NATURAL ENEMY EVALUATION

Read ch 20

INCORPORATION NATURAL ENEMIES INTO

IPM DECSION MAKING

Requirements for inclusion of biological control in crop IPM

Effective BC agents must existTarget must be important relative to other crop pestsLack of major conflict with control of key pestsTolerance of some level of target pest in cropRealistic economic injury levelSound understanding of natural enemy’s ecologySampling tools to measure natural enemy abundance

Modification of IPM thresholds to reflect information about current natural enemy densities or ratios in the crop

Monitoring tomato fruitworm (Helicoverpa zea) in processing tomatoes in CA. Ratios of black (parasitized)/white (healthy) eggs are used in a sequential sampling plan to assess need for pesticides

Modification of IPM thresholds to reflect information about current natural enemy densities or ratios in the crop

Modification of IPM thresholds-second example

The pest threshold of apple blotch leafminer (13 mines/100 leaves of 1st gen.) can be relaxed in view of 1st gen larval parasitism, which can be determined by timely sampling

ASSESSING IMPACT OFCLASSICAL BIOLOGICAL

CONTROL AGENTS

ASSESSING IMPACT OF CLASSICAL BIOLOGICAL CONTROL AGENTS

Experimental methods based on “with and without” treatment plots

1. Before and after plot2. Geographic release and control plots3. Cage exclusion4. Chemical exclusion

ASSESSING IMPACT OFCLASSICAL BIOLOGICAL CONTROL AGENTS

METHOD #1.“BEFORE AND AFTER”

ASSESSMENT OF PEST DENSITY OR CROP LOSS

Example #1Invasion of

citrus blackfly(insert name)into Florida

and its control via parasitoid introduction

Black= nymphs

Citrus blackfly (nymphs are black normally), with exit holes of introduced parasitoids

Citrus blackfly parasitoid: Amitus hesperidum, one of two species introduced for pest control in Florida

Sampling to measure density and parasitism before and after introduction

Yard citrus is main pest reservoir

Citrus blackfly nymphal density “before and after” parasitioid introduction

Post-project pest density

Citrus blackfly nymphal density“before and after” parasitioid introductionPre-project pest density

Transition phase as parasitism rises

CMFA. hesperidusP. opulenta

Example #2Olive scale, Parlatoria oleae in CA, before parasitoid introduction

“Before BC” = 43% culls

“After BC” = 0.3% culls, 99% reduction

Olive scale, Parlatoria oleae,in CA, after parasitoid introduction

1.You need to start observations in control plots up to several years before natural enemy releases begin

2. Since there may be strong climatic differences between the “before” and the “after” years, it is best to continue some “before” sites without releases (partial geographic design)

Potential problems or limitations with the “before and after” design

ASSESSING IMPACT OFCLASSICAL BIOLOGICAL CONTROL AGENTS

METHOD #2. Geographically separated release and control plots

Assessment of pest density at sites with releases vs ones with no releases of the

agent to be evaluated

Insert picture of ck lifestages from C drive

Effect of Chilocorus kuwanae on euonymus scale, Control Site (one of 15)- no predator release

Plant died in fall

Insert picture of ck lifestages from C drive

Predator build up

Scale decline

Effect of Chilocorus kuwanae on euonymus scale.Release site– one of 15

Predator build up

Scale decline

Insert picture of ck lifestages from C drive

Predator build up

Effect of Chilocorus kuwanae on euonymus scale (Unaspis euonymi), averaged over all research sites,

1991-1993 in New England

Predator presentPredator absent

Conclusion: sites with increasing scale typically lacked the predator, while sites with the predator typically decreased in scale density

Summary

Geographical plots to measure effect of phorid fly on fire ants and effect of rapid range expansion on exp. design

Range expansion post release of P. tricuspis in two years (black 1999, dark grey 2000, and light grey, 2001)

As a consequence of rapid expansion of fly after release, control plots had to be relocated to somewhat distant country (Madison, upper left

1. You need to define a pool of plots and assign control or release treatments to them AT RANDOM (this is often overlooked)

2. Control plots are sometimes invaded by the natural enemy as it disperses. To avoid this, greater separation must be used. This may cause control plots to enter new ecological zones or if control plots are grouped to enhance separation from release plots, this conflicts with random assignment.

Potential problems or limitations with the “geographically separated plots” design

ASSESSING IMPACT OFCLASSICAL BIOLOGICAL CONTROL AGENTS

METHOD #3. Cage Exclusion

Assessment of pest density inside and outside cages excluding natural enemy

3 treatments-open cage, closed cage, no cage

Cage exclusion to measure pest density with and without the key natural enemy

Closed cage initially impregnated with DDT to kill off any pre-existing parasitoids in scales

Open cage is intended to allow full parasitoids full access to scales

Uncaged branch is a check on cage effects

Cage exclusion and CA red scale

Leaf clip cages are used to assess

impacts of parasitoids on

sedentary species such as scale,

mealybugs and whiteflies

Pest mortality inside and outside leaf clip cages

Example #1: Effect of parasites on CA red scale on ivy

Cage exclusion example #2: control of cassava mealybug (Phenococcus manihoti) in Africa by Epidinocarsis lopezi

Cage exclusion example #2: control of cassava mealybug(Phenococcus manihoti) in Africa by Epidinocarsis lopezi

Note log scale

>97% pest reduction

1. Temperature may be increased inside cages, causing pests to increase faster, perhaps raising pest population density.

2. Humidity may be increased inside cages, leading to higher rates of mortality from fungal pathogens.

3. Pest progeny will be confined inside cages, raising pest density by restricting pest dispersal

Potential problems or limitations with the “cage exclusion” design

ASSESSING IMPACT OFCLASSICAL BIOLOGICAL CONTROL AGENTS

METHOD #4. Pesticide Exclusion

Assessment of pest density in plots sprayed with a pesticide that kills the

natural enemy but not the pest vs. unsprayed areas with the natural enemy

Use of pesticides to exclude natural enemy being evaluated

Pesticides must1. Kill the natural enemy2. Be safe to the pest3. Not stimulate pest reproduction

Use of DDT to exclude

natural enemies of California red scale

Example #1 of chemical exclusion

70-fold pest increase

Example #2 of chemical exclusion

Use of carbaryl to exclude predators of Pacific mite in California vineyards

>30-fold pest increase

Example #3: Use of pesticides to exclude natural enemies of cassava mealybug

The residues of some pesticides stimulate the population growth of such groups as mites or

1.2 eggs for controls

One of the problems in chemical exclusion: hormolygosis

1.6 eggs for low residue treatment

0.6 eggs for highresidue treatment

1. It may be difficult to find a pesticide that has not effect on the pest but kills the natural enemy (easy if pest is a weed).

2. Pesticides are likely to eliminate the whole natural enemy complex, not just the newly introduced species.

3. Some pesticides stimulate pest fecundity, raising pest density.

Potential problems or limitations with the “pesticide exclusion” design

Ants that tend honey-dew-producing Homopera can exclude natural enemies in some cases, acting as a

“biological check”

ASSESSING IMPACT OF CLASSICAL BIOLOGICAL CONTROL AGENTS

Using life tables

Life tables

1. Provide an organizational framework for data collection and comparison

2. Allow contributions of specific mortalities in restricting population growth to be assessed

3. Allow separate effects of contemporaneous mortalities to be distinguished

4. Series allow detection of density dependent action of sources of mortality

Types of designs using life tables1. Single- is a minimal description of what

happened in one generation

2. Series- a long series of life tables for one population over many generations can be used to look for mortality factors that act in a density dependent way

3. Paired- pairs of life tables, for populations with and without some feature of interest (such as a new parasitoid) allows the impact of the factor to be assessed

Cohort vs population based life tables

1. Cohort life table- is based on a group of individuals that are created for the purpose of tracking their fate. They are synchronized and do not suffer exactly the same fates as the real population since that may be more protracted over time

2. Real population life table- based on samples drawn from the population of interest in some representative way

Cohort lifetables of sessile species such as leafminers are

readily obtained, as a group can be

marked and easily reencountered later

Mines of apple blotch leafminer (Phyllonorycter crataegella,Lepidoptera: Gracillariidae) on apple

Sessile vs mobile stages or species

Cohort of immatures of apple blotch leafminer(Phyllonorycter crataegella, Lepidoptera: Gracillariidae)

Mines retain a story of what happened to the pest, which can be sorted out by what life stages, cast skins or residues are found

sap larva-pest

pupa-pest

pest pupal skins outside mines

larva of parasitoid

cadaver of pest larva

Sample life table

How much mortality is enough for pest control?

R0 is the best measure

Paired lifetable-example #1- apple blotch leafminers

Unsprayedplot

Lifetable apple blotch leafminer (Phyllonorycter crataegella) on unsprayed trees in Buckland, MA, 1981

65% larval parasitism

Popl’n increased by 1.8X

Manipulation of previous life table for apple blotchleafminer (Phyllonorycter crataegella) (unsprayed trees in

Buckland, MA, 1981) with parasitism omitted

UnsprayedPlot, parasitism omitted

Popl’n increased 8.6X

Real field lifetable for apple blotch leafminer(Phyllonorycter crataegella) on sprayed trees (leafminer

resistant but parasitoids not resistant)

Sprayed Plot, parasitism eliminated by pesticides

Popl’n increased 8.5X

Contrasting the Ro values for populations with and with the natural enemy of interest in paired life tables

Ro values

• Unsprayed wild orchard 1.8X• Table #1, parasitism removed 8.6X• Sprayed orchard-

chemical check 8.5X

Conclusion: prediction made based on lifetable from wild orchard matches actual pest population growth in sprayed orchard

Paired lifetable-example #2-whiteflies on poinsettia

B. tabaci, B strain adult and nymphs

Eretmocerus eremicus, parasitoid of the whitefly Bemisia tabaci used in poinsettia via augmentative BC

Paired cohort lifetables to measure impact of Eretmocerus eremicus on Bemisia tabaci in greenhouse poinsettia

Defined leaf areas are repeatedly photographed and survival of individual whitefly nymphs recorded

Whitefly nymph

Life table for the whitefly Bemisia tabaci on poinsettia in the absence of parasitoid releases

75% egg-to-adult-survival, R0 = 67 (90 eggs/F)

Life table for the whitefly Bemisia tabaci on poinsettia with Eretmocerus eremicus releases

8% egg-to-adult-survival, R0 = 7.2 (90 eggs/F)

Use of life table data to determine if a source of mortality acts in a density dependent manner

Rates of mortality to winter moth from the tachind parasitoid Cyzenis albicans over 6 years. Note upward change in % from 1982-84

In principle, compensatory change in mortality by subsequent density dependent factors can nullify effects of newly imposed mortality, as for example a release of

an egg parasitoid (e.g., Trichogramma releases)

Eggs Larvae Pupae Ro (given fertility of 20 per F)

Natural popl’n In-100kill-5%Out-95

In-95kill-50%Out-48

In-48% F-50F Out-24

480 eggsRo 4.8

Trichogramma releases added

In-100kill-50%+5%Out-45

In-45kill-20%Out-36

In-36% F-50F Out-18

360 eggsRo 3.6

Consequences Increases egg mortality 10-fold (5 to 55%)

Reduces damaging stage (larvae by 53%)

But reduced new year’s popl’n only by 25%

Construction of life tables

How does one build accurate lifetables?

Previous life table examples were based on cohorts (a marked set of individuals), not samples from a population

If insects are not sessile, you usually can’t follow the fate of a set of individuals (a cohort). Rather, you have to estimate

the current numbers of each life stage by sampling

If insects are not sessile, you usually can’t follow the fate of a set of individuals (a cohort). Rather, you have to estimate

the current numbers of each life stage by sampling

Change in density over time of Heliothis spp. eggs and larvae

Density vs. numbers entering a stage

Components of a Lifetable

Data Used to Construct table• Number entering each life stage (lx)• Number dying, for each cause, in each stage (dx)

Calculations made from data• Apparent mortality (qx) = dxi/lxi for a factor and

stage• Irreplaceable mortality- dx/lx (stage 1)• Marginal rate of mortality- rate of death for each

factor modified (when there are 2 or more sources of mortality acting together) to reflect underlying attack rates

Life tables are not built on density date. They ask for numbers (summed over a generation) that enter each life

stage and how many die in each stage

In a generation, how many Colorado potato beetle eggs get laid?

Density and Recruitment

Estimating CPB egg density and recruitment in a potato field- stakes mark sampling locations

Measuring lx for a stage

How can we measure the numbers of eggs per plant that get laid by a whole generation of Colorado potato beetles?

Double Sample Method

1. Density– select sample plants and count egg masses per plant on each date

2. Remove all egg masses found on plants sampled to estimate density

3. Recruitment. On next sample date (3 or 4 days later), recheck old density plants. Any egg masses found on these plants had to have been laid during the period between sample date. This is a recruitment value (# eggs laid per plant per time period).

Comparison of egg density over a series of dates and numbers of new eggs laid per day in each time period

(recruitment to the stage)

Year 1

Year 2

Density and Recruitment

Blow up of part of data on previous slide

Year 2, 1st

GenerationYear 2, 2nd

GenerationPeak density 275 (38%

of correct number)

400 (25%)

Recruitment (number entering the stage)

729 1603

Relationship between peak density and number of eggs laid per generation (recruitment) for

Colorado potato beetle

Measuring dx for a stage

How can we measure the numbers of eggs per plant that get parasitizedby a parasitoid species over the course of a whole pest generation?

Measuring the host’s dx from parasitism is the same as measuring the lx to the lifestage of the immature parasitoid

Host popl’n

Imm. Par. popl’n

PHost gain/day

Imm par. gain/day

Loss to next host stage

Loss to parasite emgergence from host

P

PLoss to death

PLoss to death

Edovum puttleri, a eulophid, new association egg parasitoid or Colorado potato beetle from Colombia

Measuring losses to parasitism(dx parasitism from E. puttleri for Colorado potato beetle)

To measure total losses to parasitism:1. Measure host lx to the egg stage as above2. Measure semiweekly parasitoid attacks by

rearing eggs recruited in each time interval to detect newly parasitized eggs

3. Sum all parasitized eggs in recruitment samples for the whole egg generation (this is both parasitoid lx to immature stage and dx from parasitism for the host) (assumes eggs not vulnerable to parasitism after one time period)

4. Generational loss to parasitism (qx of lifetable) is dx parasitism/lx CPB egg

\

Numbers of Colorado potato beetles laid per day and parasitized per day over a whole generation = total loss (dx)

to egg parasitism

Cotesia glomerata is an imported parasitoid of Pieris rapae, a pest of cabbage

Measuring losses to parasitism example #2(dx parasitism from C. glomerata for P. rapae larvae)

Pieris rapae larvae parasitized by Cotesia glomerata, plus cocoons from another previously parasitized larva

Measuring losses to parasitism #2(dx parasitism from C. glomerata for P. rapae larvae)

1. Measure host lx to 1st larval instar (relatively non mobile between leaves) using the double sample methods described for CPB example)

2. Dissect all larvae (all instars) found on plants sampled to measure P. rapae density.

3. Among dissected larvae (step 2), count only those with small parasitoid eggs (= young enough to be laid between sample dates).

4. Over whole insect generation, sum larvae with small parasitoid eggs (step 3) and divide by total host larvae (step1)

Summation for a generation of the total P. rapae larvae entering the larval stage and the total number dying from

C. glomerata parasitism

Can good life table lx values be extracted from samples of an insect stage’s density?

Only the graphical method of Southwood has ever been widely used to obtain lx values from density sample data

Big issue: as mortality in the stage increases, the value obtained becomes progressively TOO SMALL

Separating Simultaneous Mortalities

Recovering rates of attack from observations on rates of death, when two mortality agents act simultaneously

The fate of this group is the critical factor

Method is called “marginal rate of mortality”

When two factors act together, the discrepancy between apparent mortality and the marginal rate is important only when both attack rates are large

WHEN DEATH LEAVES NO TRACE: COUNTING LOSSES WHEN CADAVERS ARE MISSING

OR NOT DISTINCTIVE

1. Predation on non-attached prey stages2. Host feeding by parasitoids

Predacious Hemiptera attacking a caterpillar

When the dead caterpillar falls to the ground, there is nothing to mark that it ever existed

Early option: ELISA

Current methods:PCR-for prey DNA

An alternative is to seek remains of prey inside predators

Caterpillar of apple blotch leafminer (Phyllonorycter crataegella) that was killed by a host feeding parasitoid

Blood binding neck of caterpillar to skin of mine

Host fed individuals are findable in field samples only if cadavers are stuck to plant or contained in leafmine, gall, etc