The concept of a niche is crucial for understanding species interactions within a community, particularly those involving competition. Two or more species sharing the same habitat will experience niche overlap. The degree of overlap determines the intensity of competition between them. If two species have very similar niches (i.e., they utilize the same resources in the same way), they will compete strongly, potentially leading to one species outcompeting the other or to resource partitioning where they evolve to use resources differently.

Example: Darwin's finches on the Galapagos Islands. Darwin observed different species of finches, each with beaks adapted to different food sources. While all finches share the same general habitat (the Galapagos Islands), their beaks represent different niches. Some finches have large, strong beaks for cracking seeds, while others have smaller, more delicate beaks for eating insects. This niche partitioning allows different finch species to coexist on the same islands, reducing competition and increasing overall biodiversity. Their beaks are a direct result of natural selection shaping their niches.

The data collected from the 100 randomly selected plots can be used to estimate the total biodiversity of the forest by calculating an estimate of the species richness and species abundance, and then extrapolating these values to the entire forest area. Here's a breakdown of the process:

1. Species Richness (S): For each of the 100 plots, the researcher identifies and records all the different species present. This gives a count of the number of species in each plot. The total number of species observed across all 100 plots is the total species richness (S).
2. Species Abundance (N): For each species identified, the researcher estimates the abundance (number of individuals or biomass) of that species within each plot. This can be done through direct counting, quadrat sampling (counting individuals within a defined area), or other appropriate methods.
3. Estimating Biodiversity in the Forest: The biodiversity of the forest can be estimated using the following formulas:

• Species Richness (S): The total number of different species observed across all 100 plots is considered an estimate of the total species richness of the forest. If, for example, the researcher identified 250 different species across the 100 plots, they would estimate the forest has a species richness of 250.
• Species Abundance: The abundance of each species can be estimated by calculating the average abundance across all plots where that species was found. For example, if a species was found in 60 plots with an average abundance of 10 individuals per plot, the estimated total abundance of that species in the forest would be 60 plots x 10 individuals/plot = 600 individuals.
• Diversity Indices: More sophisticated measures of biodiversity, such as the Shannon-Weiner diversity index (H) or Simpson's diversity index (D), can be calculated using the species richness and abundance data. These indices provide a more comprehensive assessment of biodiversity, taking into account both the number of species and their relative abundance. The formulas for these indices are:

Shannon-Weiner Index (H): H = - Σ (pi * ln(pi)) where pi is the proportion of individuals belonging to species i.

Simpson's Index (D): D = 1 - Σ (pi^2) where pi is the proportion of individuals belonging to species i.

Extrapolation: The key to estimating the biodiversity of the entire forest is the assumption that the 100 randomly selected plots are representative of the whole forest. If this assumption is valid, the estimates of species richness and abundance from the plots can be extrapolated to the entire forest area. The larger the sample size (in this case, 100 plots), the more reliable the extrapolation will be.

Here are three methods a researcher could use to assess biodiversity in a woodland, along with the level of biodiversity they assess and the information they provide:

1. Quadrat Sampling (Species Diversity & Ecosystem/Habitat Diversity): The researcher would randomly select several quadrats (defined areas) within the woodland. Within each quadrat, they would identify and count all the plant species present. This directly assesses species diversity (number of species) and provides information about ecosystem/habitat diversity if different quadrat locations represent different habitat types within the woodland (e.g., sunny glades vs. shaded areas). The data can be used to calculate species richness and evenness.
2. Camera Trapping (Species Diversity): The researcher would place camera traps throughout the woodland to capture images of animals that visit the area. By analyzing these images, they can identify the different animal species present and estimate their relative abundance (frequency of sightings). This primarily assesses species diversity, particularly for animals that are difficult to observe directly. It provides information on the presence and relative abundance of different animal species.
3. Soil Analysis (Genetic Diversity - Indirect): While not a direct measure of genetic diversity within a species, soil analysis can provide indirect insights. By analyzing soil DNA, researchers can assess the diversity of soil microorganisms. This can indicate the health and stability of the woodland ecosystem, which is linked to the genetic diversity of the plants and animals that depend on it. A diverse microbial community is often associated with a healthy ecosystem and can support the genetic diversity of the woodland's inhabitants. This method assesses the diversity of the soil microbiome, which indirectly reflects the health and resilience of the woodland ecosystem and its associated species.