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This suggests predictive limits of our approach,
in that more-connected systems with more soil
evaporation and less-connected systems with
less soil evaporation will produce similar con-
tinental output flux isotope ratios.
The terrestrial hydrologic partitioning esti-
mated here corresponds to a total transpiration
of 55,000 ± 12,000 km
3
per year (mean ± 1 SD), a
total soil evaporation of 5000 ± 4000 km
3
per
year, and a total surface water evaporation of
2000 ± 2000 km
3
per year, assuming an inter-
ception of 23,000 ± 10,000 km
3
per year ( 27)
and a continental precipitation of 115,000 ±
2000 km
3
per year (28) (Fig. 3). The transpired
fraction determined here is consistent with
previous meta-analyses (Fig. 1C) and places an
observational constraint on transpiration esti-
mates from global Earth system models, which
range between 38 and 80% (4–6, 29). The frac-
tion of total evapotranspiration flux occurring
from surface waters, 2.9%, is also consistent with
values from global Earth system models, which
range from 2 to 4% when reported (29). Globally,
tropical forests provide the bulk of continental
transpiration, although these regions contribute
modest amounts of soil and surface water evap-
oration as well.
Transpiration fluxes form the primary link
between the water and carbon cycles, with water
lost from plant stomata du ring carbon assimila-
tion (i.e., plant water use efficiency) being a critical
factor determining ecosystem function and pro-
ductivity. Although we estimate that plant tran-
spiration is a majority of the evapotranspiration
flux, our results demonstrate that previous par-
titioning approaches may overestimate the con-
tribution of transpiration, because they do not
consider evaporation from multiple catchment
water pools and their connectivity. Furthermore,
isotopic partitioning approaches are sensitive to
bulk flux estimates and their uncertainti es, as
well as assumptions about interception rates, with
larger interception isotopically indistinguishable
from increased transpiration because both fluxes
areoftenassumedtobeunfractionatedrelativeto
their source waters (6, 20). Because a majority of
evaporation occurs from soils and not open
waters, more knowledge is needed of the role of
ecosystem structure and microclimate in deter-
mining sub-canopy evaporation rates.
Finally, the partial hydrologic disconnect be-
tween bound and mobile waters, which our es-
timates suggest is substantial and pervasive at
the global scale, has implications for prediction
and monitoring of both water quantity and qual-
ity within streams and rive rs. The hydrologic and
hydrochemical properties of surface water sys-
temsarestronglyinfluencedbyphysicalflow
paths within the near surface, and the low con-
nectivity found here suggests, for example, that
stream biogeochemistry may be less sensitive to
soil zone processes than it would be if hydrologic
connectivity were higher . Although we determined
a single average connectivity value, connectivity
varies with geography and in time as preferential
flow paths are activated and deactivated through-
out the year (30). Indeed, the relation between the
connectivity metric and soil-water transit time dis-
tributions is likely to be complex. Given the ubiq-
uitous nature of both water quantity and water
quality issues affecting watersheds worldwide, an
improved understanding of hydrologic connectivity
at variety of temporal and spatial scales is essential.
REFERENCES AND NOTES
1. T. H. Syed, J. S. Famiglietti, D. P. Chambers, J. K. Willis,
K. Hilburn, Proc. Natl. Acad. Sci. U.S.A. 107,17916–17921 (2010).
2. B. D. Newman et al., Water Resour. Res. 42, W06302 (2006).
3. S. Jasechko et al., Nature 496, 347–350 (2013).
4. L. Wang, S. P. Good, K. K. Caylor, Geophys. Res. Lett. 41,
6753–6757 (2014).
5. S. J. Sutanto et al., Hydrol. Earth Syst. Sci. Discuss. 11,
2583–2612 (2014).
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115–117 (2014).
7. J. J. McDonnell, Wiley Interdiscip. Rev . Water 1,323–329 (2014).
8. M. Stieglitz et al., Global Biogeochem. Cycles 17, 1105 (2003).
9. M. Weiler, J. J. McDonnell, Water Resour. Res. 43,W03403(2007).
10. J. R. Brooks, H. R. Barnard, R. Coulombe, J. J. McDonnell,
Nat. Geosci. 3, 100–104 (2009).
11. G. R. Goldsmith et al., Ecohydrology 5, 779–790 (2012).
12. F. M. Phillips, Nat. Geosci. 3,77–78 (2010).
13. G. Dongmann, H. W. Nürnberg, H. Förstel, K. Wagener,
Radiat. Environ. Biophys. 11,41–52 (1974).
14. H. Craig, L. I. Gordon, in Stable Isotopes in Oceanographic
Studies and Paleotemperatures , E. Tongiori, Ed. (Consiglio
Nazionale Delle Richerche Laboratorio Di Gelogica Nucleare,
Pisa, Italy, 1965), pp. 9–130.
15. J. J. Gibson, T. W. D. Edwards, Global Biogeochem. Cycles 16,
10-1–10-14 (2002).
16. J. R. Brooks et al., Limnol. Oceanogr. 59, 2150–2165 (2014).
17. X. F. Wang, D. Yakir, Hydrol. Processes 14, 1407–1421 (2000).
18. E. A. Yepez et al., Agric. For. Meteorol. 132, 359–376 (2005).
19. S. P. Good et al., Water Resour. Res. 50, 1410–1432 (2014).
20. A. M. J. Coenders-Gerrits et al., Nature 506,E1–E2 (2014).
21. D. R. Schlaepfer et al., Ecosphere 5, art61 (2014).
22. Materials and methods are available as supplementary
materials on Science Online.
23. S. P. Good, D. Noone, N. Kurita, M. Benetti, G. J. Bowen,
Geophys. Res. Lett. 10.1002/2015GL064117 (2015).
24. J. Worden et al., Atmos. Meas. Tech. 5, 397–411 (2012).
25. G. J. Bowen, J. Revenaugh, Water Resour. Res. 39,1–13 (2003).
26. S. P. Good, K. Soderberg, L. Wang, K. K. Caylor, J. Geophys. Res.
117, D15301 (2012).
27. D. Wang, G. Wang, E. N. Anagnostou, J. Hydrol. (Amst.) 347,
308–318 (2007).
28. R. F. Adler et al., J. Hydrometeorol. 4,1147–1167 (2003).
29. L. Wang-Erlandsson, R. J. Van Der Ent, L. J. Gordon,
H. H. G. Savenije, Earth Syst. Dyn. 5, 441–469 (2014).
30. I. Heidbüchel, P. A. Troch, S. W. Lyon, M. Weiler,
Water Resour. Res. 48, W06520 (2012).
AC KN OW LE D GM E NT S
This project was funded by the NSF Macrosystems Biology
program, grant EF-01241286, and the U.S. Department of Defense.
D.N. acknowledges the support of the NSF Climate and Large Scale
Dynamic program as part of a Faculty Early Career Development
award (AGS-0955841). Support and resources from the Center for
High Performance Computing at the University of Utah are also
gratefully acknowledged. Bulk flux data used in this study are
available online from NASA (http://precip.gsfc.nasa.gov/,
http://gmao.gsfc.nasa.gov/merra/) and the Woods Hole
Oceanographic Institute (http://oaflux.whoi.edu/). Global surface
vapor isotope data are available as supplementary information in
(23). The model code and input data files used in this study are
available at http://waterisotopes.org.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/349/6244/175/suppl/DC1
Materials and Methods
Figs. S1 to S3
References (31–37)
7 January 2015; accepted 2 June 2015
10.1126/science.aaa5931
CLIMATE CHANGE
Climate change impacts
on bumblebees converge
across continents
Jeremy T. Kerr,
1
* Alana Pindar,
1
Paul Galpern,
2
Laurence Packer,
3
Simon G. Potts,
4
Stuart M. Roberts,
4
Pierre Rasmont,
5
Oliver Schweiger,
6
Sheila R. Colla,
7
Leif L. Richardson,
8
David L. Wagner,
9
Lawrence F. Gall,
10
Derek S. Sikes,
11
Alberto Pantoja
12
†
For many species, geographical ranges are expanding toward the poles in response to
climate change, while remaining stable along range edges nearest the equator. Using
long-term observations across Europe and North America over 110 years, we tested for
climate change–related range shifts in bumblebee species across the full extents of their
latitudinal and thermal limits and movements along elevation gradients. We found
cross-continentally consistent trends in failures to track warming through time at species’
northern range limits, range losses from southern range limits, and shifts to higher
elevations among southern species. These effects are independent of changing land uses
or pesticide applications and underscore the need to test for climate impacts at both
leading and trailing latitudinal and thermal limits for species.
B
iological effects of climate change threaten
many species (1), necessitating advances in
techniques to assess their vulnerabilities
(2). In addition to shifts in the timing of
species’ life cycles, warming has caused
range expansion toward the poles and higher
elevations (3–6). Climate impacts could cause
losses from parts of species’ trailing range margins
(7), but those losses are infrequent ly observed (4).
Such responses depend on species’ traits, such as
SCIENCE sciencema g.org 10 JULY 2015 • VOL 349 ISSUE 6244 177
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heat or cold tolerance, that reflect shared evolu-
tionary history and climatic origins (e.g., tropical
or temperate) of taxa (8, 9). Climate change can
interact with other threats, like land-use intensi-
fication, to alter species’ responses to emerging
conditions (10). Such global changes can alter or
erode ecological services provided by the affected
species (11). Few species assemblages contribute
more to these services than bumblebees (Bombus),
many of which are declining (12, 13). No study has
yet evaluated climate change impacts across the
latitudinal and thermal limits of such a large spe-
cies assemblage spanning two continents.
We assembled a database of ~423,000 georef-
erenced observations for 67 European and North
American bumblebee species (fig. S1 and tables S1
and S2). Species observations were gathered from
the Global Biodiversity Information Facility (171,4 79
North American and 192,039 European records)
(14), Bumblebees of North America (15)(153,023
records), and the Status and Trends of European
Pollinators Collaborative Project (237,586 records).
We measured differences in species’ northern
and southern range limits, the warmest or coolest
temperatures occupied, and their mean elevations
in three periods (1975 to 1986, 1987 to 1998, and
1999 to 2010) (figs. S2 to S4) relative to a baseline
period (1901 to 1974) (16). We investig ated whether
land use affected these results. Finally, we used
high-resolution pesticide application data avail-
able in the United States after 1991 to investigate
whether total pesticide or neonicotinoid applica-
tions accounted for changes in bumblebee species’
range or thermal limits (table S3). Tests used
phylogenetic generalized least-squares models
(PGLS), using a phylogenetic tree constructed
from nuclear and mitochondrial markers (17), and
accounted for differences in sampling intensity
between time periods (Table 1).
If species expanded their northern range limits
to track recent warming, their ranges should show
positive (northward) latitudinal shifts, but cool
thermal limits should be stable through time. In
contrast to expectations and responses known
from other taxa (4), there has been no change in
the northern limits of bumblebee distributions
in North America or Europe (Fig. 1A). Despite
substantial warming (~ +2.5°C), bumblebee spe-
cies have also failed to track warming along their
cool thermal limits on both continents (Fig. 1B
and Table 1). These failures to track climate change
occur in parallel in regions that differ in their
intensities of human land use (e.g., Canada and nor-
thern Europe), which had no direct or interaction-
based effect in any statistical model (Table 1).
If bumblebee species climate responses resem-
ble most terrestrial ectotherm taxa (4), their south-
ern range limits should have remained stable with
increasing temperatures along species’ warm ther-
mal limits. However, bumblebee species’ range
losses from their historical southern limits have
been pronounced in both Europe and North
America, with losses growing to ~300 km in south-
ern areas on both continents (Fig. 1C). Throughout
North America, species also experienced range
losses from the warmest areas they historically
occupied, while European species’ range losses
extend across the warmest regions (where mean
temperatures exceed ~15°C) (Fig. 1D). These re-
sponses showed a significant phylogenetic signal,
with closely related bumblebee species showing
increasingly similar range shifts from southern
and warm thermal limits (Table 1). As with fail-
ures to expand northward or into cooler areas,
land-use changes do not relate to range losses
from bumblebee species’ southern or warm ther-
mal limits.
178 10 JULY 2015 • VOL 349 ISSUE 6244 sciencemag.org SCIENCE
1
Department of Biology, University of Ottawa, Ottawa, ON,
Canada, K1N6N5.
2
Faculty of Environmental Design,
University of Calgary, Calgary, Alberta, Canada.
3
Department
of Biology, York University, Toronto, Ontario, Canada.
4
School of Agriculture, Policy and Development, The
University of Reading, Reading, UK.
5
Department of Zoology,
Université de Mons, Mons, Belgium.
6
Department of
Community Ecology, Helmholtz Centre for Environmental
Research, Halle, Germany.
7
Wildlife Preservation Canada,
Guelph, Ontario, Canada.
8
Gund Institute, University of
Vermont, Burlington, VT, USA.
9
Department of Ecology and
Evolutionary Biology, University of Connecticut, Storrs, CT,
USA.
10
Peabody Museum of Natural Histo ry, Entomology
Division, Yale University, New Haven, CT, USA.
11
University of
Alaska Museum, University of Alaska Fairbanks, Fairbanks,
AK, USA.
12
United States Department of Agriculture,
Agricultural Research Service, Subarctic Agricultural
Research Unit, Fairbanks, AK, USA.
*Corresponding author. E-mail: jkerr@uottawa.ca †Present
address: United Nations Food and Agriculture Organization,
Santiago, Chile.
Table 1. PGLS models showing climate change and interactive effects
on North American and European bumblebees. Changes in latitude (km
north of equator), thermal (°C), or elevation (m) variables observed by 1999
to 2010 for ea ch species (relative to the 1901 to 1974 baseline period) are
regressed against predictors listed on the left. Models reported in each col-
umn were selected using AIC, which can include statistically nonsignificant
variables. Sample sizes in each time period (median n per species = 536)
were tested but excluded using AIC. Variable coefficients are given, with SEs
in parentheses. A dash indicates that this variable was not part of the AIC-
selected model. Ordinary least squares (OLS) regression summary statistics
(adjusted R
2
) are provided to enable comparison with PGLS results; OLS co-
efficients are similar.
Latitude Thermal
Elevation
Predictors
Northern Southern Cool Warm
Intercept –268.3 (614.7) 657.8 (150.4) 2.436 (0.5) 657.8 (150.4) 1075 (340.7)
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Latitudinal or thermal limit (1901–1974) 0.04 (0.08) –0.12 (0.04) –0.009 (0.05) 0.19 (0.1) –
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Mean latitude (1999–2010) –––––0.21 (0.07)
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Covariates
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Continent 1158 (1039) ––10.59 (2.24) 384.5 (504.1)
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
D Crop land (1999–2010) –4.25 (7.68) ––––
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
D Pasture (1999–2010) –43.1 (60.71) ––––
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Interactions with continent
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Thermal or latitudinal limits (1901–1974) –0.12 (0.14) –––0.47 (0.12) –
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
D Crop land (1999–2010) –9.38 (41.73) ––––
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
D Pasture (1999–2010) 74.95 (74.35) ––––
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Mean latitude (1999–2010) –––––0.03 (0.1)
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Models of trait evolution
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
AIC (Independent) 915.5 863.2 291.3 274.5 863.6
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
AIC (Brownian motion) 962.4 897.4 339.3 293.9 916.4
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
AIC (Ornstein-Uhlenbeck) 917.5 861.8 293.3 264.9 865.4
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
AIC (Pagel) 915.3 862.2 293.1 273 860.8
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Pagel’s l –0.15 0.49 0.04 0.64 –0.1
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Equivalent OLS regression summary statistics
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
Adjusted R
2
0.15 0.14 –0.01 0.30 0.28
............ ................ ................ ................ ............... ................ ................ ................ ............. ................ ............... ................ ................ ................ ................ ............... ................ ................ ................ ................ ............... .......
RESEARCH | REPORTS
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Species with southern geographical ranges re-
treated to higher elevations across Europe and
North America (Table 1 and Fig. 2), consistent with
observations of range losses from their southern
range limits. Elevation shifts are larger in Europe
[i.e., Akaike’s information criterion (AIC)–based
model selection includes a small continental ef-
fect; intercept for Europe, 1459 m (366 SE); North
America, 1074 m (340 SE) (Fig. 2)]. Europe’smoun-
tainous areas are oriented predominantly east-west,
potentially inducing more pronounced upslope
shifts. Mean elevations of observations for south-
ern species have risen ~300 m since 1974. Observed
shifts along elevation gradients vary considera-
bly among species (3) but follow a coherent geo-
graphical pattern. Mean elevations among northern
species in Europe and North America shifted lower .
Over recent decades, alpine tree lines have advanced
upslope in response to human activities, geomor-
phological factors, and warming (18), potentially
overtaking nesting, overwintering, and forage hab-
itats in historically open areas. High-elevation hab-
itat changes could contribute to generalist pollinator
declines in mountainous areas (19), particularly
among bumblebee species whose ranges have not
expanded from their cold thermal limits.
In addition to land-use changes, we investigated
whether pesticide use affected shifts in thermal and
latitudinal range limits among bumblebees. Spa-
tially detailed, annual pesticide measurements,
including neonicotinoid insecticides, were available
for the United States after 1991. Neither total
pesticide nor neonicotinoid applications there
relate to observed shifts in bumblebee species’
historical ranges or thermal limits (table S1).
Neonicotinoid effects known from individual and
colony levels certainly contribute to pollinator de-
clines and could degrade local pollination services.
Neonicotinoid effects on bumblebees have been
demonstrated experimentally using field-realistic
treatments (20). These locally important effects do
not “scale up” to explain cross-continental shifts
along bumblebee species’ thermal or latitudinal
limits. The timing of climate change–related shifts
among bumblebee species underscores this obser-
vation: Range losses from species’ southern limits
and failures to track warming conditions began
before widespread use of neonicotinoid pesticides
(figs. S2 and S3).
Regional analyses suggest that latitudinal range
shifts toward the poles are accelerating in most
speciesgroups(3), while their trailing range mar-
gins remain relatively stable (4). Assemblages
showing pronounced northward range expan-
sions and limited southern-range losses, like
butt erflies, originated and diversified in tropical
climates and retain ancestral tolerances to warmer
condition s (21). Those species’ warming-related
extinction risks in temperate environment s are
low (8) but increase toward warmer areas where
climatic conditions resemble those under which
they evolved (7, 22). Drawing on comprehensive
range data, bumblebee species show opposite range
responses across continents relative to most ter-
restrial assemblages (4): rapid losses from the
south and lagging range expansions in the north.
Mechanisms leading to observed lags in range
responses at species’ northern or cool thermal
limits require urgent evaluation. Colonization of
previously unoccupied areas and maintenance of
new populations strongly affect whether species
track shifting climatic conditions (23), capacities
that appear insufficient among bumblebees. Ob-
served losses from species’ southern or warm
boundaries in Europe and North America, and
associated phylogenetic signals, are consistent
with ancestral limitations of bumblebees’ warm
thermal tolerances and evolutionary origins in
cool Palearctic conditions (24). Warming-related
extreme events cause bumblebee population losses
(25)byimposingdemandsfor energetically cost-
ly behavioral thermoregulation, even at high lati-
tudes and elevations (26). Such effects are not
yet observed for European bumblebees in cooler
regions, where species generally experience tem-
peratures exceeding those observed historically
within their ranges (Fig. 1D) (10). Range losses
there will likely accelerate without mitigation
from climatic refugia (27).
SCIENCE sciencema g.org 10 JULY 2015 • VOL 349 ISSUE 6244 179
Fig. 1. Climate change responses of 67 bumblebee species across full latitudinal and thermal lim-
its in Europe and North America. For each measur ement , the y axis shows differences in the latitude
of species’ range limits [(A) Northern, (C) Southern] or thermal limits [(B)Cool;(D) Warm], respectiv ely,
by 1999 to 2010 relativ e to baseline conditions for 1901 to 1974. Each point represents the mean of five
observations at the latitudinal or thermal limits for one bumblebee species (gr een circles for Europe and
pink for North America). Null expectations (dashed lines) are for no temporal change in latitudinal or
thermal limits. Range e xpansions from species’ historical northern limits (A) are indicated by positive
values, and positive values indicate range losses from species’ southern limits (B). Temperature changes
show whether bumblebee species are tracking differences along their thermal limits through time (no
change) , falling behind (positive values ), or retr eating mor e rapidly than mean conditions detect (negativ e
values ). Confidence bands (95%) f or regr essi on models (i.e ., with and withou t continent + interaction
against latitudinal or thermal change terms) with the lowest AIC are shown.
Fig. 2. Change in elevation of 67 bumblebee
species by 1999 to 2010 relative to their mean
latitude. Eleva tions are calculated using mean ele-
vations across species observations. The slopes are
similar between continents (according to regr ession
and PGLS analyses). The confidence bands (95%)
of regression slopes are shown.
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Climatechangeappearstocontributedistinc-
tively, and consistently, to accumulating range
compression among bumblebee species across
continents. Experimental relocation of bumble-
bee colonies into new areas could mitigate these
range losses. Assessments of climate change on
species’ ranges need to account for observations
across the full extent of species’ latitudinal and
thermal limits and explicitly test for interactions
with other global change drivers.
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ACK NOW LED GME NTS
This research was funded by the Natural Sciences and Engineering
Research Council of Canada strategic network (CANPOLIN:
Canadian Pollination Initiative) and Discovery Grant support and
University of Ottawa Research Chair in Macroecology and
Conservation to J.T.K. We are grateful to anonymous reviewers
whose comments improved this paper and to P. Williams for advice
and perspectives during development of the research. All data and
supporting scripts are available from Dryad Digital Repository:
doi:10.5061/dryad.gf774.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/349/6244/177/suppl/DC1
Materials and Methods
Supplementary Text
Supplementary Acknowledgments
Figs. S1 to S4
Tables S1 to S3
References (28–55)
15 January 2015; accepted 21 May 2015
10.1126/science.aaa7031
PLACE CELLS
Autoassociative dynamics in the
generation of sequences of
hippocampal place cells
Brad E. Pfeiffer* and David J. Foster†
Neuronal circuits produce self-sustaining sequences of activity patterns, but the precise
mechanisms remain unknown. Here we provide evidence for autoassociative dynamics in
sequence generation. During sharp-wave ripple (SWR) events, hippocampal neurons
express sequenced reactivations, which we show are composed of discrete attractors.
Each attractor corresponds to a single location, the representation of which sharpens
over the course of several milliseconds, as the reactivation focuses at that location.
Subsequently, the reactivation transitions rapidly to a spatially discontiguous location. This
alternation between sharpening and transition occurs repeatedly within individual SWRs
and is locked to the slow-gamma (25 to 50 hertz) rhythm. These findings support
theoretical notions of neural network function and reveal a fundamental discretization
in the retrieval of memory in the hippocampus, together with a function for gamma
oscillations in the control of attractor dynamics.
I
n the well-known Hopfield model, a network
of recurrently excitable neurons stores dis-
cretememoriesasstableactivitypatterns
(attractors) to which partial patterns are
guaranteed to converge, based on synaptic
weights reflecting correlations between neu-
rons in the same pattern (“autoassociation”)
(1). Sequences of patterns can also be stored,
based on weights reflecting correlations be-
tween different patterns (“heteroassociation”),
but are generally unsustainable because any
noise leads to divergence in subsequent patterns.
A solution is to combine fast autoassociation for
each pattern with slower heteroassociation for
successive patterns, allowing each pattern to
be corrected via attractor network dynamics
before transitioning to the next pattern in the se-
quence (2, 3). This process should result in “jumpy”
sequences that sharpen individual pattern rep-
resentations before transitioning to successive
patterns; however, direct evidence is lacking, due
largely to the difficulty of obtaining data from
very large ensembles of neurons express ing inter-
nally generated sequences recorded at the time
resolution of neuronal dynamics.
Hippocampal SWR-associated place-cell se-
quences (4–10), often termed “replay,” are a unique
experimental model in which neurons with well-
defined receptive fields are activated outside those
receptive fields and in specific temporal sequen-
ces corresponding to physical trajectories through
space, all while the animal is stationary, and thus
in the absence of corresponding sequences of
stimuli or behaviors. We recently develo pe d meth -
ods to record simultaneously from very large num-
bers of hippocampal neurons (up to 263) with
place fields in a single environment (10), and we
applied these recording techniques to examine
the fine structure of SWR-associated place-cell
sequences to investigate the underlying mech-
anisms of this form of memory expression and
explore the circuit-level dynamics of an attrac-
tor system in vivo.
We recorded bilateral ensemble activity from
dorsal hippocampal neurons (figs. S1 and S2) of
five rat subjects across multiple recording ses-
sions as they explored open arenas or linear tracks
(Fig.1,A,B,G,andH).Weobtainedsimultaneous
recordings from large populations of hippocam-
pal neurons in each recording session (80 to 263
units per session; mean ± SEM = 159.2 ± 11.8 units
per session), allowing us to accurately decode
spatial informat ion from the hippocampal ensem-
ble activity patterns using a memory-less, uniform-
prior Bayesian decoding algorithm (fig. S3) (5, 10).
We identified SWRs that encoded temporally com-
pressed spatial trajectories through the current
environment (Fig. 1, C to F and I to L, and fig. S4)
(10), which we term “trajectory events” rather than
“replay” to reflect the observation that SWRs do
not always represent a perfect replay of imme-
diately prior behavior but instead reflect a more
broad array of spatial paths (8–10). Across all
sessions in the open field and linear track, we
identified 815 and 564 SWR events, respectively,
that met our criteria to be classified as trajectory
events.
Consistent with prior reports (5), trajectory events
displayed average velocities in a relatively nar-
row range (Fig. 2A); however , when we examined
trajectory events on a finer time scale, we ob-
served discontinuous trajectories, alternating be-
tween immobility (in which consecutive decoding
frames represented the same location) and rapid
movement (in which consecutive frames repre-
sented a sequential path of unique positions; fig.
180 10 JULY 2015 • VO L 34 9 IS SUE 6244 sciencemag.org SCIENCE
Solomon H. Snyder Department of Neuroscience, Johns
Hopkins University School of Medicine, Baltimore, MD, USA.
*Present address: Department of Neuroscience, University of
Texas Southwestern Medical Center, Dallas, TX, USA.
†Corresponding author. E-mail: david.foster@jhu.edu
RESEARCH | REPORTS
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(6244), 177-180. [doi: 10.1126/science.aaa7031]349Science
2015)Lawrence F. Gall, Derek S. Sikes and Alberto Pantoja (July 9,
Schweiger, Sheila R. Colla, Leif L. Richardson, David L. Wagner,
Simon G. Potts, Stuart M. Roberts, Pierre Rasmont, Oliver
Jeremy T. Kerr, Alana Pindar, Paul Galpern, Laurence Packer,
continents
Climate change impacts on bumblebees converge across
Editor's Summary
, this issue p. 177Science
change.
because of their origins in a cooler climate, and suggest an elevated susceptibility to rapid climate
are experiencing shrinking distributions in the southern ends of their range. Such failures to shift may be
across North America and Europe over the past 110 years. Bumblebees have not shifted northward and
looked at data on bumblebeeset al.shifts, and these species might experience more rapid declines. Kerr
for species to shift their ranges poleward or up in elevation. Not all species, however, can make such
Responses to climate change have been observed across many species. There is a general trend
Bucking the trend
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