In recent decades, the close correlation between urban development and the concept of sustainability has become increasingly evident and important. This is demonstrated by European Union policies concerning EU cities and the United Nations 2030 Agenda for Sustainable Development, including sustainable development goal (SDG) 11: Sustainable cities and communities. In the context of increasing urbanization, it is essential to find innovative methods to manage urban living systems and to establish a standard method for assessing the environmental performance of cities and their infrastructures. A unified and complete methodology for assessing policies for urban sustainability that takes into consideration urban complexity is currently lacking. In this paper, we integrate the Urban Metabolism and Lice Cycle Assessment approach to assess urban sustainability by developing a multi-dimensional measure framework applied to cities. Our aim is to provide a holistic view of the city and unveiling the interconnections among a set of urban dimensions identified by means of an approach based on complex systems science and complex networks. We also propose a specific survey to investigate the city in a multi-dimensional perspective and suggest key indicators based on network centrality measures for investigating and comparing the interconnections among a set of urban dimensions specifically identified (e.g. energy, material, transport). Finally, a case study based on Beijing is considered to show potential applications.

Industrial and mining land is an important heat source in mining cities, however this heat has not been separated from that of built-up land in most studies of the thermal environment. This study measures the response of land surface temperature (LST) to patch characteristics and the configurations of four sub-classified industrial and mining land parcels in Wu’an, a mining city in China. We use Landsat-8 and ZY03 satellite imagery to obtain the LST and land use type. A heating rate index (Tm) is defined as the increase in LST per 1 ha increase in patch area. Multivariate regression analyses is used to analyse the relationship between thermal indicators and landscape metrics, both at the patch and village scales. Our results indicate that, at the patch scale, the mean LST (LSTm) can be described well by nonlinear binary functions of the area and landscape shape index (LSI), with different forms for each type of industrial and mining land patch. When the LSI does not vary, LSTm increases with increasing area, within area thresholds (0–50 ha for smelter or processing lands, and 5–30 ha for mining lands), while for a constant area, the LSTm decreased with increasing LSI. Below the critical LSI (~2.2), more complex patch shape had lower Tm values. Above the critical LSI, Tm do not increase or decrease as LSI increased. At the village scale, patch configuration of industrial and mining land has an effect on (LSTv). The landscape percentage and aggregation index are both directly correlated with the LST of villages that contained industrial and mining land. Our findings show that the degree of LSTv response to a land patch configuration metrics change is, in order, iron and smelter land > coal processing land > coal mining land > iron ore mining land. This study provides an improved understanding of the thermal environmental response to mining activities, and can serve as a reference for future industrial layouts and land use planning in mining cities.

Agricultural drought loss models provide services for the rapid risk assessment of agricultural disasters, and regional disaster prevention and mitigation efforts. This paper takes wheat as an example, and chooses counties dominated by rain-fed farmland in Henan Province as the study area. Counties dominated by rain-fed farmland are determined by setting a rain-fed threshold that is related to the proportion of the effective irrigation area to the cultivated land area. Modeling samples are screened by considering both drought occurrence time and wheat yield reductions. Under different thresholds (30%, 40%, 50% and 60%), we use the yield loss ratio as the dependent variable and 24 standardized precipitation evapotranspiration index parameters as independent variables to build drought loss models using both a multivariate stepwise regression model and a random forest model. Yield loss ratio from 1990 to 2015 is calculated by decomposing historical wheat yield time series. 24 standardized precipitation evapotranspiration index variables are 1–3 months’ time scale standardized precipitation evapotranspiration index during the growth period (from October to May of the following year) of winter wheat in Henan Province. The results show that the random forest-derived model outperforms the stepwise regression model in all tests. The accuracy of all the models increases with an increase of the proportion of the rain-fed threshold. When the rain-fed threshold is 60%, the R2 values of the random forest model and the multivariate stepwise regression equation are 0.720 and 0.523, respectively. The validation results show that the mean absolute error and the root mean square error of the multivariate stepwise regression are 1.38 times and 1.31 times larger than the mean absolute error and the root mean square error from the random forests model. Moreover, both models identify that standardized precipitation evapotranspiration indices in October (sowing/planting stage) and February (overwintering stage) are important variables. However, the multivariate stepwise regression model fails to recognize the importance of standardized precipitation evapotranspiration indices during April–May (filling stage).

Land degradation due to anthropic factors is the reduction of its actual or potential productivity. Nowadays, this topic is a major concern, as it affects more than one third of the soil in the world. This work presents an empirical assessment of the anthropic disturbance level (ADL) for agricultural and livestock production systems. This assessment is obtained by mapping the characteristics of land use and management practices by using five specific indicators and integrating them into a global indicator (ADL score). Earthworm populations (good indicators of soil quality) in soils under different production systems are studied to determine if the population changes are attributable to the intensity of land use and management practices. A correlation model between ADL, edaphic properties, and earthworm population characteristics is developed by using samples of 20 sites in Santa Fe province, Argentina. The inclusion of ADL allowed finding a consistent correlation structure. The results also showed that earthworm density, species diversity, and activity change at the different sites were highly sensitive to anthropic disturbance. Based on this data-driven model, the ADL can be estimated by measuring edaphic and biological data on a soil sample to monitor soil conditions for different production systems. Thus, ADL monitoring would allow deciding how to continue using and managing the land to improve its sustainability.

The wide range of factors involved in environmental health and the complexity of interactions between all environmental determinants require the validation of multidimensional approaches. While the development of composite indices is receiving growing attention by scientists and public authorities, the concept continues to lack transposability and robustness partly due to varying conceptualizations and/or methodologies. This review aims to promote harmonizing practices governing the first step of development of composite index, namely identification and characterization of the dimensions and variables that are included in environmental health indices. A review of available literature (more than 1500 studies) was conducted to identify the composite indices developed to assess territorial determinants from an environmental health perspective. This process made it possible to identify 23 spatialized composite indices and to assess a total of 329 variables. This diversity highlights that the absence of a common framework can lead to a strong subjectivity and limit comparisons between different environmental health indices. The specificity and the availability of certain variables would limit the transposability of indices. In light of current knowledge, this review proposes a consolidated methodological framework based on a categorization of variables into dimensions and sub-dimensions related to heath, environment, social, economics, services and policy. To characterize the sub-dimensions, several variables are possible and can be chosen according to the availability and/or accessibility of the data. The adaptation of a composite index to a specific territory or to a specific issue would then be effective through the included variables. This also aims to be transposable to any spatial unit (country, region, census tract). This work is a first step towards a proposal of guidelines designed to provide a consensual framework that could facilitate the exploitation of environmental health indices. This transparency could also increase the understanding and adoption of these tools by public authorities and general public.

Predicting the cost of environmental governance is an essential process in environmental protection. However, the existing cost prediction methods face several challenges, including the necessity of considering the causality of environmental governance, the importance of distinguishing environmental indicators, and the difficulty of collecting environmental data. In order to address these challenges, a novel rule-based system, called the extended belief rule-based (EBRB) system, is first introduced to establish the basic framework of cost prediction. Then, a combination of structure learning and parameter learning, or joint learning, is developed to improve the performance of the EBRB system. Finally, a new cost prediction method based on the improved EBRB system is proposed for environmental governance. To verify the effectiveness of the new cost prediction method, an experimental study is carried out to compare the predicted cost of environmental governance in 29 provinces of China. The comparative analyses demonstrate that the new cost prediction method can not only provide a desired level of accuracy, but also exhibit excellent robustness that makes it better than some existing cost prediction methods.

Objective measurement of the supply-demand of ecosystem services and sustainable ecosystem management has received increasing attention from the scientific community and the general public. This study explored changes in the supply-demand of ecosystem services and their natural and social driving mechanisms using spatial analysis methodologies as well as the relationship between the supply-demand of ecosystem services and environmental justice in coastal regions in China. In this study, the ecosystem service supply-demand index (ESSDI) was proposed based on the ecosystem services provision index and the land development index. Results indicated that although the imbalance in the supply-demand pattern of ecosystem services was serious, the spatial imbalance in the supply-demand pattern of ecosystem services improved from 2000 to 2015. Notwithstanding that the correlation coefficient between natural factors and ESSDI was higher, the development of the economy and the improvement of the quality of the population also had a substantial effect on ESSDI and the improvement of environmental quality. The spatial imbalance in ESSDI also caused serious environmental injustice as a result of differences in natural background, national policies, development gaps, trade, and industrial shifts. However, the implementation of some ecological compensation projects changed the spatial imbalance in ESSDI and relieved the environmental injustice. This research supports auxiliary decision-making for the sustainable management of regional ecosystems.

Herbivory plays a major role in shaping community dynamics across freshwater, marine and terrestrial habitats, by controlling patterns of abundance and distribution of primary producers, including seaweeds. In the context of biological invasions, the proliferation of non-native seaweeds has been often attributed to limited grazing by native herbivores on introduced species (“Enemy Release Hypothesis”, ERH). In our study, we aimed to explore the potential of an abundant generalist herbivore (the sea urchin Paracentrotus lividus) to graze on non-native in comparison to native macroalgae species. For this purpose, we used manipulative experiments to assess sea urchin preference on native and non-native seaweed species present in the northwest coast of Portugal. Specifically, we determined the preferences of P. lividus on brown seaweeds i.e. Laminaria ochroleuca, Saccorhiza polyschides and the non-native Undaria pinnatifida, and red seaweeds i.e. Chondrus crispus, Mastocarpus stellatus and the non-native Grateloupia turuturu. For each group of seaweed species, sea urchin preference and biomass consumption were examined. The nutritional (organic carbon and nitrogen) and chemical (phenolic content) features were also analysed, in order to assess their effect on sea urchin feeding. According to the results, P. lividus did not show a specific preference for any of the different seaweeds of each phylum. These results suggest that P. lividus is a generalist herbivore, not exerting a differential grazing pressure on non-native seaweeds when compared to native ones.

Microcystis and Dolichospermum are two major genera of bloom-forming cyanobacteria in Lake Chaohu. However, it is little known the way in which the two cyanobacteria adjust their own morphological and physiological characteristics to maintain their biomass. In the study, the phytoplankton community in Lake Chaohu was investigated monthly from June 2017 to May 2018, and the morphological and physiological characteristics of dominant species were also determined. The results showed that Microcystis aeruginosa and Dolichospermum flos-aquae were the two dominant cyanobacteria species in Lake Chaohu. The ranges of the cell diameter and colony size of M. aeruginosa were small throughout the whole year, but those of D. flos-aquae changed significantly over time and in different regions of the lake. There were significant negative relationship between cell diameter and colony size of D. flos-aquae, which indicated that D. flos-aquae was more dependent on morphological adjustment to adapt environmental changes than M. aeruginosa. The biomass of the two algae increased with increasing cell diameter and colony size, indicating that the two algae might maintain their biomass by regulating their morphology. The photosynthetic activity in the cyanobacteria peaked in summer, late winter and early spring. The consistency of the trends in the photochemical activity and biomass of the two algae indicated that the two algae can adapt to environmental changes by changing their photosynthetic characteristics. These findings will be helpful in improving our understanding of the population dynamics of M. aeruginosa and D. flos-aquae and their environmental adaptation mechanisms in Lake Chaohu.

Nonsan Stream is a tributary of the Geum River that is strongly affected by urbanization and agricultural activities in the watershed. The present study was performed to diagnose the chemical and biological health of the stream from 2010 to 2016 using the multi-metric water pollution index (WPI) and index of biotic integrity (IBI) models. Monthly and annual variations in stream water chemistry were directly influenced by the monsoon season. Inputs of total phosphorus, suspended solids, and ionic substances, as well as dilution of total nitrogen, occurred during July and August. Chlorophyll increase was largely influenced by total phosphorus (R2 = 0.43, p < 0.01), total nitrogen: total phosphorus ratio (R2 = 0.54, p < 0.01), and electrical conductivity (R2 = 0.81, p < 0.01). Principal component analysis showed that the abundances of omnivorous and tolerant species were higher than those of insectivorous and sensitive species due to nutrient and organic matter pollution in the stream. Fish composition analysis showed that Zacco platypus (pale chub, Cyprinidae) was the most dominant fish species in the stream, while sensitive fish species disappeared over time. Analysis of the Pearson correlation network revealed that water quality parameters were negatively correlated with trophic and tolerance indicators. Average multi-metric values of water pollution index from 2010 to 2016 ranged from 15 to 19 in the stream, indicating fair to poor chemical health. The modeled values of the multi-metric index based on biological integrity matched those of the chemical model. Moreover, the results of trophic and tolerance indicator analyses matched the chemical results. This outcome suggested that biological ecosystem health is directly influenced by chemical pollution.

In forest management, the sustainability of a multitude of economic, ecological and socio-economic impacts and services is assessed. The role of forests in the global carbon (C) cycle is almost exclusively assessed through an ecosystem approach that relates emissions from timber harvesting and removals through biomass growth and C sequestration. While harvested wood leads to a reduction in the C-stock in forest C-pools, the use of wood for energy and production of wood-based materials results in significant emission reductions by substituting in place of greenhouse gas (GHG) intensive fuels and energy-intensive non-wood materials respectively. We present a new indicator approach for the assessment of sustainable forest management that includes the entire emissions and removals associated with the harvested wood along the wood product value chain and thus represents represents C-sustainability with respect to emissions and removals. The indicator is implemented in two variants. If information available on wood use and the carbon offsets associated with it is sufficient, the indicator can be derived by presenting emissions from wood harvesting and removals from wood use in the form of a carbon balance (C-balance indicator). If the balance is even or if removals predominate, forest management fulfills the requirement of C-sustainability. If the information is insufficient, the emissions from the wood harvest and possible wood processing losses are summed up, and the necessary displacement factor (DF) is calculated, which is necessary to compensate the corresponding emissions by carbon offsets of the wood use (DF-indicator). A comparison of the necessary displacement factor with common carbon offsets of typical wood uses allows an assessment of C-sustainability. The effectiveness of the C-indicator for assessing sustainable forest management is illustrated by two case studies. We found that in order to achieve carbon neutrality, substitution factors between 1.9 (lignite) and 2.5 (gas) are necessary, depending on the fossil fuel substituted. If no energetic substitution is assumed, the DF increases to a value of 3.3. In situations with high harvest losses, the necessary DFs well exceed values that can be achieved even under very positive assumptions; C-sustainability is therefore not met. Both approaches allow an assessment of C-sustainability beyond forest boundaries by giving appropriate weight to the importance of wood use in the carbon cycle. In addition to the implication of the indicator to evaluate SFM, we claim that this indicator is responsive to the emerging global forest related international processes and their reporting requirements such as Reducing Emissions from Deforestation and forest Degradation in Developing Countries (REDD+) as well as nationally appropriate mitigation actions (NDCs, Low Emission Development Strategy).

Exploring the watershed subdivision threshold in Soil and Water Assessment Tool model is crucial for accurate hillslope soil erosion estimation, which has been rarely explored. This study investigated impacts of watershed partitioning on simulation of hillslope sediment generation and its spatial variations, and determined a partition threshold using three newly proposed evaluation indicators (coefficient of spatial variations for hillslope sediment generation, change rate of hillslope sediment generation, and change rate of coefficient of spatial variations for hillslope sediment generation). The Xixi basin in the south-eastern coastal region of China was taken as an example, and was partitioned into eleven different watershed delineations. Effects of watershed subdivision on model parameter aggregation and areal distribution of hydrological response units were analyzed. The results indicated that (1) the previous method revealing the effects of watershed subdivision on hillslope sediment generation, which was only based on watershed outlet data, and the partition threshold determined by the method, were unreliable. (2) Hillslope sediment generation was seriously affected by watershed subdivision levels, increasing the number of sub-watersheds would decrease the modelled amount of hillslope sediment generation and increase its spatial variations. The partition threshold for the average sub-watershed area was 1.6% of the total watershed area and the number of sub-watersheds was 61 as determined from our new indicators, which resulted in stable amount of hillslope sediment generation and its spatial distribution, thus, the hillslope sediment generation could be adequately simulated by this method. (3) The hydrological response unit area, which was strongly related to the change in hillslope sediment generation, was very sensitive to watershed subdivision until the level of watershed subdivision approached the threshold.

The scarcity of knowledge about both biodiversity (Linnean shortfall) and the geographic distributions of species (Wallacean shortfall) makes it hard to conduct biomonitoring programs and studies that seek to explain biodiversity patterns. One way to overcome this difficulty consists in the utilization of data with a lower taxonomic resolution, an approach called taxonomic sufficiency or Higher Taxon Approach (HTA). The main aim of this study was to evaluate, through a systematic review and meta-analysis, whether the HTA is reliable. We also evaluated whether the strength of the HTA (i.e., the relationship between datasets at high and low taxonomic resolutions) depended on different factors such as taxonomic level (genus, family, order and class), ecosystem type (aquatic and terrestrial), biological group (e.g., invertebrates, vertebrates and plants), spatial extent and higher taxa to species richness ratio (φ). We found that the HTA was a reliable approach in revealing species richness and compositional patterns independently of biological groups and ecosystem types. As expected, the strength of the HTA in describing biodiversity patterns decreased as the taxonomic resolution decreased. The strength of the HTA increased with the spatial scale of the studies. The φ was the main predictor of the HTA. Therefore, the use of higher taxonomic level (e.g., genera) is a reliable approach to save time and resources in biomonitoring programs and differs in this regard from other approaches that have already been tested in other studies (e.g., biological surrogacy). While the high HTA efficacy do not replace refined species level information which is crucial for basic and applied ecological studies, we advocate the use of the HTA especially for biological groups with high φ and for biomonitoring programs targeting large spatial extents.

Quantification of aboveground biomass (AGB) from local and regional to global scales is significant for sustainable ecosystem management, and object-based analysis technique is attractive in AGB studies. This study extended the AGB inversion researches on short vegetation species at a relatively fine scale using a homogenous vegetation response unit (HVRU)-oriented analysis approach, and explored a convenient scheme to determine diagnostic indicators. In the meantime, spectral, structural and geographic indicators related to AGB were derived from multisource data to generate HVRUs using multiresolution segmentation technology; the utilities of different variable types, regression algorithms and research scales were further evaluated in HVRU-based AGB modeling to determine the diagnostic indicators. Results showed that, inversion accuracies based on mean variables were much higher than those based on texture variables. Partial least squares regression (PLSR) and support vector regression (SVR) provided similar accuracies, in general, SVR performed better in digesting texture variables and time consumption, while PLSR was more suitable for mean variables. Compared to spectral indicators alone, the introduction of non-spectral indicators weakened accuracies at the scale of 50; at the scale of 100, 150 and 200, integrating spectral and structural indicators significantly improved accuracies on the whole; additional geographic indicators showed adverse impacts in most cases. The optimal inversion accuracy with R2cv of 0.83, RMSEcv of 0.20, RPD of 2.34 was realized based on the mean variables of spectral and structural indicators at the scale of 150 using PLSR, furthermore, FVC, OSAVI and (1/R742nm)'' contributed much more than other indicators in modeling. Effective indicators excavated in this study could be directly applied for AGB survey in Yancheng National Nature Reserve and similar areas in China’s coastal mudflat. The HVRU-based analysis approach fusing the advantages of multisource data and multiresolution segmentation technology has broad application prospects especially for research objects which are hard to quantify.

Wind power generation has always been considered as clean energy, according with national ecological civilization construction and responding to climate change. However, from the perspective of resource conservation, wind farms directly or indirectly occupy a large amount of land resources along the entire life cycle. Based on the emergy analysis, this paper estimates the ecological footprint of a wind farm in Dalian, evaluates its sustainability, and analyzes the ecological footprint of the four phases which include wind turbines production and transportation, construction, operation and maintenance, and demolition during the life cycle of the wind farm. It was concluded that the emergy carrying capacity of the wind farm was 3879.57 hm2/a, and the emergy ecological footprint was 5117.59 hm2/a. The wind farm was in an ecological deficit. Among the four phases, the ecological footprint of construction was the largest (60.93%), wind turbines production and transportation phase (33.77%) took the second, followed by operation and maintenance (4.59%) and demolition (0.71%). The main contribution of the materials was steel, followed by concrete, ecological protection investment, fiber glass, land occupation and epoxy resin. Finally, in order to illustrate the way to achieve sustainable development of the wind farm, uncertainty analysis and scenario analysis were carried out. It was found that when 62% of the recycled materials were used for wind turbines production, the wind farm realized ecological balance. Reducing the solar transformity (UEV) of steel and concrete can also decrease the ecological footprint of the wind farm.

As natural resources are becoming one of the factors hindering economic development, the utilization efficiency of natural resources should be improved and ecological stress should be reduced to ensure sustainable economic development. In this study, the ecological stress index and ecological efficiency were quantified using the ecological footprint model to describe the current status of sustainability and utilization efficiency of natural resources. The economic agglomeration was determined by conducting global spatial autocorrelation analysis using Defense Meteorological Program/Operational Line-Scan System (DMSP/OLS) nighttime light data. The relationship between economic agglomeration and ecological efficiency of China was then illustrated in a four-partite graph, and the results show that (1) the per-capita ecological footprints vary between the different provinces; however, the composition of the ecological footprint is similar between the provinces, and demonstrates the demand for fossil energy land is highest, while that for fishing ground is lowest. (2) All provinces are facing ecological overload, excluding Qinghai, and Shanghai, Tianjin, and Beijing are enduring the most severe ecological overload. (3) The ecological efficiency differs greatly between the different provinces of China, and the ecological efficiency values tend to decrease from the eastern coast to the inland region. (4) The economic activities in every province of China are spatially agglomerated, and the degree of agglomeration differs significantly. (5) There is a positive correlation between economic agglomeration and ecological efficiency, indicating that promoting economic agglomeration is an effective method of improving the utilization efficiency of natural resources. The relationship between economic agglomeration and ecological efficiency observed in this paper will provide a reference for optimizing the spatial distribution of economic activities and a theoretical basis for synchronizing environmental protection with economic development in China.

Carbon inventories are urgently needed for understanding climate dynamics and implementing climate mitigation strategies, but such data is scarce in for forest ecosystem of Himalaya. Therefore, the present study focuses to supplement the existing information by estimating carbon stocks in temperate forests dominated by Cedrus deodara along the altitudinal gradients in the Central Himalaya. Three altitudes i.e., lower (1750 m), middle (1900 m) and upper (2050 m) of the stand was considered to understand and estimate biomass and soil carbon storage potential of the forests based on the standard protocol. The results showed that the soil carbon stock (SOC) decreased significantly with increasing soil depths and altitudes. Litterfall production in the forests varied adversely with altitude. Above and below-ground biomass carbon stock also decreased along with altitude. The study observed that the total carbon stock (soil, trees and forest floor) of the Cedrus deodara forest in different altitudes was 395.4 t ha−1 (lower altitude), 321.6 t ha−1 (middle altitude) and 282.5 t ha−1 (upper altitude). The estimates of the Cedrus forests would provide guidelines for estimating carbon for forest-based mitigation activities in the Himalayan region.

In order to perform a Water Scarcity Footprint (WSF), as per the ISO standard 14,046 (2014), it is necessary to multiply a volume of water consumed in a specific region, with the corresponding local water scarcity indicator. The use of these factors is recommended in priority at the native scale at which these factors were developed, which is the watershed scale. Alternatively, country-scale factors are provided to accommodate practitioners who may not have access to the exact watershed from which the water consumption they are assessing is located. However, such factors come with very large uncertainty. In several cases the practitioner may have access to a higher spatial resolution information, such as a state, province or department within one country. For this reason, the present work proposes sub-national AWARE factors for the entire globe, using sub-national borders, as an in-between resolution of watersheds and countries. Factors are provided for 3428 administrative regions, per month as well as annually, based on the three original weighted averages provided by AWARE: agri, non-agri and unknown. This scale is often more practical than the watershed scale, which may be more challenging to identify, and should lead to the use of a more-spatially resolved water footprint metric.

Freshwaters are constantly facing ecosystem functioning alterations and loss of biodiversity driven by multiple anthropogenic and natural stressors, that by acting simultaneously create complex interactions, affecting the quantity and quality of water resources. Stream biofilms are complex communities, which are exposed to these alterations and, in addition, are naturally stressed by invertebrate grazing. Therefore, they are expected to reflect these impacts through shifts in community structure, composition and function. Here we used a mesocosm system to assess the single and interacting effect of major anthropogenic stressors acting in Mediterranean streams (i.e. flow stagnation and sewage contamination) in combination with a biological natural stressor (i.e. grazing) on the main assemblages composing biofilm (i.e. bacteria, fungi and algae) by assessing communities’ relative abundance through Denaturing Gradient Gel Electrophoresis (DGGE) operational taxonomic units (OTUs). Biofilm was submitted to the three stressors, in a full-factorial design (2 flow conditions × 2 contamination conditions × 2 grazing settings) in a 5-week experiment. Molecular data showed that the combined effect of anthropogenic stressors (flow stagnation and sewage contamination) induced unequal OTUs responses on biofilm assemblages, with antagonistic effects for bacteria, synergistic for fungi and additive for algae. Sewage and grazing interaction were significant for all groups revealing a negative effect (antagonistic) on bacteria and algae diversity but positive on fungi diversity (synergistic). The same overall response pattern was also found for the triple co-occurring stressors, which increased fungi diversity while decreasing algae and bacteria. In stream ecosystems in which low flow conditions and sewage contamination prevail in the presence of natural herbivory, algae and bacterial diversity may be severely affected, while fungal diversity may be surprisingly enhanced. Consequently, shifts in the relative proportions could led to the unbalancing of ecosystem processes (e.g., photosynthesis, nutrient mineralization) defined by the microbial communities.

Mohammadi (2019) claimed that Vitense et al. (2019) did not adequately report random forest parameter values, listing several parameters from the Python library scikit-learn as examples. Here, I explain that these parameters either were reported in the original paper or are not relevant for the software (RF++) and approach we employed. I further emphasize that the scikit-learn library should not be used to replicate the analysis in Vitense et al. (2019).

Floodplain systems are widespread in the Amazon and several studies have evaluated mercury biogeochemistry in these systems. The Amazon region can be considered a “hotspot” for the study of mercury (Hg), due to the naturally high concentration of the pollutant in its old soils and the anthropogenic emissions from small-scale gold mining activities. The periodic flooding influences the availability and distribution of Hg contamination. Therefore, it is possible that flooding can influence accumulation of Hg in fish. The aim of this study was to evaluate total Hg (THg) concentration in four seasons (rising water, high water, falling water and low water) in six fish guilds in a lentic part of a floodplain system (Puruzinho Lake). We hypothesized that fish guilds would show different seasonal patterns of THg concentration variation. A total of 2016 fish specimens were sampled over the four seasons. Two-way ANOVA indicated an interaction between guilds and seasonality (F = 7.3; p < 0.001), suggesting different seasonal patterns of THg concentration variation among the guilds. Two major patterns were identified. Herbivorous, detritivorous, carnivorous and piscivorous fish guilds showed higher concentrations during the rising and low water seasons, while planktivorous and omnivorous guilds showed lower THg levels during the low water season. There were no significant differences in THg levels between the early and late periods of each season.

The increasing demand for natural resources due to urban growth has led to sharp conflicts between urban land demand and environmental protection, causing problems such as decrease in the carbon storage of terrestrial ecosystems. To achieve sustainable urban development, China has implemented spatial regulation to restrict urban growth, but there have been various conflicts among multiple spatial regulation plans. In this study, we explored the impact of urban growth on carbon storage under integrated spatial regulation in Wuhan, China by combining the SLEUTH and InVEST (Integrated Valuation of Environmental Services and Tradeoffs) models. Firstly, the SLEUTH model was adopted to simulate the urban growth under different scenarios from 2015 to 2030. Then, the InVEST model was employed to explore the impacts of urban growth on regional carbon storage. The results showed that the integrated spatial regulation can reduce the growth rate of urban areas, decrease the use of natural resources by urban land, and alleviate the conflicts between the construction needs of urban development and environmental protection. Besides, under integrated spatial regulation, the encroachment rate of high carbon density land by urban growth is gradually slowed down, effectively reducing the loss of regional carbon storage. These findings indicate that the implementation of urban development strategies under integrated spatial regulation will better facilitate regional sustainable development than traditional urban planning.

The toxic factor (TF) is a key parameter commonly used to evaluate the potential ecological risk index (RI) of heavy metals. However, it remains largely unknown whether this traditional TF is applicable to the ecological risk assessment of heavy metal to soil microorganisms, which are essential to ecological functions and ecosystem sustainability. Here, based on the TF values of 5 and 30 for copper (Cu) and cadmium (Cd), respectively, we constructed soil microcosms to establish a gradient of ecological risk levels, and different combinations of Cu and Cd concentrations were used at each ecological risk level. We found that bacterial abundance and functional diversity significantly decreased and the metal resistance gene (MRG) abundances increased with the increasing RI level. At the same RI level, the bacterial abundance and functional diversity decreased while MRG abundances increased with increasing Cu concentrations, suggesting that the ecological risk to soil bacterial community was more relevant to Cu, rather than Cd. The traditional TF of Cu used to calculate the RI might be underestimated if it is used for evaluation of the risk to soil bacteria. Our calibration analysis revealed that the TF of Cu should be adjusted to 6 during the assessment of the RI for soil bacterial community in heavy-metal contaminated area.

The present study is an attempt to understand the role of Nepalese alder (Alnus nepalensis D. Don) in litter production, leaf litter decomposition, leaf area index (LAI) and rate of forest floor respiration as indicators of carbon flux in white oak (Quercus leucotrichophora A. Camus) forests. Five forest stands of oak mixed alder (OMA) and five oak without alder (OWA) were selected along a basal area gradient under similar ecological conditions in Indian central Himalaya. We estimated monthly variations in litterfall, leaf litter decomposition and leaf area index (LAI) in Q. leucotrichophora and A. nepalensis in OMA and OWA stands. Soil and litter CO2 effluxes were estimated seasonally using the soda-lime absorption method. The seasonal pattern of litterfall showed a bimodal trend in OMA and unimodal trend in OWA forest stands. Forest floor biomass (leaf, twig, reproductive parts, herbaceous and miscellaneous litter) was highest in summer (707–1080 g/m2) followed by winter (414–678 g/m2) and rainy (281–541 g/m2) seasons in OMA stands. In OWA stands, forest floor biomass was highest in summer (463–646 g/m2) followed by rainy (321–411 g/m2) and winter (149–279 g/m2) seasons. Total litter production, leaf litter decomposition rates, change in LAI and soil respiration were significantly higher (p < 0.01) for OMA stands than OWA stands. The maximum annual LAI max ranged from 4.10 to 6.78 m2 m−2 in OMA stands and from 1.9 to 3.06 m2 m−2 in OWA stands. Total soil respiration (TSR), litter-free soil respiration (LFSR) and litter respiration (LR) were also higher (p < 0.01) in OMA compared to OWA stands. Mean rate of TSR, LFSR, and LR was 280.18 mg m−2h−1, 180.45 mg m−2h−1, and 46.40 mg m−2h−1 in OMA and 188.46 mg m−2h−1, 150.1 mg m−2h−1, 38.35 mg m−2h−1, respectively, in OWA stands and for both recorded highest in monsoon season. The results indicate that A. nepalensis enhances litter production, LAI, leaf litter decomposition, and forest floor respiration in the oak forests, which in turn would result in increased carbon flux.

Salt marshes are worldwide recognized for their unique and important ecological role. According to the Intergovernmental Panel on Climatic Change (IPCC), the frequency and intensity of warming events will rise due to global changes. Halophyte plants, inhabiting salt marshes are known for being highly tolerant to various abiotic stresses, nonetheless, although it has been acknowledged that the tolerance response is species specific there could also be differences at the population level. The present work aims to determine if the life history of two halophytes (Halimione portulacoides and Spartina patens), namely in terms of contaminant exposure, influences inter-populational heat tolerance. For this purpose, individuals from both species collected at pristine and contaminated sites were exposed to normal and increased temperature conditions, and its physiological fitness evaluated throughout biochemical and biophysical analysis. The photobiological traits, pigment and fatty acid profiles and oxidative stress biomarkers analyses of warming treated individuals, indicate that chronic heavy metal pre-conditioning significantly influences the heat stress tolerance of the native halophyte plants. Halimione portulacoides individuals collected at heavy metal contaminated salt marsh appeared more tolerant to heat stress. On the other hand, the invasive S. patens from the pristine site showed higher tolerance to heat stress. Thus, the pre-conditioning influences the tolerance mechanisms can affect the way in which salt marsh communities will evolve in the future, possibly being different through salt marshes. The contaminated marsh can be more resilient to invasion than the non-contaminated marsh, since the native species has benefited from this pre-conditioning. Moreover, and from the ecophysiological point the set of biophysical and biochemical indicators were tested, show a high efficiency in describing the ecophysiological traits, both between species and populations, facing different pre-conditioning histories.

The aquatic Coleoptera in a Mediterranean climate region were studied in order to assess the main factors affecting the assemblage structure. We tested the effects of different land use types (native forest, agriculture and urban) on environmental features and water beetles in Chilean streams. Eighteen stream sites were sampled in the austral summer of 2016. Physical variables including water temperature, dry width of channel, altitude, proportion of fine sediment, gravel/pebbles and boulders were significantly different among land uses. Eight Coleoptera taxa were collected across all sites; five were recorded in native forest and agriculture streams and three in urban streams. The riffle beetles Stethelmis chilensis, Austrelmis sp., Austrolimnius sp. and Tychepsephenus felix were found in native forest and in low density in agriculture streams. Hydraenidae appear to be the most tolerant taxon, dominant in agriculture and urban streams. Canonical analysis of principal coordinates based on environmental features and Coleoptera density data showed a clear separation of native forest streams from agriculture and urban area streams. Using distance-based linear models, cobble proportion was the most important variable structuring the water beetle assemblage. Our findings suggest that aquatic beetles are good ecological indicators of anthropogenic land use types in aquatic ecosystems in Mediterranean streams. This is the first study using aquatic coleopteran species for Chilean Mediterranean streams. Our study highlights the importance of the native forest for the conservation of stream insects.

Light fraction (LF) and permanganate-oxidizable C (POXC) demonstrate high reliability as indicators for monitoring soil functioning in response to changes in soil organic carbon (SOC). However, mechanisms affecting the amount and composition of labile fractions and their relationship with SOC content at regional scales have not been thoroughly studied. The aim of this study was to examine the spectral features associated with these labile organic matter fractions in samples collected from 75 sites under different soil types, land use and climatic conditions in Chile. Topsoil was analyzed for total C and N content, aggregate stability, and texture. Additionally, the spectral properties of LF material and whole soils were analyzed using diffuse reflectance mid-infrared spectroscopy (MidIR). Our results show that LF shared a similar spectral composition but with different band intensities across climatic regimes. LF spectra were associated with O-alkyl C in cool and rainy areas, whereas a relative accumulation of aromatic structures was found in warmer areas. Whole soils spectra showed that SOC, POXC and aggregability were related to the prevalence of aliphatic and polysaccharides compounds in colder areas. While in warm arid areas, the stabilization of aliphatic compounds was found to be related to clay minerals. Furthermore, we found that POXC and SOC content were closely related and changes in POXC were affected by variations in climate conditions. The understanding of spectral features linked to labile SOC fractions on at larger geographical scale will contribute to the development of sustainable land management options for the prevention of land degradation in the context of adaptation to climate change.

The abilities of estuarine species to respond to salinity fluctuations by behavioural and physiological responses can determine the maintenance of populations, particularly in the context of climate change. The native clams Ruditapes decussatus and Venerupis corrugata, the native cockle Cerastoderma edule and the introduced clam Ruditapes philippinarum are important resources in Galician (NW Spain) coast. As inhabitants of estuaries, these species are exposed to frequent salinity fluctuations as a result of heavy rains. This study investigated the short-term sublethal effects of salinity drops on their physiological (scope for growth, SFG) and behavioural (valve closure and burrowing activity) responses. Bivalves were exposed to simulated tidal cycles and similar salinities to the field conditions, i.e., four salinity ramps (5–20, 10–25, 15–30 and 30–30) during six days over three different periods of the year (autumn, winter and spring). The overall response was the same for all species under the lower salinities (5, 10 and 15), with a dramatic reduction of pumping activity, SFG and burrowing. Results differed among species under the higher salinities (20, 25 and 30). While C. edule was the most affected species in autumn showing no recovery despite having higher SFG compared to the venerids, R. decussatus was more resistant in all seasons despite having the lowest SFG compared to the rest of species. In winter, V. corrugata was the most vulnerable due to lower SFG at the lowest salinities. All species showed a compensation pattern in spring that led to non-recovery of individuals. Burrowing ability had similar patterns to SFG in autumn and winter but differed in spring, when recovery was the general pattern. The decrease of burrowing ability at lower salinities during stress seen to some degree in all species can increase vulnerability to predation. Results suggest that differential responses of lower activity over time could be related to the physiological condition and habitat preferences of each species and should be taken in consideration for management plans in the context of climate change. The results drive a discussion of the usefulness of SFG as the metric with which to assess salinity stress in adult bivalves and the need in future research to increase frequency and duration of stresses in concert with variables such as food availability.

Ecological uniqueness is one aspect of beta (β) diversity that shows the relative contribution of sites (local contribution to beta diversity – LCBD) and taxa (species contribution to beta diversity – SCBD) in the formation of a unique environment in terms of species composition, and may be directly related to habitat quality. Our objective was to evaluate the uniqueness of fish communities from streams in modified areas of the Amazon, and to investigate the main environmental predictors at local and landscape scale. We sampled 58 streams in the Capim river basin (Pará, Brazil) located within areas of preserved forest, reduced-impact logging, conventional logging and pasture. We found greater β diversity and higher LCBD in the pasture areas, making this land use the largest contributor to β diversity in the study area. At local scale, this high contribution was primarily influenced by environmental heterogeneity, thalweg depth, percentage of land use and cover (all positively), and volume of large woody debris in the riverbed (negatively). This indicates that the β diversity of fish is highly affected by streams with greater thalweg depth, possibly due to the reduced amount of large woody structures entering streams within pasture areas. These streams also showed greater environmental heterogeneity due to the large variation in disturbance levels of this area, which renders their sites suitable for potential occupation by different species, making them high contributors (high LCBD) and also leading them to present high β diversity. On the other hand, areas with a higher percentage of forest at landscape scale (including preserved forest, reduced-impact logging and conventional logging) were the main contributors, while pasture areas had a higher percentage of exposed soil. We did not find any association between SCBD values and the habit of the species, as the taxa that contributed most to β diversity can be classified as having reduced niches (specialists) as well as broader niches (generalists). We conclude that modified areas may contribute substantially to β diversity because they have a distinct species combination, however different patterns can be observed at local and landscape scales.

The recognition of spatial heterogeneity as well as of areas of low and high biodiversity through spatial techniques is essential to guide decision-making regarding the conservation and management of natural areas. In this context, reliable maps of biodiversity across sampling sites can be useful tools. Many ecological studies, which have dealt with a spatial approach for biodiversity, have focused only on one specific biodiversity aspect at a time, such as species richness or species evenness, yielding a partial overview of this complex concept. To solve this issue, we propose a spatial functional data analysis approach to diversity profiles for assessing spatial biodiversity and identifying groups of sampling sites which are similar in spatial patterns. Specifically, the functional distance-based LISA algorithm has been extended to the case of diversity profiles in lattice, after smoothing the discretized curves and specifying a suitable distance measure. The proposed spatial clustering algorithm has been applied to a real data set involving tree species diversity in a fully censured plot in the Harvard Forest, New England region. Our approach provides a useful method for identifying areas of low and high biodiversity, with the potential to address the monitoring of environmental policies. Indeed, we think that a classification of diversity profiles, which takes into account the spatial dependence, would permit a more homogeneous partition of sampling stations with a substantial noise reduction in supporting conservation planning.

Modelling tree habitat suitability (HS) is a common practice to assess tree species distribution across a broad range of spatial scales. However, it is seldom used to test the extent that modelled HS-scores (probability of species occurrence) can represent on-the-ground measures of species-structural characteristics. In this study, we compare four parametric and non-parametric models generated with the R-package, sdm, to assess the potential for these models to estimate tree species distribution of balsam fir [Abies balsamea (L.) Mill.] in naturally-growing forests across an extensive landscape. Central to this development are inventory plot data of species presence-absence and four abiotic factors linked to plant growth and distribution. The study’s abiotic factors include: (1) photosynthetically active radiation; (2) growing degree-days; (3) relative extractable soil water content; and (4) near-surface wind speed, all expressed spatially at 30-m resolution. To gauge whether modelled HS can explain structural characteristics in balsam fir-dominated stands, field-based estimates of site index (SI) and cumulative aboveground biomass (ABG) were compared against independently-derived HS-scores. The results showed that: (i) random forest was the most successful at representing species distribution of balsam fir among the four methods considered; (ii) overall growing conditions for balsam fir was observed to be most favourable on north-facing slopes, particularly in the northwest part of the target landscape, where near-surface air temperatures are cooler, soils are moderately wetter, and wind speeds are lower; (iii) tree-based calculations of SI were partially characterised by patterns in modelled HS-scores, due to scale differences (i.e., from individual tree to 30-m grid cells) and an inadequate number of sample trees; and (iv) patterns of cumulative AGB were more accurately represented by species HS. Modelled HS-scores, as potential indicators of tree species habitat preference, AGB, and species distribution, can offer key ecological information essential to inform forest management and conservation planning at the landscape level.

Despite its importance for ecology and conservation, we are still far from understanding how environmental variability interacts with intrinsic factors and individual specialization to determine trophic strategies of long-lived taxa, mostly due to difficulties in studying the same animals over extended periods. Here, by yearly consistently sampling the first primary feather of 99 Cory’s shearwaters (Calonectris borealis) foraging in the Canary Current (CC) upwelling ecosystem, we provide robust evidence on the individual changes of isotopic ratios of carbon (δ13C) and nitrogen (δ15N) over 17 years. We reported a slight longitudinal decline of δ13C values throughout 2001–2017, even after being adjusted for the marine Suess effect (linked to the increasing CO2 emissions). Although CC is often considered to be overexploited by industrial fisheries, we could not detect a decline in Cory’s shearwater trophic level indicating a change in the trophic web structuring, as revealed by δ15N. We found negative correlations of δ13C and δ15N with the CC upwelling intensity, indicating annual variability in baseline isotopic levels propagates through the food chain and it integrates in predators’ tissues. Low individual repeatabilities among years at population level indicates low long-term specialization, suggesting long-lived individuals foraging on highly productive areas can adjust their foraging strategies and diet according to environmental variability. However, individual-level repeatabilities in isotopic values showed a range of individual specialization within the population, indicating most individuals are generalist and a few of them highly specialized. First, although we found a clear influence of the upwelling intensity on the trophic ecology of birds, we could not detect any temporal trend in the trophic level of the Cory’s shearwater population, suggesting a stability in the structure of the pelagic food web of the CC over the last two decades despite the fishing pressure. Second, the existence of individual specialization highlights the importance of considering the repeated sampling of individuals to detect small changes in the trophic ecology of a population. Finally, the coexistence of individuals with different degree of specialization (from extremely flexible [generalists] to highly consistent individuals [specialists]) within a population can have deep implications on the capacity of populations to cope with environmental change.

Biotic indices are tools to assess the ecological status of marine systems, and can be based on different metrics (such as ecological groups, specific diversity). The present study applies five biotic indices based on living (stained) benthic foraminiferal assemblages to assess ecological conditions in a wide area of the Mediterranean Sea in the heavily polluted Gulf of Gabes and along the western and eastern coasts of the Djerba Island, with the eastern coast considered as “pristine”. The applied indices are based on epiphytic foraminifera e.g., the “long versus short life span” index (ILS) and the modified FoRAM-Index (FI’), benthic foraminiferal assemblage composition and diversity, e.g., the Tolerant Species index (TSstd), the Foram Stress Index (FSI), the diversity Index (exp(H’bc)) and the newly developed Foram-AZTI Marine Biotic Index (Foram-AMBI). This latter index was tested using species of benthic foraminifera presently assigned to ecological groups in the Mediterranean Sea. All indices confirm good ecological conditions eastern of Djerba. The FI’ and ILS, % TSstd, FSI, indicate a severe anthropogenic impact especially in the central part of the Gulf of Gabes, next to the phosphate industry of Skhira and along the western coast of Djerba Island and confirm direct observations and geochemical data. The indices based on epiphytic foraminifera seem to better reflect actual environmental conditions in the investigated region because of the extended Posidonia oceanica meadow in the area. The exp(H’bc) reflects lower degrees of pollution. The application of the Foram-AMBI index presents limitations that prevent its current use. It strongly underestimates the pollution impact along the Tunisian coast, and additional work is needed to increase the number of species attributed to ecological groups and to possibly modify the coefficients to be used in its equation.

The aim of this study was to use an integrated, multi-indicator approach to assess and compare the biotic integrity of the polluted rivers flowing into the hyper-eutrophic Roodeplaat Dam. This included seasonal assessments of physical, chemical and biological parameters of the water and sediment, as well as a habitat, macro-invertebrate and histology-based fish health assessment. The results showed that all three rivers are polluted by metals and faecal coliform bacteria and high nutrient levels which explains the eutrophic status of the receiving impoundment. The Edendalespruit showed higher metal concentrations while the Pienaars River showed higher nutrients and Chlorophyll-a levels. Semi-volatile organic compounds were detected in sediment samples from all three rivers. The macroinvertebrate assessment confirmed that the water quality of the rivers are negatively impacted with most of the sites classified as “fair” to “poor” based on the number of taxa identified and their sensitivity scores. The histological assessment of six different target organs of two indicator fish species indicated a definite tissue response, especially in terms of the liver alterations in Clarias gariepinus. The percentage prevalence of alterations varied across rivers and seasons with no discernible pattern identified. The assessment of both abiotic and biotic indicators indicated that this system is plagued by a number of negative impacts and that management initiatives should focus especially on the activities influencing the Pienaars River to mitigate the continuing eutrophication of the Roodeplaat Dam.

Since the UNCED‘s call for the creation of sustainability indicators many such have been put forth in the literature. One of the more successful ones, in terms of popularity, is the Ecological Footprint (EF). Much criticism has been directed at the EF, not least the carbon uptake component (CF). The CF typically makes up around 50% of global EF and is the sole cause for its overshot – i.e. results indicating unsustainable consumption. The aim of this study was to assess the accuracy of the data used for the calculation of CF. The study finds that the data is lacking in accuracy to the point that stating that CF or EF is any given number at any given time is misleading. The reasons for this uncertainty are identified as use of estimates and averages for the calculations as well as discrepancy between data collected locally and data from international databanks. CF or EF results should thus always be prefaced with caveats regarding the uncertainty involved in the estimation. The lack of caveats in EF dissemination is worrying and has led to the most serious criticism of the method to date, that of it fulfilling the criteria for pseudo-science for failing to disclose uncertainties in calculations and results. This study suggests that the reason for this failure may be traced to the Global Footprint Network (GFN) being both a think tank actively promoting the use of EF, and the world’s largest research unit into the methodology. This can lead to uncertainties being down played in dissemination not to confuse current users of the method or dissuade new ones. The study further raises questions about the accuracy of GHG estimates in general since they are often based on the same IPCC default emission factors and activity data as used by the GFN.

Several studies have proposed to predict Species Richness (SR) by combining the predictions of independent Species Distributions Models (SDMs) (the predict first-assemble later strategy). Alternative methods propose to combine outputs from SDMs differently, by either summing predicted presence probabilities at each location, or summing binary presence predictions after thresholding the probabilities. Species can occupy various proportions of their suitable habitats (i.e, have various levels of habitat saturation), which can cause discrepancy when predicting their presences through SDMs. Furthermore, these discrepancies can be increased when combining the predictions of individual SDMs to predict SR. In this article, we performed simulations of species distributions with varying habitat saturation (i.e., the amount of suitable habitat occupied by a species), and we compared observed richness with that predicted by the alternative approaches. We found that probability-based richness is not biased by the level of habitat saturation, while threshold-based richness over-predicts richness at low habitat saturation and under-predicts it as high habitat saturation. Probability-based richness should thus be used in priority when predicting species richness locally. Nonetheless, threshold-based richness represents species richness constrained by environmental filtering only and thus is a useful indicator of potential species richness when species fully saturate their habitats. Thus the systematic comparison of probability-based and threshold-based richness predictions can reveal the importance of habitat saturation and can thus help identify community assembly mechanisms at play.

Stream water quality assessments are conducted by analysing invertebrate communities as a biological quality element (BQE). In Denmark, water quality assessments of streams are often estimated according to the Danish Stream Fauna Index (DSFI). The conventional DSFI method is time consuming and requires highly specialized expertise for species identification of the relevant indicator invertebrates. Furthermore, conventional species identification of relevant indicators may be hampered by differences in, or lack of, developmental stages or due to damages during the sampling process. Metabarcoding has the potential to overcome the challenges associated with conventional morphology-based species identification. Using high-throughput DNA sequencing, metabarcoding of invertebrates collected from stream water provides an alternative to the expertise of taxonomic experts. The present study applies metabarcoding using universal invertebrate primers targeting the mitochondrial cytochrome oxidase I (COI) gene to determine stream water qualities. The obtained community profiles were compared to conventional water quality assessments according to the Danish Stream Fauna Index (DSFI). Multivariate data analysis of obtained sequences resulted in distinct clusters of taxonomic units, which reflected the stream water quality as defined by the DSFI. In conclusion, the present study supports the knowledge that invertebrates are efficient as BQE for stream water quality assessment. DNA sequencing by metabarcoding provided a unique fingerprint of the studied communities of invertebrates and was successful in describing the stream water quality.

An invasive goldenrod, Solidago altissima, is abundant in urban environment of Japan and a threat to native biodiversity. Aerial remote sensing approach would be useful for effective monitoring and management of the species. However, it is challenging for remote sensing technique to detect a specific single plant species with a small crown and population size in highly heterogeneous urban environments. This study investigated the ability to upscale the in-situ hyperspectral reflectance signature obtained at crown level for landscape scale detection. Field hyperspectral sensors were used to obtain the spectral signatures of Solidago altissima and its surrounding features at crown scale (5 cm × 5 cm, instantaneous field of view (IFOV)). By using the first derivative analysis, the hyperspectral indices namely SAFI (S. altissima flower index) was developed based on the sensitive and peak reflectance of full blooming flower. This index was subsequently applied to identify the blooming S. altissima at population scale (1 m × 1 m) and landscape scale (5 m × 5 m) using field hyperspectral sensor and satellite data respectively. The results at crown scale showed that SAFI was able to discriminate S. altissima from their surrounding features with average probability of 72%. At the population scale with (1 m plot), SAFI can discriminate plot with different S. altissima flower distribution starting from 45%dominance. At landscape scale, effective detection was found at SAFI value over 0.3 to 0.5. The sites which hadhigh SAFI values but no actual presence of S. altissima were often under intensive land management such as frequent mowing. We conclude that the detection of S. altissima distribution at landscape scale by direct upscaling crown scale hyperspectral signature was possible using high resolution satellite image with availability of green(~480 nm) and yellow (~600 nm) spectrum bands at fine resolution (~5 m). The detection, however, were influenced by the phenology state of the flowering stages, community size and adjacent plant that possesses similar carotenoid characteristics.

Although climate change has emerged as a major threat to biodiversity, few assessments exist of the sensitivity of desert riparian forests to climate in extremely arid plain areas. We mapped tree growth-climate correlations (time period: 1957–2015) to identify the sensitivities of Populus euphratica (P. euphratica), a dominant and important tree species in the desert riparian forest in the lower Tarim River, an extremely arid area in China, to climate change. The results indicated that groundwater depth was the key factor for controlling tree growth in the study area. In order to accurately assess sensitivity to climate change of the desert riparian forest, it was necessary to consider the effects of hydrological fluctuations on P. euphratica because the sensitivities of tree growth to precipitation and temperature depended on changes in the groundwater environment. The groundwater depth threshold to distinguish the sensitivity of climate change to P. euphratica growth was 6–7 m. Moreover, warming would accelerate desert riparian forest decline when the groundwater depth was more than 6 m because an anomalously high temperature would intensify evapotranspiration and the shortage of soil moisture during the growing season, and offset the benefits from precipitation, which would cause more drought if groundwater was not compensated by sufficient runoff. Therefore, global warming would bring great disadvantages to desert riparian forests if the current mode of the ecological water conveyance project (EWCP) continues to be implemented without a fixed time and water amount in the lower Tarim River, which could not steadily decrease and actively maintain shallow groundwater depth.

Agriculture productions play significant roles in economic development. Extreme weather events, especially drought under climate change conditions, can affect future crop production. Nowadays, researchers are trying to apply modeling approaches for estimating future changes on amounts of crop yields. This study employed pooled panel data to simulate the most effective meteorological drought indices, economic and meteorological variables on rainfed wheat yield. The observation period was 1990–2016 for several meteorological data, besides SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) drought indices in monthly and yearly scales. The available economic variables during the study period were yearly guaranteed wheat prices (Rial/kg) and area under cultivation (ha). In this research, first, the most effective variables were selected according to the efficiency criteria and stepwise regression. Then by using pooled panel data, a relation was estimated between yield and the independent variables. Finally, with future downscaled variables, the amount of wheat yield was determined for the next 20 years (2019–2038). The GFDL- ESM2M and MIROC5 models under RCP45 and RCP85 were run, and MIROC5 under RCP45 was selected as the best model, for the evaluation period. The results revealed that guaranteed wheat prices, yearly precipitation and sunshine hours, the area under cultivation, and SPI of October were identified as the most effective variables on wheat yield through the Panel model. By using the projection weather variables and the pooled panel model, we achieved that the amount of rainfed wheat yield would be increased over two next decades at Mashhad, Sabzevar, and Torbat H. locations.

NPP/GPP ratio is related to the allocation of photosynthesized products by plants. The examination of this ratio and its exterior controlling factors are very critical for understanding the carbon cycle. China covers a large area and contains diverse ecosystems and climate conditions, while, extensive studies of the NPP/GPP ratio and its exterior controls have not yet been conducted. This study examines the 16-year (2000–2015) mean NPP/GPP ratio distribution in space, its spatiotemporal changes, its response to exterior controls in each ecosystem, and how ongoing climate changes affect the NPP/GPP ratio over time. This study found, cold and dry environments usually contain vegetation with high NPP/GPP ratios, while the opposite is true for warm and wet environments. However, the highest NPP/GPP values do not always occur in the coldest and driest environments, and the lowest values do not always occur in the warmest and wettest environments. The NPP/GPP ratio weakly increased over the past 16 years. Generally, both moderate precipitation and temperature can increase NPP/GPP ratio. Temporally, more grids are positively correlated with changes in precipitation and negatively correlated with changes in temperature.

We investigated between 2014 and 2016 the concentrations of PCBs, DDTs and HCB and trace elements in blubber and skin samples of Australian humpback and snubfin dolphins collected from the Fitzroy River estuary and Port Curtis, Queensland, Australia. The concentrations of PCBs, DDTs and HCB from this study were compared with results from samples collected in 2009 and 2010 from the same areas. Our results showed that the concentrations of PCBs, DDTs and HCB have increased over time. A large proportion (~68%) of the sampled population accumulated organochlorines contaminants above thresholds over which immunosuppression and reproductive anomalies are known to occur. Results also suggest a general enrichment of some trace elements above natural levels in humpback and snubfin dolphins from the Fitzroy River estuary and Port Curtis. Extensive flooding in adjacent catchments is the most likely primary factor responsible for increased distribution and bioavailability of these contaminants into coastal waters. Land-based pollution from the adjacent catchments is an ongoing threat to coastal systems and particularly for top predators such as dolphins in the Great Barrier Reef. Climate change models predict increasing frequency and intensity of extreme rainfall events that will impact coastal Queensland and the Great Barrier Reef. Reduction of land-based persistent pollutants into coastal waters of the Great Barrier Reef will be critical for the health and survival of humpback and snubfin dolphins in this region.

Coal mining and utilization have always been a great impact on landscape ecology. Many studies have identified the impact of surface mining through landscape changes, but the spatial distribution and extent of the impacts are considered less in existing studies. The objective of this study is to determine the spatial distribution and extent of the impact of surface mining on the Landscape Ecological Health (LEH) of semi-arid grasslands. The research area of this paper is Shengli surface mines, Xilinhot City, Inner Mongolia Autonomous Region, China. The main steps of the research method include LEH assessment, buffer analysis, Landscape Ecological Function Contribution Rate (LEFCR) and Modified Landscape Disturbance Index (MLDI) measurement. The results indicated that: 1) the theoretical reference range of the impact of Shengli surface mines on the semi-arid grassland LEH is 5 km. 2) The proposed MLDI can effectively monitor the disturbance of surface mining on landscape ecology. 3) The heavily affected area is mainly the open-pit landscape; the moderately affected area is mainly the dumping landscape and the mining area industrial square landscape, and the slightly affected area is mainly a small part of the dumping landscape and the surrounding grassland landscape of the mining area. The spatial distribution of the impact of surface mining on the landscape ecology of semi-arid grasslands is heterogeneous, which should be taken into account in landscape ecological management, restoration, and planning. This paper may serve as a reference for the study of the disturbances and impacts of human activities on landscape ecology.

Investigation of leaf water enrichment may improve our understanding of evapotranspiration partitioning and material exchange between the inside and the outside of leaves. In this study, with measurements of diel variations in δ18O of bulk leaf water (δL,b) of Platycladus orientalis, two theories (the Péclet effect and the two-pool model) describing leaf water enrichment were assessed. The proportional difference between observed and modeled leaf water enrichment, f, was estimated using both isotopic-steady-state (ISS) and non-steady-state (NSS) models to test the two theories. The results showed that differences between ISS- and NSS-modeled leaf water enrichment at the evaporative sites (Δl,e) were not significant (p > 0.05), thus estimated f was not significantly (p > 0.05) influenced by assuming ISS or NSS conditions. Under both ISS and NSS conditions, no significant (P > 0.05) positive f–Tr (leaf transpiration rate) relationships were found on either diel or diurnal time scales, indicating that the Péclet effect is not required to describe bulk leaf water enrichment. Therefore, it would be adequate to combine the simple two-pool model with the ISS model to predict δ18O of bulk leaf water of P. orientalis. These results may have implications for parameterizing the carbon cycle, past climates, and evapotranspiration partitioning models.

The optical properties of lichens have been traditionally explored in the context of geological mapping where the encrustation of lichens on rocks may influence the detection of minerals of interest. As of today, few studies have looked into the potential of using the optical properties of lichens to classify them; however, none has investigated the classification of tropical lichens using spectroscopy. Here we explore the use of the visible-near infrared reflectance (VNIR; 450–1000 nm) to discriminate Neotropical corticolous lichens; the most abundant lichens in tropical forests. Reflectance measurements on lichens and their bark substrate were performed on 282 lichens samples of 32 species attached to their host's bark. Using these measurements, we first explored the degree of spectral mixing of bark and lichens by linear unmixing each lichen spectrum with the corresponding average species spectrum and bark spectrum. Overall, the results reveal that the lichen signatures tend to mask the spectral contributions from bark; however, there are some specific groups of species with high bark mixing probably due to their nature and the similarities between the lichen and bark spectra. Next, we classified the lichen spectra based on growth forms and taxonomic ranks (i.e., family, genus, species) using five machine learning classifiers. This analysis was conducted on raw reflectance spectra and wavelet-transformed spectra to enhance the absorption features prior to classification. As expected, the classification of lichen spectra is less accurate at species-specific levels, rather than higher taxonomic ranks. The wavelet transformation was found to enhance the general performance of classification; however, the accuracy of the classification depends on the classifier. Of the classifiers used in this study, linear discrimination applied to reflectance spectra presents the highest performance at the species level. Our results reveal the potential of using the VNIR reflectance as a method to discriminate Neotropical lichens. The introduced methodology may be conducted in the field, thus allowing the monitoring of lichen communities in forests; thereby furthering the current understanding of the role of lichens in ecosystem functioning.

Due to their sensitivity and dramatic declines, freshwater mussels are prime targets for conservation and environmental monitoring. For this, however, information is needed on life history and ecological traits, which is lacking in many taxa, including threatened species. Species recently described or recognized as valid are of particular concern, due to the shortage of even basic knowledge. A case in point is the recently recognized and Near Threatened dolphin freshwater mussel Unio delphinus Spengler, 1793, which is endemic to the western Iberian Peninsula and has suffered marked population declines. To overcome information gaps for U. delphinus, we carried out a holistic biological study across the species range, aiming to: i) estimate the area of occupancy (AOO) and extent of occurrence (EOO) based on updated distribution data taken from the literature and recent surveys; ii) estimate growth patterns from biometrical (shell dimensions and growth annuli) measurements taken on specimens from seven populations; iii) estimate sex ratios from gonad tissue biopsies collected on specimens from eight populations; iv) estimate gametogenesis and sex ratio through histological examination of gonad and gill tissues collected monthly for a year, from a single population; and v) determine host species from infestation trials of glochidia with co-occurring fish species. We estimated an EOO of 706 km2 and an AOO of 61 km2, which together with data on declines assigns the species to the Endangered category using IUCN criteria. Unio delphinus was found to grow faster and to be shorter-lived (up to 11 years, maturity at around 2 years old) than other European freshwater mussels. Growth and life span are similar across the range in lotic habitats, but different from that in lentic habitats. The larvae of U. delphinus may attach to most co-occurring fish species, but only native species were effective hosts. Native cyprinids, especially those from the genus Squalius, seem to be the primary hosts. Overall, the information provided contributes to a better conservation status assessment, selection of conservation and rehabilitation areas, guidance for the establishment of propagation programs and better timing for specimens’ manipulation including monitoring and possible translocations. The framework presented here highlights the importance of basic biological studies to define good ecological and physiological status.

Land use/cover change (LUCC) in the Bohai Rim coastal zone has accelerated as a result of rampant economic development, which has directly caused many negative effects on ecosystem functions and services. Based on multi-temporal land use data (2000, 2005, 2010 and 2015), the benefit transfer method was used to assess the ecosystem service value (ESV) of the Bohai Rim coastal zone, and the impact of LUCC on ecosystem services was studied. Multi-scenario simulations for 2025 were conducted using the future land use simulation model. The result of the analysis showed that during the period from 2000 to 2015, the total ESV lost was 22.09 billion yuan, representing a decrease of 3.80%. The spatial distribution of the ESV showed a certain regularity, with obvious characteristics of a land-sea gradient change. As the distance from the coastline increased, the ESV per unit area gradually declined. Compared with those in 2015, the total ESVs of the socio-economic development scenario and the business-as-usual scenario in 2025 showed a declining trend, while they increased under the ecological protection priority scenario. Under the ecological protection priority scenario, regulating services and support services increased significantly, but those declined dramatically under the socio-economic development scenario. The patterns of LUCC are the main reasons for the decrease in ESV. This research provides a theoretical basis and support for the development and utilization of coastal space and the improvement of “ecological-economic-social” benefits; additionally, the results provide support for scientific decision-making services for the sustainable use of resources in the coastal zone and for the sustainable management of ecosystems.

How are urban residents distributed and agglomerated across different-sized cities? This question can be addressed by studying urban system or Zipf’s law. Using Chinese data for the period of 1990–2017, this paper contributes to the literature by exploring the evolution of urban system, and more importantly deviations from Zipf's law. It is found that (1) The size distribution of Chinese cities, as expected, did not follow Zipf’s law until very recently. Both large cities and small towns were under-sized relative to the Zipf’s law, implying possible loss of agglomeration economies; (2) When the top 10% large cities are excluded, the Chinese data fit the Zipf's law better, demonstrating the adverse role of government interference in containing the growth of mega-cities; (3) By and large, the distribution has been moving closer or further away from Zipf’s law, corroborating with policy directions in the relevant five-year plans of the central government; and (4) Government interferences helped enlarge while market forces helped reduce the numerical deviations of individual cities from Zipf’s law. The last three analytical findings are the first in the literature since no previous studies have focused on the issue of why urban system in China does not follow Zipf’s law. In particular, no earlier efforts have been made to model numerical deviations from Zipf’s law, as attempted in this paper. The results appeal for removal of government interferences and furthering market-based reforms in order to reap economies of agglomeration.

Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency Envisat MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008–2011, 2017, and 2018. A total of 2321 lakes across the contiguous United States were included in the analysis. We examined four different thresholds to define when a waterbody is classified as experiencing a bloom. Across these four thresholds, we explored variability in bloom percentage with changes in seasonality and lake size. As a validation of algorithm performance, we analyzed the agreement between satellite observations and previously established ecological patterns, although data availability in the wintertime limited these comparisons on a year-round basis. Changes in cyanobacterial bloom percentage at the national scale followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a maximum in fall, and decreased through the winter. Wintertime data, particularly in northern regions, were consistently limited due to snow and ice cover. With the exception of the Southeast and South, regional patterns mimicked patterns found at the national scale. The Southeast and South exhibited an unexpected pattern as cyanobacterial bloom percentage reached a maximum in the winter rather than the summer. Lake Jesup in Florida was used as a case study to validate this observed pattern against field observations of chlorophyll a. Results from this research establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States. In addition, methods presented in this study can be tailored to fit the specific requirements of an individual system or region.