Computer models and analysis in insect ecology, behavior, and chemical ecology
Dr. John A. Byers
Computer implemented models are important in understanding ecological and behavioral phenomena/processes as well as in analyzing and revealing patterns and correlations of observational data. Models are best suited to simplify natural complexity in order to understand the significant factors functioning in various biological systems. Secondarily, models can be used to predict outcomes based on many observed variables, but natural variation in the parameters can interact in unpredictable ways causing small to large errors in predictions. Computers are often linked with scientific measurement equipment and act in recording, analyzing and presenting data. In addition to modeling, computer programs are used in specific and specialized applications of data analysis with statistical methods and graphical methods. All areas of ecology and in fact science utilize models and computer analyses and this reliance will likely increase in the future. Dr. John Byers is an entomologist (B.S. Colorado State Univ; Ph.D. Univ. Calif. Berkeley; postdoc Lund Univ. Sweden; USDA-ARS research scientist, Arizona) with a long experience in certain aspects of modeling and computer analysis in studies of entomology in the laboratory and field.
Level of course will be adjusted to the knowledge level and capability of the students participating.
Course plan (subject to change):
Discussion of general models, computer models, programming, and computer analysis.
Programming: (a) antique QuickBASIC, (b) web Javascript, (c) application Java
Alternatives to programming: Commercial software (advantages and disadvantages)
Models in ecology: (a) dispersal, (b) encounter-rate, (c) vertical flight distribution, and others
Models in behavior: (a) minimum allowed distance (MAD), (b) Dirichlet Tessellation, (c) orientation, (d) video analysis, and others
Models in chemoecology: (a) plume and upwind orientation, (b) pheromone lures, others
Models in pest management: semiochemical traps for detection, monitoring, mating disruption, and mass trapping; synchronous and asynchronous areawide treatments
Analysis: video analysis, image analysis, iterative equations, other commercial software
Practical applications of models in studies in Israel
2 point Graduate course limited to 30 students (preferably 20) – about 14 two-hour periods (once per week)
4-6 sentences abstract:
An introductory course on how computer models and programming are important in the understanding of biological processes in the fields of entomology, ecology, behavior, and chemical ecology. Computer programs are used (1) in modeling, (2) in analyzing observational data, and (3) in control of scientific equipment to record, analyze and present data. In addition to the above, the course will present basic problems in several areas of ecology, chemical ecology, and entomology. The course will demonstrate how these problems can be understood, analyzed, and solved by computer programs that lead to new questions and design of experiments. The instructor, Dr. John Byers, is an entomologist (B.S., M.S., Ph.D.) with a long experience in Sweden and USA on aspects of modeling and computer analysis in the laboratory and field.
The weeks below will comprise one 2-hour period each week with a presentation of results of the indicated published journal articles along with demonstration of the model’s programs on personal computer. Models are nearly all visual and therefor intuitively understood. Questions and discussion about the assumptions and usefulness of each model will be entertained.
More detailed Course plan:
Week 1
Introduction to each other of students and Dr. Byers. Discussion of levels of knowledge of the students and their goals in general.
What are models? Discussion of general models, computer models, programming, and computer analysis.
Week 2
Introduction to types of programming to implement models:
a) BASIC language (QuickBASIC, other similar):
Byers, J. A. 1991. BASIC algorithms for random sampling and treatment randomization. Computers in Biology and Medicine 21:69-77
Byers, J. A. 1993. Randomization algorithms in BASIC for experimental design. Computers in Biology and Medicine 23:167-176
Byers, J. A. 1996. Random selection algorithms for spatial and temporal sampling. Computers in Biology and Medicine 26:41 52
b) Javascript and Java applets in Internet web browsers
Byers, J.A. 2002. Internet programs for drawing moth pheromone analogs and searching literature database. Journal of Chemical Ecology 28: 807-817
Week 3
Introduction to types of programming to implement models (continued):
a) Java applets and Java applications
Byers, J. A. 1996. Taxokey: A universal taxonomic key program using DOS text files and graphics. Computer Applications in the Biosciences 12:185-189
Byers, J. A. 2004. Equations for nickel-chromium wire heaters of column transfer lines in gas chromatographic-electroantennographic detection (GC-EAD). Journal of Neuroscience Methods 135:89-93
Byers, J. A. 2005. Chemical constraints on the evolution of olfactory communication channels of moths. Journal of Theoretical Biology 235:199-206.
b) Alternatives to programming: Commercial software (advantages and disadvantages)
Week 4
Models in spatial ecology:
Minimum allowed distance (MAD):
a) Byers, J. A. 1984. Nearest neighbor analysis and simulation of distribution patterns indicates an attack spacing mechanism in the bark beetle, Ips typographus (Coleoptera: Scolytidae). Environmental Entomology 13:1191 1200.
Density analysis:
b) Byers, J. A. 1992. Grid cell contour mapping of point densities: bark beetle attacks, fallen pine shoots, and infested trees. Oikos 63:233-243.
Dirichlet tessellation:
c) Byers, J. A. 1992. Dirichlet tessellation of bark beetle spatial attack points. Journal of Animal Ecology 61:759-768.
Week 5
Models in dispersal ecology:
a) Byers, J. A. 1993. Simulation and equation models of insect population control by pheromone-baited traps. Journal of Chemical Ecology 19:1939-1956.
b) Byers, J. A. 2000. Wind-aided dispersal of simulated bark beetles flying through forests. Ecological Modelling 125:231-243.
c) Byers, J. A. 2001. Correlated random walk equations of animal dispersal resolved by simulation. Ecology 82:1680-1690.
Week 6
Models in encounter ecology (predator-prey and mate/host finding):
EAR – effective attraction radius:
a) Byers, J. A., Anderbrant, O., and Löfqvist, J. 1989. Effective attraction radius: A method for comparing species attractants and determining densities of flying insects. Journal of Chemical Ecology 15:749-765
Encounter rate of mate finding and host plant finding
b) Byers, J. A. 1991. Simulation of mate-finding behaviour of pine shoot beetles, Tomicus piniperda. Animal Behaviour 41:649-660
c) Byers, J. A. 1996. An encounter rate model for bark beetle populations searching at random for susceptible host trees. Ecological Modelling 91:57-66
d) Byers, J. A. 1999. Effects of attraction radius and flight paths on catch of scolytid beetles dispersing outward through rings of pheromone traps. Journal of Chemical Ecology 25:985-1005.
Week 7
Models in pest management using encounter-rate models (detection, monitoring, mating disruption, and mass trapping):
a) Byers, J. A. 2007. Simulation of mating disruption and mass trapping with competitive attraction and camouflage. Environmental Entomology 36:1328-1338
b) Byers, J. A. 2008. Active space of pheromone plume and its relationship to effective attraction radius in applied models. Journal of Chemical Ecology 34:1134-1145
c) Byers, J. A. 2009. Modeling distributions of flying insects: Effective attraction radius of pheromone in two and three dimensions. Journal of Theoretical Biology 256:81-89
Vertical flight distribution:
d) Byers, J. A. 2011. Analysis of vertical distributions and effective flight layers of insects: Three-dimensional simulation of flying insects and catch at trap heights. Environmental Entomology. 40:1210-1222
e) Byers, J. A. 2012. Estimating insect flight densities from attractive trap catches and flight height distributions. Journal of Chemical Ecology 38:592-601
f) Byers, J. A. 2012. Modelling female mating success during mass trapping and natural competitive attraction of searching males or females. Entomologia Experimentalis et Applicata 145:228-237.
g) Byers, J. A. and Naranjo, S. E. 2014. Detection and monitoring of pink bollworm moths and invasive insects using pheromone traps and encounter rate models. Journal of Applied Ecology 51(4):1041-1049.
Week 8
Models in pest management in Israel:
a) Levi−Zada, A., Sadowsky, A., Dobrinin, S., Ticuchinski, T., Fefer, D., Dunkelblum, E., and Byers, J.A. 2017. Monitoring and mass-trapping methodologies using pheromones: The lesser date moth Batrachedra amydraula. Bulletin of Entomological Research in press
b) Byers, J.A., Maoz, Y., and Levi-Zada, A. 2017. Attraction of the Euwallacea sp. near fornicatus to quercivorol and to infestations in avocado. Journal of Economic Entomology 110:1512-1517.
c) Push-pull
Week 9
Models in chemoecology:
a) plume and upwind orientation: Byers, J. A. 1996. Temporal clumping of bark beetle arrival at pheromone traps: Modeling anemotaxis in chaotic plumes. Journal of Chemical Ecology 22:2133-2155
b) Right-left orientation
c) Dose-response: Byers, J. A. 2013. Modeling and regression analysis of semiochemical dose-response curves of insect antennal reception and behavior. J. Chem. Ecol. 39(8):1081-1089
d) Byers, J.A. 2015. Earwigs (Labidura riparia) mimic rotting-flesh odor to deceive vertebrate predators. Science of Nature 102:38; doi: 10.1007/s00114-015-1288-1.
Week 10
Models in pest management: synchronous and asynchronous areawide treatments:
Byers, J. A., and Castle, S. J. 2005. Areawide models comparing synchronous versus asynchronous treatments for control of dispersing insect pests. Journal of Economic Entomology 98:1763-1773
Week 11
Analysis: video analysis, image analysis
Week 12
Professional software: iterative equations, mathematical, statistical
Week 13
Group work: Literature review of specific models of interest to each student group
Week 14
Miscellaneous models:
a) Pheromone isolation and identification: Byers, J. A. 1992. Optimal fractionation and bioassay plans for isolation of synergistic chemicals: the subtractive-combination method. Journal of Chemical Ecology 18:1603-1621.
b) Population genetic model:Byers, J. A. 2012. A population genetic model of evolution of host-mate attraction and nonhost repulsion in a bark beetle Pityogenes bidentatus. Psyche: a Journal of Entomology vol. 2012, ID 529573, pp. 1-9
c) Color analysis: Byers, J. A. 2006. Analysis of insect and plant colors in digital images using Java software on the Internet. Annals of the Entomological Society of America 99:865-874
Dr. John A. Byers
Computer implemented models are important in understanding ecological and behavioral phenomena/processes as well as in analyzing and revealing patterns and correlations of observational data. Models are best suited to simplify natural complexity in order to understand the significant factors functioning in various biological systems. Secondarily, models can be used to predict outcomes based on many observed variables, but natural variation in the parameters can interact in unpredictable ways causing small to large errors in predictions. Computers are often linked with scientific measurement equipment and act in recording, analyzing and presenting data. In addition to modeling, computer programs are used in specific and specialized applications of data analysis with statistical methods and graphical methods. All areas of ecology and in fact science utilize models and computer analyses and this reliance will likely increase in the future. Dr. John Byers is an entomologist (B.S. Colorado State Univ; Ph.D. Univ. Calif. Berkeley; postdoc Lund Univ. Sweden; USDA-ARS research scientist, Arizona) with a long experience in certain aspects of modeling and computer analysis in studies of entomology in the laboratory and field.
Level of course will be adjusted to the knowledge level and capability of the students participating.
Course plan (subject to change):
Discussion of general models, computer models, programming, and computer analysis.
Programming: (a) antique QuickBASIC, (b) web Javascript, (c) application Java
Alternatives to programming: Commercial software (advantages and disadvantages)
Models in ecology: (a) dispersal, (b) encounter-rate, (c) vertical flight distribution, and others
Models in behavior: (a) minimum allowed distance (MAD), (b) Dirichlet Tessellation, (c) orientation, (d) video analysis, and others
Models in chemoecology: (a) plume and upwind orientation, (b) pheromone lures, others
Models in pest management: semiochemical traps for detection, monitoring, mating disruption, and mass trapping; synchronous and asynchronous areawide treatments
Analysis: video analysis, image analysis, iterative equations, other commercial software
Practical applications of models in studies in Israel
2 point Graduate course limited to 30 students (preferably 20) – about 14 two-hour periods (once per week)
4-6 sentences abstract:
An introductory course on how computer models and programming are important in the understanding of biological processes in the fields of entomology, ecology, behavior, and chemical ecology. Computer programs are used (1) in modeling, (2) in analyzing observational data, and (3) in control of scientific equipment to record, analyze and present data. In addition to the above, the course will present basic problems in several areas of ecology, chemical ecology, and entomology. The course will demonstrate how these problems can be understood, analyzed, and solved by computer programs that lead to new questions and design of experiments. The instructor, Dr. John Byers, is an entomologist (B.S., M.S., Ph.D.) with a long experience in Sweden and USA on aspects of modeling and computer analysis in the laboratory and field.
The weeks below will comprise one 2-hour period each week with a presentation of results of the indicated published journal articles along with demonstration of the model’s programs on personal computer. Models are nearly all visual and therefor intuitively understood. Questions and discussion about the assumptions and usefulness of each model will be entertained.
More detailed Course plan:
Week 1
Introduction to each other of students and Dr. Byers. Discussion of levels of knowledge of the students and their goals in general.
What are models? Discussion of general models, computer models, programming, and computer analysis.
Week 2
Introduction to types of programming to implement models:
a) BASIC language (QuickBASIC, other similar):
Byers, J. A. 1991. BASIC algorithms for random sampling and treatment randomization. Computers in Biology and Medicine 21:69-77
Byers, J. A. 1993. Randomization algorithms in BASIC for experimental design. Computers in Biology and Medicine 23:167-176
Byers, J. A. 1996. Random selection algorithms for spatial and temporal sampling. Computers in Biology and Medicine 26:41 52
b) Javascript and Java applets in Internet web browsers
Byers, J.A. 2002. Internet programs for drawing moth pheromone analogs and searching literature database. Journal of Chemical Ecology 28: 807-817
Week 3
Introduction to types of programming to implement models (continued):
a) Java applets and Java applications
Byers, J. A. 1996. Taxokey: A universal taxonomic key program using DOS text files and graphics. Computer Applications in the Biosciences 12:185-189
Byers, J. A. 2004. Equations for nickel-chromium wire heaters of column transfer lines in gas chromatographic-electroantennographic detection (GC-EAD). Journal of Neuroscience Methods 135:89-93
Byers, J. A. 2005. Chemical constraints on the evolution of olfactory communication channels of moths. Journal of Theoretical Biology 235:199-206.
b) Alternatives to programming: Commercial software (advantages and disadvantages)
Week 4
Models in spatial ecology:
Minimum allowed distance (MAD):
a) Byers, J. A. 1984. Nearest neighbor analysis and simulation of distribution patterns indicates an attack spacing mechanism in the bark beetle, Ips typographus (Coleoptera: Scolytidae). Environmental Entomology 13:1191 1200.
Density analysis:
b) Byers, J. A. 1992. Grid cell contour mapping of point densities: bark beetle attacks, fallen pine shoots, and infested trees. Oikos 63:233-243.
Dirichlet tessellation:
c) Byers, J. A. 1992. Dirichlet tessellation of bark beetle spatial attack points. Journal of Animal Ecology 61:759-768.
Week 5
Models in dispersal ecology:
a) Byers, J. A. 1993. Simulation and equation models of insect population control by pheromone-baited traps. Journal of Chemical Ecology 19:1939-1956.
b) Byers, J. A. 2000. Wind-aided dispersal of simulated bark beetles flying through forests. Ecological Modelling 125:231-243.
c) Byers, J. A. 2001. Correlated random walk equations of animal dispersal resolved by simulation. Ecology 82:1680-1690.
Week 6
Models in encounter ecology (predator-prey and mate/host finding):
EAR – effective attraction radius:
a) Byers, J. A., Anderbrant, O., and Löfqvist, J. 1989. Effective attraction radius: A method for comparing species attractants and determining densities of flying insects. Journal of Chemical Ecology 15:749-765
Encounter rate of mate finding and host plant finding
b) Byers, J. A. 1991. Simulation of mate-finding behaviour of pine shoot beetles, Tomicus piniperda. Animal Behaviour 41:649-660
c) Byers, J. A. 1996. An encounter rate model for bark beetle populations searching at random for susceptible host trees. Ecological Modelling 91:57-66
d) Byers, J. A. 1999. Effects of attraction radius and flight paths on catch of scolytid beetles dispersing outward through rings of pheromone traps. Journal of Chemical Ecology 25:985-1005.
Week 7
Models in pest management using encounter-rate models (detection, monitoring, mating disruption, and mass trapping):
a) Byers, J. A. 2007. Simulation of mating disruption and mass trapping with competitive attraction and camouflage. Environmental Entomology 36:1328-1338
b) Byers, J. A. 2008. Active space of pheromone plume and its relationship to effective attraction radius in applied models. Journal of Chemical Ecology 34:1134-1145
c) Byers, J. A. 2009. Modeling distributions of flying insects: Effective attraction radius of pheromone in two and three dimensions. Journal of Theoretical Biology 256:81-89
Vertical flight distribution:
d) Byers, J. A. 2011. Analysis of vertical distributions and effective flight layers of insects: Three-dimensional simulation of flying insects and catch at trap heights. Environmental Entomology. 40:1210-1222
e) Byers, J. A. 2012. Estimating insect flight densities from attractive trap catches and flight height distributions. Journal of Chemical Ecology 38:592-601
f) Byers, J. A. 2012. Modelling female mating success during mass trapping and natural competitive attraction of searching males or females. Entomologia Experimentalis et Applicata 145:228-237.
g) Byers, J. A. and Naranjo, S. E. 2014. Detection and monitoring of pink bollworm moths and invasive insects using pheromone traps and encounter rate models. Journal of Applied Ecology 51(4):1041-1049.
Week 8
Models in pest management in Israel:
a) Levi−Zada, A., Sadowsky, A., Dobrinin, S., Ticuchinski, T., Fefer, D., Dunkelblum, E., and Byers, J.A. 2017. Monitoring and mass-trapping methodologies using pheromones: The lesser date moth Batrachedra amydraula. Bulletin of Entomological Research in press
b) Byers, J.A., Maoz, Y., and Levi-Zada, A. 2017. Attraction of the Euwallacea sp. near fornicatus to quercivorol and to infestations in avocado. Journal of Economic Entomology 110:1512-1517.
c) Push-pull
Week 9
Models in chemoecology:
a) plume and upwind orientation: Byers, J. A. 1996. Temporal clumping of bark beetle arrival at pheromone traps: Modeling anemotaxis in chaotic plumes. Journal of Chemical Ecology 22:2133-2155
b) Right-left orientation
c) Dose-response: Byers, J. A. 2013. Modeling and regression analysis of semiochemical dose-response curves of insect antennal reception and behavior. J. Chem. Ecol. 39(8):1081-1089
d) Byers, J.A. 2015. Earwigs (Labidura riparia) mimic rotting-flesh odor to deceive vertebrate predators. Science of Nature 102:38; doi: 10.1007/s00114-015-1288-1.
Week 10
Models in pest management: synchronous and asynchronous areawide treatments:
Byers, J. A., and Castle, S. J. 2005. Areawide models comparing synchronous versus asynchronous treatments for control of dispersing insect pests. Journal of Economic Entomology 98:1763-1773
Week 11
Analysis: video analysis, image analysis
Week 12
Professional software: iterative equations, mathematical, statistical
Week 13
Group work: Literature review of specific models of interest to each student group
Week 14
Miscellaneous models:
a) Pheromone isolation and identification: Byers, J. A. 1992. Optimal fractionation and bioassay plans for isolation of synergistic chemicals: the subtractive-combination method. Journal of Chemical Ecology 18:1603-1621.
b) Population genetic model:Byers, J. A. 2012. A population genetic model of evolution of host-mate attraction and nonhost repulsion in a bark beetle Pityogenes bidentatus. Psyche: a Journal of Entomology vol. 2012, ID 529573, pp. 1-9
c) Color analysis: Byers, J. A. 2006. Analysis of insect and plant colors in digital images using Java software on the Internet. Annals of the Entomological Society of America 99:865-874
- Teacher: ג'ון-אלן באיירס