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Supplemental Material 1

Article title: Geohistorical records of the Anthropocene in Chile

Eugenia M. Gayo1,2,3*

, Virginia B. McRostie4, Roberto Campbell

4, Carola Flores

1,5, Antonio

Maldonado5,6

, Mauricio Uribe-Rodriguez7, Patricio I. Moreno

1,8, Calogero M. Santoro

9, Duncan A.

Christie1,10

, Ariel A. Muñoz1,11

, Laura Gallardo1,12

1 Center for Climate and Resilience Research (CR2, FONDAP 15110009), Chile

2 Laboratory

for Stable Isotope Biogeochemistry, Departamento de Oceanografía, Universidad de

Concepción, Concepción, Chile

3 Center of Applied Ecology and Sustainability (CAPES)

4 Programa de Antropología, Instituto de Sociología, Pontificia Universidad Católica de Chile,

Santiago, Chile

5 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile

6 Instituto de Investigación Multidisciplinario en Ciencia y Tecnología, Universidad de La Serena,

La Serena, Chile & Departamento de Biología Marina, Universidad Católica del Norte, Coquimbo,

Chile

7 Departamento de Antropología, Facultad de Ciencias Sociales, Universidad de Chile, Santiago,

Chile

8 Instituto de Ecología y Biodiversidad, Departamento de Ciencias Ecológicas, Universidad de

Chile, Santiago, Chile

9 Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Chile

10 Instituto de Conservación Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia,

Chile

11 Institute of Geography, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile

12 Departamento de Geofísica, Universidad de Chile, Santiago, Chile

Corresponding author: Eugenia M. Gayo (emgayo@uc.cl)

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List of Contents:

Table S1. Geohistorical records considered here for reconstructing human-driven transformations on Chilean

ecosystems over the past 3000 years. The “Biophysical pattern” column specifies anthropogenic

transformations in environmental patterns/processes that were informed by each geohistorical record. (*) and

(**) indicate proxy data available only for northern and central Chile, respectively.

Text S1. Paleodemographic reconstructions: datasets, procedures and methodological caveats.

Figure S1. Evaluation of research/sampling biases in the accumulation of chronometric data in the

regional datasets. Legend (A) SPDs obtained from resampled datasets. (B) Absolute frequency distributions per 100-year bin

for calibrated ages from full databases.

Figure S2. Evaluation of taphonomic bias for the Northern Chile dataset. Legend (A) Absolute frequency distributions for uncorrected (red bars) and corrected (blue bars) calibrated

ages. (B) Density plots for corrected (red curve) and uncorrected (blue curve) data.

Figure S3. Evaluation of taphonomic bias for the Central Chile database. Legend (A) Absolute frequency distributions for uncorrected (red bars) and corrected (blue bars) calibrated

ages. (B) Density plots for corrected (red curve) and uncorrected (blue curve) data.

Figure S4. Calibration effects on summed probability distributions (SPDs) over the past 3,500 years.

Legend A) Age-range bar analysis. Horizontal dashed line shows the 199.8 years threshold estimated at 2-

sigma calibration. B) Slope analysis for the SHCAL13 calibration curve. Dashed horizontal lines show upper

(2.7) and lower (-0.9) normalized thresholds.

Table S2. Range and duration for radiocarbon plateaus in SHCAL13 calibration curve. (*) indicates

discontinuous flattening intervals.

Table S3. Range and duration of steepening in the SHCAL13 calibration curve. (*) indicates discontinuous

steepening intervals.

Text S2. Detection of regime shifts in air quality and fire events

Table S4. Proxy records used to generate time-series for the detection of regime shifts in air quality and fire

activity in northern and central Chile, respectively.

Table S5. Chile´s historical census data for the period 1865 AD- 1982 AD (Bravo et al., 2013; CELADE,

2005; McCaa, 1972). (*) and (**) indicate census data interpolated for northern and central Chile,

respectively.

Cited References

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Table S1. Geohistorical records considered here for reconstructing human-driven transformations

on Chilean ecosystems over the past 3000 years. The “Biophysical pattern” column specifies

anthropogenic transformations in environmental patterns/processes that were informed by each

geohistorical record. (*) and (**) indicate proxy data available only for northern and central Chile,

respectively.

Proxy data Type of geohistorical record Biophysical pattern

Archaeobotanical record Archeological Biotic-resources exploitation, biotic

introductions/extirpations, genetic diversity

Archaeometric and descriptive

data from zooarchaeological and

bio-anthropological records

Archeological Biotic-resources exploitation, nutrient cycling

(natural fertilizers), biotic introductions /

extirpations

Charcoal records from lacustrine,

peat-bog or ice cores

Paleoenvironmental Fire regime, biotic-resources exploitation

Cultivation fields and irrigation

structures

Archeological Land-use, biotic-resources exploitation,

management and exploitation of water resources,

deforestation

Diatom record from lacustrine

sediments (**)

Paleoenviormental Water quality, soil erosion

Earthworks, vial networks,

agrarian settlements with

architecture

Archeological Land-use, anthropogenic soils, nutrient cycling

(natural fertilizers), biotic-resources exploitation

Geochemistry of lacustrine records

(**)

Paleoenvironmental Soil erosion, fire regime, water quality,

deforestation

Heavy metal concentrations in ice,

peat-bogs and/or lacustrine cores

Paleoenvironmental Air quality

Human settlement patterns Archeological Land-use, anthropogenic soils, management and

exploitation of water resources

Macrofossils from rodent-middens

and leaf-litter deposits (*)

Paleoenvironmental Biotic-resources exploitation, biotic

introductions / extirpations

Metal-working devices and

artifacts

Archeological Air quality

Pollen record from lacustrine or

peat-bog deposits

Paleoenvironmental Biotic-resources exploitation, biotic

introductions / extirpations, deforestation

Pottery Archeological Contamination by non-degradable materials

Shell-middens Archeological Anthropogenic soils, marine fauna exploitation,

land-use, nutrient cycling (?)

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Spherical carbonaceous particles

in lacustrine sediments (**)

Paleoenviormental Air quality

Tree-rings (**) Paleoenvironmental Fire regime

Text S1. Paleodemographic reconstructions: datasets, procedures and

methodological caveats.

Regional datasets (Datasets 1-2 in Supplemental Data 1) include radiometric and/or

thermoluminescence dates for the last 3000 14

C yrs BP that have been obtained from remains with

unambiguous anthropogenic origin and recovered strictly in archaeological contexts: charcoal,

human tissues, artefacts on mineral (i.e. ceramics) and organic materials. Dates on marine and

terrestrial ecofacts were also considered (Campbell and Quiroz, 2015; Gayo et al., 2015). To

produce high spatial and temporal resolution datasets for the Northern and Central Chile regions,

we concatenated available independent databases for both areas. We accepted the quality, reliability

and stratigraphic association of chronometric determinations contained within these sources. We

vetted, however, dates with laboratory errors greater than 200 years.

Long-term paleodemographic trends for northern Chile were reconstructed by combining 709

chronometric determinations (Dataset 1, Supplemental Data 1) contained in the “South Central

Andes Radiocarbon Database” (SCAR; Gayo et al., 2015) and the compilation presented by

Troncoso and Pavlovic (2013). SCAR includes 675 radiocarbon dates from 270 archaeological sites

situated along the hyperarid coastal Atacama Desert and the semi-arid Altiplano (18º-25ºS). From

the dataset by Troncoso and Plavovic (2013), we considered 24 thermoluminescence and 10

radiometric ages from 13 archeological sites lying between Copiapó and Huasco valleys (see Fig. 1

in the main text). Roughly 1.4% of 14

C dates (n=10 entries) contained in the Northern dataset derive

from marine vestiges (e.g shells, Supplemental Data 1).

The Central Chile database comprises 555 chronological determinations derived from 310

archeological sites, including 374 thermoluminescence and 181 radiocarbon ages (Dataset 2,

Supplemental Data 1). Again, radiocarbon dates from marine samples are under-represented with 8

entries (1.81%). The resulting concatenated database is unevenly distributed over space. Most of

14C-dates come from sites located at 35°30'S - 42°S (Campbell and Quiroz, 2015), but a few

radiometric reports exist (n=36) north of 35°S (Falabella et al., 2007; Sanhueza et al., 2003;

Troncoso and Pavlovic, 2013). Thermoluminescence data mostly contribute to the region between

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29° and 34°S (Ávalos et al., 2006; Falabella et al., 2015; 2007; Sanchez, 2001; 2006; Sanhueza et

al., 2003; Troncoso and Pavlovic, 2013), concentrated mainly around the Elqui-Choapa transverse

valleys (n= 92 dates) and the area of the Intermediate Depression flanked by the Maipo and

Cachapoal rivers (n= 239 dates) (see Fig 1 in the main text).

We calculated summed probabilities distribution (SPD) of calibrated data through a Bayesian

approach, which allows us to combine a diverse array of chronological data to temporally constrain

past events (Michczyski and Pazdur, 2003). We used the “BchronDensityFast” command in Bchron

4.2.6 package (Parnell et al., 2008) for R (R-Development-Core-Team, 2016), which adjusts a

Bayesian Gaussian mixture model to concurrently obtain SPD from multiple radiocarbon and

thermoluminescence ages. Radiometric determinations on terrestrial samples and

thermoluminescence dates were calibrated using the Southern Hemisphere SHCAL13 (Hogg et al.,

2013) and the NORMAL (Parnell et al., 2008) calibration curves, respectively. This later calibration

dataset is available in the Bchron 4.2.6 for treating normally distributed ages (e.g

thermoluminescence). Marine 14

C-ages were calibrated with the MARINE13 curve (Reimer et al.,

2013) and without applying any corrections for local reservoir effect.

Regional plots for SPD were obtained by exporting data generated by Bchron 4.2.6 into SciDAVis

version 1.14 (Benkert et al., 2016). Since the method sums and merges probabilities for independent

dates according to their uncalibrated age and associated standard error, subsequent distribution

curves usually extend below and above the lower and upper values contained within a given

database (Supplemental Data 1). Hence, plots for regional long-term paleodemographic trends

extend up to 3500 cal yrs BP and onwards towards the present.

The use of dates as a proxy for paleodemographic trends relies on archeological (Buchanan et al.,

2008; Rick, 1987; Smith et al., 2008; Surovell and Brantingham, 2007) and ecological (Gayo et al.,

2015) principles. Because resource exploitation is a density-dependent process, then high (low)

demographic levels in past populations should result in amplified (reduced) deposition of datable

materials/artefacts in existing archaeological sites over a region. Recently, Chaput and Gajewski

(2016) demonstrate that this methodology reproduces long-term demographic patterns that mimic

population numbers/densities modeled from historical data contained within the History Database

of the Global Environment database (aka HYDE, Klein Goldewijk et al., 2010; Klein Goldewijk et

al., 2011).

Inferences about the intensity of human activities should be derived from the analysis of major

trends in SPD plots (i.e. population events or demographic phases), which are defined by main

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modes in the temporal distribution of archaeological calibrated ages (Gamble et al., 2005; Shennan

and Edinborough, 2007; Smith et al., 2008). Thus, our interpretations focus on major peaks (or

troughs) that inform us about definitive intervals of intensified (reduced) resource exploitation as

human activities and demographic levels remained high (low) over a given landscape. This is

because truncated or low amplitude changes in SPD could arise from biases in the accumulation of

cultural dates instead of variations in the rate of resource utilization by Prehistoric populations

(Gamble et al., 2005).

This method is not without its critics (Contreras and Meadows, 2014; Crombé and Robinson, 2014;

Hiscock and Attenbrow, 2016; Torfing, 2015) as some biases might blur the relationship between

accumulated frequencies of chronometric determinations on archaeological remains and past

settlement intensities. Archeological research/sampling biases and reduced database size could

compromise the robustness and reproducibility of reconstructed patterns (Michczynska et al., 2007;

Michczynska and Pazdur, 2004). We rule out distorting effects of sample size or research bias in

our reconstructions. The resulting concatenated databases for both regions are large enough,

approximating to randomly sampled sets that exceed the critical minimum number of entries

(n=107), to produce reliable reconstructions over a short time interval (i.e. 3000 years)

(Michczynska and Pazdur, 2004; Timpson et al., 2014; Williams, 2012). Earlier works presenting

the structure and properties of the individual datasets considered here (i.e. the original data sources),

demonstrate the negligible contribution of research limitations in reconstructing paleodemographic

trends from SPDs. In fact, Gayo et al. (2015) dismiss the impact of a diverse array of “scientific

biases” on the long-term paleodemographic reconstruction derived from the SCAR dataset. More

importantly, however, by implementing a re-sampling procedure, these authors verify that the

overall trend in the SPD for northern Chile remained insensitive to redundancies or hiatuses in the

accumulation of 14

C-dates. In the case of central Chile, temporal trends in the accumulation of dates

have been discussed in light of the dynamics of the regional archeological research (Campbell and

Quiroz, 2015), and, in turn, these have been interpreted in terms of “paleodemographic” (human

population dynamics) or “archaeo-demographic” (i.e. research bias) events. A similar discussion is

presented in Falabella et al. (2015) for the reconstruction of the demographic dynamics based on the

distribution of the thermoluminescence dates that integrate our unified central Chile database.

Because our inferences are based on the combination of different individual databases for each

region, we assessed whether this “merging procedure” resulted in imbalanced datasets. To this end,

we resampled our concatenated regional datasets following the re-sampling procedure implemented

in Gayo et al. (2015). By reducing the number of multiple overlapping dates per archeological site,

we were able to examine the contribution of oversampling in reconstructed demographic trends. To

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evaluate the impact of research bias, the absolute frequency distribution of chronometric entries for

full concatenated regional databases was calculated by bucketing median calibrated ages into 100-yr

bins (Timpson et al., 2014).

The resampling procedure resulted in important reductions (>28%) in the number of entries for

northern (nresampled= 455 dates) and central Chile (nresampled= 401 dates). Even so, SPDs from these

resampled datasets (Fig. S1A) are remarkably equivalent to those obtained with full sets (Fig. 2a

and Fig. 3a in the main text). In fact, summed probabilities obtained with full and resampled

datasets are significantly and strongly correlated, with Pearson's correlation coefficient of 0.97 and

0.98 (p <0.01) for northern and central regions, respectively. This implies that oversampling biases

do not affect our interpretations on the long-term demographic history of the Pre-Columbian

societies that inhabited Chile throughout the past 3000 years. The continuous distribution of

calibrated ages in the northern dataset (Fig. S1B) over the last 3300 cal yrs BP confirms the notion

that changes in the accumulation of dates reflect unbiased fluctuations in the regional human

population. Two short-lived gaps (100 years) in the accumulation of dates are evident in the central

Chile dataset at 2700 and 2500 cal yrs (Fig. S1B). Because three 14

C-dates are recorded by 2600 cal

yrs BP between the Limari and Imperial basins (Fig. 1 main text), these hiatuses seem to be related

to biases imposed by research interests or efforts. Conversely, the major 300-yr hiatus detected

between 3300 and 3000 cal yrs BP apparently represents a real paleodemographic process as

verified by exhaustive archeological surveys conducted in the area (Campbell and Quiroz, 2015).

Still, we are aware that emerging patterns for the intensity of human activities during the past 400

years should be considered with caution, even when frequency histograms show that reconstructed

demographic dynamics for northern and central Chile extend up to 200 cal yrs and 100 cal yrs BP,

respectively (Fig S1B). Infectious diseases brought by Europeans, societal frictions (Casanueva,

2001; Evans, 2001; Koch et al., 2019) and/or loss of social resilience to abrupt climate changes

(Byers et al., 2018) may have contributed to this overall population crash after the 16th century.

Nevertheless, we suspect that research biases could partially explain the sharp reduction in the

accumulation of chronological entries in all regional datasets from 400 cal years BP onwards. It is

well-known that archaeologists traditionally disregard dating cultural remains from historical times

(Smith et al., 2008).

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Figure S1. Evaluation of research/sampling biases in the accumulation of chronometric data in the

regional datasets. Legend (A) SPDs obtained from resampled datasets. (B) Absolute frequency distributions per 100-year bin

for calibrated ages from full databases.

Stratigraphic incompleteness of archeological sequences and/or the expected loss of archeological

deposits over time could also exert significant influences on the temporal accumulation of

chronometric data (Peros et al., 2010; Surovell and Brantingham, 2007; Surovell et al., 2009;

Timpson et al., 2014; Williams, 2012). Because of the preferential preservation of more recent

records, the distribution of dates could adopt an exponential decay function (see Surovell et al.,

2009). The calibration process to obtain SPD of 14

C-dates is another source of uncertainty that is

brought about by changes in the slope of the calibration curve, ultimately related to fluctuations in

atmospheric radiocarbon levels (Bamforth and Grund, 2012; McFadgen et al., 1994; Reyes and

Cooke, 2011; Steele, 2010; Thorndycraft and Benito, 2006; Williams, 2012). Actually, during

periods in which 14

C varies highly (little), radiocarbon cliffs (plateaus) occur in the corresponding

section of the calibration curve, which can produce spurious peaks (troughs) in the SPD.

Several procedures, however, have been devised to overcome or minimize much of these biases,

which have opened new possibilities for reconstructing more reliable paleodemographic patterns

(Buchanan et al., 2008; Michczynski and Michczynski, 2006; Peros et al., 2010; Shennan et al.,

2013; Steele, 2010; Surovell and Brantingham, 2007; Surovell et al., 2009; Timpson et al., 2014;

Williams, 2012). Here, we followed the standard procedure introduced by Williams (2012) to

evaluate the contribution of taphonomic biases on reconstructed long-term paleodemography

dynamics. Specifically, each full database was corrected for taphonomic loss by applying a square

root non-linear model on the real frequency of calibrated ages per 250-year bin (Surovell and

Brantingham, 2007; Surovell et al., 2009; Williams, 2012).

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We restricted the evaluation of calibration effects to the SHCAL13. The NORMAL calibration

curve is exempt from wiggles with a slope of 1 for the past 50000 years (Parnell et al., 2008). The

impacts of the MARINE13 curve should be minimal due to the scarce and discontinuous

representation of marine materials in both regional datasets. To test the relationship between

radiocarbon plateaus and troughs in SPDs, we performed an age-range bar analysis at 2-sigma

calibration confidence interval (Guilderson et al., 2005; Williams, 2012), whereas the effects of

calendar age steps in the SHCAL13 curve were examined through the slope analysis discussed and

proposed by Williams (2012).

Our results indicate that taphonomic bias does not exert an important influence on reconstructed

regional-scale intensity of human activities. Distributions of corrected and uncorrected frequencies

of dates for each region through the past 3500 years are fundamentally similar (Fig. S2a-S3a).

Similarly, corrected and uncorrected frequency densities are in broad agreement (Fig. S2b-S3b).

These results suggest that long-term patterns in SPD plots reflect regional variations in

paleodemographic trends rather than changes in preservation or destruction of archaeological sites.

The age-range bar analysis for the SHCAL13 curve reveals a 199.8-year threshold (= x̄+) at 2-

sigma calibrated age range (x̄= 132.4, = 67.3). Hence, age bars that exceed this threshold represent

periods affected by radiocarbon plateaus in the calibration curve. Only five sections in the

calibration curve resulted problematic (Fig. S4a, Table S2), but most of them are discontinuous

and/or short-lived (100 years). In the northern reconstruction, neither the protracted radiocarbon

plateau at 2680-2400 cal yrs BP nor the one detected by 2200-2100 cal yrs BP produces minor

troughs that alter the general trend inferred for the intensity of human activities (Fig. S1A, Fig. 2

and Fig 3 in the main text). None of the radiocarbon plateaus detected for SHCAL13 results in

artificial flattening in the central Chile SPD curves (Fig. S1A, Fig. 3 main text). This is an expected

result as >67% for the regional dataset is composed by thermoluminescence dates.

We identified 47 transient sections (<100 years) of the SHCAL13 curve in which estimated slope

values exceed normalized upper and lower thresholds (Fig. S4b, Table S3). Overall, these

radiocarbon cliffs produce negligible spikes in SPDs from full (Fig. 2 and Fig 3, main text) and

resampled (Fig. S1A) datasets, which in turn suggests that major increasing trends in regional

curves represent genuine increments in past human activities. For instance, throughout the event of

increased paleodemographic levels detected over both regions by 500-700 cal yrs BP, brief and

low-amplitude peaks appear at about 605-545 cal yrs BP (Fig 2-3 main text).

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Figure S2. Evaluation of taphonomic bias for the Northern Chile dataset. Legend (A) Absolute frequency distributions for uncorrected (red bars) and corrected (blue bars) calibrated

ages. (B) Density plots for corrected (red curve) and uncorrected (blue curve) data.

Figure S3. Evaluation of taphonomic bias for the Central Chile database. Legend (A) Absolute frequency distributions for uncorrected (red bars) and corrected (blue bars) calibrated

ages. (B) Density plots for corrected (red curve) and uncorrected (blue curve) data.

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Figure S4. Calibration effects on summed probability distributions (SPDs) over the past 3,500 years.

Legend A) Age-range bar analysis. Horizontal dashed line shows the 199.8 years threshold estimated at 2-

sigma calibration. B) Slope analysis for the SHCAL13 calibration curve. Dashed horizontal lines show upper

(2.7) and lower (-0.9) normalized thresholds.

Table S2. Range and duration for radiocarbon plateaus in SHCAL13 calibration curve. (*) indicates

discontinuous flattening intervals

Lower range limit (Calibrated Age BP) Upper range limit (Calibrated Age BP) Duration (years)

100 215 115 *

2100 2200 100

2405 2740 335 3000 3040 40 *

3110 3220 110 *

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Table S3. Range and duration of steepening in the SHCAL13 calibration curve. (*) indicates

discontinuous steepening intervals

Lower range limit (Calibrated Age BP) Upper range limit (Calibrated Age BP) Duration (years)

15 25 10 *

70 90 20 *

110 170 60 200 230 30

270 365 95 *

400 405 5 440 445 5

490 505 15 *

545 605 60 625 630 5

650 680 30

725 730 5 790 800 10

860 870 10

915 930 15

955 960 5

1010 1025 15

1055 1070 15 1130 1135 5

1175 1215 40

1265 1270 5 1295 1305 10

1385 1390 5

1530 1535 5 1645 1660 15

1695 1705 10 *

1745 1770 25 * 1825 1850 25

1925 1930 5

1965 1970 5 1995 2005 10

2025 2030 5

2125 2130 5

2145 2180 35

2220 2245 25 2265 2270 5

2295 2310 15

2340 2360 20 2485 2490 5

2555 2560 5

2585 2640 55 2735 2755 20 *

2900 2920 20

3075 3095 20 * 3280 3305 25 *

3420 3450 30 *

3475 3500 25

Text S2. Detection of regime shifts in air quality and fire events

We generated time-series for heavy metal paleopollution and past fire activity (Table S4) by

selecting published proxy records that met the criteria described in the main text. These data were

obtained either from the principal author of the primary source, online databases (NOAA, Global

Charcoal Database, Mendeley), or supplementary materials. The time-series for paleopollution in

northern Chile aggregates sequential trace-element characterizations of stratified sedimentary

records (peat-bogs, lakes) and ice-cores from the western Andean slope (12°-20°S; Cooke et al.,

2007; 2009; 2011; 2013; Eichler et al., 2017; 2015; Uglietti et al., 2015) and Patagonia (De

Vleeschouwer et al., 2014). Because we are interested in the contribution of anthropogenic

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emissions to the inventory of metalloids derived from traditional metallurgical activities (Cu, Ag,

Pb, Hg), only data reported as crustal-normalized concentrations (Enrichment Factors or EFs) or

background flux-ratios were considered (Table S4). An independent time-series for air-quality

changes in the Santiago basin for the period 1852 AD – 2002 AD was constructed by gathering

influxes of spheroidal carbonaceous particles (SCPs) in four lakes located close to El Teniente

mining operation (33°S) (von Gunten et al., 2009). The time-series for past fire activity is based on

influxes of micro-charcoal particles (<120 μm) in closed lacustrine basins (Heusser, 1990; Martel-

Cea et al., 2016; Villa-Martínez et al., 2003), which collectively inform on “regional” fires

(Whitlock and Larsen, 2001) occurring along Mediterranean central Chile between 33° and 34°S.

Additionally, we aggregated the absolute frequency of fire events per 100 years inferred from the

Austrocedrus chilensis tree-ring series from the Cachapoal basin (34°S, Bustos-Schindler et al.,

2010).

To produce equally spaced data, heavy-metal pollution proxy records were matched to the

chronology of the Illimani ice-core (Eichler et al., 2017) through linear interpolation between two

adjacently measured geochemical samples. The Illimani record provides Cu EFs at 50-yr intervals

from the onset of the Pre-Columbian metallurgy (3125 cal yrs BP) to the Industrial period (2005

AD). Nevertheless, most of geochemical data could not be interpolated up to 3125 cal yrs BP due to

their limited temporal extent (Table S4). Flux-ratios and EFs were then min-max normalized (1-0),

and all of these proxy records were merged into a single time-series by averaging normalized values

for each 50-year interval. Therefore, we obtained a time-series for a paleopollution index that

describes concurrently the trajectory for anthropogenic emissions of Hg, Pb, Cu and Ag in northern

Chile from 2005 AD to 3125 cal yrs BP (Dataset 3, Supplemental Data 1). SCP and paleofire time-

series for central Chile were generated following these same procedures. However, because of the

sparse temporal-resolution of lacustrine records, data were spaced at 100-year and 5-year intervals

for paleofire and SCPs time-series, respectively (Table S4, Datasets 4-5).

We used the sequential t-test algorithm proposed by Rodionov (2004) to detect statistically

significant changes in the mean of our paleopollution and paleofire time-series. A cutoff length of 3

intervals (i.e. 150 years) and a significance p-value of 0.05 were defined to identify long-term

regime shifts in metal air pollution over the past 3125 cal yrs BP. The probability level was

maintained when analyzing long-term changes in the fire activity and SCPs pollution, but we

applied cutoff lengths of 5 (500 years) and 3 (15 years) intervals, respectively. For comparative

purposes, we also looked into regime shift in fire activity during the past 7000 cal yrs BP (cutoff

window= 1000 years, p-value= 0.05) by considering the Tagua Tagua micro-charcoal record (Fig.

5a in main text; Heusser, 1990), which represents the largest charcoal time-series available for

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central Chile. All regime shift analyses were implemented using the SRSD v.3.2 package for Excel

(Rodionov, 2004; 2006).

Table S4. Proxy records used to generate time-series for the detection of regime shifts in air quality

and fire activity in northern and central Chile, respectively.

A- Northern Chile: Paleopollution time-series

Record Metal Unit Temporal coverage Reference Access

Laguna Lobato Pb, Ag Enrichment factor 1995 AD - 1225 cal yrs BP Cooke et al. (2011) Provided by Colin Cooke

Karukinka peat-bog

Cu Crustal-normalized Cu/La

2005 AD - 3125 cal yrs BP De Vleeschouwer

et al. (2014) Provided by Anja Eichler

Laguna

Pirhuacocha Cu Flux ratios 2005 AD - 1375 cal yrs BP Cooke et al. (2007) Provided by Colin Cooke

Illimani ice-core Cu Enrichment factor 1975 AD - 3125 cal yrs BP Eichler et al.

(2017) Provided by Anja Eichler

Illimani ice-core Pb Enrichment factor 1975 AD - 1875 cal yrs BP Eichler et al.

(2015) NOAA paleoclimate database

Laguna Negrita Hg Flux ratios 2005 AD - 3075 cal yrs BP Cooke et al. (2013) Supplementary material in the

original article

Laguna Yanacocha core

1

Hg Flux ratios 2005 AD - 3075 cal yrs BP Cooke et al. (2009) Provided by Colin Cooke

Laguna Yanacocha core

2

Hg Flux ratios 2005 AD - 3125 cal yrs BP Cooke et al. (2009) Provided by Colin Cooke

Chungara Lake Hg Flux ratios 2005 AD – 2675 cal yrs BP Guedron et al.

(2019) data.mendely.com/datasets

Quelccaya ice-

core

Cu, Ag,

Pb Enrichment factor 1985 AD - 1225 cal yrs BP

Uglietti et al.

(2015) NOAA paleoclimate database

B- Central Chile: Paleofire time-series

Record Proxy

record Unit Temporal coverage Reference Access

Lagunas Aculeo, El Ocho,

Ensueño, Del

Inca, Negra

Spheriodal

carbon particles

Influx

(particles/ cm2/yr) 1852 AD – 2002 AD

von Gunten et al.

(2009) Provided by Lucien Von Gunten

Laguna Tagua Tagua

Charcoal Influx (mm2/cm2/yr)

1710 AD – 2400 cal yrs BP Heusser (1990) Global Charcoal Database

Laguna Aculeo Charcoal Influx (particles/cm2/yr)

1950 AD - 3500 cal yrs BP Villa-Martínez et

al. (2003) Global Charcoal Database

Laguna Chepical Charcoal Influx

(particles/cm2/yr) 1950 AD – 3000 cal yrs BP

Martel-Cea et al.

(2016) Provided by Alejandra Martel-Cea

Austrocedrus

chilensis stands Tree-rings

Absolute frequency

of fires 1950 AD – 1450 AD

Bustos-Schindler et

al. (2010) Extracted from the original article

To perform statistical correlations with heavy-metal paleopollution or fire activity,

paleodemographic time-series for the northern and central regions were resampled by estimating

median values of obtained SPDs at 50-yr and 100-yr intervals, respectively. Specifically, we

evaluated the relationship between these patterns and population levels during Pre-Columbian (425

– 3500 cal yrs BP) and historical (1575 AD – 2005 AD) times. Because the metallurgy production

in northern Chile was likely driven by economic pressures of centralized states since the Inca period

S15

(Armesto et al., 2010; Zori, 2019), regional population levels considered for the period 1450 AD -

2005 AD derive from a national-scale reconstruction. This reconstruction partly emerges from the

SPDs of chronometric data from central and northern regions that cover the period 1425 AD - 1825

AD. The SPDs of these calibrated data was constructed according to the procedure described in the

Text S1. The resulting time-series for northern Chile was then complemented with official census

data for the period 1865 AD - 2005 AD (Table S5) so that trends in heavy metal pollution for

historical times could be correlated with reliable estimations for population levels. The years

between censuses were linearly interpolated. The SPDs time-series for central Chile was

complemented with the interpolated census data for the year 1950 AD (Table S5). To aggregate

SPDs and census data, Pre-Columbian and historical demographic estimates were min-max

normalized, and then merged additively into an index of demographic levels. All Spearman´s

correlations analyses were performed using the ggpubr v. 0.2 for R (R-Development-Core-Team,

2016).

Table S5. Chile´s historical census data for the period 1865 AD- 1982 AD (Bravo et al., 2013;

CELADE, 2005; McCaa, 1972). (*) and (**) indicate census data interpolated for northern and

central Chile, respectively.

Year AD Northern (# inhabitants) Central (#inhabitants) National total (# inhabitants)

2012 806,438 14,224,031 15,030,469

2005 (*) 719,982 13,856,653 14,576,635

2002 682,930 13,699,205 14,382,135

1995 (*) 604,195 12,611,624 13,215,820

1992 570,452 12,145,518 12,715,970

1985 (*) 492,121 10,876,888 11,369,010

1982 458,551 10,333,190 10,791,741

1975 (*) 382,161 9,070,707 9,452,868

1970 327,597 8,168,934 8,496,531

1960 239,305 6,810,423 7,049,728

1952 182,902 5,485,753 5,668,655

1950 (**) 249,269 5,284,091 5,533,360

1940 581,105 4,275,781 4,856,886

1930 553,460 3,598,373 4,151,833

1925 (*) 534,700 3,338,617 3,873,317

1920 515,940 3,078,861 3,594,801

1907 491,066 2,540,158 3,031,224

1895 378,805 2,238,357 2,617,162

1885 142,865 2,091,136 2,497,797

1875 71,489 1,662,516 2,075,971

1865 78,895 1,681,111 1,760,006

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