Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long‐lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time‐series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population‐level time‐series from 974 species in 66 countries. The mean and median time‐series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well‐studied species, MASTREE+ includes extensive replication of time‐series across geographical and climatic gradients. Here we describe the open‐access data set, available as a.csv file, and we introduce an associated web‐based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long‐lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics.

Context: Ecological connectivity is key to mitigating global change impacts such as isolation and climate change effects on populations, especially in historically modified biomes such as European forests. The term connectivity is widely used in conservation, but the multiple methods for measuring it, as well as the scales and objects of study to which it can be applied, make it difficult to understand and compare research results. Objectives: Our aim was to enhance the understanding of connectivity studies in European forests to inform conservation efforts, guide future research, and identify potential knowledge gaps, through a systematic review of connectivity studies in European forests. Methods: We did a systematic review on the literature of connectivity studies in European forests. We classified the studies according to the methods used. Then, we extracted information on study characteristics and context relevant for our purpose. Results: Our literature search identified 142 relevant articles, more than half of these studied connectivity through structural measures (based only on landscape attributes). The three most used measures were area-based methods, Euclidean distances, and spatial indices (structural), followed by species and graph theory-based models (functional measures, including species data). Most studies focused on large Western European countries, and birds and mammals as focal taxa. Generally, functional measures were more frequent in publications that had the study of connectivity as a primary objective. Conclusions: Research priorities and recommendations to guide future studies were identified such as the inclusion of structural and functional measures to bridge the gap between approaches, retaining the benefits of both and helping to uncover relationships between them; and as the need to address temporal connectivity, especially for less mobile species.

The Leaf-Area-Index (LAI) is commonly used to characterize the plant canopy and is a fundamental indication of plant vitality and photosynthetic activity. The forest health status is not only vital for economical reasons, but also has a significant impact on global carbon sequestration. The LAI has a highly dynamic character among deciduous forests and is prone to significant seasonal fluctuations. Accurate continuous LAI measurements do provide valuable information on growth characteristics, but they require considerable measurement effort. In this study, we tested a novel method that would allow for continuous low-effort LAI parameterizations. For our study we used temperature measurements from 2011 to 2019 obtained at two meteorological stations: Station one is an open land station, station two is located inside a forest stand characterized by European beech (measurements were undertaken as part of the ICP Forests program), both are located in Klausen Leopoldsdorf (Austria). We chose the difference in daily maximum temperature between the two sites for our LAI parametrization (LAI_par) since the forest canopy has a significant impact on local radiation conditions. We were able to identify phenological events such as leaf unfolding, the end of leaf growth, and the beginning and end of defoliation by examining at the average course of the year for LAI_par. The resultant LAI_par values were compared to annual values derived from hemispheric photographs taken near the stand temperature sensor. For the years 2011–2017, we found a strong correlation of 0.93 between LAI measures and LAI_par, which dropped to 0.69 after adding the year 2018 and 0.32 after adding 2019. We further compared the phenological events obtained from LAI_par to phenological observations. The impact of forests on their site climate, according to our findings, can be utilized to identify phenological and growth characteristics. The proposed method, however, is not a replacement for conventional LAI measurements.

Seven long-term observation sites have been established in six regions across Austria, covering major ecosystem types such as forests, grasslands, and wetlands across a wide bioclimatic range. The purpose of these observations is to measure key ecosystem parameters serving as baselines for assessing the impacts of extreme climate events on the carbon cycle. The datasets collected include meteorological variables, soil microclimate, CO₂ fluxes, and tree stem growth, all recorded at high temporal resolution (15–60 min) between 2019 and 2021 (including 1 year of average climate conditions and 2 comparatively dry years). The DOIs of the dataset can be found in the “Data availability” section. The sites will be integrated into the European Research Infrastructure for Integrated European Long-Term Ecosystem, Critical Zone, and Socio-Ecological Research (eLTER RI). Subsequently, new data covering the variables presented here will be continuously available through the data integration portal. This step will allow the data to reach their full potential for research on drought-related ecosystem carbon cycling.​​​​​​​

Changing climatic circumstances bring more frequent and intense extreme weather events that significantly impact forests in various ways. Since forests are the largest terrestrial sinks for carbon, and are among the richest biological environments on Earth, the goals of understanding the related challenges and improving the forest resilience is high on the agenda to mitigate climate change and save biodiversity. Achieving these goals requires access to data to derive vitality and health of trees, monitor and forecast tree growth, environmental conditions data, as well as suitable data modelling approaches.Within our research, we exploited a wide set of data sources originating and ranging from remote sensing to in-situ measurement equipment allowing us to address the tree growth and health from different spatial and temporal points of view.The data from dendrometers provided us with the high frequency (hourly), intraday variation of tree radial growth for assessing long-term growth and instantaneous changes in growth. These data are of extreme value, as no other means to monitor trees on such a high temporal resolution with a very high sensitivity exits. However, to understand the variations in these data, which directly show variation in the tree growth, especially in the context of extreme or sudden changes, they are evaluated within the environmental context. The environmental high-quality data were collected directly from forest sites selected from the Europe-wide Forest monitoring program (ICP-Forests), which has been providing high-quality data on the vitality and adaptability of trees, nutrient cycles, water balance, etc. Furthermore, satellite Earth Observation (EO) data for single-tree detection and monitoring forest disturbances like selective logging and drought impacts have been likewise exploited, to explore if they may have an impact on the individual tree growth. We show that a CNN-based U-Net model trained on Very High Resolution (VHR) imagery demonstrates strong potential for identifying tree crowns and validating changes in forest structure. However, challenges such as limited training data diversity and low resolution for small trees underscore the need for further refinements.Finally, terrestrial laser scanning (TLS) technique delivers single tree point-clouds not only allowing extraction of traditional tree features like diameters at different heights, tree height and crown dimensions, but also providing the possibility of statistical approaches for calculation of various metrics, e.g., point-cloud percentiles along the tree height and tree competition.We describe the approaches on leveraging these, the challenges we have encountered (e.g., data gaps, errors in data, co-location), how we approached them, and all in the context of developing predictive AI-based, climate sensitive tree growth models, to support forest management on a local, regional and national level, and thus empowering response to minimize potentially harmful consequences for modern societies in line with the UN Sustainable Development Goals.