DOI: 10.5061/dryad.3bk3j9ks5
title: Feral pig (Sus scrofa) disturbance facilitates establishment of resource-acquisitive species in Hawaiian forest understories

description: In this study, we quantify the effects of leaf traits and dispersal
 attributes on species responses to pig soil disturbance at two spatial
 scales – 0.5 m2 patches embedded along 20 m transects within sites –
 across a gradient of pig density in a Hawaiian montane wet forest using
 Bayesian mixed models.  Native and non-native species
 demonstrated divergent responses, with increasing presence and abundance
 of non-native species in the understory as soil disturbance within patches
 and sites increased. Dominant patterns in measured traits tracked the leaf
 economic spectrum (LES), with non-native species tending toward
 resource-acquisitive traits. Species with resource-acquisitive traits,
 regardless of identity, were favored with disturbance and responded
 positively to light availability in disturbed sites. Models showed species
 primarily dispersed by wind were more prevalent in disturbed patches and
 sites than those dispersed by endozoochory, while seed mass had no effect.
description: From Methods outlined in the publication:
 Data Collection The impacts of pig activity
 and disturbance vary by scale, and effects on community composition are
 mediated in part by differences in establishment success due to
 disturbance within habitat patches. To capture these effects, we used a
 hierarchical design incorporating pig density at the zone level, our
 broadest spatial scale, and pig soil disturbance (i.e. physically
 overturned soil as a result of pig activity) at two consecutively smaller
 scales: (i) within sites nested within zones (hereafter, Dsite), and (ii)
 within habitat patches nested within sites (hereafter, Dpatch). Four zones
 with 1-km radii, roughly equal to conservative estimates of pig home-range
 size in Hawaiian forests, were established within the study site (Fig. 1A)
 (Diong, 1982). To capture patterns in understory composition and soil
 disturbance at the site- and patch-scale, we surveyed 30 sites within each
 zone using a stratified-random design (1 additional site was accidentally
 surveyed in Zone A, leading to a total of 121 sites). Sites with &lt;
 50% canopy coverage were excluded to target the understory under forested
 conditions. At each site, we established a 20-m transect oriented along a
 random azimuth from 0 – 360 degrees to capture site-scale community
 composition and disturbance. We then uniformly distributed 0.5 m x 0.5 m
 quadrats (hereafter, patches) at 3-m intervals along the transect starting
 from the 2-meter mark, for a total of 6 patches per site representing
 individual habitat patches where plant species establishment might occur.
 14 patches were discarded from the analysis due to trees or other
 obstructions preventing quadrat establishment at locations along the
 transect, resulting in a total of 712 patch-scale measurements. This
 nested, hierarchical approach allowed us to untangle the effects of pig
 disturbance across multiple spatial scales and to test the underlying
 impacts of disturbance on vegetation community dynamics.
 Disturbance and Vegetation Characterization
 To quantify pig density within zones, we conducted a camera trap
 survey during Summer 2019 designed to estimate density following Rowcliffe
 et al. (2014). We randomly generated 20 potential trapping locations
 within each zone and deployed cameras at a random subset of 4-6 locations
 over three 10–14-day trapping periods. The absence of pigs at the fenced
 zone D was confirmed by camera traps at 7 locations over the final two
 trapping periods. Camera traps were programmed to collect a set of three
 pictures for every trigger with a 1-second delay between triggers. Cameras
 were attached to trees within a 20-m radius from each sampling location in
 an area where pig activity was evident from signs of trails, rooting
 activity, and scat to avoid underestimation of population size (Rowcliffe
 et al., 2008). Cameras were attached ~50 cm above the base of a tree and
 were oriented to have a capture distance of 5 m and to capture images
 within an angle of ~45 degrees. To calculate pig density estimates, we fit
 a random encounter model (REM), which assumes the trapping rate (number of
 photographs taken per unit time) provides quantitative information about
 the density of a species (Rowcliffe et al., 2008, 2014). This model
 estimates population density from random encounters with camera traps
 while parameterizing covariates that affect trapping rate. We used a
 bootstrapping approach to integrate sampling (location-specific
 variability) and non-sampling (i.e., model dependencies) error in our
 final density estimates (see SM). To capture site- and
 patch-specific patterns of pig disturbance, we conservatively defined
 disturbed area as that with visible signs of mechanical disturbance to
 surface soil structure from digging, wallowing, and trampling activity.
 Other ungulates that could be associated with these disturbances were not
 detected in the camera trap survey and are presumed to be absent. Pig
 trails, defined here as patches and pathways with clear evidence of pig
 use but without overturned soil, were excluded. To calculate disturbance
 extent within patches (Dpatch), we estimated the percent of disturbed area
 within each patch and assigned estimates to cover categories of 0%, 0–10%,
 10–25%, 25–50%, 50–75%, and 75–100%. Estimates are presented as the median
 value for each category to reduce measurement bias. To capture disturbance
 at the site scale (Dsite), we independently measured disturbance along
 each 20-m transect at each site by summing the length (within 0.05 m) of
 transect segments intersecting disturbed soil as defined above. As nurse
 logs are important for native woody species establishment in Hawaiian wet
 forests (Rehm et al., 2021; Santiago, 2000), we quantified nurse log area
 using the same methods as disturbance (NLpatch in patches, NLsite in
 sites). We estimated light availability using
 hemispherical photographs taken with a 180° fisheye camera lens at three
 points along each transect, each located between quadrat pairs. Using the
 Gap Light Analyzer software (Frazer et al., 1999), we calculated canopy
 openness for each photograph, which we used as a metric for light
 availability. For patch-scale measurement, we assigned light availability
 values (hereafter, Lightpatch) to pairs of patches located most closely to
 where each photograph was taken, and averaged all three measurements for
 site-scale estimates (hereafter, Lightsite). Finally,
 we assessed the composition of understory vascular plants at both the
 patch- and site-scale. At the patch scale, we identified individuals
 &lt; 2 m tall and rooted within each quadrat to assess species
 occurrence (i.e. presence/absence) within habitat patches. At the
 site-scale, we evaluated patterns of community composition by estimating
 understory species cover using a point-intercept approach. Vascular plant
 species intercepted between 0–2 m in height were recorded at intervals of
 0.5 m for a total of 41 points per site. Individuals were identified to
 the species level except for Peperomia, Coprosma, and Adenophorus, which
 were identified to the genus level because characteristics for
 species-level identification were typically missing. Flowering plant
 taxonomy was based on Wagner (1991) with updated nomenclature according to
 Smith and Brown (2018), and pteridophyte taxonomy was based on Palmer
 (2002). Trait data collection To
 understand how plant responses to pig activity and disturbance are
 influenced by differences in plant ecological function, we measured
 functional traits associated with leaf construction and dispersal. Leaf
 investment strategies can be identified by covariance in leaf traits
 linked to maximum photosynthetic rate and growth, such as specific leaf
 area (SLA), leaf carbon to nitrogen ratio (C:N), leaf thickness, and leaf
 dry matter content (LDMC) (Díaz et al., 2016; Reich, 2014; Wright et al.,
 2004). To measure these characteristics, we sampled fully expanded leaves
 (≥5 per individual) from 6 haphazardly selected individuals of each
 species where possible, distributed across at least 3 pig-activity zones.
 Due to the presence of Sphagnum in Kohala (Schomaker, 2017), 3 individuals
 of each species were selected to be rooted in Sphagnum where possible to
 account for differences in N concentrations in Sphagnum and non-Sphagnum
 areas. All leaves were collected, weighed, placed in moist plastic bags,
 and leaf area and thickness were measured within 48 hours of collection.
 Leaf area was measured using a LI-COR area meter. Leaves were weighed for
 fresh mass, dried at 60°C for at least 48 hours or until fully dry,
 weighed again for dry mass, ground using a Wiley mill, and sent to the
 University of New Mexico Center for Stable Isotopes (Albuquerque, NM) for
 %C and %N content using accelerator mass spectrometry. SLA was calculated
 by dividing leaf area by dry mass, and LDMC was calculated by dividing dry
 mass by fresh mass. Lycophytes were removed from the analysis due to
 incongruencies in comparing lycophyte microphylls to megaphylls of other
 species. Average seed mass for angiosperms was extracted from Yoshinaga et
 al. (unpub) and published sources from the TRY Plant Traits Database
 (Kattge et al., 2020) (SM). For species for which data were not available,
 we averaged available seed mass values among the most closely related
 species in the genus. Analyses involving seed mass and dispersal were
 conducted with pteridophytes removed.
description: # Feral pig (Sus scrofa) disturbance facilitates establishment of
 resource-acquisitive species in Hawaiian forest understories
 Datasets Included: 1\)
 abundance ('hits' from point-intercept estimates) of understory
 plant species at sites (named "Site-scale.csv"): * Site: Site
 where data were collected (treat as a factor) * Dsite: site-scale
 disturbance given by summed lengths of transect segments intersecting
 disturbance at each site (in meters) * NLsite: nurse log given by summed
 lengths of transect segments intersecting nurse logs (in meters) *
 Pig_Density: population density estimates given by REM (individuals per
 km2) * Lightsite: canopy openness from 3 hemispheric photographs averaged
 across the site (%) The species-level abundances are in the remaining
 columns, given as 'hits' from point-intercept approach. 2\)
 presence/absence of plant species rooted in patches (named
 "Patch-scale.csv"): * Site: Site where data were collected
 (treat as a factor) * Patch: Patch where observations were taken, treat as
 factor * Zone: Zone where sites were located, treat as factor * NLpatch:
 nurse log area visual % cover estimates in patches, given as median values
 of cover classes (in %) * Dpatch: patch-scale disturbance of visual %
 cover estimates in patches, given as median values of cover classes (in %)
 * Dsite: site-scale disturbance given by summed lengths of transect
 segments intersecting disturbance at each site (in meters) * Lightpatch:
 canopy openness from hemispherical photograph taken at the site, given as
 median values for cover classes (in %) * Pig_Density: population density
 estimates given by REM (individuals per km2) The species-level
 presence/absence in patches are given in the remaining columns 3\) mean
 trait values of plants encountered in the survey (names
 "Trait_data.csv"). Species in rows, mean trait estimates in
 columns. * sla: Specific leaf area measured as projected leaf area divided
 by dry mass (mm2/mg) * ldmc: Leaf dry matter content measured as dry
 mass/fresh mass (mg/mg) * thickness: leaf thickness (cm) * CN: Carbon to
 nitrogen ratios by %, estimated given by AMS * seed_mass: Seed mass
 estimates (in mg) * dispersal: Wind or animal dispersed (categorical) *
 Identity: native or non-native (categorical) 4\) code for running Bayesian
 phylogenetic generalized linear mixed models (PGLMM's) ##
 Sharing/Access information Contact Michael Peyton
 ([mspeyton@wisc.edu](mailto:mspeyton@wisc.e)) for more information, or for
 camera trap data used to estimate pig population density ## Code/Software
 Please see manuscript for model specifications and required packages, code
 runs PGLMM's in r.