Heat and desiccation tolerances predict bee abundance under climate change – Nature.com

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Climate change could pose an urgent threat to pollinators, with critical ecological and economic consequences. However, for most insect pollinator species, we lack the long-term data and mechanistic evidence that are necessary to identify climate-driven declines and predict future trends. Here we document 16 years of abundance patterns for a hyper-diverse bee assemblage1 in a warming and drying region2, link bee declines with experimentally determined heat and desiccation tolerances, and use climate sensitivity models to project bee communities into the future. Aridity strongly predicted bee abundance for 71% of 665 bee populations (species × ecosystem combinations). Bee taxa that best tolerated heat and desiccation increased the most over time. Models forecasted declines for 46% of species and predicted more homogeneous communities dominated by drought-tolerant taxa, even while total bee abundance may remain unchanged. Such community reordering could reduce pollination services, because diverse bee assemblages typically maximize pollination for plant communities3. Larger-bodied bees also dominated under intermediate to high aridity, identifying body size as a valuable trait for understanding how climate-driven shifts in bee communities influence pollination4. We provide evidence that climate change directly threatens bee diversity, indicating that bee conservation efforts should account for the stress of aridity on bee physiology.
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Kristin M. Conrad, Valerie E. Peters & Sandra M. Rehan
Maryse Vanderplanck, Baptiste Martinet, … Denis Michez
Panagiotis Theodorou, Sarah-Christine Herbst, … Robert J. Paxton
All datasets generated and/or analysed during the current study are publicly available. Long-term bee monitoring data are available via the Environmental Data Initiative (EDI) at https://doi.org/10.6073/pasta/cdc8381b8b2be97188daeffcd6310e9b. Also available via EDI are the SEV-LTER meteorological data (https://doi.org/10.6073/pasta/decdaa0c695cb2070c73f5b684a32e73), plant phenology data (https://doi.org/10.6073/pasta/ceb693495ef57b8b1ba075ca1ee0f7ed), and plant biomass data (https://doi.org/10.6073/pasta/5d6fa085c3d31bc1bc352081ec9e839a). Bee body mass, life history trait, and physiological tolerance data are available via the Open Science Framework (OSF) at https://doi.org/10.17605/OSF.IO/H2YV6. Projected future climate data are available from ClimateNA at https://climatena.ca/.
Computer code used in the analyses is available via Zenodo at https://doi.org/10.5281/zenodo.8412361 (ref. 92).
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Funding was provided by the NSF Long-Term Ecological Research programme (DEB-1655499), the Southwestern Association of Naturalists, the University of New Mexico (UNM) Department of Biology, the UNM Graduate and Professional Student Association, and an NSF REU Site Award to S. Collins (DBI-1950237). The authors thank M. Aizen, F. Bozinovic, M. Dillon, R. Irwin, V. Martinson, H. Wearing and N. Williams for providing feedback that improved the manuscript; B. Wolf for equipment and advice on physiological measurements; M. Litvak, T. Duman, K. Hall and L. Baur for help with climate and plant community analyses; and D. Lightfoot, J. Bettinelli, O. M. Carril, J. McLaughlin, B. Turnley, A. Garcia and R. Martinez for their contributions to laboratory and field data collection.
Department of Biology, University of New Mexico, Albuquerque, NM, USA
Melanie R. Kazenel, Karen W. Wright, Kenneth D. Whitney & Jennifer A. Rudgers
Washington State Department of Agriculture, Yakima, WA, USA
Karen W. Wright
USDA-ARS Pollinating Insects Research Unit, Utah State University, Logan, UT, USA
Terry Griswold
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M.R.K. created the conceptual framework, collected bee physiology and morphology data, analysed the data, and wrote the manuscript. K.W.W. designed the long-term bee monitoring study and completed specimen collection and identification. T.G. provided taxonomic expertise. J.A.R. and K.D.W. contributed to conceptualization, statistical analyses and writing. All authors helped to revise the manuscript.
Correspondence to Melanie R. Kazenel.
The authors declare no competing interests.
Nature thanks Bryan Danforth, Baptiste Martinet, Nicole Miller-Struttman and Justin Sheffield for their contribution to the peer review of this work.
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(a, b) Trends for the monsoon season in Socorro, NM, USA. In (a), points show the 6-month integrated aridity index, calculated from temperature and precipitation data recorded since 1900, with positive values indicating hotter and drier conditions relative to the mean. In (b), points show the coefficient of variation (CV) in the aridity index for non-overlapping 5-yr time windows (adapted from Rudgers et al., Ecology, 2018). (c) Predicted future monsoon season aridity trends for the Sevilleta National Wildlife Refuge (NM, USA) under low, moderate, and high CO2 emissions scenarios (RCP 2.6, 4.5, and 8.5, respectively), using projected future climate data from six General Circulation Models (ACCESS 1.0, CanESM2, CCSM 4.0, CNRM-CM5, CSIRO-Mk3.6.0, and INM-CM4). RCP 2.6 data were only available for the CanESM2 GCM. Positive and negative values indicate hotter/drier and cooler/wetter conditions relative to the historic mean (2002–2019), respectively. In all panels, error bands represent 95% confidence intervals.
Left: Map of sampling sites at the Sevilleta National Wildlife Refuge, NM, USA (beige polygon in upper map). Bees were sampled in three focal ecosystem types: Chihuahuan Desert shrubland (green points), Chihuahuan Desert grassland (black points), and plains grassland (blue points). To sample bees, we installed one passive funnel trap at each end of five 200 m transects/site; traps are indicated by colored points in the lower panel. Maps were generated via ArcGIS v. 10.1 (ESRI 2012, Redlands, CA) using the World Imagery basemap93 (sources: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community; accessed 23 February 2022 via https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9). Upper right: Differences between sites in climate conditions (table) and plant community composition (figures). Table values are results from paired, two-sided t-tests comparing temperature and precipitation metrics from the month of greatest difference between the Plains and Chihuahuan Desert meteorological stations. Figures are NMDS plots94 illustrating differences among ecosystems in plant cover for all species, and with the dominant species removed. Lower right: (a) Images of the environmental chamber used to assess thermal and desiccation tolerances of bees. The chamber consisted of an insulated ice chest (IceKool, Queensland, Australia). In the chamber, air temperature was controlled using a 162-W Peltier device (model AC-162, TE Technology, Traverse City, MI) and a custom-built controller that incorporated a TE Technology digital display (MP-2986) and control card (TC-36-25-RS486). (b) Traps used for bee collection. Each consisted of a 946 mL paint can filled with ~275 mL of propylene glycol and topped with a plastic automotive funnel (funnel height = 10 cm, top diameter = 14 cm, bottom diameter = 2.5 cm). The funnels’ interiors were painted with either blue or yellow fluorescent paint (Krylon, Cleveland, OH or Ace Hardware, Oak Brook, IL). Each trap was placed on a 45 cm high platform that was surrounded by a 60 cm high chicken wire cage to prevent wildlife and wind disturbance.
(a) Aridity index (inverse SPEI) as a function of maximum air temperature for the period leading up to the monsoon season (April–September), for the historic period (2002–2020) in the plains and Chihuahuan Desert ecosystems, and for 2021–2100 under three predicted future climate scenarios (RCP 2.6, 4.5, and 8.5) for the midpoint between ecosystems, using data from six General Circulation Models (ACCESS 1.0, CanESM2, CCSM 4.0, CNRM-CM5, CSIRO-Mk3.6.0, and INM-CM4). The red bar with an asterisk on the x-axis indicates the critical thermal maximum (CTMax) of the least thermally tolerant bee taxon in the dataset. The error band represents the 95% confidence interval. (b) Year-to-year variation in the aridity index calculated using two different PET estimation methods (Thornthwaite and Penman) for the spring and monsoon seasons in the plains ecosystem and Chihuahuan Desert ecosystems.
Phylogeny of the 339 bee species collected at the Sevilleta National Wildlife Refuge (NM, USA) from 2002–2019, with direction of predicted future change in abundance from 2002–2100 based on averaged projections from six General Circulation Models of global climate (white = insufficient data).
(a) Change in community-weighted mean (CWM) bee body mass with monsoon season aridity (inverse SPEI) and over time in the combined historic and predicted future datasets, for low (RCP 2.6) and high (RCP 8.5) climate change scenarios. Points represent means and error bars indicate s.e.m. for the linear or quadratic effect of aridity or year on CWM body mass using results from each of six General Circulation Models (GCMs; listed on y-axis). RCP 2.6 data were only available for the CanESM2 GCM. Positive and negative aridity values indicate hotter/drier and cooler/wetter conditions relative to the historic mean (2002–2019), respectively. Statistical results are from mixed effects models (see Methods). (b) Change over time in total bee abundance across study sites, using long-term historic data and predicted future data for low and high climate change scenarios (RCP 2.6 and 8.5). Each point represents the sum for each ecosystem × year combination of all species-level mean predicted abundance values that were calculated by averaging across predictions from the six GCMs. Points are colored by monsoon aridity averaged across the six GCMs. Positive and negative aridity values indicate hotter/drier and cooler/wetter conditions relative to the historic mean (2002–2019), respectively. Error bands represent 95% confidence intervals. Statistics are from linear regression analysis.
Points represent means and error bars indicate s.e.m. (mean n = 15 bee individuals/species/year; see Methods for sample sizes per species). Statistical results are from linear regressions. Mean body mass did not change over time within any species.
Relationship between spring or monsoon season aridity and proportion of forb and shrub individuals in flower from long-term plant phenology data (2002–2019), in three focal ecosystem types. Positive and negative aridity values indicate hotter/drier and cooler/wetter conditions relative to the mean, respectively. Error bands represent 95% confidence intervals. Statistical results are from mixed effects models.
Mean air temperature and total monthly precipitation trends for the plains ecosystem and Chihuahuan Desert ecosystems (grassland and shrubland) at the Sevilleta National Wildlife Refuge, for each month averaged across the years 2002–2019 (top) and for each month within each year (bottom).
Monthly aridity index (inverse SPEI) as a function of four other climate variables (temperature, precipitation, relative humidity, and vapor pressure deficit) for each month of the year (1–12). Data are from two meteorological stations at the Sevilleta National Wildlife Refuge (Chihuahuan Desert and Plains). Error bands represent 95% confidence intervals.
This file contains: Supplementary Tables 1–8, showing results from statistical analyses, with corresponding captions; a description of methods and results related to calculating potential evapotranspiration (PET) using two different estimation methods; and Supplementary Fig. 1, showing climate sensitivity function graphs.
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Kazenel, M.R., Wright, K.W., Griswold, T. et al. Heat and desiccation tolerances predict bee abundance under climate change. Nature (2024). https://doi.org/10.1038/s41586-024-07241-2
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DOI: https://doi.org/10.1038/s41586-024-07241-2
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