What ecology is and how ecologists work

Ecology is the scientific study of the interactions that determine where organisms live and how many of them there are. That definition, adapted from the Canadian ecologist Charles Krebs, is deliberately broad. It covers a single caribou trying to stay warm in a January wind, a population of snowshoe hares rising and crashing over ten years, the dozens of plant species competing for light on a prairie, and the carbon moving between the atmosphere, the boreal forest and the peat beneath it. BIOL-108 introduced ecology in one lesson as the final layer of the story of evolution. This course takes ecology apart properly: how the physical environment limits life, how populations grow and are regulated, how species compete, eat and help one another, how communities assemble and change, and how energy and nutrients move through ecosystems. The last lesson applies all of it to the global changes now reshaping North American ecosystems.

This first lesson sets out what ecology studies, the levels at which it works, and the methods ecologists use to turn observations into reliable knowledge. It ends with a short worked example of an ecological study from question to conclusion, which the rest of the course builds on.

Ecology is not environmentalism

In everyday speech, "ecology" is often used to mean caring for the environment. The two are related but not the same. Ecology is a science: it asks how nature works and tests its answers against evidence. Environmentalism is a set of values and political positions about how people should treat nature. Good environmental policy needs good ecology, because a policy built on a wrong idea of how a lake or forest works will fail. But ecology itself does not tell us what we ought to value. An ecologist can predict how much a river's fish population will fall if a dam is built; whether the dam should be built depends also on economics, law, Indigenous rights and public values. Keeping the science separate from the values makes both clearer.

Levels of ecological organization

Ecologists work at several levels of organization, and each level has its own questions and methods. Moving up the levels, new properties appear that do not exist at the level below: a single fish has a body temperature, but only a population has a birth rate, and only a community has a species richness.

Two terms are worth fixing early. The biotic environment is the living part: other organisms of the same and different species. The abiotic environment is the non-living part: temperature, water, light, soil, nutrients, wind and fire. Most ecological questions involve both. A plant's growth depends on sunlight and soil nitrogen (abiotic) but also on the insects that eat its leaves and the fungi that help its roots absorb water (biotic).

The central questions: distribution and abundance

Krebs's definition focuses on two things: distribution (where organisms are found) and abundance (how many there are). Almost every ecological question reduces to one of these. Why does the sugar maple reach its northern limit near the boundary between the mixed forest and the boreal forest? Why were there millions of passenger pigeons in the nineteenth century and none today? Why do some years bring outbreaks of spruce budworm that defoliate whole forests? Distribution and abundance are linked: a species is absent from places where it cannot maintain a population, and rare where conditions only just allow it to persist.

Ecologists recognize several kinds of explanation for a species' distribution. It may be limited by dispersal: the species has never reached a suitable place, as when a lake isolated after the last glaciation lacks a fish species that could live there. It may be limited by the physical environment: the place is too cold, too dry or too acidic. It may be limited by other species: a competitor, predator or disease excludes it. Or it may be limited by habitat selection: the species could live there but chooses not to. Transplant experiments, which move organisms beyond their current range to see whether they survive and reproduce, are the classic test of these explanations.

Scale in space and time

Ecological processes happen at very different scales. A leaf adjusts its pores within minutes; a population of lemmings may rise and fall over three or four years; a forest recovers from fire over a century; ranges of tree species shifted northward over thousands of years after the ice sheets melted. The scale at which you observe changes what you see. A small quadrat in a forest may show a seedling dying in the shade of a parent tree, while a satellite image of the same forest shows a stable cover of trees over decades. Neither view is wrong; they answer different questions. Good ecological studies state their scale explicitly and avoid drawing conclusions at a scale they did not measure.

How ecologists know: three approaches

Ecologists use three broad approaches, and the strongest conclusions come from combining them.

Observation and description. Careful observation of patterns in nature is where most ecological questions start. Long-term monitoring is especially valuable: the Hudson's Bay Company fur records that revealed the ten-year cycle of lynx and snowshoe hare, or decades of breeding bird surveys, show patterns no short study could. Observations reveal correlations, but correlation alone cannot show cause, because many variables change together in nature.

Experiments. In a manipulative experiment, the researcher changes one factor and compares the result with controls where it was left alone. Field experiments, such as excluding grazers with fences or adding nutrients to whole lakes, test causes in realistic conditions. The Experimental Lakes Area in northwestern Ontario, where whole lakes have been manipulated since 1968, showed in the 1970s that phosphorus caused algal blooms, which helped drive limits on phosphorus in detergents. Laboratory experiments offer more control but less realism. A good experiment has replication (several independent units per treatment), randomization (treatments assigned by chance) and controls.

Models. A model is a simplified description of a system, often written as equations. Models make assumptions explicit, produce testable predictions and let us explore situations we cannot easily observe, such as a population over 200 years. This course uses many small mathematical models written in Python, from exponential growth to predator–prey cycles. A model is judged by how well its predictions match data, not by how realistic it looks.

Hypotheses, predictions and replication

A scientific hypothesis is a proposed explanation that makes testable predictions. "Moose browsing limits the height of balsam fir on Isle Royale" predicts that fir inside fences that exclude moose will grow taller than fir outside. If the prediction fails, the hypothesis is weakened; if it succeeds repeatedly, our confidence grows but never becomes absolute. Ecology often deals with multiple working hypotheses at once, since several causes may act together.

Replication matters because nature is variable. If you fence one plot and leave one plot open, any difference might be due to soil or chance. Pseudoreplication, a term introduced by Stuart Hurlbert in 1984, is the error of treating repeated measurements within one experimental unit as if they were independent replicates. Measuring 50 trees inside a single fenced plot gives 50 measurements but only one replicate of the treatment. Statistical analysis, starting with means, variation and tests of difference, is essential, and the examples in this course use Python to compute them.

import numpy as np
from scipy import stats

# Invented data: seedlings per plot after 5 years in 10 paired plots
fenced = np.array([42, 37, 51, 29, 46, 39, 55, 33, 48, 40])
open_  = np.array([18, 22, 25, 15, 27, 19, 30, 14, 26, 21])
diff = fenced - open_
t, p = stats.ttest_rel(fenced, open_)
print("mean fenced:", fenced.mean(), " mean open:", open_.mean())
print("mean difference:", diff.mean(), " sd of differences:", round(diff.std(ddof=1), 2))
print(f"paired t = {t:.2f}, p = {p:.6f}")

mean fenced: 42.0  mean open: 21.7
mean difference: 20.3  sd of differences: 4.0
paired t = 16.04, p = 0.000000

The paired test asks whether the differences within pairs are consistently above zero. Here every fenced plot has more seedlings than its partner, and the probability of seeing differences this large by chance alone is tiny. The design deals with the problem that the forest is patchy: each fenced plot is compared with an open plot in similar conditions.

Ecological knowledge from Indigenous peoples

Long before ecology became an academic discipline, Indigenous peoples across North America built detailed knowledge of the species and ecosystems they depended on, tested over many generations. This knowledge, often called Indigenous knowledge or traditional ecological knowledge, includes observations of animal movements, plant phenology, fire use and population changes, together with values and responsibilities toward the land. Researchers increasingly work with Indigenous knowledge holders as partners rather than as sources of data. The Mi'kmaw concept of Etuaptmumk, or two-eyed seeing, introduced by Elder Albert Marshall, describes learning to see with the strengths of Indigenous knowledge in one eye and Western science in the other. Lesson 12 returns to how these ways of knowing work together in practice, for example in caribou and fire management.

How this course is organized

The course moves up the levels of organization. Lessons 2 and 3 cover climate, biomes and how individual organisms cope with their physical environment. Lessons 4 to 6 cover populations: growth and regulation, life histories and demography, and populations spread across landscapes. Lessons 7 to 9 cover interactions between species and the structure of communities. Lesson 10 covers how communities change over time and space, and lesson 11 covers ecosystems. Lesson 12 applies the course to global change. Specialized topics have their own courses: field methods in BIOL-430, northern and boreal ecology in BIOL-440, aquatic ecology in BIOL-460, conservation biology in BIOL-367 and population genetics in BIOL-322.