Non-systematically collected, a.k.a. opportunistic, species observations are accumulating at a high rate in biodiversity databases. Occupancy models have arisen as the main tool to reduce effects of limited knowledge about effort in analyses of opportunistic data. These models are generally using long closure periods (e.g. breeding season) for the estimation of probability of detection and occurrence. Here we use the fact that multiple opportunistic observations in biodiversity databases may be available even within days (e.g. at popular birding localities) to reduce the closure period to one day in order to estimate daily occupancies within the breeding season. We use a hierarchical dynamic occupancy model for daily visits to analyse opportunistic observations of 71 species from nine wetlands during 10 years. Our model derives measures of seasonal site use within seasons from estimates of daily occupancy. Comparing results from our "seasonal site use model" to results from a traditional annual occupancy model (using a closure criterion of two months or more) showed that our model provide more detailed biologically relevant information. For example, when the aim is to analyse occurrences of breeding species, an annual occupancy model will over-estimate site use of species with temporary occurrences (e.g. migrants passing by, single itinerary prospecting individuals) as even a single observation during the closure period will be viewed as an occupancy. Alternatively, our model produce estimates of the extent to which sites are actually used. Model validation based on simulated data confirmed that our model is robust to certain changes and variability in sampling effort and species detectability. We conclude that more information can be gained from opportunistic data with multiple replicates (e.g. several reports per day almost every day) by reducing the time window of the closure criterion to acquire estimates of occupancies within seasons.

Non-systematically collected, a.k.a. opportunistic, species observations are accumulating at a high rate in biodiversity databases. Occupancy models have arisen as the main tool to reduce effects of limited knowledge about effort in analyses of opportunistic data. These models are generally using long closure periods (e.g. breeding season) for the estimation of probability of detection and occurrence. Here we use the fact that multiple opportunistic observations in biodiversity databases may be available even within days (e.g. at popular birding localities) to reduce the closure period to one day in order to estimate daily occupancies within the breeding season. We use a hierarchical dynamic occupancy model for daily visits to analyse opportunistic observations of 71 species from nine wetlands during 10 years. Our model of daily colonization-extinction dynamics of species estimates within-season dynamics in occupancy at sites. We use the model to derive estimates of site use within seasons, in contrast to an annual occupancy estimates as produced in previous analyses of opportunistic data. Comparing results from our “seasonal site use model” to results from a traditional annual occupancy model (using a closure criterion of two months or more) showed that our model provide more detailed biologically relevant information. For example, when the aim is to analyse occurrences of breeding species, an annual occupancy model will over-estimate site use of species with temporary occurrences (e.g. migrants passing by, single itinerary prospecting individuals) as even a single observation during the closure period will be viewed as an occupancy. On the other hand, an occupancy model with within-season dynamics, such as our seasonal site use model will produce estimates of the extent to which sites are actually used. Model validation based on simulated data confirmed that our model is robust to certain changes and variability in sampling effort and species detectability. We conclude that for opportunistic data with many records (e.g. several reports per day almost every day), more information can be gained by reducing the time window of the closure criterion to acquire estimates of occupancies within seasons.

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