Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse)

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was ~0.2 during 1986-2010. A higher area-averaged LAI change (~0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform

Drought characterization in the Northeast Brazil: a multiscale watershed analysis and remote sensing monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESDrought is a recurrent phenomenon in the Northeast Brazil (NEB) region, especially in its semiarid inlands, which are characterized by a remarkable climate variability, the expansion of desertification areas and persistent water-use conflicts. Furthermore, future climate change projections indicate that drought events will become more frequent and intense, exerting more pressure over the already vulnerable region. Although several drought studies have been carried out at the NEB, some important methodological aspects inherent to data quality and control, specificities of the used techniques, and spatial scale still need to be further discussed. Therefore, the objective of this thesis is to characterize different aspects of drought in the NEB considering different spatial scales, meteorological data characteristics, and remote sensing monitoring alternatives. This characterization was carried out in the São Francisco watershed (SFW), which presents a remarkable climate diversity, in the Piranhas-Açu watershed (PAW), which is mostly located in the semiarid NEB, and in desertification hotspots. In the first study, we thoroughly validated Climate Research Unit Time Series (CRU TS) gridded precipitation and potential evapotranspiration data over the SFW. CRU TS data presents overall good correlation with observed data. Then, we compared the applicability of the evaporation deficit and the Standardized Precipitation-Evapotranspiration Index as drought indices in the SFW. Results show that periods of water shortage are becoming more frequent and more intense in the coastal and middle zones of the basin, indicating an expansion of aridity. In the second study, we used gap-filled observed rainfall time series in order to propose a comprehensive climatological analysis in the PAW. A rainfall anomaly index was used to identify drought events, which are mostly associated with El Niño events and the anomalous warming of the Tropical North Atlantic Ocean. Finally, in the third study, different stochastic models were tested in order to forecast remotely sensed Normalized Difference Vegetation Index data (MOD13A2 product) over six desertification hotspots in the NEB. Results show that the tested models satisfactorily forecast short-term dry and degraded vegetation states. The results of this thesis contribute to the current general knowledge associated with drought assessment over semiarid regions, and the specific results of each study can be further explored by management agencies or local entities in the development of specific strategies to face and adapt to drought in the NEB

Reconstructing 800 years of summer temperatures in Scotland from tree rings

We thank The Carnegie Trust for the Universities of Scotland for providing funding for Miloš Rydval’s PhD. The Scottish pine network expansion has been an ongoing task since 2007 and funding must be acknowledged to the following projects: EU project ‘Millennium’ (017008-2), Leverhulme Trust project ‘RELiC: Reconstructing 8000 years of Environmental and Landscape change in the Cairngorms (F/00 268/BG)’ and the NERC project ‘SCOT2K: Reconstructing 2000 years of Scottish climate from tree rings (NE/K003097/1)’.This study presents a summer temperature reconstruction using Scots pine tree-ring chronologies for Scotland allowing the placement of current regional temperature changes in a longer-term context. ‘Living-tree’ chronologies were extended using ’subfossil’ samples extracted from nearshore lake sediments resulting in a composite chronology > 800 years in length. The North Cairngorms (NCAIRN) reconstruction was developed from a set of composite blue intensity high-pass and ring-width low-pass chronologies with a range of detrending and disturbance correction procedures. Calibration against July-August mean temperature explains 56.4% of the instrumental data variance over 1866-2009 and is well verified. Spatial correlations reveal strong coherence with temperatures over the British Isles, parts of western Europe, southern Scandinavia and northern parts of the Iberian Peninsula. NCAIRN suggests that the recent summer-time warming in Scotland is likely not unique when compared to multi-decadal warm periods observed in the 1300s, 1500s, and 1730s, although trends before the mid-16th century should be interpreted with some caution due to greater uncertainty. Prominent cold periods were identified from the 16th century until the early 1800s – agreeing with the so-called Little Ice Age observed in other tree-ring reconstructions from Europe - with the 1690s identified as the coldest decade in the record. The reconstruction shows a significant cooling response one year following volcanic eruptions although this result is sensitive to the datasets used to identify such events. In fact, the extreme cold (and warm) years observed in NCAIRN appear more related to internal forcing of the summer North Atlantic Oscillation.Publisher PDFPeer reviewe

A simplified climate change model and extreme weather model based on a machine learning method

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change

The CORDEX.be initiative as a foundation for climate services in Belgium

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level

Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation

Australia's 2019–2020 'Black Summer' bushfires burnt more than 8 million hectares of vegetation across the south-east of the continent, an event unprecedented in the last 200 years. Here we report the impacts of these fires on vascular plant species and communities. Using a map of the fires generated from remotely sensed hotspot data we show that, across 11 Australian bioregions, 17 major native vegetation groups were severely burnt, and up to 67–83% of globally significant rainforests and eucalypt forests and woodlands. Based on geocoded species occurrence data we estimate that >50% of known populations or ranges of 816 native vascular plant species were burnt during the fires, including more than 100 species with geographic ranges more than 500 km across. Habitat and fire response data show that most affected species are resilient to fire. However, the massive biogeographic, demographic and taxonomic breadth of impacts of the 2019–2020 fires may leave some ecosystems, particularly relictual Gondwanan rainforests, susceptible to regeneration failure and landscape-scale decline

Fish Invasions in the World's River Systems: When Natural Processes Are Blurred by Human Activities

Because species invasions are a principal driver of the human-induced biodiversity crisis, the identification of the major determinants of global invasions is a prerequisite for adopting sound conservation policies. Three major hypotheses, which are not necessarily mutually exclusive, have been proposed to explain the establishment of non-native species: the “human activity” hypothesis, which argues that human activities facilitate the establishment of non-native species by disturbing natural landscapes and by increasing propagule pressure; the “biotic resistance” hypothesis, predicting that species-rich communities will readily impede the establishment of non-native species; and the “biotic acceptance” hypothesis, predicting that environmentally suitable habitats for native species are also suitable for non-native species. We tested these hypotheses and report here a global map of fish invasions (i.e., the number of non-native fish species established per river basin) using an original worldwide dataset of freshwater fish occurrences, environmental variables, and human activity indicators for 1,055 river basins covering more than 80% of Earth's surface. First, we identified six major invasion hotspots where non-native species represent more than a quarter of the total number of species. According to the World Conservation Union, these areas are also characterised by the highest proportion of threatened fish species. Second, we show that the human activity indicators account for most of the global variation in non-native species richness, which is highly consistent with the “human activity” hypothesis. In contrast, our results do not provide support for either the “biotic acceptance” or the “biotic resistance” hypothesis. We show that the biogeography of fish invasions matches the geography of human impact at the global scale, which means that natural processes are blurred by human activities in driving fish invasions in the world's river systems. In view of our findings, we fear massive invasions in developing countries with a growing economy as already experienced in developed countries. Anticipating such potential biodiversity threats should therefore be a priority