DOI: 10.5061/dryad.w3r2280md
title: Environmental conditions and call-broadcast influence detection of eastern forest owls during standardized surveys

description: Owls provide ecosystem services and play crucial roles in the environment
 making them important to monitor and study. However, standardized methods
 for most species do not exist, and we lack understanding of the effects of
 many environmental variables and call-broadcast on detection probability
 of owls. We performed a multispecies occupancy analysis of owl monitoring
 data collected from 2004 – 2013 across the state of Maine to examine the
 effects of environmental variables, conspecific and heterospecific
 call-broadcast, and general survey protocols on detection of three forest
 owls: Northern Saw-whet Owl (Aegolius acadicus), Barred Owl (Strix varia),
 and Great Horned Owl (Bubo virginianus). We found that environmental
 variables such as cloud cover, precipitation, temperature, time of night,
 and wind had species-specific effects on detection probability, and
 ambient noise decreased detection probability for all species. We did not
 find support for effects of snow cover on detection of any species. We
 also found that conspecific call-broadcast increased detection of each
 species, while heterospecific call-broadcast had variable affects.
 Specifically, we found that Long-eared and Barred Owl broadcast increased
 the detection of Northern Saw-whet Owl, and our results suggest additional
 heterospecific effects may exist. Our study showed that compared to the
 Maine Owl Monitoring Program, surveys examining all three of our focal
 species can increase efficiency and lower disturbance by only broadcasting
 Long-eared and Barred Owl calls during a 10-minute survey. We recommend
 that future owl surveys take into account species-specific effects of
 conspecific and heterospecific call-broadcast, and use our results when
 designing survey protocols that include one or more of our focal
 species. 
description: Maine Owl Monitoring Program MOMP consisted
 of a pilot study during 2002 and 2003 to evaluate methods and study design
 for a primary study conducted from 2004 to 2013. The main goals of the
 program were to (1) examine owl abundance and distribution in Maine, (2)
 evaluate survey methods and monitoring effectiveness, and (3) build a
 long-term citizen-science project through development of a network of
 skilled volunteers (Hodgman & Gallo 2004). <br>     Survey
 routes were established across the state (Figure 1) using the Maine
 DeLorme Atlas (Olathe, KA) grid system to delineate survey blocks that
 were further divided into four equal quadrants. Within each quadrant, two
 survey routes were established, and participants were asked to sample
 their assigned route once during a 6.5-week survey period that started on
 the first Friday of March each year. All surveys were conducted between
 2400 – 0500 hours, and not all routes were sampled equally or continuously
 across the study years. Because we focus exclusively on detection
 probability, which is survey-specific (described further below), we assume
 that our inferences related to survey-level detection are independent of
 variable route sampling among years.<br>     Along each route,
 participants established ten survey locations that were separated by a
 minimum distance of 1 mile, which we assumed was sufficient for
 independence among survey points. We believe this assumption is valid, as
 a distance of 1-mile between points exceeds mean home range size for each
 of our focal species (Mazur and James 2000, Churchill et al. 2002, Bennett
 and Bloom 2005), making it unlikely that the same owl was detected at
 multiple points in a single night. During each survey, participants
 recorded start time, and quantified temperature, cloud cover, snow cover,
 wind, noise, and precipitation. Temperature was estimated using handheld
 thermometers or from regional weather records. Cloud cover represented the
 percentage of the sky covered by clouds, estimated to the nearest 10%.
 Snow cover was recorded along a categorical gradient where 1 = no snow
 cover, 2 = patchy or partial snow cover, and 3 = complete ground cover.
 Wind speed was approximated following the Beaufort scale from 0 – 5 where
 0 = &lt; 1 mph, 1 = 1 – 3 mph, 2 = 4 – 7 mph, 3 = 8 – 12 mph, 4 = 13 –
 18 mph, and 5 = 19 – 24 mph winds. Noise was quantified on a scale from 1
 – 4, where 1 = relatively quiet, 2 = moderate noise but not affecting
 ability to hear owls, 3 = loud noise that may affect detection of owls,
 and 4 = excessive loud noise that probably affects detection of owls.
 Precipitation was scored categorically as either 0 – 1, where 0 = none or
 1 = drizzle. Surveys were not conducted in high winds (&gt; 3 on the
 Beaufort scale) or during precipitation &gt; 1 on the
 scale. <br>     Surveys began with 3 minutes of passive listening,
 followed by a call-broadcast 20 seconds in duration and a 1-minute
 40-second listening period. At the start of the 5th minute of the survey,
 a second call of a different species was used, followed by a 5-minute
 40-second listening period  . This longer listening period was designed to
 allow more time for Barred and Northern Saw-whet Owls to respond before
 playing Great Horned Owl (a potential predator of smaller owls) in order
 to mediate any possible negative effects of Great Horned Owl
 call-broadcast on Barred and Northern Saw-whet Owl detection. At minute 11
 a final call of a third species was broadcast, followed by another
 1-minute 40-second listening period, the end of which marked the
 completion of the survey. All calls were broadcast for 20-seconds. During
 2004 – 2006, Northern Saw-whet, Barred and Great Horned Owl calls were
 played, in that order. From 2007 – 2013, the Northern Saw-whet Owl call
 was replaced with a Long-eared Owl call. During each survey, participants
 recorded all owls heard or seen in each of the 16 sampling intervals,
 which consisted of 60-second listening intervals (n=10), 20-second
 call-broadcast intervals (n=3), and 40-second listening intervals (n=3;
 the remainder of the minute post-broadcast). During initial years,
 participants used cassette tapes with recordings of the calls of each
 focal species, while during later years, the option to use a 13-minute
 recording that was structured to play different owl calls at the designed
 times was offered. Participants chose to either use this new method, or
 continue using the cassette tapes.  Modeling
 Procedures We constructed species-specific detection
 histories, using owl detections during each listening interval (i.e., 20-,
 40-, or 60-second listening period) as temporally-repeated observations
 within each 13-minute survey. We examined the effects of environmental
 covariates and call-broadcast on species detection probabilities (p) using
 a multispecies occupancy framework (Rota et al. 2016) in program MARK
 (White and Burnham 1999, Skelly et al. 2018). Under this formulation, the
 observation (y) of a particular species i at a specific site j during any
 given survey k is described as<br> y_ijk |z_ij=Bernoulli(z_ij
 p_ijk)<br> where z reflects a vector of species-specific latent
 occupancy states that account for the probability of
 occurrence/co-occurrence for each species (see Rota et al. 2016 for
 additional details). Estimates of p are then modelled under a binomial
 distribution which, given the hierarchical nature of the model, are
 conditional on site-occupancy of the species in question as defined by z.
 In practice p can be derived under a generalized linear modelling
 framework as<br> logit(p_ijk )=β_0+β_1 X_1+β_2 X_2…+β_t
 X_t<br> where a logit link distribution is typically used, and
 species-, survey-, or site-specific heterogeneity in p may be accommodated
 by the effects (β) of a sequence of categorical or continuous predictor
 variables. <br>     In the context of our survey design and data, we
 used the discrete listening periods (20-, 40- or 60-second intervals) as
 each k survey replicate, where p then reflected the probability of
 detection for each species during any single interval within the 13-minute
 survey. While occupancy models are often fit using repeated visits to a
 site, replicated observations within visits are another well-established
 survey design that may be used in an occupancy context (MacKenzie and
 Royle 2006). In this case, the occupancy parameter reflects the
 probability of species presence/absence during the survey, as the
 assumption of closure among survey replicates is almost certainly met
 (MacKenzie and Royle 2006, Rota et al. 2009). For our purposes, this
 survey design implies that occupancy reflected the probability that one or
 more owls of each species was present at the survey stop during the
 survey, and p gave the probability that an owl was observed and recorded
 (primarily through vocalization) during each listening interval, given
 presence. <br>     Our objectives for this paper were strictly
 related to detection probability so we used a general structure for the
 occupancy component of the model that allowed independent annual estimates
 of occupancy for each of the three focal species. In practice, we fit this
 model structure using a year*species interaction, where year was modeled
 as a categorical fixed effect, and the interaction allowed occupancy to
 vary among years independently for each species. We also included an
 additional term in the model to allow for additive effects of
 co-occurrence for each species-pair, as well as all Northern Saw-whet,
 Barred, and Great Horned Owl three species in aggregate. This model
 accommodated differences in occupancy for each species/year combination,
 with constant rates of co-occurrence among species for the duration of the
 study. We elected not to incorporate explicit multi-year dynamics in the
 model (e.g., Hines et al. 2014) to reduce model complexity given the
 multi-species design, and because we felt accommodating the temporal
 dynamics with fixed year effects was appropriate given that our primary
 objective centered on detection probability rather than occupancy. We held
 the occupancy structure constant across all models and occupancy
 parameters reached convergence in all analyses, but for the purpose of
 this paper we did not interpret the occupancy terms further.<br>   
  To assess the fit of our data to the structure of the multi-species
 occupancy model, we used the occuMulti function in the package unmarked
 (Fiske and Chandler 2011) in program R to fit a multi-species occupancy
 model to our data, and then conducted a Chi-squared goodness-of-fit test
 (MacKenzie and Bailey 2004, Warton et al. 2017) using the parboot function
 to implement a parametric bootstrap with 500 replications (Fiske and
 Chandler 2011). We used a model with independent year effects on the
 single-species occupancy and detection parameters, and an additional
 intercept-only structure for the species interaction terms. This model
 form matched the base structure of all models we constructed using Program
 MARK (i.e., the general model without covariates). Results of these tests
 suggest the data conformed to the model structure, as bootstrapped
 replicates did not deviate from the expected distributions given the model
 (p=0.39), suggesting an adequate fit of the model to the data (Fiske and
 Chandler 2011).<br>     We only examined detection probability of
 Northern Saw-whet, Barred, and Great Horned Owl; all other species
 recorded during MOMP surveys had a sample size of &lt; 100 detections,
 and were therefore not considered. We modeled all effects independently
 (i.e., a unique Beta coefficient) for each species because our goals were
 to understand species-specific changes in detection in response to
 environmental and call-broadcast effects. All covariates were
 z-standardized (mean = 0.0, SD = 1.0). We used Akaike information
 criterion adjusted for small sample size (AICc) to rank the detection
 models following a criterion of ∆AICc &lt; 2.0 for models to be
 competitive.  Modeling Environmental
 Covariates We began model selection for the detection
 probability component of the analysis by running models with additive or
 interactive effects of species, ordinal date, and year, and tested
 quadratic effects of ordinal date. From this model set, we selected the
 best-performing model and used this as a base structure upon which we
 built all further models. We first constructed a correlation matrix of all
 covariates in program R (version 3.4.2), and found that no two covariates
 were highly correlated (Pearson’s r &lt; 0.5). We then tested
 individual models with additive effects of the following 7 covariates on
 detection of each species: time of survey expressed as time since
 midnight, temperature, cloud cover, snow cover, wind, noise, and
 precipitation. We combined covariate effects that performed better than
 the base model into a singular comprehensive model, from which all
 inference was made. We used the Beta coefficients (β) and their 95%
 confidence intervals to further evaluate species-specific covariate
 support (95% CI does not overlap zero), and interpreted relative
 differences in effects among species (i.e., differences in
 species-specific β for each supported variable). 
 Modeling Effects of Call-broadcast To
 determine the effects of broadcast on detection of the three species of
 owl, we used the best model from our base model selection (combined
 species, date and year effects) and created individual models with effects
 for each of the four species’ calls, applied independently (i.e., unique β
 coefficients) to detection probability for each of the three focal
 species. We examined the effect of each species’ call-broadcast on
 detection of each species, where we modeled a unique detection probability
 for the 20-second period of call-broadcast and each subsequent sampling
 interval until the next species’ call began. For Great Horned Owl, the
 effect of conspecific? call-broadcast was modeled to include the 20-second
 call-broadcast and the remaining sampling intervals until the end of the
 survey. Effects   of Northern Saw-whet Owl and Long-eared Owl
 call-broadcast were limited to the years in which they were played (NSWO =
 2004 – 2006; LEOW = 2007 – 2013). We accomplished this in practice using a
 dummy variable in the design matrix, where values of ‘1’ were applied only
 to sampling intervals during the years in which the call-broadcasts for
 each respective species were played. The resulting β coefficients for this
 effect reflected the differences in detection probability between
 call-broadcast intervals associated with the species during the years in
 which their calls were played, and all other intervals not associated with
 the call-broadcast for that species. Although detections of Long-eared Owl
 were too infrequent to analyze, we still included the effects of
 Long-eared Owl calls on our three focal species because they were played
 for seven years of the surveys, and it has been shown that heterospecific
 calls can affect detection of other owl species (Bosakowski and Smith
 1998). <br>     Based on preliminary results, we combined all four
 of call-broadcast structures   (the independent call-broadcast structures
 of Northern Saw-whet, Long-eared, Barred, and Great Horned Owl into one
 comprehensive model, and made further inferences and interpretations from
 this combined model. In this model we also included a general effect of
 the call broadcast interval (i.e., was any call actively being broadcast)
 on the detection of all species, because we reasoned that the speaker
 broadcast could muffle the sound of an immediately responding owl.
 Finally, we ran a post-hoc model to specifically examine the effects of
 Barred Owl call-broadcast on Northern Saw-whet Owl detection during the
 years in which Northern Saw-whet owls were not broadcast (2007 – 2013).
 This allowed us to evaluate whether the effect of Barred Owl
 call-broadcast on Northern Saw-whet Owl detection was still supported
 without the bias of an earlier broadcast of Northern Saw-whet Owl calls,
 given that Barred Owl calls were always broadcast after Northern Saw-whet
 owls during 2004 – 2006. Modeling Survey Duration and
 Structure To examine how effective surveys were at
 detecting species presence, and to determine the most efficient and
 effective survey structure for detecting each species singly as well as in
 aggregate, we calculated a cumulative detection probability, p*,
 as <br>  〖p^*〗_i=1-(∏_(1:n)▒〖(1-p_k)〗)<br> which derived the
 probability that each i species was detected at least once during a survey
 of n listening intervals, where p is the probability of detection during a
 single listening interval (k). We used estimates of pk from the most
 competitive model identified by our call-broadcast model selection, and
 calculated p* under a variety of scenarios to explore how potential
 differences in call-broadcast structure and survey length could affect
 detection of each species during the survey. Given that models included a
 year effect, we used estimates of pk from years that were closest to the
 mean detection probability value for each species. We also assumed mean
 values for all environmental covariates for this assessment. <br>   
  We first calculated a unique p* for each combination of successive survey
 lengths (e.g., 1 interval, 2 intervals, etc.) under the existing protocol
 for each of the three focal species, and we repeated this for both when
 Northern Saw-whet Owl was played first and when Long-eared Owl was played
 first. We then explored hypothetical alternative survey structures where
 we assumed only two species’ calls were broadcast (i.e., either Northern
 Saw-whet or Long-eared plus Barred Owl but excluding Great Horned Owl),
 and where we assumed only one species’ call was broadcast (Northern
 Saw-whet or Long-eared only but excluding the two other species; Table 1).
 We also explored a hypothetical survey structure consisting of only
 passive listening, to which we compared all alternative call-broadcast
 structures. In each of these hypothetical scenarios, we calculated p* for
 all possible survey lengths. We report change in cumulative detection
 probability as a function of increased survey duration, and evaluated
 differences in relative ability to detect each species among survey
 structures based on the number of intervals required for a species’
 detection probability to reach values of both p* &gt; 0.50 and p*
 &gt; 0.95. In cases where two survey structures achieved the same p*
 during the same survey interval, we considered the survey structure with
 the fewest number of call-broadcasts to be more ideal, because use of
 fewer calls could limit disturbance and chance of human error.
 Collectively this exercise allowed us to evaluate optimal sampling designs
 for detecting owl presence during single visit surveys.<br>