10.5061/DRYAD.WM37PVMPN
Smith, Olivia
0000-0002-9404-0243
Michigan State University
Kennedy, Christina M.
The Nature Conservancy
Echeverri, Alejandra
Stanford University
Karp, Daniel
University of California, Davis
Latimer, Christopher
The Nature Conservancy
Taylor, Joseph
University of Georgia
Wilson-Rankin, Erin
University of California, Riverside
Owen, Jeb
Washington State University
Snyder, William
University of Georgia
Complex landscapes stabilize farm bird communities and their expected
ecosystem services
Dryad
dataset
2021
Campylobacter
evenness
multi-functional landscapes
Sustainable agriculture
wild birds
FOS: Agricultural sciences
landscape
ecosystem services
United States Department of Agriculture
https://ror.org/01na82s61
2015-51300-24155
United States Department of Agriculture
https://ror.org/01na82s61
2016-04538
Washington State University
https://ror.org/05dk0ce17
Carl H. Elling Endowment
2021-12-27T00:00:00Z
2021-12-27T00:00:00Z
en
243840 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
2.1 Study system Across four years (2016–2019), we surveyed bird
communities on a total of 27 farms in Oregon (n = 15) and Washington (n =
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
(e.g., Fig. 2; Fig. S1; range: 2.19% to 95.7% seminatural). 2.2 Bird point
count surveys 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 (e.g., Fig.
2c-d; Smith et al. 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 et al. 2020b). If structures
interfered with visual detectability of birds, the observer moved within
points to see around structures (Šálek et al. 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). 2.3 Ecosystem service and disservice provider classification 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 Campylobacter spp. prevalence and crop
contacts/survey point from Smith et al. (in press). Briefly, Smith et al.
(in press) estimated Campylobacter 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 Campylobacter spp. prevalence and number
of crop contacts/survey point for understudied bird species. Our analyses
used the estimated prevalence of Campylobacter spp. because it is the most
common foodborne pathogen found in birds (Smith et al. 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 Campylobacter 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 et al. 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 Campylobacter 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 et al. (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 et al. 2015; Smith et al. 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
et al. 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” (n = 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 et al. 2015). Therefore, low popularity scores likely indicate
lack of awareness rather than a disservice per se. 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 et al. (2021). Smith et al. (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 et al. 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, n = 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. 2.4 Local, farm, and landscape
complexity 2.4.1 Local complexity 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 (>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 et al. 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. 2.4.2 Farm-wide High Nature Value index We measured farm
intensification/extensification, or conservation-friendly management
practices, by modifying the High Nature Value index (Pointereau et al.
2010; Smith et al. 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 et al. (2021) for full details on our modification.
Each farm had one High Nature Value score to represent management across
years. 2.4.3 Landscape complexity 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 et al. 2020b). 2.5 Final
service and disservice provider index derivation 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 et al. 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.e., 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.
Data S1: Species service and disservice classifications. All bird species
used in analyses and their ecosystem service and disservice provider
classifications. Tab 2 has a meta-data key. Data S2: Sentiment analysis.
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. Data S3:
Data used in analyses. 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.