DOI: 10.5061/dryad.9cnp5hqfw
title: Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models

description: 1. Pollination is a key ecosystem service for global agriculture but
 evidence of pollinator population declines is growing. Reliable spatial
 modelling of pollinator abundance is essential if we are to identify areas
 at risk of pollination service deficit and effectively target resources to
 support pollinator populations. Many models exist which predict pollinator
 abundance but few have been calibrated against observational data from
 multiple habitats to ensure their predictions are accurate. 2. We selected
 the most advanced process-based pollinator abundance model available and
 calibrated it for bumblebees and solitary bees using survey data collected
 at 239 sites across Great Britain. We compared three versions of the
 model: one parameterised using estimates based on expert opinion, one
 where the parameters are calibrated using a purely data-driven approach
 and one where we allow the expert opinion estimates to inform the
 calibration process. 3. All three model versions showed significant
 agreement with the survey data, demonstrating this model's potential
 to reliably map pollinator abundance. However, there were significant
 differences between the nesting/floral attractiveness scores obtained by
 the two calibration methods and from the original expert opinion scores.
 4. Our results highlight a key universal challenge of calibrating
 spatially-explicit, process-based ecological models. Notably, the desire
 to reliably represent complex ecological processes in finely mapped
 landscapes necessarily generates a large number of parameters, which are
 challenging to calibrate with ecological and geographical data that is
 often noisy, biased, asynchronous and sometimes inaccurate. Purely
 data-driven calibration can therefore result in unrealistic parameter
 values, despite appearing to improve model-data agreement over initial
 expert opinion estimates. We therefore advocate a combined approach where
 data-driven calibration and expert opinion are integrated into an
 iterative Delphi-like process, which simultaneously combines model
 calibration and credibility assessment. This may provide the best
 opportunity to obtain realistic parameter estimates and reliable model
 predictions for ecological systems with expert knowledge gaps and patchy
 ecological data.
description: <strong>TransectSurveyData.csv</strong>
 This file consists of counts of bees observed along walked
 transects conducted between 2011 and 2016 at 239 sites across Great
 Britain. It is a processed composite dataset derived from multiple
 studies. The references for these original studies are listed in Table S1
 of the supplementary material of Gardner et al. (2020; the publication in
 'Methods in Ecology and Evolution' associated with this dataset)
 and section 2.1 in the main text of that publication details how the
 original observational data from those studies was processed to obtain
 this composite dataset. Briefly, where multiple transects were walked on a
 given site survey, we report the total transect length and total number of
 bees observed, where the bees are separated into four guilds (ground
 nesting bumblebees, tree nesting bumblebee, ground nesting solitary bees,
 cavity nesting solitary bees) according to the nesting preference of the
 species recorded (see Gardner et al. 2020 for details).
   <strong>Model parameter csvs
 (attract.csv; av.csv; distances.csv; floralCover.csv; growth.csv; lfn.csv;
 poll_names.csv)</strong> These files contain the
 model parameter values used to run the process-based pollinator model used
 in Gardner et al. (2020). The model itself is publicly available at
 https://github.com/yclough/ecodeal and is derived from Haussler et al. (2017) and Lonsdorf et al. (2009). See Gardner et al. (2020) for details of how these parameter values are derived and reference sources for their values. The parameter values contained in the files 'attract.csv' and 'floralCover.csv' are the values derived from the expert opinion questionnaire (nmax=10, see Gardner et al. 2020 for details).
description: <strong>TransectSurveyData.csv</strong>
 We strongly recommend using the original source datasets (see
 Gardner et al. 2020 for references), rather than our processed composite
 dataset, since these are far more detailed, not amalgamated to
 survey-level totals, generally include higher taxonomic resolution and
 often include ancillary data on survey weather conditions etc.
 The column contents of the file are as follows:
 1. Site - name of survey site 2. X - British
 National Grid x co-ordinate for survey site 3. Y -
 British National Grid y co-ordinate for survey site 4.
 Method - survey methodology (transect in all cases) 5.
 TransectDuration_min - total duration of transect survey in minutes. In
 many (but not all) cases, this is obtained by converting the reported
 transect length into a duration assuming a 50m transect takes 10min to
 survey. 6. Year - the year the survey was carried
 out 7. Week - the week of the year the survey was
 carried out, where this value ranges in integers between 1 and 52 and the
 week beginning 1st January is designated as week 1. 8.
 Day - the day of the year the survey was carried out, where this value
 ranges in integers between 1 and 365 and 1st January is designated as day
 1. 9. GroundNestingBumblebees - the total number of
 ground nesting bumblebees observed during the survey
 10. TreeNestingBumblebees - the total number of tree nesting
 bumblebees observed during the survey 11.
 GroundNestingSolitaryBees - the total number of ground nesting solitary
 bees observed during the survey 12.
 CavityNestingSolitaryBees - the total number of cavity nesting solitary
 bees observed during the survey NB: Columns 9-12 can
 contain non-integer values. This occurs when bees were recorded as
 'Bombus unknown' or 'solitary unknown'. In which case,
 they were apportioned between the nesting guilds according to the
 proportion of known species observed from each guild on that survey, as
 described in Gardner et al. (2020).  
 <strong>lfn.csv</strong> This
 model input file lists the numeric code assigned to each landcover class
 (lu).
 <strong>poll_names.csv</strong>
 This model input file lists the species code assigned to each bee
 guild, its life history (solitary or social) and the floral periods (P1,
 P2, P3) during which its reproductive females (q) and workers (w; if
 social) are actively foraging.
 <strong>av.csv</strong> This model
 input file lists the maximum nest density used for each species code.
 Units = nests per ha.
 <strong>distances.csv</strong>
 This model input file lists the foraging and dispersal (activity
 = 'nesting') distances used for each species code. Units =
 m. <strong>growth.csv</strong>
 This model input file lists the growth parameters used for each
 species code. See Gardner et al. (2020) for parameter
 descriptions.
 <strong>floralCover.csv</strong>
 This model input file lists the expert opinion floral cover
 scores used for each landcover code in each floral period (P1, P2, P3).
 Columns with *_b represent mean scores across all experts. Columns with
 *_l represent mean value minus standard error. Columns with *_u represent
 mean value plus standard error. The permitted range for scores is 0.0 -
 100.0. 
 <strong>attract.csv</strong> This
 model input file lists the expert opinion floral attractiveness scores
 (Flor_*) and nesting attractiveness scores (Nest_*) used for each species
 code with each landcover code in each floral period (P1, P2, P3). Columns
 with *_b represent mean scores across all experts. Columns with *_l
 represent mean value minus standard error. Columns with *_u represent mean
 value plus standard error. The permitted range for floral attractiveness
 scores is 0.0 - 20.0. The permitted range for nesting attractiveness
 scores is 0.0 - 1.0.