Abstract
We propose a model to consider data dependencies and assess spatial and temporal variability in land use specific floral coverage across landscapes. Data dependence arising from repeated measurements across the flowering season is taken into account using hierarchical Archimedean copulas, where the correlation is assumed to be stronger within seasonal periods than between periods. For each seasonal period, a bounded probability distribution is assigned to capture spatial variability in floral cover. The model uses a Bayesian approach and can assess land-use-specific floral covers by integrating experts judgments and field data. The model is applied to assess floral covers in four land use types in southern Sweden, where seasonal variability is captured by dividing the season into two periods according to winter oilseed rape flowering. Floral cover is updated using Markov Chain Monte Carlo sampling based on data from 16 landscapes and 2 years, with repeated measures available from each of the two seasonal periods. Our results indicate that considering data dependence improved the estimation of floral cover based on data observed during a season. Different copula families specifying multivariate probability distributions were tested, and no family had a consistently higher performance in the four tested land use types. Uncertainty in both mode and variability of floral cover was higher when data dependence were accounted for. Posterior modes of floral covers in semi-natural grassland were higher than in field edges, but both expert’s best guesses were higher than these estimates. This confirms previous findings in expert elicitation processes that experts may fail to discriminate extreme values on a bounded range. Floral cover in flower strips were estimated to be smaller/higher than semi-natural grasslands early/late in the season. The mode of floral cover in oil seed rape was estimated to be close to 100%, and higher than estimates provided by expert judgment. Floral covers for different land use classes are key parameters when quantifying floral resources at a landscape level whose assessments rely on both expert judgment and field measurements.
Highlights
Several studies have produced evidence of declines in diversity and abundances of some pollinators, including managed and unmanaged bees (Biesmeijer et al 2006; Potts et al 2010)
Even for a specific climatic region, the variability of floral coverage for a given land use class can be large from one site to the other, due to the type of species growing in each site, and to differences in management and environmental conditions like solar radiation and soil moisture
We proposed a model to estimate the floral cover in different land use types by taking into account the dependency between data observed at repeated times in the season
Summary
Several studies have produced evidence of declines in diversity and abundances of some pollinators, including managed and unmanaged bees (Biesmeijer et al 2006; Potts et al 2010). Floral resource availability varies over a growing season, since different plant species are blooming in spring compared to early and late summer. This variability is conceptualized in floral resource assessments by dividing a season into different floral periods. This approach is used for example in landscape assessments of pollinator abundances using spatially-explicit models (Lonsdorf et al 2009). These models take into account the spatial structure of the landscape and the distance between nests and floral resources. Both spatial and temporal modelling of floral resources is important when assessing floral resources availability at landscape level
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