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The length of the growing season in temperate and boreal forests has a strong effect on the global carbon balance. Yet, our current understanding of the drivers of phenological processes such as autumn leaf senescence in deciduous trees is not sufficient for making reliable estimates of future growing-season lengths under climate change. While temperature has been shown to be an important driver of autumn leaf senescence, recent evidence suggests that the concept of carbon sink limitation might help to reduce unexplained variation in leaf senescence predictions. According to the carbon sink limitation hypothesis, senescence is regulated by the balance of the plant carbon source and the plant carbon sink, so that senescence occurs later when carbon inputs (source) are low and earlier when there is a low carbon demand (sink). In our experiment, we manipulated carbon source–sink dynamics in birch seedlings (Betula pendula L.) to evaluate the evidence for an effect of carbon sink limitation on autumn leaf senescence in a widespread deciduous tree. Specifically, we removed leaves and/or buds from the seedlings and monitored the effects on autumn net photosynthesis and leaf senescence. In agreement with the carbon sink limitation hypothesis, we observed that a decrease in the carbon source through a high degree of leaf removal increased autumn leaf-level photosynthesis by ~14% and postponed senescence by 5.5 ± 2.4 days. Yet, we did not see significant effects of the lower- and medium-degree defoliation treatments. Further, we did not observe an effect of bud removal on either photosynthesis or senescence, which was likely caused by the fact that our bud removal treatment did not considerably change the plant carbon sink. At least partly, our results are in line with the hypothesis of carbon sink limitation as a driver of growing-season length and move the scientific field closer to narrowing a main uncertainty in climate change predictions.

The degree to which elevated CO

The degree to which elevated CO2 concentrations (e[CO2]) increase the amount of carbon (C) assimilated by vegetation plays a key role in climate change. However, due to the short-term nature of CO2 enrichment experiments and the lack of reconciliation between different ecological scales, the effect of e[CO2] on plant biomass stocks remains a major uncertainty in future climate projections. Here, we review the effect of e[CO2] on plant biomass across multiple levels of ecological organization, scaling from physiological responses to changes in population-, community-, ecosystem-, and global-scale dynamics. We find that evidence for a sustained biomass response to e[CO2] varies across ecological scales, leading to diverging conclusions about the responses of individuals, populations, communities, and ecosystems. While the distinct focus of every scale reveals new mechanisms driving biomass accumulation under e[CO2], none of them provides a full picture of all relevant processes. For example, while physiological evidence suggests a possible long-term basis for increased biomass accumulation under e[CO2] through sustained photosynthetic stimulation, population-scale evidence indicates that a possible e[CO2]-induced increase in mortality rates might potentially outweigh the effect of increases in plant growth rates on biomass levels. Evidence at the global scale may indicate that e[CO2] has contributed to increased biomass cover over recent decades, but due to the difficulty to disentangle the effect of e[CO2] from a variety of climatic and land-use-related drivers of plant biomass stocks, it remains unclear whether nutrient limitations or other ecological mechanisms operating at finer scales will dampen the e[CO2] effect over time. By exploring these discrepancies, we identify key research gaps in our understanding of the effect of e[CO2] on plant biomass and highlight the need to integrate knowledge across scales of ecological organization so that large-scale modeling can represent the finer-scale mechanisms needed to constrain our understanding of future terrestrial C storage.

Soil nitrogen is a prominent determinant of plant growth, with nitrogen (N) availability being a key driver of terrestrial carbon sequestration. The local availability of soil N is thus crucial to our understanding of broad-scale trends in soil fertility, productivity, and carbon dynamics. Here, we provide global, high-resolution maps of current and future (2050) potential net nitrogen mineralization (N-min), revealing global patterns in soil N availability. Highest mineralization rates are found in warm and moist tropical regions, leading to a strong latitudinal gradient in N-min. We observed a positive correlation of N-min rates with human population density and net primary productivity. Projected climate conditions for 2050 suggest that N availability will further decrease in areas of low N availability and increase in areas of high N availability, thereby intensifying current global trends. These results shed light on the core processes governing productivity at a global scale, providing an opportunity to improve the accuracy of plant biomass and climate models.

W dniach 12–14 marca 2019 r. w Instytucie Badawczym Leśnictwa odbyła się XI Sesja Zimowej Szkoły Leśnej (ZSL), której tematem było „Zastosowanie geoinformatyki w leśnictwie”. Współorganizatorem Sesji była Dyrekcja Generalna Lasów Państwowych. Merytoryczne aspekty XI Sesji zostały przygotowane i opracowane przez Radę Programową Zimowej Szkoły Leśnej w składzie: prof. dr hab. Andrzej Grzywacz, prof. dr hab. Jacek Hilszczański, dr inż. Krzysztof Janeczko, prof. dr hab. Andrzej Klocek, mgr inż. Jacek Przypaśniak, mgr inż. Marek Roman, dr inż. Jacek Sagan, dr hab. Krzysztof Stereńczak, prof. IBL (przewodniczący), mgr inż. Adam Wasiak, prof. dr hab. Tomasz Zawiła-Niedźwiecki. Rada Programowa określiła nie tylko zakres problematyki XI Sesji, ale także propozycje tematyczne referatów zamawianych oraz wytypowania ich autorów. W trakcie XI Sesji referaty wygłosili zaproszeni goście zagraniczni reprezentujący Remote Sensing Research and Consulting Ltd. (Szwajcaria), Wyższą Szkołę Techniczną w Zurychu (Szwajcaria), Norweski Uniwersytet Przyrodniczy – NMBU, Federalny Instytut Badawczy Lasu, Śniegu i Krajobrazu (Szwajcaria), Uniwersytet we Fryburgu (Niemcy), Uniwersytet Florencki (Włochy), Instytut Geografii i Geoekologii i Instytut Technologii w Karlsruhe (Niemcy), Instytut Lasu im. Sukaczewa Syberyjskiej Filii Rosyjskiej Akademii Nauk, Federalne Centrum Badawcze «Krasnoyarsk Science Center SB RAS» i Syberyjski Uniwersytet Federalny (Rosja) oraz Uniwersytet w Cambridge (Wielka Brytania). Poza gośćmi z zagranicy na Zimowej Szkole Leśnej wystąpiło szerokie grono autorów z Polski, prezentujących wiele przykładów wykorzystania geoinformatyki w leśnictwie. W tym miejscu składam serdeczne podziękowanie autorom za przygotowanie i wygłoszenie interesujących referatów, a także za opracowanie tekstów wystąpień, które znajdą Państwo w niniejszych materiałach posesyjnych. W XI Sesji ZSL wzięło udział prawie 400 osób reprezentujących m.in.: kierownictwo Lasów Państwowych oraz jednostki organizacyjne PGL LP i Ministerstwo Środowiska. Najliczniejszą grupę słuchaczy Szkoły stanowili zarówno geomatycyreprezentujący szeroko rozumiany świat nauki, jak i firmy zajmujące się wdrożeniami i praktyką, pracownicy nadleśnictw oraz biur urządzania lasu i geodezji leśnej, na co dzień korzystających z narzędzi geoinformatycznych. Liczne grono stanowili przedstawiciele ośrodków naukowych, a także studentów polskich wydziałów leśnych.

Combating against climate change requires unified action across all sectors of society. However, this collective action is precluded by the ‘consensus gap’ between scientific knowledge and public opinion. A growing body of evidence suggests that facts do not persuade people to act. Instead, it is visualization - the ability to simulate relatable scenarios - is the most effective approach for motivating behavior change. Here, we exemplify this approach, using current climate projections to enable people to visualize cities of the future, rather than describing intangible climate variables. Analyzing city pairs for 520 major cities of the world, we characterize which cities will most closely resemble the climate conditions of which other major cities by 2050. On average, most cities will resemble cities that are over 1000km south, and 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. We predict that London’s climate in 2050 will resemble Barcelona’s climate today, Madrid will resemble to Marrakesh, Moscow to Sofia, Seattle to San Francisco, Stockholm to Budapest, Tokyo to Changsha, etc. Our approach illustrates how complex climate data can be packaged to provide tangible information. By allowing people to visualize their own climate futures, we hope to empower citizens, policy makers and scientists to visualize expected climate impacts and adapt decision making accordingly.

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.