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 < 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.

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 (Days²); (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^th and 97.5^th 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.