Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

The greening and browning of Alaska based on 1982–2003 satellite data

Aim To examine the trends of 1982–2003 satellite‐derived normalized difference vegetation index (NDVI) values at several spatial scales within tundra and boreal forest areas of Alaska. Location Arctic and subarctic Alaska. Methods Annual maximum NDVI data from the twice monthly Global Inventory Modelling and Mapping Studies (GIMMS) NDVI 1982–2003 data set with 64‐km² pixels were extracted from a spatial hierarchy including three large regions: ecoregion polygons within regions, ecozone polygons within boreal ecoregions and 100‐km climate station buffers. The 1982–2003 trends of mean annual maximum NDVI values within each area, and within individual pixels, were computed using simple linear regression. The relationship between NDVI and temperature and precipitation was investigated within climate station buffers. Results At the largest spatial scale of polar, boreal and maritime regions, the strongest trend was a negative trend in NDVI within the boreal region. At a finer scale of ecoregion polygons, there was a strong positive NDVI trend in cold arctic tundra areas, and a strong negative trend in interior boreal forest areas. Within boreal ecozone polygons, the weakest negative trends were from areas with a maritime climate or colder mountainous ecozones, while the strongest negative trends were from warmer basin ecozones. The trends from climate station buffers were similar to ecoregion trends, with no significant trends from Bering tundra buffers, significant increasing trends among arctic tundra buffers and significant decreasing trends among interior boreal forest buffers. The interannual variability of NDVI among the arctic tundra buffers was related to the previous summer warmth index. The spatial pattern of increasing tundra NDVI at the pixel level was related to the west‐to‐east spatial pattern in changing climate across arctic Alaska. There was no significant relationship between interannual NDVI and precipitation or temperature among the boreal forest buffers. The decreasing NDVI trend in interior boreal forests may be due to several factors including increased insect/disease infestations, reduced photosynthesis and a change in root/leaf carbon allocation in response to warmer and drier growing season climate. Main conclusions There was a contrast in trends of 1982–2003 annual maximum NDVI, with cold arctic tundra significantly increasing in NDVI and relatively warm and dry interior boreal forest areas consistently decreasing in NDVI. The annual maximum NDVI from arctic tundra areas was strongly related to a summer warmth index, while there were no significant relationships in boreal areas between annual maximum NDVI and precipitation or temperature. Annual maximum NDVI was not related to spring NDVI in either arctic tundra or boreal buffers.     

Scientific Wealth in Middle East and North Africa: Productivity,Indigeneity, and Specialty in 1981–2013

Several developing countries seek to build knowledge-based economies by attempting to expand scientific research capabilities. Characterizing the state and direction of progress in this arena is challenging but important. Here, we employ three metrics: a classical metric of productivity (publications per person), an adapted metric which we denote as Revealed Scientific Advantage (developed from work used to compare publications in scientific fields among countries) to characterize disciplinary specialty, and a new metric, scientific indigeneity (defined as the ratio of publications with domestic corresponding authors) to characterize the locus of scientific activity that also serves as a partial proxy for local absorptive capacity. These metrics—using population and publications data that are available for most countries–allow the characterization of some key features of national scientific enterprise. The trends in productivity and indigeneity when compared across other countries and regions can serve as indicators of strength or fragility in the national research ecosystems, and the trends in specialty can allow regional policy makers to assess the extent to which the areas of focus of research align (or not align) with regional priorities. We apply the metrics to study the Middle East and North Africa (MENA)—a region where science and technology capacity will play a key role in national economic diversification. We analyze 9.8 million publication records between 1981–2013 in 17 countries of MENA from Morocco to Iraq and compare it to selected countries throughout the world. The results show that international collaborators increasingly drove the scientific activity in MENA. The median indigeneity reached 52% in 2013 (indicating that almost half of the corresponding authors were located in foreign countries). Additionally, the regional disciplinary focus in chemical and petroleum engineering is waning with modest growth in the life sciences. We find repeated patterns of stagnation and contraction of scientific activity for several MENA countries contributing to a widening productivity gap on an international comparative yardstick. The results prompt questions about the strength of the developing scientific enterprise and highlight the need for consistent long-term policy for effectively addressing regional challenges with domestic research.     

Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests

The terrestrial forest ecosystems in the northern high latitude region have been experiencing significant warming rates over several decades. These forests are considered crucial to the climate system and global carbon cycle and are particularly vulnerable to climate change. To obtain an improved estimate of the response of vegetation activity, e.g., forest greenness and tree growth, to climate change, we investigated spatiotemporal variations in two independent data sets containing the dendroecological information for this region over the past 30 years. These indices are the normalized difference vegetation index (NDVI3g) and the tree‐ring width index (RWI), both of which showed significant spatial variability in past trends and responses to climate changes. These trends and responses to climate change differed significantly in the ecosystems of the circumarctic (latitude higher than 67°N) and the circumboreal forests (latitude higher and lower than 50°N and 67°N, respectively), but the way in which they differed was relatively similar in the NDVI3g and the RWI. In the circumarctic ecosystem, the climate variables of the current summer were the main climatic drivers for the positive response to the increase in temperatures showed by both the NDVI3g and the RWI indices. On the other hand, in the circumboreal forest ecosystem, the climate variables of the previous year (from summer to winter) were also important climatic drivers for both the NDVI3g and the RWI. Importantly, both indices showed that the temperatures in the previous year negatively affected the ecosystem. Although such negative responses to warming did not necessarily lead to a past negative linear trend in the NDVI3g and the RWI over the past 30 years, future climate warming could potentially cause severe reduction in forest greenness and tree growth in the circumboreal forest ecosystem.     

Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.     

Effects of Whaling on the Structure of the Southern Ocean Food Web: Insights on the “Krill Surplus” from Ecosystem Modelling

The aim of this study was to examine the ecological plausibility of the “krill surplus” hypothesis and the effects of whaling on the Southern Ocean food web using mass-balance ecosystem modelling. The depletion trajectory and unexploited biomass of each rorqual population in the Antarctic was reconstructed using yearly catch records and a set of species-specific surplus production models. The resulting estimates of the unexploited biomass of Antarctic rorquals were used to construct an Ecopath model of the Southern Ocean food web existing in 1900. The rorqual depletion trajectory was then used in an Ecosim scenario to drive rorqual biomasses and examine the “krill surplus” phenomenon and whaling effects on the food web in the years 1900–2008. An additional suite of Ecosim scenarios reflecting several hypothetical trends in Southern Ocean primary productivity were employed to examine the effect of bottom-up forcing on the documented krill biomass trend. The output of the Ecosim scenarios indicated that while the “krill surplus” hypothesis is a plausible explanation of the biomass trends observed in some penguin and pinniped species in the mid-20^th century, the excess krill biomass was most likely eliminated by a rapid decline in primary productivity in the years 1975–1995. Our findings suggest that changes in physical conditions in the Southern Ocean during this time period could have eliminated the ecological effects of rorqual depletion, although the mechanism responsible is currently unknown. Furthermore, a decline in iron bioavailability due to rorqual depletion may have contributed to the rapid decline in overall Southern Ocean productivity during the last quarter of the 20^th century. The results of this study underscore the need for further research on historical changes in the roles of top-down and bottom-up forcing in structuring the Southern Ocean food web.     

Canopy and litter ant assemblages share similar climate–species density relationships

Tropical forest canopies house most of the globe''s diversity, yet little is known about global patterns and drivers of canopy diversity. Here, we present models of ant species density, using climate, abundance and habitat (i.e. canopy versus litter) as predictors. Ant species density is positively associated with temperature and precipitation, and negatively (or non-significantly) associated with two metrics of seasonality, precipitation seasonality and temperature range. Ant species density was significantly higher in canopy samples, but this difference disappeared once abundance was considered. Thus, apparent differences in species density between canopy and litter samples are probably owing to differences in abundance–diversity relationships, and not differences in climate–diversity relationships. Thus, it appears that canopy and litter ant assemblages share a common abundance–diversity relationship influenced by similar but not identical climatic drivers.     

Validation of the Modes of Transmission Model as a Tool to Prioritize HIV Prevention Targets: A Comparative Modelling Analysis

Background

The static Modes of Transmission (MOT) model predicts the annual fraction of new HIV infections acquired across subgroups (MOT metric), and is used to focus HIV prevention. Using synthetic epidemics via a dynamical model, we assessed the validity of the MOT metric for identifying epidemic drivers (behaviours or subgroups that are sufficient and necessary for HIV to establish and persist), and the potential consequence of MOT-guided policies.

Methods and Findings

To generate benchmark MOT metrics for comparison, we simulated three synthetic epidemics (concentrated, mixed, and generalized) with different epidemic drivers using a dynamical model of heterosexual HIV transmission. MOT metrics from generic and complex MOT models were compared against the benchmark, and to the contribution of epidemic drivers to overall HIV transmission (cumulative population attributable fraction over t years, PAF_t). The complex MOT metric was similar to the benchmark, but the generic MOT underestimated the fraction of infections in epidemic drivers. The benchmark MOT metric identified epidemic drivers early in the epidemics. Over time, the MOT metric did not identify epidemic drivers. This was not due to simplified MOT models or biased parameters but occurred because the MOT metric (irrespective of the model used to generate it) underestimates the contribution of epidemic drivers to HIV transmission over time (PAF_5–30). MOT-directed policies that fail to reach epidemic drivers could undermine long-term impact on HIV incidence, and achieve a similar impact as random allocation of additional resources.

Conclusions

Irrespective of how it is obtained, the MOT metric is not a valid stand-alone tool to identify epidemic drivers, and has limited additional value in guiding the prioritization of HIV prevention targets. Policy-makers should use the MOT model judiciously, in combination with other approaches, to identify epidemic drivers.     

Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century

Changes to forest production drivers (light, water, temperature, and site nutrient) over the last 55 years have been documented in peer‐reviewed literature. The main objective of this paper is to review documented evidence of the impacts of climate change trends on forest productivity since the middle of the 20th century. We first present a concise overview of the climate controls of forest production, provide evidence of how the main controls have changed in the last 55 years, followed by a core section outlining our findings of observed and documented impacts on forest productivity and a brief discussion of the complications of interpreting trends in net primary production (NPP). At finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground‐based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. Of the 49 papers reporting forest production levels we reviewed, 37 showed a positive growth trend, five a negative trend, three reported both a positive and a negative trend for different time periods, one reported a positive and no trend for different geographic areas, and two reported no trend. Forests occupy ≈52% of the Earth's land surface and tend to occupy more temperature and radiation‐limited environments. Less than 7% of forests are in strongly water‐limited systems. The combined and interacting effects of temperature, radiation, and precipitation changes with the positive effect of CO₂, the negative effects of O₃ and other pollutants, and the presently positive effects of N will not be elucidated with experimental manipulation of one or a few factors at a time. Assessments of the greening of the biosphere depend on both accurate measurements of rates (net ecosystem exchange, NPP), how much is stored at the ecosystem level (net ecosystem production) and quantification of disturbances rates on final net biome production.     

Dynamic Modelling of Northeast China Transect Responses to Global Change-A Regional Vegetation Model Driven by Remote Sensing Information

A remote sensing driven dynamic simulation model was developed for terrestrial ecosystems. The model was encoded in C language under the environment of SPAMOD, a spatial simulation tool developed under MS Windows. The model was applied to Northeast China Transect to simulate the dynamics of green and non-green biomass of 12 vegetation categories as well as soil water of 3 layers. The green biomass was converted to normalized difference vegetation index (NDVI) of AVHRR remote sensing, and compared with the observed NDVI from 1986 to 1990. The model was also compared with ground measurements of biomass and productivity along the transect. Ambient CO2 concentration, monthly mean air temperature and monthly precipitation were regarded as the three basic driving variables for global change study. The model also included the effects of temperature and precipitation on sunshine fracti6n, relative humidity, radiation, soil water and eventually plant growth. For each CO2 and climatic scenario, the model was run for an equilibrium solution. The results indicated that the natural vegetation of the transect was very sensitive to variation of temperature and CO2 concentration. With CO2 remained unchanged and temperature increased by 4 CE, the induced increase in evapotranspiration could reduce the average biomass and net primary productivity (NPP) over the whole transect by 32.1% and 41.9 % respectively. In contrast, a 20 % increase in precipitation alone could lead to an increase of the average biomass and NPP by 8.1% and 13.4% respectively. Under the present climatic conditions, CO2 doubling could increase the average biomass and NPP by 12.2% and 17.1% respectively. Because of compensation between the positive effects of CO2 and precipitation increase and the negative effect of temperature increase, a comprehensive interaction among CO2 doubling, a 20% increase of precipitation and a 4 ℃ increase of temperature altogether can lead to approximately a 2% reduction in the biomass and NPP of the natural vegetation over the whole transect.     

An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China

This study focuses on relationships between the phenological growing season of plant communities and the seasonal metrics of Normalized Difference Vegetation Index (NDVI) at sample stations and pixels overlying them, and explores the procedure for determining the growing season of terrestrial vegetation at the regional scale, using threshold NDVI values obtained by surface–satellite analysis at individual stations/pixels. The cumulative frequency of phenophases has been calculated for each plant community and each year in order to determine the growing season at the three sample stations from 1982 to 1993. The precise thresholds were arbitrarily set as the dates on which the phenological cumulative frequency reached 5% and 10% (for the beginning) and 90% and 95% (for the end). The beginning and end dates of the growing season were then applied each year as time thresholds, to determine the corresponding 10-day peak greenness values from NDVI curves for 8-km² pixels overlying the phenological stations. According to a trend analysis, a lengthening of the growing seasons and an increase of the integrated growing season NDVI have been detected in the central part of the research region. The correlation between the beginning dates of the growing season and the corresponding threshold NDVI values is very low, which indicates that the satellite-sensor-derived greenness is independent of the beginning time of the growing season of local plant communities. Other than in spring, the correlation between the end dates of the growing season and the corresponding threshold NDVI values is highly significant. The negative correlation shows that the earlier the growing season terminates, the larger the corresponding threshold NDVI value, and vice versa. In order to estimate the beginning and end dates of the growing season using the threshold NDVI values at sites without phenological data from 1982 to 1993, we calculated the spatial correlation coefficients between NDVI time-series at each sample station and other contiguous sites year by year. The results provide the spatial extrapolation area of the growing season for each sample station. Thus, we can use the threshold NDVI value obtained at one sample station/pixel for a year to determine the growing season at the extrapolation sites with a similar vegetation type for the same year. Received: 25 October 2000 / Revised: 19 June 2001 / Accepted: 19 June 2001     

Dissecting Variation in Biomass Conversion Factors across China’s Forests: Implications for Biomass and Carbon Accounting

Biomass conversion factors (BCFs, defined as the ratios of tree components (i.e. stem, branch, foliage and root), as well as aboveground and whole biomass of trees to growing stock volume, Mg m⁻³) are considered as important parameters in large-scale forest biomass carbon estimation. To date, knowledge of possible sources of the variation in BCFs is still limited at large scales. Using our compiled forest biomass dataset of China, we presented forest type-specific values of BCFs, and examined the variation in BCFs in relation to forest type, stand development and environmental factors (climate and soil fertility). BCFs exhibited remarkable variation across forest types, and also were significantly related to stand development (especially growing stock volume). BCFs (except Stem BCF) had significant relationships with mean annual temperature (MAT) and mean annual precipitation (MAP) (P<0.001). Climatic data (MAT and MAP) collectively explained 10.0–25.0% of the variation in BCFs (except Stem BCFs). Moreover, stronger climatic effects were found on BCFs for functional components (i.e. branch, foliage and root) than BCFs for combined components (i.e. aboveground section and whole trees). A general trend for BCFs was observed to decrease and then increase from low to high soil fertility. When qualitative soil fertility and climatic data (MAT and MAP) were combined, they explained 14.1–29.7% of the variation in in BCFs (except Stem BCFs), adding only 4.1–4.9% than climatic data used. Therefore, to reduce the uncertainty induced by BCFs in forest carbon estimates, we should apply values of BCFs for a specified forest type, and also consider climatic and edaphic effects, especially climatic effect, in developing predictive models of BCFs (except Stem BCF).     

Climatic factors controlling interannual variability of the onset of vegetation phenology in the northern Sub-Saharan Africa from 1988 to 2013

Satellite-based evaluations of change in vegetation phenology have been explored extensively but land cover-specific climate factors driving these anomalous changes are not fully understood in northern Sub-Saharan Africa. In this study, we identified the climatic factors controlling the start of the season (SOS) extracted from GIMMS NDVI from 1988 to 2013 with onset of rainy season (ORS), annual mean temperature (Temp) and precipitation (PP) through the stepwise regression analysis. The results showed that the SOS shifted towards a late onset in a northward direction with distinct earlier and later trends in grassland and cropland, respectively. The stepwise regression has successfully built a model between SOS and its drivers in 46.0% of the total pixels, where its primary factor differed regionally across land covers. The ORS explained the local anomalous SOS change primarily at 44.7% of the pixels where the model was built. Although the ORS was the primary dominant factor in savannah and cropland, the Temp and PP were leading in grassland and shrubland, respectively, and all factors contributed evenly in evergreen forest. The difference of land cover-specific primary factor implicates complex process in dependency of local vegetation phenology on physiological traits and climate regime across land cover in Sub-Saharan Africa.     

Spatio-Temporal Variation in Vegetation Biomass and Its Relationships with Climate Factors in the Xilingol Grasslands,Northern China

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=10¹² g) over a total area of 19.6×10⁴ km² and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m², with 62.6 g/m² in AGB and 442.8 g/m² in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R²=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R²=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R²=0.76, P<0.0001) but decreased with an increasing GST (R²=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R²=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors.     

Effect of climate change,CO2 trends,nitrogen addition,and land‐cover and management intensity changes on the carbon balance of European grasslands

Several lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE‐GM a process‐based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance [i.e., net biome production (NBP)] of European grasslands over 1991–2010 on a 25 km × 25 km grid. The modeled average trend in NBP is 1.8–2.0 g C m^?2 yr^?2 during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36–43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has ‘inadvertently’ enhanced soil C sequestration and reduced N₂O and CH₄ emissions by 1.2–1.5 Gt CO₂‐equivalent, offsetting more than 7% of greenhouse gas emissions in the whole European agricultural sector during the period 1991–2010. Land‐cover change, climate change and rising CO₂ also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Changes in nitrogen addition (including fertilization and atmospheric deposition) are found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual – nonattributed – term (22–26% of the trend due to all drivers) indicating negative interactions between drivers.     

黄土高原泾河流域长时间序列的归一化植被指数动态变化及其驱动因素分析

泾河流域土地开发历史悠久, 是黄土高原水土流失的典型区域。研究气候变化和人类活动影响下泾河流域的植被覆盖变化及其原因, 对黄土高原的植被恢复、水土保持和景观管理等都具有重要意义。该研究应用GIMMS归一化植被指数NDVI、土地覆盖分类数据和气候数据, 采用趋势分析和相关分析方法, 研究了泾河流域1982-2005年植被覆盖变化趋势及其驱动因素。研究表明: 泾河流域24年间79.64%的区域NDVI无显著变化趋势, NDVI趋势显著增加的区域占16.33%, 主要集中在流域中部和南部, NDVI趋势显著减小的区域占4.03%, 主要集中在流域北部。流域所有气象站点的降水量均无显著变化趋势, 气温均呈显著升高趋势。分析发现气候变化不能很好地解释NDVI趋势的空间分异, 人为因素更为重要。从土地利用分析结果来看, NDVI不同趋势下各土地利用类型比例无明显变化, 但NDVI显著增加区以耕地为主, 显著减小区以草地为主, 由此推断NDVI的显著增加趋势主要由耕地NDVI增加引起, 显著减小趋势可能与林地减少和草地退化有关。通过分析不同分区的土地利用数据和社会经济资料, 着重探讨了造成植被覆盖显著变化趋势的人为因素。     

Productivity–diversity patterns in arctic tundra vegetation

Productivity has long been argued to be a major driver of species richness patterns. In the present study we test alternative productivity–diversity hypotheses using vegetation data from the vast Eurasian tundra. The productivity–species pool hypothesis predicts positive relationships at both fine and coarse grain sizes, whereas the productivity–interaction hypothesis predicts unimodal patterns at fine grain size, and monotonic positive patterns at coarse grain size. We furthermore expect to find flatter positive (productivity–species pool hypothesis) or more strongly negative (productivity–interaction hypothesis) relationships for lichens and bryophytes than for vascular plants, because as a group, lichens and bryophytes are better adapted to extreme arctic conditions and more vulnerable to competition for light than the taller‐growing vascular plants. The normalised difference vegetation index (NDVI) was used as a proxy of productivity. The generally unimodal productivity–diversity patterns were most consistent with the productivity–interaction hypothesis. There was a general trend of decreasing species richness from moderately to maximally productive tundra, in agreement with an increasing importance of competitive interactions. High richness of vascular plants and lichens occurred in moderately low productive tundra areas, whereas that of bryophytes occurred in the least productive tundra habitats covered by this study. The fine and coarse grain richness trends were surprisingly uniform and no variation in beta diversity along the productivity gradient was seen for vascular plants or bryophytes. However, lichen beta diversity varied along the productivity gradient, probably reflecting their sensitivity to habitat conditions and biotic interactions. Overall, the results show evidence that productivity–diversity gradients exist in tundra and that these appear to be largely driven by competitive interactions. Our results also imply that climate warming‐driven increases in productivity will strongly affect arctic plant diversity patterns.     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

Objective Assessment of Nuclear and Cortical Cataracts through Scheimpflug Images: Agreement with the LOCS III Scale

PurposeTo assess nuclear and cortical opacities through the objective analysis of Scheimpflug images, and to check the correlation with the Lens Opacity Classification System III (LOCS III).MethodsNuclear and cortical opacities were graded according to the LOCS III rules after pupil dilation. The maximum and average pixel intensity values along an elliptical mask within the lens nucleus were taken to analyse nuclear cataracts. A new metric based on the percentage of opaque pixels within a region of interest was used to analyse cortical cataracts. The percentage of opaque pixels was also calculated for half, third and quarter areas from the region of interest’s periphery.ResultsThe maximum and average intensity values along the nucleus were directly proportional to the LOCS III grade: The larger the LOCS III value, the larger maximum and average intensity ones. These metrics showed a positive and significant correlation with the LOCS grade: The larger the LOCS grade, the higher was percentage of opaque pixels along the cortex within the same mask’s size. This metric showed a significant correlation to the LOCS grade.ConclusionThe metrics used to assess nuclear opacities showed good correlation with the LOCS III. The percentage of opaque pixels showed to be a useful metric to measure objectively the severity of the cortical opacity. These metrics could be implemented in an algorithm to detect and grade lens opacities automatically and objectively.     

Using a proxy of plant productivity (NDVI) to find key periods for animal performance: the case of roe deer

Animals in seasonal environments are affected by climate in very different ways depending on season and part of the climatic effects operates indirectly through the plants. Vegetation conditions in spring and summer are regarded as decisive for the reproductive success and the offspring's condition of large herbivores, but objective ways to determine key periods during the growing season have not been done often due to limitations in plant data. Using satellite data (NDVI), we determined how plant productivity from birth to fall influences the following winter body mass of roe deer fawns. We do this in two populations, the first inhabiting the low productive Chizé reserve in south western France with an oceanic climate and the second from Trois Fontaines, a highly productive forest with continental climate in east France. The effect of plant productivity was similar for male and female fawn mass, as expected from the weak intensity of sexual selection in roe deer life history traits. We found contrasting results between sites, with a strong effect of plant productivity in spring (April-May) in the Chizé population, but no effect in the Trois Fontaines population. The relatively low variability in winter fawn body mass could account for the absence of NDVI effects at Trois Fontaines. However, such results might also point to a limitation in the use of the NDVI, since the relationship between the canopy and the plant productivity at the ground level might be weak in the highly productive forest of Trois Fontaines.