Supporting Information

Asch 10.1073/pnas.1421946112 SI Text Relationships Between Phenology, Climate, Oceanic Conditions, and Precision of Estimates of Phenological Change and Evaluation of Bias Ecological Traits. The effects of three basin-scale climate oscillations due to Gaps in Sampling. A sensitivity analysis based on Monte Carlo (e.g., ENSO, PDO, and NPGO) and three local environmental simulation was conducted to quantify observation error related to variables (e.g., SST, zooplankton volume, and coastal upwelling) on noise from variations in larval abundance and the sparse, seasonal fish phenology were examined. ENSO events were characterized sampling resolution of CalCOFI. First, the seasonal distribution of a with the Oceanic Niño Index (ONI) produced by the National simulated larval species was modeled with a Gaussian curve where Centers for Environmental Prediction (www.cpc.ncep.noaa.gov/ June 15 was initially the mean date of larval occurrence and there products/analysis_monitoring/ensostuff/ensoyears.shtml). This index was a 1-mo SD. This standard deviation implied that 95% of larvae was selected because it defines the onset and termination of El would be observed within ±2 mo of the mean. The Gaussian curve Niño and La Niña on a monthly basis. The PDO index was obtained was multiplied by 10 to more realistically approximate larval from the Joint Institute for the Study of the Atmosphere and Ocean abundance in the CalCOFI region. Variations in larval abundance (57) (research.jisao.washington.edu/pdo/PDO.latest). The NPGO that could affect the calculation of seasonal central tendency (CT) index was developed and made available by Emanuele Di Lorenzo were stochastically generated from a coefficient of variation (CV) at the Georgia Institute of Technology (30) (www.o3d.org/npgo/ ranging between 0.05 and 0.60. Twelve CVs separated by 0.05 in- npgo.php). SST and mesozooplankton displacement volume were tervals were used. This range was selected based on the empirical measured at CalCOFI stations where ichthyoplankton samples 3 CV of monthly larval abundance for representative species sam- were collected. Gelatinous organisms with biovolumes >5cm pled by CalCOFI (e.g., Argentina sialis, Engraulis mordax, Lipolagus were excluded from measurements of zooplankton volume (41). ochotensis, Scomber japonicus, Tetragonurus cuvieri). Next, shifts in Zooplankton volume from 1969 to 1977 was multiplied by a cor- phenology at rates of 0–12 d/decade were simulated over six de- rection factor of 1.366 to account for changes in net type and tow cadal time steps. For each time step, the CT was calculated from depth (80). The monthly Bakun upwelling index from 33°N and monthly mean values extracted from the underlying continuous 119°W was obtained from the Environmental Research Division Gaussian distribution. This process was repeated 1,000 times for at the Southwest Fisheries Science Center (83) (www.pfel.noaa. each CV and rate of phenological change. The 90% and 95% CIs gov/products/PFEL/modeled/indices/upwelling/upwelling.html). This of CT were calculated based on these simulated values to assess the index estimates offshore Ekman volume transport based on at- precision of this estimate of phenological change. mospheric pressure fields. At 33° N and 119° W, the Bakun up- For the largest CV considered, the 90% and 95% CIs of CT welling index overestimates offshore transport due to a discontinuity always fell within ±2.8–3.1 and ±3.3–3.6 d/decade, respectively, of in the atmospheric pressure gradient related to the presence of the simulated change in phenology (Tables S3 and S4). The 90% coastal mountains (83). Nevertheless, this index is still correlated CIs corresponded closely to the minimum rate of change observed with upwelling measured with the QuikSCAT scatterometer at among species categorized as displaying earlier or later phenology r = 0.6 (86). Also, the CT of QuikSCAT upwelling over the (i.e., −2.8 d/decade for earlier species; 3.0 d/decade for later CalCOFI region during the 2000s fell within the 95% CI of the species). This finding confirmed that, despite the coarse, sea- Bakun upwelling index CT from this decade (Fig. S4). sonal sampling resolution of CalCOFI, this study was able to obtain Each climate index was partitioned into three categorical reliable estimates of which species exhibited shifts in phenology. variables for use in ANOVA. The ONI was divided into La Niña, A second potential source of error stemmed from the fact that El Niño, and neutral periods. PDO categories included (i)a no CalCOFI surveys were conducted in May, September, or negative PDO between 1951 and 1976; (ii) a positive PDO be- December during the 2000s. These data gaps could skew esti- tween 1977 and 1998; and (iii) a second, negative PDO in 1999– mates of CT from this decade and bias interdecadal trends. To 2002 and 2007–2008 (87). The years 2003–2006 were excluded evaluate this bias, I recalculated CT after removing these months because there were insufficient data to construct a monthly time from the entire time series so that all decades would have an series of larvae during this second positive PDO. Positive, neu- identical monthly distribution of data. The first principal compo- tral, and negative NPGO periods were defined on a monthly nent of this new dataset accounted for 29.9% of variance and basis according to whether this index had a value ≥0.5, between continued to show a progression toward earlier phenology (Fig. 0.5 and −0.5, or ≤−0.5, respectively. These thresholds were se- S2B). However, phenological advancement was no longer evident lected so that there were an approximately equal number of when examining the mean CT for all 43 species (Fig. S2D). For months in each NPGO category. Data on each fish phenophase the three phenology groups, decadal trends in CT were extremely were aggregated into bins corresponding to different phases of similar regardless of whether the missing months were included or the climate indices. CT anomalies of the fish phenophases were excluded from the analysis, indicating resiliency to this potential then calculated for each phase of the climate indices. Initially, source of bias (Fig. S2 E–J). In addition to being able to detect crossed, two-way ANOVAs were performed using climate index divergent trends obscured by the mean, this is another reason why and phenology group as independent variables. Because signifi- this analysis principally focused on the three phenology groups cant interactions were observed between most phenology groups when investigating long-term, ecological changes. and climate indices, separate one-way ANOVAs were performed Measurements of CalCOFI SST and mesozooplankton dis- to evaluate climate effects on each phenology group. Lilliefors’ placement volume were also unavailable during May, September, test, Bartlett’s test, and plots of residuals were used to ascertain and December in the 2000s. Again, these months were removed whether the assumptions of normality, homoscedasticity, and from the time series in its entirety, and CT was recalculated to independence of residuals were met (82). Lilliefors’ test in- detect potential biases resulting from gaps in sampling. Stepwise dicated that the assumption of normality was violated for the multiple regressions between oceanic variables and phenology ANOVA examining ENSO effects on the earlier phenology groups produced broadly consistent results regardless of whether group. As a result, a nonparametric Kruskall–Wallis test was the full dataset or the bias corrected dataset was used (Table S5). performed in lieu of ANOVA. If an ANOVA or Kruskall–Wallis As a result, Fig. 3 only presents results from the full time series. test revealed a significant climate effect, Tukey–Kramer multiple

Asch www.pnas.org/cgi/content/short/1421946112 1of9 comparison tests were used post hoc to determine which climate formed to detect major modes of variability among ecological phases exhibited significant phenological differences. variables. Categorical variables (i.e., taxonomic order, adult fish CalCOFI SST, CalCOFI zooplankton volume, and the Bakun habitat, cross-shore distribution, biogeographic affinity, and upwelling index were selected as the local environmental vari- fishing status) were converted into dummy variables before PCA ables to be examined due to known interannual-to-decadal var- (82). Each variable was then standardized based on its mean and iations in their seasonality (19, 21, 22, 25) and their potential to SD to ensure that all variables were given equal weight in the affect fish phenology through physiological and trophic pathways. PCA (82). Correlations between the first two principal compo- The CT of these variables was calculated decadally for years nents of the ecological characteristics and changes in larval fish between 1951 and 2008. Unlike upwelling and larval fish abun- CT were assessed. The PCA revealed three groups of fishes with dance, measurements of SST and zooplankton volume never similar ecological characteristics (Fig. 4A). Phenological trends approached zero during any month of the year. This pattern over time were examined for each of these groups. weakened the weighting of these variables by month when cal- This study also explored whether species that underwent shifts culating the CT, resulting in a CT skewed toward the middle in phenology were more likely to change their geographic range in of the year. To increase the influence of monthly weights, response to fluctuating climatic conditions. This analysis entailed a the minimum monthly mean value (e.g., 13 °C for SST and 3 3 comparison between the results of this study and those of Hsieh 38 cm /1,000 m of seawater strained for zooplankton volume) et al., who investigated climate-related shifts in the distribution of was subtracted from the decadally averaged time series before cal- 35 of the species examined here (Table S2) (50, 51). One-tailed culating the CT. After computing the CT of each variable, its SE binomial tests were used to evaluate whether there was a >50% was estimated with a bootstrap approach (82). Forward stepwise, probability that species in each phenology group exhibited a multiple regressions were used to examine the relationship be- range shift. tween the CT anomalies of environmental variables and the CT Last, this study examined whether there was an interaction be- anomalies of fishes in each phenology group. A first-order, au- tween decadal trends in phenology and changes in the abundance of toregressive (AR1) terms was also included as a variable in these larval fishes. Mean abundance of larvae was calculated on a decadal regressions. The Akaike information criterion (AIC) was used to determine which variables resulted in the best regression fit. basis for each of the 51 phenophases of CalCOFI fishes. Each Nine characteristics of species related to their phylogeny, ecology, phenophase was then categorized as exhibiting an increasing de- and fishery status were evaluated to assess whether they affected cadal trend in abundance, no change, or a decreasing trend using rates of phenological change. These characteristics included taxo- the same classification that was applied to generate the three fish nomic order, month of maximal larval abundance, trophic level, phenology groups. Namely, phenophases with correlations between ≥ habitat use by adult fishes, cross-shore distribution, biogeographic mean decadal abundance and year that exceeded r 0.5 were classified as displaying an increasing trend in abundance; corre- affinity, fishing status, the log10 transformed frequency of larval lations of r ≤−0.5 were indicative of a decreasing trend in abun- occurrence during CalCOFI surveys, and the loge(x + 1) trans- formed amplitude of a species’ mean seasonal cycle. Tables S1 and dance, and correlations with intermediate values were categorized 2 S2 describes classification of fishes with respect to these charac- as exhibiting no long-term, linear trend. A χ test of independence teristics. Taxonomic orders were only examined if data were was performed to determine whether fishes in any phenology available for at least five phenophases in that order. Initially, the group were more likely than would be expected by random chance mesopelagic category of habitats used by adult fishes was farther to display a trend in abundance. The N − 1 correction described by subdivided into vertically migrating and nonmigratory mesopelagic Campbell (88) was used in the χ2 test to account for small sample species. These classifications were later merged because no sig- size. The results of this test were not significant (χ2 = 2.7, df = 4, nificant difference was observed between migratory and non- P = 0.62; Table S6), indicating that there was no interaction be- migratory mesopelagic fishes in terms of phenological changes. tween phenology groups and the likelihood of fish species un- Because several ecological traits were correlated, a PCA was per- dergoing decadal declines or increases in abundance.

Point Conception Los 34ºN Angeles

32ºN

30ºN 124ºW 122ºW 120ºW 118ºW

Fig. S1. Sites where larval fish abundance was sampled. The rectangular box in the inset map shows the location of the study region relative to the West Coast of North America.

Asch www.pnas.org/cgi/content/short/1421946112 2of9 0.5 A 0.5 B

0 0

−0.5 −0.5

Principal component 1 1955 1965 1975 1985 1995 2005 1955 1965 1975 1985 1995 2005

10 10 C D 5 5

0 0

−5 −5

CT anomaly (days) −10 −10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010

E F 20 20

0 0

−20 −20 CT anomaly (days) 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010

G H 20 20

0 0

−20 −20 CT anomaly (days) 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010

I J 20 20

0 0

−20 −20 CT anomaly (days) 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

Fig. S2. Decadal trends in the CT of larval fishes based on the full dataset (Left) and a partial dataset where May, September, and December were removed (Right). The partial dataset was used to examine any biases due to lack of sampling during these months in the 2000s. (A and B) Eigenvectors for each decade from the first principal component of the CT of larval fishes. Decadal means and SEs of the CT of all fish phenophases (C and D; n = 290), phenophases with earlier phenology (E and F; n = 110), phenophases with no long-term, linear trend in phenology (G and H; n = 128), and phenophases with later phenology (I and J; n = 52).

A B C 20 20 20

0 0 0

−20 −20 −20

CT anomaly (days) Neg Neut Pos Neg Neut Pos Neg Neut Pos

Fig. S3. Effects of the NPGO on the seasonal CT of fish species displaying earlier phenology (A; n = 60), no long-term, linear change in phenology (B; n = 66), and later phenology (C; n = 27). In each box plot, the darkened line indicates the median; boxes show the interquartile range; whiskers indicate the expected extent of 99% of the data for a Gaussian distribution, and crosses show outliers. Neg, NPGO index ≤−0.5; Neut, NPGO index between −0.5 and 0.5; Pos, NPGO index ≥0.5. ANOVA results for earlier species: F2,57 = 1.6, P = 0.22; species without a linear trend: F2,63 = 0.2, P = 0.81; later species: F2,24 = 0.5, P = 0.61.

Asch www.pnas.org/cgi/content/short/1421946112 3of9 7 A

6.8

6.6

6.4

Upwellling CT (month) 6.2

6 1950 1960 1970 1980 1990 2000 2010

7.5 B

7

6.5

6

5.5 Zooplankton CT (month)

5 1950 1960 1970 1980 1990 2000 2010 Year

Fig. S4. Decadal changes in the CT of the Bakun upwelling index at 33°N and 119°W (A) and zooplankton volume from CalCOFI (B); 95% CIs from bootstrap analysis are shown. The white circle in A denotes the decadal CT of upwelling velocity within the CalCOFI area based on data from the QuikSCAT scatterometer.

Asch www.pnas.org/cgi/content/short/1421946112 4of9 Table S1. Ecological characteristics of larval fish species Month(s) of maximum Cross-shore Species larval abundance Adult habitat distribution (89) Biogeographic affinity Adult trophic level

Clueiformes Engraulidae Engraulis mordax March Epipelagic Coastal-Oceanic Wide distribution (50, 89) 3.0* (91) Clupeidae Sardinops sagax April Epipelagic Coastal-Oceanic Warm-water (50) 2.7* (90, 91) Argentiniformes Argentinidae Argentina sialis March Demersal Coastal Wide distribution (20) 3.1* (90) Microstomatidae Bathylagus pacificus March Mesopelagic Oceanic Cool-water (51) 3.3* Bathylagus wesethi July Mesopelagic Oceanic Cool-water (51) 3.2 (91) Leuroglossus stilbius March Mesopelagic Coastal-Oceanic Wide distribution (50) 3.3* (90) Lipolagus ochotensis March Mesopelagic Oceanic Cool-water (51) 3.4* Stomiiformes Gonostomatidae Cyclothone signata March Mesopelagic Oceanic Warm-water (51) 3.0* September Sternoptychidae Argyropelecus sladeni March November Mesopelagic Oceanic Wide distribution (51) 3.1* Danaphos oculatus November Mesopelagic Oceanic Wide distribution (20) 3.0* Phosichthyidae Vinciguerria lucetia August Mesopelagic Oceanic Warm-water (51) 3.0* Stomiidae Chauliodus macouni August Mesopelagic Oceanic Cool-water (51) 4.1* Idiacanthus antrostomus August Mesopelagic Oceanic Wide distribution (51) 3.8* Stomias atriventer March Mesopelagic Oceanic Warm-water (51) 4.0* Aulopiformes Paralepididae Lestidiops ringens August Mesopelagic Oceanic Wide distribution (20) 4.1* Myctophiformes Myctophidae Ceratoscopelus townsendi August Mesopelagic Oceanic Wide distribution (51) 3.5* Diogenichthys atlanticus April September Mesopelagic Oceanic Warm-water (51) 3.1* Nannobrachium regale July Mesopelagic Oceanic Wide distribution (20) 3.2* Nannobrachium ritteri March Mesopelagic Oceanic Wide distribution (51) 3.4* Protomyctophum crockeri January Mesopelagic Oceanic Cool-water (51) 3.2 (91) Stenobrachius leucopsarus March Mesopelagic Oceanic Cool-water (51) 3.6* Symbolophorus californiensis April July Mesopelagic Oceanic Cool-water (51) 3.1* Tarletonbeania crenularis March June Mesopelagic Oceanic Cool-water (51) 3.1* Triphoturus mexicanus September Mesopelagic Oceanic Warm-water (51) 3.3* Gadiformes Merlucciidae Merluccius productus February Epipelagic Coastal-Oceanic Wide distribution (89) 3.8* (90, 91) Stephanoberyciformes Melamphaidae Melamphaes lugubris March Mesopelagic Oceanic Warm-water (51) 3.2 (91) September Scorpaeniformes Scorpaenidae Sebastes aurora May Demersal Coastal Cool-water (50, 89) 3.3 (91) Sebastes diploproa October Demersal Coastal Cool-water (89) 3.3 (91) Sebastes goodei February Demersal Coastal Cool-water (89) 3.5* Sebastes jordani February Demersal Coastal Cool-water (50, 89) 3.2* (90) Sebastes paucispinis January Demersal Coastal Cool-water (50, 89) 3.5* (90) Perciformes Carangidae Trachurus symmetricus June Epipelagic Coastal-Oceanic Wide distribution (50, 89) 3.7* (90) Pomacentridae Chromis punctipinnis August Demersal Coastal Warm-water (50, 89) 2.7* (90) Labridae Oxyjulis californica August Demersal Coastal Cool-water (89) 3.1*

Asch www.pnas.org/cgi/content/short/1421946112 5of9 Table S1. Cont. Month(s) of maximum Cross-shore Species larval abundance Adult habitat distribution (89) Biogeographic affinity Adult trophic level

Scombridae Scomber japonicus May Epipelagic Coastal-Oceanic Warm-water (50) 3.4* (90) Centrolophidae Icichthys lockingtoni June Epipelagic Coastal-Oceanic Wide distribution (50) 3.7* (90) Tetragonuridae Tetragonurus cuvieri October Epipelagic Coastal-Oceanic Cool-water (50, 89) 3.8* (90) Paralichthyidae Citharichthys sordidus February Demersal Coastal Cool-water (89) 3.4* October Citharichthys stigmaeus October Demersal Coastal Cool-water (89) 3.4* Paralichthys californicus March July Demersal Coastal Warm-water (50, 51) 4.5* (90) Pleuronectidae Lyopsetta exilis April Demersal Coastal Cool-water (50, 89) 3.5* (90) Parophrys vetulus March Demersal Coastal Cool-water (89) 3.3* (90) Pleuronichthys verticalis March Demersal Coastal Warm-water (50) 3.1*

References and databases consulted to categorize species are shown in parentheses or as footnotes. Month of maximum larval abundance for each phenophase is based on mean seasonal patterns for the entire time series. *Source: www.fishbase.ca.

Asch www.pnas.org/cgi/content/short/1421946112 6of9 Table S2. Ecological characteristics, decadal trends in phenology, and responses to temperature among larval fish species Climate-related changes in Trend in phenology Change in phenology Species Fishing status* geographic distribution r (d/decade) per 1-d shift in SST CT (d)

Clueiformes Engraulidae Engraulis mordax Fished Yes (50) −0.39 −3.0 −0.5 Clupeidae Sardinops sagax Fished Yes (50) −0.31 −11.1 4.1 Argentiniformes Argentinidae Argentina sialis Unfished Yes (50) 0.26 2.0 0.3 Microstomatidae Bathylagus pacificus Unfished No (51) 0.84 4.6 −0.5 Bathylagus wesethi Unfished No (51) 0.62 4.4 0.1 Leuroglossus stilbius Unfished Yes (50) 0.23 1.2 −0.5 Lipolagus ochotensis Unfished Yes (51) −0.75 −3.2 0.7 Stomiiformes Gonostomatidae Cyclothone signata Unfished Yes (51) −0.27 −1.7 1.5 −0.64 −7.0 8.0 Sternoptychidae Argyropelecus sladeni Unfished Yes (51) −0.66–0.62 −3.8 1.4 −7.9 0.4 Danaphos oculatus Unfished N/A −0.76 −9.3 1.2 Phosichthyidae Vinciguerria lucetia Unfished Yes (51) −0.53 −5.6 1.5 Stomiidae Chauliodus macouni Unfished Yes (51) −0.11 −0.6 0.2 Idiacanthus antrostomus Unfished No (51) −0.44 −3.0 0.3 Stomias atriventer Unfished No (51) 0.22 1.2 0.1 Aulopiformes Paralepididae Lestidiops ringens Unfished N/A −0.65 −7.3 1.5 Myctophiformes Myctophidae Ceratoscopelus townsendi Unfished No (51) −0.07 −0.7 0.8 Diogenichthys attanticus Unfished No (51) −0.70 −7.6 1.0 0.51 3.0 −0.6 Nannobrachium regale Unfished N/A 0.10 0.7 0.1 Nannobrachium ritteri Unfished Yes (51) −0.87 −4.8 0.7 Protomyctophum crockeri Unfished Yes (51) −0.25 −1.4 0.6 Stenobrachius leucopsarus Unfished Yes (51) −0.30 −1.7 0.9 Symbolophorus californiensis Unfished Yes (51) −0.90 −8.2 1.2 −0.62 −4.1 1.0 Tarletonbeania crenularis Unfished Yes (51) −0.47 −2.4 −0.4 0.68 5.7 0.4 Triphoturus mexicanus Unfished Yes (51) −0.94 −6.2 0.8 Gadiformes Merlucciidae Merluccius productus Fished Yes (50) −0.66 −3.0 −0.04 Stephanoberyciformes Melamphaidae Melamphaes lugubris Unfished Yes (51) 0.06 0.4 0.6 0.53 5.6 −0.2 Scorpaeniformes Scorpaenidae Sebastes aurora Fished Yes (50) −0.83 −5.4 0.6 Sebastes diploproa Fished N/A −0.78 −12.3 5.7 Sebastes goodei Fished N/A 0.58 6.7 −1.6 Sebastes jordani Fished No (50) −0.51 −2.8 0.2 Sebastes paucispinis Fished No (50) 0.29 1.6 −0.5 Perciformes Carangidae Trachurus symmetricus Fished No (50) −0.87 −6.7 1.1 Pomacentridae Chromis punctipinnis Unfished Yes (50) 0.55 3.0 −0.8

Asch www.pnas.org/cgi/content/short/1421946112 7of9 Table S2. Cont. Climate-related changes in Trend in phenology Change in phenology Species Fishing status* geographic distribution r (d/decade) per 1-d shift in SST CT (d)

Labridae Oxyjulis californica Unfished N/A −0.75 −6.9 0.2 Scombridae Scomber japonicus Fished Yes (50) −0.64 −1.0 1.7 Centrolophidae Icichthys lockingtoni Unfished No (50) −0.64 −5.6 1.1 Tetragonuridae Tetragonurus cuvieri Unfished No (50) 0.28 3.5 0.4 Paralichthyidae Citharichthys sordidus Fished N/A 0.08 0.5 0.4 † 0.50 5.4 −2.2 Citharichthys stigmaeus Unfished N/A −0.005 16.0† −0.8† Paralichthys californicus Fished Yes (50) 0.92 7.2 −1.2 0.34 3.4 −1.4 Pleuronectidae Lyopsetta exilis Unfished No (50) −0.12 −0.7 −0.3 Parophrys vetulus Fished Yes (50) −0.25 −1.8 0.1 Pleuronichthys verticalis Unfished No (50) 0.87 5.6 −0.5

References and databases consulted to categorize species are shown in parentheses or as footnotes. Also shown are Pearson correlation coefficients r and the slope (e.g., trend in phenology) from regressions between CT anomalies and decade. Species were divided into phenology groups, such that r ≥ 0.5 indicated a shift toward later phenology; r ≤−0.5 indicated earlier phenology, and; −0.5 < r < 0.5 indicated no long-term, linear change in phenology. Slopes are also shown from regressions between the CT of fish species and the CT of SST. n = 6. For species with two phenophases, separate slopes and r are included for each seasonal peak in larval abundance. *Source: www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/annual-landings/index. † First-order, autoregressive (AR1) term included in final regression.

Table S3. Sensitivity of CT for detecting changes in mean date of larval fish abundance (rates of phenological change: 0–8 d/decade) Simulated phenological change (d/decade)

CV 012345678

0.05 −0.3–0.3 0.7–1.3 1.7–2.3 2.7–3.3 3.7–4.3 4.7–5.3 5.8–6.2 6.7–7.3 7.7–8.3 0.10 −0.5–0.5 0.5–1.5 1.5–2.5 2.4–3.6 3.5–4.5 4.5–5.5 5.5–6.5 6.5–7.5 7.4–8.5 0.15 −0.8–0.7 0.3–1.8 1.2–2.8 2.2–3.7 3.2–4.8 4.2–5.8 5.1–6.8 6.2–7.8 7.2–8.8 0.20 −1.0–1.1 −0.1–2.1 0.9–3.1 1.9–4.1 3.0–5.1 3.9–6.0 4.8–7.1 5.9–8.1 6.9–9.1 0.25 −1.3–1.4 −0.3–2.2 0.7–3.4 1.6–4.3 2.6–5.4 3.5–6.4 4.7–7.4 5.7–8.4 6.7–9.4 0.30 −1.6–1.7 −0.7–2.6 0.4–3.5 1.4–4.6 2.4–5.6 3.5–6.5 4.3–7.6 5.3–8.6 6.4–9.6 0.35 −2.0–1.9 −0.9–2.9 −0.1–3.9 1.1–5.0 2.1–5.9 3.0–7.1 4.0–7.8 5.2–8.9 6.1–9.9 0.40 −2.1–2.4 −1.3–3.2 −0.1–4.5 0.8–5.2 1.9–6.2 2.8–7.5 3.9–8.2 4.8–9.3 5.6–10.2 0.45 −2.6–2.4 −1.6–3.5 −0.6–4.4 0.5–5.4 1.3–6.7 2.4–7.6 3.3–8.3 4.6–9.8 5.6–10.5 0.50 −2.6–3.0 −2.0–3.8 −0.7–4.9 0.2–5.9 1.1–6.8 2.3–7.8 3.3–8.9 4.1–9.9 4.9–11.0 0.55 −3.2–3.3 −2.0–4.1 −1.0–5.2 0.2–6.6 1.1–7.2 1.9–8.0 3.1–9.4 3.7–10.1 4.5–11.1 0.60 −3.4–3.3 −2.3–4.5 −1.5–5.3 −0.2–6.6 0.5–7.4 1.6–8.5 2.6–9.7 3.5–10.5 4.6–11.3

This table shows 95% CIs of the CT when detecting simulated shifts in phenology. CIs were generated with 1,000 Monte Carlo simulations each for fishes whose abundance varied based on 12 sets of CVs and 13 rates of phenological change.

Asch www.pnas.org/cgi/content/short/1421946112 8of9 Table S4. Sensitivity of CT for detecting changes in mean date of larval fish abundance (rates of phenological change: 9–12 d/decade) Simulated phenological change (d/decade)

CV 9101112

0.05 8.7–9.3 9.8–10.3 10.7–11.3 11.7–12.3 0.10 8.5–9.5 9.5–10.5 10.5–11.5 11.4–12.5 0.15 8.2–9.8 9.2–10.8 10.2–11.8 11.2–12.9 0.20 8.0–10.0 8.9–11.0 9.9–12.1 11.0–13.0 0.25 7.6–10.2 8.5–11.3 9.6–12.3 10.6–13.4 0.30 7.3–10.6 8.3–11.6 9.3–12.8 10.4–13.8 0.35 7.2–10.9 8.1–11.9 8.9–13.0 10.1–14.0 0.40 6.6–11.4 7.8–12.1 8.8–13.1 9.8–14.2 0.45 6.4–11.6 7.6–12.7 8.5–13.6 9.5–14.5 0.50 6.3–11.7 7.0–12.9 8.3–13.9 9.2–15.0 0.55 5.8–12.1 6.7–13.0 7.9–14.1 8.8–15.0 0.60 5.8–12.3 6.5–13.5 7.7–14.5 8.5–15.3

This table shows 95% CIs of the CT when detecting simulated shifts in phenology. CIs were generated with 1,000 Monte Carlo simulations each for fishes whose abundance varied based on 12 sets of CVs and 13 rates of phenological change.

Table S5. Stepwise multiple regressions between the three phenology groups and local oceanic conditions Phenology group Regression equation df AIC F

Species with earlier phenology

Full dataset Y = 0.0436 + 0.9120XSST + 0.2355XZoo − 0.3823XAR1 86 75.2 26.6***

Bias-corrected dataset Y = -0.0250 + 1.2574XSST + 0.3690XZoo 87 116.6 10.2*** Species with no long-term, linear trend in phenology Full dataset Y = 0.0121 105 N/A N/A

Bias-corrected dataset Y = -0.0093 − 0.2898XAR1 104 152.7 9.3** Species with later phenology

Full dataset Y = 0.0518 − 0.4556XSST + 1.3534XUpw 40 33.8 7.5** Bias-corrected dataset Y = 0.0268 − 1.2713XSST + 1.8838XUpw 40 44.3 11.0***

In the regression equations, Y refers to CT anomalies of species in each phenology group, whereas XSST, XZoo, and XUpw are the CT of CalCOFI SST, CalCOFI mesozooplankton volume, and the Bakun upwelling index, respectively. XAR1 is a first-order autoregressive term. Only the final regression model with the lowest AIC is shown. The bias-corrected dataset removes all data from May, September, and December, because missing data during these months in the 2000s could potentially affect results of this analysis. N/A, statistic could not be estimated because the null model provided the best fit to the data. Significance levels of regressions are indicated as follows: **P < 0.01; ***P < 0.001.

Table S6. Contingency table showing the interaction between decadal trends in phenology and larval abundance Decadal trends in mean abundance

Decadal trends in phenology Increase No change Decrease Total

Earlier 7 (7.84) 8 (8.24) 5 (3.92) 20 No change 11 (8.63) 8 (9.06) 3 (4.31) 22 Later 2 (3.53) 5 (3.71) 2 (1.76) 9 Total 20 21 10 51 Test results: χ2 = 2.7; df = 4; P = 0.62

The number of fish phenophases observed in each category is shown above. Numbers in parentheses indicate expected values if phenophases were randomly assigned to each category. The χ2 test of independence was performed using the N − 1 adjustment for small sample size (88).

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