DOI: 10.5061/dryad.x69p8czv3
title: Assessing the impacts of livestock grazing on upland bird breeding territories using drone surveys

description: Ground nesting birds are sensitive to habitat structure, so understanding
 this relationship is fundamental to managing habitat to maintain or
 enhance bird populations. We used an existing long-term, large-scale
 experiment with routine monitoring of meadow pipit territories to assess
 the capability of drone-based remote sensing as a means of capturing
 relevant habitat information. Normalised Difference Vegetation Index
 (NDVI) captured differences in stocking density between treatments and
 autumn measured NDVI was well correlated to field measurements of
 vegetation height and density. Spring and autumn NDVI were negatively
 correlated due to dominant tussock-forming species dying back over winter.
 Meadow pipit apparent territory size was positively correlated to autumn
 NDVI and to the difference between autumn and spring NDVI. Apparent
 territory size was larger where there was more tussocky vegetation that
 comprise the areas least preferred for foraging. The long-lived nature of
 tussocks likely constrains the effect of the grazing treatments on meadow
 pipit breeding apparent territory size. Drone-based remote sensing of
 habitat characteristics appears to be a powerful way forward to understand
 bird-habitat associations.
description: <em>Study system</em> The
 long-term Glen Finglas grazing experiment (56°16’N, 4°24’W) was
 established in 2002 on a patchwork of upland, unimproved plant communities
 including upland wet heathland (according to the UK National Vegetation
 Classification, Rodwell 1991, 1992) mainly (in order of area) U5
 <em>Nardus stricta–G. saxatile</em> grassland
 (<em>Nardus</em> grassland), M15 <em>Scirpus
 cespitosus</em> (now <em>Trichophorum
 cespitosum</em>)–<em>Erica tetralix</em> wet heath), the
 mire communities M6 <em>Carex echinata–Sphagnum
 recurvum/auriculatum</em> (<em>Carex</em> mire), M25
 <em>Molinia caerulea–Potentilla erecta</em> (together with
 small areas of M23 <em>Juncus effusus/acutiflorus-Galium
 palustre</em> called <em>Molinia</em> mire in this
 paper), U4 <em>Festuca ovina–Agrostis capillaris–Galium
 saxatile</em> grassland (<em>Agrostis-Festuca</em>
 grassland), and U20 <em>Pteridium aquilinum–G. saxatile</em>
 (bracken). The climate is cool and humid (mean temperatures for January
 and July were 3.04 and 14.12°C, respectively, for 1981–2010 and the mean
 annual rainfall 2,230 mm (Perry and Hollis, 2005), and the soils are
 humus-iron podzols with peaty gleys (Pakeman et al. 2019). Shrubs have
 recently spread in plots in one block (see below) and affected the
 composition of the bird community (Malm et al. 2020).
 The experiment used a randomised block design made up of six
 blocks (A-F) in three groups of two, with each block formed of four plots,
 each plot being 3.3 ha. Plots were fenced in the winter of 2002-03 and the
 plots in each block were allocated randomly to one of four grazing
 treatments in spring 2003. The treatments are High - a tripling of sheep
 numbers to 2.7 ewe ha<sup>-1</sup>, Continued - grazing at the
 same low intensity to the management prior to the establishment of the
 experiment at three ewes per plot (0.9 ewe ha<sup>–1</sup>) to
 act as the control for the experiment, Mixed – a partial substitution of
 sheep by cattle (0.6 sheep ha<sup>-1</sup>) and two cows each
 with a single, suckling calf grazed for four 4 weeks in autumn
 (September-October) to give the same year-round grazing intensity as the
 Continued treatment, and None - no livestock present to represent
 agricultural abandonment. Sheep were removed during the winter (November
 to April) and for short periods for routine farming operations. All
 experimental blocks were mainly surrounded by similar mosaics of the
 vegetation communities represented in the experiment and managed similarly
 to the Continued treatment. Vegetation height data have
 been collected in two ways. Firstly, as part of detailed vegetation
 composition recording 25 fixed points were established per plot on the
 intersections of a square grid at 40 m intervals (Pakeman et al. 2019). At
 each fixed point, a vertical pin-frame of five pins, orientated
 consistently (1 m east) from the fixed point, was used to measure the
 cover of individual plant species and litter and the height of first
 contact (total 125 pins per plot). Sampling has been carried out every
 three years, including in 2020 when strict movement restrictions were in
 place due to Covid-19, in late July/early August prior to cattle being
 introduced to the plots. Secondly, an annual estimate of vegetation height
 and density was taken in late August/early September at 81 points per plot
 (Evans et al. 2015), the 25 fixed points plus intermediates on a 20 m
 grid. Three measures of maximum vegetation height were made at each point
 using a stick marked at 5-cm intervals (at arm’s length, to the front and
 either side of the observer). The visibility of white marks at 5-cm
 intervals on the vertically held stick was recorded to measure vegetation
 density. <em>Bird census</em>
 Within each plot, meadow pipit territories were mapped between
 April and June each year (since 2002) using an adaptation of the standard
 Breeding Bird Survey methodology (BBS, Heywood et al. 2023). In contrast
 to the BBS methodology, transects were spaced at much shorter distances,
 generally 25-45 m depending on the size and shape of each block, and were
 walked more or less parallel to the outer boundaries of each block. Bird
 activity was recorded on A3 field maps using the Common Bird Census
 symbology (Gilbert et al., 1998) in a single band which stretched as far
 as reliable identification was possible, typically within 100 m, but
 generally took place within the block being walked, paying particular
 attention to bird breeding behaviour, such as song flight, alarm calls,
 food or faecal sac carrying, territorial encounters and using nest
 locations where detected (Dennis et al. 2005). Detailed
 work at the start of the experiment found that observed pipit breeding
 behaviour centres around nest locations (Evans et al. 2005) and that they
 stay close to the nest when foraging (~75% observations within 60m;
 Douglas et al. 2008), with average distances reducing to 29.3 m ±2.89
 (1SE) in treatment (i) when provisioning nestlings (Vandenberghe et al.
 2009). Thus, we are confident that observed activity is indicative of a
 territory even if the nest location has not been discovered. Efforts are
 made to avoid double-recording, given the short distances between adjacent
 transects. All bird species are recorded but meadow pipit being by far the
 most abundant species at the study site, the latter only was used in the
 present analyses. Bird surveys in 2021 were carried out
 in blocks A/B on 28<sup>th</sup> April, 5th,
 11<sup>th</sup>, 25<sup>th</sup> May and
 1<sup>st</sup> June; in blocks C/D on
 29<sup>th</sup> April, 7<sup>th</sup>,
 13<sup>th</sup>, 27<sup>th</sup> May and
 2<sup>nd</sup> June; and in blocks E/F on
 26<sup>th</sup> April, 2<sup>nd</sup>,
 9<sup>th</sup>, 24<sup>th</sup> and
 30<sup>th</sup> May. Surveys took place only in suitable
 weather conditions, i.e., not in winds exceeding 8 m
 s<sup>-1</sup> and not in heavy rain. During the initial
 weeks, the higher areas of the plots could still be covered in snow; such
 days were equally avoided or surveyed later in the morning after the snow
 had melted. Following the completion of fieldwork, the registrations from
 all visits to a block were transcribed onto territory maps and grouped
 into apparent territories on the basis of clusters taking note especially
 of birds that had been recorded simultaneously and likely associated with
 different apparent territories as well as territorial encounters. We use
 the phrase “apparent territory” as they are based on a limited set of
 observations and subsequent interpretation. We concede that pipits
 occasionally use areas outside of these apparent territories and that
 territorial boundaries may be fluid. However, our adapted BBS methodology
 is suitable for determining territory size for our purposes as the
 frequency of visits is higher and spacing of transects is closer, allowing
 a more detailed mapping of territories across the experimental plots.
 Furthermore, birds were all surveyed and identified by the same team, with
 territory maps finalised by the same observer (DME) since 2002, and the
 geo-spatial spread of observations used to delineate the apparent
 territory area. Thus, we are confident that comparing territories based on
 the spread of breeding bird observations is robust within the context of
 this analysis. The transcribed maps from 2021 showing apparent territories
 were scanned and saved as jpegs. These were then georeferenced in ArcGIS
 Pro 3.3 (ESRI, Redlands, USA) and subsequently digitised.
 <em>NDVI data capture</em> On the
 1<sup>st</sup>, 2<sup>nd</sup> and
 22<sup>nd</sup> of April 2021, and again on the
 2<sup>nd</sup> and 8<sup>th</sup> of September
 2021 aerial imaging surveys were conducted at the experimental site. The
 flight planning software Ground Station Pro 2.0.17 (DJI, Shenzhen,
 Guangdong, China) was used to demarcate each experimental site and
 conditions for the survey set as follows: height above ground level: 120
 m, resulting in a ground sample distance of <em>c.</em> 7 cm
 (approximate, as terrain height varied within survey areas); image
 overlap: 80% forward, 70% side overlap. The unmanned aerial vehicle (UAV)
 deployed was a Phantom 4 Multispectral RTK (DJI, Shenzhen, Guangdong,
 China) linked to RTK (real-time kinematic positioning) base station during
 data collection, which carries a payload of one RGB and five monochromatic
 sensors (Filters: Blue (B): 450 nm ± 16 nm; Green (G): 560 nm ± 16 nm; Red
 (R): 650 nm ± 16 nm; Red edge (RE): 730 nm ± 16 nm; Near-infrared (NIR):
 840 nm ± 26 nm) and has an upward facing irradiance sensor for radiometric
 normalisation purposes. Flight lines were adjusted to be perpendicular to
 the angle of the sun (course angle option in Ground Station Pro). 
 Pix4Dmapper, Education Licence (Pix4D, SA, United States) was used to
 create Digital Surface models (DSM) prior to orthomosaic generation
 (structure from motion algorithms). Pix4Dmapper was also used to generate
 reflectance maps (incorporating the irradiance data) from the
 monochromatic images, that were then used to calculate and create NDVI
 maps, which were exported as GEOTIFF files. The 120 m flight altitude was
 more than twice the threshold (50 m) where disturbance affects have been
 detected on nesting birds (Cantu de Leija et al. 2023). Since 2008 there
 has been little between year variability in vegetation height, though
 heights have slowly increased (Pakeman et al. 2019). Consequently, the
 NDVI survey should be representative of adjacent years.
 <em>Data processing</em> All data
 processing was completed using ArcGIS software version 10.8.1 and all
 spatial datasets were projected using the WGS1984_UTM_Zone_30N coordinate
 system to match that of the drone imagery.  Each meadow pipit apparent
 territory was assigned a unique ID and the area of each apparent territory
 calculated.  Subsequently, the tabulate intersection tool was used to
 calculate the area of each territory within each of the experimental
 plots.  As some territories crossed the boundaries between experimental
 plots, the percentage of each territory contained in each plot was
 calculated using the ArcGIS field calculator.  The number of territories
 in each plot were calculated using the frequency function.
 Spatial layers containing the experiment plots, apparent meadow
 pipit territories and the locations of the 81 vegetation sampling points
 in each plot were overlayed on the NDVI maps produced from the drone
 imagery.  The zonal statistics function in ArcGIS was used to produce
 tables containing the mean NDVI and standard deviation at the plot and
 territory scale as well as within a 2m radius of each of the vegetation
 sampling points in each plot.  This was performed using the Spring and
 Autumn NDVI maps. Vegetation height and density values
 from the 2021 annual structural measurements and height measurements from
 the 2020 pin frame survey were later joined to the NDVI tables at the
 respective scale of plot, meadow pipit apparent territory and vegetation
 sample point.  Where meadow pipit apparent territories crossed the
 boundary between plots, they were assigned the plot which contained more
 than half the apparent territory.
description: # Assessing the impacts of livestock grazing on upland bird breeding
 territories using drone surveys
 [https://doi.org/10.5061/dryad.x69p8czv3](https://doi.org/10.5061/dryad.x69p8czv3) ## Description of the data and file structure ### Files and variables #### File: Combined\_Plot.csv **Description:**  ##### Variables * Block: Experimental block (A to F) * Treatment: Coded treatment (I High 9 sheep per plot, 2.7 sheep/ha; II Low sheep 3 per plot, 0.9 sheep/ha, III Mixed sheep and cattle, grazing offtake equivalent to II, IV no livestock grazing) * Plot_ID: Combination of Block and Treatment codes. * SPR_NDVI_MEAN: Mean NDVI from spring image at the plot level. Unitless, scale -1 to 1. * SPR_NDVI_STD: Standard deviation of NDVI across the plot in spring. Unitless, scale -1 to 1. * AUT_NDVI_MEAN: Mean NDVI from autumn image at the plot level. Unitless, scale -1 to 1. * AUT_NDVI_STD: Standard deviation of NDVI across the plot in autumn. Unitless, scale -1 to 1. * 2020PinFrameHeight: Mean height of first contact of pin drops from the 2020 detailed vegetation sampling. Unit: centimetres (cm). * 2020StdDevHeight: Standard deviation of height of first contact across the plot. Unit: centimetres (cm). * 2021_Structure_MEANDensity: Mean density recorded during autumn structure measurements across the plot. Unit: centimetres (cm). * 2021_Structure_STDDensity: Standard deviation of density recorded during autumn structure measurements. Unit: centimetres (cm). * 2021_Structure_MEANHeight: Mean height recorded during autumn structure measurements across the plot. Unit: centimetres (cm). * 2021_Structure_STDHeight: Standard deviation of height recorded during autumn structure measurements. Unit: centimetres (cm). #### File: Combined\_Territories.csv **Description:**  ##### Variables * Territory: Territory label * SPR_NDVI_MEAN: Mean NDVI from spring image for each territory. Unitless, scale -1 to 1. * SPR_NDVI_STD: Standard deviation of NDVI across each territory in spring. Unitless, scale -1 to 1. * AUT_NDVI_MEAN: Mean NDVI from autumn image for each territory. Unitless, scale -1 to 1. * AUT_NDVI_STD: Standard deviation of NDVI across each territory in spring. Unitless, scale -1 to 1. * Allocation: Plot allocation for each territory. NA Territory mainly outside the experimental area, so not allocated to plot. * Block: Experimental block (A to F). NA Territory mainly outside the experimental area, so not allocated to a block. * Trt: Coded treatment (I High 9 sheep per plot, 2.7 sheep/ha; II Low sheep 3 per plot, 0.9 sheep/ha, III Mixed sheep and cattle, grazing offtake equivalent to II, IV no livestock grazing). NA Territory mainly outside the experimental area, so not allocated to a treatment. * Stocking: Sheep stocking rate (sheep/ha) equivalent. NA Territory mainly outside the experimental area, so not allocated to a grazing level. * NDVI_DIFF: Difference between mean spring and mean autumn NDVI for each territory. Unitless, scale -1 to 1. #### File: Combined\_VegPoints.csv **Description:**  ##### Variables * ID: Code for each recording point from the autumn structure survey. A-F = block, I-IV = treatment, 1-81 point number. * SPR_NDVI_MEAN: Mean NDVI from spring image for circle of 2 m radius around each point. Unitless, scale -1 to 1. * SPR_NDVI_STD: Standard deviation of NDVI from spring image for circle of 2 m radius around each point. Unitless, scale -1 to 1. * AUT_NDVI_MEAN: Mean NDVI from autumn image for circle of 2 m radius around each point. Unitless, scale -1 to 1. * AUT_NDVI_STD: Standard deviation of NDVI from autumn image for circle of 2 m radius around each point. Unitless, scale -1 to 1. * Block: Experimental block (A-F) * Plot: Coded treatment (I High 9 sheep per plot, 2.7 sheep/ha; II Low sheep 3 per plot, 0.9 sheep/ha, III Mixed sheep and cattle, grazing offtake equivalent to II, IV no livestock grazing) * Point: Structure recording points within each plot (1 to 81) * 2021StructureMEANDensity: Mean density recorded during autumn structure measurements at the point. Unit: centimetres (cm). * 2021StrcutureSTDDensity: Standard deviation of density recorded during autumn structure measurements at the point. Unit: centimetres (cm). * 2021StructureMEANHeight: Mean height recorded during autumn structure measurements at the point. Unit: centimetres (cm). * 2021StructureSTDHeight: Standard deviation of height recorded during autumn structure measurements at the point. Unit: centimetres (cm). * Comm: National Vegetation Classification community at each point: U5 *Nardus stricta–G. saxatile* grassland (*Nardus* grassland), M15 *Scirpus cespitosus* (now *Trichophorum cespitosum*)–*Erica tetralix* wet heath), the mire communities M6 *Carex echinata–Sphagnum recurvum/auriculatum* (*Carex* mire), M25 *Molinia caerulea–Potentilla erecta* (together with small areas of M23 *Juncus effusus/acutiflorus-Galium palustre* called *Molinia* mire in this paper), U4 *Festuca ovina–Agrostis capillaris–Galium saxatile* grassland (*Agrostis-Festuca* grassland), and U20 *Pteridium aquilinum–G. saxatile* (bracken). NA Could not be allocated to a community. ## Code/software Files are .csv files ## Access information Other publicly accessible locations of the data: * N/A Data was derived from the following sources: * N/A