Relative Abundance Calculator: Formula & Steps | StatsUnlock

Relative abundance is the share of the total that belongs to one species or one isotope. Divide the count of that item by the total count of all items, then multiply by 100 to get a percentage.

If you count 100 birds and 40 are bulbuls, the relative abundance of the bulbul is 40%. The number tells you how common an item is compared with everything else in the same sample. It does not tell you how many there are in the area.

Rule of thumb: all relative abundances in one sample add up to 100%. A species above 50% dominates the community. A species below 1% is rare. In chemistry the same sum, taken over isotopes, is called percent abundance.

Relative abundance is how common one thing is compared with all the other things counted in the same sample. In ecology the things are species and the sample is a community: a transect, a plot, a camera trap grid, or a water sample. In chemistry the things are the isotopes of one element. In microbiology they are the bacteria in a sequenced sample. The meaning is the same in every field: a part divided by the whole.

A simple definition: the relative abundance of a species is the number of individuals of that species divided by the total number of individuals of all species. Multiply by 100 and you have a percentage. Leave it as a fraction between 0 and 1 and you have a proportion, usually written pᵢ.

Where the number comes from. You need two things: a count for each species, written nᵢ, and the total of all counts, written N. Nothing else enters the formula. That is also its weakness. If the survey missed quiet or shy species, their share is too low and every other share is too high.

What relative abundance tells you. It tells you which species are common and which are rare, how evenly the individuals are shared out, and whether one species dominates. It lets you compare two sites that were sampled with different effort, because a percentage does not grow just because you walked a longer transect.

What it does not tell you. It does not tell you how many animals live in the area. That is absolute abundance, and it needs a method such as distance sampling or mark and recapture. A species can keep the same numbers and still fall in relative abundance when another species increases.

Why ecologists use it and not a simple count. Raw counts depend on effort. Two hours of survey will give more birds than one hour. Shares are much more stable, so they are the starting point for nearly every diversity index. Shannon, Simpson, Pielou and Berger-Parker all take the relative abundances pᵢ as their only input.

4.1 What your relative abundance values say

The most abundant species at the study site is ___ with a relative abundance of ___%. The sample holds ___ individuals of ___ species.

4.2 Is one species dominating?

The Berger-Parker dominance value is ___, which means the commonest species holds ___% of all individuals. The top ___ species together make up 50% of the sample.

4.3 How evenly the individuals are shared

Pielou evenness is ___ and the Shannon index is ___. These two numbers summarise the whole list of relative abundances in one value each.

4.4 How far you can trust each percentage

With a total of ___ individuals, the ___% confidence interval for the commonest species runs from ___% to ___%. The rarest species was seen ___ times.

4.5 What relative abundance actually tells you

A relative abundance is a share of a sample. A value of 40% means that four in every ten individuals you recorded belong to that species. It is the plainest way to describe the structure of a community: which species are common, which are scarce, and how steeply the numbers fall from the first to the last.

Because it is a share, it is not tied to survey effort in the way a raw count is. Double the length of a transect and the counts roughly double, but the shares stay close to where they were. That is why relative abundance is the standard way to compare sites, seasons and studies that used different effort.

4.6 What does relative abundance not tell you?

It cannot tell you population size. A species with 40% relative abundance might be 40 birds or 40,000. It also cannot tell you whether a species truly increased. All the shares must add up to 100%, so when one species declines, the shares of all the others go up, even if their real numbers did not change at all. This is the main trap in reading relative abundance over time, and it is the reason microbiome studies now pair relative abundance with a measure of total load (Gloor et al., 2017; Vandeputte et al., 2017).

4.7 What is the difference between species richness and relative abundance?

Species richness is the number of species. Relative abundance is how the individuals are divided among those species. Two forests can each hold 20 species and still be very different places: one with every species at about 5%, the other with one species at 80% and nineteen species sharing the rest. Richness is the same. The relative abundance pattern is not. Together, richness and relative abundance are the two parts of species diversity, and a diversity index such as Shannon or Simpson combines both in one number (Hill, 1973).

4.8 What sampling does to the numbers

Small samples exaggerate. A species seen once in a sample of 20 gets a relative abundance of 5%, but its true share could be anywhere from about 1% to 24%. Rare species are also easy to miss completely, which pushes the shares of the common species upward. As a guide, you need about 300 individuals to have a 95% chance of recording a species whose true share is 1%.

Detection matters as much as sample size. Loud, bold or large species are recorded more often than quiet ones, so their relative abundance in the notebook is higher than in the forest. Camera trap studies meet the same problem, which is why a relative abundance index from photographs should be compared only within one study and one method (Sollmann et al., 2013).

4.9 What is the difference between percent abundance and relative abundance?

For isotopes the two terms are often used for the same thing, but they are not always the same number. Percent abundance is the share of all atoms of an element that are one isotope, and the values for all isotopes add up to 100%. On a mass spectrum, relative abundance is often plotted on a different scale, where the tallest peak, called the base peak, is set to 100 and every other peak is given as a percentage of it. Chlorine shows the difference well: its percent abundances are 75.76% and 24.24%, and the same data on a base-peak scale read 100 and 32.

Both scales describe the same ratio, about 3 to 1. To move from the base-peak scale to percent abundance, divide each peak by the sum of all peaks and multiply by 100. The chemistry mode of this calculator does that step for you and then works out the average atomic mass.

4.10 Practical judgement

Report each relative abundance with the total count N, so the reader can judge its precision. Do not rank species that differ by only a few individuals. Compare sites only when the same method, season and observer effort were used. When shares change between years, check the raw counts before you say that a species increased or declined. And remember that a relative abundance describes your sample first and the community second.

▶ Run the analysis above to auto-fill all five examples and the poster text with your results.

This flowchart shows how relative abundance is calculated, step by step, from raw field counts to the final verdict. Every box uses the same worked example as the Excel, R and Python sections: 100 birds of 6 species counted on one 2 km transect in dry deciduous forest.

Check any box yourself: the Excel sheet in Section 9, the R script in Section 10 and the Python script in Section 11 all return 40.0% for the bulbul, d = 0.400 and J′ = 0.851 for this dataset.

1. Choose what you are measuring. Leave the first button on Species counts for ecology data, or press Isotopes for chemistry. The two modes use the same formula but show different results.
2. Enter your data. In the default tab, type or paste comma-separated entries such as Bulbul: 40, Babbler: 25, Myna: 15. New lines, tabs and semicolons also work, so two columns pasted from Excel are fine. Plain numbers are accepted and are named Species 1, Species 2 and so on.
3. Name each site. The name box above every data box is editable. Type something useful such as Interior transect, March and it appears in the tables, the charts and the downloads.
4. Add more sites to compare. Press Add site to compare two or more communities side by side. Each site gets its own percentages, and the calculator adds a comparison table and the percentage similarity between sites.
5. Or upload a file. On the Upload tab, choose a CSV or Excel file, pick the column that holds the species names, and click every count column you want. Each column you click becomes one site.
6. Try a sample dataset. Twelve built-in datasets cover an even forest community, a fragment dominated by one species, a tiny sample, a camera trap grid, a two-site comparison, microbiome reads and a messy field sheet.
7. Fill in the study area name. This is optional. When you type it, the name is used in all five report sentences, the poster text, the conclusion and both downloads.
8. Set the options. Choose what was counted, the number of decimal places, the confidence level, and the percentage below which a species is called rare.
9. Press Calculate Relative Abundance. Nothing is calculated until you press the button. If you change any input afterwards, the results are cleared so you never read old numbers.
10. Read the results and export. Check the summary cards and the full table, then the four charts. Sections 4, 5 and 18 fill in with your own numbers. Use Download Doc for a text report or Download PDF for a print-ready page.

Excel has no function called relative abundance, but you do not need one. The whole calculation is one division. What goes wrong is the detail: a total that is not locked with dollar signs, a blank row that changes the total, and percentages that are formatted twice. This section shows the sheet at each stage, using the same six-species bird example as the rest of the page.

9.1 The functions you need

9.2 Step by step

9.3 The awkward cases

Zero counts. A species with a count of 0 has a relative abundance of 0%, which is fine. The problem comes in the Shannon formula, because LN(0) returns an error. Wrap the log so that zero rows add nothing:

In Excel 2019 and earlier, confirm this formula with Ctrl + Shift + Enter. The classic mistake is to delete the zero row to make the error go away and then forget that the species was searched for and not found.

Several sites in one sheet. Put each site in its own column and divide by the total of that column, not by the grand total. With sites in B, C and D and totals in row 8:

Here only the row is locked, so the formula can be filled both down and across. Locking the column as well is the classic mistake, and it makes every site a share of site 1.

Isotopes and average atomic mass. With isotope masses in A2:A3 and their abundances in B2:B3, the average atomic mass is a weighted mean:

For chlorine, masses 34.96885 and 36.96590 with abundances 75.76 and 24.24 return 35.453. Dividing by SUM(B2:B3) means the abundances can be percentages, fractions or peak heights. To go the other way and find the abundance of the lighter isotope from the average mass, use =(A3-B5)/(A3-A2) with the average mass in B5.

9.4 Why your Excel answer might differ from R or Python

9.5 Charting relative abundance in Excel

1. Sort the table so the most abundant species is at the top.
2. Select the species names and the relative abundance column.
3. Choose Insert → Charts → Bar → Clustered Bar. A bar chart is easier to read than a pie chart once there are more than five species.
4. Right-click the vertical axis, choose Format Axis and tick Categories in reverse order, so the commonest species sits at the top.
5. Add the axis title Relative abundance (%) and put N in the chart title. A share without its sample size is the most common reason for a reviewer comment.

For a rank-abundance curve, plot rank on the horizontal axis and relative abundance on the vertical axis as a line chart, then set the vertical axis to a logarithmic scale. Chart 3 in Section 3 of this page draws it for you.

9.6 Excel errors you will probably hit

In R the relative abundance of a vector of counts is one line, counts / sum(counts) * 100. That line alone is rarely what you should report, because it gives no sample size, no precision and no summary of dominance. The script below is complete and runs from top to bottom. It calculates the relative abundance of every species with a Wilson confidence interval, adds dominance, the Shannon index and evenness, prints a plain-English verdict, and saves one publication-ready figure at 300 dpi.

10.1 The complete script

R, base only, saves relative_abundance.png

# ============================================================
# Relative abundance of species, with one publication-ready plot
# StatsUnlock - https://statsunlock.com/relative-abundance-calculator/
# Base R only. No packages to install.
# ============================================================

# ---- 1. Your data ------------------------------------------
# Replace with your own counts, or read a CSV that has the
# columns "species" and "count":
#   dat    <- read.csv("counts.csv")
#   dat    <- dat[!is.na(dat$count), ]
#   counts <- setNames(dat$count, dat$species)
counts <- c(Bulbul = 40, Babbler = 25, Myna = 15,
            Robin = 10, Barbet = 6, Coucal = 4)

site <- "Dry deciduous forest transect"
conf <- 0.95

# ---- 2. Relative abundance ---------------------------------
counts <- sort(counts, decreasing = TRUE)   # most abundant first
N <- sum(counts)
if (N <= 0) stop("The total count is 0, so there is nothing to divide by.")
p   <- counts / N          # proportion, 0 to 1
ra  <- p * 100             # relative abundance, percent
cum <- cumsum(ra)          # cumulative share, percent

# ---- 3. Wilson confidence interval for each share ----------
z      <- qnorm(1 - (1 - conf) / 2)
den    <- 1 + z^2 / N
centre <- (p + z^2 / (2 * N)) / den
half   <- z * sqrt(p * (1 - p) / N + z^2 / (4 * N^2)) / den
lo     <- pmax(0, centre - half) * 100
hi     <- pmin(1, centre + half) * 100

# ---- 4. Dominance, diversity and evenness ------------------
pp      <- p[p > 0]                 # leave out zero counts before the log
S       <- length(pp)               # species richness
d       <- max(p)                   # Berger-Parker dominance
H       <- -sum(pp * log(pp))       # Shannon index, natural log
J       <- if (S > 1) H / log(S) else NA   # Pielou evenness
simpson <- 1 - sum(pp^2)            # Simpson diversity, 1 - D

# ---- 5. Printed summary ------------------------------------
tab <- data.frame(species       = names(counts),
                  count         = as.numeric(counts),
                  rel_abund_pct = round(as.numeric(ra), 2),
                  cum_pct       = round(as.numeric(cum), 2),
                  ci_low        = round(as.numeric(lo), 1),
                  ci_high       = round(as.numeric(hi), 1))
cat(sprintf("Site: %s\n", site))
cat(sprintf("Total individuals N = %d, species richness S = %d\n", as.integer(N), S))
print(tab, row.names = FALSE)
cat(sprintf("Berger-Parker d = %.3f\n", d))
cat(sprintf("Shannon H' = %.3f, Pielou J' = %.3f, Simpson 1-D = %.3f\n", H, J, simpson))

verdict <- if (d >= 0.50) {
  "one species holds half or more of all individuals, so dominance is high"
} else if (d >= 0.25) {
  "the commonest species is clearly ahead but does not hold the majority, so dominance is moderate"
} else {
  "no species stands far above the others, so dominance is low"
}
cat(sprintf("Verdict: %s is the most abundant species at %.1f%%, and %s.\n",
            names(counts)[1], ra[1], verdict))

# ---- 6. One figure, 300 dpi --------------------------------
colours <- c("#16a34a", "#2563eb", "#ea580c", "#9333ea", "#ca8a04",
             "#0d9488", "#db2777", "#475569", "#0891b2", "#b45309")
bar_cols <- rep(colours, length.out = length(counts))

png("relative_abundance.png", width = 3000, height = 1350, res = 300)
par(mfrow = c(1, 2), mar = c(4.5, 6.5, 3, 1.5))

# Left panel: sorted bars with confidence intervals
bp <- barplot(rev(as.numeric(ra)), horiz = TRUE, names.arg = rev(names(counts)),
              las = 1, col = rev(bar_cols), border = "white",
              xlim = c(0, max(hi) * 1.22),
              xlab = "Relative abundance (%)",
              main = sprintf("Share of each species (N = %d)", as.integer(N)))
arrows(rev(lo), bp, rev(hi), bp, angle = 90, code = 3, length = 0.04)
text(rev(hi) + 1, bp, sprintf("%.1f%%", rev(ra)), adj = 0, cex = 0.8)

# Right panel: rank-abundance curve on a log scale
par(mar = c(4.5, 4.5, 3, 1))
plot(seq_len(S), ra[seq_len(S)], log = "y", type = "b", pch = 19,
     col = "#2563eb", lwd = 2, xaxt = "n",
     xlab = "Species rank (1 = most abundant)",
     ylab = "Relative abundance (%), log scale",
     main = sprintf("Rank-abundance curve (J' = %.2f)", J))
axis(1, at = seq_len(S))
grid()

dev.off()
cat("Saved relative_abundance.png\n")

10.2 What each part does

10.3 What the figure shows

The left panel is a sorted bar chart of relative abundance with one bar per species and a thin error bar for the 95% confidence interval. Bars whose error bars overlap a lot should not be ranked against each other with confidence. The right panel is the rank-abundance curve on a log scale. A steep fall from rank 1 to rank 2 means one species dominates. A gentle slope means the individuals are shared evenly. For the example data the line falls steadily from 40% to 4%, which matches the moderate dominance and high evenness in the printed verdict.

10.4 Common R problems and fixes

10.5 Useful one-liners

Python gives the relative abundance in one line as well, counts / counts.sum() * 100, and it carries the same risk of being reported with nothing beside it. The script below is complete and runnable. It calculates the share of every species with a Wilson confidence interval, adds dominance, the Shannon index and evenness, prints a verdict in plain English, and saves one 300 dpi figure ready for a paper or a slide.

Install with pip install numpy matplotlib if you do not already have them. Add pandas only if you use the commented CSV lines.

11.1 The complete script

Python, numpy and matplotlib, saves relative_abundance.png

# ============================================================
# Relative abundance of species, with one publication-ready plot
# StatsUnlock - https://statsunlock.com/relative-abundance-calculator/
# Needs numpy and matplotlib only.
# ============================================================
import numpy as np
import matplotlib.pyplot as plt

# ---- 1. Your data ------------------------------------------
# Replace these two lines with your own species and counts,
# or read a CSV that has the columns "species" and "count":
#   import pandas as pd
#   df = pd.read_csv("counts.csv").dropna(subset=["count"])
#   species = df["species"].tolist()
#   counts = df["count"].to_numpy(dtype=float)
species = ["Bulbul", "Babbler", "Myna", "Robin", "Barbet", "Coucal"]
counts = np.array([40, 25, 15, 10, 6, 4], dtype=float)

site = "Dry deciduous forest transect"
z = 1.959964          # 1.644854 for 90%, 2.575829 for 99%

# ---- 2. Relative abundance ---------------------------------
order = np.argsort(-counts, kind="stable")      # most abundant first
species = [species[i] for i in order]
counts = counts[order]

N = counts.sum()
if N <= 0:
    raise SystemExit("The total count is 0, so there is nothing to divide by.")
p = counts / N                 # proportion, 0 to 1
ra = p * 100                   # relative abundance, percent
cum = np.cumsum(ra)            # cumulative share, percent

# ---- 3. Wilson confidence interval for each share ----------
den = 1 + z**2 / N
centre = (p + z**2 / (2 * N)) / den
half = z * np.sqrt(p * (1 - p) / N + z**2 / (4 * N**2)) / den
lo = np.clip(centre - half, 0, 1) * 100
hi = np.clip(centre + half, 0, 1) * 100

# ---- 4. Dominance, diversity and evenness ------------------
pp = p[p > 0]                  # leave out zero counts before the log
S = int(pp.size)               # species richness
d = p.max()                    # Berger-Parker dominance
H = -(pp * np.log(pp)).sum()   # Shannon index, natural log
J = H / np.log(S) if S > 1 else float("nan")   # Pielou evenness
simpson = 1 - (pp**2).sum()    # Simpson diversity, 1 - D

# ---- 5. Printed summary ------------------------------------
print(f"Site: {site}")
print(f"Total individuals N = {N:.0f}, species richness S = {S}")
print(f"{'Species':<12}{'Count':>7}{'RA %':>9}{'Cum %':>9}{'95% CI':>16}")
for s, n, r, c, a, b in zip(species, counts, ra, cum, lo, hi):
    print(f"{s:<12}{n:>7.0f}{r:>9.2f}{c:>9.2f}{a:>9.1f} to {b:<5.1f}")
print(f"Berger-Parker d = {d:.3f}")
print(f"Shannon H' = {H:.3f}, Pielou J' = {J:.3f}, Simpson 1-D = {simpson:.3f}")

if d >= 0.50:
    verdict = "one species holds half or more of all individuals, so dominance is high"
elif d >= 0.25:
    verdict = "the commonest species is clearly ahead but does not hold the majority, so dominance is moderate"
else:
    verdict = "no species stands far above the others, so dominance is low"
print(f"Verdict: {species[0]} is the most abundant species at {ra[0]:.1f}%, and {verdict}.")

# ---- 6. One figure, 300 dpi --------------------------------
colours = ["#16a34a", "#2563eb", "#ea580c", "#9333ea", "#ca8a04", "#0d9488",
           "#db2777", "#475569", "#0891b2", "#b45309"]
bar_cols = [colours[i % len(colours)] for i in range(len(species))]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4.4))

ypos = np.arange(len(species))[::-1]
ax1.barh(ypos, ra, color=bar_cols, edgecolor="white")
ax1.errorbar(ra, ypos, xerr=[ra - lo, hi - ra], fmt="none", ecolor="#111827",
             elinewidth=1, capsize=3)
ax1.set_yticks(ypos)
ax1.set_yticklabels(species)
ax1.set_xlabel("Relative abundance (%)")
ax1.set_title(f"Share of each species (N = {N:.0f})")
for y, r, h in zip(ypos, ra, hi):
    ax1.text(h + 1, y, f"{r:.1f}%", va="center", fontsize=9)
ax1.set_xlim(0, hi.max() * 1.22)

rank = np.arange(1, S + 1)
ax2.plot(rank, ra[:S], marker="o", color="#2563eb", linewidth=1.8)
ax2.set_yscale("log")
ax2.set_xticks(rank)
ax2.set_xlabel("Species rank (1 = most abundant)")
ax2.set_ylabel("Relative abundance (%), log scale")
ax2.set_title(f"Rank-abundance curve (J' = {J:.2f})")
ax2.grid(True, which="both", linewidth=0.4, alpha=0.5)

fig.suptitle(site, fontsize=11, y=1.02)
fig.tight_layout()
fig.savefig("relative_abundance.png", dpi=300, bbox_inches="tight")
print("Saved relative_abundance.png")

11.2 What each part does

11.3 What the figure shows

The figure has the same two panels as the R version. On the left are the sorted bars with 95% confidence intervals and a percentage label at the end of each bar. On the right is the rank-abundance curve on a log scale, with Pielou evenness in the title. Read the left panel to see which species are common. Read the right panel to see the shape of the community: steep means dominated, flat means even.

11.4 Common Python problems and fixes

11.5 Useful one-liners

Run it from a terminal with python relative_abundance.py. In Jupyter, delete the savefig line and add plt.show() to see the figure under the cell.

12.1 Relative abundance and the terms it is confused with

Conclusion: relative abundance is always a share of a total. If the number you have is divided by area or by effort, it is a density or a rate, not a relative abundance. The camera trap RAI is a rate per unit effort and its values do not add up to 100 (O'Brien et al., 2003).

12.2 Working bands used by this calculator

Conclusion: these bands are working rules for this tool, chosen so that the labels are easy to read. They are not a published standard. Dominance and evenness depend on the habitat and the group studied, so compare your values with other studies of the same kind before you call a community dominated (Berger & Parker, 1970; Hill, 1973).

12.3 How precise is one relative abundance? 95% Wilson intervals

Conclusion: at N = 20 a share of 25% could really be anything from about 11% to 47%. At N = 1,000 the same share is known to within about 3 percentage points. Precision depends on N far more than on the share itself (Wilson, 1927).

12.4 How many individuals you need to record a rare species

Conclusion: a sample of 100 individuals will often miss species below about 3%. Missing species make the shares of the recorded species look larger than they are, which is why richness and relative abundance from small samples must be read with care (Gotelli & Colwell, 2001; Preston, 1948).

12.5 Natural percent abundance of common isotopes

Conclusion: these are the representative values in the IUPAC tables (Meija et al., 2016a, 2016b). Natural samples vary a little, which is why the atomic mass of some elements is now given as a range. Use the values your textbook or exam board gives if they differ in the last digit.

12.6 The same data on three scales

Conclusion: proportion and percent are the same number with the decimal point moved. The base-peak scale is different: it divides by the largest value, not by the total, so it does not add up to 100. Always say which scale a relative abundance is on.

12.7 What relative abundance is a share of, by field

Conclusion: the formula never changes, but the meaning of the total does. For sequence reads the total carries no biological meaning at all, so a rise in one taxon forces a fall in the others (Gloor et al., 2017; Vandeputte et al., 2017).

Study design: relative abundance needs one count per species from one defined sample, collected with the same method and effort for every species. The eighteen points below take you from planning to a clean data sheet.

Plan

1. Decide what your relative abundance will be a share of. Write down the group (for example all diurnal birds), the area and the season before you start. A share has no meaning without its total.
2. Choose one method and keep it. Use point counts of 10 minutes with a 50 m radius, line transects of 1 to 2 km, or 20 m × 20 m vegetation plots. Do not mix methods inside one sample.
3. Plan for at least 100 individuals per site. Below 30 individuals the confidence intervals are too wide to rank species. Add points or repeat visits until you reach the target.
4. Spread the sampling units. Place points at least 250 m apart and transects at least 500 m apart so that the same bird is not counted twice.
5. Fix the time window. Count birds from sunrise to about 3 hours after sunrise. Run camera traps for at least 30 days per station. Keep the same window at every site.

Kit

6. Carry a printed data sheet with one row per species. Add columns for site, date, start time, observer, weather and tally marks. Pre-print the expected species to speed up recording.
7. Use binoculars of 8 × 42 and a rangefinder. The rangefinder lets you check the 50 m radius, which stops distant loud species from inflating their share.
8. Record position with a GPS unit or phone. Save every point or transect start and end to within 5 m, so the survey can be repeated.
9. Bring a regional field guide and a sound recorder. Record unknown calls and identify them later. Do not guess in the field.
10. Carry a tally counter for flocks. Large flocks are the biggest source of counting error, and one flock can change every relative abundance on the sheet.

Collect

11. Count individuals, not sightings. If the same bird is seen three times, it is one individual. For camera traps, treat photos of one species at one station within 30 minutes as one event.
12. Collect the species counts for relative abundance in one pass. Record every individual of every species in the group. Do not skip common species because they are boring: they are the largest part of the total.
13. Write unidentified individuals on their own row. Label them "Unidentified warbler", not as a guess. Decide later whether to keep them in the total.
14. Record zeros for target species you looked for. A zero says the species was searched for and not found. A blank says nothing was recorded.
15. Note anything that changes detection. Wind above 20 km/h, rain, traffic noise and dense fog all lower the count of quiet species and bias relative abundance toward loud ones.

Check

16. Add up the total count for relative abundance on the same day. Turn tally marks into numbers and add the column while you still remember the survey. Check that the total matches the sum of the rows.
17. Merge duplicate names before you calculate relative abundance of species. "Red-vented Bulbul", "RV Bulbul" and "Bulbul" must become one row. Use one checklist of names for the whole project.
18. Keep the raw counts with the percentages. Store species, count, site, date, method and effort in one file. A table of percentages alone cannot be checked or combined with later surveys.

Relative abundance is the right measure when your question is about the make-up of a sample, not its size.

Use relative abundance when:

• ✓ You have a count for each species from one defined sample.
• ✓ You want to know which species are common and which are rare.
• ✓ You need to compare sites or seasons sampled with different effort.
• ✓ You are preparing the input for a diversity index such as Shannon or Simpson.
• ✓ You have isotope peak heights and need percent abundance or average atomic mass.

Use a different measure when:

• ✗ You need to know how many animals live in an area. Use a density or population estimate.
• ✗ You have camera trap photos and trap nights. Use the relative abundance index (RAI), which divides by effort.
• ✗ You recorded only presence in plots, not counts. Use relative frequency.
• ✗ You want one number for the whole community. Use a diversity index or evenness.
• ✗ You want to know whether a bacterial taxon truly increased. Pair relative abundance with a measure of absolute abundance.

Real-world examples:

• Forest bird monitoring: a reserve compares the share of forest specialists in interior and edge transects each year.
• Grassland restoration: a team tracks the relative abundance of native grasses against invasive grasses by cover.
• Gut microbiome study: a lab reports the relative abundance of bacterial phyla from sequence reads in each sample.
• Chemistry class: students read two peaks from a chlorine mass spectrum and calculate the average atomic mass.

Sampling design guidance: use the same method, the same effort and the same season at every site you plan to compare. Aim for 100 or more individuals per site. Use at least three independent sampling units per site so you can report the variation between them.

Which related metric should you choose?

Assumptions

1. Every individual is counted once. Double counting a flock or a repeat visitor inflates that species and lowers all the others.
2. All species are equally easy to detect. This is rarely true. Loud, large or bold species are over-represented, so shares describe what was detected.
3. Identification is correct. Two species lumped as one, or one species split in two, changes several shares at once.
4. The sample is one community. Pooling two habitats or two seasons gives shares that describe neither.
5. Individuals are recorded independently. The confidence interval assumes this. Animals in flocks or herds are not independent, so true intervals are wider than shown.
6. The same effort was used for every species. A special search for one rare species breaks the share for all.
7. For isotopes, every isotope of the element is included. Leaving one out makes every percent abundance too high.

Limitations

• Relative abundance says nothing about population size or density.
• The shares are tied together. One species cannot rise without at least one other falling, even when real numbers have not changed.
• Small samples miss rare species, which pushes the shares of common species upward.
• Shares based on individuals, cover and biomass differ for the same community, so they must not be mixed or compared with each other.
• Sequence read shares depend on gene copy number and primer bias as well as on the number of cells.
• The dominance and evenness labels on this page are working bands, not published thresholds.
• The calculator describes one sample. It does not test whether two sites differ. For that, use a chi-square test of the count table or a permutation test on replicate samples.

▶ Run the analysis above to generate your conclusion. The four blocks below fill in with your own values.

What was found

This analysis calculated relative abundance for ___ using ___ individuals of ___ species. The most abundant species was ___ at ___%.

Ecological significance

Dominance (d = ___) and evenness (J′ = ___) indicate ___.

Conservation and management points

• ___
• ___
• ___

Next steps

• ___
• ___
• ___

A relative abundance calculator does a very small piece of arithmetic: one count divided by a total. The value of the result depends on what went into that total. Count every individual once, use the same effort for every species, report N beside every percentage, and read changes over time against the raw counts. With those habits a simple table of shares becomes a reliable picture of a community, and a sound base for richness, evenness and diversity indices.

More free statistics and ecology tools are listed at statsunlock.com.

The following peer-reviewed references support the methods used in this relative abundance calculator, covering species abundance distributions, dominance and evenness, confidence intervals for proportions, compositional data, and the isotopic abundance of the elements.

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2. Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecological Monographs, 27(4), 325–349. https://doi.org/10.2307/1942268
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4. Fisher, R. A., Corbet, A. S., & Williams, C. B. (1943). The relation between the number of species and the number of individuals in a random sample of an animal population. Journal of Animal Ecology, 12(1), 42–58. https://doi.org/10.2307/1411
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11. Meija, J., Coplen, T. B., Berglund, M., Brand, W. A., De Bièvre, P., Gröning, M., Holden, N. E., Irrgeher, J., Loss, R. D., Walczyk, T., & Prohaska, T. (2016a). Atomic weights of the elements 2013 (IUPAC Technical Report). Pure and Applied Chemistry, 88(3), 265–291. https://doi.org/10.1515/pac-2015-0305
12. Meija, J., Coplen, T. B., Berglund, M., Brand, W. A., De Bièvre, P., Gröning, M., Holden, N. E., Irrgeher, J., Loss, R. D., Walczyk, T., & Prohaska, T. (2016b). Isotopic compositions of the elements 2013 (IUPAC Technical Report). Pure and Applied Chemistry, 88(3), 293–306. https://doi.org/10.1515/pac-2015-0503
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