Browsing by Subject "UNCERTAINTY"

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  • Laurila-Pant, Mirka; Mäntyniemi, Samu; Östman, Örjan; Olsson, Jens; Uusitalo, Laura; Lehikoinen, Annukka (2021)
    Ecological indicator approaches typically compare the prevailing state of an ecosystem component to a reference state reflecting good environmental conditions, i.e. the desirable state. However, defining the reference state is challenging due to a wide range of uncertainties related to natural variability and measurement error in data, as well as ecological understanding. This study propose a novel probabilistic approach combining historical monitoring data and ecological understanding to estimate the uncertainty associated with the boundary value of an ecological indicator between good and poor environmental states. Bayesian inference is used to estimate the epistemic uncertainty about the true state of an indicator variable during an historical reference period. This approach replaces the traditional boundary value with probability distribution, indicating the uncertainty about the boundary between environmental states providing a transparent safety margin associated with the risk of misclassification of the indicator's state. The approach is demonstrated by applying it to a time-series of an ecological status indicator, 'Abundance of coastal key fish species', included in HELCOM's Baltic Sea regional status assessment. We suggest that acknowledgement of the uncertainty behind the final classification leads to more transparent and better-informed decision-making processes.
  • Holmgren, Noel Michael Andre; Norrstrom, Niclas; Aps, Robert; Kuikka, Sakari (2014)
  • Keronen, Petri; Reissell, Anni; Chevallier, Frederic; Siivola, Erkki; Pohja, Toivo; Hiltunen, Veijo; Hatakka, Juha; Aalto, Tuula; Rivier, Leonard; Ciais, Philippe; Jordan, Armin; Hari, Pertti; Viisanen, Yrjo; Vesala, Timo (2014)
  • Kiuru, Petri; Ojala, Anne; Mammarella, Ivan; Heiskanen, Jouni; Erkkila, Kukka-Maaria; Miettinen, Heli; Vesala, Timo; Huttula, Timo (2019)
    Freshwater lakes are important in carbon cycling, especially in the boreal zone where many lakes are supersaturated with the greenhouse gas carbon dioxide (CO2) and emit it to the atmosphere, thus ventilating carbon originally fixed by the terrestrial system. The exchange of CO2 between water and the atmosphere is commonly estimated using simple wind-based parameterizations or models of gas transfer velocity (k). More complex surface renewal models, however, have been shown to yield more correct estimates of k in comparison with direct CO2 flux measurements. We incorporated four gas exchange models with different complexity into a vertical process-based physico-biochemical lake model, MyLake C, and assessed the performance and applicability of the alternative lake model versions to simulate air-water CO2 fluxes over a small boreal lake. None of the incorporated gas exchange models significantly outperformed the other models in the simulations in comparison to the measured near-surface CO2 concentrations or respective air-water CO2 fluxes calculated directly with the gas exchange models using measurement data as input. The use of more complex gas exchange models in the simulation, on the contrary, led to difficulties in obtaining a sufficient gain of CO2 in the water column and thus resulted in lower CO2 fluxes and water column CO2 concentrations compared to the respective measurement-based values. The inclusion of sophisticated and more correct models for air-water CO2 exchange in process-based lake models is crucial in efforts to properly assess lacustrine carbon budgets through model simulations in both single lakes and on a larger scale. However, finding higher estimates for both the internal and external sources of inorganic carbon in boreal lakes is important if improved knowledge of the magnitude of CO2 evasion from lakes is included in future studies on lake carbon budgets.
  • Niemi, Tero J.; Warsta, Lassi; Taka, Maija; Hickman, Brandon; Pulkkinen, Seppo; Krebs, Gerald; Moisseev, Dmitri N.; Koivusalo, Harri; Kokkonen, Teemu (2017)
    Rainfall-runoff simulations in urban environments require meteorological input data with high temporal and spatial resolutions. The availability of precipitation data is constantly increasing due to the shift towards more open data sharing. However, the applicability of such data for urban runoff assessments is often unknown. Here, the feasibility of Finnish Meteorological Institute's open rain gauge and open weather radar data as input sources was studied by conducting Storm Water Management Model simulations at a very small (33.5 ha) urban catchment in Helsinki, Finland. In addition to the open data sources, data were also available from two research gauges, one of them located on-site, and from a research radar. The results confirmed the importance of local precipitation measurements for urban rainfall-runoff simulations, implying the suitability of open gauge data to be largely dictated by the gauge's distance from the catchment. Performance of open radar data with 5 min and 1 km' resolution was acceptable in terms of runoff reproduction, albeit peak flows were constantly and flow volumes often underestimated. Gauge adjustment and advection interpolation were found to improve the quality of the radar data, and at least gauge adjustment should be performed when open radar data are used. Finally, utilizing dual-polarization capabilities of radars has a potential to improve rainfall estimates for high intensity storms although more research is still needed. (C) 2017 Elsevier B.V. All rights reserved.
  • Eyherabide, Hugo Gabriel; Samengo, Ines (2018)
    The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activitythe encoding phaseand subsequently transforms such activity into adequate responses to the original stimulithe decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.
  • Minunno, Francesco; Peltoniemi, Mikko; Harkonen, Sanna; Kalliokoski, Tuomo; Makinen, Harri; Makela, Annikki (2019)
    Policy-relevant forest models must be environment and management sensitive and provide unbiased estimates of predicted variables over their intended areas of application. While empirical models derive their structure and parameters from representative data sets, process-based model (PBM) parameters should be evaluated in ranges that have a biological meaning independently of output data. At the same time PBMs should be calibrated against observations in order to obtain unbiased estimates and an understanding of their predictive capability. By means of model data assimilation, we Bayesian calibrated a forest model (PREBAS) using an extensive dataset that covered a wide range of climatic conditions, species composition and management practices. PREBAS was calibrated for three species in Finland: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies [L.] H. Karst.) and Silver birch (Betula pendula L.). Data assimilation was strongly effective in reducing the uncertainty of PREBAS parameters and predictions. A country-generic calibration showed robust performances in predicting forest variables and the results were consistent with yield tables and national forest statistics. The posterior predictive uncertainty of the model was mainly influenced by the uncertainty of the structural and measurement error.
  • Li, Xuefei; Wahlroos, Outi Marjatta; Haapanala, Sami; Pumpanen, Jukka; Vasander, Harri; Ojala, Anne; Vesala, Timo; Mammarella, Ivan (2020)
    Many wetlands have been drained due to urbanization, agriculture, forestry or other purposes, which has resulted in a loss of their ecosystem services. To protect receiving waters and to achieve services such as flood control and storm water quality mitigation, new wetlands are created in urbanized areas. However, our knowledge of greenhouse gas exchange in newly created wetlands in urban areas is currently limited. In this paper we present measurements carried out at a created urban wetland in Southern Finland in the boreal climate. We conducted measurements of ecosystem CO2 flux and CH4 flux (FCH4) at the created storm water wetland Gateway in Nummela, Vihti, Southern Finland, using the eddy covariance (EC) technique. The measurements were commenced the fourth year after construction and lasted for 1 full year and two subsequent growing seasons. Besides ecosystemscale fluxes measured by the EC tower, the diffusive CO2 and CH4 fluxes from the open-water areas (FwCO(2) and FwCH(4), respectively) were modelled based on measurements of CO2 and CH4 concentration in the water. Fluxes from the vegetated areas were estimated by applying a simple mixing model using the above-mentioned fluxes and the footprintweighted fractional area. The half-hourly footprint-weighted contribution of diffusive fluxes from open water ranged from 0% to 25.5% in 2013. The annual net ecosystem exchange (NEE) of the studied wetland was 8.0 g C-CO2 m(-2) yr(-1), with the 95% confidence interval between 18:9 and 34.9 g C-CO2 m(-2) yr(-1), and FCH4 was 3.9 g C-CH4 m(-2) yr(-1), with the 95% confidence interval between 3.75 and 4.07 g C-CH4 m(-2) yr(-1). The ecosystem sequestered CO2 during summer months (June-August), while the rest of the year it was a CO2 source. CH4 displayed strong seasonal dynamics, higher in summer and lower in winter, with a sporadic emission episode in the end of May 2013. Both CH4 and CO2 fluxes, especially those obtained from vegetated areas, exhibited strong diurnal cycles during summer with synchronized peaks around noon. The annual FwCO(2) was 297.5 g C-CO2 m(-2) yr(-1) and FwCH(4) was 1.73 g C-CH4 m(-2) yr(-1). The peak diffusive CH4 flux was 137.6 nmol C-CH4 m(-2) s(-1), which was synchronized with the FCH4. Overall, during the monitored time period, the established storm water wetland had a climate-warming effect with 0.263 kgCO(2)-eqm(-2) yr(-1) of which 89% was contributed by CH4. The radiative forcing of the open-water areas exceeded that of the vegetation areas (1.194 and 0.111 kgCO(2)-eqm(-2) yr(-1), respectively), which implies that, when considering solely the climate impact of a created wetland over a 100-year horizon, it would be more beneficial to design and establish wetlands with large patches of emergent vegetation and to limit the areas of open water to the minimum necessitated by other desired ecosystem services.
  • Yi, Chuixiang; Ricciuto, Daniel; Li, Runze; Wolbeck, John; Xu, Xiyan; Nilsson, Mats; Aires, Luis; Albertson, John D.; Ammann, Christof; Arain, M. Altaf; de Araujo, Alessandro C.; Aubinet, Marc; Aurela, Mika; Barcza, Zoltan; Barr, Alan; Berbigier, Paul; Beringer, Jason; Bernhofer, Christian; Black, Andrew T.; Bolstad, Paul V.; Bosveld, Fred C.; Broadmeadow, Mark S. J.; Buchmann, Nina; Burns, Sean P.; Cellier, Pierre; Chen, Jiquan; Ciais, Philippe; Clement, Robert; Cook, Bruce D.; Curtis, Peter S.; Dail, D. Bryan; Dellwik, Ebba; Delpierre, Nicolas; Desai, Ankur R.; Dore, Sabina; Dragoni, Danilo; Drake, Bert G.; Dufrene, Eric; Dunn, Allison; Elbers, Jan; Eugster, Werner; Falk, Matthias; Feigenwinter, Christian; Flanagan, Lawrence B.; Foken, Thomas; Frank, John; Fuhrer, Juerg; Gianelle, Damiano; Goldstein, Allen; Goulden, Mike; Granier, Andre; Gruenwald, Thomas; Gu, Lianhong; Guo, Haiqiang; Hammerle, Albin; Han, Shijie; Hanan, Niall P.; Haszpra, Laszlo; Heinesch, Bernard; Helfter, Carole; Hendriks, Dimmie; Hutley, Lindsay B.; Ibrom, Andreas; Jacobs, Cor; Johansson, Torbjoern; Jongen, Marjan; Katul, Gabriel; Kiely, Gerard; Klumpp, Katja; Knohl, Alexander; Kolb, Thomas; Kutsch, Werner L.; Lafleur, Peter; Laurila, Tuomas; Leuning, Ray; Lindroth, Anders; Liu, Heping; Loubet, Benjamin; Manca, Giovanni; Marek, Michal; Margolis, Hank A.; Martin, Timothy A.; Massman, William J.; Matamala, Roser; Matteucci, Giorgio; McCaughey, Harry; Merbold, Lutz; Meyers, Tilden; Migliavacca, Mirco; Miglietta, Franco; Misson, Laurent; Moelder, Meelis; Moncrieff, John; Monson, Russell K.; Montagnani, Leonardo; Montes-Helu, Mario; Moors, Eddy; Moureaux, Christine; Mukelabai, Mukufute M.; Munger, J. William; Myklebust, May; Nagy, Zoltan; Noormets, Asko; Oechel, Walter; Oren, Ram; Pallardy, Stephen G.; Kyaw, Tha Paw U.; Pereira, Joao S.; Pilegaard, Kim; Pinter, Krisztina; Pio, Casimiro; Pita, Gabriel; Powell, Thomas L.; Rambal, Serge; Randerson, James T.; von Randow, Celso; Rebmann, Corinna; Rinne, Janne; Rossi, Federica; Roulet, Nigel; Ryel, Ronald J.; Sagerfors, Jorgen; Saigusa, Nobuko; Sanz, Maria Jose; Mugnozza, Giuseppe-Scarascia; Schmid, Hans Peter; Seufert, Guenther; Siqueira, Mario; Soussana, Jean-Francois; Starr, Gregory; Sutton, Mark A.; Tenhunen, John; Tuba, Zoltan; Tuovinen, Juha-Pekka; Valentini, Riccardo; Vogel, Christoph S.; Wang, Shaoqiang; Wang, Weiguo; Welp, Lisa R.; Wen, Xuefa; Wharton, Sonia; Wilkinson, Matthew; Williams, Christopher A.; Wohlfahrt, Georg; Yamamoto, Susumu; Yu, Guirui; Zampedri, Roberto; Zhao, Bin; Zhao, Xinquan (2010)
  • Hällfors, Maria H.; Vaara, Elina M.; Hyvärinen, Marko; Oksanen, Markku; Schulman, Leif E.; Siipi, Helena; Lehvävirta, Susanna (2014)
    Intentional moving of species threatened by climate change is actively being discussed as a conservation approach. The debate, empirical studies, and policy development, however, are impeded by an inconsistent articulation of the idea. The discrepancy is demonstrated by the varying use of terms, such as assisted migration, assisted colonisation, or managed relocation, and their multiple definitions. Since this conservation approach is novel, and may for instance lead to legislative changes, it is important to aim for terminological consistency. The objective of this study is to analyse the suitability of terms and definitions used when discussing the moving of organisms as a response to climate change. An extensive literature search and review of the material (868 scientific publications) was conducted for finding hitherto used terms (N = 40) and definitions (N = 75), and these were analysed for their suitability. Based on the findings, it is argued that an appropriate term for a conservation approach relating to aiding the movement of organisms harmed by climate change is assisted migration defined as follows: Assisted migration means safeguarding biological diversity through the translocation of representatives of a species or population harmed by climate change to an area outside the indigenous range of that unit where it would be predicted to move as climate changes, were it not for anthropogenic dispersal barriers or lack of time. The differences between assisted migration and other conservation translocations are also discussed. A wide adoption of the clear and distinctive term and definition provided would allow more focused research on the topic and enable consistent implementation as practitioners could have the same understanding of the concept.
  • Oinonen, Soile; Hyytiäinen, Kari; Ahlvik, Lassi; Laamanen, Maria; Lehtoranta, Virpi; Salojarvi, Joona; Virtanen, Jarno (2016)
    This paper puts forward a framework for probabilistic and holistic cost-effectiveness analysis to provide support in selecting the least-cost set of measures to reach a multidimensional environmental objective. Following the principles of ecosystem-based management, the framework includes a flexible methodology for deriving and populating criteria for effectiveness and costs and analyzing complex ecological-economic trade-offs under uncertainty. The framework is applied in the development of the Finnish Programme of Measures (PoM) for reaching the targets of the EU Marine Strategy Framework Directive (MSFD). The numerical results demonstrate that substantial cost savings can be realized from careful consideration of the costs and multiple effects of management measures. If adopted, the proposed PoM would yield improvements in the state of the Baltic Sea, but the overall objective of the MSFD would not be reached by the target year of 2020; for various environmental and administrative reasons, it would take longer for most measures to take full effect.
  • Oinio, Ville; Bäckström, Pia; Uhari-Väänänen, Johanna; Raasmaja, Atso; Piepponen, Timo; Kiianmaa, Kalervo (2017)
    R**esults from animal gambling models have highlighted the importance of dopaminergic neurotransmission in modulating decision making when large sucrose rewards are combined with uncertainty. The majority of these models use food restriction as a tool to motivate animals to accomplish operant behavioral tasks, in which sucrose is used as a reward. As enhanced motivation to obtain sucrose due to hunger may impact its reward-seeking effect, we wanted to examine the decision-making behavior of rats in a situation where rats were fed ad libitum. For this purpose, we chose alcohol-preferring AA (alko alcohol) rats, as these rats have been shown to have high preference for sweet agents. In the present study, AA rats were trained to self-administer sucrose pellet rewards in a two-lever choice task (one pellet vs. three pellets). Once rational choice behavior had been established, the probability of gaining three pellets was decreased over time (50%, 33%, 25% then 20%). The effect of D-amphetamine on decision making was studied at every probability level, as well as the effect of the dopamine D-1 receptor agonist SKF-81297 and D-2 agonist quinpirole at probability levels of 100% and 25%. D-Amphetamine increased unprofitable choices in a dose-dependent manner at the two lowest probability levels. Quinpirole increased the frequency of unprofitable decisions at the 25% probability level, and SKF-82197 did not affect choice behavior. These results mirror the findings of probabilistic discounting studies using food-restricted rats. Based on this, the use of AA rats provides a new approach for studies on reward-guided decision making. (C) 2017 Elsevier B.V. All rights reserved.
  • Minviel, Jean Joseph; Sipilainen, Timo (2018)
    The existing literature on the subsidy-efficiency nexus is almost exclusively based on static modelling and thus ignores the inter-temporal nature of production decisions. The present paper contributes to this literature by developing a dynamic stochastic frontier model, which is then estimated using a sample of French farms over the period 1992-2011. For comparison purposes, the static counterpart of the dynamic model is also estimated. The results indicate that, in the dynamic case as well as in the static one, public subsidies are negatively associated with farm technical efficiency. Nevertheless, these linkages are found to be weak, and they are much weaker when dynamic aspects are taken into account.
  • Sabbatini, Simone; Mammarella, Ivan; Arriga, Nicola; Fratini, Gerardo; Graf, Alexander; Hoertriagl, Lukas; Ibrom, Andreas; Longdoz, Bernard; Mauder, Matthias; Merbold, Lutz; Metzger, Stefan; Montagnani, Leonardo; Pitacco, Andrea; Rebmann, Corinna; Sedlak, Pavel; Sigut, Ladislav; Vitale, Domenico; Papale, Dario (2018)
    The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in tenns of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods.
  • Heikkinen, Liine; Äijälä, Mikko; Dällenbach, Kaspar; Chen, Gang; Garmash, Olga; Aliaga, Diego; Graeffe, Frans; Räty, Meri; Luoma, Krista; Aalto, Pasi; Kulmala, Markku; Petäjä, Tuukka; Worsnop, Douglas; Ehn, Mikael (2021)
    The Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth-atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012-2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 mu gm(-3)) into three sub-categories - low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) - proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ngm 3 h 1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-yearlong time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.
  • Reside, April E.; VanDerWal, Jeremy; Moilanen, Atte; Graham, Erin M. (2017)
    With the high rate of ecosystem change already occurring and predicted to occur in the coming decades, long-term conservation has to account not only for current biodiversity but also for the biodiversity patterns anticipated for the future. The trade-offs between prioritising future biodiversity at the expense of current priorities must be understood to guide current conservation planning, but have been largely unexplored. To fill this gap, we compared the performance of four conservation planning solutions involving 662 vertebrate species in the Wet Tropics Natural Resource Management Cluster Region in north-eastern Australia. Input species data for the four planning solutions were: 1) current distributions; 2) projected distributions for 2055; 3) projected distributions for 2085; and 4) current, 2055 and 2085 projected distributions, and the connectivity between each of the three time periods for each species. The four planning solutions were remarkably similar (up to 85% overlap), suggesting that modelling for either current or future scenarios is sufficient for conversation planning for this region, with little obvious trade-off. Our analyses also revealed that overall, species with small ranges occurring across steep elevation gradients and at higher elevations were more likely to be better represented in all solutions. Given that species with these characteristics are of high conservation significance, our results provide confidence that conservation planning focused on either current, near-or distant-future biodiversity will account for these species.
  • Glaus, Peter; Honkela, Antti; Rattray, Magnus (2012)
    Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression. Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions. Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python. The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.
  • Parviainen, Tuuli; Goerlandt, Floris; Helle, Inari; Haapasaari, Päivi; Kuikka, Sakari (2021)
    The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.
  • Viskari, Toni; Laine, Maisa; Kulmala, Liisa; Mäkelä, Jarmo; Fer, Istem; Liski, Jari (2020)
    Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied whether state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurement time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades-long projections by at least 1 tCha(-1). Our results show the benefits of implementing SDA methods for forecasting SOC as well as highlight implementation aspects that need consideration and further research.