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Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

El objetivo del estudio fue determinar la capacidad de almacenamiento de carbono en especies de pastos naturales altoandinos, entre enero de 2012 y marzo de 2013. Se definieron dos zonas de muestreo en los distritos de Huasicancha y Chicche de la provincia de Huancayo, Junín. La primera de las zonas se localizó en el paraje Pumahuasi (18L 466456E 8628580N) y la segunda en Vista alegre (18L 464886E 8642964N), entre 3 845 y 3 870 metros de altitud. Se recolectaron 10 plantas completas al azar, entre abril y mayo, considerando el momento de máximo crecimiento de las plantas. Las muestras fueron lavadas y secadas al ambiente durante 15 días, completándose el secado en una estufa a 60 °C, durante 48 horas. La determinación del % de materia seca se realizó por comparación entre los pesos iniciales y finales de las muestras procesadas. Mientras que la determinación del % de carbono se realizó a través del método de Walkley-Black. Los resultados de la correlación de peso entre biomasa aérea y biomasa radicular fueron altamente significativos r = 0,9856** y b = 3,4507. El porcentaje del peso de la raíz respecto al de la biomasa aérea osciló entre 27,93% y 30,20%, respectivamente. El contenido de carbono expresado como porcentaje varió según la parte de la planta y el lugar de procedencia.

Natural ecosystems benefit human communities by providing ecosystem services such as water purification and crop pollination. Mapping ecosystem service values has become popular, but most are static snapshots of average value. Estimating instead the economic impacts of specific ecosystem changes can better inform typical resource decisions. Here we develop an approach to mapping marginal values, those resulting from the next unit of ecosystem change, across landscapes. We demonstrate the approach with a recent model of crop pollination services in Costa Rica, simulating deforestation events to predict resulting marginal changes in pollination services to coffee farms. We find that marginal losses from deforestation vary from zero to US$700/ha across the landscape. Financial risks for farmers from these losses and marginal benefits of forest restoration show similar spatial variation. Marginal values are concentrated in relatively few forest parcels not identified using average value. These parcels lack substitutes: nearby forest parcels that can supply services in the event of loss. Indeed, the marginal value of forest parcels declines exponentially with the density of surrounding forest cover. The approach we develop is applicable to any ecosystem service. Combined with information on costs, it can help target conservation or restoration efforts to optimize benefits to people and biodiversity.

The lignocellulosic C4 perennial crop miscanthus and, more particularly, one of its species, Miscanthus × giganteus, are especially interesting for bioenergy production because they combine high biomass production with a low environmental impact. However, few varieties are available, which is risky due to disease susceptibility. Gathering worldwide references, this review shows a high genotypic and environmental variability for traits of interest related to miscanthus biomass production and composition, which may be useful in breeding programs for enhancing the availability of suitable clones for bioenergy production. The M. × giganteus species and certain clones in the Miscanthus sinensis species seem particularly interesting due to high biomass production per hectare. Although the industrial requirements for biomass composition have not been fully defined for the different bioenergy conversion processes, the M. × giganteus and Miscanthus sacchariflorus species, which show high lignin contents, appear more suitable for thermochemical conversion processes. In contrast, the M. sinensis species and certain M. × giganteus clones with low lignin contents were interesting for biochemical conversion processes. The M. sacchariflorus species is also interesting as a progenitor for breeding programs, due to its low ash content, which is suitable for the different bioenergy conversion processes. Moreover, mature miscanthus crops harvested in winter seem preferred by industry to enhance efficiency and reduce the expense of the processes. This investigation on miscanthus can be extrapolated to other monocotyledons and perennial crops, which may be proposed as feedstocks in addition to miscanthus.

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Recurrent cycles of climatic change during the Quaternary period have dramatically affected the population genetic structure of many species. We reconstruct the recent demographic history of the coyote ( Canis latrans ) through the use of Bayesian techniques to examine the effects of Late Quaternary climatic perturbations on the genetic structure of a highly mobile generalist species. Our analysis reveals a lack of phylogeographic structure throughout the range but past population size changes correlated with climatic changes. We conclude that even generalist carnivorous species are very susceptible to environmental changes associated with climatic perturbations. This effect may be enhanced in coyotes by interspecific competition with larger carnivores.

Additional file 1: Table S1. Base temperature, upper temperature limit and crop water needs used to calculate the season temperature and water balance index. Fig. S1 Comparisons between the observed and modeled yields. Fig. S2 Same as Fig. 4 but for the M3-GSOC dataset combination. Fig. S3 Same as Fig. 4 but for the SPAM-HWSD dataset combination. Fig. S4 Same as Fig. 4 but for the SPAM-GSOC dataset combination. Fig. S5 The potentials of additional SOC stock and yield improvement (the M3-HWSD dataset combination). Fig. S6 Same as Fig. S5 but for the M3-GSOC dataset combination. Fig. S7 Same as Fig. S5 but for the SPAM-HWSD dataset combination. Fig. S8 Same as Fig. S5 but for the SPAM-GSOC dataset combination. Fig. S9 Current N application rate and estimated inorganic N input savings. Fig. S10 Estimated regional crop production increase and inorganic N input savings in relation to estimated additional SOC build-up.