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Transpiration, a major component of total evaporation from vegetated surfaces, is an unavoidable consequence of photosynthetic carbon fixation. Because of limiting soil moisture and competition for solar radiation plants invest most of their fixed carbon into structural and hydraulic functions (roots and stems) and solar radiation absorption (leaves). These investments permit individuals to overshadow competitors and provide for transport of water from the soil to the leaves where photosynthesis and transpiration occur. Often low soil moisture or high evaporative demand limit the supply of water to leaves reducing photosynthesis and thus transpiration. The absorption of solar radiation for photosynthesis and dissipation of this energy via radiation, heat, mass and momentum fluxes represents the link between photosynthesis and climate. Recognition of these relationships has led to the development of hydro/energy balance models that are based on the physiological ecology of photosynthesis. We discuss an approach to study vegetation-climate interactions using photosynthesis-centric models embedded in a GCM. The rate at which a vegetated area transpires and photosynthesizes is determined by the physiological state of the vegetation, its amount and its type. The latter two are specified from global satellite data collected since 1982. Climate simulations have been carried out to study how this simulated climate system responds to changes in radiative forcing, physiological capacity, atmospheric CO2, vegetation type and variable vegetation cover observed from satellites during the 1980's. Results from these studies reveal significant feedbacks between the vegetation activity and climate. For example, vegetation cover and physiological activity increases cause the total latent heat flux and precipitation to increase while mean and maximum air temperatures decrease. The reverse occurs if cover or activity'decreases. In general climate response of a particular region was dominated by local processes but we also find evidence that plausible climate-vegetation scenarios lead to changes in global atmospheric circulation and strong non-local influences in some cases.

2 episodes of surgical debridement during the first month.The lesions began to improve in the second month of medical therapy, when skin grafting was performed.The patient remained well and was discharged after almost 3 months.Medications were continued for a total period of 6 months.The patient has been well for >2 years.P. insidiosum in tissues resembles agents of zygomycosis morphologically but, unlike the latter, rarely stains with hematoxylin and eosin.Various immunostainings also help identify the organism (4).With the exception of the facial-cranial form of the disease in the United States (5), most cases in Thailand occur in patients with chronic hemolytic anemia; thalassemia-hemoglobinopathy is the most common underlying disease.Major clinical manifestations include ocular and craniofacial infections in healthy children, arteritis usually originating from lower extremities, and chronic subcutaneous abscesses.Treatment options reported to be successful include supersaturated potassium iodide for the chronic cutaneous form (2), a combination of terbinafine and itraconazole in a single case of an acute, severe ocular, subcutaneous infection (6), and therapeutic vaccination for severe infections involving major arteries (7,8) in conjunction with surgery.The first case suggested that, with chronic pythiosis involving the aorta, effective management is difficult.In the second case, the laboratory was familiar with P. insidiosum isolation procedures; therefore, a quick diagnosis was made and early treatment was instituted.This early form of cutaneous pythiosis is rarely diagnosed properly by most clinical and pathologic laboratories.Human cases probably occur worldwide (9) but are underrecognized and thus, misdiagnosed (5).More research into the pathogenesis, diagnosis, and new treatment modalities is urgently needed.

No abstract is provided for this article.

The Vegetation Health index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and from the brightness temperatures, both derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. VH users rely on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. This Letter explores this hypothesis with data from Mongolia incorporating information from six different ecosystems. It was found that the northern ecosystems are characterized by positive correlations, implying that rising temperature favourably influences vegetation activity. It is concluded that the VHI should be used with caution, especially in high latitude regions. Acknowledgements The authors wish to express appreciation to the United States Agency for International Development for funding this project (Grant No. TAMOU‐00‐C20‐010).

Variability in precipitation regimes at seasonal and longer time scales strongly influences ecosystem dynamics in arid and semi‐arid regions. In this paper, we use time series of global precipitation and satellite normalized difference vegetation index (NDVI) data to analyze joint spatial and temporal variability between terrestrial ecosystems and precipitation regimes. Accumulated monthly rainfall anomalies are quantified using a standardized precipitation index (SPI), which provides a better measure of ecologically significant precipitation excess or deficit at growing season time scales relative to monthly precipitation data. Results from canonical correlation analysis reveal geographically extensive patterns of joint NDVI‐SPI variability suggestive of strong climate‐biosphere coupling. Further, leading modes of covariability are shown to be related to large‐scale ocean‐atmosphere circulation anomalies. These results illustrate the global extent and sensitivity of ecosystems susceptible to climate change‐induced perturbations in precipitation regimes.

<strong>Data </strong><strong>description</strong> This file is a 100 m resolution map of % tree canopy cover per pixel for Africa in 2019. Tree cover values range from 0-100 % and nodata areas are masked as -1. The map is derived from predictions of tree cover using 3 m PlanetScope imagery, which were resampled to 1 m, and then aggregated to 100 m. Predictions at 1 m resolution (or anything between 1 and 100 m) are available on request. The image data was processed from raw scenes into merged 1x1 degree mosaics. Differences in the quality of raw scenes and processed mosaics can lead to artifacts in the predicted tree cover, visible as seamlines along scene or mosaic edges. Therefore the current version of this map should not be seen as a final product representing the exact tree cover at each location, but as a proof of concept of what is possible with PlanetScope data. The main benefit of this map is the inclusion of cover from scattered single trees in sparse cover areas such as savannahs. Here it can serve as a complement to existing forest cover maps to extend the mapping to include non-forest trees. <strong>License</strong> This tree cover map is made freely available for non-commercial purposes. All usage of the data must be attributed and should be cited with the paper citation. Please see the NICFI license for full terms of usage, available at: https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf <strong>Version differences</strong> We recommend to always use the latest version. <strong>We are continuously improving the quality and consistency of the tree cover mapped</strong>, and are expecting to release regular version updates, based on an improved model and mosaics. We also plan to release different years in the future. <strong>Version 0.1: ps_africa_treecover_2019_100m_v0.1</strong> This version was used for the analyses in the paper. It used the first version of the mosaics, which suffered from the inclusion of lower-quality scenes and missing data. Known issues - missing data coverage: incomplete mosaics and mosaics missing scenes - inconsistent predictions between mosaics: visible mosaic edges, see Zimbabwe - underprediction in very sparse cover areas - overprediction of cover in denser shrublands - overprediction (artifacts) in mountains, desert dunes, croplands and some urban areas - flowering trees in closed forests are mapped as gaps - occasional confusion between understory or shrubs and trees in wood- and shrublands <strong>Version 1.0: ps_africa_treecover_2019_100m_v1.0</strong> This is the latest version available as of March 2023. It is based on improved mosaics and a revised model, leading to better consistency both between and within mosaics. However the current model underpredicts solid tree cover in shrublands and woodlands, leading to an overall underprediction in lower rainfall areas. Improvements over v0.1: - improved mosaic quality - reduced artifacts in croplands and urban areas (though some remain) - better consistency of forest area mapping - better detection rate of small trees in dry areas Known issues - underprediction of tree clusters in shrublands - some (few) false predictions of small trees in dry areas - occasional inconsistent predictions within mosaics: seamlines along scene edges - areas of underprediction in dense tropical forest due to lower quality scenes, see DRC - overprediction (artifacts) in mountains, and occasionally desert dunes - flowering trees in closed forests are mapped as gaps - occasional confusion between understory or shrubs and trees in wood- and shrublands - trees without leaves may not be mapped correctly