2.2 | Field Methods
2.2.1 | Longspur
Settlement
We deployed 24 autonomous acoustic recorders (Wildlife Acoustics model
SM4, Maynard, MA; hereafter “song meters”) to assess settlement
patterns of territorial male longspurs on the breeding grounds. We
consulted local biologists and used observations from the USFWS Breeding
Bird Survey (BBS) and eBird (Sauer et al., 2020; Sullivan et al., 2020)
to identify locations previously used by longspurs. We deployed 8 and 16
song meters in 2020 and 2021, respectively, with half (4 in 2020, 8 in
2021) in crop fields and half in native grassland sites. We selected
sites that were no more than 25 km apart to minimize regional variation
in weather patterns between site types. We deployed song meters on 7
April and retrieved them on 30 April after territory establishment
(With, 2021). We affixed song meters to 1.8-m t-posts at a height of 1.2
m and covered each microphone with an extra layer of foam to reduce
recorded wind noise.
We programmed song meters to collect a 3-minute recording every half
hour starting 15 minutes before sunrise and ending by 09:00 hours to
coincide with morning breeding choruses of longspurs (With, 2021),
resulting in six 3-minute recordings collected each morning. Upon
removal from the field, a trained technician manually reviewed each
3-minute recording and documented longspur presence. We excluded any
recordings ≥ 25% obscured by wind or other noise.
2.2.2 | Longspur
Abundance
Initial Occurrence Surveys. – To identify areas used by
thick-billed longspurs, we randomly generated 100, 64-ha sampling plots
in both crop and native sites. We only included survey plots that had
rangeland productivity ≤1,100 kg ha-1 (Lipsey and
Naugle, 2017) and that were located within land parcels where we secured
permission to conduct fieldwork. Plots were separated by ≥200 meters to
ensure independence. Random plots in crop fields that contained
>1 crop type were discarded because different crop types
grow at different rates, potentially confounding results. In native
sites, we discarded plots if they contained badlands or water bodies
covering ≥1/4 of the plot because longspurs do not nest in such areas
(DuBois, 1937; Felske, 1971; With and Webb, 1993).
We conducted initial surveys within the 64-ha plots during 30 Apr–11
May, 2020–21. We surveyed ≥25 plots in each site type each year.
Observers walked a U-shaped line transect within each plot, starting 200
meters inward from a randomly selected plot corner (Figure 3). We
identified species and recorded perpendicular distance and direction
from the transect line for each bird or group of birds seen or heard to
maintain consistency with distance sampling methods. Estimated distances
were recorded in bins: 0–25, 26–50, 51–75, 76–100, and 101–200
meters. We walked at a pace of 2–3 km hr-1 and
completed each transect within 40 minutes. Observers were trained to
avoid double-counting birds. Surveys began one half hour before sunrise
and were completed by 10:00 hours. We did not conduct surveys if wind
speed was >25 km hr-1 or it was raining.
We recorded survey covariates including observer, cloud cover,
temperature, wind speed, date, GPS starting point, and transect
start/end times.
Abundance surveys. – Within initial plots occupied by longspurs,
we generated smaller, 16-ha survey plots within which we monitored
longspur populations for the remainder of the nesting season. We
identified occupied crop fields, randomly selected ≥20 of these fields,
and generated a single 16-ha plot within the center of each selected
field. This ensured crop plots were ≥200 m from field edges, roads, and
other plots. In native sites, we delineated large patches of occupied
habitat by tracing the extent of occupied areas on foot with a GPS unit
and later transferred this information to ArcMap 10.7.1 (ESRI, 2019).
Patches were discovered after determination of longspur occupancy during
initial surveys, and patch edges were defined by presence/absence of
singing longspurs and were typically coupled with apparent changes in
vegetation composition. We then overlaid a grid of 16-ha cells over
occupied patches and used ArcMap to randomly select ≥20 cells from these
patches. Only non-adjacent cells were used to ensure plots were ≥200 m
apart. Each site selection process allowed us to select only occupied
sites and guaranteed independence.
We conducted six rounds of line transect surveys within each 16-ha
survey plot during 10 May–15 July 2020–21. Survey rounds were
separated by ≥5 d. Observers walked a U-shaped line transect within each
plot, starting 100 meters inward from a randomly selected plot corner
(Figure 4). We collected data as described above but limited
observations to thick-billed longspurs and distance bins included 0–25,
26–50, 51–75, and 76–100 m. We completed each transect within 30
minutes and surveys began 15 minutes before sunrise and ended at 9:00
hours.
2.2.3 | Nest Phenology, Survival, and Reproductive
Output
Nest Searching. – We searched for nests during 9 May–22 July,
2020 and 5 May–8 July, 2021 to assess reproductive effort throughout
the entire nesting season (With, 2021). Nest searching began at sunrise
and ended at 11:00 hours on days without precipitation and observers
were randomly assigned a group of plots to search each morning.
Observers alternated between crop and native sites during subsequent
days and used behavioral observations to find nests (Martin and Geupel,
1993; Winter et al., 2003). We observed longspurs from a distance of ≥30
m and moved to a new plot after 60 minutes if no female longspurs were
detected. In addition, we supplemented behavioral nest searching with
standard rope dragging methods (Klett et al., 1986; Koford, 1999).
Nest Monitoring. – Upon finding a nest, we recorded the
geographical coordinates and marked the nest location with 15-cm bamboo
stakes placed 2 m north and east of the nest to aid in relocation. Nests
were checked every 2–4 days until fledging or failure (Martin and
Geupel, 1993; Ralph, 1993). We recorded adult behavior, number of eggs
and young, number of brown-headed cowbird (Molothrus ater ) eggs
or nestlings, date, time, observer, time spent at nest, and any relevant
notes. We aged nestlings according to developmental cues described in
Jongsomjit et al. (2007) so the nest could be checked on predicted date
of fledging. We considered a nest failed if eggs were gone before
expected hatch date, if nestlings disappeared before nearing expected
fledge date, or if dead nestlings or depredated eggs were found in or
near the nest. A nest was considered successful if ≥1 chick fledged. We
deemed nests successful if nearby adults were observed feeding
fledglings, ≥1 fledgling was observed, territorial adults were present
with food or directed aggressive behaviors toward observers, or fecal
material was present and nestlings had reached the appropriate age to
fledge (Ralph, 1993; Jones et al., 2010).
2.2.4 | Habitat
Conditions
We collected vegetation measurements at two spatial scales, the nest
site and the survey plot (16-ha). Measures were collected at each nest
within 3 days of fledge or expected fledge for failed nests. In
addition, we randomly selected 3 and 10 habitat sampling points within
the 16 ha survey plots in crop fields and native prairie sites,
respectively. Vegetation conditions in crop fields were fairly
homogenous and required fewer sampling points (Swicegood, 2022). We
measured vegetation conditions three times throughout the longspur
breeding season, once in May, June, and July. At each sampling point, we
recorded visual obstruction readings (VOR) in each cardinal direction
from a distance of 4 m and a height of 1 m (Robel et al., 1970). We
measured overlapping percent cover of grass, forb, shrub, litter, and
bare ground within a 20 × 50 cm sampling frame at the sampling point and
at 4 locations 0.5 m from the point in each cardinal direction
(Daubenmire, 1959). Cover was recorded within six percentage classes
(0%, 1–5%, 6–25%, 26–50%, 51–75%, 76–95%, and 96–100%). We
listed all plant species, lichen, and spikemoss in order of decreasing
abundance, found within a 2-m radius of the point center. We measured
litter depth (mm) in the northwest corner of the Daubenmire frame and
recorded the species, distance (m), and height (cm) of the nearest shrub
within 25 m as shrubs influence nest density or detection of nests for
many grassland passerines (Davis, 2005; Pulliam et al., 2021).
2.3 | Analyses
2.3.1 | Longspur Settlement
We used multi-season occupancy models to evaluate whether longspur
settlement patterns differed between crop and native sites (MacKenzie et
al., 2003). Multi-season occupancy models use detection/non-detection
data collected with a robust design (Pollock, 1982) to estimate initial
occupancy and subsequent rates of local colonization (e.g., settlement)
and extinction (e.g., site abandonment) while accounting for spatially
variable detection probability (MacKenzie et al., 2003; Mackenzie,
2006). The design used k secondary survey periods within Tprimary periods; each day represented a primary period and each 3-minute
recording a closed secondary period (i.e., 6 secondary periods occurred
over 24 days in each year).
We fitted multi-season occupancy models using the ‘colext’ function in R
package ‘unmarked’ (Fiske and Chandler, 2011; Kéry and Chandler, 2016)
and used information theory to evaluate support for competing models
representing hypotheses about detection probability, initial occupancy,
and settlement patterns (MacKenzie et al., 2003). We evaluated support
for our a priori models in a phased approach. First, we evaluated
how well a fully parameterized model fit the data and estimated a
variance inflation factor (ĉ ) using the mb.gof.test in the
R package ‘AICcmodavg’. Bootstrapping was based on 500 simulations to
generate a chi-squared statistic and to calculate average ĉ ,
where a ĉ value >1 indicates overdispersion in the
data, but much higher values (>4) may indicate lack-of-fit
(Mazerolle, 2020). We found evidence of moderate overdispersion
(ĉ = 1.9) and inflated estimated standard errors by\(\sqrt{}\)ĉ and based subsequent model evaluation and inference
on the quasi-Akaike’s Information Criterion adjusted for finite samples
(QAICc; Burnham and Anderson, 2002).
We developed models that evaluated the effects of survey conditions on
detection probability. Variables hypothesized to influence detection
included daily precipitation, minimum daily temperature, minutes past
sunrise, and Julian day (Table 1). We evaluated a quadratic effect of
minutes past sunrise because bird detections were previously found to be
highest mid-morning (With, 2021). Because all detection covariates we
measured are known to affect the detectability of songbirds, we used a
backward selection approach based on QAICc to eliminate
uninformative parameters (Pagano and Arnold, 2009; Arnold, 2010;
Montgomery et al., 2021). Models with large relative weights
(wi ) and QAICc values ≤2 from the
best-fit model were considered equally parsimonious (Devries et al.,
2008; Arnold, 2010; Burnham et al., 2011). After we identified a
parsimonious sub-model for detection, it was retained in subsequent
evaluations of occupancy and settlement.
Because some birds had already arrived at the study area prior to song
meter deployment, we evaluated whether initial occupancy differed by
habitat type (crop vs. native) before evaluating the effects of habitat
type and Julian day on settlement probability (Table 1). In addition to
these main effects, our candidate set for settlement probability
included a model with an interaction between habitat type and Julian day
because we hypothesized that settlement rates would change over the
season differentially by habitat type. We hypothesized that abandonment
rates would be extremely low; once territorial longspurs arrive at a
breeding site post-migration, they are unlikely to abandon the site
(With, 2021). Therefore, we did not include any models with covariates
on abandonment rates. Model selection was again based on
QAICc (Burnham and Anderson, 2002). We used empirical
Bayes methods to derive estimates of latent occupancy from the most
parsimonious model for each primary period from predicted posterior
distributions using the ‘ranef’ function in R package ‘unmarked’ (Fiske
and Chandler, 2011). All analyses were performed using R Statistical
Software (v 4.1.2; R Core Team 2021).
2.3.2 | Longspur Abundance
We used open-population distance sampling models to estimate longspur
abundance in crop and native sites and assess whether abundance changed
differentially throughout the breeding season (Royle et al., 2004;
Sollmann et al., 2015). Distance sampling is a common method for
estimating abundance or density of wildlife populations and allows
simultaneous estimation of detection probability without requiring
repeat site visits (Buckland et al., 2001). Open-population distance
sampling models allow explicit modeling of population dynamics over
space and time, where data from repeat distance sampling surveys are
used and populations are assumed open between survey periods (Sollmann
et al., 2015).
We fitted open-population distance sampling models using the
‘distsampOpen’ function in R package ‘unmarked’ (Fiske and Chandler,
2011) and used information theory to evaluate support for competing
models representing hypotheses about detection, initial abundance, and
trends in abundance over the breeding season (Sollmann et al., 2015). We
evaluated support for our a priori models in a phased approach.
First, we used null models with the ‘trend’ dynamics parameterization to
estimate the best-fitting detection function and mixture type based on
our data. We then evaluated how well a fully parameterized model fit the
data and estimated a variance inflation factor (ĉ ) using theNmix.gof.test in the R package ‘AICcmodavg’ from 500 bootstrapped
simulations. Because the negative binomial model can overestimate
population abundance (Ver Hoef and Boveng, 2007; Kery and Royle, 2015),
we used the Poisson distribution for all subsequent models, inflating
estimated standard errors by \(\sqrt{}\)ĉ and basing model
evaluation and inference on the quasi-Akaike’s Information Criterion
adjusted for finite samples (QAICc; Burnham and
Anderson, 2002). We found evidence of moderate overdispersion using the
Poisson distribution (ĉ = 1.9 for 2020 data, ĉ = 1.7 for
2021 data).
We developed models to evaluate the effects of survey conditions on
detection probability. Variables hypothesized to influence detection
probability included observer, wind speed, temperature, and start time
(minutes past sunrise; Table 2). We evaluated a quadratic effect of
start time because bird detections are usually highest 1–2 hours after
sunrise (With, 2021). Initial screening indicated that detection was
variable across observers, so we separated observers into 2 groups for
each year (‘high’ and ‘low’ detection rates) based on relative
coefficient estimates from a full model to reduce the number of
parameters in candidate models while retaining large observer effects on
detection. We used the backward selection approach described previously
to eliminate uninformative parameters and identify a parsimonious
sub-model for detection probability, which was retained in subsequent
evaluations of abundance and seasonal trend.
We evaluated if initial abundance and seasonal trends differed by
habitat type (crop vs. native; Table 2). We developed models that
included the effect of habitat type on both initial abundance and trend,
as well as all submodels. Model selection was again based on
QAICc (Burnham and Anderson, 2002). We used Bayesian
methods to derive true abundance estimates from the most parsimonious
model for each survey round from predicted posterior distributions using
the ‘ranef’ function in R package ‘unmarked’ (Fiske and Chandler, 2011).
We analyzed data separately for the two years because differences in
weather and drought conditions were likely to produce different
population responses.
2.3.3 | Nest Phenology, Survival, and Reproductive
Output
Nest Phenology. – For each nest, we calculated initiation date
as the day the last egg was laid, although actual initiation of
incubation is variable for passerines (Hébert, 2002; Badyaev et al.,
2003). Initiation date was estimated based on clutch size, hatch date,
or chick age and assuming an incubation period of 12 d (With, 2021). For
nests found after clutch completion but destroyed before hatch, we
assumed initiation to be 6 d prior to the midpoint of the active period.
We plotted nest initiation dates to visualize patterns of nest
initiation between crop and native sites and to assess differences
between years.
Nest Survival. – We used the nest survival model in program MARK
to model daily nest survival rate (DSR) and we fitted models in the R
package ‘RMark’ (White and Burnham, 1999; Rotella et al., 2004; Laake,
2013). We built and evaluated a set of competing models representinga priori hypothesized relationships between DSR and habitat type
(crop or native), nest initiation date, and year (2020, 21). We
evaluated 15 models with all combinations of habitat type, initiation
date, and year (Table 3). We also included a model with a quadratic
effect of initiation date because other studies have shown DSR to be
higher or lower mid-season (Weintraub et al., 2016; Skagen et al.,
2018). We predicted DSR may exhibit a pseudo-threshold response in crop
sites only, being low for nests initiated early and leveling off after
fields were planted. Therefore, we included a model with a
pseudo-threshold effect of initiation date and one including an
interaction term with habitat type. We evaluated the relative support of
models using Akaike’s Information Criterion corrected for finite sample
size (AICc). Supported models with large model weights
(AICc wi ) and
AICc values ≤2 from the best fit model were considered
parsimonious; when supported models differed by one parameter, we
considered this parameter uninformative (Arnold, 2010; Burnham et al.,
2011). To estimate nest survival probability, we used a 26-day nesting
cycle beginning with the start of the laying period and multiplied DSR
for each daily interval over a 25-day period from nest initiation to
fledging (e.g., DSR25 for constant model). We
calculated standard error for nest survival estimates using the Delta
method (Powell, 2007).
Reproductive Output. – We calculated an index of nest density
for each plot by dividing the number of nests located in each plot by
the total search effort (hours) for that plot. We report the mean and
standard deviation of relative nest density for each habitat type (crop
vs. native). Incidental nests located outside of survey plots and nests
found via rope dragging methods were excluded from this calculation.
Importantly, we were unable to account for detectability of nests with
behavioral search methods and it is possible detectability differed in
crop and native sites. Detectability almost certainly differed by
observer (Diefenbach et al., 2003; Giovanni et al., 2011); therefore,
observers were rotated through different plots each day.
We tabulated maximum clutch size for all nests with known fates and the
number of young fledged per successful nest. The number of young fledged
was recorded as the number of chicks present 8–10 d after hatching, the
typical fledging time for longspurs (With, 2021), unless some dead and
some live fledglings were found during the final visit. We developed a
set of generalized linear models to analyze the effects of habitat type
and initiation date on the number of young fledged per successful nest
using a Poisson distribution with a log link. We included an interaction
term to test if the number of young fledged differed by both habitat
type and initiation date (Table 4). Nests were removed from analysis if
the number of young fledged was unknown. We evaluated relative model
support AICc and used the best-fitting model to estimate
the number of young fledged per successful nest.
2.3.4 | Habitat
Conditions
We used generalized linear models to test hypotheses that specific
vegetation attributes differed significantly between crop and native
sites, longspur habitat changed structurally over the summer as plants
grew, and such changes were more extreme in crop sites than in native
sites. Variables included VOR, bare ground cover, grass and forb cover,
litter cover and litter depth. For proportional response data (e.g.,
percent coverages), we used the binomial distribution and logit link
function to fit GLMs (Chen et al., 2017). For all other vegetation
measures, including VOR and litter depth, we used the identity link and
log transformed the response variables to meet the assumptions of linear
regression (Dunn and Smyth, 2018). For each habitat variable, we built
and evaluated the same set of competing models representing a
priori hypothesized relationships between habitat type and survey round
(Appendix I).
We evaluated relative model support using AICc.
Supported models with large model weights (AICcwi ) and AICc values ≤2 from the
best fit model were considered equally parsimonious (Burnham et al.,
2011). When a supported model differed from a top model by a single
parameter, the additional parameter was considered uninformative
(Arnold, 2010). We based inferences on effect sizes from a single top
model and calculated model averaged estimates when models shared support
(ΔAICc ≤2; Burnham et al., 2011).
3 | RESULTS
3.1 | Longspur Settlement
We deployed song meters at 8 and 16 sites in 2020 and 2021,
respectively, half in crop fields and half in native sites. Due to
equipment malfunction and failure of longspurs to establish territories
at some sites, we were able to obtain data from 2 song meters in native
sites and 4 song meters in crop sites in 2020, and 7 song meters in
native sites and 7 song meters in crop sites in 2021. Overall, we
collected >37 hr of useable recordings in 2020 and
>100 hr in 2021.
Detection probability. – The top model for detection probability
contained an effect of Julian day, minimum temperature, and a quadratic
effect of minutes past sunrise (QAICcwi = 0.97; Table 1). Detection probability
increased with Julian day (ꞵ = 0.99 ± 0.13 SE) and increased in response
to minimum temperature (ꞵ = 0.08 ± 0.02). Detection probability was
highest at ~90 – 100 minutes past sunrise, or 1.5 hours
after sunrise (Figure 5).
Initial occupancy and settlement probability. – We found no
evidence for an effect of habitat type on initial occupancy with the
null model carrying virtually all support (QAICcwi = 0.98; Table 1). We found no evidence that
settlement probability differed by habitat type with the model
containing an effect of Julian day carrying virtually all support
(QAICc wi = 0.98). Settlement
probability increased for both habitat types with Julian day (ꞵ = 2.24 ±
0.68). Derived estimates of true occupancy for both crop and native
sites increased from 0.52 (± 0.17 SE) on 7 April to 0.99 (± 0.01) on 30
April (Figure 6).
3.2 | Longspur
Abundance
In 2020, we conducted initial occurrence surveys in 80 plots (36 crop
and 44 native); 67% of crop and 20% of native plots were occupied. In
2021, we conducted initial surveys in 62 plots (35 crop and 27 native);
91% of crop and 33% of native plots were occupied. In 2020, we
conducted 287 longspur abundance surveys at 24 crop sites and 22 native
sites during 14 May – 19 July. We observed 5.4 ± 4.4 (mean ± SD) male
longspurs in crop sites and 4.2 ± 3.3 in native sites. In 2021, we
conducted 325 surveys at 25 crop sites and 25 native sites during 10 May
– 14 July. We observed an average of 3.8 ± 3.2 and 3.2 ± 2.3 males per
plot in crop and native sites, respectively. Most crop plots contained
spring wheat (28 plots); we surveyed 4 summer fallow plots in 2020 and 8
in 2021 (Table 5).
Detection probability. – For both years, the top model contained
an effect of observer group (Table 2). Detection probability was lower
for observer group 2 and effect sizes were -1.67 ± 0.54 SE in 2020 and
-0.95 ± 0.39 in 2021 (Figure 7). Confidence intervals for the effect
sizes for other covariates on detection overlapped 0; therefore, only
observer group was retained in subsequent abundance modeling (Arnold,
2010).
Initial abundance and seasonal trends. – We found support for an
effect of habitat type on both initial abundance and seasonal trend for
data collected in 2020 (QAICc wi= 0.91; Table 2). Expected initial abundance in crop sites was 17.4 ±
4.1SE birds per plot and the estimated seasonal trend was λ = 0.84 ±
0.04, indicating that abundance decreased by 16% over the season.
Empirical estimates of true abundance for crop sites decreased from 16.8
(95% CI = 15.7–18.0) during the first survey round to 6.5 (5.6–7.8)
during the sixth round. Expected initial abundance in native sites was
8.6 ± 2.0 birds per plot and increased slightly during the season (λ =
1.02 ± 0.05). Derived empirical abundance for native was 8.7 (95% CI =
7.8–9.7) during the first survey round and 9.4 (8.4–10.7) during the
sixth round (Figure 8).
In 2021, we found no support for an effect of habitat type on either
initial abundance or seasonal trend, with the null model carrying the
most support (QAICc wi = 0.54;
Table 2). Because of model uncertainty, we averaged results across all
four supported candidate models. Expected initial abundance was similar
in crop and native sites (12.5 ± 3.3 SE) and seasonal population sizes
did not change much during the season (λ = 1.03 ± 0.04 SE in crop sites;
1.01 ± 0.04 in native sites). Derived estimates of true abundance for
crop sites increased slightly from 12.3 (95% CI = 11.1–13.3) during
the first survey round to 15.1 (13.2–17.0) during the sixth round.
Derived estimates of true abundance for native sites were fairly stable
across the season (12.7 (95% CI = 11.5–14.1) during the first survey
round; 12.1 (10.8–13.4) during the sixth round; Figure 8).
3.3 | Nest Phenology, Survival, and Reproductive
Output
We located 240 longspur nests, 111 in crop sites and 129 in native
sites. Of these, 174 were located using behavioral cues of adults, 14
using rope-dragging methods, and 52 were incidental finds while
observers were conducting other fieldwork. We spent 515 hours behavioral
searching in crop fields and 421 hours behavioral searching on native
sites, for a total of 936 hours nest searching using behavioral cues.
Using 2–3 observers, we spent 76.5 person-hours rope dragging in crop
fields and 22.5 person-hours rope dragging in native sites, for a total
of 99 rope dragging person-hours.
Of the 240 nests, 222 had known fates (96 crop, 126 native). For the 18
remaining nests, we were unable to determine nest fate due to either
conflicting clues at the nest site or weather events/farming operations
preventing timely nest checks near expected fledge date. We were able to
estimate the number of young fledged for 87 successful nests, 41 crop
and 46 native. Apparent nest success was 44% in crop sites and 37% in
native sites. Predation was the main cause of nest failure in both crop
fields and native plots (Table 6). Other causes included weather,
farming operations (crop only), and abandonment. Brown-headed cowbird
(Molothrus ater ) parasitism rates were 1.8% and 7.8% in
crop and native sites, respectively.
Nest Phenology. – Patterns of nest initiation were similar
within crop and native sites each year, but median initiation dates in
native sites were 6–11 d later than median dates in crop sites (Figure
9). In addition, the first and third quartiles were 6–10 d later in
native sites. In 2020, median initiation date was 29 May (IQR = 25 d, n
= 68) and 9 June (IQR = 26 d, n = 71) in crop fields and native sites,
respectively. Longspurs nested through mid-July and there were two
prominent peaks in nest initiation. In 2021, median date of initiation
was 28 May (IQR = 17 d, n = 28) in crop sites and 3 June (IQR = 13 d, n
= 55) in native sites. Nesting slowed significantly in late-June –
early-July and there was only one main peak in nest initiation. Notably,
the interquartile distance for initiation dates was 32% shorter in crop
sites and 50% shorter in native sites during the 2021 drought year than
during 2020.
Nest Survival. – The null model of constant daily nest survival
was the best supported in the candidate set (AICcwi = 0.18; Table 3). Models including effects of
habitat type, year, and initiation date, including models with different
functional forms of initiation date, had approximately equal support as
the null model, indicating that these parameters were uninformative.
Average daily nest survival estimated from the null model was 0.944 ±
0.005SE and estimated nest survival over the 26-day exposure period
(DSR25) was 0.236 ± 0.028.
Reproductive Output. – Relative nest density (±SD) was 0.153 ±
0.215 nests/hour/plot in crop sites and 0.233 ± 0.317 nests/hour/plot in
native sites. Mean clutch sizes ± SD were 3.5 ± 0.8 and 3.3 ± 0.8 for
nests occurring in crop fields and native sites, respectively. The mean
number of young fledged per successful nest was 3.0 ± 1.1 SD in crop
sites and 2.8 ± 0.9 in native sites. The null model was the best
supported model in our candidate set of generalized linear models for
number of young fledged per successful nest (AICcwi = 0.49; Table 4), indicating that neither nest
initiation date nor habitat type was related to the number of young
fledged. Models including the effects of habitat type and initiation
date had approximately equal support as the null model, indicating that
these parameters were uninformative. Estimated from the null model, the
average number of young fledged per successful nest in both crop and
native sites was 2.90 ± 0.18 SE.
3.4 | Habitat
Conditions
We observed significant differences in vegetation conditions between
crop and native sites that varied across survey rounds (Figure 10).
Visual obstruction reading (VOR) estimates (cm ± SE) in 2020 changed
from 0.81 ±1.42 in May to 17.81 ±1.43 in July in crop sites and from
1.95 ±1.51 to 2.61 ±1.46 in native sites. In 2021, VOR estimates (cm ±
SE) changed from 0.72 ±1.35 to 1.48 ±1.35 in crop sites and from 0.68
±1.34 to 0.28 ±1.34 in native sites. Bare ground coverage was
significantly lower on native sites than crop sites during both years.
Estimated bare ground (% ± SE) in 2020 was 45 ±6 in crop fields and 10
±4 in native sites. In 2021, estimated bare ground was 42 ±6 in crop
fields and 14 ±4 in native sites.
Estimated litter coverage in 2020 (% ± SE) was 25 ±5 in crop fields and
8 ±3 in native sites. In 2021, estimated litter coverage was 26 ±5 in
crop fields and 11 ±3 in native sites. Estimated litter depth in 2020
(mm ± SE) changed from 4.66 ±1.30 in May to 0.58 ±1.31 in July in crop
sites and from 1.57 ±1.34 to 1.05 ±1.32 in native sites. In 2021,
estimates changed from 2.75 ±1.12 in May to 1.35 ±1.12 in July in crop
sites and from 1.01 ±1.12 to 0.91 ±1.12 in native sites. Models of
residual, forb, and grass cover indicated that these vegetation
conditions were similar across habitat types and survey rounds. The same
results were true for grass cover when we only compared native sites to
wheat crop types (e.g., all crop plots classified as forb were removed).
4 | DISCUSSION
Collectively, our results did not support the hypothesis that crop
fields are ecological traps for breeding thick-billed longspurs because,
compared with longspur use of native grassland sites, there was no
evidence of preference for crop habitat or of suppressed reproduction in
crop fields. Specifically, settlement patterns of singing males were
similar between crop and native sites and relative nest density was
slightly lower in crop sites, providing no evidence for preferential
selection of crop habitat. Nest survival, average clutch size, and the
number of young fledged were similar between crop and native sites,
providing no evidence for suppressed reproduction in crop fields.
Additionally, precipitation and associated vegetation growth appeared to
mediate longspur abundance and use of crop fields. Longspur abundance
decreased throughout the breeding season in crop fields during a normal
year (2020) as plant biomass increased whereas abundance did not
decrease during a drought year (2021). Annual variation in timing of
seeding coupled with drought effects on vegetation may increase the
unpredictability of crop habitat among years.
We found that median nest initiation dates occurred 6–11 days earlier
in crop sites despite similar settlement patterns for the two habitat
types. Longspurs appeared to shift timing of nesting in crop sites, and
perhaps this phenological shift is beneficial in habitat that changes
structurally to become unsuitable late in the breeding season. Based on
our results, crop sites may provide thick-billed longspur populations
with viable alternative nesting opportunities in an area where native
habitat has been reduced.
4.1 | Crop Fields as Potential
Traps
We observed similar settlement patterns within breeding territories in
crop fields and native prairie, indicating that selection cues and
preference of longspurs were similar between habitat types. Although
50% of our study plots were occupied prior to song meter deployment in
early April, increases in daily longspur occupancy were similar across
habitat types and all sites were occupied by 27 April. Also, our nest
density index was 29% lower in crop sites, though estimated precision
was low and confidence intervals overlapped. Together, these findings
suggest a similar preference of longspurs for crop fields and native
rangeland habitats.
All measures of reproductive output (nest survival, clutch size, number
of young fledged per successful nest) were similar between habitat
types. Although we observed higher early nest failures in crop sites as
a result of farming activities (e.g., seeding, discing, and plowing),
thick-billed longspurs are quick to renest (<10 d) (Mickey,
1943; Felske, 1971; With, 2021), and we often found new nests close to
failed nest locations. While the most common cause of nest failure in
both habitat types was predation, higher predation rates on native sites
resulted in overall similar nest survival rates (~24%)
in crop fields and native prairie habitat.
In contrast to expectations, some farming activities, including rolling
(field leveling) and spraying, did not result in nest damage or
abandonment, and harvest of crops occurred too late in the season to
affect nesting longspurs. Longspur nest bowls were constructed below the
soil surface; farming activities such as rolling that did not disturb
the soil did not negatively affect nests (n=9) regardless of nest stage.
Most (>95%) of our crop fields were sprayed with
herbicides (glyphosate, 2-4 dichlorophenoxyacetic acid [2-4D]) twice
per season and ≥5 fields were also sprayed once with organic
fertilizers. Herbicide application typically consisted of a pre-spray to
eliminate weeds around the time of seeding and a second application in
June when plants were 12–15 cm tall (M. Sather, USFWS, pers. comm.).
Although nests were active when fields were sprayed, spraying did not
directly result in losses of eggs or nestlings. However, we did not
assess potential indirect effects of herbicide and fertilizer spraying
on nestling growth rates or subsequent fledgling survival.
Flooding and hail destroyed nests in both crop (n=8) and native sites
(n=4). Nest abandonment was often due to partial predation, weather,
brown-headed cowbird parasitism, and possibly frequent disturbance by
predators or perceived predators. On a few occasions in native sites
(n=3 nests), we found all nestlings apparently uninjured but laying
outside the nest. These nestlings never survived and were never returned
to the nest by adult longspurs. We suspect this to be the activity of
brown-headed cowbirds or other passerine nest predators (Pietz and
Granfors, 2000; Pietz et al., 2012).
4.2 | Longspur Abundance and Use of Crop
Fields
Precipitation and vegetation structure appeared to mediate longspur
abundance in crop fields but not native sites. Longspur abundance was
relatively stable throughout the season within native sites in both
years and averaged 8–12 birds per plot (0.63 birds per ha). In a season
of normal precipitation (2020), longspur abundance was higher in crop
sites than native sites early in the season (April–May) when crop
biomass was low but declined with the growth of crops. In contrast,
longspur abundance increased slightly throughout the breeding season in
crop fields during a drought year (2021) when crop growth was minimal.
Because rates of nest abandonment were ubiquitously low, declining
abundances of longspurs across time imply reduced nesting attempts in
crop sites during a year of normal precipitation, though we could not
confirm this with unmarked birds.
Longspurs used all types of crop fields in our study area, including
lentil, pea, flax, wheat, canola, mixed cover crop, and summer fallow.
Although we didn’t have sufficient sample plots in summer fallow
treatments (n = 12) to include this as a separate category in our
analyses, we consistently observed fewer longspurs in summer fallow
fields compared to other crop types. We found very few nests in summer
fallow fields over both years (n = 10). Summer fallow fields were
planted in strips, with fallow sections intermixed with planted
sections. Fields planted in the narrowest strips, and hence having more
abrupt edges, were rarely used by longspurs (on 4–5 out of 6 surveys we
detected 0 birds). Lower abundance of longspurs in these areas is
consistent with avoidance of habitat edges in grassland birds (Johnson
and Igl, 2001; Renfrew et al., 2005; Sliwinski and Koper, 2012; Thompson
et al., 2015). In addition, fallow portions are tilled multiple times
during the breeding season to control weeds. Therefore, nests in
unplanted portions of summer fallow fields have a higher risk of being
destroyed later in the season, unlike nests in annual crop sites. It is
also possible that frequent tilling of fallow fields results in
different soil conditions, generating different invertebrate resource
availability than that found in minimum-tilled fields (Stinner and
House, 1990; Kladivko, 2001).
4.3 | Timing of Nesting
In native prairie habitats, longspurs select territories on south-facing
slopes during the early breeding season where snow melts and the ground
warms faster (Felske, 1971; Greer, 1988; Shaffer et al., 2019). Bare
ground cover was higher in crop fields than native sites throughout the
breeding season, and exposed soils warm faster than vegetated soils
(Song et al., 2013). Although territory settlement phenology was similar
between habitat types, median nest initiation dates during both years
occurred 6–11 days earlier in crop fields than in native sites. Thus,
earlier warming of crop fields may allow earlier nest initiation and egg
laying resulting from favorable microclimatic conditions or an earlier
invertebrate food supply (Felske, 1971; Greer and Anderson, 1989).
However, we did not assess thermal or other microclimatic conditions at
nests. In addition, the range of nest initiation dates and therefore
length of nesting period was significantly shorter during the drought
year in 2021. Longspurs are known to forego nesting or experience lower
reproductive success during periods of extreme drought (Felske, 1971;
Shaffer et al., 2019). Our results indicate that longspurs may initiate
nests earlier in crop than native sites but experience a shorter
breeding period in both site types during drought.