Large Temporal Scales Research Articles

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Xylem anatomical responses of Larix Gmelinii and Pinus Sylvestris influenced by the climate of Daxing'an mountains in Northeastern China.

Wood anatomy and plant hydraulics play a significant role in understanding species-specific responses and their ability to manage rapid environmental changes. This study used the dendro-anatomical approach to assess the anatomical characteristics and their relation to local climate variability in the boreal coniferous tree species Larix gmelinii (Dahurian larch) and Pinus sylvestris var. mongolica (Scots pine) at an altitude range of 660m to 842m. We measured the xylem anatomical traits (lumen area (LA), cell wall thickness (CWt), cell counts per ring (CN), ring width (RW), and cell sizes in rings) of both species at four different sites Mangui (MG), Wuerqihan (WEQH), Moredagha (MEDG) and Alihe (ALH) and investigated their relationship with temperature and precipitation of those sites along a latitude gradient. Results showed that all chronologies have strong summer temperature correlations. LA extremes were mostly associated with climatic variation than CWt and RWt. MEDG site species showed an inverse correlation in different growing seasons. The correlation coefficient with temperature indicated significant variations in the May-September months at MG, WEQH, and ALH sites. These results suggest that climatic seasonality changes in the selected sites positively affect hydraulic efficiency (increase in the diameter of the earlywood cells) and the width of the latewood produced in P. sylvestris. In contrast, L. gmelinii showed the opposite response to warm temperatures. It is concluded that xylem anatomical responses of L. gmelinii and P. sylvestris showed varied responses to different climatic factors at different sites. These differences between the two species responses to climate are due to the change of site condition on a large spatial and temporal scale.

Recognition and completeness: two key metrics for judging the utility of citizen science data

Biodiversity citizen science data are being collected at unprecedented scales, and are key for informing conservation and research. Species‐level data typically provide the most valuable information, but recognition of specimens to species level from photographs varies among taxa. We examined a large dataset of Australian photographic observations of terrestrial invertebrates uploaded to iNaturalist to quantify recognition to species across different taxa. We also quantified the proportion of Australian species that have been uploaded to iNaturalist. Across 1,013,171 observations covering 14,663 species (17.8% completeness), 617,045 (60.9%) were recognized to species. Dragonflies/damselflies and butterflies were the best‐recognized and most complete taxa, and therefore represent the best groups for researchers and managers intending to use existing iNaturalist data at large spatial and temporal scales. The recruitment of additional experts to identify records, and enhanced support for accessible resources for hard‐to‐identify taxa, will likely increase recognition for other taxa.

Global Leaf Chlorophyll Content Dataset (GLCC) from 2003–2012 to 2018–2020 Derived from MERIS and OLCI Satellite Data: Algorithm and Validation

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics.

Effectiveness of acoustic indices as indicators of vertebrate biodiversity

Effective monitoring tools are key for tracking biodiversity loss and informing management intervention strategies. Passive acoustic monitoring promises to provide a cheap and effective way to monitor biodiversity across large spatial and temporal scales, however, extracting useful information from long-duration audio recordings still proves challenging. Recently, a range of acoustic indices have been developed, which capture different aspects of the soundscape, and may provide a way to estimate traditional biodiversity measures. Here we investigated the relationship between 13 acoustic indices obtained from passive acoustic monitoring and biodiversity estimates of various vertebrate taxonomic groupings obtained from manual surveys at six sites spanning over 20 degrees of latitude along the Australian east coast. We found a number of individual acoustic indices that correlated well with species richness, Shannon’s diversity index, and total individual count estimates obtained from traditional survey methods. Correlations were typically greater for avian and total vertebrate biodiversity than for anuran and non-avian vertebrate biodiversity. Acoustic indices also correlated better with species richness and total individual count than with Shannon’s diversity index. Random forest models incorporating multiple acoustic indices provided more accurate predictions than single indices alone. Out of the acoustic indices tested, cluster count, mid-frequency cover and spectral density contributed the greatest predictive ability to models. Our results suggest that models incorporating multiple acoustic indices could be a useful tool for monitoring certain vertebrate groups. Further work is required to understand how site-specific variables can be incorporated into models to improve predictive capabilities and how to improve the monitoring of taxa besides avians, particularly anurans.

A POD-based reduced order model applied to 1D shallow water equations

Many environmental problems involving free surface flow can be solved using the shallow water equations (SWE) often involving high computational costs due to the large spatial and temporal scales of the events. In recent times, reduced order models (ROM) techniques are increasingly used to improve the computational efficiency of simulation models. The Proper Orthogonal Decomposition (POD) method provides an orthogonal basis for representing a given set of data and constructing the ROM by means of the method of snapshots. In this work, a POD-based intrusive ROM strategy is applied to the 1D SWE. The main goal of this work is to build a simulation model able to reproduce realistic scenarios. We analyse the computational improvement and the accuracy of the ROM results with respect to those of the full-order model (FOM).

The iNaturalist platform as a source of data to study amphibians in Brazil

Based on debilitating recent budget cuts for science, Brazilian researchers had to find alternative ways to continue scientific production. Here we provide a perspective for the use of citizen-science data deposited in the iNaturalist platform as an alternative source of data to support biodiversity research. Observations contributed by volunteers can be analyzed at large spatial and temporal scales and can respond to questions in behavioral and population ecology. We analyzed this potential through the example of Brazilian amphibians, a group that is less studied worldwide than birds. In fact, to our knowledge, only two studies have been published that are based on citizen-science data for Brazilian amphibians. At the time of writing, the iNaturalist platform has over 14,800 research grade observations from Brazil, representing 698 species, a number increasing daily. Compared to other species-rich countries, volunteer-collected datasets from Brazil cover a relatively high taxonomic diversity (61%), providing a plethora of valuable data. Despite this potential, there are large spatial gaps in sampling in Brazil. Here we encourage established and budding herpetologists not only to use the platform to retrieve data, but also to contribute to iNaturalist actively, with new observations, as well as by identifying species in existing records.

Conceptual Framework for Urban Development Dynamics Dealing with Geo-Environmental Coastal Hazards (On the Northern Coast of the Nile Delta in Egypt)

The Geo-Environmental Coastal Hazards (GECH) causing serious damages in many areas around the world, are among the most important hazards due to its numerous effects. The coastal areas are important attractions for populations and activities. These areas are characterized with the phenomenon of sea-level rise that varies in its rate of change at large spatial and temporal scales, so it is difficult to be predicted locally [1]. As a result, the climate changes’ hazards affect the future of urban development processes in coastal areas. Based on the foregoing, the main question of the research is whether the potential effects of hazards have been taken into account while planning the coastal areas in Egypt and their integration into the development approaches or not. Hence, the research emphasizes the concept of dynamism and flexibility of coastal planning that may be compatible with these variables, and may be positively reflected on the urban plans of coastal cities. The research methodology tackles studying, analyzing, and evaluating coastal hazards and their implications on the Egyptian development. The analytical framework discusses the international case studies dealing with hazards from a developmental point of view, to formulate a proposed dynamic framework for developing the Egyptian coastal areas, which are exposed to coastal hazards, taking into account the temporal dimension and expected geo-environmental hazards, which vary according to the Sea Level Rise (SLR) scenarios. This framework is applied in the most dangerous low areas, such as the Egyptian Delta coast, reaching the gap of lacking dynamics and flexible procedures dealing with the changeable coastal geo-environmental hazards over different time periods.

An evaluation of platforms for processing camera‐trap data using artificial intelligence

Abstract Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost‐effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera‐trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera‐trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera‐trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy‐to‐use interfaces. We review key characteristics of four AI platforms—Conservation AI, MegaDetector, MLWIC2: Machine Learning for Wildlife Image Classification and Wildlife Insights—and two auxiliary platforms—Camelot and Timelapse—that incorporate AI output for processing camera‐trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either ‘blank’ or ‘animal’. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera‐trap‐data processing by allowing users to filter their dataset into subsets (e.g. of certain taxonomic groups or blanks) that can be verified using bulk actions. By reviewing features of popular AI‐powered platforms and sharing an open‐source GitBook that illustrates how to manage AI output to evaluate model performance, we hope to facilitate ecologists' use of AI to process camera‐trap data.

Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale

The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.

Designing a large-scale track-based monitoring program to detect changes in species distributions in arid Australia.

Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track-based monitoring designs at detecting change in species distributions in arid Australia. We collated presence-absence records from 550 2-ha track-based plots for 11 vertebrates over 13years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2-ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest-tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade-off with the number of sites sampled and requires further consideration. Nonetheless, our research suggests that track-based surveying is an effective and logistically feasible approach to monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.

A framework for quantifying forest wildfire hazard and fuel treatment effectiveness from stands to landscapes

BackgroundWildland fires are fundamentally landscape phenomena, making it imperative to evaluate wildland fire strategic goals and fuel treatment effectiveness at large spatial and temporal scales. Outside of simulation models, there is limited information on how stand-level fuel treatments collectively contribute to broader landscape-level fuel management goals. Our objective here is to present a framework designed to measure fuel treatment effectiveness from stands to landscapes to inform fuel treatment planning and improve ecological and social resilience to wildland fire.ResultsOur framework introduces the concept of a fuel management regime, an iterative and cumulative evaluation from the stand to the landscape of fire hazard, fuel treatments, and wildland fire behavior and effects. We argue that the successfulness of fuel treatments within this regime must be evaluated based on pre-treatment fire hazard and post-wildland fire fuel treatment outcomes over large spatial and temporal scales. Importantly, these outcomes can be evaluated from the stand level to across a landscape through time, based on preidentified management objectives that define condition-based criteria that account for social values and environmental and ecological indicators used to determine the effectiveness of fuel treatments within a fuel management regime.ConclusionsEvaluating the cumulative ability of fuel treatments to change landscape patterns of fire behavior and effects is challenging. By quantifying fire hazard, followed by evaluating outcomes of wildfires on environmental and ecological indicators and social values, it becomes possible to assess how individual fuel treatments placed within the context of a fuel management regime are effective based on desired conditions that address management objectives. This conceptual framework offers a much-needed middle-ground planning, monitoring, and reporting approach between overly simplistic annual reporting summaries of the area treated, number of fires, and burned area and detailed fire simulation modeling outcomes by putting individual treatments and fires in the context of current and desired vegetative conditions and social values. Our fuel treatment effectiveness framework examines the state of fuels through the lens of fire hazard and connects fuels to subsequent fire behavior and effects over time and space. The framework provides a way to focus regional and national fuel management planning efforts toward creating fuel management regimes that increase social and ecological resilience from wildfire.

Effects of roots cohesion on regional distributed slope stability modelling

We present a regionally distributed slope stability modelling for shallow landslides considering the effect of plant roots. Our modelling is based on the physically-based distributed slope stability model, HIRESSS (HIgh REsolution Slope Stability Simulator). Thanks to the parallel structure, our code is well suited to perform fast assessment of slope stability over large spatial and temporal scales. The reinforcing effect of plant roots is implemented into the code by considering the local plant species and their spatial variations. The probabilistic nature of the stability of slopes is considered by performing Monte Carlo simulations. Our model is applied to two study areas in Italy. The numerical results are compared with the collected data from these areas. The model predictions show that the effect of plant root is sensitive to rainfall intensity and duration. Our model prediction shows only minor influence of plant root on slope stability particularly for the soil conditions near saturation.

Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.

Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.

Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field.

An along-the-track interferometric synthetic aperture radar (ATI-SAR) system can estimate the radial velocity of a moving target on the ground and on a sea surface current. This acquires the interference phase by combining two composite SAR images obtained by two antennas spatially separated along the direction of movement of the platform. The key to retrieving the sea surface current is to remove the interference of sea surface waves, wind-generated current, and Bragg phase velocity in the interference Doppler velocity. Previous methods removed the surface waves, Bragg phase velocity, and other interferences based on externally-assisted wind fields (e.g., ECMWF), using the M4S or other models. However, the wind fields obtained from ECMWF and other external information are often average results of a large temporal and spatial scale, while the images obtained from SAR are high-resolution images of sea surface transients, which are quite different in time and space. This paper takes the SAR image data of the Gaofen-3 satellite as the research object and employs an SAR-based wind field retrieval method to obtain an SAR-observed transient wind field. Combined with the CDOP model, the interference of Doppler velocities, such as the sea surface wave, wind-generated current, and Bragg wave phase velocity, was calculated and subtracted from the Doppler velocity, to obtain the sea surface velocity result. Then, the current field measured by the shore-based HF radar was compared with that obtained by correcting the ATI Doppler velocity based on the SAR retrieved wind field and the ECMWF wind field. The comparison of results indicated that the wind field correction result based on the SAR retrieved wind field was closer to the current field measured by the shore-based HF radar than the wind field correction result based on the ECMWF wind field.

Low-cost open-source recorders and ready-to-use machine learning approaches provide effective monitoring of threatened species

Passive acoustic monitoring is a powerful tool for monitoring vocally active taxa. Automated signal recognition software reduces the expert time needed for recording analyses and allows researchers and managers to manage large acoustic datasets. The application of state-of-the-art techniques for automated identification, such as Convolutional Neural Networks, may be challenging for ecologists and managers without informatics or engineering expertise. Here, we evaluated the use of AudioMoth — a low-cost and open-source sound recorder — to monitor a threatened and patchily distributed species, the Eurasian bittern (Botaurus stellaris). Passive acoustic monitoring was carried out across 17 potential wetlands in north Spain. We also assessed the performance of BirdNET — an automated and freely available classifier able to identify over 3000 bird species — and Kaleidoscope Pro — a user-friendly recognition software — to detect the vocalizations and the presence of the target species. The percentage of presences and vocalizations of the Eurasian bittern automatically detected by BirdNET and Kaleidoscope Pro software was compared to manual annotations of 205 recordings. The species was effectively recorded up to distances of 801–900 m, with at least 50% of the vocalizations uttered within that distance being manually detected; this distance was reduced to 601–700 m when considering the analyses carried out using Kaleidoscope Pro. BirdNET detected the species in 59 of the 63 (93.7%) recordings with known presence of the species, while Kaleidoscope detected the bittern in 62 recordings (98.4%). At the vocalization level, BirdNet and Kaleidoscope Pro were able to detect between 76 and 78%, respectively, of the vocalizations detected by a human observer. Our study highlights the ability of AudioMoth for detecting the bittern at large distances, which increases the potential of that technique for monitoring the species at large spatial scales. According to our results, a single AudioMoth could be useful for monitoring the species' presence in wetlands of up to 150 ha. Our study proves the utility of passive acoustic monitoring, coupled with BirdNET or Kaleidoscope Pro, as an accurate, repeatable, and cost-efficient method for monitoring the Eurasian bittern at large spatial and temporal scales. Nonetheless, further research should evaluate the performance of BirdNET on a larger number of species, and under different recording conditions (e.g., more closed habitats), to improve our knowledge about BirdNET's ability to perform bird monitoring. Future studies should also aim to develop an adequate protocol to perform effective passive acoustic monitoring of the Eurasian bittern.

Multi-decadal changes in the at-sea distribution and abundance of black-browed and light-mantled sooty albatrosses in the southwest Pacific Ocean

Abstract Many long-term studies have reported changes in seabird abundance and distribution in response to climate change and various anthropogenic activities. However, a greater understanding of how species are responding to change over large spatial and temporal scales are required—particularly at high latitudes such as the Southern Ocean. We examined black-browed Thalassarche melanophris (BBAL) and light-mantled sooty Phoebetria palpebrata albatross (LMSA) observations spanning over 50 years. Both species have a wide-ranging distribution in a rapidly changing Southern Ocean. We used generalized additive models (GAMs) to investigate environmental drivers of their abundance and occurrence. Our results show that climate indices, sea surface temperature and sea surface height are the main drivers influencing the distribution and abundance of both species. The abundance of BBAL southeast of Australia was observed to be decreased substantially whereas no significant change was observed in the abundance of LMSA. Both species demonstrated contrasting distributions along their latitudinal gradient with BBAL showing early stages of a southward range shift. Our analyses suggest that responses to climate change are species-specific. These rare, long-term data have provided an understanding of species’ responses to past changes in the marine environment and can provide critical information for future conservation and management.

Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology

Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning models are currently used to automate classification of organisms into taxonomic units. However, imprecision in these classifiers introduces a measurement noise that is difficult to control and can significantly hinder the analysis and interpretation of data. We overcome this limitation through ensembles of Data-efficient image Transformers (DeiTs), which we show can reach state-of-the-art (SOTA) performances without hyperparameter tuning, if one follows a simple fixed training schedule. We validate our results on ten ecological imaging datasets of diverse origin, ranging from plankton to birds. The performances of our EDeiTs are always comparable with the previous SOTA, even beating it in four out of ten cases. We argue that these ensemble of DeiTs perform better not because of superior single-model performances but rather due to smaller overlaps in the predictions by independent models and lower top-1 probabilities, which increases the benefit of ensembling.

A meta-analysis of environmental responses to freshwater ecosystem restoration in China (1987–2018)

Understanding how abiotic and biotic components respond to aquatic ecosystem restoration is pivotal for sustainable development in the face of economic development and global environmental change. However, the post-restoration monitoring and evaluation of aquatic ecosystems across large spatial and temporal scales is underfunded or not well documented, especially outside of Europe and North America. We present a meta-analysis of abiotic and biotic indices to quantify post-restoration (2 months–13 years) effects from reported aquatic restoration projects throughout the China-mainland, incorporating 39 lentic and 36 lotic ecosystems. Decreases in dissolved nutrients (total nitrogen, ammonia nitrogen and total phosphorus) post-restoration were rapid, but tended to slow down after about 9.3 years. Response ratios summarizing biodiversity responses (incorporating phytoplankton, invertebrates, vascular plants, fish and birds) typically lagged behind abiotic changes, suggesting longer timescales are needed for biotic indices to recover. Time since restoration interacted with lentic project size showing that, even with the same proportional efforts of restoration, larger lentic ecosystems responded much more slowly than smaller ones. Spatial heterogeneity, reflecting the effects of different restoration approaches (e.g., sewage interception, polluted sediment dredging, artificial wetlands, etc.), had a significantly stronger effect on biotic than abiotic indices, particularly in rivers compared to standing waters. This reflects the complexity of fluvial ecosystem dynamics and hints at a limitation in the reinstatement of ecological processes in these systems to overcome issues such as dispersal limitations. Overall, the different timelines and processes by which abiotic and biotic indices recover after restoration should be taken into account when defining restoration targets and monitoring programs. Our study illustrates the value of long-term aquatic ecosystem monitoring, especially in China given the scale and magnitude of ongoing restoration investments in the country.

Macrophysiology for decision‐making

AbstractIn an 1854 lecture, TH Huxley suggested that physiology should be measured by its contribution to science, its practical worth and the extent to which it should be made a branch of education. Since macrophysiology, or the investigation of variation in physiological traits over large geographical and temporal scales and the ecological implications of this variation, is well‐established and approaching its third decade, these questions are posed for it here. The field is, of course, much older, but, over the last two decades, has contributed substantially to fundamental eco‐evolutionary research and to understanding the conservation consequences of physiological variation at regional and global scales. Its greatest successes have been in understanding variation in thermal and metabolic responses of animals and plants to variable environments and the significance thereof in the context of the climate crisis. By contrast, much less progress has been made in understanding the regulation of water in the context of currently changing environments and projections of further impacts associated with ongoing global change. The development of new datasets of physiological traits across broad spatial scales has been a slower enterprise than the further compilation of trait databases from extant data. Surprising insights have emerged from genomics‐based microbial macrophysiology, an approach which has much potential to span all of life's variety. The findings of macrophysiological investigations have been taken up within policy reports and/or implementation actions, such as those of the IPCC and the Antarctic Treaty System, that have resulted in benefits to society and to the environment. Yet, as is the case with other areas of research, much remains to be done to develop further strategies to counteract the anti‐science attitudes that are now so prevalent globally.