Global biodiversity monitoring: From data sources to Essential Biodiversity Variables

Essential Biodiversity Variables (EBVs) consolidate information from varied biodiversity observation sources. Here we demonstrate the links between data sources, EBVs and indicators and discuss how different sources of biodiversity observations can be harnessed to inform EBVs. We classify sources of primary observations into four types: extensive and intensive monitoring schemes, ecological field studies and satellite remote sensing. We characterize their geographic, taxonomic and temporal coverage. Ecological field studies and intensive monitoring schemes inform a wide range of EBVs, but the former tend to deliver short-term data, while the geographic coverage of the latter is limited. In contrast, extensive monitoring schemes mostly inform the population abundance EBV, but deliver long-term data across an extensive network of sites. Satellite remote sensing is particularly suited to providing information on ecosystem function and structure EBVs. Biases behind data sources may affect the representativeness of global biodiversity datasets. To improve them, researchers must assess data sources and then develop strategies to compensate for identified gaps. We draw on the population abundance dataset informing the Living Planet Index (LPI) to illustrate the effects of data sources on EBV representativeness. We find that long-term monitoring schemes informing the LPI are still scarce outside of Europe and North America and that ecological field studies play a key role in covering that gap. Achieving representative EBV datasets will depend both on the ability to integrate available data, through data harmonization and modeling efforts, and on the establishment of new monitoring programs to address critical data gaps

Expression of the stem cell marker ALDH1 in BRCA1 related breast cancer

Introduction The BRCA1 protein makes mammary stem cells differentiate into mature luminal and myoepithelial cells. If a BRCA1 mutation results in a differentiation block, an enlarged stem cell component might be present in the benign tissue of BRCA1 mutation carriers, and these mammary stem cells could be the origin of BRCA1 related breast cancer. Since ALDH1 is a marker of both mammary stem cells and breast cancer stem cells, we compared ALDH1 expression in malignant tissue of BRCA1 mutation carriers to non-carriers. Methods Forty-one BRCA1 related breast cancers and 41 age-matched sporadic breast cancers were immunohistochemically stained for ALDH1. Expression in epithelium and stroma was scored and compared. Results Epithelial (P=0.001) and peritumoral (P=0.001) ALDH1 expression was significantly higher in invasive BRCA1 related carcinomas compared to sporadic carcinomas. Intratumoral stromal ALDH1 expression was similarly high in both groups. ALDH1 tumor cell expression was an independent predictor of BRCA1 mutation status. Conclusion BRCA1 related breast cancers showed significantly more frequent epithelial ALDH1 expression, indicating that these hereditary tumors have an enlarged cancer stem cell component. Besides, (peritumoral) stromal ALDH1 expression was also more frequent in BRCA1 mutation carriers. ALDH1 may therefore be a diagnostic marker and a therapeutic target of BRCA1 related breast cancer

Expression of estrogen receptor beta in the breast carcinoma of BRCA1 mutation carriers

<p>Abstract</p> <p>Background</p> <p>Breast cancers (BC) in women carrying mutations in BRCA1 gene are more frequently estrogen receptor negative than the nonhereditary BC. Nevertheless, tamoxifen has been found to have a protective effect in preventing contralateral tumors in BRCA1 mutation carriers. The identification of the second human estrogen receptor, ERβ, raised a question of its role in hereditary breast cancer. The aim of this study was to assess the frequency of ERα, ERβ, PgR (progesterone receptor) and HER-2 expression in breast cancer patients with mutated <it>BRCA1 </it>gene and in the control group.</p> <p>Methods</p> <p>The study group consisted of 48 women with <it>BRCA1 </it>gene mutations confirmed by multiplex PCR assay. The patients were tested for three most common mutations of BRCA1 affecting the Polish population (5382insC, C61G, 4153delA). Immunostaining for ERα, ERβ and PgR (progesterone receptor) was performed using monoclonal antibodies against ERα, PgR (DakoCytomation), and polyclonal antibody against ERβ (Chemicon). The EnVision detection system was applied. The study population comprised a control group of 120 BC operated successively during the years 1998–99.</p> <p>Results</p> <p>The results of our investigation showed that <it>BRCA1 </it>mutation carriers were more likely to have ERα-negative breast cancer than those in the control group. Only 14.5% of <it>BRCA1</it>-related cancers were ERα-positive compared with 57.5% in the control group (<it>P </it>< 0.0001). On the contrary, the expression of ERβ protein was observed in 42% of <it>BRCA1</it>-related tumors and in 55% of the control group. An interesting finding was that most hereditary cancers (75% of the whole group) were triple-negative: ERα(-)/PgR(-)/HER-2(-) but almost half of this group (44.4%) showed the expression of ERβ.</p> <p>Conclusion</p> <p>In the case of <it>BRCA1</it>-associated tumors the expression of ERβ was significantly higher than the expression of ERα. This may explain the effectiveness of tamoxifen in preventing contralateral breast cancer development in <it>BRCA1 </it>mutation carriers.</p

Environmental stratifications as the basis for national, European and global ecological monitoring

status: publishe

Environmental stratifications as the basis for national, European and global ecological monitoring

There is growing urgency for integration and coordination of global environmental and ecological data and indicators required to respond to the ‘grand challenges’ the planet is facing, including climate change and biodiversity decline. A consistent stratification of land into relatively homogenous strata provides a valuable spatial framework for comparison and analysis of ecological and environmental data across large heterogeneous areas. We discuss how statistical stratification can be used to design national, European and global biodiversity observation networks. The value of strategic ecological survey based on stratified samples is first illustrated using the United Kingdom (UK) Countryside Survey, a national monitoring programme that has measured ecological change in the UK countryside for the last 35 years. We then present a design for a European-wide sampling design for monitoring common habitats, and discuss ways of extending these approaches globally, supported by the recently developed Global Environmental Stratification. The latter provides a robust spatial analytical framework for the identification of gaps in current monitoring efforts, and systematic design of new complementary monitoring and research. Examples from Portugal and the transboundary Kailash Sacred Landscape in the Himalayas illustrate the potential use of this stratification, which has been identified as a focal geospatial dataset within the Group on Earth Observation Biodiversity Observation Network (GEO BON)

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conserva-tion action, it is critical to map and monitor changes in the type and extent of land cover/use and habitatclasses, which can be related to human pressures over time. Satellite Earth observation (EO) data andtechniques offer the opportunity to detect such changes. Yet association with field information and expertinterpretation by ecologists is required to interpret, qualify and link these changes to human pressure.There is thus an urgent need to harmonize the technical background of experts in the field of EO dataanalysis with the terminology of ecologists, protected area management authorities and policy makers inorder to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIRframework, providing a terminology to relate the concepts of state, pressures, and drivers with the appli-cation of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e.changes in land cover and/or habitat type and/or condition). Four broad categories of changes in stateare identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentationand changes in landscape connectivity, and changes in plant community structure. These categories ofchange in state can be mapped through EO analyses, with the goal of using expert judgement to relatechanges in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of pro-tected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysedto (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pres-sure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) themost appropriate spatial and temporal EO data sources as well as interpretations from ecologists andfield data useful in connection with EO data analysis. We provide detailed examples for two protectedareas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expertinterpretation to relate such change to specific anthropogenic pressures. We conclude with a discussionof the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures,suggesting additional research efforts required in this direction

Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

There is an increasing need of effective monitoring systems for habitat quality assessment. Methodsbased on remote sensing (RS) features, such as vegetation indices, have been proposed as promisingapproaches, complementing methods based on categorical data to support decision making.Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge-driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000site.We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitatsof a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the severalquality indicators.Three indicators were mainly explained by predictors related to landscape and neighbourhood struc-ture. Overall, competing models based on the products of the automated knowledge-driven system hadthe best performance to explain quality indicators, compared to models based on manually classifiedland cover data.The system outputs in terms of both land cover classes and spectral/landscape indices were consideredin the study, which highlights the advantages of combining EO data with RS techniques and improvedmodelling based on sound ecological hypotheses. Our findings strongly suggest that some features ofhabitat quality, such as structure and habitat composition, can be effectively monitored from EO datacombined with in-field campaigns as part of an integrative monitoring framework for habitat statusassessment

Using life strategies to explore the vulnerability of ecosystem services to invasion by alien plants

Invasive plants can have different effects of ecosystem functioning and on the provision of ecosystem services, from strongly deleterious impacts to positive effects. The nature and intensity of such effects will depend on the service and ecosystem being considered, but also on features of life strategies of invaders that influence their invasiveness as well as their influence of key processes of receiving ecosystems. To address the combined effect of these various factors we developed a robust and efficient methodological framework that allows to identify areas of possible conflict between ecosystem services and alien invasive plants, considering interactions between landscape invasibility and species invasiveness. Our framework combines the statistical robustness of multi-model inference, efficient techniques to map ecosystem services, and life strategies as a functional link between invasion, functional changes and potential provision of services by invaded ecosystems. The framework was applied to a test region in Portugal, for which we could successfully predict the current patterns of plant invasion, of ecosystem service provision, and finally of probable conflict (expressing concern for negative impacts, and value for positive impacts on services) between alien species richness (total and per plant life strategy) and the potential provision of selected services. Potential conflicts were identified for all combinations of plant strategy and ecosystem service, with an emphasis for those concerning conflicts with carbon sequestration, water regulation and wood production. Lower levels of conflict were obtained between invasive plant strategies and the habitat for biodiversity supporting service. The added value of the proposed framework in the context of landscape management and planning is discussed in perspective of anticipation of conflicts, mitigation of negative impacts, and potentiation of positive effects of plant invasions on ecosystems and their services