Physiological and ecological variables measured at the high and low pCO2 reef sections

Experiments have shown that ocean acidification due to rising atmospheric carbon dioxide concentrations has deleterious effects on the performance of many marine organisms. However, few empirical or modelling studies have addressed the long-term consequences of ocean acidification for marine ecosystems. Here we show that as pH declines from 8.1 to 7.8 (the change expected if atmospheric carbon dioxide concentrations increase from 390 to 750 ppm, consistent with some scenarios for the end of this century) some organisms benefit, but many more lose out. We investigated coral reefs, seagrasses and sediments that are acclimatized to low pH at three cool and shallow volcanic carbon dioxide seeps in Papua New Guinea. At reduced pH, we observed reductions in coral diversity, recruitment and abundances of structurally complex framework builders, and shifts in competitive interactions between taxa. However, coral cover remained constant between pH 8.1 and ~7.8, because massive Porites corals established dominance over structural corals, despite low rates of calcification. Reef development ceased below pH 7.7. Our empirical data from this unique field setting confirm model predictions that ocean acidification, together with temperature stress, will probably lead to severely reduced diversity, structural complexity and resilience of Indo-Pacific coral reefs within this century.

Pollen data and pollen-based climate reconstruction for the KTK1 pollen record from Lake Kotokel

In this study a radiocarbon-dated pollen record from Lake Kotokel (52°47' N, 108°07' E, 458 m a.s.l.) located in southern Siberia east of Lake Baikal was used to derive quantitative characteristics of regional vegetation and climate from about 15 kyr BP (1 kyr = 1000 cal. yr) until today. Quantitative reconstruction of the late glacial vegetation and climate dynamics suggests that open steppe and tundra communities predominated in the study area prior to ca. 13.5 kyr BP and again during the Younger Dryas interval, between 12.8 and 11.6 kyr BP. The pollen-based climate reconstruction suggests lower-than-present mean January (~ -38 °C) and July (~ 12 °C) temperatures and annual precipitation (~ 270-300 mm) values during these time intervals. Boreal woodland replaced the primarily open landscape around Kotokel three times at about 14.8-14.7 kyr BP, during the Allerød Interstadial between 13.3-12.8 kyr BP and with the onset of the Holocene interglacial between 11.5 and 10.5 kyr BP, presumably in response to a noticeable increase in precipitation, and in July and January temperatures. The maximal spread of the boreal forest (taiga) communities in the region is associated with a warmer and wetter-than-present climate (Tw ~ 17-18 °C, Tc ~ -19 °C, Pann ~ 500-550 mm) that occurred ca. 10.8-7.3 kyr BP. During this time interval woody vegetation covered more than 50 % of the area within a 21x21 km window around the lake. The pollen-based best modern analogue reconstruction suggests a decrease in woody cover percentages and in all climatic variables about 7-6.5 kyr BP. Our results demonstrate a gradual decrease in precipitation and mean January temperature towards their present-day values in the region around Lake Kotokel since that time.

Is the Russell Cycle a true cycle? Multidecadal zooplankton and climate trends in the western English Channel

The Russell Cycle is one of the classical examples of climate influence on biological oceanography, represented as shifts in the marine plankton over several decades with warm and cool conditions. While the time-series data associated with the phenomenon indicate cyclical patterns, the question remains whether or not the Russell Cycle should be considered a “true cycle”. Zooplankton time-series data from 1924 to 2011 from the western English Channel were analysed with principal component (PC), correlation and spectral analyses to determine the dominant trends, and cyclic frequencies of the Russell Cycle indicators in relation to long-term hydroclimatic indices. PC1 accounted for 37.4% of the variability in the zooplankton data with the main contributions from non-clupeid fish larvae, southwestern zooplankton, and overall zooplankton biovolume. For PC2 (14.6% of data variance), the dominant groups were northern fish larvae, non-sardine eggs, and southern fish larvae. Sardine eggs were the major contributors to PC3 (representing 12.1% of data variance). No significant correlations were observed between the above three components and climate indices: Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and local seawater temperature. Significant 44- and 29-year frequencies were observed for PC3, but the physical mechanisms driving the cycles are unclear. Harmonic analysis did not reveal any significant frequencies in the physical variables or in PCs 1 and 2. To a large extent, this is due to the dominant cycles in all datasets generally being long term (>50 years or so) and not readily resolved in the examined time frame of 88 years, hence restricting the ability to draw firm conclusions on the multidecadal relationship between zooplankton community dynamics in the western English Channel and environmental indices. Thus, the zooplankton time-series often associated and represented as the Russell Cycle cannot be concluded as being truly cyclical.

Is the Russell Cycle a true cycle? Multidecadal zooplankton and climate trends in the western English Channel

The Russell Cycle is one of the classical examples of climate influence on biological oceanography, represented as shifts in the marine plankton over several decades with warm and cool conditions. While the time-series data associated with the phenomenon indicate cyclical patterns, the question remains whether or not the Russell Cycle should be considered a “true cycle”. Zooplankton time-series data from 1924 to 2011 from the western English Channel were analysed with principal component (PC), correlation and spectral analyses to determine the dominant trends, and cyclic frequencies of the Russell Cycle indicators in relation to long-term hydroclimatic indices. PC1 accounted for 37.4% of the variability in the zooplankton data with the main contributions from non-clupeid fish larvae, southwestern zooplankton, and overall zooplankton biovolume. For PC2 (14.6% of data variance), the dominant groups were northern fish larvae, non-sardine eggs, and southern fish larvae. Sardine eggs were the major contributors to PC3 (representing 12.1% of data variance). No significant correlations were observed between the above three components and climate indices: Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and local seawater temperature. Significant 44- and 29-year frequencies were observed for PC3, but the physical mechanisms driving the cycles are unclear. Harmonic analysis did not reveal any significant frequencies in the physical variables or in PCs 1 and 2. To a large extent, this is due to the dominant cycles in all datasets generally being long term (>50 years or so) and not readily resolved in the examined time frame of 88 years, hence restricting the ability to draw firm conclusions on the multidecadal relationship between zooplankton community dynamics in the western English Channel and environmental indices. Thus, the zooplankton time-series often associated and represented as the Russell Cycle cannot be concluded as being truly cyclical.

Linking environmental variables with regional- scale variability in ecological structure and standing stock of carbon within UK kelp forests

Kelp forests represent some of the most productive and diverse habitats on Earth. Understanding drivers of ecological patterns at large spatial scales is critical for effective management and conservation of marine habitats. We surveyed kelp forests dominated by Laminaria hyperborea (Gunnerus) Foslie 1884 across 9° latitude and >1000 km of coastline and measured a number of physical parameters at multiple scales to link ecological structure and standing stock of carbon with environmental variables. Kelp density, biomass, morphology and age were generally greater in exposed sites within regions, highlighting the importance of wave exposure in structuring L. hyperborea populations. At the regional scale, wave-exposed kelp canopies in the cooler regions (the north and west of Scotland) were greater in biomass, height and age than in warmer regions (southwest Wales and England). The range and maximal values of estimated standing stock of carbon contained within kelp forests was greater than in historical studies, suggesting that this ecosystem property may have been previously undervalued. Kelp canopy density was positively correlated with large-scale wave fetch and fine-scale water motion, whereas kelp canopy biomass and the standing stock of carbon were positively correlated with large-scale wave fetch and light levels and negatively correlated with temperature. As light availability and summer temperature were important drivers of kelp forest biomass, effective management of human activities that may affect coastal water quality is necessary to maintain ecosystem functioning, while increased temperatures related to anthropogenic climate change may impact the structure of kelp forests and the ecosystem services they provide.

Linking environmental variables with regional- scale variability in ecological structure and standing stock of carbon within UK kelp forests

Kelp forests represent some of the most productive and diverse habitats on Earth. Understanding drivers of ecological patterns at large spatial scales is critical for effective management and conservation of marine habitats. We surveyed kelp forests dominated by Laminaria hyperborea (Gunnerus) Foslie 1884 across 9° latitude and >1000 km of coastline and measured a number of physical parameters at multiple scales to link ecological structure and standing stock of carbon with environmental variables. Kelp density, biomass, morphology and age were generally greater in exposed sites within regions, highlighting the importance of wave exposure in structuring L. hyperborea populations. At the regional scale, wave-exposed kelp canopies in the cooler regions (the north and west of Scotland) were greater in biomass, height and age than in warmer regions (southwest Wales and England). The range and maximal values of estimated standing stock of carbon contained within kelp forests was greater than in historical studies, suggesting that this ecosystem property may have been previously undervalued. Kelp canopy density was positively correlated with large-scale wave fetch and fine-scale water motion, whereas kelp canopy biomass and the standing stock of carbon were positively correlated with large-scale wave fetch and light levels and negatively correlated with temperature. As light availability and summer temperature were important drivers of kelp forest biomass, effective management of human activities that may affect coastal water quality is necessary to maintain ecosystem functioning, while increased temperatures related to anthropogenic climate change may impact the structure of kelp forests and the ecosystem services they provide.

Chapter Six – The Increase of Exotic Zoonotic Helminth Infections: The Impact of Urbanization, Climate Change and Globalization

Zoonotic parasitic diseases are increasingly impacting human populations due to the effects of globalization, urbanization and climate change. Here we review the recent literature on the most important helminth zoonoses, including reports of incidence and prevalence. We discuss those helminth diseases which are increasing in endemic areas and consider their geographical spread into new regions within the framework of globalization, urbanization and climate change to determine the effect these variables are having on disease incidence, transmission and the associated challenges presented for public health initiatives, including control and elimination.

Transient simulations for present and last interglacials using a comprehensive coupled climate model CCSM3 (Community Climate System Model 3.0), links to model result files in NetCDF format

Transient simulations are widely used in studying the past climate as they provide better comparison with any exisiting proxy data. However, multi-millennial transient simulations using coupled climate models are usually computationally very expensive. As a result several acceleration techniques are implemented when using numerical simulations to recreate past climate. In this study, we compare the results from transient simulations of the present and the last interglacial with and without acceleration of the orbital forcing, using the comprehensive coupled climate model CCSM3 (Community Climate System Model 3). Our study shows that in low-latitude regions, the simulation of long-term variations in interglacial surface climate is not significantly affected by the use of the acceleration technique (with an acceleration factor of 10) and hence, large-scale model-data comparison of surface variables is not hampered. However, in high-latitude regions where the surface climate has a direct connection to the deep ocean, e.g. in the Southern Ocean or the Nordic Seas, acceleration-induced biases in sea-surface temperature evolution may occur with potential influence on the dynamics of the overlying atmosphere. The data provided here are from both accelerated and non-accelerated runs as decadal mean values.

Perceptions of climate change, environmental variability and the role of agricultural adaptation strategies by small-scale farmers in Africa: the case of Mwanga district in Northern Tanzania

Abstract The potential impacts of climate change and environmental variability are already evident in most parts of the world, which is witnessing increasing temperature rates and prolonged flood or drought conditions that affect agriculture activities and nature-dependent livelihoods. This study was conducted in Mwanga District in the Kilimanjaro region of Tanzania to assess the nature and impacts of climate change and environmental variability on agriculture-dependent livelihoods and the adaptation strategies adopted by small-scale rural farmers. To attain its objective, the study employed a mixed methods approach in which both qualitative and quantitative techniques were used. The study shows that farmers are highly aware of their local environment and are conscious of the ways environmental changes affect their livelihoods. Farmers perceived that changes in climatic variables such as rainfall and temperature had occurred in their area over the period of three decades, and associated these changes with climate change and environmental variability. Farmers’ perceptions were confirmed by the evidence from rainfall and temperature data obtained from local and national weather stations, which showed that temperature and rainfall in the study area had become more variable over the past three decades. Farmers’ knowledge and perceptions of climate change vary depending on the location, age and gender of the respondents. The findings show that the farmers have limited understanding of the causes of climatic conditions and environmental variability, as some respondents associated climate change and environmental variability with social, cultural and religious factors. This study suggests that, despite the changing climatic conditions and environmental variability, farmers have developed and implemented a number of agriculture adaptation strategies that enable them to reduce their vulnerability to the changing conditions. The findings show that agriculture adaptation strategies employ both planned and autonomous adaptation strategies. However, the study shows that increasing drought conditions, rainfall variability, declining soil fertility and use of cheap farming technology are among the challenges that limit effective implementation of agriculture adaptation strategies. This study recommends further research on the varieties of drought-resilient crops, the development of small-scale irrigation schemes to reduce dependence on rain-fed agriculture, and the improvement of crop production in a given plot of land. In respect of the development of adaptation strategies, the study recommends the involvement of the local farmers and consideration of their knowledge and experience in the farming activities as well as the conditions of their local environment. Thus, the findings of this study may be helpful at various levels of decision making with regard to the development of climate change and environmental variability policies and strategies towards reducing farmers’ vulnerability to current and expected future changes.

Role of climate on barn owls road-kill likelihood and the effect on population viability in future climate change

Roads represent a new source of mortality due to animal-vehicle risk of collision threatening log-term populations’ viability. Risk of road-kill depends on species sensitivity to roads and their specific life-history traits. The risk of road mortality for each species depends on the characteristics of roads and bioecological characteristics of the species. In this study we intend to know the importance of climatic parameters (temperature and precipitation) together with traffic and life history traits and understand the role of drought in barn owl population viability, also affected by road mortality in three scenarios: high mobility, high population density and the combination of previous scenarios (mixed) (Manuscript). For the first objective we correlated the several parameters (climate, traffic and life history traits). We used the most correlated variables to build a predictive mixed model (GLMM) the influence of the same. Using a population model we evaluated barn owl population viability in all three scenarios. Model revealed precipitation, traffic and dispersal have negative relationship with road-kills, although the relationship was not significant. Scenarios showed different results, high mobility scenario showed greater population depletion, more fluctuations over time and greater risk of extinction. High population density scenario showed a more stable population with lower risk of extinction and mixed scenario showed similar results as first scenario. Climate seems to play an indirect role on barn owl road-kills, it may influence prey availability which influences barn owl reproductive success and activity. Also, high mobility scenario showed a greater negative impact on viability of populations which may affect their ability and resilience to other stochastic events. Future research should take in account climate and how it may influence species life cycles and activity periods for a more complete approach of road-kills. Also it is important to make the best mitigation decisions which might include improving prey quality habitat.

Global interactions between fire and vegetation, human activities and climate

Doutoramento em Engenharia Florestal - Instituto Superior de Agronomia - UL

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.

Remotely-sensed changes in the primary productivity of migratory caribou calving grounds and summer pasture: the mixed influences of climate change and caribou herbivory

Nous avons utilisé la télédétection pour examiner comment l’abondance du caribou migrateur pouvait influencer la quantité de ressources alimentaires, et comment ces changements pouvaient affecter la dynamique de population et les patrons d’utilisation de l’espace des caribous. Nous avons évalué les relations entre le caribou et ses ressources alimentaires pour l’aire de mise bas et l’aire d’estivage du troupeau Rivière-George (TRG) du nord du Québec et du Labrador (Canada) entre 1991 et 2011. Nous avons modélisé les relations entre la productivité primaire et des variables climatiques, nous permettant d’isoler les effets d’autres facteurs, comme la pression de broutement des caribous. Nous avons trouvé une relation négative entre la densité de caribous et la productivité primaire à grande échelle, suggérant que la pression de broutement par les caribous pouvait réduire l’abondance des ressources alimentaires et contribuer à la dégradation de l’habitat. Une forte tendance au réchauffement durant la période d’étude, couplée avec un déclin de la taille de population du TRG, a cependant entrainé une productivité primaire plus élevée. Cette hausse de la productivité primaire pourrait représenter un rétablissement de la végétation suite à la réduction de la pression de broutement et/ou un effet du réchauffement climatique.