Mutualism and community structure

Not every interaction between species is a contest. In a mutualism, both partners gain. Most land plants depend on fungi in their roots, most flowering plants on animals that carry their pollen, and reef corals on algae living inside their cells. Mutualisms are as fundamental to ecosystems as predation and competition, though they were studied less for most of the twentieth century. This lesson first examines mutualism and facilitation, then turns to the community level: how ecologists describe which species are present and how common they are, how to compare communities, how food webs are structured, and how certain species, such as sea otters and wolves, exert effects on their communities far out of proportion to their abundance.

Mutualism

Mutualisms vary in how tightly the partners depend on each other. In obligate mutualisms, neither partner can survive without the other: lichens are partnerships between a fungus and an alga or cyanobacterium, and the yucca and the yucca moth of the southwestern deserts depend entirely on each other, the moth pollinating the flower and laying its eggs in the ovary where some seeds feed its larvae. In facultative mutualisms, partners benefit but can survive alone, as with many pollinators and the plants they visit.

Mycorrhizal fungi are perhaps the most important mutualists on land. More than 80 per cent of plant species form them. In ectomycorrhizae, typical of pines, spruces, birches and oaks, the fungus wraps the root tips in a sheath and grows between the root cells; in arbuscular mycorrhizae, typical of grasses and most herbs, the fungus grows into the root cells. The fungal threads (hyphae) extend far into the soil, absorbing phosphorus and water from a much larger volume than roots alone could reach. Research led by Suzanne Simard at the University of British Columbia showed that carbon can move between trees through shared fungal networks, though how much this matters for forest communities, and claims that trees deliberately share resources, are actively debated.

Mutualisms are not pure cooperation. Each partner is selected to gain as much as possible at the least cost, so cheaters arise: some orchids attract pollinators with the promise of nectar but offer none, and some yucca moths lay so many eggs that larvae eat most of the seeds. Plants can sanction cheaters, for example by aborting flowers with too many moth eggs or cutting oxygen supply to root nodules whose bacteria fix little nitrogen.

Facilitation

A related interaction is facilitation, in which one species makes the environment more suitable for another, often without itself benefiting. In harsh environments, nurse plants shelter seedlings from wind, heat and grazers: young saguaro cacti in the Sonoran Desert survive mainly in the shade of palo verde trees. The stress-gradient hypothesis proposes that facilitation becomes more important, and competition less, as environmental stress increases, a pattern seen in alpine and salt-marsh plants. Facilitation also drives succession, as lesson 10 shows.

Describing communities

A community is all the interacting species in an area. The simplest description is species richness: the number of species present. But richness ignores how common each species is. Two forests may each contain ten tree species, but one may be 91 per cent sugar maple while the other has all ten species in similar numbers. Evenness measures how equally individuals are spread among species, and diversity indices combine richness and evenness. BIOL-108 introduced the Shannon index (\( H' \)) and Simpson's index. This lesson adds three tools that ecologists use constantly: rank-abundance curves, rarefaction and beta diversity.

A rank-abundance curve plots species from most to least abundant, with abundance on a log scale. Communities with high evenness have flat curves; communities dominated by a few species have steep ones. Most communities have a few common species and many rare ones.

Species richness depends heavily on sampling effort: the more individuals you count, the more species you find, because rare species turn up only in large samples. Comparing richness between sites sampled with different effort is therefore misleading. Rarefaction solves this by repeatedly drawing random subsamples of a fixed size from each sample and calculating the expected number of species, so sites can be compared at the same sample size.

import numpy as np

rng = np.random.default_rng(208)
# Invented bird counts from two forest sites (individuals per species)
site_a = np.array([60, 41, 30, 22, 15, 11, 8, 6, 4, 3, 2, 2, 1, 1, 1, 1])
site_b = np.array([25, 18, 14, 9, 6, 4, 3, 2, 1, 1, 1])

def rarefy(counts, n, reps=2000):
    pool = np.repeat(np.arange(counts.size), counts)
    return np.mean([np.unique(rng.choice(pool, n, replace=False)).size for _ in range(reps)])

def shannon(c):
    p = c / c.sum(); return -(p * np.log(p)).sum()

for name, c in [("A", site_a), ("B", site_b)]:
    print(f"site {name}: {c.sum()} birds, {c.size} species observed, Shannon H' = {shannon(c):.2f}")
for n in (20, 50, 84):
    print(f"expected species in {n:3d} birds:  A = {rarefy(site_a, n):5.2f}   B = {rarefy(site_b, n):5.2f}")

site A: 208 birds, 16 species observed, Shannon H' = 2.10
site B: 84 birds, 11 species observed, Shannon H' = 1.93
expected species in  20 birds:  A =  7.73   B =  7.07
expected species in  50 birds:  A = 10.64   B =  9.54
expected species in  84 birds:  A = 12.52   B = 11.00

Site A has 16 species to site B's 11, but it was sampled far more heavily. Rarefied to the same 84 birds, the gap shrinks considerably. Whether the remaining difference is real would need replicate samples and a statistical test.

Comparing communities: beta diversity

Robert Whittaker distinguished diversity within a site (alpha diversity), across a whole region (gamma diversity), and the change in species composition between sites (beta diversity). High beta diversity means sites have very different species, as along a gradient from valley bottom to mountain top. Two simple similarity indices use presence and absence: the Jaccard index is the number of shared species divided by the total number of species found at either site, and the Sørensen index gives shared species double weight.

Here \( a \) and \( b \) are the numbers of species at each site and \( c \) the number shared.

sites = {
    "valley":  {"sugar maple", "red oak", "basswood", "white pine", "beech", "hemlock", "ironwood"},
    "slope":   {"sugar maple", "red oak", "white pine", "hemlock", "red maple", "white birch"},
    "ridge":   {"red pine", "white pine", "red oak", "jack pine", "blueberry", "white birch"},
}
names = list(sites)
for i in range(len(names)):
    for j in range(i + 1, len(names)):
        A, B = sites[names[i]], sites[names[j]]
        c = len(A & B)
        jac = c / len(A | B); sor = 2 * c / (len(A) + len(B))
        print(f"{names[i]:6s} vs {names[j]:6s}: shared {c}, Jaccard {jac:.2f}, Sorensen {sor:.2f}")
gamma = len(set().union(*sites.values()))
alpha = sum(len(s) for s in sites.values()) / len(sites)
print(f"gamma = {gamma}, mean alpha = {alpha:.2f}, Whittaker beta = gamma/alpha = {gamma/alpha:.2f}")

valley vs slope : shared 4, Jaccard 0.44, Sorensen 0.62
valley vs ridge : shared 2, Jaccard 0.18, Sorensen 0.31
slope  vs ridge : shared 3, Jaccard 0.33, Sorensen 0.50
gamma = 12, mean alpha = 6.33, Whittaker beta = gamma/alpha = 1.89

Food webs

A food web maps who eats whom in a community. Organisms are grouped into trophic levels: primary producers (plants, algae), primary consumers (herbivores), secondary consumers (predators of herbivores) and so on, with decomposers processing dead material from every level. Real food webs are tangled, because many species eat at several levels (omnivory) and some, like the young of large fish, change diet as they grow. Ecologists describe food webs with simple statistics. Connectance is the fraction of all possible feeding links that actually occur; for \( S \) species and \( L \) links it is \( L/S^2 \). Most real food webs have connectance between about 0.05 and 0.3, and food chains are short, rarely more than four or five links, partly because energy is lost at each step, as lesson 11 explains.

links = {
    "hare": ["plants"], "moose": ["plants"], "caribou": ["lichens"], "vole": ["plants"],
    "insects": ["plants"], "lynx": ["hare"], "fox": ["hare", "vole"],
    "owl": ["hare", "vole"], "weasel": ["vole"], "wolf": ["moose", "caribou", "hare"],
    "songbird": ["insects"], "hawk": ["songbird", "vole"],
}
species = set(links) | {p for v in links.values() for p in v}
S = len(species); L = sum(len(v) for v in links.values())
print(f"species S = {S}, feeding links L = {L}, connectance L/S^2 = {L / S**2:.3f}, links per species = {L / S:.2f}")
basal = sorted(species - set(links))
print("basal species:", basal)

species S = 14, feeding links L = 17, connectance L/S^2 = 0.087, links per species = 1.21
basal species: ['lichens', 'plants']

Keystone species and trophic cascades

In 1966, Robert Paine reported an experiment on the rocky shores of Washington state. He removed the predatory sea star Pisaster ochraceus from a stretch of shore and left a nearby stretch untouched. Without the sea star, mussels spread and crowded out most other species; within a few years, the number of species dropped from 15 to 8. Paine called Pisaster a keystone species: one whose effect on its community is large relative to its abundance, like the keystone at the top of an arch that holds the other stones in place. Since 2013, sea star wasting disease has killed vast numbers of sea stars along the Pacific coast from Alaska to Mexico, including Pisaster and the sunflower star, allowing sea urchins to multiply in places.

Sea otters are another keystone predator. Hunted almost to extinction for their fur by the early twentieth century, they were reintroduced to the west coast of Vancouver Island between 1969 and 1972 using animals from Alaska. Where otters returned, they ate sea urchins, urchin numbers fell, and kelp forests regrew, providing habitat for fish and absorbing carbon. Where otters were absent, urchins grazed kelp down to barren rock. This chain of effects running from a top predator down through herbivores to plants is a trophic cascade. The return of otters brought conflict as well as benefits: they also eat the clams, crabs and urchins harvested by coastal First Nations and commercial fishers, and Indigenous communities have called for a role in managing otters.

The reintroduction of grey wolves to Yellowstone National Park in 1995 and 1996 is often presented as a textbook trophic cascade: wolves reduced elk numbers and made elk avoid risky areas, allowing willow and aspen to regrow along streams, which in turn benefited beavers and songbirds. The story is more contested than popular accounts suggest. Elk declined for several reasons, including hunting by people, bears and cougars and drought, and studies disagree on how much willow and aspen have recovered and how much of it is due to wolves. Some researchers argue that the cascade is weaker or more local than first claimed, and that hydrology changed after beavers and wolves were lost for decades. The debate illustrates how hard it is to establish cause in large, unreplicated natural experiments.

Not every important species is a predator. Foundation species, such as eelgrass, reef-building corals, kelp and dominant trees, create the physical structure of a community. Ecosystem engineers modify habitat physically: beavers turn streams into ponds and wetlands, changing water flow, nutrient cycling and the species present for decades.