“Tracking the Impact” is a comprehensive four-year landscape-scale wildlife survey program conducted by the Chilterns Conservation Board (see Chilterns AONB - Tracking the Impact). The program, carried out in the Central Chilterns area, aims to identify long-term trends in the distribution and abundance of bird, butterfly, and plant populations. This dashboard has been developed using the survey results to explore avian biodiversity and community composition at the site.
Tracking the Impact uses volunteers to comprehensively survey over 70 1km squares. The following breakdown provides information regarding survey effort and how it influences the survey results.
Welcome to AVIBIO, the first avian integrated biodiversity monitoring platform, exclusively designed for British birds. Developed with a focus on ecosystem function, AVIBIO provides a comprehensive suite of tools for analysing and understanding biodiversity across three key facets: taxonomic, functional, and phylogenetic diversity. Our approach sets us apart, emphasising the significance of integrated monitoring, which surpasses conventional methods by accounting for species’ varying phylogenetic positions and ecological traits.
AVIBIO brings together widely recognised diversity measures for each facet, coupled with carefully curated trait selection for an ecosystem function-orientated experience. Use the diversity indices to track the impact of conservation efforts, land development, or habitat degradation on biodiversity. Monitor functional diversity to understand the efficiency of resource utilisation at your site, with low functional diversity indicating reduced ecosystem productivity, functionality, and invasion resistance. Explore the phylogenetic measures to evaluate how distinct the evolutionary history of your community is, with higher values supporting ecosystem stability and productivity.
Explore descriptive measures that dissect functional and community composition at your site. Gain valuable insights into the presence and dominance of functional groups, identify missing guilds, and empower targeted management initiatives for enhancing biodiversity and ecosystem function.
Our holistic approach aligns with increasing calls from the conservation community to measure biodiversity beyond a simple species count, and reflects our commitment to conserving both species-rich and functionally robust ecosystems. Navigate through the tabs above to explore different facets of biodiversity and community composition. For additional information about the dashboard, please visit the About section.
Embark on a journey of comprehensive biodiversity monitoring with AVIBIO, and contribute to the preservation of our natural heritage.
Taxonomic, or species-level, diversity is used to infer enhanced ecosystem function on the basis that the introduction of a new species to the community is likely to enhance the trait diversity at a given site. However, taxonomic diversity’s efficiency as a predictor of ecosystem function is hindered by its underlying assumption that all species contribute equally to ecosystem functionality.
Functional and phylogenetic measures are much more efficient at deducing ecosystem function, as they account for varying functional traits and phylogenetic positions of species. Nevertheless, taxonomic diversity remains a crucial component of integrated biodiversity monitoring and stands as the standard diversity measure in conservation practices.
Three measures of taxonomic diversity have been included: species richness, the inverse Simpson’s diversity index, and the Shannon-Weiner diversity index.
Species richness is the number of species present at a site, and does not account for abundance.
Simpson’s diversity index, a weighted arithmetic mean of proportional abundance, measures the probability that two randomly selected individuals from a community will belong to the same species. The inverse index is applied here, meaning that the higher the value, the higher the diversity.
The Shannon-Weiner index, also weighted by relative abundance, is based on measuring uncertainty. If a community has low diversity, the uncertainty of prediction is low; a randomly sampled species is most likely going to be the dominant species. However, if diversity is high, uncertainty is high.
Functional diversity examines trait variation within a community, representing the diversity of species niches and functions. Low functional diversity indicates limited trait diversity, reducing ecosystem function, stability, and productivity, and heightening invasion risk.
Functional richness is measured by the convex-hull-based index, FRic (Villéger et al., 2008), and the dendrogram-based index, FD (Petchey and Gaston, 2007), which reflect the range and size of trait space within any given community, aiming to estimate the amount of niche space filled. High functional richness indicates a community that sustains a wider niche space, and is better able to exploit resources as a result.
Functional evenness (FEve; Villéger et al., 2008) measures the degree of distribution of abundance within the niche space, and is constrained between 0 and 1. A value of 1 indicates complete evenness in the distribution of individuals within the trait space. Though this rarely happens in nature, an increasing FEve value suggests that species abundances in trait space are becoming more even, allowing for effective utilisation of the entire range of resources.
Functional divergence (FDiv; Villéger et al., 2008) accounts for the distribution of abundance within the functional space, indicating the level of niche differentiation within a community. High functional divergence indicates enhanced niche differentiation, improving ecosystem function by decreasing competition for similar niches, and enhancing niche complementarity and resource utilisation.
Functional dispersion (FDis; Laliberte and Legendre, 2010), and Rao’s quadratic entropy (RaoQ; Rao, 1982) aim to assess the dispersion of species in trait space by their relative abundances. High functional dispersion reflects a broader spread of species in the trait space, suggesting a diverse range of ecological strategies within the community.
To allow for assessment of how the facets interact, they have been scaled between 0-1 and plotted on a graph (left), with the exception of FEve which is already constrained between 0-1.
Community-weighted means (CWM) identifies the dominant trait values within a community (see above) and generates a proportional breakdown of abundance-weighted trait distribution (see below). As abundance fluctuations of dominant species (or groups) drive ecosystem service delivery, as purported by the mass-ratio hypothesis, community-weighted means identify the traits and groups which are likely to contribute most to ecosystem functioning.
For example, if the dominant functional group at your site is 7 (comprised of many species, including crows, pigeons, gulls), and the dominant family at your site is corvidae (crows), it is likely that members of the crow family are contributing heavily towards ecosystem service and function. As birds can also contribute both ecosystem services (e.g nutrient transport, scavenging) and disservices (e.g. being crop pests themselves), this measure can be used to inform appropriate conservation management.
The traits we have chosen to include taxonomic position (family and order), the primary habitat association of birds found at the site, and information relating to their trophic niche, primary lifestyle, and designated functional groupings.
Phylogenetic diversity assesses the distinctiveness of evolutionary histories within a community, explaining more variation in ecosystem function and stability than species richness alone. As many traits show a phylogenetic signal, higher phylogenetic diversity suggests a higher trait diversity, enhancing ecosystem functionality.
The indices of phylogenetic diversity included were phylogenetic diversity (PD; Faith, 1992), taxonomic diversity (delta), and taxonomic distinctness (delta*; Clarke and Warwick, 1998).
Phylogenetic diversity is calculated as the sum of branch lengths of a community’s phylogenetic dendrogram, and does not account for abundance.
Taxonomic diversity (delta) and distinctness (delta*) both measure the phylogenetic dissimilarity of a community, and account for abundance distributions within the phylogenetic space. Taxonomic diversity (delta) is the average taxonomic distance between any two organisms, chosen at random from the sample. It is empirically related to the Shannon-Weiner Diversity Index, but has an added component of taxonomic separation. In contrast, taxonomic distinctness (delta*) is the average path length between two randomly chosen individuals, conditional on them being from two different species. It does not have any contribution from species diversity.
Site: 2020
Number of functional groups present in Britain: 17
Number of functional groups present: 12
Missing functional groups: 5, 12, 8, 16, 17
Site: 2021
Number of functional groups present in Britain: 17
Number of functional groups present: 16
Missing functional groups: 16
Site: 2022
Number of functional groups present in Britain: 17
Number of functional groups present: 16
Missing functional groups: 16
Site: 2023
Number of functional groups present in Britain: 17
Number of functional groups present: 16
Missing functional groups: 16 What is Functional Group Richness?
Functional Group Richness (FGR) calculates the number of avian functional groups at a site and reports which groups are missing. Functional group classification was conducted using hierarchical clustering, and traits were chosen with an ecosystem-function focused approach in mind (See About for more information).
It must be emphasised that FGR is not included as a measure of functional diversity, but to support site managers in identifying which functional groups are missing, and where targeted management could be used to enhance functional diversity.
Management Information
The table below contains management information that may be useful with regards to enhancing biodiversity at your site, including functional group classifications, habitat associations, nesting information, and dietary preferences of all British birds.
Use the descriptive information generated by the community weighted means and functional group richness measures to identify conservation targets. For example, if your site is dominated primarily by one particular functional group, as identified by CWM, targeting management initiatives towards alternate functional groups may enhance functional diversity at the site.
Use the management information below to determine appropriate measures. For example, functional group 16 is the only group missing from the site, and is comprised of mostly coastal and marine species. As these are unlikely to be found in the Chilterns due to habitat filtering, it would be more worthwhile to invest in measures to support groups which are present, but under-represented within the community. Functional groups 3, 5, 8, and 15 may be more appropriate to target.
Biodiversity is declining at a rapid rate, and along with it, the vital ecosystem services this diversity provides to humanity. The Convention on Biological Diversity (CBD) has set a number of global targets to be achieved by 2030 in order to minimise such declines, including planning and managing areas to reduce biodiversity and ecosystem service loss, build ecosystem resilience, and minimise the impacts of invasive alien species (see https://www.cbd.int/gbf/targets/). The CBD targets also include goals regarding tools and solutions for wide scale implementation of biodiversity monitoring. Of these suggestions, key themes include integrating biodiversity and its multiple values in decision making, strengthening monitoring capacities, and ensuring that the best data, information, and knowledge is available to decision makers.
The most widely used biodiversity measures focus on taxonomic diversity, which evaluates biodiversity at the species level. Whilst useful, this diversity measure assumes that all species are equal, and does not account for the diverse ecological functions and phylogenetic positions of species. As a result, there have been calls to integrate alternative facets of biodiversity into widescale biodiversity monitoring, including functional and phylogenetic diversity analyses.
As these analyses account for differences between species, they are far superior in predicting ecosystem function and service provision (Mammola et al., 2021). For example, functional diversity represents the diversity of species’ ecological niches and functions, with high functional diversity increasing invasion resistance, ecosystem productivity, and resilience through enhanced resource utilisation (Mason et al., 2005). Despite this, it is significantly under-represented in protected areas (Devictor et al., 2010), and may be under greater threat from anthropogenic activity than taxonomic diversity (Flynn et al., 2009). Similarly, phylogenetic diversity also encompasses trait variation above the species level (Cadotte et al., 2011), increasing ecosystem stability and explaining variation in ecosystem function and stability better than species richness alone (Liu et al., 2021; Cadotte et al., 2012).
This dashboard has been created to support integrated biodiversity monitoring in two key ways. Firstly, it supports integrated monitoring of all facets of biological diversity, ensuring that we conserve both species rich AND functionally robust ecosystems. Secondly, this dashboard was created with the express purpose of providing decision makers with all of the necessary data and information to make informed conservation decisions, with minimal input and effort. All the user needs to do is enter survey data, and the dashboard will break down functional and community composition, calculate the various diversity indices, and return graphical representations. This allows for easy integration of biodiversity information and monitoring into habitat management, conservation action, or land development. We believe that this dashboard represents a pivotal step towards achieving the Convention on Biological Diversity’s 2030 targets, ensuring better biodiversity outcomes.
What data cleaning and processing has been performed?
With regards to the survey data, some species entries have been altered. Domestic Greylags are simply noted as Greylag Geese, and Carrion x Hooded Crow hybrids are referred to solely as Hooded Crows.
With regards to the diversity analyses, trait data is not available for Jackdaw. As a result, Jackdaw have been removed from the diversity analyses entirely.
Lastly, with regards to the management information, some of the nest data has been inferred. Missing data for the Lesser Redpoll (Acanthis cabaret) was inferred from the Common Redpoll (Acanthis flammea), data for the Tundra Bean Goose (Anser serrirostris) was inferred from the Taiga Bean Goose (Anser fabalis), and data for the Hooded Crow (Corvus cornix) was inferred from the Carrion Crow (Corvus corone).
What trait data was included in the functional analyses?
The functional diversity analyses were made possible using Tobias et al. (2022)’s AVONET trait database, which contains trait information for over 11,000 bird species globally.
As trait choice highly influences the outcomes of functional diversity analyses, careful consideration was taken to include appropriate traits for an ecosystem-function focused approach. A principal coordinate analysis was used to identify uncorrelated, well represented morphological traits, as trait correlation can inflate functional redundancy. The traits included were beak (nares), tarsus, and secondary length, and mass. Similarly, categorical traits relating to how birds facilitate ecosystem functioning, including their trophic niche and primary lifestyle, were included. This was supported by a multiple correspondence analysis, which identified them as well represented, uncorrelated traits.
How was functional group richness calculated?
The functional group classifications were designated using hierarchical clustering, Gower distance, and complete linkage. The traits used were those that were incorporated in the functional diversity analyses.
FGR was previously used as a measure of functional diversity, but has proved inefficient in comparison to the continuous functional diversity indices. This is because designating species into discrete categories inaccurately represents the continuous nature of trait diversity, vastly oversimplifying functional diversity analyses.
What traits were analysed in community-weighted means?
Community weighted means identify the dominant trait values within a community, and generate a proportional breakdown of trait distribution, weighted by abundance. The traits incorporated were those related to phylogenetic position (family and order), and information regarding how species interact with their environment and resources (habitat association, trophic niche, primary lifestyle, and functional group). As dominant species or groups drive ecological functions (Winfree et al., 2015), the dominant groups are likely to be those facilitating ecosystem service delivery. This is known as the mass-ratio hypothesis.
Anderson, M. J., K. E. Ellingsen, and B. H. McArdle. 2006. Multivariate dispersion as a measure of beta diversity. DOI: https://doi.org/10.1111/j.1461-0248.2006.00926.x
Cadotte, M.W., Carscadden, K. and Mirotchnick, N., 2011. Beyond species: functional diversity and the maintenance of ecological processes and services. DOI: https://doi.org/10.1111/j.1365-2664.2011.02048.x
Cadotte, M.W., Dinnage, R. and Tilman, D., 2012. Phylogenetic diversity promotes ecosystem stability. DOI: https://doi.org/10.1890/11-0426.1
Clarke, K.R. and Warwick, R.M., 1998. A taxonomic distinctness index and its statistical properties. DOI: https://doi.org/10.1046/j.1365-2664.1998.3540523.x
Devictor, V., Mouillot, D., Meynard, C., Jiguet, F., Thuiller, W. and Mouquet, N., 2010. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. DOI: https://doi.org/10.1111/j.1461-0248.2010.01493.x
Faith, D.P., 1992. Conservation evaluation and phylogenetic diversity. DOI: https://doi.org/10.1016/0006-3207(92)91201-3
Flynn, D.F., Gogol‐Prokurat, M., Nogeire, T., Molinari, N., Richers, B.T., Lin, B.B., Simpson, N., Mayfield, M.M. and DeClerck, F., 2009. Loss of functional diversity under land use intensification across multiple taxa. DOI: https://doi.org/10.1111/j.1461-0248.2008.01255.x
Laliberté, E. and Legendre, P., 2010. A distance‐based framework for measuring functional diversity from multiple traits. DOI: https://doi.org/10.1890/08-2244.1
Liu, Y., Zhang, M., Peng, W., Qu, X., Zhang, Y., Du, L. and Wu, N., 2021. Phylogenetic and functional diversity could be better indicators of macroinvertebrate community stability. DOI: https://doi.org/10.1016/j.ecolind.2021.107892
Mammola, S., Carmona, C.P., Guillerme, T. and Cardoso, P., 2021. Concepts and applications in functional diversity. DOI: https://doi.org/10.1111/1365-2435.13882
Mason, N.W., Mouillot, D., Lee, W.G. and Wilson, J.B., 2005. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. DOI: https://doi.org/10.1111/j.0030-1299.2005.13886.x
Petchey, O.L. and Gaston, K.J., 2007. Dendrograms and measuring functional diversity. DOI: https://doi.org/10.1111/j.0030-1299.2007.15894.x
Rao, C.R., 1982. Diversity and dissimilarity coefficients: a unified approach. DOI: https://doi.org/10.1016/0040-5809(82)90004-1
Tobias, J.A., Sheard, C., Pigot, A.L., Devenish, A.J., Yang, J., Sayol, F., Neate‐Clegg, M.H., Alioravainen, N., Weeks, T.L., Barber, R.A. and Walkden, P.A., 2022. AVONET: morphological, ecological and geographical data for all birds. DOI: https://doi.org/10.1111/ele.13898
Villéger, S., Mason, N.W. and Mouillot, D., 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. DOI: https://doi.org/10.1890/07-1206.1
Winfree, R., W. Fox, J., Williams, N.M., Reilly, J.R. and Cariveau, D.P., 2015. Abundance of common species, not species richness, drives delivery of a real‐world ecosystem service. DOI: https://doi.org/10.1111/ele.12424