(a) Define regression. Explain different methods and applications of regression analysis. (20 marks) (b) Define food chain…

Regression is a statistical technique that models how a dependent variable (Y) changes with one or more independent variables (X), allowing prediction and inference. It differs from correlation, which only measures the strength and direction of association, by estimating an equation and quantifying the effect of X on Y. The simplest form is linear regression, Y = a + bX + e, fitted by least squares, which minimises the sum of squared residuals. Multiple regression, Y = b0 + b1X1 + b2X2 + …, separates the effects of several factors; logistic regression, logit(p) = b0 + b1X, models binary outcomes such as presence/absence; polynomial regression, Y = a + bX + cX², captures curvilinear trends. The coefficient of determination (R²) measures goodness of fit, i.e. the proportion of variance in Y explained by the model, while standard error indicates uncertainty in estimates. In ecology, regression underpins species distribution models that use temperature, rainfall and elevation to predict climate-change impacts on Himalayan biodiversity; in epidemiology it relates disease incidence to climate or vector density; in wildlife management it forecasts population trends from census and habitat data.

A food chain is a linear sequence of energy transfer from producers to consumers, each trophic level losing most energy as heat so that only about 10% is passed on, producing ecological pyramids that narrow upward. Pyramids of number, biomass and energy therefore reflect these losses. A grazing chain begins with living plants: in grasslands, grass → grasshopper → frog → snake → hawk; similar chains operate in Western Ghats grassland and forest-edge habitats. A detritus chain begins with dead organic matter: on a forest floor, leaf litter → earthworm → centipede → shrew; in Sundarbans mangroves, mangrove leaf litter → detritivorous crabs or fish → fish → crocodile. These chains show how energy and nutrients move through ecosystems, but real communities are better represented by food webs, because most organisms feed at more than one level.

Altruism is behaviour in which an actor incurs a cost (C) while a recipient gains a benefit (B). Kinship explains its evolution through inclusive fitness, the sum of direct and indirect fitness: an allele can spread if the actor’s relatedness to the recipient (r) makes rB > C, Hamilton’s rule. In meerkats, alarm calls expose the caller to predators (C), but alert kin (B), so calls are favoured when relatedness is high. Indian blue-bearded bee-eaters show cooperative breeding: helpers delay their own reproduction to feed nestlings, gaining indirect fitness by aiding siblings or parents. In Apis cerana honeybees, eusociality involves workers forgoing reproduction to maintain the colony; because queens are polyandrous, worker-worker relatedness is lower than 0.75 and often below 0.5, so the advantage cannot be reduced to full-sister relatedness. Instead, workers gain inclusive fitness from helping the queen and colony when relatedness to colony members, queen control and colony-level benefits make rB exceed C. Reciprocal altruism extends the logic to non-kin: vampire bats share blood meals with helpers who have fed them, favoured by repeated encounters and memory. This explains why altruism is common in family groups and rare where kinship is weak. Thus altruism is a cost-benefit strategy shaped by kinship, reciprocity and ecological constraints.

Together, these concepts show how zoology links statistical and behavioural ecology to conservation. Regression models can forecast how warming will shift Himalayan species ranges and disease risk, while understanding food chains and altruistic kinship helps protect energy flows, social colonies and cooperative species in protected areas such as Western Ghats and Sundarbans. Such evidence-based approaches support monitoring by the Wildlife Institute of India and state forest departments, enabling targeted interventions for climate-vulnerable Himalayan fauna.