Dataset Information

A field experiment characterizing variable detection rates during plant surveys.

ABSTRACT: Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

SUBMITTER: Hauser CE

PROVIDER: S-EPMC9303269 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The environmental DNA (eDNA) method is the practice of collecting environmental samples and analyzing them for the presence of a genetic marker specific to a target species. Little is known about the sensitivity of the eDNA method. Sensitivity is the probability that the target marker will be detected if it is present in the water body. Methods and tools are needed to assess the sensitivity of sampling protocols, design eDNA surveys, and interpret survey results. In this study, the sensitivity of the eDNA method is modeled as a function of ambient target marker concentration. The model accounts for five steps of sample collection and analysis, including: 1) collection of a filtered water sample from the source; 2) extraction of DNA from the filter and isolation in a purified elution; 3) removal of aliquots from the elution for use in the polymerase chain reaction (PCR) assay; 4) PCR; and 5) genetic sequencing. The model is applicable to any target species. For demonstration purposes, the model is parameterized for bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) assuming sampling protocols used in the Chicago Area Waterway System (CAWS). Simulation results show that eDNA surveys have a high false negative rate at low concentrations of the genetic marker. This is attributed to processing of water samples and division of the extraction elution in preparation for the PCR assay. Increases in field survey sensitivity can be achieved by increasing sample volume, sample number, and PCR replicates. Increasing sample volume yields the greatest increase in sensitivity. It is recommended that investigators estimate and communicate the sensitivity of eDNA surveys to help facilitate interpretation of eDNA survey results. In the absence of such information, it is difficult to evaluate the results of surveys in which no water samples test positive for the target marker. It is also recommended that invasive species managers articulate concentration-based sensitivity objectives for eDNA surveys. In the absence of such information, it is difficult to design appropriate sampling protocols. The model provides insights into how sampling protocols can be designed or modified to achieve these sensitivity objectives.

Project description:Plant-soil feedback (PSF) has gained attention as a mechanism promoting plant growth and coexistence. However, most PSF research has measured monoculture growth in greenhouse conditions. Translating PSFs into effects on plant growth in field communities remains an important frontier for PSF research. Using a 4-year, factorial field experiment in Jena, Germany, we measured the growth of nine grassland species on soils conditioned by each of the target species (i.e., 72 PSFs). Plant community models were parameterized with or without these PSF effects, and model predictions were compared to plant biomass production in diversity-productivity experiments. Plants created soils that changed subsequent plant biomass by 40%. However, because they were both positive and negative, the average PSF effect was 14% less growth on "home" than on "away" soils. Nine-species plant communities produced 29 to 37% more biomass for polycultures than for monocultures due primarily to selection effects. With or without PSF, plant community models predicted 28%-29% more biomass for polycultures than for monocultures, again due primarily to selection effects. Synthesis: Despite causing 40% changes in plant biomass, PSFs had little effect on model predictions of plant community biomass across a range of species richness. While somewhat surprising, a lack of a PSF effect was appropriate in this site because species richness effects in this study were caused by selection effects and not complementarity effects (PSFs are a complementarity mechanism). Our plant community models helped us describe several reasons that even large PSF may not affect plant productivity. Notably, we found that dominant species demonstrated small PSF, suggesting there may be selective pressure for plants to create neutral PSF. Broadly, testing PSFs in plant communities in field conditions provided a more realistic understanding of how PSFs affect plant growth in communities in the context of other species traits.

Project description:BackgroundThere are 1.1 billion smokers worldwide, and each year, more than 8 million die prematurely because of cigarette smoking. More than half of current smokers make a serious quit every year. Nonetheless, 90% of unaided quitters relapse within the first 4 weeks of quitting due to the lack of limited access to cost-effective and efficient smoking cessation tools in their daily lives.ObjectiveThis study aims to enable quantified monitoring of ambulatory smoking behavior 24/7 in real life by using continuous and automatic measurement techniques and identifying and characterizing smoking patterns using longitudinal contextual signals. This work also intends to provide guidance and insights into the design and deployment of technology-enabled smoking cessation applications in naturalistic environments.MethodsA 4-week observational study consisting of 46 smokers was conducted in both working and personal life environments. An electric lighter and a smartphone with an experimental app were used to track smoking events and acquire concurrent contextual signals. In addition, the app was used to prompt smoking-contingent ecological momentary assessment (EMA) surveys. The smoking rate was assessed based on the timestamps of smoking and linked statistically to demographics, time, and EMA surveys. A Poisson mixed-effects model to predict smoking rate in 1-hour windows was developed to assess the contribution of each predictor.ResultsIn total, 8639 cigarettes and 1839 EMA surveys were tracked over 902 participant days. Most smokers were found to have an inaccurate and often biased estimate of their daily smoking rate compared with the measured smoking rate. Specifically, 74% (34/46) of the smokers made more than one (mean 4.7, SD 4.2 cigarettes per day) wrong estimate, and 70% (32/46) of the smokers overestimated it. On the basis of the timestamp of the tracked smoking events, smoking rates were visualized at different hours and were found to gradually increase and peak at 6 PM in the day. In addition, a 1- to 2-hour shift in smoking patterns was observed between weekdays and weekends. When moderate and heavy smokers were compared with light smokers, their ages (P<.05), Fagerström Test of Nicotine Dependence (P=.01), craving level (P<.001), enjoyment of cigarettes (P<.001), difficulty resisting smoking (P<.001), emotional valence (P<.001), and arousal (P<.001) were all found to be significantly different. In the Poisson mixed-effects model, the number of cigarettes smoked in a 1-hour time window was highly dependent on the smoking status of an individual (P<.001) and was explained by hour (P=.02) and age (P=.005).ConclusionsThis study reported the high potential and challenges of using an electronic lighter for smoking annotation and smoking-triggered EMAs in an ambulant environment. These results also validate the techniques for smoking behavior monitoring and pave the way for the design and deployment of technology-enabled smoking cessation applications.International registered report identifier (irrid)RR2-10.1136/bmjopen-2018-028284.

Project description:BackgroundField translocation experiments (i.e., the introduction of seeds or seedlings of different species into different localities) are commonly used to study habitat associations of species, as well as factors limiting species distributions and local abundances. Species planted or sown in sites where they naturally occur are expected to perform better or equally well compared to sites at which they do not occur or are rare. This, however, contrasts with the predictions of the Janzen-Connell hypothesis and commonly reported intraspecific negative plant-soil feedback. The few previous studies indicating poorer performance of plants at sites where they naturally occur did not explore the mechanisms behind this pattern.Aims and methodsIn this study, we used field translocation experiments established using both seeds and seedlings to study the determinants of local abundance of four dominant species in grasslands. To explore the possible effects of intraspecific negative plant-soil feedback on our results, we tested the effect of local species abundance on the performance of the plants in the field experiment. In addition, we set up a garden experiment to explore the intensity of intraspecific as well as interspecific feedback between the dominants used in the experiment.Key resultsIn some cases, the distribution and local abundances of the species were partly driven by habitat conditions at the sites, and species performed better at their own sites. However, the prevailing pattern was that the local dominants performed worse at sites where they naturally occur than at any other sites. Moreover, the success of plants in the field experiment was lower in the case of higher intraspecific abundance prior to experimental setup. In the garden feedback experiment, two of the species performed significantly worse in soils conditioned by their species than in soils conditioned by the other species. In addition, the performance of the plants was significantly correlated between the two experiments, suggesting that plant-soil feedback is a likely explanation of the patterns observed in the field.ConclusionsAll of the results indicate that intraspecific negative plant-soil feedback, either biotic or abiotic, may be a key factor determining the performance of the plants in our field translocation experiment. The possible effects of negative feedback should thus be considered when evaluating results of translocation experiments in future studies.

Project description:The contamination of soil with total petroleum hydrocarbons (TPH) may result in dramatic consequences and needs great attention, as soil rehabilitation would need more effort from a sustainability perspective. However, there is still no known general method since the remediation technology is strictly site-specific. Adaptive biological system dynamics can play a key role in understanding and addressing the potential of situ-specific biological combinations for soil pollutants removal. The potential worst-case of TPH contamination reflects soil affected by heavy industrial activities, such as oil refineries. Therefore, the experimental trial was conducted on a 2,000 m2 area from a contaminated site located in northern Italy. We evaluated the remediation potential over time (270 days) assessing (i) the phytoremediation efficiency of two species of Poaceae (Festuca arundinacea Schreb. and Dactylis glomerata L.) and two species of Fabaceae (Medicago sativa L. and Lotus corniculatus L.) and (ii) the role of the indigenous bacteria flora and endo-mycorrhizae consortium addition in plant growth promotion. We also induced resistance to contamination stress in a field experiment. Thirty-three indigenous bacteria selected from the contaminated soils showed marked plant growth promotion. Moreover, functional metagenomics confirmed the metabolic capability of hydrocarbon-degrading microorganisms living in the polluted soil. Our data showed that soil enzymatic activities increased with hydrocarbon degradation rate after 60 days. Both Poaceae and Fabaceae resulted in remarkable remediation potential. Stress markers and antioxidant activity indicated that the selected plant species generally need some time to adapt to TPH stress. In conclusion, our evaluation implied both the rhizosphere effects and functional features of the plant and suggested that plants should (i) have marked tolerance to specific contaminants, (ii) be characterized by an extensive root system, and (iii) be susceptible to arbuscular mycorrhizal fungi (AMF) infection.

Dataset Information

A field experiment characterizing variable detection rates during plant surveys.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets