Nutrient Dilution and Climate Cycles Underlie Declines in a Dominant Insect Herbivore

Supplementary Information for

Nutrient dilution and climate cycles underlie declines in a dominant insect herbivore

Ellen A. R. Welti, Karl A. Roeder, Kirsten M. de Beurs, Anthony Joern, Michael Kaspari

Ellen A. R. Welti Email: [email protected]

This PDF file includes:

Supplementary text Figures S1 to S18 Tables S1 to S12 SI References

1 www.pnas.org/cgi/doi/10.1073/pnas.1920012117 Supplementary Information Text Supplementary Methods and Materials.

Long-term datasets Grasshopper sampling. The KNZ time series of short-horned grasshoppers (Family: Acrididae) spans 1983-1991 and 1996-2017 (1). Sampling locations and effort are more consistent starting in 1996 and time series analysis assumes no gaps in the data; thus our time series analysis uses data from 1996- 2017 (22 years). Sampling of grasshoppers in watersheds grazed by bison began in 2002; time series of grasshoppers in bison grazed watersheds spans 2002-2017 (16 years). We analyzed average total grasshopper abundance/sample/year in ungrazed and bison grazed watersheds separately as time series began in different years and calculated standard error using watersheds as replicates. As previous work on this system has examined the effects of fire and grazing on grasshopper communities (2, 3), we focus on grasshopper responses to climate, plant quantity, and plant quality. However, we note that grazed watersheds (Fig. 1 B; 67.4 grasshoppers/sample +/- 5.1 SE from 2002-2017) tend to have higher grasshopper abundances than ungrazed watersheds (Fig. 1 A; 58.2 grasshoppers/sample +/- 4.7 SE from 2002-2017). Our analysis includes the most consistently sampled 13 watersheds (ungrazed watershed codes: 1D, SpB, 2C, 2D, 4D, 4F, 20B; grazed watershed codes: N1A, N1B, N4A, N4D, N20A, and N20B) corresponding to four fire frequencies (burned every 1, 2, 4, and 20 years). Within each watershed, grasshoppers were sampled at two locations, twice per year (usually mid-July and early August) for 40 samples/watershed/year in total. Each of the 40 samples contained grasshoppers collected with 20 sweeps (800 sweeps/watershed/year) along a random transect using 38 cm sweepnets. In years prior to 1988, more samples were taken from each watershed, and samples were additionally taken earlier in the growing season, in June. For watersheds included in sampling in the early time series (1D, 2C, 2D, 4B, and 4F), we removed June samples prior to calculating average grasshopper abundance/ sample for a more consistent comparison across years.

Plant biomass. Plant biomass on three KNZ ungrazed watersheds, consisting of 1, 4, and 20 year fire frequency treatments (watershed codes: 1D, 4B, and 20B), was collected annually at the end of the growing season and is available from 1984-2015 (4). Live grasses, forbs, woody plants, and previous year's dead vegetation are clipped from 0.1 m2 quadrats in 20 plots/ watershed/year, dried and weighed. We examined live grasses (graminoids) and live forbs, as these represent the majority of host plants for KNZ acridids (3). Dried samples of live grass and forb biomass were averaged across the three sampled watersheds.

Climate cycles. Monthly climate cycle indices from 1983-2018 of the NAO and the ENSO using the Multivariate ENSO Index (5) were extracted from the National Oceanic and Atmospheric Administration database (noaa.gov). PDO values were sourced from the Joint Institute for the Study of the Atmosphere and Oceans, University of Washington (http://research.jisao.washington.edu/pdo/). Following (6), we summarized climate indices seasonally, by averaging values for winter (DJF), spring (MAM), and summer (JJA). Fall indices were not used as they are least likely to influence seasonal biotic dynamics such as plant and insects in temperate zones (6) and because they cause multi-collinearity (Variance Inflation Factor >5) when including in models as predictors (7).

Temperature and precipitation. Temperature and precipitation data are collected at the KNZ headquarters meteorological station and stored by the Climate and Hydrology Database Project with support from NSF LTER, the USGS, and the USDA forest service (https://climhy.lternet.edu/). We examined the relationships between climate cycle oscillations of NAO, PDO and ENSO and mean monthly temperature (°C) and cumulative monthly precipitation (mm) for the years 1983-2018 for the same month and a one-month lag to detect ongoing climate oscillation inertia (8). We examined relationships between seasonal winter, spring, and summer cumulative precipitation (mm) and mean temperatures (°C) and grasshopper abundances for both time series using multiple regression. A model including negative coefficients of spring and summer temperature and precipitation and positive coefficients of winter precipitation and temperature explained 48% of the variance in grasshopper abundance in the longer time series in ungrazed watersheds whereas seasonal indices of temperature and precipitation were not individually strongly correlated with grasshopper abundance (Table S11 A and

Fig. S18). Seasonal temperature and precipitation were not predictive of grasshopper abundances in the shorter time series in grazed watersheds (Table S11 B).

Plant chemistry. Elemental composition of end of season foliar grass tissue from watershed 1D was analyzed using combustion analysis for percent N and using hot plate digestion and inductively coupled plasma atomic emission spectroscopy (ICP-AES) for concentrations of metals (ppm) at the Cornell Nutrient Analysis Laboratory (https://cnal.cals.cornell.edu/). We provide elemental concentrations of N, P, K, Na, and Mg in Table S12. We conducted as principle component analysis (PCA) to summarize variation of these elements using the R package vegan (9), and determined the number of informative PCA axes using a broken stick model (10).

Detecting and correcting for cycles Spectral analysis. Spectral analysis is used to detect the presence of significant cycles and their period length. To meet time series analysis assumptions (11), for each time series and prior to spectral analysis, grasshopper abundance was log transformed and detrended. We performed spectral analysis using the MultiTaper Method (MTM) and used harmonic F-tests to determine cycle significance (12, 13) with the R package “astrochron” (14). MTM has several advantages over periodogram approaches (e.g. the Fast Fourier Transform), especially reducing the problem of periodogram leakage (15) and detection of frequencies when the background contains signals beyond white noise (16). Cycles are considered significant when they meet both the 90% MTM harmonic confidence level and the red noise confidence level is within +/- one-half of the power spectrum bandwidth resolution (13). We report significant cycles for each watershed sampled, each feeding guild, the 21 grasshopper species with >100 sampled individuals, and for mean abundances within grazing treatment.

Decomposition. Following cycle detection, log-transformed (but not detrended) grasshopper abundances from both the ungrazed and the grazed time series were decomposed into the cycle, and de- cycled trend (trend + remainder) using Seasonal and Trend decomposition using Loess (STL) using the “stl” function in Program R (17). In order to accommodate a multi-year cycle, sampling frequency (typically samples/year to decompose annual cycles) was set to the cycle period detected using the MTM rounded to the nearest integer and is generalized to samples/cycle.

Analysis of large-scale climate oscillations

AICc analysis. To compare models using winter, spring and summer averages of the climate cycle indices of PDO, NAO, and ENSO on average grasshopper abundance/sample, we used Akaike’s Information Criterion corrected for small sample size (AICc) (18). Climate indices are considered important drivers of grasshopper abundance when the null model is not included in top models (∆AICc<2). Models containing one additional parameter to the top model are not considered competitive unless they substantially improve log likelihood (18, 19). Relative Importance Values (RIVs), a model averaging statistic, were calculated to rank predictor variables. AICc analysis was conducted using the R package “MuMIn” (20).

Climate oscillations, temperature, and precipitation. NAO, PDO, and ENSO are global-scale cycles, but their impact varies by location. For example, when ENSO is in a La Niña phase, Kansas is drier and warmer, while the north central USA is generally colder (21). Thus, the effects of large-scale climate oscillations on global insect populations should vary geographically and produce cycles with variable period length. In this system, NAO and PDO but not ENSO are weakly linked to local monthly temperature and precipitation while the positive relationship with NAO and negative relationship with PDO in climate signals for both time series characterize colder and drier conditions (Fig. S14 and Fig. S15) favoring increased grasshopper abundances (22), especially in the central and southern USA (23). The temporal decline observed in grasshopper abundances is also evident in the climate signal from the 2 longer time series in ungrazed watersheds (F(1,20)=0.21, R =0.21, P=0.03), and exhibited a trend in the 2 climate signal from grazed watersheds (F(1,14)=4.1, R =0.23, P=0.06). Thus, shifts toward warmer and wetter conditions may explain decreasing grasshopper abundances. Seasonality also likely plays a role,

3 with the joint climate signal predicting the longer time series increasing with winter temperatures but other relationships weak but suggesting joint climate signals are indicative of colder and drier conditions (Fig. S16 and Fig. S17).

Climate oscillations and nutrient dilution as joint drivers SEM analysis. We used a Structural Equation Model (SEM) to examine direct and indirect relationships related to grasshopper abundances in the watershed 1D, in which we analyzed plant chemistry, for all years in which data from grasshopper abundances and plant nutrient data were both available (1985, 1986, 1988-1991, and 1996-2016) Our a priori SEM contained three linear models: 1) the climate cycle index for ungrazed watersheds and nutrient dilution PC1 as predictors of grasshopper abundance, 2) grass biomass as a predictor of nutrient dilution PC1, and 3) the climate cycle index for ungrazed watersheds as a predictor of grass biomass. Variables in this model displayed no evidence of variance inflation (all VIF<3) (7). SEM was conducted using the R package lavaan (24).

All analyses were run in R ver. 3.6.1 (25).

Fig S1. Grasshopper abundances in the seven ungrazed watersheds from 1982-2017 (A) and the six bison grazed watersheds from 2002-2017 (B). Significant cycle frequencies and tests of change over time after accounting for cycles for grasshopper abundance in individual watersheds from continuous time series (1996-2007 for ungrazed watersheds) are provided in Table S2.

Fig S2. Grasshopper feeding guild abundance trends over time and for both time series include mixed- feeders (A), grass-feeders (B), and forb-feeders (C). Significant cycle frequencies and tests of change over time for grasshopper feeding guilds are provided in Table S3.

Fig S3. Trends in abundance over time for mixed-feeding grasshopper species (with >100 individuals sampled) in both time series include Melanoplus bivittatus (A), Melanoplus confusus (B), Melanoplus femurrubrum (C), Melanoplus sanguinipes (D), and Schistocerca lineata (E). Significant cycle frequencies and tests of change over time for mixed-feeding species are provided in Table S4.

Fig S4. Trends in abundance over time for grass-feeding grasshopper species (with >100 individuals sampled) in both time series include Ageneotettix deorum (A), Mermiria bivitatta (B), Mermiria picta (C), Orphulella speciosa (D), Pardalophora haldemanii (E), Phoetaliotes nebrascensis (F), Pseudopomala brachyptera (G), and Syrbula admirabilis (H). Significant cycle frequencies and tests of change over time for grass-feeding species are provided in Table S5.

Fig S5. Trends in abundance over time for forb-feeding grasshopper species (with >100 individuals sampled) in both time series include Arphia xanthoptera (A), Camphylacantha olivacea (B), Hesperotettix speciosus (C), Hesperotettix viridis (D), Hypochlora alba (E), Melanoplus keeleri (F), Melanoplus packardii (G), and Melanoplus scudderi (H). Significant cycle frequencies and tests of change over time for forb-feeding species are provided in Table S6.

Fig. S6. Grasshopper abundance in ungrazed watersheds displayed one significant harmonic F-test peak. MTM power spectra and harmonic F-tests show peaks for grasshopper abundance on ungrazed KNZ watersheds from 1996-2017 and have a significant peak (harmonic CL=94.7, rednoise CL= 93.4) with a frequency (cycles/year) of 0.2, characterizing a 5-year cycle (A & C). For grasshopper abundance in grazed watersheds, one significant F-test peak (harmonic CL=99.9, rednoise CL= 85.6) was detected, describing a grasshopper abundance cycle at a frequency of 0.188 and period of 5.3 years (B & D). The F-test peak occurring around a frequency of 0.08 in ungrazed watersheds (C) does not occur on the power spectrum above the local robust red noise background estimate (A) and therefore is not considered significant.

Figure S7. The seasonal climate indices predicting grasshopper abundances in the ungrazed watersheds were tested for relationships with grass and forb biomass (Table S8) which revealed a relationship between summer NAO and grass biomass. Grass biomass negatively correlated with summer NAO.

2 Figure S8. Average grass biomass increased over time (F(1,30)=24, R =0.44, P<0.001), nearly doubling from the first 5 years (mean=159 g/m2/yr) to the last 5 years (mean=304 g/m2/yr), and driving an increase 2 in the joint biomass of grasses and forbs. Forb biomass exhibiting no temporal pattern (F(1,30)=2, R =0.06, P=0.17). Joint forb and grass biomass increased by 60% from the first 5 years (mean=224 g/m2/yr) to the last 5 years (mean=358 g/m2/yr). Error bar represent one standard error using watersheds as replicates.

2 Figure S9. Mean annual temperature (°C) on KNZ increased from 1983-2017 (A; F(1,33)=6.5, R =0.16, P=0.02). Cumulative annual precipitation (mm) on KNZ from 1983-2017 exhibited no temporal trend (B; 2 F(1,33)=0.2, R =0.01, P=0.67). The coefficient of variation (CV) of mean monthly temperature tended to 2 decline from 1983-2017 (C; F(1,33)=3.8, R =0.1, P=0.06). The CV of mean monthly cumulative precipitation 2 from 1983-2017 exhibited no temporal trend (D; F(1,33)=0.02, R <0.001, P=0.9).

Figure S10. The concentrations of elemental nutrients in end of growing season grass tissue from watershed 1D varied over time. P, K, Na, and Mg are shown with log-scaled axes and were log 2 transformed prior to linear regression analysis. Tissue % nitrogen (A; F(1,29)=28.6, R =0.5, P<0.001), 2 2 phosphorus ppm (B; F(1,29)=29.6, R =0.51, P<0.001), potassium ppm (C; F(1,29)=22.2, R =0.43, P<0.001), 2 and sodium ppm (D; F(1,29)=13.8, R =0.32, P<0.001) all decreased over time. Grass tissue magnesium 2 ppm had no significant temporal trend (E; F(1,29)=2.1, R =0.07, P=0.16). % N decreased by 42% from the first 5 years (mean=0.68%) to the last 5 years (mean=0.48%) of sample analysis. P ppm decreased by 58% from the first 5 years (mean=1004 ppm) to the last 5 years (mean=637ppm). K ppm decreased by 54% from the first 5 years (mean=8965 ppm) to the last 5 years (mean=5826 ppm). Na ppm decreased by 90% from the first 5 years (mean=42 ppm) to the last 5 years (mean=22 ppm).

Figure S11. Our a priori Structural Equation Model of grasshopper abundance per sample, grass biomass in g/m2, nutrient dilution PC1, all in watershed 1D, and the climate cycle index for ungrazed watersheds (spring ENSO + summer NAO – winter PDO) from the years 1985, 1986, 1988-1991 and 1996-2015 (the years from which all data from all variables was available) had a moderate fit to the data (χ2(df=2) = 2.89, P = 0.24, TLI = 0.92). Grasshopper abundances were log transformed. The proportion of variance explained in each predicted variable is given as the R2 value and standardized path estimates are provided next to each path. Line thickness indicates size of path estimates. Red arrows indicate positive and black arrows indicate negative relationships. Significant relationships are indicated with solid arrows whereas insignificant arrows are indicated with dashed arrows. Model estimates, standard errors, and p-values are provided in Table S10.

Figure S12. Conceptual model illustrating the potential effects of the presence cycles on linear regression slopes in time series. Solid black trend lines represent correct slopes whereas dashed red lines represent trend lines biased by the relationship between cycle phase and when time series start and stop. Cyclic patterns do not affect slope estimates when time series start and stop at the same end of the cycle (A & D). However, when time series start and stop at opposite cycle ends of a cycle, cycles bias linear regression slopes, and can cause detection of linear trends where none occur (B). The potential for error increases when fewer cycles are sampled (C). Likewise, when cycles and temporal trends co-occur, slope sign (+/-) is likely unaffected, but the time sampling starts and stops in relation to cycle phase may still cause error in slope estimation (E) and, again, the severity of potential error increases in time series encompassing fewer cycles (F).

Figure S13. The Principle Component Analysis of the elemental composition of N, P, K, Na, and Mg in grass tissue collected on the KNZ watershed 1D from 1985, 1986, and 1988-2016 (A) has one significant axis as shown using a broken stick model (B). The significant axis (PC1) is negatively correlated with N, P, K, and Na concentrations in grass tissue and is referred to as nutrient dilution PC1.

Figure S14. Correlations between mean monthly temperature (A, B, and C), cumulative monthly precipitation (D, E, and F), and monthly climate oscillations of Multivariate ENSO Index (MEI), NAO and 2 PDO. Mean monthly temperature decreased with NAO (B; F(1,430)=15.7, R =0.04, P<0.001). Cumulative 2 annual precipitation decreased with NAO (E; F(1,430)=19.4, R =0.04, P<0.001). All other relationships are nonsignificant.

Figure S15. Correlations between mean monthly temperature (A, B, and C), cumulative monthly precipitation (D, E, and F) and climate cycle oscillations of Multivariate ENSO Index (MEI), NAO and PDO from the previous month to examine lag effects. Mean monthly temperature decreased with NAO from the 2 previous month (B; F(1,429)=8.4, R =0.02, P=0.004) and increased with PDO values from the previous 2 month (C; F(1,427)=5.7, R =0.01, P=0.02). Cumulative monthly precipitation increased with PDO from the 2 previous month (F; F(1,427)=9.3, R =0.02, P=0.002). All other relationships are nonsignificant.

Figure S16. Comparison of combined cycle indices predicting grasshopper abundances in ungrazed watersheds (spring ENSO + summer NAO - winter PDO) with seasonal measures consisting of mean winter temperature (A), mean spring temperature (B), mean summer temperature (C), cumulative winter precipitation (D), cumulative spring precipitation (E), and cumulative summer precipitation (F) for 1983- 2017. The combined cycle signal predicting grasshopper abundance in ungrazed watersheds and was 2 significantly positively correlated with winter temperatures (A; F(1,33)=9.1, R =0.22, P=0.005).

Figure S17. Comparison of combined cycle indices predicting grasshopper abundances in bison grazed watersheds (summer NAO - spring PDO) with seasonal measures consisting of mean winter temperature (A), mean spring temperature (B), mean summer temperature (C), cumulative winter precipitation (D), cumulative spring precipitation (E), and cumulative summer precipitation (F) for 1983-2017. The combined cycle signal predicting grasshopper abundances in grazed watersheds was not significantly correlated with seasonal temperature and precipitation measures.

Figure S18. Seasonal precipitation and temperature measures included in multiple regression (Table S11 A) of grasshopper abundances in ungrazed watersheds. Individual linear relationships between grasshopper abundances in ungrazed watersheds and cumulative summer precipitation (A), cumulative spring precipitation (B), cumulative winter precipitation (C), mean summer temperature (D), mean spring temperature (E), and mean winter temperature (F) were not significantly correlated. The relationship between grasshopper abundance in ungrazed watersheds and mean spring temperature (E) exhibited a 2 trend (F(1,20)=3.3, R =0.14, P=0.08) and was significant when the outlier (the year 2012 when the mean 2 spring temperature was 17.2°C) was removed (F(1,19)=18.8, R =0.5, P<0.001).

Table S1. Meta-analyses and long-term studies of CO2 dilution of plant nutrients. A Web of Science search of the terms “nutrient”, “plant”, “dilution”, and “CO2” located 19 meta-analyses and five long-term (≥20 years) observational studies of change in plant elemental chemistry with increasing atmospheric CO2. Type categories include M = meta-analysis and O = observational study. For meta-analyses, time refers to years of publication for included studies while for observational studies, time refers to years in which data were collected. We did not include a column listing plants with increasing nutrient concentrations with increasing atmospheric CO2 as this only occurred in one study indicated by the * in which %K increased slightly in wheat grains. The ** indicates that results are only listed for elements with n>5. The *** indicates insufficient statistical power for many plants. The **** indicates that the decline in %N in plant tissue began ca. 1926 while plant %N from 1876-1926 displayed no temporal trend.

Plants with Declining Plants with No Change in Citation Type Location Time Element (s) Concentrations Concentrations tree, forb, legume, C3 (26) M Global 1989-2000 N and C4 grass (27) M NA 1984-1998 N 29 wild species 4 wild species B, Ca, Cd, Cu, Fe, K, Mg, Mn, wheat grain (Ca, Cd, Cu, (28)* M Global 1980-2016 N, Na, P, S, Zn Fe, Mg, Mn, N, P, S, Zn) wheat grain (B, Na) 9 wild woody species, 4 Pinus ponderosa, 7 wild woody (29) M Global 1978-1997 N wild non-woody species species woody, non-woody, legume, non-legume (N); non-woody, non- (30) M Global 1991-2013 N and P legume (P) woody, legume (P) Avena, Arabidopsis, Hordeum, Triticum, (31) M Global 1983-2000 N Gossypium (seeds) Oryza, Glycine (seeds) C4, and C3 non-legume (32) M Global 1976-2012 N plants C3 legume Ca, Cu, Fe, K, 19 herbaceous, 11 Mg, Mn, N, P, woody plants, and 5 (33) M NA 1992-2000 S, Zn wheat cultivars Ca, Cu, Fe, K, Mn, Mg, N, P, (34) M Global 1986-2014 S, Zn C3 plants (35) M Global 1989-2003 N C3 plants Al, B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, N, Ni, crops (Al, Ca, Co, Fe, K, (36)** M Global NA P, Pb, S, Si, Zn Mg, Mn, N, Ni, P, S, Zn) crops (B, Cu, Mo, Pb, Si) C3 grains, wheat, rice, barley, C4 grains, sorghum, root vegetable, potato, legumes, beans, oilcrops, (37)*** M Global NA N maize, peas, rapeseed soy, fruit Japan, wheat, rice, pea (Fe, N, USA, Zn); soybean, maize sorghum (Fe, N, Zn); soybeans, (38) M Australia 1998-2010 N, Fe, Zn (Fe, Zn) maize (N) 1994- Sweden, 1996, (39) M Germany 2004-2008 Cd, Mn, N, Zn wheat Average of 58 species in (40) M Global 1984-2003 N 27 families Average of many (41) M Global 1990-2016 N, P species (N) Average of many species (P) (42) M NA NA Ca, K, Mg, N, P crops (Ca, Mg, N, P) crops (K) barley, rice, wheat, (43) M Global 1982-2005 N soybean, potato (44) M Global 1980-2011 N wheat (45) O England 1876-2008 Se, I grass (46) O England 1845-2005 Cu, Fe, Mg, Zn wheat 24 forb, C3 and C4 grass and woody (47)**** O KS, USA 1876-2008 N species B, Cu, Fe, Mg, (48) O OR, USA 1995-2015 Zn wheat USA, Solidago canadensis (49) O Canada 1842-2014 N (pollen)

Table S2. Grasshopper abundance cycle signals in all 13 sampled watersheds. All watersheds had exactly one significant cycle with a harmonic confidence level >80%. The significant cycle varied from a frequency of 0.18-0.21 matching a period of 4.78-5.71 years. Grasshopper abundances were log transformed and detrended prior to spectral analysis. Following significant frequency detection using spectral analysis, the cycle (to the nearest integer as allowed by STL analysis) was removed from the raw series using decomposition. WS indicates name of KNZ watershed, Fire indicates fire return interval in years, Trt indicates the time series (U=ungrazed, G=grazed), Total= total grasshoppers sampled in the watershed, Length= continuous length of the time series in years, Freq= significant frequency, Period= length of cycle in years, Har. CL= harmonic confidence level in percent, RN CL= red noise confidence level in percent. We provide the corrected slope estimate and F test of the relationships between the de- cycled abundance and year. Grasshopper abundance in nine watersheds significantly declined over time while all 13 had negative slopes. Plots of grasshopper abundances in each watershed over time are provided in Fig. S1.

WS Fire Trt Total Length Freq Period Har. CL RN CL Slope F test

F(1,20)=16.2, 1D 1 U 7185 22 0.2 5 99.9 87 -0.027 R2=0.45, P<0.01 F(1,20)=11.8, SpB 1 U 6556 22 0.2 5 98.9 94.3 -0.023 R2=0.37, P=0.003 F(1,20)=6.8, 2C 2 U 6392 22 0.2 5 99.5 89.3 -0.023 R2=0.26, P=0.02 F(1,20)=2.8, 2D 2 U 6394 22 0.19 5.24 95.8 84.1 -0.017 R2=0.12, P=0.11 F(1,20)=0.1, 4B 4 U 4384 22 0.2 5 94.3 77.7 -0.003 R2<0.01, P=0.8 F(1,20)=6.2, 4F 4 U 4693 22 0.19 5.24 98.6 86.3 -0.025 R2=0.24, P=0.02 F(1,20)=0.6, 20B 20 U 4500 22 0.21 4.78 99.1 82.2 -0.006 R2=0.03, P=0.44 F(1,14)=13.8, N1A 1 G 5615 16 0.19 5.33 99.8 61.1 -0.025 R2=0.5, P=0.003 F(1,14)=30, N1B 1 G 4212 16 0.19 5.33 99.8 80 -0.04 R2=0.68, P<0.001 F(1,14)=12.7, N4A 4 G 4470 16 0.19 5.33 99.1 73.9 -0.035 R2=0.47, P=0.003 F(1,14)=7.3, N4D 4 G 3718 16 0.19 5.33 99.8 78.9 -0.028 R2=0.34, P=0.02 2 F(1,14)=5, R =0.26, N20A 20 G 4289 16 0.18 5.71 99.2 59.9 -0.018 P=0.04 F(1,14)=1.2, N20B 20 G 3200 16 0.19 5.33 99.4 81.2 -0.01 R2=0.08, P=0.29

Table S3. Grasshopper abundance cycle signals by feeding type. The abundance of each grasshopper feeding guild (mixed-feeder, grass-feeder, forb-feeder) with each time series (grazed and ungrazed) had exactly one significant cycle with a harmonic confidence level >80%. Species feeding guild follows determinations in (2). The significant cycle varied from a frequency of 0.18-0.2 matching a period of 5-5.71 years. Grasshopper abundances were log transformed and detrended prior to spectral analysis. Following significant frequency detection using spectral analysis, the cycle (to the nearest integer as allowed by STL analysis) was removed from the raw series using decomposition. Diet indicates grasshopper feeding guild, Trt indicates the time series (U=ungrazed, G=grazed), Total= total grasshoppers sampled within the feeding guild and time series, Freq= significant frequency, Period= length of cycle in years, Har. CL= harmonic confidence level in percent, RN CL= red noise confidence level in percent. We provide the corrected slope estimate and F test of the relationship between the de- cycled abundance and year. All feeding guilds in both time series significantly declined over time. Plots of grasshopper feeding guild abundance over time are provided in Fig. S2.

Diet Trt Total Freq Period Har. CL RN CL Slope F test 2 Mixed U 2019 0.2 5 95.7 71 -0.028 F(1,20)=4.3, R =0.18, P=0.05 2 Mixed G 4326 0.2 5 97.4 84.1 -0.021 F(1,20)=5.5, R =0.28, P=0.03 2 Grass U 31622 0.2 5 99.2 87.7 -0.019 F(1,20)=7.3, R =0.27, P=0.01 2 Grass G 11219 0.1875 5.333 99.9 73.7 -0.031 F(1,20)=18.7, R =0.57, P<0.001 2 Forb U 6463 0.2 5 99.5 74.3 -0.017 F(1,20)=6.9, R =0.26, P=0.02 2 Forb G 9959 0.175 5.714 99.9 62 -0.028 F(1,20)=24.4, R =0.64, P<0.001

Table S4. Grasshopper abundance cycle signals for mixed-feeding species. We conducted time series analysis on the abundance of mixed-feeding grasshopper species (with >100 total caught individuals) for each time series (grazed and ungrazed). Grasshopper abundances were log transformed and detrended prior to spectral analysis. Following significant frequency detection using spectral analysis, when cycles were present, the cycles (period to the nearest integer) were removed from the raw series using decomposition. The two significant frequencies in the time series of Melanoplus femurrubrum in ungrazed watersheds were removed sequentially. Species refers to grasshopper species, Trt indicates the time series (U=ungrazed, G=grazed), Total= total abundance of the grasshopper species sampled in the time series, Freqs= significant frequencies, Period= length of cycle in years, Har. CL= harmonic confidence level in percent, RN CL= red noise confidence level in percent. We provide the corrected slope estimate and F test of the relationship between the de-cycled abundance and year when cycles are present, and slope and F test of the log10 +1 transformed abundance and year in the absence of significant frequencies. Change indicates the change in grasshopper abundance over time (- = decline, + = increase, NS(-) = no significant change with a negative slope, NS(+) = no significant change with a positive slope). Plots of mixed-feeding grasshopper species abundance over time are provided in Fig. S3.

Species Trt Total Freqs Period Har. CL RN CL Slope F test Change

Melanoplus 0.2, 5, 89.8, 70.1, F(1,20)=5.6, femurrubrum U 1270 0.255 3.929 89.4 86 -0.016 R2=0.22, P=0.03 - 2 Melanoplus F(1,14)=1, R =0.07, femurrubrum G 2746 0.213 4.706 99.9 81.6 0.006 P=0.33 NS (-)

Melanoplus F(1,20)=4.1, bivittatus U 474 0.191 5.238 98.9 83.5 -0.009 R2=0.17, P=0.05 - Melanoplus - F(1,14)<0.1, bivittatus G 636 0.175 5.714 94.3 93.7 0.0002 R2<0.01, P=0.98 NS (-)

Melanoplus F(1,20)=113.8, confusus U 141 0.155 6.471 94.2 72.6 -0.009 R2=0.85, P<0.001 -

Melanoplus F(1,14)=4.7, confusus G 544 NS -0.032 R2=0.25, P=0.05 - 2 Melanoplus F(1,20)=7, R =0.26, sanguinipes U 42 NS -0.004 P=0.02 - Melanoplus F(1,14)=11.5, sanguinipes G 268 NS -0.023 R2=0.45, P=0.004 - Schistocerca F(1,20)=6.1, lineata U 54 0.218 4.583 86.2 71.8 0.005 R2=0.23, P=0.02 + Schistocerca F(1,14)=2.8, lineata G 50 0.188 5.333 85.8 88.5 0.005 R2=0.17, P=0.17 NS (+)

Table S5. Grasshopper abundance cycle signals for grass-feeding species. We conducted time series analysis on the abundance of grass-feeding grasshopper species (with >100 total caught individuals) for each time series (grazed and ungrazed). Grasshopper abundances were log transformed and detrended prior to spectral analysis. Following significant frequency detection using spectral analysis, when cycles were present, the cycles (period to the nearest integer) were removed from the raw series using decomposition. The two significant frequencies in the time series of Mermiria picta in ungrazed watersheds were removed sequentially. Species refers to grasshopper species, Trt indicates the time series (U=ungrazed, G=grazed), Total= total abundance of the grasshopper species sampled in the time series, Freqs= significant frequencies, Period= length of cycle in years, Har. CL= harmonic confidence level in percent, RN CL= red noise confidence level in percent. We provide the corrected slope estimate and F test of the relationship between the de-cycled abundance and year when cycles are present, and slope and F test of the log10 +1 transformed abundance and year in the absence of significant frequencies. Change indicates the change in grasshopper abundance over time (- = decline, + = increase, NS(-) = no significant change with a negative slope, NS(+) = no significant change with a positive slope). Plots of grass-feeding grasshopper species abundance over time are provided in Fig. S4.

Species Trt Total Freqs Period Har. CL RN CL Slope F test change

Phoetaliotes F(1,20)=10.4, nebrascensis U 16428 0.191 5.238 97.7 85.2 -0.011 R2=0.34, P=0.004 -

Phoetaliotes F(1,14)=40.2, nebrascensis G 3415 0.188 5.333 99.8 84.8 -0.031 R2=0.74, P<0.001 -

Orphulella F(1,20)=24.1, speciosa U 12180 0.2 5 99.7 88 -0.027 R2=0.55, P<0.001 -

Orphulella F(1,14)=11.9, speciosa G 4682 0.188 5.333 99.9 87.2 -0.027 R2=0.46, P=0.004 - Syrbula F(1,20)=1.1, admirabilis U 1074 0.191 5.238 99.9 88.2 -0.008 R2=0.05, P=0.31 NS (-) Syrbula F(1,14)=0.1, admirabilis G 1156 0.188 5.333 96.1 77.4 0.003 R2=0.01, P=0.74 NS (-) Mermiria F(1,20)=0.1, bivittata U 719 0.218 4.583 91.4 80.2 -0.002 R2<0.01, P=0.76 NS (-)

Mermiria F(1,14)=9.6, bivittata G 358 0.225 4.444 99.2 95.9 -0.024 R2=0.41, P=0.008 - 2 Ageneotettix F(1,20)=2.2, R =0.1, deorum U 102 NS -0.006 P=0.15 NS (-) Ageneotettix F(1,14)=8.4, deorum G 701 0.175 5.714 88 95.5 -0.027 R2=0.37, P=0.01 NS (-) 2 F(1,20)=4.9, R =0.2, Mermiria picta U 294 0.2 5 97 85.7 -0.008 P=0.04 - 2 0.15, 6.667, 93.7, 71.4, F(1,14)=4, R =0.22, Mermiria picta G 245 0.338 2.962 96 63.7 -0.011 P=0.06 NS (-) Pardalophora F(1,20)=0.2, haldemanii U 12 NS 0.0002 R2=0.008, P=0.7 NS (+) Pardalophora F(1,14)=1.3, haldemanii G 124 NS -0.006 R2=0.09, P=0.27 NS (-) Pseudopomala F(1,20)=1.2, brachyptera U 89 NS -0.003 R2=0.06, P=0.28 NS (-) 2 Pseudopomala F(1,14)=9.5, R =0.4, brachyptera G 22 NS -0.007 P=0.008 -

Table S6. Grasshopper abundance cycle signals for forb-feeding species. We conducted time series analysis on the abundance of forb-feeding grasshopper species (with >100 total caught individuals) for each time series (grazed and ungrazed). Grasshopper abundances were log transformed and detrended prior to spectral analysis. Following significant frequency detection using spectral analysis, when cycles were present, the cycles (period to the nearest integer) were removed from the raw series using decomposition. Species refers to grasshopper species, Trt indicates the time series (U=ungrazed, G=grazed), Total= total abundance of the grasshopper species sampled in the time series, Freq= significant frequency, Period= length of cycle in years, Har. CL= harmonic confidence level in percent, RN CL= red noise confidence level in percent. We provide the corrected slope estimate and F test of the relationship between the de-cycled abundance and year when cycles are present, and slope and F test of the log10 +1 transformed abundance and year in the absence of significant frequencies. Change indicates the change in grasshopper abundance over time (- = decline, + = increase, NS(-) = no significant change with a negative slope, NS(+) = no significant change with a positive slope). Plots of forb-feeding grasshopper species abundance over time are provided in Fig. S5. Species Trt Total Freq. Period Har. CL RN CL Slope F test Change Campylacantha F(1,20)=2, olivacea U 1651 0.218 4.583 97.7 65.2 -0.013 R2=0.09, P=0.17 NS (-) Campylacantha F(1,14)=1.2, olivacea G 3156 NS -0.026 R2=0.08, P=0.29 NS (-) F(1,20)=18.7, Melanoplus R2=0.48, keeleri U 1948 0.2 5 98.7 80.1 -0.02 P<0.001 - F(1,14)=21.6, Melanoplus R2=0.61, keeleri G 2557 0.163 6.154 98.2 88.3 -0.038 P<0.001 - F(1,20)=1, Hypochlora alba U 890 0.209 4.782 98.9 59 -0.007 R2=0.05, P=0.32 NS (-) F(1,14)=0.3, Hypochlora alba G 1004 0.138 7.27 95.8 59.6 0.003 R2=0.02, P=0.58 NS (+) Melanoplus F(1,20)=3.5, scudderi U 703 0.164 6.111 89.4 83.2 -0.0162 R2=0.15, P=0.08 NS (-)

Melanoplus F(1,14)=5.1, scudderi G 676 0.175 5.714 99.2 77.4 -0.013 R2=0.27, P=0.04 - Hesperotettix F(1,20)=3.4, speciosus U 547 0.464 2.157 90 98.5 -0.016 R2=0.15, P=0.08 NS (-) Hesperotettix F(1,14)=1, speciosus G 612 0.163 6.154 92 81.2 0.008 R2=0.07, P=0.32 NS (+) Hesperotettix F(1,20)<0.1, viridis U 102 NS -0.0005 R2<0.01, P=0.86 NS (-) Hesperotettix F(1,14)=2.9, viridis G 218 0.188 5.333 86.8 93 -0.012 R2=0.14, P=0.11 NS (-) Arphia F(1,20)=0.6, xanthoptera U 47 NS 0.001 R2=0.03, P=0.45 NS (+) Arphia F(1,14)=0.4, xanthoptera G 102 NS -0.002 R2=0.03, P=0.52 NS (-) Melanoplus F(1,20)<0.1, packardii U 34 NS -0.0004 R2<0.01, P=0.97 NS (-) Melanoplus F(1,14)=2.8, packardii G 106 0.175 5.714 99.9 72.7 -0.004 R2=0.17, P=0.12 NS (-)

Table S7. Top climate cycle index models of grasshopper abundance. Top models consist of those with ΔAICc<2 predicting mean grasshopper abundance/sample in ungrazed watersheds from 1996-2017 (A) and bison grazed watersheds from 2002-2017 (B). AIC statistics include: AICc = AIC statistic, adjusted for small sample size, LL = log likelihood, df=degrees of freedom, ∆AICc = AICc minus top model AICc, wi = model weight, R2 = adjusted R2, and P = p-value. The Relative Importance Values (RIVs) of the variables occurring in the top model for ungrazed watersheds (A) were all >0.5 (RIVs: summer NAO=0.68, winter PDO=0.62, spring ENSO=0.54; the next highest-ranking predictor variable, spring PDO, had an RIV=0.32). RIVs for summer NAO=0.76 and for spring PDO=0.73 in the models for grazed watersheds (B), while the next highest-ranking predictor variable, summer PDO, had a RIV=0.22. While the alternate model for ungrazed watersheds, and the two alternates for grazed also met the criterion of ∆AICc<2, they all contained one additional parameter to the top models without strongly improving model log likelihood. This combined with the lower RIVs of the additional parameters led us to not consider the alternate models as equally parsimonious to the top models. Seasonal climate indices in the top models were combined into one climate index, which predicted grasshoppers within the same time series (see Climate signal in Methods and Large-scale climate oscillations as cause of cycles in Results). Moreover, the combined climate cycle indices for ungrazed watersheds correlates with grasshopper abundance in 2 the grazed watersheds (F(1,14)=6.1, R =0.3, P=0.03) and the climate index for grazed watersheds 2 correlates with grasshopper abundance in ungrazed watersheds (F(1,20)=5, R =0.2, P=0.04).

2 predictor variables AICc LL df ΔAICc wi R P

A.) Ungrazed +spring ENSO, +summer NAO, 15.4 -0.82 5 0 0.11 0.34 0.015 -winter PDO +spring ENSO, +summer NAO, 17.3 0.14 6 1.94 0.04 0.36 0.019 -winter PDO, -spring PDO B.) Bison grazed +summer NAO, -spring PDO 2.7 4.46 4 0 0.15 0.48 0.006 +summer NAO, -spring PDO, 3.8 6.09 5 1.1 0.09 0.54 0.006 +summer PDO +summer NAO, -spring PDO, +winter NAO 3.9 6.03 5 1.22 0.08 0.54 0.006

Table S8. ANOVAs of plant biomass and seasonal climate indices. Seasonal climate indices in the top model for the longer time series, predicting grasshopper abundances in ungrazed grasslands were tested as predictors of average grass (A) and forb (B) biomass from ungrazed watersheds. The table provides degrees of freedom (df), sums of squares (Sum Sq), F statistic (F), and p-value (P). A scatterplot of the relationship between summer NAO and grass biomass is provided in Fig. S7.

df Sum Sq F P A.) Grasses summer NAO 1 362.3 5.6 0.02 spring ENSO 1 0.02 >0.1 0.95 winter PDO 1 96.5 1.5 0.23 residual 28 1806.3 B.) Forbs summer NAO 1 2 2.1 0.16 spring ENSO 1 2.8 0.7 0.4 winter PDO 1 9.1 2.4 0.14 residual 28 108.3

Table S9. PCA axes and element correlations. Correlations between principle component axes from the principle component analysis of elemental concentrations of N, P, K, Na, and Mg in grass tissue from the watershed 1D on KNZ depicted in Fig. S13.

Element PC1 PC2 PC3 PC4 PC5 N -0.892 0.139 -0.236 0.318 -0.168 K -0.915 -0.066 0.01 -0.368 -0.152 Mg 0.237 0.966 0.072 -0.066 -0.032 Na -0.723 0.022 0.68 0.104 0.064 P -0.9 0.166 -0.304 -0.043 0.261

Table S10. Structural Equation Model parameters. Standardized path estimates, standard errors, and p-values for the SEM depicted in Fig. S11.

Response Predictor Estimate Std. Error P grasshopper abundance nutrient dilution PC1 -0.064 0.03 0.02 grasshopper abundance climate index 0.155 0.05 0.001 nutrient dilution PC1 grass biomass 0.854 0.17 <0.001 grass biomass climate index -0.28 0.26 0.29

Table S11. Top seasonal temperature and precipitation models of grasshopper abundance. Multiple regression results of season mean temperature and cumulative precipitation effects on mean grasshopper abundance/sample in ungrazed watersheds from 1996-2017 (A) and bison grazed watersheds from 2002-2017 (B). The full model of all season measures of temperature and precipitation 2 was correlated with grasshopper abundance in ungrazed watersheds (A; F(6,15)= R =0.48, P=0.01). Regression relationships between grasshopper abundance in ungrazed watersheds and seasonal precipitation (winter, spring, and summer cumulative precipitation) and temperature (winter, spring and summer mean temperature) measures included in top models are shown in Fig. S18. Grasshopper abundances in grazed watersheds were not correlated with season temperature and precipitation (F(6,9)= R2<0.01, P=0.59) but coefficients have the same signs (+/-) as grasshopper abundance responses to climate in ungrazed watersheds (B).

predictor variables Estimate Std. Error t-value P A.) Ungrazed summer precipitation -0.006 0.002 -2.73 0.015 spring precipitation -0.005 0.002 -2.37 0.03 winter precipitation 0.01 0.005 2.26 0.04 summer temperature -0.128 0.067 -1.87 0.08 spring temperature -0.111 0.046 -2.41 0.03 winter temperature 0.067 0.033 2.04 0.06 A.) Grazed summer precipitation -0.003 0.003 -0.92 0.38 spring precipitation -0.002 0.003 -0.63 0.54 winter precipitation 0.002 0.007 0.28 0.78 summer temperature -0.102 0.096 -1.06 0.32 spring temperature -0.082 0.058 -1.42 0.19 winter temperature 0.045 0.046 0.93 0.36

Table S12. Elemental chemistry. N, P, K, Na, and Mg concentrations from grass tissue collected on the watershed 1D at KNZ for 1985, 1986, and 1988-2016.

year % N ppm K ppm Mg ppm Na ppm P 1985 0.615 6739.08 1427.91 65.871 743.49 1986 0.860 11226.90 1391.67 69.913 1266.02 1988 0.626 9652.42 1177.58 37.420 752.56 1989 0.834 9375.26 1377.91 13.893 1278.99 1990 0.469 7831.64 1239.35 24.692 980.25 1991 0.557 8717.77 1272.92 39.551 929.12 1992 0.652 9007.01 1175.33 44.447 976.59 1993 0.635 8398.54 1193.17 63.073 930.49 1994 0.651 8498.93 1224.13 48.196 884.69 1995 0.607 8887.37 1244.74 49.574 828.47 1996 0.612 8142.62 1656.95 57.646 920.92 1997 0.657 7631.91 1270.95 55.013 1096.86 1998 0.631 9076.12 1321.86 77.707 929.78 1999 0.651 9713.80 1185.49 58.518 880.94 2000 0.542 9053.13 1357.22 50.022 874.41 2001 0.658 7987.81 1479.49 57.702 889.29 2002 0.517 6698.60 1375.80 50.028 670.00 2003 0.551 5986.16 1394.78 54.281 762.23 2004 0.506 5944.68 1309.73 18.467 604.48 2005 0.580 6520.04 1487.19 17.538 867.35 2006 0.661 6888.95 1410.07 16.457 821.62 2007 0.465 5057.30 1406.22 18.441 630.06 2008 0.439 5993.98 1417.17 17.453 673.74 2009 0.467 5973.74 1420.56 18.851 599.01 2010 0.395 4391.50 1397.87 13.919 521.43 2011 0.452 7895.17 1385.49 37.166 562.55 2012 0.499 2991.22 1185.59 16.878 419.40 2013 0.496 8540.78 1254.85 37.416 765.14 2014 0.457 5853.54 1508.42 18.336 657.86 2015 0.457 5438.55 1402.40 19.020 584.68 2016 0.513 6305.95 1309.73 18.438 759.45

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