Suomessa kunnilla ja kaupungeilla on maankäyttäjinä ja maankäytön suunnittelusta vastaavina tahoina merkittävä asema ja vastuu luontokadon pysäyttämisessä. Siihen tarvitaan uusia keinoja jo käytössä olevien lisäksi. Ekologinen kompensaatio on yksi lisäkeino ehkäistä ja vähentää luontokatoa, jota väistämättä syntyy esimerkiksi asumiseen ja liikkumiseen liittyvän rakentamisen ja muun luontoa nakertavan toiminnan myötä. Ekologisessa kompensaatiossa ihmistoiminnan aiheuttama haitta luonnon monimuotoisuudelle hyvitetään parantamalla elinympäristön tilaa tai suojelemalla heikennysuhan alla olevia luontokohteita toisaalla. Lieventämishierarkian mukaan ensisijaista on välttää ja lieventää luonnolle aiheutettavaa haittaa ja käyttää kompensaatiota jäljelle jäävän, väistämättömän luontohaitan hyvittämiseen. Ekologisen kompensaation yhtenä keskeisenä tavoitteena mainitaan usein luonnon monimuotoisuuden kokonaisheikentämättömyys (no net loss of biodiversity)...

The rate and extent of global biodiversity change is surpassing our ability to measure, monitor and forecast trends. We propose an interconnected worldwide system of observation networks — a global biodiversity observing system (GBiOS) — to coordinate monitoring worldwide and inform action to reach international biodiversity targets.

In boreal forests, European aspen (Populus tremula L.) is a keystone species that hosts a variety of accompanying species including epiphytic lichens. Forest management actions have led to a decrease in aspen abundance and subsequent loss of suitable habitats of epiphytic lichens. In this study, we evaluate the environmental responses of epiphytic lichen species richness and community composition on aspen, focusing on the potential of remote sensing by combined hyperspectral imaging and airborne laser scanning to identify suitable habitats for epiphytic lichens. We measured different substrate and habitat parameters in the field (e.g., aspen diameter and bark pH) and by remote sensing (e.g., mean canopy height and tree species composition of the surrounding forest) in the study area in Southern Finland that includes protected and non-protected forest. We used linear regression and the Hierarchical Model of Species Communities (HMSC) to compare how the different parameters explain and predict lichen species richness and community composition, respectively. We show that coarse predictions of epiphytic lichen community composition can be made using parameters extracted from remote sensing data. Estimated mean canopy height, tree density, dominant tree species and tree species diversity of the stand predicted the species community on aspens slightly better than field parameters. Remote sensing variables calculated over a larger area (30 m radius) always outperformed the same variables calculated over a smaller area (10 m radius) in predicting community composition, highlighting the cost-efficiency of remote sensing compared to covering a similar area with on-ground measurements. These results are encouraging for the prospects of using remote sensing data to direct field inventories and to map potential high-biodiversity habitats. Aspen bark pH was the only parameter affecting species richness regardless of whether the forest was protected or not, whereas, interestingly, the effects of tree diameter, height and furrow depth were only significant in protected areas. Our results also underline the importance of protected areas, since they hosted a higher tree-specific number of epiphytic lichen species, and red listed species, than non-protected areas.

During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation.

The rates of ecosystem degradation and biodiversity loss are alarming and current conservation efforts are not sufficient to stop them. The need for new tools is urgent. One approach is biodiversity offsetting: a developer causing habitat degradation provides an improvement in biodiversity so that the lost ecological value is compensated for. Accurate and ecologically meaningful measurement of losses and estimation of gains are essential in reaching the no net loss goal or any other desired outcome of biodiversity offsetting. The chosen calculation method strongly influences biodiversity outcomes. We compare a multiplicative method, which is based on a habitat condition index developed for measuring the state of ecosystems in Finland to two alternative approaches for building a calculation method: an additive function and a simpler matrix tool. We examine the different logic of each method by comparing the resulting trade ratios and examine the costs of offsetting for developers, which allows us to compare the cost-effectiveness of different types of offsets. The results show that the outcomes of the calculation methods differ in many aspects. The matrix approach is not able to consider small changes in the ecological state. The additive method gives always higher biodiversity values compared to the multiplicative method. The multiplicative method tends to require larger trade ratios than the additive method when trade ratios are larger than one. Using scoring intervals instead of using continuous components may increase the difference between the methods. In addition, the calculation methods have differences in dealing with the issue of substitutability.

Biodiversity crisis calls for better knowledge of the status of biodiversity, but also drivers and pressures behind the ecosystem change and the vulnerability of habitats. There is growing demand for the biodiversity data for decision-making on national, regional and global scales. However, the operationalized biodiversity and ecosystem monitoring systems which can integrate remote sensing, in situ and modelling data are still rare despite of that there are good co-operation initiatives such as Australian TERN and US NEON in place. In Finland, we have launched the Finnish Ecosystem Observatory (FEO) as a reaction to such a need to upgrade and modernize biodiversity and ecosystem monitoring schemes. We have started a development of national research and monitoring infrastructure/platform which can integrate different data types and provide timely data and knowledge for various decision-making purposes. In this short paper, we describe three case studies as examples of different data types and different monitoring needs: i) habitat mapping for large area, ii) hydrological condition of aapa mires, and iii) automated Aspen mapping for boreal forests. We will evaluate the possibilities to operationalize the production of such data sets in a long-term as well as the challenges and bottlenecks related either to data, processing or applicability. Finally, we will also discuss how FEO and similar kind of biodiversity monitoring platforms could be further developed, and how the network of such observatories could co-operate in the future.

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive. We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km2 contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks). Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm) (Viinikka et al. 2020; Mäyrä et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.

Importance of biodiversity is increasingly highlighted as an essential part of sustainable forest management. As direct monitoring of biodiversity is not possible, proxy variables have been used to indicate site’s species richness and quality. In boreal forests, European aspen (Populus tremula L.) is one of the most significant proxies for biodiversity. Aspen is a keystone species, hosting a range of endangered species, hence having a high importance in maintaining forest biodiversity. Still, reliable and fine-scale spatial data on aspen occurrence remains scarce and incomprehensive. Although remote sensing-based species classification has been used for decades for the needs of forestry, commercially less significant species (e.g., aspen) have typically been excluded from the studies. This creates a need for developing general methods for tree species classification covering also ecologically significant species. Our study area, located in Evo, Southern Finland, covers approximately 83km2, and contains both managed and protected southern boreal forests. The main tree species in the area are Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), with relatively sparse and scattered occurrence of aspen. Along with a thorough field data, airborne hyperspectral and LiDAR data have been acquired from the study area. We also collected ultra high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors. Our aim is to gather fundamental data on hyperspectral and multispectral species classification, that can be utilized to produce detailed aspen data at large scale. For this, we first analyze species detection at tree-level. We test and compare different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks), with specific emphasis on accurate and feasible aspen detection. The results will show, how accurately aspen can be detected from the forest canopy, and which bandwidths have the largest importance for aspen. This information can be utilized for aspen detection from satellite images at large scale.

Using spatial prioritization, we identify priority areas for the expansion of the global protected area network. We identify a set of unprotected key biodiversity areas (KBAs) that would efficiently complement the current protected area network in terms of coverage of ranges of terrestrial vertebrates. We show that protecting a small fraction (0.36%) of terrestrial area within KBAs could increase conservation coverage of ranges of threatened vertebrates by on average 14.7 percentage points. We also identify areas outside both the protected area and KBA networks that would further complement the priority KBAs. These areas are likely to hold populations of species that are poorly protected or covered by KBAs, and where on-the-ground surveys might confirm suitability for KBA designation or protection.

Offsets for compensating biodiversity loss are increasingly suggested as a system for allocating responsibilities onto those actors who contribute to the loss. As the mechanism is outlined as a new opportunity, the expectations need to be analyzed relative to the ensuing changes in rights and responsibilities over biodiversity degradation, conservation and restoration. In this paper we conduct an analysis of rights and responsibilities using literature and empirical material. Our empirical case is in Finland, where ecological compensation and biodiversity offsets represent an emerging avenue for conservation. We find that rights to conservation, property and economic activity have generally not been explicitly addressed in parallel, and that the focus has been on evaluating biodiversity loss through ecological assessment or as an ethical notion. Offsetting literature focuses on developer rights to a predictable operational environment rather than on human rights to biodiversity or the property rights of offset suppliers. At the same time, the literature on offsets analyzing the responsibilities over management, avoiding degradation and meeting societal expectations, has placed much emphasis on governance and control by authorities. These analyses result in doubts and criticism of the capacity of governance arrangements to reach the set targets. Echoing the literature, the Finnish case shows that even though the mechanism is framed as a way to place the responsibility onto developers, numerous responsibilities are expected to be taken by authorities or a yet non-existing mediating actor, while developer rights are expected to be secured and landowner rights are either mostly assumed not to change, or not addressed at all. Our study shows that the assumptions on rights and responsibilities need to be exposed to empirical analysis, to support the design of meaningful new institutional arrangements.

Complementarity and cost-efficiency are widely used principles for protected area network design. Despite the wide use and robust theoretical underpinnings, their effects on the performance and patterns of priority areas are rarely studied in detail. Here we compare two approaches for identifying the management priority areas inside the global protected area network: 1) a scoring-based approach, used in recently published analysis and 2) a spatial prioritization method, which accounts for complementarity and area-efficiency. Using the same IUCN species distribution data the complementarity method found an equal-area set of priority areas with double the mean species ranges covered compared to the scoring-based approach. The complementarity set also had 72% more species with full ranges covered, and lacked any coverage only for half of the species compared to the scoring approach. Protected areas in our complementarity-based solution were on average smaller and geographically more scattered. The large difference between the two solutions highlights the need for critical thinking about the selected prioritization method. According to our analysis, accounting for complementarity and area-efficiency can lead to considerable improvements when setting management priorities for the global protected area network.

Internationally coordinated expansion of the global protected area network to 17% could triple the average protection of species ranges and ecoregions; if projected land-use changes and consequent habitat loss until 2040 occur, currently feasible protection levels will not be achievable, and more than 1,000 threatened species face reductions in the range of over 50%. Protected areas are intended to mitigate pressures on biodiversity caused by anthropogenic factors such as habitat loss. To that end, an internationally agreed target aims to extend the protected area network to cover 17% of the world's land area by 2020. But biodiversity is unevenly distributed between countries and habitats, raising the question of which areas should be protected to maximize the effectiveness. Federico Montesino Pouzols et al. show that internationally coordinated expansion of the protected area network to the 17% target could triple the average protection of species ranges and ecoregions. However, within-country prioritization is considerably less efficient. Moreover, taking into account projected land-use changes and consequent habitat loss until 2040, current levels of protection will not be feasible to maintain, and over 1,000 threatened species face reductions in their range of over 50%. Thus, the authors suggest that for effective biodiversity conservation, land-use policy and protected area decisions must be coordinated at an international level. Protected areas are one of the main tools for halting the continuing global biodiversity crisis1,2,3,4 caused by habitat loss, fragmentation and other anthropogenic pressures5,6,7,8. According to the Aichi Biodiversity Target 11 adopted by the Convention on Biological Diversity, the protected area network should be expanded to at least 17% of the terrestrial world by 2020 ( http://www.cbd.int/sp/targets ). To maximize conservation outcomes, it is crucial to identify the best expansion areas. Here we show that there is a very high potential to increase protection of ecoregions and vertebrate species by expanding the protected area network, but also identify considerable risk of ineffective outcomes due to land-use change and uncoordinated actions between countries. We use distribution data for 24,757 terrestrial vertebrates assessed under the International Union for the Conservation of Nature (IUCN) ‘red list of threatened species’9, and terrestrial ecoregions10 (827), modified by land-use models for the present and 2040, and introduce techniques for global and balanced spatial conservation prioritization. First, we show that with a coordinated global protected area network expansion to 17% of terrestrial land, average protection of species ranges and ecoregions could triple. Second, if projected land-use change by 2040 (ref. 11) takes place, it becomes infeasible to reach the currently possible protection levels, and over 1,000 threatened species would lose more than 50% of their present effective ranges worldwide. Third, we demonstrate a major efficiency gap between national and global conservation priorities. Strong evidence is shown that further biodiversity loss is unavoidable unless international action is quickly taken to balance land-use and biodiversity conservation. The approach used here can serve as a framework for repeatable and quantitative assessment of efficiency, gaps and expansion of the global protected area network globally, regionally and nationally, considering current and projected land-use pressures.

In the tenth Conference of Parties to the Convention on Biological Diversity (CBD) held in Nagoya in 2010, it was decided that 17% of terrestrial and 10% of marine areas should be protected globally by 2020. It was also stated that conservation decision-making should be based on sound science. Here, we review how recent scientific literature about spatial conservation prioritization analyses and macro-ecology corresponds to the information needs posed by the Aichi Biodiversity Target 11. A literature search was performed in Web of Science to identify potentially relevant research articles published in 2010-2012. Additionally, we searched all articles published since 2000 in five high-profile scientific journals. The studies were classified by extent and resolution, and we evaluated the type and breadth of data that was utilized (This information is included in a supplementary table to facilitate further research). Implementation of the Aichi Targets would best be supported by broad-extent, high-resolution, and data-rich studies that can directly support realistic decision-making about allocation of conservation efforts at sub-continental to global extents. When looking at all evaluation criteria simultaneously, we found little research that directly supports the analytical needs of the CBD. There are many narrow-extent, low-resolution, narrow-scope, or theoretically-aimed studies that are important in developing theory and local practices, but which are not adequate for guiding conservation management at a continental scale. Even national analyses are missing for many countries. Global-extent, high-resolution analyses using broad biodiversity and anthropogenic data are needed in order to inform decision making under the CBD resolutions. (C) 2014 Associacao Brasileira de Ciencia Ecologica e Conservacao. Published by Elsevier Editora Ltda.