BackgroundRafting of organisms on floating objects, long recognised as a key process in biogeography and evolution, has undergone tremendous change with the rapid increase of ocean litter (plastics and other human‐made materials). Resulting increases in raft longevity and abundance expand opportunities for marine species' dispersal. Here, we present a conceptual framework for the role of benthic stopovers

AimBroad biodiversity patterns (e.g., the latitudinal diversity gradient) are cornerstones of ecology that are fundamental in understanding the distribution of life on the planet. In the deep sea, declining faunal abundance/biomass with depth and a mid‐continental slope diversity maximum are among the most well‐defined patterns. However, they have largely been tested with samples of sediment faunal

AimTo compare field‐based evidence of plant and animal responses to fire with remotely sensed signals of fire heterogeneity and post‐fire biomass recovery.LocationSouth‐eastern Australia; New South Wales.Time Period2019–2022.Major Taxa StudiedA total of 982 species of plants and animals, in eight taxonomic groups: amphibians, birds, fish, insects, mammals, molluscs, plants and reptiles.MethodsWe collated

AimNon‐native plants have the potential to harm ecosystems. Harm is classically related to their distribution and abundance, but this geographical information is often unknown. Here, we assess geographical commonness as a potential indicator of invasive status for non‐native flora in the United States. Geographical commonness could inform invasion risk assessments across species and ecoregions.LocationConterminous

We examine how the massive exodus of Venezuelans and an amnesty program aimed at their integration affected income distribution in Colombia. Using RIF regressions in an instrumental variables approach, we find that the exodus had a negative effect on the lower tail of native Colombians’ income distribution, while the amnesty program partially mitigated this impact. Our findings posit downgrading as

Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction

Deep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph

High-resolution land cover (LC) data are essential for ecological monitoring and resource management, especially in heterogeneous landscapes containing diverse LC types. With the growing of available LC products, a comprehensive evaluation of their classification accuracy and spatial consistency is important for users’ selection and application. In this study, we compared eight widely used LC products

Timely monitoring of crop conditions is essential for optimizing and assessing agricultural management. Vegetation indices (VIs) derived from remote sensing data can be useful for assessing crop conditions on a large spatial scale. Traditional crop condition assessments compare a VI in the current year to a baseline VI, averaged over multiple years. However, comparing VIs across years by calendar day

Reservoirs play a significant role in the mobilization of water resources in Burkina Faso, contributing to the management and availability of water for various purposes. Operational management of reservoirs requires accurate and timely water level information, which remote sensing can provide cost-effectively and with limited resources. In this study, the surface area of 20 reservoirs is first determined

The wave phenomena in SAR images are able to provide water depth information. SAR ocean images are often characterized by unclear wave texture and strong speckle noise, which will hinder the bathymetric inversion. Denoising and texture enhancement are two strategies to improve image quality. However, noise reduction may blur textures, while texture enhancement may amplify noise. To address this, we

Accurate estimation of evapotranspiration (ET) is essential for effective water resource management, particularly in arid and semi-arid areas. Advancements in remote sensing technology have made ET models indispensable, offering high-resolution spatial and temporal assessments. Contextual models such as the Surface Energy Balance Algorithm for Land (SEBAL) are particularly valuable for ET estimation

Under global warming, the Yellow River Basin (YRB), serving as an ecological barrier and climate-sensitive region in northern China, faces severe challenges such as frequent extreme droughts and floods, as well as intensifying water resource supply–demand conflicts. To systematically assess the evolution of droughts and floods in the YRB, this study utilizes observational data from 137 meteorological

Public transport is commonly considered a solution to car dependence, aiming to address environmental degradation and social problems which car dependence creates in cities. However, first and last-mile connectivity problems are significant barriers to public transport ridership. Addressing first-mile and last-mile barriers may well reduce reliance on private cars and lead to a corresponding decrease

To address the vulnerability of complex transportation networks during sudden events and attacks, this study focuses on the road network of Fucheng District in Mianyang City and proposes an adaptive topological expansion optimization model to enhance the original road network data. Densely populated region adjustments were considered to calculate the composite vulnerability index of nodes. The analysis

To promote low-carbon transit, cities are increasingly adopting public-private partnerships to offer bikeshare services. But some neighborhoods use bikeshare services more than others, raising questions about how equitable these public programs' rollout has been. We examined the entire temporal directed network of individual rides from Boston's Bluebikes program, tracking bikers' starting and ending

AimPagel's (lambda) is a useful tool in ecology and evolution for describing trait evolution, imputing missing species' data, and generalising ecological relationships beyond their study system. Here, we review the various applications and interpretations of , highlight common misconceptions, and show how confusion in defining and using can mislead our interpretation of ecological and evolutionary

Pinseel, E., Sabbe, K., Verleyen, E. and Vyverman, W. (2024), A New Dawn for Protist Biogeography. Global Ecol Biogeogr, 33: e13925. https://doi.org/10.1111/geb.13925 In the originally published article, the reference by Kotyk et al. was given incorrectly. The correct reference is as follows: Kotyk, M., W. A. Bourland, M. Soviš, et al. 2024. ‘Morphology Matters: Congruence of Morphology and Phylogeny

This study examines the impact of bus station quality on users' travel choices, with a specific focus on the hedonic aspects related to architectural design and passenger services offered. It is widely shown that a safer, reliable, and comfortable transport service fosters greater trust in the transport system and leaves users with a positive perception of ease of movement within a geographical area

This article explores the social inclusiveness of Sustainable Urban Mobility Plans (SUMPs), a policy tool introduced by the European Commission to inspire local governments in developing long-term sustainable visions to address the mobility needs of everyone. The aim of SUMPs is to accelerate the shift to sustainable mobility and achieve the complete decarbonisation of European Mobility by 2050. While

The increasing interconnectivity of power grids and electric vehicle (EV) charging stations exposes them to the ever-growing threat of cyberattacks. This paper proposes a multifaceted approach that addresses the interdependencies between power grids, charging stations, and EVs. We explore a new EV routing challenge that includes regulations for charging and vehicle-to-grid (V2G) discharging. We estimate

Efficient public transportation requires innovative planning and operational strategies. Accurate demand forecasting is crucial, as it is influenced by complex, non-linear interactions of various spatial and temporal factors. This study proposes a neuro-fuzzy inference and deep learning models to predict public transport demand in Mashhad's traffic zones for enhanced operational planning. The model's

Despite the steady decline in the total number of pedestrian crashes in Korea, the pedestrian fatality rate per 100,000 people remains high compared to the Organization for Economic Cooperation and Development (OECD) average. As the data of traffic crashes is gradually accumulated every year, various machine learning methodologies are needed to analyze this data. This study proposed a new algorithmic

Cities are characterized by high skill levels and workers improve their skills through learning. This article presents novel stylized facts on learning behavior and cities. I use Japanese survey data that provide distinctively rich first-hand information about the frequency, purpose, and subject of off-the-job learning. First, people engage in learning more frequently in denser cities. Second, people

Artisanal and Small-Scale mining (ASM) provides essential livelihoods for many in developing countries but often lacks regulation, leading to environmental issues such as water pollution and deforestation. Timely and accurate mapping of ASM activities is vital for responsible mining that benefits the environment and local communities. Synthetic Aperture Radar (SAR) is crucial for regular ASM monitoring

Buildings are crucial to cities, and tomographic synthetic aperture radar (TomoSAR) is an important tool for monitoring the heights, linear deformations and thermal amplitudes of buildings. However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps

In the processing of Interferometric synthetic aperture radar (InSAR) data, two-dimensional (2-D) phase unwrapping (PU) is crucial for ensuring the quality of InSAR data inversion. Traditional methods, based on the assumption of phase continuity, often struggle with abrupt terrain changes and the influence of severe noise, leading to poor performance or failure. To address these challenges, this paper

Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to

In recent advancements, deep learning-based methods for change detection have demonstrated rapid capabilities to identify alterations across extensive regions, underscoring significant research and application potential in remote sensing change detection. Nonetheless, these methods currently encounter limitations in feature extraction, often leading to blurred edges and challenges in identifying small-scale

Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works

AimForests dominated by non‐native trees are becoming increasingly common. However, their impact on biodiversity remains uncertain, with a debate on whether they represent ‘green deserts’ or secondary habitats for biodiversity. We addressed this question by evaluating the patterns and ecological drivers of taxonomic and functional understory diversity between black pine (Pinus nigra) forests within

The article addresses the issue of spatiotemporal patterns of parking in the centre of Łódź. The discussion focuses on the interrelationships between the configuration of the road network and the distribution and utilization of parking spaces. The study employs the Space Syntax methodology and the DBSCAN clustering algorithm, with an additional analysis of the spatial distribution and intensity of

Machine learning approaches to unlocking the TOD-metro ridership relationship have attracted great attention due to the strong capability of such approaches to handle the underlying nonlinearity and complexity in this relationship. Considering the peculiarities of spatiotemporal heterogeneity in metro ridership, however, one prominent challenge remains unsettled, namely, the issue that traditional

In an effort to decarbonize the transport sector, many countries are implementing policies to increase the uptake of Zero-Emission Vehicles (ZEVs). While ZEV adoption is on the rise, it is not occurring at the same rate geographically. With some exceptions, rural areas are adopting ZEVs at much slower rates compared to urban areas. It is likely that unique rural geographies require special policy considerations

Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO2). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for

AimThe term ‘megafire’ is increasingly used to describe large fires worldwide. We proposed a size‐based definition of megafire—fires exceeding 10,000 ha arising from single or multiple related ignition events. A recent perspective in Global Ecology and Biogeography argues against a size‐based definition of megafire and suggest that the term is too emotive for scientific use. We highlight that many

AimIntroduced species can establish in climates outside of their native niche and undergo ‘niche shifts’. However, studies of niche shifts generally rely on above‐ground climate data, neglecting the potential buffering effect of ground‐level or soil climates.LocationGlobal.Time PeriodPresent.Major Taxa StudiedFormicidae.MethodsHere, we investigated the impact of soil temperatures on niche shifts in

AimTesting the impact of climate on diversification is a major goal of evolutionary biology. Birth‐death models like palaeoenvironment‐dependent diversification (PDD) models, for example, allow exploring the potential correlations between diversification dynamics and past environmental changes, such as temperature, among other abiotic variables. So far, such studies have been limited to proxy‐derived

AimThe tendency for animals in warmer climates to be longer‐limbed (Allen's Rule) is widely attributed to the demands of thermoregulation. The role of thermoregulation in structuring bird wings, however, has been overshadowed by the selective demands placed on wings by flight. We test whether occurrence in warmer climates is associated with longer wing bones.LocationGlobal.Time PeriodCurrent.Major

There is an emerging awareness that the transition towards sustainable transportation systems may contribute to widening social disparities, especially related to access to opportunities and services among marginalised communities manifested as transport exclusion. However, mechanisms that lead to or compound transport exclusion are less understood and empirics uncovering how marginalised communities

Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets

AimThe carbon–climate feedback of terrestrial ecosystems plays a key role in determining atmospheric carbon dioxide concentrations. Ecosystem respiration (ER) has been demonstrated to be more sensitive than gross primary productivity (GPP) to increasing temperature, leading to positive carbon–climate feedback. However, the direction and magnitude of the feedback are unclear across diverse thermal regimes

AimMultivariate spatio‐temporal models are widely applicable, but specifying their structure is complicated and may inhibit wider use. We introduce the R package tinyVAST from two viewpoints: the software user and the statistician.InnovationFrom the user viewpoint, tinyVAST adapts a widely used formula interface to specify generalised additive models and combines this with arguments to specify spatial

Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic

Recent studies have demonstrated the potential of light detection and ranging (LiDAR) from uncrewed aerial vehicles (UAVs) for assessing forest structures. Maximizing data completeness and representativeness is essential to accurately retrieve key structural parameters. However, knowledge on how data acquisition approaches affect canopy volume exploration is sparse. This study investigated the effects

Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified

Recent geopolitical developments, including the Russia-Ukraine war, the US-China trade war, and China's Belt and Road Initiative, have significantly altered the transportation landscape of Central Asia. Traditional corridors like the Trans-Siberian Corridor through Russia have become less viable, accelerating the rise of new alternatives, such as the Trans-Caspian Corridor (TCC). This corridor, connecting

Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. However, classical CNN-based methods primarily concentrate on information within a fixed-size neighborhood and a standard square region, neglecting long-range and global information. As non-Euclidean data, the topological structure enables flexible construction

AimSpecies richness of local communities is often considered to be disconnected from the macroevolutionary diversification of lineages operating at much larger spatiotemporal scales. However, local communities occupy patches of habitat types, many of which persist across large spatiotemporal scales. We hypothesise that high local species richness of specific habitat types could result from ongoing

AimUnderstanding the distribution of foliar fungal diseases is crucial to predicting their impact on ecosystems and their future spread. However, the relative importance of abiotic and biotic factors in determining variation in pathogens between plant communities remains controversial. Here, we tested four hypotheses: warmer, wetter climates, higher soil fertility and dominance by fast‐growing plants

AimMost process‐based forest simulation models used to project the impacts of climate change on forest dynamics rely on thermal transfer functions (TTF) that describe the relationship between temperature and growth. However, these functions have faced criticism, undermining model forecasts and highlighting the need for more robust TTF based on large empirical datasets. In this study, we modelled growth

AimThe priming effect (PE) refers to changes in the decomposition of native soil organic carbon induced by exogenous carbon inputs. Specifically, an increase in native soil organic carbon decomposition is termed positive PE, whereas a decrease is referred to as negative PE. In this study, we aimed to investigate how positive and negative PE respond to experimental warming and the factors controlling

AimMicrobial carbon use efficiency (CUE) is one of the key indicators for the formation and release of soil carbon. CUE can be divided into exogenous CUE (CUEex, efficiency in using external carbon sources measured by e.g. 13C or 14C labeling) and endogenous CUE (CUEen, efficiency in using internal carbon sources measured by 18O labeling). Global changes strongly influence CUE, which response depends

Collective human mobility is an important phenomenon within defined geographic areas for specific mobility patterns. Studies have been conducted to understand human mobility patterns and the determinants, including the built environment, and socioeconomic factors. However, there is a dearth of systematic investigation focusing on the dual-directional nature of collective human mobility in places, referring

The first and last mile (FLM) travel is a challenge that affects urban rail usage and in some cases increases dependence on personal vehicles for accessing transit nodes. While previous studies have explored FLM travel, there is limited research that thoroughly examines the influence of street-level built environment factors on mode choice behavior and how these factors affect groups based on car dependency

Accurate and effective coastal wetland classification using hyperspectral remote sensing technology is crucial for their conservation, restoration, and sustainable development. However, the large scale variance of land covers in complex wetland scenes poses challenges for existing methods and leads to misclassifications. Additionally, existing methods encounter difficulties in practical wetland classification

AimWe addressed the role of climate and historical biogeography on the temperate‐tropical divide in Mexico, also known as the “Mexican Transition Zone” (MTZ). We asked: (1) How phylogenetic structure and species composition vary across the MTZ, (2) What roles dispersal, in situ speciation and climatic filtering play in assembling regional floras, and (3) How historical biogeographic events and climate

This study investigates gender differences in the operational strategies of ride-hailing drivers in Suzhou, China, using identity-confirmed trip data from October 2023. Four novel operational metrics (workload, earning capacity, operational efficiency, and willingness to work far from home) were developed to quantify gender-based operational preferences between male drivers and female drivers, who

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AimPredicting the future distribution of invasive species is a current challenge for biodiversity assessment. Species distribution models (SDMs) have long been the state‐of‐the‐art to evaluate suitable areas for new invasions, but they may be limited by truncated niches and the uncertainties of species dispersal. Here, we developed a framework based on hierarchical SDMs and dispersal simulations to