Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management.

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

The enhancement of flowery-like aroma in green tea under optimized processing conditions by sensory-directed flavor analysis.

Flowery-like aroma are positive contributors to green tea. Here, the optimal processing conditions for green tea with flowery-like aroma were designed using spreading time, fixation time and drying temperature as three factors designed by response surface methodology (RSM), and the response value of aroma sensory evaluation score. The volatiles in batches of tea samples were analyzed by GC-MS. The optimal quality was obtained with a flowery-like aroma by RSM under a spreading time of 8.97 h, fixation time of 162.3 s, and drying temperature of 103.32 °C. GC-O and odor activity values further revealed floral-like volatiles, including decanal, linalool oxide, ß-lonone, geraniol, (Z)-jasmone, linalool, nonanal, and benzeneacetaldehyde. The recombination of these floral volatiles confirmed the consistency with the floral green tea. Furthermore, the extending spreading duration (8-10 h), reducing fixation duration (160-190 s), and increasing drying temperature (100-115 °C) promote their accumulation in green tea. This study provides new perspectives for the precise enhancement of floral odorants for green tea.

Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea.

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.

Revelation of volatile contributions in green teas with different aroma types by GC-MS and GC-IMS.

Aroma types of green teas associate with their commercial prices and consumer acceptance, mainly including floral-like (HX), chestnut-like (LX), and fresh (QX) aromas. However, the volatile differences and specificities in these aroma types are still unclear. Herein, Taiping Houkui green teas with HX, LX, and QX aromas were processed separately with the same fresh tea leaves. Ninety-four and seventy-eight volatiles were detected and identified by headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), respectively. Candidate differential volatiles among the tea samples were determined by the variable importance in projection (VIP) of the partial least squares-discriminant analysis (PLS-DA) and were further confirmed by the relative odor activity value (ROAV) and odor description. The volatiles 1-hexanol, linalool oxide (furanoid), linalool, geraniol, (E)-ß-ionone, isoamyl acetate, and 2-methylpropanal enriched in HX and contributed to the floral-like aroma, while 3-methylbutanal, 2-ethyl-1-hexanol, indole, ß-damascone, and cedrol enriched in LX and contributed to the chestnut-like aroma. This study reveals the specificities and contributions of volatiles in green teas with different aromas, thus providing new insights into the molecular basis of different flavored teas, benefiting for their precision processing and targeted quality control.

A phenology of fear: Investigating scale and seasonality in predator-prey games between wolves and white-tailed deer.

Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale-sensitive inferences in predator-prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white-tailed deer and canid predators (coyotes and wolves), we used a large and year-round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk-response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade-offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a "phenology of fear." Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra-annual variation in predator-prey interactions may be common in seasonal environments.