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Monitoring agriculture by remote sensing enables large‐scale evaluation of biomass production across space and time. The normalized difference vegetation index (NDVI) is used as a proxy for green biomass. Here, we used satellite‐derived NDVI of arable farms in the Netherlands to evaluate changes in biomass following conversion from conventional to organic farming. We compared NDVI and the stability of NDVI across 72 fields on sand and marine clay soils. Thirty‐six of these fields had been converted into organic agriculture between 0 and 50 years ago (with 2017 as reference year), while the other 36 were paired control fields where conventional farming continued. We used high‐resolution images from the Sentinel‐2 satellite to obtain NDVI estimates across 5 years (January 2016–October 2020). Overall, NDVI did not differ between conventional and organic management during the time series, but NDVI stability was significantly higher under organic management. NDVI was lower under organic management in sandy, but not in clay, soils. Organic farms that had been converted less than ~19 years ago had lower NDVI than conventional farms. However, the difference diminished over time and eventually turned positive after ~19 years since the conversion. NDVI, averaged across the 5 years of study, was positively correlated to soil Olsen‐P measured from soil samples collected in 2017. We conclude that NDVI in organic fields was more stable than in conventional fields, and that the lower biomass in the early years since the transition to organic agriculture can be overcome with time. Our study also indicates the role of soil P bioavailability for plant biomass production across the examined fields, and the benefit of combining remote sensing with on‐site soil measurements to develop a more mechanistic understanding that may help us navigate the transition to a more sustainable type of agriculture. [ABSTRACT FROM AUTHOR]

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Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The time‐series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long‐term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time‐series data sets. Methods: In this study, a long time‐series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High‐Resolution Radiometer) time‐series NDVI products using the Random Forest (RF) downscaling model. Results: Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7. Conclusions: The long time‐series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long‐term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

In this Technical Advance, we describe a novel method to improve ecological interpretation of remotely sensed vegetation greenness measurements that involved sampling 24,395 Landsat pixels (30 m) across 639 km of Alaska's central Brooks Range. The method goes well beyond the spatial scale of traditional plot‐based sampling and thereby more thoroughly relates ground‐based observations to satellite measurements. Our example dataset illustrates that, along the boreal‐Arctic boundary, vegetation with the greatest Landsat Normalized Difference Vegetation Index (NDVI) is taller than 1 m, woody, and deciduous; whereas vegetation with lower NDVI tends to be shorter, evergreen, or non‐woody. The field methods and associated analyses advance efforts to inform satellite data with ground‐based vegetation observations using field samples collected at spatial scales that closely match the resolution of remotely sensed imagery. [ABSTRACT FROM AUTHOR]

Grasslands are one of the most common land‐cover types, providing important ecosystem services globally, yet few studies have examined grassland critical‐zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely‐sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up‐, mid‐, and down‐slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co‐varied with soil CO2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO2 fluxes were inhibited by high moisture content in the down‐slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post‐rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid‐slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical‐zone environments on hillslopes. Plain Language Summary: Globally, grasslands are both common and important landscapes, but less studies have investigated the influence of hillslope processes on these environments and their properties. This study investigated a coastal grassland on a hillslope at Point Reyes National Seashore, California, by combining data sets from different disciplines, covering satellite, field, and laboratory measurements. The site could be grouped into four environmental zones with different properties along the hillslope. Satellite measurements revealed that plants were more active in the wetter, down‐slope throughout the dry summer months. Soil carbon content was not linked directly to soil moisture. Yet, soil carbon dioxide emissions were related to soil moisture, displaying three different behaviors depending on the moisture level. First, soil carbon dioxide emission was lower in the down‐slope during the wet months (negative relationship), but then behaved similarly at all slope locations during the summer and early fall, and increased when it rained (positive relationship). The clustering analysis showed that our site varied significantly over a small distance (<8 m elevation and 150 m distance) and time (1 yr). Beyond the investigation of this specific site, this study highlights an approach for combining data sets to study ecosystems along hillslopes. Key Points: The study used a critical‐zone approach to combine multidisciplinary data sets and characterise a California coastal grasslandHeterogeneity was large over a short distance (<150 m) and time (1 yr) and could be clustered by landscape position into four distinct zonesSoil CO2 fluxes exhibited contrasting responses to soil moisture, which differed with slope and season at the grassland [ABSTRACT FROM AUTHOR]

Culicoides imicola is the main vector of viral diseases of livestock in Europe such as bluetongue (BT), African horse sickness and epizootic haemorrhagic disease. Climatic factors are the main drivers of C. imicola occurrence and its distribution might be subject to rapid shifts due to climate change. Entomological data, collected during BT surveillance, and climatic/environmental variables were used to analyse ecological niche and to model C. imicola distribution and possible future range shifts in Italy. An ensemble technique was used to weigh the performance of machine learning, linear and profile methods. Updated future climate projections from the latest phase of the Climate Model Intercomparison Project were used to generate future distributions for the next three 20‐year periods, according to combinations of general circulation models and shared socioeconomic pathways and considering different climate change scenarios. Results indicated the minimum temperature of the coldest month (BIO 6) and precipitation of the driest‐warmest months (BIO 14) as the main limiting climatic factors. Indeed, BIO 6 and BIO 14 reported the two highest values of variable importance, respectively, 9.16% (confidence interval [CI] = 7.99%–10.32%), and 2.01% (CI = 1.57%–2.44%). Under the worst‐case scenario of climate change, C. imicola range is expected to expand northward and shift away from the coasts of central Italy, while in some areas of southern Italy, environmental suitability will decrease. Our results provide predictions of C. imicola distribution according to the most up‐to‐date future climate projections and should be of great use to surveillance management at regional and national scales. [ABSTRACT FROM AUTHOR]

Prioritizing restoration opportunities effectively across entire riverscape networks (i.e., riverine landscape including floodplain and stream channel networks) can be difficult when relying on in‐channel, reach‐scale monitoring data, or watershed‐level summaries that fail to capture riverscape heterogeneity and the information necessary to implement restoration actions. Leveraging remote sensing and geospatial tools to develop spatially continuous information across nested hierarchical scales may support increased understanding of local riverscape reaches in their broader network context. Using riparian (vegetation) and geomorphic (elevation) indicators to assess status of riverscape health, along with a measure of restoration capacity (valley bottom area), could be adapted to fit specific management goals related to riverscape restoration. Frameworks using remotely sensed vegetation and elevation data to prioritize restoration continuously across riverscapes at restoration‐relevant, reach‐scales may uphold the ecosystem services provided by riverscapes. By incorporating local knowledge and identifying caveats for using these datasets, continuous inferences can be applied at network scales (watershed to regional extent and reach‐scale resolution) to prioritize restoration over a wide variety of ecoregions. This article is categorized under:Water and Life > Conservation, Management, and AwarenessWater and Life > MethodsWater and Life > Stresses and Pressures on Ecosystems [ABSTRACT FROM AUTHOR]

Global climate change has altered the timing of seasonal events (i.e., phenology) for a diverse range of biota. Within and among species, however, the degree to which alterations in phenology match climate variability differ substantially. To better understand factors driving these differences, we evaluated variation in timing of nesting of eight Arctic‐breeding shorebird species at 18 sites over a 23‐year period. We used the Normalized Difference Vegetation Index as a proxy to determine the start of spring (SOS) growing season and quantified relationships between SOS and nest initiation dates as a measure of phenological responsiveness. Among species, we tested four life history traits (migration distance, seasonal timing of breeding, female body mass, expected female reproductive effort) as species‐level predictors of responsiveness. For one species (Semipalmated Sandpiper), we also evaluated whether responsiveness varied across sites. Although no species in our study completely tracked annual variation in SOS, phenological responses were strongest for Western Sandpipers, Pectoral Sandpipers, and Red Phalaropes. Migration distance was the strongest additional predictor of responsiveness, with longer‐distance migrant species generally tracking variation in SOS more closely than species that migrate shorter distances. Semipalmated Sandpipers are a widely distributed species, but adjustments in timing of nesting relative to variability in SOS did not vary across sites, suggesting that different breeding populations of this species were equally responsive to climate cues despite differing migration strategies. Our results unexpectedly show that long‐distance migrants are more sensitive to local environmental conditions, which may help them to adapt to ongoing changes in climate. [ABSTRACT FROM AUTHOR]

The displacement of a river to a new position within its adjacent floodplain is called avulsion, and here we examine how a newly recognized style, called retrogradational avulsion, affects the surrounding floodplain in tropical rainforests using remote sensing. Retrogradational avulsions begin with a channel blockage that causes self‐propagating upstream dechannelization and flooding. While this flooding results in vegetation die‐off and floodplain sedimentation, few quantitative measurements of disturbance by retrogradational avulsions exist. Here, we first focus on land‐cover change following a single retrogradational avulsion in Papua New Guinea from 2012 to 2021. During the avulsion, the river dechannelized 892 m upstream, and the parent channel width doubled. Using maximum likelihood image classification, we observed healthy vegetation fluctuated around 4.3 km2, vegetation regrowth peaked in 2017 at 3.2 km2, dead vegetation peaked in 2013 at 2.1 km2, and visible extent of deposited sediment was greatest in 2015 at 0.44 km2. We also examined 19 other retrogradational avulsions in Papua New Guinea and South America using NDVI. The area of floodplain disturbance (i.e., vegetation die‐off and possible sedimentation) for each avulsion ranged from <1 to >13 km2 and scaled with the dechannelization area. Comparing our plan‐view disturbance results with FABDEM digital‐elevation data and ICESat‐2 surface elevation measurements, we hypothesize floodplain disturbance extent is a function of topographic relief. Our results also suggest that retrogradational avulsions, on average, perturb larger areas of forest compared to blowdowns, suggesting this might be an important disturbance regime that influences gap‐filling regeneration in tropical rainforests. [ABSTRACT FROM AUTHOR]

Built‐up land changes along slope gradients (BCSG) are widespread globally, driven by natural environmental and socio‐economic variations. This process has profound effects on vegetation cover and is closely related to the United Nations Sustainable Development Goals. However, the mechanisms involved in the BCSG remain to be elucidated, and the impacts of BCSG on vegetation cover need to be quantified. The BCSG from 1990 to 2020 was described through slope spectrum analysis by taking the Three Gorges Reservoir Area (TGRA) as an example. Then, the Normalized Difference Vegetation Index (NDVI) was used as an indicator of vegetation cover, and its influence by BCSG was explored through a multiple linear regression model. The built‐up land in the TGRA from 1990 to 2020 was first dominated by slope climbing (SC) and then by horizontal expansion, and the intensity of built‐up land SC showed a general trend of weakening over time. During the 30 years, the intensity of built‐up land SC weakened and then strengthened in townships with mainly intermountain plains, while gradually decreasing in townships with mainly hills and townships with mainly mountains. Socio‐economic factors, natural factors, and regional special events drive the BCSG in TGRA. Built‐up land SC in the TGRA will have a negative impact on NDVI but to a progressively lesser degree from 1990 through 2020. The built‐up land SC in townships with mainly hills and mountains has a negative effect on NDVI. The built‐up land SC in townships with mainly intermountain plains has a positive impact on NDVI. These results can provide a scientific basis for formulating reasonable vertical management policies for built‐up land and help reduce the ecological risks caused by land development. [ABSTRACT FROM AUTHOR]

Human activity and climate change are degrading the environmentally fragile Loess Plateau in dry and semiarid regions. Land deterioration threatens human and ecological existence. To prevent additional land degradation and ensure the ecological development and quality of arable land in the region, China launched "Grain for Green" in the late 1990s. This effort greatly boosted Loess Plateau vegetation. However, land degradation is complex, and so we must also examine natural and social variables to prevent additional degradation. Thus, this study presents a comprehensive index system to quantify land degradation on the Loess Plateau and uses machine learning to anticipate high‐risk locations. The project improved land degradation, and the spatial distribution of degradation risk is high in the northern and low in the eastern and southern regions of the Plateau. Gross Domestic Product and population density are the main drivers of land degradation. Industrialization and urbanization have raised the risk of land degradation, which now accounts for 1%–2% of the area. This study emphasizes sustainable land management in the Loess Plateau, a critical area for sustainable development in China. The integrated assessment indicator system and random forest modeling machine learning help grasp the current status and future preventive measures. The outcome of this study advances the Loess Plateau land degradation research and sustainable land management. The research findings possess significant scientific reference value in terms of mitigating and managing land degradation in environmentally vulnerable regions worldwide. [ABSTRACT FROM AUTHOR]

The glacier database provides an opportunity to track its dynamics and address issues linked with water resources, hazards and sustainability. To better understand the dynamics of Eastern Dhauliganga basin, we mapped the clean and debris‐covered glaciers by using satellite images from Landsat‐series and Sentinel 2. The band‐ratio based methods—NDSI (Normalised Difference Snow Index) and NDVI (Normalised Difference Vegetation Index) were used to map the clean ice glaciers. The debris‐covered glaciers were delineated using topographic parameters and thermal band of Landsat TM. The glacier extent and length change were monitored for 25 glaciers in the basin, of which 13 are clean ice and 12 are debris‐covered glaciers. During the time period from 1994 to 2018, higher area loss was observed for clean ice glaciers (±20.6%) compared with debris‐covered glaciers (±10.9%). The clean‐ice glaciers retreated at a rate of 4.8–33.7 m/year, whereas debris‐covered glaciers retreated at a rate of 2.9–28.8 m/year during 1994–2018. [ABSTRACT FROM AUTHOR]

Ecological restoration programs such as the "Grain for Green Project" (GFGP) have significantly reduced soil erosion and increased vegetation cover on the Loess Plateau (LP) of China over the last decades. The LP has become the paragon of ecological restoration and soil and water conservation across the world and has been highlighted by numerous reports in the literature. However, there is a lack of "seeing is believing" evidence (that is, a picture is worth a thousand words) depicting the effectiveness of the ecological restoration on the LP. Rephotography (repeat photography) was used in this study to compare historical and current photographs of the same locations to explore the landscape changes associated with ecological restoration. Twenty‐nine photo pairs between 1925 and 2021 were compiled and combined with satellite imagery and the deduced Normalized Difference Vegetation Index (NDVI) between 1986 and 2021. This allowed the establishment of a rephotography library reflecting the achievements of ecological remediation and reconstruction on the LP. This in turn provides intuitive and detailed evidence for the ecological environment construction of the LP and resonates with the notion of "lucid waters and lush mountains being invaluable assets". In addition to presenting a great opportunity to investigate landscape change over time, which is difficult to discern using other approaches, the study provides a scientific reference and a basis for government decision‐making. The potential use of rephotography for calibrating remote sensed data was also discussed. Good agreement was found between the NDVI derived from rephotography of large areas and NDVI derived from satellite imageries, which may facilitate the accurate reconstruction of time series NDVI. [ABSTRACT FROM AUTHOR]

Riparian zones in drylands provide important refugia for plants but depend on groundwater and thus are subject to local temporal and spatial variability in abiotic controls. In lieu of costly field‐based sampling, we used readily available data to establish site–scale interannual relationships among riparian plant health and the abiotic factors that control their water balance for a historically persistent wetland adjoining the Santa Clara River in southern California, USA. Non‐linear generalized additive model (GAM) analysis of plant health, represented using the normalized difference vegetation index (NDVI), confirmed robust relationships among plant health and various geomorphological and hydrological factors over multi‐decadal timeframes, including years since last high‐flow event, intra‐year groundwater elevation changes and magnitude of 2‐year cumulative surface water inflows. Geomorphic controls are related to years with high flows that cause extensive scour and deposition that re‐set riparian plant communities. Relationships with dry‐season groundwater declines reflect direct plant access to sub‐surface moisture. Hydrological dependence via cumulative inflow magnitude indicates the dependency of groundwater elevations on sufficient winter recharge to prevent precipitous groundwater decline. GAMs‐based inflection point analysis of surface water inflows versus groundwater elevations confirmed that the cumulative magnitude of multi‐year inflows is critical in avoiding catastrophic groundwater declines and that large flood events drive groundwater recovery. We show that abiotic controls on plant health can be derived from readily available data and that non‐linear analysis better represents the complexity of these scalar controls. Our analysis has relevance for ecosystem management of human‐altered rivers and climate change adaptation. [ABSTRACT FROM AUTHOR]

Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long‐term vegetation GIMMS Normalized Difference Vegetation Index (NDVI) data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detailed human activities with some innovations. The results show that (1) NDVI shows an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PI: 1982–2000) than after it (PII: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate‐driven promotion had the strongest effect, followed by human‐driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis shows that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PI and economization during PII. [ABSTRACT FROM AUTHOR]

Single phenological measures, like the average rate of phenological advancement, may be insufficient to explain how climate change is driving trends in animal populations. Here, we develop a multifactorial concept of spring phenology—including the onset of spring, spring duration, interannual variability, and their temporal changes—as a driver for population dynamics of migratory terrestrial species in seasonal environments. Using this conceptual model, we found that effects of advancing spring phenology on animal populations may be buffered or amplified depending on the duration and interannual variability of spring green‐up, and those effects are modified by evolutionary and plastic adaptations of species. Furthermore, we compared our modelling results with empirical data on normalized difference vegetation index‐based spring green‐up phenology and population trends of 106 European landbird finding similar associations. We conclude how phenological changes are expected to affect migratory bird populations across Europe and identify regions that are particularly prone to suffer population declines. [ABSTRACT FROM AUTHOR]

The start of vegetation growing season (SOS) plays an important role in the energy cycle between the land and atmosphere. Due to the limited temporal span of a single satellite sensor through time, the continuous variation of the SOS over 40 years has not been adequately quantified. Using the overlapping periods (2001–2015) between the Global Inventory Modeling and Mapping Studies (GIMMS) (1982–2015) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (2001–2021) data sets, we construct an NDVI data set covering the period 1982–2021 on the Mongolian Plateau and further construct a map of relative climatic constraint on the SOS (divided into "temperature‐constrained," "precipitation‐constrained," and "other" regions) for quantifying SOS variability. We show that the constructed NDVI data set has high consistency and continuity with earlier GIMMS NDVI data. Regions with the SOS constrained by temperature account for 55.3% of the plateau and are located in northwestern and northeastern cold areas, while regions with the SOS constrained by precipitation constitute over 34.7% and are located in central and southwestern drier regions. Importantly, the temperature‐constrained SOS has continuously and significantly advanced, with a total advance of 4.8 days over 40 years. In contrast, the precipitation‐constrained SOS reversed from advancing to delaying in 2005. This suggests that differentiating the climatic constraint on the SOS might be a practical treatment for reducing the uncertainties in the SOS trends in previous studies. Interestingly, the precipitation‐constrained SOS does not significantly correlate to both the chilling and forcing temperatures, indicating less dependency of the SOS on chilling, which may not have been well considered previously. Plain Language Summary: Shifts in the spring phenology of plants have profound effects on ecosystem structure and function. Trends in spring phenology have become increasingly controversial as observational periods have extended. To reduce the uncertainties in the controversial changes of spring phenology and explore the trends of the start of the growing season (SOS) over 40 years on the Mongolian Plateau, we divided the plateau into three subregions and quantified the main climatic constraints on spring phenology. Based on these divisions and quantifications, we observed different trends of spring phenology in different subregions. We can infer that warming could continuously advance spring phenology in the future in areas where the spring phenology is constrained by temperature, but the magnitude of the advance may be smaller than that estimated before the 1990s. However, the advance of spring phenology in regions where the spring phenology is constrained by precipitation will be delayed with continuous warming. Due to none of the single remotely sensed normalized difference vegetation index (NDVI) data sets covering sufficiently long periods to predict future trends of spring phenology, the construction of a consistent and continuous NDVI time series using the overlapping periods of different available sensors, especially for regions with an annual NDVI > 0.1, is encouraged. Key Points: Per‐pixel linear model is sufficient to calibrate Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) to Global Inventory Modeling and Mapping Studies (GIMMS) NDVI in areas with an annual NDVI > 0.10, for start of vegetation growing season (SOS) trend through timeRegion with SOS constrained by temperature is 55.3% of the Mongolian plateau, while region with SOS constrained by precipitation is 34.7%SOS in temperature‐constrained region has continuously advanced, and SOS in precipitation‐constrained region has delayed from 1982 to 2021 [ABSTRACT FROM AUTHOR]

While many studies on insect diversity report declines, others show stable, fluctuating or increasing trends. For a thorough understanding of insect trends and their effects on ecosystem functioning, it is important to simultaneously assess insect richness, abundance and biomass, and to report trends for multiple taxa.We analysed insect richness, abundance and biomass data for all insects and for eight insect taxa (Buprestidae, Cerambycidae, Carabidae, other Coleoptera, Aculeata, other Hymenoptera, Heteroptera and Lepidoptera) from 42 sites across Switzerland from 2000 to 2007, representing three major habitat types in Switzerland (agricultural, unmanaged [open and forested] and managed forest habitats). As potential drivers of temporal patterns, we evaluated weather‐ and land‐use‐related factors. As predictors, we included temperature and precipitation as well as the vegetation index and the habitat type, respectively.We found a consistent pattern of stable or increasing trends for richness, abundance and biomass of insects in total and the eight taxa over 8 years. Both overall patterns and six out of eight taxa (except for Cerambycidae and Lepidotpera) showed the highest values in agricultural habitats. However, when accounting for elevation, there was no difference in open habitats regardless of whether they were used agriculturally.Habitat types were the most important predictors, followed by weather‐ and vegetation‐related factors. Modelled responses to mean temperature were unimodal, whereas the standard deviation of temperature showed positive and precipitation negative effects. Longer time series are needed to draw robust inferences and to investigate potential negative effects of future warming. [ABSTRACT FROM AUTHOR]

Aim: Changes in surface thaw onset in Arctic permafrost can regulate terrestrial ecosystem dynamics. Permafrost surface thaw onset has advanced significantly because of strong Arctic warming. However, surface thaw onset change effects on vegetation during the different growing season stages remain unclear. Location: Arctic and subarctic permafrost region. Time Period: 1982–2016. Major Taxa Studied: Arctic vegetation. Methods: This study investigated surface thaw onset change effects on vegetation growth in the Arctic permafrost region by analysing the relationships between surface thaw onset dates and vegetation indices obtained via satellite remote sensing from 1982 to 2016. Results: The leaf area index (LAI) from April to June was negatively correlated (−0.16 area‐averaged partial correlation coefficient) with surface‐thawed dates in over 78.6% of the Arctic permafrost region, and the LAI from August to October was positively correlated (0.17 area‐averaged partial correlation coefficient) with surface‐thawed dates in over 79.9% of the Arctic permafrost region. These distinct relationships suggest that earlier thaw onset may have a positive effect on vegetation growth during the early stage of the growing season. However, earlier thaw onset can limit vegetation growth during the late stage of the growing season. The different relationships between surface thaw onset and LAI can be explained by moisture availability. This difference occurred because 67.3% of the Arctic permafrost region was more constrained by temperature during the early stage of the growing season, therefore, the earlier thaw onset alleviated this temperature constraint and benefited vegetation growth. During the late stage of the growing season, 47.7% of the Arctic permafrost region was more constrained by moisture. Main Conclusions: Our results suggest that surface thaw onset change effects on vegetation vary by period, and this difference requires careful consideration when predicting ecosystem changes in permafrost regions. [ABSTRACT FROM AUTHOR]

Increasing temperatures and winter precipitation can influence the carbon (C) exchange rates in arctic ecosystems. Feedbacks can be both positive and negative, but the net effects are unclear and expected to vary strongly across the Arctic. There is a lack of understanding of the combined effects of increased summer warming and winter precipitation on the C balance in these ecosystems. Here we assess the short‐term (1–3 years) and long‐term (5–8 years) effects of increased snow depth (snow fences) (on average + 70 cm) and warming (open top chambers; 1–3°C increase) and the combination in a factorial design on all key components of the daytime carbon dioxide (CO2) fluxes in a wide‐spread heath tundra ecosystem in West Greenland. The warming treatment increased ecosystem respiration (ER) on a short‐ and long‐term basis, while gross ecosystem photosynthesis (GEP) was only increased in the long term. Despite the difference in the timing of responses of ER and GEP to the warming treatment, the net ecosystem exchange (NEE) of CO2 was unaffected in the short term and in the long term. Although the structural equation model (SEM) indicates a direct relationship between seasonal accumulated snow depth and ER and GEP, there were no significant effects of the snow addition treatment on ER or GEP measured over the summer period. The combination of warming and snow addition turned the plots into net daytime CO2 sources during the growing season. Interestingly, despite no significant changes in air temperature during the snow‐free time during the experiment, control plots as well as warming plots revealed significantly higher ER and GEP in the long term compared to the short term. This was in line with the satellite‐derived time‐integrated normalized difference vegetation index of the study area, suggesting that more factors than air temperature are drivers for changes in arctic tundra ecosystems. [ABSTRACT FROM AUTHOR]

Climate change is contributing to declines of insects through rising temperatures, altered precipitation patterns, and an increasing frequency of extreme events. The impacts of both gradual and sudden shifts in weather patterns are realized directly on insect physiology and indirectly through impacts on other trophic levels. Here, we investigated direct effects of seasonal weather on butterfly occurrences and indirect effects mediated by plant productivity using a temporally intensive butterfly monitoring dataset, in combination with high‐resolution climate data and a remotely sensed indicator of plant primary productivity. Specifically, we used Bayesian hierarchical path analysis to quantify relationships between weather and weather‐driven plant productivity on the occurrence of 94 butterfly species from three localities distributed across an elevational gradient. We found that snow pack exerted a strong direct positive effect on butterfly occurrence and that low snow pack was the primary driver of reductions during drought. Additionally, we found that plant primary productivity had a consistently negative effect on butterfly occurrence. These results highlight mechanisms of weather‐driven declines in insect populations and the nuances of climate change effects involving snow melt, which have implications for ecological theories linking topographic complexity to ecological resilience in montane systems. [ABSTRACT FROM AUTHOR]

A relative lack of standardised long-term monitoring data often limits the ability of African conservancies to quantify their efficacy to protect wildlife. In this study, we combined eight 2-km long transects surveyed monthly between October 2017 and March 2020 (total 240 transects sampled) with a hierarchical multi-species and multi-season distance sampling modelling framework to: (1) estimate monthly density of an ensemble of 10 different large herbivores and (2) understand how species respond to changes in vegetation productivity and time across the Naboisho Conservancy in the Greater Mara Ecosystem, Kenya. We documented a total of 55,298 individuals from 6830 animal groups. The median coefficient of variation for all density estimates was 27.67%. On average, number of groups was higher at intermediate levels of vegetation productivity. We found that in general, populations of large herbivores remained stable in the conservancy during the study period, except for wildebeest which decreased in density. We showed that multi-species monitoring frameworks can be used to understand how species abundance changes across space and time, providing indications as to how they are responding to environmental dynamics or management prescriptions--both of which are valuable tools for under-resourced wildlife conservancies. [ABSTRACT FROM AUTHOR]

In a largely deforested landscape, thousands of small pockets of indigenous forest remain in Ethiopia's northern highlands. These "church forests" are maintained by Ethiopian Orthodox church communities, and they provide valuable reserves of indigenous species diversity and ecosystem services. However, there are important trade‐offs between the different ecosystem services church forests provide: the size of community‐made clearings within church forests used for prayer and other church services have to be balanced with services derived from intact forest cover and dense vegetation. This research uses a spatial, social–ecological model to examine patterns in church forest size and vegetation density across 2743 church forests in the Amhara Region. Results suggest that larger internal clearings correlate with larger forest area, and clearings also scale with population size. We also find that greater elevation and distance to population centers are associated with higher church forest density, while greater community wealth is associated with reduced forest density and area. These trends initially suggest that internal clearings can support conservation efforts, but they are nuanced by prevalent non‐native Eucalyptus planting and increasing internal deforestation for graveyard space for wealthier communities. Additionally, we find that internal clearings are an important source of omitted variable bias in estimating the relationship between density and area in church forests. As a consequence, incorporating church clearings into social–ecological models leads to meaningful differences in church forest density and area estimates. Ultimately, this study suggests that church forests in Ethiopia may be sustained rather than consumed from within. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) as a source of the oldest world cultures, flows through vast arid and semi‐arid regions of China. Its fragile vegetation is suffering the obvious effect from regional climate change, and easily turns from a critical state into qualitatively different modes of change, which implies significant impacts on humans and ecosystems. Our results show that in the period of 1982–2015, the normalized difference vegetation index (NDVI) appeared significantly discontinuous changes by stagnant warming and strengthening wetting, especially in the central YRB (104.5°–111° E, 35.5°–38° N). The NDVI in this region shifted from slowly increasing (5.42 × 10−4 a−1) to rapidly increasing (5.13 × 10−3 a−1) around 2003, called the turning point (TP). Such shift change is mainly a result of increased moderate rain, with frequency trend changed from 0.083 times·a−1 to 0.324 times·a−1. Meanwhile, these changes on vegetation led to a reversal of the gross primary productivity (GPP) from decreasing to increasing. Such results indicated the vulnerable ecosystem of the central YRB has played a positive contribution to the carbon balance, and more sustainable management of vegetation is required for the ecological development and engaging adaptive strategy. [ABSTRACT FROM AUTHOR]

There is mounting evidence from experimental studies that drought and nutrient enrichment can interact to impact the biodiversity and productivity of terrestrial ecosystems. Whether such interactive effect influences plant diversity and the temporal stability of community productivity of natural ecosystems is unknown. To fill this knowledge gap, we combined a field survey of plant diversity and soil conditions with remote sensing temporal estimates of primary productivity in grasslands along a natural gradient in northern China. We found that aridity and soil ammonium (NH4+‐N) interacted to influence temporal stability of NDVI. That is, the relationship between ammonium and temporal stability of NDVI shifted from positive to negative due to increased standard deviation of NDVI with increasing aridity. Species richness was not related to temporal stability because it influenced the mean and standard deviation of NDVI proportionally. As a result, soil fertility outweighed the contribution of species richness to temporal stability. Our study demonstrates the synergistic effect of aridity and soil fertility, but not species richness, on temporal stability along a large natural gradient. Predicting how environmental drivers affect diversity and the stable provisioning of ecosystem services in real‐world ecosystems therefore requires a better understanding of the complex interactions among environmental drivers. [ABSTRACT FROM AUTHOR]

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance‐based remotely sensed optical signals at northern latitude or high‐altitude regions are readily confounded by snow coverage, hampering applications of satellite‐based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness‐based indices and ecosystem productivity of many evergreen‐dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global‐scale photosynthesis monitoring efforts using remotely sensed vegetation indices. [ABSTRACT FROM AUTHOR]

Vegetation is a key component controlling soil accretion in coastal wetlands through production of belowground organic matter and enhanced deposition of mineral sediments. Vegetation structure is a proxy for wetland health and degradation that can be monitored at large scales with remote sensing. Among different multispectral indices, the Normalized Difference Vegetation Index (NDVI) is generally used for this purpose. Using Google Earth Engine (GEE), NDVI time‐series are extracted around 45 monitoring stations of the Coastwide Reference Monitoring System (CRMS) located in Terrebonne Bay, Louisiana, USA. NDVI tends to increase from saline to freshwater wetlands. Using these NDVI observations and in situ measurements of salinity, soil accretion rates, and geomorphic metrics (i.e., elevation, distance from the bay or from the nearest channel bank), empirical models were developed to derive maps of organic mass accumulation rates and salinity. The analysis shows that NDVI can be used to reproduce the salinity gradient in Terrebonne Bay, as the index captures differences in vegetation cover, which depend on salinity. A negative relationship between NDVI and organic accumulation mass rates is also found, indicating that saline marshes tend to accumulate more organic material compared to fresh wetlands. Plain Language Summary: Coastal wetlands need to increase their elevation to counterbalance relative sea level rise. This process is achieved through deposition of mineral sediment and organic matter produced by local vegetation. The latter contribution becomes fundamental in areas lacking mineral supply. In this study, we show that it is possible to leverage on the Normalized Difference Vegetation Index (NDVI), a very common remote sensing indicator, to derive the contribution of vegetation to soil accumulation. Moreover, the same index can be used to derive the distribution of salinity. These results can be achieved because NDVI is related to the general plant type, which changes when we move from saline to freshwater wetlands. Key Points: Average NDVI values are used to derive distribution of salinity and organic mass accumulation rates in Terrebonne Bay, LouisianaNDVI reflects different plant types in the wetlands of Terrebonne BayOn average 0.065 g cm−2 yr−1 of organic mass are accumulated on a yearly basis in Terrebonne Bay [ABSTRACT FROM AUTHOR]

Background: Wastewater from sewage treatment plants contains high levels of nutrients, which can be used for plant nutrition. Classical wastewater treatment plants use complex microbial consortia of autotrophic and heterotrophic microorganisms for biological wastewater treatment. Certain autotrophic microalgae (e.g., species of the genera Chlorella, Scenedesmus, and Pediastrum) accumulate nutrients from wastewater very effectively. Aims: We investigated the potential of microalgae biomass obtained from a prototype wastewater treatment plant as a source of nutrients for crops, focusing on nitrogen. Methods: We provided wheat plants with different levels of algae biomass equivalent to 60, 120, and 180 kg N per hectare or with mineral fertilizer (N, P, and K) equivalent to the amounts contained in the algal biomass. Physiological and phenotypic traits were measured during growth, including vegetation indices, photosynthetic performance, growth, and nitrogen use efficiency (NUE). In addition, the adundances of Bacteria, Archaea and fungi and genes of ammonium oxidizing Bacteria and Archaea were determined in the rhizosphere of differently fertilized plants. Results: Microalgal application at fertilizer levels of 120 and 180 kg N ha–1 showed significantly improved physiological performance, growth, yield and nutrient uptake compared to the unfertilized control. Nevertheless, their yields and NUE were lower than with the application of equal amounts of mineral fertilization, while the adundance of rhizosphere microbes and ammonia‐oxidizing microorganisms were not significantly affected. Conclusions: Microalgae from wastewater treatments form a suitable source of organic fertilizer for wheat plants with only moderate reductions in N use efficiency compared to mineral fertilizer. [ABSTRACT FROM AUTHOR]

Recruitment is one of the fundamental drivers of ungulate population dynamics. Recruitment of neonates into an ungulate population can be influenced by a wide range of abiotic, biotic, and anthropogenic factors. Our objective was to examine which environmental variables most influenced pronghorn (Antilocapra americana) fawn recruitment, as measured by fawn:doe ratios, across six subpopulations in Idaho spanning 35 years (1984–2018) of herd composition surveys. Using a retrospective analysis, we examined the influence of precipitation, minimum and maximum temperatures, Palmer drought severity, normalized difference vegetation index (NDVI), vegetation, and elevation across five biological time periods of interest (one and two months pre‐parturition, one and two months post‐parturition, and breeding). Environmental factors, especially the variance in the greenness of vegetation (i.e., NDVI) during the two months post‐parturition (during lactation), were the main drivers of fawn recruitment for subpopulations occupying mountain valleys. Pronghorn fawn recruitment in these mountain valleys was dependent on the condition of the lactating female as influenced by the quality of vegetation, which would subsequently influence fawn growth rates. In contrast, pronghorn recruitment at lower elevations was driven by several environmental variables (i.e., NDVI, drought severity, minimum and maximum temperatures, forb, and grass cover) during the month pre‐parturition. Environmental variables at lower elevations were influencing the condition of the female during gestation which, if favorable, would result in higher birth weights and subsequent increased fawn survival. At intermediate elevation sites, results were mixed with fawn recruitment at one site influenced by low temperatures, which could induce hypothermia in fawns, while recruitment at the other site was influenced by drought severity during gestation (two months pre‐parturition). Influence of these environmental variables was also related to the timing of pronghorn arriving at the high elevation sites (i.e., arriving just prior to fawning); hence, effects were predominantly post‐parturition. In contrast, low elevation sites either had resident pronghorn or earlier arrival; thereby, effects on fawn recruitment were mostly from pre‐parturition variables. Given the range of environmental factors influencing pronghorn subpopulations, managers will need to consider the timing, intensity, and variability of environmental conditions as conditions were spatially and temporally explicit. [ABSTRACT FROM AUTHOR]

Over the past several decades, various trends in vegetation productivity, from increases to decreases, have been observed throughout Arctic–Boreal ecosystems. While some of this variation can be explained by recent climate warming and increased disturbance, very little is known about the impacts of permafrost thaw on productivity across diverse vegetation communities. Active layer thickness data from 135 permafrost monitoring sites along a 10° latitudinal transect of the Northwest Territories, Canada, paired with a Landsat time series of normalized difference vegetation index from 1984 to 2019, were used to quantify the impacts of changing permafrost conditions on vegetation productivity. We found that active layer thickness contributed to the observed variation in vegetation productivity in recent decades in the northwestern Arctic–Boreal, with the highest rates of greening occurring at sites where the near‐surface permafrost recently had thawed. However, the greening associated with permafrost thaw was not sustained after prolonged periods of thaw and appeared to diminish after the thaw front extended outside the plants' rooting zone. Highest rates of greening were found at the mid‐transect sites, between 62.4° N and 65.2° N, suggesting that more southernly sites may have already surpassed the period of beneficial permafrost thaw, while more northern sites may have yet to reach a level of thaw that supports enhanced vegetation productivity. These results indicate that the response of vegetation productivity to permafrost thaw is highly dependent on the extent of active layer thickening and that increases in productivity may not continue in the coming decades. [ABSTRACT FROM AUTHOR]

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the 'LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how 'LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents. [ABSTRACT FROM AUTHOR]

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

Overpopulation in conservation areas with large populations of elephants reduces the diversity of the thicket vegetation of the Eastern Cape Province of South Africa. Monitoring species' specific impacts is a difficult task for reserves. This study investigates whether the Normalised Difference Vegetation Index (NDVI) can be used to evaluate the health of thicket in the Addo Elephant National Park successfully. To achieve this, different browsing intensities of elephants were used to establish a gradient of the full range of utilisation levels on thicket. These were obtained at different distances from the nearest available water sources for Mesic and Valley Thicket types. NDVI values were correlated to an elephant utilisation gradient, which was, in turn, directly related to distance from a water source. The lowest NDVI values (<0.2) were observed adjacent to watering holes where vegetation was completely degraded due to trampling by the daily congregation of animals. The highest NDVI values (>0.5 representing the densest stands of thicket vegetation) were obtained at distances exceeding 7 km from a water source where elephant densities were the lowest within the park. NDVI values could, therefore, be used to identify and monitor gradients of elephant impact on the integrity of thicket. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM products (i.e., IMERG) provide new‐generation satellite precipitation measurements. For urban contexts, however, the issues of its bias and insufficient resolutions still exist. This study aims to develop high‐precision and high‐resolution (e.g., 0.01°/1 h) data for a metropolitan region based on IMERG and gauge precipitations. The original IMERG product is evaluated using hourly in situ precipitations from 47 gauges. A spatial downscaling‐calibration (DC) technique is then developed to enhance the IMERG using the Normalized Difference Vegetation Index. The results show the limited capability of IMERG to capture sub‐daily precipitation and high‐intensity precipitation. The proposed DC method significantly improves IMERG performance, with correlation coefficient (CC) increasing from 0.07 to 0.75, and probability of detection improving from 0.34 to 0.90 at the hourly scale. In terms of spatial rainfall distribution, 86% of mean absolute error and 80% of RMSE are improved with CC increasing from 0.07 to 0.91 on average. Additionally, the calibrated downscaled product provides finer information in local areas, capturing three times more spatial variabilities of urban precipitation against the original IMERG input data. The results highlight the necessity of improving urban observations for flood risk management at fine spatiotemporal resolutions. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy of corn yield forecasts using machine learning with satellite and weather data. In addition, the study examines the incremental value of these forecasts to augment the World Agricultural Supply and Demand Estimates (WASDE) forecast. To illustrate the potential of machine learning methods for agricultural forecasting, publicly available data are collected from 1984 to 2021 for national corn yield, state corn yield, satellite variables, and weather variables and used with the XGBoost algorithm. The results show that the XGBoost model performed about the same but did not outperform the WASDE corn yield forecasts over a 12‐year out‐of‐sample period. The incremental value analysis results suggest that the XGBoost and WASDE forecasts capture similar information, and no incremental information exits. Although the XGBoost model does not outperform the WASDE August forecast, it is near real‐time and can be produced using publicly available data. The results indicate that the XGBoost machine learning models can produce reasonably accurate crop yield forecasts. [ABSTRACT FROM AUTHOR]

We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions. Key Points: We present the satellite vegetation index time series flood (SatVITS‐Flood) model, which uses SatVITS metrics to detect rare floods in hyperarid regionsSatVITS‐Flood was tested in four sites over three continents using 40‐year hydrological data and three different satellite platformsThe model's accuracy was 78%, with flood volume NSE = 0.79 and duration of R2 = 0.69. Our model provides new data for climate‐hydrology research [ABSTRACT FROM AUTHOR]

Global drylands are experiencing booming development of centralized photovoltaics (PV), which aims to address the dual challenges posed by climate change and energy transformation. In dryland areas with large‐scale deployment of solar PV infrastructure, vegetation was reported to experience drastic changes. However, the long‐term dynamic changes and driving mechanisms have not been thoroughly studied yet. Quantitatively distinguishing the disturbances of climate change and PV plant deployment on vegetation change is the key to understanding the environmental impact of clean energy development and formulating adaptive ecological recovery measures. To understand this, we selected the Gonghe solar power project in northern China, one of the largest dryland PV plants in the world, as a case study. Specifically, satellite‐derived Normalized Difference Vegetation Index (NDVI) and meteorological data from ground stations were used to analyze the changing patterns of vegetation growth and climatic factors. The relative contributions of climatic factors and PV plant deployment to NDVI change were quantified by multiple regression analysis. The results indicated that vegetation has increased gradually since 2000, with the rate of vegetation recovery doubled during the PV expansion period. From 2013 to 2020, climate change was the main driver of increased vegetation (56%), followed by the expansion of solar PV infrastructures (44%). Vegetation inside PV arrays increased 1.4 times faster than outside, mainly because the PV panels improve the efficiency of rainwater utilization in summer and reduce the negative impact of excessive sunlight in the growing season. In addition, vegetation management practices like grazing can further enhance carbon sequestration and create sustainable livelihood opportunities, achieving sustainable economic, social, and ecological development. The novelty of the study lies in the proposed framework to quantify the impact of solar PV programs on vegetation in dryland allowing easy interpretations of vegetation dynamics under clean energy development and climate change, which provide scientific references for clean energy planning and ecological recovery in arid areas. [ABSTRACT FROM AUTHOR]

Non‐human primate species are threatened worldwide. Their population declines go along with the loss of ecological functions such as seed dispersal that plays a crucial role in plant regeneration. Restoring essential habitat structures could thus not only protect primates, but also facilitate forest regeneration. We used classical vegetation description on the ground and a remote sensing analysis to describe habitat use of crowned lemurs (Eulemur coronatus), a seed‐dispersing primate endemic to northern Madagascar. Our aim was to find vegetation characteristics important for lemurs that might be targeted in a restoration approach. For this, we applied both methods in differently degraded forest types. Both classical vegetation description and remote sensing analysis were able to distinguish these forest types. The habitat use of our two study groups was associated consistently with vegetation structures measured on a small scale such as tree height and density of thick trees. In contrast, vegetation productivity and water content derived from satellite imagery on a larger scale could not consistently explain habitat use of lemurs. Thus, measurements on the ground can identify suitable microhabitats that do not show on the satellite imagery scale. These microhabitats might be very important conservation elements to create buffer zones and corridors. Further, they might attract seed‐dispersing species into degraded areas targeted for forest restoration, acting as natural regeneration nuclei. The potential of these microhabitats for conservation would not be recognized when analyses were based solely on landscape analyses on large scales. [ABSTRACT FROM AUTHOR]

Soil erosion has been a persistent problem in high‐latitude regions and may worsen as climate change unfolds and encourages increased anthropogenic exploitation. We propose that soil moisture is likely to shape future erosion trends, as moisture stress reduces the capacity of vegetation cover to retard erosive processes. However, the spatial variability of soil moisture in high‐latitude soils—and the ways in which this variability drives the spatial distribution of erosion features—is poorly understood. We addressed this knowledge gap with a study of andosol erosion in southern Iceland. Our study used a combination of high‐resolution (<3 cm) remote sensing data (using normalised difference vegetation index (NDVI) and normalised difference red edge as metrics of plant vitality) and long‐term, in situ measurements of soil moisture to unpick the relationship between moisture stress, vegetation vitality and patchy soil erosion. Mean NDVI increased with distance from eroded areas, varying from ~0.6 in vegetated areas on the margins of erosion patches to ~0.8 in areas >10 m from eroded terrain. We found lower moisture availability close to existing erosion features: mean volumetric soil moisture content varied from 17% (proximal to erosion patch) to 36% (distal to erosion patch). We also found that variability in soil moisture decreased with distance from eroded areas: the coefficient of variation (CV) in soil moisture varied from 0.33 (proximal to erosion patch) to 0.13 (distal to erosion). Our findings indicate that the margins of erosion patches have a stressful soil environment due to exposure to the atmosphere. The vegetation in these locations grows less vigorously, and the exposed soil becomes more vulnerable to erosion, leading to erosion patch expansion and coalescence. If these conditions hold more generally, they may represent a feedback mechanism that facilitates the lateral propagation of soil erosion in high‐latitude regions. [ABSTRACT FROM AUTHOR]

Recent studies highlight the potential of climate change refugia (CCR) to support the persistence of biodiversity in regions that may otherwise become unsuitable with climate change. However, a key challenge in using CCR for climate resilient management lies in how CCR may intersect with existing forest management strategies, and subsequently influence how landscapes buffer species from negative impacts of warming climate. We address this challenge in temperate coastal forests of the Pacific Northwestern United States, where declines in the extent of late‐successional forests have prompted efforts to restore old‐growth forest structure. One common approach for doing so involves selectively thinning forest stands to enhance structural complexity. However, dense canopy is a key forest feature moderating understory microclimate and potentially buffering organisms from climate change impacts, raising the possibility that approaches for managing forests for old‐growth structure may reduce the extent and number of CCR. We used remotely sensed vegetation indices to identify CCR in an experimental forest with control and thinned (restoration) treatments, and explored the influence of biophysical variables on buffering capacity. We found that remotely sensed vegetation indices commonly used to identify CCR were associated with understory temperature and plant community composition, and thus captured aspects of landscape buffering that might instill climate resilience and be of interest to management. We then examined the interaction between current restoration strategies and CCR, and found that selective thinning for promoting old‐growth structure had only very minor, if any, effects on climatic buffering. In all, our study demonstrates that forest management approaches aimed at restoring old‐growth structure through targeted thinning do not greatly decrease buffering capacity, despite a known link between dense canopy and CCR. More broadly, this study illustrates the value of using remote sensing approaches to identify CCR, facilitating the integration of climate change adaptation with other forest management approaches. [ABSTRACT FROM AUTHOR]

Contrary to the general "greening of the Arctic", the Siberian Indigirka Lowlands show strong "browning" (a decrease in the Normalized Difference Vegetation Index or "NDVI") in various recent satellite records. Since greening and browning are generally indicative of increases and losses in photosynthetically active biomass, this browning trend may have implications for the carbon balance and vegetation of this Arctic tundra region. To explore potential mechanisms responsible for this trend break from general Arctic greening, we studied timeseries of Landsat summer maximum NDVI, weather data, and high‐resolution maps of vegetation compositional change, topography, geomorphology and hydrology. We find that a significant proportion of browning (lower summer NDVI) is explained by moisture dynamics, with high snow depths and resulting floods as well as summer drought coinciding with low NDVI. Relations between seasonal weather variables and NDVI are spatially heterogeneous, with floodplains, drained thaw lake basins and Yedoma ridges showing different patterns of association with weather variables. Low summer NDVI after high snowfall was particularly evident in floodplains, likely explained by early summer floods. Local small‐scale vegetation changes explained only small amounts of variance in browning rates in Landsat NDVI. Local expansion of Sphagnum vegetation in particular may have contributed to recent browning of our study site, but higher resolution NDVI timeseries are necessary to accurately constrain the role of small‐scale vegetation shifts. Overall, associations identified in this study suggest that future increases in Arctic precipitation variability and extremes may limit tundra greening, but to different extents even across comparatively small topographical contrasts. Plain Language Summary: Across Arctic regions, satellite images show that tundra ecosystems have been "greening". This suggests that plant growth, and thereby uptake of carbon from the atmosphere, is increasing in the Arctic. The Indigirka Lowlands in Siberia provide a stark contrast; in this lowland tundra region, satellite images show "browning", which suggests a decline in plant growth. We explored why this might be the case, in order to better assess whether browning should be expected across the wider Arctic in a changing climate. We compare local browning rates, calculated as changes in spectral indices from Landsat satellite images, to annual weather data, detailed maps of local vegetation changes and maps of terrain properties such as elevation and hydrology. We find that browning is associated with high snow depths and resulting floods, but also with dry summer conditions. Browning was not strongly related to local, small‐scale changes in vegetation (the most common of which was expansion of peat moss). We also find that the degree of association with potential drivers of browning (e.g., flooding, dry summers) differs across landforms. Floodplains show particularly strong browning following high snowfall, likely explained by early summer floods. This suggests that expected increases in year‐to‐year variability and extremes in Arctic precipitation may limit future greening. Key Points: Our study site in the Indigirka Lowlands shows strong (−0.0039 NDVI units yr−1) and ubiquitous (76% of area) recent browning in Landsat NDVIHigh snowfall and low summer rainfall were associated with lower summer NDVI, with differential impacts across topographical gradientsLocal small‐scale shifts in vegetation types and thermokarst activity show weak association with Landsat NDVI trends [ABSTRACT FROM AUTHOR]

Giant reed (Arundo donax) is a prevalent invasive plant in desert riparian ecosystems that threatens wildlife habitat. From 2008 to 2018, under a United States–Mexico partnership, prescribed burns and herbicide applications were used to remove giant reed and promote native revegetation along the Rio Grande—Río Bravo floodplain in west Texas, USA, and Mexico. Our goal was to explore the effects of the removal efforts on butterfly and bird communities and their habitat along the United States portion of the Rio Grande—Río Bravo floodplain in Big Bend National Park, Texas. During spring and summer, 2016–2017, we surveyed butterflies, birds, and their habitat using ground‐collected and remotely sensed data. Using a variety of generalized linear and N‐mixture modeling routines and multivariate analyses, we found that the initial giant reed removal efforts removed key components of riparian habitat leading to reduced butterfly and bird communities. Within several years following management, giant reed levels remained low, while riparian habitat conditions and butterfly and bird communities largely rebounded, including many disturbance‐sensitive butterfly species and riparian‐associated bird species. Butterflies were most consistently associated with forb and grass cover, and birds with a remotely sensed index of greenness (the normalized difference vegetation index), several vegetation cover types, and habitat heterogeneity, habitat elements that were most common in locations that had the longest time to recover following management actions. Our results suggest that prescribed burns and herbicide applications, when used following protocols to minimize risk to wildlife, can limit the spread of giant reed in desert riparian systems and introduce habitat conditions that support diverse and abundant butterfly and bird communities. [ABSTRACT FROM AUTHOR]

Climate change, including both increasing temperatures and changing snow regimes, is progressing rapidly in the Arctic, leading to changes in plant phenology and in the seasonal patterns of plant properties, such as tissue nitrogen (N) content and community aboveground biomass. However, significant knowledge gaps remain over how these seasonal patterns vary among Arctic plant functional groups (i.e., shrubs, grasses, and forbs) and across large geographical areas. We used three years of in situ field vegetation sampling from an 80,000‐km2 area in Arctic Alaska, remotely sensed vegetation data (daily normalized difference vegetation index [NDVI]), and modeled output of snow‐free date to determine and model the seasonal trends and primary controls on leaf percent nitrogen and biomass (in grams per square meter) among Arctic vegetation functional groups. We determined relative vegetation phenology stage at a 500‐m spatial scale resolution, defined as the number of days between the date of the seasonal maximum NDVI and the vegetation field sampling date, and relative snow phenology stage (90‐m spatial scale) was determined as the number of days between the date of snow‐free ground and the sampling date. Models including relative phenology stage were particularly important for explaining seasonal variability of %N in shrubs, graminoids, and forbs. Similarly, vegetation and snow phenology stages were also important for modeling seasonal biomass of shrubs and graminoids; however, for all functional groups, the models explained only a small amount of seasonal variability in biomass. Relative phenology stage was a stronger predictor of %N and biomass than geographic position, indicating that localized controls on phenology, acting at spatial scales of 500 m and smaller, are critical to understanding %N and biomass. [ABSTRACT FROM AUTHOR]

The peak growth of plant in summer is an important indicator of the capacity of terrestrial ecosystem productivity, and ongoing studies have shown its responses to climate warming as represented in the mean temperature. However, the impacts from the asymmetrical warming, that is, different rates in the changes of daytime (Tmax) and nighttime (Tmin) warming were mostly ignored. Using 60 flux sites (674 site‐year in total) measurements and satellite observations from two independent satellite platforms (Global Inventory Monitoring and Modeling Studies [1982–2015]; MODIS [2000–2020]) over the Northern Hemisphere (≥30°N), here we show that the peak growth, as represented by both flux‐based maximum primary productivity and the maximum greenness indices (maximum normalized difference vegetation index and enhanced vegetation index), responded oppositely to daytime and nighttime warming. Tmax−Tmin+ (peak growth showed negative responses to Tmax, but positive responses to Tmin) dominated in most ecosystems and climate types, especially in water‐limited ecosystems, while Tmax+Tmin− (peak growth showed positive responses to Tmax, but negative responses to Tmin) was primarily observed in high latitude regions. These contrasting responses could be explained by the strong association between asymmetric warming and water conditions, including soil moisture, evapotranspiration/potential evapotranspiration, and the vapor pressure deficit. Our results are therefore important to the understanding of the responses of peak growth to climate change, and consequently a better representation of asymmetrical warming in future ecosystem models by differentiating the contributions between daytime and nighttime warming. [ABSTRACT FROM AUTHOR]

Spatial dependence into environmental data is an influential criterion in clustering processes, as the resulting clustering outputs depend very much upon such spatial structure. As classical methods do not take spatial dependence in consideration, the inclusion of this structure produces unexpected but more realistic results and clusters of curves that may not be similar in shape or behavior. In this paper, clustering is made using the KMSCFD algorithm for spatially correlated functional data. The methodology was developed through weighting the distance matrix between the curves with the trace‐variogram calculated with the coefficients of the basis functions resulting from a data smoothing operation. For the validation of the method, a number of simulated scenarios were tested together with an application to Normalized Difference Vegetation Index data derived from a high elevation ecosystem in the Ecuadorian Andes. Quality indices are implemented to obtain the appropriate number of clusters. The analysis showed five different regions that were latitudinally distributed. [ABSTRACT FROM AUTHOR]

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. We found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience–mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience–mortality relationships. [ABSTRACT FROM AUTHOR]

Columbian sharp‐tailed grouse (Tympanuchus phasianellus columbianus) are endemic to grassland and shrub‐steppe ecosystems of western North America, yet their distribution has contracted to <10% of their historical range. Primary threats to Columbian sharp‐tailed grouse include loss of native habitat and conversion to agriculture, reductions in habitat once provided by the Conservation Reserve Program (CRP), wildfire, and drought conditions, yet population‐level consequences of these threats and their spatio‐temporal scales of effect are poorly understood. We evaluated multi‐scale effects of land cover, weather, and fire histories on patterns of abundance and productivity for Columbian sharp‐tailed grouse populations during 1995–2020 in Idaho, USA, using mixed‐effects generalized regression and remotely sensed data. We demonstrated negative effects of fire, tree encroachment, and bare ground, positive effects of spring and summer precipitation and cover of shrubs and perennial forbs and grasses, and positive effects of CRP on grouse abundance that changed in magnitude with cover of perennials and shrubs near leks (i.e., strongest effects when average cover of shrubs and perennial forbs and grasses were less abundant). We also demonstrated per capita recruitment of Columbian sharp‐tailed grouse is positively associated with late‐summer greenness. Our results show that several suspected threats have measurable, population‐level impacts to Columbian sharp‐tailed grouse within Idaho. Moreover, our results suggest ongoing changes occurring within the core range of Columbian sharp‐tailed grouse, including loss of CRP cover to tilled agriculture and changes to wildfire and precipitation dynamics are likely to have negative effects on populations. [ABSTRACT FROM AUTHOR]

Sweetpotato is a crucial crop to guarantee food security in sub‐Saharan Africa, and drought events are considered one of the most critical factors affecting sweetpotato productivity in this region. In this study, airborne imagery based on reflectance (NDVI, CIred‐edge) and canopy temperature minus air temperature (dT) indices was used to characterize sweetpotato genotypes under drought treatments in Mozambique. Two field experiments established in rainy/hot (Trial A) and dry/cool (Trial B) seasons were assessed. In Trial A, 24 genotypes were subjected to early‐ (ESD), mid‐ (MSD) and late‐season (LSD) drought stress treatments and compared against a control. In Trial B, 120 genotypes were subjected to LSD only. The percentage of reduction in vine weight (PRVW) under drought was related primarily to temporal variation of NDVI and CI, regardless of drought treatment and seasons. dT in relation to control (dTAmp) was associated with PRVW in ESD‐Trial A and LSD‐Trial B, whereas under LSD‐Trial A, dTAmp was related to total fresh storage root weight (TRW). During the rainy/hot season, higher TRW reduction was promoted under ESD; however, under LSD, it was possible to identify productive genotypes able to withstand drought stress, highlighting their relevance for drought‐tolerance selection purposes. [ABSTRACT FROM AUTHOR]

Aim: Species‐level traits, such as body and range sizes, are important correlates of extinction risk. However, both are often related and are driven by environmental factors. Here, we elucidated links between environmental factors, body size, range size and susceptibility to extinction, across the whole order of rodents. Location: Global. Time period: Current. Major taxa studied: Rodents (order Rodentia). Methods: We compiled an unprecedentedly large database of rodent morphology, phylogeny, range size, conservation status, global climate and normalized difference vegetation index (NDVI), comprising >86% of all described species. Using phylogenetic regressions, we initially explored the environmental factors driving body size. Next, we modelled the relationship between body size and range size. From this relationship, we computed and mapped (at the assemblage level) an index of relative range size, corresponding to the deviation from the expected range size of each species, given its body size. Finally, we tested whether relative range was correlated with the risk of extinction of the species derived from an assessment by the International Union for Conservation of Nature. Results: We found that, contrary to the expectations of Bergmann's rule, the body size of rodents was mostly influenced by variation in NDVI (rather than latitude/temperature). Body size, in turn, imposed a constraint on species range size, as evidenced by a triangular relationship that was segmented at the lower bound. The relative species range size derived from this relationship highlighted four geographical regions where rodents with small relative range were concentrated globally. We demonstrated that lower relative range size was associated with increased risk of extinction. Main conclusions: Species that, given their body size, are distributed across ranges that are smaller than expected have elevated extinction risk. Therefore, investigating the relationships between environmental drivers, body size and range size might help to detect species that could become threatened in the near future. [ABSTRACT FROM AUTHOR]