Sampling and Monitoring Nematode Populations and Communities


Rev: 10/30/19
  Fundamentals Design relative to Objectives
Defining Objectives Intensity and Precision
Return to Methodology Menu Biological/Ecological Considerations Risk
Efficiency and Reliability Mechanics and Tools
Return to Management Utilities Menu Spatial Patterns Powerpoint presentation on sampling
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Fundamental Components of Sampling Program

1. Objectives should be clearly stated and understood
2. Biology and ecology of pest known
3. Efficiency and reliability of method known

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1. Objectives of monitoring/sampling for nematodes

A. Assess risk of loss

i) Determine presence or absence

a. assessment of long-term risk - perennials
b. virus-vectors
c. root crops - direct damage.
d. exotic pests

ii) Determine population abundance - relative/absolute

a. predict potential yield/damage
b. assess rate of population change (+ or -)

iii) Determine spatial patterns.

a. pattern of potential loss
b. partial treatment/management

B. Faunistic studies

i) Community structure and ecosystem analysis

a. foodweb structure and function

ii) Environmental impacts/quality /markers

a.  effects of disturbance and contaminants
b.  recovery from perturbation

iii) Collections / surveys

a.  faunal inventories
b. biodiversity studies

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2. Biological/Ecological Considerations

A. Factors Affecting Microdistribution

i) Life history strategies

a. feeding/parasitism
b. reproductive behavior
c. motility
d. energetics of system - influence on population
e. survival strategies.

ii) Food distribution

a. crop spacing
b. root morphology

iii) Ecological requirements

a. moisture
b. temperature (magnitude and stability)
c. oxygen

iv) Natural enemies

a. abundance and pattern
b. biology and aggressiveness.

B. Factors Affecting Macrodistribution

i) Crop history, management, field usage

a. host status of previous crops
b. crop sequence
c. spatial arrangement of previous crops

ii) Age of infestation

a.  time to spread from a point source

iii) Edaphic conditions

a.  soil texture patterns

iv) Drainage patterns

a. soil moisture levels
b. soil aeration

Avoid spreading through field by equipment.  Photograph taken after land-leveling operations in a sugarbeet field in Imperial County, California.

Source: I.J. Thomason

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3. Efficiency and Reliability - Optimal Sampling Methodology

Consider objectives, biology/ecology, usefulness of information, acceptable precision, time, cost.

A. Pattern

i) Organism moves to sampler

a. only over small distances in soil organisms

b. to roots of bioassay plants or to CO2 attractants.

ii) Sampler moves to organism

a. core sampling - aggregate samples

b.  for monitoring changes through time, mark and sample at the same locations each time
c. symptom assessment, e.g. gall ratings - where possible

iii) Stratification - based on macrodistribution parameters

a. minimizes variance within each stratum
b. increases confidence in estimate of mean 
c. population estimate more reflective of true population levels in each stratum
d. allows determination of spatial pattern

B. Timing

i) To maximize probability of achieving objectives

a. detect presence when populations highest
b. greatest precision when lowest? - but may be many misses!

ii) To allow evaluation and management decision

a.  prior to planting
b. end of growing season, following treatment, etc.

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4. Characteristics of Spatial Patterns

A. Uniform/Regular

i) presence of one individual negatively affects presence of others - competition for a resource.
ii) variance < mean

B. Random

i) presence of 1 individual does not influence others
ii) variance = mean

C. Aggregated/Clumped

i) presence of one individual positively affects presence of others - reproductive, social behavior.
ii) variance > mean

D. Patterns and Frequency of Population Density Classes

E. Perception of spatial pattern in relation to sample unit size

i) As sample units become smaller perception of aggregated patterns: uniform > random > aggregated

Largest sample unit detects approximately the same number of objects each time it is placed in the sample arena.  Repeated samples with the smallest sample unit have a high frequency of non detection.

ii) Consider the usefulness of information at different sample unit sizes

Detection of presence/absence, spatial pattern, etc.  Smallest sample unit provides information on spatial pattern but may require processing or analysis of a large number of samples.  Largest sample unit provides no information on spatial pattern but always detects object if it is present in the sample arena.

iii) Consider mechanics and tools for measurement
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5.  Considering the Objectives for Sampling

A. Detecting presence or absence

i) Probability of zero (non-detection) at different population density and variance.

P(0)=(1+x/k)**(-k) k=0.5-1.0
for x=10, k=0.5 P(0)=0.22 (per liter)
for x=100, k=0.5 P(0)=0.07


Note:  k is the dispersion paramater of the negative binomial distribution

B. Determining abundance

i) Probability of overestimation of damage - smaller portion of field represented by mean or population densities above mean than portion represented by population levels below mean.
ii) Problem of comparing density or prominence of two species of different
iii) Effect of compositing cores / sample units

a. reason for doing it - cost, time.
b. decrease in variance - mean ratio - the larger the sample, the more reliable - each composite sample more consistent.
c. note loss of information on spatial pattern, variability, etc.
d.  One10-core sample in 5 acres covers only a 4-millionth of the soil surface.

iv) Taylor's Power Law

a. effect of increasing cores per sample is to decrease a.  Parameter b is relatively stable;  is it a species characteristic?
b. variance vs population size

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6. Relationship Between Sampling Intensity and Precision

A.   Sample intensity to obtain specified precision

n=((t/D)2)(v/x2), where n is number of samples and x is population level.

B.  Precision for specified sampling intensity

D=SQRT((t2/n)(v/x2)) where D=half interval.

C. Increase precision by:

a. decreasing variance
b. increasing number of samples
c. increasing size of samples
but consider cost and logistics.

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7.  Risk of Financial loss - the Management Decision

A. The annual crop damage function

i) log axis, log axis

ii) critical point models

a. utility based on time of management (preplant), and relatively low population volatility or motility.

iii) effect of increasing number of samples - root-knot nematodes on cotton example

v=a*mb ;                    
v=0.2*m2.2 ;              n=((t/D)2)*(v/(m2))

for 1 sample, t=12, D=SQRT((144/1)*(v/m2))

for 100 nematodes:                 for 500 nematodes:
    v=5024                                   v=173286

relative yield for cotton: y=0.55+0.45*0.998(m-65)

One sample per stratum;  n=1, t=12
Pi = 100 nematodes Pi = 500 nematodes
variance 5024 173286
Mean relative yield 0.97 0.74
D 8.5 9.9
Population range (2Dm) 0 - 1700 0 - 9990
Yield estimate range 1 - 0.56 1 - 0.55


Two sample per stratum;  n=2, t=4.3
Pi = 100 nematodes Pi = 500 nematodes
variance 5024 173286
Mean relative yield 0.97 0.74
D 2.16 2.53
Population range (2Dm) 0 - 512 0 - 5060
Yield estimate range 1 - 0.71 1 - 0.55

B.  Perennial crop damage functions - extended planning horizon.

i)   derivation(?)

ii)  multiple point models: dosage rate, damage rate

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8.  Mechanics of Sampling

A.  Tools

Veihmeyer Sampling Tube Soil Auger Power Auger

B.  Catch efficiency

C.  Extraction efficiency

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