DOI: 10.5061/dryad.wm37pvmpn
title: Complex landscapes stabilize farm bird communities and their expected ecosystem services

description: 1. Birds play many roles within agroecosystems including as consumers of
 crops and pests, carriers of pathogens, and beloved icons. Birds are also
 rapidly declining across North America, in part due to agricultural
 intensification. Thus, it is imperative to identify how to manage
 agroecosystems to best support birds for multi-functional outcomes (e.g.,
 crop production and conservation). Both the average amounts of
 services/disservices provided and their temporal stability are important
 for effective farm planning. 2. Here, we conducted point-count surveys for
 four years across 106 locations on 27 diversified farms in Washington and
 Oregon, USA. We classified birds as ecosystem service or disservice
 providers using indices spanning supporting, regulating, provisioning, and
 cultural services/disservices. We then examined service/disservice index
 pairwise correlations and assessed the relative importance of local, farm,
 and landscape complexity on the average and temporal stability of avian
 service/disservice provider indices. 3. Generally, service provider
 indices (production benefitting birds, grower appreciation, and
 conservation scores) were positively correlated with each other. Foodborne
 pathogen risk, grower disapproval, and identity/iconic value indices were
 also positively correlated with each other. However, the crop damaging
 bird index generally had low correlations with other indices. 4. Farms
 that implemented more conservation-friendly management practices generally
 had higher average service provider indices, but farm management did not
 impact disservice provider indices, except for grower disapproval. Average
 disservice provider indices were lower on farms in complex landscapes. 5.
 Local vertical vegetation complexity tended to increase the temporal
 stability of service provider indices but did not affect the disservice
 provider indices. Greater landscape complexity was generally associated
 with increased temporal stability of service and disservice provider
 indices. Increased landscape complexity may stabilize bird communities by
 increasing bird community evenness, which in turn, positively predicted
 temporal stability of all service/disservice provider indices. 6. Policy
 implications: Our results suggest that farmers can effectively manage
 their farms to harness ecosystem services from birds through farm
 diversification. Disservices provided by birds, however, appear to be most
 negatively impacted by landscape-level complexity. Thus, greater
 incentives for farmers to increase seminatural cover at the
 landscape-scale are likely necessary to achieve multifunctional outcomes
 for conservation and agriculture.
description: <b>2.1</b><b>
 Study system</b> Across four years (2016–2019),
 we surveyed bird communities on a total of 27 farms in Oregon (<i>n
 </i>= 15) and Washington (<i>n </i>= 12; Fig. 2; Fig.
 S1) states, USA. We obtained permission to conduct surveys on all farms
 from the farm owners and/or managers. All farms fell into the Northern
 Pacific Rainforest Bird Conservation Region. Farms were highly
 diversified, largely used organic practices (20 were certified), and grew
 a range of crops (no monocultures; mean = 46.5 ± 19.8 (SD) crops grown per
 farm) including cereals, vegetables and melons, fruits and nuts, oilseed
 crops, roots, spice crops, beverage crops, medicinal crops, commercial
 flowers, and grasses and fodder crops, among others. Livestock were
 integrated into farming operations for at least one year of the study on
 18 of these farms in a variety of forms. Farms spanned a range of
 landscape contexts, from intensified agriculture to primarily seminatural
 (<i>e.g</i>., Fig. 2; Fig. S1; range: 2.19% to 95.7%
 seminatural). <b>2.2 Bird point count
 surveys</b> Bird surveys were conducted twice per
 farm each year between 20 May and 8 August 2016–2019 to coincide with peak
 produce production in the region. We moved along a south to north transect
 among farms (Fig. S1a) for each of the two annual survey periods. Survey
 one each year roughly corresponded with the nesting season along the south
 to north transect, while survey two roughly corresponded with the fledging
 and flocking periods for gregarious species. One point count location
 (“point”) with a 100-m radius was surveyed for every 4 ha of farmed land
 to maintain a consistent point density. Points were systematically
 stratified to capture the range of land uses present on farms
 (<i>e.g.</i>, Fig. 2c-d; Smith <i>et al.</i>
 2020b). Point count centers were at least 200 m apart to avoid double
 counting individuals. In total, 106 points were surveyed across the 27
 farms included in this analysis (mean per farm: 3.9 ± 3.3 (SD); range = 1
 – 14). At each point, the observer recorded the number
 of unique individuals per species seen or heard within a 100-m radius
 during a 10 min period. Surveys were conducted between sunrise and 10:45
 AM, only in the absence of heavy rain, by the same skilled observer (OMS)
 to eliminate biases due to observer differences. Additionally, points
 within farms were surveyed in a different order each visit to reduce
 detection biases due to time-of-day effects (Smith <i>et
 al.</i> 2020b). If structures interfered with visual detectability
 of birds, the observer moved within points to see around structures (Šálek
 <i>et al.</i> 2017). Because our study traded geographic
 breadth for within-season temporal replication, we were unable to account
 for detection probability in our analyses. Thus, detection probability was
 assumed to be constant during each survey period across farms. For
 conciseness, we use the term “abundance” in the article to refer to the
 number of individuals detected, but it should be noted that we may have
 missed individuals. Our research was conducted with the approval of
 Washington State University’s Institutional Animal Care and Use Committee
 (ACC protocol ASAF #04760). This manuscript uses point count data (which
 only requires passive observation of birds) and weights the point count
 estimates by foodborne pathogen prevalence estimates derived from
 mist-netting birds reported previously (Smith et al. 2020a, in
 press). <b>2.3 Ecosystem service and disservice
 provider classification</b> We calculated several
 metrics spanning supporting, regulating, provisioning, and cultural
 ecosystem services and disservices provided by birds (Fig. 1A; Table S1).
 We acknowledge that all service and disservice proxies have limitations,
 which we note for each proxy used in Table S1. We used abundance (total
 number of individuals detected during each point count survey) as a metric
 of supporting services. To estimate the risk of
 foodborne pathogen delivery to crops (regulating disservice), we generated
 a foodborne pathogen risk index. To do so, each bird observed was weighted
 by its species’ estimated <i>Campylobacter </i>spp. prevalence
 and crop contacts/survey point from Smith <i>et al.</i> (in
 press). Briefly, Smith <i>et al.</i> (in press) estimated
 <i>Campylobacter </i>spp. prevalence and number of crop
 contacts/survey point for 139 bird species by examining which of 11
 species traits were most predictive of each. They then used the
 best-supported models to predict <i>Campylobacter </i>spp.
 prevalence and number of crop contacts/survey point for understudied bird
 species. Our analyses used the estimated prevalence of
 <i>Campylobacter </i>spp. because it is the most common
 foodborne pathogen found in birds (Smith <i>et al.</i> 2020c,
 a). The crop contact score represents the estimated number of individuals
 of that species in crop fields per survey point. We accounted for both the
 estimated <i>Campylobacter </i>spp. prevalence and estimated
 crop contact rate because the probability that an individual will deposit
 pathogens on crops is the joint probability that it will carry the
 pathogen, enter crop fields, and defecate on produce (Smith <i>et
 al.</i> 2020c, a). We calculated a per point
 estimate of food safety risk as [Equation 1]. [Eqn 1]:
 Per-point foodborne pathogen risk index = Σspecies’(estimated
 <i>Campylobacter</i> spp. prevalence * crop contacts * number
 of individuals detected) To generate a proxy for
 regulating services (pest consumption and pollination) and provisioning
 disservices (full or partial consumption of crops), all bird species
 detected were assigned to a diet guild following the protocol outlined in
 Smith et al. (2020b, 2021) (Data S1). Wilman <i>et al.</i>
 (2014) assigned species to diet guilds when the diet was ≥ 50% that item,
 and we followed this definition to assign species to guilds based on the
 majority items in the diet (if ≤ 50% in any category, the species was
 considered omnivorous). We then assigned insectivorous, carnivorous, and
 nectivorous species as “production benefiters” (species potentially
 provide pest control or pollination services); and frugivorous,
 granivorous, and herbivorous species as “crop damagers” (species
 potentially inflict crop damage/loss through foraging on fruits, grains,
 seeds, or vegetation of crop plants) (Peisley <i>et al.</i>
 2015; Smith <i>et al.</i> 2021). To calculate per survey point
 estimates of production benefit services [Equation 2] and crop damage
 disservices [Equation 3], we calculated the abundance of birds falling
 into each guild and weighted those abundances by the summed proportion of
 the diet in those categories from Elton Traits 1.0 (Wilman <i>et
 al.</i> 2014). [Eqn 2]: Per-point production
 benefitting bird index = Σ[(number of individuals from insectivorous,
 carnivorous, or nectivorous species)*(Total percent of the species’ diet
 composed of invertebrates, endothermic vertebrates, carrion, plus
 nectar)] [Eqn 3]: Per-point crop damaging bird index =
 Σ[(number of individuals from granivorous, herbivorous, and frugivorous
 species)*(Total percent of the species’ diet composed of fruits, seeds,
 plus plants)] We then considered several metrics of
 cultural ecosystem services (Table S1). We first estimated identity and
 iconic value to the US population as a whole using the popularity score of
 “celebrity” birds (Schuetz & Johnston 2019). We created subsets of
 all species to include those that were ranked as “celebrity” (<i>n
 </i>= 37), which are those that have above average national interest
 when considering national-level encounter rates (“popularity”) and have
 low geographic alignment in interest, or interest outside of where they
 are found (“low congruence”). This is because prior work has demonstrated
 that people may only perceive a subset of birds around them (Belaire
 <i>et al.</i> 2015). Therefore, low popularity scores likely
 indicate lack of awareness rather than a disservice <i>per
 se</i>. Thus, we used weighted abundances of “celebrity” species by
 weighting observed abundances by the species’ continuous popularity scores
 [Equation 4]. [Eqn 4]: Per-point identity and iconic
 value index = Σcelebrity species’(number of individuals detected *
 continuous popularity score) We then generated a metric
 of cultural ecosystem service provisioning to the growers whose farms we
 surveyed using data from Smith <i>et al.</i> (2021). Smith
 <i>et al.</i> (2021) distributed a grower questionnaire survey
 to 54 farmers, including the 27 who managed farms included in this study
 alongside more farmers who managed similar farms in California, USA. These
 questionnaire surveys were conducted under the Washington State University
 Office of Research Assurances Institutional Review Board (IRB) that deemed
 it exempt from the need for IRB review (certification number 16610-001).
 Farmers provided open-ended responses to questions asking which species
 were considered beneficial or harmful to the farm and why, as well as
 which species farmers were attempting to attract/repel and why. Based on
 these open-ended data, we generated a metric of cultural ecosystem service
 provisioning to the farmers by first calculating a salience/interest
 metric similar to Schuetz & Johnston (2019) and then conducting a
 sentiment analysis (Lennox <i>et al.</i> 2019). We began by
 quantifying the number of times different species, families, suborders,
 and orders of birds were mentioned to the finest level possible (Data S2
 in this Dryad Dataset, <i>n</i> = 263 data points). Of the 263
 rows we coded, we were able to identify order in 90.4%, family in 79.8%,
 genus in 49.0%, and species in 46.8%. Considering this frequency
 distribution, we used family as the taxonomic unit in our analyses and
 excluded the comments from which family could not be inferred (e.g.,
 “songbirds” [Oscine suborder of Passeriformes] and “birds of prey”
 [representing birds from Accipitriformes, Falconiformes, and Strigiformes
 in our region]). We estimated “interest/salience” of
 those families by extracting the residuals from a linear regression of the
 total number of individuals from each family observed across farms that
 returned the grower questionnaire survey (predictor variable) against the
 number of mentions of that family by farmers (response variable) (Fig.
 S2). We did so to account for how much more, or less, interest each family
 generated than expected for a given encounter rate to mirror our
 identity/iconic value derived from Schuetz & Johnston (2019). We
 re-centered interest/salience values by adding the most negative residual
 value to make all values positive. In this way, our index of
 interest/salience captures the degree to which a given farmer is
 interested in a particular family of birds, accounting for differences in
 how likely they were to encounter a bird of a given family (as measured by
 the total number of birds observed in a family). Next,
 we conducted a sentiment analysis on the same grower questionnaire survey
 data to understand farmers’ attitudes towards the birds they mentioned.
 Two of the authors (OMS and AE) independently scored each mention to range
 from -3 (“very negative” sentiment) to 3 (“very positive” sentiment) from
 a scale that included “very negative,” “moderately negative,” “slightly
 negative,” “slightly positive,” “moderately positive,” and “very
 positive.” (See Data S2, sentiment code book). As is common in attitudinal
 studies (e.g., Thelwall & Buckley (2013); Hutto & Gilbert
 (2014)), we averaged the two sentiment scores (84% of the 263 rows were
 assigned the same score by the two coders) and averaged the sentiment
 across mentions of that family. We then generated an
 index of grower appreciation using species with positive averaged
 sentiment values and an index of grower disapproval using species with
 negative averaged sentiment values. For each, we first summed the
 abundance of individuals from each bird taxonomic family per survey point.
 We then multiplied each family’s abundance by its interest/salience score
 and by its sentiment score. For the service [Equation 5] and disservice
 [Equation 6] indices, we summed the weighted abundances across species
 with positive and negative sentiments, respectively.
 [Eqn 5]: Per-point grower appreciation index = Σfamilies with
 positive averaged sentiments(number of individuals detected * positive
 sentiment * interest/salience) [Eqn 6]: Per-point
 grower disapproval index = Σfamilies with negative averaged sentiments
 (number of individuals detected * negative sentiment *
 interest/salience) Finally, we estimated conservation
 value using the maximum Combined Conservation Score from the North
 American Bird Conservation Initiative State of North America’s Birds
 (2016). We considered ‘conservation need’ as a cultural ecosystem service
 in itself because of the greater value people assign to species in need of
 conservation (Schuetz & Johnston 2019). We used the Combined
 Conservation Score instead of species’ binary listing because only 3% of
 total observations were of species listed as at least sensitive at the
 state level (Data S1), precluding analyses. To calculate the per point
 conservation value index, we weighted abundances of each species that had
 Moderate (9-13) or High (14-20) Combined Conservation Scores by its
 Maximum Combined Conservation Score (CCSmax). We then summed across
 species’ weighted abundances for each point for each survey for the
 conservation value index [Equation 7]. [Eqn 7]:
 Per-point conservation value index (combined conservation score index) =
 Σspecies with moderate to high CCSmax (CCSmax * number of individuals
 detected) We repeated analyses using all species
 weighted by their CCSmax, which yielded similar results. Therefore, we
 refer the reader to Tables S2-S5 and Fig. S3 for results from analyses
 using all species. <b>2.4 Local, farm, and
 landscape complexity</b> <i>2.4.1 Local
 complexity</i> To capture the structural
 complexity of each survey point, we estimated the percent cover of ground
 herbaceous vegetation (0–0.5-m height class), low shrubs/crops (0.5–2 m),
 tall shrubs/crops (2–6 m), and trees (&gt;6 m) within a 10-m radius of
 each point count location’s center (Fig. S4a). We divided the 10-m radius
 circles into four equal quadrants divided along the four cardinal
 directions (Kennedy <i>et al.</i> 2010). During each survey,
 we estimated the percent vegetative cover in each height class for each of
 the four quadrants. We then averaged estimates across the four cardinal
 directions for each height group to estimate percent cover by vertical
 strata. Vegetation surveys were conducted at each bird point-count
 location at each bird survey occasion. Finally, we averaged the ground,
 shrub, tall shrub, and tree cover estimates across the 8 surveys (4 years
 x 2 repetitions per year) for each point, giving us 106 averaged values
 for each of the four vertical strata to estimate the local
 complexity. To obtain a single estimate of local
 vertical vegetation complexity for each survey point location, we
 conducted a principal components analysis using the ‘prcomp’ function in
 the ‘stats’ package in R version 3.6.3 (R Core Team 2020). First, we
 standardized values by calculating a z-score for each. The first two
 principal components (PC) combined accounted for 67.5% of the variation
 (Fig. S4b). Increasing values of PC1 (“local vertical vegetation
 complexity”; 41.0% of the variation) were associated with increased
 fullness of the shrub, tall shrub, and tree layer. Increasing values of
 PC2 (“ground cover”; 26.5% of the variation) were primarily associated
 with increased cover in the ground layer. We used PC1 in subsequent models
 because we were interested in local vertical vegetation
 complexity. <i>2.4.2 Farm-wide High Nature Value
 index</i> We measured farm
 intensification/extensification, or conservation-friendly management
 practices, by modifying the High Nature Value index (Pointereau
 <i>et al.</i> 2010; Smith <i>et al.</i> 2021). In
 brief, the High Nature Value index is a continuous metric from 1 (lowest
 conservation value/most intensive) to 30 (highest conservation value/most
 extensive). Farms are classified using 3 sub-component indices (“diversity
 of crops,” “extensive/intensive practices,” and “landscape elements”),
 which each get equal weight (10 points max). Farms that score highest on
 the “diversity of crops” indicator are typically small with high crop
 diversity and/or integrate livestock. Farms that score high on
 “extensive/intensive practices” typically use few inputs, are certified
 organic, and maintain low stocking densities of livestock. Farms that
 score high on “landscape elements” incorporate seminatural elements within
 their farms, such as hedges or wet grassland. See Smith <i>et
 al.</i> (2021) for full details on our modification. Each farm had
 one High Nature Value score to represent management across
 years. <i>2.4.3 Landscape complexity
 </i> To characterize landscape context, we
 calculated the percent seminatural land cover based on the 2016 National
 Land Cover Database (Dewitz 2019) using a 2.1 km radius buffer from each
 point count location (Fig. 2e-f) using R and FRAGSTATS 4.1 (McGarigal
 & Marks 1994; R Core Team 2020). Seminatural land cover included
 forest (deciduous, evergreen, and mixed), scrubland (dwarf scrub and
 shrub/scrub), herbaceous (grassland/herbaceous, sedge/herbaceous, lichens,
 and moss), and wetland categories (woody and emergent herbaceous
 wetlands). Categories not included in seminatural land cover were water,
 ice/snow, developed, barren, pasture/hay, and cultivated crop classes. We
 used a 2.1 km radius as the biologically relevant landscape scale (Jackson
 & Fahrig 2015) because it was the estimated weighted average home
 range size for birds detected on our farms (Smith <i>et
 al.</i> 2020b). <b><i>2.5 Final
 service and disservice provider index
 derivation </i></b> We first estimated
 ecosystem-service-and-disservice-weighted abundance indices for each point
 per survey per year (which we label “average
 ecosystem-service-and-disservice-weighted abundance indices”). Then, we
 calculated the coefficient of variation for each of the indices per survey
 point across the eight temporal replicates as an estimate of temporal
 variability. The coefficient of variation is calculated by dividing the
 standard deviation by the mean (CV = σ/µ). Stability is the inverse of the
 coefficient of variation (1/CV) (Blüthgen <i>et al.</i> 2016),
 which is the metric we used in temporal stability analyses. 
 Our results suggested that landscape context was important in
 promoting temporal stability. We hypothesized that this was due to a shift
 away from dominant, highly nomadic species (<i>i.e.</i>, an
 identity or selection effect; Fig. 1c). Therefore, we conducted analyses
 examining the relative importance of local, farm, and landscape complexity
 on evenness of the overall bird community at each survey point. We
 averaged the evenness values across the 8 surveys and repeated our
 analyses described above used to examine temporal stability. We then
 examined the influence of evenness as a predictor of temporal stability on
 each of the ecosystem-service-and-disservice-weighted abundance indices
 examined.
description: <strong>Data S1: Species service and disservice
 classifications. </strong>All bird species used in analyses and
 their ecosystem service and disservice provider classifications. Tab 2 has
 a meta-data key. <strong>Data S2: Sentiment
 analysis. </strong>Code book of grower open-ended responses to
 questions asking which species were considered beneficial or harmful to
 the farm and why, as well as which species farmers were attempting to
 attract/repel and why. Tab 1: examples of how statements were coded for
 sentiment. Tab 2: all responses from growers to our open-ended questions,
 "Please indicate which bird species
 you consider are the most beneficial to your farm and why (Please
 specify)," "Please indicate which bird species you consider are
 the most harmful to your farm and why (Please specify)," "Which
 bird species are you trying to attract to your farm and why? (Please
 specify)," and "If you use any repellent techniques, please
 indicate which species or bird groups (such as sparrows, starlings,
 finches, corvids, birds of prey, etc.) you target and why." Tab 3:
 scorer 1 (Alejandra Echeverri) coding. Tab 4: scorer 2 (Olivia Smith)
 coding. Tab 5: assigned sentiment scores and assignment of bird taxa
 (Order, Family, Genus, Species) mentioned. Tab
 6: meta-data key.
 <strong>Data S3: Data used in analyses.</strong> Tab
 1: data used for pairwise correlations between
 average ecosystem-service-and-disservice-weighted abundance indices and
 stability of ecosystem-service-and-disservice-weighted abundance indices
 (data needed to recreate main text Figure 3). Tab 2: data used for average
 ecosystem-service-and-disservice-weighted abundance index analyses. Tab 3:
 data used for stability of ecosystem-service-and-disservice-weighted
 abundance indices. Tab 4: meta-data key.