Next Article in Journal
Is Land Expropriation to Keep Agricultural Use an Effective Strategy for the Conservation of an Urban Agricultural Heritage System? Evidence from China
Next Article in Special Issue
Analysis of Landscape Character Assessment and Cultural Ecosystem Services Evaluation Frameworks for Peri-Urban Landscape Planning: A Case Study of Harku Municipality, Estonia
Previous Article in Journal
Exploring Accessibility and Its Impact in the Mountain City: A Typical Case Study of Nyingchi City, Tibet Autonomous Region, China
Previous Article in Special Issue
Assessing Preferences for Cultural Ecosystem Services in the English Countryside Using Q Methodology
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 12
Issue 2
10.3390/land12020362
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor

by
Gabriella L. Pardee
1,*,
Kimberly M. Ballare
1,2,
John L. Neff
3,
Lauren Q. Do
1,
DianaJoyce Ojeda
1,
Elisa J. Bienenstock
4,†,
Berry J. Brosi
5,†,
Tony H. Grubesic
6,†,
Jennifer A. Miller
7,†,
Daoqin Tong
8,† and
Shalene Jha
1,9
1
Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
2
Ecology and Evolutionary Biology Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA
3
Central Texas Melittological Institute, Austin, TX 78731, USA
4
Watts School of Public Service and Community Solutions, Arizona State University, Tempe, AZ 85281, USA
5
Department of Biology, University of Washington, Seattle, WA 98195, USA
6
Center for Geospatial Sciences, University of California Riverside, Riverside, CA 92521, USA
7
Department of Geography and the Environment, University of Texas at Austin, Austin, TX 78712, USA
8
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
9
Lady Bird Johnson Wildflower Center, Austin, TX 78739, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2023, 12(2), 362; https://doi.org/10.3390/land12020362
Submission received: 8 December 2022 / Revised: 12 January 2023 / Accepted: 16 January 2023 / Published: 29 January 2023
(This article belongs to the Special Issue Rural–Urban Gradients: Landscape and Nature Conservation)
Browse Figures
Review Reports Versions Notes

Abstract

:
Given widespread concerns over human-mediated bee declines in abundance and species richness, conservation efforts are increasingly focused on maintaining natural habitats to support bee diversity in otherwise resource-poor environments. However, natural habitat patches can vary in composition, impacting landscape-level heterogeneity and affecting plant-pollinator interactions. Plant-pollinator networks, especially those based on pollen loads, can provide valuable insight into mutualistic relationships, such as revealing the degree of pollination specialization in a community; yet, local and landscape drivers of these network indices remain understudied within urbanizing landscapes. Beyond networks, analyzing pollen collection can reveal key information about species-level pollen preferences, providing plant restoration information for urban ecosystems. Through bee collection, vegetation surveys, and pollen load identification across ~350 km of urban habitat, we studied the impact of local and landscape-level management on plant-pollinator networks. We also quantified pollinator preferences for plants within urban grasslands. Bees exhibited higher foraging specialization with increasing habitat heterogeneity and visited fewer flowering species (decreased generality) with increasing semi-natural habitat cover. We also found strong pollinator species-specific flower foraging preferences, particularly for Asteraceae plants. We posit that maintaining native forbs and supporting landscape-level natural habitat cover and heterogeneity can provide pollinators with critical food resources across urbanizing ecosystems.

1. Introduction

The urban-to-rural interface has long been shown to drive changes in animal community composition via shifts in food and nesting resources, alteration in biotic and abiotic factors, and the impediment of an organism’s ability to disperse into hospitable habitat [1,2]. Despite this fact, researchers have only begun examining how changes in habitat composition affect mutualistic relationships across human-modified landscapes [3,4]. This effort is complicated because natural habitats within urban areas are often highly heterogenous and thus vary in their degree of quality and connectivity, which can directly affect mutualistic interactions [5,6]. For example, past work has shown that increases in landscape-level natural habitat cover can result in higher pollination services [7] and that increases in landscape-level heterogeneity can lead to increases in mutualistic plant-ant co-occurrences [8]. Furthermore, given that human-dominated habitats, which we describe as urbanizing environments that have high population densities, are expanding in size and number due to human population growth [9], it is essential to deepen our understanding of how landscape-level habitat composition impacts important mutualistic relationships in the face of these continued global changes.
The mutualistic interaction between plants and pollinators is particularly critical in the context of global change because pollinators contribute more than 300 bn USD worldwide in ecosystem service provisioning [10], given that the majority of plant species rely on pollinators for successful reproduction [11,12]. Previous studies have shown that both local and landscape characteristics of urban parks and gardens can affect the richness and abundance of bee species [13,14,15], with potential subsequent effects on plant-pollinator interactions and pollination success. Indeed, plant-pollinator network indices have successfully predicted community stability and ecosystem function [16], particularly within human-dominated landscapes [17,18]. For example, past work has indicated that habitat fragmentation due to increased agriculture alters network structure and size, which can lead to increased extinction rates, disruption of ecosystem functioning, and functional homogenization of pollinator communities [19,20]. Further, in simplified habitats with limited resources for bees, plant-pollinator networks are often less specialized, meaning that pollinators are interacting with a larger group of plant species than in more complex habitats [21]. In contrast, a more heterogeneous habitat may allow pollinators to seek out specific flowers, leading to more specialized plant-pollinator networks [22]. Further, network indices such as foraging specialization and resource partitioning are predictive of many pollination success metrics, such as stigmatic pollen deposition [17], highlighting the high ecological value of these network quantifications.
In addition to leveraging plant-pollinator interactions for predicting function and community stability, it is essential to understand pollinator species-specific plant preferences in order to effectively restore optimal pollinator habitat. Animal foraging preferences can be beneficial to consider when designing conservation habitat [23] and are classically defined as the resources that are utilized in proportionally higher amounts than are available across a landscape [24]. For many pollinators, pollen is the fundamental food source for larval development and determines adult reproductive success [25]. As pollen is highly variable in its nutritional content across species [26], many pollinators seek out flower species that are most beneficial for their offspring [27]. Despite the value of understanding pollen preference for pollinator conservation, past network interaction research has often focused on pollinator visitation data (not pollen collection), irrespective of whether insects actually collected pollen [28]. Thus, assessment of pollen floral preference is often missing from pollinator community ecology research, even though it could provide key information for improving vegetation management efforts.
In this study, we quantify pollen-based plant-pollinator networks and pollinator species-specific pollen usage across a ~350 km rapidly urbanizing linear corridor in the southern US to determine the effects of land use change on plant-pollinator interactions and pollinator foraging preferences. We focused our sampling on bees as they are among the most effective pollinators [29,30], especially in the southern U.S. [31]. We investigated if local and landscape habitat features impact key plant-pollinator network characteristics, specifically specialization, connectance, nestedness, and pollinator generality, and quantified whether bees show pollen preferences within urban grassland habitats. We hypothesized that network foraging specialization would increase and pollinator generality would decrease with an increased amount of surrounding semi-natural habitat, indicative of more selective foraging behavior, as seen in past studies [32]. In contrast, network connectance and nestedness would increase with increased floral abundance and vegetation cover, as these characteristics indicate greater habitat complexity and food resources [33,34]. We also hypothesized that native pollinators would exhibit preferences for both native and non-native plant species, likely those within Asteraceae, as seen in other pollen preference studies conducted in Texas, U.S. [35]

2. Methods

2.1. Study System

We conducted this study across ~350 km of the rapidly urbanizing I-35 highway corridor in Central Texas, USA. This corridor, which encompasses five of the twenty fastest-growing large cities in the U.S. [36], cuts through highly fragmented savannah and grassland habitat [37]. Both the Austin and Dallas-Fort Worth metroplexes expanded by more than 4% per year from 2000 to 2010 [38], highlighting the urgent need for urban ecological research across these regions. We selected 20 grassland sites within this linear corridor (10 across each metroplex) based on past work in the Central Texas region [13]. These sites are all managed as wildlife refuges or city/state parks and are dominated by native grassland vegetation. All sites have minimal to no active disturbance regimes (e.g., fire or grazing), except for infrequent mowing in some sites that occurred less than two times per year and outside of the sampling season. Sites were located along a gradient of developed land and were separated by at least 2 km, as this captures typical maximum foraging distances for bees [39]. In each site, we established a 50 × 50 m plot to sample local habitat characteristics as well as bee and flowering plant communities. We visited each site three times during the late spring and early summer of 2013, specifically in the first two weeks of May, June, and July, as described in [13]. We chose to sample during the late spring and early summer as this period has both high pollinator abundance and low year-to-year variation in rainfall and is thus likely to produce the most representative sample of the typical bee and flowering community [13].

2.2. Local and Landscape Characteristics

Within each 50 × 50 m sampling plot, we laid out three 50 m transects, running North to South, at 15, 25, and 35 m to measure the flowering plant community and local habitat characteristics, as described in Ballare et al. [11]. Within each quadrat, we recorded the number of flowering forb species, the number of inflorescences per flowering species, and the percent cover of live ground vegetation, bare ground, rocky/impervious surface, and leaf litter. Previous studies have shown these factors to be important resources for bee nesting and foraging [15,40]. Overall, sites had a mean of 53.29% ground vegetation, 12.94% bare ground, 2.78% rocky/impervious surface, and 26.60% leaf litter, as well as a mean of 402.55 flowering inflorescences, and 5.98 flowering species.
We used the 2011 National Land Cover Database (NLCD; 30 m resolution and minimum mapping areas of five pixels: [41]) dataset to quantify the amount of semi-natural, agricultural, and developed land within a 2 km buffer of each site and calculated these values in qGIS v. 2.14 [13,42,43]. Specifically, we summed the percentage area of each of 11 land use types in 2 km buffers surrounding each site into three land cover categories representing broad differences in bee nesting and foraging habitat (as per Ritchie et al., 2016 [35]): (1) developed land, comprised of high intensity developed, medium intensity developed, low intensity developed, and developed open space (NLCD categories 21–24); (2) pasture and cropland, comprised of pasture/hay and agriculture crops (NLCD categories 81–82); and (3) semi-natural land, comprised of deciduous forest, evergreen forest, mixed forest, grassland/herbaceous, and shrub/scrub (NLCD categories 41–43, 71, 52). Finally, as habitat heterogeneity could also play a role in bee nesting and foraging behavior, we quantified heterogeneity as the total number of land use patches of all 15 possible NLCD land use types within each 2 km buffer, calculated using qGIS, as in [13]. Sites were surrounded by 0–100% developed land, 0–63% cropland, and 0–98% semi-natural habitat within a 2 km radius, with a mean of 10 land-use types within a 2 km radius.

2.3. Pollinator Sampling

We focused on wild native bees in our study, and thus did not collect non-native managed honey bees (Apis mellifera) as none of our sites contained hives and therefore had low abundances of honey bees. a. We sampled bees systematically within our 50 × 50 m plot by collecting any native bee seen actively foraging on a flower using hand nets, as done in previous pollen studies examining floral preferences [44,45,46,47]. To reduce observation bias, two trained researchers each sampled half of the plot for 15 min each, then switched sides for the remaining 15 min, for a total netting time of 1 h per site in each of three sampling rounds (May, June, and July), as in [48]. To reduce cross-contamination, each bee was placed into a separate vial upon collection, and the observer shook out the hand net to remove any pollen. All specimens were pinned, labeled with their site-round and floral association, and identified to the lowest taxonomic level. Given temporal proximity in the sampling round, we combined interactions across the three rounds as in past network studies (i.e., [20]) for a total of 20 networks.

2.4. Pollen Identification

All bees were washed for pollen load analysis by gently vortexing the entire specimen in 5.5 mL of ethanol to maximize pollen extraction, centrifuging the tube to isolate the pollen pellet, and removing 5 mL of the ethanol supernatant, as described in [35]. To create the pollen slide for each individual bee, 20 microliters of the vortexed pollen pellet, plus ethanol solution, was added to 60 microliters of fuchsin + glycerol mix [49] to dye the pollen grains for increased visibility of pollen diagnostic features, and then plated for each slide. For each slide, we scanned the sample starting from the upper left corner, moving in a snake-like pattern until we encountered up to 300 grains, a common pollen identification threshold [50]. Each pollen grain encountered was identified to species level using a pollen reference library created from the anthers of all flowering species found within our sites. We only included pollen in our counts if there were more than two occurrences of it per slide [35]. When an unknown pollen grain was encountered more than two times, it was designated with a morphospecies ID [51].

2.5. Pollen-Based Networks

We constructed quantitative pollen-transport networks by calculating four network metrics, foraging specialization (H2’), connectance, nestedness, and pollinator generality because past studies have shown that these metrics have the capacity to be influenced by local and landscape features, such as floral availability at the local scale, and amount of semi-natural habitat at the landscape scale (e.g., [17,18,33]). All metrics were calculated using the networklevel function in the bipartite package in R. Network foraging specialization (H2’) describes the degree of foraging specialization between plants and pollinators while incorporating abundances into the network [52] and ranges from 0 to 1, with 0 indicating complete foraging generalization and 1 meaning complete foraging specialization. Connectance describes network complexity [53], is calculated as the proportion of observed links out of the total possible links between plants and pollinators [54], and ranges from 0 to 1 with higher values indicating higher generalization [33]. We weighted connectance, which takes ‘link weights’ (visitation rates) into account in order to capture the shape of the visitation distribution [55]. Nestedness describes the degree to which specialist species (those with few links) interact with generalist species (those with many links [56], and has a value of 0 to 100, where 0 represents complete randomization and 100 represents perfect nestedness [57]. We weighted nestedness by visitation rates using the NODF function [58]. Pollinator generality describes diet breadth and is calculated as the mean number of pollinators per plant species [59].
We performed all statistical analyses used in this study in the statistical program R, version 3.5.1 (R Core Team 2018). We modeled the impacts of local and landscape characteristics on bee abundance, species richness, and the four network metric response variables using linear mixed models with a Gaussian distribution in the lme4 package [60]. Because the percentage of developed, cropland, and semi-natural habitat were highly collinear, we opted to keep only the percentage of semi-natural habitat, as in past studies [61]. Likewise, because the percent cover of leaf litter and rocky surfaces were highly collinear with the percent ground vegetation, we opted to keep the percent of live ground vegetation, as in [13,61]. Also, flower species richness was highly correlated with the mean number of flowers, thus we opted to keep the mean number of flowers in our analysis, as in [43]. After removing the five correlated variables, the remaining variables showed variance inflation scores (VIF, [62]) of approximately two or lower. Thus, the final variables we used in our models were percent ground vegetation, percent bare ground, flower abundance, amount semi-natural habitat within 2 km of each site, and habitat heterogeneity as fixed effects, as well as region (Dallas-Fort Worth vs. Austin Metroplex) as a random effect. All predictor variables were rescaled. We then tested all combinations of the fixed effect variables and conducted model selection via Akaike’s information criterion (AIC) using the MuMIn package [63]. We constructed our final models by averaging all models within delta AIC < 2 of the top model [64] also using MuMin.

2.6. Pollen Preference Analysis

We assessed pollen preference for the five most abundant bee species across our sites: Halictus ligatus Say, Melissodes coreopsis Robertson, Melissodes tepaneca Cresson, Svastra petulca Cresson, and Xylocopa virginica Linnaeus. Though Svastra petulca and Melissodes tepaneca are considered Asteraceae specialists based on the composition of pollen masses used for nest provisioning [65], nest provisions do not fully describe all floral associations by adult bees, and body pollen collected from these species has revealed that adult bees visit a variety of hosts during foraging bouts [66]. Thus, pollen-based preference studies provide a great tool for understanding foraging behavior, beyond nest provision to include nectar foraging, among both specialist and generalist bee species. Preference analyses require individual and site-level replication, which is why we focused on these five species. In order to conservatively assess pollen preference, we only included bees that had a minimum of 50 pollen grains on their bodies (as per [35]) (mean individuals collected per species: 49.1; mean number of sites per species: 11.1). Pollen preference describes a condition where a pollen species is found on a pollinator species’ body significantly more than expected relative to its landscape availability [67,68] and is assessed via traditional habitat use methods [69]. We used classical compositional analyses of habitat use [24] for each species using the adehabitatHS package [70]. Specifically, to test if pollen species usage was significantly nonrandom relative to availability per site, we compared a matrix of floral species availability per site (proportion of floral cover, as measured from the vegetation surveys) with a second matrix of proportional pollen load for each plant species per bee. We evaluated the relationship between the two matrices using a randomization test (500 repetitions) where significant preference for one species over each other species was assessed [24] as in past pollen usage studies [35,67,68].

3. Results

Across all sites, we collected a total of 624 bees comprising 87 different species. The most common bee species collected were: Svastra petulca (12.8%), Melissodes coreopsis (9.77%), Halictus ligatus (7.05%), Xylocopa virginica (6.73%), and Melissodes tepaneca (5.29%). We recorded more than 35,000 inflorescences comprising 128 different plant species. Of the 624 bees, 546 carried at least 2 grains of pollen on their body. All totaled, we screened 104,000 pollen grains, with 60 species identified to species/family level, and 8 unknown morphospecies, for a total of 68 species/morphospecies used in our analysis. The most commonly encountered pollen types were Chaerophyllum tainturieri Hook. (13.96%), Gaillardia pulchella Foug. (10.04%), and Croton monanthogynus Michx. (9.83%). Overall, 92% of pollen found on all bees was from plant species that are native to Texas.

3.1. Predictors of Pollinator Community and Pollen-Based Networks

We found that foraging specialization increased with increased landscape heterogeneity (p = 0.038, Table 1; Figure 1A). We also found that generality declined with increased proportion of semi-natural habitat surrounding the sites (p = 0.001; Table 1; Figure 1B). Further, we found that connectance decreased with increased amounts of ground vegetation (p = 0.012; Table 1; Figure 1C). We did not find any significant effect of local or landscape characteristics on nestedness (p > 0.07 in all cases; Table 1). No local or landscape characteristic significantly predicted bee abundance or species richness (p > 0.272; Supplementary Table S1).

3.2. Pollen Preference Analysis

We found that all five focal bee species exhibited significant pollen preferences. Halictus ligatus (λ = 0.209, p = 0.002; Figure 2) showed the strongest preference for Helianthus annuus L., Ratibida columniformis (Nutt.) Wooton & Standl, and Erigeron modestus A. Gray. In contrast, Melissodes tepaneca (λ = 0.456, p = 0.018; Figure 2) had the strongest preference for Convolvulus equitans Benth., Ratibida columniformis, and Gaillardia pulchella. The congener Melissodes coreopsis (λ = 0.288, p = 0.002; Figure 2) had the strongest preference for Cirsium texanum Buckley, Gaillardia pulchella, and Rudbeckia hirta. Similarly, Svastra petulca (λ = 0.377, p = 0.002; Figure 2) had the strongest preference for Gaillardia pulchella, Rudbeckia hirta, and Helianthus annuus. Finally, Xylocopa virginica (λ = 0.129, p = 0.002; Figure 2) had the strongest preference for Solanum elaeagnifolium Cav., Convolvulus equitans, and Asclepias viridiflora Raf.

4. Discussion

Through the examination of pollen-based networks and pollen preference analyses, our study provides much-needed insight into bee foraging patterns and preferences across rapidly urbanizing landscapes. We found that the degree of plant-pollinator foraging specialization was significantly driven by surrounding landscape composition (2 km radius), while network connectance was driven by within-site ground vegetation. Specifically, pollinator generality was higher in sites with greater levels of surrounding semi-natural habitat cover, and pollinator specialization was higher in sites with greater landscape heterogeneity. In contrast, we found that connectance, a measure of network complexity, was lower in sites with greater ground vegetation cover. We also documented significant and distinct pollen preferences for five bee species inhabiting the same study region, highlighting the need for maintaining high flowering plant diversity to meet distinct pollinator resource needs. Our findings demonstrate the importance of landscape-level natural habitat cover and heterogeneity in shaping plant-pollinator network interactions and reveal the critical role of multiple native forb species for bee pollen collection within urbanizing landscapes.

4.1. Landscape-Level Habitat Composition Shapes Network Structure

First, we show that the degree of landscape heterogeneity surrounding a grassland habitat patch critically impacts the structure of plant-pollinator interaction networks. We documented greater network foraging specialization with increased landscape heterogeneity. As network foraging specialization describes niche partitioning [71,72], we show that higher habitat heterogeneity reduces the overlap of shared resources among plants and pollinators, likely facilitating inter-species coexistence due to reduced competition for both pollinators and plants. This pattern has been observed in at least one other plant-pollinator study, where greater habitat diversification across the landscape resulted in higher network foraging specialization, largely driven by segregated niches among pollinators [73]. Landscape heterogeneity can provide a variety of different nesting and feeding resources to support a functionally diverse bee community [73], which has indeed been shown to increase the number of plant species receiving visits due to niche partitioning based on pollinator functional needs [74].
In addition to greater foraging specialization, we found that pollinator generality, which describes diet breadth, declined with increased semi-natural habitat cover, indicating that highly urbanized areas with less semi-natural habitat may drive bees to forage more generally, as seen in other human-dominated landscapes [16,75]. This outcome may be due to the fact that pollinators in human-dominated landscapes are spatially constrained to visit those plant species available within cities, even if they are not preferred, and may even experience greater metabolic costs to travel in these human-altered landscapes [76]. For example, Redhead et al., [32] found that pollinator generality was significantly higher in agriculturally dominated landscapes, where bee diet was more generalized in habitats with lower flower species richness and diversity. Similarly, past work has shown that increased landscape-level semi natural habitat cover can facilitate movement across agricultural landscapes for better foraging and nesting resources [77,78], potentially allowing bees to visit more preferred plant species.

4.2. Local Vegetation Drives Higher Network Connectance

The one local habitat driver of plant-pollinator network interactions was ground vegetation, where connectance was lower when live ground vegetation cover was higher. Connectance is a measure of network complexity and is associated with community stability [79,80] and past studies have found that increased floral display [34,81] and floral richness [33] may lead to greater network connectance. While we found no relationship with floral abundance, we posit that ground vegetation is likely negatively correlated with other bee nesting materials, which have also been shown to shape pollinator communities [82]. Past work has indeed shown that dead litter and reduced ground vegetation can be an important resource for stem-nesting and ground-nesting bees, respectively [83], thus greater nest proximity could increase network connectance. Because connectance is indicative of how well communities will recover from environmental disturbances [84], improving network connectance within urban grasslands could potentially buffer communities from continued land-use change. Based on our results, we posit that managing for enhanced nesting resources for bees, via the maintenance of litter or reduction of vegetation overgrowth may play an important role in shaping plant-pollinator networks. Improved pollinator nesting habitat may be particularly important in cities like Austin and Dallas-Fort Worth, where human population growth is expected to increase by over 50% between 2010 and 2050 [85], especially given that human population growth typically leads to increases in impervious ground cover [86].

4.3. Pollinators Show Distinct Floral Preferences for Native Flowers in Urban Landscapes

We also found that bees exhibited significant preferences for a number of native forb species, many within the Asteraceae family. Interestingly, across our five focal bee species, all of the top three preferred plant species were native to the study region. Further, out of all the pollen found on bees, only 5% of the plant species were non-native to Texas, showing that bees in our system primarily forage on native plants, as seen in more rural grasslands within our study region [35]. This pattern has also been documented in agricultural hedgerows on the US west coast [87], and remnant and restored heathlands in Europe [88], among other systems. While the five bee species in our study differed in their preferences, most preferred to forage on Asteraceae plants, with a number of bee species focusing on Gaillardia pulchella, and Rudbekia hirta. One caveat to our study is that though Svastra petulca and Melissodes tepaneca are Asteraceae specialists, the other three bee species used in our pollen preference study are generalist foragers, and their pollen preference might vary based on resources and across systems with different flowering species [89]. However, our findings are similar to another pollen preference study conducted within more rural grasslands in the same bioregion [35]. Further, several other studies conducted throughout South America, Europe, and South Africa have also documented Asteraceae plants as important resources for bees across ecosystems [90,91,92,93]. Asteraceae flowers have compound inflorescences that yield high amounts of pollen and nectar resources for bees and often have a colorful and large floral display [93], which might make them attractive to many different bee species. For example, G. pulchella is prevalent across Central Texas and has been found in pollen stores in nests of both specialist and generalist bees indicating that this is an important pollen source for a diverse bee assemblage [94]. Finally, though Xylocopa virginica bees were both collected from and shown to prefer Asclepias viridiflora in our study, bees do not actively collect pollen from milkweed [95]. Instead, milkweed is a rich nectar resource for bees [95] so it is likely that Xylocopa virginica picked up milkweed pollen while nectaring. Thus, sites containing a variety of Asteraceae species as well as plants known to offer high nectar rewards will likely support a more diverse bee community [96].

4.4. Conclusions and Conservation Applications

Overall, our study reveals the importance of both local and landscape habitat management for the structure of plant-pollinator interaction networks. We also highlight the critical role of native forbs for native bee pollen collection within urbanizing grassland ecoregions. Based on our findings, we suggest that land managers conserve and promote semi-natural habitat and native plant abundance within urban areas, not only because native plants make up most native bee pollen collections, but also because our network results indicate that pollinators become more specialized as natural habitat becomes more available. These recommendations could also benefit the plant community given that higher pollinator foraging specialization [97], and greater natural cover [98,99] could increase conspecific pollen deposition on stigmas. Our results and others indicate that maintaining native floral resources and natural habitat patches can support native pollinators and their foraging interactions across rapidly urbanizing landscapes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land12020362/s1, Table S1: Results of local and landscape drivers on bee abundance and species richness.

Author Contributions

Conceptualization, G.L.P., K.M.B. and S.J.; methodology, K.M.B., L.Q.D., D.O., J.L.N. and S.J.; software, G.L.P.; validation, G.L.P., K.M.B. and S.J.; formal analysis, G.L.P. and S.J.; investigation, G.L.P., K.M.B. and S.J.; resources, G.L.P., K.M.B., L.Q.D., D.O., J.L.N. and S.J.; data curation, G.L.P., K.M.B., L.Q.D., D.O., J.L.N. and S.J.; writing—original draft preparation, G.L.P. Pardee; writing—review and editing, G.L.P., K.M.B., J.L.N., L.Q.D., D.O., E.J.B., B.J.B., T.H.G., J.A.M., D.T. and S.J.; visualization, G.L.P.; supervision, G.L.P., K.M.B. and S.J.; project administration, G.L.P., K.M.B. and S.J.; funding acquisition, K.M.B., E.J.B., B.J.B., T.H.G., J.A.M., D.T. and S.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Texas Parks and Wildlife Department, Texas Ecolabs, the Native Plant Society of Texas Ann Miller Gonzales Graduate Research Grant, and the Graduate Program in Ecology, Evolution, and Behavior at the University of Texas at Austin and the National Science Foundation. Additionally, the research reported here was funded in whole or in part by DEVCOM ARL, ARO through a Multidisciplinary University Research Initiative Grant (#W911NF1910231). The research, interpretations, and perspectives reported here are those of the authors and should not be attributed to the Army or the Department of Defense.

Data Availability Statement

Data available upon request from corresponding author.

Acknowledgments

We would like to thank individual landowners, Texas State Parks, and the Lower Colorado River Authority for allowing access to sites. We thank R. Ruppel, C. Glinka, A. Ritchie, K. Merrill, B. French, N. Vojnovich, M. Rolbiecki, T. Ortega, L. Stevens, and N. Fogel for assistance with the field collection of insects and vegetation surveys, and assistance with insect preservation and curation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fehlmann, G.; Oriain, M.J.; Fürtbauer, I.; King, A.J. Behavioral Causes, Ecological Consequences, and Management Challenges Associated with Wildlife Foraging in Human-Modified Landscapes. Bioscience 2021, 71, 40–54. [Google Scholar] [CrossRef] [PubMed]
  2. Šálek, M.; Drahníková, L.; Tkadlec, E. Changes in Home Range Sizes and Population Densities of Carnivore Species along the Natural to Urban Habitat Gradient. Mamm. Rev. 2015, 45, 1–14. [Google Scholar] [CrossRef]
  3. Theodorou, P. The Effects of Urbanisation on Ecological Interactions. Curr. Opin. Insect. Sci. 2022, 52, 100922. [Google Scholar] [CrossRef] [PubMed]
  4. Pellissier, L.; Albouy, C.; Bascompte, J.; Farwig, N.; Graham, C.; Loreau, M.; Maglianesi, M.A.; Melián, C.J.; Pitteloud, C.; Roslin, T.; et al. Comparing Species Interaction Networks along Environmental Gradients. Biol. Rev. 2018, 93, 785–800. [Google Scholar] [CrossRef] [Green Version]
  5. Baldock, K.C.R.; Goddard, M.A.; Hicks, D.M.; Kunin, W.E.; Mitschunas, N.; Morse, H.; Osgathorpe, L.M.; Potts, S.G.; Robertson, K.M.; Scott, A.V.; et al. A Systems Approach Reveals Urban Pollinator Hotspots and Conservation Opportunities. Nat. Ecol. Evol. 2019, 3, 363–373. [Google Scholar] [CrossRef] [Green Version]
  6. Barr, A.E.; van Dijk, L.J.A.; Hylander, K.; Tack, A.J.M. Local Habitat Factors and Spatial Connectivity Jointly Shape an Urban Insect Community. Landsc. Urban Plan. 2021, 214, 104177. [Google Scholar] [CrossRef]
  7. Jakobsson, A.; Ågren, J. Distance to Semi-Natural Grassland Influences Seed Production of Insect-Pollinated Herbs. Oecologia 2014, 175, 199–208. [Google Scholar] [CrossRef]
  8. Corro, E.J.; Ahuatzin, D.A.; Jaimes, A.A.; Favila, M.E.; Ribeiro, M.C.; López-Acosta, J.C.; Dáttilo, W. Forest Cover and Landscape Heterogeneity Shape Ant–Plant Co-Occurrence Networks in Human-Dominated Tropical Rainforests. Landsc. Ecol. 2019, 34, 93–104. [Google Scholar] [CrossRef]
  9. Seto, K.C.; Fragkias, M.; Güneralp, B.; Reilly, M.K. A Meta-Analysis of Global Urban Land Expansion. PLoS ONE 2011, 6, e23777. [Google Scholar] [CrossRef]
  10. IPBES. The Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on Pollinators, Pollination, and Food Production; Potts, S.G., Imperatriz-Fonseca, V.L., Ngo, H.T., Eds.; Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services: Bonn, Germany, 2016. [Google Scholar]
  11. Ollerton, J.; Winfree, R.; Tarrant, S. How Many Flowering Plants Are Pollinated by Animals? Oikos 2011, 120, 321–326. [Google Scholar] [CrossRef]
  12. Joosten, H.; de Klerk, P. Importance of Pollinators in Changing Landscapes for World Crops. Proc. R. Soc. B Biol. Sci. 2007, 274, 303–313. [Google Scholar]
  13. Ballare, K.M.; Neff, J.L.; Ruppel, R.; Jha, S. Multi-Scalar Drivers of Biodiversity: Local Management Mediates Wild Bee Community Response to Regional Urbanization. Ecol. Appl. 2019, 29, e01869. [Google Scholar] [CrossRef]
  14. Ayers, A.C.; Rehan, S.M. Supporting Bees in Cities: How Bees Are Influenced by Local and Landscape Features. Insects 2021, 12, 128. [Google Scholar] [CrossRef] [PubMed]
  15. Egerer, M.; Cecala, J.M.; Cohen, H. Wild Bee Conservation within Urban Gardens and Nurseries: Effects of Local and Landscape Management. Sustainability 2020, 12, 293. [Google Scholar] [CrossRef] [Green Version]
  16. Spiesman, B.J.; Inouye, B.D. Habitat Loss Alters the Architecture of Plant-Pollinator Interaction Networks. Ecology 2013, 94, 2688–2696. [Google Scholar] [CrossRef] [Green Version]
  17. Weiner, C.N.; Werner, M.; Linsenmair, K.E.; Bluthgen, N. Land-Use Impacts on Plant-Pollinator Networks: Interaction Strength and Specialization Predict Pollinator Declines. Ecology 2014, 95, 466–474. [Google Scholar] [CrossRef] [PubMed]
  18. Theodorou, P.; Albig, K.; Radzevičiūtė, R.; Settele, J.; Schweiger, O.; Murray, T.E.; Paxton, R.J. The Structure of Flower Visitor Networks in Relation to Pollination across an Agricultural to Urban Gradient. Funct. Ecol. 2017, 31, 838–847. [Google Scholar] [CrossRef]
  19. Jauker, F.; Jauker, B.; Grass, I.; Steffan-Dewenter, I.; Wolters, V. Partitioning Wild Bee and Hoverfly Contributions to Plant–Pollinator Network Structure in Fragmented Habitats. Ecology 2019, 100, e02569. [Google Scholar] [CrossRef]
  20. Grass, I.; Jauker, B.; Steffan-Dewenter, I.; Tscharntke, T.; Jauker, F. Past and Potential Future Effects of Habitat Fragmentation on Structure and Stability of Plant–Pollinator and Host–Parasitoid Networks. Nat. Ecol. Evol. 2018, 2, 1408–1417. [Google Scholar] [CrossRef]
  21. Tommasi, N.; Biella, P.; Maggioni, D.; Fallati, L.; Agostinetto, G.; Labra, M.; Galli, P.; Galimberti, A. DNA Metabarcoding Unveils the Effects of Habitat Fragmentation on Pollinator Diversity, Plant-Pollinator Interactions, and Pollination Efficiency in Maldive Islands. Mol. Ecol. 2022, 1–11. [Google Scholar] [CrossRef]
  22. Moreira, E.F.; Boscolo, D.; Viana, B.F. Spatial Heterogeneity Regulates Plant-Pollinator Networks across Multiple Landscape Scales. PLoS ONE 2015, 10, e0123628. [Google Scholar] [CrossRef] [PubMed]
  23. Vaudo, A.D.; Patch, H.M.; Mortensen, D.A.; Tooker, J.F.; Grozinger, C.M. Macronutrient Ratios in Pollen Shape Bumble Bee (Bombus Impatiens) Foraging Strategies and Floral Preferences. Proc. Natl. Acad. Sci. USA 2016, 113, E4035–E4042. [Google Scholar] [CrossRef] [Green Version]
  24. Aebischer, N.J.; Robertson, P.A.; Kenward, R.E. Compositional Analysis of Habitat Use from Animal Radio-Tracking Data. Ecology 1993, 74, 1313–1325. [Google Scholar] [CrossRef]
  25. Thorp, R.W. The Collection of Pollen by Bees. In Pollen and Pollination; Danfi, A., Hesse, M., Pacini, E., Eds.; Springer Vienna: Vienna, Austria, 2000; pp. 211–223. [Google Scholar]
  26. Roulston, A.H.; Cane, J.H.; Buchmann, S.L. What Governs Protein Content of Pollen: Pollinator Preferences, Pollen-Pistil Interactions, or Phylogeny? Ecol. Monogr. 2000, 70, 617–643. [Google Scholar] [CrossRef]
  27. Vaudo, A.D.; Tooker, J.F.; Patch, H.M.; Biddinger, D.J.; Coccia, M.; Crone, M.K.; Fiely, M.; Francis, J.S.; Hines, H.M.; Hodges, M.; et al. Pollen Protein: Lipid Macronutrient Ratios May Guide Broad Patterns of Bee Species Floral Preferences. Insects 2020, 11, 132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Barker, D.A.; Arceo-Gomez, G. Pollen Transport Networks Reveal Highly Diverse and Temporally Stable Plant-Pollinator Interactions in an Appalachian Floral Community. AoB Plants 2021, 13, plab062. [Google Scholar] [CrossRef]
  29. Garibaldi, L.A.; Steffan-dewenter, I.; Winfree, R.; Aizen, M.A.; Bommarco, R.; Cunningham, S.A.; Kremen, C.; Carvalheiro, L.G.; Harder, L.D.; Afik, O.; et al. Wild Pollinators Enhance Fruit Set of Crops Regardless Honey Bee Abundance. Science (1979) 2013, 339, 1608–1611. [Google Scholar] [CrossRef]
  30. Winfree, R.; Williams, N.M.; Dushoff, J.; Kremen, C. Native Bees Provide Insurance against Ongoing Honey Bee Losses. Ecol. Lett. 2007, 10, 1105–1113. [Google Scholar] [CrossRef]
  31. Cusser, S.; Haddad, N.M.; Jha, S. Unexpected Functional Complementarity from Non-Bee Pollinators Enhances Cotton Yield. Agric. Ecosyst. Environ. 2021, 314, 107415. [Google Scholar] [CrossRef]
  32. Redhead, J.W.; Woodcock, B.A.; Pocock, M.J.O.; Pywell, R.F.; Vanbergen, A.J.; Oliver, T.H. Potential Landscape-Scale Pollinator Networks across Great Britain: Structure, Stability and Influence of Agricultural Land Cover. Ecol. Lett. 2018, 21, 1821–1832. [Google Scholar] [CrossRef] [Green Version]
  33. Cusser, S.; Goodell, K. Diversity and Distribution of Floral Resources Influence the Restoration of Plant-Pollinator Networks on a Reclaimed Strip Mine. Restor. Ecol. 2013, 21, 713–721. [Google Scholar] [CrossRef]
  34. Russo, L.; Shea, K. Deliberately Increased Network Connectance in a Plant-Pollinator Community Experiment. J. Complex Netw. 2017, 5, 473–485. [Google Scholar] [CrossRef] [Green Version]
  35. Ritchie, A.D.; Ruppel, R.; Jha, S. Generalist Behavior Describes Pollen Foraging for Perceived Oligolectic and Polylectic Bees. Environ. Entomol. 2016, 45, 909–919. [Google Scholar] [CrossRef] [PubMed]
  36. Guo, J.; Zhang, M. Exploring the Patterns and Drivers of Urban Expansion in the Texas Triangle Megaregion. Land 2021, 10, 1244. [Google Scholar] [CrossRef]
  37. Day, F.A.; Vaughan, J.W. The Evolving Landscape of the Austin-San Antonio Corridor. In Landscapes, Identities, and Development; Roca, Z., Claval, P., Agnew, J., Eds.; Routledge: New York, NY, USA, 2011; pp. 275–285. [Google Scholar]
  38. Frey, W.H. Population Growth in Metro America since 1980: Putting the Volatile 2000s in Perspective; Metropolitan Policy Program at Brookings: Washington, DC, USA, 2012. [Google Scholar]
  39. Greenleaf, S.S.; Williams, N.M.; Winfree, R.; Kremen, C. Bee Foraging Ranges and Their Relationship to Body Size. Oecologia 2007, 153, 589–596. [Google Scholar] [CrossRef]
  40. Birdshire, K.R.; Carper, A.L.; Briles, C.E. Bee Community Response to Local and Landscape Factors along an Urban-Rural Gradient. Urban. Ecosyst. 2020, 23, 689–702. [Google Scholar] [CrossRef] [Green Version]
  41. Homer, C.J.; Fry, A.; Barnes, C.A. The National Land Cover Database. US Geol. Surv. Fact Sheet 2012, 3020, 1–4. [Google Scholar]
  42. QGIS Development Team. QGIS Geographic Information System Open Source Geospatial Foundation Project. 2014. Available online: https://qgis.org/en/site/ (accessed on 23 September 2022).
  43. Quistberg, R.D.; Bichier, P.; Philpott, S.M. Landscape and Local Correlates of Bee Abundance and Species Richness in Urban Gardens. Environ. Entomol. 2016, 45, 592–601. [Google Scholar] [CrossRef]
  44. Lane, I.G.; Portman, Z.M.; Herron-Sweet, C.H.; Pardee, G.L.; Cariveau, D.P. Differences in Bee Community Composition between Restored and Remnant Prairies Are More Strongly Linked to Forb Community Differences than Landscape Differences. J. Appl. Ecol. 2022, 59, 129–140. [Google Scholar] [CrossRef]
  45. de Manincor, N.; Hautekèete, N.; Mazoyer, C.; Moreau, P.; Piquot, Y.; Schatz, B.; Schmitt, E.; Zélazny, M.; Massol, F. How Biased Is Our Perception of Plant-Pollinator Networks? A Comparison of Visit- and Pollen-Based Representations of the Same Networks. Acta Oecologica 2020, 105, 103551. [Google Scholar] [CrossRef]
  46. Tur, C.; Vigalondo, B.; Trøjelsgaard, K.; Olesen, J.M.; Traveset, A. Downscaling Pollen-Transport Networks to the Level of Individuals. J. Anim. Ecol. 2014, 83, 306–317. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Alarcón, R. Congruence between Visitation and Pollen-Transport Networks in a California Plant-Pollinator Community. Oikos 2010, 119, 35–44. [Google Scholar] [CrossRef]
  48. Urbanowicz, C.; Virginia, R.A.; Irwin, R.E. The Response of Pollen-Transport Networks to Landscape-Scale Climate Variation. Polar. Biol. 2017, 40, 2253–2263. [Google Scholar] [CrossRef]
  49. O’Connell, M.; Jordan, Z.; McGilvray, E.; Cohen, H.; Liere, H.; Lin, B.B.; Philpott, S.M.; Jha, S. Reap What You Sow: Local Plant Composition Mediates Bumblebee Foraging Patterns within Urban Garden Landscapes. Urban. Ecosyst. 2021, 24, 391–404. [Google Scholar] [CrossRef]
  50. Williams, N.M.; Kremen, C. Resource Distributions among Habitats Determine Solitary Bee Offspring Production in a Mosaic Landscape. Ecol. Appl. 2007, 17, 910–921. [Google Scholar] [CrossRef] [PubMed]
  51. Joosten, H.; de Klerk, P. What’s in a Name? Some Thoughts on Pollen Classification, Identification, and Nomenclature in Quaternary Palynology. Rev. Paleobot. Palynol. 2002, 122, 29–45. [Google Scholar] [CrossRef]
  52. Blüthgen, N.; Menzel, F.; Blüthgen, N. Measuring Specialization in Species Interaction Networks. BMC Ecol. 2006, 6, 9. [Google Scholar] [CrossRef] [Green Version]
  53. Olesen, J.M.; Bascompte, J.; Dupont, Y.L.; Jordano, P. The Modularity of Pollination Networks. Proc. Natl. Acad. Sci. USA 2007, 104, 19891–19896. [Google Scholar] [CrossRef] [Green Version]
  54. Jordano, P. Patterns of Mutualistic Interactions in Pollination and Seed Dispersal: Connectance, Dependence Assymetries, and Coevolution. Am. Nat. 1987, 129, 657–677. [Google Scholar] [CrossRef]
  55. van Altena, C.; Hemerik, L.; de Ruiter, P.C. Food Web Stability and Weighted Connectance: The Complexity-Stability Debate Revisited. Theor. Ecol. 2016, 9, 49–58. [Google Scholar] [CrossRef] [Green Version]
  56. Staniczenko, P.P.A.; Kopp, J.C.; Allesina, S. The Ghost of Nestedness in Ecological Networks. Nat. Commun. 2013, 4, 13191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Dormann, C.F.; Fründ, J.; Blüthgen, N.; Gruber, B. Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks. Open Ecol. J. 2009, 2, 7–24. [Google Scholar] [CrossRef]
  58. Ulrich, W.; Almeida-Neto, M.; Gotelli, N.J. A Consumer’s Guide to Nestedness Analysis. Oikos 2009, 118, 3–17. [Google Scholar] [CrossRef]
  59. Novella-Fernandez, R.; Rodrigo, A.; Arnan, X.; Bosch, J. Interaction Strength in Plant-Pollinator Networks: Are We Using the Right Measure? PLoS ONE 2019, 14, e0225930. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Bates, D.; Mächler, M.; Bolker, B.M.; Walker, S.C. Fitting Linear Mixed-Effects Models Using Lme4. J. Stat. Softw. 2015, 67, 1–47. [Google Scholar] [CrossRef]
  61. Cohen, H.; Philpott, S.M.; Liere, H.; Lin, B.B.; Jha, S. The Relationship between Pollinator Community and Pollination Services Is Mediated by Floral Abundance in Urban Landscapes. Urban. Ecosyst. 2021, 24, 275–290. [Google Scholar] [CrossRef]
  62. Fox, J.; Weisberg, S.; Adler, D.; Bates, D.; Baud-Bovy, G.; Ellison, S. Package “Car”. 2016. Available online: https://cran.r-project.org/web/packages/car/index.html (accessed on 23 September 2022).
  63. Barton, K. Package “MuMin”. 2009. Available online: https://cran.r-project.org/web/packages/MuMIn/index.html (accessed on 23 September 2022).
  64. Grueber, C.E.; Nakagawa, S.; Laws, R.J.; Jamieson, I.G. Multimodel Inference in Ecology and Evolution: Challenges and Solutions. J. Evol. Biol. 2011, 24, 699–711. [Google Scholar] [CrossRef]
  65. Cane, J.H.; Sipes, S. Floral Specialization by Bees: Analytical Methods and a Revised Lexicon for Oligolecty. In Plant-Pollinator Interactions: From Specialization to Generalization; Waser, N., Ollerton, J., Eds.; University of Chicago Press: Chicago, IL, USA, 2006; pp. 99–112. [Google Scholar]
  66. Minckley, R.L.; Roulston, T.H. Incidental Mutualisms and Pollen Specialization among Bees. In Plant-Pollinator Interactions: From Specialization to Generalization; Waser, N.M., Ollerton, J., Eds.; Chicago Press: Chicago, IL, USA, 2006; pp. 69–98. [Google Scholar]
  67. Davis, E.S.; Reid, N.; Paxton, R.J. Quantifying Forage Specialisation in Polyphagic Insects: The Polylectic and Rare Solitary Bee, Colletes Floralis (Hymenoptera: Colletidae). Insect Conserv. Divers. 2012, 5, 289–297. [Google Scholar] [CrossRef]
  68. Jha, S.; Stefanovich, L.; Kremen, C. Bumble Bee Pollen Use and Preference across Spatial Scales in Human-Altered Landscapes. Ecol. Entomol. 2013, 38, 570–579. [Google Scholar] [CrossRef] [Green Version]
  69. Johnson, D.H. The Comparison of Usage and Availability Measurements for Evaluating Resource Preference. Ecology 1980, 61, 65–71. [Google Scholar] [CrossRef]
  70. Calenge, C. The Package “Adehabitat” for the R Software: A Tool for the Analysis of Space and Habitat Use by Animals. Ecol. Modell. 2006, 197, 516–519. [Google Scholar] [CrossRef]
  71. Blüthgen, N. Why Network Analysis Is Often Disconnected from Community Ecology: A Critique and an Ecologist’s Guide. Basic. Appl. Ecol. 2010, 11, 185–195. [Google Scholar] [CrossRef]
  72. Morozumi, C.; Loy, X.; Reynolds, V.; Schiffer, A.; Morrison, B.; Savage, J.; Brosi, B. Simultaneous Niche Expansion and Contraction in Plant–Pollinator Networks under Drought. Oikos 2022, e09265. [Google Scholar] [CrossRef]
  73. Gómez-Martínez, C.; González-Estévez, M.A.; Cursach, J.; Lázaro, A. Pollinator Richness, Pollination Networks, and Diet Adjustment along Local and Landscape Gradients of Resource Diversity. Ecol. Appl. 2022, 32, e2634. [Google Scholar] [CrossRef] [PubMed]
  74. Frund, J.; Dormann, C.F.; Holzschuh, A.; Tscharntke, T. Bee Diversity Effects on Pollination Depend on Functional Complementarity and Niche Shifts. Ecology 2013, 94, 2042–2054. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Doré, M.; Fontaine, C.; Thébault, E. Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale. Glob. Chang. Biol. 2021, 27, 1266–1280. [Google Scholar] [CrossRef] [PubMed]
  76. Bolin, A.; Smith, H.G.; Lonsdorf, E.V.; Olsson, O. Scale-Dependent Foraging Tradeoff Allows Competitive Coexistence. Oikos 2018, 127, 1575–1585. [Google Scholar] [CrossRef] [Green Version]
  77. Hopfenmüller, S.; Steffan-Dewenter, I.; Holzschuh, A. Trait-Specific Responses of Wild Bee Communities to Landscape Composition, Configuration and Local Factors. PLoS ONE 2014, 9, e104439. [Google Scholar] [CrossRef] [Green Version]
  78. Mallinger, R.E.; Gibbs, J.; Gratton, C. Diverse Landscapes Have a Higher Abundance and Species Richness of Spring Wild Bees by Providing Complementary Floral Resources over Bees’ Foraging Periods. Landsc. Ecol. 2016, 31, 1523–1535. [Google Scholar] [CrossRef]
  79. Rohr, R.P.; Saavedra, S.; Bascompte, J. On the Structural Stability of Mutualistic Systems. Science (1979) 2014, 345, 1–9. [Google Scholar] [CrossRef] [Green Version]
  80. Thébault, E.; Fontaine, C. Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks. Science (1979) 2010, 329, 853–856. [Google Scholar] [CrossRef] [PubMed]
  81. Senapathi, D.; Goddard, M.A.; Kunin, W.E.; Baldock, K.C.R. Landscape Impacts on Pollinator Communities in Temperate Systems: Evidence and Knowledge Gaps. Funct. Ecol. 2017, 31, 26–37. [Google Scholar] [CrossRef]
  82. Griffin, S.R.; Bruninga-Socolar, B.; Gibbs, J. Bee Communities in Restored Prairies Are Structured by Landscape and Management, Not Local Floral Resources. Basic. Appl. Ecol. 2021, 50, 144–154. [Google Scholar] [CrossRef]
  83. Potts, S.G.; Vulliamy, B.; Roberts, S.; O’Toole, C.; Dafni, A.; Ne’eman, G.; Willmer, P. Role of Nesting Resources in Organising Diverse Bee Communities in a Mediterranean Landscape. Ecol. Entomol. 2005, 30, 78–85. [Google Scholar] [CrossRef]
  84. Baumgartner, M.T. Connectance and Nestedness as Stabilizing Factors in Response to Pulse Disturbances in Adaptive Antagonistic Networks. J. Theor. Biol. 2020, 486, 110073. [Google Scholar] [CrossRef]
  85. Potter, L.B.; Hoque, N. Texas Population Projections 2010–2050; Office of the State Demographer, 2014. Available online: https://demographics.texas.gov/Resources/publications/2019/20190128_PopProjectionsBrief.pdf (accessed on 23 September 2022).
  86. Dutta, D.; Rahman, A.; Paul, S.K.; Kundu, A. Impervious Surface Growth and Its Inter-Relationship with Vegetation Cover and Land Surface Temperature in Peri-Urban Areas of Delhi. Urban Clim. 2021, 37, 100799. [Google Scholar] [CrossRef]
  87. Morandin, L.A.; Kremen, C. Hedgerow Restoration Promotes Pollinator Populations and Exports Native Bees to Adjacent Fields. Ecol. Appl. 2013, 23, 829–839. [Google Scholar] [CrossRef] [Green Version]
  88. Forup, M.L.; Henson, K.S.E.; Craze, P.G.; Memmott, J. The Restoration of Ecological Interactions: Plant-Pollinator Networks on Ancient and Restored Heathlands. J. Appl. Ecol. 2008, 45, 742–752. [Google Scholar] [CrossRef] [Green Version]
  89. Layek, U.; Manna, S.S.; Karmakar, P. Pollen Foraging Behaviour of Honey Bee (Apis mellifera L.) in Southern West Bengal, India. Palynology 2020, 44, 114–126. [Google Scholar] [CrossRef]
  90. Oleques, S.S.; Vizentin-Bugoni, J.; Overbeck, G.E. Influence of Grazing Intensity on Patterns and Structuring Processes in Plant–Pollinator Networks in a Subtropical Grassland. Arthropod Plant Interact. 2019, 13, 757–770. [Google Scholar] [CrossRef]
  91. Mayer, C.; Soka, G.; Picker, M. The Importance of Monkey Beetle (Scarabaeidae: Hopliini) Pollination for Aizoaceae and Asteraceae in Grazed and Ungrazed Areas at Paulshoek, Succulent Karoo, South Africa. J. Insect. Conserv. 2006, 10, 323–333. [Google Scholar] [CrossRef]
  92. van Klink, R.; Nolte, S.; Mandema, F.S.; Lagendijk, D.D.G.; WallisDeVries, M.F.; Bakker, J.P.; Esselink, P.; Smit, C. Effects of Grazing Management on Biodiversity across Trophic Levels–The Importance of Livestock Species and Stocking Density in Salt Marshes. Agric. Ecosyst. Environ. 2016, 235, 329–339. [Google Scholar] [CrossRef]
  93. Torres, C.; Galetto, L. Are Nectar Sugar Composition and Corolla Tube Length Related to the Diversity of Insects That Visit Asteraceae Flowers? Plant Biol. 2002, 4, 360–366. [Google Scholar] [CrossRef] [Green Version]
  94. Neff, J.L. Components of Nest Provisioning Behavior in Solitary Bees (Hymenoptera: Apoidea). Apidologie 2008, 39, 30–45. [Google Scholar] [CrossRef] [Green Version]
  95. Ivey, C.T.; Martinez, P.; Wyatt, R. Variation in Pollinator Effectiveness in Swamp Milkweed, Asclepias Incarnata (Apocynaceae). Am. J. Bot. 2003, 90, 214–225. [Google Scholar] [CrossRef] [Green Version]
  96. Warzecha, D.; Diekötter, T.; Wolters, V.; Jauker, F. Attractiveness of Wildflower Mixtures for Wild Bees and Hoverflies Depends on Some Key Plant Species. Insect Conserv. Divers 2018, 11, 32–41. [Google Scholar] [CrossRef] [Green Version]
  97. Brosi, B.J.; Briggs, H.M. Single Pollinator Species Losses Reduce Floral Fidelity and Plant Reproductive Function. Proc. Natl. Acad. Sci. USA 2013, 110, 13044–13048. [Google Scholar] [CrossRef] [Green Version]
  98. Klein, A.M.; Brittain, C.; Hendrix, S.D.; Thorp, R.; Williams, N.; Kremen, C. Wild Pollination Services to California Almond Rely on Semi-Natural Habitat. J. Appl. Ecol. 2012, 49, 723–732. [Google Scholar] [CrossRef]
  99. Vaca-Uribe, J.L.; Figueroa, L.L.; Santamaría, M.; Poveda, K. Plant Richness and Blooming Cover Affect Abundance of Flower Visitors and Network Structure in Colombian Orchards. Agric. Entomol. 2021, 23, 545–556. [Google Scholar] [CrossRef]
Land 12 00362 g001 550
Figure 1. The significant relationships between the local and landscape factors and plant-pollinator network metrics: (A) landscape heterogeneity impacts on specialization, (B) % semi-natural habitat impacts on pollinator generality, and (C) % ground vegetation cover impacts on connectance. Grey shading represents 95% confidence intervals.
Figure 1. The significant relationships between the local and landscape factors and plant-pollinator network metrics: (A) landscape heterogeneity impacts on specialization, (B) % semi-natural habitat impacts on pollinator generality, and (C) % ground vegetation cover impacts on connectance. Grey shading represents 95% confidence intervals.
Land 12 00362 g001
Land 12 00362 g002 550
Figure 2. Results of the pollen preference analysis for the five most commonly occurring bee species. The color gradient represents preference with darker colors indicating higher preference and lighter colors indicating lower preference. * Denotes plants that are used solely for nectaring and not for pollen collection.
Figure 2. Results of the pollen preference analysis for the five most commonly occurring bee species. The color gradient represents preference with darker colors indicating higher preference and lighter colors indicating lower preference. * Denotes plants that are used solely for nectaring and not for pollen collection.
Land 12 00362 g002
Table
Table 1. The final results of the averaged linear mixed models with AIC < 2 for our pollen-based plant-pollinator networks. The full models included the following predictor variables: percent ground vegetation, percent bare ground, percent flower abundance, percent semi-natural habitat within a 2 km radius, and landscape heterogeneity.
Table 1. The final results of the averaged linear mixed models with AIC < 2 for our pollen-based plant-pollinator networks. The full models included the following predictor variables: percent ground vegetation, percent bare ground, percent flower abundance, percent semi-natural habitat within a 2 km radius, and landscape heterogeneity.
ResponseEstimateStd. ErrorZ Valuep ValueR2 Value
H2’
Landscape heterogeneity0.10030.0442.0720.038--
R2GLMM (m)--------0.262
Connectance
% Ground vegetation−0.0710.0232.7800.005--
% Bare ground−0.0250.0240.9680.333--
R2GLMM (m)--------0.421
Nestedness
% Bare ground−0.0180.0420.4140.679--
Landscape heterogeneity−0.0290.0520.5440.586--
R2GLMM (m)--------0.208
Generality
% Semi-natural habitat−0.6070.1713.2700.001--
Landscape heterogeneity−0.1570.1970.2030.437--
R2GLMM (m)--------0.528
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pardee, G.L.; Ballare, K.M.; Neff, J.L.; Do, L.Q.; Ojeda, D.; Bienenstock, E.J.; Brosi, B.J.; Grubesic, T.H.; Miller, J.A.; Tong, D.; et al. Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor. Land 2023, 12, 362. https://doi.org/10.3390/land12020362

AMA Style

Pardee GL, Ballare KM, Neff JL, Do LQ, Ojeda D, Bienenstock EJ, Brosi BJ, Grubesic TH, Miller JA, Tong D, et al. Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor. Land. 2023; 12(2):362. https://doi.org/10.3390/land12020362

Chicago/Turabian Style

Pardee, Gabriella L., Kimberly M. Ballare, John L. Neff, Lauren Q. Do, DianaJoyce Ojeda, Elisa J. Bienenstock, Berry J. Brosi, Tony H. Grubesic, Jennifer A. Miller, Daoqin Tong, and et al. 2023. "Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor" Land 12, no. 2: 362. https://doi.org/10.3390/land12020362

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop