EU 2020 Biodiversity Strategy

Policy Document
in the Communication: Our life insurance, our natural capital: an EU biodiversity strategy to 2020 (COM(2011) 244) the European Commission has adopted a new strategy to halt the loss of biodiversity and ecosystem services in the EU by 2020. There are six main targets, and 20 actions to help Europe reach its goal. The six targets cover: - Full implementation of EU nature legislation to protect biodiversity - Better protection for ecosystems, and more use of green infrastructure - More sustainable agriculture and forestry - Better management of fish stocks - Tighter controls on invasive alien species - A bigger EU contribution to averting global biodiversity loss

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Landscape fragmentation pressure and trends in Europe This indicator measures landscape fragmentation due to transport infrastructure and sealed areas. Unlike the previous indicator on fragmentation status, this updated version uses the TeleAtlas® Multinet data set to ensure the statistical comparability of the time series. While the Open Street Map data set is a valuable source of the street network available for the general public, there are still inconsistencies in this data set for some regions of Europe, which render it secondary to the TeleAtlas data set. As in the previous version, this indicator is based on the effective mesh size method  (Jaeger, 2000) . For some species, the effective mesh size (meff) can be interpreted as the area that is accessible when beginning to move from a randomly chosen point inside a landscape without encountering anthropogenic barriers such as transport routes or built-up areas. However, it should be stressed that for many species that can fly, or are effective dispersers in others ways, man-made structures may not act as barriers.  The combination of all barriers in a landscape is referred to as the fragmentation geometry (FG) hereafter. The meff value expresses the probability that any two points chosen randomly in an area are connected. Hence, meff is a measure of landscape connectivity, i.e. the degree to which movements between different parts of the landscape are possible. The larger the meff, the more connected the landscape. The indicator addresses the structural connectivity of the landscape and does not tackle functional, species-specific connectivity. The effective mesh density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by fragmentation geometry. It gives the effective number of meshes (or landscape patches) per 1 000 km 2 , in other words the density of the meshes. The seff value is 1 000 km 2 /meff, hence the number of meshes per 1 000 km 2 . The more barriers fragmenting the landscape, the higher the effective mesh density. The values of meff and seff are reported within the cells of a 1 km 2 regular grid. The value of meff is area-proportionally additive, hence it characterises the fragmentation of any region considered, independently of its size, and thus can be calculated for a combination of two or more regions. It has several advantages over other metrics: It addresses the entire landscape matrix instead of addressing individual patches. It is independent of the size of the reporting unit and its values can be compared among reporting units of differing sizes. It is suitable for comparing the fragmentation of regions with differing total areas and with differing proportions occupied by housing, industry and transportation structures. Its reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics is limited, as they only partially meet the following criteria: intuitive interpretation; mathematical simplicity; modest data requirements; low sensitivity to small patches; detection of structural differences; mathematical homogeneity (i.e. intensive or extensive).
Pathways of introduction of marine non-indigenous species to European seas This indicator describes the processes (pathways) that result in the transfer of alien species from one location to another. The identification and categorisation of pathways follows the Convention on Biological Diversity (CBD) classification as interpreted by the IUCN (2017). A hierarchical approach has been adopted to describe the pathways, based on the framework developed by Hulme et al. (2008). The main pathways shown above, in which a NIS arrive, can be subdivided into the following categories: importation of a commodity; arrival of a transport vector; or spread from a neighbouring region. The principal pathways were identified under the CBD process (UNEP, 2014) as quoted below. Pathways: 'Transport–Stowaway' refers to the moving of live organisms attached to shipping - transporting vessels and associated equipment and media. This pathway is further subdivided (in Figure 2 and Figure 3) into  three sub-groups. The physical means of transport-stowaway include various conveyances, ballast water and sediments (short: T-S/BALLAST WATER), bio-fouling of ships, boats, offshore oil and gas platforms and other water vessels (short: T-S/HULL FOULING), dredging, angling or fishing equipment (short: T-S/OTHER). Recreational boating is also included under this pathway. 'Release in nature' refers to the intentional introduction of live alien organisms for the purpose of human use in the natural environment. Examples include for biological control; erosion control (and dune stabilisation); fishing or hunting in the wild; landscape 'improvement'; and the introduction of threatened organisms for conservation purposes. 'Escape from confinement’; refers to the movement of (potentially) invasive alien species from confinement (e.g. aquaria; aquaculture and mariculture facilities; scientific research or breeding programmes). Through this pathway, the organisms were initially purposefully imported or otherwise transported to the confined conditions, but then escaped from such confinement, unintentionally. This may include the accidental or irresponsible release of live organisms from confinement, including cases such as the disposal of live food into the environment or the use of live baits in an unconfined water system. 'Transport–Contaminant' refers to the unintentional movement of live organisms as contaminants of a commodity that is intentionally transferred through international trade, development assistance, or emergency relief. This includes pests and fisheries as well as contaminants of other products. ‘Corridor’ refers to the movement of alien organisms into a new region following the construction of transport infrastructures, in whose absence the spread would not have been possible. Such trans-bio-geographical corridors include international canals (connecting river catchments and seas). The secondary natural dispersal of invasive alien species that have been introduced by various  pathways is unaided. Information on the mechanisms of the secondary spread of invasive alien species, after their introduction is not considered in this analysis. Trends in primary pathways over 6 year periods should tell a story when combined with management policies applied/implemented over the last decades. In reality, there could be other factors involved, for example: the decrease could be partly explained by the decreasing pool of potential NIS species not yet arrived in Europe; another factor could be variations in sampling effort, available expertise; the decrease may also be a temporal phenomenon etc.. These aspects are no yet considered in this analysis.
Landscape fragmentation pressure from urban and transport infrastructure expansion This indicator is based on the Effective Mesh Size (Jaeger 2000) method .  For some species, the effective mesh size (meff) can be interpreted as the area that is accessible when beginning to move from a randomly chosen point inside a landscape without encountering man-made barriers such as transport routes or built-up areas. However, it should be stressed that for many species that can fly, or are effective dispersers in others ways, man-made structures may not act as barriers. The combination of all barriers in a landscape is called Fragmentation Geometry (FG) hereafter. The meff value expresses the probability that any two points chosen randomly in an area are connected. Hence, meff is a measure of landscape connectivity, i.e. the degree to which movements between different parts of the landscape are possible. The larger the meff, the more connected the landscape. The indicator addresses structural connectivity of the landscape and does not tackle functional, species specific connectivity. The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by Fragmentation Geometry. It gives the effective number of meshes (or landscape patches) per 1 000 km 2 , in other words, the density of the meshes. The seff value is calculated as 1 000 km 2 /meff, hence, the number of meshes per 1 000 km 2 . The more barriers fragmenting the landscape, the higher the effective mesh density. meff and seff are reported within the cells of a 1 km 2 regular grid. meff is area-proportionally additive, hence it characterises the fragmentation of any region considered, independently of its size, and thus can be calculated for a combination of two or more regions. The meff has several advantages over other metrics: It addresses the entire landscape matrix instead of addressing individual patches. It is independent of the size of the reporting unit and its values can be compared among reporting units of differing sizes. It is suitable for comparing the fragmentation of regions with differing total areas and with differing proportions occupied by housing, industry and transportation structures. It's reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics was limited as they only partially met the following criteria: Intuitive interpretation; Mathematical simplicity; Modest data requirements; Low sensitivity to small patches; Detection of structural differences; Mathematical homogeneity (i.e., intensive or extensive).
Vegetation productivity The indicator addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.  References: Jönsson P., Eklundh L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences 30 (2004) 833–845. Eklundh L., Jönsson P., 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham Jin, H., Eklundh, L. 2014. A physically based vegetation index for improved monitoring of plant phenology, Remote Sensing of Environment, 152, 512 – 525. Karkauskaite, P., Tagesson, T., Fensholt, R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone, Remote Sensing, 9 (485), 21 pp. Jin, H.X.; Jönsson, A.M.; Bolmgren, K.; Langvall, O.; Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sensing of Environment 2017,198, 203-212. Abdi, A. M., N. Boke-Olén, H. Jin, L. Eklundh, T. Tagesson, V. Lehsten and J. Ardö (2019). First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. International Journal of Applied Earth Observation and Geoinformation 78: 249-260. Jin, H., A. M. Jönsson, C. Olsson, J. Lindström, P. Jönsson and L. Eklundh (2019). New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. International Journal of Biometeorology 63(6): 763-775.
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