Red Queen Coevolution on Fitness Landscapes

Species do not merely evolve, they also coevolve with other organisms. Coevolution is a major force driving interacting species to continuously evolve ex- ploring their fitness landscapes. Coevolution involves the coupling of species fit- ness landscapes, linking species genetic changes with their inter-specific ecological interactions. Here we first introduce the Red Queen hypothesis of evolution com- menting on some theoretical aspects and empirical evidences. As an introduction to the fitness landscape concept, we review key issues on evolution on simple and rugged fitness landscapes. Then we present key modeling examples of coevolution on different fitness landscapes at different scales, from RNA viruses to complex ecosystems and macroevolution.Comment: 40 pages, 12 figures. To appear in "Recent Advances in the Theory and Application of Fitness Landscapes" (H. Richter and A. Engelbrecht, eds.). Springer Series in Emergence, Complexity, and Computation, 201

Evaluation de l'équilibre énergétique d'une steppe semi-aride à partir d'une télédétection optique et micro-ondes

[Notes_IRSTEA]graph. [Departement_IRSTEA]GTThe estimation of energy balance of plant covers is interesting in hydrology and climatology. The surface energy transfer models are a good way to estimate energy flows on the vegetation and soil (vegetation density, soil structure, soil moisture content). It is possible to use optics (visible and thermal infrared spectrum) and active microwaves (SAR) which are remote sensing data gathered by satellite sensors and which give simultaneously soil and vegetation data, which can be used in these models.L'estimation des flux d'énergie des couverts végétaux est d'un grand intérêt pour l'hydrologie et la climatologie. Les modèles de transfert d'énergie de surface sont un moyen d'estimation des flux d'énergie basé sur la végétation et le sol (densité de la végétation, structure du sol, humidité du sol). Il est possible d'utiliser l'optique (spectre dans le visible et l'infrarouge thermique) et les micro-ondes actives (SAR) qui sont des données de télédétection obtenues par capteurs sur satellite qui donnent simultanément les données du sol et de la végétation, qui peuvent être utilisées dans ces modèles

A GLOBAL RAIN-DRIVEN SOIL EROSION INVESTIGATION BASED ON SIMULATED BREAKPOINT PRECIPITATION

Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data as opposed to breakpoint precipitation data, which better preserves precipitation characteristics such as intensity and duration. Due to their wider availability, erosion modeling applications and risk assessments are typically based on fixed-interval data. However, these applications could be subject to substantial erosion underestimation bias related to time-averaged precipitation. Alternatively, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset of climate parameters from more than 10,000 long-term climate stations in numerous countries was evaluated for use in parameterizing CLIGEN simulations. CLIGEN-simulated event characteristics and derived statistics such as annual rainfall erosivity were compared to high-quality, high-resolution NOAA-ASOS precipitation data where available. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs for simple bare soil scenarios with site-specific soil textures taken from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most critical climate factor for determining sediment yield, and combined CLIGEN precipitation factors had approximately as much predictive power as soil texture. Average annual rainfall erosivity values were calculated for each location and were 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively. This finding is in agreement with the latest research on the topic. © 2022 American Society of Agricultural and Biological Engineers.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Satellite Assessment of Early-Season Forecasts for Vegetation Conditions of Grazing Allotments in Nevada, United States

The extent and heterogeneity of rangelands in the state of Nevada (United States) pose a challenging situation for land managers when determining stocking levels for livestock grazing. Overutilization can cause lasting environmental damage, while underutilization can create unnecessary economic hardship for livestock operators. An improved ability to forecast vegetation stress later in the growing season would allow resource managers to better manage the tradeoffs between ecological and economic concerns. This research maps how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year by analyzing 15 yr of enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectroradiometer sensor, cumulative monthly precipitation, and the Palmer drought severity index. Land cover classes within the grazing allotments that are not relevant to grazing were removed from the analysis, as well as areas that showed > 50% change in EVI since these likely represented transitions or disturbances that were not related to interannual climate variability. The datasets were gridded at spatial resolutions from 4 to 72 km, and the correspondence between image and meteorological datasets was found to improve as measurements were averaged over larger areas. A 16-km sampling grid was judged to provide the best balance between predictive ability and spatial precision. The average R2 of regressions between the vegetation index and meteorological variables within each of the 16-km grid cells was 0.69. For most of Nevada, the ability to predict vegetation conditions for the entire growing season (February-September) generally peaks by the end of May. However, results vary by region, with the northeast particularly benefiting from late-season data. Regressions were performed with and without very wet years, and the ability to make early predictions is better when including wet years than in dry to typical conditions. © 2017 The Society for Range Management. Published by Elsevier Inc. All rights reserved.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

Optical-microwave synergy for estimating surface sensible heat flux over a semi-arid rangeland

This study reports the first results of the Walnut Gulch' 92 experiment concerning the combined use of radar backscattering (ERS-1) and thermal infrared (Landsat TM) data to estimate surface sensible heat flux. The purpose is to use the radar-thermal synergy to retrieve both vegetation and soil temperatures required by a two-layer type model. The first step investigates the potential use of ERS-1 SAR images for surface soil moisture monitoring of the watershed using five calibrated images acquired during the year 1992 (dry to wet conditions). Results show that despite the typical low level of biomass of semi-arid rangeland, an attenuation of the soil backscatter (up to 2 dB) can occur during the rainy season mainly due to the vegetation characteristics. A statistical relationship is then used to retrieve the volumetric surface soil moisture from ERS-1 backscattering (sensitivity of 0.23 dB / % moisture) with a resulting root mean square error (RMSE) of 1.3% of soil moisture. In a second step a semi-empirical approach based on energy balance relates soil temperature Ts to this estimated surface soil moisture with an accuracy of 1.3 °C. Vegetation temperature is then deduced from both Ts and Landsat TM composite temperature Tr in order to estimate sensible heat flux according to the two-layer model. To extend the validation data set, additional Ts and Tr values are also obtained from ground soil moisture measurements and thermal aircraft flights respectively. The overall low RMSE of 35 W/m² obtained between ground and remote sensible heat flux confirms the potentiality of radar/thermal synergy over semi-arid sparse vegetation for energy fluxes estimate. / Nous étudions ici comment l'utilisation combinée de données radar (ERS-1) et infrarouge thermique (Landsat TM) permet d'estimer le flux de chaleur sensible sur le bassin versant de Walnut Gulch (Arizona) en 1992. L'objectif est d'utiliser la synergie radar-thermique pour retrouver les températures du sol et de la végétation requises par un modèle de flux à deux couches. Nous analysons dans un premier temps les potentialités des images radar d'ERS-1 pour suivre l'humidité du sol du bassin versant à partir de 5 images radar calibrées acquises pendant l'année 1992. Bien que cette steppe semi-aride soit caractérisée par une végétation peu développée, une atténuation importante de la rétrodiffusion du sol par la végétation a été observée pendant la saison humide (jusqu'à 2 dB). Une relation empirique est alors calibrée pour retrouver l'humidité de surface du sol (sensibilité de 0.23 dB / % d'humidité) avec un écart-type résiduel de 1.3% d'humidité. La température de surface du sol Ts est ensuite déduite de l'humidité de surface par une approche semi-empirique basée sur le bilan d'énergie avec une précision de 1.3°C. La température de la végétation est alors obtenue en combinant l'estimation de Ts et la température composite Tr mesurée par Landsat TM, ce qui permet finalement l'estimation du flux de chaleur sensible par le modèle à deux couches. Des données issues de prélèvements au sol (humidité de surface) et de vols avions (thermique) sont également utilisées pour accroitre le jeu de données. Le flux de chaleur sensible estimé par cette approche multi-capteur montre un bon accord avec les mesures effectuées sur le terrain (écart-type résiduel de 35 W.m-2)

Synergie optique - micro-ondes pour l'estimation du flux de chaleur sensible d'une steppe semi-aride

[Departement_IRSTEA]GT [TR1_IRSTEA]GMA1-Fonctionnement hydrologique des bassins et des réseaux hydrographiquesInternational audienceThis study reports the first results of the Walnut Gulch' 92 experiment concerning the combined use of radar backscattering (ERS-1) and thermal infrared (Landsat TM) data to estimate surface sensible heat flux. The purpose is to use the radar-thermal synergy to retrieve both vegetation and soil temperatures required by a two-layer type model. The first step investigates the potential use of ERS-1 SAR images for surface soil moisture monitoring of the watershed using five calibrated images acquired during the year 1992 (dry to wet conditions). Results show that despite the typical low level of biomass of semi-arid rangeland, an attenuation of the soil backscatter (up to 2 dB) can occur during the rainy season mainly due to the vegetation characteristics. A statistical relationship is then used to retrieve the volumetric surface soil moisture from ERS-1 backscattering (sensitivity of 0.23 dB / % moisture) with a resulting root mean square error (RMSE) of 1.3% of soil moisture. In a second step a semi-empirical approach based on energy balance relates soil temperature Ts to this estimated surface soil moisture with an accuracy of 1.3 °C. Vegetation temperature is then deduced from both Ts and Landsat TM composite temperature Tr in order to estimate sensible heat flux according to the two-layer model. To extend the validation data set, additional Ts and Tr values are also obtained from ground soil moisture measurements and thermal aircraft flights respectively. The overall low RMSE of 35 W/m² obtained between ground and remote sensible heat flux confirms the potentiality of radar/thermal synergy over semi-arid sparse vegetation for energy fluxes estimate.Nous étudions ici comment l'utilisation combinée de données radar (ERS-1) et infrarouge thermique (Landsat TM) permet d'estimer le flux de chaleur sensible sur le bassin versant de Walnut Gulch (Arizona) en 1992. L'objectif est d'utiliser la synergie radar-thermique pour retrouver les températures du sol et de la végétation requises par un modèle de flux à deux couches. Nous analysons dans un premier temps les potentialités des images radar d'ERS-1 pour suivre l'humidité du sol du bassin versant à partir de 5 images radar calibrées acquises pendant l'année 1992. Bien que cette steppe semi-aride soit caractérisée par une végétation peu développée, une atténuation importante de la rétrodiffusion du sol par la végétation a été observée pendant la saison humide (jusqu'à 2 dB). Une relation empirique est alors calibrée pour retrouver l'humidité de surface du sol (sensibilité de 0.23 dB / % d'humidité) avec un écart-type résiduel de 1.3% d'humidité. La température de surface du sol Ts est ensuite déduite de l'humidité de surface par une approche semi-empirique basée sur le bilan d'énergie avec une précision de 1.3°C. La température de la végétation est alors obtenue en combinant l'estimation de Ts et la température composite Tr mesurée par Landsat TM, ce qui permet finalement l'estimation du flux de chaleur sensible par le modèle à deux couches. Des données issues de prélèvements au sol (humidité de surface) et de vols avions (thermique) sont également utilisées pour accroitre le jeu de données. Le flux de chaleur sensible estimé par cette approche multi-capteur montre un bon accord avec les mesures effectuées sur le terrain (écart-type résiduel de 35 W.m-2)