DOI: 10.5061/dryad.905qfttvw
title: Data from: Breeding Ammospiza nelsoni (Nelson’s Sparrow) exploits both saltmarsh and hayfields in northern habitats

description: Ammospiza nelsoni subvirgata (Acadian subspecies of Nelson’s Sparrow)
 breeds in saltmarshes from northern Massachusetts to New Brunswick and
 eastern Quebec. In the Canadian Maritimes, this subspecies also
 successfully breeds in diked agricultural lands (i.e., “dikeland”) that
 were originally created by Acadian settlers in the 1600s. Little is known
 about the reasons for or consequences of using dikeland for breeding. To
 fill this knowledge gap, we tracked male and female sparrows, and
 monitored nest fates in natural saltmarsh and human-made dikeland
 habitats. We collected fecal samples from adults and nestlings to examine
 which habitat type they were foraging in, and we also quantified
 vegetative cover. We hypothesized that flood risk in saltmarshes played an
 important role in the decision of A. n. subvirgata to nest in dikeland
 given that the saltmarsh is regularly inundated with tidal water. Based on
 nest monitoring, we estimated higher overall nest success in dikeland than
 saltmarshes. Fecal sample analysis showed distinct differences in diet
 between individuals using dikeland compared to saltmarshes. We also
 observed differences in vegetation. These results suggest that A. n.
 subvirgata are able to take advantage of a readily available human-made
 habitat for breeding. With rising sea levels and increased storm events
 threatening coastal habitats, it is important to understand if
 coastal-breeding birds can adapt to changes and what trade-offs exist for
 individuals who shift to alternative habitats.
description: <strong>Study site</strong> We
 conducted our study near Beaubassin Research Station (hereafter
 “Beaubassin”) in Aulac, New Brunswick (45°50'51.08",
 -64°17'7.27") from 2021 to 2023, and in Dorchester, New
 Brunswick (45°53'32.01", -64°32'27.2") in 2023 only.
 Beaubassin is surrounded by dikeland, with natural and restored
 saltmarshes along the shore of the bay (Figure 1). The site in Dorchester
 was added to the study after we found a <em>A. n.
 subvirgata</em> nest there in 2023. The Dorchester site includes two
 large impoundments (human-made diked wetlands), directly adjacent to a
 large dike that separates a narrow saltmarsh along the Memramcook River.
 Beyond the impoundments are dikeland used for cattle pasture and
 hayfields. We did not conduct any radio-tracking or banding of adults at
 the Dorchester site, but monitored several nests at this site in 2023. The
 area we covered in Dorchester had a much narrower saltmarsh, measuring
 approximately 130 m wide, than the one near Beaubassin, where the
 saltmarsh extends up to 700 m from the dike. Although the dikeland or
 agricultural land area was over 7 km<sup>2</sup>, we
 restricted our nest searching efforts to the narrow area (approximately
 10% of the total) on or near the dike because these lands were publicly
 accessible. <strong>Radio-telemetry
 home range estimation</strong> <em>A. n.
 subvirgata </em>are difficult to monitor: they have soft songs,
 often sing and move within dense vegetation, and nest near the ground. We
 therefore used a combination of radio telemetry and color-banding to track
 female and male sparrows in 2021 and 2022. To capture sparrows, we set up
 multiple mist-nets in either dikeland or saltmarsh. In the saltmarsh, most
 nets were set inside ditches because <em>A. n.
 subvirgata,</em> when disturbed, would often fly into the ditches
 and could be coaxed into the net. In the dikeland, if a ditch was not
 present, we set multiple nets parallel to the dike or near shrubs or small
 trees where the net would be camouflaged. We had limited success with
 playback or passive netting and did not use these techniques beyond a few
 initial attempts. Once captured, we tagged individuals
 with Lotek nano tags (Lotek NTQB2-2-M) registered with the Motus network
 (Taylor et al. 2017). In 2021, we used the clip-and-glue method (Raim
 1978; Diemer et al. 2014), but switched to leg-loop harnesses (Rappole and
 Tipton 1991) in 2022 due to retention issues with the clip-and-glue
 method. The clip-and-glue method involved clipping some interscapular
 feathers, gluing a small piece of cotton fabric to the tag to improve
 adhesion, then gluing the tag to the clipped area on the individual’s
 back. The leg-loop harness method involved creating two adjacent loops out
 of elastic cord then gluing on the tag. Harnesses were sized to fit the
 individual, therefore, we made the harnesses in the field while holding
 the individual. All individuals successfully flew away after tagging and
 we confirmed that individuals continued their normal activities after
 relocating them using radio telemetry. The weight of the tags with the
 glue, cotton fabric, and cord was &lt; 0.4 g, which is &lt;5% of
 handled individuals’ weights (range: 14.5 – 22 g). With the leg-loop
 harness method, we had one mortality due to entanglement of the tag
 antenna in grass, a problem that has been reported with this method in the
 past, particularly for species that spend a lot of time on the ground
 among grass (Hill and Elphick 2011, Choi et al. 2021).
 We also banded each individual with a unique combination of color
 bands to ease identification during resighting, and one metal band with a
 unique federally issued number. For each individual, we collected
 information on weight (g), wing length (mm), fat score (0-5), age (i.e.,
 hatch-year or after hatch-year) and sex, determined by the presence of a
 cloacal protuberance or brood patch. In 2022, we also measured tarsus
 length (mm), tail length (mm), and collected a fecal sample from each
 individual. All birds were handled and banded following animal use
 protocols of the University of New Brunswick (AUP #s: 21034, 22028, 23025)
 and under banding permit #10801 E. We recorded
 locations of individuals every 2-3 days throughout the breeding season and
 at different times of day. We used handheld radio telemetry equipment
 (Lotek SRX800 receiver and 8-element Yagi antenna) ensured the tag was on
 the individual by resighting the color bands or tag or flushing the
 individual from the location and confirming that the tag location moved.
 We also included locations of individuals of individuals that died or
 whose tags dropped if we could confirm the color band combination
 (<em>n </em>= 68 locations). Once located, we marked the
 location using a handheld Garmin GPS and recorded coordinates, time, and
 date. We did not track individuals on days with rain, heavy fog, or a high
 risk of lightning We created kernel density estimates
 derived from each individual’s utilization distribution using
 “adehabitatHR,” an R package for home range analysis (Calenge 2019). We
 only included individuals with ≥ 5 locations (<em>n </em>=
 50), which is the minimum number of locations required by “adehabitatHR”
 to create home ranges. We used R package “sp” to convert the individuals’
 locations to a SpatialPointsDataFrame object (Pebesma and Bivand 2005),
 one of the two data types that can be used with “adehabitatHR”. Then we
 used “adehabitatHR” to create 50% kernel density estimates to look at home
 ranges for each individual (with ≥ 5 locations; range 5 – 26 locations;
 mean = 13 locations). The 50% kernel density estimate is usually
 considered representative of the core range of an individual, and the area
 where it spend most of its time. We also created kernel density estimates
 at the 95% level to represent the whole home range with 5% removed to deal
 with extreme values. We focussed on the 50% core home ranges for analysis
 because the 95% whole home ranges included substantial overlap that made
 it difficult to interpret visually. <strong>Nest
 searching and monitoring</strong> In 2023, we
 searched for <em>A. n. subvirgata </em>nests in dikeland and
 saltmarsh habitats from 16 June - 23 August. Nest searching was both
 opportunistic (i.e., while completing other tasks in the field) and
 systematic (i.e., dedicated searching). Nearly all nests were found by
 first flushing the female off the nest. We used a variety of methods to
 find nests: walking through the habitat, rope-dragging, searching with the
 assistance of a thermal camera, and following a female carrying food. We
 found that a combination of multiple methods was the most useful. For
 example, in many cases, we would split up and walk around the habitat
 watching for behavioral signs of females with nests nearby, such as
 carrying food or flushing late (i.e., taking flight when someone was very
 close to the bird). When one was encountered, we would leave a marking
 flag and return to the area with the rope. Then, after flushing the female
 close to the nest with the rope, we would use the thermal camera to try
 and pinpoint the exact location of the nest. We used an AGM Global Vision
 Thermal Monocular ASP-Micro TM160 to assist with nest searching efforts.
 This follows the recommendation of Galligan et al. (2003) of pairing a
 handheld thermal camera with traditional nest-searching methods. Once we
 found a nest, we marked the location with two flags placed 2m on either
 side of the nest. We checked nests once every three
 days, unless weather or logistics prevented a check on the third day, in
 which case we would check on the second or fourth day. During each nest
 check, we determined if the nest was still active and gathered information
 on the number of eggs or nestlings, age of nestlings, and if eggs were
 warm or cold. We also noted if the female flushed when we approached,
 which can suggest the nest is still active. If the nest was not active, we
 tried to determine the cause of failure, for example, if the nest itself
 was destroyed and eggs were smashed, we assumed it was depredated, or if
 the nest was wet and the area around the nest was flattened from a recent
 high tide, then we assumed the nest was flooded. We also monitored tides
 to determine when flooding was likely, and we monitored weather,
 particularly rain events, to determine when nest wetness might be due to
 rain rather than flooding. Two high tide events resulted in failures at
 many of our nests; on 6 July at approximately 0145h, the height of the
 tide at a local tidal station (Pecks Point, 45°45'0",
 -64°28'58.8") reached 13.02 m, and on 4 August at approximately
 0130h, the tide reached a height of 13.28 m (Government of Canada
 2024). <strong>Nest
 survival</strong> We concluded whether each nest
 had failed or fledged based on observations at the nest. If we determined
 that at least one nestling had fledged, then we assigned a fate of
 “fledged’ (i.e., a successful nest). For each failed nest, we assigned a
 nest failure type: flooded, depredated, or unknown. Flooded nests were due
 to high tides and could be determined by nest wetness during nest checks
 (especially if there was no precipitation in the past 24 hours) and by
 using tide tables. We considered a nest to be depredated if there were
 signs of disturbance at the nest or broken shells. If we could not
 determine the nest fate, we assigned a nest fate of “unknown”. Some nest
 failures occurred after an extreme rain event, and some of “unknown” nest
 failures may have been due to this extreme weather event, which did not
 coincide with a particularly high tide. Nest failure due to heavy rains
 and winds has been reported in other tidal marsh nesting sparrows
 (Greenberg et al. 2006) We weighed, measured, and
 collected fecal samples from nestlings once they were 7-8 days old. We
 used chick aging guides from the Saltmarsh Sparrow Research Initiative
 (Robinson 2021) and the Saltmarsh Habitat and Avian Research Program
 (SHARP 2021). The former reference guide is specific to <em>A.
 caudacata</em>, and the latter uses examples of <em>A.
 caudacata</em>, <em>A. n. subvirgata</em>, and
 <em>A. maritimus</em> (Seaside Sparrow). We found that our
 population of <em>A. n. subvirgata</em> were developmentally
 more advanced (by one day) than the examples in the two guides . We
 weighed nestlings (g), and measured wing chord (mm), tarsus (mm), and the
 length of the outermost primary feather (P9; mm). We
 included 35 nests in our survival analyses. After removing a nest from the
 analysis that we suspected had already failed (and the clutch had not been
 completed by the female) at the time we found the nest. We created several
 models looking at nest success with a combination of three variables:
 exposure days, habitat, and nesting cycle. We measured exposure days from
 the first day an egg was laid, and therefore exposed to threats or could
 potentially fail, to the day the young successfully fledge. Habitat was a
 binary variable of saltmarsh or dikeland. Nesting cycle was also a binary
 variable; the first-cycle nests (nests that failed or fledged before 5
 July), and second-cycle nests (nests that failed or fledged after 5 July).
 In our study area, the first series of very high tides since the beginning
 of the nesting season occurred around 5 July, and six saltmarsh nests
 failed. About 10 days after these high tides, we found 10 new nests, which
 we suspect were due to females renesting after failures. In three cases,
 the nests were &lt;5 m from a recently failed nest. We considered any
 nest in the egg stage after 6 July to be a second-cycle nest. We included
 nesting cycle in the models because, although the nesting cycle of
 <em>A. n. subvirgata</em> are not known to synchronized with
 lunar cycles (Shriver et al. 2007), it appeared that nest timing was at
 least loosely associated with the tide, and, therefore, we tested for an
 effect. For <em>A. n. subvirgata</em>, nesting cycle is
 estimated to be 23 days (4 days for egg-laying, 9 days for incubating, and
 10 days to fledging; Shriver et al. 2007). We used a
 discrete proportional hazards approach and fit binomial regression models
 with a complementary log-log link and exposure days as an offset term to
 estimate daily nest survival probability (de Zwaan and Martin 2018). We
 compared models using corrected Akaike’s Information Criterion (AICc),
 which corrects for small sample sizes to avoid overfitting (Hurvich and
 Tsai 1993), and we considered models to  have different support if ΔAICc
 was &gt;2. We used the complementary log-log link because it fit the
 data better than a logistic curve; the complementary log-log link is
 appropriate for time series data when the timing of an event (e.g., nest
 failure) cannot be precisely determined, but can be assigned to a time
 interval (e.g., a calendar day; Heisey et al. 2007). We used parameter
 estimates from the best-supported model to calculate overall nest success.
 We then back-calculated daily nest survival by taking the inverse
 exponential of the overall nest success based on the average nesting cycle
 for <em>A. n. subvirgata</em>.
 <strong>Diet</strong> We collected
 fecal samples from all adults banded in 2022 and all nestlings banded in
 2023 to determine if adults were foraging in saltmarsh or dikeland
 habitats, both for their own dietary needs and to feed nestlings. To
 determine foraging location, we analyzed δ<sup>13</sup>C. We
 also looked at d<sup>15</sup>N values, which provide
 information about the trophic position of prey items. In 2022, adult
 sparrows were placed in a brown paper bag instead of the usual cloth bag.
 We placed all nestlings from the same nest into a single paper bag.
 Usually, individuals released a fecal sample into the bag before we
 removed them to band, tag, and measure. The paper bags with the samples
 were then labeled and frozen. At the Stable Isotope in
 Nature Laboratory (SINLAB) at the University of New Brunswick, we analysed
 carbon isotopic signatures of fecal samples from adult and nestling
 <em>A. n. subvirgata</em> collected in 2022 (adults:
 <em>n</em> = 27) and 2023 (nestlings:<em> n</em> =
 16). Samples were dried in an oven at 60°C for 48 – 72 hours, then were
 ground, prepared in tin capsules (8 x 5mm; Elemental Analysis), and
 weighed (± 0.001 g). We submitted 49 samples, of which four were blind
 duplicates, to the SINLAB who used Continuous Flow-Isotope Ratio Mass
 Spectrometry to obtain d<sup>13</sup>C and
 d<sup>15</sup>N ratios. To ensure quality and comparability of
 data, samples were measured alongside standard and reference materials.
 Carbon and nitrogen ratios were calibrated to international standards
 (Vienna Peedee Belemnite for carbon, atmospheric air for nitrogen), and
 are reported in delta notation (d) parts per thousand (per mille ‰). The
 SINLAB calculated analytical error as 0.2‰ for δ<sup>13</sup>C
 and 0.3‰ for δ<sup>15</sup>N based on repeat analyses of
 certified standard materials: bovine liver standard (developed by SINLAB,
 δ<sup>13</sup>C = −18.76‰, δ<sup>15</sup>N =
 7.17‰), muskellunge muscle (developed by SINLAB,
 δ<sup>13</sup>C = −22.30‰, δ<sup>15</sup>N =
 14.00‰), Nicotinamide (batch 237264, δ<sup>13</sup>C =
 −32.53‰, δ<sup>15</sup>N = 2.10‰). These standards were
 calibrated against secondary reference materials: USGS61 (caffeine,
 certified by USGS, δ<sup>13</sup>C = −35.05‰,
 δ<sup>15</sup>N = -2.87‰), CH7 (polyethylene foil, certified
 by IAEA, δ<sup>15</sup>N = 20.3‰), N2 (ammonium sulfate,
 certified by IAEA, δ<sup>13</sup>C = −32.15‰).
 In habitats like dikeland and saltmarsh, which are isotopically
 different, d<sup>13</sup>C values from fecal samples can
 provide information on where the individual was foraging because of
 dominant photosynthetic pathways by freshwater or terrestrial
 (C<sub>3</sub>) and saltmarsh (C<sub>4</sub>)
 vegetation (Brittain et al. 2012). Lower δ<sup>13</sup>C
 values would suggest a diet with a terrestrial or freshwater origin
 indicative of vegetation using the C<sub>3</sub>
 photosynthetic pathway, while higher δ<sup>13</sup>C values
 suggest a diet with a saltwater origin from vegetation using the
 C<sub>4</sub> photosynthetic pathway (Kelly 2000). Nitrogen
 values provided information on trophic position of prey items, which can
 be used as a proxy for food quality, as protein content is generally
 positively correlated with trophic position in arthropods (Wilder et al.
 2013). To compare carbon and nitrogen values from
 nestling fecal samples between habitats, we used Welch’s Two-sample
 t-tests with ɑ = 0.05. We did not run analyses on fecal samples from
 adults because sample sizes for individuals using mainly dikeland
 (<em>n</em> = 4) or using both habitats
 (<em>n</em> = 2) were too small, so we instead report the
 means. <strong>Vegetation
 surveys</strong> We sampled 300 vegetation
 quadrats (0.5 m x 0.5 m square; Jones et al. 2021) at 100 locations
 between 6-15 June 2023, when females were choosing nest site locations for
 first-cycle nests. In each quadrat, we made the same measurements at five
 points: each of the four corners, and the center of the quadrat using a
 meter-long ruler (SHARP 2019). For each point in the quadrat, we measured
 the average height of vegetation crown (an estimate based on the height of
 continuous vegetation around the ruler; cm), species and height (cm) of
 tallest plants, and thatch depth (cm). We defined thatch as the horizontal
 layers of dead vegetation from previous years. We randomly selected one
 location for each adult <em>A. n. subvirgata</em> that was
 tagged in 2021 and 2022 and had at least five locations based on radio
 tracking or resighting. Therefore, half of the vegetation quadrats were
 completed at a location, where a unique individual was observed during the
 2021 or 2022 breeding season (<em>n</em> = 50). For the other
 50 quadrat locations, we created a polygon outlining the study area, then
 overlayed a grid of points on the polygon with 100 m spacing. We used
 random sampling with no replacement to select locations and removed any
 points that were located in water. To assess differences in vegetation
 structure between saltmarsh and dikeland habitats, we used Welch’s
 Two-sample t-tests to compare means, and F-tests to compare variances,
 with ɑ = 0.05. All analyses were completed in R version
 4.3.1 (R Core Team 2023). All outliers were defined as values above Q3 +
 1.5*IQR or below Q1 – 1.5*IQR. <strong>Literature
 cited</strong> Brittain, R. A., A. Schimmelmann,
 D. F. Parkhurst, and C. B. Craft (2012). Habitat use by coastal birds
 inferred from stable carbon and nitrogen isotopes. Estuaries and Coasts
 35:633–645. Calenge, C. (2006). The package
 “adehabitat” for the R software: a tool for the analysis of space and
 habitat use by animals. Ecological Modelling 197:516-519.
 Choi, D. Y., A. C. Fish, C. E. Moorman, C. S. Deperno, and J. M.
 Schillaci (2021). Breeding-season survival, home-range size, and habitat
 selection of female Bachman’s Sparrows. Southeastern Naturalist
 20:105–116. de Zwaan, D. R., and K. Martin (2018).
 Substrate and structure of ground nests have fitness consequences for an
 alpine songbird. Ibis 160:790–804. Government of Canada
 (2024). <em>Pecks Point (00190)</em>. Retrieved January 30,
 2024, from https://tides.gc.ca/en/stations/00190.
 Greenberg, R., C. S. Elphick, J. C. Nordby, C. Gjerdrum, H.
 Spautz, W, G. Shriver, B. Shmeling, B. Olsen, P. Marra, N. Nur, and M.
 Winter (2006). Flooding and predation: Trade-offs in the nesting ecology
 of tidal-marsh sparrows. Studies in Avian Biology<em>
 </em>32:96-109. Heisey, D. M., T. R. Shaffer, and
 G. C. White (2007). The ABCs of nest survival: theory and application from
 a biostatistical perspective. Studies in Avian Biology 34:13-33.
 Hurvich, C. M., and C. Tsai (1993). A corrected Akaike
 Information Criterion for vector autoregressive model selection. Journal
 of Time Series Analysis 14:271–279. Jones, S. F., C. N.
 Janousek, M. L. Casazza, J. Y. Takekawa, and K. M. Thorne (2021). Seasonal
 impoundment alters patterns of tidal wetland plant diversity across
 spatial scales. Ecosphere 12:1–19. Kelly, J. F. (2000).
 Stable isotopes of carbon and in the study of avian and mammalian trophic
 ecology. Canadian Journal of Zoology 78:1–27. QGIS.org
 (2023). QGIS Geographic Information System. QGIS Association. http://www.qgis.org R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Raim, A. (1978). A radio transmitter attachment for small passerine birds. <em>Bird-banding</em> 4:326-332. Rappole, J. H. and A. R. Tipton. (1991). New harness design for attachment of radio transmitters to small passerines. <em>Journal of Field Ornithology </em>3:335-337. SHARP (2019). Nest vegetation measurements – standard operating procedure. Saltmarsh Habitat and Avian Research Program. SHARP (2021). Chick Aging Protocol. Saltmarsh Habitat & Avian Research Program Standard Operating Procedure. Saltmarsh Habitat and Avian Research Program. Shriver, W. G., P. D. Vickery, T. P. Hodgman, and J. P. Gibbs (2007). Flood tides affect breeding ecology of two sympatric Sharp-tailed Sparrows. Auk 124:552–560. Taylor, P. D., T. L. Crewe, S. A. Mackenzie, D. Lepage, Y. Aubry, Z. Crysler, G. Finney, C. M. Francis, C. G. Guglielmo, D. J. Hamilton, R. L. Holberton, P. H. Loring, G. W. Mitchell, R. D. Norris, J. Paquet, R. A. Ronconi, J. R. Smetzer, P. A. Smith, L. J. Welch, and B. K. Woodworth (2017). The Motus Wildlife Tracking System: a collaborative research network to enhance the understanding of wildlife movement. Avian Conservation & Ecology 12:8. Wilder, S. M., M. Norris, R. W. Lee, D. Raubenheimer, and S. J. Simpson (2013). Arthropod food webs become increasingly lipid-limited at higher trophic levels. Ecology Letters 16:895–902.
description: # Ammospiza nelsoni subvirgata data from breeding season in Canadian
 Maritimes 2021 - 2023
 [https://doi.org/10.5061/dryad.905qfttvw](https://doi.org/10.5061/dryad.905qfttvw) ## Description of the data and file structure Data are included in five CSV files that can be read into R (or opened in any application that opens CSV files, e.g., Microsoft Excel). There are two CSV files for fecal sample data, one CSV file for nest survival data, one CSV file for tracking data for home range analyses, and one CSV file with vegetation data from surveys completed at bird locations and random locations (used in this study) and at nest sites (used in an adjacent study). ### Files and variables #### File: ORNITH-APP-24-098\_fecal\_data\_NoDup.csv **Description:** CSV file containing data from stable isotope analyses of Ammospiza nelsoni subvirgata (Acadian Nelson's Sparrow) fecal samples. Isotope analysis completed by the Stable Isotopes in Nature Lab (SINLAB) at the University of New Brunswick. Fecal samples came from adult birds in 2022 and nestlings in 2023. Duplicates are removed in this file. The other file contains duplicate samples. Samples were collected from adults Beaubassin Research Station (45°50'51.08", -64°17'7.27"). Nestling fecal samples were colelcted near Beaubassin Research Station and Dorchester (45°53'32.01", -64°32'27.2"). ##### Variables * id: an identifier created by me (nest name or band #) for each bird or nest * type: bird (i.e., fecal from an individual adult sparrow) or nest (i.e., fecal from nestlings) * habitat: dikeland (dl) vs saltmarsh (sm) * sinlab_id: a unique identifier for the sample created by the SINLAB * date: date sample was processed in SINLAB * row: position in the analytical run for that particular day; samples are weighed into 96-well ELISA trays. * amount: weight of the sample in grams * C02: the area of the chromatographic peak for CO2, related to the amount of CO2 gas detected by the mass spectrometer and reported in in volts(V), a function of the weight of tissue used and the total amount of carbon (%C) it contains. * N2_ampl: the height of the chromatographic peak for N2, related to the amount of N2 gas detected by the mass spectrometer in volts (V), a function of the weight of tissue used and the total amount of nitrogen (%N) it contains. * d13C: the carbon stable isotope ratio, presented as per mille differences in delta notation. A function of the isotopic difference between the sample and the international standard (VPDB) according to the formula: *13C = [(Rsample/Rstandard)-1]*1000*, where R denotes the ratio of the heavy to light (13C/12C) isotopes in the sample and international standard, respectively. * d15N: the nitrogen stable isotope ratio, presented as per mille differences in delta notation. A function of the isotopic difference between the sample and the international standard (AIR) according to the formula: *15N = [(Rsample/Rstandard)-1]*1000*, where R denotes the ratio of the heavy to light (15N/14N) isotopes in the sample and international standard, respectively. * percent_C: percent of carbon in the sample by weight; calculated via comparison of CO2 amplitudes with standards with known %C * percent_N: percent of nitrogen in the sample by weight; calculated via comparison of N2 amplitudes with standards with known %N * C/N: ratio of carbon to nitrogen in the sample; simple division of %C by %N. * prep: describes combination of mass spectrometer and peripheral used to analyze sample. #### File: ORNITH-APP-24-098\_fecal\_data.csv **Description:** CSV file containing data from stable isotope analyses of Ammospiza nelsoni subvirgata (Acadian Nelson's Sparrow) fecal samples. Isotope analysis completed by the Stable Isotopes in Nature Lab (SINLAB) at the University of New Brunswick. Fecal samples came from adult birds in 2022 and nestlings in 2023. Duplicates are removed in this file. The other file contains duplicate samples. Samples were collected from adults Beaubassin Research Station (45°50'51.08", -64°17'7.27"). Nestling fecal samples were colelcted near Beaubassin Research Station and Dorchester (45°53'32.01", -64°32'27.2"). Includes duplicates of samples. Also includes "comment" column. ##### Variables * id: an identifier created by me (nest name or band #) for each bird or nest. * type: bird (i.e., fecal from an individual adult sparrow) or nest (i.e., fecal from nestlings). * habitat: dikeland (dl) vs saltmarsh (sm). * sinlab_id: a unique identifier for the sample created by the SINLAB. * date: date sample was processed in SINLAB. * row: position in the analytical run for that particular day; samples are weighed into 96-well ELISA trays. * amount: weight of the sample in grams. * C02: the area of the chromatographic peak for CO2, related to the amount of CO2 gas detected by the mass spectrometer and reported in in volts(V), a function of the weight of tissue used and the total amount of carbon (%C) it contains. * N2_ampl: the height of the chromatographic peak for N2, related to the amount of N2 gas detected by the mass spectrometer in volts (V), a function of the weight of tissue used and the total amount of nitrogen (%N) it contains. * d13C: the carbon stable isotope ratio, presented as per mille differences in delta notation. A function of the isotopic difference between the sample and the international standard (VPDB) according to the formula: *13C = [(Rsample/Rstandard)-1]*1000*, where R denotes the ratio of the heavy to light (13C/12C) isotopes in the sample and international standard, respectively. * d15N: the nitrogen stable isotope ratio, presented as per mille differences in delta notation. A function of the isotopic difference between the sample and the international standard (AIR) according to the formula: *15N = [(Rsample/Rstandard)-1]*1000*, where R denotes the ratio of the heavy to light (15N/14N) isotopes in the sample and international standard, respectively. * percent_C: percent of carbon in the sample by weight; calculated via comparison of CO2 amplitudes with standards with known %C * percent_N: percent of nitrogen in the sample by weight; calculated via comparison of N2 amplitudes with standards with known %N * C/N: ratio of carbon to nitrogen in the sample; simple division of %C by %N. * prep: describes combination of mass spectrometer and peripheral used to analyze sample. * comment: comment column with additional notes. #### File: ORNITH-APP-24-098\_nest\_data.csv **Description:** CSV file with Ammospiza nelsoni subvirgata (Acadian Nelson's Sparrows) nest survival data for 2023. Nests were monitored in two habitats throughout the nesting season near Beaubassin Research Station (45°50'51.08", -64°17'7.27") and Dorchester (45°53'32.01", -64°32'27.2") in southeastern New Brunswick. All dates in ordinal date or day of year with January 1st = 1. ##### Variables * nest_id: unique identified for each nest. * clutch_initiation: day that first egg was laid (ordinal date). * clutch_size: number of eggs/nestlings. * hatch: day that first egg hatched (ordinal date). * finish_date: day that nest fledged or failed (ordinal date). * age_at_fledge: age of nestlings at fledge, where applicable. * exposure_days: total number of days that eggs or nestlings were exposed to potential failure (i.e., days as eggs or nestlings that could face depredation, flooding, or other risks). * brood_size: number of nestlings (does not include unhatched eggs). * success: binary, 0 = failed, 1 = at least one nestling fledged successfully. * habitat: binary, 0 = saltmarsh, 1 = dikeland * end_type: 0: No active contents 1: Undisturbed/normal 2: Partially destroyed by flood 3: Completely destroyed by flood 4: Partially depredated 5: Completely depredated 6: Partially failed unknown 7: Completely failed unknown 8: Fledged 9: Unknown (if failed/fledged) 10: Never had eggs * stage_at*_*end: stage of nest when it failed or succeeded (early_egg, later_egg, nestling, fledge); splits incubation into early and late stage * stage: stage of nest when it failed or succeeded, does not split incubation (incubation, nestling, fledge) * ibutton: binary, y = yes, n = no, whether or not the nest had an iButton placed in it for temperature monitoring * complete: if the nest was complete; only one nest we removed due to "incomplete" because we think it failed before we found it * cycle: two nestings cycle (0 = first nesting cycle, 1 = second nesting cycle)           Missing data : NA #### File: new\_nest\*\_\*dat.csv \**Description: **Same as ORNITH-APP-24-098_nest_data.csv but with an additional column "active". Did not include this CSV file in the original Dryad submission - added after. ##### Variables Same as ORNITH-APP-24-098_nest_data.csv (see above) but includes column "active". * active: binary, 1 = active, 0 = inactive. 1 for every day that the nest was active (i.e., eggs or nestlings) and 0 for the final day when the nest fledged or failed and was, therefore, no longer active. #### File: ORNITH-APP-24-098\_tracking\_locations.csv \**Description: **Tracking data from handheld radio telemetry tracking female and male adult Ammospiza nelsoni subvirgata (Acadian Nelson's Sparrows) near Beaubassin Research Station (45°50'51.08", -64°17'7.27") in 2021 and 2022 during the breeding season. ##### Variables * id: colour band combo for each adult sparrow (i.e. unique identifier) * y: latitude * x: longitude * day: day of month location was taken * month: month location was taken * year: year (2021 or 2022) location was taken * time: time of day location was taken * tag: Lotek tag number * pt.name: unique name of point (corresponds to point name in GPS unit); tag number + date (e.g., 163JUL26). * sex: f = female, m = male #### File: ORNITH-APP-24-098\_veg.csv **Description:** CSV file with Ammospiza nelsoni subvirgata (Acadian Nelson's Sparrows) habitat data from 2023. Include data from vegetation surveys around nests that were monitored in two habitats throughout the nesting season near Beaubassin Research Station (45°50'51.08", -64°17'7.27") and Dorchester (45°53'32.01", -64°32'27.2") in southeastern New Brunswick. Also includes vegetation monitored at a randomly selected point for each adult bird that was tagged in 2021 and 2022 chosen from tracked locations. Includes random points selected in the unused areas of habitat throughout the study area (Beaubassin only). Each measurement is repeated 5 times, at each corner of the quadrat square (0.5 x 0.5 m square) and at the center of the square. ##### Variables * gps_point: name used in GPS unit; center point of vegetation survey * pt_num: each vegetation survey included 3 randomly chosen points around the center point. Nest points included an additional survey at the nest (center point). * habitat: SM = saltmarsh, DL = dikeland * bird: whether the point was a bird's location ("bird"), nest location ("nest") or random location ("random"). * lat: latitude * long: longitude * day: day of year that survey was completed * month: month that survey was completed * year: year (2023) that survey was completed * avg_ht1: first measurement of average height (cm) (i.e., approximation of average height of the vegetation around the ruler) * avg_ht2: second measurement of average height (cm) * avg_ht3: third measurement of average height (cm) * avg_ht4: fourth measurement of average height (cm) * avg_ht5: fifth measurement of average height (cm) * tallest1: first measurement of tallest single piece of vegetation touching the ruler (cm) * tallest2: second measurement of tallest single piece of vegetation touching the ruler (cm) * tallest3: third measurement of tallest single piece of vegetation touching the ruler (cm) * tallest4: fourth measurement of tallest single piece of vegetation touching the ruler (cm) * tallest5: fifth measurement of tallest single piece of vegetation touching the ruler (cm) * tallest1_sp: first species of tallest single piece of vegetation touching the ruler (cm) * tallest2_sp: second species of tallest single piece of vegetation touching the ruler (cm) * tallest3_sp: third species of tallest single piece of vegetation touching the ruler (cm) * tallest4_sp: fourth species of tallest single piece of vegetation touching the ruler (cm) * tallest5_sp: fifth species of tallest single piece of vegetation touching the ruler (cm) * thatch_depth1: first depth of thatch at rule (cm) * thatch_depth2: second depth of thatch at rule (cm) * thatch_depth3: third depth of thatch at rule (cm) * thatch_depth4: fourth depth of thatch at rule (cm) * thatch_depth5: fifth depth of thatch at rule (cm) * thatch: % cover of thatch in quadrat * wrack: % cover of wrack in quadrat * poop: % cover of animal feces in quadrat * bare: % cover of bare ground in quadrat * water: % cover of open water (puddle or edge of pond) in quadrat For all remaining columns, % cover of **(column name)** in the quadrat. Most are plants identified to species, some only identified to genus, and some we were unable to identify. Spartina alterniflora, Spartina patens, Lysimachia maritima, Limonium carolinianum, Epilobium spp, Distichlis spicata, Puccinellia americanus, Spartina pectinata, Juncus gerardii, Alopecurus pratensis, Plantago maritima, Triglochin maritima, Poa compressa, Poa pratensis, Poa palustris, Calamagrostis canadensis, Scirpus cyperinus, Agrostis sp , grass sp3 (unknown grass species #3), grass_sp4 (unknown grass species #4), Anthoxanthum odoratum, grass_sp6 (unknown grass species #6), grass_sp7 (unknown grass species #7), Hierochloe odorata, Sporobolus michauxianus, grass_sp10 (unknown grass species #10), grass_sp11 (unknown grass species #11), grass_sp12 (unknown grass species #12), grass_sp13 (unknown grass species #13), Phalaris arundinacea, Phleum pratense, Elymus repens, Bromus inermis, Hordeum vulgare, Carex Spp, Scirpus spp, Cyperus spp, Bolboschoenus maritimus, Luzula multiflora, Valeriana officinalis, Aronia arbutifolia, Rosa Spp, Vicia cracca, Galium Spp, Fragaria vesca, Chamerion angustifolium, Trifolium Sp, Rumex acetosella, Spiraea alba, Viola Spp, Pilosella officinarum, Taraxacum, Arctium spp, Atriplex spp, Rubus hispidus, Salicornia maritima, Wildflower spp, Aster Spp, Tanacetum vulgare, Tragopogon pratensis, Solidago sempervirens, Solidago spp, Moss spp, sphagnum moss, Achillea millefolium, Moehringia lateriflora, Rubus idaeus, Forbs, Convolvulus spp, Persicaria sagittata, Cirsium arvense, Sphagnum fimbriatum, mushroom spp, Vaccinium oxycoccos, Vaccinium angustifolium, Juncus spp, Ranunculus repens, Vulpia spp, Polytrichum commune, plant_sp2 (unknown plant species #2), grass_sp14 (unknown grass species #14), Filipendula ulmaria, thin_grass (unknown thin grass species), Sonchus arvensis. ## Code/software All analyses were completed in R version 4.3.1. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [https://www.R-project.org/](https://www.R-project.org/) Home range map created using QGIS version 3.18.1-Zürich. QGIS.org (2023). QGIS Geographic Information System. QGIS Association. [http://www.qgis.org](https://www.qgis.org). CSV files created using Microsoft Excel version 16.90.