Application of ecological models in entomology: a view from Brazil

Application of ecological models in entomology: a view from Brazil Wesley A. C. Godoy University of São Paulo "Luiz de Queiroz" College of Agriculture Piracicaba, São Paulo, Brazil - wacgodoy@usp.br

Working with ecological models in different places and areas Medical and forensic entomology Agricultural and forest entomology Universidade Estadual Paulista University of São Paulo - ESALQ Luiz de Queiroz College of Agriculture

Overview Part I: blowflies as a study model to investigate intra and interspecific interactions Population dynamics: a scenario involving exotic and native blowfly species Population dynamics applied to forensic entomology Intraguild predation Tri-trophic interactions Part II: combining population theory with biological control and integrated pest management (IPM) Ecological basis for modelling pests and natural enemies Concept of economic injury level A preliminary model combining host-parasitoid theory and IPM Inserting spatial dimension into the system Experiments focused on potential natural enemies for mass production

Population dynamics: a scenario involving exotic and native blowfly species

Importance of blowflies Vector of diseases Larval therapy Myiasis Forensic entomology

and finally, as an experimental model to study population dynamics in laboratory

Life cycle of blowflies Carrion

Modelling biology and ecology of flies N t 1 1 2 F( N t ) S( N t ) N t Fecundity Survival Prout & McChesney, 1985

Density dependence N t 1 1 2 F( N t ) S( N t ) N t F * f N t * s Nt e S e F* S* f s N(t) N(t)

Population size Population size Different values for fecundity and survival produce different dynamics 2000 1800 1600 1400 1200 900 800 700 600 1000 800 600 400 200 Exotic blowfly species 500 400 300 200 Native blowfly species 0 0 10 20 30 40 50 60 Generations 100 0 50 100 150 200 250 300 Generations

Part I: blowflies as a study model to investigate intra and interspecific interactions Population dynamics: a scenario involving exotic and native blowfly species Population dynamics applied to forensic entomology Intraguild predation Tri-trophic interactions Part II: combining population theory with biological control and integrated pest management (IPM) Ecological basis for modelling pests and natural enemies Concept of economic injury level A preliminary model combining host-parasitoid theory and IPM Inserting spatial dimension into the system Experiments focused on potential natural enemies for mass production

Forensic applications

How can ecological models provide useful information for forensic sciences? Showing what factors govern diversity and abundance of insects

Three important ecological factors: Diversity and abundance of blowflies Interspecific and trophic interactions Psychoactive drugs or medicines and population dynamics of blowflies Diversity and abundance influence strength of interactions demographic parameters depend on resources available and influence dynamic behaviours Influence of drugs on demographic parameters

Comparing demographic parameters influenced by drugs with the Prout & McChesney model N 1 t 1 F( Nt ) S( Nt ) N 2 t F S * * e e f N t s N t 1. Amphetamine (stimulant drug) 2. Phenobarbital (anticonvulsant, sedative and hypnotic) 3. Methanol (organic solvent) 4. Oxycodone (analgesic)

Table 1. Exponential regression analysis of fecundity and survival for the control, phenobarbital, methanol and amphetamine treatments Control Phenobarbital Methanol Amphetamine F S F S F S F S Y intercepts 26.74 0.81 22.87 0.90 27.12 0.54 27.45 0.60 RC 0.0009 0.00163 0.0006 0.002 0.0009 0.001 0.0009 0.001 r 2 0.66 0.80 0.54 0.90 0.65 0.90 0.61 0.89 ANOVA 445 40.60 264 94.64 414 80.59 345 81.53 P < 0.001; F = fecundity; S = survival; RC= Regression coefficient

Fecundity and survival influenced or not by drugs in C. albiceps Control Phenobarbital Fecundity Survival

Fecundity and survival influenced or not by drugs in C. albiceps Methanol Amphetamine Fecundity Survival

Table 1. Exponential regression analysis of fecundity and survival for the control, phenobarbital, methanol and amphetamine treatments Control Phenobarbital Methanol Amphetamine F S F S F S F S Without prey Y intercepts 26.74 0.81 22.87 0.90 27.12 0.54 27.45 0.60 RC 0.0009 0.00163 0.0006 0.002 0.0009 0.001 0.0009 0.001 r 2 0.66 0.80 0.54 0.90 0.65 0.90 0.61 0.89 ANOVA 445 40.60 264 94.64 414 80.59 345 81.53 P < 0.001; F = fecundity; S = survival; RC= Regression coefficient Table 2. Exponential regression analysis of fecundity and survival in oxycodone, phenobarbital, methanol and amphetamine treatments with the addition of C. megacephala prey Oxycodone Methanol Amphetamine With prey F S F S F S Y intercepts 29.15 0.87 23.34 0.57 28.14 0.77 RC 0.0008 0.002 0.0006 0.001 0.0009 0.001 r 2 0.54 0.83 0.50 0.86 0.59 0.89 ANOVA 228 48.98 216 63.31 272 70.97 P < 0.001; F = fecundity; S = survival; RC= Regression coefficient

Fecundity and survival influenced or not by prey consumption Without prey With prey Fecundity Survival

Table 3. Percentage of predation of C. albiceps on C. megacephala without choice of prey Predation rate on C. megacephala Time Control Phenobarbital Oxycodone Amphetamine Methanol 30 27.5 52.5 12.5 12.5 47.15 60 17.5 8 20 7.5 12.5 90 7.5 8 32.5 12.5 5 120 7.5 2.5 7.5 17.5 15 150 2.5 7.5 12.5 2.5 5 180 5 2.5 5 17.5 0 Total 67.5 81 90 70 85

Intraguild predation Predator Prey

Intraguild predation equations

Satiation intensity

Attack intensity

Tri trophic interactions investigated IGP: Intraguild predation

Interactions investigated with experiments IG-prey survival in absence of IG predator IG-prey survival in presence of IG predator IG-predator survival in absence of IG prey IG-predator survival in presence of IG prey IG - Intraguild

IG prey alone IG predator alone IG predator and prey IG prey and parasitoid IG predator and parasitoid IG predator, prey and parasitoid

Nomenclature for the ecological model n e = time from oviposition to hatching = 1 day n l1 = development time for 1st and 2nd larval instars n l2 = development time for o 3rd Instar n l = n l1 + n l2 = 4 days n p = pupal time = 4 days n a = adult time = 7 days Species: Chrysomya megacephala (PREY): 1 Chrysomya albiceps (PREDATOR): 2 Nasonia vitripennis (PARASITOID): W

Functions for the model IGP by L 2 n on L 1 n Cannibalism on L 2n, IGP ( ), cannibalism ( ) and parasitism ( ) f 1 and f 2 with values between 1 and 0.5

Parasitism Number of pupae parasitized = Maximum number of pupae parasitized for 1 day

Model description Age of fly E, L,P ou A Species Egg Larva Pupa Natural mortality IGP and cannibalism Adult 3rd Instar: beginning of Beginning of simulation 1st day Following day interactons between flies Pupae

Parasitism Natural mortality Natural mortality Interactions with parasitoids Surviving pupae reaches adult phase Oviposition by flies New life cycle

Parasitoid equation k = cycle length h = sex ratio (eggs) q = eggs per day Natural mortality Days since the beginning of the experiment

Density of blowfly species long to generation Prey + 1 parasitoid Prey + 10 parasitoids Initial population Size = 300 Initial population Size = 100 Predator + 1 parasitoid Predator + 10 parasitoids Gray bars = larvae and pupae of blowflies, White bars = dead individuals, Black lines = parasitoids

Only IG prey and predator Prey: bars Predator: black line high IGP and low cannibalism high IGP and high cannibalism low IGP and low cannibalism low IGP and high cannibalism

Prey Predator IG prey, predator and parasitoids high IGP and low cannibalism high IGP and high cannibalism parasitoid low IGP and low cannibalism low IGP and high cannibalism parasitoid

Starting from a host parasitoid model with functional response type II 1200 1000 800 600 400 200 densityindependent survival of parasitoid propagules at generation t 0 1 11 21 31 41

If N(t+1) < threshold (L) If N(t+1) threshold (L) q1 = reduction of host population by other methods q2 = parasitoid release rate = number of released parasitoids L = economic threshold Tang & Cheke, 2008

Introducing integrated pest management (IPM) strategies into the model 1200 1000 800 600 + 400 200 0 1 11 21 31 41

30 Population dynamics without IPM strategies 25 20 N,P 15 10 H P 5 0 1 11 21 31 41 51 61 71 81 91 Population dynamics taking into account IPM strategies 25 L = 15 N,P 20 15 10 H P 5 0 1 6 11 16 21 26 31 36 Tempo

Now including migration by using coupled lattice model Diffusion type I Host Density independent Diffusion type II Host Density dependent H < Economic threshold: white; H Economy threshold: gray; H Injury level: black

without IPM and migration with IPM with IPM and migration

Relationships between pest and potential predators

Experiments to compare the best diet for natural enemies

Experiments focused on potential natural enemies for mass production M =

Population dynamics of Podisus nigrispinus structured in life stages maintained in artificial diet N Life cycle stages

Population dynamics of P. nigrispinus structured in life stages maintained in Drosophila melanogaster N Life cycle stages

Population dynamics of P. nigrispinus structured in life stages maintained in Chrysomya putoria N Life cycle stages

Current projects by graduate students Fennel and cotton with colored fibers intercropping, pest and natural enemies (Master thesis) Trophic interactions between Spodoptera frugiperda (corn caterpillar) and natural enemies (Master thesis) Trophic interactions between soybean bug and their parasitoids (phd thesis) Intraguild predation in Diaphorina citri and their natural enemies: citrus and sorghum intercropping (phd thesis) Population dynamics of forest pest and natural enemies (phd thesis) Trophic interactions between predator stink bugs and crop pests (phd thesis) Functional response and predator prey dynamics in coccinelids and aphids (posdoc)

Thank you