DOI: 10.5061/dryad.73n5tb2wp
title: Data for: Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany

description: Aim: Recent studies suggest insect declines in parts of Europe; however,
 the generality of these trends across different taxa and regions remains
 unclear. Standardized data are not available to assess large-scale,
 long-term changes for most insect groups but opportunistic citizen science
 data is widespread for some. Here, we took advantage of citizen science
 data to investigate distributional changes of Odonata. Location: Germany
 Methods: We compiled over 1 million occurrence records from different
 regional databases. We used occupancy-detection models to account for
 imperfect detection and estimate annual distributions for each species
 during 1980–2016 within 5 x 5 km quadrants. We also compiled data on
 species attributes that were hypothesized to affect species’ sensitivity
 to different drivers and related them to the changes in species’
 distributions. We further developed a novel approach to cluster groups of
 species with similar patterns of distributional change to represent
 multi-species indicators. Results: More species increased (45%) than
 decreased (29%) or remained stable (26%) in their distribution (i.e.,
 number of occupied quadrants). Species showing increases were generally
 warm-adapted species and/or running water species, while species showing
 decreases were cold-adapted species using standing water habitats such as
 bogs. Time-series clustering defined five main patterns of change – each
 associated with a specific combination of species attributes, and
 confirming the key roles of species’ temperature and habitat preferences.
 Overall, our analysis predicted that mean quadrant-level species richness
 has increased over most of the time-period. Main conclusions: Trends in
 Odonata provide mixed news – improved water quality, coupled with positive
 impacts of climate change, could explain the positive trends of many
 species. At the same time, declining species point to conservation
 challenges associated with habitat loss and degradation. Our study
 demonstrates the great value of citizen science and the work of natural
 history societies for assessing large-scale distributional change.
description: Datafile 1 - Traits and trends.csv (1) Odonata
 trait data are from various sources as follows: <i>Distribution</i>:
 We estimated species’ European geographic range size as the number of
 occupied grids (50 x 50 km) in a published atlas (Boudot & Kalkman
 2015). <i>Species temperature
 preference</i>: Species’ temperature preferences were calculated by
 overlaying each species’ European distribution with an average temperature
 map from E-OBS v. 19e (Cornes, van der Schrier, van den Besselaar,
 & Jones, 2018) following other studies (Jiguet, Gadot, Julliard,
 Newson, & Couvet, 2007). For each species, we calculated the mean
 of the mean daily temperatures of occupied grid cells. While we call this
 variable “temperature preference”, its calculation did not aim to estimate
 species’ optimal temperatures but rather to place species on a gradient
 from those preferring cooler temperature to those preferring warmer
 temperatures. <i>Life-history</i>:
 Data on voltinism, i.e., number of generations per year, was compiled from
 Corbet et al. (2006), complemented by expert knowledge within the
 co-author team. We applied a weighted mean of fuzzed-coded species
 affinities (values assigned to multiple categories reflecting the relative
 commonness of that category for the species, summing to 10 across all
 categories) to voltinism categories: multivoltine (coded as 5), bivoltinie
 (4), univoltine (3), semivoltine (2), and partivoltine (1). This weighted
 mean ranged between 1 (for a fully partivoltine species) and 5 (for a
 fully multivoltine species). <i>Phenology</i>:
 Mean start dates of the main flight period were taken from Boudot
 & Kalkman (2015), which provided start date at a resolution of
 monthly tertiles. Species’ phenologies vary geographically but the data
 presented was usually for Bavaria, southern Germany. However, like for
 temperature preference, the aim was to create a variable that placed
 species on a gradient from those appearing early in the year to those
 appearing later. <i>Habitat</i>: Main
 habitat preferences were classified according to descriptions in Dijkstra
 (2006) and Boudot & Kalkman (2015). Each species was coded to
 whether they use the following habitats: streams, rivers, ponds, lakes,
 ditches, canals, fenland, bogs, forest and quarries/pits. <i>Morphology</i>:
 Hind wing length (median of lower and upper values) was taken from
 Dijkstra (2006). <i>Threat-level</i>:
 We compiled data on the 2015 red list classification for each species in
 Germany (Ott et al., 2015). We aligned the German categories with the
 international IUCN categories following Jansen et al. (2020).
   (2) Trend data Trends were estimated using
 occupancy-detection models (see the main paper for full details). In brief
 the trends estimates represent the slope of a regression line through the
 predicted annual occupancy proportions for each species.   Datafile 2   -
 AnnualOccupancies Annual occupancies (between 0 and
 1) reflect the predicted proportion of survey quadrants (c. 5 x 5 km)
 occupied by each species each year between 1980 and 2016. Occupancies are
 derived from an occupancy-detection model, including site as a random
 effect and year as a fixed effect, fit using JAGS. The predicted
 occupancies (z), which account for variation in detection, are then summed
 across all survey qudrants in the analysis to reflect a mean nationwide
 occupancy for each species each year. The data frame contains summary
 statistics (mean, sd and quantiles) of the posterior distributions for
 these occupancy estimates.
description: Please read the full methods section of the paper in Diversity
 and Distributions before use. The paper is open-access.