Plant beta-diversity across biomes captured by imaging spectroscopy

JA, Cavender-Bares J. Plant diversity across dimensions: Coupling biodiversity measures from the ground and the sky

Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Dispersion-assisted high-dimensional photodetector

Intensity, polarization and wavelength are intrinsic characteristics of light. Characterizing light with arbitrarily mixed information on polarization and spectrum is in high demand1-4. Despite the extensive efforts in the design of polarimeters5-18 and spectrometers19-27, concurrently yielding high-dimensional signatures of intensity, polarization and spectrum of the light fields is challenging and typically requires complicated integration of polarization- and/or wavelength-sensitive elements in the space or time domains. Here we demonstrate that simple thin-film interfaces with spatial and frequency dispersion can project and tailor polarization and spectrum responses in the wavevector domain. By this means, high-dimensional light information can be encoded into single-shot imaging and deciphered with the assistance of a deep residual network. To the best of our knowledge, our work not only enables full characterization of light with arbitrarily mixed full-Stokes polarization states across a broadband spectrum with a single device and a single measurement but also presents comparable, if not better, performance than state-of-the-art single-purpose miniaturized polarimeters or spectrometers. Our approach can be readily used as an alignment-free retrofit for the existing imaging platforms, opening up new paths to ultra-compact and high-dimensional photodetection and imaging.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064 nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

The past and future of ecosystem restoration in China

For decades, China has implemented restoration programs on a large scale, thanks to its capacity to set policy and mobilize funding resources. An understanding of China's restoration achievements and remaining challenges will help to guide future efforts to restore 30% of its diverse ecosystems under the Kunming-Montreal Global Biodiversity Framework. Here we summarize the major transitions in China's approach to ecosystem restoration since the 1970s, with a focus on the underlying motivations for restoration, approaches to ecosystem management, and financing mechanisms. Whereas China's restoration efforts were predominantly guided by the delivery of certain ecosystem functions and services in earlier decades, more recently it has come to emphasize the restoration of biodiversity and ecosystem integrity. Accordingly, the focal ecosystems, approaches, and financing mechanisms of restoration have also been considerably diversified. This evolution is largely guided by the accumulation of scientific evidence and past experiences. We highlight the key challenges facing China's restoration efforts and propose future directions to improve restoration effectiveness, with regard to goal setting, monitoring, stakeholder involvement, adaptive management, resilience under climate change, and financing.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

PREMISE

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.

METHODS

Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.

RESULTS

Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.

CONCLUSIONS

Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

High-Precision Ultra-Long Air Slit Fabrication Based on MEMS Technology for Imaging Spectrometers

The increasing demand for accurate imaging spectral information in remote sensing detection has driven the development of hyperspectral remote sensing instruments towards a larger view field and higher resolution. As the core component of the spectrometer slit, the designed length reaches tens of millimeters while the precision maintained within the μm level. Such precision requirements pose challenges to traditional machining and laser processing. In this paper, a high-precision air slit was created with a large aspect ratio through MEMS technology on SOI silicon wafers. In particular, a MEMS slit was prepared with a width of 15 μm and an aspect ratio exceeding 4000:1, and a spectral spectroscopy system was created and tested with a Hg-Cd light source. As a result, the spectral spectrum was linear within the visible range, and a spectral resolution of less than 1 nm was obtained. The standard deviation of resolution is only one-fourth of that is seen in machined slits across various view fields. This research provided a reliable and novel manufacturing technique for high-precision air slits, offering technical assistance in developing high-resolution wide-coverage imaging spectrometers.

Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species

Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits, and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.

Plants under pressure: the impact of environmental change on plant ecology and evolution

Plants have demonstrated tremendous resilience through past mass extinction events. However, anthropogenic pressures are rapidly threatening plant survival. To develop our understanding of the impact of environmental change on plant ecology and evolution and help solve the current biodiversity crisis, BMC Ecology and Evolution has launched a new article Collection titled "Plants under Pressure".

Characterization of the impacts of hydro-dams on wetland inundations in Southeast Asia

Inundations of wetlands play a significant role in wetland ecosystems, but they are vulnerable to hydrological alterations. In Southeast Asia, many hydro-dams, which significantly alter the hydrology, have been built, but little is known about the influences of dams on wetland inundations. In this study, we quantified the characteristics of inundations and related the alterations to the dams by distinguishing them from influences of climate variabilities and local human activities. A multi-sensor approach using Landsat 8, Sentinel-1, and MODIS was devised to delineate the weekly inundations of 362 Southeast Asian wetlands from 2014 to 2021. The four hydrological characteristics (cyclical patterns, trends, intra-annual variability, and amplitude of inundations) were quantified, and the alteration of the characteristics caused by dams was separated from climate variabilities and local human activities using correlation analysis and logistic regression models. The results found that cyclical patterns, trends, intra-annual variability, and amplitude of wetland inundations changed significantly over the period, but the magnitudes vary significantly depending on their geographic locations with respect to the dams. Findings showed that dams critically affect the wetlands even though dams are located distantly from the dams. This indicates that wetlands should be monitored and conserved for reducing the influences of dams. This study advances our understanding of the effects of dams on wetlands by using the multi-sensor approach and distinguishing them from climate variabilities and local human activities.

The Spectral Species Concept in Living Color

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

The potential of mineral weathering of halophilic-endophytic bacteria isolated from Suaeda salsa and Spartina anglica

Bacteria have the abilities of salt tolerant, mineral weathering and plant growth promoting can promote the growth of plants in saline lands. However, few reports of the mineral weathering capacity of halophilic-endophytic bacteria, raising the question of whether the halophilic-endophytic weathering bacteria are fundamentally distinct from those in plants communities. In this study, we isolated and characterized halophilic bacterial strains from the roots and leaves of Suaeda salsa and Spartina anglica with respect to their mineral weathering pattern, role in the promoting plant growth, community structure, and their changes in these two plants. Using improved Gibbson medium, we obtained 156 halophilic bacterial strains, among which 92 and 64 strains were isolated from the S. salsa and S. anglica samples, respectively. The rock weathering patterns of the isolates were characterized using batch cultures that measure the quantity of Si, Al, K, and Fe released from crystal biotite under aerobic conditions. Significantly, the biomass and capacity of the mineral weathering of the halophilic-endophytic bacteria were different in the plants. The abundance of the halophilic-endophytic bacterials in the Suaeda salsa was significantly greater than Spartina anglica, whereas the mineral weathering bacterial in the Suaeda salsa was similar to the Spartina anglica. Furthermore, the proportion of plant growth-promoting bacteria in the Suaeda salsa was higher than Spartina anglica. Phylogenetic analyses show that the weathered minerals were inhabited by specific functional groups of bacteria (Halomonas, Acinetobacter, Burkholderia, Alcaligenes, Sphingobium, Arthrobacter, Chryseobacterium, Paenibacillus, Microbacterium, Ensifer, Ralstonia and Enterobacter) that contribute to the mineral weathering. The changes in halophilic endophytes weathering communities between the two plants were attributable not only to major bacterial groups but also to a change in the minor population structure.