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.
2018
Authors
Steffen AdlerAbstract
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Authors
Hermine Mitter Martin Schönhart Franz Sinabell Anja Techen Katharina Helming Benjamin Leon Bodirsky Ian Holman Kasper Kok Heikki Lehtonen Adrian Leip Hermann Lotze- Kampen Erik Mathijs Bano Mehdi Melania Michetti Klaus Mittenzwei Lillian Øygarden Jörg A. Priess Pytrik Reidsma Rudiger Schaldach Erwin Schmid Heidi A. WebberAbstract
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Authors
Monika Suškevičs Matthias Buchecker Sebastian Eiter Dina Stober Igor Kuvač Berthe Jongejan Stanislav Martinat Cheryl de BoerAbstract
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Authors
Kaishan Song Zhidan Wen Yingxing Shang Hong Yang Lili Lyu Ge Liu Chong Fang Jia Du Ying ZhaoAbstract
As a major fraction of carbon in inland waters, dissolved organic carbon (DOC) plays a crucial role incarbon cycling on a global scale. However, the quantity of DOC stored in lakes and reservoirs was notclear to date. In an attempt to examine the factors that determine the DOC storage in lakes and reservoirsacross China, we assembled a large database (measured 367 lakes, and meta-analyzed 102 lakes from fivelimnetic regions; measured 144 reservoirs, and meta-analyzed 272 reservoirs from 31 provincial units) ofDOC concentrations and water storages for lakes and reservoirs that are used to determine DOC storagein static inland waters. We found that DOC concentrations in saline waters (Mean/median ± S.D: 50.5/30.0 ± 55.97 mg/L) are much higher than those in fresh waters (8.1/5.9 ± 6.8 mg/L), while lake DOCconcentrations (25.9/11.5 ± 42.04 mg/L) are much higher than those in reservoirs (5.0/3.8 ± 4.5 mg/L). Interms of lake water volume and DOC storage, the Tibet-Qinghai lake region has the largest water volume(552.8 km3), 92% of which is saline waters, thus the largest DOC (13.39 Tg) is stored in these alpine lakeregion; followed by the Mengxin lake region, having a water volume of 99.4 km3in which 1.75 Tg DOCwas stored. Compared to Mengxin lake region, almost the same amount of water was stored in East Chinalake region (91.9 km3), however, much less DOC was stored in this region (0.43 Tg) due to the lower DOCconcentration (Ave: 3.45 ± 2.68 mg/L). According to our investigation, Yungui and Northeast lake regionshad water storages of 32.14 km3and 19.44 km3respectively, but relatively less DOC was stored in Yungui(0.13 Tg) than in Northeast lake region (0.19 Tg). Due to low DOC concentration in reservoirs, especiallythese large reservoirs having lower DOC concentration (V > 1.0 km3: 2.31 ± 1.48 mg/L), only 1.54 Tg wasstored in a 485.1 km3volume of water contained in reservoirs across the entire country.
Abstract
The forest understory is often associated with rapid rates of carbon and nutrient cycling, but cost-efficient quantification of its biomass remains challenging. We tested a new field technique for understory biomass assessment using an off-the-shelf handheld laser rangefinder. We conducted laser sampling in a pine forest with an understory dominated by invasive woody shrubs, especially Rhamnus frangula L. Laser sampling was conducted using a rangefinder, mounted on a monopod to provide a consistent reference height, and pointed vertically downward. Subsequently, the understory biomass was measured with destructive sampling. A series of metrics derived from the airborne LiDAR literature were evaluated alone and in combination for prediction of understory biomass using best-subsets regression. Resulting fits were good (r2 = 0.85 and 0.84 for the best single metric and best additive metric, respectively, and R2 = 0.93 for the best multivariate model). The results indicate that laser sampling could substantially reduce the need for costly destructive sampling within a double-sampling context.
Authors
Anita SønstebyAbstract
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Authors
Anita SønstebyAbstract
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Authors
Gregor Kalinkat, Tea Ammunet Madeleine Barton Andrea Battisti Sanford D. Eigenbrode Jane Uhd Jepsen Philipp Lehmann Seppo Neuvonen Pekka Niemelä Bjørn Økland John s. Terblanche Christer BjörkmanAbstract
Recently, Deutsch and colleagues(1) projected future increases in yield losses to insect pests of the three globally most important staple crops under different climate change scenarios. Their results are based on model simulations parameterized with thermal sensitivity analyses of population growth and metabolic rates from a geographically and functionally diverse set of insect species taken from an earlier study(2). A subset of the original data compilation was then used to estimate the direct impact of warming on insect fitness across latitudes(3). More precisely, the derived thermal-dependence of fitness for globally distributed terrestrial insect species was integrated with projected geographic distribution of climate change for the next century (3). These models were then deployed in the new study investigating potential yield losses in three specific crops(1). We submit that Deutsch and colleagues rely on inadequate empirical data for their model parameterization to estimate pest-related crop losses. Strikingly, their source data did not include major pest taxa of the focal staple crops and were not restricted to herbivores despite that temperature-dependence of metabolic and performance responses are known to vary substantially among trophic levels and functional groups(4,5). Hence, the inferences drawn by Deutsch and colleagues(1) may be unreliable. Mitigating potential climate change responses of pest organisms jeopardizing future food security can only be successful if they are based on high-quality information relevant to the crop system in question(6). References 1. C. A. Deutsch et al., Science. 361, 916–919 (2018). 2. M. R. Frazier, R. B. Huey, D. Berrigan, Am. Nat. 168, 512–520 (2006). 3. C. A. Deutsch et al., Proc. Natl. Acad. Sci. 105, 6668–6672 (2008). 4. A. I. Dell, S. Pawar, V. M. Savage, Proc. Natl. Acad. Sci. 108, 10591–10596 (2011). 5. A. I. Dell, S. Pawar, V. M. Savage, J. Anim. Ecol. 83, 70–84 (2014). 6. P. Lehmann et al., bioRxiv (2018), doi:10.1101/425488.