ICAR-IASRI MSc and PhD (Agricultural Statistics, Computer Application, Bioinformatics) · India · ICAR-Indian Agricultural Statistics Research Institute with the IARI Graduate School
Original practice questions written to the published syllabus · not past papersCounting 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
How our questions for ICAR-IASRI MSc and PhD spread across the units of Statistical Ecology, Environmental & Fisheries Statistics. Difficulty: 10 easy, 10 medium, 0 hard.
| Spatial patterns: random, clumped and regular; dispersion indices | 2 | |
| Quadrat, line-transect and distance sampling | 2 | |
| Capture-recapture: Lincoln-Petersen and open-population models | 2 | |
| Diversity indices: Shannon, Simpson and species richness estimators | 2 | |
| Species-abundance and species-area models | 2 | |
| Population dynamics models: logistic and Leslie matrix | 2 | |
| Fish stock assessment: growth (von Bertalanffy), mortality and yield-per-recruit | 2 | |
| Forest inventory: plot sampling, point sampling and volume estimation | 2 | |
| Environmental monitoring designs and trend detection | 2 | |
| Extreme value analysis of rainfall and floods | 2 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
Quadrat counts with variance much greater than the mean indicate a pattern that is:
B. Clumped
Poisson (random) has variance = mean; regular patterns have variance < mean.
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)
Frequency, the proportion of quadrats containing a species, is a poor measure of abundance mainly because it:
A. Depends strongly on quadrat size
Larger quadrats are more likely to contain at least one individual.
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)
100 fish are marked; later 80 are caught, of which 20 are marked. The Lincoln–Petersen estimate is:
A. 400
N = MC/R = 100 × 80/20 = 400.
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)
Genetic identification from hair or faeces in capture–recapture studies acts as:
D. A natural mark for individuals
Non-invasive genetic sampling gives individual capture histories.
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)
A rank–abundance plot that is a straight line on a log scale indicates which model?
C. Geometric series
Each species takes a constant fraction of remaining resources.
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)
In the logistic model dN/dt = rN(1 − N/K), population growth is fastest at:
B. N = K/2
rN(1 − N/K) is maximised at K/2.
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)
Stratifying a forest inventory by forest type mainly:
D. Reduces within-stratum variance
Homogeneous strata give more precise estimates.
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)
L-moments are preferred for estimating extreme value parameters in hydrology mainly because:
B. Robustness with short records
They are less influenced by single outliers than ordinary moments.
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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