DOI: 10.5061/dryad.d2547d87n
title: Data from: Movement of avian predators points to biodiversity hotspots in agricultural landscape

description: Global agricultural landscapes are witnessing a concerning decline in
 biodiversity, and this trend is predicted to persist. To safeguard these
 biodiversity-rich areas, it's crucial to pinpoint hotspots
 effectively. In doing so, we utilized various species of birds of prey as
 suitable sentinel animals due to their mobility and dependence on prey
 diversity and abundance. Between 2019 and 2021, we tracked 62 individuals
 from four predator species using GPS loggers in Estonian farmland.
 Dividing the study area into 50-meter grids and overlaying them with
 tracked individuals' locations enabled us to differentiate between
 hotspots of their activity and control sites. We conducted surveys on
 amphibians, birds, small mammals, and plant diversity to determine if
 avian predator activity hotspots correlated with overall biodiversity. Our
 findings revealed significantly higher diversity and abundance in the
 surveyed groups within activity hotspots compared to control sites. These
 hotspots continued to be frequented by raptors in the subsequent year,
 albeit not two years later. In conclusion, multispecies GPS telemetry of
 avian predators emerges as an objective, dependable, and spatially
 accurate biodiversity indicator. With the accumulation of movement data,
 we anticipate increased interest and adoption of this approach in
 biodiversity monitoring.
description: <strong>GPS Telemetry Dataset</strong>
 Starting from April 2019, we trapped individuals of four
 different species of birds of prey in their known breeding territories. We
 used large mistnets with stuffed specimens of large avian top predators
 (white-tailed eagle and eagle owl) to provoke the attack of the focal
 species near their nest sites and to catch them in the net. We managed to
 trap and track 62 individuals of four different species (Table S1). As
 much as 59 individuals were caught during this project, but we also used
 additional data from three lesser spotted eagles that were caught earlier
 in the same area and were still transmitting data (Väli et al., 2020).
   We used 15 – 30-g GPS-loggers with solar panels
 (Ornitela, Lithuania), which were selected according to the body mass of
 each studied bird so as not to exceed the 3% threshold that is currently
 considered as acceptable by many bioethics committees. GPS loggers were
 attached to birds as backpacks that were sewn at the sternum with Teflon
 ribbon. Handling time reached 30 – 60 minutes per individual. In any case
 there was no mortality in following months and none individuals deserted
 their brood. GPS data was collected at 3 to 60-minute intervals depending
 on the battery level of each individual’s logger. Marsh harriers and
 lesser spotted eagles flew extensively and charged their tags through
 sunlight, so they mostly were able to acquire data with 3 to 10-minute
 intervals. Common buzzards and northern goshawks spent around 90% of their
 time perching and mostly under canopies (Mirski & Väli, 2021).
 Therefore, their charging ability was much lower, and GPS fixes were
 usually acquired in 10 to 60-minute intervals, depending on the
 individual. <strong>Hotspot
 designation</strong> To test whether the raptors’
 space use would lead us to biodiversity hotspots, we first had to
 designate the hotspots of raptors activity – i.e. sites of highest use by
 different species and different individuals followed by GPS telemetry. For
 comparison, we also chose control sites. To designate both, we divided the
 study area into 50-m × 50-m grids, drawn in QGIS 3.22 and subtracted the
 forest, waterbodies, and peatland layers. The chosen grid size is a few
 times higher than the GPS positioning accuracy and precise enough to
 register selected habitat patches with sufficient detail.
 Activity hotspots and control sites were chosen in a few sets and
 progressed with our knowledge on space use by tracked birds. Data on the
 first half of birds marked with GPS tags in 2019 (n=35) was used to
 designate pairs of hotspots and control sites for the biodiversity survey
 (bird survey) carried out in 2020. Chosen activity hotspots were then
 completed with the subsequent years based on movement data from early
 spring 2020 (April) and late spring 2020 (May). Activity hotspots for
 biodiversity surveys in 2021 were chosen using data from all of 2020,
 early spring of 2021 (April), and late spring of 2021 (May) (see Table S2
 for the number in each set). We considered a grid cell as an activity
 hotspot when at least three different individuals of two different species
 were using it in a given season (see Fig. 1C). If more such grid squares
 were located in the adjacent position, we chose the square with the
 highest number of species recorded inside. Squares with obvious perching
 posts (such as single trees or pylons) were discarded, as at a given
 spatial scale it may attract birds of prey more as suitable perching or
 roosting site than as foraging site. When choosing between squares with
 the same number of species, we chose one with a greater number of
 individuals (with the highest priority) or observations (with secondary
 priority). To avoid choosing sites that were too close to each other (bird
 prey survey involved detectability radius of at least 100-m from the grid
 centre), we discarded other activity hotspots or control site in grid
 cells closer than 450 m from the chosen activity hotspot.
 Control sites were designated as farmland areas with similar
 characteristics to the hotspots and were physically accessible to the
 individuals we tracked. We selected them as pairs with an activity
 hotspot, always located  500 meters from their centers. The procedure
 began with the extraction of centroids of activity hotspots and the
 creation of 500-meter buffers around them. We initiated the search for a
 control site starting from the first grid square intersected by the buffer
 at a 0-degree azimuth from an activity hotspot, moving clockwise. The
 control site was the first suitable cell found, with the following
 exclusions: 1) cells that had more than one observation of tracked raptors
 in a given spring (to avoid similarities with hotspots), 2) cells that
 were closer than 500 meters to already chosen activity hotspots and other
 control sites in the same season, and 3) cells containing landcover other
 than farmland. Hotspots and control sites were
 designated using part of the tracking data in a given season or even
 utilizing data from the previous season. To verify the disparities in
 raptor activity at these sites, once the breeding season data was
 completed, we tallied the number of all observations, the count of
 different individuals and species, and the number of different days these
 sites were used by the tracked birds during the season, within each grid
 square (all done in QGIS 3.22). To accomplish this, we didn't employ
 the raw dataset. Instead, we resampled the data into 15-minute intervals
 using the <em>amt</em> package in R (Signer et al., 2019).
 This approach allowed us to objectively assess the accuracy of activity
 hotspot selections, irrespective of variations in data collection among
 the best-charging individuals.
 <strong>Biodiversity surveys </strong>
 We measured the species richness and abundance of amphibians,
 birds, and small mammals, as well as the species richness of vascular
 plants. The sample sizes differed between the studied groups because of
 large differences of labour in inventories of various taxa from 15-min
 bird counts to 48-h small-mammal trappings. However, the ratio between
 activity hotspots and control sites was always drawn in equal
 proportions. We conducted the inventory of vascular
 plants in July–September 2020 – 2021. In total, we studied 120 50-m × 50-m
 plots (68 in 2020, 52 in 2021; Fig. S1). In each plot, we assessed the
 species composition and the coverage of each species in five
 1-m<sup>2</sup> squares and estimated species richness as a
 total species list of all five. We also calculated the Jaccard index as a
 diversity measure to estimate the species similarity between the squares
 (Maguard 2004). The plant species list of five 1-m<sup>2</sup>
 squares were compared in pairs, and the average Jaccard index was
 calculated for one plot. The smaller the average value of the Jaccard
 index, the greater the difference was in the species composition between
 the squares, and the greater the species diversity was in the 50-m × 50-m
 plot. We conducted bird inventories in 74 plots in the
 first year and in the same number in the next year following the
 methodology of point counts (Bibby et al., 2000). We counted birds twice
 in a season (in the first half of May and in the first half of June) and
 averaged the numbers. The observer stayed at the centre of a 50-m × 50-m
 plot for 15 minutes and recorded all birds within a 100-m radius (Fig.
 S2). Hence, the actual studied area was larger than the 50-m × 50-m plot
 because we expected that the observer may scare birds away from the
 plot. We studied small mammals in 92 plots (46 plots
 per study year). The main dataset was collected by trapping small mammals
 in August and September. In each 50-m × 50-m plot, we conducted trapping
 in five 1-m<sup>2</sup> squares (Fig. S2). In each
 1-m<sup>2</sup> square, we placed a set of traps consisting of
 two snap-traps, two box-traps (Sherman large; 23×9×8
 cm<sup>3</sup>), and one trap-hole (a cone made of a
 bottomless plastic bottle with a depth of about 25 cm and an upper
 diameter of about 10 cm). Each trap was baited with a piece of black
 bread. We trapped small mammals for two consecutive nights in each plot.
 We set the traps up in the evening and checked them in the next morning.
 We counted amphibians in the course of other inventories (birds, mammals,
 vascular plants) in a total of 92 plots (46 annually). Observers were
 walking along five 50-m long transects, in each covering 10-m stripe of
 the ground in their eyesight.
description: # Movement of avian predators points to biodiversity hotspots in
 agricultural landscape 
 This dataset is linked to the original article by Paweł Mirski, Ülo Väli
 and co-authors under the same title as the dataset. The article can be
 found here: Data was
 gathered in 2019-2021 in Tartu county, Estonia. The dataset comprise of
 two parts: 1) GPS telemetry data on avian meso-predators movement in
 Estonian agricultural landscape near Tartu, 2) biodiversity survey results
 in hotspots of avian predators activity and in control sites. Data
 curators are: part 1) - Paweł Mirski, University of Białystok,
 [p.mirski@uwb.edu.pl](mailto:p.mirski@uwb.e) part 2) - Ülo Väli, Estonian
 University of Life Sciences, [ylo.vali@emu.ee ](mailto:ylo.vali@emu.e) ##
 Description of the data and file structure For details, see the Methods
 chapter of the "Movement of avian predators points to biodiversity
 hotspots in agricultural landscape" article by Mirski et al. Tab.1
 Tracking data is comprised of following variables: x - longitude y -
 latitude t - time of GPS fix acquisition id - identificator of the
 individual species - english name of the species tracked Tab.2
 Biodiv_survey is comprised of following variables: id - studied site
 unique identificator type - hotspot or control site year - the year it was
 studied habitat - habitat type classified to one of main classes: cereal,
 grassland, unmanged or edge if the site was the ecotone between two of
 those classes phase - the hotspots and control sites were chosen in few
 steps across 2019 and 2020. This field describes the phase of the study,
 when a given site was chosen to study amphibians_spec - number of
 amphibian species recorded at the given site amphibians_numb - number of
 amphibians recorderd at a given site birds_spec - total number of bird
 species recorded during two controls in the given season birds_avg_numb -
 average number of birds recorded during two controls in the given season
 small_mammals_spec - number of small mammals species recorded at the given
 site small_mammals_numb - number of small mammals recorded at the given
 site flora_avg_Jaccard - simmilarity of flora at a given site, calculated
 with Jaccard index and averaged from 5 plots inside the studied site
 flora_spec_rich - total number of plants recorderd at a given site
 flora_avg_spec_rich - number of plants recorded at a given site averaged
 from 5 plots inside the studied site NAs in the table result from
 different number of surveys devoted to different taxonomic groups. In
 example, small mammal surveys were the most laborius and required 48 hours
 of trapping, while birds were counted twice per season, but the count took
 only 15 minutes. Therefore there are only few NAs in bird survey columns,
 but relatively a lot in small mammal surveys. <br> ## Sharing/Access
 information The data of biodiversity inventories are deposited in the
 PlutoF data repository and available at
 . The raw movement data
 of raptors is deposited in the Movebank data repository
 (data ID: 2055190494 and 1161886125).