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This paper investigates the consequences of ignoring the clustered data structure on allometric models. Clustered data, in the form of multiple trees sampled from multiple forest stands is commonly used to develop biomass allometric models. Of 102 reviewed papers published between 2012 and 2016 that reported biomass allometric models, 84 (82%) have used a clustered sampling design. However, in as many as 80% of these, the clustered data structure was ignored, potentially violating the independence assumption in ordinary least squares methods. The consequences of ignoring clustered data structure were empirically validated using two clustered biomass datasets (of 110 and 220 trees, with the cluster size of 5 and 10 trees respectively). We showed that when Intraclass Correlation Coefficient (ICC) was higher than zero, ignoring the clustered data structure returned underestimated standard errors, affecting further the confidence interval and t-test results. The underestimation level depended on ICC (which shows the variance proportion that was caused by the forest stand) and on cluster size (the number of trees sampled from one forest stand). We also showed that using first-order autocorrelation tests, such as the traditional Durbin-Watson statistic, to detect the autocorrelation due to clustered structure could be misleading as the test may show lack of autocorrelation even though ICC is different from zero. In conclusion, when ICC is higher than zero, ignoring the clustered data structure yields over-confident biomass predictions (due to underestimated confidence interval) and/or incorrect research conclusions (due to overestimated evidence against null hypothesis in t-test). Therefore, using a modelling approach that accounts for the hierarchical structure of the data is highly recommended when any form of clustering can be identified, even if the autocorrelation is not significant. [ABSTRACT FROM AUTHOR]

Introduction: In connection to food insecurity, adaptation of new techniques or alteration of regular behavior is executed that translates to coping strategies. This paper has used data from food security and nutrition surveillance project (FSNSP), which collects information from a nationally representative sample in Bangladesh on coping behaviors associated with household food insecurity. To complement the current understanding of different coping strategies implemented by the Bangladeshi households, the objective of this paper has been set to examine the demographic and socio-economic characteristics of the food insecure households which define their propensity towards adaptation of different types of coping strategies. Methodology: FSNSP follows a repeated cross-sectional survey design. Information of 23,374 food insecure households available from February 2011 to November 2013 was selected for the analyses. Coping strategies were categorized as financial, food compromised and both. Multinomial logistic regression was employed to draw inference. Results: Majority of the households were significantly more inclined to adopt both multiple financial and food compromisation coping strategies. Post-aman season, educational status of the household head and household women, occupation of the household’s main earner, household income, food insecurity status, asset, size and possession of agricultural land were found to be independently and significantly associated with adaptation of both financial and food compromisation coping strategies relative to only financial coping strategies. The relative risk ratio of adopting food compromisation coping relative to financial coping strategies when compared to mildly food insecure households, was 4.54 times higher for households with moderate food insecurity but 0.3 times lower when the households were severely food insecure. Whereas, households were 8.04 times and 4.98 times more likely to adopt both food compromisation and financial relative to only financial coping strategies if moderately and severely food insecure respectively when compared to being mildly food insecure. Conclusion: Households suffering from moderate and severe food insecurity, are more likely to adopt both financial and food compromisation coping strategies. [ABSTRACT FROM AUTHOR]

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. [ABSTRACT FROM AUTHOR]

Plant cell walls development is an integrated process during which several components are deposited successively. In the cell walls in grass, the accessibility of structural polysaccharides is limited by the cell walls structure and composition mainly as a result of phenolic compounds. Here, we studied the patterns of cell walls establishment in the internode supporting the ear in three distinct maize genotypes. The developmental patterns observed in the internode cell walls in terms of its composition are reported with an emphasis on lignification, p-coumaroylation and feruloylation. We combined biochemical and histological approaches and revealed that internode cell walls development in maize before flowering is characterized by the rapid deposition of secondary cell walls components and robust lignification in both the pith and the rind. After flowering and until silage maturity, the slow deposition of secondary walls components occurs in the cortical region, and the deposited lignins are rich in β-O-4 bonds and are highly p-coumaroylated. We conclude the paper by proposing a revised spatiotemporal model based on that proposed by Terashima et al. (1993) for cell walls development in grass. [ABSTRACT FROM AUTHOR]

When the unmanned aerial vehicle (UAV) is used for aerial spraying, the downwash airflow generated by the UAV rotor will interact with the crop canopy and form a conical vortex shape in the crop plant. The size of the vortex will directly affect the outcome of the spraying operation. Six one-way spraying were performed by the UAV in a rice field with different but random flying altitude and velocities within the optimal operational range to form different vortex patterns. The spraying reagent was clear water, which was collected by water sensitive paper (WSP), and then the WSP was analyzed to study the droplets deposition effects in different vortex states. The results showed that the formation of the vortex significantly influenced the droplet deposition. To be specific, the droplet deposition amount in the obvious-vortex (OV) state was about 1.5 times of that in the small-scale (SV) vortex state, and 7 times of that in the non-vortex (NV) state. In the OV state, the droplets mainly deposited directly below and on both sides of the route. The deposition amount, coverage rate and droplet size increased from top to bottom of the crops with the deposition amount, coverage rate, and volume median diameter (VMD) ranging 0.204–0.470 μL/cm2, 3.31%-7.41%, and 306–367μm, respectively. In the SV state, droplets mainly deposited in the vortex area directly below the route. The deposition amount in the downwind direction was bigger than that in the upwind direction. The maximum of deposition amount, coverage rate and droplet size were found in the middle layer of the crops, the range are 0.177–0.334μL/cm2, 2.71%-5.30%, 295–370μm, respectively. In the NV state, the droplet mainly performed drifting motion, and the average droplet deposition amount in the downwind non-effective region was 29.4 times of that in the upwind non-effective region and 8.7 times of the effective vortex region directly below the route. The maximum of deposition amount, coverage rate and droplet size appeared in the upper layer of the crop, the range are 0.006–0.132μL/cm2, 0.17%-1.82%, 120–309μm, respectively, and almost no droplet deposited in the middle and lower part of the crop. The coefficient of variation (CV) of the droplet deposition amount was less than 40% in the state of obvious-vortex and small-scale vortex, and the worst penetration appeared in the non-vortex amounting to 65.97%. This work offers a basis for improving the spraying performance of UAV. [ABSTRACT FROM AUTHOR]

This paper investigates how former hunter-gatherers living along the southern North Sea coast in NW Europe adapted to long-term and short-term climatic and environmental changes at the beginning of the Holocene. It is argued that contemporaneous hunter-gatherers repeatedly changed their hunting equipment in response to changing climate and environment, not just for functional reasons but mainly driven by socio-territorial considerations. Based on a Bayesian analysis of 122 critically selected radiocarbon dates a broad chronological correlation is demonstrated between rapid changes in the design and technology of stone projectiles and short but abrupt cooling events, occurring at 10.3, 9.3 and 8.2 ka cal BP. Combined with the rapid sea level rises and increased wildfires these climatic events probably impacted the lifeways of hunter-gatherers in such a way that they increasingly faced resource stress and competition, forcing them to invest in the symbolic defense of their social territories. [ABSTRACT FROM AUTHOR]

Artificial surfaces are routinely used instead of leaves to enable a reductionist approach in phyllosphere microbiology, the study of microorganisms residing on plant leaf surfaces. Commonly used artificial surfaces include, flat surfaces, such as metal and nutrient agar, and microstructured surfaces, such as isolate leaf cuticles or reconstituted leaf waxes. However, interest in replica leaf surfaces as an artificial surface is growing, as replica surfaces provide an improved representation of the complex topography of leaf surfaces. To date, leaf surfaces have predominantly been replicated for their superhydrophobic properties. In contrast, in this paper we investigated the potential of agarose, the elastomer polydimethylsiloxane (PDMS), and gelatin as replica leaf surface materials for phyllosphere microbiology studies. Using a test pattern of pillars, we investigated the ability to replicate microstructures into the materials, as well as the degradation characteristics of the materials in environmental conditions. Pillars produced in PDMS were measured to be within 10% of the mold master and remained stable throughout the degradation experiments. In agarose and gelatin the pillars deviated by more than 10% and degraded considerably within 48 hours in environmental conditions. Furthermore, we investigated the surface energy of the materials, an important property of a leaf surface, which influences resource availability and microorganism attachment. We found that the surface energy and bacterial viability on PDMS was comparable to isolated Citrus × aurantium and Populus × canescens leaf cuticles. Hence indicating that PDMS is the most suitable material for replica leaf surfaces. In summary, our experiments highlight the importance of considering the inherent material properties when selecting a replica leaf surface for phyllosphere microbiology studies. As demonstrated, a PDMS replica leaf offers a control surface that can be used for investigating microbe-microbe and microbe-plant interactions in the phyllosphere, which will enable mitigation strategies against pathogens to be developed. [ABSTRACT FROM AUTHOR]

This paper aims to develop a novel computational technique for the detection of the transit through the anaerobic threshold. This technique uses only cardiac intervals derived from the electrocardiogram and is based on algebraic relationships between RR and QRS intervals. Electrocardiograms are measured during the load and the recovery processes. Algebraic relationships between cardiac intervals are used not only to identify the anaerobic threshold but also to characterise individual features of the person during the transit through the threshold. The ratio between carbon dioxide and oxygen in the exhaled air is used to validate the results. The algebraic relationship between cardiac intervals serves as a stand-alone indicator for both the determination of the anaerobic threshold and the characterization of the performance of the person during the load and the recovery processes. [ABSTRACT FROM AUTHOR]

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives. [ABSTRACT FROM AUTHOR]

Southern bluefin tuna (SBT) is a valuable species that has been subject to high exploitation rates since the 1950s. In 2011, the spawning stock biomass was estimated to be at a historically low level, at only 5% of pre-fished biomass. A key component for managing and rebuilding the stock is having reliable, fishery-independent estimates of juvenile abundance. This paper describes how such estimates have been constructed from aerial surveys of juvenile (age 2–4) SBT conducted annually in the Great Australian Bight from 1993–2000 and 2005–2009. During these surveys, observers flew along pre-set transect lines searching for surface schools of SBT. Data were collected on the location and biomass of SBT sightings, and on the environmental conditions present during the survey. Sea surface temperature (SST) was found to correlate with the size (biomass) of schools, and several environmental variables, SST and wind speed in particular, were found to correlate with the number of sightings (presumably by affecting the ability of observers to see surface schools as well as whether fish were present at the surface). In addition, observers changed over time and differed in their aptitude for spotting tuna. Thus, generalized linear mixed models (GLMMs) were used to standardize the sightings and biomass data to a common set of observers and environmental conditions in order to produce an annual time series of relative abundance estimates. These estimates, which form one of two key inputs to the management procedure used by the international Commission for the Conservation of Southern Bluefin Tuna to set the global catch quota, suggest juvenile abundance was highest in the first years of the survey (1993–1996), after which it declined and fluctuated around a level about four times lower. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics. [ABSTRACT FROM AUTHOR]

The objective of this work is to develop a simple methodology for high resolution crop suitability analysis under current and future climate, easily applicable and useful in Least Developed Countries. The approach addresses both regional planning in the context of climate change projections and pre-emptive short-term rural extension interventions based on same-year agricultural season forecasts, while implemented with off-the-shelf resources. The developed tools are applied operationally in a case-study developed in three regions of Guinea-Bissau and the obtained results, as well as the advantages and limitations of methods applied, are discussed. In this paper we show how a simple approach can easily generate information on climate vulnerability and how it can be operationally used in rural extension services. [ABSTRACT FROM AUTHOR]

Transmission of zoonotic cutaneous leishmaniasis (ZCL) depends on the presence, density and distribution of Leishmania major rodent reservoir and the development of these rodents is known to have a significant dependence onenvironmental and climate factors. ZCL in Tunisia is one of the most common forms of leishmaniasis. The aim of this paper was to build a regression model of ZCL cases to identify the relationship between ZCL occurrence and possible risk factors, and to develop a predicting model for ZCL's control and prevention purposes. Monthly reported ZCL cases, environmental and bioclimatic data were collected over 6 years (2009–2015). Three rural areas in the governorate of Sidi Bouzid were selected as the study area. Cross-correlation analysis was used to identify the relevant lagged effects of possible risk factors, associated with ZCL cases. Non-parametric modeling techniques known as generalized additive model (GAM) and generalized additive mixed models (GAMM) were applied in this work. These techniques have the ability to approximate the relationship between the predictors (inputs) and the response variable (output), and express the relationship mathematically. The goodness-of-fit of the constructed model was determined by Generalized cross-validation (GCV) score and residual test. There were a total of 1019 notified ZCL cases from July 2009 to June 2015. The results showed seasonal distribution of reported ZCL cases from August to January. The model highlighted that rodent density, average temperature, cumulative rainfall and average relative humidity, with different time lags, all play role in sustaining and increasing the ZCL incidence. The GAMM model could be applied to predict the occurrence of ZCL in central Tunisia and could help for the establishment of an early warning system to control and prevent ZCL in central Tunisia. [ABSTRACT FROM AUTHOR]

Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. A promising option is eco-functional intensification through organic farming, an approach based on using and enhancing internal natural resources and processes to secure and improve agricultural productivity, while minimizing negative environmental impacts. In this concept an active soil microbiota plays an important role for various soil based ecosystem services such as nutrient cycling, erosion control and pest and disease regulation. Several studies have reported a positive effect of organic farming on soil health and quality including microbial community traits. However, so far no systematic quantification of whether organic farming systems comprise larger and more active soil microbial communities compared to conventional farming systems was performed on a global scale. Therefore, we conducted a meta-analysis on current literature to quantify possible differences in key indicators for soil microbial abundance and activity in organic and conventional cropping systems. All together we integrated data from 56 mainly peer-reviewed papers into our analysis, including 149 pairwise comparisons originating from different climatic zones and experimental duration ranging from 3 to more than 100 years. Overall, we found that organic systems had 32% to 84% greater microbial biomass carbon, microbial biomass nitrogen, total phospholipid fatty-acids, and dehydrogenase, urease and protease activities than conventional systems. Exclusively the metabolic quotient as an indicator for stresses on microbial communities remained unaffected by the farming systems. Categorical subgroup analysis revealed that crop rotation, the inclusion of legumes in the crop rotation and organic inputs are important farming practices affecting soil microbial community size and activity. Furthermore, we show that differences in microbial size and activity between organic and conventional farming systems vary as a function of land use (arable, orchards, and grassland), plant life cycle (annual and perennial) and climatic zone. In summary, this study shows that overall organic farming enhances total microbial abundance and activity in agricultural soils on a global scale. [ABSTRACT FROM AUTHOR]

The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), is a serious invasive species that significantly damages plants of approximately 60 families around the world. It is originally from North America and has also been introduced to other continents. Our goals were to create a current and future potential global distribution map for this pest under climate change with MaxEnt software. We tested the hypothesis of niche conservatism for P. solenopsis by comparing its native niche in North America to its invasive niches on other continents using Principal components analyses (PCA) in R. The potentially suitable habitat for P. solenopsis in its native and non-native ranges is presented in the present paper. The results suggested that the mean temperature of the wettest quarter and the mean temperature of the driest quarter are the most important environmental variables determining the potential distribution of P. solenopsis. We found strong evidence for niche shifts in the realized climatic niche of this pest in South America and Australia due to niche unfilling; however, a niche shift in the realized climatic niche of this pest in Eurasian owing to niche expansion. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). [ABSTRACT FROM AUTHOR]

This paper introduces a new approach–the Principal Component Gradient Analysis (PCGA)–to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA. [ABSTRACT FROM AUTHOR]

The warming trend in the Arctic region is expected to cause drastic changes including permafrost degradation and vegetation shifts. We investigated the spatial distribution of ice content and stable isotopic compositions of water in near-surface permafrost down to a depth of 1 m in the Indigirka River lowlands of northeastern Siberia to examine how the permafrost conditions control vegetation and microtopography in the Taiga–Tundra boundary ecosystem. The gravimetric water content (GWC) in the frozen soil layer was significantly higher at microtopographically high elevations with growing larch trees (i.e., tree mounds) than at low elevations with wetland vegetation (i.e., wet areas). The observed ground ice (ice-rich layer) with a high GWC in the tree mounds suggests that the relatively elevated microtopography of the land surface, which was formed by frost heave, strongly affects the survival of larch trees. The isotopic composition of the ground ice indicated that equilibrium isotopic fractionation occurred during ice segregation at the tree mounds, which implies that the ice formed with sufficient time for the migration of unfrozen soil water to the freezing front. In contrast, the isotopic data for the wet areas indicated that rapid freezing occurred under relatively non-equilibrium conditions, implying that there was insufficient time for ice segregation to occur. The freezing rate of the tree mounds was slower than that of the wet areas due to the difference of such as soil moisture and snow cover depends on vegetation and microtopography. These results indicate that future changes in snow cover, soil moisture, and organic layer, which control underground thermal conductivity, will have significant impacts on the freezing environment of the ground ice at the Taiga–Tundra boundary in northeastern Siberia. Such changes in the freezing environment will then affect vegetation due to changes in the microtopography of the ground surface. [ABSTRACT FROM AUTHOR]

Black leaf streak disease, or black Sigatoka, is caused by the fungus Pseudocercospora fijiensis, and has been identified as a major constraint to global production of banana and plantain. We fitted a climatic niche model (CLIMEX) for P. fijiensis to gain an understanding of the patterns of climate suitability, and hence hazard from this disease. We then calibrated the climate suitability patterns against the results of an expert elicitation of disease pressure patterns. We found a moderately strong non-linear relationship between modelled climate suitability for P.°fijiensis and the expert ratings for disease pressure. The strength of the relationship provides a cross-validation between the CLIMEX model and the expert elicitation process. The bulk of global banana production experiences high potential threat from P. fijiensis, and the higher yielding areas for banana and plantain production are at greatest threat. By explicitly considering the role of irrigation we have been able to identify how strategic irrigation could be used to support banana production in areas that are at low risk from P. fijiensis. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the management and preservation of species and the ecosystem, a new approach in freshwater fish conservation and the life trajectory of a jaguar.

Four chronologies of the bivalve species Glycymeris pilosa have been constructed along a 300 km gradient of the eastern coastal Adriatic Sea, all of which span the common period of 1982–2015. The chronologies are compared to local and remote environmental drivers suspected to influence the biology of the system, including air and seawater temperature, precipitation and freshwater discharge. The Adriatic-Ionian Bimodal Oscillating System (BiOS), a key oceanographic feature quantified by satellite-derived absolute dynamic topography, is also compared to the chronologies. The chronologies at the two southern sites are more strongly influenced by local river discharge, while the two northern chronologies are more strongly influenced by BiOS. These results highlight the broadscale importance of BiOS to the Adriatic system as well as the heterogeneity of nearshore environmental and drivers of growth. These G. pilosa chronologies provide unique multidecadal, continuous, biological time series to better understand the ecology and fine-scale variability of the Adriatic with potential for other shallow, semi-enclosed seas. [ABSTRACT FROM AUTHOR]

Climate-related studies have generally focussed upon physiologically well-defined ‘mechanistic’ traits rather than ‘functional’ ones relating indirectly to resource capture. Nevertheless, field responses to climate are likely to typically include both ‘mechanistic’ specialization to climatic extremes and ‘functional’ strategies that optimize resource acquisition during less climatically-severe periods. Here, this hypothesis was tested. Seventeen traits (six ‘functional’, six ‘mechanistic’ and five ‘intermediate’) were measured from 19 populations of oleaster (wild olive) along a climatic gradient in Morocco. Principal components analysis of the trait dataset identified size and the ‘worldwide leaf economics spectrum’ as PCA axes 1 and 2. However, contrary to our prediction, these axes, and commonly-measured ‘functional’ traits, were little correlated with climate. Instead, PCA 3, perhaps relating to water-use and succulence, together stomatal density, specific leaf water content and leaf shape, patterned with altitude, aridity, rainfall and temperature. We concluded that, at least for slow-growing species, such as oleaster, ‘mechanistic’ traits are key to identifying mechanisms of climatic restriction. Meaningful collaboration between ‘mechanistic’ and ‘functional’ disciplines provides the best way of improving our understanding of the global impacts of climate change on species distribution and performance. [ABSTRACT FROM AUTHOR]

Extreme weather events affect the development and survival of disease pathogens and vectors. Our aim was to investigate the potential effects of heat waves on the population dynamics of Asian tiger mosquito (Aedes albopictus), which is a major vector of dengue and Zika viruses. We modeled the population abundance of blood-fed mosquito adults based on a mechanistic population model of Ae. albopictus with the consideration of diapause. Using simulated heat wave events derived from a 35-year historical dataset, we assessed how the mosquito population responded to different heat wave characteristics, including the onset day, duration, and the average temperature. Two important observations are made: (1) a heat wave event facilitates the population growth in the early development phase but tends to have an overall inhibitive effect; and (2) two primary factors affecting the development are the unusual onset time of a heat wave and a relatively high temperature over an extended period. We also performed a sensitivity analysis using different heat wave definitions, justifying the robustness of the findings. The study suggests that particular attention should be paid to future heat wave events with an abnormal onset time or a lasting high temperature in order to develop effective strategies to prevent and control Ae. albopictus-borne diseases. [ABSTRACT FROM AUTHOR]

Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed–as part of dairy cows’ compound feed–to dairy cows in the Netherlands. Three different climate models, one aflatoxin B1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean aflatoxin M1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. Results from all combinations of carryover and climate models suggest a similar or slight increase (up to 0.6%) of the chance of finding aflatoxin M1 in milk above the EC limit of 0.05 μg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B1 in maize and related aflatoxin M1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product. [ABSTRACT FROM AUTHOR]

Sister centromere fusion is a process unique to meiosis that promotes co-orientation of the sister kinetochores, ensuring they attach to microtubules from the same pole during metaphase I. We have found that the kinetochore protein SPC105R/KNL1 and Protein Phosphatase 1 (PP1-87B) regulate sister centromere fusion in Drosophila oocytes. The analysis of these two proteins, however, has shown that two independent mechanisms maintain sister centromere fusion. Maintenance of sister centromere fusion by SPC105R depends on Separase, suggesting cohesin proteins must be maintained at the core centromeres. In contrast, maintenance of sister centromere fusion by PP1-87B does not depend on either Separase or Wapl. Instead, PP1-87B maintains sister centromeres fusion by regulating microtubule dynamics. We demonstrate that this regulation is through antagonizing Polo kinase and BubR1, two proteins known to promote stability of kinetochore-microtubule (KT-MT) attachments, suggesting that PP1-87B maintains sister centromere fusion by inhibiting stable KT-MT attachments. Surprisingly, C(3)G, the transverse element of the synaptonemal complex (SC), is also required for centromere separation in Pp1-87B RNAi oocytes. This is evidence for a functional role of centromeric SC in the meiotic divisions, that might involve regulating microtubule dynamics. Together, we propose two mechanisms maintain co-orientation in Drosophila oocytes: one involves SPC105R to protect cohesins at sister centromeres and another involves PP1-87B to regulate spindle forces at end-on attachments. [ABSTRACT FROM AUTHOR]

A simultaneous treatment of lignocellulosic biomass (LCB) and low density oxodegradable polyethylene (LDPEoxo) was carried-out using Pleurotus ostreatus at microcosm scale to obtain biotransformed plastic and oxidized lignocellulosic biomass. This product was used as raw matter (RM) to produce biochar enriched with phosphate solubilizing bacteria (PSB). Biochar potential as biofertilizer was evaluated in Allium cepa culture at greenhouse scale. Experiments including lignocellulosic mix and LDPEoxo were performed for 75 days in microcosm. Biotransformation progress was performed by monitoring total organic carbon (TOC), CO2 production, laccase (Lac), manganese peroxidase (MnP), and lignin peroxidase (LiP) enzymatic activities. Physical LDPEoxo changes were assessed by atomic force microscopy (AFM), scanning electron microscopy (SEM) and static contact angle (SCA) and chemical changes by Fourier transform infrared spectroscopy (FTIR). Results revealed P. ostreatus was capable of LCB and LDPEoxo biotransformation, obtaining 41% total organic carbon (TOC) removal with CO2 production of 2,323 mg Kg-1 and enzyme activities of 169,438 UKg-1, 5,535 UKg-1 and 5,267 UKg-1 for LiP, MnP and Lac, respectively. Regarding LDPEoxo, SCA was decreased by 84%, with an increase in signals at 1,076 cm-1 and 3,271 cm-1, corresponding to C-O and CO-H bonds. A decrease in signals was observed related to material degradation at 2,928 cm-1, 2,848 cm-1, agreeing with CH2 asymmetrical and symmetrical stretching, respectively. PSB enriched biochar favored A. cepa plant growth during the five-week evaluation period. To the best of our knowledge, this is the first report of an in vitro circular production model, where P. ostreatus was employed at a microcosmos level to bioconvert LCB and LDPEoxo residues from the agroindustrial sector, followed by thermoconversion to produce an enriched biochar with PSB to be used as a biofertilizer to grow A. cepa at greenhouse scale. [ABSTRACT FROM AUTHOR]

Recently available downscaled ocean climate models for the Bering Sea and Aleutian Arc offer the opportunity to assess climate vulnerability for upper trophic level consumers such as marine birds. We analyzed seasonal and annual spatial projections from three climate models for two physical climate variables (seawater temperature and sea ice) and three forage variables (large copepods, euphausiids, and benthic infauna), comparing projected conditions from a recent time period (2003–2012) to a future time period (2030–2039). We focused the analyses on core areas within globally significant Important Bird Areas, and developed indices of the magnitude of projected change and vulnerability agreement among models. All three climate models indicated a high degree of change for seawater temperature warming (highest in the central and eastern Aleutian Islands) and ice loss (most significant in the eastern Bering Sea) across scales, and we found those changes to be significant for every species and virtually every core area assessed. There was low model agreement for the forage variables; while the majority of core areas were identified as climate vulnerable by one or more models (72% for large copepods, 73% for euphausiids, and 94% for benthic infauna), very few were agreed upon by all three models (only 6% of euphausiid-forager core areas). Based on the magnitude-agreement score, euphausiid biomass decline affected core areas for fulmars, gulls, and auklets, especially along the outer shelf and Aleutian Islands. Benthic biomass decline affected eiders along the inner shelf, and large copepod decline was significant for storm-petrels and auklets in the western Aleutians. Overall, 12% of core areas indicated climate vulnerability for all variables assessed. Modeling and interpreting biological parameters to project future dynamics remains complex; the strong signal for projected physical changes raised concerns about lagged responses such as distribution shifts, breeding failures, mortality events, and population declines. [ABSTRACT FROM AUTHOR]

As climate change continues to exert increasing pressure upon the livelihoods and agricultural sector of many developing and developed nations, a need exists to understand and prioritise at the sub national scale which areas and communities are most vulnerable. The purpose of this study is to develop a robust, rigorous and replicable methodology that is flexible to data limitations and spatially prioritizes the vulnerability of agriculture and rural livelihoods to climate change. We have applied the methodology in Vietnam, Uganda and Nicaragua, three contrasting developing countries that are particularly threatened by climate change. We conceptualize vulnerability to climate change following the widely adopted combination of sensitivity, exposure and adaptive capacity. We used Ecocrop and Maxent ecological models under a high emission climate scenario to assess the sensitivity of the main food security and cash crops to climate change. Using a participatory approach, we identified exposure to natural hazards and the main indicators of adaptive capacity, which were modelled and analysed using geographic information systems. We finally combined the components of vulnerability using equal-weighting to produce a crop specific vulnerability index and a final accumulative score. We have mapped the hotspots of climate change vulnerability and identified the underlying driving indicators. For example, in Vietnam we found the Mekong delta to be one of the vulnerable regions due to a decline in the climatic suitability of rice and maize, combined with high exposure to flooding, sea level rise and drought. However, the region is marked by a relatively high adaptive capacity due to developed infrastructure and comparatively high levels of education. The approach and information derived from the study informs public climate change policies and actions, as vulnerability assessments are the bases of any National Adaptation Plans (NAP), National Determined Contributions (NDC) and for accessing climate finance. [ABSTRACT FROM AUTHOR]

Reliable population estimates are fundamental to the conservation of endangered species. We evaluate here the use of photo-identification (photo-ID) and mark-recapture techniques for estimating the population size of the endangered Saimaa ringed seal (Phoca hispida saimensis). Photo-ID data based on the unique pelage patterns of individuals were collected by means of camera traps and boat-based surveys during the molting season in two of the species' main breeding areas, over a period of five years in the Pihlajavesi basin and eight years in the Haukivesi basin. An open model approach provided minimum population estimates for these two basins. The results indicated high survival rates and site fidelity among the adult seals. More accurate estimates can be obtained in the future by increasing the surveying effort both spatially and temporally. The method presented here proved effective for evaluating population size objectively, whereas the results of the current snow lair censuses are dependent on varying winter conditions, for instance. We therefore suggest that a photo-ID-based non-invasive mark-recapture method should be used for estimating Saimaa ringed seal abundances in order to ensure reliable, transparent population monitoring under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Synchronizing chaotic oscillators has been a challenge to guarantee successful applications in secure communications. That way, three synchronization techniques are applied herein to twenty two chaotic oscillators, three of them based on piecewise-linear functions and nineteen proposed by Julien C. Sprott. These chaotic oscillators are simulated to generate chaotic time series that are used to evaluate their Lyapunov exponents and Kaplan-Yorke dimension to rank their unpredictability. The oscillators with the high positive Lyapunov exponent are implemented into a field-programmable gate array (FPGA), and afterwards they are synchronized in a master-slave topology applying three techniques: the seminal work introduced by Pecora-Carroll, Hamiltonian forms and observer approach, and open-plus-closed-loop (OPCL). These techniques are compared with respect to their synchronization error and latency that is associated to the FPGA implementation. Finally, the chaotic oscillators providing the high positive Lyapunov exponent are synchronized and applied to a communication system with chaotic masking to perform a secure image transmission. Correlation analysis is performed among the original image, the chaotic channel and the recovered image for the three synchronization schemes. The experimental results show that both Hamiltonian forms and OPCL can recover the original image and its correlation with the chaotic channel is as low as 0.00002, demonstrating the advantage of synchronizing chaotic oscillators with high positive Lyapunov exponent to guarantee high security in data transmission. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is one of the most commonly used gas euthanasia agents in mice, despite reports of aversion and nociception. Inert gases such as nitrogen (N2) may be a viable alternative to carbon dioxide. Here we compared behavioural and electrophysiological reactions to CO2 or N2 at either slow fill or rapid fill in C57Bl/6 mice undergoing gas euthanasia. We found that mice euthanised with CO2 increased locomotor activity compared to baseline, whereas mice exposed to N2 decreased locomotion. Furthermore, mice exposed to CO2 showed significantly more vertical jumps and freezing episodes than mice exposed to N2. We further found that CO2 exposure resulted in increased theta:delta of the EEG, a measure of excitation, whereas the N2 decreased theta:delta. Differences in responses were not oxygen-concentration dependent. Taken together, these results demonstrate that CO2 increases both behavioural and electrophysiological excitation as well as producing a fear response, whereas N2 reduces behavioural activity and central neurological depression and may be less aversive although still produces a fear response. Further studies are required to evaluate N2 as a suitable euthanasia agent for mice. [ABSTRACT FROM AUTHOR]

In flies, the olfactory information is carried from the first relay in the brain, the antennal lobe, to the mushroom body (MB) and the lateral horn (LH). Olfactory associations are formed in the MB. The LH was ascribed a role in innate responses based on the stereotyped connectivity with the antennal lobe, stereotyped physiological responses to odors, and MB silencing experiments. Direct evidence for the functional role of the LH is still missing. Here, we investigate the behavioral role of the LH neurons (LHNs) directly, using the CO2 response as a paradigm. Our results show the involvement of the LH in innate responses. Specifically, we demonstrate that activity in two sets of neurons is required for the full behavioral response to CO2. Tests of the behavioral response to other odors indicate the neurons are selective to CO2 response. Using calcium imaging, we observe that the two sets of neurons respond to CO2 in a different manner. Using independent manipulation and recording of the two sets of neurons, we find that the one that projects to the superior intermediate protocerebrum (SIP) also outputs to the local neurons within the LH. The design of simultaneous output at the LH and the SIP, an output of the MB, allows for coordination between innate and learned responses. [ABSTRACT FROM AUTHOR]

Thousand grain weight is one of the components determining wheat grain yield. It represents the average value of individual grain weights which depends on position within the ear and on positon within the spikelet. Our objective was to quantify the influences of individual floret anthesis date, of carpel weight at anthesis and of rate and duration of grain filling, on variation in individual final grain weight. Two bread wheat cultivars were grown in a greenhouse and their ears were sampled from anthesis through to harvest. Each ear was divided into three parts—basal, central and apical—where the two proximal grains were dissected from each of two spikelets. We analysed (i) the flowering time shift within the ear and within the spikelet; and (ii) the growth kinetics during grain filling in relation to position along the ear. For both cultivars, florets located in the central part of the ear were the first to reach anthesis followed by those in the apical part and then the basal part. Within a spikelet, the floret located nearest the rachis flowered first followed by the more distal ones. We found no significant systematic effect of flowering time-shift on final grain weight. Nevertheless, grains in the central part were heavier than the basal ones (9.75% smaller) and than the apical ones (18.25% smaller). These differences were explained mainly by differences in mean grain filling rates. Analysis of growth kinetics enabled an improved explanation of the variability of individual grain weight along the ear. [ABSTRACT FROM AUTHOR]

Managers need information about the vulnerability of historical plant communities, and their potential future conditions, to respond appropriately to landscape change driven by global climate change. We model the climate envelopes of plant communities on the Kenai Peninsula in Southcentral Alaska and forecast to 2020, 2050, and 2080. We assess 6 model outputs representing downscaled climate data from 3 global climate model outputs and 2 representative concentration pathways. We use two lines of evidence, model convergence and empirically measured rates of change, to identify the following plausible ecological trajectories for the peninsula: (1.) alpine tundra and sub-alpine shrub decrease, (2.) perennial snow and ice decrease, (3.) forests remain on the Kenai Lowlands, (4.) the contiguous white-Lutz-Sitka spruce complex declines, and (5.) mixed conifer afforestation occurs along the Gulf of Alaska coast. We suggest that converging models in the context of other lines of evidence is a viable approach to increase certainty for adaptation planning. Extremely dynamic areas with multiple outcomes (i.e., disagreement) among models represent ecological risk, but may also represent opportunities for facilitated adaptation and other managerial approaches to help tip the balance one way or another. By reducing uncertainty, this eclectic approach can be used to inform expectations about the future. [ABSTRACT FROM AUTHOR]

Protein extracts obtained from cells or tissues often require removal of interfering substances for the preparation of high-quality protein samples in proteomic analysis. A number of protein extraction methods have been applied to various biological samples. TCA/acetone precipitation and phenol extraction, a common method of protein extraction, is thought to minimize protein degradation and activity of proteases as well as reduce contaminants like salts and polyphenols. However, the TCA/acetone precipitation method relies on the complete pulverization and repeated rinsing of tissue powder to remove the interfering substances, which is laborious and time-consuming. In addition, by prolonged incubation in TCA/acetone, the precipitated proteins are more difficult to re-dissolve. We have described a modified method of TCA/acetone precipitation of plant proteins for proteomic analysis. Proteins of cells or tissues were extracted using SDS-containing buffer, precipitated with equal volume of 20% TCA/acetone, and washed with acetone. Compared to classical TCA/acetone precipitation and simple acetone precipitation, this protocol generates comparable yields, spot numbers, and proteome profiling, but takes less time (ca. 45 min), thus avoiding excess protein modification and degradation after extended-period incubation in TCA/acetone or acetone. The modified TCA/acetone precipitation method is simple, fast, and suitable for proteomic analysis of various plant tissues in proteomic analysis. [ABSTRACT FROM AUTHOR]

Background: Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction. Methods and findings: We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM’s Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach. Conclusions: We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised. [ABSTRACT FROM AUTHOR]

Freshwater fish populations are rapidly declining globally due to the impacts of rapid climate change and existing non-climatic anthropogenic stressors. In response to these drivers, freshwater fishes are responding by shifting their distribution range, altering the timing of migration and spawning and through demographic processes. By 2050, the mean daily air temperature is predicted to increase by 2 to 3 degrees C in insular Newfoundland and by 3 to 4 degrees C in Labrador. Mean daily precipitation is also projected to increase in all locations, with increased intensity projected for several regions. To mitigate negative consequences of these changes, managers require analytical approaches that describe the vulnerability of fish to climate change. To address this need, the current study adopts the National Marine Fisheries Service vulnerability assessment framework to characterize the vulnerability of freshwater fishes in Newfoundland and Labrador. Twelve vulnerability indicators were developed from an extensive literature review and applied to the assessment. Experts were solicited using an online questionnaire survey and scores for exposure, sensitivity and adaptive capacity were collated and analyzed to derive a final vulnerability score and rank for each species. The analysis showed one species to be of high—very high vulnerability, two species were highly vulnerable while four species were moderately vulnerable to climate change. The result provides insight into the factors that drive vulnerability of freshwater fishes in the region, this information is significant to decision-makers and other stakeholders engaged in managing freshwater fish resources in Newfoundland and Labrador. [ABSTRACT FROM AUTHOR]

Knowledge about the relative importance of influencing-factors on rice yield gap is crucial to rice production, especially in southwestern China where topography is extremely complicated. In the current study, the data of rice yield from a total of 76 experiments were collected in 2008 and 2009 in Chongqing, southwest China. For each location, two treatments with fertilizer and without fertilizer were carried out, each treatment was performed with three replications, and yield gap was calculated using fertilized yield minus unfertilized yield. Seventeen influencing-factors including variety, fertilization, climate, terrain, and soil properties were obtained at each location. Regression tree (RT) model were employed to investigate relative important of influencing-factors to rice yield gap variability. The result of Pearson correlation analysis suggested yield gap of rice was positively correlated with sunshine hours, phosphorous and potassium fertilizers, while negatively correlated with soil available nitrogen content. The results of RT showed that the selected influencing-factors explained about 74.1% of rice yield gap variation. Meanwhile, the result also indicated variety followed by others had more influence on rice yield gap variation. Our findings analyzed by regression model at a regional scale suggested that more precise fertilization recommendation should be formulated based on comprehensive factors (e.g., soil, climate, terrain, variety), which reasonably guided farmer and government for rice production. [ABSTRACT FROM AUTHOR]

Drosophila suzukii is a horticultural pest on a global scale which causes both yield and economic losses on a range of soft- and stone-fruit. Tackling this pest is problematic but exploiting behavioral rhythms could increase the impact of control. To do this, a better understanding of behavioral patterns is needed. Within this study we aimed to investigate rhythms in reproductive behavior of wild D. suzukii under natural conditions in the field. Environmental parameters were also recorded to decipher how they influence these rhythms. Assays were then performed on laboratory cultures, housed under artificial conditions mimicking the temperature and light cycles, to see if these patterns were reproducible and rhythmic. We were able to promote field like oviposition patterns within the laboratory using realistic temperature and light cycles regardless of variations in other factors including substrate, humidity, and lighting type. Locomotion activity was also recorded under these mimicked conditions to identify how this behavior interacts with oviposition rhythms. Both our field and laboratory assays show that oviposition behavior is likely under the control of the circadian clock and primarily occurs during the day. However, consistent with prior reports we observed that these patterns become crepuscular when day-time temperature peaks exceeded 30°C. This was also found within locomotion rhythms. With an increased understanding of how these behaviors are influenced by environmental conditions, we highlight the importance of using realistic temperature and light cycles when investigating behavioral patterns. From an increased understanding of D. suzukii behavior we increase our ability to target the pest in the field. [ABSTRACT FROM AUTHOR]

There is worldwide concern about the status of elasmobranchs, primarily as a result of overfishing and bycatch with subsequent ecosystem effects following the removal of top predators. Whilst abundant and wide-ranging, blue sharks (Prionace glauca) are the most heavily exploited shark species having suffered marked declines over the past decades, and there is a call for robust abundance estimates. In this study, we utilized depth data collected from two blue sharks using pop-up satellite archival tags, and modelled the proportion of time the sharks were swimming in the top 1-meter layer and could therefore be detected by observers conducting aerial surveys. The availability models indicated that the tagged sharks preferred surface waters whilst swimming over the continental shelf and during daytime, with a model-predicted average proportion of time spent at the surface of 0.633 (SD = 0.094) for on-shelf, and 0.136 (SD = 0.075) for off-shelf. These predicted values were then used to account for availability bias in abundance estimates for the species over a large area in the Northeast Atlantic, derived through distance sampling using aerial survey data collected in 2015 and 2016 and modelled with density surface models. Further, we compared abundance estimates corrected with model-predicted availability to uncorrected estimates and to estimates that incorporated the average time the sharks were available for detection. The mean abundance (number of individuals) corrected with modelled availability was 15,320 (CV = 0.28) in 2015 and 11,001 (CV = 0.27) in 2016. Depending on the year, these estimates were ~7 times higher compared to estimates without the bias correction, and ~3 times higher compared to the abundances corrected with average availability. When the survey area contains habitat heterogeneity that may affect surfacing patterns of animals, modelling animals’ availability provides a robust alternative to correcting for availability bias and highlights the need for caution when applying “average” correction factors. [ABSTRACT FROM AUTHOR]

In the European Union (EU) millions of laboratory mice are used and killed for experimental and other scientific purposes each year. Although controversially discussed, the use of carbon dioxide (CO2) is still permitted for killing rodents according to the Directive 2010/63/EU. Within the scope of refinement, our aim was to investigate if isoflurane and sevoflurane are an appropriate alternative killing method to CO2 in mice. Different concentrations of CO2 (filling rates of 20%, 60%, 100%; CO2 20, 60, 100), isoflurane (Iso 2%, 5%) and sevoflurane (Sevo 4.8%, 8%) were compared in two mouse strains (NMRI, C57Bl/6J) using a broad spectrum of behavioral parameters, including the approach-avoidance test, and analyzing blood for stress parameters (glucose, adrenaline, noradrenaline). We focused in our study on the period from the beginning of the gas inlet to loss of consciousness, as during this period animals are able to perceive pain and distress. Our results show that only higher concentrations of CO2 (CO2 60, 100) and isoflurane (5%) induced surgical tolerance within 300 s in both strains, with CO2 100 being the fastest acting inhalant anesthetic. The potency of halogenated ethers depended on the mouse strain, with C57Bl/6J being more susceptible than NMRI mice. Behavioral analysis revealed no specific signs of distress, e. g. stress-induced grooming, and pain, i. e. audible vocalizations, for all inhalant gases. However, adrenaline and noradrenaline plasma concentrations were increased, especially in NMRI mice exposed to CO2 in high concentrations, whereas we did not observe such increase in animals exposed to isoflurane or sevoflurane. Escape latencies in the approach-avoidance test using C57Bl/6J mice did not differ between the three inhalant gases, however, some animals became recumbent during isoflurane and sevoflurane but not during CO2 exposure. The rise in catecholamine concentrations suggests that CO2 exposure might be linked to a higher stress response compared to isoflurane and sevoflurane exposure, although we did not observe a behavioral correlate for that. Follow-up studies investigating other fast-acting stress hormones and central anxiety circuits are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Human activities are exposing organisms not only to direct threats (e.g. habitat loss) but also to indirect environmental pressures such as climate change, which involves not just directional global warming but also increasing climatic variability. Such changes will impact whole communities of organisms and the possible effects on population dynamics have raised concerns about increased extinction rates. Conservation-minded approaches to extinction risk vary from range shifts predicted by climate envelope models with no population dynamics to population viability analyses that ignore environmental variability altogether. Our modelling study shows that these extremes are modelling responses to a spectrum of environmental sensitivity that organisms may exhibit. We show how the survival curve plays a major role in how environmental variability leads to population fluctuations. While it is often supposed that low-fecundity organisms (those with high parental investment) will be the most vulnerable to climate change, it is those with high fecundity (low parental investment) that are likely to be more sensitive to such changes. We also find that abundance variations in high fecundity populations is driven primarily by fluctuations in the survival of early life stages, the more so if those environmental changes are autocorrelated in time. We show which types of conservation actions are most appropriate for a number of real populations. While the most effective conservation actions for organisms of low fecundity is to avoid killing them, for populations with high fecundity (and low parental investment), our study suggests conservation should focus more on protecting early life stages from hostile environments. [ABSTRACT FROM AUTHOR]

A coupled numerical model is developed to examine aggregative behavior in instances where the behavior not only responds to the environment, but the environment responds to the behavior such as fish schooling and penguin huddling. In the coupled model, the full Navier-Stokes equations are solved for the wind field using a finite difference method (FDM), and coupled to a smoothed particle hydrodynamics (SPH) model adapted to simulate animal behavior (penguins are individual particles in the SPH). We use the model to examine the dynamics of penguin huddling as a purely individual fitness maximizing behavior. SPH is a mesh-free Lagrangian method driven by local interactions between neighboring fluid particles and their environment allowing particles to act as free ranging ‘animals’ unconstrained by a computational grid that implicitly interact with one another (a critical element of aggregative behavior). The coupled model is recomputed simultaneously as the huddle evolves over time to update individual particle positions, redefine the properties of the developing huddle (i.e., shape and density), and adjust the wind field flowing through and around the dynamic huddle. This study shows the ability of a coupled model to predict the dynamic properties of penguin huddling, to quantify biometrics of individual particle “penguins”, and to confirm communal penguin huddling behavior as an individualistic behavior. [ABSTRACT FROM AUTHOR]