.

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One of the goals of forming regional socio-economic systems is to improve the interaction of nature and society by improving the processes of nature management. Therefore, it is necessary to study changes in the environmental status of natural ecosystems of the region under economic activity pressure. Natural ecosystems are complex systems, and methods of mathematical modelling are applied to study its changes and to predict its state. This article presents modern methods of mathematical modelling of natural ecosystems.
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Hunt, Melody J. "Essential Considerations for Development of Estuarine Ecological Modeling Tools." In 10th International Conference on Estuarine and Coastal Modeling. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40990(324)13.

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Fechner, Wim, and Eelco Herder. "Digital Nudging for More Ecological Supermarket Purchases." In UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3450614.3464620.

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Nevanpää, Tiina, and Nancy Law. "Pupil's ecological reasoning with help of modeling tool." In Proceeding of the 2006 conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1139073.1139088.

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Lezzi, D., R. Rafanell, E. Torres, R. D. Giovanni, I. Blanquer, and R. M. Badia. "Programming Ecological Niche Modeling Workflows in the Cloud." In 2013 Workshops of 27th International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2013. http://dx.doi.org/10.1109/waina.2013.6.

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Chen, Miao, Umashanthi Pavalanathan, Scott Jensen, and Beth Plale. "Modeling heterogeneous data resources for social-ecological research." In the 13th ACM/IEEE-CS joint conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2467696.2467737.

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Reports on the topic "Ecological modeling"

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Fleishman, Erica, and Karen Seto. Applications Of Remote Sensing To Ecological Modeling. American Museum of Natural History, 2009. http://dx.doi.org/10.5531/cbc.ncep.0169.

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Satellite data offer panoramic and long-term perspectives that assist us in managing ecosystems to meet social, economic, and ecological objectives. In particular, remote sensing data are becoming increasingly common and critical inputs to ecological models. Models help us to understand ecological patterns and processes and the affect of human actions on those phenomena. Remote sensing has changed how ecologists approach the modeling process, e.g., new capacity to track severe events like hurricanes has improved our ability to prepare, respond, and mitigate effectively. In this module, we offer a review of model usage in ecology, and introduce some of the advantages and constraints of integrating satellite remote sensing with ecological models, (e.g., landscape metrics, climate, invasive species). Although remote sensing data cannot meet all modeling needs or objectives, they often can contribute to the process of conserving and managing biodiversity and ecological function.
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Swannack, Todd M., J. C. Fischenich, and David J. Tazik. Ecological Modeling Guide for Ecosystem Restoration and Management. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada572123.

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Hernandez-Abrams, Darixa, Carra Carrillo, and Todd Swannack. Scenario analyses in ecological modeling and ecosystem management. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44840.

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Ecosystem management and restoration practitioners are challenged with complex problems, diverse project goals, multiple management alternatives, and potential future scenarios that change the systems of interest. Scenario analysis aids in forecasting, evaluating, and communicating outcomes of potential management actions under different plausible conditions, such as land-use change or sea level rise. However, little guidance exists for practitioners on the utility and execution of scenario analysis. Therefore, this technical note highlights the usefulness of scenario analysis as a tool for addressing uncertainty in potential project outcomes. The mechanics of the scenario-analysis process are explained, and examples of different types of scenario analyses are described for context on the breadth of its use. Lastly, two hypothetical case studies of scenario analysis in ecological modeling are presented showing a semiquantitative approach for assessing anadromous fish and a quantitative approach examining freshwater mussel habitat. Overall, this technical note provides a brief review of the utility and application of scenario analyses in the context of ecological modeling and ecosystem management decision-making.
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Carrillo, Carra, S. McKay, Safra Altman, and Todd Swannack. Ecological model development : Toolkit for interActive Modeling (TAM). Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45101.

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Ecological models provide crucial tools for informing many aspects of ecosystem restoration and management, ranging from increasing understanding of complex ecological functions to prioritizing restoration sites and quantifying benefits for project reporting. The diversity of ecosystem types and restoration objectives often precludes the use of existing models; as such, model development is commonly required to inform restoration decision-making. Index-based habitat models are a common approach for assessing ecosystem condition. These models relate habitat quality to species’ distributions. Habitat suitability (quality) typically ranges on a scale from 0 to 1. Habitat models have been developed to assess habitat suitability for specific taxa, communities, or ecosystem functions. Restoration-project timelines often require that these models be developed rapidly and in conjunction with many external stakeholders or partners. Here, the Toolkit for interActive Modeling (TAM) is proposed as a platform for rapidly developing index-based models, particularly for US Army Corps of Engineers’ (USACE) ecosystem-restoration or mitigation planning processes. The TAM is a consistent quantitative framework that allows for development of a generic platform for index-based model development.
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Gertner, George. Error and Uncertainty Analysis for Ecological Modeling and Simulation. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada483511.

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Gertner, George. Error and Uncertainty Analysis for Ecological Modeling and Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada363719.

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Gonzalez, Logan, Christopher Baker, Stacey Doherty, and Robyn Barbato. Ecological modeling of microbial community composition under variable temperatures. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48184.

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Soil microorganisms interact with one another within soil pores and respond to external conditions such as temperature. Data on microbial community composition and potential function are commonly generated in studies of soils. However, these data do not provide direct insight into the drivers of community composition and can be difficult to interpret outside the context of ecological theory. In this study, we explore the effect of abiotic environmental variation on microbial species diversity. Using a modified version of the Lotka-Volterra Competition Model with temperature-dependent growth rates, we show that environmentally relevant temperature variability may expand the set of temperature-tolerance phenotype pairs that can coexist as two-species communities compared to constant temperatures. These results highlight a potential role of temperature variation in influencing microbial diversity. This in turn suggests a need to incorporate temperature into predictive models of microbial communities in soil and other environments. We recommend future work to parameterize the model applied in this study with empirical data from environments of interest, and to validate the model predictions using field observations and experimental manipulations.
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McKay, S., Nate Richards, and Todd Swannack. Ecological model development : evaluation of system quality. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45380.

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Ecological models are used throughout the US Army Corps of Engineers (USACE) to inform decisions related to ecosystem restoration, water operations, environmental impact assessment, environmental mitigation, and other topics. Ecological models are typically developed in phases of conceptualization, quantification, evaluation, application, and communication. Evaluation is a process for assessing the technical quality, reliability, and ecological basis of a model and includes techniques such as calibration, verification, validation, and review. In this technical note (TN), we describe an approach for evaluating system quality, which generally includes the computational integrity, numerical accuracy, and programming of a model or modeling system. Methods are presented for avoiding computational errors during development, detecting errors through model testing, and updating models based on review and use. A formal structure is proposed for model test plans and subsequently demonstrated for a hypothetical habitat suitability model. Overall, this TN provides ecological modeling practitioners with a rapid guide for evaluating system quality.
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King, A. W., T. L. Ashwood, B. L. Jackson, H. I. Jager, and C. Hunsacker. Ecological Modeling and Simulation Using Error and Uncertainty Analysis Methods (Project CS-1097). Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada363681.

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Harris, Aubrey, Nathan Richards, and S. McKay. Defining levels of effort for ecological models. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47642.

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While models are useful tools for decision-making in environmental management, the question arises about the level of effort required to develop an effective model for a given application. In some cases, it is unclear whether more analysis would lead to choosing a better course of action. This technical note (TN) examines the role of ecological model complexity in ecosystem management. First, model complexity is examined through the lens of risk informed planning. Second, a framework is presented for categorizing five different levels of effort that range from conceptual models to detailed predictive tools. This framework is proposed to enhance communication and provide consistency in ecological modeling applications. Third, the level of effort framework is applied to a set of models in the Middle Rio Grande River system to demonstrate the framework’s utility and application. Ultimately, this TN seeks to guide planners in determining an appropriate level of effort relative to risks associated with uncertainty and resource availability for a given application.
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