DOI: 10.5061/dryad.rm2n805
title: Data from: Recent drought and tree mortality effects on the avian community in southern Sierra Nevada: a glimpse of the future?

description: Birds respond rapidly to changes in both habitat and climate conditions
 and thus are good indicators of the ecological effects of a changing
 climate, which may include warmer temperatures, changing habitat
 conditions, and increased frequency and magnitude of extreme events like
 drought. We investigated how a widespread tree mortality event concurrent
 with a severe drought influenced the avian community of the Sierra Nevada
 mountain range in California. We assessed and compared the separate
 effects of climate stresses and altered habitat conditions on the avian
 community and used this information to evaluate the changes that are
 likely to occur in the near future. We built tree mortality maps from
 freely available Landsat imagery with Google Earth Engine. We analyzed
 avian point counts from 2010 to 2016 in the southern Sierra Nevada, to
 model temperature, water deficit, and tree mortality effects on the
 abundances of 45 bird species, and then used these models to project
 abundances into the future based on three climate projections. A large
 portion of the avian community, 47%, had a positive relationship with
 temperature increase, compared to 20% that responded negatively. More
 species (36%) declined with drier conditions than increased (29%). More
 species declined in response to high tree mortality (36%) than increased
 (9%). A preponderance of species adapted to colder temperatures (higher
 elevation) had negative responses to high tree mortality and water
 deficit, but positive responses to increasing temperature. We projected
 the highest total bird abundances in the future under the warmest climate
 scenario that we considered, but habitat modification (e.g., tree
 mortality) and water deficit could offset the positive influence of
 temperature for many species. As other studies have shown, climate warming
 may lead to substantial but idiosyncratic effects on wildlife species that
 could result in community composition shifts. We conclude that future
 climate conditions may not have a universally negative effect on
 biodiversity in the Sierra Nevada, but probable vegetation changes and
 increased likelihood of extreme events such as drought should be
 incorporated into climate‐smart forest and wildlife management decisions.
description: data formatted for
 analysis_Roberts et al. 2019 EcoAppsBird survey detections
 and covariates file. Each row in this spreadsheet is a detection of a
 single species at a particular distance (in some cases may be more than
 one individual). Fields in this file include: PointYrID (a unique
 identifier for each point visited in a given year); Point (unique
 identifier for each field location); Transect (unique identifier for
 groups of points arranged in a 5-point transect); Year (calendar year in
 which the detections were recorded); Site (unique identifier for a pair of
 two 5-point transects in close proximity); Rep2 (visit identifier in
 sequential order across all years of surveys); Rep1 (visit identifier for
 sequential visit within the same calendar year, 1-2-3 are only possible
 values); Date (calendar date of the visit); DOY (numerical identifier of
 day of year on which the visit took place, possible values = 1-365); TOD
 (numerical format of time of day); Count (number of individuals detected);
 Spp (species identified, conforming to 4-letter AOU codes); Detection Cue
 (song=S, visual=V, call=C, drumming=D, hum=H, juvenile=J); Distance
 (distance in meters at first detection); Temp (temperature in degrees
 celsius); Wind (Beaufort scale estimated wind
 measurment).Site covs.Roberts et al.
 2019 EcoAppsSite covariates file.
 Fields include: PointYrID (unique identifier for each point visited in
 each year); Forested (visual estimate from Google Earth imagery of the
 forest condition); ConDieOffGIS (visual estimate from GoogleEarth imagery
 and field survey estimates of the percent of standing trees within 100m of
 the bird survey plot center that have succumbed to beetle mortality);
 ConDieOfffield (visual estimate by field survey crew members of the
 percent of standing trees within 100m of the bird survey plot center that
 have succumbed to beetle mortality); CWHR1 (California Wildlife Habitat
 Relationship forest type classification); Point (unique point identifier);
 Year (calendar year in which the visit occurred); MaxOfVisit (number of
 visits in a single calendar year in which bird survey data were gathered);
 latitude (geographic coordinate WGS84/NAD83); longitude (geographic
 coordinate WGS84/NAD83); elevation (elevation in meters sampled from DEM);
 elevres (elevation residual from a regression of elevation vs. latitude
 for entire Bioregional Monitoring project sample); aspect (degrees aspect,
 0-360); slope (% of vertical); SRI (Solar Radiation Index); southness
 (proportion of completely south-facing aspect [180 degrees]); ShrubCov
 (shrub cover, field estimated % cover); TreeCov (tree cover, field
 estimated % cover); BA (basal area, square feet per acre); g30SNAGS (count
 of snags greater than 30cm in diameter), totSNAGS (count of snags greater
 than 10cm in diameter); MaxDBH (diameter in inches of largest trees within
 50m); PineProp (proportion of total tree cover that is either Sugar Pine,
 Ponderosa Pine, or Jeffrey Pine); NDWI (normalized difference wetness
 index); dNDWI (difference in normalized difference wetness index from
 baseline value in 2009); cwd (climatic water deficit in mm H2O); dCWD
 (difference in CWD from baseline value in 2009); tmn (average daily
 minimum temperature in june); tmx (average daily maximum temperature in
 June); dtmn (difference in average daily minimum temperature in June from
 baseline value in 2009); dtmx (difference in average daily maximum
 temperature in June from baseline value in 2009).species
 listList of species included
 in this analysis. Fields include Spp (4-letter AOU code); model (best
 fitting distsamp model detection function)Site covs
 CNRM85bCovariates file for
 future predictions of species abundances based on the CNRM climate
 projections. All covariates as described in "Site covs.Roberts et al.
 2019 EcoApps.csv" file except that temperature (TMN, TMX, dDMN, dTMX)
 data are sampled from future predictions according to the CNRM85
 scenario.Site covs
 GFDLB1bCovariates file for
 future predictions of species abundances based on the CNRM climate
 projections. All covariates as described in "Site covs.Roberts et al.
 2019 EcoApps.csv" file except that temperature (TMN, TMX, dDMN, dTMX)
 data are sampled from future predictions according to the GFDLB1
 scenario.Site covs
 MIROC85bCovariates file for
 future predictions of species abundances based on the CNRM climate
 projections. All covariates as described in "Site covs.Roberts et al.
 2019 EcoApps.csv" file except that temperature (TMN, TMX, dDMN, dTMX)
 data are sample from future predictions according to the MIROC85
 scenario.