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. 2016 Oct 11;3:160087. doi: 10.1038/sdata.2016.87

Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors

Brian Helmuth 1,a, Francis Choi 1, Allison Matzelle 1, Jessica L Torossian 1, Scott L Morello 2, KAS Mislan 3, Lauren Yamane 4, Denise Strickland 5,*, P Lauren Szathmary 5,*, Sarah E Gilman 6, Alyson Tockstein 5,*, Thomas J Hilbish 5, Michael T Burrows 7, Anne Marie Power 8, Elizabeth Gosling 9, Nova Mieszkowska 10,*, Christopher DG Harley 11, Michael Nishizaki 12,*, Emily Carrington 12, Bruce Menge 13, Laura Petes 13,*, Melissa M Foley 13,*, Angela Johnson 13, Megan Poole 13, Mae M Noble 13,*, Erin L Richmond 13,*, Matt Robart 13,*, Jonathan Robinson 13, Jerod Sapp 13, Jackie Sones 14, Bernardo R Broitman 15, Mark W Denny 16, Katharine J Mach 16,*, Luke P Miller 16,*, Michael O’Donnell 16,*, Philip Ross 17, Gretchen E Hofmann 18, Mackenzie Zippay 18,*, Carol Blanchette 18,*, JA Macfarlan 18,*, Eugenio Carpizo-Ituarte 19, Benjamin Ruttenberg 19,*, Carlos E Peña Mejía 19,*, Christopher D McQuaid 20, Justin Lathlean 20, Cristián J Monaco 20, Katy R Nicastro 20,*, Gerardo Zardi 20
PMCID: PMC5058338  PMID: 27727238

Abstract

At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10–30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0–2.5 °C, during daily fluctuations that often exceeded 15°–20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on ‘habitat-level’ measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.

Subject terms: Ecophysiology, Marine biology, Climate-change impacts

Background & Summary

Increasingly, researchers are emphasizing the need to consider physiological mechanisms when forecasting the effects of global climate change on organisms and ecosystems1–3. Specifically, studies have highlighted a need to understand how environmental conditions vary in space and time4 in addition to the details of how organisms respond to those variables5–8 as a means of evaluating inter- and intraspecific vulnerability (‘winners and losers’)9,10, the probability of invasion by non-native species11,12, changes in patterns of abundance and distribution13,14, and declines in biodiversity15 and ecosystem services16.

Notably, there is concern that simple correlations between environmental measurements (such as air, land surface and sea surface temperature) and species distributions may fail under the novel conditions presented by climate change17, highlighting the need to extrapolate from experiments conducted under controlled conditions to projections of future climate impacts3,18. There has also been an emphasis on considering the cumulative impacts of physiological stress14,19 on patterns of growth20 and reproduction21 rather than focusing solely on lethal extremes19.

However, making connections between the lab and field can be far more complex than is often assumed4. For example, a number of theoretical and empirical studies have explored the often over-riding importance of spatial and temporal variability in environmental parameters9,22, which is not captured when experiments are based only on monthly, yearly or decadal averages23,24. Moreover, while large-scale measurements of environmental conditions made by satellites, buoys, and weather stations provide critical insights into rates of environmental change on large scales25, at a proximal level these habitat-level measurements may not always serve as good indicators of physiological stress4,26. In fact, the only ‘environmental signals’ that matter to an organism are those that the organism actually experiences27. Making connections across scales that span from organismal to biogeographic is no easy matter, but is crucial if we are to effectively forecast ongoing responses to environmental change28,29.

One of the most obvious examples of the complex ways climate defines weather patterns, and weather then drives niche-level organismal responses30, is how climate change is ultimately reflected as changes in plant and animal body temperatures. The vast majority of organisms on Earth are ectothermic poikilotherms, so that their body temperatures and thus levels of physiological performance change with ambient environmental conditions. For terrestrial and intertidal ectotherms (and even some shallow-water corals31), body temperatures are driven by multiple environmental parameters, most notably solar radiation, air and water temperatures and wind speed32–34. The structure of an organism’s microhabitat, and especially its exposure to direct solar radiation, can have enormous implications for its body temperature, such that animal temperatures are only close to air temperature in fully shaded microhabitats26,35. While many animals can behaviourally select among these microhabitats as a means of thermoregulation36, others are functionally sessile and thus have body temperatures determined by very local topography. To further complicate matters, the size, morphology and colour of organisms, as well as their ability to form aggregations37,38 can affect heat exchange so that two organisms exposed to identical microclimatic conditions can have very different body temperatures39,40. To contend with these issues, multiple authors have developed heat budget models that factor-in the characteristics of the organism26,33,41 to predict body temperatures using weather data as inputs.

An alternative approach—and one that is required to validate biophysical (heat budget) models—is to use in situ sensors specifically tailored to record temperatures relevant to the organism being studied, either directly or through the use of biomimics42. Biomimetic sensors (biomimics) match the thermal characteristics (size, morphology, colour, material properties) of living organisms43,44, serving as an effective tool for recording organismal body temperature in their natural environment45,46. Here we report on a long-term data set of temperatures recorded by biomimetic loggers thermally matched to bivalves (mussels) in the intertidal zone, one of the most physiologically harsh habitats on Earth. Over the course of a 24-hr period, intertidal animals and algae are alternately exposed to water at high tide and to air, wind and solar radiation at low tide. Thus, their temperature not only depends on local weather conditions but also on the timing and duration of low tide47. We have previously shown, for example, that consistent differences in the timing of low tide relative to high levels of solar radiation create geographic mosaics in low tide temperature, where mussel body temperatures at higher latitude sites can be much higher than those at low latitude sites40,47,48. As ecosystem engineers49 mussels in particular have a large influence on the stability and biodiversity of the intertidal community and so quantifying their survival and physiological performance has significant ecosystem-level consequences50,51.

Methods

We used biomimetic loggers to estimate temperatures of the mussels Mytilus californianus (West coast of North America), M. edulis and Geukensia demissa (East coast of North America), M. chilensis (Chile), Perna perna (South Africa) and P. canaliculus (New Zealand). We also deployed unmodified commercial loggers directly on rock surfaces at multiple sites (Australia, Ireland, Mexico, Scotland, U.K., U.S.) that recorded temperatures relevant to barnacles, newly settled mussels and other organisms that are sufficiently small that their temperatures mirror those of the underlying rock52.

Each biomimetic sensor (‘Robomussel’; Fig. 1) consisted of either a commercially-available TidbiT logger (TB132-20+50 and UTB1-001; Onset Computer Corporation, Pocasset, MA) encased in black-coloured polyester resin (Evercoat Premium Marine Resin, Illinois Tool Works, Inc.), or a real mussel shell filled with silicone and encasing a Tidbit or a Thermochron iButton logger (DS1922L-F5; Maxim Integrated, San Jose, California). Both instruments are factory calibrated: Tidbit loggers have a reported accuracy of 0.21 °C and a stability (drift) of 0.1 °C per year (http://www.onsetcomp.com/products/data-loggers/utbi-001) and ibuttons have an accuracy of 0.5 °C (https://datasheets.maximintegrated.com/en/ds/DS1922L-DS1922T.pdf); the drift is reported by the manufacturer to be negligible, especially when compared to the ~2 °C accuracy of the biomimic loggers (see Technical Validation below). Because of loss due to waves, each logger was typically used for only 2–3 years. Details on logger designs and field tests are described in detail in previous publications44,45,53. In brief, logger thermal characteristics were calculated using empirical measurements of shell and tissue mass against length from adult Mytilus californianus collected on the west coast of North America. In addition to morphology (which determines convective heat flux) and colour (which affects solar heat load), the primary consideration is the maintenance of thermal inertia (the tendency of an object to resist temperature change as a function of external forcing). Mass/length relationships were combined with measurements of the specific heat capacity of shell and tissue to estimate total thermal inertia as a function of size45. This was then compared to the thermal mass of polyester resin mussels of different lengths. The point where the two curves intersect is~8 cm shell length; this was the size of the epoxy loggers. Silicone molds were cast from a representative 8 cm mussel, and were in turn used to pour two-part polyester resin (Evercoat) around the commercial TidbiT logger.

Figure 1. Epoxy ‘robomussel’ biomimetic logger (~8 cm in length) deployed in growth position in a Mytilus californianus bed.

Figure 1

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Loggers were designed to match the thermal characteristics of bivalves and were typically made of epoxy (as shown) but real shells filled with silicone were also used, especially for smaller (4 cm) mussels.

In some cases, iButton loggers were encased in ~8 cm mussel shells filled with silicone, which has a mass*specific heat similar to that of water. Comparisons of these instruments against adjacent mussels showed that silicone-filled shells recorded temperatures within ~1 °C of living animals54. However, these loggers were considerably less durable and required more frequent maintenance (~bimonthly) than epoxy mussels (every 6–10 months), and so were used only infrequently at most sites. At some sites where the targeted mussel species is smaller (e.g., M. edulis in the Gulf of Maine), we used 4 cm mussel shells. Loggers of differing size were never used at the same site, and are distinguished from one another in the database. Nevertheless, any direct comparison between data collected by loggers of different sizes should be made with caution, as size can affect mussel temperature by several degrees55.

Robomussels were deployed primarily on hard rock substrate, in growth position (posterior upward) in intact beds using Z-spar splash zone epoxy putty (Fig. 1). Care was taken to ensure that the logger was completely surrounded by other mussels, as tests showed that loggers deployed as solitary individuals tended to yield anomalously high readings. On the east coast of North America, loggers were also deployed at soft sediment (marsh) sites in mud substrate by attaching the loggers to dowel rod.

Deployment began in 1998 at the Hopkins Marine Station in Pacific Grove, California54, and was expanded to other sites beginning in 2000 (Table 1 (available online only), Fig. 2). Total deployment time varied by location, ranging from less than a year to almost 18 years (average deployment time of 4 years). The number of loggers deployed and lost due to wave dislodgement also varied at each site, but a standard protocol was to deploy at least 3 loggers in the middle of mussel beds on horizontal, unshaded surfaces. At most sites, loggers were deployed at the upper edge of the mussel bed (‘upper’), half way between the upper and mid levels (‘upper mid’), mid level (‘mid’), half way between the mid and lower edge of the bed (‘lower mid’) and at the bottom of the mussel bed (‘lower’).

Table 1. Metadata describing sites where loggers were deployed and type of biomimetic sensor deployed.

Site Code Site name Region Country Logger type Latitude Longitude
AUNSBE Bermagui New South Wales Australia Tidbit −36.4229 150.0824
AUNSCB Cape Byron New South Wales Australia Tidbit −28.6337 153.6382
AUNSGB Garie Beach New South Wales Australia Tidbit −34.1734 151.0646
AUNSHP Haycock Point New South Wales Australia Tidbit −36.9501 149.9416
AUNSKI Kiama New South Wales Australia Tidbit −34.6649 150.8557
AUNSMR Mimosa Rocks New South Wales Australia Tidbit −36.5842 150.0510
AUNSPM Port Macquarie New South Wales Australia Tidbit −31.4620 152.9366
AUQLNH Noosa Heads Queensland Australia Tidbit −26.3793 153.1026
AUVICP Cape Paterson Victoria Australia Tidbit −38.6743 145.6331
AUVIKI Kilcunda Victoria Australia Tidbit −38.5519 145.4669
AUVIMA Mallacoota Victoria Australia Tidbit −37.5734 149.7659
CABCSI Seppings Island British Columbia Canada 8 cm epoxy Robomussel 48.8391 −125.2076
CLCOTE El Temblador Coquimbo Chile 8 cm epoxy Robomussel −29.5000 −71.3200
CLCOGU Guanaqueros Coquimbo Chile 8 cm epoxy Robomussel −30.1800 −71.4700
CLCOPT Punta Talca Coquimbo Chile 8 cm epoxy Robomussel −30.9200 −71.5000
GBENSP Swanage Peveril Pt England United Kingdom ibutton 50.6122 −2.1375
GBSCBK Back Scotland United Kingdom ibutton 56.4519 −5.4483
GBSCDS Dunstaff Scotland United Kingdom ibutton 56.4549 −5.4418
GBSCPP Pump Scotland United Kingdom ibutton 56.4531 −5.4428
IECLBH Black Head Clare Ireland ibutton 53.1542 −9.2648
IECLCQ Coolsiva Quay Clare Ireland ibutton 53.1428 −9.2261
IEGWBA Ballynahown Galway Ireland ibutton 53.2217 −9.5083
IEMYDO Dooega Mayo Ireland ibutton 53.9210 −10.0204
IEMYSA Saula Mayo Ireland ibutton 53.9535 −9.9271
MXBCAB Punta Abreojo Baja Mexico 8 cm epoxy Robomussel, Tidbit 26.7262 −113.5450
MXBCBE Baja Escorpion Baja Mexico 8 cm epoxy Robomussel 26.2377 −112.4786
MXBCBM Bajamar Baja Mexico 8 cm epoxy Robomussel 31.9803 −116.7939
MXBCBT Baja Tortugas Baja Mexico 8 cm epoxy Robomussel, Tidbit 27.6849 −114.9365
MXBCCR Los Cerritos Baja Mexico 8 cm epoxy Robomussel, Tidbit 23.3288 −110.1811
MXBCER Erendira Baja Mexico 8 cm epoxy Robomussel 31.3203 −116.4362
MXBCES Esmeralda Baja Mexico 8 cm epoxy Robomussel 28.5168 −114.0724
MXBCOJ Los Ojitos Baja Mexico 8 cm epoxy Robomussel 28.8823 −114.4238
MXBCPB Punta Baja Baja Mexico 8 cm epoxy Robomussel 29.9497 −115.8134
MXBCPM Punta Morro Baja Mexico 8 cm epoxy Robomussel 31.8614 −116.6678
NZAKAW Anawhata Auckland New Zealand 8 cm epoxy Robomussel −36.9170 174.4500
NZAKPK Pakiri Auckland New Zealand 8 cm epoxy Robomussel −36.2598 174.7531
NZCBBT Box Thumb Canterbury New Zealand 8 cm epoxy Robomussel −43.3506 172.7902
NZCMWW Whau Whau Beach Coromandel New Zealand 8 cm epoxy Robomussel −36.7789 175.7486
NZNLEB Elliots Beach Northland New Zealand 8 cm epoxy Robomussel −35.1148 173.9604
NZNLHH Herekino Harbour Northland New Zealand 8 cm epoxy Robomussel −35.2946 173.1571
NZNLMB Maunganui Bluff Northland New Zealand 8 cm epoxy Robomussel −36.7516 173.5695
NZWCWB Woodpecker Bay West Coast New Zealand 8 cm epoxy Robomussel −42.0190 171.2271
USCAAG Alegria California USA 8 cm epoxy Robomussel, ibutton 34.4672 −120.2770
USCABD Bodega Reserve California USA 8 cm epoxy Robomussel, ibutton 38.3185 −123.0740
USCABH Boat House California USA 8 cm epoxy Robomussel 34.7188 −120.6088
USCACA Cambria California USA 8 cm epoxy Robomussel 35.5400 −121.0929
USCACI Bird Rock California USA 8 cm epoxy Robomussel 33.4514 −118.4861
USCACM Cape Mendocino California USA 8 cm epoxy Robomussel 40.3480 −124.3650
USCACP Coal Oil pt California USA 8 cm epoxy Robomussel, ibutton 34.4067 −119.8783
USCAFR Fraser California USA 8 cm epoxy Robomussel 34.0627 −119.9192
USCAHS Hopkins California USA 8 cm epoxy Robomussel, ibutton 36.6219 −121.9053
USCAJA Jalama California USA 8 cm epoxy Robomussel, ibutton 34.4952 −120.4969
USCALL Lompoc Landing California USA 8 cm epoxy Robomussel, ibutton 34.7191 −120.6089
USCALS Lompoc South California USA 8 cm epoxy Robomussel, ibutton 34.7143 −120.6075
USCAPD Piedras California USA 8 cm epoxy Robomussel 35.6658 −121.2867
USCAPR Prisoners Harbor California USA 8 cm epoxy Robomussel 34.0204 −119.6866
USCATL Trailer California USA 8 cm epoxy Robomussel 34.0517 −119.9032
USCATR Terrace Point California USA 8 cm epoxy Robomussel 41.0621 −124.1493
USCAVD Trinidad California USA 8 cm epoxy Robomussel 41.0621 −124.1493
USCAVL Valley California USA 8 cm epoxy Robomussel 33.9837 −119.6658
USCAWL Willows California USA 8 cm epoxy Robomussel 33.9618 −119.7549
USMADC Dorothy Cove Massachusetts USA 4 cm shell robomussel 42.4238 −70.9208
USMAFR Forest River Massachusetts USA 4 cm shell robomussel 42.4976 −70.8868
USMANP East Point Massachusetts USA 4 cm shell robomussel 42.4200 −70.9022
USMAOP Obear Park Massachusetts USA 4 cm shell robomussel 42.5451 −70.9014
USMAPH Pumphouse Massachusetts USA 4 cm shell robomussel 42.4168 −70.9067
USORBB Boiler Bay Oregon USA 8 cm epoxy Robomussel, ibutton 44.8306 −124.0601
USORCA Cape Arago Oregon USA 8 cm epoxy Robomussel 43.3066 −124.4024
USORFC Fogarty Creek Oregon USA 8 cm epoxy Robomussel 44.8373 −124.0585
USORSH Strawberry Hill Oregon USA 8 cm epoxy Robomussel, ibutton 44.2499 −124.1136
USSCOL Oyster Landing South Carolina USA 8 cm Shell Robomussel 33.3495 −79.1888
USWACC Colins Cove Washington USA 8 cm epoxy Robomussel, ibutton 48.5494 −123.0060
USWACP Cattle Point Washington USA 8 cm epoxy Robomussel, ibutton 48.4514 −122.9618
USWALB Landing Beach Washington USA 8 cm epoxy Robomussel 48.3938 −124.7355
USWASD Strawberry Point Washington USA 8 cm epoxy Robomussel 48.3914 −124.7384
ZAECCA Cape St Francis Eastern Cape South Africa 8 cm epoxy Robomussel −34.2095 24.8374
ZAECJO Jongensfontein Eastern Cape South Africa 8 cm epoxy Robomussel −34.4198 21.3574
ZAECKB Kidd's Beach Eastern Cape South Africa 8 cm epoxy Robomussel −33.1475 27.7033
ZAECKE Kenton-on-sea Eastern Cape South Africa 8 cm epoxy Robomussel −33.6942 26.6678
ZAECMB Morgans Bay Eastern Cape South Africa 8 cm epoxy Robomussel −32.7111 28.3396
ZAECSK Skoenmakerskop Eastern Cape South Africa 8 cm epoxy Robomussel −34.0408 25.5333
ZAKNBA Ballito KwaZulu-Natal South Africa 8 cm epoxy Robomussel −29.4838 31.2592
ZAKNPE Port Edward KwaZulu-Natal South Africa 8 cm epoxy Robomussel −31.0480 30.2302
ZANCHO Hondeklipbaai Northern Cape South Africa 8 cm epoxy Robomussel −30.3061 17.2707
ZAWCBT Brenton Western Cape South Africa 8 cm epoxy Robomussel −34.0752 23.0239
ZAWCDO Doringbaai Western Cape South Africa 8 cm epoxy Robomussel −31.8004 18.2298
ZAWCKB Keurboom Western Cape South Africa 8 cm epoxy Robomussel −34.0050 23.4553
ZAWCPB Plettenberg Western Cape South Africa 8 cm epoxy Robomussel −34.0616 23.3798
ZAWCPN Paternoster Western Cape South Africa 8 cm epoxy Robomussel −32.8019 17.8983
ZAWCRO Robberg Western Cape South Africa 8 cm epoxy Robomussel −34.1022 23.3808
ZAWCYZ Yzerfontein Western Cape South Africa 8 cm epoxy Robomussel −33.3467 18.1531
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Figure 2. Map of logger deployment sites.

Figure 2

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Colors indicate approximate length of deployment, which ranged from one or two seasons to almost 18 years. Insets show (a) West and (b) East coasts of the United States and (c) New Zealand.

Loggers were programmed to record at intervals of 10–30 min and left in the field for periods up to 9 months before they were removed for downloading, and replaced with another logger. Every effort was made to place this new logger in precisely the same position in the bed as the logger being retrieved. All logger clock times were set to GMT. In the U.S., the absolute tidal elevation (height above chart datum) was measured with a Trimble R8 GNSS GPS system capable of sub-cm resolution. Temperature records were also used to record wave swash by comparing sudden drops in temperature (an indication of first wave splash following exposure at low tide) against predicted tidal elevations. The measurements of ‘Effective Shore Level’ can subsequently be compared against buoy records of significant wave height in order to estimate wave splash as a function of nearshore wave height at each site56,57.

Code availability

Code written in R58 was used to trim data recorded by each logger before and after deployment. A separate software program (SiteParser) is also available on the Northeastern website to determine the incidence of wave splash56,57. This is accomplished by comparing rapid (user-defined) drops in temperature, indicative of the return of the tide, against predicted (Xtide software, www.flaterco.com/xtide) or measured (tidesandcurrents.noaa.gov) tide height for each site. By comparing these measurements against measured logger tidal elevations, it is possible to calculate the ‘effective shore level’ of a logger as a function of nearshore wave height56. This also provides a method of dividing logger temperatures into aerial and submerged records. Notably, the choice of temperature drop determines both the accuracy of the division between aerial and submerged records, as well as the total amount of data available. Specifically, the choice of a larger temperature drop tends to increase certainty as to temperature divisions, but can restrict the amount of data to days when such drops are observed. For this reason, the database provides data that have not been analyzed in this manner, but instead provides tools for the user to do so. A link to the open source SiteParser software program is provided on the Northeastern database website, along with links to all metadata including (when available) logger elevations.

Data Records

Data from all loggers are archived in two databases. The first is a searchable database maintained by Northeastern University (www.northeastern.edu/helmuthlab/Research/Database.html) and provides unrestricted access to data as well as to associated links such as the SiteParser software described above. Metadata for each microsite are included as a downloadable spreadsheet, which includes, for each site: Country, Region, Site name, and GPS coordinates (Table 1 (available online only)). The metadata file also includes information specific to each microsite, including: Biomimic logger type (unmodified ibutton, unmodified TidBit, epoxy [8 cm] mussel logger, shell (silicone-filled) mussel logger [4 or 8 cm length]), Substrate (rocky, muddy, tidepool), Tidal elevation zone (low, lower mid, mid, upper mid, or upper), Wave exposure (protected or exposed), and Start and end dates (Table 2). At the Northeastern website, data can be viewed and downloaded using a series of drop-down menus (Fig. 3). Given the range of selections, the database provides the range of dates over which data meeting those criteria are available (this information is also included in the metadata file). Data from each logger can be downloaded as raw data, as well as daily, monthly or annual maxima, minima and averages. Note that data include both aerial and submerged temperatures, but raw data can be parsed using the software provided. In instances where multiple microsites meet the selected criteria, the program takes the average at each time point from the maximum number of loggers available before calculating summary statistics. Data from all microsites can be downloaded as raw data to avoid this averaging procedure.

Table 2. Data descriptors.

Parameter Description Unit
Logger type Type of biomimic or logger Text
Site Code 6-character site identification code (1st 2-characters: country; 2nd 2-characters: state; 3rd 2-characters: site) Text
Site Name Name of the site Text
Region Name of the region or jurisdiction Text
Country Name of the country Text
Latitude Latitude of the site Decimal degree
Longitude Longitude of the site Decimal degree
Zone Intertidal zone (low, lower mid, mid, upper-mid, upper) Text
Substrate Additional characteristics: muddy intertidal, rocky intertidal Text
Wave Exposure Wave exposure of logger (exposed or protected) Text
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Figure 3. Northeastern database showing dropdown menus.

Figure 3

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Users select Biomimic type (e.g., 8 cm epoxy logger); Country and Region (e.g., state); Site name; Intertidal zone (e.g., upper, mid, lower); Substrate type; Wave exposure, and Data statistic (raw, mean, maximum, or minimum over ranges of daily, monthly or yearly).

Raw data in text file format as well as associated metadata are also archived in a public repository (Data Citation 1). Files are organized in to a series of subfolders organized by Country, Region and Site (Table 1 (available online only)). Metadata identical to those available at the Northeastern site are also included as a downloadable file. Each data file contains information specific to the microsite in its header, and follows a 10 letter/6 number naming convention as follows: BM (indicating biomimetic logger database); Logger type (RM for mussel loggers [‘Robomussels’] or RB for unmodified loggers [‘Robobarnacles’]); 6 letter site code (Table 1 (available online only); Country, Region, Site); two-digit microsite ID and four digit Year.

Technical Validation

Comparisons of logger temperatures against tissue temperatures of adjacent live mussels made using thermocouples are presented in four publications44,45,54,59. The first compared temperatures recorded by a thermistor with the tip embedded in a silicone-filled shell against point measurements made from adjacent mussels in the field in Pacific Grove, California and found an average difference of ~0.75 °C (ref. 54). The second involved a more comprehensive set of tests of epoxy (polyester) loggers in both the field and in a wind tunnel fitted with a heat lamp45. In the laboratory experiments, the average difference between loggers and live mussels in artificial beds was ~2.2 °C (ref. 45). Notably, the average difference between live mussels and unmodified loggers (TidbiTs) in the same experiment was 14.6 °C. Field-tests yielded similar results, with an average error of 2.7 °C between robomussels and live mussels45. A follow-up study with additional laboratory tests over a wider range of temperatures (10–50 °C) reported a Root Mean Square Error (RMSE) of 3.84 °C with a correlation coefficient of 0.89 between loggers and live mussels, with a bias of 0.8 °C where loggers tended to overestimate temperatures slightly under extreme conditions44. Finally, iButton loggers placed in the middle of silicone-filled Geukensia demissa shells were tested in a wind tunnel in artificial beds under a range of wind speeds; results showed average differences of ~1.0–1.5 °C (ref. 59).

Usage Notes

Portions of the logger data presented here have been used in multiple field studies, and have provided context for laboratory studies. At small scales, biomimetic loggers (both loggers that we deployed as well as similar loggers made by other researchers) have been used to record differences in temperature among microhabitats (shaded and unshaded surfaces) and tidal elevations (Fig. 4) and the results compared to measurements of biochemical indicators of stress such as heat shock proteins54,60, gene expression61, reproductive condition62, and to the fine-scale distribution of native and non-native species63. At biogeographic scales, robomussels have been used to document thermal mosaics across large latitudinal gradients40,48 (Fig. 5) and the results related to patterns of mortality64, physiological stress65–67 and growth68,69, as well as interspecific differences in physiological stress39 and geographic distribution70. Measurements from mussel biomimetics have been used to test heat budget models that estimate animal temperature using data from weather stations and satellites71–73. Robomussels have also been used as part of controlled laboratory experiments that strive to replicate realistic field conditions37,74. Finally robomussel data can be used to estimate wave splash and water temperature56,57, although in this regard they do not present a major advantage over unmodified loggers.

Figure 4. Monthly average daily maximum temperature at low, mid and upper intertidal elevations at a relatively wave-protected bench in Boiler Bay, Oregon.

Figure 4

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Figure 5. Monthly average daily maximum temperature (for the hottest month of each year at each site) at mid intertidal elevations along the west coast of the United States (2007–2014).

Figure 5

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Additional Information

How to cite this article: Helmuth, B. et al. Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors. Sci. Data 3:160087 doi: 10.1038/sdata.2016.87 (2016).

Supplementary Material

sdata201687-isa1.zip (11.9KB, zip)

Acknowledgments

The collection of robomussel data has primarily been supported by grants from NSF (IBN-9985878, OCE-0323364, OCE-0926581 and IBN- 1557868); NASA (NNG04GE43G, NNX07AF20G, NNX11AP77G), NOAA (NA04NOS4780264), the National Geographic Society to B.H., N.S.F. IBN-1557868 to B.H. and M.Z., and the David and Lucile Packard Foundation to PISCO, the Partnership for Interdisciplinary Studies of Coastal Oceans (B.M., G.E.H., C.B.) and by the Chilean Ministry of Economics (MINECON NC120086) to B.B. This work includes research supported by the South African Research Chairs Initiative of the Department of Science and Technology and the National Research Foundation (C.D.M.). This is publication number 468 of the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) and 343 of the Marine Science Center, Northeastern University. Numerous people helped to collect loggers over the last 18 years, and we are very grateful for their help: Tameka Breland, Susan Bolte, Nick Burnett, Cari Cardoni, Ryan Craig, Sean Craig, Colette Dryden, Tara Fitzhenry, Kristi Gardner, Shawn Gerrity, Chris Haas, Steven Hawkins, Maxine Henry, Lindsay Hunter, Scott Johnson, Nicole Kish, Ruth Milston-Clements, Gayle Murphy, Tom O’Keefe, Susanne Pender, Cathy Pfister, Camryn Pennington, Brittany Poirson, Sylvain Pincebourde, Eric Sanford and Morgan Timmerman-Helmuth. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of any of the authors' institutions or agencies.

Footnotes

The authors declare no competing financial interests.

Data Citations

  1. Helmuth B. 2016. Dryad. http://dx.doi.org/10.5061/dryad.6n8kf

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Data Citations

  1. Helmuth B. 2016. Dryad. http://dx.doi.org/10.5061/dryad.6n8kf

Supplementary Materials

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