DOI: 10.5061/dryad.sf7m0cg5n
title: Arresting the spread of invasive species in continental systems

description: Invasive species are a primary threat to biodiversity and are challenging
 to manage once populations become established. But removing them is
 further complicated when invasions occur in continental, mixed-ownership
 systems. We demonstrate a rare conservation success: the regional-scale
 removal of an invasive predator – the barred owl (Strix varia) – to
 benefit the spotted owl (S. occidentalis) in California, USA. Barred owl
 site occupancy declined six-fold from 0.19 to 0.03 following one year of
 removals, and site extinction (0.92) far exceeded colonization (0.02).
 Spotted owls recolonized 56% of formerly occupied territories within one
 year, contrasting starkly with removals conducted after barred owls
 achieved high densities in the Pacific Northwest. Thus, our study averted
 the otherwise likely extirpation of California spotted owls by barred owl
 competition. Collectively, leveraging technological advances in population
 monitoring, early intervention, targeting defensible biogeographic areas,
 and fostering public-private partnerships will reduce invasive
 species-driven extinction of native fauna in continental systems.
description: Barred owls and barred x spotted owl hybrids (hereafter
 “hybrids”) were located for removals using a combination of (1) intensive
 passive acoustic surveys on national forests in the northern Sierra Nevada
 where densities are highest and (2) active broadcast surveys across public
 and private land across the entire Sierra Nevada. Removal success was
 monitored using passive acoustic surveys in the high-density northern
 Sierra Nevada. Thus, while our test of the feasibility of barred owl
 removals was focused on the northern Sierra Nevada, success in this area
 is assumed to be indicative of the success and feasibility of removals in
 the areas of much lower density of the Sierra Nevada.
 We conducted passive acoustic surveys at 346 sites in 2018, 983
 sites in 2019, and 267 sites in 2020 across 6,000
 km<sup>2</sup> of national forest land in the northern Sierra
 Nevada (Wood et al. 2019, 2020). Each site was a 4
 km<sup>2</sup> hexagonal grid cell – the approximate size of
 barred owl territories in the region (Wood et al. 2020; Figure 2). The 346
 sites surveyed in 2018 were selected systematically with approximately one
 of every five of the 1500 available sites receiving a survey; these sites
 were considered “pre-removal” in our test of removal effectiveness and
 provided locations of barred owls for removals. Typically, these sites
 were separated by at least one non-surveyed site to reduce the probability
 of detecting the same individuals at multiple sites (i.e., minimize false
 detections). The 983 sites surveyed in 2019 included 330 of those surveyed
 in 2018 and an additional 653 intervening sites intended to locate as many
 barred owls as possible – and thus encompassed most (65%) of national
 forests in the northern Sierra Nevada (Figure 2). Surveys conducted in
 2019 were not used to assess the effectiveness of removals (see below).
 The 267 sites surveyed in 2020 were a subset of those surveyed in 2018
 (owing to COVID-19 restrictions) and were treated as “post-removal” sites
 in our test of removal effectiveness. At each site, we conducted two to
 five passive acoustic surveys, five to seven nights in duration. We
 deployed two to three autonomous recording units (ARUs; Swift Recorder,
 Cornell Lab of Ornithology, Ithaca, New York, USA) per site in areas of
 high topographic relief with ARUs operating from 2000 to 0600 hours at a
 sample rate of 32 kHz. We scanned audio data using Program Raven Pro 2.0
 (Cornell Lab of Ornithology, Ithaca, New York, USA) and used a previously
 developed sliding window template detector applied to our audio data to
 identify barred owl eight-note territorial calls (Wood et al. 2019). This
 template detector yields &gt;0.98 probability of detecting at least
 one barred owl call within a bout of calling (Wood et al. 2019). We
 manually reviewed all possible detections to confirm the identification of
 barred owls. Active surveys were conducted across the
 Sierra Nevada in areas with known, historic barred owl detections using
 digitally broadcasted barred and spotted owl vocalizations. We typically
 surveyed multiple points within 1 km of historic detections for 10 minutes
 each. These detections were supplemented by soliciting information on
 barred owl detections from management agencies and other researchers that
 survey for owls in the region. In concert, we expected that our passive
 acoustic surveys in the northern Sierra Nevada and the extensive active
 surveys conducted by our and other groups throughout the Sierra Nevada
 would locate a high fraction of the territorial barred owl population in
 the region. We lethally removed barred owls and hybrids
 from 2018 to 2020, following field protocols established by Diller et al.
 (2014). Individuals with vertical barring on breast feathers that produced
 distinct eight-note calls were identified as pure barred owls, while
 individuals with bars and spots on their breast feathers that produced
 territorial calls that were neither distinctly barred owl or spotted owl
 were identified as hybrids (see Fig. S1 in Wood et al. [2021]).
 We tested for declines in barred owl occupancy following
 experimental removals using a multi-season occupancy model (MacKenzie et
 al. 2003) parameterized with detection/non-detection data from passive
 acoustic survey data from 2018 and 2020, when removals were conducted
 before or after the acoustic surveys, respectively. Our primary sampling
 periods were the two seasons (May to August, 2018 and 2020) and sites were
 restricted to those 267 sites surveyed in both the pre-removal (2018) and
 post-removal (2020) years. We excluded data from 2019 because removals and
 acoustic surveys occurred simultaneously, violating the assumption of
 closure. We designated two to five secondary sampling periods within each
 primary sampling period which reflected the five-to-seven night ARU
 deployments. We estimated
 <i>p</i>,<i><sub> </sub></i>the
 probability of detecting a barred owl at a site given that the site was
 occupied, ψ, the probability of barred owl site occupancy, and ε, the
 probability of an occupied site going extinct from 2018 to 2020, and
 derived an estimate of γ, the probability of site colonization from 2018
 to 2020. We used Akaike’s information criterion, corrected for small
 sample size (AIC<sub>c</sub>), to compare models and
 considered models with ΔAIC<sub>c</sub> &lt; 2 to have
 strong support from our data. We tested for heterogeneity in <i>p
 </i>between primary sampling periods (years) and among secondary
 sampling periods (surveys), and for changes in barred owl occupancy from
 2018 to 2020 by allowing ψ to vary by year. We considered all combinations
 of sub-model parameters to find the model structure with the most support
 (Doherty et al. 2012). We used packages <i>RMark</i> and
 <i>xlsx</i> in Program R 4.0.2 (R Core Development Team
 2020).
description: Our R script will run the single-species multi-season occupancy
 model from the spreadsheet (bdo_occupancy.xlsx), but the "core
 &lt;- read.xlsx" line will need to be changed to the correct
 pathway on whoever's computer is trying to use this
 dataset. On the spreadsheet: 
 "Cell_ID" refers to the site ID
 "ch" refers to the detection histories for both
 primary sampling periods combined (ch_2018 and ch_2020) with 0 = barred
 owl not detected, 1 = barred owl detected, and . = not surveyed during the
 particular secondary sampling period.
 "core" refers to whether sites were surveyed in both
 primary sampling periods (2018 and 2020) with 1 = yes, and 0 =
 no. Dates refer to the initial Julian Date of each
 secondary sampling period, which we tested for as a covariate, but did not
 find support for in our modeling.