Characterizing the species composition of European Culicoides vectors by means of the Köppen-Geiger climate classification

Brugger, Katharina; Rubel, Franz

doi:10.1186/1756-3305-6-333

Cited by 33 publications

(32 citation statements)

References 20 publications

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“…The northward surge of the African–Asian bluetongue virus from its previously accepted northern limit in the peri‐Mediterranean region and the change of vector species from Culicoides imicola (Diptera: Ceratopogonidae) to the Culicoides obsoletus and Culicoides pulicaris complexes were unprecedented and have been described as the result of climate change (Brugger & Rubel, ; Scolamacchia et al. , ; Ganter, ), although this assumption has not been verified.…”

Section: Invasion Of Arthropods and Emergence Of Arthropod‐borne Disementioning

confidence: 99%

A look into the Medical and Veterinary Entomology crystal ball

Dantas‐Torres

Cameron

Colwell

et al. 2014

Medical Vet Entomology

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Abstract. Medical and Veterinary Entomology (MVE) represents a leading periodical in its field and covers many aspects of the biology and control of insects, ticks, mites and other arthropods of medical and veterinary importance. Since the first issue of the journal, researchers working in both developed and developing countries have published in MVE, with direct impact on current knowledge in the field. An increasing number of articles dealing with the epidemiology and transmission of vector-borne pathogens have been published in MVE, reflecting rapid changes in vector distribution, pathogen transmission and host-arthropod interactions. This article represents a gaze into the crystal ball in which we identify areas of increasing interest, discuss the main changes that have occurred in the epidemiology of parasitic arthropods since the first issue of MVE, and predict the principal scientific topics that might arise in the next 25 years for scientists working in medical and veterinary entomology.

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Section: Invasion Of Arthropods and Emergence Of Arthropod‐borne Disementioning

confidence: 99%

A look into the Medical and Veterinary Entomology crystal ball

Dantas‐Torres

Cameron

Colwell

et al. 2014

Medical Vet Entomology

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“…By discretizing a multitude of local climates (LCs) into a manageable number of climate types (CTs; a list of all acronyms is given in Table 1), classification simplifies the spatial variability of climates into a form that is more meaningful and easier to analyze. Thus, climate classification provides intuitive and valuable insight into the relationships between climate and Earth's physical and biological systems, such as erosion (Peel et al 2001), soils (Rohli et al 2015), the biota (Baker et al 2010;Garcia et al 2014), and distributions of invasive species (Werier and Naczi 2012) and virus vectors (Brugger and Rubel 2013). Climate classification is also used to provide visualization of global climate datasets (Fraedrich et al 2001;Diaz and Eischeid 2007;Zhang and Yan 2014;Chen and Chen 2013;Spinoni et al 2015) in order to illustrate climate change in terms of shifting geographical boundaries of major climate types.…”

Section: Introductionmentioning

confidence: 99%

On Using a Clustering Approach for Global Climate Classification

Netzel

Stepinski

2016

Journal of Climate

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Classifying the land surface into climate types provides means of diagnosing relations between Earth's physical and biological systems and the climate. Global climate classifications are also used to visualize climate change.Clustering climate datasets provides a natural approach to climate classification, but the rule-based Köppen-Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clusteringbased classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. These classifications are compared between themselves and to the KGC using the information-theoretic V measure. It is found that the best classifications are obtained using three climate variables (temperature, precipitation, and temperature range), a data normalization that takes into account the skewed distribution of precipitation values, and the partitioning around medoids clustering algorithm. Two such classifications are compared in detail between each other and to the KGC. About half of the climate types found by clustering can be matched to the familiar KGC classes, but the rest differ in their climatic character and spatial distribution. Finally, it is demonstrated that clustering-based classification results in climate types that are internally more homogeneous and externally more distinct than climate types in the KGC.

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“…Amongst the most well known and widely used are the International Geosphere-Biosphere Program DISCover Global 1 km Land Cover classification (IGBP-DIS) (Loveland et al, 2000), Global Land Cover 2000 (Bartholomé and Belward, 2005), and more recently the land cover map developed within the European Space Agency's Climate Change Initiative (ESA CCI) (Poulter et al, 2015;. Similarly, climate classification schemes cluster regions with similar climate conditions and are also widely used to stratify geographical regions with different climatic expectations (Baker et al, 2009;Brugger and Rubel, 2013;Garcia et al, 2014;Herrando-Pérez et al, 2014). Here, the best known is probably the Köppen-Geiger climate classification (Köppen, 1936), which has been modified many times in recent decades (e.g.…”

Section: Introductionmentioning

confidence: 99%

Global hydro-climatic biomes identified via multitask learning

et al. 2018

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Abstract. The most widely used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the characteristic interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multitask learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based datasets indicate that, without the need to prescribe any land cover information, the identified regions of coherent climate–vegetation interactions agree well with the expectations derived from traditional global land cover maps. The resulting global hydro-climatic biomes can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate–vegetation interactions in Earth system models.

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Characterizing the species composition of European Culicoides vectors by means of the Köppen-Geiger climate classification

Cited by 33 publications

References 20 publications

A look into the Medical and Veterinary Entomology crystal ball

A look into the Medical and Veterinary Entomology crystal ball

On Using a Clustering Approach for Global Climate Classification

Global hydro-climatic biomes identified via multitask learning

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