DOI: 10.5061/dryad.hdr7sqvqq
title: Restored off-channel pond habitats create thermal regime diversity and refuges within a Mediterranean-climate watershed

description: Cool-water habitats provide increasingly vital refuges for cold-water fish
 living on the margins of their historical ranges; consequently, efforts to
 enhance or create cool-water habitat are becoming a major focus of river
 restoration practices. However, the effectiveness of restoration projects
 for providing thermal refuge and creating diverse temperature regimes at
 the watershed scale remains unclear. In the Klamath River in Northern
 California, the Karuk Tribe Fisheries Program, the Mid-Klamath Watershed
 Council, and the U.S. Forest Service constructed a series of off-channel
 ponds that recreate floodplain habitat and support juvenile coho salmon
 (Oncorhynchus kisutch) and steelhead (Oncorhynchus mykiss) along the
 Klamath River and its tributaries. We instrumented these ponds and applied
 multivariate auto-regressive time series models of fine-scale temperature
 data from ponds, tributaries, and the mainstem Klamath River to assess how
 off-channel ponds contributed to thermal regime diversity and thermal
 refuge habitat in the Klamath riverscape. Our analysis demonstrated that
 ponds provide diverse thermal habitats that are significantly cooler than
 creek or mainstem river habitats, even during severe drought. Wavelet
 analysis of long-term (10 years) temperature data indicated that thermal
 buffering (i.e. dampening of diel variation) increased over time but was
 disrupted by drought conditions in 2021. Our analysis demonstrates that in
 certain situations, human-made off-channel ponds can increase thermal
 diversity in modified riverscapes even during drought conditions,
 potentially benefiting floodplain-dependent cold-water species.
 Restoration actions that create and maintain thermal regime diversity and
 thermal refuges will become an essential tool to conserve biodiversity in
 climate-sensitive watersheds. 
description: Data Collection In July 2020, we deployed 30
 temperature sensors (HOBO MX2201, Onset Corporation, Massachusetts)
 programmed to measure temperature every 15 minutes in ponds and creeks. We
 placed 1-4 sensors in each pond to capture local-scale temperature
 variation. Sensors were installed at approximately ⅓ the water depth (at
 time of placement), except for two sensors in Goodman Pond, one in Upper
 Lawrence Pond, and one in Lower Lawrence Pond, where sensors were placed
 on the bottom of the pond. We chose these locations to capture within-pond
 variation in thermal habitat, to maximize access and safety, and to
 facilitate future monitoring. We also placed 1 sensor in the creek
 upstream of the outlet of each pond. We placed sensors between 7 and 13
 July 2020 and read them out between 11 and 13 July 2021. We removed
 incomplete sensor time series (n = 6 pond sensors and n = 3 creek sensors)
 resulting either from sensor malfunction or sensors that were no longer
 submerged because of drought-related decreases in water level. In ponds
 and creeks with multiple sensors, we averaged the remaining sensor
 readings to obtain an average time series per site. In the 5 ponds with
 only one sensor, we used that sensor’s time series. We calculated and
 modeled daily temperature means (instead of using sub-daily data) to avoid
 having to account for diel periodicity in the MAR models (Hampton et al.
 2013; Holmes et al. 2023), which would have made these models
 unnecessarily complex. Water levels in the pond
 fluctuated throughout the year, leading to different depths for the
 sensors throughout the study period, which could influence temperatures.
 We removed from analysis sensors that were completely out of the water
 (thus, recording air temperature rather than water temperature) because of
 depth fluctuations. To understand how well the remaining sensors represent
 thermal habitats in the ponds, we took post hoc temperature-depth profiles
 in June 2023 at several locations in each pond. 
 Klamath River temperature data were collected by the Karuk Tribe
 and accessed with permission from the Karuk Tribe Water Quality Department
 (Accessed 27 September 2022). We used data between May 2020 and February
 2021. We used a combination of data readings from the Seiad Valley station
 as well as interpolated data using a linear regression from the Orleans
 station when Seiad Valley data was unavailable (5.5% of Seiad Valley data
 was interpolated). Additionally, we obtained air temperature time series
 from the National Oceanic and Atmospheric Administration’s Climate Data
 Online database for Siskiyou County, CA (NOAA 2020). We used the Slater
 Butte air sensor, located relatively close (13 km) to our study sites in
 Seiad Creek. Although these two sites differ in elevation (1423 m vs. 430
 m), we expected fluctuations in air temperature at these two locations to
 be correlated, and we note that our models quantify the effects of
 fluctuations around the mean rather than absolute values of air
 temperature (see next section). Also, we measured DO in a single location
 in each pond over several days in July 2020 (Figure S3), and we took post
 hoc DO and temperature-depth profiles in each pond in June 2023 (Figure
 S2).  Thermal diversity To analyze
 variation in thermal regimes across the riverscape, we used multivariate
 auto-regressive model (MAR) models. The MAR model is a time series model
 that takes advantage of temporal correlation in environmental variables to
 estimate the effects of a particular driver, while also accounting for
 stochastic process error (Holmes et al. 2014; Ives et al. 2003; Ruhí et
 al. 2015). A MAR model in the matrix form can be expressed as
 follows: X<sub>t</sub> =
 BX<sub>t-1</sub> +  Cc<sub>t-1</sub> +
 w<sub>t</sub>,  where w<sub>t</sub> ~ MVN(0,
 Q)  where temperature at a given day
 (X<sub>t</sub>) is a function of temperature the previous day
 (X<sub>t-1</sub>) plus sensitivity to a covariate, here
 variation in air temperature (Cc<sub>t-1</sub>); and process
 error (w<sub>t</sub>). As a covariate
 (c<sub>t-1</sub>), we used a time series of air temperature
 with a one-day time lag, after examining support for other lags (results
 not shown); and the C matrix captured site-specific sensitivity to air
 temperature. In turn, process error (w<sub>t</sub>) was drawn
 from a multivariate normal distribution, with mean zero and covariance
 matrix Q. In our case, Q captured stochasticity in water temperature
 (i.e., temporal variation in water temperature that was unrelated to air
 temperature). B is an interaction matrix that can model the effect of each
 state on itself (diagonal parameters) and on each other (off-diagonal
 parameters). In our case, we set off-diagonal parameters to zero (as we
 did not expect sites to interact with each other) and estimated the
 diagonal parameters, often used to capture “density-dependence” in
 population processes, or pull-back to mean.  To test
 our prediction that off-channel ponds have significantly different thermal
 regimes compared to creeks and the mainstem, we developed four MAR model
 hypotheses that represent different levels of complexity in thermal
 regimes (as in Leathers et al. 2022). Each hypothesis was tested by
 manipulating the matrices of the MAR model, capturing stochastic or
 ‘unexplained’ variation (Q matrix), and deterministic or
 covariate-explained variation (C matrix). This strategy allowed modeling
 mean daily temperatures among pond, creek, and river habitats in different
 ways. The first hypothesis was that all sites had different levels of
 stochastic and deterministic variability (i.e., as many thermal regimes as
 sites). The second hypothesis was that each habitat type (pond, creek, and
 river) had some typical level of stochastic and deterministic variability,
 but sites within the same habitat type did not differ from each other. The
 third hypothesis predicted that stochastic and deterministic variability
 depended on the watershed (Horse Creek vs. Seiad Creek vs. Klamath River),
 but not the specific site or habitat type. The fourth hypothesis predicted
 that all sites would have the same level of stochastic and deterministic
 variability (i.e., a single, watershed-level thermal regime). We used
 Akaike’s Information Criterion corrected for small sample size (AICc) to
 compare support for the different hypotheses. All data and covariate data
 was z-scored, and model outputs were examined for normality and
 autocorrelation of residuals via the Autocorrelation Function (ACF). We
 used the MARSS package version 3.11.3 (Holmes et al. 2021) in R (R
 Development Core Team 2021). Thermal
 buffering To quantify thermal buffering of ponds
 (relative to creeks), we compared daily maximum temperatures (averaged
 across all sensors in a site) in each pond and creek during the three
 hottest months of 2020 (15 July – 15 September), and then ran a one-way
 ANOVA of temperature ~ site. We repeated the same process for the winter,
 focusing on daily minimum temperatures during the three coldest months (15
 December 2020 – 15 February 2021). We ensured that model residuals met
 assumptions of normality and homogeneity of variances.
 We also assessed daily thermal buffering capacity of ponds and
 creeks by calculating the coefficient of variation (CV) for each day,
 using 15-minute temperature data. We then averaged daily CVs for each site
 over the yearlong study period. We used mean CV values to calculate the
 ratio of creek to pond CV for each pond/tributary pairing. If the
 creek:pond CV ratio was equal or less than 1, that suggested no
 significant buffering took place. If the ratio was greater than 1, we
 considered the pond to “buffer” thermal fluctuations compared to the
 creek. Thermal stabilization over time
 We used wavelet analysis to examine thermal regimes in the
 frequency and time domains and to determine whether some scales of
 variation strengthened over time. Although the wavelet method does not
 require pre-specifying a frequency of interest, here we focused on
 temperature variation at diel (24-hour) and seasonal scales (12-months),
 and asked whether diel and seasonal variation changed over the years. We
 interpolated missing values in the historical temperature datasets for
 Alexander and Stender Ponds (3.3% and 3.9% of days, respectively) via an
 autoregressive integrated moving average model (ARIMA) and a Kalman
 filter. An ARIMA model is generally expressed as ARIMA(p, d, q), where p
 is the order of the autoregressive model, i.e. the dependence of the model
 on prior values; d is the order of non-seasonal differences, i.e. degree
 of differencing of raw observations; and q is the order of the moving
 average, i.e. the model’s dependence on longer-term values and stochastic
 “shocks”. After identifying the best-fit ARIMA model, we used the Kalman
 filter to interpolate missing data. We then ran wavelets on the complete
 time series, using the WaveletComp package in R (Roesch &
 Schmidbauer 2018). We used the Morlet wavelet function and compared
 observed power to a null background generated with red noise (i.e.
 temporally autocorrelated data).
description: # Restored off-channel pond habitats create thermal regime diversity and
 refuges within a Mediterranean-climate watershed
 This study used water
 temperature data to understand how human-made off-channel floodplain ponds
 in the Klamath River watershed in Northern California contribute to
 thermal regime diversity and create thermal refuge habitat. We used
 multivariate auto-regressive time series models and wavelet analysis to
 assess how ponds created thermal regime diversity in the riverscape over
 time. Datasets belonging to the Karuk Tribe and the Mid Klamath Watershed
 Council are only available upon request. Please contact the corresponding
 author ([jessie_moravek@berkeley.edu](mailto:jessie_moravek@berkeley.e))
 for assistance accessing these datasets. ## Pond Temperature Data Pond and
 creek temperature data were obtained via a series of temperature sensors
 (HOBO MX2201) placed in 9 ponds and 2 creeks from July 2020-July 2021.
 Water temperature was measured every 15 minutes. Sensors in the same pond
 or creek were averaged to create a single time series for each site.
 Klamath River temperature data was collected by the Karuk Tribe and
 accessed with permission from the Karuk Tribe Water Quality Department
 (Accessed 27 September 2022). We used data between May 2020 and February
 2021. We used a combination of data readings from the Seiad Valley station
 as well as interpolated data using a linear regression from the Orleans
 station when Seiad Valley data was unavailable (5.5% of Seiad Valley data
 was interpolated). Water temperature was measured every hour. This dataset
 is available upon request. #### Dataset: quarterHour\_condensed.rds ####
 Dataset description: Matrix of site x 15 minute water temperatures. Sites
 include off-channel ponds and creeks. #### Dataset:
 daily\_means\_condensed.rds (dataset available upon request) #### Dataset
 description: Matrix of site x daily mean water temperatures. Sites include
 off-channel ponds, creeks, and the Klamath River Row Names: AP: Alexander
 Pond SP: Stender Pond Durazo: Durazo Pond LS: Lower Seiad Pond May: May
 Pond FG: Fish Gulch Pond GP: Goodman Pond UL: Upper Lawrence Pond LL:
 Lower Lawrence Pond SC: Seiad Creek HC: Horse Creek KSV: Klamath Seiad
 Valley #### Dataset: daily\_maxs\_condensed.rds (dataset available upon
 request) #### Dataset description: Matrix of site x daily maximum water
 temperatures. Sites include off-channel ponds, creeks, and the Klamath
 River Row Names: AP: Alexander Pond SP: Stender Pond Durazo: Durazo Pond
 LS: Lower Seiad Pond May: May Pond FG: Fish Gulch Pond GP: Goodman Pond
 UL: Upper Lawrence Pond LL: Lower Lawrence Pond SC: Seiad Creek HC: Horse
 Creek KSV: Klamath Seiad Valley #### Dataset: daily\_mins\_condensed.rds
 (dataset available upon request) #### Dataset description: Matrix of site
 x daily minimum water temperatures. Sites include off-channel ponds,
 creeks, and the Klamath River Row Names: AP: Alexander Pond SP: Stender
 Pond Durazo: Durazo Pond LS: Lower Seiad Pond May: May Pond FG: Fish Gulch
 Pond GP: Goodman Pond UL: Upper Lawrence Pond LL: Lower Lawrence Pond SC:
 Seiad Creek HC: Horse Creek KSV: Klamath Seiad Valley ## Air Temperature
 Data The air temperature dataset was downloaded from the National Oceanic
 and Atmospheric Administrations Climate Data Online database for the
 Slater Butte air sensor in Siskiyou County, CA. #### Dataset:
 SlaterButteAirTemp\_2020\_2021\_lag.csv #### Dataset description: Columns:
 Station: Station code Name: Name of station Date: Date of air temperature
 measurement TAVG: Average daily temperature (in Fahrenheit) TMAX: Maximum
 daily temperature (in Fahrenheit) TMIN: Minimum daily temperature (in
 Fahrenheit) ## Historical Pond Temperature Data Historical temperature
 data from Alexander and Stender Ponds was provided by the Mid Klamath
 Watershed Council (MKWC). Temperature data were collected hourly in these
 ponds from 2010-2021 using HOBO U22 sensors. Datasets available upon
 request. #### Dataset: Alexander\_Historical\_2.csv (dataset available
 upon request) #### Dataset description: Alexander Pond historical
 temperature data Columns: Date_Time: Date and time of collection Temp:
 Water temperature (in Celsius) #### Dataset: Stender\_Historical\_2.csv
 (dataset available upon request) #### Dataset description: Stender Pond
 historical temperautre data Columns: Date_Time: Date and time of
 collection Temp: Water temperature (in Celsius) ## Dissolved Oxygen Data
 DO and temperature depth profiles were taken using a YSI DO200M in June
 2023. DO was measured using HOBO DO probes for several days in each pond
 in July 2020. #### Dataset: DepthProfiles\_All.csv #### Dataset
 description: Temperature and Dissolved oxygen (DO) depth profiles taken in
 June 2023 Columns: site: Pond name profile: Replicate of depth profile
 depth_cm: Depth of temperature/DO reading (in cm) do_percent: Percent DO
 (in %) do_mgL: DO (in mg/L) temp_c: Water temperature (in Celsius) conduc:
 Conductivity (in SpC) #### Datasets: Sen212\_DO.csv; Sen213\_DO.csv;
 Sen214\_DO.csv; Sen215\_DO.csv; Sen216\_DO.csv; Sen217\_DO.csv ####
 Dataset description: Dissolved oxygen (DO) over several days in July 2020
 Columns: UID: Unique identifier for each reading date_time: Date and time
 of reading DO_mg/L: DO (in mg/L) temp_c: Water temperature (in Celsius) ##
 Miscellaneous data Miscellaneous data pulled into separate datasets for
 easier figure creation #### Dataset: daily\_means.RDS #### Dataset
 description: Matrix of daily mean water temperatures x site. This data set
 is not "condensed", meaning it has each sensor time series
 listed individually instead of averaging all sensors in the same site. We
 used this dataset only for visualization. ## Code/Software The script
 MoravekEtAl_DryadUpload.Rmd is an R Markdown script that uses the above
 datasets to reproduce the analysis in this manuscript.