Chasing and surfing seasonal waves: Avian migration through the US tracks land surface phenology in fall, but not spring.

Climate change is altering the timing of seasonal events for many taxa. There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration. We tested whether migratory passage-observed via radar-consistently tracked land surface variables and phenophases across latitudes in the US Central Flyway in both spring and fall. The daily temperatures, precipitation and vegetation greenness occurring on 10%, 50% and 90% cumulative passage dates changed substantially with latitude, indicating that most migrants experienced rapidly changing conditions as they headed north or south. Temperature did not limit the progression of migration in either season. Peak spring migration in the southern US occurred nearly 40 days after the spring green wave, the northward progression of vegetation growth, but nearly caught up to green-up at 48° N. Spring migration phenology may have evolved to prioritize earlier arrival for breeding. Across all latitudes, peak fall migration coincided with the same land surface phenophase, an interval of 26 days prior to dormancy onset. Migrants may rely on phenological events in vegetation during fall stopovers. Considering that (a) migratory passage tracked fall land surface phenology across latitudes at a continental scale, (b) previous studies at local scales have demonstrated the importance of fruit during fall migratory stopover and (c) fruiting phenology in North America is occurring later over time while fall migration is advancing, the potential for mismatch between fall fruiting and bird migration phenology urgently needs further investigation.

Artificial light at night is a top predictor of bird migration stopover density.

As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia-making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density. In over 70% of our models, we identify skyglow as a highly influential and consistently positive predictor of bird migration stopover density across the United States. This finding points to the potential of an expanding threat to avian migrants: peri-urban illuminated areas may act as ecological traps at macroscales that increase the mortality of birds during migration.

Global shocks, cascading disruptions, and (re-)connections: viewing the COVID-19 pandemic as concurrent natural experiments to understand land system dynamics.

Context: For nearly three years, the COVID-19 pandemic has disrupted human well-being and livelihoods, communities, and economies in myriad ways with consequences for social-ecological systems across the planet. The pandemic represents a global shock in multiple dimensions that has already, and is likely to continue to have, far-reaching effects on land systems and on those depending on them for their livelihoods. Objectives: We focus on the observed effects of the pandemic on landscapes and people composing diverse land systems across the globe. Methods: We highlight the interrelated impacts of the pandemic shock on the economic, health, and mobility dimensions of land systems using six vignettes from different land systems on four continents, analyzed through the lens of socio-ecological resilience and the telecoupling framework. We present preliminary comparative insights gathered through interviews, surveys, key informants, and authors' observations and propose new research avenues for land system scientists. Results: The pandemic's effects have been unevenly distributed, context-specific, and dependent on the multiple connections that link land systems across the globe. Conclusions: We argue that the pandemic presents concurrent "natural experiments" that can advance our understanding of the intricate ways in which global shocks produce direct, indirect, and spillover effects on local and regional landscapes and land systems. These propagating shock effects disrupt existing connections, forge new connections, and re-establish former connections between peoples, landscapes, and land systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-023-01604-2.

Urbanization imprint on land surface phenology: The urban-rural gradient analysis for Chinese cities.

Rising temperature shifts plant phenology. Chinese cities, experiencing extensive expansion and intensive warming, spanning a wide latitudinal range, might provide ideal experimental opportunities for observing and predicting phenological responses to warming temperature. Using the urban-rural gradient approach, we explored urbanization imprint on land surface phenology across the entire urbanization intensity (UI) gradient ranging from 0% to 100% in 343 Chinese cities using the VIIRS Land Surface Phenology along with MODIS Land Surface Temperature (LST) products. We found prevalent advancing and delaying trends for the start of the growing season (SOS) and the end of the growing season (EOS) with increasing UI across 343 Chinese cities, respectively. Overall, the phenology shifted earlier by 8.6 ± 0.54 days for SOS, later by 1.3 ± 0.51 days for EOS, and lengthened by 9.9 ± 0.77 days for the growing season length (GSL) in urban core areas (UI above 50%) relative to their rural counterparts (UI lower than 1%). The temperature sensitivity of SOS and EOS was 10.5 ± 0.25 days earlier and 2.9 ± 0.16 days later per 1°C LST increase in spring and autumn, respectively. Moreover, the northern cities witnessed higher temperature sensitivity for SOS and EOS than the southern ones. Both spring and autumn temperature sensitivity across these 343 cities would likely decrease with future urban warming, suggesting any projections of future phenological responses to continued warming must be approached with caution.

Trends in land surface phenology across the conterminous United States (1982-2016) analyzed by NEON domains.

Tracking phenological change in a regionally explicit context is a key to understanding ecosystem status and change. The current study investigated long-term trends of satellite-observed land surface phenology (LSP) in the 17 National Ecological Observatory Network (NEON) domains across the conterminous United States (CONUS). Characterization of LSP trends was based on a high temporal resolution (3-d) time series of the two-band enhanced vegetation index (EVI2) derived from a long-term data record (LTDR) of the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We identified significant trend patterns in LSP and their seasonal climate and land use/land cover drivers for each NEON domain. Key findings include (1) the start of season (SOS) predominantly shifted later in 13 out of 17 domains (24.3% of CONUS by area) due potentially to both a lack of spring warming in the eastern United States and changes in agronomic practices over agricultural lands; (2) the end of season (EOS) became predominantly later in nine domains dominated by natural vegetation (14.1% of CONUS by area) in response to widespread warming in autumn; (3) the EOS predominantly shifted earlier in three domains (10.6% of CONUS by area) over primarily agricultural lands as potentially affected by changes in crop growth cycles; and (4) earlier shift in the SOS was mostly found in the Northwest (3.6% of CONUS by area) and was predominant only in the moist Pacific Northwest (27.7% of the domain by area) in response to more pronounced spring warming in the region. The overall patterns of SOS and EOS trends across CONUS appeared constrained by continental-scale temperature trends as characterized by a west-east dipole and the distribution of the nation's agricultural lands. In addition, seasonal trend analysis revealed that most NEON domains (15/17) became predominantly greener in part of or throughout the growing season, potentially contributed by both climate change-induced growth increase and improved agricultural productivity. The domain-wide LSP trends with their underlying drivers identified here provide important contextual information for NEON science as well as for investigations within CONUS using other distributed observatories (e.g., LTER, LTAR, FLUXNET, USA-NPN, etc.).

Drivers of urban expansion over the past three decades: a comparative study of Beijing, Tianjin, and Shijiazhuang.

Urban expansion is influenced by various natural and anthropogenic factors. Understanding the driving forces of urban expansion is crucial for modeling the process of urban expansion as well as guiding urban planning and management. Here, we quantified and compared the effects of natural, socioeconomic, and neighboring factors on urban expansion and their temporal dynamics in three large cities in the Jing-Jin-Ji Urban Agglomeration: Beijing, Tianjin, and Shijiazhuang. We used remote sensing imagery from six epochs (circa 1980, 1990, 1995, 2000, 2005, and 2010) integrated with GIS techniques and analyzed using binary logistic regression. The relative importance of the three types of driving forces was further decomposed using variance partitioning. We found that the direction and/or magnitude of effects on the drivers of urban expansion varied with both epoch and city. Natural factors placed significant constraints at early stages of urban expansion, but this constraint relaxed over time. As precursor drivers of urbanization, socioeconomic factors significantly influenced urban growth in most epochs for each city. Non-urban lands near existing urban areas were more likely to be urbanized, due to easier access to existing transportation infrastructure and other facility resources. Furthermore, with urbanization, individual effects of drivers tended to be replaced by joint effects, especially for the neighboring factors. Similarities and differences in the individual and joint effects of drivers on urban expansion across cities and through time will provide valuable information for adaptive urban development strategies in the national capital region of China.

Contemporary evolution and scaling of 32 major cities in China.

Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes. Here, we show the temporal coevolution of multiple physical, demographic, socioeconomic, and environmental attributes in individual cities, and the cross-city scaling of urban attributes at six time points (i.e., 1978, 1990, 1995, 2000, 2005, and 2010) in 32 major Chinese cities. We found that power law scaling could adequately characterize both the cross-city scaling of urban attributes across cities and the longitudinal scaling describing the temporal coevolution of urban attributes within individual cities. The cross-city scaling properties demonstrated substantial changes over time signifying evolved social and economic forces. A key finding was that the cross-city linear or superlinear scaling of urban area with population contradicts the theoretical sublinear power law scaling proposed between infrastructure and population. Furthermore, the cross-city scaling between area and population transitioned from linear to superlinear over time, and the superlinear scaling in recent times suggests decreased infrastructure efficiency. Our results demonstrate a diseconomy of scale in urban areal expansion that indicates a significant waste of land resources in the urbanization process. Future planning efforts should focus on policies that increase urban land use efficiency before continuing expansion.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system.

BACKGROUND: Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited. METHODS: The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector. RESULTS: The system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk. CONCLUSIONS: Successful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics.

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia.

BACKGROUND: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. METHODS: In this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. RESULTS: Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. CONCLUSIONS: Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.

Landsat still contributing to environmental research.

Landsat data have enabled continuous global monitoring of both human-caused and other land cover disturbances since 1972. Recently degraded performance and intermittent service of the Landsat 7 and Landsat 5 sensors, respectively, have raised concerns about the condition of global Earth observation programs. However, Landsat imagery is still useful for landscape change detection and this capability should continue into the foreseeable future.

Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis.

The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median thresholding and local adaptive thresholding (using a 7 x 7 window) successfully segmented the grey-scale images. Lacunarity analysis indicated a multifractal nature to the images, and a range of marrow space sizes with significant structure around 14-18 mm(2). Preliminary studies of in vivo images from a clinical CT scanner indicate that lacunarity analysis can follow the pattern of bone loss in osteoporosis by monitoring the homogeneity of the marrow spaces, which is related to the connectivity of the trabecular bone network and the marrow space sizes. Although the patient sample was small, derived parameters such as the maximum deviation of the lacunarity from a neutral (fractal) model, and the maximum derivative of this deviation, seem to be sufficiently sensitive to distinguish a range of bone conditions. Our results suggest that these parameters, used with bone mineral density values, may have diagnostic value in characterizing osteoporosis and predicting fracture risk.

Ecophysiological consequences of contrasting microenvironments on the desiccation tolerant moss Tortula ruralis.

Tortula ruralis is a homoiochlorophyllous-desiccation-tolerant (HDT) moss that retains all pigments when dehydrated and rapidly recovers physiological function upon rehydration. This moss forms extensive cover in exposed and shaded areas in the sandy semi-arid grasslands of Central Europe. We hypothesized that contrasting drying regimes between these microhabitats would affect plant N status, constraints to gas exchange and growth, as well as result in altered pigment concentrations and ratios, and photochemical light-response dynamics. Furthermore, we believed T. ruralis's HDT habit would limit its ability to acclimate to altered light environment. We found that sun plant T. ruralis had lower plant mass, as well as lower tissue N, C, total photosynthetic pigment concentrations and carbon isotope discrimination (Δ) values compared to shade plant counterparts. Carotenoid/chlorophyll ratios in sun plants were typical of high light-adapted tissue, but chlorophyll a/chlorophyll b ratios were lower, more characteristic of low light-adapted tissue. This unique combination of pigment responses was accompanied by sustained lower levels of optimal quantum efficiency of PSII (F v/F m) in sun plant T. ruralis, even during favorable diurnal conditions, and reduced engagement of energy-dependent thermal dissipation (NPQ). Reciprocal transplants of sun and shade plants showed that T. ruralis is capable of short-term adjustment to altered light level, as evidenced by increases in F v/F m, NPQ, and light-adapted PSII yield (φPSII) in transplanted sun plants, and concurrent decreases in sun-transplanted shade plants. However, the performance of transplanted sun plants remained consistently below that of undisturbed shade plants. These findings show that microenvironmental variation results in different patterns of resource acquisition in this HDT moss, and that growth in the open imparts greater desiccation tolerance, and the development of a greater standing engagement of slowly reversing photoprotective mechanisms. In contrast, prolonged activity and greater resource acquisition in shaded populations may allow T. ruralis to rapidly adjust to changes following disturbance to the plant canopy, fostering the persistence of T. ruralis in these semi-arid grasslands.