Biodiversity has been defined (CBD 1992, Article 2) as:
“the variability among living organisms from all sources including inter alia, terrestrial, marine and other aquatic ecosystems, and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems”.
This definition implies that lists of species, for example, will suffice to describe biodiversity but the quality of biodiversity is not revealed in a list. A more useful definition encompassing the term “biodiversity quality” is given by Hooper et al (2005), Petchey and Gaston (2002) and Feest (2006) which allows biodiversity to be characterised by reference to functional biodiversity which in turn will involve the measurement of a range of indices via a standardised sampling approach. Ecological factors such as climatic and edaphic properties, gene diversity and competition among species, and other biotic and abiotic factors are also important in determining species biodiversity and should be recorded for sites, along with the biodiversity sensu stricto.
Much of the historic work on biodiversity has been based on vegetation analysis and indeed some plants (particularly trees) do have a strong influence on the overall biodiversity value of a site but, for many groups of organisms, biodiversity value is more a function of vegetation architecture and history rather than species composition. Therefore, true assessment of biodiversity quality should also consider the non-vegetation element of biodiversity (Woodcock et al. 2007) to relate to ecosystem functionality. The non-vegetation element also contains most of the total biodiversity (e.g. invertebrates and fungi) albeit based on plant vegetation production.
The added benefit of considering non-vegetation biodiversity is that candidate groups selected can show the ability to respond to Global Climate Change (GCC) and pollution much more quickly than any vegetation measurements (Baar and Kuyper, 1998; Lilleskov, Fahey and Lovett, 2001).
Indicator systems are sets of species from different organism chosen as a group to indicate for example forest change (Thompson & Angelstam 1999). A change (species turn-over) in communities of the organism groups (assemblage structure) is often caused by altered environmental conditions. For example, the site dependency of organism groups as fungi, bryophytes, lichens, saproxylic insects and woodpeckers have been used as an indicator system for assessment of conservation value in boreal forests (Nilsson et al. 2001). With effective selection, organisms studied can include those that represent the full range of the ecosystem functional environment (soil, air, hydrology, climate and ecosystem). Such measures can be combined with more traditional techniques to provide better information. For example, the possible use of dendrochronological measurement for forest sites can give expression to the historic functioning of forests, albeit with a 30-50 year time lag for significant results and combined with recent biodiversity change as indicated by the indicator groups.
The choice of indicator organism groups and species is a difficult major decision and there are many examples of mistakes resulting in frustration and confusion (Thompson & Angelstam 1999). Currently, there is an urgent need for developing functional indicator systems for monitoring biodiversity changes due to climate change or other factors within the European Union (Watts & Handley 2010; Bässler et al. 2010). Many previous studies of charismatic indicator groups failed to critically evaluate the utility of the indicator groups that they used (Fleishman & Murphy 2009). Thus, it is important to define objectives and endpoints without mixing means and ends, i.e. policy objectives and targets (Failing & Gregory 2003). Apart from the objective, the scale of management, and the level of available knowledge should also influence the decision regarding the use of indicators in management planning (Wiens et al. 2008). Furthermore, using a limited number of indicator species is risky because of surrogacy and lack of validation (Simberloff 1998). More recently biologists are often replacing single species with indicator systems containing multiple taxonomic groups with varying functional traits and ecological roles (Boutin et al. 2009).
In Switzerland new indicator values for vascular plants, bryophytes and terricolous lichens has been published. These indicator values may be very useful for the building of functional groups (e.g. species sensitive to nitrogen pollution or climate warming) and will be applicable outside of Switzerland.
The FUNGIB Programme
This bespoke computer programme was created by Dr Alan Feest who is Senior Lecturer at the University of Bristol and a Director of Ecosulis to answer the need for compiling a set of biodiversity quality indices from a single dataset whilst presenting the data in an easily interpreted format. The underlying principle is that data from fieldwork can be presented in a clear readable format that allows statistical comparisons rather than starting from a statistical approach as other programmes do.
The programme presents the sample data in the form of a species accumulation chart plus the following biodiversity quality indices:
- Species Richness (also estimated by Chao 1 and 2 methodologies).
- Shannon Wiener Biodiversity Index (for both population numbers and relative biomass; not available in other programmes).
- Simpson’s Biodiversity Index (for both population numbers and relative biomass; not available in other programmes).
- Berger-Parker Biodiversity Index (for both population numbers and relative biomass; not available in other programmes).
- Population Density.
- Species Conservation Value Index (mean value and standard deviation and not available in other programmes).
- Biomass Index (not available in other programmes).
There is the facility in the programme to create other indices such as a nitrogen sensitivity index (Ellenberg score) where appropriate. Expressing biodiversity as numerical indices allows statistical tests for differences to be used to show significant change in population biodiversity and between group biodiversity. The programme allows species lists (such as have been used in previous biodiversity assessments) to be compiled in the normal way.
The use of a programme such as FUNGIB enables users to (among other things):
- Develop tools, including one or more biodiversity quality index of one or more indicator group of organisms, based on the biodiversity quality of different ecosystems (including fragmented habitats).
- Ensure consistency of data collection and therefore transboundary comparisons of the potential impacts of policy decisions with respect to biodiversity.
- Register change or status of biodiversity under a scenario of GCC, nitrogen deposition (measured for example as Critical Load Exceedance) and other pollution pressures and events.
- Develop guidance to aid managers of biodiversity and decision makers to utilise historical data associated with sites, as a means of determining current biodiversity quality and future management practice.
- Develop a standardised methodology to register change in biodiversity quality of sites based on site history and current baseline values.
- Strengthen and build on local institutions for site management and provide long term opportunities for the involvement of local communities in measuring and monitoring biodiversity and thereby assist with the channelling of funding programmes (e.g. REDD+ finance).
- Identify common biodiversity characteristics across the various groups of organisms, measure and provide guidelines with respect to their contribution to the biodiversity quality of a biotope.
- Develop guidelines for extending the method and results of this work to other ecosystems.
- Identify any existing relationships between biodiversity quality indices and anthropogenic effects upon functionality.
- Develop guidance on the creation of research programmes on site functionality that can be fully integrated into the international biodiversity recording initiatives such as FutMon, GEOSS, EuMon and Biodiversa.
Examples of the Application of FUNGIB
Linking SEBI 2010 Indicators
The chosen indicators for the monitoring of progress towards the Streamlining European Biodiversity Indicators (SEBI) 2010 target of reducing the rate of loss of biodiversity logically require that biodiversity is measured and that the selected indicator is also linked to other biodiversity measurements. In Phase 1 of the SEBI 2010 project a set of 26 indicators has been suggested to monitor progress towards the target. Some of the suggested indicators directly track the impact on a component of biodiversity, whereas others reflect threats to biodiversity, ecosystem integrity or sustainable use. If the link between these different indicators could be established their usefulness would be strengthened by this triangulation.
Research undertaken by Ecosulis for the European Environment Agency showed a linkage between Nitrogen Critical Load Exceedance (CLE) and butterfly population biodiversity quality, showing that CLE - at least for butterflies - is a reasonable threat indicator (Feest, 2008). The linkage was reflected in most butterfly population biodiversity quality parameters (Biomass, Population, Species Nitrogen Value Index, Species Conservation Value Index, Simpson Index and most importantly n CLE but not by Species Richness often used as a measure of biodiversity.
It was concluded in our study that a focus on characteristic species for specific habitats in bio-monitoring and future studies could help in finding useful relationships for transforming CLE into terms of loss of biodiversity. Also the quotient between the number of characteristics and other species is probably a powerful indicator, but it was not tested in the paper.
The triangulation of the two indicators tested concludes that they were well founded indicators and will have high utility in the progress towards the 2010 biodiversity target and the use of a range of biodiversity indices has allowed a more complete picture of biodiversity quality changes to be indicated.
Measuring Favourable Conservation Status of SSSIs
In October 2010, Ecosulis was awarded a contract by Natural England to prepare lower plant site dossiers and favourable conditions tables for 12 Sites of Special Scientific Interest (SSSIs) within the North-West region and East Midlands region. The service provided by the contract is to be used by Natural England to inform detailed Conservation Objectives for each of the 12 SSSIs. The Objectives are tailored to each site based on the features present to inform both future management of the sites by Natural England and consideration to plans and proposals by statutory authorities. Unlike the previous surveys used to assess these sites; a method of ‘walking about’ to record presence/absence of species, Ecosulis was able to offer a unique survey method and assessment by using FUNGIB, which allows relatively rapid (as it has a stop-rule) presence/absence surveys, in this case for both lichen and bryophyte species, at up to 20 sampling points in a site or habitat to represent the totality of the site as defined by Natural England. This method therefore provides defined valuable data for assessing site/habitat condition, and in this case, for assessing the favourable condition status of the SSSIs. It can also provide an estimate of the total number of species likely to be present so the efficiency of the survey can be accurately estimated.
Biodiversity Value of Sewage Treatment Reedbed Systems
The aim of this study was to examine the effect of increasing botanical diversity, through reed-bed planting and maintenance regimes, on sewage treatment reed-bed invertebrate biodiversity (Feest et al . in press).
Reed-bed invertebrates were identified as a good indicator group of overall site biodiversity quality and were sampled at a range of sewage treatment reed-bed sites in the same geographical area between May and August 2006. One natural reed-bed control site was also sampled. Standardised water trapping and pitfall trapping techniques were employed throughout this sampling period.
Statistical analysis of the sampling results revealed that the number of plant species recorded was inversely related to terrestrial invertebrate species richness, species conservation value index and biomass within the study sites. For example, the natural reed-bed sampled had the highest botanical diversity but the lowest terrestrial invertebrate species richness.
The data collected in the course of this study has demonstrated that sewage treatment reed-beds support a diverse range of invertebrate species, some of them being of national conservation value. This suggests that sewage treatment reed-beds may be at least as biodiverse as naturally occurring reed-beds if not more.
Biodiversity Impacts of Biomass Production
Growing any one crop in isolation will have particular environmental impacts. One obvious impact of growing energy crops as monocultures is an adverse change in biodiversity. However, one of the main crops used for biomass is short rotation coppice, which can provide better cover than is supplied by arable crops or grassland. This may actually improve biodiversity. Ecosulis in association with the University of Bristol and on behalf of Great Western Research are currently undertaking research to examine the impact on biodiversity of growing biomass crops on a range of scales. It will take the results produced in the resource evaluation and LCA determine the biodiversity consequences of greater biomass production for the South West of England.
Biodiversity and Forest Ecosystem Functionality
Ecosulis has prepared a proposal under the EU’s Funding Programme 7 relating to the assessment of biodiversity quality in forest ecosystems and its relation to functionality, climate change and pollution. The proposal includes participants from Greece, Portugal, Slovenia, Italy, Switzerland, Turkey, Serbia, Sweden, Norway, FYR Macedonia, Bosnia and Herzegovina, Cyprus, Germany and Austria who will be assessing forest functioning by monitoring indicator species in target forest types throughout Europe.
Forestfunctionality in this context was defined as the processes relating to nutrient and water cycling and tree growth and therefore covers not only the biodiversity elements but also the physical attributes, goods and services of the forest, including soils and water.
The indicator species will be identified from different organism groups chosen as indicators of forest change (Thompson & Angelstam 1999). A change (species turnover) in communities of the organism group (assemblage structure) is often caused by altered environmental conditions. For example, the site dependency of organism groups such as fungi, bryophytes, lichens, saproxylic insects and woodpeckers are used as an indicator system for assessment of conservation value in boreal forests (Nilsson et al 2001).
The proposed project will function as a pilot for the unified recording of biodiversity across Europe and as a starting point for assessing the impact of the 2010 process and also the impacts of climate change and pollution (specifically CLE) upon forest functionality. Of particular importance is the element of measuring biodiversity directly as well as by inference as is done in 26 candidate indicators listed by the SEBI 2010 Working Groups of which only two directly address the functionality of forest ecosystems (deadwood and forest growing stock; Spanos et al 2009).
The novel approach of using biodiversity quality indices will be allied with more traditional forestry measurements of species mixture, wood volume, sizes, deadwood, soil type, climatic conditions, pest infestation, management/fire history (Siemann et al 1997; Schuck et al 2004; Spanos et al 2005; Spanos and Feest 2007). Indicators and environmental indices can improve our mechanistic understanding of processes linking climate to ecosystem changes (Drinkwater et al 2010). At the end of this process it is expected that one or more sensitive and accurate biodiversity quality indices will be identified that can provide a more rapid indication of change in functionality of Mediterranean, Alpine Boreal and other forest ecosystems (including relict forests) than is currently possible (for example Sarris et al 2007).
Therefore, this programme is unique in a) its geographical spread b) its approach to biodiversity such that disparate taxa can be compared using a universal set of biodiversity quality indices c) its integration and separation of both GCC and nitrogen deposition d) the direct relationship of forest biodiversity and forest functionality and e) modelling of such issues at the local, regional, national and global levels. A scientific biodiversity network will be developed to ensure that forest workers and forestry organisations will benefit from this knowledge. As a consequence, the human capacity to manage ecosystems that support the diversity of life on earth will improve.
Future Applications of FUNGIB
Previous successful use of FUNGIB to identify biodiversity quality suggests that there is significant opportunity for expanding this tool to other species groups and habitat types. The tool can be designed so that on completion it can be rapidly expanded to cover other ecosystems if appropriate and be integrated into other biodiversity actions such as FutMon, LTER-Europe, GEOSS and SEBI 2010. The programme is designed to create an open access resource for the wider scientific community and can be linked with commitments for ongoing research developed with the national funding agencies e.g. Natura 2000.
For further information and access to the FUNGIB programme please view the following page (http://www.ecosulis.co.uk/page/fungib-programme). For other information relating to FUNGIB please email firstname.lastname@example.org.
Baar,J. Kuyper T.W. (1998) Restoration of aboveground Ectomycorrhizal Flora in stands of Pinus sylvestris (Scots Pine) in the Netherlands by the Removal of Litter and Humus. Restoration Ecology 6(3): 227-237.
Bässler, C., Müller, J., Hothorn, T., Kneib, T., Badeck, F. & Dziock, F. (2010). Estimation of the extinction risk for high-montane species as a consequence of global warming and assessment of their suitability as cross-taxon indicators. Ecol. Indicators10: 341-352.
Boutin, S., Haughland, D.L., Schieck, J., Herbers, J. & Bayne, E. (2009). A new approach to forest biodiversity monitoring in Canada. For. Ecol. Manage. 2585: S168-S175.
Drinkwater, K.F., Beaugrand, G., Kaeriyama, M., Kim, S., Ottersen, G., Perry, R.I., Pörtner, H.-O. Polovina, J.J. & Takasuka, A. (2010). On the processes linking climate to ecosystem changes. J. Marine Syst. 79: 374-388.
Failing, L. & Gregory, R. (2003). Ten common mistakes in designing biodiversity indicators for forest policy. J. Environ. Manage. 68: 121-132.
FeestA. (2008) Research to test the Integration of the Nitrogen Critical Load Exceedence Model (EG4) into the 2010 target by linking it ot the Butterfly Indicator (EG1). Research report for the European Environment Agency (EEA/BSS/07/010).
Fleishman, E. & Murphy (2009). A Realistic Assessment of the Indicator Potential of Butterflies and Other Charismatic Taxonomic Groups. Conservation Biology 23: 1109-1116.
Hooper, D.U., Chapin, F.S., Ewel, J.J., Hector, A., Inchausti, S., Lavorel, S., Lawton, J.H., Lodge, D.M., Loreau, M., Naeem, S., Schmid, B., Setala, H., Symstad, A.J., Vandermeer, J. and Wardle, D.A. (2005) Effects of biodiversity on ecosystem functiuoning: a consensus of current knowledge. Ecological Monographs 75(1) 3-35.
LilleskovE.A., Fahey T.J., Lovett G.M. (2001) Ectomycorrhizal Fungal aboveground community change over an Atmospheric Nitrogen Deposition Gradient. Ecological Applications. 11(2): 397-410
Nilsson, S.G., Hedin, J. & Nicklasson, M. (2001). Biodiversity and its assessment in boreal and nemoral forests. Scand. J. For. Res. Suppl. 3: 10-26.
Petchey, O.L. and Gaston, K.J. (2002) Functional diversity (FD), species richness and community composition. Ecology Letters 5: 402-41
SarrisD. Christodoulakis D. Korner C. (2007) Recent decline in precipitation and tree growth in the eastern Mediterranean. Global Change Biology. 13: 1187-1200.
SchuckA. Meyer P. Lier M. & Lindner M. (2004) Forest Biodiversity Indicator: Dead Wood - A Proposed Approach towards Operationalisinig the MCPFE Indicator. In Marchetti M. (ed.) Monitoring and Indicators of Forest Biodiversity in Europe, University of Firenze, pp. 49-79.
SiemannE. Haarstad J. & Tilman D. (1997) Short-term and Long-term Effects of Burning on Oak Savanna Arthropods. American Midland Naturalist. 137: 349-361.
Simberloff, D. (1998). Flagships, umbrellas, and keystones: Is single-species management passé in the landscape era? Biol. Conserv. 83: 247-257.
SpanosK.A. Feest A. (2007) A Review of the Assessment of Biodiversity in Forest Ecosystems. Journal of Management of Environmental Quality. 18 (4): 475-486.
SpanosI. Raftoyannis Y. Goudelis G. Xanthopoulou E. Samara Th. and Tsiontsis A. (2005) Effects of postfire logging on soil and vegetation recovery in a Pinus halepensis Mill. Forest of Greece. Plant and Soil. 278:171-179.
Spanos K.A. Feest A. Petrakis P.V. (2009) Improving the assessment and monitoring of forest biodiversity. Management of Environmental Quality. 20(1): 52-63.
Thompson, I.D. & Angelstam, P. (1999). Special species. IN: Hunter, M.L.Jr. (Ed.). Maintaining biodiversity in forest ecosystems. pp. 434-459. Cambridge Univ. Press.
Watts, K. & Handley, P. (2010). Developing a functional connectivity indicator to detect change in fragmented landscapes. Ecol. Indicators 10: 552-557.
WeisW, Huber C, Goetlein A (2001) Regeneration of Mature Norway Spruce Stands: Early Effects of Selective Cutting and Clear Cutting on Seepage Water Quality and Soil Fertility. Optimizing Nitrogen Management in Food and Energy Production and Environmental Protection: Proceedings of the 2nd International Nitrogen Conference on Science and Policy, TheScientificWorld 1(S2): pp. 493-499
WoodcockB. A. Potts S. G. Westbury D.G. Ramsay A. J. Lambert M. Harris S. J. & Brown V. K. (2007). The importance of sward architectural complexity in structuring predatory and phytophagous invertebrate assemblages. Ecological Entomology. 32 (3): 302-311