scholarly journals Effects of Temperature and Precipitation on Breeding Migrations of Amphibian Species in Southeastern Norway

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Børre K. Dervo ◽  
Kim Magnus Bærum ◽  
Jostein Skurdal ◽  
Jon Museth
Keyword(s):  
Climate Change ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Survival Rates ◽  
General Context ◽  
Amphibian Species ◽  
Temperature And Precipitation ◽  
Migration Phenology ◽  
Effects Of Temperature ◽  
Southern Norway

To reveal the effects of climate, a generalized linear mixed model was used to explore the variation in onset of spawning migration for the two newt speciesT. cristatusandL. vulgarisin southern Norway. Amphibians are highly influenced by the physical environment, such as temperature and rainfall. The first migrating newts were observed subsequently to the three first consecutive days with mean temperature close to or above 4°C. Further, migration ofL. vulgariswas facilitated at lower temperatures compared toT. cristatus, but the migration was dependent on higher precipitation levels. Northern populations ofT. cristatusandL. vulgarismay already benefit from a warmer climate due to increased recruitment and juvenile survival. However, an offset in the migration phenology due to climate change might further alter the recruitment and survival rates with either positive or negative outcome. Thus, variations in migration phenology for newts due to climate change may have implications for management and protection status in many systems. In a general context, we should increase emphasis on protecting newts and support increased populations and distribution.

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks

2019 ◽  
Vol 23 (11) ◽  
pp. 4763-4781 ◽  
Author(s):  
Juan Ossa-Moreno ◽  
Greg Keir ◽  
Neil McIntyre ◽  
Michela Cameletti ◽  
Diego Rivera
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Mean Squared Error ◽  
Performance Criterion ◽  
Probability Of Detection ◽  
Monthly Precipitation ◽  
Low Density ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Approximate Bayesian

Abstract. The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

scholarly journals Port Adaptation and Efficiency: An Empirical Study of Ghanaian Ports

2021 ◽  
Vol 8 (2) ◽  
pp. 36
Author(s):  
Ruhaimatu Abudu ◽  
Shiyuan Zheng ◽  
Emmanuel Anu Thompson
Keyword(s):  
Climate Change ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Climate Adaptation ◽  
Policy Recommendation ◽  
Substantial Cost ◽  
The Past ◽  
Port Efficiency ◽  
Major Factors ◽  
One Way Anova

The port being an economical leg of countries development and thereby affected by climate change creates a substantial cost to the various stakeholders since is described as a “business” hub. It is, therefore, essential that ports across the globe attribute much importance to climate adaptation and its relation to port efficiency, especially for the coming years. The need to establish the effect these variables have on each other has become paramount. For this study, an empirical analysis of Ghanaian ports is being reviewed for the past and future annual efficiencies and adaptation scores. The annual efficiencies of both ports are calculated using various variables as inputs and outputs with the DEA Frontier software to calculate the DEA-CCR of the study ports. To assess the adaptation scores of each port, questionnaires and interviews were conducted based on four (4) major factors that affect port adaptation. This research also employs the linear mixed model and one-way ANOVA to assess the means of the data groups obtained for 2009-2020 & 2021-2040 respectively. This research aimed to analyse the significant relationship between port adaptation and efficiency for past and future years while highlighting the adaptative strategies of Ghanaian ports. The concluding chapters of this research represent the data analysis, policy recommendation for the stakeholders of Ghanaian ports, and also recommendations that are deemed useful for further research.

scholarly journals Climate warming weakens local adaptation

2020 ◽  
Author(s):  
M. Bontrager ◽  
C. D. Muir ◽  
C. Mahony ◽  
D. E. Gamble ◽  
R. M. Germain ◽  
...  
Keyword(s):  
Climate Change ◽  
Local Adaptation ◽  
Average Performance ◽  
Temperature And Precipitation ◽  
Quantitative Synthesis ◽  
Effects Of Temperature ◽  
Scientific Challenge ◽  
Field Transplant ◽  
Transplant Experiments ◽  
Population Performance

AbstractAnthropogenic climate change is generating mismatches between the environmental conditions that populations historically experienced and those in which they reside. Understanding how climate change affects population performance is a critical scientific challenge. We combine a quantitative synthesis of field transplant experiments with a novel statistical approach based in evolutionary theory to quantify the effects of temperature and precipitation variability on population performance. We find that species’ average performance is affected by both temperature and precipitation, but populations show signs of local adaptation to temperature only. Contemporary responses to temperature are strongly shaped by the local climates under which populations evolved, resulting in performance declines when temperatures deviate from historic conditions. Adaptation to other local environmental factors is strong, but temperature deviations as small as 2°C erode the advantage that these non-climatic adaptations historically gave populations in their home sites.One sentence summaryClimate change is pulling the thermal rug out from under populations, reducing average performance and eroding their historical home-site advantage.

scholarly journals Polar Auxin Transport: Understanding the Effects of Temperature and Precipitation on Polar Auxin Transport in Polytrichum Ohioense Could Develop a Moss into a Climate Change Bioindicator

2014 ◽  
Vol 07 (2) ◽  
Keyword(s):  
Climate Change ◽  
Auxin Transport ◽  
Fold Increase ◽  
Polar Auxin Transport ◽  
Wild Plants ◽  
Winter Condition ◽  
Growing Seasons ◽  
Temperature And Precipitation ◽  
Warm Summer ◽  
Effects Of Temperature

In an attempt to better understand polar auxin transport in the sporophytes of Polytrichum ohioense, a 5.3 fold increase in basipetal and acropetal transport was observed when temperature and precipitation varied significantly throughout the growing seasons. Wild plants were transferred into cultures and allowed to develop in temperatures that represented a “Warm Summer, Cold Winter” condition. Spores were grown on soil and watered to represent precipitation of a “Wet Fall, Winter” year. Traditional polar auxin transport assays were done on all sporophytes to calculate the amount of auxin transported in a polar fashion. The amount of water available to the developing sporophyte appears to be critical to polar auxin transport physiology; the temperature has less of an effect. This study also suggests that as climate change occurs, moss will be unequally affected by the environmental factors. An additional benefit of this study suggests that polar auxin transport assays may be developed to monitor climatic change. Keywords: Polar auxin transport, Bryophytes, Polytrichum ohioense, climate change, monitoring

Probing the relationship between formaldehyde column concentrations and soil moisture using mixed models and attribution analysis

2020 ◽  
Author(s):  
Susanna Strada ◽  
Josep Penuelas ◽  
Marcos Fernández Martinez ◽  
Iolanda Filella ◽  
Ana Maria Yanez-Serrano ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Drought Index ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Numerical Models ◽  
Positive Trend ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

<p align="justify">In response to changes in environmental factors (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). Once released in the atmosphere, BVOCs influence levels of greenhouse gases and air pollutants (e.g., methane, ozone and aerosols), thus affecting both climate and air quality. In turn, climate change may alter BVOC emissions by modifying the driving environmental conditions and by increasing the occurrence and intensity of severe stresses that alter plant functioning. To understand and better constrain the evolution of BVOC emissions under future climates, it is important to reduce the uncertainties in global and regional estimates of BVOC emissions under present climate. Part of the uncertainty in the estimates of BVOC emissions is related to the impact that water stress might have on BVOC emissions. Field campaign, in-situ and laboratory experiments investigated the effect of different regimes of water stress (short- vs. long-term) on BVOC emissions. However, these studies provide geographically scattered and uneven results. To explore the relationship between BVOC emissions and water stress globally, we use remotely sensed soil moisture and formaldehyde, a proxy of BVOC emissions. As BVOCs include a multitude of gas tracers with lifetime ranging from few hours to days, a fully characterisation of these components is virtually impossible. Nevertheless, in the continental boundary layer, formaldehyde is an intermediate by-product of the oxidation of BVOCs, it thus provides a proxy for probing local BVOC emissions, and in particular isoprene, which accounts for about 50% of the total BVOC emissions.</p><p align="justify">In the present study, retrievals of formaldehyde from the Ozone Monitoring Instrument (OMI) are combined with observations of soil moisture, biomass, aerosols, evapotranspiration, drought index, temperature and precipitation. Firstly, we look into the linear annual trend of the selected fields. Secondly, assuming formaldehyde as the dependent variable, we apply a linear mixed model analysis that extends the application of a simple linear regression model by accounting for both fixed (i.e., explained by the independent variables) and random (i.e., due to dependence in the data) effects. The analysis of the linear trend of formaldehyde concentrations shows a positive trend over the Amazon and Central Africa and a negative trend over South Africa and Australia. Over the Amazon, formaldehyde is negatively correlated with the Standardised Precipitation-Evapotranspiration Index (SPEI), a drought index that accounts for both changes in temperature and precipitation, with positive and negative values identifying wet and dry events, respectively. The outcomes of this analysis might provide new insights in the relationship between BVOC emissions and water stress and might help in improving parameterizations that link soil moisture to BVOC emissions in numerical models.</p>

scholarly journals Climatic drivers of genotype–environment interactions in lodgepole pine based on multi-environment trial data and a factor analytic model of additive covariance

2018 ◽  
Vol 48 (7) ◽  
pp. 835-854 ◽  
Author(s):  
Nicholas K. Ukrainetz ◽  
Alvin D. Yanchuk ◽  
Shawn D. Mansfield
Keyword(s):  
Climate Change ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Lodgepole Pine ◽  
Future Climate Change ◽  
Climate Modelling ◽  
Mixed Model Analysis ◽  
Factor Analytic Model ◽  
Factor Analytic ◽  
Breeding Groups

The optimum deployment of select material from tree breeding programs is affected by the presence of genotype–environment interactions (G × E) and further complicated by future climate change. Here, we analyzed tree height data from 28 progeny test sites in a multi-environment trial (MET) dataset for two testing cycles of the lodgepole pine (Pinus contorta Douglas ex Loudon) breeding program in British Columbia to characterize one approach to investigating the climatic variables influencing G × E and the potential impacts of climate change. Linear mixed model analysis was conducted using an approximate reduced animal model with a factor analytic (FA) variance model to estimate the complex additive (co)variance structure. Test sites were grouped according to patterns of G × E, and climate modelling was employed to project historical and future deployment zones for each group. Based on these findings, it appears that breeding groups with historically wide deployment zones from northern environments will become less important as the climate warms, and therefore investment should be directed toward southern breeding groups, which will be useful across a very wide geographic range in the near to mid-term future.

scholarly journals Comparison of approaches to interpolating climate observations in steep terrains with low-density gauging networks

2018 ◽  
Author(s):  
Juan Ossa-Moreno ◽  
Greg Keir ◽  
Neil McIntyre ◽  
Michela Cameletti ◽  
Diego Rivera
Keyword(s):  
Spatial Data ◽  
Spatial Interpolation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Mean Squared Error ◽  
Performance Criterion ◽  
Probability Of Detection ◽  
Low Density ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model

Abstract. The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges, coupled with complex spatial variations in climate. Increasingly, spatial data sets (i.e. satellite and gridded products) and new computational tools are used to address this problem, by assisting with the spatial interpolation of ground observations. This paper presents a comparison of approaches of different complexity to spatially interpolate precipitation and temperature time-series in the upper Aconcagua catchment in central Chile. A Generalised Linear Mixed Model whose parameters are estimated through approximate Bayesian inference is compared with three simpler alternatives: Inverse Distance Weighting, Lapse Rates and a method based on WorldClim data. The assessment is based on a leave-one-out cross validation, with the Root Mean Squared Error being the primary performance criterion for both climate variables, while Probability of Detection and False Alarm Ratio are also used for precipitation. Results show that for spatial interpolation of the expected values of temperature and precipitation, the WorldClim approach may be recommended as being the more accurate, easy to apply and relatively more robust to tested reductions in the number of estimation gauges, particularly for temperature. The Generalised Linear Mixed Model has comparable performance when all gauges were included, but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

scholarly journals Effects of Temperature, Wetness Duration, and Leaflet Age on Infection of Strawberry Foliage by Phomopsis obscurans

Plant Disease ◽  
2003 ◽  
Vol 87 (5) ◽  
pp. 579-584 ◽  
Author(s):  
M. Nita ◽  
M. A. Ellis ◽  
L. V. Madden
Keyword(s):  
Prediction Model ◽  
Environmental Conditions ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Disease Incidence ◽  
Leaf Wetness ◽  
Controlled Environment ◽  
Mixed Model Analysis ◽  
Time Of Infection ◽  
Effects Of Temperature

Temperature, leaf wetness, and leaflet age effects on infection of strawberry foliage by Phomopsis obscurans were examined in controlled-environment experiments. A mid-season (‘Honeoye’) and early-season (‘Earliglow’) cultivar were used. Tested temperatures were 10, 15, 20, 25, 30, and 35°C, and tested wetness periods were 5, 10, 15, 20, 25, and 35 h. Leaflets were labeled based on age: 0 to 1, 2 to 6, and 7 to 14 days old. Effects of temperature, wetness duration, and leaflet age on the logit of disease incidence and severity were quantified using a linear mixed model analysis of variance (ANOVA). Age, wetness duration, and their interaction significantly affected these measures of disease. Disease intensity decreased dramatically with increasing leaflet age at the time of infection, indicating a decrease in susceptibility with maturation of foliage, and increased with increasing wetness duration. Temperature only affected disease incidence with ‘Honeoye’. A prediction model was developed for leaflet infection based on ANOVA results. Coefficients of determination were approximately 0.8 for both cultivars and measures of disease (incidence and severity), indicating that disease could be described accurately based on environmental conditions and leaflet age.

Dynamics of moisture content in spruce–feather moss and spruce–Sphagnum organic layers during an extreme fire season and implications for future depths of burn in Clay Belt black spruce forests

2014 ◽  
Vol 23 (4) ◽  
pp. 490 ◽  
Author(s):  
Aurélie Terrier ◽  
William J. de Groot ◽  
Martin P. Girardin ◽  
Yves Bergeron
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Black Spruce ◽  
Weather Conditions ◽  
Fire Disturbance ◽  
Fire Season ◽  
Organic Layer ◽  
Feather Moss

High moisture levels and low frequency of wildfires have contributed to the accumulation of the organic layer in open black spruce (Picea mariana)–Sphagnum dominated stands of eastern boreal North America. The anticipated increase in drought frequency with climate change could lead to moisture losses and a transfer of the stored carbon back into the atmosphere due to increased fire disturbance and decomposition. Here we studied the dynamics of soil moisture content and weather conditions in spruce–feather moss and spruce–Sphagnum dominated stands of the boreal Clay Belt of eastern Canada during particularly dry conditions. A linear mixed model was developed to predict the moisture content of the organic material according to weather, depth and site conditions. This model was then used to calculate potential depth of burn and applied to climate model projections to determine the sensitivity of depth of burn to future fire hazards. Our results suggest that depth of burn varies only slightly in response to changes in weather conditions in spruce–Sphagnum stands. The reverse holds true in spruce–feather moss stands. In conclusion, our results suggest that spruce–Sphagnum stands in the boreal Clay Belt may be resistant to an increase in the depth of burn risk under climate change.

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