DOI: 10.5061/dryad.cnp5hqcd4
title: Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach

description: Rapid changes in thermal environments are threatening many species
 worldwide. Thermal acclimatisation processes may partially buffer species
 from the impacts of these changes, but currently the knowledge about the
 temporal dynamics of acclimatisation remains limited. Acclimatisation
 phenotypes are typically determined in laboratory conditions that lack the
 variability and stochasticity that characterize the natural environment.
 Through a Distributed Lag Non-linear Model (DLNM), we use field data to
 assess how the timing and magnitude of past thermal exposures influence
 thermal tolerance. We apply the model to two Scottish freshwater
 Ephemeroptera species living in natural thermal conditions. Model results
 provide evidence that rapid heat hardening effects are dramatic and
 reflect high rates of change in temperatures experienced over recent hours
 to days. In contrast, temperature change magnitude impacted
 acclimatisation over the course of weeks but had no impact on short term
 responses. Our results also indicate that individuals may de-acclimatise
 their heat tolerance in response to cooler environments. Based on the
 novel insights provided by this powerful modelling approach, we recommend
 its wider uptake among thermal physiologists to facilitate more nuanced
 insights in natural contexts, with the additional benefit of providing
 evidence needed to improve the design of laboratory experiments. 
description: The data are a collection of Serratella ignita and Baetis rhodani
 Critical Thermal maxima (CTmax) values.  <em>S.
 ignita</em> (Poda 1761) and <em>B. rhodani</em> (Pictet
 1843) are common and ubiquitous freshwater invertebrates across Europe.
 Both species usually produce a single generation per year, with eggs
 overwintering (hatching: ~March/April) and adults emerging from April to
 September, depending on local thermal conditions (see, López-Rodríguez et
 al., 2009; Raddum & Fjellheim, 1993) with non-overlapping
 generations in Scotland (Maitland, 1965; Morgan & Egglishaw,
 1965).  Larvae of both species were collected from four sites in Scotland,
 in rivers upstream and downstream from two natural lakes (Rescobie Loch
 and Loch Morie) and two artificial reservoirs created by damming (Loch of
 Lintrathen and Glenquey Reservoir), giving a total of 8 sampling locations
 . Larval sampling took place within a circle of 5 m radius from the
 loggers, and within the same riffle, using kick sampling. Subsequent
 local-scale spatial mapping of temperatures suggests that within-riffle
 temperatures are generally homogenous. Once collected, larvae were placed
 in containers filled with river water and immediately transported to the
 laboratory in cooler bags (average trip 2h by car). Throughout the
 transport, we assumed minor variation in the water temperature since the
 last temperature experienced in the field. CT<sub>max</sub>
 values were established in experiments using high performance Grant Optima
 TX150 circulating water baths with programmable heating settings. Larvae
 were always handled using plastic spoons, to prevent damage. They were
 placed in 8 plastic cups (7x15 cm), one for each location. Additional
 plastic cups were used if the number of individuals exceeded 30. Cups were
 filled with respective site water in order to preserve thermal conditions
 and minimise physiological stress. The cups were then placed in the water
 bath. Prior to experiments, all individuals were equilibrated at 10°C for
 10 minutes. During each thermal trial, samples were subjected to a
 constant water temperature increase of 0.1°C/minute, which was monitored
 and controlled by Grant TX150 programmable circulating water bath with C2G
 cooling attachment (Grant Instruments, Shepreth, UK).
 CT<sub>max</sub> values for each individual were recorded when
 no physical response occurred after three consecutive prods (Becker
 & Genoway, 1979); these individuals were then removed from cups
 while the remaining individuals continued to be monitored. All the
 CT<sub>max </sub>experiments were performed on the day of
 sample collection. No behavioural interactions between individuals were
 noted during trials.  Water temperature was
 continuously recorded at each sampling location with HOBO Pendant
 UA-002-64 data loggers (Onset Corp. Bourne, MA, USA, accuracy +/-0.2°C) on
 a time step of 15 min over the 28 days prior to each larval sample
 collection. Loggers were placed carefully in locations with running water
 (riffles) to have good mixing through the water column, and in areas with
 representative and homogenous morphology of each stream far from sources
 of potential thermal alteration such as confluences.
description: # Data from: Determining critical periods for thermal acclimatisation
 using a Distributed Lag Non-linear Modelling approach ## Description of
 the data and file structure We include here the three data sets on which
 the analysis has been performed.  1. "allCT" dataset contains
 all the Critical Thermal maxima value for samples of Serratella ignita and
 Baetis rhodani. Refer to the data description and the main manuscript for
 detailed information on the sampling method and CTmax estimation. The data
 set reports: "date" (sampling date); "nsampl"
 (progressive number of sampling location-specific); "site" (site
 of sampling, respectively Loch of Linthrathen, Loch Morie, Loch Rescobie
 and Glenquei reservoir); "location" (upstream or downstream
 sampling location); CTmax (Critical Thermal maxima in °C); size (larvae
 body size in cm); "sp" (species, respectively "si" for
 Serratella ignita, "br" for Baetis rhodani). is a freeform
 section for you to describe how the data are structured and how a
 potential consumer might use them. Be as descriptive as necessary. Keep in
 mind that users of your data might be new to the field and unfamiliar with
 common terminology, metrics, etc. Water temperature has been collected in
 slightly different way in 2016 and 2017. From here the different structure
 of the data frames. The code avaialb eon GitHub allow to homogenize the
 data structure (see below). 2. "temp_data_16" contains water and
 air temperature data recorded for each sampling location during the
 experiment time with HOBO water temperature loggers in 2016. The data set
 report "day" (day of the year), "hour" (UTC+1),
 "minute" (water temperature recorded every 15 min) and
 "date" (i.e., year) of each observation. "site" and
 "type" report the site name and the type of site (i.e., dammed
 or natural reservoir). The column "WUS" and "WDS"
 report the upstream and downstream water temperature respectively;
 "AUS" and "ADS" report the air temperature upstream
 and downstream of the lakes. Air temperature was not used in this study;
 logger for air temperature were placed ~15 mt far from the water
 temperature loggers on the river bank, shaded with aluminium foils to
 avoid direct insulation. All temperature are reported in °C.  3.
 "temp_data_17" contains water temperature data recorded for each
 sampling location during the experiment time with HOBO water temperature
 loggers in 2017. The data set report "day" (day of the year),
 "hour" (UTC+1), "site" and "type" report the
 site name and the type of site (i.e., dammed or natural reservoir). The
 column "WUS" and "WDS" report the upstream and
 downstream water temperature respectively, recorded with an hurly
 time-step. "progr" reports the data and time of each observation
 in dd/mm/YYYY hh:mm format. ## Sharing/Access information All the analysis
 reported in the paper has been performed in R. Code and step by step guide
 to reproduce the data analysis are available
 at [https://github.com/monviso/Determining-Critical-Periods-for-Thermal-Accliamtion-Using-a-DLNM-approach](https://github.com/monviso/Determining-Critical-Periods-for-Thermal-Accliamtion-Using-a-DLNM-approach)