Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
Authors
D. Vanella D.S. Intrigliolo S. Consoli G. Longo-Minnolo G. Lizzio R.C. Dumitrache E. Mateescu Johannes Deelstra J.M. Ramírez-CuestaAbstract
The reliability of short-term weather forecast provided by COSMO model in simulating reference evapotranspiration (ET0) was evaluated in 7 study sites distributed in 4 countries (Italy, Norway, Romania and Spain). The main objective of the study was to assess the optimal scenario for calculating ET0, using the FAO-56 Penman-Monteith (PM) equation, by separately considering the accuracy in the use of “past” and “forecast” data input. Firstly, each forecasted variable (air temperature, Tair; relative humidity, RH; wind speed, u2; solar radiation, Rs) and ET0 were compared with in situ observations at hourly and daily scales. Moreover the seasonality effect in the forecast performance was evaluated. Secondly, simulated ET0 were computed every three days with: (i) a “past scenario” that used the observed data input measured in situ during the previous three days, (ii) a “forecast scenario” that used the forecasted input variables for the next three days; and compared with (iii) actual ET0 obtained from the in situ measured data. A general good agreement was found between observed and forecasted agro-meteorological parameters at the different explored time-scales. The best performance was obtained for Tair and Rs, followed by RH and u2. Globally, the comparison between ET0 from the measured and forecasted data input showed high performance, with R2 and RMSE of 0.90 and 0.68 mm d−1. ET0 simulations resulted more accurate using the “forecast scenario” (1.7% overestimation), rather than using the “past scenario” (2.6% underestimation). These results open promising perspectives in the use of forecast for ET0 assessment for different agriculture practices and particularly for irrigation scheduling under water scarcity conditions.
Abstract
To support decision-makers considering adopting integrated pest management (IPM) cropping in Norway, we used stochastic efficiency analysis to compare the risk efficiency of IPM cropping and conventional cropping, using data from a long-term field experiment in southeastern Norway, along with data on recent prices, costs, and subsidies. Initial results were not definitive, so we applied stochastic efficiency with respect to a function, limiting the assumed risk aversion of farmers to a plausible range. We found that, for farmers who are risk-indifferent to moderately (hardly) risk averse, the conventional system was, compared to IPM, less (equally) preferred.
Authors
Steen Magnussen Ronald E. McRoberts Johannes Breidenbach Thomas Nord-Larsen Göran Ståhl Lutz Fehrmann Sebastian SchnellAbstract
Background: Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling (SRS) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS, Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results: The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS. The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion: Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar. Keywords: Spatial autocorrelation, Linear trend, Model based, Design biased, Matérn variance, Successive difference replication variance, Geary contiguity coefficient, Random site effects
Authors
Isobel Phoebus John Boulanger Hans Geir Eiken Ida Marie Luna Fløystad Karen Graham Snorre Hagen Anja Sorensen Gordon StenhouseAbstract
Wildlife managers conduct population inventories to monitor species, particularly those at-risk. Although costly and time consuming, grid-based DNA hair-snag sampling has been the standard protocol for grizzly bear inventories in North America, while opportunistic fecal DNA sampling is more commonly used in Europe. Our aim is to determine if low-cost, low-effort scat sampling along roads can replace the current standard. We compare two genetic non-invasive techniques using concurrent sampling within the same grid system and spatially explicit capture–recapture. We found that given our methodology and the present status of fecal genotyping for grizzly bears, scat sampling along roads cannot replace hair sampling to estimate population size in low-density areas. Hair sampling identified the majority of individual grizzly bears, with a higher success rate of individuals identified from grizzly bear samples (100%) compared to scat sampling (14%). Using scat DNA to supplement hair data did not change population estimates, but it did improve estimate precision. Scat samples had higher success identifying species (98%) compared with hair (80%). Scat sampling detected grizzly bears in grid cells where hair sampling showed non-detection, with almost twice the number of cells indicating grizzly bear presence. Based on our methods and projected expenses for future implementation, we estimated an approximate 30% cost reduction for sampling scat relative to hair. Our research explores the application of genetic non-invasive approaches to monitor bear populations. We recommend wildlife managers continue to use hair-snag sampling as the primary method for DNA inventories, while employing scat sampling as supplemental to increase estimate precision. Scat sampling may better indicate presence of bear species through greater numbers and spatial distribution of detections, if sampling is systematic across the entire area of interest. Our findings speak to the management of other species and regions, and contribute to ongoing advances of monitoring wildlife populations.
Authors
Fredrik Ronquist Mattias Forshage Sibylle Häggqvist Dave Karlsson Rasmus Hovmöller Johannes Bergsten Kevin Holston Tom Britton Johan Abenius Bengt Andersson Peter Neerup Buhl Carl-Cedric Coulianos Arne Fjellberg Carl-Axel Gertsson Sven Hellqvist Mathias Jaschhof Jostein Kjærandsen Seraina Klopfstein Sverre Kobro Andrew Liston Rudolf Meier Marc Pollet Matthias Riedel Jindřich Roháček Meike M. Schuppenhauer Julia Stigenberg Ingemar Struwe Andreas Taeger Sven-Olof Ulefors Oleksandr Varga Phil Withers Ulf GärdenforsAbstract
No abstract has been registered
Authors
Frode VeggelandAbstract
No abstract has been registered
Authors
Philipp Lehmann Tea Ammunet Madeleine Barton Andrea Battisti Sanford D. Eigenbrode Jane Uhd Jepsen Gregor Kalinkat Seppo Neuvonen Pekka Niemelä John S. Terblanche Bjørn Økland Christer BjörkmanAbstract
Although it is well known that insects are sensitive to temperature, how they will be affected by ongoing global warming remains uncertain because these responses are multifaceted and ecologically complex. We reviewed the effects of climate warming on 31 globally important phytophagous (plant‐eating) insect pests to determine whether general trends in their responses to warming were detectable. We included four response categories (range expansion, life history, population dynamics, and trophic interactions) in this assessment. For the majority of these species, we identified at least one response to warming that affects the severity of the threat they pose as pests. Among these insect species, 41% showed responses expected to lead to increased pest damage, whereas only 4% exhibited responses consistent with reduced effects; notably, most of these species (55%) demonstrated mixed responses. This means that the severity of a given insect pest may both increase and decrease with ongoing climate warming. Overall, our analysis indicated that anticipating the effects of climate warming on phytophagous insect pests is far from straightforward. Rather, efforts to mitigate the undesirable effects of warming on insect pests must include a better understanding of how individual species will respond, and the complex ecological mechanisms underlying their responses.
Abstract
Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves
Authors
Raimondas Mozūraitis David Hall Nina Trandem Baiba Ralle Kalle Tunström Lene Sigsgaard Catherine Baroffio Michelle Fountain Jerry Cross Atle Wibe Anna-Karin Borg-KarlsonAbstract
The strawberry blossom weevil (SBW), Anthonomus rubi, is a major pest in strawberry fields throughout Europe. Traps baited with aggregation pheromone are used for pest monitoring. However, a more effective lure is needed. For a number of pests, it has been shown that the attractiveness of a pheromone can be enhanced by host plant volatiles. The goal of this study was to explore floral volatile blends of different strawberry species (Fragaria x ananassa and Fragaria vesca) to identify compounds that might be used to improve the attractiveness of existing lures for SBW. Floral emissions of F. x a. varieties Sonata, Beltran, Korona, and of F. vesca, were collected by both solid-phase microextraction (SPME) and dynamic headspace sampling on Tenax. Analysis by gas chromatography/mass spectrometry showed the floral volatiles of F. x ananassa. and F. vesca were dominated by aromatic compounds and terpenoids, with 4-methoxybenzaldehyde (p-anisaldehyde) and α-muurolene the major compounds produced by the two species, respectively. Multi-dimensional scaling analyses separated the blends of the two species and explained differences between F. vesca genotypes and, to some degree, variation between F. x ananassa varieties In two-choice behavioral tests, SBW preferred odors of flowering strawberry plants to those of non-flowering plants, but weevils did not discriminate between odors from F. x ananassa and F. vesca flowering plants. Adding blends of six synthetic flower volatiles to non-flowering plants of both species increased the preference of SBW for these over the plants alone. When added individually to non-flowering plants, none of the components increased the preference of SBW, indicating a synergistic effect. However, SBW responded to 1,4-dimethoxybenzene, a major component of volatiles from F. viridis, previously found to synergize the attractiveness of the SBW aggregation pheromone in field studies.
Authors
Sekhar Udaya NagothuAbstract
The final chapter in the book summarizes the main messages from the preceding chapters. It analyses the diverse views of the bioeconomy concept and supports the view that sustainable bioeconomy development has the potential to change the way we produce and consume natural resources while reducing the negative impacts on the environment. However, there are always risks associated with any new paradigm, hence, it is necessary to ensure transparency in the process, consider the interests of the most vulnerable groups and introduce genuine stakeholder management from the start. Whether, and to what extent, bioeconomy can contribute to the SDGs is a debatable issue. However, several case studies in the book do support the idea that bioeconomy can help in achieving several SDGs. The chapter also highlights the importance of sustainability indicators, including ecological (i.e., the local ecological footprint, total organic carbon, soil nitrogen, transport of minerals from land to rivers and oceans and other ecosystem services), economic and social sustainability indices in the context of bioeconomy development. Their measurement and monitoring are essential to ensure that we are on the sustainable development path. The chapter suggests possible measures to overcome constraints or risks associated with bioeconomy and proposes the necessary conditions required for sustainable bioeconomy development.