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.
2024
Authors
Stefano PulitiAbstract
No abstract has been registered
Abstract
In the present work we have investigated the effects of abiotic and biotic factors on the growth and quality of carrots. The experiment tested how precipitation above field capacity (WATER) vs. no precipitation (DROUGHT) affect carrot growth and storability. Each treatment period lasted three weeks. We found no yield difference between the treatments at harvesting the carrots (6.6 vs. 6 t daa‑1) and the proportion of fresh roots was generally around 85%. High precipitation, especially in the latter part of the growth period, resulted in a higher proportion of cracked roots, number of roots with a lighter colour, rot in the upper part of the root and the occurrence of enlarged cork cells. After storage, we did not see any difference between the different treatments in the proportion of fresh roots. There was a slight tendency for tip rot to increase during drought at the end of the season. The soil content of phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and sodium (Na) was reduced by high water supply, especially early in the season. The nutrient content in the roots was generally less affected by treatments than the soil mineral content. We found that the content of K and manganese (Mn) was higher at high water supply and the content of zinc (Zn) and ion (Fe) lower. The dry matter content was lowest in the treatments with a high-water supply. As the precipitation influences the soil content of some minerals, we looked at how low pH, low Ca content in the soil, would influence carrot growth. High soil pH (7.4 vs. 5.5) resulted in a higher proportion of roots with fingers when harvesting, but a lower proportion of roots with tip rot after storage (7.8 vs. 3.3%) as well as a higher proportion of healthy roots (83% vs. 67%). The conclusion is that the climatic changes where periods with high precipitation and with drought occur more often require attention to cultivation methods to reduce the negative effects.
Authors
Andrea FickeAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Elena L. Zvereva Benjamin Adroit Tommi Andersson Craig R. A. Barnett Sofia Branco Bastien Castagneyrol Giancarlo Maria Chiarenza Wesley Dáttilo Ek del-Val Jan Filip Jory Griffith Anna L. Hargreaves Juan Antonio Hernández-Agüero Isabelle L. H. Silva Yixuan Hong Gabriella Kietzka Petr Klimeš Max Koistinen Oksana Y. Kruglova Satu Kumpula Paula Lopezosa Marti March-Salas Robert J. Marquis Yuri M. Marusik Angela T. Moles Anne Muola Mercy Murkwe Akihiro Nakamura Cameron Olson Emilio Pagani-Núñez Anna Popova Olivia Rahn Alexey Reshchikov Antonio Rodriguez-Campbell Seppo Rytkönen Katerina Sam Antigoni Sounapoglou Robert Tropek Cheng Wenda Guorui Xu Yu Zeng Maxim Zolotarev Natalia A. Zubrii Vitali Zverev Mikhail V. KozlovAbstract
Aim Long-standing theory predicts that the intensity of biotic interactions increases from high to low latitudes. Studies addressing geographic variation in predation on insect prey have often relied on prey models, which lack many characteristics of live prey. Our goals were to explore global latitudinal patterns of predator attack rates on standardised live insect prey and to compare the patterns in predation on live insects with those on plasticine prey models. Location Global forested areas. Time Period 2021–2023. Major Taxa Arthropods, birds. Methods We measured predation rates in 43 forested locations distributed across five continents from 34.1° S to 69.5° N latitude. At each location, we exposed 20 sets of three bait types, one set per tree. Each set included three live fly larvae (maggots), three live fly puparia and three plasticine models of the puparia. We used glue rings to isolate half of the sets from non-flying predators. Results Arthropod attack rates on plasticine prey decreased linearly from low to high latitudes, whereas attack rates on maggots had a U shaped distribution, with the lowest predation rates at temperate latitudes and the highest rates at tropical and boreal latitudes. This difference emerged from intensive predator attacks on live maggots, but not on plasticine models, in boreal sites. Site-specific attack rates of arthropod predators on live and plasticine prey were not correlated. In contrast, bird attack rates on live maggots and plasticine models were positively correlated, but did not show significant latitudinal changes. Main Conclusions Latitudinal patterns in predation differ between major groups of predators and between types of prey. Poleward decreases in both arthropod and combined arthropod and bird predation on plasticine models do not mirror patterns of predation on our live prey, the latter likely reflecting real patterns of predation risk better than do patterns of attack on artificial prey.
Abstract
No abstract has been registered
Authors
Bert van der Veen Robert Brian O'Hara Francis K. C. Hui Knut Anders HovstadAbstract
The ecological niche is a fundamental concept in ecology that can be used in order better understand species relationships. The overlap in species niches provides a measure of the likelihood for species to co-occur. Most approaches that quantify niche overlap have been based on distance and similarity indices, for pairwise combinations of species. In this paper, we suggest that niche overlap can be calculated from the predictions of a model. Using a statistical model to predict niche overlap provides various benefits, includes the possibility to adjust the model to properties of the data. We demonstrate this using an example dataset of an ecological community of Foraminifera species, to which we fit a generalized linear latent variable model (GLLVM). GLLVMs are a flexible class of models that allow to estimate the distribution of species using both measured environmental predictors and residual covariation between species. We demonstrate how to calculate niche overlap from GLLVMs for any combination of species, and separately for different environments. Predicting niche overlap from a model further expands the toolset available to ecologists for the exploration of species co-occurrence patterns.
Abstract
No abstract has been registered
Authors
Ståle Haaland Josef Hejzlar Bjørnar Eikebrokk Geir Orderud Ma. Cristina Paule‐Mercado Petr Porcal Jiří Sláma Rolf David VogtAbstract
Over the past four decades, an increase in Dissolved Natural Organic Matter (DNOM) and colour, commonly referred to as browning, has been noted in numerous watercourses in the northern hemisphere. Understanding the fluctuations in DNOM quality is a prerequisite for gaining insights into the biogeochemical processes governing DNOM fluxes. Such knowledge is also pivotal for water treatment plants to effectively tailor their strategies for removing DNOM from raw water. The specific ultraviolet absorbance (sUVa) index has been a widely applied measurement for assessing DNOM quality. The sUVa index is the UV absorbance (OD254) of water normalized for DNOM concentration. We have used a long-term dataset spanning from 2007 to 2022, taken from the Malše River in South Bohemia, to model DNOM and the sUVa index. We have applied regression models with a process-oriented perspective and have also considered the influence of climate change. Both DNOM and the sUVa index is positively related to temperature, runoff and pH, and negatively related to ionic strength over the studied period. Two distinct model approaches were employed, both explaining about 40% of the variation in sUVa over the studied period. Based on a moderate IPCC monthly climate scenario, simulations indicate that both DNOM and the sUVa index averages remain fairly stable, with a slight increase in winter season minima projected towards the year 2099. A slight decline in summer season maxima is simulated for DNOM, while the sUVa summer maximum remain stable. These findings suggest a robust resilience in both DNOM and the sUVa index against anticipated changes in temperature and runoff for the Malše River in South Bohemia.
Authors
Jaime Candelas Bielza Lennart Noordermeer Erik Næsset Terje Gobakken Johannes Breidenbach Hans Ole ØrkaAbstract
Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m2 in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r2) of 0.74, 0.79 and 0.51 for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and deciduous species, respectively. The dominant species was predicted with median values of overall accuracy, quantity disagreement and allocation disagreement of 0.90, 0.07 and 0.00, respectively. Predicted species-specific volumes yielded average values of RMSD of 63, 48 and 23 m3/ha (relative RMSD of 39%, 94% and 158%) and r2 of 0.84, 0.60 and 0.53 for spruce, pine and deciduous species, respectively. In one of the districts with independent validation plots of mean size 3700 m2, predictions of the dominant species were compared to results obtained through manual photo-interpretation. The model predictions gave greater accuracy than manual photo-interpretation. This study highlights the utility of RS data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery.