Bivalve habitat restoration is growing in geographic extent and scale globally. While addressing the wide‐scale loss of these biogenic habitats is still a key motivation behind restoration efforts, stakeholders and funders are increasingly drawn to shellfish restoration for the many ecosystem services these habitats provide. There is clear evidence for the provision of ecosystem services from species targeted for restoration in the USA, in particular Crassostrea virginica. Ecosystem services, however, remain largely unquantified or even undescribed for the majority of other species targeted for restoration. A structured review of the literature was undertaken and supplemented by expert knowledge to identify which ecosystem services are documented in the following other bivalve species targeted for restoration: Ostrea edulis, Ostrea angasi, Crassostrea rhizophorae, Perna canaliculus, Modiolus modiolus, Mytilus edulis, Mytilus platensis, Crassostrea gigas, Ostrea denselamellosa, Crassostrea ariakensis, and Crassostrea sikamea. Key knowledge gaps in quantifying ecosystem services and the ecosystem engineering properties of habitat‐building bivalves contributing to the provision of ecosystem services were identified. Ecosystem services with the potential to be widely applicable across bivalve habitat‐building species were identified. Though there is evidence that many of the ecosystem engineering properties that underpin the provision of ecosystem services are universal, the degree to which services are provided will vary between locations and species. Species‐specific, in situ, studies are needed in order to avoid the inappropriate transfer of the ecosystem service delivery between locations, and to further build support and understanding for these emerging targets of restoration.

Conventional methods for soil element content determination based on laboratory analyses are costly and timeconsuming. A soil reflectance spectrum is an alternative approach for soil element content estimation with the advantage of being rapid, non-destructive, and cost effective. Visible/near-infrared spectra (350 nm to 2500 nm) were measured from 105 soil samples originating from 30 apple orchards on the Jiaodong peninsula. The Savitzky-Golay (FD-SG) technique for spectral data was implemented to reduce the signal noise. Logarithm of the reciprocal of reflectance (logR) and the first derivative transformation (DR) were used to accentuate the features and to prepare the data for use in quantitative estimation models. The SI (sum index), DI (difference index), PI (product index), RI (ratio index), and NDI (normalized difference index) were calculated to extract sensitive waveband combinations that are significantly related to soil element contents. Soil element contents were retrieved based on sensitive waveband combinations by multiple linear stepwise regression (MLSR) and partial least square (PLSR) models. The results showed that DR performed better than logR −1 in eliminating the interfering factors of soil particle size and spectral noise. The MLSR and PLSR calibration models based on PI performed better than those based on SI or DI did. The MLSR performed better than PLSR in estimating soil elemental content. The contents of total nitrogen (TN), arsenic (As), and mercury (Hg) could be estimated well using MLSR and PLSR calibration models developed with PI. The MLSR calibration model developed with PI performed well in estimating available potassium (A-K) content. However, the contents of available phosphorus (A-P), ammonium nitrogen (NH 4 + -N), nitric nitrogen (NO 3 --N), and soil organic matter (SOM) could not be estimated using MLSR or PLSR calibration models. These outcomes will provide the theoretical basis and technical support for estimations of soil element content using visible/near-infrared spectra. Although they were shown to be useful in apple orchards of the Jiaodong peninsula, these models and methods should be further tested in soil samples from other regions and countries to prove their validity.

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Renewable high-density spiro-fuels are synthesized from lignocellulose-derived cyclic ketones for the first time, which show higher density, higher neat heat of combustion and lower freezing point compared with other biofuels synthesized from the same feedstock, and thus represent a new type of renewable high-density fuel attractive for practical applications.

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