This data release includes tabular data files. The dataset consists of four input data tables (Appendices A1-A4) for a Lake Superior EcoPath with EcoSim (EwE; http://ecopath.org) model parameterized to the early 21st century using 2001-2016 collections. The data presented here are primarily intended to development a static ecosystem model representing a snapshot of Lake Superior circa 2005 when the bulk of information was collected. Input data were compiled from multiple federal, state, provincial, and tribal agencies, academic institutions, published reports, theses, and peer review journal articles. This dataset includes results from lake-wide Cooperative Science and Monitoring Initiative surveys (CSMI; CSMI 2020) of Lake Superior undertaken in 2005-06, 2011 and 2016. We provide results of three lake-wide acoustic surveys (2003-2006, 2011 and 2016) that provided biomass estimates (kg/ha) of pelagic prey (cisco, bloater, kiyi and rainbow smelt); the later two (2011 and 2016) were part of the larger CSMI efforts. Our inputs on fish rely heavily on data included in Isaac (2010) who used USGS bottom trawl samples. These trawl data had been included in a previously released data set (Great Lakes Science Center 2019). We include these samples in the current release because we made a QA/QC effort to verify that the results of Isaac (2010) could be reproduced. We found that biomass estimates were close to that reported by Isaac (2010) for most groups (see associated metadata record process step 8 for details), but not exact. We opted to use biomass estimates as reported in Isaac (2010) for four species because his production to biomass (P/B) we’re using in the EcoPath model are explicitly linked to the biomass estimates provided by Isaac (2010). Collectively, these data represent the best available estimates of lake-wide population characteristics (biomass, production, consumption, diet, harvest, etc.) across trophic levels from bacteria to sea lamprey. Readers interested in learning more details about this data compilation and balancing of this Lake Superior ecosystem model should read the associated manuscript (Matthias, et al.), which is noted in the metadata cross reference section. References: Cooperative Science and Monitoring Initiative (CSMI). 2020. U.S. Environmental Protection Agency. https://www.epa.gov/great-lakes-monitoring/cooperative-science-and-monitoring-initiative-csmi [accessed 10/82020] Isaac, E.J., 2010. An Evaluation of the Importance of Mysis relicta to the Lake Superior Fish Community. University of Minnesota - Duluth, Duluth, MN. https://conservancy.umn.edu/handle/11299/93161 [accessed 10/8/2020]. Matthias, B.G., T.R. Hrabik, J. Hoffman, M. Seider, D. Yule, M. Sierszen, and P. Yurista. In review. Trophic transfer efficiency in the Lake Superior food web: assessing the impacts of non-native species. Journal of Great Lakes Research. USGS (U.S. Geological Survey), 2019. Great Lakes Research Vessel Operations 1958-2018. (ver. 3.0, April 2019): U.S. Geological Survey data release. Available from: https://doi.org/10.5066/F75M63X0 [accessed 10/8/2020].

This data release includes tabular data files. The dataset consists of four input data tables (Appendices A1-A4) for a Lake Superior EcoPath with EcoSim (EwE; http://ecopath.org) model parameterized to the early 21st century using 2001-2016 collections. The data presented here are primarily intended to development a static ecosystem model representing a snapshot of Lake Superior circa 2005 when the bulk of information was collected. Input data were compiled from multiple federal, state, provincial, and tribal agencies, academic institutions, published reports, theses, and peer review journal articles. This dataset includes results from lake-wide Cooperative Science and Monitoring Initiative surveys (CSMI; CSMI 2020) of Lake Superior undertaken in 2005-06, 2011 and 2016. We provide results of three lake-wide acoustic surveys (2003-2006, 2011 and 2016) that provided biomass estimates (kg/ha) of pelagic prey (cisco, bloater, kiyi and rainbow smelt); the later two (2011 and 2016) were part of the larger CSMI efforts. Our inputs on fish rely heavily on data included in Isaac (2010) who used USGS bottom trawl samples. These trawl data had been included in a previously released data set (Great Lakes Science Center 2019). We include these samples in the current release because we made a QA/QC effort to verify that the results of Isaac (2010) could be reproduced. We found that biomass estimates were close to that reported by Isaac (2010) for most groups (see associated metadata record process step 8 for details), but not exact. We opted to use biomass estimates as reported in Isaac (2010) for four species because his production to biomass (P/B) we’re using in the EcoPath model are explicitly linked to the biomass estimates provided by Isaac (2010). Collectively, these data represent the best available estimates of lake-wide population characteristics (biomass, production, consumption, diet, harvest, etc.) across trophic levels from bacteria to sea lamprey. Readers interested in learning more details about this data compilation and balancing of this Lake Superior ecosystem model should read the associated manuscript (Matthias, et al.), which is noted in the metadata cross reference section. References: Cooperative Science and Monitoring Initiative (CSMI). 2020. U.S. Environmental Protection Agency. https://www.epa.gov/great-lakes-monitoring/cooperative-science-and-monitoring-initiative-csmi [accessed 10/82020] Isaac, E.J., 2010. An Evaluation of the Importance of Mysis relicta to the Lake Superior Fish Community. University of Minnesota - Duluth, Duluth, MN. https://conservancy.umn.edu/handle/11299/93161 [accessed 10/8/2020]. Matthias, B.G., T.R. Hrabik, J. Hoffman, M. Seider, D. Yule, M. Sierszen, and P. Yurista. In review. Trophic transfer efficiency in the Lake Superior food web: assessing the impacts of non-native species. Journal of Great Lakes Research. USGS (U.S. Geological Survey), 2019. Great Lakes Research Vessel Operations 1958-2018. (ver. 3.0, April 2019): U.S. Geological Survey data release. Available from: https://doi.org/10.5066/F75M63X0 [accessed 10/8/2020].

Contains the model output from Spadyno, a benthic habitat modelling and display package developed for the NWSJEMS project. The model predicts benthic habitat recovery under various fishing regimes. Two different modelling approaches can be selected by the user. The software also allows the user to vary environmental parameters such as cyclone activity and currents. Accompanying the model output on the production CD is source code, input data and documentation. The model is written in C and the output is in Visual Basic.

This project was developed for the Ningaloo Research Program (NRP) to explore the effects of managing recreational fishing, which is perhaps the most important extractive activities in the Ningaloo Marine Park. The project used simulation techniques known as Management Strategy Evaluation (MSE) to explore the consequences of a range of management actions, under a series of alternative future scenarios on the management of a major target species on Ningaloo Reef, spangled emperor (Lethrinus nebulosus). The results of the scenarios are examined against the objectives set out by management and other stakeholders in the park. A simulation model, known as ELFSim, was used. ELFSim is a decision support software system designed to evaluate options for conservation and harvest management, and includes a number of key components: a population dynamics model of target species that captures the full life history (including larval dispersal, reproduction, development, and habits) of the target species, a model of fishing dynamics that captures the exploitation pattern due to fishing behaviour, a management model that simulates the implementation of management actions. ELFSim was developed for other coral reef fisheries where commercial fishing was the primary fishing activity, and in this sought to develop a simulation model of recreational fishing dynamics. This model was agent-based, meaning that individual recreational fishing boats were represented in the model, and a range of management measures were tested on the ability to manage these virtual recreational fishers. These management measures, derived from stakeholder workshops include the effect of increasing the no take sanctuary zones, and restricting the fishing in sanctuary zones that occurs from shore. The effectiveness of these management actions in the simulation model was measured against the management objectives of the stakeholders. Management objectives were classified according to ecological (conservation) objectives, or social and economic objectives. The results showed that the current management arrangement perform adequately against the range of ecological and social objectives. However, for other management actions, the results showed the inherent trade-off that exists between the ecological objective and the social objectives.

This project was developed for the Ningaloo Research Program (NRP) to explore the effects of managing recreational fishing, which is perhaps the most important extractive activities in the Ningaloo Marine Park. The project used simulation techniques known as Management Strategy Evaluation (MSE) to explore the consequences of a range of management actions, under a series of alternative future scenarios on the management of a major target species on Ningaloo Reef, spangled emperor (Lethrinus nebulosus). The results of the scenarios are examined against the objectives set out by management and other stakeholders in the park. A simulation model, known as ELFSim, was used. ELFSim is a decision support software system designed to evaluate options for conservation and harvest management, and includes a number of key components: a population dynamics model of target species that captures the full life history (including larval dispersal, reproduction, development, and habits) of the target species, a model of fishing dynamics that captures the exploitation pattern due to fishing behaviour, a management model that simulates the implementation of management actions. ELFSim was developed for other coral reef fisheries where commercial fishing was the primary fishing activity, and in this sought to develop a simulation model of recreational fishing dynamics. This model was agent-based, meaning that individual recreational fishing boats were represented in the model, and a range of management measures were tested on the ability to manage these virtual recreational fishers. These management measures, derived from stakeholder workshops include the effect of increasing the no take sanctuary zones, and restricting the fishing in sanctuary zones that occurs from shore. The effectiveness of these management actions in the simulation model was measured against the management objectives of the stakeholders. Management objectives were classified according to ecological (conservation) objectives, or social and economic objectives. The results showed that the current management arrangement perform adequately against the range of ecological and social objectives. However, for other management actions, the results showed the inherent trade-off that exists between the ecological objective and the social objectives.

This study produced qualitative models that assembled stakeholder perceptions of various assets and issues within the Peel Harvey estuarine system including water quality, wading birds, blue swimmer crabs and governance. The models were developed through workshops with a wide variety of stakeholders, including community groups, government agencies, researchers, managers and non-government organisations, and discussions with individuals or small groups following the workshops. Each model was used to assess the current situation and the drivers of change that were negatively impacting the focal asset. Potential management strategies were then identified and the ‘best case management strategy’, where both model stability and asset management were improved, was incorporated in a ‘future’ model. Common themes that arose throughout this process were the need to improve water quality throughout the estuary and nearby rivers by reducing nutrient input from various sources, and the need to alter current governance structures to allow effective environmental management.