v2.1.2

Great Plains Population & Trend Simulator

Great Plains Population & Trend Simulator Documentation

Overview

The Great Plains Population & Trend Simulator (GPPTS) is a sophisticated ecological modeling tool designed for analyzing and predicting bird species populations across the Great Plains region. This application integrates complex population models with environmental data to provide insights into species trends and abundance patterns.

Key Features

  • Population trend modeling and visualization: Generate and visualize species-specific population trends
  • Environmental scenario testing: Model the effects of environmental changes on populations
  • Multi-model comparison capabilities: Compare different scenarios and management strategies
  • Spatial analysis and visualization: Interactive mapping and spatial data integration
  • Customizable confidence intervals: Adjust statistical confidence levels for analysis
  • Interactive mapping interface: Draw or upload regions for analysis

Quick Start Guide

Follow these steps to begin using the application:

  1. Launch the Application
    • Access via modern web browser
    • Ensure stable internet connection
    • Check system requirements
  2. Select a Species
    • Choose from curated list of Great Plains species
    • Consider species-specific data availability
    • Review any model limitations for selected species
  3. Define Study Area
    • Upload shapefiles or draw regions directly on map
    • Define Region of Interest (ROI)
    • Specify Management Region within ROI
  4. Set Parameters
    • Adjust environmental variables
    • Set confidence intervals
    • Define temporal range
  5. Run Scenario
    • Execute model
    • Review visualizations
    • Analyze results
  6. Save & Compare
    • Save scenarios for future reference
    • Compare multiple scenarios
    • Export results as needed

Detailed User Guide

Getting Started

System Requirements

  • Web Browser: Chrome (v90+), Firefox (v88+), Safari (v14+), Edge (v90+)
  • Internet Connection: Stable broadband connection required
  • Screen Resolution: Minimum 1024x768
  • Memory: 4GB RAM recommended

Browser Compatibility

Browser Minimum Version Notes
Chrome 90+ Recommended browser
Firefox 88+ Fully supported
Safari 14+ Fully supported
Edge 90+ Fully supported

Interface Navigation

Main Tabs

  • Run Model

    Primary interface for creating and executing scenarios:

    • Species selection
    • Parameter adjustment
    • Region definition
    • Model execution

  • Compare Models

    Tool for comparing multiple scenarios:

    • Select multiple scenarios
    • Visual comparison
    • Statistical analysis
    • Export capabilities

  • Documentation

    Comprehensive guide and reference material:

    • User guides
    • Technical documentation
    • Troubleshooting
    • FAQs

Running a Scenario

Step-by-Step Guide

1. Name Your Scenario

Begin by providing a descriptive name for your scenario:

  • Use clear, descriptive names (e.g., 'TBLO_Cropland_Reduction_2030')
  • Include relevant details like species code, main variable changed, target year
  • Avoid special characters except underscores (_)

2. Define Regions

Two methods available for defining study regions:

Region of Interest (ROI)
  • Drawing Method:
    • Select 'Draw on Map' option
    • Use drawing tools to define area
    • Double-click to complete polygon
    • Can be edited or redrawn as needed
  • Shapefile Upload:
    • Prepare .zip file containing all shapefile components
    • Required files: .shp, .shx, .dbf, .prj
    • Optional files: .cpg, .sbn, .sbx
    • Must be in valid coordinate system
    • Must be topologically correct and closed, with no gaps and self-intersections or overlapping polygons within the same layers
    • Must be within Great Plains Simulator the study region
Management Region
  • Must be within or equal to ROI
  • Defines area where management changes will be applied
  • Same drawing/upload options as ROI
  • Will be validated against ROI boundaries

3. Select Model Type

Base Model (2010-present)
  • Data Sources:
    • Rangeland Analysis Platform (RAP)
    • Road density data
    • Conservation practice information
  • Characteristics:
    • Longer temporal coverage
    • Comprehensive environmental variables
    • Validated against historical data
NFWF Model (2017-present)
  • Additional Features:
    • NFWF management practice integration
    • Enhanced conservation metrics
    • Specialized habitat parameters
  • Limitations:
    • Shorter temporal coverage
    • Specific to NFWF project areas
    • May have different variable sensitivities

Technical Documentation

Model Architecture

Core Components

1. Population Model

The population model integrates multiple components to estimate species abundance and trends:

  • Occupancy Dynamics
    • Initial occupancy (ψ) estimation
    • Colonization (γ) and extinction (ε) processes
    • Temporal persistence patterns
  • Abundance Estimation
    • Density-dependent factors
    • Habitat capacity calculations
    • Spatial distribution patterns
  • Trend Calculation
    • Year-to-year population changes
    • Long-term trajectory analysis
    • Confidence interval estimation
2. Environmental Integration

Environmental factors are incorporated through various parameters:

  • Land Cover Variables
    • Crop cover percentage
    • Grassland types and extent
    • Woody vegetation coverage
    • Annual vs. perennial grass composition
  • Infrastructure Impacts
    • Road density effects
    • Development pressure
    • Habitat fragmentation metrics
  • Management Practices
    • Conservation actions
    • Land use changes
    • Restoration activities

Statistical Framework

Model Parameters
Parameter Symbol Description
Occupancy ψ Probability of species presence
Detection p Probability of detecting species when present
Abundance N Estimated population size
Growth Rate λ Population change rate
Model Equations

Key mathematical relationships in the model:

  • Occupancy Dynamics: ψ(t+1) = ψ(t) * (1 - ε) + (1 - ψ(t)) * γ
  • Population Growth: N(t+1) = N(t) * λ(t)
  • Environmental Effects: logit(ψ) = β₀ + β₁X₁ + β₂X₂ + ... + βₙXₙ

Data Sources

Environmental Data

  • Rangeland Analysis Platform (RAP)
    • Annual vegetation cover
    • Temporal resolution: Annual
    • Spatial resolution: 30m
    • Coverage: 2010-present
  • Road Network Data
    • Source: OpenStreetMap and TIGER/Line
    • Updates: Annual
    • Density calculations: km/km²
  • Conservation Practice Database
    • NFWF project data
    • Conservation easements
    • Management histories

Species Data

  • Survey Data
    • Breeding Bird Survey
    • eBird observations
    • Targeted surveys
  • Historical Records
    • Long-term monitoring
    • Research studies
    • State wildlife data
  • Expert Knowledge
    • Habitat associations
    • Behavioral patterns
    • Population constraints

Computational Methods

Algorithm Overview

1. Initialization
  • Parameter loading and validation
  • Spatial data processing
  • Environmental variable preparation
  • Model configuration setup
2. Processing Pipeline
  1. Data Preparation:
    • Coordinate system alignment
    • Raster processing
    • Variable scaling
  2. Model Execution:
    • Parameter estimation
    • Population modeling
    • Uncertainty calculation
  3. Result Compilation:
    • Spatial aggregation
    • Temporal trending
    • Uncertainty bounds

Troubleshooting

Common Issues

1. Upload Problems

Issue Possible Cause Solution
Shapefile upload fails Missing required files Ensure .zip contains all required files (.shp, .shx, .dbf, .prj)
Invalid geometry error Self-intersecting polygons Check and repair geometry in GIS software
Coordinate system error Incompatible projection Ensure shapefile is in a supported coordinate system (preferably WGS 84)

2. Processing Errors

Error Message Cause Resolution
Memory allocation error Study area too large Reduce extent of analysis area
Model convergence failure Invalid parameter combinations Check parameter ranges and constraints
Visualization error Data processing incomplete Ensure all model steps completed successfully

Performance Optimization

  • Browser Performance:
    • Clear browser cache regularly
    • Limit number of active browser tabs
    • Use recommended browser versions
  • Data Management:
    • Optimize shapefile sizes
    • Use appropriate study area extents
    • Consider temporal range carefully
  • Model Execution:
    • Run scenarios sequentially
    • Save results before comparing
    • Monitor system resources

Support and Updates

Contact Information

Version Information

Version Release Date Key Changes
0.0.8 2023-12-15 Initial Release to NGPJV Partners for Feedback
1.0.0 2024-01-12 Public Release
1.1.0 2024-02-18 Incorporated Sliders
1.2.0 2024-03-15 Incorporated Error Messages
1.3.0 2024-05-17 Updated Plots
2.0.0 2024-10-30 Added ability to draw regions of interst
2.1.0 2024-10-31 Added ability to compare multiple models
2.1.1 2024-11-01 Updated Documentation
2.1.2 2025-02-19 Corrected shapefile validation errors and optimized processing and validation checks of shapefiles and drawn areas. Updated code to reflect changes in R dependency packages.
2.2.0 Planned Add Custom Colors for Plots

Appendix B: Variable Definitions

Variable Definition Units Range
Crop_pct Percentage of cropland cover % 0-100
shrb Shrubland cover % 0-100
tree Tree canopy cover % 0-100
agfc Annual grass cover % 0-100
pgfc Perennial grass cover % 0-100
Road_km Road density km/km² ≥0

Appendix C: Model Assumptions

  • Spatial Assumptions:
    • Spatial independence between sites
    • Uniform habitat quality within pixels
    • Representative sampling of landscape
  • Temporal Assumptions:
    • Markovian population dynamics
    • Annual time steps adequate
    • Consistent seasonal timing
  • Population Assumptions:
    • Closed population during sampling
    • Density-dependent effects
    • Representative detection

References

Primary Sources

  1. Latif, Q. et al. (2024). [Forthcoming publication on model development]
  2. Bird Conservancy of the Rockies. (2023). Integrated population models.
  3. Northern Great Plains Joint Venture. (2023). Conservation planning tools.

Additional Resources