1.1 Key Concepts in Ecological Sampling

• Population – A group of individuals of the same species living in a specific area at a given time, capable of interbreeding.

  • Example: A herd of elephants in Amboseli National Park

• Biotic – The living components of an ecosystem, such as plants, animals, fungi, and microorganisms, that interact with each other and their environment.

  • Example: Trees, birds, bacteria, and fungi in a forest

• Abiotic – The non-living physical and chemical factors in an ecosystem, such as sunlight, temperature, water, soil, and air, which influence living organisms.

  • Example: Sunlight, temperature, soil, and rainfall in a desert ecosystem.

• Population Density – The number of individuals of a species per unit area or volume in a given habitat. It helps in understanding species distribution and resource competition.

  • Example: If a forest has 50 deer in 10 square kilometers, the population density is 5 deer per square kilometer

• Sampling – A method used to estimate population size, distribution, or diversity in an ecosystem by studying a small representative portion rather than the entire population.

  • Example: Scientists using a quadrat to count the number of plant species in a grassland.

• Habitat – The natural environment in which an organism lives, including all biotic and abiotic factors that influence its survival and reproduction.

  • Example: A coral reef is the habitat of clownfish and sea anemones

• Sampling Frame: A list or map of all possible sampling units from which samples are selected.

  • Example: A map showing all plots within a national park.

1.2 Population

• Is a group of organisms of one species or the same species that interbreed and live in a place at the same time.

  • Eg The elephant or deer population

• On the other hand, a biological population refers to a group of organisms of a particular species living in a certain area or a particular community.

• Ecologists often ask questions about factors that affect the size and growth of a population.For instance ,allegators living in a swamp make up a population because they are members of the same species living in the same geographical area at a particular time

• As ecologists our interests is on the several factors that influence the population size and how it changes overtime.

• This may include availability of food and space,weather conditions and the breeding pattern.

• In studying how these factors affect the populations geographical boundaries w/c may be natural or artificial boundaries.

  Population density

• Is the no. of individuals of a particular species per unit area or volume.

  • Eg The no. of antelopes per square KM in a national park.

Population = No. of individuals/ Unit area

1.3 Sampling

• A sample is a subject of a population or it is a segment or piece that is a representation of a whole.

• Sampling is a process used in statistical analysis in w/c a predetermined no. of observations will be taken from a larger population.

• The methodology used to sample from a larger population will depend on the type of analysis that will be performed and this may include:

  • Simple random sampling     

  • Stratified random sampling

  • Systematic random sampling

  • Judgemental sampling

  • convenient sampling

  • Purposive sampling

  • Cluster random sampling

NB: A sample should always be representative of the general population.

1.4 Ecosystem

• It is composed of all communities and their associated physical environments , including the physical,chemical and biological processes.

• Ecosystems may sustain themselves entirely through photosynthetic activity, energy flow through food chain and nutrient recycling.

• This includes the biotic and abiotic factors in an area.

1.5 Habitat

• Is an ecological/environmental area that is inhabited by a particular species or animal or plant or other type of organism.

• It is a natural environment in w/c an organism lives or the physical environment that surrounds a species population.

• N/B : Ecologists studies, like any other scientific study involves a no. of processes:

  • Identify a problem

  • Define the entities to be studied

  • Design an experimental study

  • Select the sampling or data collection procedure

  • Obtain the representative sample

  • Observe or measure the sample to obtain to obtain data

  • Objectively analyse data

  • Interpret and draw conclusion from the data

  • Report your findings

1.6 Ecological sampling

• Ecological sampling refers to the process of selecting a subset of organisms, locations, or observations from an ecosystem in order to study ecological patterns and processes.

• Because it is usually impossible to measure every organism in nature, scientists collect samples that represent the larger ecological system.

• Ecological sampling helps researchers answer questions such as:

  • How many organisms exist in a habitat?

  • How are species distributed in space?

  • How does biodiversity vary across environments?

  • How do environmental factors influence species abundance?

• Ecologists would like to collect quantitative information about the habitat, community, ecosystem and population.

• However, it impractical to monitor the entire habitat or obtain measurements for all organisms for all organisms in a given area.

• For instance, suppose we would like to draw conclusions of the body weight of mice in a given habitat, but it is impossible to weigh all the mice and use this information to get the weight of the total population.

• The entire data of interest are in the weights of all mice w/c is referred to as statistical population while the measured portion is called statistical sample.

• This should not be confused with the biological population.

• To have a truly representative sample, samples should be taken randomly from the population.

• Frequently ecologists notice a distinct pattern that may be related to one related to one or more factors at the two sides eg. The vegetation in one field may be very different from the one found in another field or the species found under the oak trees may be different from those found under the ash trees or the trees or the species found upstream and downstream of an outflow pipe discharging into a river may seem to differ between the two.

• Therefore, to make valid comparisons, samples need to be taken from both sides.

• If an investigator chooses where to sample, then the sample, will be subjective or biased and therefore we need to ensure that we use random sampling that ensures unbiased samples being taken from the sides.

1.7 Reason for sampling in Ecology

1. Practicality & Feasibility

• Large Populations: Counting every individual in a population is often impossible, especially for highly mobile or widespread species.

  • It is usually not practical to count every member of the population eg population of bees in a bee hive or houseflies.

  • In an ideal world, when investigating say mosquitoes, in two different sites, you could count every single mosquito in the site, the problem is that this can take forever and also become boring and therefore we need to sample and estimate the population.

  • You might estimate the no. of mosquitoes in each site by counting the number in several small areas and then multiplying up to calculate the value for each site.

• Time & Resource Constraints: Full censuses require significant time, labor, and financial resources. Sampling allows efficient data collection.

2. Representative Data Collection

• Generalization: Proper sampling techniques ensure that collected data accurately represent the entire population or habitat.

• Minimizing Bias: Random and systematic sampling help reduce bias, providing a more accurate ecological picture.

3. Ecological Monitoring & Conservation

• Tracking Changes Over Time: Sampling helps monitor population trends, species distributions, and ecosystem changes due to climate change, habitat destruction, or conservation efforts.

• Early Detection of Issues: Changes in sampled populations can signal problems such as pollution, habitat degradation, or invasive species presence.

4. Statistical Inference & Modeling

• Estimating Population Size & Density: Sampling methods like quadrats, transects, and mark-recapture allow scientists to estimate abundance and distribution. The idea is to maximise the usefulness of your data while minimising the effort required to collect them.

• Predictive Modeling: Data from samples can be used to model ecological interactions, species distributions, and future trends

5. Ethical & Minimal Impact Research

• Non-invasive Studies: Instead of disturbing entire populations, ecologists can gather enough information from small samples to minimize environmental impact.

• Sustainable Research: Reducing disruption to ecosystems ensures long-term viability of research sites and conservation efforts.

1.8 Standard Sampling Methods

• Usually, the goal of ecological sampling is to summarise the characteristics of the individual units in a biological population.

  • For example, the characteristic of interest might be the weight and sex of individual animals in a population in a particular area.

• The summary of these characteristics then consists of the estimates of the mean and standard deviations of the weight of the animals and an estimate of the proportion of females.

• In statistics, population is defined as the collection of all items that are of interest in an investigation.

• These items may include, individual animals or plants but they could also be small plots of land, pieces of rock or groups of animals.

• In statistical theory it is crucial or important that the items that make up the population are sampled using an appropriate procedure.

  • For this reason, these items are often called sample units.

• Sometimes, population sizes are small enough to allow every item to be examined and this provides us with a census.

• However, population of interest in ecology are usually large enough to make census impractical.

• The measures that are used to summarise a population are referred to as population parameters.

• On the other hand those corresponding to sample values are called sample statistic.

i) Simple Random Sampling

The use of random sampling is important whenever inferences are to be made about population parameters on the basis of the sample results because of the need to base the inferences on the laws of probabilities.

In this connection, it must be appreciated that random sampling is not the same as haphazard selection of the units using a well defined and carefully carried out randomization procedure that ensures that all the possible samples of the repaired sizes are equally likely to be chosen.

Even though this the case the fact is with ecological sampling, the strictly random selection.

This may be with or without replacement.

Generally, sampling without replacement is preferable to sampling with replacement because it gives slightly more accurate estimation of population parameters.

However, the difference between the population size is much larger than the sample size.

Example; Sampling plants in a large study area

Suppose it is required to estimate the density of plants of certain species in a large study area, one approach is to set up a grid and consider the area that consists of quadrants that this produce.

The quadrants are then the sampling units that make up the population of interest .

The list of this units is called sampling frame.

To sample the next step is to decide on the sample size.

There different methods that can be used to decide on the sample size.

Eg using the estimated proportion of the population we can compute the sample size required.

This sample is usually calculated using a formula that relies on the level of accuracy.

ii) Stratified random sampling

Simple random sampling leaves so much room especially when the sample size is small.

It might for example be clear that the no of sample unit in different geographical area does not match population sizes in those area, with parts of the population bring under sampled while others being, over sampled.

One way to overcome this problem while keeping the advantages of random sampling.

In stratified random sampling, the units in the population an divided into non-overlapping strata where simple random samples are selected from each of these strata.

Usually, there is nothing to lose by using this compilated type of sampling but there are potential gains with it.

i.e, i) If individuals within strata are rather more similar, than individuals in general, the estimates of the overall population mean will have a smaller standard error than can be obtained from a simple random sample.

     ii)There may be value in having separate estimates of population parameters for different strata.

     iii)Stratification makes it possible to sample different parts of the population in different ways, w/c makes some set saving possible.

>On the other hand, Stratified sampling design has problems when there are errors in allocating sample units to the strata.

This may occur if the allocation is made using a mark that is not completely accurate.

Therefore, when sample units are visited in the field it may be found that some are not in the expected strata.

Reclassifying those units to the correct strata will mean that all population units the new strata no longer have the same probability of being sampled.

This implies that the sampling design has to be changed and thereby introducing some estimation bias.

Secondly, after the data has being collected, it is desired to do some analysis, in the same form of stratification.

At times one may use simple random sampling even though the data was collected using a stratified random sampling.

This may be a problem although studies have shown that using simple random sampling with limited or no stratification is still possible.

3. Systematic Random Sampling

This can be carried out whenever a population can be listed in order or it covers well defined spatial area.

In this method, every kth item of the list can be sampled, starting at an item chose at random from the 1st kth position.

For so defined spatial areas, sampling points can be set out at a systematic pattern to cover the area.

There are 2 main reasons why systematic sampling is used compared to simple random sampling.

i)It is usually easier to carry out than simple random sampling.

ii)It seems likely that a systematic sampling will be more representative than a simple random sample hence being more precise because it gives a uniform-coverage of all the population of interest.

>Systematic sampling has an advantage of not allowing any simple determination of the level of sampling errors unless it is assumed that the items in the population are more or less random in their order.

4. Cluster Sampling

Groups of sample units, that are close in some sense are randomly sampled together of then all measured.

The idea is to reduce the cost of sampling each unit so that more units can be measured than would have been possible if they were all sampled individually.

This advantage is to offset to some extend the tendency of sample units that are close together to have similar measurements.

Thus in general, a cluster sample of ‘n’ units will give estimates that are less precise than a simple random sample of ‘n’ units.

Nevertheless, cluster sampling may give better value than the sampling of individual units in terms of what is obtained from a fixed total sampling effort.

5. Multi-stage sampling

The sample units are regarded is falling within hierarchical structure.

Random sampling is then at various levels, within the structure.

For instance, suppose there is interest in estimating the mean of some water quality variable, in the lakes of a large area, such as a whole country, then the country can be divided into primary sampling units consisting of states, or provinces or counties.

Each primary sampling unit, might then consist of a no. of sub-counties and each sub-county might contain a no. of words with a certain no of lakes.

A three stage sample of lakes will then be obtained by first randomly sampling the counties and then sub counties within the counties and them words within the sub counties sample and finally the lakes within the words.

Note:

Sampling is fundamental to ecological research as it allows efficient, representative, and minimally invasive study of populations and ecosystems. Through careful sampling, ecologists can make accurate inferences, guide conservation efforts, and improve our understanding of biodiversity and environmental change.

1.9 Principles of Good Ecological Sampling

Good sampling should be:

• Representative: Samples should represent the entire study area.

• Replicated: Multiple samples should be collected.

• Randomized: Sampling locations should avoid bias.

• Standardized: Methods should be consistent.

• Reproducible: Other researchers should be able to repeat the study

1.10 Applications of Ecological Sampling

Ecological sampling is used in:

• Biodiversity studies: Estimating species richness.

• Conservation: Monitoring endangered species.

• Agriculture: Estimating crop pests.

• Environmental monitoring: Detecting ecosystem changes.

• Climate change research: Studying species shifts.

3.1 What is distance sampling?

• Distance sampling is an extension of plot sampling, where not all animals in the covered region are detected.

• Distance sampling is a widely-used group of closely related methods for estimating the density and/or abundance of biological populations.

• Its name derives from the fact that the information used for inference are the recorded distances to objects of interest (usually animals) obtained by surveying lines or points.

• In the case of lines the perpendicular distances to detected animals are recorded, while in the case of points the radial distances from the point to detected animals are recorded.

• A key underlying concept is that the probability of detecting an animal decreases as its distance from the observer increases.

• Much of distance sampling methodology is concentrated on detection functions, which model the probability of detecting an animal, given its distance from the transect.

• The main methods are line transects and point transects (also called variable circular plots).

3.2 Methods of distance sampling

The term ‘distance sampling’ covers a range of methods for assessing abundance:

1. Line transect sampling, in which the distances sampled are distances of detected objects (usually animals) from the line along which the observer travels

2. Point transect sampling, in which the distances sampled are distances of detected objects (usually birds) from the point at which the observer stands

3. Cue counting, in which the distances sampled are distances from a moving observer to each detected cue given by the objects of interest (usually whales)

4. Migration counts, in which the ‘distances’ sampled are actually times of detection during the migration of objects (usually whales) past a watch point