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In which scenario does mark-recapture fail?

High mortality between marking and recapture violates the closure and equal-survival assumptions of the simple Lincoln–Petersen estimator. If marked individuals die disproportionately or many die before mixing, the number of marked recaptures R falls. Because N is estimated as MC/R, an artificially small R inflates the population estimate. Even equal mortality among marked and unmarked animals can undermine the interpretation if population size changes substantially during the interval. A closed population, uniform mixing, and equal marking or capture probabilities support rather than defeat the method. Marking itself must not increase mortality; otherwise marked animals cease to represent the population. Investigators reduce this problem by using harmless marks, shortening the interval, and applying multi-occasion survival models when closure is unrealistic. “Failure” need not mean no estimate can be calculated—it means the resulting estimate is biased or refers ambiguously to population size at different times. Reliable capture–recapture inference depends on preserving the marked fraction except through the same sampling processes experienced by all individuals.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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If dispersion pattern is random, variance:mean ratio is:

Independent random placement of organisms is commonly modeled by a Poisson distribution. A defining property of the Poisson distribution is equality of its mean and variance, so the variance-to-mean ratio is expected to be 1. Values substantially below 1 indicate underdispersion or uniform spacing, because counts vary less than chance predicts. Values above 1 indicate overdispersion or clumping, because some units contain aggregations while others are nearly empty. An observed ratio will rarely equal exactly 1 in a finite sample; statistical tests evaluate whether the departure is greater than expected by sampling variation. The result also depends on quadrat size and spatial scale. A population may appear random at one grain even if it is clustered within smaller patches or regularly arranged at very short distances. Thus the ratio of 1 is a theoretical null expectation, not proof that no ecological processes operate. It means that, at the sampled scale, attraction and repulsion do not produce detectable departure from independent spatial placement.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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High variance in population densities suggests:

A high variance among equal-area counts relative to their mean indicates that individuals are concentrated unevenly across space. Clumped populations produce many quadrats with few or no individuals and a smaller number with very high counts, inflating variance above the Poisson random expectation. The variance-to-mean ratio therefore exceeds 1 under aggregation. Uniform dispersion gives unusually similar counts and a ratio below 1, while random dispersion gives a ratio near 1. “High variance” should ideally be interpreted relative to the mean rather than in isolation, because variance naturally changes with average abundance and units. Patchy resources, social behavior, offspring remaining near parents, or localized suitable habitat can all create clumps. Population decline is a temporal trend and cannot be inferred merely from spatial variance at one census. Sampling scale also matters: large quadrats may average over fine-scale clusters, while very small quadrats may emphasize them. Formal tests or spatial point-pattern methods can distinguish a genuine aggregated process from variation expected through finite sampling.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Which parameter is NOT needed for mark-recapture estimation?

The Lincoln–Petersen estimate requires three counts: M, the number initially marked and released; C, the total number captured on the second occasion; and R, the marked individuals found within that second capture. The number unmarked in the second sample is C − R, so it is not mathematically independent but can help determine C when only marked and unmarked counts are recorded. Population sex ratio is unnecessary for the basic estimator because the marked proportion, not sex composition, drives N ≈ MC/R. Sex ratio becomes relevant only if capture probabilities differ by sex, sampling targets one sex, or separate demographic estimates are desired. More generally, all animals should have comparable chances of capture, marks should persist, and the population should remain effectively closed between samples. If one sex is disproportionately trappable, ignoring that heterogeneity can bias the estimate, but measuring sex ratio is still not a required parameter in the formula. The distinction is between data needed algebraically and biological covariates that may be useful for diagnosing violations of model assumptions.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Deme is defined as:

A deme is a local interbreeding population: individuals share a geographic area and constitute a reproductive community with appreciable random mating relative to individuals outside the group. In population genetics, the idealized deme is often treated as panmictic, meaning mating is random with respect to genotype within that local unit. This permits allele-frequency models such as Hardy–Weinberg expectations to be applied, while migration connects different demes. Real demes may deviate from perfect random mating because of inbreeding, assortative mating, social structure, or spatial subdivision; the term still emphasizes a local breeding group rather than genetic identity. A group explicitly defined by nonrandom mating contradicts the simplest panmictic ideal. Members also need not possess identical genes, and a fixed dispersal pattern is not part of the definition. Demes are useful units for studying gene flow, drift, local adaptation, and metapopulation structure. Small demes experience stronger genetic drift, whereas migration can homogenize allele frequencies among them. Thus “random mating population” is the closest concise description, with the understood qualification that it is a local population model.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Recolonization of a previously extinct patch is an example of:

The rescue effect and recolonization are related but distinct. Rescue occurs when immigrants enter a patch that is still occupied and prevent its local population from going extinct. Recolonization occurs after extinction, when dispersers establish a new population in an empty patch. Therefore, the wording “recolonization of a previously extinct patch” describes colonization following local extinction, not rescue in its strict ecological meaning. Movement of colonists is migration or dispersal, making that listed alternative closer to the mechanism, although “recolonization” itself would be the precise term if offered. This distinction matters in metapopulation models: rescue reduces the extinction probability of occupied patches, whereas recolonization increases the transition rate from empty to occupied. Both depend on landscape connectivity and sources of migrants. The preserved key conflates these processes and is scientifically inaccurate under standard definitions. A patch that has already lost every individual cannot be rescued from extinction; it can only be colonized again. Clear terminology helps separate persistence within patches from restoration of occupancy across the patch network.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Which sampling method assumes closed population?

The simplest two-sample Lincoln–Petersen mark–recapture model assumes demographic and geographic closure between sampling occasions. No births, deaths, immigration, or emigration should change the population or alter the proportion of marked individuals. Under closure, the marked fraction after thorough mixing remains interpretable, allowing M/N ≈ R/C and N ≈ MC/R. Quadrat and transect methods sample spatial units and do not inherently require a closed population in the same formal sense, although movement during counting can still create error. Satellite methods track individuals and may explicitly measure movement rather than assume its absence. Closure is an approximation whose plausibility depends on study duration and boundary design; investigators can shorten intervals or choose natural boundaries to improve it. Open-population capture–recapture models, such as Cormack–Jolly–Seber approaches, relax closure and estimate survival or recruitment from multiple occasions. Other assumptions remain important even in a closed study: marks must persist, capture probabilities should be comparable, marking must not affect organisms, and individuals must mix before recapture.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Which factor contributes most to clumped distribution?

Social warning systems can favor group formation because individuals gain information about approaching predators from nearby conspecifics. Alarm calls, collective vigilance, and dilution of individual risk make aggregation beneficial, producing clumped dispersion. A group may detect threats sooner than a solitary organism, while each member can spend less time scanning and more time feeding. Such behavioral attraction yields patches of high abundance separated by unoccupied space and often a variance-to-mean ratio above 1. Allelopathy creates local inhibition and is more likely to generate uniform spacing. Wind dispersal may approximate randomness under homogeneous conditions, and “random reproduction” provides no directed mechanism for aggregation. Predator-warning benefits are not the only cause of clumping; patchy resources, breeding colonies, family groups, and restricted offspring dispersal are common alternatives. Nor does every predator response cause aggregation, since territorial or cryptic species may separate. Among the listed factors, however, shared warning and vigilance provide the clearest positive interaction that draws or retains individuals together and thereby increases spatial clustering.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Dispersion pattern determined by neutral interactions is:

Random dispersion arises when individuals are positioned independently at the scale of observation, with neither strong attraction nor strong repulsion among neighbors. This can occur when resources are relatively homogeneous, propagules disperse without directional bias, and interactions among individuals are weak or neutral. Quadrat counts then approximate a Poisson distribution, whose variance equals its mean. Uniform dispersion instead reflects negative interactions such as territoriality, competition, or allelopathy, while clumped dispersion reflects attraction, limited dispersal, social grouping, or patchy resources. “Neutral” does not mean that no ecological processes operate; it means that those processes do not create systematic spatial dependence detectable at the chosen scale. Environmental heterogeneity can create clumping even when organisms do not interact, and changing quadrat size can alter the apparent pattern. Therefore randomness is a statistical null model that should be tested against observed counts or point locations. A variance-to-mean ratio near 1 supports it, whereas ratios below or above 1 indicate regularity or aggregation, respectively.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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Which estimate method uses nested grids for sampling immobile species?

Quadrat sampling estimates abundance or density by placing frames or grid cells of known area and counting organisms within them. It is especially appropriate for immobile or slow-moving species such as plants, corals, barnacles, and many soil organisms because individuals remain associated with a sampling unit during observation. Nested quadrats place progressively larger frames around the same point or subdivide a larger grid, allowing investigators to examine species–area relationships, choose an efficient quadrat size, or measure pattern at multiple scales. A line transect records organisms along a path and is useful for gradients, while mark–recapture estimates mobile populations from repeated captures. GPS mapping can record locations but is not itself the nested-grid estimator. Representative placement is essential: random, systematic, or stratified designs reduce selection bias, and sufficient replication captures spatial heterogeneity. Density per unit area can be extrapolated to the habitat only when sampled units represent the larger area. Quadrat size also affects observed dispersion, because patterns visible at one spatial grain may disappear at another.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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The term 'metapopulation' was coined by:

Richard Levins introduced the term and formal concept of a metapopulation in the late 1960s while developing models of populations occupying temporary habitat patches. His classic formulation tracked the fraction of suitable patches occupied, balancing colonization of empty patches against extinction of occupied ones. This abstraction showed how a species could persist regionally even when individual local populations repeatedly vanished. Robert MacArthur and E. O. Wilson developed island biogeography, a closely related theory balancing immigration and extinction in island species richness, but they did not coin “metapopulation.” Their work strongly influenced landscape and patch ecology, which may explain the plausible distractors. Levins’s model also established a threshold condition: colonization must be sufficiently strong relative to extinction for nonzero regional occupancy to persist. Modern metapopulation theory extends the framework by including patch area, isolation, habitat quality, rescue effects, demographic stochasticity, and explicit dispersal. The historical attribution matters because it connects the word to a specific theoretical shift—from treating populations as spatially continuous units to viewing them as networks of locally dynamic patches.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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The 'rescue effect' refers to:

The rescue effect occurs when immigrants enter a small, declining local population and reduce its probability of extinction. New arrivals add individuals directly, can increase reproduction by supplying mates, and may restore genetic variation or reduce inbreeding. The patch remains occupied throughout; immigration “rescues” it before abundance reaches zero. This differs from recolonization, which establishes a population in a patch after local extinction has already occurred. In metapopulation models, both processes depend on connectivity, but they affect occupancy in different ways: rescue lowers the local extinction rate, whereas recolonization raises the colonization rate of empty patches. Predator control and resource reallocation are management actions, not the defining mechanism. Increased reproduction may contribute after immigrants arrive, but immigration is the causal link between patches. Very high connectivity can also synchronize populations or spread disease, so more dispersal is not always beneficial. The rescue effect is strongest when donor populations provide migrants and the recipient patch remains suitable enough for those migrants to survive and reproduce.

Ref: Ecology: Concepts and Applications, Molles, 9th Ed., Ch. 9

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