Читать книгу Reconciling agricultural production with biodiversity conservation - Группа авторов - Страница 11
ОглавлениеThe challenge of monitoring biodiversity in agricultural landscapes at the EU level
M. L. Paracchini, European Commission, Joint Research Centre (JRC), Italy; S. Condé, European Topic Centre on Biological Diversity – Muséum National d’Histoire Naturelle, France; R. D’Andrimont, European Commission, Joint Research Centre (JRC), Italy; B. Eiselt, European Commission, Eurostat, Luxembourg; O. Fernandez Ugalde, E. Gervasini and A. Jones, European Commission, Joint Research Centre (JRC), Italy; V. Kovacevic, European Commission, DG Environment, Belgium; R. Oppermann, Institut für Agrarökologie und Biodiversität (IFAB), Germany; A. Orgiazzi, M. Van der Velde, C. Polce and C. Rega, European Commission, Joint Research Centre (JRC), Italy; C. Van Swaay, De Vlinderstichting, The Netherlands; and P. Voříšek, Czech Society for Ornithology, Czech Republic
2Farmland birds and butterflies
6Monitoring under the Habitats Directive 92/43/EEC
7European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL)
10New approaches and technologies
12Where to look for further information
Following the acknowledgement that biodiversity in agricultural lands globally and in the European Union (EU) has been strongly impacted by the intensification of agricultural practices (Dudley and Alexander, 2017; IPBES, 2019), many efforts have been carried out to revert the trend, starting with agri-environmental schemes becoming compulsory for EU Member States in 1992 (EU Regulation 2078/92) (Batary et al., 2015) aimed at reducing pressures from agriculture in order to meet environmental objectives such as the protection or enhancement of biodiversity, the improvement of soil, water, landscape and air quality, climate change mitigation and adaptation.
Through the Common Agricultural Policy (CAP) cycles that followed, the concern for biodiversity has been embedded into the legislation as a target in general (e.g. protection and enhancement of biodiversity in axis 2 of rural development policy) and in specific terms (e.g. high nature value farming and forestry) (2006/144/EC). Persisting concerns about the fate of biodiversity, which emerged most evidently in the public consultation on modernizing and simplifying the common agricultural policy launched by the European Commission (EC) in 20171, have been embedded in the legislative proposal for the CAP post-2020 (COM (2018) 392 final), which identifies as one of its nine priorities to ‘contribute to the protection of biodiversity, enhance ecosystem services and preserve habitats and landscapes’.
In parallel, environmental legislation through the decades has targeted rare and threatened species, and rare natural habitats (EEC, 1979; EEC, 1992; European Parliament and the Council of the European Union, 2009): as a result, the European Union currently hosts the largest coordinated network of protected areas in the world, the Natura 2000 Network2. By adding the concept of restoration to the protection concept, the legislation of the past two decades has widened the scope, addressing all habitats and not only those more endangered and ecologically valuable. This started with the Commission’s proposal to the Gothenburg European Council (EC, 2001), which calls for protecting and restoring habitats and natural systems and halting the loss of biodiversity by 2010, a concept which was reinforced in the EU Biodiversity Strategy to 2020 (EC, 2011). The latter, introducing the concept of ecosystem services, makes all habitats possible targets for restoration. In particular, target 3, which relates to agriculture specifically, defines the goal of maximizing 'areas under agriculture across grasslands, arable land and permanent crops that are covered by biodiversity-related measures under the CAP so as to ensure the conservation of biodiversity and to bring about a measurable improvement in the conservation status of species and habitats that depend on or are affected by agriculture and in the provision of ecosystem services as compared to the EU2010 Baseline, thus contributing to enhance sustainable management'. The EU Biodiversity Strategy to 2030 reinforces this line of action by dedicating a whole section to bringing nature back to agricultural land (EC, 2020 – Section 2.2.2)3.
Lastly, legislative requirements that contribute to biodiversity preservation also include the so-called 'environmental safeguards' directives, requiring formal environmental assessments to be carried out for projects (under Directive 85/337/EEC4 and following amendments), and plans/programs (under Directive 2001/42/EC5) with potential detrimental effects, including on biodiversity and habitats.
To assess the effectiveness of such efforts different techniques are applied, spanning from the analysis of case study areas (Kettunen and Ten Brink, 2006; Kleijn et al., 2006) to the use of proxies (Alliance Environnement, 2017) or models (Kok et al., 2018). In this frame, there is, overall, a lack of data recorded through monitoring efforts, EU wide assessments are in fact presently relying on a limited set of surveyed data: farmland birds (Gregory et al., 20056), grassland butterflies (EEA, 2013a) and the reporting under the Birds and Habitats Directive (EC, 2015).
Biodiversity decline, and in particular the loss of genetic diversity, is within the nine global-scale processes that are essential to maintain the earth system in a resilient and accommodating state defined by Steffen et al. (2015), one of the two processes laying outside the safe operating space. Despite the urgency to revert the trend and the efforts from the policy side to incorporate the concern, signals are not encouraging (EC, 2020). Better targeting and improved assessments need filling knowledge gaps and using updated and detailed data, covering different taxa. Moreover, in the frame of planning, implementing, monitoring and assessing EU policy, sources of information should cover the entire European Union, and should be based on a harmonized approach for data collection. Establishing surveys is an important way to guarantee that such information becomes available.
The EU Biodiversity Strategy to 2020 defines biodiversity as ‘the unique variety of life on our planet’, the UN Convention on Biological Diversity 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’ (UN, 1993). Such definitions suggest a complexity that is probably the reason why it has been so difficult to put in place a large-scale monitoring system that includes surveys of the main components of biodiversity. Surveys can be burdensome and therefore the costs can exceed current financial and administrative capacity, especially when an entire continent should be covered. Nevertheless, initiatives and pilots are ongoing, to enlarge the available data pool.
This chapter reviews where we stand in surveying biodiversity in agricultural areas at the EU level as well as plans to increase monitoring efforts. The sections that follow describe established surveys, ongoing pilots and plans for new surveys at the EU scale. A multiplicity of information is available at local/regional/national scale, but this chapter focusses on the long and winding road to wall-to-wall coverage of the European Union. At the end, the point can be made on where we will stand in the short-medium term with our knowledge of agro-biodiversity in the European Union, and which gaps still need to be filled to appropriately and sufficiently describe biodiversity dynamics.
2Farmland birds and butterflies
2.1 The Pan-European common bird monitoring scheme
Many countries of the European Union are characterized by a long-lasting tradition of bird-watching, on which scientifically grounded countrywide surveys were nested. Countries such as Finland, Sweden and Denmark organized a countrywide monitoring scheme in 1975, the United Kingdom in the 1960s. Others started later on, but nowadays all EU countries except Malta have a monitoring scheme in place.
In 2002, the Pan-European Common Bird Monitoring Scheme (PECBMS) was started as a joint initiative of the European Bird Census Council (EBCC) and BirdLife International, with the aims of: collecting data on European common bird species from national monitoring schemes and calculate European common bird indices and indicators, to raise awareness and to feed the policy process; support the national coordinators in setting up the schemes, and guarantee a harmonized approach in the calculation of the indices; explore the relation between population trends and main driving forces (EBCC, 2019).
Most surveys are carried out through point or line transect counts, where the selection of the plots to be surveyed (each plot containing one or more point or transect) is made either following rigorous statistical procedures (e.g. systematic selection, stratified random selection) or a free choice approach (Table 1). The surveyor visits the assigned location one or more times during the year, in predetermined time windows (e.g. 10 May–20 June as in the Italian survey), and records the individuals seen or heard. A thorough statistical analysis is followed to identify errors and outliers.
Table 1 Principal characteristics of national breeding birds monitoring schemes
| Country | Generic breeding bird monitoring scheme | Contributes to PECBMS | Start year | Field survey methods | Selection of plots | Number of species |
| Austria | Yes | Yes | 1998 | Point counts | Free choice | 85 |
| Belgium | Yes | Yes | 1990 | Point counts | Stratified random, other | 134 |
| Bulgaria | Yes | Yes | 2004 | Line transects | Stratified random | 63 |
| Croatia | Yes | Not yet | 2015 | Point counts | Stratified semi-random | 30 |
| Cyprus | Yes | Yes | 2006 | Line transects | Other | 45 |
| Czech Republic | Yes | Yes | 1982 | Point counts, line transects | Free choice, stratified random | 218 |
| Denmark | Yes | Yes | 1975 | Point counts | Free choice | 143 |
| Estonia | Yes | Yes | 1983 | Point counts | Free choice | 90 |
| Finland | Yes | Yes | 1975 | Point counts, line transects, other | Systematic, other | 140 |
| France | Yes | Yes | 1989 | Point counts | Other | 150 |
| Germany | Yes | Yes | 1989 | Line transects, point counts, territory mapping | Free choice, stratified random | 100 |
| Greece | Yes | Yes | 2007 | Point counts | Stratified random | 233 |
| Hungary | Yes | Yes | 1999 | Point counts | Stratified random | 420 |
| Ireland | Yes | Yes | 1998 | Line transects | Stratified random | 55 |
| Italy | Yes | Yes | 2000 | Point counts | Random | 103 |
| Latvia | Yes | Yes | 1995 | Point counts, territory mapping | Random, systematic, other | 90 |
| Lithuania | Yes | Yes | 1994 | Point counts | Stratified semi-random | 70 |
| Luxembourg | Yes | Yes | 2002 | Point counts, territory mapping, line transect | Random, stratified random | 120 |
| Malta | No | |||||
| Netherlands | Yes | Yes | 1984 | Territory mapping, point counts, line transects | Free choice, random, stratified random | 100 |
| Poland | Yes | Yes | 2000 | Line transects | Stratified random | 110 |
| Portugal | Yes | Yes | 2004 | Point counts | Stratified random | 64 |
| Romania | Yes | Yes | 2006 | Point counts | Stratified semi-random | 70 |
| Slovak Republic | Yes | Yes | 1994 | Point counts | Free choice | 100 |
| Slovenia | Yes | Yes | 2007 | Line transects | Stratified non-random | 29 |
| Spain | Yes | Yes | 1996 | Point counts, line transects | Stratified random, other | 200 |
| Sweden | Yes | Yes | 1975 | Point counts, line transects | Free choice, systematic | 180 |
| United Kingdom | Yes | Yes | 1966 | Territory mapping, line transects | Free choice, stratified random | 111 |
The extraordinary component of PECBMS are the thousands volunteers who count the birds in the field, each year, according to a methodology standardized at national level. The data are sent to the national offices, where, using TRIM software made available by PECBMS (van Strien et al., 2001; Statistics Netherlands, 2017), calculate the national species indices and trends. PECBMS combines national species indices with supra-national indices for individual species for the European Union and its main regions (new and old European Union, and West, South, North, Central and East Europe), plus Europe as a whole. All indices are annually updated, and EU indices are regularly sent to and published by EUROSTAT7. Thirty-nine species compose the Farmland Bird Index (FBI, Table 2). These are common species, which are dependent from agroecosystems for feeding and nesting, and as such are considered to be a descriptor of the state of agroecosystems. The index is a composite, multispecies index calculated using Monte Carlo simulations as described in Soldaat et al. (2017).
Table 2 Species composing the EU farmland bird index
| Alauda arvensis | Emberiza melanocephala | Passer montanus |
| Alectoris rufa | Falco tinnunculus | Perdix perdix |
| Anthus campestris | Galerida cristata | Petronia petronia |
| Anthus pratensis | Galerida theklae | Saxicola rubetra |
| Bubulcus ibis | Hirundo rustica | Saxicola torquatus |
| Burhinus oedicnemus | Lanius collurio | Serinus serinus |
| Calandrella brachydactyla | Lanius minor | Streptopelia turtur |
| Carduelis cannabina | Lanius senator | Sturnus unicolor |
| Ciconia ciconia | Limosa limosa | Sturnus vulgaris |
| Corvus frugilegus | Melanocorypha calandra | Sylvia communis |
| Emberiza cirlus | Miliaria calandra | Tetrax tetrax |
| Emberiza citrinella | Motacilla flava | Upupa epops |
| Emberiza hortulana | Oenanthe hispanica | Vanellus vanellus |
Of the monitoring initiatives presented in this paper, this is the only one that has a wall-to-wall coverage of the European Union, with a sampling density sufficient to derive statistically meaningful information at different scales (EU, national, regional), on different ecosystem types (farmland, forest; montane birds and mire birds for North Europe indicators are under development) or climate change (Gregory et al., 2009).
The information is valuable and widely used, since it is considered that, being at the top of the food chain, birds are indicators of the environment’s health. The farmland bird indicator is to date the most widespread biodiversity indicator used in EU policies (impact indicator of the CAP8; condition indicator of the Monitoring and Assessment of Ecosystems and their Services – MAES9), indicator frameworks (Streamlining European Biodiversity Indicators – SEBI10; agri-environmental indicators – AEIs11; OECD agri-environmental indicators12) and global assessments (e.g. FAO, 2019).
2.2 European breeding bird atlas
The EBCC has organized a second European Breeding Bird Atlas (EBBA2). This project, complementary to the PECBMS, brings information on spatial distribution and abundance of all bird species in Europe including European Russia and Turkey (www.ebba2.info). The outputs of the project which has collected field data from 2013 to 2017 are the maps of distribution and abundance of each species in a grid of 50 km×50 km, and maps of modelled probability of occurrence of selected species in a grid of 10 km×10 km. The atlas also provides maps of change in distribution in comparison with the 1980s, when the first atlas was produced. Information about the spatial distribution of species has been shown to have an extremely high value for policy, nature conservation and research (Herrando et al., 2019). The EBBA2 outputs will be published in 2020.
2.3 Butterflies
The experience of bird monitoring has paved the way to other initiatives aimed at planning and putting in place large-scale surveys. One of these is butterfly monitoring. Butterfly populations are in fact highly sensitive to environmental change, providing an early warning of impacts on ecosystems. Moreover, being insects, they are part of the food chain and they are also important pollinators. The process of establishing monitoring schemes in EU countries and improving the Grassland Butterfly Index is very similar to the PECBMS experience. Some countries had developed their own Butterfly Monitoring Scheme already in the nineties (e.g. Belgium – Flanders, the Netherlands, Finland, Spain – Catalonia), with the United Kingdom at the forefront, having started its monitoring activities in 1976 (Brereton et al., 2009).
Butterflies are counted along line transects, by experts or skilled volunteers. The counts take place several times a year (in the majority of cases 10–20 times), under well-defined weather conditions and times of the day. Transects are distributed either following a statistical procedure for random selection, but in the majority of cases are free choice of volunteers (Brereton et al., 2009). A transect is on average 1 km long, and it may intersect one habitat type or more. The surveyor should walk the transect every week during the butterfly season, counting the individuals seen within an imaginary box of 5 m wide, 5 m high and 5 m ahead of the observer (Sevilleja et al., 2019).
The early 2000s were a topical moment for butterfly monitoring: van Swaay et al. (2006) associated butterfly species to biotopes, highlighting the importance of grasslands, in particular dry grasslands, as the most species-rich biotopes, but at the same time as the biotopes hosting the largest numbers of threatened species. Van Swaay and van Strien (2005) proposed the development of a European grassland butterfly indicator, which followed the protocol of the European common bird indicator (Gregory et al., 2005) and made use of the same TRIM programme to calculate national indices. Finally, Butterfly Conservation Europe was founded, to coordinate monitoring efforts and support national offices in setting up their monitoring schemes (van Swaay et al., 2006).
Through the years that followed, more countries initiated a monitoring scheme, and the European Grassland Butterfly Indicator was updated every two years from 2008 onwards (EEA, 2013a; Van Swaay et al., 2019).
More recently, the European Commission has launched a pilot project (Assessing ButterFlies in Europe – ABLE) to increase the number of monitored countries, expanding the surveys in particular in South and East of the European Union, to have a more representative index. Moreover, ABLE is meant to develop indices for other habitat types besides grasslands, and to support the monitoring network through online resources. The initial period of funding is two years (until November 2020), and the partnership is composed of Butterfly Conservation Europe, the Centre for Ecology and Hydrology (UK), the Helmholtz Centre for Environmental Research (Germany), Dutch Butterfly Conservation (the Netherlands) and Butterfly Conservation (UK) (more information available at www.butterfly-monitoring.net).
In 2018 the first pilot of a EU-wide grassland survey was carried out in the frame of the Land Use/Cover Area-Frame Survey (LUCAS). LUCAS is a harmonized land cover and land-use data collection exercise that extends over the whole of the EU’s territory. Data are gathered through direct observations made by surveyors on the ground or through photo-interpretation, aimed primarily at collecting information on land use and land cover. The whole LUCAS 2018 survey was based on 337 854 points/observations, of which 238 077 were in-field and 99 777 photo-interpreted (EUROSTAT, 2019).
The survey started in 2009 and through the years different modules were plugged-in to exploit the LUCAS infrastructure and collect data on relevant environmental variables. Two of the modules cover grasslands and soils (for the latter see Section 5).
During spring/summer 2018, 2173 grassland points were surveyed in 26 countries for a total of 134 surveyors involved. In parallel, 36 expert botanists visited a subsample of 730 points, and their data served as control points to evaluate the reliability of the survey (Sutcliffe et al., 2019) (Fig. 1). The points were selected among those that in the previous survey LUCAS 2015 were identified as grasslands, and their distribution is statistically significant at the level of environmental zones.
Figure 1 Expert botanists (a, c) and corresponding surveyors (b, d) transects. © European Union, LUCAS 2018.
The reason for having a grassland survey derives from the awareness that most EU grasslands are not described in any database to a degree that may be used in the policy process. For example, there is no information about their ecological quality. Protected habitats are monitored in the frame of the Habitats Directive (see Section 6) and the list of grassland habitats, though exhaustive, covers about 30% of EU grassland extent. The remaining 70% is described mostly in terms of geographical distribution and biomass production. Information on species is completely lacking.
Points were surveyed on a transect of 20 m × 2.5 m (Fig. 2), if they had at least 50% grassland coverage. Walking alongside the transect line, the surveyor should survey the vegetation of 1.25 m to the left and 1.25 m to the right. The starting point and the transect should be at least 5 m inside the grassland parcel, for the surveyed vegetation to be representative of the parcel and not of the edge. The transect should always originate from a LUCAS point, but the technical specifications contained information on how to reposition the transect if, given the starting point and the required orientation, it was not completely contained in the parcel.
Figure 2 Diagram of the theoretical location of the transect and the enlarged transect with respect to the LUCAS point (Eurostat 2018).
The timing of the survey was carefully planned, according to grassland phenology in the European Union. To allow the identification of the plants, the surveyor had to visit the site before the first cut or before the start of grazing. The surveys were therefore carried out following a latitudinal time gradient, from mid-April in Mediterranean South until mid-July in Boreal Scandinavia North.
The parameters list was the product of a consultation of EC services in need of information, to support environmental, agricultural and energy EU policies. The results of the consultation were translated into parameters to be surveyed.
These included information on:
• site characteristics (altitude, exposition, inclination, type of surface, moisture),
• a general description of vegetation (vigour, layers, presence of graminoids, forbs, woody plants layer, bare ground, age – older or younger than 5 years etc.),
• a more specific description of the flowering forbs (number of flowering forb species, flower density),
• identification of EUNIS habitat type (https://eunis.eea.europa.eu/about), and
• key species (plant indicator species) and structural plant species.
Concerning the last bullet point, 41 key species or key species groups were identified, and grouped in 10 lists with 20 species groups each for the different biogeographic zones characterizing the European Union. The revised list in the forthcoming surveys is reduced to 12 key species groups, identical for all the biogeographic zones, as this number was considered to be representative and suitable for non-botanist surveyors receiving a specific training to be able to recognize individual species. Such key species lists are assumed to be a direct indicator of the grasslands’ ecological quality. A grassland module is currently planned as part of LUCAS 2022, on 20000 points.
The raising awareness of the decline of insects (Hallmann et al., 2017; Sanchez-Bayo and Wyckhuys, 2019) and pollinators in particular (Goulson et al., 2015; Potts et al., 2010), the quantification of the losses and of the impacts on ecosystem services, the increased understanding of the causes and the awareness of knowledge gaps (IPBES, 2016) are factors that have strongly influenced the availability of funds for research as well as the adoption by the European Commission, on 1 June 2018, of the EU initiative on pollinators (EC, 2018).
The main aims of the initiative are to improve the scientific knowledge about insect pollinator decline, tackle its main known causes and strengthen collaboration between all the actors concerned. The initiative is organized around three priorities:
i improving knowledge of pollinator decline, its causes and consequences,
ii tackling the causes of pollinator decline, and
iii raising awareness, engaging society-at-large and promoting collaboration.
One of the main knowledge gaps that is identified concerns species distribution and abundance of pollinators, it is estimated in fact that half of the bee species in Europe is data deficient (IEEP & IUCN, 2018). Scattered monitoring programmes for bees and butterflies are in place, but not much is known about all other pollinator species. Under the first priority it is therefore planned that a common EU monitoring scheme is developed. Once implemented, this scheme should generate data that will enable the full assessment of the problem and the effectiveness of mitigation actions. Action 1A of the pollinators Initiative explicitly states ‘The Commission will devise and test an EU-wide pollinator monitoring scheme to ensure the provision of good quality data for assessing the status and trends of pollinator species in the EU and developing a pollinator indicator. A technical expert group will be set up to support this work’.
Discussions have started (UN WCMC, 2017; IEEP and IUCN, 2018) and an expert group has been set up and work on the monitoring scheme13 and the proposal for the indicator. Common monitoring and standardization of data and approaches to monitoring are seen as essential requirements under this premise; suggested methods for monitoring are a combination of passive methods (that do not rely on attracting insects), such as standardized transect walks, and active methods (that rely on attracting insects) such as pan-trapping.
An example of standardized transect is the bumblebee transect count method, which is similar to the butterfly method, and it is structured as follows: volunteers walk fixed transects of 1–2 km (divided into 4–10 sections with different habitat types) each month from March to October (on sunny days between 11:00 and 17:00) recording all bumblebee species in an imaginary 4 m × 4 m box on either side and in front, if necessary by catching bees in net or pot (Bumblebee Conservation Trust, 2017; Comont and Dickinson, 2018).
Interesting features are that data from new recorders are not used in the first two years to ensure data quality by allowing the recorder to develop sufficient skills in identification, and to put in place a mentoring system of experienced recorders tutoring new recorders.
The bee pan-trapping method has been standardized at the EU level by the FAO (2016): it uses bowl or pan traps, which are small plastic bowls or cups, coloured white, fluorescent blue or fluorescent yellow, filled with water mixed with a small amount of detergent, which acts as a surfactant. Twenty-four bowls should be placed on a line or transect 5 m apart, alternating the three colours and left for 24 hours or a fixed shorter period (to be noted), avoiding heavy shade. Catches are sieved and placed in 70% alcohol in container or sample bag (large-bodied non-Hymenoptera insects need to be removed first) which can be sent to the laboratory for identification. Samples should be stored in a freezer if they are not processed within one or two days. The processing of the samples includes washing, drying, pinning, labelling and maintaining the specimens. Lastly, the data from the specimens should be entered into a database system, together with validation and double-checking procedures.
The species to be monitored should include both common (with regional-specific lists) and key indicator species, and cover different pollinator groups, including wild bees, hoverflies and butterflies.
In parallel, a monitoring scheme for the products of the beehive (pollen, nectar, honeybee bread and honey) could be rolled out to monitor pesticide residues and other environmental pollutants.
Soil represents a complex habitat sustaining a huge diversity of organisms that are structured by and embedded within the physical matrix (Geisen et al., 2019). Despite its importance for a range of ecosystems and ecosystem services, from nutrient cycle regulation to soil erosion control (Barrios, 2007), it is recognized that our knowledge of the soil habitat is limited (Jeffery et al., 2010). Currently, there are no specific policy measures or designated protection areas in the European Union targeting soil biodiversity. In a perspective of filling this gap, a pilot campaign was launched within the soil module of the 2018 ‘Land Use/Cover Area frame statistical Survey’ (LUCAS Soil), to test a sampling protocol for soil biodiversity. The campaign collected samples from 1 000 locations with diverse land cover and use. This is currently the most extensive EU assessment of soil biodiversity, based on DNA meta-barcoding (Orgiazzi et al., 2015).
The main aims of the survey are:
i to be able to develop a quantitative indicator of soil biodiversity, based on the genetic signatures,
ii to look for correlations between the DNA evidence and land cover/land use, especially in intensive agricultural areas,
iii to match the DNA data with residues of plant protection products. Information on the latter is available from a separate analysis of the concentrations of 70 active ingredients and metabolites in around 3000 LUCAS samples, and
iv eventually to expand the analysis to look at functional groups.
The DNA meta-barcode analysis will cover different types of soil-living organisms, from micro-organisms to macrofauna, and precisely (Orgiazzi et al., 2018):
• bacteria and archaea – target region 16S ribosomal DNA (rDNA),
• fungi – target region internal transcribed spacer (ITS), and
• eukaryotes other than fungi – target region 18S ribosomal DNA (rDNA).
Possibly, nematodes, arthropod mesofauna, and earthworms will be included. The protocol defined by the Earth Macrobiome Project (EMP, 2017) will be applied.
Soil samples should be frozen as soon as possible after collection. This requires precise logistical arrangements (Fernandez-Ugalde et al., 2017): the surveyors need to prepare freezer packs well in advance of the sampling and place them in a polystyrene box the day of the survey, wash the sampling material before each sampling with alcohol and water and wear plastic gloves during the collection of the sample. Once sealed in the polystyrene box, the sample, wherever the sampling point is located in Europe, should reach the final storage location preferably within 48 hours.
The reference for locating the sampling point is the LUCAS grid. Soil samples should be taken within a maximum of 100 m distance from the LUCAS point and in the same field where the LUCAS point is located. Surveyors use a metallic ring that they press in the soil with the help of a mallet to extract the soil sample. Ideally, a sample is taken at the LUCAS point, and mixed with four other samples collected 2 m away from the point in each of the main cardinal directions (Fig. 3). A subsample of soil is extracted and put in a plastic jar, labelled and sealed; the jar is put in the polystyrene box together with the freezer packs, labelled and sealed with tape.
Figure 3 Spatial distribution of samples to be collected for soil biodiversity analysis per each visited LUCAS point.
Initial results should be available during 2020. All information will be hosted by the European Soil Data Centre (ESDAC). This will include options for the online generation of maps as well as the entire methodology. Other parties (e.g. national organisations, NGOs) who wish to make the database grow through their own contribution may adopt the same protocol (Orgiazzi et al., 2018).
6Monitoring under the Habitats Directive 92/43/EEC
The Habitats Directive 92/43/EEC is aimed at maintaining or restoring ‘at favourable conservation status, natural habitats and species of wild fauna and flora of Community interest', which are listed in the Annexes of the Directive. Such requirements involve (i) defining ‘conservation status’; (ii) knowing where the habitat/species is found and (iii) what its conservation status is.
Conservation status is defined as ‘the overall assessment of the status of a habitat type or a species at the scale of a Member State’s biogeographical or marine region’ (DG Environment, 2017) and more precisely Article 1 of the Directive links the term to the extent of the area in which the habitat/species is found, the surface of the habitat area, its structure and functions (in case of habitat), the size of the population, its age structure, mortality and reproduction (of species) (EC, 2009).
Article 17 of the Habitats Directive (CEC, 1992) requires the Commission to draw up, every six years, a composite report based on the national reports delivered by the Member States of the European Union, including ‘in particular information concerning the conservation measures referred to in Article 6(1) as well as evaluation of the impact of those measures on the conservation status of the natural habitat types of Annex I and the species in Annex II and the main results of the surveillance referred to in Article 11’. The report should be available for the other EU institutions and the public in general.
Three of these reports are available (the fourth to be released in 2020), two of which focus on the conservation status of the habitat types and species included in the Annexes to the Directive for the periods 2001–2006 and 2007–2012. The assessment of conservation status takes as a reference the concept of favourable conservation status, defined as ‘a situation where a habitat type or species is prospering (in both quality and extent/population) and with good prospects to continue to do so in the future’, which is the overall objective of the Directive.
The conservation status of a species in the Habitats Directive (Article 1(i)) will be taken as ‘favourable’ when:
• population dynamics data on the species concerned indicate that it is maintaining itself on a long-term basis as a viable component of its natural habitats,
• the natural range of the species is neither being reduced nor is likely to be reduced for the foreseeable future, and
• there is, and will probably continue to be, a sufficiently large habitat to maintain its populations on a long-term basis.
The conservation status of a habitat in the Habitats Directive (Article 1(e)) will be taken as ‘favourable’ when:
• its natural range and areas it covers within that range are stable or increasing,
• the specific structure and functions which are necessary for its long-term maintenance exist and are likely to continue to exist for the foreseeable future, and
• the conservation status of its typical species is favourable as defined in (i).
Four parameters have been identified for evaluating the conservation status, which, combined, provide the overall assessment. The parameters are:
• species: range, population, habitat for the species, future prospects, and
• habitat types: range, area, structure and functions, future prospects.
These four parameters are combined to assess conservation status for individual species and habitats using the following four categories: ‘favourable’ (FV), ‘unfavourable-inadequate’ (U1), ‘unfavourable-bad’ (U2) and ‘unknown’ (XX). Trends are equally assessed on the basis of a combination of the individual trends of the four parameters listed above, classified as: increasing, decreasing, stable and unknown.
Of particular interest for this chapter is the fact that in addition to the respective conservation status, trends and presence per biogeographical and MS provided in tabular format, distribution maps for species and habitats should be provided as geospatial information with a 10 km cell resolution. This allows using the geospatial layers for EU-wide assessments of the status of agroecosystems (Masante et al., 2015). Interestingly, pressures and/or threats are recorded as well, including a ranking of its impact on the conservation status of species for each pressure/threat (Table 3).
Table 3 Pressure categories in the list of pressures and threats (from the explanatory notes and guidelines for the period 2013–2018, http://cdr.eionet.europa.eu/help/habitats_art17)
| Pressure code | Pressure category | Note |
| A | Agriculture | Includes pressures and threats caused by agricultural practice. |
| B | Forestry | Includes pressures and threats caused by forestry activities, including thinning, wood harvesting, pest control in trees. |
| C | Extraction of resources (minerals, peat, non-renewable energy resources) | Includes pressures related to extraction of materials, such as mining or quarrying, pollution or waste disposal. |
| D | Energy production processes and related infrastructure development | Includes pressures related to production of energy, for example, the construction and operation of power plants, water-use for energy production, waste from energy production, activities and infrastructure related to renewable energy. |
| E | Development and operation of transportation and service corridors | Includes pressures related to transportation of materials or energy, such as construction of infrastructure, pollution and disturbances or increased mortality due to traffic. |
| F | Development, construction and use of residential, commercial, industrial and recreational infrastructure and areas | Includes pressures related to development, construction and use of residential, commercial, industrial and recreational infrastructure, for example, infrastructural changes on existing built areas, expansion of built areas, land use and hydrological changes for urban or industrial development, disturbances or pollution due to residential, commercial, industrial or recreational infrastructure. Includes also pressures related to sport, tourism and leisure activities and infrastructure. |
| G | Extraction and cultivation of biological living resources (other than agriculture and forestry) | Includes pressures linked to uses of biological resources other than agriculture and forestry. |
| H | Military action, public safety measures, and other human intrusions | Includes pressures related to public safety and other human intrusions. |
| I | Invasive and problematic species | Includes pressures related to problematic inter-specific relationships with non-native species which cannot be associated with other pressure categories. Includes also problematic relationships with native species, which came out of balance due to human activities. |
| J | Mixed source pollution | Includes pollution which cannot be associated with other pressure categories. |
| K | Human-induced changes in hydraulic conditions | Includes hydrological and physical modifications of water bodies, which cannot be associated with other pressures categories. |
| L | Natural processes (excluding catastrophes and processes induced by human activity or climate change) | Includes natural processes, such as natural succession, competition, trophic interaction and erosion. |
| M | Geological events, natural catastrophes | Includes pressures such as natural fires, storms and tsunamis. |
| N | Climate change | Includes pressures related to climate change. |
7European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL)
European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) is a rapid approach based on an in situ survey of plots with a size of 500 m × 500 m (25 ha). This plot size yields an optimum ratio between survey effort and detailedness, and effectively represents an agricultural landscape. The development of this survey is part of the monitoring activities coordinated by the European Commission (DG Environment).
The survey aims at recording information on characteristics of agricultural land in each plot, and namely on grassland, cropland and landscape elements. The survey is carried out during optimum vegetation conditions (peak of the growing season). Land cover is mapped, including landscape elements, surveyed parameters include the description of the site (e.g. habitat coding, type of land cover), cropland (e.g. coverage of crops, wild plants, bare soil, number of flowering forbs, key species list) and grasslands (parameters harmonized with LUCAS Grasslands protocol). A nature value is assigned to all mapped elements.
Four 20 m transects are walked, with an observation frame of 1.25 m to the left and 1.25 m to the right of the surveyor. During the transect walks, the surveyors will record the presence of potential key plant species given in a list as well as information on the structure of the fields. This information will serve as an additional qualification of the land-use intensity, which is estimated in the main sheet for each parcel and as biodiversity record which enables further data interpretation.
The parameters are to be assessed in the field and deliver results at the area level (area and types of land cover/landscape elements, change in landscape structure, nature values for the types of land cover/landscape elements, area per EUNIS habitat type etc.) and at the transect level (coverage of wild plants in arable land, number of key species in grassland, arable land and fallow land, species composition, flower density etc. and respective changes in these parameters). As in the case of the LUCAS grassland survey, training of field surveyors is of utmost importance for ensuring data quality.
EMBAL is set to be the first large-scale survey to provide information on biodiversity values in arable land and a detailed mapping and identification of landscape elements. It is also set to be the first survey applying the landscape approach. By including as well the qualitative estimate of the natural value of the area, once the survey is established and repeated it will provide evidence of the trajectory of evolution of agricultural landscapes with regard to some aspects of biodiversity (increase or decrease), as shown by previous experiences on which the survey is based (IFAB, 2017; Hünig and Benzler, 2017). The EMBAL methodology14was piloted in 2020, and is going to be fine-tuned as necessary. The pilot is also used for testing potential integration of soil sampling into the EMBAL framework. Sampling squares are aligned with the LUCAS grid. Overall, the two surveys are harmonized, and complement each other in depicting the state of biodiversity in agricultural areas.
‘Alien species’ are defined as live specimens of a species, subspecies or lower taxon of animals, plants, fungi or micro-organisms introduced outside their natural range, including any part, gametes, seeds, eggs or propagules of such species, as well as any hybrids, varieties or breeds that might survive and subsequently reproduce15.
Alien species causing damage to agriculture are harmful organisms (insects, mites, nematodes, bacteria, parasitic plants, fungi, viruses and virus-like organisms) regulated under the European phytosanitary legislation, and stem from Council Directive 2000/29/EC16and related decisions laying down emergency or control measures for specific pests and diseases under this specific remit (e.g. Xylella fastidiosa, Dryocosmus kuriphilus, Anoplophora chinensis and A. glabripennis). This Directive has been repealed by Regulation (EU) 2016/203117, whose requirements entered into force on 14 December 2019. The compulsory monitoring for agriculture-related alien species (open field crops, nurseries, and greenhouses) is laid down in technical annexes and must be implemented by official controls performed by Member States National Plant Protection Organizations. Producers are subject to specific obligations in relation to the health of their crops, and shall immediately notify the responsible official body of the Member State concerned of any unusual occurrence of harmful organisms, symptoms or any other plant abnormality, and collaborate with official authorities. Official surveillance and systematic inspections are conducted at the place of production in the most appropriate periods considering the organisms’ biology, supplemented by laboratory analysis. The organization of the phytosanitary surveillance and organisms involved varies and reflects Member States’ administrative organization.
Invasive Alien Species (IAS) constitute one of the most important threats to biodiversity, causing worldwide severe ecological and socio-economic impacts. Target 2.2.10 of the the EU Biodiversity Strategy for 2030 (EC, 2020) puts particular effort on addressing invasive alien species (IAS) and the protection of native biodiversity. Recognizing the need for a coordinated set of actions to prevent, control and mitigate IAS impact, the European Parliament and the Council adopted the EU Regulation 1143/2014 (the IAS Regulation), which gives priority to a list of species, named as IAS of Union concern, selected by scientific and legislative bodies after an evaluation procedure and the analysis of specific pest risk assessments. Species are included in this list, among others, because they can cause a significant damage to biodiversity justifying the adoption of dedicated measures at Union level. Three Implementing Commission Regulations18have listed 66 alien species (30 animals and 36 plants) as of Union concern by 2019, which are affecting terrestrial, freshwaters and marine (2 species) environments. Under the IAS Regulation, EU Member States competent authorities must prevent the introduction and spread of IAS of Union concern, enforce effective early detection and rapid eradication mechanisms for new introductions and adopt management measures for species already widely spread.
Article 16 of the IAS Regulation requires the mandatory notification of early detections of listed species to the European Commission and to the other Member States. Detailed and up-to-date spatial information (baselines) on the distribution of 49 IAS of Union concern in Europe have been prepared by the European Alien Species Information Network of JRC (https://easin.jrc.ec.europa.eu/easin/) (baseline for the 17 species listed by Regulation (EU) 2019/1262 is under preparation) working in close collaboration with EU Member States competent authorities, setting a geographic baseline of their current distribution19. The baselines allow a shared knowledge of the IAS distribution, a better planning of the surveillance activity, the official notification of new detections, and foster the collaboration among MS. These baselines are also available in electronic format as shapefiles (https://easin.jrc.ec.europa.eu/easin/Documentation/Baseline, Fig. 4).
Figure 4 Distribution map of Vespa velutina nigrithorax in Europe at 10 km ×10 km grid level, from EASIN data. Picture: Didier Descouens – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=26722179.
IAS are also considered by the EU Framework Directive for the evaluation of 'good ecological status' of freshwaters (Directive 2000/60/EC)20 and by the Marine Strategy Framework Directive under Descriptor 2 (non-indigenous species) (Directive 2008/56/EC)21. Farmers are sometimes actively involved in monitoring and management of IAS, even if not regulated by law, such as Fallopia japonica, Japanese knotweed, like it happens, for example, in Ireland (Teagasc Invasive Alien Species Working Group).
Specific professional groups of farmers are more directly involved in detection and control of IAS. This is the case of the Asian hornet, Vespa velutina, which sees an active role of beekeepers in many EU countries in detecting the presence of the invasive insect, which preys on honeybees, deploying a network of sentinel hives provided with hornet-traps (e.g. interregional network in northern Italy: http://www.stopvelutina.it/nord-italia-nasce-lover-la-rete-di-sorveglianza-per-la-velutina/).
Many LIFE and Interreg projects in Europe (e.g. LIFE STopVespa, LIFE Invasaqua, LIFE Asap, Interreg Invalis, Interreg ACECA, http://ec.europa.eu/environment/life/project/Projects/index. Cfm, https://www.interregeurope.eu/invalis/, https://camaloteaceca.eu/?page_id=347) are also dedicated to the monitoring, eradication and management of invasive alien species in different environments, or restoration activities which entail the removal of IAS, for example, weeds or invasive plants, preserving natural areas and protecting agriculture from their further spread.
The environmental DNA (e-DNA) is progressively being used to detect the presence of IAS, for example, Procambarus fallax f. virginalis, Pseudorasbora parva, Vespa velutina (Takahashi et al., 2018), while many sequenced genomes are already available in gene banks (https://www.ncbi.nlm.nih.gov/), including for Lithobates catesbeianus and Heracleum mantegazzianum.
A great potential for early detection of IAS and provision of massive data is offered by the contribution of volunteer citizens through Citizen Science, nowadays a consolidated activity, which favours citizens’ awareness and active engagement in IAS management. A repository of Citizen Science projects in Europe which target alien species is available in EASIN (https://easin.jrc.ec.europa.eu/easin/CitizenScience/Projects). Following this line, JRC has developed a smartphone application called ‘Invasive Alien Species Europe’ allowing citizens to report species of Union concern across Europe, where species list, identification factsheets, and guidelines are kept up-to-date (http://digitalearthlab.jrc.ec.europa.eu/app/invasive-alien-species-europe). Data gathered via the App, after validation, are uploaded and available through EASIN geodatabase.
The monitoring frameworks presented in this chapter do not represent the only sources of information on biodiversity currently available, but those for which regular updating is either in place or planned.
The Biodiversity Information System for Europe (BISE, https://biodiversity.europa.eu/info) provides information at the European level in relation to the Biodiversity targets for the European Union, and as regards data it collects and makes available data sources, statistics and maps related to land, water, soil, air, marine, agriculture, forestry, fisheries, tourism, energy, land use and transport. Most of these data are made available under projects or programmes that do not foresee a regular update.
A structured approach to farmland monitoring is a main outcome of the BioBio Project22. The authors Geijzendorffer et al. (2015) and Herzog et al. (2012), starting from the assumption that no single all-inclusive index for biodiversity can be devised23, proposed a framework composed by 15 indicators that describe genetic, species and habitat diversity in farmland. The indicators set is a result of thorough scientific screening and testing in 12 case-study regions with various farm types and farming systems across Europe, as well as regular stakeholder consultation (Herzog et al., 2012).
Such framework can be used as a reference versus the EU-wide monitoring efforts to check what data gaps are and what should still be improved. Table 4 briefly summarizes BioBio indicators, with the exclusion of farm management indicators.
Table 4 BioBio indicators (source: Herzog et al., 2012)
| Genetic diversity | Species diversity | Habitat diversity |
| Number and amount of different breeds | Vascular plants | Habitat richness |
| Cultivar diversity | Wild bees and bumblebees | Habitat diversity |
| Origin of crops | Spiders | Patch size |
| Earthworms | Linear habitats | |
| Crop richness | ||
| Shrub habitats | ||
| Tree habitats | ||
| Semi-natural habitats |
Species diversity is the category in which efforts at the EU level are more advanced and going in the right direction. Plants, pollinators and soil organisms are covered by monitoring efforts either already in place or under development. To these, birds and butterflies must be added, and the progress on the European bat indicator is worth mentioning (EEA, 2013b; Battersby, 2010). Compared to other taxa, though, it must be noted that soil biodiversity is still the less known.
Indicators of habitat diversity can be partially derived from the EMBAL monitoring system (e.g. habitat richness, habitat diversity, patch size). Moreover, the Copernicus Land Monitoring Service, part of the European Union’s Earth Observation Programme24, is providing geographical information on land cover and its changes, land use and vegetation state which can be used to derive indicators on habitat diversity and structural habitat characteristics, such as the presence of landscape elements (tree rows, hedges and groups of trees). The LUCAS module on landscape features in 2022 will collect in-situ information on smaller landscape elements in agricultural areas to complement information from Copernicus. Data (small woody features and hedgerows, grass fringes, ditches, small ponds, stone walls and terraces) will be collected on 93.000 points with the purpose to provide quantitative information at EU and Member State level. The potential of the new Sentinel imagery applied to habitat monitoring has still to be fully exploited. It is important to note that the mentioned approaches have their limitations in terms of resolution or density of sampling points, and provide results that are mostly applicable at country level or macro-regions (e.g. LUCAS grasslands, LUCAS soil, EMBAL), and satellite images are constrained by the resolution of the images (both geometric and spectral).
Despite the efforts, the first column in Fig. 2 remains substantially empty. Though projects aiming at the conservation of genetic material do exist, at global (e.g. Svalbard Global Seed Vault, https:// www.seedvault.no/), national (RIBES, the Italian network of seed banks,
http://www.reteribes.it/) and local (https://www.communityseedbanks.org/the-csb-map/) levels, an overview at EU level of the number, amount and geographical distribution of traditional breeds, cultivars, landraces, wild crop relatives, traditional and ancient varieties is not yet available. The FAO reports that ‘although crop wild relatives represent about 13 percent of the world’s gene bank holdings, about 70 percent of such species are still missing’ (FAO, 2018). To a great degree, the quantification of that part of biodiversity that is directly embedded into agriculture and constitutes its core, as well as an insurance for agricultural ecosystems to remain resilient and adaptive, still has to happen.
What analysed so far concerns initiatives in place or planned. Before reaching some conclusions, it is worth looking into the near future and understand which substantial improvements in biodiversity monitoring can be expected from the operational use of new technologies.
10New approaches and technologies
Recent and ongoing technological developments are reshaping our capacity to monitor biodiversity. Digital connectedness, cataloguing, and storage, combined with advanced analytical techniques and fast computation, provide frameworks that can gather observations across large areas. In Europe, LifeWatch ERIC was established as a European Research Infrastructure Consortium25to provide continued support to the scientific community studying biodiversity. LifeWatch ERIC is building virtual, instead of physical, e-laboratories supplied by the most advanced facilities to capture, standardize, integrate, analyse and model biodiversity (Basset and Los, 2012).
Flexibility and scalability allow designing of monitoring networks for specific tailor-made purposes. Importantly, these frameworks allow expert biologists, citizen scientists, as well as relative non-experts to make valuable contributions (e.g. Chandler et al., 2017). Indeed, these developments facilitate contributions by expert volunteers, who have contributed to biodiversity monitoring for centuries (McKinley et al., 2017). Several tools exist. Functionality varies from geo-locating ones’ own observations to sharing and mapping across the globe. The most well-known citizen-science platform in this context is probably iNaturalist (www.inaturalist.org). iNaturalist provides a generic platform to catalogue species. Another example, which started in 2010, is Pl@ntNet (https://plantnet.org).
Pl@ntNet allows a user to identify a flower, plant or tree species by photographing them with a smartphone (Joly et al., 2014). Specific examples relate to identifying weeds, cultivated and/or ornamental plants, invasive species (Botella et al., 2018) or a focus on specific geographic regions.
Using computer-vision-based algorithms relies on massive amounts of good-quality training data. Pl@ntNet for instance is able to identify a wide range of species with increasing accuracy through a novel collection and validation approach. The Pl@ntNet algorithm continuously learns and improves its accuracy. Each new picture that is submitted provides new data to train the algorithm, benefitting from user feedback stating whether the correct species was identified (or not). These developments highlight that ‘automated plant identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems’ (Bonnet et al., 2018). This will drastically reduce the time needed to generate significant biodiversity data flows.
Clearly, there are also limitations. For example, essential taxonomic details may (currently) not be visible on a picture. Resembling a Turing test, Pl@ntNet pitted its algorithms against botanical experts (Bonnet et al., 2018). One of the conclusions was the need for details to make certain taxonomic distinctions.
Ethical considerations also arise. How best to safeguard the geo-location of a protected red-listed species – say an orchid notoriously difficult to find? Surveying is thus undergoing changes. There is still a huge potential in Citizen Science and crowdsourcing. Maintaining such monitoring over longer time periods is one of the challenges. Furthermore, tapping this potential requires having procedures to set standards and for thorough quality. This starts at the collection of observations. The detection and monitoring of pollinator communities, for instance, provides guidance and training to guarantee minimum identification skills of contributing surveyors (LeBuhn et al., 2016). Besides building inventories of species occurrence, near real-time observations underpin better process understanding of why and how species move through landscapes and interact with their environment. Voluntary contributions tracking birds, for instance, have revealed that migratory tracks have been shifting due to climate change (Cooper et al., 2014). New technologies also enable fast and efficient ways to gather data. One example is the efficient collection and extraction of information from pictures with street-level cameras (e.g. d’Andrimont et al., 2018).
Green infrastructures critical for biodiversity can now be mapped at relevant temporal and spatial scales. For example, Lucas et al. (2019) used LiDAR to identify linear vegetation elements in a rural landscape. While new approaches have been developed to retrieve in situ data, the satellite remote sensing community, via the Group on Earth Observations Biodiversity Observation Network (GEO BON, https://geobon.org/ebvs) has developed the concept of Essential Biodiversity Variables (EBVs) (Pereira et al., 2013). They provide the first level of abstraction between low-level primary observations and high-level indicators of biodiversity. So far, there are six EBV classes (genetic composition, species populations, species traits, community composition, ecosystem function and ecosystem structure) with 21 EBV candidates. The EBVs are defined as the derived measurements required to study, report and manage biodiversity change. The EBVs should broker between monitoring initiatives and decision makers.
Some lessons can be learnt from the various experiences of running monitoring schemes and surveys at the supranational level. The first is that setting up a monitoring scheme takes time. Setting up large-scale surveys from planning to execution takes a few years, and this is the case of all surveys described in this chapter. Preparing the sampling scheme, the survey protocol, identifying the funding bodies, running contracts, finding surveyors, actually running the survey and finally preparing the final database and a first analysis of the data easily takes three to five years, and more if, as in the case of birds and butterflies, national schemes have to be set up individually. Therefore, the operational phase should start as soon as the knowledge gap is identified.
Secondly, the role of volunteers is essential. Whether skilled ornithologists and entomologists participating in counts, or people willing to use apps to communicate species and location of plants and animals, biodiversity monitoring cannot occur without volunteers. The cost for monitoring would simply be too high if tens of thousands of volunteers needed to be paid. Moreover, Schmeller et al. (2009) show that volunteer-based schemes can yield unbiased results, if surveys are appropriately designed and the number of volunteers is sufficiently high. In this regard, the raising awareness that biodiversity is under threat will hopefully convey growing interest into active participation in surveys.
The potential of new technologies is high and not yet fully exploited. We can expect in a near future much higher capacity of automatic recognition of plants and animals, improved algorithms for processing images sent by smartphones, higher availability of centralized databases, platforms to store and retrieve information. This will greatly reduce the time needed to be spent on the ground (e.g. taking photos of plants rather than recognizing in the field each single species), but planning, post-processing and maintenance of the infrastructure will still take a considerable share of resources.
Lastly, there is a point that is often undervalued: taxonomy. In order to correctly identify a specimen, a reference must exist, and species must have been named and classified. Moreover experts must be sufficiently trained to correctly classify specimens, and research on this topic should be reported correctly. It is recognized that taxonomy especially for insects and micro-organisms is not complete (FAO, 2016; Audisio, 2017), that there is a lack of taxonomists and experts with 10–15 years of experience in recognizing species (FAO, 2016), that taxonomic research is often lacking accurate and replicable taxonomic identification (Packer et al., 2018). Also in this case the important role of amateur taxonomists is recognized.
In conclusion, three main pillars can be identified in the complex architecture of (farmland) biodiversity monitoring: the experts, who hold the scientific knowledge of biodiversity and tools to measure it; the decision makers and funding bodies, who take decisions about what to monitor and support monitoring activities and monitoring architecture; and the citizens, essential in information recording.
12Where to look for further information
It is clear that the effort for reaching a sufficient level of biodiversity monitoring is a collective and specialised effort, that requires coordination among many actors at different levels. The improvement of scientific knowledge is supported by research funding, for example, under the EU Research and Innovation programmes (Horizon 2020, Horizon Europe). Moreover, bridging science and technology eases active citizenship involvement. Monitoring schemes can be set up on the basis of existing scientific knowledge, and improved at each survey round. It is a process that takes time, for example, the LUCAS grassland survey developed in approximately seven years from the discussions about the approach to take in 2015, to a first test survey in 2018, to an enlarged survey on 20 000 transects planned for 2022. The involvement of scientific communities (e.g. birds, lepidoptera, pollinators) holding the necessary knowledge about individual taxa or habitats is key. Coordination and support provided by governance bodies (national ministries, EU bodies) is needed to provide the framework for the implementation of the surveys (e.g. funding, coordination) and to secure their repetition through time.
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Notes
2 (https://ec.europa.eu/environment/nature/natura2000/index_en.htm)
3 https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1590574123338&uri=CELEX:52020DC0380
4 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:01985L0337-20090625
5 https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32001L0042
6 https://pecbms.info/trends-and-indicators/indicators)
7 https://ec.europa.eu/eurostat/web/products-datasets/-/t2020_rn130
9 https://ec.europa.eu/environment/nature/knowledge/ecosystem_assessment/pdf/5th%20MAES%20report.pdf
11 https://ec.europa.eu/eurostat/web/agriculture/agri-environmental-indicators
12 https://stats.oecd.org/Index.aspx?QueryId=77269&lang=en
13 Draft report title "Proposal for an EU Pollinator Monitoring Scheme", authors: Potts S. (University of Reading, UK), Dauber J. (Thünen Institute, Germany), Hochkirch A. (Trier University, Germany), Oteman B. (Dutch Butterfly Conservation, Netherlands), Roy D. (UK Centre for Ecology & Hydrology, UK), Ahrne K. (Swedish University of Agricultural Sciences, Sweden), Biesmeijer K. (Naturalis Biodiversity Center, Netherlands), Breeze T. (University of Reading, UK), Carvell C. (UK Centre for Ecology & Hydrology, UK), Ferreira C. (IUCN, Belgium), Fitzpatrick U. (National Biodiversity Data Centre, Ireland), Isaac N. (UK Centre for Ecology & Hydrology, UK), Kuussaari M. (Finnish Environment Institute, Finland), Ljubomirov T. (Bulgarian Academy of Sciences, Bulgaria), Maes J. (European Commission Joint Research Centre, Italy), Ngo H. (IPBES, Germany), Pardo A. (Centre for Ecology, Evolution and Environmental Changes, Portugal), Polce C. (European Commission Joint Research Centre, Italy), Quaranta M. (Council for Agricultural Research and Agricultural Economy Analysis, Italy), Settele J. (Helmholtz-Centre for Environmental Research - UFZ, Germany), Sorg M. (Entomological Society Krefeld, Germany), Stefanescu C. (Granollers Natural Sciences Museum, Spain), and Vujic A. (University of Novi Sad, Serbia)
14 https://ec.europa.eu/environment/nature/knowledge/pdf/embal_report.pdf https://ec.europa.eu/environment/nature/knowledge/pdf/embal_survey_manual.pdf
15 Art. 3 of Regulation (EU) No 1143/2014 of the European Parliament and of the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species.
OJ L 317, 4.11.2014, p. 35–55
16 Council Directive 2000/29/EC of 8 May 2000 on protective measures against the introduction into the community of organisms harmful to plants or plant products and against their spread within the community.
OJ L 169, 10.7.2000, p. 1–112
17 Regulation (EU) 2016/2031 of the European Parliament of the Council of 26 October 2016 on protective measures against pests of plants, amending Regulations (EU) No 228/2013, (EU) No 652/2014 and (EU) No 1143/2014 of the European Parliament and of the Council and repealing Council Directives 69/464/EEC, 74/647/EEC, 93/85/EEC, 98/57/EC, 2000/29/EC, 2006/91/EC and 2007/33/EC.
OJ L 317, 23.11.2016, p. 4–104
18 a Commission Implementing Regulation (EU) 2016/1141 of 13 July 2016 adopting a list of invasive alien species of Union concern pursuant to Regulation (EU) No 1143/2014 of the European Parliament and of the Council C/2016/4295.
OJ L 189, 14.7.2016, p. 4–8
b Commission Implementing Regulation (EU) 2017/1263 of 12 July 2017 updating the list of invasive alien species of Union concern established by Implementing Regulation (EU) 2016/1141 pursuant to Regulation (EU) No 1143/2014 of the European Parliament and of the Council C/2017/4755.
OJ L 182, 13.7.2017, p. 37–39
c Commission Implementing Regulation (EU) 2019/1262 of 25 July 2019 amending Implementing Regulation (EU) 2016/1141 to update the list of invasive alien species of Union concern C/2019/5360.
OJ L 199, 26.7.2019, p. 1–4
19 Tsiamis K, Gervasini E, Deriu I, D’Amico F, Katsanevakis S., Cardoso AC, 2019. Baseline distribution of species listed in the first update of Invasive Alien Species of Union concern. Publications Office of the European Union, Ispra. https://doi.org/10.2760/75328
Tsiamis K, Gervasini E, Deriu I, D’Amico F, Nunes A, Addamo A, De Jesus Cardoso A, 2017. Baseline Distribution of Invasive Alien Species of Union concern. Publications Office of the European Union, Ispra. https://doi.org/10.2760/772692
20 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32000L0060
21 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02008L0056-20170607
22 EU 7th Framework Programme, Grant no. KBBE 227161
23 BioBio webpage http://www.biobio-indicator.org/indicators.php?l=1
24 https://www.copernicus.eu/en/services/land
25 By European Commission Implementing Decision (EU) 2017/499 of 17 March 2017.
