(a) Describe various types of social groupings in primates. Write a note on importance of social grouping. (20 marks) (b) (i)…

(a) Social groupings in primates

Primate societies vary with diet, habitat and predation. The main types are:

• Solitary: individuals forage alone within overlapping home ranges, as in lorises, bushbabies and the orangutan.
• Monogamous pair: a male, a female and their young defend a territory and the male helps to carry the infant, as in gibbons (the hoolock gibbon of north-east India), siamangs and titis.
• One-male (polygynous) group: one breeding male with several females and young, while surplus males form all-male bands; examples are the Hanuman langur, gorilla and patas monkey. Takeover by a new male can lead to infanticide.
• Multi-level society: one-male units join into clans, bands and troops, as in the hamadryas baboon and gelada.
• One-female (polyandrous) group: one breeding female with several males that help to rear the young, as in tamarins and marmosets.
• Multi-male multi-female group: many adults of both sexes with dominance hierarchies, as in the rhesus macaque and savanna baboon.
• Fission–fusion community: a large community splits into temporary parties and re-unites as food and safety change, as in the chimpanzee, bonobo and spider monkey.

Importance of social grouping:

• Protection: many eyes detect predators earlier, alarm calls and mobbing deter them, and the risk to each individual is diluted.
• Food: the group finds and defends food patches and territory, and young learn foraging skills from others.
• Reproduction: easy access to mates, defence of infants, shared care (allomothering) and cooperative breeding.
• Social bonds: grooming, alliances and dominance order reduce conflict and keep cooperation among kin stable.
• These gains outweigh costs such as feeding competition and easier spread of parasites.

(b)(i) Primary and secondary pollutants

• Primary pollutants are emitted directly from a source and stay in the form released: CO, SO₂, NO, hydrocarbons, soot, lead.
• Secondary pollutants form in the atmosphere from primary ones by chemical or photochemical reactions: ozone, peroxyacetyl nitrate (PAN), and the sulphuric and nitric acids of acid rain. Example: NO₂ + sunlight → NO + O; O + O₂ → O₃.
• Primary pollutants are controlled at source; secondary ones only by cutting their precursors.

(b)(ii) Primary and secondary treatment of waste water

Primary treatment is physical. Screens remove floating debris, a grit chamber lets sand and gravel settle, and sedimentation in a primary clarifier settles suspended solids as primary sludge while oil and grease are skimmed off. It removes about 50–70% of suspended solids and 25–40% of BOD.

Secondary treatment is biological and removes dissolved organic matter.

1. The primary effluent flows into an aeration tank, where air is pumped in and the liquid is agitated.
2. Aerobic bacteria, with fungal filaments and protozoa, grow as flocs and consume the organic matter, so the BOD falls sharply.
3. In a settling tank the flocs sediment as activated sludge; a small part returns to the aeration tank as inoculum.
4. The rest goes to anaerobic digesters, where bacteria produce biogas (methane, CO₂, H₂S).
5. The treated effluent is released into a river or stream.

Trickling filters and oxidation ponds are other secondary methods.

(c) Chi-square analysis

Chi-square (χ²) analysis, introduced by Karl Pearson (1900), is a non-parametric test of whether observed frequencies differ from expected frequencies by more than chance. χ² = Σ(O − E)²/E. Degrees of freedom (df) are classes − 1 for goodness of fit, and (r − 1)(c − 1) for an r × c contingency table.

Procedure:

1. State the null hypothesis (no difference between observed and expected).
2. Work out expected frequencies from it.
3. Calculate χ² and df.
4. Compare with the table value at P = 0.05: a smaller calculated value means the null hypothesis is accepted, a larger one means it is rejected.

Conditions: counts, not percentages, of independent observations; a large sample; expected frequency of at least 5 in each class (otherwise pool classes, or apply Yates's correction when df = 1).

Applications in biology:

• Goodness of fit of genetic ratios. Mendel's F₂ flower colour gave 705 violet : 224 white (n = 929) against the expected 3:1 (696.75 : 232.25), so χ² = 0.39, below 3.84 for df = 1; the data fit.
• Independent assortment or linkage in dihybrid and test crosses (9:3:3:1, 1:1:1:1).
• Hardy–Weinberg equilibrium of a population (df = 1 for two alleles).
• Association of two attributes, such as disease and exposure, or two species in a habitat.
• Homogeneity tests, such as a 1:1 sex ratio.