DOI: 10.5061/dryad.70rxwdc6w
title: Multi-species occupancy model for estimating the probability of detecting amphibian species in Hungary

description: Research into freshwater communities often aims to link patterns of
 species distribution in ponds with underlying biotic factors. However,
 errors with species detection (e.g., false negatives) may underestimate
 distribution and bias assessments of community structure. Occupancy models
 that account for imperfect detection offer a solution to this problem.
 Here, we used three methods (call/ visual encounter surveys, dip-netting
 and newt trapping) to survey amphibians and fish (potential amphibian
 predators) at 100 ponds in an urbanised landscape in Hungary over one
 breeding season. We estimated species detection probabilities for
 amphibians (all life stages combined) and fish using multi-species
 occupancy models to gain insight into amphibian-fish relationships and
 other survey-specific variables. We detected nine amphibian and 20 fish
 species. There were relatively low but variable estimated probabilities of
 detection for amphibians (mean: 0.320, 95% Bayesian credible interval:
 0.142 – 0.598), with three species having detection rates &lt; 0.1.
 Probabilities of detection peaked in the middle of the breeding season and
 increased with survey effort. Detection probabilities of five species were
 negatively associated with the detection of fish at a pond, while there
 were positive relationships between detection and emergent vegetation
 cover. We found no substantial differences in detection rates among the
 three survey methods. The probability of detecting fish was much higher
 than for amphibians (0.588, 0.503 – 0.717) but was lower at ponds with
 high emergent vegetation where amphibian detection was higher. Our results
 underscore the importance of accounting for the imperfect detection of
 both response organisms and potentially interacting species in aquatic
 community studies. We recommend applying multi-species occupancy models to
 enable inference for both common and rare species at ponds in landscapes
 subjected to human disturbances.
description: Amphibian surveys were conducted at 100 ponds over a single
 breeding season (spring/summer) using three survey methods to detect all
 life stages: (1) call/ visual encounter surveys (VES), (2) dip-netting,
 and (3) Dewsbury newt traps. The number of dip-net sweeps and traps at a
 pond was pre-determined to be proportional to the pond surface area.
 During the surveys, we recorded water temperature, water level, wind,
 rain, detection of fish and estimated the percentage cover of emergent
 vegetation at ponds. Amphibians were identified and released at the point
 of capture.  We used Multi-species Occupancy Models
 (MSOM) with Bayesian inference were used to estimate the probabilities of
 detection at the community and individual species levels. Models were used
 to examine relationships between the probabilities of detection of any
 life stage (i.e., eggs, larvae, juveniles or adults) and nine covariates
 recorded during a survey: (1) the number of days since 19 March 2023
 (Days); (2) a quadratic relationship of the number of days
 (Days<sup>2</sup>); (3) survey effort (Effort); (4) water
 temperature (Temp); (5) water levels (Water); (6) wind speed (Wind); (7)
 rain (Rain); (8) detection/ non-detection of fish (Fish); and (9) emergent
 vegetation cover (Emergent). Survey effort was expressed as the number of
 minutes spent conducting VES, which was positively correlated with the
 number of dip-net sweeps and number of traps deployed at a
 pond.  The means, standard deviations and the
 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles
 of the posterior distributions of the model coefficients were estimated
 from the MSOM, which represents 95% Bayesian credible intervals (BCI).
 Survey covariates consisting of continuous data were standardised (mean =
 0, SD = 1) so that the relative importance of each covariate could be
 determined according to the magnitude of the coefficient. We modelled
 predictive relationships for influential covariates while holding the
 other covariates in the model at their mean values.
description: # Multi-species occupancy model for estimating the probability of
 detecting amphibian species in Hungary
 [https://doi.org/10.5061/dryad.70rxwdc6w](https://doi.org/10.5061/dryad.70rxwdc6w) This dataset enables the user to estimate the probability of detection of nine amphibian species at 100 ponds in Hungary. The data is based on field surveys conducted in 2023 that used three survey methods to detect amphibian species. Environmental and climatic conditions were recorded during each survey that comprised survey-specific covariates. Species data and survey-specific covariates were read into the model code to produce both community and individual species probabilities of detection according to each survey covariate.  ## Description of the data and file structure The dataset contains individual species detection data saved as comma delimited (.csv) files. Each of the three survey columns in each species file is detection of the species using Visual Encounter Surveys and aural census (VES), dip-netting and newt trapping techniques combined. Detection of a species includes detection of either eggs, larvae or post-metamorphic individuals. The abbreviations of the individual species and survey-specific covariates are listed below. Further information on the study design, sampling method and modelling is provided in the methods section of the associated manuscript. **Species data files** correspond with the following species: bombom = *Bombina bombina*; bufbuf = *Bufo bufo*; bufvir = *Bufotes viridis*; hylarb = *Hyla arborea*; lisvul = *Lissotriton vulgaris*; pelfus = *Pelobates fuscus*; pelcom = *Pelophylax* spp. complex; randal = *Rana dalmatina*; tridob = *Triturus dobrogicus.* The column "site" corresponds to the site names of the 100 ponds surveyed.  The columns "survey1", "survey2", "survey3" correspond with detection data from the three surveys (1 = detected; 0 = not detected). Cells with "NA" at site T9 for survey1 correspond to no survey being conducted at that site during that particular survey. Individual **survey-specific covariate data** is also saved as .csv files. The covariates include: \- days = number of days since 19 March 2023 \- effort = survey effort expressed as minutes conducting VES \- water temp = water temperature (°C) recorded at the start of a survey at the shoreline \- water = water levels recorded as the % of the maximum full water holding capacity of a pond \- wind = wind speed (calm/ light [0], moderate/ strong [1]) \- rain = rain occurrence (binary, 0/1) \- fish = detection of fish from visual surveys, dip-netting and newt traps (binary, 0/1) \- emerg = estimated % cover of emergent vegetation over the pond surface  Continuous covariates (all except wind, rain and fish) were standardised in the model code by subtracting the mean and dividing by one standard deviation. ### Files and variables #### File: Hamer\_Horanyi\_EcolEvol\_detection\_model\_1.R **Description:** multi-species occupancy model for estimating the probability of detection of nine amphibian species at 100 ponds in Hungary #### File: species\_data.zip **Description:** individual .csv files of detection data for each of the nine amphibian species detected at ponds #### File: survey\_covariates.zip **Description:** individual .csv files of survey-specific covariates recorded during each survey ## Code/software ##### Program R R Core Team, 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [https://www.R-project.org/](https://www.R-project.org/). * ###### Package R2jags Su, Y., Yajima, M., 2021. Package ‘R2jags’: using R to run ‘JAGS.’ Version 0.7-1. ##### Program JAGS Plummer, M., 2017. JAGS Version 4.3.0 user manual. [https://sourceforge.net/projects/mcmc-jags/](https://sourceforge.net/projects/mcmc-jags/).