The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Miguel B. Araújo - One of the best experts on this subject based on the ideXlab platform.
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effects of climate change on the distribution of indigenous species in oceanic islands azores
Climatic Change, 2016Co-Authors: Maria Teresa Ferreira, Miguel B. Araújo, Pedro Cardoso, Paulo A V Borges, Rosalina Gabriel, Eduardo B Azevedo, Francisco ReisAbstract:Oceanic islands host a high proportion of the world’s endemic species. Many such species are at risk of extinction owing to habitat degradation and loss, biological invasions and other threats, but little is known about the effects of climate change on island native biodiversity. The Azorean archipelago provides a unique opportunity to study species-climate-change relationships. We used Ensemble Forecasting to evaluate the current and future distribution of well-studied endemic and native bryophytes (19 species), endemic vascular plants (59 species) and endemic arthropods (128 species), for two of the largest Azorean Islands, Terceira and Sao Miguel. Using a Regional Climate Model (CIELO), and assuming the extreme scenario RCP8.5, we examined changes in the potential distributions of the species and possible loss of climate space for them. Models projected that 23 species (11 %) could lose all adequate climate on either one or both islands. Five additional species were projected to lose ≥90 % of climate space. In total, 90 % of the species were projected to lose climate space: 79 % of bryophytes, 93 % of vascular plants and 91 % of arthropods. We also found for vascular plants and arthropods a tendency for upward shift in altitude in their suitable climate space, while for bryophytes the shift was towards the coastal areas. Our results have profound implications for future conservation priorities on islands, such as for the redrawing of conservation borders of current protected areas.
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conserving the brazilian semiarid caatinga biome under climate change
Biodiversity and Conservation, 2012Co-Authors: Guilherme De Oliveira, Miguel B. Araújo, Thiago Fernado Rangel, Diogo Alagador, Jose Alexandre Felizola DinizfilhoAbstract:The Caatinga is a semiarid biome of the northeast of Brazil with only 1 % of its territory currently conserved. The biome’s biodiversity is highly threatened due to exposure to land conversion for agricultural and cattle ranch. Climate forecasts predict increases in aridity, which could pose additional threats to the biome’s biodiversity. Here, we ask if the remnants of natural vegetation in Caatinga biome, where endemic terrestrial vertebrate species occur, are likely to retain more climatic suitability under climate change scenarios than other less pristine areas of the biome. In order to assess changes in climate suitability across individual species ranges, Ensemble Forecasting was used based on seven bioclimatic envelope models, three atmosphere–ocean general circulation models, and two greenhouse emission gas scenarios for 2020, 2050, and 2080. We found that most species will gain climatic suitability in the natural vegetation remnants of the Caatinga. Such gains are even greater than the expected to occur within random sets of areas with size similar to the natural vegetation remnants. Our results suggest that natural vegetation remnants will likely play a role of climate refuges for endemic vertebrate species, so efforts should be concentrated in these regions.
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BIOMOD - A platform for Ensemble Forecasting of species distributions
Ecography, 2009Co-Authors: Wilfried Thuiller, Bruno Lafourcade, Robin Engler, Miguel B. AraújoAbstract:BIOMOD is a computer platform for Ensemble Forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.
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Ensemble Forecasting of species distributions.
Trends in ecology & evolution, 2007Co-Authors: Miguel B. Araújo, Mary NewAbstract:Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an Ensemble Forecasting framework and describe alternative approaches to the analysis of bioclimatic Ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if Ensemble forecasts are produced and analysed appropriately.
T N Palmer - One of the best experts on this subject based on the ideXlab platform.
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Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware
'American Meteorological Society', 2014Co-Authors: Pd Dueben, T N PalmerAbstract:A reduction of computational cost would allow higher resolution in numerical weather predictions within the same budget for computation. This paper investigates two approaches that promise significant savings in computational cost: the use of reduced precision hardware, which reduces floating point precision beyond the standard double- and single-precision arithmetic, and the use of stochastic processors, which allow hardware faults in a trade-off between reduced precision and savings in power consumption and computing time. Reduced precision is emulated within simulations of a spectral dynamical core of a global atmosphere model and a detailed study of the sensitivity of different parts of the model to inexact hardware is performed. Afterward, benchmark simulations were performed for which as many parts of the model as possible were put onto inexact hardware. Results show that large parts of the model could be integrated with inexact hardware at error rates that are surprisingly high or with reduced precision to only a couple of bits in the significand of floating point numbers. However, the sensitivities to inexact hardware of different parts of the model need to be respected, for example, via scale separation. In the last part of the paper, simulations with a full operational weather forecast model in single precision are presented. It is shown that differences in accuracy between the single- and double-precision forecasts are smaller than differences between Ensemble members of the Ensemble forecast at the resolution of the standard Ensemble Forecasting system. The simulations prove that the trade-off between precision and performance is a worthwhile effort, already on existing hardware
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Ensemble Forecasting
Journal of Computational Physics, 2008Co-Authors: Martin Leutbecher, T N PalmerAbstract:Numerical weather prediction models as well as the atmosphere itself can be viewed as nonlinear dynamical systems in which the evolution depends sensitively on the initial conditions. The fact that estimates of the current state are inaccurate and that numerical models have inadequacies, leads to forecast errors that grow with increasing forecast lead time. The growth of errors depends on the flow itself. Ensemble Forecasting aims at quantifying this flow-dependent forecast uncertainty. The sources of uncertainty in weather Forecasting are discussed. Then, an overview is given on evaluating probabilistic forecasts and their usefulness compared with single forecasts. Thereafter, the representation of uncertainties in Ensemble forecasts is reviewed with an emphasis on the initial condition perturbations. The review is complemented by a detailed description of the methodology to generate initial condition perturbations of the Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). These perturbations are based on the leading part of the singular value decomposition of the operator describing the linearised dynamics over a finite time interval. The perturbations are flow-dependent as the linearisation is performed with respect to a solution of the nonlinear forecast model. The extent to which the current ECMWF Ensemble prediction system is capable of predicting flow-dependent variations in uncertainty is assessed for the large-scale flow in mid-latitudes.
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the economic value of Ensemble forecasts as a tool for risk assessment from days to decades
Quarterly Journal of the Royal Meteorological Society, 2002Co-Authors: T N PalmerAbstract:Despite the revolutionary development of numerical weather and climate prediction (NWCP) in the second half of the last century, quantitative interaction between model developers and forecast customers has been rather limited. This is apparent in the diverse ways in which weather forecasts are assessed by these two groups: root-mean-square error of 500 hPa height on the one hand; pounds, euros or dollars saved on the other. These differences of approach are changing with the development of Ensemble Forecasting. Ensemble forecasts provide a qualitative tool for the assessment of weather and climate risk for a range of user applications, and on a range of time-scales, from days to decades. Examples of the commercial application of Ensemble Forecasting, from electricity generation, ship routeing, pollution modelling, weather-risk finance, disease prediction and crop yield modelling, are shown from all these time-scales. A generic user decision model is described that allows one to assess the potential economic value of numerical weather and climate forecasts for a range of customers. Using this, it is possible to relate analytically, potential economic value to conventional meteorological skill scores. A generalized meteorological measure of forecast skill is proposed which takes the distribution of customers into account. It is suggested that when customers' exposure to weather or climate risk can be quantified, such more generalized measures of skill should be used in assessing the performance of an operational NWCP system. Copyright © 2002 Royal Meteorological Society.
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extended range atmospheric prediction and the lorenz model
Bulletin of the American Meteorological Society, 1993Co-Authors: T N PalmerAbstract:The physical basis for extended-range prediction is explored using the famous three-component Lorenz convection model, taken as a conceptual representation of the chaotic extra tropical circulation, and extended by coupling to a linear oscillator to represent large-scale tropical-extra tropical interactions. The model is used to analyze the roles of time averaging and Ensemble Forecasting, and, in extended form, the impact of both anomalous tropical sea surface temperature and anomalous extratropical sea surface temperature. The conceptual paradigms and analytic calculations presented are used to interpret results from numerical weather prediction and general circulation model experiments. Some remarks on the relevance of predictability studies for the climate change problem are given.
Antoine Guisan - One of the best experts on this subject based on the ideXlab platform.
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effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk a test with plants in an arid environment
Ecological Modelling, 2014Co-Authors: Federico Luebert, Patricio Pliscoff, Hartmut H Hilger, Antoine GuisanAbstract:Abstract The effects of using alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) and its consequences on extinction risk estimates have remained insufficiently studied. Here, we modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an Ensemble Forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Our Results show that the use of different sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions and extinction risk estimates. This form of uncertainty in model-based estimates of extinction risk may need to be better acknowledged and quantified in future SDM studies.
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adapting global conservation strategies to climate change at the european scale the otter as a flagship species
Biological Conservation, 2011Co-Authors: C Cianfrani, Hector F Satizabal, Luigi Maiorano, Antoine GuisanAbstract:Abstract Climate change has created the need for new strategies in conservation planning that account for the dynamics of factors threatening endangered species. Here we assessed climate change threat to the European otter, a flagship species for freshwater ecosystems, considering how current conservation areas will perform in preserving the species in a climatically changed future. We used an Ensemble Forecasting approach considering six modelling techniques applied to eleven subsets of otter occurrences across Europe. We performed a pseudo-independent and an internal evaluation of predictions. Future projections of species distribution were made considering the A2 and B2 scenarios for 2080 across three climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HADCM3. The current and the predicted otter distributions were used to identify priority areas for the conservation of the species, and overlapped to existing network of protected areas. Our projections show that climate change may profoundly reshuffle the otter’s potential distribution in Europe, with important differences between the two scenarios we considered. Overall, the priority areas for conservation of the otter in Europe appear to be unevenly covered by the existing network of protected areas, with the current conservation efforts being insufficient in most cases. For a better conservation, the existing protected areas should be integrated within a more general conservation and management strategy incorporating climate change projections. Due to the important role that the otter plays for freshwater habitats, our study further highlights the potential sensitivity of freshwater habitats in Europe to climate change.
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adapting global conservation strategies to climate change at the european scale the otter as a flagship species
Biological Conservation, 2011Co-Authors: C Cianfrani, Hector F Satizabal, Luigi Maiorano, Antoine GuisanAbstract:Abstract Climate change has created the need for new strategies in conservation planning that account for the dynamics of factors threatening endangered species. Here we assessed climate change threat to the European otter, a flagship species for freshwater ecosystems, considering how current conservation areas will perform in preserving the species in a climatically changed future. We used an Ensemble Forecasting approach considering six modelling techniques applied to eleven subsets of otter occurrences across Europe. We performed a pseudo-independent and an internal evaluation of predictions. Future projections of species distribution were made considering the A2 and B2 scenarios for 2080 across three climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HADCM3. The current and the predicted otter distributions were used to identify priority areas for the conservation of the species, and overlapped to existing network of protected areas. Our projections show that climate change may profoundly reshuffle the otter’s potential distribution in Europe, with important differences between the two scenarios we considered. Overall, the priority areas for conservation of the otter in Europe appear to be unevenly covered by the existing network of protected areas, with the current conservation efforts being insufficient in most cases. For a better conservation, the existing protected areas should be integrated within a more general conservation and management strategy incorporating climate change projections. Due to the important role that the otter plays for freshwater habitats, our study further highlights the potential sensitivity of freshwater habitats in Europe to climate change.
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Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant.
Biological Conservation, 2010Co-Authors: Angela Lomba, Loïc Pellissier, Christophe F. Randin, Joana R. Vicente, Francisco Moreira, João P. Honrado, Antoine GuisanAbstract:Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models are not over-fitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted Ensemble approach. We further propose that this Ensemble Forecasting is conducted within a hierarchic multi-scale framework. We present two Ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales. (C) 2010 Elsevier Ltd. All rights reserved.
Wilfried Thuiller - One of the best experts on this subject based on the ideXlab platform.
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are different facets of plant diversity well protected against climate and land cover changes a test study in the french alps
Ecography, 2014Co-Authors: Wilfried Thuiller, Maya Gueguen, Damien Georges, Richard Bonet, Loic Chalmandrier, Luc Garraud, Julien Renaud, Cristina Roquet, Niklaus E Zimmermann, Sebastien LavergneAbstract:Climate and land cover changes are important drivers of the plant species distributions and diversity patterns in mountainous regions. Although the need for a multifaceted view of diversity based on taxonomic, functional and phylogenetic dimensions is now commonly recognized, there are no complete risk assessments concerning their expected changes. In this paper, we used a range of species distribution models in an Ensemble-Forecasting framework together with regional climate and land cover projections by 2080 to analyze the potential threat for more than 2500 plant species at high resolution (2.5 × 2.5 km) in the French Alps. We also decomposed taxonomic, functional and phylogenetic diversity facets into α and β components and analyzed their expected changes by 2080. Overall, plant species threats from climate and land cover changes in the French Alps were expected to vary depending on the species' preferred altitudinal vegetation zone, rarity, and conservation status. Indeed, rare species and species of conservation concern were the ones projected to experience less severe change, and also the ones being the most efficiently preserved by the current network of protected areas. Conversely, the three facets of plant diversity were also projected to experience drastic spatial re-shuffling by 2080. In general, the mean α-diversity of the three facets was projected to increase to the detriment of regional β-diversity, although the latter was projected to remain high at the montane-alpine transition zones. Our results show that, due to a high-altitude distribution, the current protection network is efficient for rare species, and species predicted to migrate upward. Although our modeling framework may not capture all possible mechanisms of species range shifts, our work illustrates that a comprehensive risk assessment on an entire floristic region combined with functional and phylogenetic information can help delimitate future scenarios of biodiversity and better design its protection.
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uncertainty in Ensemble Forecasting of species distribution
Global Change Biology, 2010Co-Authors: Wilfried Thuiller, Laetitia Buisson, Nicolas Casajus, Sovan Lek, Gael GrenouilletAbstract:Species distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an Ensemble Forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole Ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions Ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.
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BIOMOD - A platform for Ensemble Forecasting of species distributions
Ecography, 2009Co-Authors: Wilfried Thuiller, Bruno Lafourcade, Robin Engler, Miguel B. AraújoAbstract:BIOMOD is a computer platform for Ensemble Forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.
Craig Mcgowan - One of the best experts on this subject based on the ideXlab platform.
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accuracy of real time multi model Ensemble forecasts for seasonal influenza in the u s
PLOS Computational Biology, 2019Co-Authors: Nicholas G Reich, Craig Mcgowan, Logan C Brooks, Teresa K Yamana, Abhinav Tushar, Evan L Ray, Dave Osthus, Sasikiran Kandula, Willow Crawfordcrudell, Graham Casey GibsonAbstract:Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza Forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model Ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall Ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model Ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model Ensemble created by the CDC that took a simple average of all models submitted to the Forecasting challenge. This project shows that collaborative efforts between research teams to develop Ensemble Forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of Forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.
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collaborative efforts to forecast seasonal influenza in the united states 2015 2016
Scientific Reports, 2019Co-Authors: Craig Mcgowan, Matthew Biggerstaff, Michael A Johansson, Karyn M Apfeldorf, Michal Bennun, Logan C Brooks, Matteo Convertino, Madhav Erraguntla, David C FarrowAbstract:Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season Forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean Ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza Forecasting challenges and utilization of Ensemble Forecasting techniques. The mean Ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza Forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.