How many GPS locations are needed for MCP?

A minimum of 30–50 independent GPS fixes is recommended for a reliable MCP estimate. Area typically stabilizes after 30+ locations.

The 95% MCP removes the 5% most peripheral GPS locations before computing the polygon, reducing bias from occasional long-distance movements.

Can I upload a CSV file for MCP calculation?

Yes. Use the Upload tab to upload a .csv, .txt, .xlsx, or .xls file. The tool detects numeric columns automatically.

How do I report MCP home range in a research paper?

Report as: Home range was estimated using the MCP method (Mohr, 1947). The 95% MCP was X ha (N = Y locations).

What units does this MCP tool use?

Results are reported in hectares (ha) by default, with conversion to km², acres, and m². Coordinates must be in decimal degrees (WGS84).

Minimum Convex Polygon (MCP) Home Range Estimator — Free Tool

Minimum Convex Polygon (MCP) Home Range Estimator – Free Online Wildlife Tool | StatsUnlock

📍

GPS Location Data Input

Quick Load Sample Dataset:

Latitudes (decimal degrees) — comma-separated:

0 latitude values

Longitudes (decimal degrees) — comma-separated:

0 longitude values

Upload CSV or Excel file (.csv, .txt, .xlsx, .xls) – select column:

Supports .csv, .txt, .xlsx, .xls — headers detected automatically. Select latitude and longitude columns separately.

Fix / LabelLatitude (°)Longitude (°)

0 rows entered

⚙️

Analysis Configuration

Study Animal / Site / Individual Name (optional — appears in all outputs):

Area Units:

MCP Level (%):

Distance Method:

Group / Individual Name (editable):

GPS Fix Interval:

Season / Period:

📊

MCP Home Range Results

—

MCP Area

—

GPS Fixes Used

total fixes loaded

—

Perimeter

polygon perimeter

—

Hull Vertices

convex hull points

MCP Home Range — Algorithm & Formula

The Minimum Convex Polygon home range is computed in three steps:

Step 1 — Project coordinates to metres (Haversine great-circle): Δlat = (lat₂ − lat₁) × π/180; Δlon = (lon₂ − lon₁) × π/180 a = sin²(Δlat/2) + cos(lat₁)·cos(lat₂)·sin²(Δlon/2) d = 2 × R × arcsin(√a), R = 6 371 000 m Step 2 — Compute convex hull (Graham scan, O(n log n)): Hull = { p ∈ S : p is a vertex of conv(S) } Step 3 — Polygon area (Shoelace / Gauss formula): A = ½ |Σᵢ (xᵢ yᵢ₊₁ − xᵢ₊₁ yᵢ)|

A: Home range area (m², converted to ha / km² / acres)
S: Set of GPS fix locations (after outlier trimming for <100% MCP)
conv(S): Convex hull — smallest convex polygon enclosing S
xᵢ, yᵢ: Projected Cartesian coordinates of hull vertex i (metres, local tangent plane)
R: Mean Earth radius = 6,371,000 m (WGS84 spherical)
For %MCP < 100%: Points beyond the centroid-distance percentile cutoff are excluded before hull computation

Detailed Results Table

Statistic	Value	Description

—

📈

Visualizations (4 Charts)

📍 MCP Polygon Map (Projected)

📏 Distance from Centroid Distribution

🗺️ Fix Point Scatter (Spatial Distribution)

📐 MCP Area at Different Percentage Levels

🔍

Detailed Interpretation Results

▶ Run the analysis above to see detailed interpretation results.

✍️

How to Write Your Results in Research

▶ Run the analysis above to auto-fill all six reporting examples.

🎯

Conclusion

▶ Run the analysis above to generate a detailed conclusion.

🔬 Technical Notes — MCP Algorithm, Assumptions & Limitations

Algorithm: Graham Scan Convex Hull

This tool uses the Graham scan algorithm (O(n log n)) to compute the convex hull of projected GPS fixes. Points are first converted from WGS84 decimal degrees to local Cartesian coordinates (metres) using the Haversine formula, centred on the centroid of the point cloud. The Shoelace (Surveyor's) formula then gives the polygon area. This approach is accurate within ±0.5% for study areas up to ~100 km across.

Outlier Trimming for % MCP

For <100% MCP (e.g., 95% MCP), points are sorted by their Euclidean distance from the centroid in projected space and the most distant (100 − MCP)% are excluded before computing the hull. This reduces the influence of occasional exploratory forays that inflate the home range estimate.

Assumptions

Coordinates are in WGS84 decimal degrees
GPS fixes are representative of actual animal locations
Movement is not limited by geographic barriers within the polygon
Fixes are reasonably independent (no severe autocorrelation correction applied)

Limitations

MCP overestimates actual area used — treats all space within polygon as habitat
Area estimate is sensitive to sample size (n < 30 is unreliable)
Does not identify core areas or probability of use (use KDE for this)
Does not correct for GPS fix autocorrelation (use AKDE for correction)
Projection accuracy decreases for study areas >200 km across

When to Use MCP vs. KDE

Use MCP for: quick preliminary estimates, comparison with historical literature, legal/regulatory range definitions, human-wildlife conflict buffer delineation. Use KDE for: probability of use mapping, core habitat identification, habitat selection analysis, and high-frequency GPS collar datasets.

✅

When to Use MCP Home Range Analysis

✅ Appropriate Uses

Preliminary home range estimates for large mammals
Comparing home range size across individuals or populations
Setting buffer zones for protected areas
Conflict zone delineation in HWC studies
Legal / regulatory wildlife boundary descriptions
Radio-telemetry data with ≥30 locations

❌ Inappropriate Uses

Identifying core habitat use areas (use KDE)
Animals with multi-modal / discontinuous ranges
Migratory species mixing multiple seasonal ranges
Estimating habitat preference or selection
Datasets with fewer than 30 GPS fixes
Studies requiring autocorrelation correction

🦒 Example Species Applications

Large felids: tiger, leopard, lion, cheetah
Megafauna: elephant, rhino, bison, bear
Canids: wolf, wild dog, dhole (Cuon alpinus)
Raptors: eagle, hawk, harrier, owl
Ungulates: deer, antelope, boar, bison
Primates: langur, macaque, great apes

📚 Related Methods & Metrics

Kernel Density Estimation (KDE) — probabilistic core areas
Autocorrelation-corrected KDE (AKDE/ctmm)
Brownian Bridge Movement Model (BBMM)
T-LoCoH (time-constrained Local Convex Hull)
Step Selection Functions (SSF) — habitat preference
Dynamic Brownian Bridge MM (dBBMM)

❓

Frequently Asked Questions

What is a Minimum Convex Polygon (MCP)?

A Minimum Convex Polygon (MCP) is the smallest convex polygon that encloses all GPS fix locations of an animal. Originally described by Mohr (1947), it is the most widely used and simplest method for estimating home range in wildlife research. All interior angles of the polygon are ≤180° (no concavities). The MCP gives the maximum space an animal was recorded using during a study period.

How do I calculate MCP home range area using this tool?

Enter all GPS latitude and longitude coordinates in the input boxes (comma-separated), select your MCP level (95% is standard), choose area units, then click "Calculate MCP Home Range." The tool projects coordinates to metres using the Haversine formula, computes the convex hull using the Graham scan algorithm, and returns the enclosed area using the Shoelace formula — with visualizations and a publication-ready results report.

What is the 95% MCP and why is it preferred?

The 95% MCP removes the 5% most peripheral GPS fixes (those farthest from the centroid) before computing the polygon. This reduces the overestimation bias caused by occasional long-distance movements, exploratory forays, or GPS errors. The 95% MCP is the most commonly reported metric in published wildlife studies because it is more robust than the 100% MCP and enables better cross-study comparison.

How many GPS locations are needed for a reliable MCP estimate?

A minimum of 30–50 independent GPS fixes is recommended for a reliable MCP estimate. Home range area tends to increase with each new peripheral fix until the sample stabilizes (typically around 30 fixes for most species). With fewer than 15 fixes, MCP estimates are highly unreliable. Always plot an area-observation curve (area vs. number of fixes) to confirm the estimate has reached an asymptote before reporting.

What is the difference between MCP and kernel density estimation (KDE)?

MCP draws the smallest convex polygon around all points and treats the enclosed space as uniformly used. KDE models the probability density of animal locations, producing isopleths (e.g., 50% and 95% contours) that show core and peripheral use areas with varying intensity. KDE is more biologically realistic and is preferred for habitat analyses, but MCP remains widely used for its simplicity and direct comparability with decades of published literature.

Can I upload a CSV or Excel file with GPS data?

Yes. Click the "Upload File" tab and upload a .csv, .txt, .xlsx, or .xls file. The tool automatically detects column headers, identifies numeric columns, and lets you select separate columns for latitude and longitude. A preview shows the first 8 rows with the selected column highlighted. Click "Use These Columns" to load the data for analysis.

Does MCP overestimate home range size?

Yes, MCP consistently overestimates the actual area used because the polygon includes all space within the boundary — even areas the animal never visited (rivers, roads, villages). The degree of overestimation depends on how convex the true home range shape is. Using 95% MCP (removing 5% outliers) reduces this bias. KDE provides more realistic estimates of actual space use probability.

How do I report MCP home range results in a research paper?

Standard format (Journal of Wildlife Management style): "Home range was estimated using the Minimum Convex Polygon method (MCP; Mohr, 1947). The 100% MCP encompassed X ha (n = Y GPS fixes, [date range]). The 95% MCP, excluding the most peripheral 5% of fixes, was Z ha. All spatial analyses were performed in R using the adehabitatHR package (Calenge, 2006)." Always cite Mohr (1947) and report both 100% and 95% MCP values alongside sample size.

What animals is MCP home range analysis used for?

MCP is used for virtually all GPS-tracked terrestrial vertebrates: large mammals (elephants, tigers, wolves, bears, leopards, lions), medium mammals (foxes, mongooses, civets, sloth bears), birds (raptors, hornbills, pelicans, storks), reptiles (crocodiles, sea turtles, monitor lizards), and some fish tracked with acoustic telemetry. It is also used for livestock and free-roaming dogs in human-wildlife conflict studies.

What software do researchers use for home range analysis?

Common software includes: R packages adehabitatHR, ctmm, amt, and moveHMM; ArcGIS Home Range Tools extension; QGIS with animal movement plugins; Movebank (online platform for GPS data management); and Python with the shapely library for convex hull computation. This StatsUnlock tool provides instant browser-based MCP estimation without software installation — ideal for quick estimates and teaching.

📋

How to Use This MCP Home Range Estimator (10 Steps)

Collect GPS Telemetry Data

Obtain GPS fix locations from a collar, ear tag, or logger. Export as decimal degrees (WGS84). Recommended: ≥30 independent fixes across the study period. Remove obviously erroneous points (speed > species maximum, GPS dilution of precision > 5).

Choose Your Input Method

Use "Paste / Type" for comma-separated coordinates from a spreadsheet. Use "Upload File" to import your .csv or .xlsx GPS data file directly. Use "Manual Table" for small datasets you want to enter row by row with fix labels and a structured grid.

Enter Latitude and Longitude Coordinates

Paste all latitude values (decimal degrees, WGS84) in the first box and all longitude values in the second box, separated by commas. Ensure the order matches — position 1 in the latitude box must correspond to position 1 in the longitude box. Both boxes must have the same number of values.

Configure Analysis Settings

Enter the animal's name or ID in the Study Animal field. Select MCP level (95% is recommended for published studies). Choose area units (ha for most species; km² for very large-ranging species like elephants or wolves). Edit the Group / Individual Name field to match your study design.

Click "Calculate MCP Home Range"

The tool projects coordinates to local metres (Haversine), runs the Graham scan convex hull algorithm, and computes area with the Shoelace formula. If fewer than 3 valid pairs are found, an error message guides you to check your input format.

Interpret the Summary Cards

The four summary cards show: MCP area (in your selected units), number of GPS fixes used in the polygon, polygon perimeter in km, and number of convex hull vertices. Compare your area to published benchmarks for your species group.

Examine the Four Visualizations

The MCP Polygon Map shows GPS points and the convex hull boundary. The Distance from Centroid histogram reveals fix distribution and outlier pattern. The Spatial Scatter Map shows the geographic spread of fixes. The % MCP Area Chart shows how the home range area changes across 50–100% MCP levels — useful for identifying the area stabilization asymptote.

Read the Detailed Interpretation

Five dynamically generated paragraphs explain what your MCP area means ecologically, how it compares to published benchmarks, what the polygon shape reveals about movement patterns, practical conservation implications, and limitations that apply to your specific dataset.

Copy a Reporting Template

Six auto-filled reporting examples cover: ecology journal style, thesis/dissertation, plain-language policy brief, structured conference abstract, long-term monitoring report, and a full research poster panel. All values are auto-substituted. Click "📋 Copy" to copy any example to your clipboard immediately.

Download Your Report

Click "Download Report (.txt)" for a plain-text summary you can paste into your methods and results sections. Click "Download PDF" to generate a print-ready A4 report for submission, field records, or institutional reporting. Both downloads include study animal name, all key metrics, and formatted references.

📚

References

Peer-reviewed references for the Minimum Convex Polygon home range estimator, GPS telemetry methods, and animal movement ecology used in this tool:

Mohr, C. O. (1947). Table of equivalent populations of North American small mammals. American Midland Naturalist, 37(1), 223–249. https://doi.org/10.2307/2421652
Burt, W. H. (1943). Territoriality and home range concepts as applied to mammals. Journal of Mammalogy, 24(3), 346–352. https://doi.org/10.2307/1374834
White, G. C., & Garrott, R. A. (1990). Analysis of Wildlife Radio-Tracking Data. Academic Press. https://doi.org/10.1016/C2009-0-03607-3
Seaman, D. E., & Powell, R. A. (1996). An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77(7), 2075–2085. https://doi.org/10.2307/2265701
Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70(1), 164–168. https://doi.org/10.2307/1938423
Calenge, C. (2006). The package adehabitat for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling, 197(3–4), 516–519. https://doi.org/10.1016/j.ecolmodel.2006.03.017
Powell, R. A. (2000). Animal home ranges and territories and home range estimators. In L. Boitani & T. K. Fuller (Eds.), Research Techniques in Animal Ecology (pp. 65–110). Columbia University Press.
Fleming, C. H., & Calabrese, J. M. (2017). A new kernel density estimator for accurate home-range and species-range area estimation. Methods in Ecology and Evolution, 8(5), 571–579. https://doi.org/10.1111/2041-210X.12673
Laver, P. N., & Kelly, M. J. (2008). A critical review of home range studies. Journal of Wildlife Management, 72(1), 290–298. https://doi.org/10.2193/2005-589
Nilsen, E. B., Pedersen, S., & Linnell, J. D. C. (2008). Can minimum convex polygon home ranges be used to draw biologically meaningful conclusions? Ecological Research, 23(3), 635–639. https://doi.org/10.1007/s11284-007-0421-9
Fieberg, J., & Börger, L. (2012). Could you please phrase "home range" as a question? Journal of Mammalogy, 93(4), 890–902. https://doi.org/10.1644/11-MAMM-S-172.1
Horne, J. S., & Garton, E. O. (2006). Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis. Journal of Wildlife Management, 70(3), 641–648. https://doi.org/10.2193/0022-541X
Edelhoff, H., Signer, J., & Balkenhol, N. (2016). Path segmentation for beginners: An overview of current methods for detecting changes in animal movement. Movement Ecology, 4(1), 21. https://doi.org/10.1186/s40462-016-0086-5
Signer, J., Fieberg, J., & Avgar, T. (2019). Animal movement tools (amt): R package for managing tracking data. Ecology and Evolution, 9(2), 880–890. https://doi.org/10.1002/ece3.4823
Silva, I., Fleming, C. H., Noonan, M. J., Alston, J., Folta, C., Fagan, W. F., & Calabrese, J. M. (2022). Autocorrelation-informed home range estimation: A review and practical guide. Methods in Ecology and Evolution, 13(3), 534–544. https://doi.org/10.1111/2041-210X.13786