Many studies have reported the paradoxical observation of high concentrations of low-density microplastics (plastic particles < 5 mm) in deep-sea sediments despite their buoyancy. The incorporation of buoyant microplastics into marine snow has been observed to enhance microplastic settling. Previous studies on the vertical movement of buoyant microplastics have been unable to theoretically account for these ocean observations and no study has comprehensively elucidated microplastic transport pathways in the ocean from the surface to seafloor. Here, we establish a one-dimensional theoretical model, that embraces key elements of the flocculation process, to explain how marine snow acts as a vector to transport buoyant microplastics to deep water and the ocean bottom. Microplastics reach the ocean floor through multiple cycles of aggregation, settling, and disaggregation between marine snow and microplastics. Each settling cycle results in a net settling of 200-400 m. We demonstrate that microplastics with different sizes show distinct vertical settling behaviors and only microplastics less than 100 mu m in diameter can reach the ocean bottom. This theoretical model refines our ability to predict and understand the global and long-term fate, transport, and inventory of microplastics in the ocean interior, the influence of microplastics on the biological carbon pump and the efficacy of plastic management policies.

Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function but typically require extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture and physiology. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture newly developed from PointNet and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, trained on data from diverse mature European forests. Our model uses meticulously labelled data combined with voxel-based sampling, neighbourhood rescaling, and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across open datasets from boreal, temperate, Mediterranean and tropical regions, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet based approach for leaf/wood segmentation on our high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We also find consistently strong performance tested on others open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, showcasing the transferability of our model to a wide range of ecosystems and sensors.

Forests and landscapes are fundamentally interconnected, with geomorphic process being modulated by vegetation dynamics, which in turn is influenced by landscape form. Trees play a critical role in shaping landscapes by redistributing sediment across the Earth's surface via gradual processes including tree throw and root growth, and catastrophic processes such as landsliding and debris flows, where spatially variable root cohesion contributes to slope failure likelihood. Conversely, landscape morphology controls the availability of light, water and nutrients for trees and has been observed to dive significant variability in the structure and composition of forests at both local and regional scales. Until recently, our ability to disentangle these processes at broad spatial scales has been limited due to a lack of high resolution data on tree morphology. Advances in Terrestrial Laser Scanning and UAV-LiDAR systems now allow forest plots to be scanned rapidly, capturing the morphology of hundreds of trees alongside the terrain they grow on. Working across a range of European forest ecosystems, representing a range of climates, we have constructed an unprecedented 3D dataset of European forest-landscape dynamics. From plot-level scans, individual trees are segmented from the digital forest and classified by species. State of the art structural metrics are then computed at an individual, species, and regional level across each distinct climate zone. This pan-European dataset is then coupled with high resolution topographic data, to explore the fundamental linkages between landscapes and vegetation.

Rivers and their drainage basins are fundamental landscape units, and their morphology is a record of the cascade of geologic, tectonic, biological, and climatic processes acting upon them. Quantifying this cascade depends on morphometric measurements of rivers and drainage basins, and comparison of these measurements across diverse landscape settings. Here we present a new near-Global dataset of Drainage Basin Morphology, GDBM, which provides morphometric measurements of 254,966 basins and the longest river channel within them. This dataset is created by extracting channels from the 30-meter resolution Shuttle Radar Topography Mission (SRTM) topographic data which fall within Köppen-Geiger climate zones, to allow the influence of climate on river and basin morphology to be quantified. GDBM contains measurements of channel length, slope, relief, normalised concavity, basin area, basin shape and aridity. These data have been generated with minimal assumptions, focusing on identifying and classifying channels with high confidence, through the use of a conservative drainage area threshold. GDBM provides opportunities for rapid spatial analysis of channel morphology at a near-global scale and has the potential to yield continuing insight into landscape evolution across diverse climate regimes. This dataset also has potential applications across a range of Earth and environmental science domains, through the integration of additional data on, for example, forest canopy height, landcover, or soil properties to explore the spatial variability of channel and basin properties with climate.

PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive because plots are small and spread out, requiring data from multiple satellite tiles. PlotToSat processed a full year of multi-tile Sentinel-1 and Sentinel-2 data (estimated 18.3TB) at 15,962 plots from the fourth Spanish Forest Inventory in less than 24 h. PlotToSat, implemented using the Python API of Google Earth Engine, offers a new and unique workflow that is innovative due to its efficient, scalable and adaptable implementation. It supports Sentinel-1 and Sentinel-2 data, but its flexible design eases integration of additional EO datasets. New environmental modelling is expected to emerge facilitating EO time-series analyses and investigating interactive effects of environmental drivers.

The ocean is considered a sink for plastic waste, with buoyant plastics remaining at the surface for up to 50 years. Removal of plastic waste from the ocean by manpower is not feasible, but natural-based removal strategies are promising. Among these, the biological pump stands out as a crucial process responsible for transferring materials and nutrients from the surface to the deep ocean. In this context, plastic particles can be incorporated into and transported with marine snow aggregates, providing a potential mechanism for removing plastic waste from the upper ocean (human food chain) to the deep ocean. However, the longevity of plastic debris at the ocean surface remains poorly understood. To address this gap, we developed a degradation-aggregation model to predict the longevity of different types, sizes and shapes of buoyant plastics at the ocean surface. Our results show that the longevity of plastic debris is primarily determined by the time it takes for plastic debris to degrade sufficiently and become small enough (< 100 µm) to be incorporated into marine snow aggregates. Larger plastics take decades to degrade into small microplastics that can be trapped in marine snow, prolonging their presence in surface waters. Conversely, once microplastics are small enough to be incorporated into marine snow, they can reach the seafloor sediment within two years. Interestingly, our model highlights that vertical settling of microplastics occurs through multiple incorporation and settling via vector transport of marine snow, which differs from previous studies reporting oscillations of microplastics in the ocean subsurface. This study explains the mechanisms of plastic debris removal by the biological pump and the longevity of plastic debris at the ocean surface. In addition, this theoretical model can be extended to different aquatic ecosystems to predict the fate and longevity of plastic debris in different environments.

Questions of landscape scale in coupled channel–hillslope landscape evolution have been a significant focus of geomorphological research for decades. Studies to date have suggested a characteristic landscape length that marks the shift from fluvial channels to hillslopes, limiting fluvial incision and setting the length of hillslopes. The representation of real-world landscapes in slope–area plots, however, makes it challenging to identify the exact transition from hillslopes to channels, owing to the existence of an intermediary colluvial valley region. Without a rigorous explanation for the scaling of the channel hillslope transition, the use of computational models, which are forced to implement a finite grid resolution, is limited by the scaling of the physical parameters of the model relative to the grid resolution. Grid resolution is also tied to the width of channels, which is undetermined without a rigorous explanation of where channels begin. Building on existing work, we demonstrate the existence and implications of the characteristic landscape length and its relationship to grid resolution. We derive the characteristic landscape length as the horizontal length in a one-dimensional landscape evolution framework required to form an inflection point. On a two-dimensional domain, channel heads form in steady state at the characteristic area, the square of the characteristic length, independent of grid resolution. We present a box-counting fractal definition using the grid resolution, revealing that the dimension of the contributing drainage region on steady-state hillslopes is expressed as a multifractal system. In sum, channels have contributing drainage areas, therefore a dimension of 2, whereas, by definition, unchannelized locations or nodes have a dimension between zero and 2, so not a well-defined area. This conceptualization aligns with the scaling of channel width as the square root of drainage area. Since channel heads form at a resolution-independent drainage area, the width of channel heads is not explicitly defined, suggesting that the grid resolution is analogous to the property of channel head width in real-world landscapes, influenced by the particle size. We substantiate this theory with topographic analyses of Gabilan Mesa, California. These findings clarify several unresolved properties of channel–hillslope coupling, with potential for substantially improving the accuracy of coupled landscape evolution models in replicating landscape forms.

Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories are labor-intensive and limited in spatial coverage. Advances in remote sensing using drone-acquired RGB imagery combined with deep learning models have promised precise individual tree crown (ITC) segmentation; however, existing methods are frequently validated against human-annotated images, lacking rigorous independent ground truth. In this study, we generate high-fidelity validation labels from co-located Terrestrial Laser Scanning (TLS) data for drone imagery of mixed unmanaged boreal and Mediterranean forests. We evaluate the performance of two widely used deep learning ITC segmentation models - DeepForest (RetinaNet) and Detectree2 (Mask R-CNN) - on these data, and compare to performance on further Mediterranean forest data labelled manually. When validated against TLS-derived ground truth from Mediterranean forests, model performance decreased significantly compared to assessment based on hand-labelled from an ecologically similar site (AP50: 0.094 vs. 0.670). Restricting evaluation to only canopy trees shrank this gap considerably (Canopy AP50: 0.365), although performance was still far lower than on similar hand-labelled data. Models also performed poorly on boreal forest data (AP50: 0.142), although again increasing when evaluated on canopy trees only (Canopy AP50: 0.308). Both models showed very poor localisation accuracy at stricter IoU thresholds, even when restricted to canopy trees (Max AP75: 0.051). Similar results have been observed in studies using aerial LiDAR data, suggesting fundamental limitations in aerial-based segmentation approaches in closed canopy forests.

Rapid evolution through small shifts in allele frequencies at thousands of loci is a long-standing neo-Darwinian prediction but is hard to characterize in the wild. European ash tree ( Fraxinus excelsior ) populations have recently come under strong selection by the invasive fungal pathogen Hymenoscyphus fraxineus . Using genomic prediction models based on field trial phenotypes and 7985 loci, we show a shift in genomically estimated breeding values in an ancient woodland, between adult trees established before the epidemic started and juvenile trees established since. Using simulations, we estimate that natural selection has eliminated 31% of the juvenile population. Thus, we document a highly polygenic heritable microevolutionary adaptive change over a single generation in the wild.

The global increase in observed forest dieback, characterized by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find that color-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed, and cost of forest dieback monitoring.

Microplastics (MPs) are an important component of suspended particulate matter in aquatic environments with two main transport modes, that is, as individual entities or in flocs. Despite its importance to MP pollution management, understanding and predicting MP flocculation remains a challenge. In this Article, we combined a meta-analysis of published data (>2,000 measurements) with new experimental data (>4,000 measurements) to investigate which size fraction of MPs can be incorporated into and transported by flocs in the aquatic environment. The size relationship between MPs and flocs can be used to predict the flocculation of MPs in various aquatic environments, and we have proposed a mathematical model to show that small MPs (<162 m) are predominantly transported as flocs, regardless of the physicochemical characteristics of the MPs or water body. This provides valuable information to predict the transport modes of MPs, presenting a critical insight for multiple environmental settings and future pollution control strategies.

AbstractClimate is thought to affect the structure and evolution of drainage basins, but it is not clear how climate impacts the power law scaling between channel length and drainage area. Since climate controls runoff, streamflow, and erosion regimes, we looked for dependency of drainage basin morphometrics on climate within a near‐global data set. We show that increasingly arid regions have longer channels and narrower drainage basins, and power law scaling between channel length and basin area (Hack's Law) increases monotonically with aridity. We suggest these results arise due to downstream channel extension by rare large floods that erode channels into previously unchanneled terrain, yielding a morphometric signature in drylands that is preserved over long timescales due to a lack of subsequent topographic smoothing. This new understanding of drainage basin morphometrics on Earth may be used to inform interpretations of past climates on our planet and other solar system bodies.

PlotToSat is a tool that uses the Python API of Google Earth Engine (GEE) to solve the problem of creating time-series of Earth Observation (EO) data at multiple plot locations spread out within a landscape. The current version supports Sentinel-1 and Sentinel-2. It is a flexible framework though that allows easy addition of EO datasets. Using PlotToSat, we generated a Normalised Difference Vegetation Index (NDVI) time-series at 14,822 plot of the fourth Spanish National Forest Inventory (Spanish NFI). Using the non-parametric k-NN algorithm, we classified the NDVI time-series into the six dominant genera in Peninsular Spain. The classification was cross-validated using the evaluation metric F1-score. We show that re-sampling to create a balanced dataset improved classification results. The F1-score for the most common tree genera was low. This is suspected to occur due to subgroups (of tree species and/or stand structural types) being formed within each large genus class. When reducing data to use an equal number of samples per genus class in the classifier, subgroups of large genus classes become under-represented. PlotToSat processed an estimated 18.3 TB of EO data in less than 24 hours. It is useful for any application requiring EO time-series data from multiple spatially disconnected locations.

Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three‐dimensional vegetation structure at high resolution, but the scalability of using these data for large‐scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer‐level graphics processing units, provide new opportunities. Here, we apply a deep learning‐based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre‐processing such as leaf‐wood separation or quantitative reconstructions. Using modern network architectures and data augmentation techniques, and without extensive pre‐processing, we are able to achieve high overall and per‐species accuracy that is comparable or higher than in existing work while using data from a water‐limited ecosystem complicated by structural convergence and multi‐stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.

In forested landscapes, trees drive and modulate both advective and diffusive sediment transport processes. These relationships represent complex feedbacks between topography and vegetation. Root growth and tree throw drive diffusive soil creep, whilst root cohesion is a contributing factor in advective processes including landsliding and debris flows. In turn, landscape morphology modulates water, nutrient, and light availability and has been observed to cause significant variability in the structure and composition of forests across scales. To fully explore this topic, a confluence of robust, high resolution measurements of landscape and tree morphology is required alongside long term monitoring data, which has hitherto been unavailable at the appropriate spatial scale.Working across a range of European forest ecosystems, coupling long term measurements of forest structure with newly acquired high resolution topographic data, we have constructed an unprecedented 3D dataset of European forest-landscape dynamics. We segment individual trees from combined UAV LiDAR and terrestrial laser scanning campaigns, compute tree structural metrics, and link them to localised topographic metrics computed using LSDTopoTools. Using these data we explore the inter- and intra- specific relationships between topography and individual trees and demonstrate the potential to fundamentally link geomorphic and ecological process through coupled field and computational research.

Digital Hemispherical Photography images taken in 33 30 x 30 m plots across two sites in Spain. Images were taken on a 10 m grid, making 16 locations per plot (see Flynn et al., 2022 for details). At each location, DHP images were captured with three exposure settings (automatic and ± one stop exposure compensation), levelling a Canon EOS 6D full frame DSLR sensor with a Sigma EX DG F3.5 fisheye lens, mounted on a Vanguard Alta Pro 263AT tripod. For each RGB image, the blue band was extracted, as this best represents sky/ vegetation contrast. For each plot, an exposure setting was chosen based on visual assessment and pixel brightness histograms of four images indicative of the whole plot. Automatic thresholding was carried out using the Ridler and Calvard method (1978), creating a binary image of sky and vegetation.

&lt;p&gt;Large scale forest inventory plot data are key to monitor forest ecosystems, but while they provide very detailed information at tree level they are limited in resolution in both space and through time. Earth Observation (EO) data offer the opportunity for scaling up plot data and improving the temporal resolution of monitoring. However, there are significant challenges to this, including small field plot sizes, pre-processing and potential GPS errors in aligning the data, whilst the huge amount and variety of EO data introduce substantial challenges of high dimensionality, in addition to the noise of training and testing data, within any AI system. In this work, we fuse plot and Earth Observation data, demonstrating the value of embedding existing and newly EO derived metrics, and selecting the most important features to improve monitoring of forest properties at large scales.&amp;#160;&lt;/p&gt; &lt;p&gt;In this work we work with Sentinel-1 (SAR) and Sentinel-2 (optical) and inventory data from close to 10,000 plots in Spain, measured from onwards. SAR data require substantial pre-processing due to noise and acquisition, topographic and moisture effects. We used pre-processed SAR data, and filtered for non-shaded slopes, removed plots close to surface water and data collected on days with high precipitation. We masked out clouds from our optical data. After fusing the EO data, we removed disturbed areas using the Global Forest Change Collection and plots with high variability of pixels around them to reduce uncertainty due to the small sizes of the plots. As well as using standard indices (e.g., NDVI, RVI), we derive new metrics of the phenological cycle of the forest from monthly averages of indices and bands by selecting features from peaks and troughs. We reduce dimensionality using principal component analysis and random forest to select the most important features. Chosen features are used for training and evaluating a customized AI system to estimate forest variables such as total basal area, stem density, mean diameter at breast height and forest type. The code implemented in Google Earth Engine JavaScript and Python will be released as open source.&lt;/p&gt;

The recent explosion in availability of high resolution remote sensing technologies and, crucially, the tools to analyse the 3D data they produce is leading to substantial interest in using them for widespread forest structural monitoring. The level of detail contained in the entire 3D shape of trees, fully captured within these data, can generate a wide range of metrics of interest to ecologists, but the potential metrics of interest and their uncertainties have not been fully explored. In particular, the value of different technologies - whether passive or active sensors, and from the ground or the air - for accurately deriving different metrics is not well known. Working across a range of European forest ecosystems, we have constructed a unique 3D dataset of European forest structural properties from passive and active sensors. We segment individual trees from concurrent and co-located Structure from Motion photogrammetry (SfM) (passive sensor), and UAV LiDAR, and terrestrial laser scanning (active sensors) campaigns, and use these to compute tree structural metrics. We compare the ability of these different technologies to accurately measure key tree properties across a diversity gradient in multiple biomes.

With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed.Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.

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