Population parameters
Overview
Builds on previous lecture on abundance & density estimation.
Focus:
Using those data in fisheries and population models.
Exploring energy flow and predator–prey relationships.
Introducing mark–recapture techniques and vital rates (growth, mortality, etc.).
Mark–Recapture Methods
Core idea: repeat observations of individuals through time.
Allows estimation of:
Population size (N).
Growth rates.
Mortality rates (natural & non-natural).
Sex ratios, recruitment, movement patterns.
These metrics are essential for predicting population trajectories.
Why Repeated Observation Matters
One-time counts only give snapshot abundance.
Repeat sightings → track individual fates and population change through time.
Needed for population growth modelling (e.g., shark viability, recovery rates).
Mark–Recapture Techniques
1. Physical Tagging
Capture, tag, and release.
Examples:
Cattle tags (cheap, simple ID number).
Satellite tags (expensive, transmit location and movement data).
Tags provide:
Growth rate data.
Movement (migration, dispersal).
Survival estimates.
Ethical consideration: non-harmful capture preferred.
2. Photo Identification (Photo-ID)
Non-invasive method – used for species with unique markings.
Example: Whale sharks (spot patterns behind head unique like fingerprints).
Repeat photos over time = resightings.
Enables:
Estimation of population size.
Growth and size tracking.
Sex ratio assessment.
Public participation (citizen science).
Citizen Science Example
Manta Watch project:
Divers photograph manta ray undersides (unique spot patterns).
Upload images to online database.
Experts match individuals.
Allows global data collection cheaply & efficiently.
Expands sampling scale via non-scientists.
3. Case Study 1 – Manta Rays (Marshall et al.)
Location: Southern Mozambique.
Aim: Assess population structure using photographic mark–recapture.
Method:
Collected diver photos of individuals during dives.
Compared cumulative sightings with photographic resightings.
Findings:
Without distinguishing repeats → cumulative count continually increases, overestimates abundance.
With photo-ID → curve plateaus at true population size (fewer “new” individuals over time).
Identified a sex ratio of ~3.5 females per male.
Conclusion:
Photo-ID more accurate for estimating true density.
Allows detection of population structure (sex ratio, demographics).
4. Combining Methods – Case Study 2 (Brown & Roberts, Whale Sharks, Belize)
Combined cattle tags + photo-ID → greater accuracy.
Aim: Monitor size distribution and abundance over time within a Marine Protected Area (MPA).
Findings:
Over multiple years, lost large and small individuals → population decline.
MPA too small to protect migratory species; sharks leaving boundaries were vulnerable.
Conclusion:
Combining tagging types enhances data accuracy.
Revealed that protected areas must match species’ movement scale.
5. Remote Photo-ID (Dorsal Fin Identification)
Used when in-water tagging unsafe or impractical (e.g., great white sharks).
Sharks’ dorsal fins have unique notches & scars → identifiable by computer algorithms.
Enables re-sighting and population estimation.
Example: Great White Sharks, Central California (Burgess et al.):
Dorsal fins photographed over several years.
Estimated ~219 mature individuals (range 130–275).
Used dorsal fin size–body length relationships → ~5.3 m mean length.
Significance: First robust population estimate for this region.
Demonstrates safe, repeatable, long-term monitoring.
Growth and Vital Rates
Growth Rates
Measured as change in length over time:
Growth rate=L2−L1t2−t1Growth\ rate = \frac{L_2 - L_1}{t_2 - t_1}Growth rate=t2−t1L2−L1
L1,L2L_1, L_2L1,L2 = total length at different time points.
More frequent measurements → better growth rate estimate.
6. Case Study – Pacific Angel Sharks (California)
Method: Capture–mark–recapture.
Measured same individuals multiple times → developed growth curves.
Results:
Established sex-specific growth equations.
Enabled predictions of population growth potential.
Application:
Used to model how selective fishing (removing large/small individuals) affects population growth rate.
Demonstrates importance of growth data in viability analysis.
Body Condition & Juvenile Survival
7. Case Study – Scalloped Hammerheads (Hawaii, Kim Holland et al.)
Study Site: Kaneohe Bay, Oahu (nursery area).
Method:
Tagged >4,000 juveniles; recaptured 151 (labor-intensive!).
Tracked abundance and body condition over time.
Findings:
Abundance: Sharp increase (births), followed by steep decline (mortality).
Causes: predation, competition, limited shelter, and natural selection.
Body Condition: U-shaped pattern:
Initial drop (competition for food/shelter).
Later rise (mortality reduces competition → survivors healthier).
Survivors = fittest individuals, contributing to next generation.
Significance:
Demonstrates natural selection dynamics in nursery grounds.
Simple data (weight & length) can reveal strong ecological patterns.
Cost-effective research using basic tools – not all science needs expensive tech.
Population Modelling
Why Model Populations?
Allows projection of:
Population trends (growth or decline).
Effects of fishing, mortality, recruitment.
Combines vital rates (growth, fecundity, mortality) into mathematical frameworks.
8. Case Study – Great Barrier Reef Shark Population Model
Purpose: Investigate suspected illegal fishing impacts on shark populations.
Species Studied:
Whitetip reef shark.
Grey reef shark.
Model Inputs:
Growth rate.
Age at maturity.
Fecundity (reproductive output).
Total mortality (Z).
Natural mortality (M).
Method: Compare Z (total) vs M (natural) to infer non-natural mortality.
Bootstrapping Explanation
Statistical method to estimate uncertainty:
Repeatedly resample data (with replacement).
Build distribution of means and errors.
Gives more robust estimates of variability.
Used here to handle limited, incomplete shark data.
Model Results
Both species (whitetip & grey reef sharks):
Natural mortality (M) lower than total mortality (Z).
Population growth rate (λ):
Natural model → positive growth (stable/increasing).
Total mortality model → negative growth (declining).
Interpretation:
Additional mortality must come from non-natural sources → illegal fishing.
Evidence strengthened by photographs of illegally finned sharks nearby.
Significance of the GBR Study
Quantitative evidence of illegal poaching in protected areas.
Demonstrated how fisheries models can aid conservation, not just exploitation.
Combined strong field data with rigorous statistics to guide policy enforcement.
Lecture Summary
Many methods beyond visual counts:
Mark–recapture (physical/digital).
Photo-ID, tagging, growth tracking, body condition.
Allow estimation of vital rates: growth, mortality, fecundity, sex ratio.
Modern mark–recapture increasingly digital (AI image recognition, online databases).
Modelling reveals real-world impacts of human activity (e.g., illegal fishing).
Fisheries models ≠ only for harvest optimisation — they are essential conservation tools.
Key Takeaways
Repeat observation (recapture) is crucial for understanding population dynamics.
Multiple data sources (tags, photos, citizen science) improve accuracy.
Simple field techniques can yield powerful ecological insights.
Vital rates (growth, mortality, fecundity) underpin population models.
Bootstrapping enhances statistical robustness.
Population modelling can reveal hidden human impacts (e.g., poaching).
Conservation management depends on integrating data + models + theory.