DOI: 10.5061/dryad.zgmsbcckx
title: Impacts of umbrella species management on non-target species

description: Restoration of anthropogenically altered habitats has often focused on
 management for umbrella species—vulnerable species whose conservation is
 thought to benefit co-occurring species. Woody plant encroachment is a
 form of habitat alteration occurring in grasslands and shrublands around
 the globe, driven by anthropogenic shifts in disturbance regimes. One
 pervasive threat to historically widespread sagebrush communities is
 conifer expansion, which outcompetes sagebrush and can negatively affect
 sagebrush-obligate animal species. Degradation and loss of sagebrush plant
 communities in western North America have been associated with drastic
 declines in wildlife populations. The imperiled Greater Sage-Grouse is
 assumed to be an umbrella species for the sagebrush community, so habitat
 restoration, including removal of encroaching conifers, is commonly
 targeted toward sage-grouse. How this conservation action affects the
 demography of species other than age grouse is largely unknown. We
 quantified the demographic effects of landscape-level restoration of
 sagebrush communities through conifer removal on an assemblage of
 sagebrush-obligate, shrubland generalist, and woodland-associated
 songbirds. We compared songbird density and reproduction between adjacent
 restored and uncut conifer-encroached sagebrush plots in southwest
 Montana. We found and monitored nests to record nest fate and number of
 offspring produced. We found demographic benefits for sagebrush-obligate
 species in restored areas. Sage Thrashers colonized restored areas.
 Brewer’s Sparrow density was 39% higher and nest success was 63% higher in
 removal treatments, resulting in 119% higher fledgling production compared
 with uncut areas. The density of Vesper Sparrows, a shrubland generalist,
 was 308% higher, and fledgling production was 660% higher in restored
 areas. Another shrubland generalist, the White-crowned Sparrow,
 experienced 55% lower density and 37% lower fledgling production in
 conifer removal areas. Two woodland-associated species, Chipping Sparrows
 and Dark-eyed Juncos were nearly extirpated following conifer removal. A
 third woodland-associated species, the Green-tailed Towhee, experienced
 57% lower density and 69% lower fledgling production in removal than in
 non-removal areas. Synthesis and applications: Our study demonstrates the
 benefits of conifer removal for sagebrush-obligate species while
 highlighting species that may be sensitive. Umbrella species management
 can benefit co-occurring species with similar habitat associations, but
 demographic analyses for all impacted species are essential for effective
 conservation.
description: <em>Density</em> To understand how
 treatment impacted breeding pair density, we used spot-mapping to create
 territory maps in each year of the study (Chalfoun & Martin, 2007;
 Ralph et al., 1993). We surveyed all plots 3-5 times throughout the
 breeding season and mapped every bird seen and heard. The 50m grid of
 points was used to assist with the accurate identification of bird
 locations using a GPS unit and visual distance estimation. We paid
 attention to males that were counter-singing to determine territory
 boundaries. Using the spot-mapping observations and nest locations, we
 created a territory map for each species each year of the study. We
 assumed that every territory held a pair of birds and if a territory
 overlapped the plot boundary, then it was considered half of a territory.
 We used the final territory maps to estimate the number of pairs of each
 species on each plot and divided by plot area to calculate
 density. <em>Nest success and offspring
 production</em> To estimate reproductive success
 and offspring production between treatments, we located nests by observing
 parental behavior and following individuals to their nests, as well as
 systematic searching (Martin & Geupel, 1993). Once nests were
 found, we monitored them every 1-3 days, recording nest contents at each
 visit. Nests were considered failed if the nest was empty two or more days
 before the end of the mean nestling period and no evidence of fledglings
 was detected. Nests were considered successful if at least one nestling
 fledged. We recorded the egg number when the final clutch size was
 confirmed on two or more consecutive visits. We recorded the number of
 eggs hatched only if all young were accounted for and none disappeared at
 hatch. We recorded the number of fledglings based on nestling count in the
 nest within two days of average fledging age and evidence of fledging was
 observed (i.e., fledglings seen or heard, adults carrying food in the
 vicinity, or nest edge flattened). To determine whether
 more nesting attempts were possible in one of the treatment types due to a
 longer active breeding period, we calculated the mean season length for
 each species. We took the average of the earliest and the latest three
 Julian initiation dates for each species in each year to account for
 outliers and subtracted them to calculate season length. Finally, to
 understand how removal treatments may impact population dynamics and
 impact declining species, we calculated the number of fledglings produced
 per hectare. <em>Statistical
 analysis</em> We used species-specific mixed
 effects models to assess the effect of treatment on various dependent
 variables, including density, nest success, season length, number of eggs
 laid, number of eggs hatched, nestlings fledged, and fledglings produced
 per hectare. Each model included treatment as the only fixed effect. We
 used linear models to test for a treatment effect so we could account for
 temporal and spatial variation using random effects of year and plot.
 Species abundance varied between years (Table 1) and was measured at the
 plot level, so models of species density included only year as a random
 effect. For analyzing nest success, we used a logistic-exposure analysis
 with nest fate as the dependent variable (1 = success, 0 = fail) and
 exposure days accounted for in the logistic-exposure link function
 (Shaffer, 2004). We expected that varying species abundance between years
 and spatial autocorrelation may impact nest success, so we tested random
 effects of year and plot. We used AIC to select the top model and
 calculated daily nest success by taking the inverse logit of beta
 estimates from the models. For the remaining models of offspring number,
 we assumed similar spatial and temporal impacts and used the same random
 effects structure as used in nest success models. For all mixed models, we
 calculated degrees of freedom using the Satterthwaite method
 (Satterthwaite, 1941) in the jtools package (Long, 2022).
 Finally, to estimate the number of fledglings produced per
 hectare, we first calculated entire-period nest success by raising daily
 nest success to the power of the average length of the entire period of
 each nest (Mayfield, 1961) for each species in both treatments. To capture
 the variability of fledglings per hectare, we bootstrapped 1000 iterations
 of the density dataset in each treatment for each species and took their
 means. Then, we multiplied density, entire-period nest success, and number
 of fledglings per successful nest to calculate the number of fledglings
 produced per hectare. Finally, we ran linear models with treatment as a
 fixed effects and the same random effects as nest success
 models.
description: # Impacts of umbrella species management on non-target species
 [https://doi.org/10.5061/dryad.zgmsbcckx](https://doi.org/10.5061/dryad.zgmsbcckx) This dataset consists of species abundances as collected by territory mapping surveys and nest data. Data used to calculate species density, number of offspring produced, and daily nest success between treatments are included. ## Description of the data and file structure The data are set up in CSV format for easy import into R. All data analysis was conducted in R (version 4.3.0) and important packages included nlme and lme4 for modeling and ggplot for plotting. Territory data includes the year, species (SPP, common names associated with four-letter codes can be found in Table 1), plot, treatment, number of territories in the plot, and plot area in hectares. Nest data includes a row for each nest found and consists of the year, nest ID (NID), species (SPP, common names associated with four-letter codes can be found in Table 1), plot, treatment, clutch size, number of eggs hatched, number of offspring fledged, accuracy of fledgling number (Fledged_Exact; 1 indicates exact number known), comments, days the nest was active and observed (Total_Days), and nest fate (0: failed, 1: at least one nestling fledged). R code used to run logistic-exposure analysis can be found at [https://rpubs.com/bbolker/logregexp](https://rpubs.com/bbolker/logregexp)