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The mechanisms by which the soil physical structure, nutrient conditions, understory vegetation diversity and forest meteorological factors influence fine root (<2 mm diameter) characteristics mediated by soil moisture content (SMC) and soil heat flux (SHF) remain uncertain under climate change. Therefore, in this research, continuous observations were made of the fine root growth, death and turnover of cypress plantations, as well as the SMC and SHF under the management of four thinning intensities in hilly areas in central Sichuan from 2021 to 2023. The fine root data were obtained using the microroot canals (minirhizotron) in the study, and the soil hydrothermal data were obtained using the ECH2O soil parameter sensor and the PC-2R SHF data logger. In the time series, the fine root growth, death and turnover of the cypress plantations with different thinning intensities first increased and then decreased throughout the year; the vertical center of the gravity of the fine roots of cypress was concentrated in the 30–50 cm range. This research also revealed that the variability in the SMC decreased with increasing soil depth. Additionally, the SHF was transmitted from greater soil depths to the surface in unthinned cypress plantation at a rate of 0.036 per year, which decreased the heat in the fine root region. However, SHF was transmitted from the soil surface to greater depths at rates of 0.012 per year, 0.08 per year and 0.002 per year, which increased the heat in the fine root area. The redundancy analysis (RDA) and structural equation model (SEM) results indicated that the SMC and soil heat energy distribution pattern obviously affected fine root growth, death and turnover in the cypress plantation. However, the climate conditions in the forest, the characteristics of vegetation in the understory and the physical and chemical characteristics of the soil directly or indirectly affect the characteristics of the fine roots of cypress plantations with changes in thinning intensity. This research provides a basis for understanding ecosystem structure, nutrient cycling and carbon balance and may guide artificial plantation development and management. [ABSTRACT FROM AUTHOR]

Species' habitats are strongly influenced by the 3‐dimensional (3D) structure of ecosystems. The dominant technique used to measure 3D structure is Airborne Laser Scanning (ALS), a type of LiDAR (Light Detection and Ranging) technology. Airborne Laser Scanning captures fine‐scale structural information over large spatial extents and provides useful environmental predictors for habitat modeling. However, due to technical complexities of processing ALS data, the full potential of ALS is not yet realized in wildlife research, with most studies relying on a limited set of 3D predictors, such as vegetation metrics developed principally for forestry applications. Here, we highlight the full potential of ALS data for wildlife research and provide insight into how it can be best used to capture the environmental conditions, resources, and risks that directly determine a species' habitat. We provide a nontechnical overview of ALS data, covering data considerations and the modern options available for creating custom, ecologically relevant, ALS predictors. Options included the following: i) direct point cloud approaches that measure structure using grid, voxel, and point metrics, ii) object‐based approaches that identify user‐defined features in the point cloud, and iii) modeled environmental predictors that use additional modeling to infer a range of habitat characteristics, including the extrapolation of field acquired measurements over ALS data. By using custom ALS predictors that capture species‐specific resources, risks, and environmental conditions, wildlife practitioners can produce models that are tailored to a species' ecology, have greater biological realism, test a wider range of species‐environment relationships across scales, and provide more meaningful insights to inform wildlife conservation and management. [ABSTRACT FROM AUTHOR]

Plants adopt particular growth‐forms when they are exposed to extreme environmental conditions. In this study, we describe a unique woody plant growth‐form induced by large mammalian herbivores and discuss that this growth‐form could have evolved as a strategy for escaping the browser zone in herbivore driven ecosystems.We analysed responses of key architectural and morphological attributes (branching and thorn density, tree dimensions, presence of flowers and fruits) of three Eurasian spiny tree species (Malus sylvestris, Prunus cerasifera and Pyrus communis) to different levels of browsing by large herbivores in the temperate Białowieża Forest, Poland.Under high browsing pressure, studied trees displayed two distinct forms of the crown: a bottom sterile part developing into a densely branched structure with high density of thorns ('cage‐form'), and an upper reproductive part that escaped from herbivore control ('escaped‐form'). The size of cage‐form influenced the feeding behaviour of red deer (Cervus elaphus) by increasing the time deer spend foraging and increasing the bite rate. The height at which cages started to escape and their diameter matched with foraging reach of red deer.Synthesis. We argue that the frequency and cage dimensions of this woody growth‐form in the landscape could inform on the type and intensity of recent herbivory. Moreover, its distinctive inducibility suggests that this growth‐form did not emerge recently under anthropogenic pressure but could be the legacy of ancient herbivory effects. Observational evidence suggests that this growth‐form emerged in several herbivore‐driven systems around the globe and may be used to identify the dominant herbivores that control vegetation structure in these ecosystems. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Recently, there has been a surge of interest in developing innovation ecosystems within targeted industry sectors to enhance national industry capabilities. However, academic discussions on innovation ecosystems within a sectoral context are notably lacking. Past research has primarily focused on innovation ecosystems centered around a single organization, initiated and led by the same organization. This study aims to address this gap by examining the enablers of sectoral innovation ecosystems (SIE) development. A systematic literature review was conducted using 57 empirical studies from the Web of Science database between 2010-2023. The study proposes a conceptual framework with three critical enablers for developing SIE: (1) Structural configurations (structural constructs and structural alignment), (2) capabilities (organizational capabilities and ecosystem capabilities), and (3) interactions (nature of interactions and drivers of interactions). This framework provides a comprehensive understanding of how sectoral innovation ecosystems can be structured and managed to enhance industry capabilities. The study underscores the pivotal role of intermediary organizations in orchestrating SIE development, highlighting their importance in facilitating interactions and aligning structures within the ecosystem. By advocating for the complementary use of structure-based and coevolution-based approaches, the study offers a dual perspective that enriches the theoretical understanding of SIE. The systematic literature review concludes with recommendations for future research, providing a pathway for further exploration of SIE.

Introduction: With the continuous changes in climate patterns due to global warming, drought has become an important limiting factor in the development of terrestrial ecosystems. However, a comprehensive understanding of the impact of drought on soil microbial activity at a global scale is lacking. Methods: In this study, we aimed to examine the effects of drought on soil microbial biomass (carbon [MBC], nitrogen [MBN], and phosphorus [MBP]) and enzyme activity (b-1, 4-glucosidase [BG]; b-D-cellobiosidase [CBH]; b-1, 4-Nacetylglucosaminidase [NAG]; L-leucine aminopeptidase [LAP]; and acid phosphatase [AP]). Additionally, we conducted a meta-analysis to determine the degree to which these effects are regulated by vegetation type, drought intensity, drought duration, and mean annual temperature (MAT). Result and discussion: Our results showed that drought significantly decreased the MBC, MBN, and MBP and the activity levels of BG and AP by 22.7%, 21.2%, 21.6%, 26.8%, and 16.1%, respectively. In terms of vegetation type, drought mainly affected the MBC and MBN in croplands and grasslands. Furthermore, the response ratio of BG, CBH, NAG, and LAP were negatively correlated with drought intensity, whereas MBN and MBP and the activity levels of BG and CBH were negatively correlated with drought duration. Additionally, the response ratio of BG and NAG were negatively correlated with MAT. In conclusion, drought significantly reduced soil microbial biomass and enzyme activity on a global scale. Our results highlight the strong impact of drought on soil microbial biomass and carbon- and phosphorus-acquiring enzyme activity. [ABSTRACT FROM AUTHOR]

Long-term fishing bans have spurred extensive debate regarding their impacts on ecosystem structures, functions, and water qualities. However, data on the effects of specific changes induced by fishing bans on ecosystem structures, functions, and water qualities in lakes are still lacking. Therefore, the present study addresses this knowledge gap by employing an Ecopath model to assess alterations in an ecosystem's structure and function before (2011) and after (2021) the implementation of the fishing ban in Caohai Lake and its association with changes in water quality. (1) We observed a substantial reduction in the area covered by submerged aquatic vegetation after the ban, amounting to a 65% decrease in coverage compared with that before the ban, and a 60% reduction in the total ecosystem's biomass. (2) Following the ban, the number of fish species increased from 7 to 14, and this was accompanied by a rise in the fish biomass from 14.16 t·km−2 to 25.81 t·km−2; a 4.5-fold increase in the total system consumption was observed, signifying accelerated energy and material flows within the ecosystem. (3) The fishing ban exhibited no significant impact on the total nitrogen concentration; however, it significantly reduced the water's transparency and increased the total phosphorus, ammonia nitrogen, chemical oxygen demand, and chlorophyll contents (p < 0.05). This shift in nutrient dynamics fostered a transformation from a macrophyte-dominant lake to an alga-dominant lake. The fish abundance and diversity increase in closed-type macrophytic lakes, thereby accelerating energy and material flows within food webs. These findings present novel insights into the effective policy management of fishing bans within the Yangtze River Basin, thus enhancing our understanding of sustainable lake ecosystem management. [ABSTRACT FROM AUTHOR]

Abstract Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large‐scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three‐dimensional vegetation structure at local scales. Here we demonstrate the potential of high‐frequency ground‐based laser scanning to systematically monitor structural changes in vegetation. We present a proof‐of‐concept high‐temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high‐temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

The study of changes in land cover provides a better understanding of the interactions between humans and natural ecosystems. In this context, the present study focused on the dynamics of natural ecosystems in the Rif region of Northern Morocco. The methodology was based on the inspection and visual interpretation of Landsat and Google Earth image captures, the time series of five Landsat 4-8 image bands, and the Tasseled Cap indices for a random sample of 500 points from 1984 to 2022. The study found that changes affected practically the whole study region over the study period, with around a third of them being ignored due to their very tiny magnitudes or being false positives. The findings demonstrated a general declining trend in the measured changes, indicating a reduction in pressure on different ecosystems. Furthermore, this tendency may be due in part to the availability of Google Earth images during the 2000s, which has significantly reduced the number of false positives. In terms of the year of first change, only 5.7% of pixels experienced their first events after the year 2000, implying that these pixels underwent no change for at least the first 16 years of the study period. On the other hand, 2.5% of the pixels had their last events during the first ten years and have thus remained unmodified for at least 27 years. For the year 2020, the confidence rating of the visual land cover categorization is medium to high for 88.9% of pixels using high-resolution Google Earth photos, whereas the classification quality was inadequate for 64% of pixels in 1984. Despite the stresses on the ecosystems structured by shrubs/shrubs, forests, and herbaceous/shrubs caused by the different disturbances identified, the majority of these ecosystems have not been converted to new land cover classes. According to the study, agriculture is the primary driving force underlying the conversion of forests, herbaceous/shrublands, and even shrublands/shrublands. The area increases for the latter three ecosystems represent, on the one hand, their ability to regenerate themselves and, on the other, Morocco's restoration efforts.

Abstract Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling. This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics. We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study). Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools.

IntroductionAn unprecedented amount of Earth Observations and in-situ data has become available in recent decades, opening up the possibility of developing scalable and practical solutions to assess and monitor ecosystems across the globe. Essential Biodiversity Variables are an example of the integration between Earth Observations and in-situ data for monitoring biodiversity and ecosystem integrity, with applicability to assess and monitor ecosystem structure, function, and composition. However, studies have yet to explore how such metrics can be organized in an effective workflow to create a composite Ecosystem Integrity Index and differentiate between local plots at the global scale.MethodsUsing available Essential Biodiversity Variables, we present and test a framework to assess and monitor forest ecosystem integrity at the global scale. We first defined the theoretical framework used to develop the workflow. We then measured ecosystem integrity across 333 forest plots of 5 km2. We classified the plots across the globe using two main categories of ecosystem integrity (Top and Down) defined using different Essential Biodiversity Variables.Results and discussion:We found that ecosystem integrity was significantly higher in forest plots located in more intact areas than in forest plots with higher disturbance. On average, intact forests had an Ecosystem Integrity Index score of 5.88 (CI: 5.53–6.23), whereas higher disturbance lowered the average to 4.97 (CI: 4.67–5.26). Knowing the state and changes in forest ecosystem integrity may help to deliver funding to priority areas that would benefit from mitigation strategies targeting climate change and biodiversity loss. This study may further provide decision- and policymakers with relevant information about the effectiveness of forest management and policies concerning forests. Our proposed method provides a flexible and scalable solution that facilitates the integration of essential biodiversity variables to monitor forest ecosystems.

In a recent perspective (Diversity and Distributions, 29, 39–50), '10 variables' were proposed to measure vegetation structure from airborne laser scanning (ALS) for assessing species distributions and habitat suitability. We worry about this list because the variables predominantly represent variation in vegetation height, the vertical variability of vegetation biomass is insufficiently captured, and variables of vegetation cover are ill‐defined or not ecosystem agnostic. We urge for a better defined, more comprehensive and more balanced list, and for assessing which information from ALS point clouds is truly essential to measure the major dimensions of 3D vegetation structure within and across ecosystems and animal habitats. We think that the currently proposed 'list of 10 ALS metrics' is premature and that researchers and stakeholders should be cautious in adopting this list. [ABSTRACT FROM AUTHOR]

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large‐scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three‐dimensional vegetation structure at local scales. Here we demonstrate the potential of high‐frequency ground‐based laser scanning to systematically monitor structural changes in vegetation. We present a proof‐of‐concept high‐temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high‐temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways. [ABSTRACT FROM AUTHOR]

Abstract Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed. We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric. We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests. These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications.

Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling.This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics.We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study).Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools. [ABSTRACT FROM AUTHOR]

IntroductionWith the continuous changes in climate patterns due to global warming, drought has become an important limiting factor in the development of terrestrial ecosystems. However, a comprehensive understanding of the impact of drought on soil microbial activity at a global scale is lacking.MethodsIn this study, we aimed to examine the effects of drought on soil microbial biomass (carbon [MBC], nitrogen [MBN], and phosphorus [MBP]) and enzyme activity (β-1, 4-glucosidase [BG]; β-D-cellobiosidase [CBH]; β-1, 4-N-acetylglucosaminidase [NAG]; L-leucine aminopeptidase [LAP]; and acid phosphatase [AP]). Additionally, we conducted a meta-analysis to determine the degree to which these effects are regulated by vegetation type, drought intensity, drought duration, and mean annual temperature (MAT).Result and discussionOur results showed that drought significantly decreased the MBC, MBN, and MBP and the activity levels of BG and AP by 22.7%, 21.2%, 21.6%, 26.8%, and 16.1%, respectively. In terms of vegetation type, drought mainly affected the MBC and MBN in croplands and grasslands. Furthermore, the response ratio of BG, CBH, NAG, and LAP were negatively correlated with drought intensity, whereas MBN and MBP and the activity levels of BG and CBH were negatively correlated with drought duration. Additionally, the response ratio of BG and NAG were negatively correlated with MAT. In conclusion, drought significantly reduced soil microbial biomass and enzyme activity on a global scale. Our results highlight the strong impact of drought on soil microbial biomass and carbon- and phosphorus-acquiring enzyme activity.

The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8,587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product. Plain Language Summary: The amount of carbon stored in aboveground vegetation is uncertain. This uncertainty limits our ability to calculate fluxes of carbon between the land surface and the atmosphere, and prevents rigorous carbon offset crediting in forests. Much of this uncertainty is attributed to inconsistent measurement techniques and the use of Earth‐observation methods that were not designed to quantify carbon density. The Global Ecosystem Dynamics Investigation (GEDI) can largely overcome these challenges by producing measurements of vegetation height using a lidar sensor on the International Space Station. This document describes methods developed by the GEDI Science Team to convert spaceborne measurements of vegetation height into estimates of aboveground biomass density. The algorithms depend on the geographic world region and the type of vegetation that is present at a sampled location. For example, evergreen broadleaf forests of the humid tropics in South America and deciduous broadleaf forests of Europe use different algorithms. Statistical models were developed using comprehensive field measurements and simulated GEDI data. This document describes the importance of filtering GEDI data to reduce the impact of measurement artifacts on aboveground biomass predictions. Quality flags and ancillary data contained in the GEDI04_A data product ensure that the best predictions can be used. Key Points: Global Ecosystem Dynamics Investigation (GEDI) aboveground biomass density is from models trained on a comprehensive database of field measurements and simulated GEDI waveformsOn‐orbit prediction requires stratification by plant functional type and world regionQuality flags and metrics distinguish GEDI measurements that are representative of the conditions under which models were developed [ABSTRACT FROM AUTHOR]

Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed.We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric.We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests.These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications. [ABSTRACT FROM AUTHOR]

A huge proportion of the world’s population resides in urban areas along the coast. As cities expand, the ability of coastal ecosystems to provide the benefits people derive from nature, ranging from food from fisheries to coastal defense to maritime transportation and beyond, is in question. While it is well understood that coastal development changes ecosystems, quantitative insights about how terrestrial urbanization fundamentally alters ecosystem structure and function in adjacent freshwater and downstream coastal marine habitats remain rare, though a general expectation is that impacts of terrestrial urbanization will attenuate from land to freshwater to coastal marine habitats. Empirical assessments of these phenomena are especially important for species that rely on freshwater and coastal marine habitats at multiple points in their life cycles, including endangered and threatened Pacific salmon (Oncorhynchus spp.). We investigated associations between landscape-scale urbanization and ecosystem structure (biodiversity of epibenthic invertebrate taxa) and function (benthic net primary productivity and decomposition) in freshwater and coastal marine habitats across six pairs of more and less urbanized, coastal watersheds in Puget Sound, WA, USA, using principal components analysis, analysis of covariance, and Mantel tests. Greater upland urbanization was associated with greater reductions in freshwater biodiversity, measured as the density and evenness of epibenthic invertebrate families. In contrast and surprisingly, however, coastal marine biodiversity (measured as the density and evenness of epibenthic invertebrate families) tended to be higher at more urbanized sites, suggesting the potential role of low to moderate levels of urbanization-related disturbance in determining coastal marine biodiversity patterns. We found no statistical association between urbanization and freshwater and coastal marine ecosystem functions, estimated from changes in accumulated algal biomass on tiles (benthic net primary productivity) and loss of biomass from litter bags (decomposition). In addition, there was no evidence that changes in ecosystem structure and function with urbanization were more severe in freshwater than coastal marine habitats, as might be expected if the land-sea boundary diminished effects of landscape-scale urbanization. Our results suggest that the effects of urbanization can be complex and that attention to terrestrial, freshwater, and coastal marine systems in concert will produce more effective, ecosystem-based management.

The frequently used term ecology is currently interpreted in various ways. This scientific discipline has undergone relatively dynamic development since its inception, and its character is still widening and changing. Therefore, in this contribution we attempt to briefly summarize the subject and contents of current ecology, as well as to propose its more systemic and comprehensive definition. We try to enhance its present definitions especially by putting emphasis on the dynamics of nature (the dynamics of natural processes). We define current ecology as the science dealing with the structure, dynamics and functions of nature including evolution, where structure involves the distribution and abundance of individual organisms, habitats and ecosystems; dynamics include all the aspects of their life trajectories and cycles, including growth, development, reproduction or renewal, interactions and their changes, the cycling of matter, flows of energy and information and their transformations; and functions involve the properties, traits and niches of individual organisms and species in an ecosystem, as well as the properties and niches of ecosystems in the landscape, ecoregion or in the whole Earth system.

Forage fishes are an important component of marine, estuarine, and aquatic food webs that facilitate the transfer of energy and nutrients from primary producers to upper trophic levels. Previous studies of forage fishes have focused primarily on pelagic planktivorous species in pelagic environments. However, benthically associated taxa can be just as important as planktivorous species, particularly in highly productive estuarine environments that provide critical habitat for many predators. In this study, we analyzed a 20‐year forage fish community composition and abundance dataset across four eastern Gulf of Mexico estuaries spanning a broad latitudinal gradient to investigate spatiotemporal variability in community structure and quantify associations with habitat. Our analyses revealed significant regional structuring of forage fish communities, coupled with a strong association with habitat characteristics related to latitudinal effects and basal resource regime. Communities in the two northern estuaries and two southern estuaries were associated primarily with planktonically reliant and benthically reliant taxa, respectively. Despite regional differences, we uncovered a coherent annual cycle in forage fish communities across all estuaries related to seasonal shifts in abundances of several abundant and ubiquitous species. We additionally revealed significant subdecadal periodicity potentially associated with bottom‐up effects of global climatic cycles. The significant association of forage fish communities with habitat regime shown in this study underlies the importance of continued monitoring of these communities. This study represents a novel approach to assess this critical ecosystem component in diverse estuarine systems globally. [ABSTRACT FROM AUTHOR]

Abstract Forage fishes are an important component of marine, estuarine, and aquatic food webs that facilitate the transfer of energy and nutrients from primary producers to upper trophic levels. Previous studies of forage fishes have focused primarily on pelagic planktivorous species in pelagic environments. However, benthically associated taxa can be just as important as planktivorous species, particularly in highly productive estuarine environments that provide critical habitat for many predators. In this study, we analyzed a 20‐year forage fish community composition and abundance dataset across four eastern Gulf of Mexico estuaries spanning a broad latitudinal gradient to investigate spatiotemporal variability in community structure and quantify associations with habitat. Our analyses revealed significant regional structuring of forage fish communities, coupled with a strong association with habitat characteristics related to latitudinal effects and basal resource regime. Communities in the two northern estuaries and two southern estuaries were associated primarily with planktonically reliant and benthically reliant taxa, respectively. Despite regional differences, we uncovered a coherent annual cycle in forage fish communities across all estuaries related to seasonal shifts in abundances of several abundant and ubiquitous species. We additionally revealed significant subdecadal periodicity potentially associated with bottom‐up effects of global climatic cycles. The significant association of forage fish communities with habitat regime shown in this study underlies the importance of continued monitoring of these communities. This study represents a novel approach to assess this critical ecosystem component in diverse estuarine systems globally.

The Scotia Sea is a productive pelagic ecosystem in the Southern Ocean, which is rapidly changing as a consequence of global warming. Species range shifts are particularly evident, as sub-Antarctic species expand their range from North to South, potentially rearranging the structure of this ecosystem. Thus, studies are needed to determine the current extent of variation in food web structure between these two biogeographic regions of the Scotia Sea and to investigate whether the observed patterns are consistent among depth zones. We compiled a database of 10,888 feeding interactions among 228 pelagic taxa, underpinned by surveys and dietary studies conducted in the Scotia Sea. Network analysis indicated that the Northern Scotia Sea (NSS), relative to the Southern Scotia Sea (SSS), is more complex: with higher species richness (more nodes) and trophic interactions (more links) is more connected overall (greater connectance and linkage density). Moreover, the NSS is characterised by more groups of strongly interacting organisms (greater node clustering) than the SSS, suggesting a higher trophic specialisation of Antarctic compared to sub-Antarctic species. Depth also played a key role in structuring these networks, with higher mean trophic position and more dietary generalism in the mesopelagic and bathypelagic zones relative to the epipelagic zones. This suggests that direct access to primary producers is a key factor influencing the trophic structure of these communities. Our results suggest that under current levels of warming the SSS ecosystem will likely become more connected and less modular, resembling the current structure of the NSS. [ABSTRACT FROM AUTHOR]

The trophic position concept is central in system ecology, and in this study, trophic position (TP) estimates from stable-isotopes and an Ecopath mass-balance food web model for the Barents Sea were compared. Two alternative models for estimating TP from stable isotopes, with fixed or scaled trophic fractionation were applied. The mass-balance model was parametrized and balanced for year 2000, was comprised of 108 functional groups (Gs), and was based on biomass and diet data largely based on predator stomach data. Literature search for the Barents Sea Large Marine Ecosystem revealed 93 sources with stable isotope data (δ15N values) for 83 FGs, and 25 of the publications had trophic position estimated from nitrogen stable isotopes. Trophic positions estimated from the mass-balance model ranged to 5.1 TP and were highly correlated with group mean δ15N values, and also highly correlated with the original literature estimates of trophic positions from stable isotopes. On average, TP from the mass-balance model was 0.1 TP higher than the original literature TP estimates (TPSIR) from stable isotopes. A trophic enrichment factor (TEF) was estimated assuming fixed fractionation and minimizing differences between trophic positions from Ecopath and TP predicted from δ15N values assuming a baseline value for δ15N calculated for pelagic particulate organic matter at a baseline TP of 1.0. The estimated TEF of 3.0‰ was lower than the most commonly used TEF of 3.4 and 3.8‰ in the literature. The pelagic whales and pelagic invertebrates functional groups tended to have higher trophic positions from Ecopath than from stable isotopes while benthic invertebrate functional groups tended to show an opposite pattern. Trophic positions calculated using the scaled trophic fractionation approach resulted in lower TP than from Ecopath for intermediate TPs and also a larger TP range in the BS. It is concluded that TPs estimated from δ15N values using a linear model compared better to the Ecopath model than the TPs from scaled fractionation approach.

Nature-based solutions have emerged as a concept for integrating ecosystem-based approaches whilst addressing multiple sustainable development goals. However, implementing nature-based solutions is inherently complex and requires consideration of a range of environmental and socio-economic conditions that may impact on their effectiveness. This research assesses ecosystem services within the Valletta urban agglomeration, Malta, and evaluates the implications arising from existing distributional patterns. Proxy-based indicators and expert knowledge were used to map and assess a set of 14 ecosystem services. Proximity and correlation analyses were used to assess distributional inequalities arising from differentiated availability of ecosystem types with high ecosystem service capacities for groups with different socio-economic characteristics. Data relating to schooling, employment, sickness, disability, and old age, were combined to identify areas of relative advantage and disadvantage. The highest ecosystem service capacities were in the urban fringes and the lowest in dense urban cores. Private gardens and urban trees had the highest regulating ecosystem service capacities per unit area. Contrastingly, public gardens had low effectiveness for regulating ecosystem services but the highest cultural ecosystem service capacities. Availability of urban green infrastructure and tree cover differ according to socio-economic advantage, and disadvantaged communities generally had reduced proximity to ecosystems with high ecosystem service capacities. Considering these findings, we argue that urban ecosystem service assessments can support greening strategies by identifying the most effective nature-based solutions that can play a redistributive role by addressing existing inequalities in green infrastructure and ecosystem services capacities distribution in cities.

Assessing and monitoring ecosystem health are critical components of ecosystem management as they provide information on natural resource destruction and the causes and factors leading to it. The need for comprehensive and multidimensional indices for assessing and predicting the status of ecosystems has become increasingly important. The ecosystem health index (EHI) was calculated by assessing multiple criteria, including vigor, organization, and resilience criteria, in an ecotone ecosystem in Ardabil province. Different weighting approaches were used to evaluate the index value, and sub-watersheds were prioritized based on the index weighting. The EHI values varied among the studied sub-watersheds, ranging from 0.32 to 0.79, depending on the weighting method. Sub-watershed SW1 had a higher health status with a value of 0.77 compared to other studied sub-watersheds in the Iril watershed, while SW5 had a lower health status with a value of 0.34 in the equal weighting approach. The average value of the EHI for all studied sub-watersheds in the Iril watershed was found to be 0.50, indicating a relatively undesirable ecosystem health status. The EHI is a useful tool for prioritizing areas and an effective step in ecosystem management, considering the multidimensional nature of the index and the factors affecting ecosystem restoration or destruction. Assessing the extent of ecosystem degradation across various regions depends on the health status of the studied ecosystem, the feasibility of index calculation, and may involve different selection criteria. [ABSTRACT FROM AUTHOR]

Avian communities are very good indicators of integrity and stability of ecosystem structure and functions. Assessment of bird assemblages in different landscapes is therefore emphasized from an environmental monitoring viewpoint. Bird surveys were carried out from April 2015 to March 2016 to document the avian species assemblage of agricultural landscapes in Panipat, Haryana, India. Point-transect in amalgam with opportunistic encounter methods were used to collect data. A total of 101 bird species under 44 families and 15 orders were recorded from the study area. The bird species richness was highest for the order Passeriformes (48), followed by Pelecaniformes (15), Charadriiformes (6), and the remaining 12 orders. Ardeidae was the most diverse bird family in the study area. Among the recorded avifauna, 77 species were residents, 18 species were winter migrants and six species were summer migrants. Species richness was recorded to be highest in the month of January compared to the remaining months. Species richness, abundance, diversity and evenness differed significantly (P < 0.05) between seasons as well as among the agricultural landscapes. Most bird species were insectivorous (36) followed by carnivorous (26), omnivorous (24), granivorous (9), frugivorous (5) and nectarivorous (1). Painted Stork Mycteria leucocephala, Black-necked Stork Ephippiorhynchus asiaticus, Black-headed Ibis Threskiornis melanocephalus, and Alexandrine Parakeet Psittacula eupatria are four Near Threatened species found in this region. Interestingly, five species having globally declining population trends are still common in the study area. The observed richness of avian species in the study area calls for further studies on habitat preference, seasonal changes, nest ecology, and breeding biology to understand species specific roles of birds in agro-ecosystems.

Aim: Light Detection And Ranging (LiDAR) is a promising remote sensing technique for ecological applications because it can quantify vegetation structure at high resolution over broad spatial extents. Using country‐wide airborne laser scanning (ALS) data, we test to what extent fine‐scale LiDAR metrics capturing low vegetation, medium‐to‐high vegetation and landscape‐scale habitat structures can explain the habitat preferences of threatened butterflies at a national extent. Location: The Netherlands. Methods: We applied a machine‐learning (random forest) algorithm to build species distribution models (SDMs) for grassland and woodland butterflies in wet and dry habitats using various LiDAR metrics and butterfly presence–absence data collected by a national butterfly monitoring scheme. The LiDAR metrics captured vertical vegetation complexity (e.g., height and vegetation density of different strata) and horizontal heterogeneity (e.g., vegetation roughness, microtopography, vegetation openness and woodland edge extent). We assessed the relative variable importance and interpreted response curves of each LiDAR metric for explaining butterfly occurrences. Results: All SDMs showed a good to excellent fit, with woodland butterfly SDMs performing slightly better than those of grassland butterflies. Grassland butterfly occurrences were best explained by landscape‐scale habitat structures (e.g., open patches, microtopography) and vegetation height. Woodland butterfly occurrences were mainly determined by vegetation density of medium‐to‐high vegetation, open patches and woodland edge extent. The importance of metrics generally differed between wet and dry habitats for both grassland and woodland species. Main conclusions: Vertical variability and horizontal heterogeneity of vegetation structure are key determinants of butterfly species distributions, even in low‐stature habitats such as grasslands, dunes and heathlands. The information content of low vegetation LiDAR metrics could further be improved with country‐wide leaf‐on ALS data or surveys from drones and terrestrial laser scanners at specific sites. LiDAR thus offers great potential for predictive habitat distribution modelling and other studies on ecological niches and invertebrate–habitat relationships. [ABSTRACT FROM AUTHOR]

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 ​m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

Distributing fishing mortality across the widest possible range of species, stocks, and sizes in proportion to their natural productivity (i.e., balanced harvest, BH) has been suggested as a new paradigm of fisheries management to minimize the effects of fishing on the ecosystem structure while maximizing overall yield. Models that have been used to test the effects of BH, however, usually concentrate on fish and assume full alignment of fishing mortality with the productivity of each species. Here, we used the trophic-level-based approach EcoTroph to investigate the effects of BH on the biomass and catch trophic spectra of a virtual ecosystem assuming (1) a full implementation, where all trophic levels can be fished according to their productivity and (2) a more realistic implementation, where low and intermediate trophic levels are only partially exploitable by fisheries mimicking current technological and practical limitations. EcoTroph simulations show that a BH fishing pattern does not fully maintain ecosystem structure but results in small structural changes and a large total yield. The resulting catch, however, was dominated by low trophic levels (i.e., 2–2.5). Considering that fishing mortality cannot be fully aligned to all species, we observed an additional adverse impact of BH: the increase in unexploitable biomass. In contrast, protecting lower trophic levels appeared as an efficient way to limit the impact of fisheries on the highest trophic levels, which play a crucial role in ecosystem stability and biodiversity. We conclude that given our inability to align fishing mortality to the productivity of each species, BH could lead to strong adverse impacts on the ecosystem. Instead of expanding fishing pressure toward new species and trophic levels, we first should ensure the sustainable management of those that are currently harvested beyond their capacity to replenish.

Characterizing the spatial distribution and variation of species communities and validating these characteristics with data from the field are key elements for an ecosystem‐based approach to management. However, models of species distributions that yield community structure are usually not linked to models of community dynamics, constraining understanding and management of the ecosystem, particularly in data‐poor regions. Here we use a qualitative network model to predict changes in Antarctic benthic community structure between major marine habitats characterized largely by seafloor depth and slope, and use multivariate mixture models of species distributions to validate the community dynamics. We then assess how future increases in primary production associated with anticipated loss of sea‐ice may affect the ecosystem. Our study shows how both spatial and structural features of ecosystems in data‐poor regions can be analyzed and possible futures assessed, with direct relevance for ecosystem‐based management. [ABSTRACT FROM AUTHOR]

Human land use has caused substantial declines in global species richness. Evidence from different taxonomic groups and geographic regions suggests that land use does not equally impact all organisms within terrestrial ecological communities, and that different functional groups of species may respond differently. In particular, we expect large carnivores to decline more in disturbed land uses than other animal groups.We present the first global synthesis of responses to land use across functional groups using data from a wide set of animal species, including herbivores, omnivores, carnivores, fungivores and detritivores; and ranging in body mass from 2 × 10−6 g (an oribatid mite) to 3,825 kg (the African elephant).We show that the abundance of large endotherms, small ectotherms, carnivores and fungivores (although in the last case, not significantly) are reduced disproportionately in human land uses compared with the abundance of other functional groups.The results, suggesting that certain functional groups are consistently favoured over others in land used by humans, imply a substantial restructuring of ecological communities. Given that different functional groups make unique contributions to ecological processes, it is likely that there will be substantial impacts on the functioning of ecosystems. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Global biodiversity is threatened by unprecedented and increasing anthropogenic pressures, including habitat loss and fragmentation. LiDAR can become a decisive technology by providing accurate information about the linkages between biodiversity and ecosystem structure. Here, we review the current use of LiDAR metrics in ecological studies regarding birds, mammals, reptiles, amphibians, invertebrates, bryophytes, lichens, and fungi (BLF). We quantify the types of research (ecosystem and LiDAR sources) and describe the LiDAR platforms and data that are currently available. We also categorize and harmonize LiDAR metrics into five LiDAR morphological traits (canopy cover, height and vertical distribution, understory and shrubland, and topographic traits) and quantify their current use and effectiveness across taxonomic groups and ecosystems. The literature review returned 173 papers that met our criteria. Europe and North America held most of the studies, and birds were the most studied group, whereas temperate forest was by far the most represented ecosystem. Globally, canopy height was the most used LiDAR trait, especially in forest ecosystems, whereas canopy cover and terrain topography traits performed better in those ecosystems where they were mapped. Understory structure and shrubland traits together with terrain topography showed high effectiveness for less studied groups such as BLF and invertebrates and in open landscapes. Our results show how LiDAR technology has greatly contributed to habitat mapping, including organisms poorly studied until recently, such as BLF. Finally, we discuss the forthcoming opportunities for biodiversity mapping with different LiDAR platforms in combination with spectral information. We advocate (i) for the integration of spaceborne LiDAR data with the already available airborne (airplane, drones) and terrestrial technology, and (ii) the coupling of it with multispectral/hyperspectral information, which will allow for the exploration and analyses of new species and ecosystems.

The Arctic Ocean has been experiencing rapid warming, which accelerates sea ice melt. Further, the increasing area and duration of sea ice-free conditions enhance ocean uptake of CO2. We conducted two shipboard experiments in September 2015 and 2016 to examine the effects of temperature, CO2, and salinity on phytoplankton dynamics to better understand the impacts of rapid environmental changes on the Arctic ecosystem. Two temperature conditions (control:

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

Disturbances are fundamental components of ecosystems and, in many cases, a dominant driver of ecosystem structure and function at multiple spatial and temporal scales. While the effect of any one disturbance may be relatively well understood, multiple interacting disturbances can cause unexpected disturbance behavior (e.g., larger extents), altered return likelihoods, or reduced ecosystem resilience and regime shifts. Given the long‐lasting implications of such events, and the potential for changes in disturbance rates driven by climate change and increasing anthropogenic pressures, developing a broad conceptual understanding and some predictive ability regarding the likelihood of interactions between disturbances is crucial. Through a broad synthesis of the literature, and across multiple biomes, disturbance interactions are placed into a unified framework around the concept of changing ecosystem resistance (“linked interactions,” alterations to likelihood, extent, or severity) or ecosystem resilience (“compound interactions,” alterations to recovery time or trajectory). Understanding and predicting disturbance interactions requires disaggregating disturbances into their constituent legacies, identifying the mechanisms which drive disturbances behavior (or ecosystem recovery), and determining when and where those mechanisms might be altered by the legacies of prior disturbances. The potential for cascading effects is discussed, by which these interactions may extend the reach of anthropogenic or climate change‐induced alterations to disturbances beyond what is currently anticipated. Finally, several avenues for future research are outlined, as suggested from the current literature (and areas in which that literature is lacking). These include the potential for cross‐scale interactions and changing scale‐driven limitations, further work on cascading effects, and the potential for cross‐biome comparisons. Disturbance interactions have the potential to cause large, nonlinear, or unexpected changes in ecosystem structure and functioning; finding generality across these complex events is an important step in predicting their occurrence and understanding their significance.

Social-ecological systems in the Mediterranean Basin are characterised by high biodiversity and a prolonged cultural influence, leading to the co-evolution of these systems. The unique characteristics of Mediterranean social-ecological systems, current pressures leading to a decline in ecosystem services, and the need for coordinated action are recognised by policies promoting the protection and sustainable use of the region's heritage. Ecosystem assessments provide valuable information on the capacity of the Mediterranean Basin to ensure the wellbeing of its population. However, most assessments simplify the complexity of these systems, which may lead to inaccurate ecosystem services supply and flow estimations. This paper uses the Driver-Pressure-State-Impact-Response (DPSIR) model to guide an expert consultation that identifies the key characteristics of the Mediterranean social-ecological systems and analyses how these should be included in ecosystem assessments. Data collection was carried out through expert consultation with ecosystem services researchers. Multiple sources of complexity were identified, including the relationship between historical human activities, biodiversity spatio-temporal patterns, as well as the seasonal and long-term variability in ecosystem services. The importance of incorporating this complexity in ecosystem assessments for evidence-based decision-making is identified, suggesting that there is a need to adapt assessment approaches for the Mediterranean Basin social-ecological systems. [ABSTRACT FROM AUTHOR]