Forecasting daily total pollen concentrations on a global scale.

BACKGROUND: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. METHODS: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. RESULTS: The best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. CONCLUSIONS: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.

Predicting the current and future suitable distribution range of Trilocha varians (Walker, 1855) (Lepidoptera: Bombycidae) in China.

Trilocha varians is one of the major pests of Ficus spp. Based on 19 bioclimatic variables provided by the Worldclim, our study analysed the suitable distribution areas of T. varians under current and future climate changes (SSP1-2.6, SSP2-4.5, SSP5-8.5) for two periods (the 2050s and 2090s) using the maximum entropy algorithm (MaxEnt) model. Key environmental variables affecting the geographic distribution of T. varians were also identified, and the changes in the area of suitable range under current and future climate changes were compared. The results showed that the key environmental variables affecting the distribution of T. varians were temperature and precipitation, comprising annual mean temperature (bio1), temperature seasonality (standard deviation × 100) (bio4), precipitation of driest month (bio14), and precipitation of driest quarter (bio17). Under the current climatic conditions, the suitable distribution area of T. varians is within the range of 92°13'E-122°08'E, 18°17'N-31°55'N. The current high, medium, and low suitable areas for T. varians predicted by the MaxEnt model are 14.00 × 104, 21.50 × 104, and 71.95 × 104 km2, of which the high suitable areas are mainly distributed in southern Guangdong, southwestern Guangxi, western Taiwan, Hong Kong, and Hainan. Under different future climatic conditions, some of the high, medium, and low suitability zones for T. varians increased and some decreased, but the mass centre did not migrate significantly. The Pearl River Basin is predicted to remain the main distribution area of T. varians.

Forecasting the future of Fall armyworm Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) in India using ecological niche model.

The Fall armyworm, Spodoptera frugiperda is the most notorious invasive pest species on maize, recently reported in India. The continuous spread of Fall armyworms to new ecological niches raises global concern. The current study is the first in India to forecast the suitability of a habitat for S. frugiperda using a maximum entropy algorithm. Predictions were made based on an analysis of the relationship between 109 occurrence records of S. frugiperda and pertinent historical, current, and predicted climatic data for the study area. The model indicated that S. frugiperda could thrive in different habitats under the current environmental circumstances, particularly in the west and south Indian states like Maharashtra, Tamil Nadu, and Karnataka. The model predicted that areas with higher latitudes, particularly in Uttar Pradesh, Odisha, West Bengal, and some portions of Telangana, Rajasthan, Chhattisgarh, and Madhya Pradesh, as well as some tracts of northeastern states like Assam and Arunachal Pradesh, would have highly climate-suitable conditions for S. frugiperda to occur in the future. The average AUC value was 0.852, which indicates excellent accuracy of the prediction. A Jackknife test of variables indicated that isothermality with the highest gain value was determining the potential geographic distribution of S. frugiperda. Our results will be useful for serving as an early warning tool to guide decision-making and prevent further spread toward new areas in India.

Feeding ecology of Moenkhausia collettii (Steindachner, 1882) (Characiformes: Characidae) in streams in the eastern Amazon: Environmental factors and body size.

The present study aimed to characterize the diet of Moenkhausia collettii and investigate possible changes due to environmental variations and its body size in streams in the eastern Amazon. The specimens were sampled monthly between April 2019 and March 2020. They were measured for standard length (SL) and total mass (Tm) and eviscerated for analysis of stomach contents. Food items were identified and grouped into categories. Dietary aspects such as food importance index (AI%), trophic niche width, and stomach repletion index (SRI%) were evaluated. Furthermore, generalized linear models (GLMs) were used to evaluate the relation between diet and the SL, as well as between diet and the environmental variables of streams. A total of 355 specimens with SL ranging from 11.06 to 46.03 mm and weight ranging from 0.020 to 2.373 g were evaluated. Out of the 355 stomachs analysed, 88 contained material in an advanced stage of decomposition and 12 were empty. The diet of M. collettii was considered omnivorous, with a tendency toward insectivory. Formicidae was the most important category in the diet of the species, followed by immature Diptera and plant material. The GLMs showed a relationship between the diet and a set of environmental variables such as dissolved oxygen, conductivity, flow, width, depth, wood, leaf bank, and SL. The trophic niche width and feeding intensity increased with the length of the species, as well as in the period of higher precipitation, reinforcing trophic opportunism for M. collettii. Therefore, new studies that combine the traditional method of stomach content analysis, the use of stable isotopes, as well as ecomorphological attributes, are crucial for a profound understanding of the trophic ecology of the ichthyofauna in the face of natural changes occurring in their environment.

What drives the changing characteristics of phytoplankton in urban lakes: Climate, hydrology, or human disturbance?

Phytoplankton in shallow urban lakes are influenced by various environmental factors. However, the long-term coupling effects and impact pathways of these environmental variables on phytoplankton remain unclear. This is an emerging issue due to high urbanization and the resultant complex climate, lake hydrology and morphology, human interference, and water quality parameter changes. This study used Tangxun Lake, the largest urban lake in the Yangtze River Economic Belt, as an example to assess for the first time the individual contributions and coupled effects of four environmental variables and fourteen indicators on chlorophyll-a (Chla) concentrations under two scenarios from 2000 to 2019. Additionally, the influence pathways between the environmental variables and Chla concentration were quantified. The results indicated that the Chla concentration was most affected by lake hydrology and morphology, as were the total nitrogen, total phosphorus, and transparency. Especially after urbanization (2015-2019), the coupling effect of human interference, lake hydrology and morphology, and water quality parameters was strongest (18%). This is mainly due to fluctuations in the lake water level and an increase in the shape index of lake morphology, large amounts of nutrients were input, which reduced lake transparency and indirectly changed the Chla content. In addition, due to the rapid development of Wuhan city, the expansion of construction land has led to an increase in impervious surface area and a decrease in lake area. During periods of intense summer rainfall, a substantial amount of pollutants entered the lakes through surface runoff, resulting in decreased lake transparency, and elevated concentrations of nitrogen and phosphorus, indirectly increasing the Chla content. This study provides a scientific basis for aquatic ecological assessment and pollution control in urban shallow lakes.

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm's pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers' agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

Seasonal dynamics of the microbial community in a strong-flavor baijiu fermentation.

BACKGROUND: The microbial community plays a crucial role in Chinese strong-flavor baijiu (SFB) fermentation. However, the seasonal dynamics of the microbial community in the SFB fermentation system and its contribution to the unique flavor of SFB have not been fully elucidated. In this study, we investigated the seasonal dynamics of the microbial community through 16S rRNA and ITS gene sequencing. RESULTS: The results revealed significant temporal dynamics of microbial communities and environmental variables throughout the four seasons. The influence of seasons on fungal communities was found to be more significant than on bacterial communities. The diversity of bacteria was higher during the winter and summer, whereas fungal diversity was more prominent in summer and autumn. Stochastic processes maintained their dominance in microbial assembly throughout all four seasons but the significance of heterogeneous selection increased during summer for both bacteria and fungi, whereas homogeneous selection became more pronounced during winter for fungi. The pH and environmental temperature were important drivers of microbial community assembly across different seasons, primarily impacting the core genera responsible for the production of major volatile flavor compounds (VFCs), especially ethyl caproate. CONCLUSION: These findings provide new insights into the impact of seasons on microbial communities and hold promise for improving the quality-control measures for SFB brewed in different seasons. © 2024 Society of Chemical Industry.

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region.

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Macrogenetics reveals multifaceted influences of environmental variation on vertebrate population genetic diversity across the Americas.

The broad scale distribution of population-specific genetic diversity (GDP ) across taxa remains understudied relative to species diversity gradients, despite its relevance for systematic conservation planning. We used nuclear DNA data collected from 3678 vertebrate populations across the Americas to assess the role of environmental and spatial variables in structuring the distribution of GDP , a key component of adaptive potential in the face of environmental change. We specifically assessed non-linear trends for a metric of GDP, expected heterozygosity (HE ), and found more evidence for spatial hotspots and cold spots in HE rather than a strict pattern with latitude. We also detected inconsistent relationships between HE and environmental variables, where only 11 of 30 environmental comparisons among taxa groups were statistically significant at the .05 level, and the shape of significant trends differed substantially across vertebrate groups. Only one of six taxonomic groups, freshwater fishes, consistently showed significant relationships between HE and most (four of five) environmental variables. The remaining groups had statistically significant relationships for either two (amphibians, reptiles), one (birds, mammals), or no variables (anadromous fishes). Our study highlights gaps in the theoretical foundation upon which macrogenetic predictions have been made thus far in the literature, as well as the nuances for assessing broad patterns in GDP among vertebrate groups. Overall, our results suggest a disconnect between patterns of species and genetic diversity, and underscores that large-scale factors affecting genetic diversity may not be the same factors as those shaping taxonomic diversity. Thus, careful spatial and taxonomic-specific considerations are needed for applying macrogenetics to conservation planning.

Chromosome-level genome assembly and population genomic analysis reveal evolution and local adaptation in common hairfin anchovy (Setipinna tenuifilis).

Understanding the genetic structure and the factors associated with adaptive diversity has significant implications for the effective management of wild populations under threat from overfishing and climate change. The common hairfin anchovy (Setipinna tenuifilis) is an economically and ecologically important pelagic fish species, spanning a broad latitudinal gradient along marginal seas of the Northwest Pacific. In this study, we constructed the first reference genome of S. tenuifilis using PacBio long reads and high-resolution chromosome conformation capture (Hi-C) technology. The assembled genome was 798.38 Mb with a contig N50 of 1.43 Mb and a scaffold N50 of 32.42 Mb, which were anchored onto 24 pseudochromosomes. A total of 22,019 genes were functionally annotated, which accounted for 95.27% of the predicted protein-coding genes. Chromosomal collinearity analysis revealed chromosome fusion or fission events in Clupeiformes species. Three genetic groups of S. tenuifilis were revealed along the Chinese coast using restriction site-associated DNA sequencing (RADseq). We investigated the influence of four bioclimatic variables as potential drivers of adaptive divergence in S. tenuifilis, suggesting that these environmental variables, especially sea surface temperature, may play important roles as drivers of spatially varying selection for S. tenuifilis. We also identified candidate functional genes underlying adaptive mechanisms and ecological tradeoffs using redundancy analysis (RDA) and BayeScan analysis. In summary, this study sheds light on the evolution and spatial patterns of genetic variation of S. tenuifilis, providing a valuable genomic resource for further biological and genetic studies on this species and other closely related Clupeiformes.

A trait-based approach to determining principles of plant biogeography.

Lineage-specific traits determine how plants interact with their surrounding environment. Unrelated species may evolve similar phenotypic characteristics to tolerate, persist in, and invade environments with certain characteristics, resulting in some traits becoming relatively more common in certain types of habitats. Analyses of these general patterns of geographical trait distribution have led to the proposal of general principles to explain how plants diversify in space over time. Trait-environment correlation analyses quantify to what extent unrelated lineages have similar evolutionary responses to a given type of habitat. In this synthesis, I give a short historical overview on trait-environment correlation analyses, from some key observations from classic naturalists to modern approaches using trait evolution models, large phylogenies, and massive data sets of traits and distributions. I discuss some limitations of modern approaches, including the need for more realistic models, the lack of data from tropical areas, and the necessary focus on trait scoring that goes beyond macromorphology. Overcoming these limitations will allow the field to explore new questions related to trait lability and niche evolution and to better identify generalities and exceptions in how plants diversify in space over time.

Unravelling diversity, drivers, and indicators of soil microbiome of Trillium govanianum, an endangered plant species of the Himalaya.

In an era of global environmental change, conservation of threatened biodiversity and ecosystem restoration are formidable ecological challenges. The forest understory strata and the belowground soil environment including rhizospheric microbial communities, which are crucial for ecosystem functioning and overall forest biodiversity maintenance, have remained understudied. Here, we investigate the soil microbiome of Trillium govanianum - an endangered Himalayan Forest herb, to unravel the underground diversity, drivers, and potential indicators of the microbial community. We collected rhizospheric and bulk soil samples for microbiome and physicochemical analysis at three sites along an elevation gradient (2500-3300 m) in Kashmir Himalaya. Amplicon sequencing of 16 S rRNA and ITS was used to identify the bacterial and fungal soil microorganisms. We found significant differences in the structure and diversity of microbial community (bacterial and fungal) between the rhizosphere and bulk soil along the altitudinal gradient, and noticeable shifts in the nutrient level in dominant microbial phyla associated with T. govanianum. A significant difference between soil physicochemical parameters and increasing altitude suggests that microbial community structure is determined by altitude and soil type. Similarly, the microbial communities showed a significant (P < 0.05) correlation with soil physicochemical variables along the altitudinal gradient. The moisture content in bacterial and total organic carbon in fungal communities showed the most substantial impact on the physiochemical drivers. We also identify potential bacterial and fungal plant growth promoter indicator species in the soil microbiome of T. govanianum. Overall, our findings provide novel research insights that can be pivotal in designing integrated species recovery programs and long-term restoration plans for T. govanianum, with learnings for biodiversity conservation elsewhere.

Environmental Sensing in High-Altitude Mountain Ecosystems Powered by Sedimentary Microbial Fuel Cells.

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring of physical environmental parameters in high-altitude mountainous areas. The implemented power management system (PMS) uses a reference SMFC (SMFCRef) to implement a quasi-maximum power point tracking (quasi-MPPT) algorithm to harvest energy stably. As a result, while transmitting in a point-to-point wireless sensor network topology, the system achieves an overall efficiency of 59.6%. Furthermore, the control mechanisms prevent energy waste and maintain a stable voltage despite the microbial fuel cell (MFC)'s high impedance, low time response, and low energy production. Moreover, our system enables a fundamental understanding of environmental systems and their resilience of adaptation strategies by being a low-cost, ecological, and environmentally friendly alternative to power-distributed and dynamic environmental sensing networks in high-altitude mountain ecosystems with anoxic environmental conditions.

Ecological drivers of soil carbon in Kashmir Himalayan forests: Application of machine learning combined with structural equation modelling.

Soil carbon (SC) heterogeneity in mountain ecosystems is ascertained by a complex interdependency of topography, climate, edaphic features, and biotic elements, which may incite uncertainties in regional SC estimation. However, quantitative evaluations of the interplay between SC and these determinants as well as underlying possible link networks, are uncommon. Using the data set of SC along with soil properties at 0-10 and 10-20 cm depths from 135 plots under three coniferous forests, we aimed to ascertain SC heterogeneity and to elucidate how these interactions affect the SC storage, operating data-driven models (Least Absolute Shrinkage and Selection Operator [LASSO] regression and structural equation modeling [SEM]) to identify the dominant explanatory factors affecting the distribution of SC in Kashmir Himalayan forests. Average SC stocks at 0-10 cm and 10-20 cm depth intervals range from 32.41 Mg ha-1 in sub-alpine (SA) forest to 48.50 Mg ha-1 in mixed conifer (MC) forest. The findings show that SC declines significantly from 0 - 10 cm to 10-20 cm strata, consistent with other soil physico-chemical determinants other than bulk density. SEM renders better model fit (0-10 cm: R2 = 0.61; 10-20cm: R2 = 0.46) with lesser uncertainties compared to LASSO (0-10 cm: R2 = 0.55; 10-20cm: R2 = 0.37). Soil properties and topography play a key role in modulating SC stocks, with total nitrogen (TN), soil moisture (SM), and elevation being principal drivers with contrasting effects on SC storage, while climate and vegetation parameters are of lesser influence. The relative effect of majority of explanatory drivers reduces with depth while that of temperature increases. Our analyses indicate that shifts in floristic composition could have long-lasting implications on soil structure and C storage, providing valuable data for C sink management.

Monetary valuation of unsorted waste: A shadow price approach.

Improving the management of municipal solid waste (MSW) is fundamental to promote circular economy and sustainability. Unsorted waste involves negative environmental impacts which often are ignored in economic feasibility studies due to its difficult valuation. In this study the shadow price of unsorted waste using the directional distance function was estimated. This methodological approach also allowed us to compute eco-efficiency scores of a set of municipalities in the provision of MSW services. The empirical application focused on a sample of 119 Chilean municipalities. The results showed that the average shadow price of unsorted waste was 297.66 €/ton which means that the environmental cost of left waste as unsorted was 297.66 € per ton. A regression tree model illustrated that population density, tourism intensity and the generation of waste per capita significantly influenced the shadow price of unsorted waste. Moreover, it was illustrated that Chilean municipalities were very inefficient in the management of MSW since the average eco-efficiency score was 0.272. The findings from this study reveal that additional and alternative policies should be adopted to improve the management of MSW and increase its recycling rate.

Changes in species diversity along the ephemeral streams in eastern Mediterranean Mountainous Ecosystem, a case study for Ahir Mountain (Kahramanmaras-Türkiye).

Ephemeral streams are important habitats that support biodiversity, especially in mountainous ecosystems in arid and semi-arid regions, and are among those that will be most affected by global climate change. This study aimed to examine the trends in plant species diversity of ephemeral stream beds and their relationship with environmental variables (aspect, elevation, soil) in the eastern Mediterranean region.For this purpose, 40 sample plots were applied in the valley cross sections of the ephemeral stream beds (valley bottom, side and ridge) on two main slopes (north and south) in 2019. The abundance values of plant species were determined and the number of individuals in each sample plot was counted. In addition, soil samples were taken at a depth of 0-30 cm and analyzed. With the data obtained, the importance value index of all species was calculated, and the diversity and species richness of the sample plots were determined.A total of 130 plant taxa were identified in the study area. The most dominant species were Astragalus kurdicus (IVI value: 25.97), Helichrysum plicatum (21.94), Taeniatherum caput-medusae subsp. crinitum (15.51), Hordeum bulbosum (15.33), Bromus erectus (15.32), and Minuartia juniperina (14.14). Both plant richness and endemism rate showed a significant relationship with increasing elevation. Plant diversity values of south-facing slopes were higher (2.23) than those of north-facing slopes (2.21). In addition, we found that phosphorus is an important factor in the distribution of plant species along the ephemeral stream beds, and as the amount of phosphorus increases, species richness and diversity also increase.

Spatio-temporal variation in water quality and phytoplankton community structure in Changwang, Meishe, and Wuyuan Rivers in Hainan Island, China.

For the first time, this study explored spatio-temporal variation in water quality and phytoplankton community structure in Changwang, Meishe, and Wuyuan Rivers in tropical Hainan Island, China. Phytoplankton samples and water were collected between March and December 2019 and analyzed using standard methods. Two-way ANOVA revealed significant spatial and seasonal variation in physico-chemical parameters (p < 0.05). Wuyuan had high TP (0.06 ± 0.04 mg L-1), TN (1.14 ± 0.71 mg L-1), NH4+-N (0.07 ± 0.09 mg L-1), Secchi depth (2.28 ± 3.79 m), salinity (3.60±5.50 ppt), and EC (332.50 ± 219.10 µS cm-1). At the same time, Meishe had high TP (0.07 ± 0.03 mg L-1), TN (1.04 ± 0.74 mg L-1), NH4+-N (0.07 ± 0.10 mg L-1), EC (327.61 ± 63.22 µS cm-1), and turbidity (40.25 ± 21.16 NTU). In terms of seasons, spring recorded high average TP, TN, NH4+-N, COD, and DO, while summer had a high temperature, Chl-a, salinity, and EC. Generally, the physico-chemical parameters met the China water quality standard limits (GB 3838-2002). Overall, 197 phytoplankton species belonging to Cyanophyta, Chlorophyta, Cryptophyta, Bacillariophyta, Pyrrophyta, Euglenophyta, Xanthophyta, and Chrysophyta were identified, with Cyanophyta being dominant. Phytoplankton density showed spatial changes varying from 18 × 106 cell L-1 to 84 × 106 cell L-1. The phytoplankton diversity ranged from 1.86 to 2.41, indicating a mesotrophic state. One-way ANOSIM showed no significant spatial dissimilarity in phytoplankton composition (R = 0.042, p = 0.771) but indicated a significant seasonal difference (R = 0.265, p = 0.001). Therefore, SIMPER analysis revealed that Lyngbya attenuata, Merismopedia tenuissima, Cyclotella sp., Merismopedia glauca, Merismopedia elegans, and Phormidium tenue contributed to the seasonal differences. Furthermore, CCA demonstrated that TP, TN, NH4+-N, COD, Chl-a, and Secchi depth greatly influenced the phytoplankton community. This study shows the spatio-temporal variation in water quality and phytoplankton communities, useful for managing riverine quality.

Factors influencing eco-efficiency of municipal solid waste management in Chile: A double-bootstrap approach.

Improving eco-efficiency in the provision of municipal solid waste plays an important role for a sustainable economy. Eco-efficiency of municipal solid waste service providers (MSWSPs) has been generally assessed using the conventional data envelopment analysis (DEA) method. However, this approach is sensitive to data noise and has no statistical properties. To overcome these limitations, in this paper, we adopt the double-bootstrap DEA model to derive robust eco-efficiency scores. This nonparametric method allows conducting statistical inference to explore environmental factors affecting the eco-efficiency of MSWSPs. The empirical approach focused on a sample of 298 MSWSPs in Chile, a middle-income country whose policies for promoting waste recycling are incipient. The results indicated that based on the bias-corrected eco-efficiency scores, the potential saving in costs and unsorted waste could be up to 37.8% on average to generate the same level of output (recycled waste). The findings showed that dealing with data noise and uncertainly is of great importance when conducting benchmarking analysis. The region where the municipality is located, tourism, population density and waste per capita are environmental variables that significantly influenced eco-efficiency of Chilean MSWSPs. Several policy implications are discussed based on the findings of this study.

The eco-efficiency of municipalities in the recycling of solid waste: A stochastic semi-parametric envelopment of data approach.

Eco-efficiency assessment of municipal solid waste (MSW) suppliers is a useful tool in the transition to a circular economy. Furthermore, it provides evidence of the economic and environmental performance of municipalities that can be used for decision-making and/or elaboration of regulatory policies. In this study, eco-efficiency scores were computed for a sample of 140 Chilean municipalities in the provision of MSW services. In doing so, the stochastic semi-parametric envelopment of data method was applied. It is a novel technique which overcomes the limitations of parametric (stochastic frontier analysis) and non-parametric (data envelopment analysis) methods previously employed to evaluate the eco-efficiency of MSW services. The average eco-efficiency of the 140 assessed municipalities was 0.332 which indicates that they could save 66.8% of their operational costs and recycling the same amount of waste. Moreover, 61.4% of the evaluated municipalities presented an eco-efficiency score which was lower than 0.4, whereas the other municipalities (38.6% of the sample) exhibited an eco-efficiency which raged between 0.4 and 0.80. Hence, none of the municipalities assessed was identified as eco-efficient which, implies that there is room for all municipalities to reduce operational costs in the management of MSW. Population density, tourism and location of the municipality were identified as factors influencing the eco-efficiency of the municipalities in MSW management.

Non-linear models of species' responses to environmental and spatial gradients.

Species' responses to broad-scale environmental or spatial gradients are typically unimodal. Current models of species' responses along gradients tend to be overly simplistic (e.g., linear, quadratic or Gaussian GLMs), or are suitably flexible (e.g., splines, GAMs) but lack direct ecologically interpretable parameters. We describe a parametric framework for species-environment non-linear modelling ('senlm'). The framework has two components: (i) a non-linear parametric mathematical function to model the mean species response along a gradient that allows asymmetry, flattening/peakedness or bimodality; and (ii) a statistical error distribution tailored for ecological data types, allowing intrinsic mean-variance relationships and zero-inflation. We demonstrate the utility of this model framework, highlighting the flexibility of a range of possible mean functions and a broad range of potential error distributions, in analyses of fish species' abundances along a depth gradient, and how they change over time and at different latitudes.