NATURAL ENEMY EVALUATION...Cage exclusion example #2: control of cassava mealybug (Phenococcus...
Transcript of NATURAL ENEMY EVALUATION...Cage exclusion example #2: control of cassava mealybug (Phenococcus...
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