Figure
5. Species networks indicating the net strength of interactions (sum of
direct interactions and higher-order interactions per species pair) in
the most parsimonious models (ΔAIC < 2) at zero (a) and at
maximum (b) drought index. The color of the network links indicates
whether the net interaction is positive (blue) or negative (red). The
color of the species relates to its functional group including grasses
(green), non-N-fixing-forbs (brown) and N-fixers (purple). When multiple
interactions were found between the same species pair in different
models, their strengths were averaged. The thickness of the connections
indicates net interaction strength. Non-significant interactions were
excluded. For full species names, see methods section 3.1.
Discussion
Inclusion of species interactions (including HOIs) increased the
accuracy of plant performance models along the drought gradient compared
to models only including the drought effect. Direct interactions
improved the explanatory power of models more on average compared to
HOIs, though the latter were present in a larger number of the most
parsimonious models (Table S4). This finding affirms the necessity to
include at least some HOIs to improve net species interaction estimates
in complex and diverse ecological networks (Li et al., 2020; Li et al.,
2021; Martyn et al., 2021; Mayfield & Stouffer, 2017). Particularly in
plant communities subjected to drought, where species interactions can
change drastically (Grant et al., 2014; Van den Berge et al., 2014),
facilitation and/or competition may be overestimated without inclusion
of HOIs, limiting our ability to predict ecosystem trajectories (Brooker
et al., 2008). Incorporating HOI approaches in more studies could thus
improve the predictability of species performance under climate change.
Contrasting with the stress gradient hypothesis (Bertness & Callaway,
1994), more net interactions became negative on average along the
drought gradient. This occurred because direct interactions
predominantly shifted to more negative with longer drought, while HOIs
were doing the opposite, albeit without fully neutralizing intensified
competition (Fig. 4). In line with Xiao et al. (2020) and Li et al.
(2021), these findings indicate that HOIs may influence community
stability by partially counteracting increased direct competitive
effects under increasing precipitation variability. While Xiao et al.
(2020) argue that especially intraspecific HOIs play a stabilizing role,
our experimental findings suggest that interspecific HOIs may also
contribute to stabilizing community dynamics (Bairey et al., 2016;
Grilli et al., 2017; Singh & Baruah, 2021). Hence, persistence of
highly diverse communities under increasing drought stress could be
promoted by interspecies HOIs counteracting stronger direct competitive
effects, thus stabilizing community dynamics, and preventing dominance
of one single species group (Brooker et al., 2008). Since such
stabilizing HOIs are more likely to be generated in multi-species
assemblages (Grilli et al., 2017), this observation may also point at an
additional mechanism through which more diverse communities could be
better equipped to withstand climate extremes (Brooker et al., 2008; De
Boeck et al., 2018).
In terms of directionality, the majority of HOIs showed a shift towards
stronger facilitation (or weaker competition) along the drought gradient
on average (positive values in Fig. 3d), in line with the stress
gradient and dominance hypotheses (Bertness & Callaway, 1994; Coyle et
al., 2014). However, three additional species exerted direct competitive
pressures at maximum drought index on average (Fig. 4c), and more net
interactions became competitive (Fig. 5; Table 1). Hence, while the
majority of direct competitive interactions was weakened by positive
HOIs, the strengthening of some competitors may have led to a
destabilized system where more species were being outcompeted under
longer drought (Table 1; Fig. 5). This is in agreement with some studies
(Maestre et al., 2005; Olsen et al., 2016; Saccone et al., 2009), but
contradicted by others (Brooker et al., 2008; He et al., 2013). Having
more species in negative direct interactions (and more net competitive
interactions) under longer drought indeed suggests that some species
(e.g. Phleum pratense ) could become more competitive under water
shortage because of, for example, favorable resource acquisition traits
(Kraft et al., 2015; Olsen et al., 2016).
In that regard, we observed that direct interactions became more
negative with increasing Ellenberg’s nutrient indices under longer
drought (Fig. S5b), indicating that species with an affinity for
resource rich environments became involved in more direct competitive
interactions. Hence, increasing weather persistence seemed to not only
affect species interaction networks directly through intrinsic negative
responses to changes in soil water availability (i.e. Ellenberg’s
moisture indices; Fig. S5a), but also indirectly through effects of
drought on nutrient availability. These intensified direct competitive
effects may thus also reflect differences in plant resilience during
post-drought rewetting and recovery phases, when the flush of nutrients
related to Birch effects induces transient periods of high nutrient
availability (Birch, 1958; Borken & Matzner, 2009), allowing rapid
recovery of more resilient species.
However, positive interactions still made up half of all interactions
under extreme drought (Fig. 5b), and the average strength of net
interactions per functional group did not change noticeably with drought
(Table 1). Together with the striking productivity declines (Fig. S2 &
S3) and diversity losses related to the intrinsic drought sensitivity of
individual species (Reynaert et al., 2021), these observations may
indicate a primary role of environmental filtering (i.e. drought), and
only to a lesser extent shifts in species interactions as the primary
cause for aboveground productivity declines under drought (Coyle et al.,
2014; Elst et al., 2017). Following Maestre et al. (2009), we pose that
under extreme environmental stress (e.g., prolonged drought), species
could potentially also stop growing due to exceedance of intrinsic
ecological thresholds, mostly independently of the community dynamics
(i.e. facilitative or competitive interactions) in their growing
environment (De Boeck et al., 2018; Reynaert et al., 2021; Soliveres et
al., 2015). The non-linearity in survival responses (Fig. S5; Reynaert
et al. (2021)), could indeed suggest such a mechanism. Similar negative
threshold responses of aboveground plant productivity have been observed
in many terrestrial ecosystems, particularly in relation to the duration
of dry spell anomalies (Felton et al., 2021). Taken together, these
results indicate that both intrinsic responses to increasing drought and
intensifying interspecific competitive effects (Olsen et al., 2016) lead
to the observed changes in community dynamics by the end of the first
120 days of the experiment.
In line with our third hypothesis, forbs (particularly N-fixers) tended
to be more involved in facilitative interactions on average, while
grasses seemed the strongest competitors (Table 1; Fig. 5). This role of
species identity (Martyn et al., 2021; Weigelt et al., 2002) was
confirmed by the relationships with Ellenberg’s moisture and nutrient
indices at maximum drought index, with species with an affinity for
wetter and more resource rich environments experiencing or exerting more
negative HOIs or direct interactions on average, respectively (Fig.
S5a,b). Additionally, the disproportionate contribution of N-fixers to
positive interactions and grasses to negative interactions suggests that
some species (or species groups) are crucial in defining the dynamics of
complex species networks (Singh & Baruah, 2021). With the available
information, we cannot fully determine whether N-fixer importance is
because of active facilitative traits (e.g., improving water or nutrient
supply), or due to passive competitive release as these smaller species
rapidly disappeared with increasing drought stress (i.e. were more
susceptible to environmental filtering), or because they prevent other
more competitive neighbors from colonizing (e.g., via vegetative
propagation) (Maestre et al., 2003).
Some aspects of this study’s methodology should be considered. First,
the presented direct vs three-way higher-order counteracting interaction
system may not fully reveal the nature of net community dynamics.
Inclusion of HOIs beyond simple three-way terms may shed more light on
the overall strength of facilitation and competition, but was not
feasible in this study because of insufficient data (Martyn et al.,
2021). Nonetheless, previous studies have indicated that inclusion of
more complex (i.e., 4-way, 5-way, etc.) HOIs leads to progressively
diminished returns in explaining community dynamics (Li et al., 2021),
highlighting the importance of the modelled interactions. Second, the
exclusion of intraspecific and density effects likely perturbed the
estimation of interaction coefficients, since intraspecific interactions
(both direct and HOIs) and competitor density influence net community
dynamics in diverse ecosystems (Chesson, 2000; Li et al., 2021; Xiao et
al., 2020). However, because
individuals of the same species were never planted directly next to
one-another and the initial planting density was identical in every
mesocosm, we believe that the observed changes primarily reflect plant
responses in function of neighbor performance and drought (Grant et al.,
2014). Finally, the species interactions investigated here represent
short-term responses to altered persistence in summer precipitation
(Reynaert et al., 2021). Since we already observed strong differences in
species performance related to the extremity of the precipitation
regime, later assessment of interactions would likely disproportionally
reflect the effects of only a few single species since many had died
already under extreme summer drought after 120 days (Reynaert et al.,
2021). Consequently, the identified interactions likely represent the
most important ones that contributed to the observed declines of species
under the imposed climate trend.
Conclusion
Modelling and disentangling the role of HOIs in community dynamics
remains challenging because of their complexity, extensive dataset
requirements and a lack of widely applicable methodologies. More
targeted manipulation experiments filtering out the environmental noise
that perturbs the estimation of interaction coefficients could aid in
bridging the gap between theoretical modelling and meaningful ecological
understanding of diverse communities. We took a first step in that
direction, by experimentally demonstrating that species interactions,
including HOIs, can significantly change along a gradient of
increasingly persistent precipitation regimes. Inclusion of HOIs did not
only improve model explanatory power, but also shed more light on
potential stabilizing mechanisms for increasing competition under
drought. Hence, HOI approaches could further improve the accuracy of
species performance models under rapidly changing environmental
conditions, an imperative prerequisite for targeted and efficient
ecosystem management.
Conflict of interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
The corresponding data of this study will be made publicly available
online in a Zenodo repository at [INSERT DOI] and in the previously
published Dryad repository at
https://doi.org/10.5061/dryad.k98sf7m52.
References
Bairey, E., Kelsic, E. D., & Kishony, R. (2016). High-order species
interactions shape ecosystem diversity. Nat Commun, 7 (1), 12285.
doi:10.1038/ncomms12285
Ball, K. R., Power, S. A., Brien, C., Woodin, S., Jewell, N., Berger,
B., & Pendall, E. (2020). High-throughput, image-based phenotyping
reveals nutrient-dependent growth facilitation in a grass-legume
mixture. PLoS One, 15 (10), e0239673.
doi:10.1371/journal.pone.0239673
Bertness, M. D., & Callaway, R. (1994). Positive interactions in
communities. Trends Ecol Evol, 9 (5), 191-193.
doi:10.1016/0169-5347(94)90088-4
Billick, I., & Case, T. J. (1994). Higher order interactions in
ecological communities: what are they and how can they be detected?Ecology, 75 (6), 1529-1543. doi:/10.2307/1939614
Bimler, M. D., Stouffer, D. B., Lai, H. R., & Mayfield, M. M. (2018).
Accurate predictions of coexistence in natural systems require the
inclusion of facilitative interactions and environmental dependency.Journal of Ecology, 106 (5), 1839-1852.
doi:10.1111/1365-2745.13030
Birch, H. (1958). The effect of soil drying on humus decomposition and
nitrogen availability. Plant and soil, 10 (1), 9-31.
Borken, W., & Matzner, E. (2009). Reappraisal of drying and wetting
effects on C and N mineralization and fluxes in soils. Global
Change Biology, 15 (4), 808-824. doi:10.1111/j.1365-2486.2008.01681.x
Brooker, R. W., Maestre, F. T., Callaway, R. M., Lortie, C. L.,
Cavieres, L. A., Kunstler, G., . . . Michalet, R. (2008). Facilitation
in plant communities: the past, the present, and the future.Journal of Ecology, 96 (1), 18-34.
doi:10.1111/j.1365-2745.2007.01295.x
Chesson, P. (2000). Mechanisms of maintenance of species diversity.Annual review of Ecology and Systematics, 31 (1), 343-366. doi:DOI
10.1146/annurev.ecolsys.31.1.343
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogee, J., Allard, V.,
. . . Valentini, R. (2005). Europe-wide reduction in primary
productivity caused by the heat and drought in 2003. Nature,
437 (7058), 529-533. doi:10.1038/nature03972
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T.,
Friedlingstein, P., . . . Krinner, G. (2013). Long-term climate change:
projections, commitments and irreversibility. In Climate Change
2013-The Physical Science Basis: Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate
Change (pp. 1029-1136): Cambridge University Press.
Corbin, J. D., & D’Antonio, C. M. (2004). Competition between native
perennial and exotic annual grasses: Implications for an historical
invasion. Ecology, 85 (5), 1273-1283. doi:10.1890/02-0744
Coyle, J. R., Halliday, F. W., Lopez, B. E., Palmquist, K. A., Wilfahrt,
P. A., & Hurlbert, A. H. (2014). Using trait and phylogenetic diversity
to evaluate the generality of the stress-dominance hypothesis in eastern
North American tree communities. Ecography, 37 (9), 814-826.
doi:10.1111/ecog.00473
Csardi, G., & Nepusz, T. (2006). The igraph software package for
complex network research. InterJournal, complex systems, 1695 (5),
1-9.
De Boeck, H. J., Bloor, J. M. G., Kreyling, J., Ransijn, J. C. G., Nijs,
I., Jentsch, A., . . . Wardle, D. (2018). Patterns and drivers of
biodiversity-stability relationships under climate extremes.Journal of Ecology, 106 (3), 890-902. doi:10.1111/1365-2745.12897
Elst, E. M., De Boeck, H. J., Vanmaele, L., Verlinden, M., Dhliwayo, P.,
& Nijs, I. (2017). Impact of climate extremes modulated by species
characteristics and richness. Perspectives in Plant Ecology
Evolution and Systematics, 24 , 80-92. doi:10.1016/j.ppees.2016.12.007
Fay, P. A., Carlisle, J. D., Knapp, A. K., Blair, J. M., & Collins, S.
L. (2003). Productivity responses to altered rainfall patterns in a
C-4-dominated grassland. Oecologia, 137 (2), 245-251.
doi:10.1007/s00442-003-1331-3
Felton, A. J., Knapp, A. K., & Smith, M. D. (2021).
Precipitation-productivity relationships and the duration of
precipitation anomalies: An underappreciated dimension of climate
change. Glob Chang Biol, 27 (6), 1127-1140. doi:10.1111/gcb.15480
Grant, K., Kreyling, J., Heilmeier, H., Beierkuhnlein, C., & Jentsch,
A. (2014). Extreme weather events and plant-plant interactions: shifts
between competition and facilitation among grassland species in the face
of drought and heavy rainfall. Ecological Research, 29 (5),
991-1001. doi:10.1007/s11284-014-1187-5
Grilli, J., Barabas, G., Michalska-Smith, M. J., & Allesina, S. (2017).
Higher-order interactions stabilize dynamics in competitive network
models. Nature, 548 (7666), 210-213. doi:10.1038/nature23273
He, Q., Bertness, M. D., & Altieri, A. H. (2013). Global shifts towards
positive species interactions with increasing environmental stress.Ecol Lett, 16 (5), 695-706. doi:10.1111/ele.12080
Klaus, V. H., Friedritz, L., Hamer, U., & Kleinebecker, T. (2020).
Drought boosts risk of nitrate leaching from grassland fertilisation.Sci Total Environ, 726 , 137877.
doi:10.1016/j.scitotenv.2020.137877
KMI. (2019). Klimatologisch jaaroverzicht, jaar 2019. [WWW document]
URL
https://www.meteo.be/resources/climatology/pdf/klimatologisch_jaaroverzicht_2019.pdf
[accessed 5 October 2021].
Knapp, A. K., Beier, C., Briske, D. D., Classen, A. T., Luo, Y.,
Reichstein, M., . . . Weng, E. (2008). Consequences of More Extreme
Precipitation Regimes for Terrestrial Ecosystems. BioScience,
58 (9), 811-821. doi:10.1641/B580908
Kraft, N. J., Godoy, O., & Levine, J. M. (2015). Plant functional
traits and the multidimensional nature of species coexistence.Proc Natl Acad Sci U S A, 112 (3), 797-802.
doi:10.1073/pnas.1413650112
Levine, J. M., Bascompte, J., Adler, P. B., & Allesina, S. (2017).
Beyond pairwise mechanisms of species coexistence in complex
communities. Nature, 546 (7656), 56-64. doi:10.1038/nature22898
Li, Y., Bearup, D., & Liao, J. (2020). Habitat loss alters effects of
intransitive higher-order competition on biodiversity: a new
metapopulation framework. Proc Biol Sci, 287 (1940), 20201571.
doi:10.1098/rspb.2020.1571
Li, Y., Mayfield, M. M., Wang, B., Xiao, J., Kral, K., Janik, D., . . .
Chu, C. (2021). Beyond direct neighbourhood effects: higher-order
interactions improve modelling and predicting tree survival and growth.Natl Sci Rev, 8 (5), nwaa244. doi:10.1093/nsr/nwaa244
Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero
replacement strategies for compositional data with large numbers of
zeros. Chemometrics and Intelligent Laboratory Systems, 210 ,
104248. doi:10.1016/j.chemolab.2021.104248
Maestre, F. T., Bautista, S., & Cortina, J. (2003). Positive, negative,
and net effects in grass-shrub interactions in mediterranean semiarid
grasslands. Ecology, 84 (12), 3186-3197. doi:10.1890/02-0635
Maestre, F. T., Callaway, R. M., Valladares, F., & Lortie, C. J.
(2009). Refining the stress-gradient hypothesis for competition and
facilitation in plant communities. Journal of Ecology, 97 (2),
199-205. doi:10.1111/j.1365-2745.2008.01476.x
Maestre, F. T., & Cortina, J. (2004). Do positive interactions increase
with abiotic stress? A test from a semi-arid steppe. Proceedings
of the Royal Society of London. Series B: Biological Sciences,
271 (suppl_5), S331-S333. doi:10.1098/rsbl.2004.0181
Maestre, F. T., Valladares, F., & Reynolds, J. F. (2005). Is the change
of plant–plant interactions with abiotic stress predictable? A
meta‐analysis of field results in arid environments. Journal of
Ecology, 93 (4), 748-757. doi:10.1111/j.1365-2745.2005.01017.x
Martyn, T. E., Stouffer, D. B., Godoy, O., Bartomeus, I., Pastore, A.
I., & Mayfield, M. M. (2021). Identifying ”Useful” Fitness Models:
Balancing the Benefits of Added Complexity with Realistic Data
Requirements in Models of Individual Plant Fitness. Am Nat,
197 (4), 415-433. doi:10.1086/713082
Mayfield, M. M., & Stouffer, D. B. (2017). Higher-order interactions
capture unexplained complexity in diverse communities. Nat Ecol
Evol, 1 (3), 62. doi:10.1038/s41559-016-0062
Metz, J., & Tielborger, K. (2016). Spatial and temporal aridity
gradients provide poor proxies for plant-plant interactions under
climate change: a large-scale experiment. Functional Ecology,
30 (1), 20-29. doi:10.1111/1365-2435.12599
Olsen, S. L., Topper, J. P., Skarpaas, O., Vandvik, V., & Klanderud, K.
(2016). From facilitation to competition: temperature-driven shift in
dominant plant interactions affects population dynamics in seminatural
grasslands. Glob Chang Biol, 22 (5), 1915-1926.
doi:10.1111/gcb.13241
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C., & Sanderson, B.
M. (2017). Precipitation variability increases in a warmer climate.Sci Rep, 7 (1), 17966. doi:10.1038/s41598-017-17966-y
Pfleiderer, P., Schleussner, C. F., Kornhuber, K., & Coumou, D. (2019).
Summer weather becomes more persistent in a 2 degrees C world.Nature Climate Change, 9 (9), 666-+. doi:10.1038/s41558-019-0555-0
R Core Team. (2019). R: a language and environment for statistical
computing, R v.3.6.1 . Vienna, Austria: R Foundation for Statistical
Computing. [WWW document] URL https://www.R-project.org
[accessed 5 October 2021].
Reynaert, S., De Boeck, H. J., Verbruggen, E., Verlinden, M., Flowers,
N., & Nijs, I. (2021). Risk of short-term biodiversity loss under more
persistent precipitation regimes. Glob Chang Biol, 27 (8),
1614-1626. doi:10.1111/gcb.15501
Reynaert, S., Zi, L., AbdElgawad, H., De Boeck, H. J., Vinduskova, O.,
Nijs, I., . . . Asard, H. (2022). Does previous exposure to extreme
precipitation regimes result in acclimated grassland communities?Sci Total Environ, 838 (Pt 3), 156368.
doi:10.1016/j.scitotenv.2022.156368
Saccone, P., Delzon, S., Pages, J. P., Brun, J. J., & Michalet, R.
(2009). The role of biotic interactions in altering tree seedling
responses to an extreme climatic event. Journal of vegetation
science, 20 (3), 403-414. doi:10.1111/j.1654-1103.2009.01012.x
Schmitz, O. J., Grabowski, J. H., Peckarsky, B. L., Preisser, E. L.,
Trussell, G. C., & Vonesh, J. R. (2008). From individuals to ecosystem
function: toward an integration of evolutionary and ecosystem ecology.Ecology, 89 (9), 2436-2445. doi:10.1890/07-1030.1
Siepielski, A. M., Morrissey, M. B., Buoro, M., Carlson, S. M., Caruso,
C. M., Clegg, S. M., . . . MacColl, A. D. (2017). Precipitation drives
global variation in natural selection. Science, 355 (6328),
959-962. doi:10.1126/science.aag2773
Singh, P., & Baruah, G. (2021). Higher order interactions and species
coexistence. Theoretical Ecology, 14 (1), 71-83.
doi:10.1007/s12080-020-00481-8
Soliveres, S., Smit, C., & Maestre, F. T. (2015). Moving forward on
facilitation research: response to changing environments and effects on
the diversity, functioning and evolution of plant communities.Biol Rev Camb Philos Soc, 90 (1), 297-313. doi:10.1111/brv.12110
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., & Dosio, A. (2018).
Will drought events become more frequent and severe in Europe?International Journal of Climatology, 38 (4), 1718-1736.
doi:10.1002/joc.5291
Suttle, K. B., Thomsen, M. A., & Power, M. E. (2007). Species
interactions reverse grassland responses to changing climate.Science, 315 (5812), 640-642. doi:10.1126/science.1136401
Van den Berge, J., Naudts, K., De Boeck, H., Ceulemans, R., & Nijs, I.
(2014). Do interactions with neighbours modify the above-ground
productivity response to drought? A test with two grassland species.Environmental and Experimental Botany, 105 , 18-24.
doi:10.1016/j.envexpbot.2014.04.002
Van Sundert, K., Arfin Khan, M. A. S., Bharath, S., Buckley, Y. M.,
Caldeira, M. C., Donohue, I., . . . Vicca, S. (2021). Fertilized
graminoids intensify negative drought effects on grassland productivity.Glob Chang Biol . doi:10.1111/gcb.15583
Vicca, S., Gilgen, A. K., Serrano, M. C., Dreesen, F. E., Dukes, J. S.,
Estiarte, M., . . . Granier, A. (2012). Urgent need for a common metric
to make precipitation manipulation experiments comparable. New
Phytologist, 195 (3), 518-522. doi:10.1111/j.1469-8137.2012.04224.x
Walker, L. R., Clarkson, B. D., Silvester, W. B., & Clarkson, B. R.
(2003). Colonization dynamics and facilitative impacts of a
nitrogen-fixing shrub in primary succession. Journal of vegetation
science, 14 (2), 277-290. doi:DOI 10.1111/j.1654-1103.2003.tb02153.x
Weigelt, A., Steinlein, T., & Beyschlag, W. (2002). Does plant
competition intensity rather depend on biomass or on species identity?Basic and Applied Ecology, 3 (1), 85-94. doi:Doi
10.1078/1439-1791-00080
Weiher, E., & Keddy, P. A. (1995). Assembly rules, null models, and
trait dispersion: new questions from old patterns. Oikos ,
159-164.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis.New York, NY, USA: Springer.
Wickham, H., Francois, R., Henry, L., & Müller, K. (2015). dplyr: A
grammar of data manipulation. [WWW document] URL
https://dplyr.tidyverse.org/ [accessed 5 October 2021].
Wootton, J. T. (1993). Indirect Effects and Habitat Use in an Intertidal
Community - Interaction Chains and Interaction Modifications.American Naturalist, 141 (1), 71-89. doi:10.1086/285461
Xiao, J. L., Li, Y. Z., Chu, C. J., Wang, Y. S., Meiners, S. J., &
Stouffer, D. B. (2020). Higher-order interactions mitigate direct
negative effects on population dynamics of herbaceous plants during
succession. Environmental Research Letters, 15 (7), 074023.
doi:10.1088/1748-9326/ab8a88
Zhao, W. N., & Khalil, M. A. K. (1993). The Relationship between
Precipitation and Temperature over the Contiguous United-States.Journal of Climate, 6 (6), 1232-1236.
doi:10.1175/1520-0442(1993)006<1232:Trbpat>2.0.Co;2