Ecosystem Dynamics Research Articles

Learning extreme vegetation response to climate drivers with recurrent neural networks

Abstract. The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in learning and predicting vegetation response, including extreme behavior from meteorological data. While machine learning methods, particularly neural networks, have significantly advanced in modeling nonlinear dynamics, it has become standard practice to approach the problem using recurrent architectures capable of capturing nonlinear effects and accommodating both long- and short-term memory. We compare four recurrent-based learning models, which differ in their training and architecture for predicting spectral indices at different forest sites in Europe: (1) recurrent neural networks (RNNs), (2) long short-term memory networks (LSTMs), (3) gated recurrent unit networks (GRUs), and (4) echo state networks (ESNs). While our results show minimal quantitative differences in their performances, ESNs exhibit slightly superior results across various metrics. Overall, we show that recurrent network architectures prove generally suitable for vegetation state prediction yet exhibit limitations under extreme conditions. This study highlights the potential of recurrent network architectures for vegetation state prediction, emphasizing the need for further research to address limitations in modeling extreme conditions within ecosystem dynamics.

Leveraging Cloud Native Development for Ecosystem Strategy

This article explores the intersection of cloud-native development and business ecosystem strategies, highlighting how cloud-native technologies create scalable, flexible, and interoperable platforms that foster collaboration and innovation. It examines the composition and dynamics of business ecosystems, the key advantages of cloud-native development for ecosystem strategies, and practical approaches for implementing cloud-native ecosystem platforms. The paper provides insights into the transformative potential of these technologies in shaping modern business ecosystems, supported by market data, case studies, and industry trends.

Variability of trace elements in bodies of scrapers (Ephemeroptera) and predators (Plecoptera) from mountain rivers of Dzungarian Alatau (Kazakhstan) and Western Carpathians (Slovakia)

AbstractBioaccumulation of trace elements in aquatic environments can be influenced by local environmental conditions such as temperature fluctuations, pH levels, sediment composition, dissolved organic matter content, and the presence of other chemical substances. We analyzed the differences in trace elements accumulation (S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zn, Rb, Sr, Mo, Ba, and Pb) between two trophic guilds—scrapers (Ephemeroptera) and predators (Plecoptera)—of freshwater benthic macroinvertebrates collected from mountain streams in Kazakhstan and Slovakia. Trace elements in dried insect bodies were analyzed using an X-ray spectrometer, and physicochemical parameters of stream water were investigated at each sampling site. Our results showed significant differences in Fe, Ti, and Sr levels in predators from Kazakhstan and Cu levels in predators from Slovakia. Despite some trace elements showing higher concentrations in one group over another, the overall differences between regions were more pronounced. Principal component analysis (PCA) revealed that the primary factors influencing trace elements variability were associated with environmental conditions such as temperature, oxygen levels, and total dissolved solids (TDS). PCA components indicated a higher load of trace elements in the warmer, less oxygenated streams, particularly in Kazakhstan. These findings suggest that both biotic (feeding strategies) and abiotic (geographical and environmental conditions) factors significantly influence trace elements dynamics in freshwater ecosystems.

Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.

Modelling snowpack on ice surfaces with the ORCHIDEE land surface model: application to the Greenland ice sheet

Abstract. Current climate warming is accelerating mass loss from glaciers and ice sheets. In Greenland, the rates of mass changes are now dominated by changes in surface mass balance (SMB) due to increased surface melting. To improve the future sea-level rise projections, it is therefore critical to have an accurate estimate of the SMB, which depends on the representation of the processes occurring within the snowpack. The Explicit Snow (ES) scheme implemented in the land surface model Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) has not yet been adapted to ice-covered areas. Here, we present the preliminary developments we made to apply the ES model to glaciers and ice sheets. Our analysis mainly concerns the model's ability to represent ablation-related processes. At the regional scale, our results are compared to the MAR regional atmospheric model outputs and to MODIS albedo retrievals. Using different albedo parameterizations, we performed offline ES simulations forced by the MAR model over the 2000–2019 period. Our results reveal a strong sensitivity of the modelled SMB components to the albedo parameterization. Results inferred with albedo parameters obtained using a manual tuning approach present very good agreement with the MAR outputs. Conversely, with the albedo parameterization used in the standard ORCHIDEE version, runoff and sublimation were underestimated. We also tested parameters found in a previous data assimilation experiment, calibrating the ablation processes using MODIS snow albedo. While these parameters greatly improve the modelled albedo over the entire ice sheet, they degrade the other model outputs compared to those obtained with the manually tuned approach. This is likely due to the model overfitting to the calibration albedo dataset without any constraint applied to the other processes controlling the state of the snowpack. This underlines the need to perform a “multi-objective” optimization using auxiliary observations related to internal snowpack processes. Although there is still room for further improvements, the developments reported in the present study constitute an important advance in assessing the Greenland SMB with possible extension to mountain glaciers or the Antarctic ice sheet.

Empirical analysis of the influence mechanism of vegetation and environment on negative air ion in warm temperate forest ecosystems

Air pollution presents a significant threat to public health in megacities globally. Negative air ions (NAI), often referred to as “air vitamins,” are recognized for their effectiveness in alleviating the harmful effects of air pollution. Forest ecosystems serve as natural generators of NAI, with both vegetation and environmental conditions playing critical roles in the formation and persistence of NAI. Gaining a comprehensive understanding of how forest ecosystems regulate NAI production is essential for leveraging their potential to enhance air quality. However, the intricate dynamics of forest ecosystems, along with seasonal fluctuations in vegetation and environmental factors, introduce uncertainties in NAI generation. This study utilized long-term observational data to explore the relationships between environmental variables, vegetation photosynthetic capacity (using solar-induced chlorophyll fluorescence, SIF), and NAI concentrations. By employing machine learning algorithms, we analyzed the spatiotemporal distribution of NAI, identifying the key contributing factors and their relative influence within forest ecosystems. The results revealed distinct seasonal variations in NAI levels, with higher values in summer and lower in winter. SIF and PM2.5 primarily influenced NAI through direct effects across seasons, whereas ambient temperature (TA), relative humidity, photosynthetically active radiation (PAR), and soil moisture predominantly impacted SIF on NAI through indirect effects in summer. TA was the primary influencing factor in spring and winter, contributing 28% and 25%, respectively, while PAR played a more significant role in summer and autumn, accounting for 37% and 27%. Vegetation had a greater impact on NAI levels during spring and summer, contributing 66% and 62%, whereas environmental factors dominated in autumn and winter, with contributions of 83% and 89%. This study offers both a theoretical foundation and technical guidance for enhancing the role of forest ecosystems in improving air quality and human living environments.

Estimating vegetation structure and aboveground carbon storage in Western Australia using GEDI LiDAR, Landsat and Sentinel data

Abstract Worsening climate change impacts are amplifying the need for accurate estimates of vegetation structure and aboveground biomass density (AGBD) to assess changes in biodiversity and carbon storage. In Australia, increasing wildfire frequency and interest in the role of forests in the carbon cycle necessitates biomass mapping across large geographic extents to monitor forest change. The availability of spaceborne Light Detection and Ranging (LiDAR) optimised for vegetation structure mapping through the Global Ecosystem Dynamics Investigation (GEDI) provides an opportunity for large-scale forest AGBD estimates of higher accuracy. This study assessed the use of the GEDI canopy height product to predict woody AGBD across five vegetation types in Western Australia: tall eucalypt forests, eucalypt open‒woodlands, low-lying heathland, tropical eucalypt savannas, and tussock and hummock grasslands. Canopy height models were developed using random forest regressions trained on GEDI canopy height discrete point data. Predictor variables included spectral bands and vegetation indices derived from synthetic aperture radar (SAR) Sentinel‒1 data, and multispectral Landsat and Sentinel‒2 data. AGBD was subsequently estimated using power-law models derived by relating the predicted canopy heights to field AGBD plots. Mapping was conducted for 2020 and 2021. The accuracy of canopy height predictions varied with height quantiles; models underestimated the height of taller trees and overestimated the height of smaller trees. A similar underestimation and overestimation trend was observed for the AGBD estimates. The mean carbon stock was estimated at 69.0 ±12.0 MgCha-1 in the tall eucalypt forests of the Warren region; 33.8 ± 5.0 MgCha-1 for the open eucalypt woodlands in the South Jarrah region; 7.1 ± 1.4 MgCha-1 for the heathland and shrublands in the Geraldton Sandplains region; 43.9 ± 4.9 MgCha-1 for the Kimberley eucalypt savanna; and 3.9 ± 1.0 MgCha-1 for the Kimberley savanna grasslands. This approach provides a useful framework for the future development of this process for fire management, and habitat health monitoring.

Global microplastics pollution: a bibliometric analysis and review on research trends and hotspots in agroecosystems.

The prevalence of microplastics (MPs) in agricultural ecosystems poses a notable threat to dynamics of soil ecosystems, crop productivity, and global food security. MPs enter agricultural ecosystems from various sources and have considerable impacts on the physiochemical properties soil, soil organisms and microbial communities, and plants. However, the intensity of these impacts can vary with the size, shape, types, and the concentrations of MPs in the soil. Besides, MPs can enter food chain through consummation of crops grown on MPs polluted soils. In this study, we conducted a bibliometric analysis of 1636 publications on the effects of MPs on agricultural ecosystems from 2012 to May 2024. The results revealed a substantial increase in publications over the years, and China, the USA, Germany, and India have emerged as leading countries in this field of research. Social network analysis identified emerging trends and research hotspots. The latest burst keywords were contaminants, biochar, polyethylene microplastics, biodegradable microplastics, antibiotic resistance genes, and quantification. Furthermore, we have summarized the effects of MPs on various components of agricultural ecosystems. By integrating findings from diverse disciplinary perspectives, this study provides a valuable insight into the current knowledge landscape, identifies research gaps, and proposes future research directions to effectively tackle the intricate challenges associated with MPs pollution in agricultural environments.

A See‐Saw of Subduction Rate in the North Pacific Western and Eastern Subtropical Mode Water Formation Areas Induced by the Aleutian Low

AbstractSubduction of water masses serves as a critical linkage between the surface and subsurface ocean, playing a crucial role in redistributing heat, carbon, and nutrients in the ocean. This process significantly influences global climate and marine ecosystem dynamics. Analyzing five ocean reanalysis data sets, we reveal a distinctive see‐saw pattern in anomalous subduction rates between western and eastern Subtropical Mode Water (STMW) formation areas. This pattern is predominantly driven by variations in latent heat flux, which modulates the late‐winter mixed layer depth. We demonstrate that late‐winter Aleutian Low (AL) activity induces these anti‐phase variations through its impact on latent heat flux. Notably, the Pacific Decadal Oscillation (PDO) significantly influences this subduction see‐saw pattern. During PDO warm phases, AL southward shift intensifies the anti‐correlated variations. Conversely, during cold phases, AL northward shift weakens its influence on ESTMW area, resulting in a less stable anti‐phase relationship.

The impact of data resolution on dynamic causal inference in multiscale ecological networks.

While it is commonly accepted that ecosystem dynamics are nonlinear, what is often not acknowledged is that nonlinearity implies scale-dependence. With the increasing availability of high-resolution ecological time series, there is a growing need to understand how scale and resolution in the data affect the construction and interpretation of causal networks-specifically, networks mapping how changes in one variable drive changes in others as part of a shared dynamic system ("dynamic causation"). We use Convergent Cross Mapping (CCM), a method specifically designed to measure dynamic causation, to study the effects of varying temporal and taxonomic/functional resolution in data when constructing ecological causal networks. As the system is viewed at different scales relationships will appear and disappear. The relationship between data resolution and interaction presence is not random: the temporal scale at which a relationship is uncovered identifies a biologically relevant scale that drives changes in population abundance. Further, causal relationships between taxonomic aggregates (low-resolution) are shown to be influenced by the number of interactions between their component species (high-resolution). Because no single level of resolution captures all the causal links in a system, a more complete understanding requires multiple levels when constructing causal networks.

Modeling the Impact of Global Warming on Ecosystem Dynamics: A Compartmental Approach to Sustainability

Environmental degradation driven by human activities has heightened the need for sustainable development strategies that balance economic growth with ecological preservation. This study uses a compartmental model approach to examine the effects of global warming on ecosystem dynamics, focusing on how rising temperatures alter interactions across trophic levels. Three case studies of varying complexity, including a human ecosystem incorporating social and economic factors, were analyzed by integrating feedback loops between greenhouse gas emissions, temperature anomalies, and ecosystem responses. The results quantitatively demonstrate that even minor disruptions in one part of an ecosystem can cause significant instability across trophic levels, potentially driving the system to collapse in a short period. These findings from all case studies highlight the cascading impacts of global warming, underscoring the intricate relationship between climate change and ecosystem stability. Furthermore, this study offers qualitative insights into the potential consequences of climate change on biodiversity and resource availability in real ecosystems, highlighting the vulnerability of such systems and the importance of incorporating feedback mechanisms into environmental policy and decision-making processes. The approach employed in this study offers a more robust framework for understanding ecosystem responses and for developing strategies to enhance resilience against climate change, thereby protecting the long-term sustainability of ecosystems.

Enhancing Forest Canopy Height Mapping in Kaziranga National Park, Assam, by Integrating LISS IV and SAR data with GEDI LiDAR data Using Machine Learning

Abstract. This study investigates the integration of spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical imagery from Linear Imaging Self Scanning Sensor (LISS IV), Sentinel-1 Synthetic Aperture Radar (SAR) and PALSAR data for continuous forest canopy height mapping in Kaziranga National Park (KNP), Assam for the years 2018 and 2022. Four machine learning models were trained and evaluated to assess the predictive ability of LISS IV data in conjunction with SAR variables. The mean canopy height was measured at approximately 8.58 m in 2018, which increased to about 9.07m in 2022. Results reveal Extreme Gradient Boosting (XGB) as the top-performing model, achieving an RMSE of 5.47m and an R2 of 0.55. In comparison, Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) achieved RMSE values of 5.49, 5.51, and 5.73, respectively. Analysis shows a prevalent occurrence of canopy heights below 5 meters in KNP (more than 35% of the area), while taller canopies beyond 20m can be found in less than 5% of the area. This finding underscores the importance of integrating satellite data and machine learning and highlights the novel application of LISS IV data in enhancing canopy height mapping. Furthermore, it represents the first comprehensive attempt to map canopy height in KNP, laying the groundwork for further research on biomass assessment and carbon sequestration in this vital biodiversity hotspot. Overall, the study highlights the potential of leveraging advanced remote sensing technologies and machine learning approaches for improved understanding and management of forest ecosystems.

Repeat airborne laser scanning to assess canopy height changes in tropical montane forests

Abstract. Tropical montane forests are vital ecosystems globally, preserving biodiversity, carbon stocks, and capturing moisture. We employed two airborne laser scanning (ALS) data sets to study changes in montane forest canopy heights in the Taita Hills, Kenya between 2014/2015 and 2022. We studied two forests, Ngangao (129 ha) and Yale (57 ha), which encompassed both indigenous montane forest and exotic plantations. First, forest types were mapped using field observations and aerial imagery, and then, canopy height changes were analysed using canopy height models at spatial resolution (i.e. the cell size) ranging from 1 m to 20 m. The results revealed overall increase in canopy height in the studied forests, with considerable spatial variation between the forest segments and main tree species. Planted exotic tree species, particularly eucalyptus but also pine and cypress, exhibited faster growth rates than native tree species. Point density differences between the ALS data sets can cause bias to estimation of canopy height changes. However, we observed that reducing the cell size of the canopy height models from 1 m to 10 m and 20 m, decreased the positive trend between point density difference and overestimation of canopy height change due to higher point density of more recent ALS data set. These findings contribute to our understanding on spatial complexity of montane forest ecosystems dynamics and help informing forest monitoring and development of management strategies for fragmented forests in montane regions.

Uncovering the rich amphibian fauna of two semideciduous forest fragments in southwestern Bahia, Brazil

Fauna inventories reduce biodiversity knowledge gaps by providing comprehensive data on species distribution, richness, and abundance. Furthermore, they identify undocumented species and enhance understanding of ecosystem dynamics and conservation needs. The richness and abundance of amphibian species were studied in two Semideciduous Seasonal Forest areas in the municipalities of Potiraguá (Serra Azul) and Itarantim (Serra do Mandim) in southwestern Bahia, Brazil. Active visual and acoustic surveys were conducted in 24 forest interior transects, two stream transects, and two permanent ponds investigated in the study area. Opportunistic encounters during team movements were also recorded. The richness was 46 amphibian species distributed in 14 families and 26 genera. Approximately half of the species were shared between the two areas, while 11 species were exclusive to Serra Azul and another nine were found only in Serra do Mandim. Cluster analysis for 42 locations in Atlantic Forest, Caatinga, and Cerrado, in a presence/absence matrix with 216 species, revealed that the composition of the amphibians found in Serra do Mandim and Serra Azul is similar to other sampled locations in the northeastern region of Minas Gerais, close to the study site, which are considered transitional between the Atlantic Forest and the Caatinga. Our results demonstrate that the remaining forest fragments in the region, although small and isolated, still sustain a high richness of amphibians with species restricted to the Atlantic Forest and Bahia, such as Bahius bilineatus, Ololygon strigilata, Aplastodiscus weygoldti and Vitreorana eurygnatha, and others considered typical of the Caatinga, such as Leptodactylus troglodytes and Physalaemus cicada. Additionally, we sampled potential new species, filled occurrence gaps, and expanded the geographical range of Pseudis fusca.

Mycorrhizal and endophytic fungi structure forest below-ground symbiosis through contrasting but interdependent assembly processes

BackgroundInteractions between plants and diverse root-associated fungi are essential drivers of forest ecosystem dynamics. The symbiosis is potentially dependent on multiple ecological factors/processes such as host/symbiont specificity, background soil microbiome, inter-root dispersal of symbionts, and fungus–fungus interactions within roots. Nonetheless, it has remained a major challenge to reveal the mechanisms by which those multiple factors/processes determine the assembly of root-associated fungal communities. Based on the framework of joint species distribution modeling, we examined 1,615 root-tips samples collected in a cool-temperate forest to reveal how root-associated fungal community structure was collectively formed through filtering by host plants, associations with background soil fungi, spatial autocorrelation, and symbiont–symbiont interactions. In addition, to detect fungi that drive the assembly of the entire root-associated fungal community, we inferred networks of direct fungus–fungus associations by a statistical modeling that could account for implicit environmental effects.ResultsThe fine-scale community structure of root-associated fungi were best explained by the statistical model including the four ecological factors/processes. Meanwhile, among partial models, those including background soil fungal community structure and within-root fungus–fungus interactions showed the highest performance. When fine-root distributions were examined, ectomycorrhizal fungi tended to show stronger associations with background soil community structure and spatially autocorrelated patterns than other fungal guilds. In contrast, the distributions of root-endophytic fungi were inferred to depend greatly on fungus–fungus interactions. An additional statistical analysis further suggested that some endophytic fungi, such as Phialocephala and Leptodontidium, were placed at the core positions within the web of direct associations with other root-associated fungi.ConclusionBy applying emerging statistical frameworks to intensive datasets of root-associated fungal communities, we demonstrated background soil fungal community structure and fungus–fungus associations within roots, as well as filtering by host plants and spatial autocorrelation in ecological processes, could collectively drive the assembly of root-associated fungi. We also found that basic assembly rules could differ between mycorrhizal and endophytic fungi, both of which were major components of forest ecosystems. Consequently, knowledge of how multiple ecological factors/processes differentially drive the assembly of multiple fungal guilds is indispensable for comprehensively understanding the mechanisms by which terrestrial ecosystem dynamics are organized by plant–fungal symbiosis.

Early Alarm on the First Occurrence of the Southern Giant Hornet Vespa soror du Buysson, 1905 (Vespidae) in Europe

ABSTRACTAn eco‐monitoring program to assess the biodiversity of insects affected by yellow‐legged hornet (Vespa velutina) trapping in the north of the Iberian Peninsula (Spain) revealed the first occurrence of the southern giant hornet Vespa soror (Hymenoptera, Vespidae) on the European continent. We present a detailed characterization, combining morphological characteristics and molecular tools for genetic identification, as well as key information on its identification with respect to other hornets found on the Iberian Peninsula. We discuss the most plausible pathways and vectors of introduction, its potential invasiveness, and subsequent impacts on host localities. Our preliminary results raise concerns about the potential threat of V. soror to human health and ecosystem dynamics, as it is a highly predatory species on other insects and even small vertebrates. Finally, this study confirms once again the usefulness of studying insects trapped in such traps for rapid response and early detection of inland invasive species. We also propose a common Spanish name for the species, “avispón sóror”.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Strengthening the pain care ecosystem to support equitable, person-centered, high-value musculoskeletal pain care

Abstract Improving health and wellbeing outcomes for people experiencing chronic musculoskeletal pain requires collective efforts across multiple levels of a healthcare ecosystem. System-wide barriers to care equity must however be addressed (eg, lack of co-designed services; overuse of low value care/underuse of high value care; inadequate health workforce; inappropriate funding models; inequitable access to medicines and technologies; inadequate research and innovation). In this narrative review, utilizing a systems’ thinking framework, we synthesize novel insights on chronic musculoskeletal pain research contextualized through the lens of this complex, interconnected system, the “pain care ecosystem.” We examine the application of systems strengthening research to build capacity across this ecosystem to support equitable person-centred care and healthy ageing across the lifespan. This dynamic ecosystem is characterized by three interconnected levels. At its centre is the person experiencing chronic musculoskeletal pain (micro-level). This level is connected with health services and health workforce operating to co-design and deliver person-centred care (meso-level), underpinned further upstream by contemporary health and social care systems (macro-level context). We provide emerging evidence for how we, and others, are working towards building ecosystem resilience to support quality musculoskeletal pain care: at the macro-level (eg, informing musculoskeletal policy and health strategy priorities); at the meso-level (eg, service co-design across care settings; health workforce capacity); and downstream, at the micro-level (eg, person-centred care). We outline the mechanisms and methodologies utilized and explain the outcomes, insights and impact of this research, supported by real world examples extending from Australian to global settings.

Functional ecology of plant communities as a guide for vegetation management

Plant functional traits determine how individual plants cope with varying environmental conditions, and extensive literature supports their role in influencing ecosystem properties. The study of plant functional traits could help the prediction of species responses to disturbances and environmental change, thus providing invaluable knowledge to refine conservation strategies of species and vegetation. However, applications of functional ecology for vegetation management are still underrepresented.The articles in this Special Issue of FLORA offer cutting-edge insights on the variation of plant traits and ecological strategies in response to environmental drivers, such as anthropogenic disturbance, climate and land use change. The results demonstrate how trait-based approaches can be used for the prediction of climate and land use change impacts on vulnerable sets of species or on biogeochemical cycles, reinforcing the concept that functional plant ecology can help identify the most effective procedures for vegetation management, such as the choice of mowing intensity in grasslands or planning vegetation recovery after wildfires.Our knowledge of plant trait coordination at the community level is still undefined, especially concerning the integration of above- and belowground traits. This represents a missed opportunity for vegetation management, especially considering that a complete understanding of the dynamics of the entire ecosystem is crucial for complete understanding of ecosystem properties. Since plant functional traits can represent a straightforward tool for monitoring changes in ecosystem structure and functions, their use could support vegetation monitoring as required by different programs of nature conservation. It would be useful to embrace a perspective that integrates plant traits with systematic conservation planning, ensuring effective and sustainable conservation efforts.

Carbohydrate Research Mini-review Relationships between bacteria and the mucus layer

The mucus layer on epithelial cells is an essential barrier, as well as a nutrient-rich niche for bacteria, forming a dynamic, functional and symbiotic ecosystem and first line of defense against invading pathogens. Particularly bacteria in biofilms are very difficult to eradicate. The extensively O-glycosylated mucins are the main glycoproteins in mucus that interact with microbes. For example, mucins act as adhesion receptors and nutritional substrates for gut bacteria. Mucins also play important roles in immune responses, and they control the composition of the microbiome, primarily due to the abundance of complex O-glycans. In inflammation or infection, the structures of mucin O-glycans can change and thus affect mucin function, impact biofilm formation and the induction of virulence pathways in bacteria. In turn, bacteria can support host cell growth, mucin production and can stimulate changes in the host immune system and responses leading to healthy tissue function. The external polysaccharides of bacteria are critical for controlling adhesion and biofilm formation. It is therefore important to understand the relationships between the mucus layer and microbes, the mechanisms and regulation of the biosynthesis of mucins, of bacterial surface polysaccharides, and adhesins. This knowledge can provide biomarkers, vaccines and help to develop new approaches for improved therapies, including antibiotic treatments.