Come on feel the noise: Ecological foundations in stochastic bioeconomic models

2018 ◽  
Vol 31 (4) ◽  
pp. e12191
Author(s):  
Charles Sims ◽  
Richard D. Horan ◽  
Benjamin Meadows
Keyword(s):  
Bioeconomic Models

scholarly journals Fishing Power Functions in Aggregate Bioeconomic Models

1985 ◽  
Vol 2 (1) ◽  
pp. 87-107 ◽  
Author(s):  
Timothy G. Taylor ◽  
Fred J. Prochaska
Keyword(s):  
Power Functions ◽  
Bioeconomic Models

scholarly journals Integration-Valuation Nexus in Invasive Species Policy

2006 ◽  
Vol 35 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Jason F. Shogren ◽  
David Finnoff ◽  
Chris McIntosh ◽  
Chad Settle
Keyword(s):  
Invasive Species ◽  
Nonmarket Valuation ◽  
Feedback Loops ◽  
Economic Research ◽  
Policy Integration ◽  
Research Areas ◽  
Invasive Biology ◽  
Invasive Species Policy ◽  
Environmental Damages ◽  
Bioeconomic Models

This paper reviews recent work examining two topics of economic research vital for invasive species policy—integration and valuation. Integration requires bioeconomic models that blend invasive biology with economic circumstances and the feedback loops between the two systems. Valuation requires nonmarket valuation associated with human and environmental damages posed by invasive species. We argue for a second-level of integration in invasive species economics—valuation based on integration models. Policy prescriptions based on integration models need valuation work; valuation surveys need integration models—the two are complements. Valuation could be enhanced with integration in mind; integration could be made better with valuation in mind. An example from blending the two research areas is presented and its merits demonstrated.

scholarly journals An integrated approach to analyzing risk in bioeconomic models

2018 ◽  
Vol 31 (3) ◽  
pp. e12172 ◽  
Author(s):  
Biswo N. Poudel ◽  
Krishna P. Paudel
Keyword(s):  
Integrated Approach ◽  
Bioeconomic Models

scholarly journals THE ENSEMBLE KALMAN FILTER FOR MULTIDIMENSIONAL BIOECONOMIC MODELS

2015 ◽  
Vol 28 (3) ◽  
pp. 321-347 ◽  
Author(s):  
STURLA F. KVAMSDAL ◽  
LEIF K. SANDAL
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Bioeconomic Models

Bioeconomic models of marine recreational fishing

1979 ◽  
Vol 6 (2) ◽  
pp. 127-139 ◽  
Author(s):  
Kenneth E McConnell ◽  
Jon G Sutinen
Keyword(s):  
Recreational Fishing ◽  
Bioeconomic Models

Selection of a Barley Yield Model Using Information–Theoretic Criteria

Weed Science ◽  
2008 ◽  
Vol 56 (4) ◽  
pp. 628-636 ◽  
Author(s):  
Marie Jasieniuk ◽  
Mark L. Taper ◽  
Nicole C. Wagner ◽  
Robert N. Stougaard ◽  
Monica Brelsford ◽  
...  
Keyword(s):  
Functional Form ◽  
Information Criterion ◽  
Small Sample ◽  
Theoretic Approach ◽  
Wild Oat ◽  
Information Theoretic ◽  
Information Theoretic Criteria ◽  
The Impact ◽  
Selection Of ◽  
Bioeconomic Models

Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized complete block design with three replications. Barley was planted at 0, 0.5, 1, and 2 times the locally recommended seeding rate. Wild oat was planted at target infestation densities of 0, 10, 40, 160, and 400 plants m−2. Twenty-five candidate yield models were used to describe the data from each location and year using maximum likelihood estimation. Based on Akaike's Information Criterion (AIC), a second-order small-sample version ofAIC(AICc), and the Bayesian Information Criterion (BIC), most data sets supported yield models with crop density (Dc), weed density (Dw), and the relative time of emergence of the two species (T) as variables, indicating that all variables affected barley yield in most locations.AIC,AICc, andBICselected identical best models for all but one data set. In contrast, the Information Complexity criterion,ICOMP, generally selected simpler best models with fewer parameters. For data pooled over years and locations,AIC,AICc, andBICstrongly supported a single best model with variablesDc,Dw,T, and a functional form specifying both intraspecific and interspecific competition.ICOMPselected a simpler model withDcandDwonly, and a functional form specifying interspecific, but no intraspecific, competition. The information–theoretic approach offers a rigorous, objective method for choosing crop yield and yield loss equations for bioeconomic models.

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