Bumblebees in a Changing Climate: Evaluating the Effects of Temperature on Queen Performance

Bumblebees are a very essential group of pollinating insects, but their populations have declined drastically during the past decades. We need to understand why their numbers are decreasing and what can be done to reverse this trend. Climate change-related phenomena, such as changes in the overwintering temperatures and spring conditions, are among the most prominent threats to bumblebees. Queens have a special role in the lifecycle of bumblebees because they overwinter and start new colonies the next year. Their successful performance: survival, overwintering ability, longevity, immune competence, and nest establishing capability in spring, is highly important for bumblebee populations. However, the effects of climate change on bumblebee queen performance remain unknown. The main objective of this thesis was to assess how temperature affects the performance of bumblebee queens during and after overwintering. The effects of warm temperature predicted by climate change scenarios on queen survival and stress-tolerance were studied by a four-month artificial diapause of bumblebee queens at two temperatures (9°C and 1.8°C). Bumblebee colonies were also reared in a laboratory and factors affecting colony characteristics were examined. In addition, queen performance during spring was studied in a starvation experiment using two temperatures (15°C as normal; 24°C as warmer than average) and queens collected from nature right after their emergence. My research revealed how temperature affects queen performance, and queen size was found to be an important factor determining the direction of some of these effects. We found a 0.4g weight threshold for bumblebee queens to be able to survive overwintering. In addition, during mild winters, larger queens have a higher chance than smaller ones to survive through winter and also to cope with immunological stresses after overwintering. During cold conditions, which are normal in the current climatic situation, this advantage disappears. In the spring starvation experiment, the starved queens survived approximately eight days longer in 15°C than in 24°C, which means that starvation risk rises significantly with increasing spring temperature, in a situation where food is scarce due to for example frost damage or asynchrony between bumblebees and their important food plants. These results could mean that in the future climate, larger queens are better able to survive the winter, initiate their nests and start rearing their offspring. This may be problematic, because I also detected two alternative strategies of colony development that differ between large and small queens; larger queens start to lay eggs earlier at nest initiation, their colonies mature later, they produce more workers, and they have a more strongly male biased sex allocation compared with smaller queens. If larger queens have a greater change of producing offspring after a mild winter, this could lead to a significant decline in the total production of new queens at a population level. Thus, it seems that queen size could act as one mechanism regulating the population level outcomes in different temperatures. The new information presented in my thesis reinforces that basic research, monitoring, and local species conservation of bumblebees both in Finland and globally must be increased to ensure that this highly important pollinator group survives in the face of climate change.

Quantification of Toxin Biosynthesis Genes In Cyanobacteria and Dinoflagellates - Genetic Factors as Predictors of Toxin Production in the Environment

Harmful algal blooms (HABs) are events caused by the massive proliferation of microscopic, often photosynthetic organisms that inhabit both fresh and marine waters. Although HABs are essentially a natural phenomenon, they now cause worldwide concern. Recent anthropogenic effects, such as climate change and eutrophication via nutrient runoff, can be seen in their increased prevalence and severity. Cyanobacteria and dinoflagellates are often the causative organisms of HABs. In addition to adverse effects caused by the sheer biomass, certain species produce highly potent toxic compounds: hepatotoxic microcystins are produced exclusively by cyanobacteria and neurotoxic saxitoxins, also known as paralytic shellfish toxins (PSTs), by both cyanobacteria and dinoflagellates. Specific biosynthetic genes in the cyanobacterial genomes direct the production of microcystin and paralytic shellfish toxins. Recently also the first paralytic shellfish toxin gene sequences from dinoflagellate genomes have been elucidated. The public health risks presented by HABs are evident, but the monitoring and prediction of toxic events is challenging. Characterization of the genetic background of toxin biosynthesis, including that of microcystins and paralytic shellfish toxins, has made it possible to develop highly sensitive molecular tools which have shown promise in the monitoring and study of potentially toxic microalgae. In this doctoral work, toxin-specific genes were targeted in the developed PCR and qPCR assays for the detection and quantification of potentially toxic cyanobacteria and dinoflagellates in the environment. The correlation between the copy numbers of the toxin biosynthesis genes and toxin production were investigated to assess whether the developed methods could be used to predict toxin concentrations. The nature of the correlation between gene copy numbers and amount of toxin produced varied depending on the targeted gene and the producing organism. The combined mcyB copy numbers of three potentially microcystin-producing cyanobacterial genera showed significant positive correlation to the observed total toxin production. However, the presence of PST-specific sxtA, sxtG, and sxtB genes of cyanobacterial origin was found to be a poor predictor of toxin production in the studied area. Conversely, the dinoflagellate sxtA4 was a good qualitative indicator of a neurotoxic bloom both in the laboratory and in the field, and population densities reflected well the observed toxin concentrations. In conclusion, although the specificity of each potential targeted toxin biosynthesis gene must be assessed individually during method development, the results obtained in this doctoral study support the use of quantitative PCR -based approaches in the monitoring of toxic cyanobacteria and dinoflagellates.

Emergence of normative beliefs legitimizing antisocial behaviour in adolescents : the roles of monitoring, attachment, and temperament /

Beliefs about the rightness or wrongness of engaging in various antisocial acts, referred to here as nonnative beliefs legitimizing antisocial behaviour (nblab), have been shown to playa role in the emergence oflater antisocial behaviour. The current study represented an attempt to understand whether parental monitoring and parent-child attachment have differential relationships with these antisocial nonnative beliefs in adolescents of different temperaments. The participants, 7135 adolescents in 25 high schools (ages 10- 18 years, M = 15.7) completed a wide-ranging questionnaire as part of the broad Youth Lifestyle Choices - Community University Research Alliance project, whose goal is to identify and describe the major developmental pathways of risk behaviours and resilience in youth. Two aspects of monitoring (monitoring knowledge and surveillance/tracking), attachment security, and two measures of temperament (activity level and approach) were examined for main effects and in interactions as predictors of adolescent nonnative beliefs. All of these measures were based on adolescent self-ratings on either 3- or 4-point Likert-type scales. Several important results emerged from the study. Males were higher than females in nblab; parental monitoring knowledge and adolescent attachment security were negatively related to nblab; and temperamental activity level was positively related. Monitoring knowledge, the strongest of the predictors, was much more strongly related to nonnative beliefs than was parental surveillance/tracking, supporting the contention that it is how much parents actually know, and not their surveillance efforts, that predict adolescent nonnative beliefs. A surprising finding that is of the utmost importance was that, although several of the interactions tested were significant, none were considered to be of a meaningful magnitude (defined as sr^ > .01). The current study supported the suggestion that normative beliefs legitimizing antisocial behaviour are multiply determined, and the results were discussed with respect to the observed differential relations of parental monitoring, parent-child attachment, temperament, age, and gender to antisocial normative beliefs in adolescents. Also discussed were the need to test other parenting, temperament, and other variables that may be involved in the development of nblab; the need to directly test possible mechanisms explaining the links among the variables; and the usefulness of longitudinal research in determining possible directions of causality and developmental changes in the relationships.

Frontal Lobe Function and Performance Monitoring following Total Sleep Deprivation

Imaging studies have shown reduced frontal lobe resources following total sleep deprivation (TSD). The anterior cingulate cortex (ACC) in the frontal region plays a role in performance monitoring and cognitive control; both error detection and response inhibition are impaired following sleep loss. Event-related potentials (ERPs) are an electrophysiological tool used to index the brain's response to stimuli and information processing. In the Flanker task, the error-related negativity (ERN) and error positivity (Pe) ERPs are elicited after erroneous button presses. In a Go/NoGo task, NoGo-N2 and NoGo-P3 ERPs are elicited during high conflict stimulus processing. Research investigating the impact of sleep loss on ERPs during performance monitoring is equivocal, possibly due to task differences, sample size differences and varying degrees of sleep loss. Based on the effects of sleep loss on frontal function and prior research, it was expected that the sleep deprivation group would have lower accuracy, slower reaction time and impaired remediation on performance monitoring tasks, along with attenuated and delayed stimulus- and response-locked ERPs. In the current study, 49 young adults (24 male) were screened to be healthy good sleepers and then randomly assigned to a sleep deprived (n = 24) or rested control (n = 25) group. Participants slept in the laboratory on a baseline night, followed by a second night of sleep or wake. Flanker and Go/NoGo tasks were administered in a battery at 1O:30am (i.e., 27 hours awake for the sleep deprivation group) to measure performance monitoring. On the Flanker task, the sleep deprivation group was significantly slower than controls (p's <.05), but groups did not differ on accuracy. No group differences were observed in post-error slowing, but a trend was observed for less remedial accuracy in the sleep deprived group compared to controls (p = .09), suggesting impairment in the ability to take remedial action following TSD. Delayed P300s were observed in the sleep deprived group on congruent and incongruent Flanker trials combined (p = .001). On the Go/NoGo task, the hit rate (i.e., Go accuracy) was significantly lower in the sleep deprived group compared to controls (p <.001), but no differences were found on false alarm rates (i.e., NoGo Accuracy). For the sleep deprived group, the Go-P3 was significantly smaller (p = .045) and there was a trend for a smaller NoGo-N2 compared to controls (p = .08). The ERN amplitude was reduced in the TSD group compared to controls in both the Flanker and Go/NoGo tasks. Error rate was significantly correlated with the amplitude of response-locked ERNs in control (r = -.55, p=.005) and sleep deprived groups (r = -.46, p = .021); error rate was also correlated with Pe amplitude in controls (r = .46, p=.022) and a trend was found in the sleep deprived participants (r = .39, p =. 052). An exploratory analysis showed significantly larger Pe mean amplitudes (p = .025) in the sleep deprived group compared to controls for participants who made more than 40+ errors on the Flanker task. Altered stimulus processing as indexed by delayed P3 latency during the Flanker task and smaller amplitude Go-P3s during the Go/NoGo task indicate impairment in stimulus evaluation and / or context updating during frontal lobe tasks. ERN and NoGoN2 reductions in the sleep deprived group confirm impairments in the monitoring system. These data add to a body of evidence showing that the frontal brain region is particularly vulnerable to sleep loss. Understanding the neural basis of these deficits in performance monitoring abilities is particularly important for our increasingly sleep deprived society and for safety and productivity in situations like driving and sustained operations.

Wireless Sensor Networks for Paddy Field Crop Monitoring Application in Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production.This paper describes the application of wireless sensor network for crop monitoring in the paddy fields of kuttand, a region of Kerala, the southern state of India.

Development and Deployment of Wireless Sensor Network in Paddy Fields of Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the development and deployment of wireless sensor network for crop monitoring in the paddy fields of Kuttanad, a region of Kerala, the southern state of India.

Modeling the interplay between land use, bioenergy production and climate change

Land use has become a force of global importance, considering that 34% of the Earth’s ice-free surface was covered by croplands or pastures in 2000. The expected increase in global human population together with eminent climate change and associated search for energy sources other than fossil fuels can, through land-use and land-cover changes (LUCC), increase the pressure on nature’s resources, further degrade ecosystem services, and disrupt other planetary systems of key importance to humanity. This thesis presents four modeling studies on the interplay between LUCC, increased production of biofuels and climate change in four selected world regions. In the first study case two new crop types (sugarcane and jatropha) are parameterized in the LPJ for managed Lands dynamic global vegetation model for calculation of their potential productivity. Country-wide spatial variation in the yields of sugarcane and jatropha incurs into substantially different land requirements to meet the biofuel production targets for 2015 in Brazil and India, depending on the location of plantations. Particularly the average land requirements for jatropha in India are considerably higher than previously estimated. These findings indicate that crop zoning is important to avoid excessive LUCC. In the second study case the LandSHIFT model of land-use and land-cover changes is combined with life cycle assessments to investigate the occurrence and extent of biofuel-driven indirect land-use changes (ILUC) in Brazil by 2020. The results show that Brazilian biofuels can indeed cause considerable ILUC, especially by pushing the rangeland frontier into the Amazonian forests. The carbon debt caused by such ILUC would result in no carbon savings (from using plant-based ethanol and biodiesel instead of fossil fuels) before 44 years for sugarcane ethanol and 246 years for soybean biodiesel. The intensification of livestock grazing could avoid such ILUC. We argue that such an intensification of livestock should be supported by the Brazilian biofuel sector, based on the sector’s own interest in minimizing carbon emissions. In the third study there is the development of a new method for crop allocation in LandSHIFT, as influenced by the occurrence and capacity of specific infrastructure units. The method is exemplarily applied in a first assessment of the potential availability of land for biogas production in Germany. The results indicate that Germany has enough land to fulfill virtually all (90 to 98%) its current biogas plant capacity with only cultivated feedstocks. Biogas plants located in South and Southwestern (North and Northeastern) Germany might face more (less) difficulties to fulfill their capacities with cultivated feedstocks, considering that feedstock transport distance to plants is a crucial issue for biogas production. In the fourth study an adapted version of LandSHIFT is used to assess the impacts of contrasting scenarios of climate change and conservation targets on land use in the Brazilian Amazon. Model results show that severe climate change in some regions by 2050 can shift the deforestation frontier to areas that would experience low levels of human intervention under mild climate change (such as the western Amazon forests or parts of the Cerrado savannas). Halting deforestation of the Amazon and of the Brazilian Cerrado would require either a reduction in the production of meat or an intensification of livestock grazing in the region. Such findings point out the need for an integrated/multicisciplinary plan for adaptation to climate change in the Amazon. The overall conclusions of this thesis are that (i) biofuels must be analyzed and planned carefully in order to effectively reduce carbon emissions; (ii) climate change can have considerable impacts on the location and extent of LUCC; and (iii) intensification of grazing livestock represents a promising venue for minimizing the impacts of future land-use and land-cover changes in Brazil.

Climate change: a contemporary and geological perspective: introduction

This file describes the resouces to be made available through the Open Educational Resources 'C-change in GEES' project exploring the open licensing of climate change and sustainability resources in the Geography, Earth and Environmental Sciences.

Sensitivity of large-scale atmospheric analyses to humidity observations and its impact on the global water cycle and tropical and extratropical weather systems in ERA40

Reanalysis data obtained from data assimilation are increasingly used for diagnostic studies of the general circulation of the atmosphere, for the validation of modelling experiments and for estimating energy and water fluxes between the Earth surface and the atmosphere. Because fluxes are not specifically observed, but determined by the data assimilation system, they are not only influenced by the utilized observations but also by model physics and dynamics and by the assimilation method. In order to better understand the relative importance of humidity observations for the determination of the hydrological cycle, in this paper we describe an assimilation experiment using the ERA40 reanalysis system where all humidity data have been excluded from the observational data base. The surprising result is that the model, driven by the time evolution of wind, temperature and surface pressure, is able to almost completely reconstitute the large-scale hydrological cycle of the control assimilation without the use of any humidity data. In addition, analysis of the individual weather systems in the extratropics and tropics using an objective feature tracking analysis indicates that the humidity data have very little impact on these systems. We include a discussion of these results and possible consequences for the way moisture information is assimilated, as well as the potential consequences for the design of observing systems for climate monitoring. It is further suggested, with support from a simple assimilation study with another model, that model physics and dynamics play a decisive role for the hydrological cycle, stressing the need to better understand these aspects of model parametrization. .

How well do we understand and evaluate climate change feedback processes?

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

Methods and resources for climate impacts research: achieving synergy

The prediction of climate variability and change requires the use of a range of simulation models. Multiple climate model simulations are needed to sample the inherent uncertainties in seasonal to centennial prediction. Because climate models are computationally expensive, there is a tradeoff between complexity, spatial resolution, simulation length, and ensemble size. The methods used to assess climate impacts are examined in the context of this trade-off. An emphasis on complexity allows simulation of coupled mechanisms, such as the carbon cycle and feedbacks between agricultural land management and climate. In addition to improving skill, greater spatial resolution increases relevance to regional planning. Greater ensemble size improves the sampling of probabilities. Research from major international projects is used to show the importance of synergistic research efforts. The primary climate impact examined is crop yield, although many of the issues discussed are relevant to hydrology and health modeling. Methods used to bridge the scale gap between climate and crop models are reviewed. Recent advances include large-area crop modeling, quantification of uncertainty in crop yield, and fully integrated crop–climate modeling. The implications of trends in computer power, including supercomputers, are also discussed.

Nitrate transport in Chalk catchments: monitoring, modelling and policy implications

Rising nitrate levels have been observed in UK Chalk catchments in recent decades, with concentrations now approaching or exceeding legislated maximum values in many areas. In response, strategies seeking to contain concentrations through appropriate land management are now in place. However, there is an increasing consensus that Chalk systems, a predominant landscape type over England and indeed northwest Europe, can retard decades of prior nitrate loading within their deep unsaturated zones. Current levels may not fully reflect the long-term impact of present-day practices, and stringent land management controls may not be enough to avert further medium-term rises. This paper discusses these issues in the context of the EU Water Framework Directive, drawing on data from recent experimental work and a new model (INCA-Chalk) that allows the impacts of different land use management practices to be explored. Results strongly imply that timelines for water quality improvement demanded by the Water Framework directive are not realistic for the Chalk, and give an indication of time-scales over which improvements might be achieved. However, important unresolved scientific issues remain, and further monitoring and targeted data collection is recommended to reduce prediction uncertainties and allow cost effective strategies for mitigation to be designed and implemented. (C) 2007 Elsevier Ltd. All rights reserved.

A baseline appraisal of water-dependant ecosystem services, the roles they play within desakota livelihood systems and their potential sensitivity to climate change

This report forms part of a larger research programme on 'Reinterpreting the Urban-Rural Continuum', which conceptualises and investigates current knowledge and research gaps concerning 'the role that ecosystems services play in the livelihoods of the poor in regions undergoing rapid change'. The report aims to conduct a baseline appraisal of water-dependant ecosystem services, the roles they play within desakota livelihood systems and their potential sensitivity to climate change. The appraisal is conducted at three spatial scales: global, regional (four consortia areas), and meso scale (case studies within the four regions). At all three scales of analysis water resources form the interweaving theme because water provides a vital provisioning service for people, supports all other ecosystem processes and because water resources are forecast to be severely affected under climate change scenarios. This report, combined with an Endnote library of over 1100 scientific papers, provides an annotated bibliography of water-dependant ecosystem services, the roles they play within desakota livelihood systems and their potential sensitivity to climate change. After an introductory, section, Section 2 of the report defines water-related ecosystem services and how these are affected by human activities. Current knowledge and research gaps are then explored in relation to global scale climate and related hydrological changes (e.g. floods, droughts, flow regimes) (section 3). The report then discusses the impacts of climate changes on the ESPA regions, emphasising potential responses of biomes to the combined effects of climate change and human activities (particularly land use and management), and how these effects coupled with water store and flow regime manipulation by humans may affect the functioning of catchments and their ecosystem services (section 4). Finally, at the meso-scale, case studies are presented from within the ESPA regions to illustrate the close coupling of human activities and catchment performance in the context of environmental change (section 5). At the end of each section, research needs are identified and justified. These research needs are then amalgamated in section 6.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.