Generally speaking, Functional Diversity is a component of biodiversity that encompasses various microorganisms’ available properties. As a result of its potential to influence numerous elements of ecosystem functioning, functional diversity is significant from an ecological standpoint. A community’s functional richness and evenness may gauge its practical variety.
The amount of species occupying a given niche is referred to as functional richness, whereas functional evenness shows how equally the species are dispersed. For example, a rise or fall in practical variety is accompanied by a rise or fall in functional richness and evenness.
Functional richness and evenness affect ecosystem production and stability, reducing the functional diversity of the same ecosystem. An ecosystem’s output may be quantified using the sample effect and niche differentiation models.
Niche complementarity and species redundancy have also been linked to ecosystem functioning in other ways. There is a link between species richness and ecosystem function based on rivets and characteristic models. It is possible to conclude that functional diversity is a critical component of ecosystem functioning by examining the earlier models.
As a result, it is reasonable to suppose that learning about a given ecosystem shows its variety and evenness, allowing a person to get insight into its functional features. Consequently, it provides an entirely new method for doing ecology-related research that is more efficient and exact.
Which Environmental Functions Are Best Explained By Functional Diversity?
Because people have known the functional diversity meaning in the 1990s, FD has changed the study of BEF from its inception (Tilman et al. 1997). Biodiversity theories embraced FD in their explanations of causality before they were experimentally tested, recognising that species’ functional features were likely to affect the operation of ecosystems (Chapin, Schulze & Mooney, 1992).
However, in the early studies, biodiversity was defined as the number of species in a habitat (Naeem et al. 1994; Tilman & Downing 1994).
When researchers first started looking into biodiversity loss, they were inspired by how quickly it disappeared (Tilman & Downing 1994). Researchers established communities with varying numbers of species and evaluated ecosystem performance within these communities to estimate the effect of these extinctions.
The concentration on various species was considered to mimic real-world situations of biodiversity loss, where species were evenly impacted by extinction (Naeem et al. 1994).
One of the most common theories for the BEF link is that as biodiversity grows, so does the variety of functional features. An organism’s ability to harvest resources from its surroundings is determined by these characteristics (McGill et al. 2006). As FD rises, a community’s ability to divide its available resources improves.
Overyielding macroalgae were more productive when FD was included in the macroalgae community than when FD was used in isolation. An explanation for a rise in overyielding with FD, mainly when the assemblage productivity is more than that of the most productive monoculture, is challenging to come up with that does not rely on resource partitioning.
An overyielding mechanism cannot consistently be recognised via the use of the FD method, according to Griffin et al. (2009). Even though evidence for resource partitioning may be found in this method, it does not define which niche axis the resources are being partitioned along (Griffin et al. 2009). Resource partitioning may explain the BEF link, but the FD–productivity relationship does not tell what the resources are.
However, model selection approaches may explain the proportional contribution of each trait to overyielding when data for many characteristics are available (e.g., Cadotte et al. 2009; Flynn et al. 2011).
Selection effects may also be identified using functional trait-based techniques. According to this theory, certain species have unique, valuable features that enable them to take in an even more significant share of the ecosystem’s total resources than other species, which explains why they are thought to have a more substantial impact on ecosystem performance.
If species with identical trait values are contributing equally to ecosystem function, then functional trait assessments may indicate whether or not this is the case. Because multivariate FD measures are community-level, they cannot be used to identify specific species in an experiment, even though trait values might reveal the extent of selection.
What Is The Reason For The Importance Of Functional Diversity In Ecosystems?
Theoretically, a more diverse range of resource utilisation should lead to improved ecosystem performance. Traits that influence how organisms use resources are thus likely to be the most significant distinctions. Theories suggest that ecosystem function is enhanced by FD (Diaz & Cabido 2001). Studies that changed species diversity and assumed that FD was being modified simultaneously generally came up with these predictions (Petchey & Gaston 2006).
Species with complementary functional traits, such as root geometry, will be able to occupy non-overlapping spatial niches, and the total occupied niche space will increase as species diversity increases, according to Loreau (1998). Higher diversity of functional traits increases resource-use efficiency in heterogeneous environments, according to Diaz and Cabido (2001). This is one explanation for how FD affects ecosystem function.
More challenging to conduct in the real world are experiments that directly assess if there is a mechanistic link between FD and ecosystem function. Grassland communities with varying numbers of functional groups were planted, and several ecosystem functions were measured by Tilman et al. (1997) in one of the earliest tests of this type.
A more comprehensive explanation of ecosystem function was found in ecosystems’ functional diversity and composition. It was discovered that multivariate FD measures predicted variance in ecosystem function better than functional richness or species richness by Petchey, Hector, and Gaston (2004), who reanalysed six BEF studies.
Because it contains a magnitude, FD can better explain variance than richness. Even when the correlation is substantial, communities in one environment may be more functional than communities in another. Suppose FD lacks variety (i.e., high redundancy). In that case, BEF correlations should be insignificant, but if there is variation in FD, it might still explain variation in function even if richness does not.
Final Verdict
Ecosystems are judged by the extent and value of their functional diversity. It is a crucial piece of physical hardware for understanding how an ecosystem works. An ecosystem’s productivity and long-term stability are jeopardised if there is an imbalance in the available variety. Functional diversity may be employed as a measurement indicator to assess an ecosystem’s stability.
Ecosystem stability is a critical element of a dynamic and highly productive ecosystem. The more stable an ecosystem is, the more resistant it is to disruptions, and hence, the more effective it is. Therefore, an ecosystem with high functional diversity would be shown to have many different biological processes going on at the same time.
The ability to perform varied physical activities, such as a healthy agricultural sector and ecosystem bioremediation, may be attributed to an ecosystem with greater functional diversity. Ecosystem screening may be improved with functional diversity as an indicator.
Conclusion
Ecosystems are judged by the extent and value of their functional diversity. It is a crucial piece of physical hardware for understanding how an ecosystem works. An ecosystem’s productivity and long-term stability are jeopardised if there is an imbalance in the available variety. Functional diversity may be employed as a measurement indicator to assess an ecosystem’s stability.