DOI: 10.5061/dryad.c2fqz61mh
title: Impacts of an anxiolytic drug on fish behaviour and habitat use in a natural landscape

description: Pharmaceutical contaminants reaching natural aquatic ecosystems can affect
 fish behaviour, modifying activity patterns, foraging behaviour, and
 antipredator responses. While laboratory-based studies can offer key
 insights, assessing the ecological relevance of these findings requires
 field-based approaches. Therefore, we examined the effects of oxazepam, a
 widely prescribed anxiolytic drug, on the behaviour of a cyprinid fish
 (the common roach, Rutilus rutilus) in the wild, combining slow-release
 exposure implants with continuous tracking via acoustic telemetry. To add
 ecological realism, we created a landscape of fear with an uneven
 distribution of resources (macrophytes) and exposure to predators (pike,
 Esox lucius), additionally testing the effects of the drug on roach
 habitat selection and predator-prey interactions. Fish exposed to the drug
 showed an increased swimming activity and speed, but exhibited a more
 constrained spatial distribution in the pond, favoring areas with higher
 refuge availability. Both exposed and unexposed fish modified their
 habitat use in the presence of predators. Exposed fish appeared to get
 closer to the predators when these were caged, but not when predators were
 free-roaming. Our findings highlight the importance of considering
 ecological context to understand how pharmaceuticals affect fish
 behaviour, which is crucial for assessing risks at population and
 ecosystem levels. Our study had two experimental treatments: oxazepam
 exposure (control fish and oxazepam-exposed fish) and the landscape of
 fear predation treatment I. No predators, II. Caged predators, and III.
 Free-roaming predators, these stages are called conditions throughout. We
 separated the data into three data sets, one per week, representing the
 different stages of the predation treatment. Here we provide the data and
 code used for the analysis of fish positions to extract movement
 parameters and statistical analysis. There are six folders with the
 datasets and respective R scripts: data filtering, swimming activity,
 behavioral states, Habitat selection, Home range, and predator-prey
 interactions.
description: # Impacts of an anxiolytic drug on fish behaviour and habitat use in a
 natural landscape Dataset DOI:
 [10.5061/dryad.c2fqz61mh](10.5061/dryad.c2fqz61mh) ## Description of the
 data and file structure The IPonds-Landscape of fear project is a
 repository for the data and code used in the manuscript titled:
 "Impacts of an anxiolytic drug on fish behaviour and habitat use in a
 natural landscape". The project has two components: "Data"
 and "Scripts". Data contains the main datasets needed to run the
 code scripts provided in the folder called Scripts. Both components are
 organized by analyses according to the sections provided in the
 manuscript: Filtering, Activity, Home range, Behavioral states, habitat
 selection, and predator-prey interactions. The code provided is fully
 annotated. ### Files and variables #### File: R_scripts.zip
 **Description:** The compresed file contains a folder with six quarto
 files fully annotated. Each file (.QMD) contains the code used to conduct
 the analyses described in each of the respective sections in the
 manuscript: Filtering, Activity, Home range, Habitat selection, Behavioral
 states, and Predator-prey interactions. The data sets required to run the
 scripts are provided in the folder "Datasets".  Please read the
 respective material and methods sections for more details on the
 analytical approaches used. #### File: Datasets.zip Description: The
 compresed file contains a folder with six sub folders containing the data
 sets used in the R scripts. One folder per analysis.  **Filtering** Data
 set used for initial data tidying and filtering. Three files are provided:
 iponds2_2018_Tagging_: a CSV file with information about the fish and
 dates of the experiments. 9 columns. TagID: Tag ID for each fish  
 Treatment: Exposed fish: Oxazepam; Control fish: Control              
 TL_cm: Fish total length in cm Weight_g: Fish weight in g  Comments:
 important notes when the exposure happened.            Tagging Date: Date
 when fish were tagged and implanted   Release Date: Date when the roach
 was released             PikeInBoxesDate: Date when pikes were in boxes 
 PikeLooseDate: date when pike were loose               MosterPike: Date
 when big pike was added pos_DB.rds: Large dataset with the positions of
 all fish for the whole duration of the study. 16 variables tag: fish tag
 number ts: time stamp, date, time, and hour for each position x: position
 latitude y: position longitud z: position Depth x_sd: standard deviation
 of estimated position YAPS y_sd: standard deviation of estimated position
 YAPS nobs: number of receivers that heard it k: chunk (what is this, 1 hr
 bins or less) nobs10: running mean of ten day: day without time tod: time
 of day, 0-24 hr, decimal representation of time hour: hour dist: distance
 to the next observation speed: to the next observation Data_all.rds: Merge
 the data set of pos_DB.rds and iponds2_2018_Tagging\_ containing both
 positions of the fish and information on treatment and fish size.
 **Activity** A Folder including datasets used to conduct the analyses of
 swimming activity and speed. Including four datasets.
 Cummulative_dn_all: dataset used for swiming activity analyses consisting
 of the following variables: Tag: Tag number Treatment: oxazepam, Control
 Period: Day and Night Sum.t: cumulative time Condition: predator treatment
 corresponding to the consecutive weeks: No predator, caged predator, free
 predator Prop.activ_all_filt:  dataset used for activity index analyses
 consisting of the following variables: Tag: Fish tag Treatment: Control ,
 oxazepam period: Day/Night prop.mov: proportion of time in which speed  is
 higher than 0.02 m/s condition: predator treatment corresponding to the
 consecutive weeks: No predator, caged predator, free predator
 Speeds_dn_ctmm_all: dataset consisting of the following variables: Week:
 respective week Period Day and Night Id: file id output from CTMM Tag:
 fish tag ID Low: confidence interval Est: estimate High: high confidence
 interval Treatment: predator, control Condition: No predator, caged
 predator, free predator speed_hr; speed in hours distance_km: distance
 traveled km speed_ms: speed m/ s speed: speed m/ s distance_mh: m/hr
 prop.activ_all_filt: This file records individual animal movement (as
 proportion moved) under different treatments, times of day, and predator
 exposure conditions. fits_allweeks_daytime.rds : Model output CTMM.fit
 function, ctmm package. Large list of 3.1 MB with 146 elements, one per
 fish per day. The function ctmm returns a prototype ctmm movement-model
 object. By default, ctmm.loglike returns the log-likelihood of the
 model CTMM. ctmm.fit returns the estimated CTMM movement-model object with
 all of the components of CTMM plus the components listed below.  AICc: The
 approximate corrected Akaike information criterion for multivariate
 distributions with variable numbers of unknown mean and (structured)
 covariance parameters (Burnham & Anderson, Eq. 7.91). This formula is
 only exact for IID data. Loglike: The log-likelihood. Sigma: The maximum
 likelihood variance/covariance estimate (possibly debiased). For the
 endlessly diffusing BM and IOU processes, this is instead the diffusion
 rate estimate. Mu: The maximum likelihood stationary mean vector estimate.
 COV.mu: The covariance matrix of the mu estimate, assuming that the
 covariance estimate is correct. DOF.mu: The effective number of degrees of
 freedom in the estimate of mu, assuming that the autocorrelation model is
 correct. This can be much smaller than the length(data$t) if the data are
 autocorrelated. COV: Covariance of the autocovariance parameter estimate
 vector c(sigma, tau, circle), as derived (asymptotically) from
 the hessian of the log-likelihood function, and where sigma is
 parameterized in terms of its largest variance major, the ratio of the
 smallest to largest variance minor, and angle of orientation.
 Typically, sigma's major parameter is extremely correlated to tau[1],
 and sequential components of tau are slightly correlated. **Home range**
 Folder UDs: Folder with three subfolders, one per each week, containing
 the utilization distribution models, the autocorrelated kernel density
 estimate. Autocorrelated kernel density home-range estimates
 from telemetry data and a corresponding continuous-time movement model.
 UD object: a list with the sampled grid line locations r$x and r$y, the
 extent of each grid cell dr, the probability density and cumulative
 distribution functions evaluated on the sampled grid
 locations PDF & CDF, the optimal bandwidth matrix H, and the effective
 sample size of the data in DOF.H. The list also contains the effective
 sample sizes of various parameter estimates (DOF) and a parameter estimate
 table CI, with low, point, and high estimates for home range area. The
 home-range area with the fraction of inclusion level. UD. E.g., the 50%
 core home range is estimated at the level.UD=0.50, and 95% confidence
 intervals are placed on that area estimate with level=0.95.
 Telemetries.rds and Telemetries_fp.rds: A list of fish positions in
 “telemetry” format is required as input to run the autocorrelated kernel
 density function from ctmm R package. **Behavioral states** Behave_all_hr:
 output csv file including fish ID, Treatment, Condition, and the time
 spent in each of the different behaviors. Theta.estim.long.w1\_ and
 Tracks.out_w1.rds, both are output files from different steps in the
 Segmentation step of the model, which can be obtained following the
 respective R script “Behavioral states” **Habitat selection** Integrated
 resource selection functions with autocorrelation-adjusted weights on the
 RSF likelihood function, in one file per week.  rsf_all_w2, rsf_w1_all_,
 rsf_w3_all_. Each one contains the following variables: id: Fish id
 low_plant: lower limit of the 95% confidence intervals around parameter
 estimates of weighted RSFs for macrophyte presence. low_pred: lower limit
 of the 95% confidence intervals around parameter estimates of weighted
 RSFs for predator presence.         low_area: lower limit of the 95%
 confidence intervals around parameter estimates of weighted RSFs for home
 range area in Km2 low_Tp: lower limit of the 95% confidence intervals
 around parameter estimates of weighted RSFs for home range crossing time
 scale in days. low_T: lower limit of the 95% confidence intervals around
 parameter estimates of weighted RSFs for velocity persistence time scale
 in minutes. low_speed: lower limit of the 95% confidence intervals around
 parameter estimates of weighted RSFs for speed in Km/ day           ,  
 low_diff: lower limit of the 95% confidence intervals around parameter
 estimates of weighted RSFs for diffusion in Km2/day       est_plant:
 parameter estimates of weighted RSFs for macrophyte presence.            
 est_pred: parameter estimates of weighted RSFs for predator presence.   
 est_area  parameter estimates of weighted RSFs for home range area Km2
 est_T  parameter estimates of weighted RSFs for crossing time scale in
 days.        est_T: parameter estimates of weighted RSFs for velocity
 persistence time scale in minutes. est_speed: parameter estimates of
 weighted RSFs for speed. est_diff: parameter estimates of weighted RSFs
 for diffusion in Km2/day.   high_plant: Higher limit of the 95% confidence
 intervals around parameter estimates of weighted RSFs for macrophyte
 presence. high_pred: lower limit of the 95% confidence intervals around
 parameter estimates of weighted RSFs for predator presence.        
 high_area: lower limit of the 95% confidence intervals around parameter
 estimates of weighted RSFs for home range area in Km2.             
 high_Tp: lower limit of the 95% confidence intervals around parameter
 estimates of weighted RSFs for velocity persistence time scale in
 minutes.   high_T: lower limit of the 95% confidence intervals around
 parameter estimates of weighted RSFs for velocity persistence time scale
 in minutes.   high_speed: lower limit of the 95% confidence intervals
 around parameter estimates of weighted RSFs for speed in Km/ day.
 high_diff: lower limit of the 95% confidence intervals around parameter
 estimates of weighted RSFs for diffusion in Km2/day.     Treatment:
 Oxazepam and control. **Predator-prey interactions** A Folder with four
 files used to evaluate predator-prey interactions. distance_all_pikes:
 Matrix with the Euclidean distance from each fish to each of the four
 pikes encounter_rate_w3_all: dataset including the output of encounter
 probabilities from the R package ctmm. Contains the following variables:
 id: Fish id ec: encounter probability, output from cttm using the function
 encounter of the R package, which produces an array of standardized
 encounter probabilities with CIs predator: Pike ID Treatment: control and
 oxazepam Inclusion_matrix_UDS: Matrix with fish ID and 0-1 to be included
 or not in the estimation of  the conditional location distribution of
 where encounters take place (UD object) interaction pred_w3: dataset
 including the number of interactions per fish with each of the four
 predators based on a threshold of distance and duration.
 week3_interpolated.rds: Positions of fish for the week3 interpolated using
 the R package adehabitatLT to have the same number of rows, which is a
 requirement for the dynamic interaction analyses in the R package WildDI.
 **Script folder**: This folder, which corresponds to the R_scripts zip
 file, contains six QMD files with the R code fully annotated. ##
 Code/software All the analyses were run in RStudio 2023.03.0. The packages
 used are indicated at the beginning of each R script.