How do recent spatial biodiversity analyses support the convention on biological diversity in the expansion of the global conservation area network?

ABSTRACTIn 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.© 2014 Associação Brasileira de Ciência Ecológica e Conservação. Published by Elsevier Editora Ltda

Precision, Applicability, and Economic Implications: A Comparison of Alternative Biodiversity Offset Indexes

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.Peer reviewe

Biodiversiteetin mittaaminen ja uudet menetelmät

Our planet is undergoing massive global change. We are increasingly aware of the biodiversity crisis, which raises concerns about the future of nature and humankind. Targets and goals set at several multilateral environmental agreements to stop the crisis have been agreed upon, but their effective follow-up and implementation require relevant and timely biodiversity data. For this purpose, a set of policy-relevant Essential Biodiversity Variables (EBVs), describing the biological state and capturing the major dimensions of biodiversity change, have been proposed. Generating EBVs requires integration of in situ and Earth observation data. The former is collected in the field by experts, citizens, or automatic sensor networks, assisted by new technologies such as eDNA and machine learning, while the latter is measured from space or air, enabled by analysis-ready multi-sensor data and cloud computing services. As a case example for better biodiversity monitoring, the Finnish Ecosystem Observatory (FEO) is proposed. FEO will combine and standardize environmental information from different data sources, making the data, metadata and models openly available and easily accessible to users and policy makers

Using key biodiversity areas to guide effective expansion of the global protected area network

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. (c) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Gastric Bypass Promotes More Lipid Mobilization Than a Similar Weight Loss Induced by Low-Calorie Diet

Background. Recently, we found large reductions in visceral and subcutaneous fat one month after gastric bypass (GBP), without any change in liver fat content. Purpose. Firstly to characterize weight loss-induced lipid mobilization after one month with preoperative low-calorie diet (LCD) and a subsequent month following GBP, and secondly, to discuss the observations with reference to our previous published findings after GBP intervention alone. Methods. 15 morbidly obese women were studied prior to LCD, at GBP, and one month after GBP. Effects on metabolism were measured by magnetic resonance techniques and blood tests. Results. Body weight was similarly reduced after both months (mean: −8.0 kg, n = 13). Relative body fat changes were smaller after LCD than after GBP (−7.1 ± 3.6% versus −10 ± 3.2%, P = .029, n = 13). Liver fat fell during the LCD month (−41%, P = .001, n = 13) but was unaltered one month after GBP (+12%). Conclusion. Gastric bypass seems to cause a greater lipid mobilization than a comparable LCD-induced weight loss. One may speculate that GBP-altered gastrointestinal signalling sensitizes adipose tissue to lipolysis, promoting the changes observed

Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks

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.peerReviewe

Complementarity and Area-Efficiency in the Prioritization of the Global Protected Area Network

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 scoringbased 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.Peer reviewe

IL-23 plays a key role in Helicobacter hepaticus–induced T cell–dependent colitis

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract that is caused in part by a dysregulated immune response to the intestinal flora. The common interleukin (IL)-12/IL-23p40 subunit is thought to be critical for the pathogenesis of IBD. We have analyzed the role of IL-12 versus IL-23 in two models of Helicobacter hepaticus–triggered T cell–dependent colitis, one involving anti–IL-10R monoclonal antibody treatment of infected T cell–sufficient hosts, and the other involving CD4+ T cell transfer into infected Rag−/− recipients. Our data demonstrate that IL-23 and not IL-12 is essential for the development of maximal intestinal disease. Although IL-23 has been implicated in the differentiation of IL-17–producing CD4+ T cells that alone are sufficient to induce autoimmune tissue reactivity, our results instead support a model in which IL-23 drives both interferon γ and IL-17 responses that together synergize to trigger severe intestinal inflammation