Ground-dwelling arthropods of pinyon-juniper woodlands: Arthropod community patterns are driven by climate and overall plant productivity, not host tree species

Derek Andrew Uhey; Hannah Lee Riskas; Aaron Dennis Smith; Richard William Hofstetter

doi:10.1371/journal.pone.0238219

. 2020 Aug 26;15(8):e0238219. doi: 10.1371/journal.pone.0238219

Ground-dwelling arthropods of pinyon-juniper woodlands: Arthropod community patterns are driven by climate and overall plant productivity, not host tree species

Derek Andrew Uhey ^1,^*, Hannah Lee Riskas ¹, Aaron Dennis Smith ², Richard William Hofstetter ¹

Editor: Frank H Koch³

PMCID: PMC7449382 PMID: 32845929

Abstract

Pinyon-juniper (PJ) woodlands have drastically changed over the last century with juniper encroaching into adjacent habitats and pinyon experiencing large-scale mortality events from drought. Changes in climate and forest composition may pose challenges for animal communities found in PJ woodlands, especially if animals specialize on tree species sensitive to drought. Here we test habitat specialization of ground-dwelling arthropod (GDA) communities underneath pinyon and juniper trees. We also investigate the role of climate and productivity gradients in structuring GDAs within PJ woodlands using two elevational gradients. We sampled 12,365 individuals comprising 115 taxa over two years. We found no evidence that GDAs differ under pinyon or juniper trees, save for a single species of beetle which preferred junipers. Climate and productivity, however, were strongly associated with GDA communities and appeared to drive differences between sites. Precipitation was strongly associated with arthropod richness, while differences in GDA composition were associated with environmental variables (precipitation, temperature, vapor pressure, and normalized difference vegetation index). These relationships varied among different arthropod taxa (e.g. ants and beetles) and community metrics (e.g. richness, abundance, and composition), with individual taxa also responding differently. Overall, our results suggest that GDAs are not dependent on tree type, but are strongly linked to primary productivity and climate, especially precipitation in PJ woodlands. This implies GDAs in PJ woodlands are more susceptible to changes in climate, especially at lower elevations where it is hot and dry, than changes in dominant vegetation. We discuss management implications and compare our findings to GDA relationships with vegetation in other systems.

Introduction

Pinyon pine (Pinus edulis) and juniper (Juniperus spp.) co-dominate woodlands that cover 19 million hectares in the southwestern United States [1]. Composition and distribution of pinyon-juniper (PJ) woodlands have drastically changed over the last century. Juniper has increased its range 10-fold, ‘encroaching’ into grassland/sagebrush habitats [2] and pinyon has experienced massive mortality from drought and subsequent bark beetle outbreaks [3, 4]. Droughts and temperature are expected to increase across the range of PJ woodlands, further altering this habitat [5, 6] and posing challenges for animals and conservation.

Animal species often specialize on host resources such as a specific host tree species or genus (i.e. the tree specialization hypothesis, a specific version of niche theory, [7]). The two dominant tree species in PJ woodlands offer distinct resources and habitat structures for animals. Pinyon and juniper differ drastically in composition of defensive secondary compounds [8] and wood properties [9, 10], resulting in distinct litter and soil under pinyon and juniper canopies [11, 12]. Additionally, junipers tend to provide more shelter through their shrub-like growth than pinyons resulting in higher moisture and cooler soil temperatures [13]. Despite these differences, comparisons between animal communities in pinyon and juniper are few. Bird assemblages differ between pinyon and juniper [14, 15] and other animal taxa may also have similar tree species preferences. If so, the shifting composition of PJ woodlands to primarily juniper may change animal communities and biodiversity within this ecotype.

Arthropods often closely associate with vegetation types [16] and can vary between tree species and even tree genotypes [17]. Most tests of tree species-effects on arthropods come from foliar-herbivore communities which live and feed directly on the living tree (e.g. [18, 19]). But unlike foliar communities, the majority of ground-dwelling arthropod (GDA) communities do not interact directly with living trees, instead harvesting nutrients from tree litter and microbes decomposing the litter, or preying on other arthropods, while using tree architecture for habitat [20]. GDA diversity is linked to litter composition mainly through diet preferences [21, 22] and canopy architecture via habitat preferences [23, 24].

The large diversity and cryptic habitats of GDAs have generally limited our understanding of their communities [25], and only a handful of studies have described GDA communities in PJ woodlands [26–28]. This lack of information is disconcerting because GDAs mediate above- and below- ground processes such as decomposition, soil aeration, and movement of soil nutrients [29] affecting tree regeneration and carbon storage [30]. Through their contribution to nutrient cycling and plant diversity, GDAs are critical to forest health [31]. Understanding and promoting GDAs in PJ woodlands may improve preservation and management of these ecosystems.

Climate can also be a strong driver of GDA assemblages [32], as temperature and precipitation regulate and limit arthropod physiology [33]. Arthropods are thermophilic, typically increasing in richness and abundance in warmer climates [34]. However, in the arid climates of PJ woodlands, the risk of desiccation from low precipitation and high temperatures (i.e. low vapor pressure) is often the limiting factor for arthropods [34]. Temperature and precipitation vary markedly across PJ ecosystems which are distributed across a large climate zone, often spanning over 1000m in elevation locally. Understanding how GDAs are distributed across these climatic gradients can inform what future GDA communities will look like after continued warming and drying.

Our study compares GDA communities in paired pinyon and juniper trees along two elevational gradients in northern Arizona over multiple seasons, encompassing large climate and productivity differences within the same biogeographical area. We test whether GDA communities differ between pinyon and juniper, and investigate the role of climate, productivity, and season in structuring the aggregate PJ GDA communities. We analyze species-specific and whole GDA community patterns with corroborative statistical methods, giving a detailed examination of GDA responses to habitat and climate. In the face of biodiversity loss from climate change, documenting these patterns establishes a baseline for future comparisons and gives insight into the functioning of PJ ecosystems.

Materials and methods

Study sites

We examined PJ woodlands along two elevational gradients spanning ~300m on opposing sides of the San Francisco Peaks in northern Arizona (Fig 1 and S1 Table). We sampled eight sites, four per gradient, encompassing the highest, lowest, and mid-elevational ranges of pinyon (Pinus edulis) and single-seed juniper (Juniperus monosperma) co-occurrence. Other plant species within these PJ woodlands include Gambel oak (Quercus gamebelii), sagebrush (Artemisia sp.), rabbitbrush (Chrysothamnus sp.), bitterbrush (Purshia sp.), snakeweed (Gutierrezia sarothrae), many grasses and forbs (e.g., Bouteloua gracilis, Draba sp., Erigeron divergens, Microsteris gracilis, Penstemon sp., Poa fendleriana), and cacti (Escobaria sp., Opuntia sp.). The northeast gradient is located within the rain-shadow effect created by the San Francisco Peaks (highest point at 3851m elevation), and is therefore much drier than the northwestern gradient. We refer to these two gradients hereafter as the dry and wet gradients.

The relative proportions of pinyons and junipers changes across elevation [5, 6]; to describe tree composition differences among our sites we counted trees (greater than breast height) in five parallel 200m² plots at each site (Fig 2 and S1 Table). We found compositional trends typical for PJ woodlands along elevational gradients [3] with trees becoming more abundant with increasing elevations, junipers largely dominating low- to mid-elevations (i.e. 1911-2085m), and pinyons dominating the two highest elevation sites (2104m and 2200m). These two highest sites also contained small numbers of ponderosa pine (Pinus ponderosae Dougl. Ex. Laws.). While the proportion and number of trees changes among our sites, all these woodlands are characterized by generous spacing between trees with mature pinyons and junipers growing separately with wide sprawling canopies (Fig 2). These canopies commonly reach 5m in diameter, creating large areas of influence on the ground through shade and accumulated litter distinct to either pinyon or juniper. We sampled from these areas using the same three pairs of pinyon and juniper trees across five 7-day sampling periods spanning two years at each site. We purposefully chose large-canopied pinyons or junipers (>3m diameter canopy) that were not growing close (>5m) to the opposing species to avoid confounding effects. To ensure samples were representative of their respective tree species, we counted all trees within a 20m² diameter of sample locations (S1 Table), which showed that nearest tree neighbors were always the same tree species (or no other trees occurred), and within a 10m² diameter no opposing species occurred. We conducted our research on National Forest Service and private lands, with permission from Babbitt Ranches (https://www.babbittranches.com/) and project approval from the Southwestern Experimental Garden Array (https://sega.nau.edu/). Our study did not involve endangered or protected species.

Fig 2 — Tree composition of elevational sites averaged from five 200m² plots and photos of tree species and aerial (drone) view of “Blue Chute” study site (1945m).

Seasonality and weather

We sampled arthropod communities for one-week intervals five times, encompassing different seasons with vastly different weather patterns. Two sample periods occurred during dry summers (8–15 June 2018 and 27 June-4 July 2019) and one during a dry spring (7–14 April 2019); no precipitation fell within these sampling periods or during the month prior to sampling. Two other sample periods occurred during an above average monsoon season with large precipitation events during sampling (30 July -6 August 2018 and 2–9 Sept. 2018). We refer to the former three dates as dry season and the latter two as monsoon season.

Climate and plant productivity measures

We calculated climate measurements for each site using 30-year averages of annual total precipitation, average temperature, and average vapor pressure (S1 Table) with data extracted from PRISM Climate Group, Oregon State University (PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created 10 Nov. 2019). These environmental factors contribute to understanding desiccation stress, a key limit on arthropods in arid climates [33]. To estimate productivity, which is associated with resource availability, we used normalized difference vegetation index (NDVI, [35]) calculated from satellite imagery at the 250m resolution for each site and sampling period. NDVI is widely used, creating an index that ranges from zero to one with higher values indicating higher productivity [35). PRISM data were downloaded at a spatial resolution of 800 meters and extracted using R.3.2.3 and the package raster version 2.5–2 based on observed latitude and longitude of each site.

Arthropod sampling

We used pitfall trapping to sample GDAs, with a trap dug well under the canopy of each tree as close to the trunk as possible. Pitfalls are good for sampling different GDA communities with equal intensity among treatments [36]. Traps were dug to ground level and opened during sample periods, with capped traps keeping locations constant in-between sampling. Pitfall traps consisted of a long borosilicate glass tube measuring 32 mm diameter and 200 mm length filled with 100ml of propylene glycol fitted within PVC sleeves with a rain cover ~3-4cm above the ground [27]. Some samples (12 of 240) were lost to flooding. We sorted pit trap samples and deposited voucher specimens at the Northern Arizona University Forest Entomology Collection, yielding richness and abundance measures for each trap. Taxa were identified and assigned functional groups with the assistance of taxonomic experts. We excluded adult Diptera and Lepidoptera as by-catch (i.e. not GDA). We digitally cataloged all reference species in Symbiota Collection of Arthropod Network (SCAN, http://scan-bugs.org) and http://bugguide.net (url numbers in S4 Table) online data portals.

Data analysis

To investigate GDA composition in relation to categorical (i.e. tree species, date, and site) and continuous (i.e. elevation, climate, and NDVI) variables, we conducted complementary analyses on the GDA community as a whole, and separately on two dominant taxa groups (ants and beetles which together constituted 87% of individuals collected) using R.3.6.2 (R script: S1 File).

Environmental variables

To understand how climate and productivity changed across our gradients, we used simple linear regressions testing elevation against climate (temperature, precipitation, and vapor pressure) and NDVI variables. Temperature and vapor pressure were strongly correlated with elevation, while the variation between gradients caused precipitation to not correlate with elevation when all sites were considered together (Fig 3 and Table 1). NDVI was generally correlated with elevation, except during the dry April 2019 sample period (Fig 4 and Table 1). To analyze the effect of these environmental variables on GDA, we chose to test elevation (closely associated with temperature and vapor pressure), precipitation, and NDVI as separate variables, scaling them prior to analysis. These variables are not entirely independent, and we acknowledge it is difficult to fully separate their effects. However, we can infer their relative effects on GDAs by differences in patterns across sites.

Fig 3 — Averages of climate variables along elevational gradients (shapes). (A) Average temperature (red) and vapor pressure (yellow) decrease with elevation. (B) Precipitation (blue) is lower on the east gradient and increasing pattern with elevation is only evident when gradients are separated.

Table 1. Elevation and climate variable correlations.

		r	adjust-R²	p-value	F_1,6
Elevation	Av. Temperature	-0.97	0.93	<0.0001	88.98
	Av. Vapor Pressure	-0.96	0.91	0.0002	67.65
	Av. Precipitation	0.38	0.01	0.352	1.02
	NDVI June 2018	0.87	0.71	0.005	18.12
	NDVI August 2018	0.95	0.9	0.0002	60.38
	NDVI Sept. 2018	0.93	0.84	0.0008	39.02
	NDVI April 2019	0.25	0.01	0.554	0.39
	NDVI July 2019	0.94	0.87	0.0005	46.35

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Correlations of environmental variables and productivity (NDVI: normalized difference vegetation index) with elevation across eight sites.

Fig 4 — Normalized difference vegetation index (NDVI) measurements from five different dates (color) along two elevational gradients (shape). NDVI is higher during monsoon sampling periods (August and September 2018) than dry sampling periods. For all dates except April 2019, NDVI has a strong positive correlation with elevation.

GDA abundance and richness

To test whether richness and abundance of GDAs changed between pinyon and juniper trees, and along climatic gradients, we ran generalized linear mixed effect models (GLMM). GLMMs offer a flexible approach for testing multiple categorical and continuous variables, and are not subject to assumptions of sphericity. We examined abundance and species richness of the entire GDA community, two major insect taxa (ants and beetles), and remaining GDA groups (hereafter referred to as ‘others’) as our response variables. Because our response variables were count data, we ran each model with Poisson and negative binomial distributions. Using the Akaike information criterion (AIC, corroborated with AICc), we determined greater fit for the negative binomial distribution models and Poisson for GDA response variables (S2 Table). For all models, we treated scaled environmental variables (elevation, precipitation, and NDVI) and tree species as fixed effects while date and site were treated as crossed random effects. We used Wald χ² tests to exclude non-significant predictor variables and AIC score comparisons among models with all possible variable combinations to select final models (S2 Table). We checked model performance graphically via diagnostic plots [37]. We performed GLMMs with R packages lme4 [38] and arm [39].

GDA composition

We assessed individual GDAs and whole community composition responses to tree species, climate, and NDVI variables across sites and dates with the function manyglm from R package mvabund [40]. In this function, each taxon is fitted with individual generalized linear models (GLM) providing both taxa-level and global estimates of significance while controlling for multiple testing. Variance in abundance was greater than the mean for most taxa, therefore abundance of taxa j in sample i was modeled as negative binomial. Models included tree species, site, date, elevation, precipitation, and NDVI as predictor variables. We report global (entire GDA community) significance for each explanatory variable, and specific relationships of continuous variables with taxa with more than five observations. For categorical variables, we used indicator analyses for taxa-level results. We conducted indicator species analysis using R packages labdsv [41] and indicspecies [42] packages.

Ants and beetles

To determine whether patterns differed between our two most dominant taxa, we analyzed ants and beetles separately through comparison of dissimilarity matrices from averaged (across site/date/tree) data. Beetle data required a square root transformation, following which we constructed similarity matrices with Bray-Curtis similarity coefficients. Ants showed extreme variation, with many high-abundance outliers likely caused by ant nest proximity. We therefore analyzed ant data on an incidence basis with Jaccard similarity coefficients for similarity matrices. We visualized results via multi-dimensional scaling (MDS) plots. We used goodness of fit, Shepard diagrams, and stress to test satisfactory fit of ordination (S2 File). To test if ants or beetles differed by date, site, or tree species, we used permutational analysis of variance (PERMANOVA, permutations = 9999) including all factors with significant differences followed by pairwise comparisons. We checked PERMANOVAs and pairwise results against analysis of similarity (ANOSIM) and multiple response permutation procedure (MRPP) (S3 Table); all analyses agreed, we report PERMANOVA results. We fitted environmental variables to ordinations and permutations to test significance (permutations = 999). All analyses were done using R packages vegan [43] and ecodist [44].

Results

Over two years of sampling, 12,365 GDAs were captured with 115 taxa identified (S3 Table). Species accumulation curves show an asymptote for sampling both juniper and pinyon trees at each site (S1 Fig). Abundance was dominated by ants (76.0% of individuals) followed by beetles (11.0%), spiders (3.9%), a morphospecies of slender springtail (3.8%), orthopterans (1.2%), mites (1.2%), non-ant hymenopterans (1.1%), hemipterans (0.8%) and others (2.0%) (S4 Table). Mites, parasitic wasps, and pseudo-scorpions (<2% of individuals) were unable to be morphotyped and were identified to order only, with other arachnids (e.g. spiders) identified to family (<4% of specimens). All other arthropods (96.0% of specimens) were identified to species (81.2% of specimens) or morphospecies (14.8% of specimens). The most diverse GDA were beetles (52/115 taxa) followed by ants (21/115). We use the terms richness and diversity for referring to these mixed taxonomic levels. Functionally, GDA were mostly omnivores (76.0% of specimens, all ants), followed by detritivores (13.0% of specimens, mostly beetles in the family Tenebrionidae), predators (6.3%, mostly arachnids), fungivores (1.8%, mostly beetles in the family Nitidulidae), and herbivores (1.2%, mostly beetles, S4 Table).

Insect community in pinyon vs juniper

Contrary to our hypothesis, we detected no significant differences among any GDA community metric or taxa analyses between pinyon and juniper on any GDA community metric or taxa in any analysis, except a single morpho-type of Leiodes (family Leiodidae) beetle which was three times more abundant in and a significant indicator (IV = 24.3, p = 0.023) of junipers. Besides 33 rare taxa (<6 individuals collected), pinyon and juniper shared all taxa at nearly equal abundances.

Gradient and season

Multivariate GLM showed GDA communities differed significantly among sites (Deviance [Dev] = 1952, Pr (>Dev) = 0.001) and dates (Deviance [Dev] = 1634, Pr (>Dev) = 0.001). Twelve taxa were indicative of one site and 23 taxa were indicative of one date (S5 Table). For dates, pairwise differences in ants and beetles were largely between dry and monsoon dates with most dates within-seasons being similar. For sites, pairwise differences were largely between east and west gradient sites with most within-gradient sites similar (S6 Table).

Climate and productivity

GDA richness was positively correlated with precipitation (Fig 5 and Table 2). GDA compositional differences were linked to both precipitation (Deviance [Dev] = 405.9, p = 0.001) and elevation (Deviance [Dev] = 156.8, p = 0.001) and marginally to NDVI (Deviance [Dev] = 93, p = 0.056). Nine taxa increased significantly with elevation, two taxa (Forelius pruinosus and Monomorium cyaneum) decreased with elevation, and one taxa (Lycosidae) increased with precipitation (S7 Table).

Table 2. GLMM models for GDA richness and abundance.

Response variable	Predictor variable(s)	Estimate	SE	Z	P
GDA Abundance	No significant model
GDA Richness	Precipitation	0.18	0.04	4.45	<0.001
Ant Abundance	No significant model
Ant Richness	Precipitation	0.16	0.04	3.69	<0.001
Ant Richness	Elevation	-0.15	0.07	-2.067	0.038
Beetle Abundance	Precipitation	0.4	0.12	3.18	<0.001
Beetle Richness	Precipitation	0.2	0.07	3.07	0.002
Other GDAs Abundance	Precipitation	0.177	0.049	3.62	<0.001
Other GDAs Richness	Precipitation	0.344	0.126	2.74	0.006

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Final models with significant predictor variables. Comparisons included tree species, elevation, precipitation, and NDVI with site and date as crossed random effects (S2 Table). Models were run on richness and abundance of A) all arthropods, B) only ants, C) only beetles, and D) other GDAs. Intercept estimates, standard errors (SE), Z-values (Z), and p-values (P) are given for each variable.

Ant and beetle differences

Ants and beetles differed in relationships with climate variables and NDVI. Ant richness increased with elevation and precipitation, but ant abundance showed no relationships with these variables (Table 2). Ordinations of ant composition showed significant correlations with NDVI ((R² = 0.163, p = 0.004), Fig 6 and S8 Table). Beetle abundance and beetle richness increased with precipitation (Table 2), and ordinations of beetle composition showed significant correlations with elevation (R² = 0.211, p = 0.002), precipitation (R² = 0.191, p = 0.003) and NDVI (R² = 0.085, p = 0.034) (Fig 6 and S8 Table). Other GDA richness and abundance increased with precipitation (Table 2).

Fig 6 — Significant correlations of environmental variables are mapped onto ordinations. Sites show significant group differences, with most pairwise differences (S6 Table) among gradients (color). Tree species (shape) shows no significant differences (PERMANOVA, F = 1.42, p = 0.196).

Discussion

Tree species effect

Pinyon-juniper (PJ) woodlands provide habitat for a wide range of arthropods in one of the driest and hottest forest types in western North America. Pinyon and juniper trees may have unique roles in shaping and maintaining insect biodiversity. Here, we tested if ground dwelling arthropods (GDA) specialize in either pinyon or juniper niches, assuming differences between litter composition or habitat structure would create differences in local GDA communities. Despite considerable differences in tree chemistry [8], wood properties [9, 10], litter and soil characteristics [11, 12] and canopy architecture [13] of pinyon and juniper, we found only one GDA that showed affinity to one of these tree species. Our results suggest that most GDAs in PJ woodlands utilize both tree species, but our patterns should be cautiously extrapolated to PJ woodlands in other regions that contain different juniper species. A limitation of ours and most studies examining the full range of GDA diversity is taxonomic clarity for certain groups. We identified 81 percent of specimens to species but the rest were assigned to either morphospecies or higher taxa levels, which may obscure patterns for these latter groups.

The use of tree microhabitat by GDAs may be either specific or random. Our study suggests the latter for PJ woodlands, yet, there are many instances where GDAs are sensitive to changes in vegetation structure [45–49]. Canopy-closure creates microhabitats which host different GDA communities than open areas [50], including in PJ woodlands [26–28]. So, while most GDAs may not be affected by tree composition, some may be confined to canopied habitat with open habitats acting as barriers [46]. How isolated a tree is may affect what GDAs occur under it. These effects may be exacerbated in species such as ants, which typically stay close to nests [51]. Our trees varied in their level of isolation, as the densities of trees varied across sites (Fig 2). PJ woodlands typically have wide spacing between trees and our results may not be applicable to more mesic forest types with unbroken canopies.

Foliar and wood-infesting arthropod communities commonly differ among tree species [7, 17] but reasons exist to doubt whether tree species create unique GDA communities. Increasing tree diversity does not seem to increase GDA diversity [52, 53]. Many GDAs are generalists; three-quarters of specimens in our study were omnivorous ants and the remaining were largely detritivores or predators. Detritivores pull nutrients from litter and microbial turfs decomposing litter [54]. While litter quality does differ under tree species, many plant defensive compounds break down over time, meaning less specialization is required to ingest litter or microbes consuming litter, versus plant tissues [55]. We found many generalist detritivores such as darkling beetles, which may not be heavily influenced by the source of litter (e.g. [52]. With GDA prey not different among trees, there may be little pressure for predators to differ (e.g. [53]).

Only a small minority of specialist taxa are likely tied to tree species. The majority of GDA in our study were found under both tree species in surprisingly equal abundance, except one beetle (Leiodes sp.) indicative for junipers that likely specializes in subterranean fungi [56]. Pinyon and juniper host different mycorrhizal mutualists [57] potentially explaining the association. Specialized relationships like these, and therefore community differences among tree species, are more likely to arise with herbivorous or fungivorous relationships. Future studies should explore foliar and wood-infesting arthropod communities of pinyon and juniper, which are more likely to have specialist species driving trends.

Climate and productivity effects

Our elevational gradient, which only encompassed PJ woodlands, is short compared to most elevational studies, yet we still found strong climate-driven trends. Typically, temperature is posited to drive arthropod community dynamics along elevational gradients [33, 58]. Increases in temperature (and thus decreases in elevation) are associated with increased arthropod abundance and richness. Temperature at our sites showed strong correlations with beetle composition, and correlations of some taxa with elevation suggest temperature strongly affects them as well. However, elevation was not a strong predictor of GDA richness or abundance. While we cannot fully disentangle the roles of covarying environmental factors (i.e. temperature, vapor pressure, precipitation, and productivity), the comparatively strong correlations of precipitation with GDA richness and abundance, along with differences between dry/wet gradients and dry/monsoon dates, highlight the strong role of water in our system.

Arid systems commonly have “flipped” elevational patterns, where richness and abundance increase with elevation due to lack of water at low elevations [58, 59]. Our results suggest PJ woodlands fit this pattern. In arid systems like Arizona, limited water at low elevations puts those communities at immediate risk from increased severity and frequency of droughts. Across most PJ woodlands, average annual temperatures have risen over 1.0°C since 1950, warming at a much faster rate than most of the continent [60]. Further warming of 1.5–2°C and increased droughts by 2050 are predicted for the region [61]. This is expected to amplify tree mortality and shift forest compositions [5]. Recent droughts and drought-induced pests have resulted in high mortality of pinyon [3, 6, 62]. Low-elevation PJ woodlands are often the first to succumb to drought [3], and GDAs may be equally or even more drought susceptible. At higher elevations, PJ woodlands are replacing ponderosa pine forests [6, 62]. GDAs may follow this progression or precede it. Our results suggest the latter, since GDAs were not dependent on tree species.

Our results clarify GDAs vulnerabilities to environmental changes in PJ woodlands. Differences among sample dates appear to be related to seasonal precipitation patterns while differences among sites likely relate to long-term precipitation patterns. While rarely compared, climate does seem to have a stronger direct effect on certain GDA communities than primary productivity [58, 63, 64], suggesting that GDA communities respond directly to changes in climate, rather than changes to vegetation and habitat structure.

Taxa differences

Environmental patterns were not concordant among GDA community metrics (richness, abundance, and composition) and taxa (ants and beetles). Ants were extremely abundant and varied extensively, a common phenomenon for GDA surveys caused by nest and foraging trail proximity [65]. Non-results of ant (and overall GDA) abundance may be due to sampling method, as pit traps may not accurately sample ant abundance, but significant patterns were still found with ant richness. Beetles made up roughly half the diversity, showing strong abundance and richness patterns. Both ants and beetles differed in their responses to elevation, precipitation, and NDVI. These relationships changed whether groups were measured by richness, abundance, and composition. Furthermore, taxa responded differently when analyzed individually. These nuances highlight the challenge of understanding GDA communities, and studies must examine a large spectrum of measurements within a diverse range of taxa to be comprehensive.

Conclusion

GDAs are important for nutrient-cycling and support higher trophic levels; understanding their relationships to climate and habitat may be critical for effective management of PJ woodlands. Currently, most management focuses on woodland reduction to mitigate conifer encroachment on ungulate habitat or reduce wildfire risks [66]. Bombaci & Pejchar [67] highlighted the potential negatives of this strategy for wildlife and our lack of knowledge on invertebrates in PJ ecosystems. GDA communities are sensitive to changes in habitat following drought-induced [27] and fire-induced [28] mortality of pinyon. Reduction of PJ woodland, whether through anthropomorphic or environmental means, likely has consequences for GDA biodiversity and ecosystem functions. With the threat of intensified droughts and increased temperatures, we underscore the need to establish more baseline data of arthropod communities in PJ woodlands to better understand and conserve these ecosystems.

Supporting information

S1 Table. Site locations and characteristics.

Includes site coordinates, elevations, and estimates of climate variables, primary productivity, 200m² plots for estimating tree species composition at site level, and 10m² radius plots for estimating tree composition at sample level.

(XLSX)

Click here for additional data file.^{(16.2KB, xlsx)}

S2 Table. GLMM model selection showing the two-model selection approach with AIC comparisons.

All models included date and site as random effects, only predictor variables are included here.

(XLSX)

Click here for additional data file.^{(19.4KB, xlsx)}

S3 Table. PERMANOVA, ANOSIM, and MRPP results of ant and beetle communities across tree species, sites, and dates.

All analyses agreed.

(XLSX)

Click here for additional data file.^{(9.4KB, xlsx)}

S4 Table. Arthropod data showing all taxa.

Raw data and taxonomic identifications of arthropods. Diptera and Lepidoptera were not used in analysis.

(XLSX)

Click here for additional data file.^{(116KB, xlsx)}

S5 Table. Indicator analysis for tree species, site, and date.

(XLSX)

Click here for additional data file.^{(10.3KB, xlsx)}

S6 Table. Pairwise site and date differences of ant and beetle community composition.

Site differences were largely between sites on different gradients (brown = dry gradient, green = wet gradient) and date differences were largely between dates in different seasons (dry = red, monsoon = blue).

(XLSX)

Click here for additional data file.^{(11.7KB, xlsx)}

S7 Table. GLM results for each taxon with environmental variables via manyGLM function.

Each model included predictors of elevation, precipitation, and NDVI. Correlations show direction of effect.

(XLSX)

Click here for additional data file.^{(15KB, xlsx)}

S8 Table. Correlations of environmental variables with ant and beetle ordinations.

(XLSX)

Click here for additional data file.^{(9KB, xlsx)}

S1 File. Zip files with R script and output, and csv datasheets for all analyses.

(ZIP)

Click here for additional data file.^{(247.6KB, zip)}

S2 File. Table of ordination fit statistics (stress and goodness of fit) and shepard diagrams.

(DOCX)

Click here for additional data file.^{(1.6MB, docx)}

S1 Fig. Species accumulation curves.

Number of unique taxa accumulated during sampling for both tree species (pinyon and juniper) at elevational sites.

(TIF)

Click here for additional data file.^{(258KB, tif)}

Acknowledgments

We thank Michael Remke, Rebecca Hirsch, and Sneha Vissa for helping with data collection. We also thank Neil Cobb, Babbitt Ranches, the Southwestern Experimental Garden Array (SEGA, established under National Science Foundation award #1126840 and Field Stations and Marine Labs Grant #152253) for providing site access. This manuscript benefited from the input of many arthropod taxonomists who helped identify taxa including Dr. Peter Messer (ground beetles), Dr. Gary Alpert (ants), Dr. Matthew Prebus (acorn ants), Nick Fensler (spider wasps), Dr. Bill Warner (scarab and clown beetles), Dr. Ainsely Seago (Leiodidae), Dr. Donald Chandler (Anthicidae), Dr. Robert Anderson (weevils), Dr. Charlene Wood (Latridiidae), Gareth Powell (Nitidulidae), Dr. Blaine Mathison (click beetles), Anthony Cognato (bark beetles), and Vassili Belov.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Miller RF, Rose JA. Historic expansion of Juniperus occidentalis (southwest juniper) in southeast Oregon. Great Basin Nat. 1995;55: 37–45. [Google Scholar]
2.Wang J, Xiao X, Qin Y, Doughty RB, Dong J, Zou Z. Characterizing the encroachment of juniper forests into sub-humid and semi-arid prairies from 1984 to 2010 using PALSAR and Landsat data. Remote Ses. Environ. 2018;205: 166–179. [Google Scholar]
3.Clifford MJ, Cobb NS, Buenemann M. Long-term tree cover dynamics in a pinyon-juniper woodland: climate-change-type drought resets successional clock. Ecosystems. 2011;14(6): 949–962. [Google Scholar]
4.Flake SW, Weisberg PJ. Widespread mortality and defoliation of pinyon pine in central Nevada mountains. Bulletin of the Ecological Society of America. 2019;100(2): 1–5. [Google Scholar]
5.Williams AP, Allen CD, Millar CI, Swetnam TW, Michaelsen J, Still CJ, et al. Forest responses to increasing aridity and warmth in the southwestern United States. PNAS 2010;107: 21289–21294, 10.1073/pnas.0914211107 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Minott JA, Kolb TE. Regeneration patterns reveal contraction of ponderosa forests and little upward migration of pinyon-juniper woodlands. For Ecol Manag. 2020;458: 117640. [Google Scholar]
7.Erwin TL. Tropical forests: their richness in Coleoptera and other arthropod species. Coleopt Bull. 1982;36: 74–75 [Google Scholar]
8.Nowak RS, Moore DJ, Tausch RJ. Ecophysiological patterns of pinyon and juniper. Proceedings RMRS. 1998;9: 35. [Google Scholar]
9.Gottfried GJ, Severson KE. Managing pinyon-juniper woodlands. Rangelands. 1994:16(6): 234–236. [Google Scholar]
10.Miles PD, Smith WB. Specific gravity and other properties of wood and bark for 156 tree species found in North American. USDA; 2009;38. [Google Scholar]
11.Shukla MK, Lal R, Ebinger M, Meyer C. Physical and chemical properties of soils under some pinyon-juniper-oak canopies in a semi-arid ecosystem in New Mexico. J Arid Environ. 2006;66(4): 672–685. [Google Scholar]
12.Johnson BG, Verburg PS, Arnone JA. Plant species effects on soil nutrients and chemistry in arid ecological zones. Oecologia. 2016;182(1): 299–317. 10.1007/s00442-016-3655-9 [DOI] [PubMed] [Google Scholar]
13.Lin TC, Rich PM, Helsler DA, Barnes FJ. Influences of canopy geometry on near-ground solar radiation and water balances of pinyon-juniper and ponderosa pine woodlands. Am Soc Photo & Rem Sens. 1992;1: 285–285. [Google Scholar]
14.Laudenslayer WF, Balda RP. Breeding bird use of a pinyon-juniper-ponderosa pine ecotone. The Auk. 1976; 571–586. [Google Scholar]
15.Paulin KM, Cook JJ, Dewey SR. Pinyon-juniper woodlands as sources of avian diversity. 1999. [Google Scholar]
16.Lessard JP, Sackett TE, Reynolds WN, Fowler DA, Sanders NJ. Determinants of the detrital arthropod community structure: the effects of temperature and resources along an environmental gradient. Oikos. 2011;120(3): 333–343. [Google Scholar]
17.Whitham TG, et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat Rev Genet. 2006;7(7): 510–523. 10.1038/nrg1877 [DOI] [PubMed] [Google Scholar]
18.Castagneyrol B, Jactel H, Vacher C, Brockerhoff EG, Koricheva J. Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. J Appl Ecol. 2014;51(1): 134–141. [Google Scholar]
19.Wardhaugh CW, Edwards W, Stork NE. 2015. The specialization and structure of antagonistic and mutualistic networks of beetles on rainforest canopy trees. Zool J Linnean Soc. 2015;11(2): 287–295. [Google Scholar]
20.Bardgett RD. The biology of soil: a community and ecosystem approach. Oxford University Press, Oxford; 2005. [Google Scholar]
21.Hansen RA. Effects of habitat complexity and composition on a diverse litter microarthropod assemblage. Ecology. 2000;81: 1120–1132. [Google Scholar]
22.Ambrecht I, Perfecto I, Vandermeer J. Enigmatic biodiversity correlations: ant diversity responds to diverse resources. Science. 2004;304: 284–286. 10.1126/science.1094981 [DOI] [PubMed] [Google Scholar]
23.Halaj J, Ross DW, Moldenke AR. Habitat structure and prey availability as predictors of the abundance and community organization of spiders in western Oregon forest canopies. J Arachnol. 1998; 203–220. [Google Scholar]
24.Halaj J, Ross DW, Moldenke AR. Importance of habitat structure to the arthropod food‐web in Douglas‐fir canopies. Oikos. 2000;90(1): 139–152. [Google Scholar]
25.Behan-Pelletier V, Newton G. Linking soil biodiversity and ecosystem function–the taxonomic dilemma. BioSci. 1999;49: 149–153. [Google Scholar]
26.Lightfoot DC, Brantley SL, Allen CD. Geographic patterns of ground-dwelling arthropods across an ecoregional transition in the North American Southwest. West N Am Nat. 2008;68(1): 83–102. [Google Scholar]
27.Higgins JW, Cobb NS, Sommer S, Delph RJ, Brantley SL. Ground‐dwelling arthropod responses to succession in a pinyon‐juniper woodland. Ecosphere. 2014;5(1): 1–29. [Google Scholar]
28.Delph RJ, Clifford MJ, Cobb NS, Ford PL, Brantley SL. Pinyon pine mortality alters communities of ground-dwelling arthropods. West N Am Nat. 2014;74(2): 162–184. [Google Scholar]
29.Culliney TW. Role of arthropods in maintaining soil fertility. Agriculture. 2013;3(4): 629–659. [Google Scholar]
30.Wall DH, Bradford MA, St. John MG, Trofymows JA, Behan-Pelleter V, Bignell DE, et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob Change Biol. 2010;14: 2661–2677. [Google Scholar]
31.Schowalter T. Arthropod diversity and functional importance in old-growth forests of North America. Forests. 2010;8(4): 97. [Google Scholar]
32.Høye TT, Forchhammer MC. The influence of weather conditions on the activity of high-arctic arthropods inferred from long-term observations. BMC Ecology. 2008;8(1): 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Hodkinson ID. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol Rev. 2005;80(3): 489–513. 10.1017/s1464793105006767 [DOI] [PubMed] [Google Scholar]
34.Supriya K, Moreau CS, Sam K, Price TD. Analysis of tropical and temperate elevational gradients in arthropod abundance. Front Biogeogr. 2019;11(2). [Google Scholar]
35.Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jędrzejewska B, Lima M, et al. The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Climate research. 2011;46(1): 15–27. [Google Scholar]
36.Andersen AN, Hoffmann BD, Müller WJ, Griffiths AD. Using ants as bioindicators in land management: simplifying assessment of ant community responses. J Appl Ecol. 2002;39(1): 8–17. [Google Scholar]
37.Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1: 3–14. [Google Scholar]
38.Bates D, Machler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using {lme4}. J Stat Softw. 2015;67:1–48. 10.18637/jss.v067.i01 [DOI] [Google Scholar]
39.Su AGaY-S. arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. 2018.
40.Wang YI, Naumann U, Wright ST, Warton DI. mvabund–an R package for model‐based analysis of multivariate abundance data. Methods Ecol. Evol. 2012;3(3): 471–474. [Google Scholar]
41.Roberts DW. labdsv: Ordination and multivariate analysis for ecology. R package version. 2007;1(1). [Google Scholar]
42.De Cáceres M, Jansen F. indicspecies: Relationship Between Species and Groups of Sites. R package version 1.7. 5. 2015.
43.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2017. [Google Scholar]
44.Urban SCGaDL. The ecodist package for dissimilarity-based analysis of ecological data. Statistical Software. 2007;22(7):1–19. [Google Scholar]
45.Lange M, Türke M, Pašalić E, Boch S, Hessenmöller D, Müller J, et al. Effects of forest management on ground-dwelling beetles (Coleoptera; Carabidae, Staphylinidae) in Central Europe are mainly mediated by changes in forest structure. For Ecol Manage. 2014;329: 166–176. [Google Scholar]
46.Perry KI, Wallin KF, Wenzel JW, Herms DA. Forest disturbance and arthropods: Small‐scale canopy gaps drive invertebrate community structure and composition. Ecosphere. 2018;9(10), p.e02463. [Google Scholar]
47.de Abreu Pestana LF, de Souza ALT, Tanaka MO, Labarque FM, Soares JAH. Interactive effects between vegetation structure and soil fertility on tropical ground-dwelling arthropod assemblages. App Soil Ecol. 2020;155: p.103624. [Google Scholar]
48.Pardon P, Reheul D, Mertens J, Reubens B, De Frenne P, De Smedt P, et al. Gradients in abundance and diversity of ground dwelling arthropods as a function of distance to tree rows in temperate arable agroforestry systems. Agr Ecocsyst Environ. 270;2019: 114–128. [Google Scholar]
49.Černecká Ľ, Mihál I, Gajdoš P, Jarčuška B. The effect of canopy openness of European beech (Fagus sylvatica) forests on ground‐dwelling spider communities. Insect Conser Diver, 2020;13(3), pp.250–261. [Google Scholar]
50.Thorn S, Bußler H, Fritze MA, Goeder P, Müller J, Weiß I, et al. Canopy closure determines arthropod assemblages in microhabitats created by windstorms and salvage logging. For Ecol Manage. 2016;381: 188–195. [Google Scholar]
51.Plowman NS, Mottl O, Novotny V, Idigel C, Philip FJ, Rimandai M, et al. Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies. Ecography. 2020;43(3): 431–442. [Google Scholar]
52.Donoso DA, Johnston MK, Kaspari M. Trees as templates for tropical litter arthropod diversity. Oecologia. 2010;164(1), pp.201–211. 10.1007/s00442-010-1607-3 [DOI] [PubMed] [Google Scholar]
53.Vehviläinen H, Koricheva J, Ruohomäki K. Effects of stand tree species composition and diversity on abundance of predatory arthropods. Oikos. 2008;117(6): 935–943. [Google Scholar]
54.Ayres E, Dromph KM, Bardgett RD. Do plant species encourage soil biota that specialize in the rapid decomposition of their litter? Soil Biol Biochem. 2006;38(1): 183–186. [Google Scholar]
55.Illig J, Langel R, Norton RA, Scheu S, Maraun M. Where are the decomposers? Uncovering the soil food web of a tropical montane rain forest in southern Ecuador using stable isotopes (15 N). J Trop Ecol. 2005;21: 589–593. [Google Scholar]
56.Hochberg ME, Bertault G, Poitrineau K, Janssen A. Olfactory orientation of the truffle beetle, Leiodes cinnamomea. Entomol Exp Appl. 2003;109(2): 147–153. [Google Scholar]
57.Haskins KE, Gehring CA. Interactions with juniper alter pinyon pine ectomycorrhizal fungal communities. Ecology. 2004;85(10): 2687–2692. [Google Scholar]
58.Szewczyk T, McCain CM. A systematic review of global drivers of ant elevational diversity. PLoS One. 2016;11(5). [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Sanders NJ, Moss J, Wagner D. Patterns of ant species richness along elevational gradients in an arid ecosystem. Global Ecol Biogeogr. 2003;2: 93–102. [Google Scholar]
60.Walsh J, et al. Ch. 2: Our changing climate. Climate Change Impacts in the United States: The Third National Climate Assessment, Melillo JM, Richmond Terese (TC), and Yohe GW, Eds, US Global Change Research Program. 2014; 19–67. [Google Scholar]
61.Karmalkar AV, Bradley RS. Consequences of global warming of 1.5 C and 2 C for regional temperature and precipitation changes in the contiguous United States. PloS one. 2017;12(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Floyd ML, Clifford MJ, Cobb NS, Hanna D, Delph RJ, Ford P, et al. 175 of stand characteristics to drought-induced mortality in piñon-juniper woodlands in Colorado, Arizona and New Mexico. Ecol. Appl. 2009;19: 1223–1230. 10.1890/08-1265.1 [DOI] [PubMed] [Google Scholar]
63.Sanders NJ, Lessard JP, Fitzpatrick MC, Dunn RR. Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains Global Ecol Biogeogr. 2007;16(5): 640–649. [Google Scholar]
64.Uhey DA, Hofstetter RW, Remke M, Vissa S, Haubensak KA. Climate and vegetation structure shape ant communities along elevational gradients on the Colorado Plateau. Ecol Evol. 2020; 10.1002/ece3.6538 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Gotelli NJ, Ellison AM, Dunn RR, Sanders NJ. Counting ants (Hymenoptera: Formicidae): biodiversity sampling and statistical analysis for myrmecologists. Myrmecol News. 2011. [Google Scholar]
66.Redmond MD, Golden ES, Cobb NS, Barger NN. Vegetation management across Colorado Plateau BLM lands: 1950–2003. Rangeland Ecol Manag. 2014;67(6): 636–640. [Google Scholar]
67.Bombaci S, Pejchar L. Consequences of pinyon and juniper woodland reduction for wildlife in North America. For Ecol Manage. 2016;365: 34–50. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0238219.r001

Decision Letter 0

Frank H Koch

19 May 2020

PONE-D-20-11956

Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species

PLOS ONE

Dear Mr. Uhey,

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I appreciate the amount of work put in by the authors, on both the analyses and the manuscript. In particular, I found the Introduction section to be well-written, with clear explanations of the authors' objectives and expectations. Nevertheless, I think there are still some aspects to confront before the manuscript may be suitable for publication. The reviewers have provided detailed comments that should serve as an excellent guide during the revision process. Foremost, I would focus on addressing the first major concern raised by each reviewer (Reviewer 1 about sampling methods and power, Reviewer 2 regarding level of taxonomic identification), but it is important to consider and respond to all of their concerns. I also agree with the reviewers that the Discussion section should be improved and expanded. They have provided several potential jumping-off points that may be worth further examination.

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Additional Editor Comments (if provided):

In Table S5, there are cases where no arthropods were reported for a given tree on a given sampling date. For example, there is no June 2018 record for tree J20 or J24. I assume these were the samples that were lost to flooding (line 135). I suggest inserting a row for each of these samples, indicating that they were lost to flooding and so there are no data to report.

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Reviewer #1: In this manuscript, Uhey et al. examine the role of tree types, climate and productivity on the richness and abundance patterns of ground dwelling arthropods in Pinyon-Juniper woodlands. They sampled ground arthropods using pitfall traps at several sites underneath Pinyon and Juniper trees at several time points and use generalized linear mixed models and other methods to address their question. I think they posed a very interesting question. Often when we think about arthropod-plant interactions, we think of foliage-dwelling arthropods and not ground-dwelling arthropods. The authors did a really good job of explaining how ground dwelling arthropod communities could be affected by tree types in the introduction and they also collected a lot of arthropods. However, I was not entirely convinced by their sampling methods, at least as they are currently written. I think the results section needs more details as well. They used a whole host of statistical methods but do not report the results of these in sufficient detail in the manuscript. Finally, I think their discussion needs to be developed further. They left out some important papers that they should have cited and do not address the limitations of their current work or point to any future directions.

Major comments:

1. I am concerned about the sampling method used in the paper and whether the authors even had enough power to discern differences in the ground dwelling arthropod communities harbored by Pinyon vs Juniper trees. They only sampled under three Pinyon and three Juniper trees at each site, which I am not sure gives them enough power to be able to discern any differences between arthropod communities under those trees. They also chose isolated trees for sampling (also need to clarify how isolated these isolated trees were, i.e. how far from the nearest tree?). However, there might be confounding factors that lead to these trees being isolated that affect the arthropod community. I think a better study design would have been to record the density of juniper and pinyon trees in say 10 m2 radius around each pitfall trap and look at how variation in the density of juniper and pinyon trees in an area affects arthropod diversity. If they sampled an isolated juniper tree that was in an area dominated by Pinyon trees, then are they sampling ground-dwelling arthropods that might prefer juniper tree litter? I am not sure. I think they need to add more details about the relative abundance of Pinyon and Juniper trees around their pitfall traps. If all the sites had a similar density of Pinyon and Juniper trees around the pitfall traps, then I do not think they are able to test the effects of tree type at all.

2. I think it would have been interesting if the authors had also sampled foliage dwelling arthropods. That would have set up a cool comparison of how diversity patterns of foliage dwelling versus ground dwelling arthropods are affected by tree types, climate, and productivity. This might have also allayed some concerns about sampling adequacy if they had noticed an effect of tree types on foliage dwelling arthropods but not ground dwelling ones. Unless the authors already have these data, I know it might not be feasible to go and collect foliage dwelling arthropods now, but this is something they could include in the future directions section of the discussion.

3. In the first sentence of the study sites section in Methods, the authors say that they sampled along two elevational gradients spanning ~1000m. However, when I look at figure 1, it looks like all their sites are between 1900m-2200m elevation. If that is indeed the case, then the elevational gradient is just 300m. I think it is important for the authors to clarify this. I would also be careful when citing literature on elevational gradients and comparing their results to other studies on elevational gradients. This study only captures a subset of the entire gradient, so any patterns seen or not seen with regards to elevation need to be interpreted with that in mind. I am not suggesting that they should have sampled a longer gradient; given their research question with regards to Pinyon-Juniper woodlands, I think the elevational distribution of their study sites make a lot of sense. I am just suggesting a more nuanced explanation of results in terms of elevation.

4. I am unclear about the specification of the GLMMs. Date and site were used as random effects, but were they entered as two separate random effects or were they treated as crossed random effects? As far as I understand their sampling design, they should have been treated as crossed random effects. Did the authors run model diagnostics to make sure that the model specification was okay? Also, why the use of AIC instead of AICc which corrects for small sample size? Lastly, I think they should include their GLMM table in the main paper instead of putting it in the supplement. At least the table for all arthropods. The model should include standard errors in addition to the slope values. That would help understand if they had enough statistical power. I would also encourage the authors to include their R script as a supplement.

5. I think the authors need to look through the literature more carefully and make sure to cite relevant studies. For example, I was wondering if there are other studies that show an effect of tree types on ground dwelling arthropods and a quick search led me to this paper: https://doi.org/10.1111/j.0030-1299.2008.15972.x I think this paper should be cited in this study. I would advise the authors to look through the literature once again and cite other relevant studies, it will make the discussion section much stronger. Also, like I said earlier, discussing the limitations of the study and what future work should be done in this field of research should be included in the discussion.

Minor comments:

I am personally not a huge fan of acronyms within papers unless they are already universally used. So, I think it is better to just say ground dwelling arthropods instead of GDAs and Pinyon-Juniper woodlands instead of PJs. I think it makes for easier reading, but it is up to you.

Line 39: need a comma between “tree specialization hypothesis, a specific version”

Line 40: “dominant” not “dominate”

Line 62: “management of these ecosystems”

Line 116: What was the source of this climate data? Did they have weather stations or is it satellite data from Worldclim? Please describe.

Line 124: Spatial resolution of 800m seems pretty low given the proximity of the sites that were sampled. I thought there was higher resolution data available for US, but I am not sure. MODIS seems to have data at 250m resolution(https://modis.gsfc.nasa.gov/data/dataprod/mod13.php) and has EVI in addition to NDVI which has been recommended in some recent work. I do not have expertise in this domain, but I would encourage the authors to look a little bit more into this.

Line 170: “Fig 3”, not “Fig 2”

Line 177: There is a typo, it should be “Linear” not “Liner”

Line 204: Please clarify what you mean when you say ant data was relativized by maximum.

Figure 3: This figure might not be readable to people with red-green colorblindness. Please check and try to avoid the use of red and green together in a figure. This is a really useful resource to help pick colors that are colorblind friendly: https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3

Reviewer #2: In this study, the authors compare communities of ground dwelling arthropods in pinyon-juniper woodlants using specimens from pitfall traps that were repeatedly sampled over the course of two years. With an emphasis on ants and beetles, they ask whether there are distinct species assemblages based on tree type, and also evaluate the effects of climate and productivity as measured by NDVI. They find variable effects of precipitation, NDVI, and elevation depending on the taxon and metric. While I think the idea is interesting, I have three main concerns:

1. Mixed genera, morphospecies, and species.

The authors do not identify all taxa, instead frequently using morphospecies or genera. This is common and completely understandable. However, the grouping is rather uneven, such that the analyses are performed on communities composed of a rather haphazard mixture of species, morphospecies, and genera. This makes me rather skeptical of the results. For example, several speciose genera (e.g., Crematogaster) are left as genera ('Crematogaster sp.'), while other smaller genera are identified to species (e.g., Forelius). I recognize that these genera are very difficult to identify, but I do not think it is valid to treat 'Crematogaster sp' as taxonomically equivalent to Forelius mccooki in terms of community composition and dissimilarity. This applies to other arthropods as well. For example, 'Melanotus similis' is treated as a separate taxon compared to 'Melanotus sp', but most specimens are identified only to genus. I expect that the taxa identified to species may have an outsized weight on the analyses, despite representing a minority of the actual species, while genera like Crematogaster sp. or Formica sp. may be obscuring differences between the communities since many species are treated as one. Obviously it would be best to identify all specimens to species (or to morphospecies representing a few likely species for those that may not be separable). Otherwise, identifying only to genera might be a better approach.

2. Treatment of ant worker abundances

The authors acknowledge that abundances of ant workers in their samples were biased by proximity of some pitfalls to ant nests, and they accounted for this by relativizing worker abundances by the maximum. However, this does not adequately or correctly account for the relevant peculiarities of ant life histories. In particular, ant foraging behavior varies by species, and some species are more likely than others to form foraging trails (Lanan 2014) that might lead to large numbers of ants from the same colony being captured in one trap (Bestelmeyer et al 2000). With ants in pitfall traps, it is unfortunately not possible to use the observed worker abundances as reliable estimates of either local worker abundance or local colony density. There are incidence-based metrics available that could be useful. A simple alternative would be to use a presence-based metric like Jaccard dissimilarity rather than Bray-Curtis (analyses in lines 206–207).

3. Discussion topics

The discussion should also be expanded. For example, it would be good to discuss the spatial scales involved in the context of tree specificity. Ground-dwelling arthropods generally move quite a bit, so the assumption you're making is that abundance in pitfall traps is correlated with 'time spent in that microhabitat'. Isolated trees were also intentionally selected for sampling to avoid mixed canopies of pinyon and juniper. Would this affect the observed community? What if the taxa that are specialized to tree type are also less likely to travel across larger stretches of open canopy? Would you expect differences between ants and beetles, given that ants are central place foragers with a (generally) stationary nest, unlike beetles?

As just an additional comment, this dataset seems to be very well-suited to using an occupancy model to account for species that were present but happened to not be sampled, with interesting combinations across aspect, elevation, and tree type. I recognize this is not the focus of this study, but it could be an interesting avenue for the authors to explore in the future.

References:

Bestelmeyer, B. T., Agosti, D., Alonso, L. E., Brandão, R. F., Brown, W. L. J., Delabie, J. H. C., & Silvestre, R. (2000). Field Techniques for the Study of Ground-Dwelling Ants: An Overview, Description, and Evaluation. In D. Agosti, J. D. Majer, L. E. Alonso, & T. R. Schultz (Eds.), Ants: Standard Methods For Measuring and Monitoring Biodiversity (pp. 122–144). http://antbase.org/ants/publications/20339/20339.pdf

Lanan, M. (2014). Spatiotemporal resource distribution and foraging strategies of ants (Hymenoptera: Formicidae). Myrmecological News, 20, 53–70.

Minor items

27–28: briefly summarise these points here

40: "dominant"

46–47: Doesn't the preceding sentence contradict this claim? If there are few studies which are largely limited to birds, how can you state that the shifting composition is changing animal communities?

48: maybe something like "Arthropods often closely associate with vegetation types"

49: specify 'plant' or 'tree' genotypes

56: Add "The" to the start of the sentence to make it a bit more clear

60–62: This sentence is rather vague. Could you tie it to the previous sentence more, specify how preservation and management could be improved, and/or add an example with a reference?

89: 'bitterbrush'

197–198: RStudio is just the IDE / interface - I think only the R version is necessary here.

221: Specify which Supp Table

332: I think here (and throughout) it would be good to qualify 'diversity' and 'richness', as readers will generally assume these are at the species level.

Fig. 4: I think it would be clearer to show the dates as different colors and the trees as different shapes

**********

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PLoS One. 2020 Aug 26;15(8):e0238219. doi: 10.1371/journal.pone.0238219.r002

Author response to Decision Letter 0

1 Jul 2020

Dear Editor,

Please find below our responses to reviewer comments for our manuscript, “Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species”, catalog number PONE-D-20-11956. We have made extensive revisions and believe our manuscript has greatly benefited from reviewer feedback. All authors have approved of changes.

Thank you for your time and consideration,

Derek Uhey, Corresponding Author

Northern Arizona University

College of Engineering, Forestry and Natural Sciences

200 E. Pine Knoll Dr.

Flagstaff, AZ 86011 USA

Ph: 303-961-3984

Email: dau9@nau.edu

Additional Editor Comments (if provided):

We thank the editor for their suggestion and have corrected our table (now S6 Table).

We note that Figure 1 in your submission contains map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

Our map image was created by us on PowerPoint and is not copyrighted. We note that we previously had images of highway signs and have replaced those in our figure with our own, which should now meet PLOS’s license requirements.

Reviewer #1:

In this manuscript, Uhey et al. examine the role of tree types, climate and productivity on the richness and abundance patterns of ground dwelling arthropods in Pinyon-Juniper woodlands. They sampled ground arthropods using pitfall traps at several sites underneath Pinyon and Juniper trees at several time points and use generalized linear mixed models and other methods to address their question. I think they posed a very interesting question. Often when we think about arthropod-plant interactions, we think of foliage-dwelling arthropods and not ground-dwelling arthropods. The authors did a really good job of explaining how ground dwelling arthropod communities could be affected by tree types in the introduction and they also collected a lot of arthropods. However, I was not entirely convinced by their sampling methods, at least as they are currently written. I think the results section needs more details as well. They used a whole host of statistical methods but do not report the results of these in sufficient detail in the manuscript. Finally, I think their discussion needs to be developed further. They left out some important papers that they should have cited and do not address the limitations of their current work or point to any future directions.

Major comments:

We thank the reviewer for their concern about our sampling, we have addressed this with several additions and modifications in the manuscript which A) more thoroughly explains our sampling design and study system, B) adds data, explanation, and discussion of our tree composition both at the site and tree level, and C) adds evidence that tree/site combinations were adequately sampled. We elaborate below.

A) We have improved our description of our sampling design and study system on lines 120-138 in the methods and with photos in a new figure (Fig. 2). Most forests would be difficult to separate tree-species effects, as trees grow in close proximity. Lacking before was a description of why our woodlands are unique compared to forests, as wide-spacing and isolation of trees is common (Fig. 2). Our woodland system is therefore ideal in having large-areas influenced by either pinyon or juniper which both establish sprawling canopies and accumulate litter underneath. We have clarified that we purposefully choose large-canopied pinyons or junipers (>3m2 radius canopy, canopy diameters now listed under Supp. Table 1) that were not growing close (>5m2) of the opposing species to avoid confounding effects. Pit traps were placed near trunks of trees and we believe these samples to be good representatives of their respective tree species.

B) To help contextualize our system, we have measured plots as suggested by the reviewer with estimations of tree composition at each site (tree density in five parallel belt transects of 100mX20m=100m2 plot) and at each tree (tree density in 10m2 radius plots) described in the methods on lines 134-138, added as Fig. 2., and data made available in S1 Table. The tree-level data reaffirms our sampling design (no opposing tree species within >5m2 radius, 5-10m2 radius with few/no opposing tree species).

In regards to the reviewer’s concerns, we only isolated trees purposefully from opposing species; some trees were adjacent to the same species while others isolated. Roughly half of trees were isolated within a 5m2 radius and 16.7% isolated within a 10m2 radius (S1 Table). Tree composition did change across sites (Fig. 2). We feel our samples are representative of the variation found in PJ woodlands of our region and aren’t heavily influenced by confounding factors. However, we have added discussion on tree isolation and the limitations of our study on lines 424-426.

C) While the sample size of three of each tree species at each site may appear low, we believe our sampling was intensive. Each tree was sampled for five separate week-long sampling periods (total of 15 samples per tree species at each site), with a large number of arthropods captured (as mentioned by the reviewer). To further show sampling was adequate, we have added species accumulation curves that show asymptotes in sampling for each tree/site combination (S7 Fig) referenced on lines 317-318. Moreover, statistical power is also a product of variation, which was low within compared to among sites (S7 Table). All models showed good fit statistics.

We thank the reviewer for their insight which we hope to one-day test. While we did not sample foliage-dwelling arthropods, we agree it should be added to the future directions section of the discussion which now appears on lines 460-462.

We thank the reviewer, this was a typo and we have revised our methods to state our gradients span ~300m (or ~1,000ft) on line 93. We agree with adding nuance to our elevational comparisons and have contextualized our elevational findings in our discussion on lines 465-466.

We have taken the reviewer’s suggestions and improved our methodological description and reporting of our GLMM statistics. Date and site were crossed random effects, now stated in line 256 of the methods, along with a more detailed description of GLMM specification and model diagnostics (lines 256-258). We have included AICc values in S3 Table referenced on line 253 which agree with our previously reported AIC values. Our GLMM table now appears in the main text as Table 2 on lines 369-374, with standard errors reported. We’ve added our R script as supplement “S2 File”.

We thank the reviewer for their suggestion to improve the discussion and the provided resource. We have added a more thorough discussion on tree species effects on ground-dwelling arthropods on lines 403-463, based on additional literature (de Abreu Pestana et al. 2020, Černecká et al. 2020, Plowman et al. 2020, Pardon et al. 2019, Perry et al. 2018, Thorn et al. 2016, Lange et al. 2014, Pringle & Fox-Dobbs 2008, and Vehviläinen et al. 2008). We’ve also included more discussion of limitations and future work.

de Abreu Pestana, L.F., de Souza, A.L.T., Tanaka, M.O., Labarque, F.M. and Soares, J.A.H., 2020. Interactive effects between vegetation structure and soil fertility on tropical ground-dwelling arthropod assemblages. Applied Soil Ecology, 155, p.103624.

Černecká, Ľ., Mihál, I., Gajdoš, P. and Jarčuška, B., 2020. The effect of canopy openness of European beech (Fagus sylvatica) forests on ground‐dwelling spider communities. Insect Conservation and Diversity, 13(3), pp.250-261.

Lange, M., Türke, M., Pašalić, E., Boch, S., Hessenmöller, D., Müller, J., Prati, D., Socher, S.A., Fischer, M., Weisser, W.W. and Gossner, M.M., 2014. Effects of forest management on ground-dwelling beetles (Coleoptera; Carabidae, Staphylinidae) in Central Europe are mainly mediated by changes in forest structure. Forest Ecology and Management, 329, pp.166-176.

Pardon, P., Reheul, D., Mertens, J., Reubens, B., De Frenne, P., De Smedt, P., Proesmans, W., Van Vooren, L. and Verheyen, K., 2019. Gradients in abundance and diversity of ground dwelling arthropods as a function of distance to tree rows in temperate arable agroforestry systems. Agriculture, ecosystems & environment, 270, pp.114-128.

Perry, K.I., Wallin, K.F., Wenzel, J.W. and Herms, D.A., 2018. Forest disturbance and arthropods: Small‐scale canopy gaps drive invertebrate community structure and composition. Ecosphere, 9(10), p.e02463.

Plowman, N.S., Mottl, O., Novotny, V., Idigel, C., Philip, F.J., Rimandai, M. and Klimes, P., 2020. Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies. Ecography, 43(3), pp.431-442.

Pringle, R.M. and Fox‐Dobbs, K., 2008. Coupling of canopy and understory food webs by ground‐dwelling predators. Ecology Letters, 11(12), pp.1328-1337.

Thorn, S., Bußler, H., Fritze, M.A., Goeder, P., Müller, J., Weiß, I. and Seibold, S., 2016. Canopy closure determines arthropod assemblages in microhabitats created by windstorms and salvage logging. Forest ecology and management, 381, pp.188-195.

Vehviläinen, H., Koricheva, J. and Ruohomäki, K., 2008. Effects of stand tree species composition and diversity on abundance of predatory arthropods. Oikos, 117(6), pp.935-943.

Minor comments:

We appreciate the reviewer’s preference on acronyms and understand they can be cumbersome when not commonly used. However, ‘GDA’ (Meyer et al. 2015, Xiao et al. 2020, Hasin & Booncher 2020) and ‘PJ’ (Weppner et al. 2013, Carrol et al. 2016, Jahromi & Agblevor 2017, Hartsell et al. 2020, Filippelli et al. 2020) are both widely used and we believe convenient due to the repetition of these terms in our manuscript. We leave it to the editor’s preference though.

Carroll, R.W., Huntington, J.L., Snyder, K.A., Niswonger, R.G., Morton, C. and Stringham, T.K., 2017. Evaluating mountain meadow groundwater response to Pinyon‐Juniper and temperature in a great basin watershed. Ecohydrology, 10(1), p.e1792.

Filippelli, S.K., Falkowski, M.J., Hudak, A.T., Fekety, P.A., Vogeler, J.C., Khalyani, A.H., Rau, B.M. and Strand, E.K., 2020. Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin. Environmental Research Letters, 15(2), p.025004.

Hartsell, J.A., Copeland, S.M., Munson, S.M., Butterfield, B.J. and Bradford, J.B., 2020. Gaps and hotspots in the state of knowledge of pinyon-juniper communities. Forest Ecology and Management, 455, p.117628.

Hasin, S. and Booncher, K., 2020. Change in ground-dwelling arthropod communities in different agroecosystems in Wang Nam Khiao, Nakhon Ratchasima province, Thailand. Agriculture and Natural Resources, 54(2), pp.139-149.

Jahromi, H. and Agblevor, F.A., 2017. Upgrading of pinyon-juniper catalytic pyrolysis oil via hydrodeoxygenation. Energy, 141, pp.2186-2195.

Meyer, W.M., Eble, J.A., Franklin, K., McManus, R.B., Brantley, S.L., Henkel, J., Marek, P.E., Hall, W.E., Olson, C.A., McInroy, R. and Loaiza, E.M.B., 2015. Ground-dwelling arthropod communities of a sky island mountain range in southeastern Arizona, USA: Obtaining a baseline for assessing the effects of climate change. PloS one, 10(9).

Weppner, K.N., Pierce, J.L. and Betancourt, J.L., 2013. Holocene fire occurrence and alluvial responses at the leading edge of pinyon–juniper migration in the Northern Great Basin, USA. Quaternary Research, 80(2), pp.143-157.

Xiao, H., Du, C., Yuan, X. and Li, B., 2020. Multiple floods affect composition and community structure of the ground-dwelling arthropods in the riparian zone of the Three Gorges Reservoir. Ecological Indicators, 113, p.106220.

Line 39: need a comma between “tree specialization hypothesis, a specific version”

Agreed and revised as suggested by reviewer.

Line 40: “dominant” not “dominate”

Agreed and revised as suggested by reviewer.

Line 62: “management of these ecosystems”

Agreed and revised as suggested by reviewer.

Line 116: What was the source of this climate data? Did they have weather stations or is it satellite data from Worldclim? Please describe.

This was described at the end of the paragraph (data source is PRISM), but we understand that this may not read clearly, so have moved our statement on data source to follow our statement at the paragraph start (lines 159-160).

While this spatial data is course it is best seen as an estimate for relative climate information for each site. Remke et al. 2020 showed relative temperature differences predicted by PRISM were similar to observed temperature differences by weather stations. Of course finer resolution data would be better, but MODIS and other imagining sources do not provide estimates of climate parameters averaged over long time periods. Numerous studies have used PRISM data as an estimate for climatic conditions (e.g. Delph et al. 2014, Youngsteadt et al. 2015, Welti et al. 2020). Strachan and Daly (2017) show that observed spatial variation in temperature is high and complex, however, currently spatially explicit data does not exists, so PRISM still provides a reasonable alternative until more stations are used to improve spatial interpolation models (http://www.prism.oregonstate.edu/documents/pubs/2017JGR_TestingPRISMTemperature_Strachan.pdf).

Delph, R.J., Clifford, M.J., Cobb, N.S., Ford, P.L. and Brantley, S.L., 2014. Pinyon pine mortality alters communities of ground-dwelling arthropods. Western North American Naturalist, 74(2), pp.162-184.

Remke, M.J., Hoang, T., Kolb, T., Gehring, C., Johnson, N.C. and Bowker, M.A., 2020. Familiar soil conditions help Pinus ponderosa seedlings cope with warming and drying climate. Restoration Ecology.

Strachan, S. and Daly, C., 2017. Testing the daily PRISM air temperature model on semiarid mountain slopes. Journal of Geophysical Research: Atmospheres, 122(11), pp.5697-5715.

Youngsteadt, E., Dale, A.G., Terando, A.J., Dunn, R.R. and Frank, S.D., 2015. Do cities simulate climate change? A comparison of herbivore response to urban and global warming. Global Change Biology, 21(1), pp.97-105.

Welti, E.A., Prather, R.M., Sanders, N.J., de Beurs, K.M. and Kaspari, M., 2020. Bottom‐up when it is not top‐down: Predators and plants control biomass of grassland arthropods. Journal of Animal Ecology.

Line 170: “Fig 3”, not “Fig 2”

Figure numbers have changed and are correct.

Line 177: There is a typo, it should be “Linear” not “Liner”

Agreed and revised as suggested by reviewer.

Line 204: Please clarify what you mean when you say ant data was relativized by maximum.

We have changed this analysis based on reviewer’s two comment and no longer modify ant data in this manner.

Agreed and revised as suggested by reviewer using colorbrewer2.org.

Reviewer #2:

In this study, the authors compare communities of ground dwelling arthropods in pinyon-juniper woodlants using specimens from pitfall traps that were repeatedly sampled over the course of two years. With an emphasis on ants and beetles, they ask whether there are distinct species assemblages based on tree type, and also evaluate the effects of climate and productivity as measured by NDVI. They find variable effects of precipitation, NDVI, and elevation depending on the taxon and metric. While I think the idea is interesting, I have three main concerns:

1. Mixed genera, morphospecies, and species.

We appreciate the reviewer’s concern regarding level of taxonomic identity and have addressed this with several additions and revisions to our manuscript. First, during the review and revision process additional identifications of eight morphospecies were received from experts which we have updated in S6 Table (Crematogaster punctulata, Formica (neogatates complex), Myrmecocystus mimicus, Myrmica rugiventris, Crocidema arizonicus, Stachyocenmus apicalis, and Listrus senilis). Second, we have added to S6 Table rows detailing who assigned species/morphospecies identifications and links to photographs of voucher specimens posted on bugguide.net where possible. Third, we have added to our results clearer statements on numbers of species/morphospecies designations on line 315. Including the new identifications, 81% of specimens are identified to species. 15% of specimens are left at morphospecies, but 16 of those morphospecies are singleton specimens and thereby must represent single species. This level of identification is high compared to most studies investigating ground-dwelling arthropod communities (Delph et al. 2014, Meyer et al. 2015, Yekwayo et al. 2018, Ferrenberg et al. 2019, Ferreira et al. 2020).

We feel confident that morphospecies designations are reasonably representative of single species, as experts determined voucher specimens of each species/morphospecies per site. Of course it is always possible for a morphospecies designation to represent multiple cryptic species; as the reviewer points out this is a common problem among arthropod studies. Morphospecies was assigned for certain groups that are difficult to identify or in need of taxonomic revision by experts who confirmed multiple voucher specimens of morphospecies were most likely the same species.

We found few differences with ground-dwelling arthropod communities between tree species when analyzed on the species/morphospecies level, doing our analysis on genera rather than species/morphospecies (i.e. losing taxonomic information) could only make communities look more similar. We believe our taxonomic depth high in comparison to other studies and this data supports robust analyses, however we have given this issue more recognition in our manuscript by adding discussion points on morphospecies and diversity limitations on lines 414-416.

Ferreira, P.M., Andrade, B.O., Podgaiski, L.R., Dias, A.C., Pillar, V.D., Overbeck, G.E., Mendonça Jr, M.D.S. and Boldrini, I.I., 2020. Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities. PloS one, 15(1), p.e0227706.

Ferrenberg, S., Wickey, P. and Coop, J.D., 2019. Ground-Dwelling Arthropod Community Responses to Recent and Repeated Wildfires in Conifer Forests of Northern New Mexico, USA. Forests, 10(8), p.667.

Yekwayo, I., Pryke, J.S., Gaigher, R. and Samways, M.J., 2018. Only multi-taxon studies show the full range of arthropod responses to fire. PloS one, 13(4), p.e0195414.

2. Treatment of ant worker abundances

We thank the reviewer for improving our analysis; we have taken the suggestion to redo our ordination based-analyses with an incidence-based Jaccard dissimilarity metric. While this did not significantly change any result, we are glad to report more conservative estimates of ant communities which do vary considerably as the reviewer states. We have updated our methods on lines 295-297 and all figures/tables impacted by the change.

3. Discussion topics

We thank the reviewer for these excellent suggestions for discussion points. We have extensively rewritten and added much to our discussion, including the reviewer’s suggested topics. We have also identified arthropods to functional group, which has improved contextualizing our results in the discussion.

This is an interesting idea we hope to pursue in the future.

References:

Lanan, M. (2014). Spatiotemporal resource distribution and foraging strategies of ants (Hymenoptera: Formicidae). Myrmecological News, 20, 53–70.

Minor items

27–28: briefly summarise these points here

We agree and have summarized our discussion points in the lines 27-30 of the abstract.

40: "dominant"

Agreed and revised as suggested by reviewer.

As written these statements were contradictory, we have revised our second sentence to a speculation (“may”) from statement (“is”). That is, now our introduction states that the shifting composition of pinyon-juniper woodlands may change animal communities. The uncertainty is caused by limited studies comparing pinyon and juniper animal communities, setting up our study.

48: maybe something like "Arthropods often closely associate with vegetation types"

Agreed and revised as suggested by reviewer.

49: specify 'plant' or 'tree' genotypes

Agreed and revised as suggested by reviewer.

56: Add "The" to the start of the sentence to make it a bit more clear

Agreed and revised as suggested by reviewer.

60–62: This sentence is rather vague. Could you tie it to the previous sentence more, specify how preservation and management could be improved, and/or add an example with a reference?

We have replaced our sentence on lines 69-71 to be more specific and tie into the previous sentence with an added a reference (Schowalter 2017).

Schowalter, T., 2017. Arthropod diversity and functional importance in old-growth forests of North America. Forests, 8(4), p.97.

89: 'bitterbrush'

Agreed and revised as suggested by reviewer.

197–198: RStudio is just the IDE / interface - I think only the R version is necessary here.

Agreed, we have removed the RStudio reference.

221: Specify which Supp Table

Agreed and revised to specify S5 Table.

332: I think here (and throughout) it would be good to qualify 'diversity' and 'richness', as readers will generally assume these are at the species level.

Clarified on lines 316-317.

Fig. 4: I think it would be clearer to show the dates as different colors and the trees as different shapes

Agreed and changed as suggested by reviewer.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(124.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0238219.r003

Decision Letter 1

Frank H Koch

21 Jul 2020

PONE-D-20-11956R1

Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species

PLOS ONE

Dear Dr. Uhey,

The reviewers and I agree that this revised version of the manuscript is much improved from your initial submission. Reviewers 1 and 2 listed a handful of remaining minor concerns that you should address. I also have my own list of editorial or grammatical edits for you to consider (see "Additional Editor Comments", below). If you address all of these comments, then the manuscript should be suitable for publication.

I received another comment from someone other than the three reviewers of record on your submission. To summarize, this individual argued that you should have used a repeated measures ANOVA approach to analyze your data, rather than generalized linear mixed models. I disagree. Repeated measures approaches, at least in this context, tend to violate the sphericity assumption, and GLMMs offer better flexibility. Nevertheless, it may be worth including a brief sentence that explains why you chose to use GLMMs for your tests of richness and abundance. I'll leave that up to you.

Please submit your revised manuscript by Sep 04 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Frank H. Koch, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Line 38 - suggest inserting a comma after "woodlands"

Line 44 - suggest inserting a comma after "properties (9,10)"

Line 46 - suggest inserting a comma after "pinyons"

Lines 94-95 - the San Francisco Peaks are a mountain range, i.e., there is no singular San Francisco Peak. The listed elevation is for Humphreys Peak, which is the tallest peak in the range.

Line 100 - capitalize "peaks"

Line 102 - "The relative proportion of pinyons and junipers..." - there are two relative proportions, one for pinyons and another for junipers.

Line 169 - replace "collectivity" with "collectively", or maybe "together"

Line 311 - replace "habit" with "habitat"

Line 328 - replace "are" with "may", or delete "be"

Line 354 - insert commas after "gradient" and after "woodlands"

Line 359 - replace "affect" with "affects"

Line 361 - replace "can't" with "cannot"

Line 373 - insert hyphen between "Low" and "elevation"

Line 375 - make "Ponderosa" lower-case

Line 401 - replace "highlight" with "highlighted"

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: I commend the authors on doing a really thorough job with the revisions. I just noticed a few minor errors/typos that I am listing here. My only other concern is the data availability. The authors say the data is available to download as supplements or from Symbiota Collection of Arthropod Network, but no link is provided leaving the reader to locate the data themselves, which can be cumbersome. The authors included the R output on my request, but without the csv file, it is difficult to replicate their analyses. So, I would encourage the authors to make the data available in a more easily accessible format. And here are the other minor edits: 1. Line 263, I think it should say "a significant indicator" instead of "significant indicators"; 2. Line 296: Table 2 does not show that NDVI is a significant predictor of ant richness, so I think the authors should remove NDVI from this sentence; 3. Line 311, "habitat" instead of "habit"; 4. Line 328: these is an extra "be"; 5. Line 330, "exacerbated" instead of "exasperated"

Reviewer #2: The authors have greatly improved the manuscript in this draft, and they have adequately responded to the majority of my previous comments. I do still have a few concerns, but I think they should be easy to address.

First, I am still not satisfied with the handling of ant abundances. I realize it seems like I might be harping on this, but it really is a difficult quantity to measure. The authors run GLMMs for abundance of 1) all GDA, 2) only ants, and 3) only beetles. In Table 2, the estimates for all GDAs and for ants are quite similar, which makes sense because 76% of the individuals were ants. While ants are certainly a very abundant and important component of the GDA community, the observed abundances are not representative given the issues with pitfall traps, particularly with regards to samples along gradients. For example, Camponotus vicinus is not 250x as abundant under tree 135 compared to tree 166, but that is the data going into the model.

Ant workers collected in pitfall traps simply do not represent either colony abundance or worker abundance, and using worker counts as estimates of abundance will (frankly and unfortunately) result in nonsense. Using the worker abundances from pitfall traps directly just does not give the kind of information needed to estimate abundances. My suggestion would be to instead limit the abundance GLMMs to non-ant GDAs, and show results for 'Beetle abundance' and 'Other GDA abundance', where 'other' is non-ant non-beetle taxa. This is only an issue for abundances - richness should be unaffected.

Second, unless time or computing power are limiting factors, it is generally preferable to fit all possible models and compare using AICc rather than using backwards stepwise selection (208-209). However, I think there must be something missing or I am misunderstanding the analyses, as I cannot find any GLMM results for subsets of the predictor variables representing the final models. If it is the former, the results should be included and I would contend that Table 2 should only show the final models. If it is the latter, this section and the results perhaps need some clarifying.

A final minor suggestion for improved reproducibility the future: R notebooks (in RStudio) are a really nice way to provide the code and output. PDFs can be generated easily, with text, code, and output inline without the additional characters that come from pasting into a .docx file (S2). In my opinion, it makes it easier for readers, for anyone interested in trying/adapting your code, and even for the authors.

# Line items

41: 'genus'

46-49: I still feel like this needs more qualification or justification... Maybe first rephrase to state the differences found in the bird studies, or even that they found a difference, and then qualify the second sentence with something like 'if other taxa show similar microhabitat specialization, ...'

200-211: Is it correct that 'abundance' for ants here refers to the number of workers? For clarity, it would be worth stating below (230-232) that incidence was used *only* in the similarity matrices (e.g., "In contrast to the above analyses which directly used the number of ant workers collected...").

Reviewer #3: (No Response)

**********

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Reviewer #1: Yes: K. Supriya

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Reviewer #3: Yes: Heather Slinn

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PLoS One. 2020 Aug 26;15(8):e0238219. doi: 10.1371/journal.pone.0238219.r004

Author response to Decision Letter 1

5 Aug 2020

Dear Editor,

Below are our responses (in red) to the reviewers comments for the manuscript, “Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species”, catalog number PONE-D-20-11956.

We appreciate the feedback provided from both reviewers and the editor which has strengthened our manuscript. We have accepted all suggestions and revised our manuscript accordingly. All authors have approved the changes.

Thank you for your time and consideration,

Derek Uhey, Corresponding Author

Northern Arizona University

College of Engineering, Forestry and Natural Sciences

200 E. Pine Knoll Dr.

Flagstaff, AZ 86011 USA

Ph: 303-961-3984

Email: dau9@nau.edu

Editor Comments:

We thank the editor for their suggestion and understanding of our choice of statistical method. We have added a brief statement on why we choose the GLMM approach on lines 209-210 of the methods.

Additional Editor Comments (if provided):

Line 38 - suggest inserting a comma after "woodlands"

Line 44 - suggest inserting a comma after "properties (9,10)"

Line 46 - suggest inserting a comma after "pinyons"

Lines 94-95 - the San Francisco Peaks are a mountain range, i.e., there is no singular San Francisco Peak. The listed elevation is for Humphreys Peak, which is the tallest peak in the range.

Line 100 - capitalize "peaks"

Line 102 - "The relative proportion of pinyons and junipers..." - there are two relative proportions, one for pinyons and another for junipers.

Line 169 - replace "collectivity" with "collectively", or maybe "together"

Line 311 - replace "habit" with "habitat"

Line 328 - replace "are" with "may", or delete "be"

Line 354 - insert commas after "gradient" and after "woodlands"

Line 359 - replace "affect" with "affects"

Line 361 - replace "can't" with "cannot"

Line 373 - insert hyphen between "Low" and "elevation"

Line 375 - make "Ponderosa" lower-case

Line 401 - replace "highlight" with "highlighted"

We have accepted all of the above suggestions and thank the editor for these improvements.

We thank the reviewer and agree data availability can be improved. We have included all relevant CSV files as supplements (in zip file with R-script) and improved our R-script (S2 File). Unfortunately, our records in Symbiota Collection of Arthropod Network are only searchable by specimen label information. The search platform is found at the given link. We also include links to photographed specimens at bugguide.net with url #s in S6 Table. With the changes to our S2 file, along with our many supplemental materials, we believe our data is easily available.

And here are the other minor edits:

1. Line 263, I think it should say "a significant indicator" instead of "significant indicators";

2. Line 296: Table 2 does not show that NDVI is a significant predictor of ant richness, so I think the authors should remove NDVI from this sentence;

3. Line 311, "habitat" instead of "habit";

4. Line 328: these is an extra "be";

5. Line 330, "exacerbated" instead of "exasperated"

We have revised our manuscript and accepted the reviewer’s suggested line comments.

We thank the reviewer for their concerns. The accuracy of estimating ant-abundances via pit-traps is certainly debatable, given we found no significant patterns with ant or GDA abundance, we agree our non-results should be accompanied with more discussion of potential methodological bias. We believe it is still important to report our non-results on the abundance of ants and over-all GDAs but have added discussion on the short-comings of pit-traps for estimating abundance on lines 415-420. Further, we have added the ‘other’ group to our analysis as suggested by the reviewer.

We thank the reviewer for their suggestion and have updated our methods and results to strengthen our GLMM results. We fit all possible models for each GDA response variable and include AICc comparisons in S3 Table. Our methods are updated to reflect this on lines 207-221. We have taken the reviewer suggestion to have table 2 only include final models.

We are grateful for this suggestion and have included an R-notebook output in our S2 files along with CSV datasheets.

# Line items

41: 'genus'

Revised.

We have revised these sentences as suggested by reviewer.

We have clarified that use of incidence-based ant abundance was only in similarity matrices for ordination analysis.

Reviewer #3: (No Response)

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(106.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0238219.r005

Decision Letter 2

Frank H Koch

13 Aug 2020

Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species

PONE-D-20-11956R2

Dear Dr. Uhey,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Frank H. Koch, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for responding to all of the comments on the previous revision. Your manuscript is now suitable for publication, although I have some minor editorial comments, listed below:

Line 40: insert “a” before “host tree species”

Line 45: insert comma after “pinyons”

Line 48: “taxa groups” seems odd – maybe “animal taxa”?

Line 60: replace “describe” with “have described”

Line 109: “ponderosa” should be lower-case

Line 115: “chose” instead of “choose”

Lines 140-141: Did you mean to say “30-year averages of annual average precipitation…”? For precipitation, I think you’re probably working with a 30-year average of the annual total. On the other hand, it is reasonable to have a 30-year average of the annual average temperature.

Line 146: Was the spatial resolution 250 m? 250 m2 would mean each raster cell was about 16 m on a side.

Line 149: Here, you denote the raster package using single quotes. In line 221, you denote the mvabund packages using italics. In lines 216 and 229, you don’t distinguish the names of the lme4, arm, labdsv, and indicspecies packages, and the same is true in line 246 for the vegan and ecodist packages. Pick one approach and use it consistently.

Lines 192-193: Vapor pressure is represented using yellow in Fig 3; precipitation is represented using blue.

Line 199: “had” instead of “has”

Line 208: Insert “the” before “Akaike” (since it’s a singular criterion).

Line 244: “analyses” instead of “analysis”

Line 273: “differed significantly”

Line 284: “increased significantly”

Line 310: insert “the” before “Bray-Curtis”

Line 318: “forest types” instead of “forests”

Line 381: delete “arthropods” (redundant)

Line 384: “GDA” instead of “GDAs”

S1 Site descriptions.xlsx, “100X20m plots” tab – “parallel” is misspelled

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0238219.r006

Acceptance letter

Frank H Koch

17 Aug 2020

PONE-D-20-11956R2

Ground-dwelling Arthropods of Pinyon-Juniper Woodlands: Arthropod Community Patterns are Driven by Climate and Overall Plant Productivity, not Host Tree Species

Dear Dr. Uhey:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Frank H. Koch

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Site locations and characteristics.

(XLSX)

Click here for additional data file.^{(16.2KB, xlsx)}

S2 Table. GLMM model selection showing the two-model selection approach with AIC comparisons.

All models included date and site as random effects, only predictor variables are included here.

(XLSX)

Click here for additional data file.^{(19.4KB, xlsx)}

S3 Table. PERMANOVA, ANOSIM, and MRPP results of ant and beetle communities across tree species, sites, and dates.

All analyses agreed.

(XLSX)

Click here for additional data file.^{(9.4KB, xlsx)}

S4 Table. Arthropod data showing all taxa.

Raw data and taxonomic identifications of arthropods. Diptera and Lepidoptera were not used in analysis.

(XLSX)

Click here for additional data file.^{(116KB, xlsx)}

S5 Table. Indicator analysis for tree species, site, and date.

(XLSX)

Click here for additional data file.^{(10.3KB, xlsx)}

S6 Table. Pairwise site and date differences of ant and beetle community composition.

(XLSX)

Click here for additional data file.^{(11.7KB, xlsx)}

S7 Table. GLM results for each taxon with environmental variables via manyGLM function.

Each model included predictors of elevation, precipitation, and NDVI. Correlations show direction of effect.

(XLSX)

Click here for additional data file.^{(15KB, xlsx)}

S8 Table. Correlations of environmental variables with ant and beetle ordinations.

(XLSX)

Click here for additional data file.^{(9KB, xlsx)}

S1 File. Zip files with R script and output, and csv datasheets for all analyses.

(ZIP)

Click here for additional data file.^{(247.6KB, zip)}

S2 File. Table of ordination fit statistics (stress and goodness of fit) and shepard diagrams.

(DOCX)

Click here for additional data file.^{(1.6MB, docx)}

S1 Fig. Species accumulation curves.

Number of unique taxa accumulated during sampling for both tree species (pinyon and juniper) at elevational sites.

(TIF)

Click here for additional data file.^{(258KB, tif)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(124.1KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(106.1KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

[pone.0238219.ref001] 1.Miller RF, Rose JA. Historic expansion of Juniperus occidentalis (southwest juniper) in southeast Oregon. Great Basin Nat. 1995;55: 37–45. [Google Scholar]

[pone.0238219.ref002] 2.Wang J, Xiao X, Qin Y, Doughty RB, Dong J, Zou Z. Characterizing the encroachment of juniper forests into sub-humid and semi-arid prairies from 1984 to 2010 using PALSAR and Landsat data. Remote Ses. Environ. 2018;205: 166–179. [Google Scholar]

[pone.0238219.ref003] 3.Clifford MJ, Cobb NS, Buenemann M. Long-term tree cover dynamics in a pinyon-juniper woodland: climate-change-type drought resets successional clock. Ecosystems. 2011;14(6): 949–962. [Google Scholar]

[pone.0238219.ref004] 4.Flake SW, Weisberg PJ. Widespread mortality and defoliation of pinyon pine in central Nevada mountains. Bulletin of the Ecological Society of America. 2019;100(2): 1–5. [Google Scholar]

[pone.0238219.ref005] 5.Williams AP, Allen CD, Millar CI, Swetnam TW, Michaelsen J, Still CJ, et al. Forest responses to increasing aridity and warmth in the southwestern United States. PNAS 2010;107: 21289–21294, 10.1073/pnas.0914211107 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0238219.ref006] 6.Minott JA, Kolb TE. Regeneration patterns reveal contraction of ponderosa forests and little upward migration of pinyon-juniper woodlands. For Ecol Manag. 2020;458: 117640. [Google Scholar]

[pone.0238219.ref007] 7.Erwin TL. Tropical forests: their richness in Coleoptera and other arthropod species. Coleopt Bull. 1982;36: 74–75 [Google Scholar]

[pone.0238219.ref008] 8.Nowak RS, Moore DJ, Tausch RJ. Ecophysiological patterns of pinyon and juniper. Proceedings RMRS. 1998;9: 35. [Google Scholar]

[pone.0238219.ref009] 9.Gottfried GJ, Severson KE. Managing pinyon-juniper woodlands. Rangelands. 1994:16(6): 234–236. [Google Scholar]

[pone.0238219.ref010] 10.Miles PD, Smith WB. Specific gravity and other properties of wood and bark for 156 tree species found in North American. USDA; 2009;38. [Google Scholar]

[pone.0238219.ref011] 11.Shukla MK, Lal R, Ebinger M, Meyer C. Physical and chemical properties of soils under some pinyon-juniper-oak canopies in a semi-arid ecosystem in New Mexico. J Arid Environ. 2006;66(4): 672–685. [Google Scholar]

[pone.0238219.ref012] 12.Johnson BG, Verburg PS, Arnone JA. Plant species effects on soil nutrients and chemistry in arid ecological zones. Oecologia. 2016;182(1): 299–317. 10.1007/s00442-016-3655-9 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref013] 13.Lin TC, Rich PM, Helsler DA, Barnes FJ. Influences of canopy geometry on near-ground solar radiation and water balances of pinyon-juniper and ponderosa pine woodlands. Am Soc Photo & Rem Sens. 1992;1: 285–285. [Google Scholar]

[pone.0238219.ref014] 14.Laudenslayer WF, Balda RP. Breeding bird use of a pinyon-juniper-ponderosa pine ecotone. The Auk. 1976; 571–586. [Google Scholar]

[pone.0238219.ref015] 15.Paulin KM, Cook JJ, Dewey SR. Pinyon-juniper woodlands as sources of avian diversity. 1999. [Google Scholar]

[pone.0238219.ref016] 16.Lessard JP, Sackett TE, Reynolds WN, Fowler DA, Sanders NJ. Determinants of the detrital arthropod community structure: the effects of temperature and resources along an environmental gradient. Oikos. 2011;120(3): 333–343. [Google Scholar]

[pone.0238219.ref017] 17.Whitham TG, et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat Rev Genet. 2006;7(7): 510–523. 10.1038/nrg1877 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref018] 18.Castagneyrol B, Jactel H, Vacher C, Brockerhoff EG, Koricheva J. Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. J Appl Ecol. 2014;51(1): 134–141. [Google Scholar]

[pone.0238219.ref019] 19.Wardhaugh CW, Edwards W, Stork NE. 2015. The specialization and structure of antagonistic and mutualistic networks of beetles on rainforest canopy trees. Zool J Linnean Soc. 2015;11(2): 287–295. [Google Scholar]

[pone.0238219.ref020] 20.Bardgett RD. The biology of soil: a community and ecosystem approach. Oxford University Press, Oxford; 2005. [Google Scholar]

[pone.0238219.ref021] 21.Hansen RA. Effects of habitat complexity and composition on a diverse litter microarthropod assemblage. Ecology. 2000;81: 1120–1132. [Google Scholar]

[pone.0238219.ref022] 22.Ambrecht I, Perfecto I, Vandermeer J. Enigmatic biodiversity correlations: ant diversity responds to diverse resources. Science. 2004;304: 284–286. 10.1126/science.1094981 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref023] 23.Halaj J, Ross DW, Moldenke AR. Habitat structure and prey availability as predictors of the abundance and community organization of spiders in western Oregon forest canopies. J Arachnol. 1998; 203–220. [Google Scholar]

[pone.0238219.ref024] 24.Halaj J, Ross DW, Moldenke AR. Importance of habitat structure to the arthropod food‐web in Douglas‐fir canopies. Oikos. 2000;90(1): 139–152. [Google Scholar]

[pone.0238219.ref025] 25.Behan-Pelletier V, Newton G. Linking soil biodiversity and ecosystem function–the taxonomic dilemma. BioSci. 1999;49: 149–153. [Google Scholar]

[pone.0238219.ref026] 26.Lightfoot DC, Brantley SL, Allen CD. Geographic patterns of ground-dwelling arthropods across an ecoregional transition in the North American Southwest. West N Am Nat. 2008;68(1): 83–102. [Google Scholar]

[pone.0238219.ref027] 27.Higgins JW, Cobb NS, Sommer S, Delph RJ, Brantley SL. Ground‐dwelling arthropod responses to succession in a pinyon‐juniper woodland. Ecosphere. 2014;5(1): 1–29. [Google Scholar]

[pone.0238219.ref028] 28.Delph RJ, Clifford MJ, Cobb NS, Ford PL, Brantley SL. Pinyon pine mortality alters communities of ground-dwelling arthropods. West N Am Nat. 2014;74(2): 162–184. [Google Scholar]

[pone.0238219.ref029] 29.Culliney TW. Role of arthropods in maintaining soil fertility. Agriculture. 2013;3(4): 629–659. [Google Scholar]

[pone.0238219.ref030] 30.Wall DH, Bradford MA, St. John MG, Trofymows JA, Behan-Pelleter V, Bignell DE, et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob Change Biol. 2010;14: 2661–2677. [Google Scholar]

[pone.0238219.ref031] 31.Schowalter T. Arthropod diversity and functional importance in old-growth forests of North America. Forests. 2010;8(4): 97. [Google Scholar]

[pone.0238219.ref032] 32.Høye TT, Forchhammer MC. The influence of weather conditions on the activity of high-arctic arthropods inferred from long-term observations. BMC Ecology. 2008;8(1): 8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0238219.ref033] 33.Hodkinson ID. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol Rev. 2005;80(3): 489–513. 10.1017/s1464793105006767 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref034] 34.Supriya K, Moreau CS, Sam K, Price TD. Analysis of tropical and temperate elevational gradients in arthropod abundance. Front Biogeogr. 2019;11(2). [Google Scholar]

[pone.0238219.ref035] 35.Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jędrzejewska B, Lima M, et al. The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Climate research. 2011;46(1): 15–27. [Google Scholar]

[pone.0238219.ref036] 36.Andersen AN, Hoffmann BD, Müller WJ, Griffiths AD. Using ants as bioindicators in land management: simplifying assessment of ant community responses. J Appl Ecol. 2002;39(1): 8–17. [Google Scholar]

[pone.0238219.ref037] 37.Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1: 3–14. [Google Scholar]

[pone.0238219.ref038] 38.Bates D, Machler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using {lme4}. J Stat Softw. 2015;67:1–48. 10.18637/jss.v067.i01 [DOI] [Google Scholar]

[pone.0238219.ref039] 39.Su AGaY-S. arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. 2018.

[pone.0238219.ref040] 40.Wang YI, Naumann U, Wright ST, Warton DI. mvabund–an R package for model‐based analysis of multivariate abundance data. Methods Ecol. Evol. 2012;3(3): 471–474. [Google Scholar]

[pone.0238219.ref041] 41.Roberts DW. labdsv: Ordination and multivariate analysis for ecology. R package version. 2007;1(1). [Google Scholar]

[pone.0238219.ref042] 42.De Cáceres M, Jansen F. indicspecies: Relationship Between Species and Groups of Sites. R package version 1.7. 5. 2015.

[pone.0238219.ref043] 43.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2017. [Google Scholar]

[pone.0238219.ref044] 44.Urban SCGaDL. The ecodist package for dissimilarity-based analysis of ecological data. Statistical Software. 2007;22(7):1–19. [Google Scholar]

[pone.0238219.ref045] 45.Lange M, Türke M, Pašalić E, Boch S, Hessenmöller D, Müller J, et al. Effects of forest management on ground-dwelling beetles (Coleoptera; Carabidae, Staphylinidae) in Central Europe are mainly mediated by changes in forest structure. For Ecol Manage. 2014;329: 166–176. [Google Scholar]

[pone.0238219.ref046] 46.Perry KI, Wallin KF, Wenzel JW, Herms DA. Forest disturbance and arthropods: Small‐scale canopy gaps drive invertebrate community structure and composition. Ecosphere. 2018;9(10), p.e02463. [Google Scholar]

[pone.0238219.ref047] 47.de Abreu Pestana LF, de Souza ALT, Tanaka MO, Labarque FM, Soares JAH. Interactive effects between vegetation structure and soil fertility on tropical ground-dwelling arthropod assemblages. App Soil Ecol. 2020;155: p.103624. [Google Scholar]

[pone.0238219.ref048] 48.Pardon P, Reheul D, Mertens J, Reubens B, De Frenne P, De Smedt P, et al. Gradients in abundance and diversity of ground dwelling arthropods as a function of distance to tree rows in temperate arable agroforestry systems. Agr Ecocsyst Environ. 270;2019: 114–128. [Google Scholar]

[pone.0238219.ref049] 49.Černecká Ľ, Mihál I, Gajdoš P, Jarčuška B. The effect of canopy openness of European beech (Fagus sylvatica) forests on ground‐dwelling spider communities. Insect Conser Diver, 2020;13(3), pp.250–261. [Google Scholar]

[pone.0238219.ref050] 50.Thorn S, Bußler H, Fritze MA, Goeder P, Müller J, Weiß I, et al. Canopy closure determines arthropod assemblages in microhabitats created by windstorms and salvage logging. For Ecol Manage. 2016;381: 188–195. [Google Scholar]

[pone.0238219.ref051] 51.Plowman NS, Mottl O, Novotny V, Idigel C, Philip FJ, Rimandai M, et al. Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies. Ecography. 2020;43(3): 431–442. [Google Scholar]

[pone.0238219.ref052] 52.Donoso DA, Johnston MK, Kaspari M. Trees as templates for tropical litter arthropod diversity. Oecologia. 2010;164(1), pp.201–211. 10.1007/s00442-010-1607-3 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref053] 53.Vehviläinen H, Koricheva J, Ruohomäki K. Effects of stand tree species composition and diversity on abundance of predatory arthropods. Oikos. 2008;117(6): 935–943. [Google Scholar]

[pone.0238219.ref054] 54.Ayres E, Dromph KM, Bardgett RD. Do plant species encourage soil biota that specialize in the rapid decomposition of their litter? Soil Biol Biochem. 2006;38(1): 183–186. [Google Scholar]

[pone.0238219.ref055] 55.Illig J, Langel R, Norton RA, Scheu S, Maraun M. Where are the decomposers? Uncovering the soil food web of a tropical montane rain forest in southern Ecuador using stable isotopes (15 N). J Trop Ecol. 2005;21: 589–593. [Google Scholar]

[pone.0238219.ref056] 56.Hochberg ME, Bertault G, Poitrineau K, Janssen A. Olfactory orientation of the truffle beetle, Leiodes cinnamomea. Entomol Exp Appl. 2003;109(2): 147–153. [Google Scholar]

[pone.0238219.ref057] 57.Haskins KE, Gehring CA. Interactions with juniper alter pinyon pine ectomycorrhizal fungal communities. Ecology. 2004;85(10): 2687–2692. [Google Scholar]

[pone.0238219.ref058] 58.Szewczyk T, McCain CM. A systematic review of global drivers of ant elevational diversity. PLoS One. 2016;11(5). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0238219.ref059] 59.Sanders NJ, Moss J, Wagner D. Patterns of ant species richness along elevational gradients in an arid ecosystem. Global Ecol Biogeogr. 2003;2: 93–102. [Google Scholar]

[pone.0238219.ref060] 60.Walsh J, et al. Ch. 2: Our changing climate. Climate Change Impacts in the United States: The Third National Climate Assessment, Melillo JM, Richmond Terese (TC), and Yohe GW, Eds, US Global Change Research Program. 2014; 19–67. [Google Scholar]

[pone.0238219.ref061] 61.Karmalkar AV, Bradley RS. Consequences of global warming of 1.5 C and 2 C for regional temperature and precipitation changes in the contiguous United States. PloS one. 2017;12(1). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0238219.ref062] 62.Floyd ML, Clifford MJ, Cobb NS, Hanna D, Delph RJ, Ford P, et al. 175 of stand characteristics to drought-induced mortality in piñon-juniper woodlands in Colorado, Arizona and New Mexico. Ecol. Appl. 2009;19: 1223–1230. 10.1890/08-1265.1 [DOI] [PubMed] [Google Scholar]

[pone.0238219.ref063] 63.Sanders NJ, Lessard JP, Fitzpatrick MC, Dunn RR. Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains Global Ecol Biogeogr. 2007;16(5): 640–649. [Google Scholar]

[pone.0238219.ref064] 64.Uhey DA, Hofstetter RW, Remke M, Vissa S, Haubensak KA. Climate and vegetation structure shape ant communities along elevational gradients on the Colorado Plateau. Ecol Evol. 2020; 10.1002/ece3.6538 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0238219.ref065] 65.Gotelli NJ, Ellison AM, Dunn RR, Sanders NJ. Counting ants (Hymenoptera: Formicidae): biodiversity sampling and statistical analysis for myrmecologists. Myrmecol News. 2011. [Google Scholar]

[pone.0238219.ref066] 66.Redmond MD, Golden ES, Cobb NS, Barger NN. Vegetation management across Colorado Plateau BLM lands: 1950–2003. Rangeland Ecol Manag. 2014;67(6): 636–640. [Google Scholar]

[pone.0238219.ref067] 67.Bombaci S, Pejchar L. Consequences of pinyon and juniper woodland reduction for wildlife in North America. For Ecol Manage. 2016;365: 34–50. [Google Scholar]

PERMALINK

Ground-dwelling arthropods of pinyon-juniper woodlands: Arthropod community patterns are driven by climate and overall plant productivity, not host tree species

Derek Andrew Uhey

Hannah Lee Riskas

Aaron Dennis Smith

Richard William Hofstetter

Roles

Abstract

Introduction

Materials and methods

Study sites

Fig 1. Site locations.

Fig 2. Woodland composition of sites.

Seasonality and weather

Climate and plant productivity measures

Arthropod sampling

Data analysis

Environmental variables

Fig 3. Temperature, vapor pressure, and precipitation along gradients.

Table 1. Elevation and climate variable correlations.

Fig 4. NDVI along gradients.

GDA abundance and richness

GDA composition

Ants and beetles

Results

Insect community in pinyon vs juniper

Gradient and season

Climate and productivity

Fig 5. GDA richness and abundance along precipitation gradients.

Table 2. GLMM models for GDA richness and abundance.

Ant and beetle differences

Fig 6. Multi-dimensional scaling plots of PJ woodland ant (left panel) and beetle (right panel) communities using the Bray-Curtis similarity coefficient.

Discussion

Tree species effect

Climate and productivity effects

Taxa differences

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Frank H Koch

Roles

Author response to Decision Letter 0

Decision Letter 1

Frank H Koch

Roles

Author response to Decision Letter 1

Decision Letter 2

Frank H Koch

Roles

Acceptance letter

Frank H Koch

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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