The Council of Agriculture of Taiwan funded the research project and the HealthCloud app development from 2018 to 2020 [109 agricultural science - 7.5.4-supplementary-#1(1)] ([109 <img alt="Equation 4" src="/files/ftp_upload/63169/63169eq04.jpg" />-7.5.4-<img alt="Equation 5" src="/files/ftp_upload/63169/63169eq05.jpg" />-#1(1)]).

The whole protocol follows the National Taiwan University Research Ethics Committee Office&#39;s instructions for conducting human-related experiments. During participant recruitment, candidates were informed of their instructions and rights and the experiment&#39;s risks in both speech and writing, and the signed consent forms were collected. The app can be installed on smartphones and smartwatches (see Table of Materials).
1. Preparation of the psychological and physiological experiment
<ol>
	<li>Obtain the information of the experimental sites for scheduling the activity.</li>
	<li>Design experimental procedures according to the site at which the protocol is applied.</li>
	<li>Obtain ethical approval.</li>
	<li>Prepare an introduction to the experiment for participant recruitment and procedure instructions. 
	NOTE: The instructions include tasks that participants will perform and dos and don&#39;ts during the experiment.</li>
	<li>Create a new Field Name in the HealthCloud app backend website to stamp the targeted data.
	<ol>
		<li>Log into the backend website. 
		NOTE: Access to the website is now limited to cooperative researchers.</li>
		<li>Add a new Field Name at the Set Field in Admin Management in the app database to mark the data collected during the experiment (Figure 7). 
		NOTE: The target data can be easily recognized and extracted in the datasheet (Figure 5).</li>
	</ol>
	</li>
	<li>Import the questionnaire items into the configuration.
	<ol>
		<li>Log into the backend website.</li>
		<li>In Admin Management &#62; Configuration, click on the Select File button to upload questionnaires in the given format (Table 1). 
		NOTE: In Table 1, the first column is where the questions in English need to be filled in; the second is for the Chinese version, and the third column is where the question indicator can be filled in. Whether the user receives the question in English or Chinese depends on the language of the smartphone. In the present study, the language of the entire system was set to Chinese; therefore, questions were in Chinese.</li>
	</ol>
	</li>
	<li>Set the time interval for the Pop Quiz questions (the questionnaires).
	<ol>
		<li>Log into the backend website.</li>
		<li>In Admin Management &#62; Configuration, set the time interval by choosing the number of minutes in &#34;Period of Pop Quiz&#34; (Figure 5). 
		NOTE: In the present study, the time interval for repetitions of the Pop Quiz task was 5 min, but it could be set anywhere between 1 min and 72 h.</li>
	</ol>
	</li>
</ol>
2. Participant recruitment
<ol>
	<li>Recruit participants using introductory instructions. 
	NOTE: Participants were recruited through an online survey in the present study. Participants were provided with a smartphone and smartwatch (see Table of Materials), regardless of whether they had their own devices.</li>
	<li>Exclude participants who are not aged 20-36 years, pregnant or breastfeeding, color blind, and have smell-related diseases.</li>
	<li>Introduce the full content of the experiment, including the purpose of the investigation, the experimental research methods and procedures, the experimental requirements, the potential risks of the experiment, the benefits for the participants, and the participants&#39; rights.</li>
	<li>Acquire written consent from the participants.</li>
</ol>
3. Preparation of wearable devices and the app
<ol>
	<li>Download the HealthCloud app on the App Store.</li>
	<li>Ensure the mentioned tools are ready for use, have good battery life, GPS functionality, and stable Internet signals at the site.</li>
	<li>Examine the operating procedures of the app to check the heart rate function.
	<ol>
		<li>Log into the app on the phone by creating an account (Figure 8A).</li>
		<li>Ensure all real-time information, including heart rate, location (latitude and longitude), elevation, weather, the distance that the user has moved since starting, and duration since the app started, are successfully collected on the app&#39;s main page on the smartphone (Figure 8B).</li>
		<li>By pressing Set Measurements on the setting page, select the Field Name created beforehand and name the participants according to their &#34;Test No.&#34; (Figure 8C).</li>
	</ol>
	</li>
	<li>Once the devices are set, verbally instruct subjects with the following wording about how to use both the smartwatch and smartphone for proper assessment.
	<ol>
		<li>Press Start on the smartwatch to begin collecting physiological and environmental information (Figure 8D). 
		NOTE: The watch will notify the tasks by vibrating five times during the walk.</li>
		<li>Once the tasks are received, please follow the instructions on the watch.</li>
		<li>To start conducting the task, press OK! and follow the instructions shown on the watch&#39;s screen.
		<ol>
			<li>For the Pop Quiz task, rate statements from 1-5 stars to answer the psychological scale items and press Send to finish measuring.</li>
			<li>For the &#34;Photo Taking,&#34; &#34;Stroop Test,&#34; and &#34;Voice&#34; tasks, open the HealthCloud app on the phone and follow the instructions.</li>
			<li>For the HRV task, open the Breathe app and start it by clicking on Start. The HRV data will be uploaded.</li>
			<li>For the SpO2 task, open the Blood Oxygen app on the watch and start measuring by clicking on Start. The result will be uploaded. 
			NOTE: Before the walk, remind the participants with the following instruction: During the walk, you will&#160;receive questions regarding the psychological scale items in a random order, with a designated time interval. Please do not modify any settings on the phone or watch during the walk. If any error occurs, let the instructors know immediately.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Data collection
<ol>
	<li>Request participant&#39;s personal IDs for deposit in exchange for smartwatches and smartphones.</li>
	<li>Verbally remind participants to relax and enjoy the walk with the following wording: &#34;During the experiment, please relax and enjoy the whole walk; walk as if you weren&#39;t in an experiment.&#34;</li>
	<li>After the walk, collect the devices and hit Quit and Calc to finish the experiment.</li>
	<li>Provide participants with a 200 NTD equivalent gift card.</li>
</ol>
5. Data analysis
<ol>
	<li>Retrieve data from the backend website of the app.
	<ol>
		<li>Log into the backend website. Extract research data from the Data Sheet by filtering the time, field, and Tester ID.</li>
		<li>Press Export CSV to download the dataset (Figure 4).</li>
	</ol>
	</li>
	<li>Perform descriptive statistical analysis using statistical software (see Table of Materials).</li>
</ol>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Question (English)</td>
			<td colspan="2">Question (Chinese)</td>
			<td>Indicator</td>
		</tr>
		<tr>
			<td colspan="2">That is a place which is away from everyday demands and where I would be able to relax and think about what interests me.</td>
			<td colspan="2"><img alt="Equation 6" src="/files/ftp_upload/63169/63169eq06.jpg" /> 
			<img alt="Equation 7" src="/files/ftp_upload/63169/63169eq07.jpg" /></td>
			<td>Being-away</td>
		</tr>
		<tr>
			<td colspan="2">That place is fascinating; it is large enough for me to discover and be curious about things.</td>
			<td colspan="2"><img alt="Equation 8" src="/files/ftp_upload/63169/63169eq08.jpg" /> 
			<img alt="Equation 9" src="/files/ftp_upload/63169/63169eq09.jpg" /></td>
			<td>Fascination</td>
		</tr>
		<tr>
			<td colspan="2">How much do you like the setting, for whatever reason?</td>
			<td colspan="2"><img alt="Equation 3" src="/files/ftp_upload/63169/63169eq03.jpg" /></td>
			<td>Preference</td>
		</tr>
	</tbody>
</table>
Table 1: The format of the Pop Quiz questions. The psychological scale items adopted in this study were used as an example to present the format of the Pop Quiz questions.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63169/63169fig07.jpg" /> 
Figure 7: &#34;Field name&#34; setting. In the backend website, typing the new field name is needed in black, and then click on Add to mark the target data. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/63169/63169fig08.jpg" /> 
Figure 8: The operating procedures of the app. Users login. (A) Login interface of the app on the smartwatch; app on the smartwatch is started at (B). (C) The main page of the app, where the real-time data were shown. (D) The measurement was set to change the &#34;Field Name&#34; and &#34;Test No.&#34; <a href="https://www.jove.com/files/ftp_upload/63169/63169fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

The authors have nothing to disclose.

Purposes of the study and significant findings 
Wearable devices, such as smartphones and smartwatches, have been widely used to investigate physiological indicators or syndromes32,33,34, psychological states22,35; environmental information, or behaviors18,36. Most applications of smart devi...

Real-time data collection 
In daily life, people benefit from the physical environment in many ways. For example, positive outcomes, such as psychological1 and heart rate restoration2 have been widely found. In addition, the relationships between ambient factors, such as temperature and humidity, and mental health have been discussed3,4. Studies have also explored the links between physiological and psychological responses, such as heart rate and stress5,6,7,8. A wide range of evidence for psychological and physiological benefits from exposure to nature has been found in well-controlled laboratory studies9,10, which may not have represented the diverse influential factors in the field. Therefore, to measure the relationships among real-time human responses, on-site studies are considered better to reflect the real-life scenario experience and reactions to the environments than laboratory simulations11. Moreover, human reactions to environments may depend on context12. Given the importance of understanding the relationship between people&#39;s psychological and physiological health and environmental quality, a real-time self-tracking measurement that can collect various information measures is urgently needed.
Ecological momentary assessments (EMAs) or experience sampling methods (ESMs) may represent solutions for on-site studies13,14. EMAs and ESMs aim to assess humans&#39; momentary responses on-site in real-life scenarios15. By adopting self-tracking techniques, the responses, reactions, and on-site experiences can be measured freshly14. Participants are notified via signals, such as texts or notifications, to implement assessments in so-called signal-contingent sampling schemes15. The term &#34;EMA&#34; is primarily used in health-related studies13, while &#34;ESM&#34; tends to be used in leisure and outdoor recreation studies16. Nonetheless, the terms have occasionally been used interchangeably12.
The possibility of applying EMAs to environmental research studies was discussed by Beute et al.12, who pointed out that they would allow a greater variety of environments to be addressed than merely &#34;natural&#34; or &#34;urban.&#34; For example, by adopting ambulatory measurement (such as through GPS location tracking), physiological responses during a walk can be matched with real-time location datasets, providing a richer spatial resolution of environment types and environmental characteristics7. Additionally, the real-time data collection allowed by EMAs ensures a high ecological validity, providing a complementary point of view from laboratory studies.
More and more on-site empirical studies have adopted wearable devices and smartphones to monitor personal health status in daily life and research purposes17,18,19,20. Adopting both of these devices may provide more advantages than using only a smartphone12. First, the access time using smartwatches was shorter than that using phones21, which may cause a reduced interruption burden. Second, watches provide a greater body closeness than smartphones22, and phones can be used as momentary databases to save and upload data. Third, smartwatches nowadays offer multiple sensors to different parameters, such as heart rate variability, electrocardiograms (ECG), and blood pressure23,24,25,26,27. The individual and the overall aspects of human responses can infer certain activities12. Finally, smartphones are usually carried in the pocket for smartphone-based studies, and when it comes to the questionnaires, extra work must be done compared to the case using smartwatches.
However, few studies have explored the relationships between psychological and physiological outcomes and environmental information. Therefore, this study showcases adopting a non-commercial self-developed app, the HealthCloud, on wearable devices, such as smartwatches, and smartphones, to collect real-time psychological, physiological, and environmental information.
The self-developed app and wearable devices 
The app for use on wearable devices was developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), to provide more accessible and more flexible ways to track human responses and environmental data, allowing researchers to analyze further the relationships among human health and environmental information (Figure 1).
The app, based on iOS, provides multiple tasks and passive data-collection functions. The app collects self-reported data on the smartwatch, such as psychological-scale items measured through Pop Quiz questions on which users can rate their responses from one to five stars for quick and easy assessment. This type of question intervention may be considered a type of Micro interaction-EMA (&#181;EMA)-an in situ data collection method requiring less attention and having a greater response rate than smartwatch-EMA28. Sensor-monitored physiological response data, including heart rate, heart rate variability, and blood oxygen saturation level, can be measured using the functions of the iOS. Heart rate is measured through the smartwatch&#39;s optical heart sensor using a technique called photoplethysmography29. The app detects the amount of blood flow using green LED lights with light-sensitive photodiodes, and the heartbeats per minute are also calculated. The heart rate variability (HRV) and blood oxygen concentration (SpO2) can be detected using apps. For the smartphone, the tasks, such as the Stroop Test (Figure 2B), and Image Capture task (Figure 2C), and the Environment Sound task (Figure 2D), the ambient conditions data, including relative humidity, weather, and altitude, are passively collected from several Application Programming Interfaces.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63169/63169fig01.jpg" /> 
Figure 1: Overview of the app. The functions of the app on the smartwatch, smartphone, and database. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63169/63169fig02.jpg" /> 
Figure 2: The app tasks. Examples of the tasks that can be used on the app: from left to right, there is (A) The Pop-up question. (B) The Stroop Test. (C) The Image Capture task. (D) The Environment Sound task. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
All data will be uploaded to the backend website (access to cooperative researchers, see Table of Materials). The website provides several primary functions: a map display that shows users&#39; current locations and heart rate (Figure 3), a datasheet for browsing and extracting data (Figure 4), and task configurations for modifying the frequency, priority, and content of the tasks (Figure 5). With such great flexibility and a wide range of measurements, researchers can easily select the previously stated task functions according to the research objectives. In addition, the app can benefit both users and researchers. The app provides their health reports and GPS location trajectories (Figure 6) according to the questions they have answered and their chosen routes. Thus, they can obtain a quick idea of their health status on the day and keep on tracking their health data.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63169/63169fig03.jpg" /> 
Figure 3: The map displayed on the app database. The map display of the app database provides current information, including locations and heart rate, to the researchers. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63169/63169fig04.jpg" /> 
Figure 4: Datasheet on the app database. The data report of the display map in the app database, in which data can be exported by filtering the Time, Field, or Tester ID. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63169/63169fig05.jpg" /> 
Figure 5: The task configuration on the app database. The task priorities, time intervals, language, and content of the questionnaires can be modified. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63169/63169fig06.jpg" /> 
Figure 6: The health report for the app users. After using the app, the user can receive a set of individual results generated automatically. <a href="https://www.jove.com/files/ftp_upload/63169/63169fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Representative study 
To showcase the integration of diverse dimensions of data collection using the app on smartphones and smartwatches, an in situ study was conducted in 2020 at the National Taiwan University campus in Taipei City, Taiwan. Participants for the study were recruited on the social media fan page of National Taiwan University through an online form 1 week before the experiment. The form included the research purpose, process, location, participation conditions, a schematic diagram of the research device to be worn, and a space for readers to indicate their willingness to participate and the time at which they could do so. After completion, participants were notified of their experiment&#39;s exact time and location by email 2 days before the schedule. Since the research examines psychological changes, physiology, physical activity (walking), and sound and color perception, participants met the following conditions: (1) between 20-36 years old, (2) good physical and mental health, (3) not be in regular use of drugs affecting the central nervous system, (4) not be pregnant or breastfeeding, (5) have no history of cardiovascular disease, (6) can walk for more than 30 min on foot, (7) be able to identify a color.
On the day of the experiment, participants were provided with one set of smartphones and smartwatches, and a route map. Researchers presented a uniform explanation to the participants of the purpose of the research, the research process, the wearable devices, and the matters needing attention in the research process. During the walk, psychological responses were assessed using a Pop Quiz task every 5 min, and physiological responses, such as heart rate, were measured every minute by sensors in the smartwatch. After the experiment, participants were compensated with a 200 NTD equivalent gift card (~7 USD).
For the psychological measurement, this study considered landscape preferences and two aspects of the Perceived Restorative Scale Short Version30, namely, &#34;being away&#34; and &#34;fascination.&#34; These aspects were measured by asking participants to rate the statements &#34;This is a place which is away from everyday demands and where I would be able to relax and think about what interests me.&#34; and &#34;That place is fascinating; it is large enough for me to discover and be curious about things.&#34; on a five-point Likert scale from (1) &#34;strongly disagree&#34; to (5) &#34;strongly agree&#34; to measure individual perceptions of the restorative factors of the environment based on attention restoration theory31. Landscape preference was assessed using a five-point Likert scale with the single question: &#34;How much do you like the setting, for whatever reason?&#34; from (1) &#34;very little&#34; to (5) &#34;very much.&#34; The questionnaire was sent using the &#34;Pop Quiz&#34; task with a 5 min time interval, meaning participants received the questionnaire every 5 min.
For physiological measurement, heart rate (HR) while walking was used to represent the participants&#39; physiological outcomes with a 1 min time interval. Environmental information, including GPS data (latitude and longitude), temperature, relative humidity, wind speed, and wind degree, were collected through the smartphone.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Apple Watch 6</td>
			<td>Apple</td>
			<td></td>
			<td>For the use of the HealthCloud app, such as Pop-up questions, heart rates measurement.</td>
		</tr>
		<tr>
			<td>iPhone</td>
			<td>Apple</td>
			<td></td>
			<td>For the use of the HealthCloud app, such as GPS location collection, weather data colledction, data storage, data transfer.</td>
		</tr>
		<tr>
			<td>HealthCloud</td>
			<td>Self-developed</td>
			<td></td>
			<td>The HealthCloud app, adopting Apple Watch and iPhone, was developed by Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU) to track human responses. It adopted several APIs such as HealthKit, ResearchKit, Weather API and AppleWatch applications including Breathe app, and Blood Oxygen app to collect physiological status and psychological states, and environmental data in aims of further analyzing the relationships between human health and the environmental information. 
			 
			The link to the app in APP Store is as following: https://apps.apple.com/tw/app/healthcloud/id1446179518?l=en</td>
		</tr>
		<tr>
			<td>backend website</td>
			<td></td>
			<td></td>
			<td>The backend website of HealthCloud app for the use of the configuration of the tasks, data exportation, and the display of users. 
			http://healthcloud.hort.ntu.edu.tw/</td>
		</tr>
		<tr>
			<td>HealthKit</td>
			<td>Apple</td>
			<td></td>
			<td>For the use of retrieving the data of physiological responses such as heart rate, heart rate variability, and blood oxygen saturation level. 
			The link to the HealthKit: 
			https://developer.apple.com/documentation/healthkit</td>
		</tr>
		<tr>
			<td>ResearchKit</td>
			<td>Apple</td>
			<td></td>
			<td>This kit includes a variety of tasks for the use of research purposes. The functions adopted in HealthCloud app include Image Capture task, environment sound task, Stroop Test, to the Pop Questions of psychological state measurements such as perceived restorativeness scale, landscape preferences. 
			The link to the ResearchKit: 
			https://www.researchandcare.org/</td>
		</tr>
		<tr>
			<td>Weather API</td>
			<td>OpenWeather</td>
			<td></td>
			<td>For the use of collecting the real-time environmental data, including humidity, weather, global positioning system location, altitudes, etc., from the nearest weather station. 
			The link to the Weather API: 
			https://openweathermap.org/api</td>
		</tr>
		<tr>
			<td>Breathe app</td>
			<td>Apple</td>
			<td></td>
			<td>For the use of assessing the real-time heart rate variability (HRV). This app was not included in the procedures of this pilot study. However, the HealthlCloud is now capable of retrieving the HRV data collected from Breathe app. 
			The link to the Breathe app: 
			https://apps.apple.com/us/app/breathe/id1459455352</td>
		</tr>
		<tr>
			<td>Blood Oxygen app</td>
			<td>Apple</td>
			<td></td>
			<td>For the use of assessing the real-time Blood Oxygen Concentration level (SpO2). The latest version of HealthlCloud is capable of retrieving the SpO2 data collected from&#160; app. This app was not included in the procedures of this pilot study. However, 
			The measurement of Blood Oxygen app: 
			https://support.apple.com/en-us/HT211027 
			The link to the Blood Oxygen app: 
			https://apps.apple.com/us/app/breathe/id1459455352&#34;</td>
		</tr>
		<tr>
			<td>IBM SPSS Statistics 25</td>
			<td>IBM</td>
			<td></td>
			<td>For the use of statistical analysis. 
			The link to the Blood Oxygen app: 
			https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-25</td>
		</tr>
	</tbody>
</table>

an application for pairing with wearable devices to monitor personal health status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The original sample consisted of 423 individuals, of which 18 had to be excluded because of poor data quality due to instability of the beta version of the app and another 20 failed to complete all Pop Quiz questions. This led to an effective sample rate of 0.91. A dataset of 385 students (213 females, 172 males) from National Taiwan University were recruited. Participants were between 20-36 years old (M = 23.38, SD = 2.268). Regarding their psychological states, 514 ratings of preference (PREF, M =...

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An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

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2.6K Views.  National Taiwan University. The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

Video: An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during storage. These processes are the major sources of greenhouse gas (GHG) emissions from animal production systems. Techniques for measuring enteric CH4 vary from direct measurements (respiration chambers, which are highly accurate, but with limited applicability) to various indirect methods (sniffers, laser technology, which are practical, but with variable accuracy). The sulfur hexafluoride (SF6)&#160;tracer gas method is commonly used to measure enteric CH4 production by animal scientists and more recently, application of an Automated Head-Chamber System (AHCS) (GreenFeed, C-Lock, Inc., Rapid City, SD), which is the focus of this experiment, has been growing. AHCS is an automated system to monitor CH4 and carbon dioxide (CO2) mass fluxes from the breath of ruminant animals. In a typical AHCS operation, small quantities of baiting feed are dispensed to individual animals to lure them to AHCS multiple times daily. As the animal visits AHCS, a fan system pulls air past the animal&#8217;s muzzle into an intake manifold, and through an air collection pipe where continuous airflow rates are measured. A sub-sample of air is pumped out of the pipe into non-dispersive infra-red sensors for continuous measurement of CH4 and CO2 concentrations. Field comparisons of AHCS to respiration chambers or SF6 have demonstrated that AHCS produces repeatable and accurate CH4 emission results, provided that animal visits to AHCS are sufficient so emission estimates are representative of the diurnal rhythm of rumen gas production. Here, we demonstrate the use of AHCS to measure CO2 and CH4 fluxes from dairy cows given a control diet or a diet supplemented with technical-grade cashew nut shell liquid.

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Accuracy and precision of the techniques used to measure methane emissions from ruminant animals are critically important for the success of greenhouse gas mitigation efforts. This manuscript describes the principles and operation of an automated system to monitor methane and carbon dioxide mass fluxes from the breath of ruminant animals.

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during ...

Spotting Cheetahs: Identifying Individuals by Their Footprints

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor2. Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,9053 to 13,5204. The disparity is likely a result of the different techniques used in monitoring.
Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique5 is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Ward's cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results.
The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of &gt;90%.

The cheetah (Acinonyx jubatus) is an iconic, endangered species, but conservation efforts are challenged by habitat shrinkage and conflict with commercial farmers. The footprint identification technique, a robust, accurate and cost-effective image classification system, is a new approach to monitoring cheetahs.

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges ...

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered &#34;bee-safe,&#34; there is mounting evidence that fungicide residues in pollen are associated with bee declines (for both honey and bumble bee species). While the mechanisms remain relatively unknown, researchers have speculated that bee-microbe symbioses are involved. Microbes play a pivotal role in the preservation and/or processing of pollen, which serves as nutrition for larval bees. By altering the microbial community, it is likely that fungicides disrupt these microbe-mediated services, and thereby compromise bee health. This manuscript describes the protocols used to investigate the indirect mechanism(s) by which fungicides may be causing colony decline. Cage experiments exposing bees to fungicide-treated flowers have already provided the first evidence that fungicides cause profound colony losses in a native bumble bee (Bombus impatiens). Using field-relevant doses of fungicides, a series of experiments have been developed to provide a finer description of microbial community dynamics of fungicide-exposed pollen. Shifts in the structural composition of fungal and bacterial assemblages within the pollen microbiome are investigated by next-generation sequencing and metagenomic analysis. Experiments developed herein have been designed to provide a mechanistic understanding of how fungicides affect the microbiome of pollen-provisions. Ultimately, these findings should shed light on the indirect pathway through which fungicides may be causing colony declines.

Microbial consortia within bumble bee hives enrich and preserve pollen for bee larvae. Using next generation sequencing, along with laboratory and field-based experiments, this manuscript describes protocols used to test the hypothesis that fungicide residues alter the pollen microbiome, and colony demographics, ultimately leading to colony loss.

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered ...

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental ...

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Microfluidic devices are versatile tools for studying transport processes at a microscopic scale. A demand exists for microfluidic devices that are resistant to low molecular-weight oil components, unlike traditional polydimethylsiloxane (PDMS) devices. Here, we demonstrate a facile method for making a device with this property, and we use the product of this protocol for examining the pore-scale mechanisms by which foam recovers crude oil. A pattern is first designed using computer-aided design (CAD) software and printed on a transparency with a high-resolution printer. This pattern is then transferred to a photoresist via a lithography procedure. PDMS is cast on the pattern, cured in an oven, and removed to obtain a mold. A thiol-ene crosslinking polymer, commonly used as an optical adhesive (OA), is then poured onto the mold and cured under UV light. The PDMS mold is peeled away from the optical adhesive cast. A glass substrate is then prepared, and the two halves of the device are bonded together. Optical adhesive-based devices are more robust than traditional PDMS microfluidic devices. The epoxy structure is resistant to swelling by many organic solvents, which opens new possibilities for experiments involving light organic liquids. Additionally, the surface wettability behavior of these devices is more stable than that of PDMS. The construction of optical adhesive microfluidic devices is simple, yet requires incrementally more effort than the making of PDMS-based devices. Also, though optical adhesive devices are stable in organic liquids, they may exhibit reduced bond-strength after a long time. Optical adhesive microfluidic devices can be made in geometries that act as 2-D micromodels for porous media. These devices are applied in the study of oil displacement to improve our understanding of the pore-scale mechanisms involved in enhanced oil recovery and aquifer remediation.

The goal of this procedure is to easily and rapidly produce a microfluidic device with customizable geometry and resistance to swelling by organic fluids for oil recovery studies. A polydimethylsiloxane mold is first generated, and then used to cast the epoxy-based device. A representative displacement study is reported.

Microfluidic devices are versatile tools for studying transport processes at a microscopic scale. A demand exists for microfluidic devices that are ...

The Calibration and Use of Capacitance Sensors to Monitor Stem Water Content in Trees

Water transport and storage through the soil-plant-atmosphere continuum is critical to the terrestrial water cycle, and has become a major research focus area. Biomass capacitance plays an integral role in the avoidance of hydraulic impairment to transpiration. However, high temporal resolution measurements of dynamic changes in the hydraulic capacitance of large trees are rare. Here, we present procedures for the calibration and use of capacitance sensors, typically used to monitor soil water content, to measure the volumetric water content in trees in the field. Frequency domain reflectometry-style observations are sensitive to the density of the media being studied. Therefore, it is necessary to perform species-specific calibrations to convert from the sensor-reported values of dielectric permittivity to volumetric water content. Calibration is performed on a harvested branch or stem cut into segments that are dried or re-hydrated to produce a full range of water contents used to generate a best-fit regression with sensor observations. Sensors are inserted into calibration segments or installed in trees after pre-drilling holes to a tolerance fit using a fabricated template to ensure proper drill alignment. Special care is taken to ensure that sensor tines make good contact with the surrounding media, while allowing them to be inserted without excessive force. Volumetric water content dynamics observed via the presented methodology align with sap flow measurements recorded using thermal dissipation techniques and environmental forcing data. Biomass water content data can be used to observe the onset of water stress, drought response and recovery, and has the potential to be applied to the calibration and evaluation of new plant-level hydrodynamics models, as well as to the partitioning of remotely sensed moisture products into above- and belowground components.

The hydraulic capacitance of biomass is a key component of the vegetation water budget, which serves as a buffer against short and long-term drought stresses. Here, we present a protocol for the calibration and use of soil moisture capacitance sensors to monitor water content in the stems of large trees.

Water transport and storage through the soil-plant-atmosphere continuum is critical to the terrestrial water cycle, and has become a major research focus ...

A Strain Gauge Monitor (SGM) for Continuous Valve Gape Measurements in Bivalve Molluscs in Response to Laboratory Induced Diel-cycling Hypoxia and pH

An inexpensive, laboratory-based, strain gauge valve gape monitor (SGM) was developed to monitor the valve gape behavior of bivalve molluscs in response to diel-cycling hypoxia. A Wheatstone bridge was connected to strain gauges that were attached to the shells of oysters (Crassostrea virginica). The recorded signals allowed for the opening and closing of the bivalves to be recorded continuously over two-day periods of experimentally-induced diel-cycling hypoxia and diel-cycling changes in pH. Here, we describe a protocol for developing an inexpensive strain gauge monitor and describe, in an example laboratory experiment, how we used it to measure the valve gape behavior of Eastern oysters (C. virginica), in response to diel-cycling hypoxia and cyclical changes in pH. Valve gape was measured on oysters subjected to cyclical severe hypoxic (0.6 mg/L) dissolved oxygen conditions with and without cyclical changes in pH, cyclical mild hypoxic (1.7 mg/L) conditions and normoxic (7.3 mg/L) conditions. We demonstrate that when oysters encounter repeated diel cycles, they rapidly close their shells in response to severe hypoxia and close with a time lag to mild hypoxia. When normoxia is restored, they rapidly open again. Oysters did not respond to cyclical pH conditions superimposed on diel cycling severe hypoxia. At reduced oxygen conditions, more than one third of the oysters closed simultaneously. We demonstrate that oysters respond to diel-cycling hypoxia, which must be considered when assessing the behavior of bivalves to dissolved oxygen. The valve SGM can be used to assess responses of bivalve molluscs to changes in dissolved oxygen or contaminants. Sealing techniques to better seal the valve gape strain gauges from sea water need further improvement to increase the longevity of the sensors.

A Strain Gauge Monitor SGM for Continuous Valve Gape Measurements in Bivalve Molluscs in Response to Laboratory Induced Diel-cycling Hypoxia and pH

Understanding the behavioral responses of bivalve suspension-feeders to environmental variables, such as dissolved oxygen, can explain some ecosystem processes. We have developed an inexpensive, laboratory-based, strain gauge monitor (SGM) to measure valve gape responses of oysters, Crassostrea virginica, to diel-cycling hypoxia and cyclical pH.

An inexpensive, laboratory-based, strain gauge valve gape monitor (SGM) was developed to monitor the valve gape behavior of bivalve molluscs in response ...

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current measures to combat such pathogens have proved too conservative and, thus, not sufficiently effective, and they can potentially pose environmental risks. Therefore, it is extremely necessary to identify host-resistance factors to assist in controlling plant diseases naturally through the identification of resistant germplasm, the isolation and characterization of resistance genes, and the molecular breeding of resistant cultivars. In this regard, there is need to establish an accurate, rapid, and large-scale inoculation method to breed and develop plant resistance genes. The rice blast fungal pathogen Magnaporthe grisea causes severe disease symptoms and yield losses. Recently, M. grisea has emerged as a model organism for studying the mechanisms of plant-fungal pathogen interactions. Hence, we report the development of a plant virulence test method that is specific for M. grisea. This method provides for both spray inoculation with a conidial suspension and wounding inoculation with mycelium cubes or droplets of conidial suspension. The key step of the wounding inoculation method for detached rice leaves is to make wounds on plant leaves, which avoids any interference caused by host penetration resistance. This spray/wounding protocol contributes to the rapid, accurate, and large-scale screening of the pathotypes of M. grisea isolates. This integrated and systematic plant infection method will serve as an excellent starting point for gaining a broad perspective of issues in plant pathology.

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Here, we present a protocol to test plant virulence with the plant pathogen Magnaporthe grisea. This report will contribute to the large-scale screening of the pathotypes of fungi isolates and serve as an excellent starting point for understanding the resistant mechanisms of plants during molecular breeding.

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current ...

A Video Surveillance System to Monitor Breeding Colonies of Common Terns (Sterna Hirundo)

Many waterbird populations have faced declines over the last century, including the common tern (Sterna hirundo), a waterbird species with a widespread breeding distribution, that has been recently listed as endangered in some habitats of its range. Waterbird monitoring programs exist to track populations through time; however, some of the more intensive approaches require entering colonies and can be disruptive to nesting populations. This paper describes a protocol that utilizes a minimally invasive surveillance system to continuously monitor common tern nesting behavior in typical ground-nesting colonies. The video monitoring system utilizes wireless cameras focused on individual nests as well as over the colony as a whole, and allows for observation without entering the colony. The video system is powered with several 12 V car batteries that are continuously recharged using solar panels. Footage is recorded using a digital video recorder (DVR) connected to a hard drive, which can be replaced when full. The DVR may be placed outside of the colony to reduce disturbance. In this study, 3,624 h of footage recorded over 63 days in weather conditions ranging from 12.8 &#176;C to 35.0 &#176;C produced 3,006 h (83%) of usable behavioral data. The types of data retrieved from the recorded video can vary; we used it to detect external disturbances and measure nesting behavior during incubation. Although the protocol detailed here was designed for ground-nesting waterbirds, the principal system could easily be modified to accommodate alternative scenarios, such as colonial arboreal nesting species, making it widely applicable to a variety of research needs.

A Video Surveillance System to Monitor Breeding Colonies of Common Terns Sterna Hirundo

This paper describes a protocol that uses a remote video monitoring surveillance system to continuously monitor breeding colonies of ground-nesting waterbirds. The system includes five cameras monitoring individual nests and one camera monitoring the colony as a whole, and is powered by car batteries that are recharged via solar panels.

Many waterbird populations have faced declines over the last century, including the common tern (Sterna hirundo), a waterbird species with a widespread ...

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Fish models and behaviors are increasingly used in the biomedical sciences; however, fish have long been the subject of ecological, physiological and toxicological studies. Using automated digital tracking platforms, recent efforts in neuropharmacology are leveraging larval fish locomotor behaviors to identify potential therapeutic targets for novel small molecules. Similar to these efforts, research in the environmental sciences and comparative pharmacology and toxicology is examining various behaviors of fish models as diagnostic tools in tiered evaluation of contaminants and real-time monitoring of surface waters for contaminant threats. Whereas the zebrafish is a popular larval fish model in the biomedical sciences, the fathead minnow is a common larval fish model in ecotoxicology. Unfortunately, fathead minnow larvae have received considerably less attention in behavioral studies. Here, we develop and demonstrate a behavioral profile protocol using caffeine as a model neurostimulant. Though photomotor responses of fathead minnows were occasionally affected by caffeine, zebrafish&#160;were markedly more sensitive for photomotor and locomotor endpoints, which responded at environmentally relevant levels. Future studies are needed to understand comparative behavioral sensitivity differences among fish with age and time of day, and to determine whether similar behavioral effects would occur in nature and be indicative of adverse outcomes at the individual or population levels of biological organization.

Here, we present a protocol to examine larval zebrafish and fathead minnow locomotor activities and photomotor responses (PMR) using an automated tracking software. When incorporated in common toxicology bioassays, analyses of these behaviors provide a diagnostic tool to examine chemical bioactivity. This protocol is described using caffeine, a model neurostimulant.

Fish models and behaviors are increasingly used in the biomedical sciences; however, fish have long been the subject of ecological, physiological and ...