Local Spatial Scales Research Articles

Comparison of Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS).

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear. Our objective is to compare particulate matter with aerodynamic diameter () concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses. We assigned annual exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region. With a few exceptions, relative agreement of approach-specific exposure estimates was high for concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for . There was no evidence of large differences in health effects associations with among estimation approaches in analyses adjusted for climate region. Different estimation approaches produced similar spatial patterns of concentrations across the contiguous US and in areas with dense monitoring data, and effects associations were similar among estimation approaches. estimates and effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Divergent selection in a Mediterranean pine on local spatial scales

Abstract The effects of selection on an organism's genome are hard to detect on small spatial scales, as gene flow can swamp signatures of local adaptation. Therefore, most genome scans to detect signatures of environmental selection are performed on large spatial scales; however, divergent selection on the local scale (e.g. between contrasting soil conditions) has also been demonstrated, in particular for herbaceous plants. Here, we hypothesised that in topographically complex landscapes, microenvironment variability is strong enough to leave a selective footprint in the genomes of long‐lived organisms. To test this, we investigated paired south‐ versus north‐facing Pinus pinaster stands on the local scale, with trees growing in close vicinity (≤820 m distance between paired south‐ and north‐facing stands), in a Mediterranean mountain area. While trees on north‐facing slopes experience less radiation, trees on south‐facing slopes suffer from especially harsh conditions, particularly during the dry summer season. Two outlier analyses consistently revealed five putatively adaptive loci (out of 4034), in candidate genes two of which encoded non‐synonymous substitutions. Additionally, one locus showed consistent allele frequency differences in all three stand pairs indicating divergent selection despite high gene flow on the local scale. Permutation tests demonstrated that our findings were robust. Functional annotation of these candidate genes revealed biological functions related to abiotic stress response, such as water availability, in other plant species. Synthesis. Our study highlights how divergent selection in heterogeneous microenvironments shapes and maintains the functional genetic variation within populations of long‐lived forest tree species, being the first to focus on adaptive genetic divergence between south‐ and north‐facing slopes within continuous forest stands. This is especially relevant in the current context of climate change, as this variation is at the base of plant population responses to future climate.

Biodiversity survey and estimation for line-transect sampling.

Conducting biodiversity surveys using a fully randomised design can be difficult due to budgetary constraints (e.g., the cost of labour), site accessibility, and other constraints. To this end, ecologists usually select representative line transects or quadrats from a studied area to collect individuals of a given species and use this information to estimate the levels of biodiversity over an entire region. However, commonly used biodiversity estimators such as Rao's quadratic diversity index (and especially the Gini-Simpson index) were developed based on the assumption of independent sampling of individuals. Therefore, their performance can be compromised or even misleading when applied to species abundance datasets that are collected from non-independent sampling. In this study, we utilise a Markov chain model and derive an associated parameter estimator to account for non-independence in sequential sampling. Empirical tests on two forest plots in tropical (Barro Colorado, Island of Panama) and subtropical (Heishiding Nature Reserve of Guangdong, China) regions and the continental-scale spatial distribution of Acacia species in Australia showed that our estimators performed reasonably well. The estimated parameter measuring the degree of non-independence of subsequent sampling showed that a non-independent effect is very likely to occur when using line transects to sample organisms in subtropical regions at both local and regional spatial scales. In summary, based on a first-order Markov sampling model and using Rao's quadratic diversity index as an example, our study provides an improvement in diversity estimation while simultaneously accounting for the non-independence of sampling in field biodiversity surveys. Our study presents one possible solution for addressing the non-independent sampling of individuals in biodiversity surveys.

Ecological variation in invasive brown trout (Salmo trutta) within a remote coastal river catchment in northern Patagonia complicates estimates of invasion impact.

Salmonids were first introduced into the Chilean fresh waters in the 1880s, and c. 140 years later, they are ubiquitous across Chilean rivers, especially in the southern pristine fresh waters. This study examined the brown trout (Salmo trutta) and native taxa ecology in two adjacent but contrasting rivers of Chilean Patagonia. During spring 2016 and spring-fall 2017 we examined the variation in benthic macroinvertebrate and fish community composition and characterized fish size structure, stomach contents, and stable isotopes (δ13 C and δ15 N) to understand population structure, fish diet, and trophic interactions between S. trutta and native taxa. The native Galaxias maculatus (puye) dominated the fish community (74% of abundance). S. trutta was less abundant (16% of survey catch) but dominated the fish community (over 53%) in terms of biomass. S. trutta showed distinct diets (stomach content analysis) in the two rivers, and individuals from the larger river were notably more piscivorous, consuming native fish with a relatively small body size (<100-mm total length). Native fishes were isotopically distinct from S. trutta, which showed a wider isotopic niche in the smaller river, indicating that their trophic role was more variable than in the larger river (piscivorous). This study provides data from the unstudied pristine coastal rivers in Patagonia and reveals that interactions between native and introduced species can vary at very local spatial scales.

Selective logging destabilizes the functioning and composition of forest ecosystems at multiple spatial scales

Selective logging is one of the most prevalent land uses of forests worldwide, affecting biodiversity and ecosystem functioning. However, the effect of selective logging on the dual nature of temporal stability, and the scale dependence of this effect, remain to be elucidated. By conducting several decade‐long experiments in temperate forest ecosystems, we tested the effects of selective logging on aggregate and compositional stability at multiple spatial scales. As expected, forest ecosystem stability at larger spatial scales was enhanced both by the stability of local scales (i.e. α stability) and asynchronous dynamics among local communities (i.e. spatial asynchrony). We found that the negative effects of selective logging on both facets of forest stability propagated from local to larger spatial scales due to reduced α stability and the biological insurance effects of α diversity. However, both spatial aggregate and compositional asynchrony were not affected by selective logging. Interestingly, despite the selective logging, α diversity still provided biological insurance effects for maintaining aggregate and compositional stability. Our results imply that selective logging may destabilize the aggregate ecosystem functioning and species composition of forest ecosystems at local and larger spatial scales. To our knowledge, this study provides the first evidence of the scale dependence of aggregate and compositional stability of forest ecosystems in response to selective logging. Our findings suggest that forest management should avoid excessive selective logging and strive to protect forest diversity to safeguard the sustainability of the functioning and composition of natural forest ecosystems at multiple spatial scales.Keywords: aggregate stability, compositional stability, diversity, ecosystem functioning, spatial asynchrony, stability

The usefulness of food web models in the ecosystem services framework: Quantifying, mapping, and linking services supply

Coastal ecosystems provide a wide range of valuable ecosystem services (ES) for human wellbeing. Such services depend on the functioning and structure of ecosystems. Unfortunately, these ecosystems are threatened by humans, directly impairing their ability to provide these services. In order to predict such changes, we used a food web model to forecast potential spatial changes in ES supply in the Seine Bay (English Channel), due to climate change effects (CC) and the setup of an offshore wind farm (OWF). Three ES were studied, food production from fishing, top predator production for cultural purposes and the potential resistance of the ecosystem inferred from its organization. The ability of the Seine Bay ecosystem to produce food appears to be negatively impacted by the effect of climate change. Because of the important economic role of fishing in Normandy, such changes could percolate on the entire social and economic system of the bay. The Courseulles-sur-Mer offshore wind farm appears to increase the supply of services and limit the impact of climate change at the local spatial scale, which could give stakeholders insights into mitigating the effects of climate change. Such ecosystem approach enables for a more integrative view of ES supply, through the characterization of the entire system functioning.

Global Potential Distribution of Sarcophaga dux and Sarcophaga haemorrhoidalis under Climate Change

Climate change has a direct impact on biodiversity, affecting ecosystems and altering their balance. Many taxa, including insects, are likely to be affected by climate change in terms of geographic distribution. Sarcophagid flies, such as Sarcophaga dux and Sarcophaga haemorrhoidalis, are important flies because of their apparent ecological, forensic, and medical significance. Global habitat suitability varies as a result of climate change. In wildlife management, models that predict species’ spatial distribution are being used more and more, which emphasizes the need for reliable methods to evaluate their accuracy. Consequently, the statistical robustness of maximum entropy was implemented in Maxent to model the current and future global distribution of both flies, involving occurrence data of 155 and 87 points for S. dux and S. haemorrhoidalis, respectively. Based on the Pearson correlation and Jackknife test, five bioclimatic variables were used for current and future predictive models. For future models, two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050 and 2070 were applied. Both statistical parameters, AUC and TSS, were used to assess the resulting models with values equal to 0.80 (±0.01) and 0.9, respectively, for S. dux and equal to 0.86 (±0.01) and 0.92 for S. haemorrhoidalis. The resulting models for S. dux showed high and very high suitability in Europe, Tropical Africa, India, Canada, the United States from Alaska to Florida, Brazil, and Australia. In the case of S. haemorrhoidalis Europe and North and South America displayed low to medium suitability, but North Africa, including Egypt; Tropical Africa; Asia, including Saudi Arabia, India, and China; and Australia showed increased suitability. Decision-makers are put in conflict with their duties to avert destruction in the economic, medical, and ecological sectors by such anticipated models, and use these predictive models as a cornerstone for building a control strategy for such forensically important flies at local spatial scales.

An integrative salt marsh conceptual framework for global comparisons

AbstractSalt marshes occur globally across climatic and coastal settings, providing key linkages between terrestrial and marine ecosystems. However, salt marsh science lacks a unifying conceptual framework; consequently, historically well‐studied locations have been used as normative benchmarks. To allow for more effective comparisons across the diversity of salt marshes, we developed an integrative salt marsh conceptual framework. We review ecosystem‐relevant drivers from global to local spatial scales, integrate these multi‐scale settings into a framework, and provide guidance on applying the framework using specific variables on 11 global examples. Overall, this framework allows for appropriate comparison of study sites by accounting for global, coastal, inter‐, and intra‐system spatial settings unique to each salt marsh. We anticipate that incorporating this framework into salt marsh science will provide a mechanism to critically evaluate research questions and a foundation for effective quantitative studies that deepen our understanding of salt marsh function across spatial scales.

Tectonic division of the pre-cenozoic basement of the South China Sea in earth monitoring

The past five decades have witnessed satellite remote sensing become one of most efficient tools for surveying the Earth at local, regional, and global spatial scales. The nature of the basement tectonic unit is closely related to the origin and evolution of the basin. However, such research carried out in the South China Sea (SCS) is relatively limited. In this research, by analyzing the fault structure, gravity and magnetic anomaly characteristics of the SCS, the ophiolite belts, spreading edge zones and the regional deep faults are considered as the boundaries of the tectonic units, and the SCS area can be divided into seven basement tectonic units: Cathaysia massif, Indochina massif, the SCS basin, Philippine island arc belt, Nansha massif, Sunda massif and Sibu accretionary prism. These tectonic units have different tectonic sequences, sedimentary formations and metamorphic types, in addition, they affect the Cenozoic basins overlying them to form different structural types. The research results will help to further understand the pre-Cenozoic basement structure and the tectonic evolution history of the SCS.

Remote Sensing‐Based Forest Modeling Reveals Positive Effects of Functional Diversity on Productivity at Local Spatial Scale

AbstractForest biodiversity is critical for many ecosystem functions and services. Yet, it remains uncertain how plant functional diversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We integrated remote sensing and terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple spatial scales for a heterogeneous forest ecosystem in Switzerland. We initialized forest structure and composition in the ecosystem demography model (ED2) through a combination of ground‐based surveys, airborne laser scanning and imaging spectroscopy for forest patches at 10 × 10‐m spatial grain. We derived morphological and physiological forest traits and productivity from model simulations at patch‐level to relate morphological and physiological aspects of functional diversity to the average productivity from 2006 to 2015 at 20 × 20 to 100 × 100‐m spatial extent. We did this for model simulations under observed and experimental conditions (mono‐soils, mono‐cultures and mono‐structures). Functional diversity increased productivity significantly (p &lt; 0.001) across all simulations at 20 × 20 to 30 × 30 m scale, but at 100 × 100‐m scale positive relationships disappeared under homogeneous soil conditions potentially due to the low beta diversity of this forest and the saturation of functional richness represented in the model. Although local functional diversity was an important driver of productivity, environmental context underpinned the variation of productivity (and functional diversity) at larger spatial scales. In this study, we could show that the integration of remotely sensed information on forest composition and structure into terrestrial biosphere models is important to fill knowledge gaps about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.

Local habitat type influences bumble bee pathogen loads and bee species distributions.

Bumble bees (Hymenoptera: Apidae, Bombus Latreille) perform important ecological services in both managed and natural ecosystems. Anthropogenically induced change has altered floral resources, climate, and insecticide exposure, factors that impact health and disease levels in these bees. Habitat management presents a solution for improving bee health and biodiversity, but this requires better understanding of how different pathogens and bee species respond to habitat conditions. We take advantage of the washboard of repeated ridges (forested) and valleys (mostly developed) in central Pennsylvania to examine whether local variation in habitat type and other landscape factors influence bumble bee community composition and levels of 4 leading pathogens in the common eastern bumble bee, Bombus impatiens Cresson. Loads of viruses (DWV and BQCV) were found to be lowest in forest habitats, whereas loads of a gut parasite, Crithidia bombi, were highest in forests. Ridgetop forests hosted the most diverse bumble bee communities, including several habitat specialists. B. impatiens was most abundant in valleys, and showed higher incidence in areas of greater disturbance, including more developed, unforested, and lower floral resource sites, a pattern which mirrors its success in the face of anthropogenic change. Additionally, DNA barcoding revealed that B. sandersoni is much more common than is apparent from databases. Our results provide evidence that habitat type can play a large role in pathogen load dynamics, but in ways that differ by pathogen type, and point to a need for consideration of habitat at both macro-ecological and local spatial scales.

Phylogenetic and functional trait-based community assembly within Pacific Cyrtandra (Gesneriaceae): Evidence for clustering at multiple spatial scales.

Tropical rainforest communities are often characterized by a small number of species-rich genera that contribute disproportionately to the alpha diversity in these habitats. In the Pacific Basin, there are nearly 200 species of Cyrtandra, most of which are white-flowered woody shrubs that are single-island endemics. Within these island communities, multiple Cyrtandra species are commonly observed to occur sympatrically in wet forest understories, forming swarms of what appear to be ecologically similar taxa. The aim of this study was to determine whether species of these plants are randomly assembled with respect to phylogenetic relatedness and traits that are ecologically relevant. I examined assembly patterns across three Pacific archipelagoes using a combination of 10 functional traits and a well-resolved phylogeny comprising 34 species of Cyrtandra. Coexisting species were found to be more closely related and more phenotypically similar than would be expected by chance. This pattern was observed at both regional (island) and local (site) spatial scales. The retention of phylogenetic signal in floral traits and the strong influence of these traits on the observed degree of phylogenetic clustering may indicate that generalist insect pollinators act as a biotic filter on oceanic islands, driving selection for similar floral morphology among closely related species of Pacific Cyrtandra. Phylogenetic signal was also detected in leaf size, which contributed to niche clustering at both spatial scales. Coupled with a propensity for long-distance dispersal, and the restricted distribution of Cyrtandra to rainforest understories, this finding suggests that environmental filtering along this trait axis may be more important than dispersal limitation in determining species assemblages. This study supports the theory that plant species are not randomly assembled, and instead, that niche-based processes structure biodiversity at regional and local spatial scales in diverse congeneric species assemblages.

Variety among physical landscape features in natural grassland-plantation forestry mosaics promotes diverse butterfly assemblages

Various physical landscape features across local and regional spatial scales contribute to heterogeneity. This means that ostensible maintenance of physical landscape features as representatives of heterogeneity (i.e., a meso-filter conservation approach) can contribute to conservation of insect assemblages. Using butterflies as model organisms, we investigate the conservation value of specific physical landscape feature types (rocky areas, wetlands, and gullies) relative to open grasslands, across 32 study sites in a natural grassland-plantation forestry mosaic in South Africa. We found that all considered landscape feature types had equal species richness and diversity levels compared to open grasslands. Yet, β-diversity (mostly accounted for by species turnover) was overall high between rocky areas, wetlands, and open grasslands, resulting from associations between some butterfly species and certain landscape feature types. Gullies were occupied by nested assemblages of rocky areas and open grasslands, suggesting that gullies have lower conservation importance, and should be targets for restoration. Topography and butterfly food plant availability further influenced β-diversity patterns and should also be considered when adopting a meso-filter conservation approach. β-diversity was also high among similar feature types, suggesting that a variety of landscape features that encapsulates environmental variation at a landscape scale can best conserve regional butterfly assemblages. While our focus was on butterflies, we anticipate these findings to be relevant to other insect groups, given that butterflies have successfully been used as conservation surrogates for other terrestrial insect taxa (e.g., grasshoppers) in the same area.

Biogeographic patterns of biosynthetic potential and specialized metabolites in marine sediments

While the field of microbial biogeography has largely focused on the contributions of abiotic factors to community patterns, the potential influence of biotic interactions in structuring microbial communities, such as those mediated by the production of specialized metabolites, remains largely unknown. Here, we examined the relationship between microbial community structure and specialized metabolism at local spatial scales in marine sediment samples collected from the Long-Term Ecological Research (LTER) site in Moorea, French Polynesia. By employing a multi-omic approach to characterize the taxonomic, functional, and specialized metabolite composition within sediment communities, we find that biogeographic patterns were driven by local scale processes (e.g., biotic interactions) and largely independent of dispersal limitation. Specifically, we observed high variation in biosynthetic potential (based on Bray-Curtis dissimilarity) between samples, even within 1 m2 plots, that reflected uncharacterized chemical space associated with site-specific metabolomes. Ultimately, connecting biosynthetic potential to community metabolomes facilitated the in situ detection of natural products and revealed new insights into the complex metabolic dynamics associated with sediment microbial communities. Our study demonstrates the potential to integrate biosynthetic genes and metabolite production into assessments of microbial community dynamics.

Large‐scale impacts of selective logging on canopy tree beta‐diversity in the Brazilian Amazon

Abstract Selective logging is one of the largest drivers of tropical forest degradation. While logged forests often retain high alpha‐diversity of tropical trees at local spatial scales, understanding how selective logging impacts tree beta‐diversity and community composition across far larger spatial scales remains a key unresolved question. We leverage large datasets of more than 155,000 adult trees over 35 cm DBH covering 3100 ha of Amazonian rainforest to inform simulations of selective logging harvests across a gradient of logging intensity (0–40 m3 ha−1). These simulations incorporate real world price data, account for all forest damage throughout the harvest process and assume preferential harvest of the most valuable stems. We use the simulations to assess how selective logging affects canopy tree beta‐diversity and composition across large spatial scales, whether nestedness or turnover of species best explains variation in communities across space, and how the spatial scale of sampling influences observed beta‐diversity effects. Selective logging had minimal impacts on beta‐diversity across the canopy tree community, but caused substantial subtractive heterogenization in community composition for larger trees, in particular very large trees over 110 cm DBH. Turnover is the dominant component of tree beta‐diversity in unlogged and logged forests. Increasing the spatial grain of sampling reduced the observed importance of logging in explaining patterns of beta‐diversity in very large tree communities. Synthesis and applications. Minimal impacts on tree beta‐diversity across large spatial scales points towards the retention of substantial conservation value in logged tropical forests. Strong subtractive heterogenization in very large trees indicates the breakdown of broad scale patterns of composition with potential negative consequences for recruitment processes, fauna reliant upon emergent trees, and other ecosystem functions and services. Avoiding large‐scale erosion of very large tree community composition in the Amazon requires stronger conservation policies, including enforced retention or maximum cutting diameters.

Local perceptions do not follow rainfall trends: A case study in traditional Marajo island communities (eastern para state, BR)

The great current challenge for the conservation and use of natural resources refers to global climate change, because of its impacts felt in different intensities at global, regional, and local spatial scales. Within the system of environmental protection areas in Brazil, the extractive reserves ensure the sustainable use of natural resources by traditional populations, thus maintaining the cultural and biological aspects of a region. Such populations, being in close management of the surrounding environments, tend to perceive changes in ecological processes that many need for their livelihoods. The use of this perception of local populations in conjunction with academic research evidence has a high potential to allow a whole and systemic view of possible changes in natural phenomena. This study developed an integrated analysis of scientific evidence and local perceptions to understand the variation of precipitation in a community inserted in an extractive reserve in the eastern Amazon. We used 30 years of precipitation data from the Brazilian National Institute of Meteorology - INMET, the Southern Oscillation Index - SOI, and the Atlantic Meridional Mode Index - AMM. Furthermore, we applied a form to measure the population's perception of possible changes in rainfall cycles in the region. The meteorological data indicate that the region of the community has been presenting a rainfall reduction; however, people in the community do not perceive this trend. Although it is public knowledge that the global climate is undergoing changes, a fact noted after the integrating analysis of scientific evidence with local knowledge in Resexmar Soure is that the perceptions of traditional populations often focus on smaller temporal and spatial scale visions.

Riverine flood potential assessment using metaheuristic hybrid machine learning algorithms

AbstractThis study presents the performance of stand‐alone and novel hybrid models combining the feed‐forward neural network (FFNN) and extreme gradient boosting (XGB) with the genetic algorithm (GA) optimization to determine the riverine flood potential at a local spatial scale, which is represented by the Gidra river basin, Slovakia. Eleven flood factors and a robust flood inventory database, consisting of 10,000 flood and non‐flood locations, were used. Using the FFNN, XGB, GA‐FFNN and GA‐XGB models, 16.5%, 11.0%, 17.1%, and 12.3% of the studied basin, respectively, is characterized with high to very high riverine flood potential. The applied models resulted in very high accuracy, that is, AUC = 0.93 in case of the FFNN stand‐alone model and AUC = 0.96 in case of the XGB stand‐alone model. The GA algorithm was able to raise the value of AUC for the hybrid GA‐FFNN and GA‐XGB models to 0.94 and 0.97, respectively. The results of this study can be useful, especially, for the identification of the areas with the highest potential for riverine floods within the next updating of the Preliminary Flood Risk Assessment, which is being carried out based on the EU Floods Directive.

Epidemic spread on patch networks with community structure

Predicting and preparing for the trajectory of disease epidemics relies on a knowledge of environmental and socioeconomic factors that affect transmission rates on local and global spatial scales. This article discusses the simulation of epidemic outbreaks on human metapopulation networks with community structure, such as cities within national boundaries, for which infection rates vary both within and between communities. We demonstrate mathematically, through next-generation matrices, that the structures of these communities, setting aside all other considerations such as disease virulence and human decision-making, have a profound effect on the reproduction rate of the disease throughout the network. In high modularity networks, with high levels of separation between neighboring communities, disease epidemics tend to spread rapidly in high-risk communities and very slowly in others, whereas in low modularity networks, the epidemic spreads throughout the entire network as a steady pace, with little regard for variations in infection rate. The correlation between network modularity and effective reproduction number is stronger in population with high rates of human movement. This implies that the community structure, human diffusion rate, and disease reproduction number are all intertwined, and the relationships between them can be affected by mitigation strategies such as restricting movement between and within high-risk communities. We then test through numerical simulation the effectiveness of movement restriction and vaccination strategies in reducing the peak prevalence and spread area of outbreaks. Our results show that the effectiveness of these strategies depends on the structure of the network and the properties of the disease. For example, vaccination strategies are most effective in networks with high rates of diffusion, whereas movement restriction strategies are most effective in networks with high modularity and high infection rates. Finally, we offer guidance to epidemic modelers as to the ideal spatial resolution to balance accuracy and data collection costs.

The DiSCPersal model: A simple model for the small-scale atmospheric transport of spheroidal carbonaceous particles (SCPs)

Spheroidal carbonaceous particles (SCPs) are atmospherically mobile by-products of anthropogenic, high-temperature fossil fuel combustion. Since they are preserved in many geologic archives across the globe, SCPs have been identified as a potential marker for the onset of the Anthropocene. Our ability to reliably model the atmospheric dispersal of SCPs remains limited to coarse spatial scales (i.e., 102–103 km). We address this gap by developing the DiSCPersal model, a multi-iterative and kinematics-based model for dispersal of SCPs at local spatial scales (i.e., 10°–102 km). Although simple and limited by available measurements of SCPs, the model is nonetheless corroborated by empirical data of the spatial distribution of SCPs from Osaka, Japan. We find that particle diameter and injection height are the primary controls of dispersal distance, whereas particle density is of secondary importance. Further, stark differences in the modelled dispersal distances of SCPs between non-point vs. smokestack sources could explain the ambiguity of dispersal distances and the relative magnitude of long-range vs. localized sourcing of SCPs reported in the literature. This research underscores the need to incorporate understanding of the localized dispersal patterns of SCPs when interpreting their preservation in geologic archives. By extension, our findings have implications for the reliability of SCPs as a globally synchronous marker for the onset of the Anthropocene.

How has bird biodiversity changed over time? A review across spatio-temporal scales

Empirical quantification of biodiversity changes remains a challenge even in well surveyed groups such as birds. This may be because the change depends on spatio-temporal scales, specifically on spatial grain (i.e. average unit of area of the sampling or the analysis), geographic extent (i.e. size of the area of interest), temporal grain (i.e. average unit of duration of the sampling or the analysis), and temporal extent (i.e. length of the time series). Further, different metrics of biodiversity may exhibit different trends. Here we review the literature assessing the temporal trends of avian biodiversity from ca. 1900 AD to present, focusing on studies summarising trends across many locations within a larger region (i.e. spatially replicated). From each study we extracted direction of average trend (increase, decrease, stable), spatial and temporal grains and extents at which the trends have been assessed, metrics of biodiversity, and location. We then discuss the trends as a function of the spatio-temporal grains and extents they are defined at. We found 59 trends of 12 metrics, where each trend is an average of trends from multiple sites (spatial replicates). There was a tendency of biodiversity metrics to increase at local and regional spatial scales, and to decrease globally. We thus confirmed that biodiversity dynamics can have opposite trends at different spatial scales. Concerning temporal grain, it was poorly documented across the studies, with inconsistent and/or confusing definitions. We suggest a common framework to better understand the link between temporal scales and biodiversity dynamics. We have also identified underrepresented regions (those outside North America and Europe), periods (those before the 70′s), and biodiversity metrics that need further attention. We highlight the importance of considering both spatial and temporal scaling jointly in any assessment of biodiversity change, and provide guidelines for specifying spatio-temporal features (i.e. grain, lag and extent) effectively both in birds, and in other taxa.