A systematic review of vegetation phenology in Africa

Thyroid hormone concentrations in female baboons: Metabolic consequences of living in a highly seasonal environment

How female mammals adapt metabolically in response to environmental variation remains understudied in the wild, because direct measures of metabolic activity are difficult to obtain in wild populations. However, recent advances in the non-invasive measurement of fecal thyroid hormones, triiodothyronine (T3), an important regulator of metabolism, provide an opportunity to understand how female baboons living in the harsh Amboseli ecosystem in southern Kenya adapt to environmental variability and escape strict reproductive seasonality. Specifically, we assessed how a female's activity budget, diet, and concentrations of fecal T3 metabolites (mT3) changed over the course of the year and between years. We then tested which of several environmental variables (season, rainfall, and temperature) and behavioral variables (female activity budget and diet) best predicted mT3 concentrations. Finally, we determined if two important reproductive events - onset of ovarian cycling and conception of an offspring - were preceded by changes in female mT3 concentrations. We found female baboons' mT3 concentrations varied markedly across the year and between years as a function of environmental conditions. Further, changes in a female's behavior and diet only partially mediated the metabolic response to the environment. Finally, mT3 concentrations increased in the weeks prior to menarche and cycling resumption, regardless of the month or season in which cycling started. This pattern indicates that metabolic activation may be an indicator of reproductive readiness in female baboons as their energy balance is restored.

Plant phenology shifts under climate warming: a systematic review of recent scientific literature

Climate warming-driven temporal shifts in phenology are widely recognised as the foremost footprint of global environmental change. In this regard, concerted research efforts are being made worldwide to monitor and assess the plant phenological responses to climate warming across species, ecosystems and seasons. Here, we present a global synthesis of the recent scientific literature to assess the progress made in this area of research. To achieve this, we conducted a systematic review by following PRISMA protocol, which involved rigorous screening of 9476 studies on the topic and finally selected 215 studies for data extraction. The results revealed that woody species, natural ecosystems and plant phenological responses in spring season have been predominantly studied, with the herbaceous species, agricultural ecosystems and other seasons grossly understudied. Majority of the studies reported phenological advancement (i.e., preponement) in spring, followed by also advancement in summer but delay in autumn. Methodology-wise, nearly two -third of the studies have employed direct observational approach, followed by herbarium-based and experimental approaches, with the latter covering least temporal depth. We found a steady increase in research on the topic over the last decade with a sharp increase since 2014. The global country-wide scientific output map highlights the huge geographical gaps in this area of research, particularly in the biodiversity-rich tropical regions of the developing world. Based on the findings of this global synthesis, we identify the current knowledge gaps and suggest future directions for this emerging area of research in an increasingly warming world.

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Advance in the timing of the annual migration of the brown-veined white butterfly through Johannesburg, South Africa, over the period 1914-2020

During the mid-summer month of January each year, the migrating brown-veined white butterflies (Belenois aurota, Fabricius, 1973) move through Johannesburg, South Africa, on their path from the Karoo to Mozambique. The result is a short period of approximately 3 days during which the skies of Johannesburg are filled with white butterflies, a spectacle that has been recorded in print media over the past century, and social media over the past decade. In this study, we mine these traditional and social media archives to produce the first multi-decadal phenological record of butterfly migration timing for South Africa, and explore the changes in timing and the role of climate thereof. We find a statistically significant advance in timing at a rate of 2.9 days per decade (r = 0.34, p = 0.0490). The climatic drivers of shifts in migratory species arrival are difficult to detect, as they involve the role of weather at the point of departure in determining the start of flight, and the weather en route to determine the path followed. However, statistically significant relationships are found between the arrival dates and both T_min and precipitation in the month of December, and the combination thereof (r = 0.44, p = 0.0437 and r = 0.45, p = 0.0420 respectively). The findings of this study contribute to a growing literature documenting phenological shifts in South Africa, a previously under-represented region.

Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.

Identifying important ecosystem service areas based on distributions of ecosystem services in the Beijing-Tianjin-Hebei region, China

Water conservation, soil conservation, biodiversity importance, and sandstorm prevention are important ecosystem services (ES) and the core challenges to sustainable economic and societal development in the Beijing-Tianjin-Hebei (BTH) region. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and observation data, we identified high-value ES areas in the BTH region. The high-value ES areas were mainly found in the northern and southwestern parts of the region, like the Yanshan Mountain Range and the Taihang Mountain Range. The ecosystem in the northern mountains is dominated by forest and grassland, and generally provides more valuable ES than does the eastern agricultural plain. Greater species richness was mainly found in the northern mountains with low human activity intensity. Due to its proximity, the Yanshan Mountain Range is critical to the health of the local ecosystem of Beijing. High biodiversity was present in the vicinity of the national nature reserves. Compared with other regions of China, changes in the BTH region are highly intense. Reinforcement of biodiversity conservation and ecosystem restoration in areas with a high degree of ES in the BTH region are capable of effectively improving habitat quality and regional ES.

Can wind farms change the phenology of grassland in China?

Wind energy has attracted worldwide attention as a clean energy source and wind farms are rapidly increasing in number. However, operation of wind farms can affect the local climate and, consequently, local vegetation phenology. Hence, the influence of wind farms on phenology needs to be understood. In this paper, we use remote sensing MOD09GQ data to calculate phenological indexes of vegetation near a large wind farm in a semi-arid grassland area of Inner Mongolia, China. The vegetation phenology before and wind farm construction is compared, with a control area used to account for long-term climate change. The results show that the wind farm extended the growing season of vegetation in areas upwind and downwind of the wind farm. In the prevailing wind direction, the growing season was extended by 11.7 days within 4 km of the wind farm in the upwind area, by 10.0 days within the wind farm, and by 5.5 days within 4 km of the wind farm in the downwind area. The extension of the growing season is due to an earlier start of the growing season, which was mainly influenced by increases in local land surface temperatures. And such an extension will increase the evaporation from vegetation transpiration in study area, which is very likely to bring about decreases in soil moisture here. Such effects should be considered when assessing the ecological impacts of planned wind farms.

Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers

The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall.

Characterizing the Response of Vegetation Cover to Water Limitation in Africa Using Geostationary Satellites

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modeling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the "inverse texture effect" on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation-water interactions from regional to continental scales.

Cross-scale phenological monitoring in forest ecosystems: a content-analysis-based review

Phenology has been useful to better understand the climate-vegetation relationship, and it is considered an indicator of climate change impact. Phenological data have been generated through multiple remote sensing techniques and ground-based observations through professional or citizen science. The combination of both techniques is known as cross-scale phenological monitoring. However, no comparative analysis has been carried out to assess the advantages and disadvantages of each of these techniques to characterize the phenological cycle of forest ecosystem species. This work is a content-analysis-based review of scientific literature published between 2000 and 2018 related to cross-scale monitoring methods, to estimate the phenological variation in different forest ecosystems worldwide. For this study, 97 publications related to cross-scale phenological monitoring were selected. We found that 71% of the articles aimed to corroborate the data generated through satellite imagery using surface data from either phenocams, flux towers, or from citizen science networks. More publications were published by authors in the USA (30%), Canada (8%), and China (7%). The most commonly used vegetation index was the normalized difference vegetation index (65%). Some deficiencies in the evaluation of the phenological phases of autumn when compared with surface observations were found. Flux towers and phenocams were included as alternatives for ground-based monitoring. Cross-scale phenological monitoring has the potential to characterize the phenological response of vegetation accurately due to data combinations at two different observation scales. This work contributes to specifying the methodologies used in gathering phenological parameters of the world's forest ecosystems.

Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia

Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (Hevea brasiliensis) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 °C advanced SOS by ~25 days and EOS by ~14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.

Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa

Mathematical models, such as the logistic curve, have been extensively used to model the temporal evolution of biological processes, though other similarly shaped functions could be (and sometimes have been) used for this purpose. Most previous studies focused on agricultural regions in the Northern Hemisphere and were based on the Normalized Difference Vegetation Index (NDVI). This paper compares the capacity of four parametric double S-shaped models (Gaussian, Hyperbolic Tangent, Logistic, and Sine) to represent the seasonal phenology of an unmanaged, protected savanna biome in South Africa’s Lowveld, using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) generated by the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) processing system on the basis of data originally collected by National Aeronautics and Space Administration (NASA)’s Multi-angle Imaging SpectroRadiometer (MISR) instrument since 24 February 2000. FAPAR time series are automatically split into successive vegetative seasons, and the models are inverted against those irregularly spaced data to provide a description of the seasonal fluctuations despite the presence of noise and missing values. The performance of these models is assessed by quantifying their ability to account for the variability of remote sensing data and to evaluate the Gross Primary Productivity (GPP) of vegetation, as well as by evaluating their numerical efficiency. Simulated results retrieved from remote sensing are compared to GPP estimates derived from field measurements acquired at Skukuza’s flux tower in the Kruger National Park, which has also been operational since 2000. Preliminary results indicate that (1) all four models considered can be adjusted to fit an FAPAR time series when the temporal distribution of the data is sufficiently dense in both the growing and the senescence phases of the vegetative season, (2) the Gaussian and especially the Sine models are more sensitive than the Hyperbolic Tangent and Logistic to the temporal distribution of FAPAR values during the vegetative season, and, in particular, to the presence of long temporal gaps in the observational data, and (3) the performance of these models to simulate the phenology of plants is generally quite sensitive to the presence of unexpectedly low FAPAR values during the peak period of activity and to the presence of long gaps in the observational data. Consequently, efforts to screen out outliers and to minimize those gaps, especially during the rainy season (vegetation’s growth phase), would go a long way to improve the capacity of the models to adequately account for the evolution of the canopy cover and to better assess the relation between FAPAR and GPP.

Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.

Mapping Tree Species Deciduousness of Tropical Dry Forests Combining Reflectance, Spectral Unmixing, and Texture Data from High-Resolution Imagery

In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.

Agricultural productivity in relation to climate and cropland management in West Africa

The climate of West Africa is expected to become more arid due to increased temperature and uncertain rainfall regimes, while its population is expected to grow faster than the rest of the world. As such, increased demand for food will likely coincide with declines in agricultural production in a region where severe undernutrition already occurs. Here, we attempt to discriminate between the impacts of climate and other factors (e.g. land management/degradation) on crop production across West Africa using satellite remote sensing. We identify trends in the land surface phenology and climate of West African croplands between 2000 and 2018. Using the combination of a an attribution framework and residual trend anlaysis, we discriminate between climate and other impacts on crop productivity. The combined effect of rainfall, land surface temperature and solar radiation explains approximately 40% of the variation in cropland productivity over West Africa at the 95% significance level. The largest proportions of croplands with greening trends were observed in Mali, Niger and Burkina Faso, and the largest proportions with browning trends were in Nigeria, The Gambia and Benin. Climate was responsible for 52% of the greening trends and 25% of the browning trends. Within the other driving factors, changes in phenology explained 18% of the greening and 37% of the browning trends across the region, the use of inputs and irrigation explained 30% of the greening trends and land degradation 38% of the browning trends. These findings have implications for adaptation policies as we map out areas in need of improved land management practices and those where it has proven to be successful.

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979–2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

Photoperiod controls vegetation phenology across Africa

Vegetation phenology is driven by environmental factors such as photoperiod, precipitation, temperature, insolation, and nutrient availability. However, across Africa, there's ambiguity about these drivers, which can lead to uncertainty in the predictions of global warming impacts on terrestrial ecosystems and their representation in dynamic vegetation models. Using satellite data, we undertook a systematic analysis of the relationship between phenological parameters and these drivers. The analysis across different regions consistently revealed photoperiod as the dominant factor controlling the onset and end of vegetation growing season. Moreover, the results suggest that not one, but a combination of drivers control phenological events. Consequently, to enhance our predictions of climate change impacts, the role of photoperiod should be incorporated into vegetation-climate and ecosystem modelling. Furthermore, it is necessary to define clearly the responses of vegetation to interactions between a consistent photoperiod cue and inter-annual variation in other drivers, especially under a changing climate.

Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events

Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report, we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a data set of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural network-based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.

Large-scale prerain vegetation green-up across Africa

Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall-driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green-up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to-date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green-up over Africa than previously reported, with prerain green-up being the norm rather than the exception. We also show the relative sparsity of postrain green-up, confined largely to the Sudano-Sahel region. While the prerain green-up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling.

Changes in vegetation phenology on the Mongolian Plateau and their climatic determinants

Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.