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As the Earth's third pole, the Tibetan Plateau has experienced a pronounced warming in the past decades. Recent studies reported that the start of the vegetation growing season (SOS) in the Plateau showed an advancing trend from 1982 to the late 1990s and a delay from the late 1990s to 2006. However, the findings regarding the SOS delay in the later period have been questioned, and the reasons causing the delay remain unknown. Here we explored the alpine vegetation SOS in the Plateau from 1982 to 2011 by integrating three long-term time-series datasets of Normalized Difference Vegetation Index (NDVI): Global Inventory Modeling and Mapping Studies (GIMMS, 1982-2006), SPOT VEGETATION (SPOT-VGT, 1998-2011), and Moderate Resolution Imaging Spectroradiometer (MODIS, 2000-2011). We found GIMMS NDVI in 2001-2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in most parts of the western Plateau. By merging GIMMS-based SOSs from 1982 to 2000 with SPOT-VGT-based SOSs from 2001 to 2011 we found the alpine vegetation SOS in the Plateau experienced a continuous advancing trend at a rate of ~1.04 d·y⁻¹ from 1982 to 2011, which was consistent with observed warming in springs and winters. The satellite-derived SOSs were proven to be reliable with observed phenology data at 18 sites from 2003 to 2011; however, comparison of their trends was inconclusive due to the limited temporal coverage of the observed data. Longer-term observed data are still needed to validate the phenology trend in the future.

AIM: For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. LOCATION: Global. METHODS: Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30‐m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists. RESULTS: Our product offers accuracy‐weighted consensus information on the prevalence of 12 land‐cover classes within every nominal 1‐km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land‐cover information contained in the fine‐grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine‐grain land‐cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions. MAIN CONCLUSIONS: Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land‐cover products. Consequently, it surpasses single base products in the ability to capture subpixel land‐cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.

Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset.Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org.bases-doc.univ-lorraine.fr/10.5521/FORESTPLOTS.NET/2014_1Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons.Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over-or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%.Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

1. Information about when and where animals die is important to understand population regulation. In migratory animals, mortality might occur not only during the stationary periods (e.g. breeding and wintering) but also during the migration seasons. However, the relative importance of population limiting factors during different periods of the year remains poorly understood, and previous studies mainly relied on indirect evidence. 2. Here, we provide direct evidence about when and where migrants die by identifying cases of confirmed and probable deaths in three species of long-distance migratory raptors tracked by satellite telemetry. 3. We show that mortality rate was about six times higher during migration seasons than during stationary periods. However, total mortality was surprisingly similar between periods, which can be explained by the fact that risky migration periods are shorter than safer stationary periods. Nevertheless, more than half of the annual mortality occurred during migration. We also found spatiotemporal patterns in mortality: spring mortality occurred mainly in Africa in association with the crossing of the Sahara desert, while most mortality during autumn took place in Europe. 4. Our results strongly suggest that events during the migration seasons have an important impact on the population dynamics of long-distance migrants. We speculate that mortality during spring migration may account for short-term annual variation in survival and population sizes, while mortality during autumn migration may be more important for long-term population regulation (through density-dependent effects).

Aim: Comparative analyses of fire regimes at large geographical scales can potentially identify ecological and climatic controls of fire. Here we describe Australia's broad fire regimes, and explore interrelationships and trade-offs between fire regime components. We postulate that fire regime patterns will be governed by trade-offs between moisture, productivity, fire frequency and fire intensity. Location: Australia. Methods: We reclassified a vegetation map of Australia, defining classes based on typical fuel and fire types. Classes were intersected with a climate classification to derive a map of 'fire regime niches'. Using expert elicitation and a literature search, we validated each niche and characterized typical and extreme fire intensities and return intervals. Satellite-derived active fire detections were used to determine seasonal patterns of fire activity. Results: Fire regime characteristics are closely related to the latitudinal gradient in summer monsoon activity. Frequent low-intensity fires occur in the monsoonal north, and infrequent, high-intensity fires in the temperate south, demonstrating a trade-off between frequency and intensity: that is, very high-intensity fires are only associated with very low-frequency fire regimes in the high biomass eucalypt forests of southern Australia. While these forests occasionally experience extremely intense fires (> 50,000 kW m -1 ), such regimes are exceptional, with most of the continent dominated by grass fuels, typically burning with lower intensity (< 5000 kW m -1 ). Main conclusions: Australia's fire regimes exhibit a coherent pattern of frequent, grass-fuelled fires in many differing vegetation types. While eucalypts are a quintessential Australian entity, their contribution as a dominant driver of high-intensity fire regimes, via their litter and bark fuels, is restricted to the forests of the continent's southern and eastern extremities. Our analysis suggests that the foremost driver of fire regimes at the continental scale is not productivity, as postulated conceptually, but the latitudinal gradient in summer monsoon rainfall activity.

Identifying priority areas for marine vertebrate conservation is complex because species of conservation concern are highly mobile, inhabit dynamic habitats and are difficult to monitor. Many marine vertebrates are known to associate with oceanographic fronts – physical interfaces at the transition between water masses – for foraging and migration, making them important candidate sites for conservation. Here, we review associations between marine vertebrates and fronts and how they vary with scale, regional oceanography and foraging ecology. Accessibility, spatiotemporal predictability and relative productivity of front‐associated foraging habitats are key aspects of their ecological importance. Predictable mesoscale (10s–100s km) regions of persistent frontal activity (‘frontal zones’) are particularly significant. Frontal zones are hotspots of overlap between critical habitat and spatially explicit anthropogenic threats, such as the concentration of fisheries activity. As such, they represent tractable conservation units, in which to target measures for threat mitigation. Front mapping via Earth observation (EO) remote sensing facilitates identification and monitoring of these hotspots of vulnerability. Seasonal or climatological products can locate biophysical hotspots, while near‐real‐time front mapping augments the suite of tools supporting spatially dynamic ocean management. Synthesis and applications. Frontal zones are ecologically important for mobile marine vertebrates. We surmise that relative accessibility, predictability and productivity are key biophysical characteristics of ecologically significant frontal zones in contrasting oceanographic regions. Persistent frontal zones are potential priority conservation areas for multiple marine vertebrate taxa and are easily identifiable through front mapping via EO remote sensing. These insights are useful for marine spatial planning and marine biodiversity conservation, both within Exclusive Economic Zones and in the open oceans.

The structure of urban environments is known to alter local climate, in part due to changes in land cover. A growing subset of research focuses specifically on the UHI in terms of land surface temperature by using data from remote sensing platforms. Past research has established a clear relationship between land surface temperature and the proportional area of land covers, but less research has specifically examined the effects of the spatial patterns of these covers. This research considers the rapidly growing City of Phoenix, Arizona in the United States. To better understand how landscape structure affects local climate, we explored the relationship between land surface temperature and spatial pattern for three different land uses: mesic residential, xeric residential, and industrial/commercial. We used high-resolution (2.4 m) land cover data and an ASTER temperature product to examine 90 randomly selected sample sites of 240 square-meters. We (1) quantify several landscape-level and class-level landscape metrics for the sample sites, (2) measure the Pearson correlation coefficients between land surface temperature and each landscape metric, (3) conduct an analysis of variance among the three land uses, and (4) model the determinants of land surface temperature using ordinary least squares linear regression. The Pearson’s correlation coefficients reveal significant relationships between several measures of spatial configuration and LST, but these relationships differ among the land uses. The ANOVA confirmed that mean land surface temperature and spatial patterns differed among the three land uses. Although a relationship was apparent between surface temperatures and spatial pattern, the results of the linear regression indicate that proportional land cover of grass and impervious surfaces alone best explains temperature in mesic residential areas. In contrast, temperatures in industrial/commercial areas are explained by changes in the configuration of grass and impervious surfaces.

The Critical Depth Hypothesis formalized by Sverdrup in 1953 posits that vernal phytoplankton blooms occur when surface mixing shoals to a depth shallower than a critical depth horizon defining the point where phytoplankton growth exceeds losses. This hypothesis has since served as a cornerstone in plankton ecology and reflects the very common assumption that blooms are caused by enhanced growth rates in response to improved light, temperature, and stratification conditions, not simply correlated with them. Here, a nine-year satellite record of phytoplankton biomass in the subarctic Atlantic is used to reevaluate seasonal plankton dynamics. Results show that (1) bloom initiation occurs in the winter when mixed layer depths are maximum, not in the spring, (2) coupling between phytoplankton growth (μ) and losses increases during spring stratification, rather than decreases, (3) maxima in net population growth rates ( r ) are as likely to occur in midwinter as in spring, and (4) r is generally inversely related to μ. These results are incompatible with the Critical Depth Hypothesis as a functional framework for understanding bloom dynamics. In its place, a \"Dilution-Recoupling Hypothesis\" is described that focuses on the balance between phytoplankton growth and grazing, and the seasonally varying physical processes influencing this balance. This revised view derives from fundamental concepts applied during field dilution experiments, builds upon earlier modeling results, and is compatible with observed phytoplankton blooms in the absence of spring mixed layer shoaling.

AIM: To infer a forest carbon density map at 0.01° resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests. LOCATION: The study area extends from 30° N to 80° N, covering three forest biomes – temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) – over three continents (North America, Europe and Asia). METHODS: This study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventory‐based biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons. RESULTS: Our derived carbon density map agrees well with inventory data at regional scales (r² = 0.70–0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m⁻² retained in TCF and 5.80 ± 2.21 kg C m⁻² in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m⁻². Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant. MAIN CONCLUSIONS: The presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.

The urban heat island describes the phenomenon that air/surface temperatures are higher in urban areas compared to their surrounding rural areas. Numerous studies have shown that increased percent cover of greenspace (PLAND) can significantly decrease land surface temperatures (LST). Fewer studies, however, have investigated the effects of configuration of greenspace on LST. This paper aims to fill this gap using Beijing, China as a case study. PLAND along with six configuration metrics were used to measure the composition and configuration of greenspace. The metrics were calculated based on a greenspace map derived from SPOT imagery, and LST data were retrieved from Landsat TM thermal band. Ordinary least squares regression and spatial autoregression were employed to investigate the relationship between LST and spatial pattern of greenspace using the census tract as the analytical unit. The results showed that PLAND was the most important predictor of LST. A 10 % increase in PLAND resulted in approximately a 0.86 °C decrease in LST. Configuration of greenspace also significantly affected LST. Given a fixed amount of greenspace, LST increased significantly with increased patch density. In addition, the variance of LST was largely explained by both composition and configuration of greenspace. The unique variation explained by the composition was relatively small, and was close to that of the configuration. Results from this study can expand our understanding of the relationship between LST and vegetation, and provide insights for improving urban greenspace planning and management.