Temporal scale‐dependence of plant–pollinator networks

The study of mutualistic interaction networks has led to valuable insights into ecological and evolutionary processes. However, our understanding of network structure may depend upon the temporal scale at which we sample and analyze network data. To date, we lack a comprehensive assessment of the temporal scale-dependence of network structure across a wide range of temporal scales and geographic locations. If network structure is temporally scale-dependent, networks constructed over different temporal scales may provide very different perspectives on the structure and composition of species interactions. Furthermore, it remains unclear how various factors – including species richness, species turnover, link rewiring and sampling effort – act in concert to shape network structure across different temporal scales. To address these issues, we used a large database of temporally-resolved plant–pollinator networks to investigate how temporal aggregation from the scale of one day to multiple years influences network structure. In addition, we used structural equation modeling to explore the direct and indirect effects of temporal scale, species richness, species turnover, link rewiring and sampling effort on network structural properties. We find that plant–pollinator network structure is strongly temporally-scale dependent. This general pattern arises because the temporal scale determines the degree to which temporal dynamics (i.e. phenological turnover of species and links) are included in the network, in addition to how much sampling effort is put into constructing the network. Ultimately, the temporal scale-dependence of our plant–pollinator networks appears to be mostly driven by species richness, which increases with sampling effort, and species turnover, which increases with temporal extent. In other words, after accounting for variation in species richness, network structure is increasingly shaped by its underlying temporal dynamics. Our results suggest that considering multiple temporal scales may be necessary to fully appreciate the causes and consequences of interaction network structure.Fil: Schwarz, Benjamin. Albert Ludwigs University of Freiburg; AlemaniaFil: Vazquez, Diego P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Cara Donna, Paul J.. Chicago Botanic Garden; Estados UnidosFil: Knight, Tiffany M.. German Centre for Integrative Biodiversity Research; AlemaniaFil: Benadi, Gita. Albert Ludwigs University of Freiburg; AlemaniaFil: Dormann, Carsten F.. Albert Ludwigs University of Freiburg; AlemaniaFil: Gauzens, Benoit. German Centre for Integrative Biodiversity Research; AlemaniaFil: Motivans, Elena. German Centre for Integrative Biodiversity Research; AlemaniaFil: Resasco, Julian. University of Colorado; Estados UnidosFil: Blüthgen, Nico. Universitat Technische Darmstadt; AlemaniaFil: Burkle, Laura A.. Montana State University; AlemaniaFil: Fang, Qiang. Henan University of Science and Technology; ChinaFil: Kaiser Bunbury, Christopher N.. University of Exeter; Reino UnidoFil: Alarcón, Ruben. California State University; Estados UnidosFil: Bain, Justin A.. Chicago Botanic Garden; Estados UnidosFil: Chacoff, Natacha Paola. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Huang, Shuang Quan. Central China Normal University; ChinaFil: LeBuhn, Gretchen. San Francisco State University; Estados UnidosFil: MacLeod, Molly. Rutgers University; Estados UnidosFil: Petanidou, Theodora. Univversity of the Aegean; Estados UnidosFil: Rasmussen, Claus. University Aarhus; DinamarcaFil: Simanonok, Michael P.. Montana State University; Estados UnidosFil: Thompson, Amibeth H.. German Centre for Integrative Biodiversity Research; AlemaniaFil: Fründ, Jochen. Albert Ludwigs University of Freiburg; Alemani

Do the Quality and Quantity of Honey Bee-Collected Pollen Vary Across an Agricultural Land-Use Gradient?

Pollen is the source of protein for most bee species, yet the quality and quantity of pollen is variable across landscapes and growing seasons. Understanding the role of landscapes in providing nutritious forage to bees is important for pollinator health, particularly in areas undergoing significant land-use change such as in the Northern Great Plains (NGP) region of the United States where grasslands are being converted to row crops. We investigated how the quality and quantity of pollen collected by honey bees (Apis mellifera L. [Hymenoptera: Apidae]) changed with land use and across the growing season by sampling bee-collected pollen from apiaries in North Dakota, South Dakota, and Minnesota, USA, throughout the flowering season in 2015–2016. We quantified protein content and quantity of pollen to investigate how they varied temporally and across a land-use gradient of grasslands to row crops. Neither pollen weight nor crude protein content varied linearly across the land-use gradient; however, there were significant interactions between land use and sampling date across the season, particularly in grasslands. Generally, pollen protein peaked mid-July while pollen weight had two maxima in late-June and late-August. Results suggest that while land use itself may not correlate with the quality or quantity of pollen resources collected by honey bees among our study apiaries, the nutritional landscape of the NGP is seasonally dynamic, especially in certain land covers, and may impose seasonal resource limitations for both managed and native bee species. Furthermore, results indicate periods of qualitative and quantitative pollen dearth may not coincide

Supplement 1. R scripts and species lists for plants and pollinators.

<h2>File List</h2><div> <p><a href="R_function.txt">R_function.txt</a> (MD5: 3fc8d9510f355a5569ed1adf92c0f9c6)</p> <p><a href="Second_R_function.txt">Second_R_function.txt</a> (MD5: 15a475fa05e1bfa615bbb674166a39f8)</p> <p><a href="Pollinator_species_list.csv">Pollinator_species_list.csv</a> (MD5: 791e68b4b9ff6ccf79b0831a1695bd89)</p> <p><a href="Plant_species_list.csv">Plant_species_list.csv</a> (MD5: a958fc22ec3eca1d4d6384dbcda06bc4)</p> </div><h2>Description</h2><div> <p> </p> <p><b>R_function.txt</b><b>: </b>R function modified from Poisot et al. (2012) used to calculate partitions of interaction turnover according to Novotny (2009).</p> <p><b>Second_R_function.txt</b><b>:</b> Second R function modified from Poisot et al. (2012),  used for comparing more than two matrices</p> <p><b>Pollinator_species_list.csv</b><b>:</b> Pollinator species list by site. Numbers represent the weeks (1-9) during which the pollinator was present at the site</p> <p><b>Plant_species_list.csv</b><b>: </b>Plant species list by site, and results for sampling completeness. Numbers represent the weeks (1–9) during which the species was present at the site. S_Obs is the visiting pollinator richness for each plant species, and S_Est is the estimated visiting pollinator richness as calculated by the Chao estimator. </p> </div

Landscape characterization of floral resources for pollinators in the Prairie Pothole Region of the United States

Across agricultural areas of the Prairie Pothole Region (PPR), floral resources are primarily found on public grasslands, roadsides, and private grasslands used as pasture or enrolled in federal conservation programs. Little research has characterized the availability of flowers across the region or identified the primary stakeholders managing lands supporting pollinators. We explored spatial and temporal variability in flower abundance and richness across multiple grassland categories (i.e. general grassland, conservation grassland, and engineered pollinator habitat) in the PPR from 2015 to 2018 and used these data to estimate the number of flowering stems present across the region on private and public land holdings. Both flowering plant abundance and richness were greatest on engineered pollinator habitat, but this land category encompassed \u3c 0.01% of the total grassland area in the PPR. There was a steady decrease in flower abundance over the growing season across all land categories. We detected considerable variation in flower abundance and richness across grassland categories, indicating that not all natural or semi-natural covers provide similar value to pollinators. At a landscape scale, large land holdings such as privatelyowned grasslands and Conservation Reserve Program lands contributed the greatest number of flowers by an order of magnitude, though these lands collectively did not support the greatest abundance of flowers per unit area. Our research depicts spatial and temporal variation in pollinator resources across the region. Further, our research will assist managers and policy makers in understanding the role of public and private lands and conservation programs in supporting pollinators. Supplemental files attached below

Table_1_Biological effects of intraoperative radiation therapy: histopathological changes and immunomodulation in breast cancer patients.xlsx

IntroductionIntraoperative radiation therapy (IORT) delivers a single accelerated radiation dose to the breast tumor bed during breast-conserving surgery (BCS). The synergistic biologic effects of simultaneous surgery and radiation remain unclear. This study explores the cellular and molecular changes induced by IORT in the tumor microenvironment and its impact on the immune response modulation.MethodsPatients with hormone receptor (HR)-positive/HER2-negative, ductal carcinoma in situ (DCIS), or early-stage invasive breast carcinoma undergoing BCS with margin re-excision were included. Histopathological evaluation and RNA-sequencing in the re-excision tissue were compared between patients with IORT (n=11) vs. non-IORT (n=11).ResultsSquamous metaplasia with atypia was exclusively identified in IORT specimens (63.6%, p=0.004), mimicking DCIS. We then identified 1,662 differentially expressed genes (875 upregulated and 787 downregulated) between IORT and non-IORT samples. Gene ontology analyses showed that IORT was associated with the enrichment of several immune response pathways, such as inflammatory response, granulocyte activation, and T-cell activation (pConclusionIORT induces histological changes, including squamous metaplasia with atypia, and elicits molecular alterations associated with immune response and intrinsic apoptotic pathways. The increased abundance of immune-related components in breast tissue exposed to IORT suggests a potential shift towards active immunogenicity, particularly immune-desert tumors like HR-positive/HER2-negative breast cancer.</p