DOI: 10.5061/dryad.47d7wm3c8
title: High rates of anticoagulant rodenticide exposure in California Barred Owls

description: Pesticide use is pervasive and the exposure of non-target wildlife has
 been well documented over the past half century. Among pesticides,
 anticoagulant rodenticides (AR) have emerged as a particularly important
 threat in forests of the western United States, with exposure and
 mortality reported for several species of conservation concern. To further
 quantify this threat, we collected specimens of Barred Owls (Strix varia)
 and Barred Owl x Spotted Owl hybrids from the Klamath and Cascade
 Mountains and Sierra Nevada in California, USA to use as indicator species
 for environmental contamination with AR and to infer exposure of closely
 related and ecologically similar Northern and California Spotted Owls (S.
 occidentalis caurina, and S. o. occidentalis, respectively). We tested 115
 Barred Owl and 12 Barred Owl x Spotted Owl hybrid livers for eight AR
 compounds and found high rates of exposure (62%) across our study area,
 and greater than previous studies in the Pacific Northwest. In addition,
 we sampled seven ovaries from seven females and 100% tested positive for
 AR. Female Barred Owls were more likely than males to be exposed (78% and
 50%, respectively). Unlike previous studies, we found no clear link
 between illegal cannabis cultivation and AR exposure. However, Barred Owls
 sampled in proximity to the wildland-urban interface (WUI) were more
 likely to be exposed to AR. Though the exact source (e.g., cannabis
 cultivation or application around human dwellings) and location is
 unknown, the association of AR exposure with the WUI was supported from
 GPS data from Barred Owls, Northern and California Spotted Owls, and
 hybrids using the WUI for foraging. The high rate of AR exposure in Barred
 Owls and hybrids provides mounting evidence of an additional stressor that
 ARs may pose to Spotted Owls – including the first evidence for
 California Spotted Owls – and fauna native to western forest ecosystems.
description: <b>Study Area</b> We collected
 Barred Owls and hybrids from the southern Klamath and Cascade Mountains
 and from the Sierra Nevada in northern California (Figure 1) on national
 forest lands, national park lands, and private commercial timberlands
 primarily owned by Sierra Pacific Industries. There was considerable
 variation in climate, elevation, topography, and vegetation, though both
 sub-regions were predominantly composed of mixed coniferous forest,
 dominated by ponderosa pine (<i>Pinus ponderosa</i>), sugar
 pine (<i>P. lambertiana</i>),<i> </i>incense cedar
 (<i>Calocedrus decurrens</i>), Douglas fir
 (<i>Pseudotsuga menziesii</i>), and white fir (<i>Abies
 concolor</i>). Neither the U.S. Forest Service (S.C. Sawyer,
 personal communication) nor Sierra Pacific Industries (B.P. Dotters,
 personal communication) use ARs on lands they manage within our study
 area. However, there are houses in WUIs adjacent to lands where owls were
 collected, and it is not known whether ARs are used in these
 areas. <b>Tissue Collection and AR Screening
 </b> We lured territorial Barred Owls and hybrids
 by broadcasting digitally recorded Barred Owl vocalizations and collected
 them with a 12-gauge shotgun following methods described by Diller et al.
 (2014). We collected Barred Owls and hybrids under federal and state
 Scientific Collecting Permits (United States Fish and Wildlife Service
 permits MB24592D-0, MB53229B-0 and California Department of Fish and
 Wildlife permits SC-002114, SC-11963). <b> </b>We froze owls
 immediately after collecting them and stored the specimens in a –20˚C
 freezer<b> </b>until we delivered them to the Museum of
 Vertebrate Zoology (University of California, Berkeley), where we
 extracted livers and ovaries.<b> </b>We were careful to avoid
 contamination between the two organs by separating them immediately after
 they were removed from the abdominal cavity and placing them in separate
 containers. We thawed all specimens for a similar amount of time to
 extract tissues, and we left no specimen thawed for over 24
 hours.<b> </b>We shipped tissue samples to the California
 Animal Health and Food Safety Laboratory System (CAHFS; University of
 California, Davis) where they were screened for eight commonly used ARs:
 warfarin, diphacinone, chlorophacinone, coumachlor, brodifacoum,
 bromadiolone, difethialone, and difenacoum. The first four belong within
 less-acutely toxic first-generation ARs (FGAR); the latter four are more
 acutely toxic second-generation ARs (SGAR) that were created in the 1970s
 due to rodents developing resistance to first-generation ARs
 (FGARs; Buckle et al. 1994). High performance liquid chromatography-tandem
 mass spectrometry was used to screen tissue samples for AR exposure
 (whether or not any ARs were detected) and to quantify the concentration
 of ARs detected (Marek and<b> </b>Koskinen 2007). We
 classified AR exposure in livers and ovaries using the limit of detection
 (LOD), which allowed us to detect presence of AR in any sample with a
 concentration above 0.005 μg/g wet weight (ww). We quantified AR
 concentrations in liver and ovary samples using the limit of
 quantification (LOQ), which was 0.050 μg/g ww for brodifacoum and 0.020
 μg/g ww for all other ARs in owl livers, and 0.200 μg/g ww for all ARs in
 owl ovaries (Riley et al. 2007). Any sample above these LOQs could have
 concentrations quantified. These concentrations all fall below the 0.1
 μg/g ww threshold for mortality rate of 10% of individuals previously
 reported in Barred Owls (Thomas et al. 2011). When samples had
 concentrations greater than the LOD and below the LOQ, we designated those
 individuals as having “trace” exposure.
 <b>Calculating Biological Variables</b>
 We identified owls in the field as Barred Owls or hybrids based
 on both plumage and territorial vocalizations. Individuals with vertical
 barring on the breast feathers and horizontal barring around the nape that
 produced distinct two-phrase, eight-note calls (Odom and Mennill 2010)
 were identified as pure Barred Owls. Individuals with bars and spots on
 their breast feathers and that produced territorial calls that were not
 distinctly Spotted Owl or Barred Owl calls were identified as hybrids
 (Hamer et al. 1994). We classified age as either adult (≥3 years),
 sub-adult (1–2 years), or juvenile (0 years), based on adults having wider
 terminal bands than sub-adults on all flight feathers, and juveniles
 lacking most or all body contour feathers (Mazur and James 2000, J.D.
 Wiens, personal communication). We determined sex by examining gonads in
 the lab, and we assessed body condition by characterizing the amount of
 subcutaneous fat content into four categorical values, with no fat being
 our baseline (“0”), slight fat (“1”), moderate fat (“2”), and heavy fat
 (“3”). Because fat reserves in owls change throughout the year (Massemin
 et al. 1997, DeLong 2006), we obtained a corrected fat index by
 calculating the residuals of a linear regression of fat against the month
 of the year (Supplemental Material Figure S1).
 <b>Calculating Environmental Variables</b>
 We assigned owls that were collected north of the Pit River to
 the Klamath/Cascade sub-region, and owls sampled south of this river to
 the Sierra Nevada sub-region (Figure 1). We used this designation to
 differentiate Barred Owls collected within the range of the Northern
 Spotted Owl (Klamath/Cascade) or of the California Spotted Owl (Sierra
 Nevada; Barrowclough et al. 2005). We calculated remaining environmental
 variables within 2000ha circular buffers around collection locations that
 approximated Barred Owl home range size in the region that we measured
 using GPS-tagged individuals in a previous study (see Wood et al. 2020a).
 We used a combination of law enforcement databases (IERC 2019) to
 calculate the number of known cannabis cultivation sites detected from
 2004 to 2019 within the circular buffers. We also related AR exposure to a
 measure of the probability of illegal cannabis cultivation within the
 buffers, estimated from a Maximum Entropy model (Wengert et al.
 <i>in review</i>) parameterized with variables indicative of
 the suitability of growing cannabis on California’s public and private
 lands. The important variables in this predictive model included
 elevation, slope, precipitation, canopy cover, stand age, and distances to
 disturbance, fresh water, roads, and private lands, and used a resolution
 of 90m for individual cells. From the MaxEnt model we obtained an averaged
 index of cannabis cultivation suitability (ranging from 0 to 1) for each
 buffer to assess whether owls were more likely to be exposed in areas with
 more suitable conditions for cannabis cultivation.
 Additionally, we calculated the distance of each Barred Owl
 removal location to the WUI, based on 2010 census data (Radeloff et al.
 2005, http://silvis.forest.wisc.edu/data/wui-change/, accessed Aug, 2020),
 where owls that occurred within the WUI were assigned a distance of 0 km.
 Both intermix (where housing and vegetation intermingle) and interface
 (where housing occurs in the vicinity of contiguous wildland vegetation)
 components of the WUI spatial dataset were used. Four thresholds are
 defined in the WUI data provided by Radeloff et al. (2005) based on the
 level of housing density: high, moderate, low, and very low. We chose to
 use the low density WUI threshold requiring at least 6.17 housing
 units/km<sup>2</sup> because of concordance we observed with
 this threshold and buildings visible in a building footprint spatial layer
 developed from Microsoft
 (https://www.microsoft.com/en-us/maps/building-footprints). Finally, we
 calculated landownership as the proportion of the circular buffers that
 were composed of National Forest lands. Descriptive statistics of the
 environmental variables is listed in Table S1.
 <b>Characterizing Barred and Spotted Owl Foraging
 Activities</b> To characterize the distribution
 of Barred Owl foraging locations relative to environmental factors related
 to AR exposure (in this case WUIs, see below), we GPS-tagged seven Barred
 Owls and three hybrids between May and August of 2017 and 2018 in the
 northern Sierra Nevada. We used visual and vocal lures to attract Barred
 Owls and hybrids and captured them with dho-gaza nets, and applied
 Argos-enabled GPS backpack tags (Lotek Wireless, Newmarket, Ontario,
 Canada). We programmed tags to record 4–6 nighttime locations per week
 between April and August, and then to record one location per week between
 September and March. We also used locations from 24
 GPS-tagged Northern Spotted Owls and 106 California Spotted Owls to
 characterize their use of areas associated with elevated AR exposure in
 Barred Owls – and thus the potential for Northern and California Spotted
 Owl exposure rates to mirror Barred Owl rates. Northern Spotted Owl
 locations were collected in the Klamath Mountains between March and August
 of 2017, and California Spotted Owl locations were collected in the Sierra
 Nevada between May and August of 2015 through 2020 as part of previous
 studies (Jones et al. 2016, Atuo et al. 2018, Kramer et al. 2020). We used
 vocal lures to locate Spotted Owls and captured them either by hand-grab,
 pan-trap, or snare-poles, and applied GPS backpack tags (Lotek Pinpoint
 VHF 120, Newmarket, Ontario, Canada). Spotted Owl tags were programmed to
 record 5 hourly nocturnal locations per night between March and August.
 From these data, we calculated the mean proportion of locations that
 occurred within the WUI for both Northern and California Spotted Owls, as
 well as the proportion of individuals of each subspecies with at least one
 location in the WUI. We assumed the majority of these locations were
 primarily foraging locations as owls are nocturnal predators, but we
 acknowledge that other behaviors such as territory defense, resting, and
 returns to roosts and nests may be included in these locations.
 <b> </b>Additionally, we calculated the proportion of
 all known Northern Spotted Owl activity centers and all California Spotted
 Owl activity centers in the Sierra Nevada whose home ranges at least
 partially overlapped with the WUI to assess the risk of Spotted Owl
 exposure to ARs via the possibility of foraging in the WUI. We used 2.1 km
 radius home ranges for Northern Spotted Owls and 1.6 km radius home ranges
 for California Spotted Owls (Wiens et al. 2014, Blakey et al. 2019).
 Activity centers were defined as nest locations or geometric centers of
 daytime roost locations and were obtained from California Department of
 Fish and Wildlife
 (https://www.wildlife.ca.gov/Data/CNDDB/Spotted-Owl-Info, accessed Nov,
 2020). We also used both Northern and California Spotted Owl designated
 ranges (USFWS 2017) to calculate the proportion of WUI within each Spotted
 Owl subspecies’ range (only including the Sierra Nevada for California
 Spotted Owls). <b>Statistical
 Analysis</b> We used a set of generalized linear
 models (McCullagh and Nelder 1989) within an information-theoretic
 framework (Burnham and Anderson 2002) to test for associations between AR
 exposure and biological and environmental factors. Because most exposures
 were at the trace level, we modeled exposure as a binomial response
 (exposed = 1 and not exposed = 0). Biological factors consisted of species
 (pure Barred Owl versus hybrid), age, sex, and the index of body
 condition. Juvenile and un-aged owls were omitted from the generalized
 linear model because of small sample sizes. Environmental factors
 consisted of sub-region, proximity to the WUI, number of known cannabis
 cultivation sites within home ranges, the average index of predictive
 cultivation for each Barred Owl home range from the MaxEnt model, and
 landownership. We used a multi-stage secondary
 candidate strategy to select top-ranked models (Morin et al. 2020). First,
 we ran all combinations of biological models and all combinations of
 environmental models separately. We then identified supported models as
 those within 5 AIC<sub>c</sub> of the most supported model for
 each set of models. Second, we combined and evaluated support for
 variables in the top models from both the biological and environmental
 sets. In both model-selecting stages, models with uninformative variables
 (e.g., confidence intervals of variables overlap with zero) were not
 considered (Leroux 2019). We used package <i>MuMIn</i> (Bartón
 2015) in R Studio 1.3.1073 (R Core Development Team 2020) for these
 analyses. We also conducted a general Getis Ord-General
 G high/low cluster analysis (Getis and Ord 1992) to assess the degree to
 which AR exposure was more clustered than expected at random, less
 clustered than expected at random, or randomly distributed. We ran
 separate analyses for owls collected in the Klamath/Cascade sub-region and
 those collected in the northern Sierra Nevada (where the majority of
 Sierra Nevada removals were conducted), and only used locations for where
 owls were exposed, realizing that mates could be non-exposed. To reduce
 potential biases associated with sampling multiple owls from the same
 territory, owls collected within 2.52km (the radius of a 2000ha Barred Owl
 home range in the region, Wood et al. 2020a) of other owls were combined
 to single points based on the geometric centers of the points. We also
 conducted a Moran’s I spatial autocorrelation analysis with the same
 condensed points to assess the degree of concordance between different
 clustering procedures. All spatial analyses were conducted using ArcMap
 10.6.1 (ESRI Inc., Redlands, California).