High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 degrees C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990-2019) and consistent temporal trends (1950-2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 degrees C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m x 100 m) and with high accuracy (correlation >= 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.Peer reviewe

High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 degrees C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990-2019) and consistent temporal trends (1950-2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 degrees C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m x 100 m) and with high accuracy (correlation >= 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.Peer reviewe

Automation aspects for the georeferencing of photogrammetric aerial image archives in forested scenes

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1 parts per thousand with respect to flying height for data sets collected since the 1980s, and 0.2 parts per thousand for older data sets. The results show that of the studied land cover classes, "peatland without trees" changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.Peer reviewe

The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study

The Aichi Biodiversity Targets of the United Nations’ Strategic Plan for Biodiversity set ambitious goals for protecting biodiversity from further decline. Increased efforts are urgently needed to achieve these targets by 2020. The availability of comprehensive, sound and up-to-date biodiversity data is a key requirement to implement policies, strategies and actions to address biodiversity loss, monitor progress towards biodiversity targets, as well as to assess the current status and future trends of biodiversity. Key gaps, however, remain in our knowledge of biodiversity and associated ecosystem services. These are mostly a result of barriers preventing existing data from being discoverable, accessible and digestible. In this paper, we describe what regional Biodiversity Observation Networks (BONs) can do to address these barriers using the European Biodiversity Observation Network (EU BON) as an example. We conclude that there is an urgent need for a paradigm shift in how biodiversity data are collected, stored, shared and streamlined in order to tackle the many sustainable development challenges ahead. We need a shift towards an integrative biodiversity information framework, starting from collection to the final interpretation and packaging of data. This is a major objective of the EU BON project, towards which progress is being made

Case of chest-wall rigidity in a preterm infant caused by prenatal fentanyl administration

The inability to appropriately ventilate neonates shortly after their birth could be related in rare cases to chest-wall rigidity caused by the placental transfer of fentanyl. Although this adverse effect is recognized when fentanyl is administered to neonates after their birth, the prenatal phenomenon is less known. Treatment with either naloxone or muscle relaxants reverses the fentanyl effect and may prevent unnecessary excessive ventilatory settings

A nearly complete database on the records and ecology of the rarest boreal tiger moth from 1840s to 2020

Global environmental changes may cause dramatic insect declines but over century-long time series of certain species’ records are rarely available for scientific research. The Menetries’ Tiger Moth (Arctia menetriesii) appears to be the most enigmatic example among boreal insects. Although it occurs throughout the entire Eurasian taiga biome, it is so rare that less than 100 specimens were recorded since its original description in 1846. Here, we present the database, which contains nearly all available information on the species’ records collected from 1840s to 2020. The data on A. menetriesii records (N = 78) through geographic regions, environments, and different timeframes are compiled and unified. The database may serve as the basis for a wide array of future research such as the distribution modeling and predictions of range shifts under climate changes. It represents a unique example of a more than century-long dataset of distributional, ecological, and phenological data designed for an exceptionally rare but widespread boreal insect, which primarily occurs in hard-to-reach, uninhabited areas of Eurasia.Peer reviewe

Impacts of the Finnish service screening programme on breast cancer rates

<p>Abstract</p> <p>Background</p> <p>The aim of the current study was to examine impacts of the Finnish breast cancer (BC) screening programme on the population-based incidence and mortality rates. The programme has been historically targeted to a rather narrow age band, mainly women of ages 50–59 years.</p> <p>Methods</p> <p>The study was based on the information on breast cancer during 1971–2003 from the files of the Finnish Cancer Registry. Incidence, cause-specific mortality as well as incidence-based (refined) mortality from BC were analysed with Poisson regression. Age-specific incidence and routine cause-specific mortality were estimated for the most recent five-year period available; incidence-based mortality, respectively, for the whole steady state of the programme, 1992–2003.</p> <p>Results</p> <p>There was excess BC incidence with actual screening ages; incidence in ages 50–69 was increased 8% (95 CI 2.9–13.4). There was an increasing temporal tendency in the incidence of localised BC; and, respectively, a decrease in that of non-localised BC. The latter was most consistent in age groups where screening had been on-going several years or eventually after the last screen. The refined mortality rate from BC diagnosed in ages 50–69 was decreased with -11.1% (95% CI -19.4, -2.1).</p> <p>Conclusions</p> <p>The current study demonstrates that BC screening in Finland is effective in reducing mortality rates from breast cancers, even though the impact on the population level is smaller than expected based on the results from randomised trials among women screened in age 50 to 69. This may be explained by the rather young age group targeted in our country. Consideration whether to targeted screening up to age 69 is warranted.</p