Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Forfattere
Tore Krogstad Mekjell MelandSammendrag
Det er ikke registrert sammendrag
Forfattere
Ola FlatenSammendrag
Det er ikke registrert sammendrag
Forfattere
Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Forfattere
Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Anita SønstebySammendrag
Det er ikke registrert sammendrag
Forfattere
Kjersti Holt HanssenSammendrag
Det er ikke registrert sammendrag
Forfattere
Ellen Johanne Svalheim John Ingar Øverland Elin Blütecher Lars T. Havstad Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Forfattere
Atle Mysterud Erling Johan Solberg Erling Meisingset Manuela Panzacchi Geir Rune Rauset Olav Strand Bram Van Moorter Christer Moe Rolandsen Inger Maren RivrudSammendrag
The management of infectious wildlife diseases often involves tackling pathogens that infect multiple host species. Chronic wasting disease (CWD) is aprion disease that can infect most cervid species. CWD was detected in reindeer (Rangifer tarandus) in Norway in 2016. Sympatric populations of red deer(Cervus elaphus) and moose (Alces alces) are at immediate risk. However, the estimation of spillover risk across species and implementation of multispecies management policies are rarely addressed for wildlife. Here, we estimated the broad risk of CWD spillover from reindeer to red deer and moose by quantifying the probability of co-occurrence based on both (1) population density and(2) habitat niche overlap from GPS data of all three species in Nordfjella,Norway. We describe the practical challenges faced when aiming to reduce the risk of spillover through a marked reduction in the population densities of moose and red deer using recreational hunters. This involves setting the popu-lation and harvest aims with uncertain information and how to achieve them.The niche overlap between reindeer and both moose and red deer was low overall but occurred seasonally. Migratory red deer had a moderate niche over-lap with the CWD-infected reindeer population during the calving period, whereas moose had a moderate niche overlap during both calving and winter. Incorporating both habitat overlap and the population densities of the respective species into the quantification of co-occurrence allowed for more spatially targeted risk maps. An initial aim of a 50% reduction in abundance for the Nordfjella region was set, but only a moderate population decrease of less than 20% from 2016 to 2021 was achieved. Proactive management in the form of marked population reduction is invasive and unpopular when involving species of high societal value, and targeting efforts to zones with a high risk ofspillover to limit adverse impacts and achieve wider societal acceptance is important. disease management, host range, moose, multihost pathogens, niche overlap, Norway,population estimation, red deer, reindeer
Forfattere
Sofia Junttila Jonas Ardö Zhanzhang Cai Hongxiao Jin Natascha Kljun Leif Klemedtsson Alisa Krasnova Holger Lange Anders Lindroth Meelis Mölder Steffen M. Noe Torbern Tagesson Patrik Vestin Per Weslien Lars EklundhSammendrag
Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of Northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD1732 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE =9-11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.