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15 results for “economic threat”
Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"
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Supplementary material 2 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Figure S2 : Explanation note: The impact magnitude of the 48 studied alien grasses across different habitats. The impact magnitudes on the x-axis are the least-square means of the impact scores as derived from a cumulative link mixed effects model. On the y-axis are the habitat types impacted by alien grasses and in brackets is the number of species with records in that habitat. The points represent the impact magnitudes and the error bars represent 95 % confidence intervals. Letters on the right side of the confidence intervals are level groupings indicating no significant differences among the habits. Comparisons are Tukey adjusted.
Supplementary material 1 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Supplementary material 1 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Supplementary material 7 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Relative weights of predictor categories in the linear robust regression between cost data and time period
Supplementary material 11 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Summary of the outputs from the different models used for analyzing the temporal trend of invasion costs in Africa between 1970 and 2019
Supplementary material 10 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
List of species as well as their cost estimates recorded in our dataset
Supplementary material 8 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Quantitative summary of the costs reported in each African region
Supplementary material 9 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Distribution of the reliable observed costs
Supplementary material 1 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Starting dataset considered in this study
Supplementary material 2 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Data collection and filtering processes
Supplementary material 5 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Quantitative summary of the cost data and estimates
Supplementary material 4 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Conservative subset obtained following specific filtering steps applied to the starting dataset
Supplementary material 3 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Summary of the descriptive columns of the database used in this study (from Diagne et al. 2020c)
Supplementary material 6 from: Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
Categorization of recorded cost data into "damage" costs
Purity, Politics, and Polarization: Political Ideology Moderates Shifts in Moral Purity Beliefs to Economic Threat
<p>Study 1 Variables:</p> <p>Threat: Var00001 (1=threat, 2=control)</p> <p>Anxiety manipulation check: Anxiety</p> <p>Moral Foundations Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG</p> <p>Political Ideology (higher scores mean the participant is more conservative): PolitID</p> <p>Pro-life New Zealand charity: Purity_donation_true</p> <p> </p> <p>Study 2 Variables: </p> <p>Threat: Condition (1=threat, 0=control)</p> <p>Anxiety manipulation check: Anxiety</p> <p>Purity Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG</p> <p>Political Ideology (higher scores mean the participant is more conservative): POLiD</p> <p>Casual sex disapproval: xCASUAL_SEX</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.