Search results for "menetelmät"

showing 10 items of 1029 documents

Visible implant elastomer (VIE) success in early larval stages of a tropical amphibian species

2020

AbstractAnimals are often difficult to distinguish at an individual level, but being able to identify individuals can be crucial in ecological or behavioral studies. In response to this challenge, biologists have developed a range of marking (tattoos, brands, toe-clips) and tagging (PIT, VIA, VIE) methods to identify individuals and cohorts. Animals with complex life cycles are notoriously hard to mark because of the distortion or loss of the tag across metamorphosis. In frogs, few studies have attempted larval tagging and none have been conducted on a tropical species. Here, we present the first successful account of VIE tagging in early larval stages (Gosner stage 25) of the dyeing poison…

0106 biological sciencesAmphibiantägitsammakotRange (biology)Dendrobatesmedia_common.quotation_subjectlcsh:MedicineZoologyElastomertaggingBiologyvärjärinuolimyrkkysammakkoMethods research010603 evolutionary biology01 natural sciencesGeneral Biochemistry Genetics and Molecular Biologyeläintiedetoukat03 medical and health sciencesTaggingbiology.animalNeotropical frogMetamorphosiselastomer030304 developmental biologymedia_common0303 health sciencesLarvaEcologyLarval tagGeneral Neurosciencelcsh:Rmethods researchGeneral Medicinebiology.organism_classificationIndividual levelTadpoleVIEkenttätyömenetelmätneotropical frogDendrobates tinctoriuslarval tageläinten merkintäBiological dispersalimplantitGeneral Agricultural and Biological SciencesZoologyPeerJ
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Alder pollen in Finland ripens after a short exposure to warm days in early spring, showing biennial variation in the onset of pollen ripening

2017

Abstract We developed a temperature sum model to predict the daily pollen release of alder, based on pollen data collected with pollen traps at seven locations in Finland over the years 2000–2014. We estimated the model parameters by minimizing the sum of squared errors (SSE) of the model, with weights that put more weight on binary recognition of daily presence or absence of pollen. The model results suggest that alder pollen ripens after a couple of warm days in February, while the whole pollen release period typically takes up to 4 weeks. We tested the model residuals against air humidity, precipitation and wind speed, but adding these meteorological features did not improve the model pr…

0106 biological sciencesAtmospheric Science010504 meteorology & atmospheric sciencesta1171Atmospheric sciencesmedicine.disease_causeAlnus01 natural sciencesAlderPollenotorhinolaryngologic diseasesmedicineMonte Carlo resamplingPrecipitationsiitepöly0105 earth and related environmental sciencespollen seasonGlobal and Planetary Changefloweringbiologyta114kukintaAnomaly (natural sciences)ta1183food and beveragesHumidityForestryRipeningennusteetmodelingalderbiology.organism_classificationta4112leppäMonte Carlo -menetelmätAlder pollenClimatologyta1181Short exposureAgronomy and Crop Science010606 plant biology & botanyAgricultural and Forest Meteorology
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Calibrating Expert Assessments Using Hierarchical Gaussian Process Models

2020

Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background. We follow the fully Bayesian approach (the s…

0106 biological sciencesComputer sciencepäätöksentekoRECONCILIATIONInferencecomputer.software_genre01 natural sciencesSTOCK ASSESSMENTenvironmental management010104 statistics & probabilityJUDGMENTSELICITATIONkalakantojen hoito111 Mathematicstilastolliset mallitReliability (statistics)Applied Mathematicsgaussiset prosessitfisheries sciencebias correctionexpert elicitationPROBABILITY62P1260G15symbols62F15Statistics and ProbabilityarviointimenetelmätBayesian probabilityenvironmental management.Bayesian inferenceMachine learningHEURISTICSsymbols.namesakeasiantuntijatMANAGEMENT0101 mathematicsGaussian processGaussian processCATCH LIMITSbusiness.industrybayesilainen menetelmä010604 marine biology & hydrobiologyUnivariateExpert elicitationOPINIONSupra BayesArtificial intelligenceHeuristicsbusinessFISHERIEScomputerBayesian Analysis
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Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficienc…

2021

Global deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contr…

0106 biological sciencesCost effectivenessComputer scienceenvironmental effectsCost-efficiency analysiscostsmeriensuojeluCost of monitoringmonitorointimuutos010501 environmental scienceskäyttömarine monitoring tool01 natural sciencesympäristön tilakartoitusresearch methodsmethod performancestandardointichangestate of the environmentmerenkulkijatMonitoring methodsmappingmarinersReliability (statistics)General Environmental Scienceevaluationcost effectivenessCost efficiencyMarine monitoring toolComparabilityvesiekosysteemitGeneral MedicinetrackingPollutionkustannuksetmittausmenetelmätRisk analysis (engineering)ympäristövaikutuksetenvironmental changesCosts and Cost AnalysisecosystemsmeretEnvironmental Monitoringmethod standardizationevaluation methodsarviointimenetelmätOptimal costseasManagement Monitoring Policy and LawloppukäyttäjätArticlemethodsmenetelmättutkimusmenetelmätseurantaekologinen tila14. Life underwaterEcosystem0105 earth and related environmental sciencesEnd user010604 marine biology & hydrobiologyReproducibility of ResultskustannustehokkuusMethod standardizationecosystems (ecology)cost of monitoringTerm (time)ekosysteemit (ekologia)monitoringcost-efficiency analysiskustannus-hyötyanalyysiMethod performanceusearviointiEnvironmental Monitoring and Assessment
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Sustainable Mixed Cropping Systems for the Boreal-Nemoral Region

2020

Mixed cropping, including intercropping, is the oldest form of systemized agricultural production and involves the growing of two or more species or cultivars of the same species simultaneously in the same field. However, mixed cropping has been little by little replaced by sole crop systems, especially in developed countries. Some of the advantages of mixed cropping are, for example, resource use efficiency and yield stability, but there are also several challenges, such as weed management and competition. The boreal-nemoral region lies within the region 55° to 70° N. In this area, for example in Finland, the length of the thermal growing season varies from less than 105 to over 185 days. …

0106 biological sciencesGrowing seasonviljelymenetelmätcatch cropsForagelcsh:TX341-641Multiple croppingnitrogen managementHorticultureManagement Monitoring Policy and Law01 natural sciencespäällekkäisviljelyCropdouble croppingsekaviljelyCover crop2. Zero hungerGlobal and Planetary Changebiologykestävä maatalousvuoroviljelylcsh:TP368-456EcologyIntercropping04 agricultural and veterinary sciences15. Life on landbiology.organism_classificationlcsh:Food processing and manufactureboreaalinen vyöhykerelay croppingAgronomy13. Climate actiontypensidonta040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental sciencecover cropsMonocultureCroppingintercroppinglcsh:Nutrition. Foods and food supplyAgronomy and Crop Science010606 plant biology & botanyFood ScienceFrontiers in Sustainable Food Systems
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Precision, Applicability, and Economic Implications: A Comparison of Alternative Biodiversity Offset Indexes

2021

AbstractThe rates of ecosystem degradation and biodiversity loss are alarming and current conservation efforts are not sufficient to stop them. The need for new tools is urgent. One approach is biodiversity offsetting: a developer causing habitat degradation provides an improvement in biodiversity so that the lost ecological value is compensated for. Accurate and ecologically meaningful measurement of losses and estimation of gains are essential in reaching the no net loss goal or any other desired outcome of biodiversity offsetting. The chosen calculation method strongly influences biodiversity outcomes. We compare a multiplicative method, which is based on a habitat condition index develo…

0106 biological sciencesINDICATORSConservation of Natural Resourcesekologinen kompensaatioköyhtyminenBiodiversity offsettingOffset (computer science)arviointimenetelmätComputer scienceCONSERVATIONBiodiversityDIVERSITY010603 evolutionary biology01 natural sciencesOutcome (game theory)ArticleRICHNESSAdditive functionEconometricsEcosystem1172 Environmental sciencesRESTORATIONEstimationMotivationGlobal and Planetary ChangeEcology010604 marine biology & hydrobiologyMultiplicative functionkustannustehokkuusEcological compensationBiodiversity15. Life on landFINLANDluonnon monimuotoisuusPollutionBiodiversity calculation methodkompensointibiodiversiteettiECOLOGICAL EQUIVALENCEINSIGHTSHabitat destructionBiodiversity offsetting13. Climate actionPOLYPORESNo net losslaskentamallit511 EconomicsTrade ratioDEAD WOOD
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Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences

2020

Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental da…

0106 biological sciencesResearch designScientific communitySCIENTIFIC COMMUNITYMedio ambiente naturalsosiaalitieteetPsychological interventionGeneral Physics and AstronomySocial SciencesQH7501 natural sciencesEnvironmental impact//purl.org/becyt/ford/1 [https]010104 statistics & probability/706/648CredibilityPrevalenceSocial scienceComputingMilieux_MISCELLANEOUSGEMultidisciplinaryEcologyQarticleSampling (statistics)Biodiversitynäyttöön perustuvat käytännötsatunnaistetut vertailukokeetENVIRONMENTAL IMPACTResearch designResearch DesignScale (social sciences)[SDE]Environmental SciencesH1ScienceEnvironment010603 evolutionary biologyGeneral Biochemistry Genetics and Molecular BiologySocial sciencesBiastutkimusmenetelmätQH541/704/172/4081Humans0101 mathematics//purl.org/becyt/ford/1.6 [https]ympäristötieteetpoliittinen päätöksentekoClinical study designmetodologia/706/689General Chemistry15. Life on landEcologíaLiteraturePairwise comparisonObservational study/631/158luotettavuusBias; Biodiversity; Ecology; Environment; Humans; Literature; Prevalence; Research Design; Social SciencesNature Communications
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Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

2021

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…

0106 biological sciencesStatistics and ProbabilityFOS: Computer and information sciences62F15 (Primary) 62M30 60G55 (Secondary)MCMCGaussianBayesian inferenceMarkovin ketjutStatistics - Applications010603 evolutionary biology01 natural sciencesCox processMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakeregeneraatio (biologia)Applied mathematicsApplications (stat.AP)0101 mathematicsLaplace approximationStatistics - MethodologyGeneral Environmental ScienceParametric statisticsMathematicsspatial random effectsbayesilainen menetelmäMarkov chain Monte CarloFunction (mathematics)15. Life on landMissing dataMonte Carlo -menetelmätcompetition kernelLaplace's methodKernel (statistics)symbolstree regenerationpuustometsänhoitomatemaattiset mallitStatistics Probability and Uncertainty
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Fast pyrolysis of hot-water-extracted and soda-AQ-delignified okra (Abelmoschus esculentus) and miscanthus (miscanthus x giganteus) stalks by Py-GC/MS

2018

Abstract The thermochemical behavior of various samples of okra ( Abelmoschus esculentus ) and miscanthus ( Miscanthus x giganteus ) stalks (initial, hot-water-extracted, and those from sulfur-free delignification) were studied by pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). In all cases, major GC-amenable condensable products were measured semi-quantitatively and classified into several product groups. The formation of these product groups from different feedstock samples with varying mass portions of their structural constituents (carbohydrates and lignin) was investigated at 500 °C and 700 °C with a residence time of 5 s and 20 s. The main product groups were aliphatic comp…

0106 biological sciencesbiomassa020209 energypyrolysis-gas chromatographySyringol02 engineering and technologyhot-water extractionkuivatislausRaw materialcondensable products01 natural scienceschemistry.chemical_compoundokraerotusmenetelmät010608 biotechnology0202 electrical engineering electronic engineering information engineeringLigninPhenolOrganic chemistrybiomassa (teollisuus)ta116ta215Waste Management and Disposalta218soda-AQ delignificationbiologyRenewable Energy Sustainability and the EnvironmentligniiniForestryMiscanthusbiology.organism_classificationchemistrymiscanthusAbelmoschusGuaiacolAgronomy and Crop SciencePyrolysisBiomass and Bioenergy
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How to reach optimal estimates of confidence intervals in microscopic counting of phytoplankton?

2021

Abstract Present practices in the microscopic counting of phytoplankton to estimate the reliability of results rely on the assumption of a random distribution of taxa in sample preparations. In contrast to that and in agreement with the literature, we show that aggregated distribution is common and can lead to over-optimistic confidence intervals, if estimated according to the shortcut procedure of Lund et al. based on the number of counted cells. We found a good linear correlation between the distribution independent confidence intervals for medians and those for parametric statistics so that 95% confidence intervals can be approximated by using a correction factor of 1.4. Instead, the rec…

0106 biological sciencestilastomenetelmätSample (statistics)mikroskopiaAquatic Scienceluottamustasotdynamic counting010603 evolutionary biology01 natural sciencesStatisticsAcademicSubjects/SCI00970laskeminenconfidence intervalsERROREcology Evolution Behavior and SystematicsReliability (statistics)estimointiParametric statisticsMathematicsEcology010604 marine biology & hydrobiologyplanktonContrast (statistics)mikrolevätConfidence interval1181 Ecology evolutionary biologymicroscopyphytoplanktonOriginal ArticleLinear correlationJournal of Plankton Research
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