This dataset provides a snapshot of Pacific language in education in New Zealand Schools as at 1 July. It describes two levels of Pacific language learning: Pacific-medium education and Pacific language as a separate subject.

About this dataset

Context

The dataset tabulates the Pacific population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Pacific across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Pacific was 7,434, a 1.68% increase year-by-year from 2021. Previously, in 2021, Pacific population was 7,311, an increase of 0.70% compared to a population of 7,260 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Pacific increased by 894. In this period, the peak population was 7,434 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Pacific is shown in this column.
• Year on Year Change: This column displays the change in Pacific population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pacific Population by Year. You can refer the same here

Figures and Supporting information included in the article Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal

About this dataset

Context

The dataset tabulates the Pacific population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Pacific across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Pacific was 6,927, a 1.25% decrease year-by-year from 2022. Previously, in 2022, Pacific population was 7,015, a decline of 1.45% compared to a population of 7,118 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Pacific increased by 1,479. In this period, the peak population was 7,237 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Pacific is shown in this column.
• Year on Year Change: This column displays the change in Pacific population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pacific Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Pacific town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Pacific town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Pacific town was 2,781, a 0.32% decrease year-by-year from 2022. Previously, in 2022, Pacific town population was 2,790, an increase of 0.25% compared to a population of 2,783 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Pacific town increased by 252. In this period, the peak population was 2,790 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Pacific town is shown in this column.
• Year on Year Change: This column displays the change in Pacific town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pacific town Population by Year. You can refer the same here

These are unpublished figures from my 2012 doctoral dissertation. They didn't find a place in the published manuscripts, but I thought they might be useful, so I am placing them here. Please see the dissertation, linked below, for further methodological details. If you refer to these figures, please cite the dissertation as follows: Goldstein, M.C. 2012. Abundance and ecological implications of microplastic debris in the North Pacific Subtropical Gyre. Ph.D. dissertation. Scripps Institution of Oceanography at the University of California San Diego, La Jolla, CA.

Results of the Bayesian clustering approach implemented in STRUCTURE 2.3 [141] that were used to infer the number of genetic clusters (K) in the microsatellite dataset. The graph shows the mean log likelihood L(K) for K = 1–19 (±SD) and ΔK, the second order rate of change of L(K) for K = 2–18 [144]. The most likely number of cluster is K = 5, since it has the highest likelihood value (L(K)  = −2059) and a local maximum for ΔK. (EPS)

The aim of the PACIFIC study is to compare 46 AIEC bacteria isolated from Crohn's patients in France and Hong Kong between 2017 and 2022. Their adhesion and invasion to intestinal epithelial cells phenotype was tested. Colonisation of the gut of a mouse model mimicking Crohn's disease by 4 strains isolated in France and 4 strains isolated in Hong Kong was compared. The sequencing of 46 strains was carried out and the raw data used for the genomic analyses is compiled in a file. The isolated strains were also tested for antibiotic resistance and sensitivity to a cocktail of phages.

Pacific hake (Merluccius productus) is an abundant species residing along the Pacific coast from the Gulf of California to the Strait of Georgia. It is the most common groundfish in the California Current ecosystem (Helser et al. 2008). In Puget Sound, however, Pacific hake populations have declined dramatically in the past three decades (Figure 1), leading to a closure of the fishery in 1990 (Gustafson et al. 2000) and a designation by NOAA Fisheries as a Species of Concern in 1999. Because Pacific hake feed on a variety of fishes and invertebrates, and are an important prey item (for sea lions, small cetaceans, and dogfish sharks), the decline of this mid-trophic level component has important ramifications for the functioning of the Puget Sound ecosystem. Puget Sound Pacific hake are classified as part of the Georgia Basin Distinct Population Segment (DPS), which is discrete from the highly migratory coastal DPS (Figure 2a). The Biological Review Team (BRT) that reviewed the status of the Georgia Basin DPS noted that in addition to the decline in Puget Sound hake abundance, another cause for concern was a marked decrease in mean hake size and age at maturity (Gustafson et al. 2000). In contrast, these patterns were not observed as strongly in the Strait of Georgia populations (King and McFarlane 2006), which are also part of the Georgia Basin DPS. The BRT were also concerned by uncertainties in the extent of mixing among stocks of the Georgia Basin DPS (Gustafson et al. 2000). This issue is important because if mixing is limited, then the problems faced by the Puget Sound stock are more important for its potential recovery. Puget Sound hake spawn in large aggregations in a few distinct locations, which are associated with sources of freshwater. Unfortunately these sites occur in somewhat degraded areas, particularly with regard to oxygen concentration. Therefore we hypothesize that the hypoxic and otherwise degraded conditions of these spawning areas have led to depressed juvenile growth, which in turn can have detrimental consequences for the population. Woodbury et al. (1995) found that juvenile growth of the coastal stock varied from year to year and was likely related to environmental conditions. They also speculated that year-class strength might be related to early juvenile growth. Another goal of the proposed research is to produce an indicator for the Puget Sound marine ecosystem in order to aid in the ongoing development of an Integrated Ecosystem Assessment (IEA) for Puget Sound, which is a high priority for NOAA. Ecosystem indicators should be grounded in the ecology of the system, and juvenile hake growth suits this perfectly because it is not only a reflection of the state of the ecosystem, but is also reflects the viability of an integral component of the ecosystem. This proposal represents a continuation of a project we initiated last year. In our first year of research, funded by a Species of Concern grant, we obtained the following findings: 1) Otoliths sampled from recent years at the Port Susan spawning site demonstrated much reduced growth rates in the first and second years compared to otoliths sampled there 3 decades ago (Figure 3). 2) The chemical signatures of otolith edges (corresponding to the time when fish were sampled) of fish sampled from the Port Susan spawning site demonstrated strong consistency from year to year. This will enhance our ability to associate adults with their natal origin. 3) The chemical signatures of otolith cores (corresponding to natal areas) demonstrated the potential existence of at least 3 separate sources for adults sampled at the Port Susan site (Fig. 5). Here we propose to continue this research. In particular, due to staffing issues at DFO, we were not able to obtain archived otolith samples from the Strait of Georgia, which represent an important contrast for the Puget Sound population. We anticipate receiving these otoliths shortly, and.

data S1. The steady-state deformation data supporting Figure 1(a). The first and second columns show the location of point (longitude and latitude). The third, fourth, and fifth columns show the displacement induced by 2011 Tohoku-Oki Mw 9.0 earthquake (east-west, north-south, and up-down components in m). The sixth, seventh, and eighth columns show the displacement induced by 2010 Chile Mw 8.8 earthquake (east-west, north-south, and up-down components in m). data S2. Expansion of normal direction in the East Pacific Rise supporting Figure 1(b). The first and second columns show the location of point (longitude and latitude). The third and fourth columns show the displacement induced by 2011 Tohoku-Oki Mw 9.0 earthquake (the angle from east and the magnitude of displacement vector in m). The fifth and sixth columns show the displacement induced by 2010 Chile Mw 8.8 earthquake (the angle from east and the magnitude of displacement vector in m). data S3. Steady-state strain field supporting Figure 1(c). The first and second columns show the location of point (longitude and latitude). The third, fourth and fifth columns show the strain changes induced by two earthquakes (east-west, north-south, and shear components). data S4. The co-seismic and post-seismic displacement data supporting Figure 2. The first and second columns show the location of point (longitude and latitude), and the third column show the epicenter distance. The co-seismic and post-seismic displacement induced by 2011 Tohoku-Oki Mw 9.0 earthquake in the fourth column to the ninth column at different times (0, 10yr, 100yr, 1000yr,10000yr, and infinity in m).

Asia Pacific Blind Box Toys market will be USD 3277.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.0% from 2024 to 2031. The rise of collectible culture among young consumers are expected to aid the sales to USD 5485.7 million by 2031

Thin Plate Spline files (for upper and lower beaks, respectively) containing coordinates obtained from octopus beak contours to conduct shape analysis via geometric morphometrics.

simulation.sst.trend.ctrl.fiow.siow.nc is the sst trend for CTRL, FIOW and SIOW numerical model results to calculate Figure s8 in "Pacific warming pattern diversity modulated by Indo-Pacific sea surface temperature gadient". simulation.uv.trend.ctrl.fiow.siow.nc is same as simulation.sst.trend.ctrl.fiow.siow.nc, but for the horizontal wind trend used in Figure s8.

About this dataset

Context

The dataset tabulates the Pacific Grove population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Pacific Grove across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Pacific Grove was 14,757, a 0.73% decrease year-by-year from 2022. Previously, in 2022, Pacific Grove population was 14,866, a decline of 1.06% compared to a population of 15,025 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Pacific Grove decreased by 740. In this period, the peak population was 15,588 in the year 2016. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Pacific Grove is shown in this column.
• Year on Year Change: This column displays the change in Pacific Grove population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pacific Grove Population by Year. You can refer the same here

Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has panels (a), (b), (c), (d), (e), (f), with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f.

List of data provided

The datasets contains:

• Atlantic and Pacific halosteric sea level trend from CMIP6 models (1950-2014)
• Atlantic and Pacific halosteric sea level trend from Durack&Wijffels observations (1950-2019)
• Atlantic and Pacific halosteric sea level trend from EN4 observations (1950-2019)
• Atlantic and Pacific halosteric sea level trend from Ishii observations (1955-2019)
• Atlantic and Pacific thermosteric sea level trend from CMIP6 models (1950-2014)
• Atlantic and Pacific thermosteric sea level trend from Durack&Wijffels observations (1950-2019)
• Atlantic and Pacific thermosteric sea level trend from EN4 observations (1950-2019)
• Atlantic and Pacific thermosteric sea level trend from Ishii observations (1955-2019)
• Global halosteric sea level trends from Durack&Wijffels observations (1950-2019)
• Global halosteric sea level trends from EN4 observations (1950-2019)
• Global halosteric sea level trends from Ishii observations (1955-2019)
• Global halosteric sea level trends from CMIP6 multi-model mean (1950-2014)

Data provided in relation to figure

• panel_a/halosteric_trends_hist-nat.csv has data for green and black markers.
• panel_a/halosteric_trends_historical.csv has data for orange and black markers.
• panel_b/thermosteric_trends_hist-nat.csv has data for green and black markers.
• panel_b/thermosteric_trends_historical.csv has data for orange and black markers.
• panel_c/halosteric_trends_map_DW.nc has data for filled colored contours.
• panel_d/halosteric_trends_map_EN4.nc has data for filled colored contours.
• panel_e/halosteric_trends_map_Ishii.nc has data for filled colored contours.
• panel_f/halosteric_trends_map_cmip6.nc has data for filled colored contours. For panels a and b details about the data provided in relation to the figure in the header of every file.

CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.

Notes on reproducing the figure from the provided data

The observational data from here (top right panel) is taken from the file:

DurackandWijffels_GlobalOceanChanges_19500101-20191231_210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the input dataset for figure 3.28 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.

Directory Architecture:

Data:
Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations.

Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.

Scripts:
All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.

Outputs:
Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.

Figures:
All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.

Data Processing Workflow - Overview:
Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.

Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)

Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.

We report here that a manganese nodule from the Central Pacific manganese nodule province has been dated by fossil diatoms found in mud scraped from near the nodule's centre. The nodule was taken at DOMES Site B from Core B55-56 at 11°50.3'N and 137°28.2'W in a water depth of 4,892 m. It was resting on the sediment surface, with about 1.5 cm of the nodule bottom (of a total nodule height of 4.2 cm) buried in the mud. The top surface of the nodule was covered with a smooth manganese coating, but the bottom had a very rough, crusty texture. It was found that recent mud had leaked in through cracks in the nodule bottom, but that there were no pre-Pleistocene diatoms in this material. The date obtained was compared with the growth rate determined by the 230Th excess method and found to be in reasonable agreement. This study adds to the work of Harada1,2 on the biostratigraphy (mainly coccoliths) of manganese nodules.

Superimposed and aligned coordinates 2D for geometric morphometric analysis of body shape variation of the Pacific thread herring Opisthonema libertate.

17 pages. -- Figure S1: Autocorrelation of original signals and residual components of ENSO NOAA oceanic indexes (a)-(c)-(e)-(g), of TMI study cases shown in Fig. 4 (b)-(d)-(f)-(h) and of ENSO indexes derived by Takahashi et al. (2011) (i)-(j), and Sullivan et al. (2016) (k)-(l)-(m). -- Figure S2: (top panel) Hovmöller diagram depicting the zonal propagation of Kelvin waves along the equatorial Pacific Ocean between years 1993 and 2017 (both included). (bottom panel) Time series of the zonally-averaged Kelvin sea level anomalies over the equatorial Pacific Ocean (between 145°E and the Pacific coast of Colombia between -4°S and 4°N of latitude). -- Figure S3: (top panel) Hovmöller diagram illustrating the zonal propagation of wind-forcing associated to Kelvin waves along equatorial Pacific Ocean between years 1993 and 2017 (both included). (bottom panel) Time series of zonally-averaged Kelvin sea level anomalies over the equatorial Pacific Ocean (between 145°E and the Pacific coast of Colombia between -4°S and 4°N of latitude). -- Figure S4: (a)-(c) Maps of cross-correlation between the TMI and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S5: (a)-(c) Maps of cross-correlation between the ENSO 1+2 and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S6: (a)-(c) Maps of cross-correlation between the ENSO 3 and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S7: (a)-(c) Maps of cross-correlation between the ENSO 3.4 and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S8: (a)-(c) Maps of cross-correlation between the ENSO 4 and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S9: Map showing the mean SST field (shading), and mean surface wind (black arrows) during years 1988–2017. Data is described in section 3. For the sake of clarity, only 1 of every 7 arrows are shown. -- Figure S10: Mean (panels a-c-e-g) and standard deviation (panels b-d-f-h) composites of rainfall anomalies for different ENSO types. -- Figure S11: (a) Time series of 20◦C isotherm depth (z20◦C ) at 95◦W from Tropical Atmosphere Ocean project (TAO) data; (b) Time series of z20◦C at 110◦W from TAO; (c) Time series of the difference between z20◦C at 110◦W and at 95◦W; (d) Scatter diagram between z20◦C at 95◦W and at 110◦W, where the red line represents a linear fit between both depths; (e) original time series of z20◦C at 110◦W (black points) and the reconstructed time series OF z20◦C at 110◦W (sky blue solid line) after filling gaps with 95◦W data and performing a quadratic interpolation. --Figure S12: Time series of TMI4∗ (a, 20◦C isotherm depth instead of sea level), and TMI5 (b, 20◦C isotherm depth is added to the original TMI4. -- Figure S13: (a)-(b) Maps of cross-correlation between TMI4∗ (which includes the 20◦C isotherm depth instead of sea level) and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Figure S14: (a)-(b) Maps of cross-correlation between TMI5 and the time series of CHIRPS rain associated with every grid cell between years 1983 and 2017. -- Table S1: Annual mean (x) and standard deviation (σ) of rainfall from CHIRPS data during the period 1983–2017 (both included). -- Table S2: Number of CHIRPS grid cells (Ng) with time lags within the interval [-6, 0) (time lag = 0 not included) for TMI, TMI4 and ENSO oceanic indices: ENSO 1+2, ENSO 3, ENSO 3.4, ENSO 4. -- Table S3: Number of CHIRPS grid cells (Ng) with time lags within the interval [-6, 0) (time lag = 0 not included) for TMI4 and Takahashi et al. (2011) (C, P) and Sullivan et al. (2016). -- Table S4: Cross-correlation of original TMI4 and the new TMI4 (renamed as TMI4 ∗ ) and tMI5, against selected ENSO indices: ENSO 1+2, ENSO 3, ENSO 3.4 and ENSO 4, and other indices more adapted to regionally identify Central Pacific and Eastern Pacific ENSO events such as those of Takahashi et al. (2011) (C, E) and Sullivan et al. (2016) (CP, EP, Mixed), which are described in Section 3.4. -- Table S5: Same as Table S4 but for the residual components (denoted by the subscript r). -- Table S6: Number of CHIRPS grid cells (Ng) with time lags within the interval [-6, 0) (time lag = 0 not included) for the original TMI4 (without z20◦C ), TMI4∗ (which includes z20◦C instead of sea level) and TMI5. Peer reviewed

Figure of recruitment of individual species in the four climate treatments at each site.