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Molecular clocks are the basis for dating the divergence between lineages over macroevolutionary timescales (~105 to 108 years). However, classical DNA-based clocks tick too slowly to inform us about the recent past. Here, we demonstrate that stochastic DNA methylation changes at a subset of cytosines in plant genomes display a clocklike behavior. This “epimutation clock” is orders of magnitude faster than DNA-based clocks and enables phylogenetic explorations on a scale of years to centuries. We show experimentally that epimutation clocks recapitulate known topologies and branching times of intraspecies phylogenetic trees in the self-fertilizing plant Arabidopsis thaliana and the clonal seagrass Zostera marina, which represent two major modes of plant reproduction. This discovery will open new possibilities for high-resolution temporal studies of plant biodiversity.

Non-native tree species – defined as those species intentionally or unintentionally introduced by humans – have longbeen a part of the Alpine Space, providing numerous benefits, but also posing a potential threat to native biodiversityand related ecosystem services. Compared to the urban space where non-native trees comprise most tree species,the number of non-native trees in forests and plantations is relatively low. To evaluate potential risks and benefits ofnon-native trees in the Alpine Space, a transnational strategy for the responsible use and management of non-nativetrees is needed. The goals of the strategy are to tailor management practices for a sustainable and responsible useor admixture of non-native trees, to reduce the risks connected with the invasive potential of some non-native treespecies, to help forests and urban areas to adapt to climate change, and to improve coordination and cooperationregarding best practices between different regions of the Alpine Space. A proposal was developed in a four-stepprocess including expert-based assessment, stakeholder mapping, an extensive data review, and a public consulta-tion. For implementing the strategy fully, strong collaboration among diverse stakeholders is anticipated and robustgovernance and an adequate long-term and fair funding scheme is needed.

The expected benefits and potential risks of non-native tree species (NNT) to European geographic regions have polarized the opinions of experts and citizens. Benefits include climate change (CC) mitigation and adaptation, contributions to bioeconomy, urban and peri-urban green infrastructure and mitigation of natural hazards. However, NNT may become invasive and thus may pose risks to native biodiversity, ecosystem functioning or socio-economy. In critical and vulnerable ecosystems such as the Alpine Space (AS), such risks and benefits must be carefully considered before management decisions are made. Experiences in the management of NNT in urban areas, peri-urban, rural territories and forests are often region- or city-specific and rarely shared. Given the challenges in NNT management with respect to both benefits and risks, an European transnational approach is needed to qualify the role of NNT in future AS ecosystems. The objective is to provide a transnational strategy for a responsible use and sustainable management of NNT in the AS with the help of an integrated Decision Support System. The project fits within the context of national and regional site-derived policy aiming at protecting and enhancing biodiversity to ensure ecological connectivity and cultural resources while maintaining a high level of resilience and ecosystem services across the AS. Implementation activities of the ALPTREES project include developing a comprehensive database on NNT projecting the current and potential distribution of NNT in the AS under CC scenarios determining their invasive potential analyzing the different ecosystem services provided by NNT to assess the tradeoffs between risks and benefits. ALPTREES will formulate management recommendations for NNT under different climate and economic scenarios based on experience from sample plots combined with model projections, citizen science, and multiple stakeholder meetings. With the help of policy briefs, a handbook of lessons le

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.

This chapter outlines how crowdsourcing and Google Earth have been used to create the first global crwdsourced map of human impact. Human impact in this context refers to the degree to which the landscape has been modified by humans as visible from satellite imagery on Google Earth. As human impact is measured on a continuum, it could be used to indicate the wildest areas on the Earth. This bottom-up approach to mapping using the crowd is in contrast to more traditional GIS-based wilderness mapping methods, which integrate proximity-based layers of remoteness and indicators of biophysical naturalness in a top-down manner. Data on human impact were collected via a number of different data collection campaigns using Geo-Wiki, a tool for visualization, crowdsourcing and validation of global land cover. An overview of the crowdsourced data is provided, along with the resulting map of human impact and a visual comparison with the map of human footprint.