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ASRB NET Statistical Ecology, Environmental & Fisheries Statistics: practice questions

ASRB NET - Agricultural Statistics (and Computer Application / Bioinformatics) · India · Agricultural Scientists Recruitment Board (ICAR)

Original practice questions written to the published syllabus · not past papers
10worked here, free
21written to this exam in this subject
10syllabus units

What this subject covers

Counting what cannot be counted directly: capture-recapture and distance sampling, quadrat and line-transect methods, diversity indices and species-abundance models, fish stock assessment, forest inventory sampling and environmental monitoring designs.

In the exams: AICE-JRF Ag Stat Unit 5 'livestock and fishery statistics' (present); ICAR MSc/PhD elective; light in national exams

Where the 21 questions fall

How our questions for ASRB NET spread across the units of Statistical Ecology, Environmental & Fisheries Statistics. Difficulty: 1 easy, 19 medium, 1 hard.

Spatial patterns: random, clumped and regular; dispersion indices3
Quadrat, line-transect and distance sampling2
Capture-recapture: Lincoln-Petersen and open-population models2
Diversity indices: Shannon, Simpson and species richness estimators2
Species-abundance and species-area models2
Population dynamics models: logistic and Leslie matrix2
Fish stock assessment: growth (von Bertalanffy), mortality and yield-per-recruit2
Forest inventory: plot sampling, point sampling and volume estimation2
Environmental monitoring designs and trend detection2
Extreme value analysis of rainfall and floods2

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Spatial patterns: random, clumped and regular; dispersion indices · medium

For quadrat counts with mean 4 and variance 10, the moment estimate of the negative binomial k is about:

  1. 0.4
  2. 2.7
  3. 6.0
  4. 1.6
Answer and working

B. 2.7

k = x̄²/(s² − x̄) = 16/6 ≈ 2.67; small k means strong aggregation.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

2 · Spatial patterns: random, clumped and regular; dispersion indices · medium

In Taylor's power law s² = a x̄ᵇ, a value b > 1 indicates:

  1. Aggregation that increases with density
  2. A random pattern
  3. A regular pattern
  4. No relationship between variance and mean
Answer and working

A. Aggregation that increases with density

b = 1 corresponds to Poisson-like variance; b > 1 to aggregation.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

3 · Spatial patterns: random, clumped and regular; dispersion indices · medium

Morisita's index is often preferred to the variance-to-mean ratio because it:

  1. Needs no field data at all to compute
  2. Always equals one for any pattern
  3. Quadrat size affects it less
  4. Works only for regular patterns
Answer and working

C. Quadrat size affects it less

Its value for a given pattern changes less with quadrat size.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

4 · Quadrat, line-transect and distance sampling · medium

The critical assumption of conventional distance sampling is that:

  1. Objects on the line are always seen
  2. All objects within the whole strip are detected
  3. Detection does not depend on distance
  4. Objects move towards the observer
Answer and working

A. Objects on the line are always seen

g(0) = 1; detection is allowed to fall with distance.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

5 · Quadrat, line-transect and distance sampling · medium

Animals that move away from the observer before being detected cause distance-sampling density estimates to be:

  1. Biased
  2. Unbiased
  3. Exactly doubled
  4. Unaffected
Answer and working

A. Biased

Detection at original positions is assumed; evasive movement typically biases density downwards.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

6 · Capture-recapture: Lincoln-Petersen and open-population models · medium

If marked animals become trap-shy, the Lincoln–Petersen estimate will tend to be:

  1. Too low
  2. Unbiased
  3. Zero
  4. Too high
Answer and working

D. Too high

Fewer recaptures (smaller R) inflate MC/R.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

7 · Capture-recapture: Lincoln-Petersen and open-population models · easy

The same logic as capture–recapture is used for estimating human populations from overlapping lists, where it is called:

  1. Systematic sampling
  2. Cluster sampling
  3. Ratio estimation
  4. Multiple-systems estimation
Answer and working

D. Multiple-systems estimation

Overlaps between lists play the role of recaptures.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

8 · Diversity indices: Shannon, Simpson and species richness estimators · medium

Compared with Simpson's index, Shannon's index is more sensitive to:

  1. Dominant species
  2. Rare species
  3. Sample location
  4. Body size of individuals
Answer and working

B. Rare species

Simpson's index weights common species heavily.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

9 · Species-abundance and species-area models · hard

Why do species–area extrapolations tend to overestimate immediate extinctions after habitat loss?

  1. Species–area curves always underestimate richness
  2. Islands have no species
  3. Habitat loss increases species
  4. Species persist in the matrix; losses lag
Answer and working

D. Species persist in the matrix; losses lag

The matrix is not an ocean, and extinction debts take time.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

10 · Population dynamics models: logistic and Leslie matrix · medium

Why do modern harvest strategies often target fishing levels below those giving MSY?

  1. MSY is always too low to support any fishery
  2. Harvesting below MSY gives no catch
  3. Carrying capacity is always known
  4. MSY is uncertain
Answer and working

D. MSY is uncertain

Precautionary reference points allow for uncertainty and risk of collapse.

Further reading: Krebs, Ecological Methodology (1999); Buckland et al., Introduction to Distance Sampling (2001); Magurran, Measuring Biological Diversity (2004); Sparre and Venema, FAO Fisheries Technical Paper 306/1 (1998); Coles, Statistical Modeling of Extreme Values (2001)

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Read the subject

Statistical Ecology, Environmental & Fisheries StatisticsThe full study manual, free ASRB NETFormat, sections and how it has changed

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Statistical Ecology, Environmental & Fisheries Statistics for other exams

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