Lectures 2 & 3 - Field Methods, Estimating Abundance, Scale Sensitivity

LECTURES 2&3 - FIELD METHODS, ESTIMATING ABUNDANCE & SCALE SENSITIVITY

Dr. Joanna Zigouris

Saint Mary's University

Learning Objectives

  • Estimate population size.

  • Understand when to apply quadrat sampling versus mark-recapture methods.

  • Grasp the role of scale in ecology.

Estimating Population Size

  • Complete counting of individuals (true census) is often impossible.

  • Most methods rely on sampling techniques:

    • Quadrat Sampling

    • Line Transect Sampling

    • Mark-Recapture

Quadrat Sampling

  1. Define the sampling area and randomly select locations.

  2. Place quadrat (fixed area) and count all individuals within it.

  3. Calculate the average number per quadrat, then multiply by the size of the sampling area.

    • Typically involves 1 m x 1 m or 0.5 m x 0.5 m quadrats.

    • This method works best when individuals are relatively sessile.

Random Plots in Quadrat Sampling

  • Importance of random sampling in ecological studies:

    • Example from Denali National Park, with researchers using randomly located quadrats.

    • Sampling must include diverse areas (both "good" and "bad" sites) to avoid overestimating population size.

Line-Transect Surveys

  • These surveys offer a method for counting species along a linear path.

Challenges with Quadrats

  • Quadrats are less effective for sampling mobile animal populations.

Mark-Recapture Method

  1. Capture and Mark: First stage involves capturing and marking individuals.

  2. Resampling: In second stage, animals are recaptured to determine the proportion that are marked.

Photographic and Genetic Mark-Recapture Techniques

  • Photographic: Utilizes images for identification (Durban et al. 2010).

  • Genetic: Use of genetic markers to track population dynamics.

Lincoln-Peterson Estimator

  • Formula:

    • N = (M * n) / m

      • N = Estimated population size

      • M = Number marked on first visit

      • n = Total captured second visit

      • m = Number recaptured (with marks).

Lincoln-Peterson Index Details

  • Additional details about the estimator:

    • M = Number marked in first sample (t1).

    • m = Number of animals recaptured in second sample (t2).

    • n = Total animals captured in second sample (t2).

    • N = Estimated population (unknown value).

Chapman Estimator

  • Provides correction for small sample sizes:

    • Formula: N = ((M + 1)(n + 1) - 1) / (m + 1)

      • Where M = number marked in first sample, n = animals captured in second visit, m = animals recaptured.

Assumptions of Mark-Recapture Method

  1. No change in population size during sampling period.

  2. All individuals have an equal chance of being captured and recaptured.

    • Characteristics like being trap-shy or trap-happy can affect this.

  3. Sufficient time must allow for mixing of marked individuals.

  4. Marks should remain intact until the second sampling.

Assessing Estimate Accuracy

  • Variance formula to determine accuracy:

    • Variance = ((M + 1)(n + 1)(m - 1)(n - m))

    • Standard Deviation calculations and confidence intervals could be computed.

Precision and Accuracy

  • Relationships:

    • Low precision can lead to low accuracy, and vice versa.

    • High precision typically indicates higher accuracy in estimations.

Population Dispersion Patterns

  • Characterizes spacing of individuals:

    • Random

    • Clustered

    • Evenly spaced

Scale Sensitivity in Ecology

Key Concepts

  • Grain: Size of the sampling unit or resolution.

  • Extent: Size of the study area.

Questions on Scale Sensitivity

  • Optimal quadrat size for estimating population density?

  • Are species distributed randomly, evenly, or clumpily?

  • Evaluating pros and cons associated with quadrat sizes.

Conducting Field Experiments

  • Always employ random sampling to assess abundance and distribution.

Properties of Random Sampling:

  1. Unbiased: Each unit has an equal chance of being selected.

  2. Independent: Selection of one unit does not affect the others.

Bias in Sampling Procedures

  • Example of potential biases:

    • Students placing quadrats in "typical" areas, leading to unintentional biases based on subjective perceptions.

Random Sampling Examples

  • Options that represent random sampling:

    • Blindly dialing phone numbers for surveys.

    • Selecting students by surname through a random letter pick.

    • Voluntary census forms.

Case Study: Eastern Grey Squirrel Population

  • Drey density correlates with squirrel density.

  • Study focused on estimating densities and landscape features' effects on drey densities.

Scale-Sensitive Density Estimations

  • Research demonstrates:

    • Drey density declines with area extent.

    • Finer scales reveal higher density estimates compared to coarser scales.

Conclusion: Correlation Between Density and Landscape Features

  • Drey density has variable correlation with building density:

    • Effective at coarse scales, but not when analyzing finer scales.