The authors are thankful to Manuel Molina (UAM) and Cristina Ronquillo (MNCN-CSIC) for the help provided during the calibration tests, and to Bel&#233;n Est&#233;banez (UAM) for her help during the sampling campaigns.

1. Assembly of the Datalogger
<ol>
	<li>Prepare a soldering iron and a spool of solder wire. Wait for the soldering iron to heat up and moisten the cleaning sponge.</li>
	<li>Cut the pin header strips to the desired length and solder them into the sockets for the temperature and humidity sensor, the microcontroller, and the RTC clock and microSD breakout modules.
	<ol>
		<li>To solder, preheat the desired join with the tip of the soldering iron.</li>
		<li>Then, apply a small amount of material from the solder wire, enough to fill up the junction.</li>
		<li>Finally, remove the soldering iron and wait for the junction to cool down.</li>
	</ol>
	</li>
	<li>Assemble the components to the circuit board using the same procedure as in step 1.2, following the markings of the PCB and the component references specified in the Table of Materials (see Figure 1 for an assembly scheme).
	<ol>
		<li>First, solder the resistors. Then, solder the sockets for the operational amplifiers, the SHT7X sensor, and the RTC clock and microSD breakout modules.</li>
		<li>Next, solder the two transistors. The board also needs to be soldered now, using pin headers. Finally, solder the connectors to the board.</li>
	</ol>
	</li>
	<li>Solder the SHT7X humidity/temperature sensor into a pin header or extension cable to reinforce the leads.</li>
	<li>Prepare a multimeter in the continuity testing or conductivity testing mode. Use the multimeter to verify that there are no short circuits between any of the pins or connections.
	<ol>
		<li>Doublecheck the positive and negative terminals of the power supply. Also, verify that each solder joint creates a stable connection between the component pins and the copper tracks of the circuit. 
		&#8203;NOTE: This step is very important; do not skip it.</li>
	</ol>
	</li>
	<li>Connect the battery terminals and&#160;cable clips to the board using a&#160;screwdriver.
	<ol>
		<li>First, use any cutting tool to strip ~4 mm of each wire end, exposing the conductive core. Next, introduce each cable into the appropriate terminal and tighten the screw with the Phillips screwdriver.</li>
		<li>Ensure and doublecheck the correct polarity of the cables, especially those of the power supply. Test the strength of the connection by pulling the cables slightly, verifying that everything is firmly connected.</li>
	</ol>
	</li>
	<li>To further reduce power consumption, remove the power LED of the microcontroller board by either desoldering or cutting off the LED diode from the board.</li>
	<li>Finally, mount the BtM board in a weatherproof enclosure to keep moisture away from the electronics.
	<ol>
		<li>Fit the enclosure with the battery pack, connecting it to the positive and negative terminals. Mount the humidity/temperature sensor outside of the box, leaving it connected to the BtM board.</li>
		<li>Route the eight pairs of crocodile clips needed for conductance measurements to the outside of the weatherproof enclosure. Last, clip each moss strand with the crocodile clips.</li>
	</ol>
	</li>
</ol>
2. Loading the Software
<ol>
	<li>Download and install the integrated development environment (IDE) 1.0.6 from the website19. The microcontroller used is an open-source physical computing platform and it comes with its own IDE. It is important to download the adequate version since there are known compatibility problems with some of the required libraries.</li>
	<li>Download the necessary libraries from the GitHub repository18: DS3234, DS3234lib3, PowerSaver, SdFat, and Sensirion.</li>
	<li>Download the main source code for the datalogger from the GitHub repository18.</li>
	<li>Open the clock.ino file to set up the current time and date. Edit the parameters for the function RTC.setDateTime with the current time and date using the following format: 
	 
	RTC.setDateTime(DD,MM,YY,hh,mm,ss); // Date: DD/MM/YY hh:mm:ss 
	 
	Here, DD is day, MM is month, YY is year, hh is hour, mm is minutes, and ss is seconds.</li>
	<li>Then, upload the clock program to the BtM board, plugging the USB-to-Serial adapter (FTDI breakout) into the microcontroller programming ports and using a mini-USB-to-USB cable to connect the board to the computer. Finally, first press Verify and, then, Upload in the IDE.</li>
	<li>Open the datalog project in the IDE and modify the datalog.ino file. Set up the start time for the logger editing the following variables: 
	 
	int dayStart = DD, hourStart = hh, minStart = mm 
	 
	Here, DD is the number of the day, hh is the starting hour of the measurements, and mm the minute of the start. 
	NOTE: The code to set up a specific time should look like this: 
	 
	RTC.setDateTime(DD,MM,YY,hh,mm,ss);// Date 01/12/17 12:00:00</li>
	<li>Set the interval between measurements (in seconds) modifying the value of the variable interval.</li>
</ol>
3. Set-up of the Measurement Probes
<ol>
	<li>Place the crocodile clips at a central position of the communities in the cases of bryophytes, fruticose lichens and foliose lichens (Figure 2). For fruticose lichens, attach the clips in the thallus and, for mosses, directly on the stem of an individual. In the case of foliose lichens, place the clips on the border of the thallus.</li>
	<li>Keep a minimum distance of ca. 5 mm between electrodes. Ensure that the clips are not easily detached before starting measurements.</li>
</ol>
4. Calibration for Conductance Measurements
<ol>
	<li>To ensure that the specimens are dry, perform the calibration at noon, on a day with low air relative humidity, preceded by at least one, and preferably two, dry days.</li>
	<li>Select a community of moss or lichens that is healthy and well-structured.</li>
	<li>Connect the datalogger to the moss or lichen, following the steps in section 3 of this protocol.</li>
	<li>Start the measurements (turn on the datalogger) and leave the BtM board running for approximately 3 min to stabilize the recorded values.</li>
	<li>Perform a precalibration test to estimate the amount of water required in each watering event. Connect the clips to the sample and add water until the conductance reaches a value that does not increase with the addition of water. This is the maximum conductance value of that sample. This value will be used to establish the watering steps of the calibration (see step 4.7.1).</li>
	<li>Wait until the conductance measures return to the initial values (the samples are dry).</li>
	<li>Then, add water sequentially with a small spray.
	<ol>
		<li>Moisten the samples with a quantity of water equivalent to 1/10 of the amount of water required to achieve the maximum conductance (see step 4.5) in the sample.</li>
		<li>Wait until the moss or lichen fully absorbs the water and the conductance measurements are stable before watering again (~1 min between each watering event).</li>
		<li>Repeat until the conductance reaches the maximum value (saturation) and the moss or lichen is fully hydrated. 
		NOTE: Each calibration test should take around 15 min, depending on the interval between the waterings, which should be 1 - 2 min.</li>
	</ol>
	</li>
	<li>After finishing the calibration, take the microSD card from the BtM board and copy the data file to a computer. 
	NOTE: The logged values can then be used as a baseline for the experiments. It is also necessary to do this step to verify that the set-up is correctly registering the conductance of the samples, just before running the actual experiment.</li>
</ol>
5. Alternative Calibration for Lab Experiments
<ol>
	<li>Fully hydrate the community of moss or lichen until an excess of external water is observed. To ensure that the community is fully hydrated, keep the community moist for 30 min.</li>
	<li>Connect the datalogger to the moss or lichen, following the steps in section 3 of this protocol.</li>
	<li>Start the measurements and leave the BtM board running for approximately 3 min to stabilize the recorded values.</li>
	<li>Wait until the conductance reaches the minimum value (desiccation) and the moss or lichen is no longer conducting electricity. 
	NOTE: Each calibration could last at least 1 h, but the duration is highly variable depending on the species. Measurements should be taken until a minimum conductance value is achieved.</li>
</ol>

<ol>
	<li>Fontaneto, D., Hortal, J., Ogilvie, L. A., Hirsch, P. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+Biogeography:+Is+Everything+Small+Everywhere.">Microbial Biogeography: Is Everything Small Everywhere.</a> Microbial Ecological Theory: Current Perspectives. , 87-98 (2012).</li><li>Proctor, M. C. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Desiccation-tolerance+in+bryophytes:+a+review.">Desiccation-tolerance in bryophytes: a review.</a> The Bryologist. 110 (4), 595-621 (2007).</li><li>Lindo, Z., Gonzalez, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Bryosphere:+An+Integral+and+Influential+Component+of+the+Earth's+Biosphere.">The Bryosphere: An Integral and Influential Component of the Earth's Biosphere.</a> Ecosystems. 13 (4), 612-627 (2010).</li><li>Elbert, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Contribution+of+cryptogamic+covers+to+the+global+cycles+of+carbon+and+nitrogen.">Contribution of cryptogamic covers to the global cycles of carbon and nitrogen.</a> Nature Geoscience. 5, 459-462 (2012).</li><li>Magill, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moss+diversity:+New+look+at+old+numbers.">Moss diversity: New look at old numbers.</a> Phytotaxa. 9 (1), 167-174 (2014).</li><li>S&#246;derstr&#246;m, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=World+checklist+of+hornworts+and+liverworts.">World checklist of hornworts and liverworts.</a> PhytoKeys. (59), 1-828 (2016).</li><li>L&#252;cking, R., Hodkinson, B. P., Leavitt, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+2016+classification+of+lichenized+fungi+in+the+Ascomycota+and+Basidiomycota+-+Approaching+one+thousand+genera.">The 2016 classification of lichenized fungi in the Ascomycota and Basidiomycota - Approaching one thousand genera.</a> The Bryologist. 119 (4), 361-416 (2016).</li><li>Bowker, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biological+Soil+Crust+Rehabilitation+in+Theory+and+Practice:+An+Underexploited+Opportunity.">Biological Soil Crust Rehabilitation in Theory and Practice: An Underexploited Opportunity.</a> Restoration Ecology. 15 (1), 13-23 (2007).</li><li>Wilske, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+CO2+exchange+of+biological+soil+crusts+in+a+semiarid+grass-shrubland+at+the+northern+transition+zone+of+the+Negev+desert,+Israel.">The CO2 exchange of biological soil crusts in a semiarid grass-shrubland at the northern transition zone of the Negev desert, Israel.</a> Biogeosciences Discussions. 5 (3), 1969-2001 (2008).</li><li>Wardle, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linking+vegetation+change,+carbon+sequestration+and+biodiversity:+insights+from+island+ecosystems+in+a+long-term+natural+experiment.">Linking vegetation change, carbon sequestration and biodiversity: insights from island ecosystems in a long-term natural experiment.</a> Journal of Ecology. 100 (1), 16-30 (2012).</li><li>Lindo, Z., Nilsson, M. -. C., Gundale, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bryophyte-cyanobacteria+associations+as+regulators+of+the+northern+latitude+carbon+balance+in+response+to+global+change.">Bryophyte-cyanobacteria associations as regulators of the northern latitude carbon balance in response to global change.</a> Global Change Biology. 19 (7), 2022-2035 (2013).</li><li>Coxson, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impedance+Measurement+of+Thallus+Moisture+Content+in+Lichens.">Impedance Measurement of Thallus Moisture Content in Lichens.</a> The Lichenologist. 23 (1), 77-84 (1991).</li><li>Raggio, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Continuous+chlorophyll+fluorescence,+gas+exchange+and+microclimate+monitoring+in+a+natural+soil+crust+habitat+in+Tabernas+badlands,+Almeria,+Spain:+progressing+towards+a+model+to+understand+productivity.">Continuous chlorophyll fluorescence, gas exchange and microclimate monitoring in a natural soil crust habitat in Tabernas badlands, Almeria, Spain: progressing towards a model to understand productivity.</a> Biodiversity and Conservation. 23 (7), 1809-1826 (2014).</li><li>Weber, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+calibration+of+a+novel+sensor+to+quantify+the+water+content+of+surface+soils+and+biological+soil+crusts.">Development and calibration of a novel sensor to quantify the water content of surface soils and biological soil crusts.</a> Methods in Ecology and Evolution. 7 (1), 14-22 (2016).</li><li>Raggio, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolic+activity+duration+can+be+effectively+predicted+from+macroclimatic+data+for+biological+soil+crust+habitats+across+Europe.">Metabolic activity duration can be effectively predicted from macroclimatic data for biological soil crust habitats across Europe.</a> Geoderma. 306, 10-17 (2017).</li><li>Young, M. H., Fenstermaker, L. F., Belnap, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+water+content+dynamics+of+biological+soil+crusts.">Monitoring water content dynamics of biological soil crusts.</a> Journal of Arid Environments. 142, 41-49 (2017).</li><li>. GitHub - united-ecology/btmboard Available from: <a target="_blank" href="https://github.com/united-ecology/btmboard">https://github.com/united-ecology/btmboard</a> (2018)</li><li>. Arduino - Software Available from: <a target="_blank" href="https://www.arduino.cc/en/Main/Software">https://www.arduino.cc/en/Main/Software</a> (2018)</li></ol>

The authors have nothing to disclose.

To our knowledge, this is the first time that a datalogger to measure temperature, humidity, and conductance simultaneously as a proxy of the water content of poikilohydric organisms has been designed based on an open-access platform. The BtM datalogger is easy to build and cost-effective, and also provides high-quality measurements of air humidity, temperature, and impedance data using minimal power.
The simple assembly is one of the main advantages of this datalogger. As it is an open-source project, we provide the data-logging software and a detailed scheme of its structure, together with a nontechnical manual for building a ready-to-use BtM datalogger. This makes the method accessible to any research group, even to those that do not work with an engineer or specialized technicians. Besides, the assembly of each datalogger requires just about 1 hour if the printed board circuit is used and about 4 hours if the circuit is mounted by the researchers. Additionally, the BtM datalogger is highly cost-efficient. The estimated cost of the components of each unit is approximately 100 euros, a fairly low price that can be reduced even further in large-scale projects by assembling batches of several dataloggers.
Although there have been several recent methodological developments aimed at implementing devices that measure different aspects related to the physiological activity of nonvascular cryptogam communities, the BtM fills an important knowledge gap. Raggio et al.15 employ Moni-Da, a monitoring system that obtains physiological and microclimatic information. The physiological activity is collected through chlorophyll a fluorescence, a method widely used in the laboratory to estimate the activity of photosynthetic organisms. Although this method is highly accurate, it is significantly more expensive than the BtM datalogger. Besides, the monitoring system is a private company product, which cuts back the autonomy of the research group.
The two other methods that have recently been published are also based on estimating water content of nonvascular cryptogams. The first is based on thermal measurements (a dual-probe heat pulse (DPHP) method). Although promising results have recently been shown by Young et al.16, the lack of any specific scheme in the paper makes assembling it without specialized knowledge highly challenging. Lastly, Weber et al.14 presented a sensor called the biocrust wetness probe (BWP), which is very similar to the BtM datalogger. However, they do not provide any scheme for its construction, which hinders the possibility of building the datalogger without the assistance of a specialist. We overcome this issue by providing not only the construction scheme but also the circuit board to assemble the datalogger. Interestingly, the BtM can be easily modified to measure biocrusts, isolated individuals, or cushions, just by changing the crocodile clips (for lichen or bryophyte individuals/cushions) to copper alloy electrode pins (for the biocrusts). If necessary, only part of the crocodiles can be replaced, allowing direct comparisons between the two measurement probe types.
When interpreting the results, the relationship between activity and water content should be carefully addressed, because the BtM does not directly measure photosynthesis. Photosynthesis and activity are closely related in nonvascular cryptogams since a dry poikilohydric organism is in metabolic cease and a wet one is active. However, the degree of photosynthetic activity cannot be inferred directly from the water content, even though a higher metabolic activity - and, thus, a higher photosynthetic activity - can be expected in a well-hydrated organism.
Critical Steps: 
Despite the ease of assembly, there are some critical steps in the protocol that should be carefully addressed by researchers when mounting the sensor. First, as emphasized in the protocol, it is quite easy to produce short circuits when soldering, which, in the worst case, could result in serious damages to the microcontroller. It is very important to check for their presence with a multimeter and to remove them before connecting the batteries. We recommend using the provided PCB design since it significantly simplifies the process and may be the best option to overcome this issue. Second, not all IDE versions are compatible with the libraries required for this datalogger. It is important to download the proper one (1.0.6) to avoid any compatibility issues. Third, it is important to notice the polarity of the batteries. A polarity inversion could result in serious damages to the hardware. Fourth, calibration is a critical step. The BtM datalogger is designed so that the higher resolution coincides with the moment in which the cryptogam goes from dry to wet state. This implies that the conductance values saturate long before the sample is saturated in water. However, if the study at hand requires a higher accuracy around other values, it can be modified. Measures beyond one order of magnitude from this reference require the resistor to be changed and a recalibration process (see below). As the environmental temperature can affect the accuracy of the measurements, we recommend taking into account this factor when calibrating. To do so, the calibration should be done at low temperatures to check for changes in the measurement accuracy and stability (see Coxson12 for temperature effects).
Modifications: 
Although most of the components of the BtM are fixed, some can be easily modified in the field without resoldering. The simplest modification is to replace the crocodile clips for other probe or measurement systems. For example, instead of the crocodile clips, a probe with two pins, such as the one suggested in Weber et al.14, can be used.
In remote environments, where changing the batteries may not be possible within the needed frequency, batteries could be complemented with a solar panel to power the BtM datalogger for longer periods.
By changing the reference resistors employed to measure the conductance, the rank of higher resolution can be easily modified to higher or lower values. If modified, we highly recommend a precise recalibration. Also, in the source code, the RValue variable, which is programmed for a resistor value of 330 K&#937;, must be assigned to the new corresponding value (datalog.ino).
Conclusion: 
Nonvascular cryptogam communities are highly diverse and play a number of different key ecological roles, so understanding their relationships with the abiotic environment is a crucial issue. The BtM datalogger has several applications that will help advance the knowledge of these relationships. For example, it will help deepen insights about the conditions where these organisms are acting as either carbon sinks or carbon sources. The fluctuations between these two roles are strongly related to abiotic conditions such as temperature and moisture3, but large amounts of data are needed to describe and understand the variations of that relationship at a global scale. This requires dense sensor networks that are possible only if they rely on low-cost and easy-to-implement equipment.
To summarize, this device is a useful tool for ecological research groups and overcomes the technical constraints of designing and building a datalogger. The combination of these two factors may lead to a popularization in the use of dataloggers to measure the water relations of nonvascular cryptogams in situ. This, in turn, can boost the establishment of medium and long-term monitoring networks. Developing these networks is essential to assessing the response of nonvascular cryptogams to local and regional environmental factors, as well as to determine their role in ecosystem processes (e.g., nutrient cycles, community assembly) and their most likely response in light of the changes on climatic and anthropic factors associated with global change.

Communities of nonvascular cryptogams are a ubiquitous and an often-neglected part of terrestrial ecosystems1. They are made up of an aggregate of very different small-sized organisms among which bryophytes and lichens are the outstanding primary producers. These two groups of organisms share a physiologic characteristic that makes them unique: poikilohydry, or the inability to actively control their internal water content. This has profound implications for their physiological processes since the metabolism ceases when the cells are dried out in response to low levels of humidity and resumes when the environment is humid again2. As a consequence, nonvascular cryptogams avoid drought instead of coping with it2, which allows these communities to survive in a wide range of environments from cold and hot deserts to the tropics3,4.
Besides, they also show relatively simple structures and have low nutrient requirements. These characteristics make them highly sensitive to microclimatic conditions. In fact, nonvascular cryptogams often occupy a niche space that is unavailable to vascular plants of a larger size, forming ecosystems in miniature that constitute an important part of the world&#39;s diversity. Bryophytes and lichens alone include almost 40,000 species (ca. 20,000 bryophytes sensu lato5,6 and ca. 20,000 lichens7). Furthermore, their contribution to the Earth&#39;s biodiversity is even larger since their communities offer shelter for a vast number of species of fungi, including a diverse flora of free-living and mycorrhizal fungi, N-fixating cyanobacteria growing as epiphytes, and a myriad of micro-invertebrates, such as tardigrades, collembola, myriapods, insects, and mites that take advantage of the water retention capacity and buffered conditions inside these miniature ecosystems.
Communities of non-vascular cryptogams also contribute to the regulation of biogeochemical carbon cycles. In dry ecosystems, the so-called biological soil crusts cover up to 40% of their surface8 and play a major role as carbon sinks. A recent review estimated that biological soil crusts of dry environments could be fixing 7% of all carbon fixed by terrestrial vegetation. Besides, in other ecosystems where either bryophytes or lichens or a combination of both are the primary producers - like some boreal forest systems or peat bogs - they produce between 30% and 100% of the total net primary productivity10,11. They are also important in ecosystems in which these organisms are not dominant, such as temperate forests. Indeed, forest floor bryophytes had an annual carbon uptake equivalent of about 10% forest floor respiration in a New Zealand temperate rainforest. Further, they are also important for nitrogen fixation, since the cyanobacteria living as epiphytes in these communities could be fixating almost 50% of the global amount of biological nitrogen4.
Due to the dependence of their physiologic activity on the availability of water in the surrounding environment, both the diversity of nonvascular cryptogam communities and their functions in the ecosystems are strongly dependent on water content2. Note that, since they cannot actively control the water content in their tissues, their roles in carbon balance and nitrogen fixation are coupled with the hydration and desiccation cycles and, therefore, depend on the interval and periodicity of the dry-wet cycles. Thus, knowing the water content status of these organisms in real-time is key to understand the functions performed by cryptogams in the ecosystems.
Despite its importance, the development of methods to measure the water content and physiological activity in poikilohydric organisms has been relatively slow. In 1991, Coxson12 made a first approach to directly measure the water content of lichens. After that, there was a gap in this kind of study until a recent development, when several works have provided methods to approximate measures of the physiologic status of nonvascular cryptogams13,14,15,16. Nevertheless, such knowledge is still scarce and scattered, and these works are mostly focused on soil crusts4,8. However, bryophytes and lichens also play a relevant role in many other ecosystems, particularly at temperate, boreal, and polar regions1, and their importance is significant not only in soil communities but also for epiphytic communities growing on trees and saxicolous communities on rocks. This lack of research is partially linked to the absence of commercially available measurement dataloggers, which forces research groups to build their own equipment. Developing a datalogger requires specific knowledge that most ecologists do not have, so it substantially increases the cost of implementing the relatively large measuring networks needed to gather representative data on the performance of nonvascular cryptogams along environmental and habitat gradients.
In this paper, we present a simple and cost-effective method to build a datalogger capable of measuring the conductance of nonvascular cryptogamic organisms simultaneously with the ambient temperature and humidity. It is programmed to record autonomously for relatively extended periods of time (up to two months) and is rugged enough to withstand harsh outdoor field conditions. Due to its simplicity, it will be a useful tool for ecologists and field biologists without specialized training in the development of dataloggers or those research groups that lack specialized staff. Therefore, this datalogger has the potential to help popularize the use of this type of device.
We developed a low-power and low-cost datalogger able to measure the conductance from up to eight different sources and record the environmental temperature and relative humidity simultaneously. The device is designed after Coxson&#39;s design12 and implemented on an open-source platform (Table of Materials). The aim was to prioritize the ease of assembly and power efficiency and to facilitate the maintenance of long-term installations. The design is derived from an article by Open Source Building Science Sensors (OSBSS)17. This design was modified by incorporating additional circuitry to read out the impedance of cryptogams and making it more compact and easier to manufacture.
The result is BtM board (Bryolichen Temperature Moisture board), an open-source printed circuit board18. Each board is controlled by a high energy-efficient microcontroller (Table of Materials). Environmental temperature and relative humidity data are gathered through a temperature and humidity sensor that comes precalibrated and, aside from its low power consumption, has an adequate price-performance ratio.
The board uses a digital communication protocol (standard SPI serial) to manage the measurement cycle. A real-time clock (DS3234) mounted on each board provides accurate timing. In order to reduce energy consumption, the processor remains in standby mode most of the time. Each time data needs to be collected, the real-time clock activates the processor and triggers the logging process. The real-time clock is also used to accurately record the date and time of each data case.
Up to eight moss and/or lichen samples can be logged in parallel using a single BtM board. When the experiment is set up, two crocodile-clip electrode probes are applied to each moss/lichen sample. Then, a voltage divider between each electrode and a resistor reference with a known value (330 K&#937; in this case) are used. This resistor value was selected through calibration and based on previous measures of the cryptogams. It provides a resolution of one order of magnitude around the reference value (100 - 1,000 K&#937;). The voltage drop is buffered and then read with the microcontroller using its analog ports (A0 - A7)18. The voltage is calculated by applying the following formula.
Vi = (ADCi x VCC) / 1023
Here, ADCi is the raw value from the ADC (Analog-to-Digital converter) of channel i, VCC is the power supply voltage (3.3 V in this case), and 1023 is the range of the ADC output. The resulting voltage Vi is then used in combination with Ohm&#39;s law to calculate the resistance (Ri, &#937;) and conductance (G, S) of each moss sample.
Ri = (VCC x RL) / Vi - RL
G = 1 / Ri
Here, RL is the value of the resistor reference (330 K&#937; in this case). The microcontroller&#39;s onboard software incorporates all these equations, so it can directly register the values of resistance and conductance.
The board also collects measurements of the ambient temperature and humidity using sensors. Then, each data point is written to a log file on a microSD card. A microSD TransFlash breakout board was mounted on each BtM board for this purpose. Finally, the microSD card can be manually collected after the experiment. All data points can be transferred to a computer for further analysis.

<table><tbody><tr><td>BtMboard circuit (PCB)</td><td></td><td></td><td>1</td></tr><tr><td>Arduino Pro Mini 328 3.3 V (APM)</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>FTDI Basic Breakout</td><td>SparkFun</td><td></td><td>1</td></tr><tr><td>MiniUSB to USB cable adapter</td><td></td><td></td><td>1</td></tr><tr><td>TLC274 operational amplifier</td><td>Texas Instruments</td><td></td><td>2</td></tr><tr><td>2.54 mm breakout pin strip</td><td></td><td></td><td>1</td></tr><tr><td>330 KOhm resistor</td><td></td><td></td><td>8</td></tr><tr><td>330 Ohm resistor</td><td></td><td></td><td>2</td></tr><tr><td>10 KOhm resistor</td><td></td><td></td><td>1</td></tr><tr><td>2N3904 Transistor</td><td></td><td></td><td>2</td></tr><tr><td>Bornier connector, 2x1 5.08 mm</td><td></td><td></td><td>9</td></tr><tr><td>1.5 V AA battery</td><td></td><td></td><td>3</td></tr><tr><td>3xAA battery holder with switch</td><td></td><td></td><td>1</td></tr><tr><td>Sensirion SHT71</td><td>Sensirion</td><td></td><td>1</td></tr><tr><td>DS3234 RTC Breakout (clock)</td><td>SparkFun</td><td></td><td>1</td></tr><tr><td>CR1225 3 V Coin-cell battery</td><td></td><td></td><td>1</td></tr><tr><td>MicroSD Transflash breakout</td><td>SparkFun</td><td></td><td>1</td></tr><tr><td>Crocodile clip connector</td><td></td><td></td><td>16</td></tr><tr><td>Weatherproof enclosure box</td><td></td><td></td><td>1</td></tr><tr><td>12 AWG stranded cable spool</td><td></td><td></td><td>1</td></tr><tr><td>Cutting pliers</td><td></td><td></td><td>1</td></tr><tr><td>30 W soldering iron</td><td></td><td></td><td>1</td></tr><tr><td>Solder wire spool</td><td></td><td></td><td>1</td></tr><tr><td>Arduino IDE 1.0.6</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>Arduino library DS3234</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>Arduino library DS3234lib3</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>Arduino library Powersaver</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>Arduino library SdFat</td><td>Arduino</td><td></td><td>1</td></tr><tr><td>Arduino library Sensirion</td><td>Arduino</td><td></td><td>1</td></tr></tbody></table>

btm, a low-cost open-source datalogger to estimate the water content of nonvascular cryptogams

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the carbon and nitrogen cycles in many ecosystems. Being poikilohydric organisms, they do not actively control their internal water content and need a humid environment to activate their metabolism. Therefore, studying water relationships of nonvascular cryptogams is crucial to understand both their diversity patterns and their functions in the ecosystems. We present the BtM datalogger, a low-cost open-source platform for the study of the water content of nonvascular cryptogams. The datalogger is designed to measure ambient temperature, humidity, and conductance from up to eight samples simultaneously. We provide a design for a printed circuit board (PCB), a detailed protocol to assemble the components, and the required source code. All this makes the assembly of the BtM datalogger accessible to any research group, even to those without previous specialized knowledge. Therefore, the design presented here has the potential to help popularize the use of this type of device among ecologists and field biologists.

We analyzed the changes in conductance in two species of mosses, Dicranum scoparium Hedw. and Homalothecium aureum (Spruce) H. Rob. (Figure 3), during the calibration process in lab conditions. Mats of the two mosses were kept for 24 h in silica gel and placed in an artificial substrate (i.e., wadding) that kept their original structure (Figure 2). Then, the samples were watered 15x to 20x with a spray in 1 min intervals. Each watering event consisted of ca. 0.1 mL of water. In both species, a high correlation between the water added and the sample conductance (D. scoparium rS = 0.88, p &#60; 0.001; H. aureum rs = 0.87, p &#60; 0.001) was observed. There was a high increase in the conductance (from 0% to 25% at least) just in the first water addition, and the measures reached their maximum conductance at 4 mL for D. scoparium and 10 mL for H. aureum. It is important to remark that the relationship between the quantity of water and conductance is logarithmic. Therefore, the values of conductance need to be transformed to have a linear relationship between both variables, and their relationship should be modeled using nonlinear regression.
We found some variability among the samples (see the different colors in Figures 3a and 3b), although all samples belonging to the same species drew a similar curve. The variation between samples can be attributed to differences in biomass and morphology of the patches. Samples in the field are very likely to show this type of variability, so taking several measures of each community type is recommended. Not surprisingly, the highest variability was found among species, since species differ in several fundamental traits (e.g., the aggregation of the mats or morphology). To control for intra- and interspecies variability, we recommend calibrating each clip until achieving the maximum conductance values and, then, rescaling the results for each clip so that the values go from 0 to 100. Consider that absolute conductance values depend on the distance between clips and the basal conductance of the stems, so the values they provide are not directly comparable.
The amount of water added in each watering event of the calibration process is crucial and will strongly affect the results. Here, the aim was to have several watering events in the range of maximum accuracy of the BtM. We present an example of a calibration curve when too much water is added in each step (Figure 4). If the sample is overwatered in the first watering event, the increase in the conductance cannot be appreciated and the calibration will be inaccurate. This may lead to biases in the range where nonvascular cryptogams are active, which are the most interesting measurements taken with the BtM.
We also analyzed the desiccation curve of the same two species (H. aureum and D. scoparium), to provide an alternative calibration procedure. Mats of the two mosses were watered overnight to ensure they were fully saturated. Then, a representative stem of each mat was extracted and placed in a stable, controlled environment and the conductance was recorded continuously. As for the other calibration measure, the values of conductance need to be transformed to have a linear relationship between both variables, and their relationship should be modeled using nonlinear regression.
Figures 5a and 5b show the dessication curves of H. aureum and the D. scoparium variability among samples of the same species. The intra- and interspecies variability found were quite large and, as in the other calibration procedure, could be attributed to differences in biomass and morphology of each stem. To control for it, we recommend performing at least three measurements per species. Absolute conductance values are not directly comparable in this calibration procedure, as they also depend on the distance between clips and basal conductance of the stems.
We present an example of field data after a rain event occurred between June 23 - 26, 2014. We show the daily variation in the percentage of conductance (Figure 6a), relative humidity (Figure 6b), and precipitation (Figure 6c) for one species of moss (Syntrichia ruralis (Hedw.) F. Weber &#38; D. Mohr). There was a strong relationship between the conductance of the moss, the precipitation events, and the relative humidity of the air. During the period analyzed, there were two peaks in the conductance and humidity as a consequence of two precipitation events. The first one occurred just before midnight of June 23 and the second one after the midday of June 24. About 8 h after the first rain event, we observed a decline in the relative humidity of the air, followed by a sudden drop in the moss conductance that goes below 25%. The second rain event was smaller and, consequently, produced a smaller peak in conductance. After this rain event, the moss did not dry out immediately but stayed hydrated while the humidity was above 75%.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58700/58700fig1.jpg" /> 
Figure 1: Assembly schematic of the BtM datalogger. The schematic includes a picture of the BtM board and the placement of each component on the board. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig1large.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/58700/58700fig2.jpg" /> 
Figure 2: Correct placement of the clips in a moss (Homalothecium aureum). The image shows how to place the clips to maintain a minimal distance between the clips without damaging the bryophyte. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58700/58700fig3.jpg" /> 
Figure 3: The response of conductance to water addition. These panels show the response of conductance to water addition in&#160;(a) Dicranum scoparium and (b) H. aureum. The colors show the different replicates. The data points are the average of the log-transformed conductance in an interval between 10 and 30 s after the watering event. The error bars represent the standard deviation of the data in that interval. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig3large.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/58700/58700fig4.jpg" /> 
Figure 4: Response of log-transformed conductance to water addition in D. scoparium when the amount of added water is too large to allow calibration. The error bars represent the standard deviation of the data in that interval. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig4large.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/58700/58700fig5.jpg" /> 
Figure 5: Desiccation curves. These panels show the desiccation curves of&#160;(a) D. scoparium and (b) H. aureum. The data points are the average of the log-transformed conductance measured every 30 s. Black points show the mean of the three replicates and the error bars represent the standard deviation of the data in that interval. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig5large.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/58700/58700fig6.jpg" /> 
Figure 6: Daily variation in a moss&#39; (Syntrichia ruralis) conductance, precipitation, and relative humidity. The measures were taken in soil communities of the Cantoblanco, Campus of the Universidad Aut&#243;noma de Madrid, Spain. The conductance and relative humidity were measured with the BtM prototype, while the precipitation data comes from a weather station placed a few meters away from the measurement location. <a href="https://www.jove.com/files/ftp_upload/58700/58700fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Date/Time</td>
			<td>Temp(C)</td>
			<td>RH(%)</td>
			<td>Conductance(KMho)</td>
		</tr>
		<tr>
			<td>11/03/18 12:00</td>
			<td>26.6</td>
			<td>66.6</td>
			<td>139.53</td>
		</tr>
		<tr>
			<td>11/03/18 12:00</td>
			<td>26.6</td>
			<td>66.4</td>
			<td>167.92</td>
		</tr>
		<tr>
			<td>11/03/18 12:00</td>
			<td>26.8</td>
			<td>66.4</td>
			<td>199.14</td>
		</tr>
		<tr>
			<td>11/03/18 12:00</td>
			<td>26.9</td>
			<td>66.4</td>
			<td>212.75</td>
		</tr>
		<tr>
			<td>11/03/18 12:00</td>
			<td>26.6</td>
			<td>66.6</td>
			<td>217.15</td>
		</tr>
		<tr>
			<td>11/03/18 12:01</td>
			<td>26.9</td>
			<td>66.7</td>
			<td>218.93</td>
		</tr>
		<tr>
			<td>11/03/18 12:01</td>
			<td>27</td>
			<td>66.8</td>
			<td>139.53</td>
		</tr>
		<tr>
			<td>11/03/18 12:01</td>
			<td>27.1</td>
			<td>66.9</td>
			<td>164.28</td>
		</tr>
		<tr>
			<td>11/03/18 12:01</td>
			<td>27.1</td>
			<td>67.3</td>
			<td>194.21</td>
		</tr>
		<tr>
			<td>11/03/18 12:01</td>
			<td>27.3</td>
			<td>67.3</td>
			<td>209.28</td>
		</tr>
	</tbody>
</table>
Table 1: Example of the BtM output.

Watch this Scientific Journal Video about BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams at JoVE.com

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

We present a simple and cost-effective method to build an open-source datalogger that measures the conductance of nonvascular cryptogams together with the environmental temperature and humidity. We describe the hardware design of the datalogger and provide step-by-step assembly instructions, the list of required open-source logging software, the code to run the datalogger, and a calibration protocol.

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the ...

btm-low-cost-open-source-datalogger-to-estimate-water-content

Research

JoVE Journal

Environment

8.0K Views.  Real Jardín Botánico (CSIC-RJB). Niet-invasieve cryptogamen zijn cruciaal voor verschillende ecosysteemfuncties. Zij zijn bijvoorbeeld verantwoordelijk voor bijna 50% van de biologisch vaste stikstof. We hebben een datalogger ontworpen om vragen op het gebied van cryptogamic ecologie aan te pakken.De methoden meten ter plaatse de periode waarin deze organismen gehydrateerd blijven, met betrekking tot bryophyte activiteit met milieuomstandigheden. Het belangrijkste voordeel van deze techniek is dat het een lage kosten,...

Video: BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled Earth-system processes. Such knowledge is imperative in improving predictions of hydro-biogeochemical cycles, especially under climate change scenarios. We present an experimental method, designed to capture sub-surface heterogeneity of an initially homogeneous soil system. This method is based on destructive sampling of a soil lysimeter designed to simulate a small-scale hillslope. A weighing lysimeter of one cubic meter capacity was divided into sections (voxels) and was excavated layer-by-layer, with sub samples being collected from each voxel. The excavation procedure was aimed at detecting the incipient heterogeneity of the system by focusing on the spatial assessment of hydrological, geochemical, and microbiological properties of the soil. Representative results of a few physicochemical variables tested show the development of heterogeneity. Additional work to test interactions between hydrological, geochemical, and microbiological signatures is planned to interpret the observed patterns. Our study also demonstrates the possibility of carrying out similar excavations in order to observe and quantify different aspects of soil-development under varying environmental conditions and scale.

This study presents an excavation method for investigating subsurface hydrological, geochemical, and microbiological heterogeneity of a soil lysimeter. The lysimeter simulates an artificial hillslope which was initially under homogeneous condition and had been subjected to approximately 5,000 mm of water over eight cycles of irrigation in an 18-month period.

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled ...

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Vernal ponds, also referred to as vernal pools, provide critical ecosystem services and habitat for a variety of threatened and endangered species. However, they are vulnerable parts of the landscapes that are often poorly understood and understudied. Land use and management practices, as well as climate change are thought to be a contribution to the global amphibian decline. However, more research is needed to understand the extent of these impacts. Here, we present methodology for characterizing a vernal pond's morphology and detail a monitoring station that can be used to collect water quantity and quality data over the duration of a vernal pond's hydroperiod. We provide methodology for how to conduct field surveys to characterize the morphology and develop stage-storage curves for a vernal pond. Additionally, we provide methodology for monitoring the water level, temperature, pH, oxidation-reduction potential, dissolved oxygen, and electrical conductivity of water in a vernal pond, as well as monitoring rainfall data. This information can be used to better quantify the ecosystem services that vernal ponds provide and the impacts of anthropogenic activities on their ability to provide these services.

Understanding the ecosystem services and processes provided by vernal ponds and the impacts of anthropogenic activities on their ability to provide these services requires intensive hydrologic monitoring. This sampling protocol using in-situ monitoring equipment was developed to understand the impact of anthropogenic activities on water levels and quality.

Vernal ponds, also referred to as vernal pools, provide critical ecosystem services and habitat for a variety of threatened and endangered species. ...

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 ...

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. However, at the soil surface these variables are highly dynamic and standard sensors do not explicitly measure temperature or moisture in the upper few millimeters of the soil profile. This paper describes methods for manufacturing simple, inexpensive sensors that simultaneously measure the temperature and moisture of the upper 5 mm of the soil surface. In addition to sensor construction, steps for quality control, as well as for calibration for various substrates, are explained. The sensors incorporate a Type E thermocouple to measure temperature and assess soil moisture by measuring the resistance between two gold-plated metal probes at the end of the sensor at a depth of 5 mm. The methods presented here can be altered to customize probes for different depths or substrates. These sensors have been effective in a variety of environments and have endured months of heavy rains in tropical forests as well as intense solar radiation in deserts of the southwestern U.S. Results demonstrate the effectiveness of these sensors for evaluating warming, drying, and freezing of the soil surface in a global change experiment.

Accurately measuring temperature and water content of the upper 5 mm of the soil surface can improve our understanding of environmental controls on biological, chemical, and physical processes. Here we describe a protocol for manufacturing, calibrating, and conducting measurements with soil surface temperature and moisture sensors.

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. ...

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

In Situ Soil Moisture Sensors in Undisturbed Soils

Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology.
Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations.
The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.

In Situ Soil Moisture Sensors in Undisturbed Soils

The determination of soil water content is a critical mission requirement for many state and federal agencies. This protocol synthesizes multi-agency efforts to measure soil water content using buried in situ sensors.

Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture ...

A Low-Cost Method of Measuring the In Situ Primary Productivity of Periphyton Communities of Lentic Waters

Measuring the in situ primary productivity of periphyton during the growing season gradient can elucidate the quantitative effect of environmental drivers (mainly phosphorus concentration and light intensity) and species composition on primary productivity. Primary productivity is mainly driven by light intensity, temperature, availability of nutrients, and distribution of the ionic species of the carbonate system in the respective depths of the euphotic zone. It is a complex system that is very difficult to simulate in the laboratory. This cheap, transportable, and easy-to-build floating barge allows measuring the primary productivity accurately-directly under the actual natural conditions. The methodology is based on measuring the primary productivity in real time using noninvasive oxygen sensors integrated into tightly sealed glass jars, enabling online oxygen flux monitoring and providing new insights into metabolic activities. Detailed seasonal in situ measurements of gross primary productivity of microbial mats (or other benthic organisms) can improve current knowledge of the processes controlling primary productivity dynamics in lentic waters.

A Low-Cost Method of Measuring the In Situ Primary Productivity of Periphyton Communities of Lentic Waters

Presented here is a cost-effective and transportable method/facility for measuring the primary productivity of microbial mats under actual in situ environmental temperature and light conditions. The experimental setup is based on widely available materials and can be used under various conditions while offering the advantages of laboratory-based models.

Measuring the in situ primary productivity of periphyton during the growing season gradient can elucidate the quantitative effect of environmental drivers ...

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus playing a critical role in regulating the methane cycle. The wetlands in South Texas, which are subject to frequent weather events, fluctuating salinity levels, and anthropogenic activities due to climate change, influence methane cycling. Despite the ecological importance of these processes, methane cycling in South Texas coastal wetlands remains insufficiently explored. To address this gap, we developed and optimized a method for detecting genes related to methanogens and methanotrophs, including mcrA as a biomarker for methanogens and pmoA1, pmoA2, and mmoX as biomarkers for methanotrophs. Additionally, this study aimed to visualize the spatial and temporal distribution patterns of methanogen and methanotroph abundance utilizing the geographic information system (GIS) software ArcGIS Pro. The integration of these molecular techniques with advanced geospatial visualization provided critical insights into the spatial and temporal distribution of methanogen and methanotroph communities across South Texas wetlands. Thus, the methodology established in this study offers a robust framework for mapping microbial dynamics in wetlands, enhancing our understanding of methane cycling under varying environmental conditions, and supporting broader ecological and environmental change studies.

The protocol detects key methane-cycling genes in South Texas coastal wetlands and visualizes their spatial distribution to enhance understanding of methane regulation and its environmental impacts in these dynamic ecosystems.

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus ...

Measuring Spatial and Temporal Ca2+ Signals in Arabidopsis Plants

Developmental and environmental cues induce Ca2+ fluctuations in plant cells. Stimulus-specific spatial-temporal Ca2+ patterns are sensed by cellular Ca2+ binding proteins that initiate Ca2+ signaling cascades. However, we still know little about how stimulus specific Ca2+ signals are generated. The specificity of a Ca2+ signal may be attributed to the sophisticated regulation of the activities of Ca2+ channels and/or transporters in response to a given stimulus. To identify these cellular components and understand their functions, it is crucial to use systems that allow a sensitive and robust recording of Ca2+ signals at both the tissue and cellular levels. Genetically encoded Ca2+ indicators that are targeted to different cellular compartments have provided a platform for live cell confocal imaging of cellular Ca2+ signals. Here we describe instructions for the use of two Ca2+ detection systems: aequorin based FAS (film adhesive seedlings) luminescence Ca2+ imaging and case12 based live cell confocal fluorescence Ca2+ imaging. Luminescence imaging using the FAS system provides a simple, robust and sensitive detection of spatial and temporal Ca2+ signals at the tissue level, while live cell confocal imaging using Case12 provides simultaneous detection of cytosolic and nuclear Ca2+ signals at a high resolution.

Measuring Spatial and Temporal Ca2+ Signals in Arabidopsis Plants

Ca2+ signaling regulates diverse biological processes in plants. Here we present approaches for monitoring abiotic stress induced spatial and temporal Ca2+ signals in Arabidopsis cells and tissues using the genetically encoded Ca2+ indicators Aequorin or Case12.

Developmental and environmental cues induce Ca2+ fluctuations in plant cells. Stimulus-specific spatial-temporal Ca2+ patterns are sensed by cellular Ca2+ ...

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Samenvatting

Meer video's verkennen