Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Domains of scale in cumulative effects of energy sector development on boreal birds

Context

Industrial development in Canada's boreal forest creates cumulative environmental effects on biodiversity. Some effects may be scale-dependent, creating uncertainty in understanding and hindering effective management.

Objectives

We estimated cumulative effects of energy sector development on distributions of sixteen migratory songbird species at multiple spatial scales within the boreal region of Alberta, Canada, and evaluated evidence for scale domains in species responses.

Methods

We used a hierarchical, multi-scale sampling and modelling framework to compare effects of oil and gas footprint on songbirds at five spatial scales. We used Bayesian Lasso to facilitate direct comparison of parameter estimates across scales, and tested for differences in grouped parameter estimates among species.

Results

We found consistent scale-dependent patterns across species, showing variable responses to development occurring at the smallest scale, little effect at intermediate scales, and stronger, mainly positive effects at the largest scales. Differences in grouped parameter estimates across scales showed strong evidence for scale domains in the response of songbirds to energy sector development.

Conclusions

We concluded that variable effects at the smallest scale represented individual habitat selection, while larger scale positive effects reflected expanding distributions of open habitat- and disturbance-associated species in areas of high oil and gas footprint. Our results show that single-scale analyses do not reflect population processes occurring at other scales. Future research on linking patterns at different scales is required to fully understand cumulative effects of land use change on wildlife populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10980-023-01779-8.

A multiscale analysis of the spatially heterogeneous relationships between non-point source pollution-related processes and their main drivers in Chaohu Lake watershed, China

A better understanding of the relationships between non-point source (NPS) pollution-related processes and their drivers will help to develop scientific watershed management measures. Although various studies have explored the drivers' impact on NPS pollution-related processes, quantitative knowledge of the properties within these relationships is still needed. This study uses the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to produce three related processes of NPS pollution, quick flow (QF), nitrogen export (NE), and sediment export (SE), in the upstream watershed of Chaohu Lake, China. The spatial distributions of QF, NE, and SE and their responses to multiple natural-socioeconomic drivers at nine spatial scales (1 km2, 10 km2, 20 km2, 30 km2, 50 km2, 75 km2, 100 km2, 200 km2, and town) were compared. The results showed that the spatial scale has little impact on the spatial distributions of NPS pollution-related processes. Across the nine scales, the socioeconomic drivers related to agricultural activities, area proportions of cultivated land (cultivated) and paddy field (paddy), have dominant impacts on NE, while the topographical drivers, the connectivity index (IC) and slope, have dominant impacts on both SE and QF. The magnitudes of single and paired natural-socioeconomic drivers' impacts on NPS pollution-related processes increase logarithmically or linearly with increasing spatial scale, but they tend to reach a stable threshold at a certain coarse scale. Our results emphasized the necessity and importance of embracing spatial scale effects in watershed water environmental management.

Land-use diversity can better predict urban spontaneous plant richness than impervious surface coverage at finer spatial scales

Urban spontaneous plants, that are not intentionally propagated by humans and do not belong to the remnants of the natural habitats, not only occur in green spaces but are also distributed in diverse microhabitats in impervious surface areas. Impervious surface coverage is commonly used in studies on spontaneous plant diversity patterns in human-dominated landscapes; however, the role of habitat diversity (i.e., land-use diversity) has been overlooked. Here, we surveyed spontaneous plant composition and land uses (12 types) in 321 0.25 ha sampling sites on the Chongming District islands, Shanghai, to determine the role of land-use diversity in explaining species richness. We examined the linear relationships between species richness and land-use diversity, and quantified the importance of impervious surface coverage and land-use diversity using the random forest (RF) method. All these analyses were conducted for spatial scales from 0.25 to 5 ha in 0.25 ha increments. We found an overall positive relationship between species richness and land-use diversity, and the RF model predicted approximately 50% of the species richness variation at the smallest spatial scale. However, the positive relationship weakened with spatial scale increase, and a rapid decline in explanatory power occurred for all predictor variables in the RF model. Besides impervious surface coverage, both the vegetated and non-vegetated land-use diversity contributed substantially to the prediction of species richness at finer spatial scales. The findings clarify how land-use diversity, both in green spaces and impervious surface areas, affect urban spontaneous plant richness and should be considered in urban biodiversity conservation strategies at the neighborhood scale.

Influence of landscape features on urban land surface temperature: Scale and neighborhood effects

Higher land surface temperature (LST) in cities than its surrounding areas presents a major sustainability challenge for cities. Adaptation and mitigation of the increased LST require in-depth understanding of the impacts of landscape features on LST. We studied the influences of different landscape features on LST in five large cities across China to investigate how the features of a specific urban landscape (endogenous features), and neighboring environments (exogenous features) impact its LST across a continuum of spatial scales. Surprisingly, results show that the influence of endogenous landscape features (E_endo) on LST can be described consistently across all cities as a nonlinear function of grain size (g_s) and neighbor size (n_s) (E_endo = βn_sg_s^-0.5, where β is a city-specific constant) while the influence of exogenous features (E_exo) depends only on neighbor size (n_s) (E_exo = γ-εn_s^0.5, where γ and ε are city-specific constants). In addition, a simple relationship describing the relative strength of endogenous and exogenous impacts of landscape features on LST was found (E_endo > E_exo if n_s > kg_s^2/5, where k is a city-specific parameter; otherwise, E_endo < E_exo). Overall, vegetation alleviates 40%-60% of the warming effect of built-up while surface wetness intensifies or reduces it depending on climate conditions. This study reveals a set of unifying quantitative relationships that effectively describes landscape impacts on LST across cities, grain and neighbor sizes, which can be instrumental towards the design of sustainable cities to deal with increasing temperature.

Direct effects of selection on aboveground biomass contrast with indirect structure-mediated effects of complementarity in a subtropical forest

Understanding the multiple biotic and abiotic controls of aboveground biomass (AGB) is important for projecting the consequences of global change and to effectively manage carbon storage. Although large-scale studies have identified the major environmental and biological controls of AGB, drivers of local-scale variation are less well known. Additionally, involvement of multiple causal paths and scale dependence in effect sizes potentially confounds comparisons among studies differing in methodology and sampling grain. We tested for scale dependence in evidence supporting selection, complementarity and environmental factors as the main determinants of AGB variation over a 50 ha study extent in subtropical China, modelling this at four sampling grains (0.01, 0.04, 0.25 and 1 ha). At each grain, we used piecewise structural equation models to quantify the direct and indirect effects of environmental (topographic and edaphic properties) and forest attributes (structure, diversity and functional traits) on AGB, while controlling for spatial autocorrelation. Direct scale-invariant effects on AGB were evident for structure and community-mean traits, supporting dominance of selection effects. However, diversity had strong indirect effects on AGB via forest structure, particularly at larger sampling grains (≥ 0.25 ha), while direct effects only emerged at the smallest grain size (0.01 ha). The direct and indirect effects of edaphic and topographic factors were also important for explaining both forest attributes and AGB across all scales. Although selection effects appeared to be more influential on ecosystem function, ignoring indirect causal pathways for diversity via structural attributes risks overlooking the importance of complementarity on ecosystem functioning, particularly as sampling grain increases.

Scale dependence of effective geochemical rates in weathering mine waste rock

Hydrogeochemical models for the prediction of drainage quality from full-scale mine waste-rock piles are often parameterized using data from small-scale laboratory or field experiments of short duration. Yet, many model parameters and processes (e.g., sulfide-oxidation rates) vary strongly with the spatiotemporal dimensions of the experiment: the "upscaling" of prediction models remains a critical challenge for mine-waste management worldwide. Here, we investigate scale dependence in laboratory and field experiments that spanned orders-of-magnitude in size (i.e. 2 kg to 100,000 kg) at the Antamina mine in Peru. Normalized drainage mass loading rates systematically decreased with increasing scale, irrespective of waste-rock type. A process-based reactive-transport model was used to simulate observed rates and reproduce the geochemical composition of drainage across scales. Long-term trends in drainage quality could be quantitatively reproduced when the model was parameterized with mostly scale- and experiment-specific measured bulk properties or literature values, leaving geochemical rate coefficients the sole calibrated model parameters. Analysis of these fitted parameters revealed that the scale dependence of geochemical rates was largely explained by reactive mineral surface area. This work demonstrates that practical drainage quality predictions for full-scale waste-rock piles can be established from readily available bulk parameters determined at multiple scales.

Do habitat preferences improve fitness? Context-specific adaptive habitat selection by a grassland songbird

Animals are predicted to prefer high-quality over low-quality habitats, but adaptive habitat selection is less straightforward than often assumed. Preferences may improve only specific fitness metrics at particular spatial scales, with variation across time or between sexes. Preferences sometimes even reduce fitness. We investigated the context specificity of adaptive habitat selection, studying dickcissels (Spiza americana)-a polygynous songbird-as a model. From 2014 to 2015, we measured male and female habitat preferences at two scales (territories and landscape patches) on 21 grassland patches in Ringgold County, Iowa, USA. We tested whether preferences improved four fitness metrics-polygyny, avoidance of brood parasitism by brown-headed cowbirds (Molothrus ater), fledgling productivity, and offspring condition. Both sexes preferred territories where offspring attained superior condition and patches where parasitism was infrequent. Females preferred patches where nests produced more fledglings, and in 2014, males on preferred (i.e., early-established) territories attracted more mates and produced more fledglings. However, males on non-preferred (i.e., late-established) territories were more successful in 2015. This inconsistency may have arisen because females were abundant and nest-predation rates were low in May-June 2014, allowing early-settling males to produce many young. In 2015, however, females were more abundant and nests more successful later in the breeding season. Our results show that habitat preferences do not uniformly improve fitness, and some benefits differ between sexes. Moreover, preference-fitness relationships only manifest at specific scales, and annual variation in population and predation dynamics can limit consistency. Detecting adaptive habitat selection thus requires multi-year measurements and careful consideration of relevant scales.

Phylogeny and ecological processes influence grass coexistence at different spatial scales within the steppe biome

Phylogenetic analyses are essential for disentangling how environmental filtering and competition determine species coexistence across spatial scales. Inner Mongolia steppe has strong environmental gradients, but how the phylogenetic relatedness of co-occurring species and phylogenetic signals of functional traits change across spatial scales remains unclear. We investigated the phylogenetic structure of grass assemblages along environmental gradients from regional to local scales, and measured functional traits within assemblages. We compared phylogenetic signals of plant traits between the same numbers of species randomly selected from the regional pool and species observed at the local scale, did phylogenetic principal component analysis to infer the main factors driving species coexistence, and examined the key plant trait-environment relationships across the phylogeny to reveal ecological adaptation mechanisms. Regionally, grass species were phylogenetically clustered with contrasting climate preferences. With decreasing spatial scales, species richness declined, changing from phylogenetically clustered to overdispersed, and phylogenetic signals of plant traits became weaker. At the local scale, grass assemblages were structured by soil water content and neighbor density, and the trait-environment relationships were less clear than those at the regional scale. This study demonstrated that at smaller scales, co-occurring grass species in the steppe tended to be more phylogenetically overdispersed, and that phylogenetic signals of plant functional traits became weaker with increasing abiotic and biotic interactions. Our findings contributed evidence for understanding species coexistence and maintenance at scales spanning regional to local communities in the East Asia steppe biome.

Hierarchical partitioning of fish diversity and scale-dependent environmental effects in tropical coastal ecosystems

The spatial structure of the fish diversity and site-scale and landscape-scale environmental effects were investigated across hierarchical levels in tropical coastal ecosystems. Total diversity (γ) was hierarchically partitioned into α and β components using both the additive and multiplicative methods. A model selection based on the AICc was applied to generalized linear mixed models relating diversity measures to environmental variables and including random effects for hierarchical levels and season. Short-term seasonal effects were negligible. Spatial effects were more relevant at the site level and negligible at the subregion level, due to the high spatial heterogeneity and the natural pooling of ecosystems, respectively. Site-scale environmental effects were more relevant at the subregion level, with eutrophic conditions (continental influence) favoring the species richness (α and γ) and higher absence of species (β^A) in oligotrophic conditions (marine influence). At the system level, the positive effect of the distance from the ocean on γ and higher β^A in oligotrophic conditions reinforced the positive continental influence on fish diversity. Environmental homogenization processes were most likely associated with the negative effect of the pasture cover on α at the system level, and γ and β^A at the site level. The negative effect of the forest cover on the later diversity measure evidenced its relevance to maintain richer but more similar assemblages, whereas the positive continental influence was most likely due to the loss of stenohaline marine species. This study evidenced that disentangling spatial, land use, and marine vs. continental effects on diversity components is critical to understand the primary determinants of the fish diversity in tropical coastal ecosystems.

Scale-specific drivers of kelp forest communities

Identifying spatial scales of variation in natural communities and the processes driving them is critical for obtaining a predictive understanding of biodiversity. In this study, we focused on diverse communities inhabiting productive kelp forests on shallow subtidal rocky reefs in southern California, USA. We combined long-term community surveys from 86 sites with detailed environmental data to determine what structures assemblages of fishes, invertebrates and algae at multiple spatial scales. We identified the spatial scales of variation in species composition using a hierarchical analysis based on eigenfunctions, and assessed how sea surface temperature (SST), water column chlorophyll, giant kelp biomass, wave exposure and potential propagule delivery strength contributed to community variation at each scale. Spatial effects occurring at multiple scales explained 60% of the variation in fish assemblages and 52% of the variation in the assemblages of invertebrates and algae. Most variation occurred over broad spatial scales (> 200 km) consistent with spatial heterogeneity in SST and potential propagule delivery strength, while the latter also explained community variation at medium scales (65-200 km). Small scale (1-65 km) community variation was substantial but not linked to any of the measured drivers. Conclusions were consistent for both reef fishes and benthic invertebrates and algae, despite sharp differences in their adult mobility. Our results demonstrate the scale dependence of environmental drivers on kelp forest communities, showing that most species were strongly sorted along oceanographic conditions over various spatial scales. Such spatial effects must be integrated into models assessing the response of marine ecosystems to climate change.

Mathematical Modeling of Avidity Distribution and Estimating General Binding Properties of Transcription Factors from Genome-Wide Binding Profiles

The shape of the experimental frequency distributions (EFD) of diverse molecular interaction events quantifying genome-wide binding is often skewed to the rare but abundant quantities. Such distributions are systematically deviated from standard power-law functions proposed by scale-free network models suggesting that more explanatory and predictive probabilistic model(s) are needed. Identification of the mechanism-based data-driven statistical distributions that provide an estimation and prediction of binding properties of transcription factors from genome-wide binding profiles is the goal of this analytical survey. Here, we review and develop an analytical framework for modeling, analysis, and prediction of transcription factor (TF) DNA binding properties detected at the genome scale. We introduce a mixture probabilistic model of binding avidity function that includes nonspecific and specific binding events. A method for decomposition of specific and nonspecific TF-DNA binding events is proposed. We show that the Kolmogorov-Waring (KW) probability function (PF), modeling the steady state TF binding-dissociation stochastic process, fits well with the EFD for diverse TF-DNA binding datasets. Furthermore, this distribution predicts total number of TF-DNA binding sites (BSs), estimating specificity and sensitivity as well as other basic statistical features of DNA-TF binding when the experimental datasets are noise-rich and essentially incomplete. The KW distribution fits equally well to TF-DNA binding activity for different TFs including ERE, CREB, STAT1, Nanog, and Oct4. Our analysis reveals that the KW distribution and its generalized form provides the family of power-law-like distributions given in terms of hypergeometric series functions, including standard and generalized Pareto and Waring distributions, providing flexible and common skewed forms of the transcription factor binding site (TFBS) avidity distribution function. We suggest that the skewed binding events may be due to a wide range of evolutionary processes of creating weak avidity TFBS associated with random mutations, while the rare high-avidity binding sites (i.e., high-avidity evolutionarily conserved canonical e-boxes) rarely occurred. These, however, may be positively selected in microevolution.