Effect of climate and ENSO events on Prosopis pallida forests along a climatic gradient

Effect of climate and ENSO events on Prosopis pallida forests along a climatic gradient Abstract Extreme rainfall events, such as the El Niño-Southern Oscillation (ENSO), are responsible to a large extent for the processes of tree establishment and tree growth in the North Peruvian dryland forest. Prosopis pallida (algarrobo) is the dominant species of the dryland forest on the Peruvian Pacific coast. Dendrochronological data from living populations have shown its response to climatic events. The aim of this work was to study local differences in P. pallida growth responses to ENSO events through dendrochronological data. To do so, three algarrobo populations within a gradient of increasing temperature and precipitation from West to East were selected. Tree-ring data were correlated with the monthly temperature and precipitation from each location and with the 3.4 and 1 + 2 ENSO indices. Inland populations showed the highest correlation with the climatic conditions. The summer rainfall (January), spring temperature of the previous year, and summer temperature of the current year were significantly correlated with growth. All populations showed a significant increase in the tree-ring index during ENSO events. However, growth in no-ENSO years was also high in populations closer to the coast. Our results indicate that the proximity to the Andes Mountains, distance to the Pacific coast and distribution of algarrobo in this area make marginal inland populations more sensitive to climatic variations and ENSO events. We conclude that the P. pallida response to the climate in Northern Peru is the result of both strong climatic events and local conditions, which are estimated most accurately with the 1 + 2 ENSO index. Introduction In Northern Peru, dryland forests represent 41 per cent of all land cover. Thousands of rural families depend directly on their ecosystem services, which are valued at 21.8 million US dollars per year (Orihuela and Albán, 2012). Continuous land degradation here has reduced vegetation cover, crop productivity, livestock numbers and wildlife (Bonkoungou, 2001). As a result, and considering the present conditions, it has been stated that the North Peruvian dryland forests will disappear in the next 20 years due to timber overexploitation, overgrazing and climate change (Orihuela and Albán, 2012). The most important species in this ecosystem is Prosopis pallida (Humb. & Bonpl. ex Willd.) Kunth (hereafter referred to as algarrobo), representing 61 per cent of all plant cover in the dryland forest. Algarrobo is a key species since birds, mammals, plants, fungi and microbial organisms interact and survive using these trees as a nest, food source and refuge (Ruiz et al., 2008; Biederman and Boutton, 2009; Rosi et al., 2009). The climatic conditions in this region are controlled by the Humboldt Current, which creates stable and dry conditions with minimal rainfall events. This dry phase is interrupted by the El Niño Southern Oscillation (ENSO), which creates a wet phase of 1–2 years of high annual rainfall and unstable climatic conditions. Plant phenology, species biodiversity and ecological processes are shaped by this climatic cycle (Tapley and Waylen, 1990; Holmgren et al., 2006; López et al., 2008). For algarrobo, the wet phase promotes germination, growth, competition and grazing for 1 year or, in some exceptional cases, 2 years. The dry phase promotes survival over tree establishment for an uncertain period of 4–8 years. Thus, the ENSO has been recognized as a trigger of long-lasting shifts in arid vegetation, switching the landscape from perennial herbs to dryland forest according to the grazing pressure (Holmgren et al., 2001) and the resistance of Prosopis sp. to dry conditions. Nowadays, ENSO events have become relatively easy to study (Xue et al., 2017). A significant increase in the sea surface temperature (SST) in the Pacific Ocean, especially in the 3.4 region, is commonly used as an indicator of extreme precipitation on the Pacific coast of the USA (Trenberth and Stepaniak, 2001). This event is responsible for the period of greatest growth in Peruvian dryland forests (Rodríguez et al., 2005), and a significant relationship between SST and algarrobo growth is expected. However, the global and local climatic conditions could also modify plant growth. Recently, climate change has been a major concern, and a change in ENSO frequency is expected because of it (Wang et al., 2017). An increase in mean annual temperature might restrict plant physiologal processes, unless it is associated with an increase in mean annual precipitation. The other climatic events involved, such as the Interdecadal Pacific Oscillation or the Madden–Julian oscillation, make the forecasting of the ENSO a hard task, regardless of the increase in monitoring power (Squeo et al., 2007). For South America, the 1 + 2 region (0°−10°S, 90°W–80°W) of the Pacific Ocean is commonly used to determine local precipitation events and the progress of the ENSO (Vuille et al., 2015) but its relationship with algarrobo tree growth is still untested. Dendrochronological techniques have been used to determine key variables such as tree age, annual growth, basal area increment (BAI; Schöngart, 2008; Brienen and Zuidema, 2006) and growth–climate relationships, in order to understand forest establishment and its ecological response to climatic variability (López et al., 2013; Paredes-Villanueva et al., 2013). The earliest dendrochronological studies of Prosopis were made in Argentina using P. alba Griseb, P. ferox Griseb and P. flexuosa DC. as model species (Morales et al., 2005; Villagra et al., 2005; Ferrero et al., 2014). Those research works defined the basic ring structure, which is related to the presence of terminal parenchyma and large-diameter vessels (Villalba et al., 2000). Based on these studies, algarrobo tree-ring data have been shown to be a good indicator of rainfall and temperature change and of how climatic variability affects forest functionality (Morales et al., 2005), with genetic variability and local climatic conditions playing an important role in the population response to rainfall events (Ramawat, 2009). In Peru, dendrochronological studies of algarrobo have shown its positive response to ENSO events (López et al., 2005; López et al., 2006) and its potential for archeological and current climatic studies (Ghezzi and Rodríguez, 2015). Even though ENSO variability through time and space has been described in depth in the last few years (Van Leeuwen et al., 2013; Rustic et al., 2015), variability among algarrobo’s populations in the response to ENSO events has not been assessed, and the role of local climatic and geographic characteristics has not been studied. In this study, we use dendrochronological techniques to evaluate the impact of precipitation, temperature and ENSO events on North Peruvian populations of P. pallida across a climatic gradient from the coast to inland. The results provide information about the variability of the response to ENSO events among P. pallida populations, and regarding the ecological impact of this climatic anomaly on forest growth. The specific objectives of this study were (1) to analyze the tree growth variability in P. pallida populations and its potential for dendrochronological studies, (2) to assess the relationships among tree growth, local climatic conditions and ENSO events, and (3) to determine whether the population response to ENSO events changes across the climatic gradient and, if so, why. Specifically, we hypothesize that coastal population growth will benefit from the Pacific Ocean humidity and reduced air temperature regardless of the limited rainfall. While inland populations will be more sensitive to the dry and wet ENSO cycle due to the vertical stratification of temperature at higher altitudes and the high precipitation during the ENSO events. Therefore, mean radial growth in coastal populations will remain steady even in dry years, whereas mean radial growth in inland populations will be lower than coastal populations in dry years and higher in wet years. Materials and methods Study site The study site is located in the dryland forests of the North Peruvian coast (5°S). Normally, the Humboldt Current reduces the SST along the South American coast, which reduces evaporation and therefore precipitation (to below 300 mm annually) on the tropical coast of Northern Peru. The SST is highly correlated to the air temperature due to the thermal inertia of the Pacific Ocean, and this creates highly stable climatic conditions that closely fit a sine wave with a phase length of 1 year and amplitude of 7−9°C (Rollenbeck et al., 2015). During ENSO events (wet phase), the Humboldt Current weakens and the SST and air temperature rise, which increases precipitation in this region to above 2000 mm annually and creates highly unstable climatic conditions. In recent years, major ENSO events were recorded in 1953, 1958, 1964, 1972, 1982, 1987, 1992 and 1998. Three algarrobo populations were selected in January 2015 for this study (Figure 1; Table 1): the coastal population of Rinconada (RIN) forest in Paita, the University of Piura forest (PI) in Piura and the inland population of Ignacio Távara (IT) community in Chulucanas. These lie along a climatic gradient, where mean annual rainfall and temperature increase from the coast to the inland areas (Table 1). Rainfall occurs in summer (between December and March), when the mean daily temperature reaches its peak, while the lowest temperature occurs in the dry season in July (Figure 2). Differences between populations increase exponentially during the ENSO event, when mean annual rainfall and temperature are far higher in inland populations than in coastal populations (Figures 1 and 2). During the dry phase, the Humboldt Current reduces evaporation from the sea, and the wind field removes most of the dew. Therefore, there is no significant presence of fog in these dryland ecosystems (Rollenbeck et al., 2015). Figure 1 View largeDownload slide Geographic location of the populations studied in Northern Peru across the climatic gradient during the dry (up) and wet (down) phase of the ENSO cycle. Figure 1 View largeDownload slide Geographic location of the populations studied in Northern Peru across the climatic gradient during the dry (up) and wet (down) phase of the ENSO cycle. Table 1 Geographical, topographical and morphological characteristics of three P. pallida populations. Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Climatic data for the 1963–2014 period was obtained from the ‘CRU TS 3.1 database’, and the groundwater deep was obtained from the ‘Regional groundwater inventory of Piura’. See the text for further references View Large Table 1 Geographical, topographical and morphological characteristics of three P. pallida populations. Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Climatic data for the 1963–2014 period was obtained from the ‘CRU TS 3.1 database’, and the groundwater deep was obtained from the ‘Regional groundwater inventory of Piura’. See the text for further references View Large Figure 2 View largeDownload slide Monthly (up) and annual (down) rainfall precipitation for the RIN (dotted), PI (dashed) and IT (solid) populations. Figure 2 View largeDownload slide Monthly (up) and annual (down) rainfall precipitation for the RIN (dotted), PI (dashed) and IT (solid) populations. The IT and RIN populations have grown naturally under human management to improve pod production, while the PI population has grown as part of a reforestation program with a similar management strategy. In all cases, forest management has consisted only of scheduled pruning, without the use of fertilizers or pest control strategies. Despite this, the populations differ morphologically, functionally and structurally, and there is high genetic variability among them (Palacios et al., 2011). Regarding the soil texture, the sand content exceeds 98 per cent in all three locations, and the soil pH is slightly acid (6.45–6.69). Underground water deep was shallow in RIN and deeper in IT, following the climatic gradient as well (Alvarez et al., 2002, 2004). Field sampling and sample preparation Ten cross-sections per population were collected, with the approval of National Forest and Wildlife Service (SERFOR – www.serfor.gob.pe) and the nearby rural communities. The relatively small number of tree samples analyzed in this study might be seen as a limitation. However, the North Peruvian dryland forest is a scarce and endangered ecosystem. Thus, tree logging, especially for older trees, is forbidden. At each site, care was taken to select trees more than 30 years old, growing under similar microsite and competition conditions, and located at least 20 m apart from each other, across a rectangular area of 1-ha. Sections were taken at breast height (1.30 m) specifically for this study. Cross-section samples were air-dried and polished with sandpaper of different intensity, following the grain standards of the Federation of European producers of abrasives (https://www.fepa-abrasives.com/), from P24 to P2500, until the terminal parenchyma was clearly visible under a binocular microscope. Up to two radii were selected in each cross-section. The tree-ring width was measured digitally in each radius within each section, for all the selected trees. Due to the irregular radial growth of P. pallida, tree rings were analyzed across the entire disk to detect false or double rings. The correlation coefficient among samples was also considered during the elimination and addition, respectively, of double and missing rings. Because of these necessary corrections, radii from the same disk were averaged to avoid pseudo replication. The ring width measurement and crossdating were performed using CooRecorder and CDendro 7.8 (2014 – Cybis Elektronik & data AB). To compare population growth and the impact of ENSO events, the mean BAI was calculated using tree-ring data, with the formula BAI = π (R2t – R2t−1) (mm2 year−1) where R is the radius of the tree and t is the year of tree-ring formation, for each population. An annual tree-ring width chronology was built for each site. All tree-ring series were prewhitened and biweighted to remove variation not related to climatic factors during the standardization process, thereby allowing determination of the correlation between series within each population. To build the population chronology, individual tree-ring width series were double detrended according to the methodology applied for López et al. (2005) for P. pallida. First, using a negative exponential curve and then by fitting a cubic smoothing spline with a 50-per cent frequency response. Dendrochronological statistics were calculated for the common interval 1990–2010, to compare growth features among the study sites (Fritts, 1976). The mean and standard deviation (SD) of the raw tree-ring width data were calculated to compare growth among populations and with other Prosopis species. The first-order autocorrelation of the tree-ring width raw data (AC1) was calculated to determine the degree of independence of the tree-ring growth series from each other through time. The mean sensitivity (MSx) of the residual series was calculated to study the year-to-year tree-ring variability. The mean correlation of the common period (MCCP) was calculated to show the consistency of the dendrochronological results. The expressed population signal (EPS) of the residual width series was calculated to determine the common relationship with external factors. Finally, the percentage of the variance explained by the first and second principal component (PC 1 and PC 2) was calculated with all individual residual series for successive 20-year periods lagged 1 year to determine the main source of variation and its differences between populations (Fekedulegn et al., 2002; Cook and Pederson, 2011). MCCP, BAI, MSx and PCA were calculated using the ‘dplR’ package of R software (Bunn, 2008, 2010). Climate data and climate–growth relationship To analyze climatic trends at a regional scale and quantify climate–growth relationships, we used monthly climatic data (mean temperatures and total precipitation) from the CRU TS 3.1 dataset for the period 1963–2014, produced by the Climate Research Unit (http://www.cru.uea.ac.uk/). Monthly climatic data was correlated with the tree-ring index from September of the previous year to July of the current year. This dataset corresponds to the interpolated data of instrumental records recorded by a dense network of local meteorological stations, which have been subjected to homogeneity tests and relative adjustments, and finally gridded onto a 0.5° network (Mitchell and Jones, 2005). Furthermore, ENSO indices from the 3.4 (5°N–5°S, 170°W–120°W) and 1 + 2 (0°–10°S, 90°W–80°W) regions from the NOAA organization (http://www.noaa.gov/) – which record climatic fluctuations and the mean SST in the Pacific – were considered to study the ENSO influence on regional precipitation patterns and tree growth (Trenberth and Stepaniak, 2001). Monthly SST indices from the 1 + 2 region were correlated with the mean chronology of each population from August of the previous year to July of the current year. Seasonal SST means were calculated for the Niño 3.4 and 1 + 2 regions and then correlated with the tree-ring width and the first axis of variation of the PCA for each population. Correlations between the climate data and each population chronology were calculated using the ‘dcc’ function of the ‘treeclim’ package of R software (R Development Core Team, 2013). Results Tree growth The P. pallida populations showed high tree-growth variability, with a low tree-ring index under average climatic conditions (dry phase) that was 3-fold higher in ENSO years (wet phase). This growth pattern was consistent among populations, with a significant response to ENSO events over time, but the response varied between populations (Figure 3). Thus, the tree-ring width index during ENSO events (1.8) was similar to that in normal years (1.2) at RIN and PI (costal populations). Meanwhile, the tree-ring width index was greater (2.5) during ENSO events than in normal years (0.8) at IT (inland population) where growth seems to be more ENSO-dependent (Figure 4). Standard deviation was relatively low in RIN and PI, while it was high at IT. Accordingly, AC1 was significantly lower in IT than other populations. The mean sensitivity was high for all populations but within the range of P. pallida data reported previously. Peaks of the mean sensitivity were present in ENSO years, but did not appear consistently among populations, indicating again a differential response in each site (Figure 5). Similarly, MCCP was low, but within the range of tropical species. The EPS of the residual width series was high and similar between populations (Table 2). In the PCA, a high amount of the variance was explained in the first factor for all populations. Therefore, a high amount of variability is solely explained by a common source. Despite this, there was no common trend in PC 1 among the populations (Figure 6), suggesting that they responde differently to the source of variation. Whereas PC 2 was significantly low and indicated that other sources of variability played a minor role in the total common variance. Figure 3 View largeDownload slide Basal area increment (mm2) for the inland and coastal populations over time. Vertical line indicates recorded ENSO events. The trend is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 3 View largeDownload slide Basal area increment (mm2) for the inland and coastal populations over time. Vertical line indicates recorded ENSO events. The trend is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 4 View largeDownload slide Chronology of the tree-ring width index for P. pallida populations. The trend of this index is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 4 View largeDownload slide Chronology of the tree-ring width index for P. pallida populations. The trend of this index is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 5 View largeDownload slide Temporal trend in the variance explained by the first principal component analysis for Rinconada (34 year subinterval), Piura (26 years subinterval) and Ignacio Tavara (17 years subinterval). Figure 5 View largeDownload slide Temporal trend in the variance explained by the first principal component analysis for Rinconada (34 year subinterval), Piura (26 years subinterval) and Ignacio Tavara (17 years subinterval). Table 2 Characteristics of the tree-ring chronologies for the common period. Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 SD, standard deviation; MSx, mean sensitivity of the residual ring width series; MCCP, mean correlation of the common period; EPS, population signal of the residual series; PC 1, the first axis of the principal component analysis; PC 2, the second axis of the principal component analysis View Large Table 2 Characteristics of the tree-ring chronologies for the common period. Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 SD, standard deviation; MSx, mean sensitivity of the residual ring width series; MCCP, mean correlation of the common period; EPS, population signal of the residual series; PC 1, the first axis of the principal component analysis; PC 2, the second axis of the principal component analysis View Large Figure 6 View largeDownload slide Correlations of monthly total precipitation and monthly mean temperature with tree-ring index during the growing season of P. pallida. *P < 0.05. The growing season is marked with a horizontal line. Figure 6 View largeDownload slide Correlations of monthly total precipitation and monthly mean temperature with tree-ring index during the growing season of P. pallida. *P < 0.05. The growing season is marked with a horizontal line. Interaction between growth and local climate The tree-growth index was positively correlated with the precipitation in January of the growth year, the highest monthly precipitation of every year, and the precipitation during the ENSO rainfalls (Figure 7). The correlation coefficient was nonsignificant at RIN, was significant at PI (r = 0.40), and was higher at IT (r = 0.67) (Figure 7), showing the importance of the climatic gradient from the coast to inland areas and the differential response of each population to the environmental conditions. Similarly, the tree-growth index correlation with monthly temperature showed significant differences between populations. For instance, there were no significant correlations in the case of RIN. While at PI, it showed a significant relationships with the spring temperature of the previous year (October, r = 0.39; December, r = 0.32). At IT, the tree-growth index was positively correlated with the mean monthly spring temperature of the previous year (October, r = 0.50, November, r = 0.44, December, r = 0.47) and the summer temperature of the current year (January, r = 0.32) (Figure 7). Figure 7 View largeDownload slide Correlations of mean sea temperature in the Niño 1 + 2 region with tree-ring index of P. pallida in each population. *P < 0.05. Figure 7 View largeDownload slide Correlations of mean sea temperature in the Niño 1 + 2 region with tree-ring index of P. pallida in each population. *P < 0.05. Population response to ENSO events The monthly correlation between the tree-growth index and SST index (1 + 2 ENSO) showed no correlation with sea temperature in any month at RIN. Whereas the PI tree-growth index and sea temperature in August (r = 0.38) and September (r = 0.40) were positively correlated. Similarly, tree-growth index was significant for IT in August (r = 0.55), September (r = 0.56), February (r = 0.61) and March (r = 0.53) (Figure 7). Populations RIN and PI showed no significant correlation with the SST in the 1 + 2 or 3.4 ENSO region in any season. While at IT, the tree-growth index was significantly correlated with the mean sea temperature index (1 + 2 ENSO) in the spring of the previous year (r = 0.53) and in the summer of the current year (r = 0.57). Also, for IT the first factor of the PCA was also highly correlated with the sea temperature during these periods; however, this relationship was not significant (r = 0.79 and r = 0.71, respectively; Table 3). Table 3 Pearson correlation coefficient between PC 1 and the tree-growth index of each population, and the mean sea surface temperature indices from the Niño 1 + 2 and Niño 3.4 geographic areas. ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 *P < 0.05. Table 3 Pearson correlation coefficient between PC 1 and the tree-growth index of each population, and the mean sea surface temperature indices from the Niño 1 + 2 and Niño 3.4 geographic areas. ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 *P < 0.05. Discussion Our results show that algarrobo has a common dendrochronological signal with distinctive population response. The coastal population does not seem to correlate with any climate indices, while inland population dendrochronological signals are highly correlated with the local climatic conditions and the SST in the Niño 1 + 2 region. Thus, suggesting that the heterogeneous distribution of precipitation along the North Peruvian coast can hinder the effects of the ENSO phenomenon. Prosopis pallida tree growth Despite what previous dendrochronological studies have suggested (Towner, 2002; Speer, 2010), tropical species are able to provide good dendrochronological results. The dendrochronological characteristics of each population were similar to those found in previous studies (López et al., 2005; López et al., 2006). The mean sensitivity values (0.9) were quite high, even in comparison with Prosopis ferox (0.26) (Morales et al., 2001) or Prosopis caldenia (0.40) (Bogino and Jobbágy, 2011), but were similar to those reported in P. pallida by López (2006) (up to 0.85) and Rodríguez (2005) (up to 0.73). Even though this suggests that variation with time is independent of external factors, it has been proposed that sensitivity values are inefficient estimators of the coefficient of variation (Bunn et al., 2013). Our results also show low autocorrelation values and relatively small standard deviations, considering the impact of ENSO events (López et al., 2008), which are also considered good indicators of sensitivity in dendrochronological sequences. The warmer and wetter site IT, showed the lowest autocorrelation and the highest SD. This suggests that annual tree-ring growth differ greatly throughout time and could be particularly more sensitive in inland populations than in coastal sites. The MCCP was relatively low, but in line with other Prosopis sp. like P. ferox (0.33) (Morales et al., 2001), P. caldenia (0.43) (Bogino and Jobbágy, 2011), or even previous P. pallida studies (0.40) (Rodríguez et al., 2005; López et al., 2006) and tropical dendrochronological studies made in Polylepis besseri (0.43) (Gareca et al., 2010), and Machaerium scleroxylon (0.47) (Paredes-Villanueva et al., 2013). The relatively small number of samples did not reduce the significance of our results, considering that similar AC1 and sensitivity values were found by Lopez (2005) for P. pallida. Therefore, the P. pallida populations show a common dendrochronological signal, and it reaches the standard of other tropical dendrochronological studies. The high amount of variance located in the PC 1 suggest most of the variability comes from a common source, the climatic conditions; similar results were found (45 per cent) for Machaerium scleroxylon (Paredes-Villanueva et al., 2013), and P. caldenia (46 per cent) (Bogino and Jobbágy, 2011). Thus, P. pallida potential for the study of ENSO variation through time and its effect on plant ecosystems makes it the right candidate for dendrochronology on the South Pacific coast. Above a certain threshold, a rise in temperature should have a negative effect on tree-ring growth, especially during the dry season (Salazar et al. 2018). IPCC reports suggest that a temperature increase will be linked to an increase in rainfall events and in ENSO frequency, in a long-term future (Bates et al., 2008; Wang et al., 2017). However, this has not been the case yet. The BAI and tree-growth index have shown minimal increases in recent years. This confirms that the climatic conditions along the North Peruvian Pacific coast have become more limiting to growth in the last decade, a time characterized by long periods of drought, an increase in mean temperature, and limited rainfall events (Caycho and Lavado, 2014; Caycho et al., 2016). Similar changes have been registered for other forests of the Pacific coast. In North America, a 4-year drought on the Pacific coast has affected the Californian Mediterranean forest, and represents an exceptionally severe drought in the context of the last millennium (Griffin and Anchukaitis, 2014). Weakening of the East Asian summer monsoon has caused droughts in Northern China (Cai et al., 2015). The source of these long-term arid conditions is a significant increase in SST. The continuous warming of the Pacific Ocean since the 1950s has been detected (Levitus et al., 2000), alongside an increase in the SST in the Indian and Southern Oceans (Gille, 2002). In Peru, the effect of the increasing SST has been a progressive increase in the temperature in the Andes Mountains during the last 50 years, with subsequent increases in the air temperature in inland and higher-elevation locations (Vuille et al., 2015). Thus, our BAI and tree-growth index results show that the warming of the Pacific Ocean has started to threaten algarrobo growth and survival, especially for inland populations such as IT. Differences in growth–climate relationships among populations The correlations between the tree-growth index and climate differed among populations. For instance, there was no correlation between monthly rainfall and tree growth for RIN, while significant correlation with January rainfall from the current year was found for PI and IT. The difference in plant response to rainfall events among populations could be mostly related to the total annual precipitation, which is 4-fold higher at IT than at RIN or PI. This suggests that the annual precipitation events at RIN and PI are not intense enough to generate a physiological response and these populations probably rely on phreatic layers of underground water (Ramawat, 2009), which occur at <5 m depth for RIN and at 10 m for PI (Alvarez et al., 2002, 2004). This is also the case for Prosopis flexuosa in Argentina where the climate–growth relationships also depend on the groundwater depth (Giantomasi et al., 2013). Thus, lowland populations, similar to RIN and PI, are less responsive to rainfall and are influenced more by other growth-control factors like temperature. Climatic conditions play a more important role in Prosopis growth in high-altitude populations, for which precipitation in January is the main source of available water and underground water is less accessible because it is located at a depth of 50-m (Alvarez et al., 2002). Alongside the depth of the underground water, differences among sites in the rate of herbivory could explain growth variability among populations. Precipitation events may increase insect outbreaks and reduce growth, counteracting the effect of increased water availability (Koprowski and Duncker, 2012). During the ENSO wet phase, plant growth can shift into a high biomass state and overcome herbivory (Gutiérrez et al., 2007). There are no studies about insect outbreak in Northern Peru and its spatial variability across the dryland forest. Its effect on tree-ring growth is important (Ferrero et al., 2013), and could be an interesting force that should be look into in the future to understand population differences. On the South American Pacific coast, air temperature and SST are closely related, and they remain low during the dry season due to the Humboldt Current (Yu and Mechoso, 1999). Sea breezes reduce the mean temperature at coastal sites and create a gradient of temperature from the coast to inland sites. The wind field flows east and reduces cloud formation at coastal sites, allowing higher precipitation at inland sites (Mendelssohn and Schwing, 2002). This creates a climatic gradient between the coast and the inland areas in which temperature and precipitation are positively correlated (Rollenbeck et al., 2015). The SST increases in summer, along with the air temperature, and rainfall events occur (a total of 50–250 mm per year), with lower values on the coast (50–100 mm) than in inland territories (200–250 mm). Our results confirm the importance of summer rainfall for algarrobo annual growth but also show that mean monthly temperature could be an indicator of growth at inland sites. These precipitation events are enough to ensure algarrobo establishment, by promoting fast root growth and allowing water extraction from deeper soil layers (Squeo et al., 2007). For P. pallida dryland forest, the mean temperature could be an indicator of positive climatic conditions for growth – stimulating the beginning of the growing season and enhancing tree-ring width – as our results show a significant, positive effect of higher mean monthly temperatures on growth for IT and PI. The role of ENSO events in Prosopis pallida radial growth variability The relationships between the SST and major climatic phenomena – such as the ENSO, the interdecadal Pacific oscillation (IPO), or the Madden–Julian oscillation – indicate that the Pacific Ocean has a great heat capacity and thermal inertia, being a major contributor to the long-term climatic conditions (Cai et al., 2015). The SST has been remarkably useful in the study of the intensity and frequency of the ENSO; specifically, in the 3.4 region of the Pacific Ocean where the first signs of water warming up and flowing from West to East can be recorded and used as an indicator of the ENSO. Thus, the forecasting and monitoring of ENSO events have become relatively easy (Xue et al., 2017); but, regardless of the predictive power of these indices, a rise in the SST in the Pacific Ocean does not necessarily have an impact on plant growth. The ENSO events in 1975, 2005, 2010 and 2016 did not have a significant effect on summer precipitation (Caycho et al., 2016; McPhaden et al., 2014). Accordingly, our results show that the SST of the 3.4 region does not seem to have a significant correlation with P. pallida tree growth. Instead, variation in the SST of the 1 + 2 region, which is geographically closer, seems to be a more reliable indicator of precipitation events and is probably responsible for changes in local conditions. This result sets the limits of the potential dendroarchaeological use of P. pallida, suggesting it may not be a good indicator of SST variation in the central Pacific. Moreover, only the IT population exhibited a high correlation with the SST in the 1 + 2 region of the Pacific Ocean. Other P. pallida populations, such as RIN and PI, clearly benefit from ENSO events but their growth is not limited to extreme rainfall events. Three factors probably contribute to the differential response of the IT population to rainfall events. (1) Close proximity to the Pacific coast increases humidity and reduces air temperature, producing climatic conditions favorable for growth even with minimal rainfall. This allows growth in the RIN and PI populations during the dry phase of the no-ENSO years, while inland populations, like IT, have restricted growth because they are more susceptible to higher temperatures. (2) The increase in air temperature near the Andes Mountains should also play a role, because the climatic conditions in this area are highly correlated with the SST. Thus, higher temperatures at inland and higher-elevation sites, like IT, are the result of a strong vertical stratification of temperature in the atmosphere, which has been described along the Chilean and Peruvian coasts (Vuille et al., 2015). Finally, (3) it is possible that these differences are due to the regional distribution of algarrobo. Marginal populations usually have greater sensitivity to climate and show a strong response to both temperature and precipitation (Cook and Kalriukstis, 1990; de Ridder et al., 2013). The IT population is located farther from the coast and can be considered a marginal population in the P. pallida regional distribution. Therefore, it may be more sensitive to climate (temperature and precipitation) variation. Based on these results, the search for algarrobo timber remains in archeological studies could provide enough dendrochronological data to reconstruct ENSO events from ~2000 years ago. Algarrobo could also be used to study the Medieval Climate Anomaly (MCA), and whether it was a global event or a regional North Atlantic phenomenon. Recent results indicate that MCA and SST data are more likely to be related due to the century-scale variability. The presence of medieval log remains from the Inca periods, alongside seashell and coral remains (Rustic et al., 2015), could aid the elucidation of whether the MCA event had a significant impact in the South Pacific Ocean and whether it interacted with ENSO events. Conclusion Our results show that P. pallida populations on the North Peruvian coast are a good candidate for dendrochronological reconstructions, especially for the detection of current and historical strong climatic events such as the ENSO. Due to the wind field and Humboldt Current dynamic, coastal sites received lower precipitation than inland sites. Despite this, the growth of inland populations showed a closer relationship with summer precipitation and temperature than that of coastal populations. The climatic dependency of inland populations is the result of a deeper groundwater level and high temperatures that create stressful conditions for growth. Similarly, these populations showed higher sensitivity to ENSO events and Ocean surface temperature indicators, such as the 1 + 2 ENSO index, due to their proximity to the Andes Mountains. Acknowledgements We thank the biologist Luis Urbina and the undergraduate students that helped during field data collection. We also acknowledge the scientific support from the University of Córdoba – Campus de Excelencia CEIA3. Conflict of interest statement None declared. Funding This work was funded by Programa Nacional de Innovación para la Competitividad y Productividad (PNICP), also known as INNOVATE Peru (grant number: 404-PNICP-PIBA-2014). 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Abstract

Abstract Extreme rainfall events, such as the El Niño-Southern Oscillation (ENSO), are responsible to a large extent for the processes of tree establishment and tree growth in the North Peruvian dryland forest. Prosopis pallida (algarrobo) is the dominant species of the dryland forest on the Peruvian Pacific coast. Dendrochronological data from living populations have shown its response to climatic events. The aim of this work was to study local differences in P. pallida growth responses to ENSO events through dendrochronological data. To do so, three algarrobo populations within a gradient of increasing temperature and precipitation from West to East were selected. Tree-ring data were correlated with the monthly temperature and precipitation from each location and with the 3.4 and 1 + 2 ENSO indices. Inland populations showed the highest correlation with the climatic conditions. The summer rainfall (January), spring temperature of the previous year, and summer temperature of the current year were significantly correlated with growth. All populations showed a significant increase in the tree-ring index during ENSO events. However, growth in no-ENSO years was also high in populations closer to the coast. Our results indicate that the proximity to the Andes Mountains, distance to the Pacific coast and distribution of algarrobo in this area make marginal inland populations more sensitive to climatic variations and ENSO events. We conclude that the P. pallida response to the climate in Northern Peru is the result of both strong climatic events and local conditions, which are estimated most accurately with the 1 + 2 ENSO index. Introduction In Northern Peru, dryland forests represent 41 per cent of all land cover. Thousands of rural families depend directly on their ecosystem services, which are valued at 21.8 million US dollars per year (Orihuela and Albán, 2012). Continuous land degradation here has reduced vegetation cover, crop productivity, livestock numbers and wildlife (Bonkoungou, 2001). As a result, and considering the present conditions, it has been stated that the North Peruvian dryland forests will disappear in the next 20 years due to timber overexploitation, overgrazing and climate change (Orihuela and Albán, 2012). The most important species in this ecosystem is Prosopis pallida (Humb. & Bonpl. ex Willd.) Kunth (hereafter referred to as algarrobo), representing 61 per cent of all plant cover in the dryland forest. Algarrobo is a key species since birds, mammals, plants, fungi and microbial organisms interact and survive using these trees as a nest, food source and refuge (Ruiz et al., 2008; Biederman and Boutton, 2009; Rosi et al., 2009). The climatic conditions in this region are controlled by the Humboldt Current, which creates stable and dry conditions with minimal rainfall events. This dry phase is interrupted by the El Niño Southern Oscillation (ENSO), which creates a wet phase of 1–2 years of high annual rainfall and unstable climatic conditions. Plant phenology, species biodiversity and ecological processes are shaped by this climatic cycle (Tapley and Waylen, 1990; Holmgren et al., 2006; López et al., 2008). For algarrobo, the wet phase promotes germination, growth, competition and grazing for 1 year or, in some exceptional cases, 2 years. The dry phase promotes survival over tree establishment for an uncertain period of 4–8 years. Thus, the ENSO has been recognized as a trigger of long-lasting shifts in arid vegetation, switching the landscape from perennial herbs to dryland forest according to the grazing pressure (Holmgren et al., 2001) and the resistance of Prosopis sp. to dry conditions. Nowadays, ENSO events have become relatively easy to study (Xue et al., 2017). A significant increase in the sea surface temperature (SST) in the Pacific Ocean, especially in the 3.4 region, is commonly used as an indicator of extreme precipitation on the Pacific coast of the USA (Trenberth and Stepaniak, 2001). This event is responsible for the period of greatest growth in Peruvian dryland forests (Rodríguez et al., 2005), and a significant relationship between SST and algarrobo growth is expected. However, the global and local climatic conditions could also modify plant growth. Recently, climate change has been a major concern, and a change in ENSO frequency is expected because of it (Wang et al., 2017). An increase in mean annual temperature might restrict plant physiologal processes, unless it is associated with an increase in mean annual precipitation. The other climatic events involved, such as the Interdecadal Pacific Oscillation or the Madden–Julian oscillation, make the forecasting of the ENSO a hard task, regardless of the increase in monitoring power (Squeo et al., 2007). For South America, the 1 + 2 region (0°−10°S, 90°W–80°W) of the Pacific Ocean is commonly used to determine local precipitation events and the progress of the ENSO (Vuille et al., 2015) but its relationship with algarrobo tree growth is still untested. Dendrochronological techniques have been used to determine key variables such as tree age, annual growth, basal area increment (BAI; Schöngart, 2008; Brienen and Zuidema, 2006) and growth–climate relationships, in order to understand forest establishment and its ecological response to climatic variability (López et al., 2013; Paredes-Villanueva et al., 2013). The earliest dendrochronological studies of Prosopis were made in Argentina using P. alba Griseb, P. ferox Griseb and P. flexuosa DC. as model species (Morales et al., 2005; Villagra et al., 2005; Ferrero et al., 2014). Those research works defined the basic ring structure, which is related to the presence of terminal parenchyma and large-diameter vessels (Villalba et al., 2000). Based on these studies, algarrobo tree-ring data have been shown to be a good indicator of rainfall and temperature change and of how climatic variability affects forest functionality (Morales et al., 2005), with genetic variability and local climatic conditions playing an important role in the population response to rainfall events (Ramawat, 2009). In Peru, dendrochronological studies of algarrobo have shown its positive response to ENSO events (López et al., 2005; López et al., 2006) and its potential for archeological and current climatic studies (Ghezzi and Rodríguez, 2015). Even though ENSO variability through time and space has been described in depth in the last few years (Van Leeuwen et al., 2013; Rustic et al., 2015), variability among algarrobo’s populations in the response to ENSO events has not been assessed, and the role of local climatic and geographic characteristics has not been studied. In this study, we use dendrochronological techniques to evaluate the impact of precipitation, temperature and ENSO events on North Peruvian populations of P. pallida across a climatic gradient from the coast to inland. The results provide information about the variability of the response to ENSO events among P. pallida populations, and regarding the ecological impact of this climatic anomaly on forest growth. The specific objectives of this study were (1) to analyze the tree growth variability in P. pallida populations and its potential for dendrochronological studies, (2) to assess the relationships among tree growth, local climatic conditions and ENSO events, and (3) to determine whether the population response to ENSO events changes across the climatic gradient and, if so, why. Specifically, we hypothesize that coastal population growth will benefit from the Pacific Ocean humidity and reduced air temperature regardless of the limited rainfall. While inland populations will be more sensitive to the dry and wet ENSO cycle due to the vertical stratification of temperature at higher altitudes and the high precipitation during the ENSO events. Therefore, mean radial growth in coastal populations will remain steady even in dry years, whereas mean radial growth in inland populations will be lower than coastal populations in dry years and higher in wet years. Materials and methods Study site The study site is located in the dryland forests of the North Peruvian coast (5°S). Normally, the Humboldt Current reduces the SST along the South American coast, which reduces evaporation and therefore precipitation (to below 300 mm annually) on the tropical coast of Northern Peru. The SST is highly correlated to the air temperature due to the thermal inertia of the Pacific Ocean, and this creates highly stable climatic conditions that closely fit a sine wave with a phase length of 1 year and amplitude of 7−9°C (Rollenbeck et al., 2015). During ENSO events (wet phase), the Humboldt Current weakens and the SST and air temperature rise, which increases precipitation in this region to above 2000 mm annually and creates highly unstable climatic conditions. In recent years, major ENSO events were recorded in 1953, 1958, 1964, 1972, 1982, 1987, 1992 and 1998. Three algarrobo populations were selected in January 2015 for this study (Figure 1; Table 1): the coastal population of Rinconada (RIN) forest in Paita, the University of Piura forest (PI) in Piura and the inland population of Ignacio Távara (IT) community in Chulucanas. These lie along a climatic gradient, where mean annual rainfall and temperature increase from the coast to the inland areas (Table 1). Rainfall occurs in summer (between December and March), when the mean daily temperature reaches its peak, while the lowest temperature occurs in the dry season in July (Figure 2). Differences between populations increase exponentially during the ENSO event, when mean annual rainfall and temperature are far higher in inland populations than in coastal populations (Figures 1 and 2). During the dry phase, the Humboldt Current reduces evaporation from the sea, and the wind field removes most of the dew. Therefore, there is no significant presence of fog in these dryland ecosystems (Rollenbeck et al., 2015). Figure 1 View largeDownload slide Geographic location of the populations studied in Northern Peru across the climatic gradient during the dry (up) and wet (down) phase of the ENSO cycle. Figure 1 View largeDownload slide Geographic location of the populations studied in Northern Peru across the climatic gradient during the dry (up) and wet (down) phase of the ENSO cycle. Table 1 Geographical, topographical and morphological characteristics of three P. pallida populations. Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Climatic data for the 1963–2014 period was obtained from the ‘CRU TS 3.1 database’, and the groundwater deep was obtained from the ‘Regional groundwater inventory of Piura’. See the text for further references View Large Table 1 Geographical, topographical and morphological characteristics of three P. pallida populations. Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Variable Rinconada Piura Ignacio Tavara Latitude 4°54′19.13″S 5°10′43.28″S 5°12′22.66″S Longitude 81°0′59.88″O 80°38′7.46″O 80°11′32.46″O Altitud (m a.s.l.) 93 72 153 Distance from the sea (km) 13.72 59.24 108.83 Annual rainfall (mm) 18.7 45 202 Mean annual temperature 22.5 23.6 24.2 Tree DAHC (m) 0.30 ± 0.5 0.28 ± 0.04 0.32 ± 0.05 Tree height (m) 11.1 ± 0.5 9.1 ± 0.7 8.8 ± 0.4 Soil texture Sandy Sandy Sandy Soil pH 6.51 ± 0.07 6.69 ± 0.04 6.45 ± 0.1 Groundwater deep (m) 5 10 50 Climatic data for the 1963–2014 period was obtained from the ‘CRU TS 3.1 database’, and the groundwater deep was obtained from the ‘Regional groundwater inventory of Piura’. See the text for further references View Large Figure 2 View largeDownload slide Monthly (up) and annual (down) rainfall precipitation for the RIN (dotted), PI (dashed) and IT (solid) populations. Figure 2 View largeDownload slide Monthly (up) and annual (down) rainfall precipitation for the RIN (dotted), PI (dashed) and IT (solid) populations. The IT and RIN populations have grown naturally under human management to improve pod production, while the PI population has grown as part of a reforestation program with a similar management strategy. In all cases, forest management has consisted only of scheduled pruning, without the use of fertilizers or pest control strategies. Despite this, the populations differ morphologically, functionally and structurally, and there is high genetic variability among them (Palacios et al., 2011). Regarding the soil texture, the sand content exceeds 98 per cent in all three locations, and the soil pH is slightly acid (6.45–6.69). Underground water deep was shallow in RIN and deeper in IT, following the climatic gradient as well (Alvarez et al., 2002, 2004). Field sampling and sample preparation Ten cross-sections per population were collected, with the approval of National Forest and Wildlife Service (SERFOR – www.serfor.gob.pe) and the nearby rural communities. The relatively small number of tree samples analyzed in this study might be seen as a limitation. However, the North Peruvian dryland forest is a scarce and endangered ecosystem. Thus, tree logging, especially for older trees, is forbidden. At each site, care was taken to select trees more than 30 years old, growing under similar microsite and competition conditions, and located at least 20 m apart from each other, across a rectangular area of 1-ha. Sections were taken at breast height (1.30 m) specifically for this study. Cross-section samples were air-dried and polished with sandpaper of different intensity, following the grain standards of the Federation of European producers of abrasives (https://www.fepa-abrasives.com/), from P24 to P2500, until the terminal parenchyma was clearly visible under a binocular microscope. Up to two radii were selected in each cross-section. The tree-ring width was measured digitally in each radius within each section, for all the selected trees. Due to the irregular radial growth of P. pallida, tree rings were analyzed across the entire disk to detect false or double rings. The correlation coefficient among samples was also considered during the elimination and addition, respectively, of double and missing rings. Because of these necessary corrections, radii from the same disk were averaged to avoid pseudo replication. The ring width measurement and crossdating were performed using CooRecorder and CDendro 7.8 (2014 – Cybis Elektronik & data AB). To compare population growth and the impact of ENSO events, the mean BAI was calculated using tree-ring data, with the formula BAI = π (R2t – R2t−1) (mm2 year−1) where R is the radius of the tree and t is the year of tree-ring formation, for each population. An annual tree-ring width chronology was built for each site. All tree-ring series were prewhitened and biweighted to remove variation not related to climatic factors during the standardization process, thereby allowing determination of the correlation between series within each population. To build the population chronology, individual tree-ring width series were double detrended according to the methodology applied for López et al. (2005) for P. pallida. First, using a negative exponential curve and then by fitting a cubic smoothing spline with a 50-per cent frequency response. Dendrochronological statistics were calculated for the common interval 1990–2010, to compare growth features among the study sites (Fritts, 1976). The mean and standard deviation (SD) of the raw tree-ring width data were calculated to compare growth among populations and with other Prosopis species. The first-order autocorrelation of the tree-ring width raw data (AC1) was calculated to determine the degree of independence of the tree-ring growth series from each other through time. The mean sensitivity (MSx) of the residual series was calculated to study the year-to-year tree-ring variability. The mean correlation of the common period (MCCP) was calculated to show the consistency of the dendrochronological results. The expressed population signal (EPS) of the residual width series was calculated to determine the common relationship with external factors. Finally, the percentage of the variance explained by the first and second principal component (PC 1 and PC 2) was calculated with all individual residual series for successive 20-year periods lagged 1 year to determine the main source of variation and its differences between populations (Fekedulegn et al., 2002; Cook and Pederson, 2011). MCCP, BAI, MSx and PCA were calculated using the ‘dplR’ package of R software (Bunn, 2008, 2010). Climate data and climate–growth relationship To analyze climatic trends at a regional scale and quantify climate–growth relationships, we used monthly climatic data (mean temperatures and total precipitation) from the CRU TS 3.1 dataset for the period 1963–2014, produced by the Climate Research Unit (http://www.cru.uea.ac.uk/). Monthly climatic data was correlated with the tree-ring index from September of the previous year to July of the current year. This dataset corresponds to the interpolated data of instrumental records recorded by a dense network of local meteorological stations, which have been subjected to homogeneity tests and relative adjustments, and finally gridded onto a 0.5° network (Mitchell and Jones, 2005). Furthermore, ENSO indices from the 3.4 (5°N–5°S, 170°W–120°W) and 1 + 2 (0°–10°S, 90°W–80°W) regions from the NOAA organization (http://www.noaa.gov/) – which record climatic fluctuations and the mean SST in the Pacific – were considered to study the ENSO influence on regional precipitation patterns and tree growth (Trenberth and Stepaniak, 2001). Monthly SST indices from the 1 + 2 region were correlated with the mean chronology of each population from August of the previous year to July of the current year. Seasonal SST means were calculated for the Niño 3.4 and 1 + 2 regions and then correlated with the tree-ring width and the first axis of variation of the PCA for each population. Correlations between the climate data and each population chronology were calculated using the ‘dcc’ function of the ‘treeclim’ package of R software (R Development Core Team, 2013). Results Tree growth The P. pallida populations showed high tree-growth variability, with a low tree-ring index under average climatic conditions (dry phase) that was 3-fold higher in ENSO years (wet phase). This growth pattern was consistent among populations, with a significant response to ENSO events over time, but the response varied between populations (Figure 3). Thus, the tree-ring width index during ENSO events (1.8) was similar to that in normal years (1.2) at RIN and PI (costal populations). Meanwhile, the tree-ring width index was greater (2.5) during ENSO events than in normal years (0.8) at IT (inland population) where growth seems to be more ENSO-dependent (Figure 4). Standard deviation was relatively low in RIN and PI, while it was high at IT. Accordingly, AC1 was significantly lower in IT than other populations. The mean sensitivity was high for all populations but within the range of P. pallida data reported previously. Peaks of the mean sensitivity were present in ENSO years, but did not appear consistently among populations, indicating again a differential response in each site (Figure 5). Similarly, MCCP was low, but within the range of tropical species. The EPS of the residual width series was high and similar between populations (Table 2). In the PCA, a high amount of the variance was explained in the first factor for all populations. Therefore, a high amount of variability is solely explained by a common source. Despite this, there was no common trend in PC 1 among the populations (Figure 6), suggesting that they responde differently to the source of variation. Whereas PC 2 was significantly low and indicated that other sources of variability played a minor role in the total common variance. Figure 3 View largeDownload slide Basal area increment (mm2) for the inland and coastal populations over time. Vertical line indicates recorded ENSO events. The trend is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 3 View largeDownload slide Basal area increment (mm2) for the inland and coastal populations over time. Vertical line indicates recorded ENSO events. The trend is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 4 View largeDownload slide Chronology of the tree-ring width index for P. pallida populations. The trend of this index is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 4 View largeDownload slide Chronology of the tree-ring width index for P. pallida populations. The trend of this index is given by the Loess smoothing curve (thick line). Vertical lines indicate recorded ENSO events. Figure 5 View largeDownload slide Temporal trend in the variance explained by the first principal component analysis for Rinconada (34 year subinterval), Piura (26 years subinterval) and Ignacio Tavara (17 years subinterval). Figure 5 View largeDownload slide Temporal trend in the variance explained by the first principal component analysis for Rinconada (34 year subinterval), Piura (26 years subinterval) and Ignacio Tavara (17 years subinterval). Table 2 Characteristics of the tree-ring chronologies for the common period. Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 SD, standard deviation; MSx, mean sensitivity of the residual ring width series; MCCP, mean correlation of the common period; EPS, population signal of the residual series; PC 1, the first axis of the principal component analysis; PC 2, the second axis of the principal component analysis View Large Table 2 Characteristics of the tree-ring chronologies for the common period. Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 Variables Rinconada Piura Ignacio Távara No of Tree (radii) 10 (10) 11 (11) 10 (14) Age (years) 51 50 44 Tree-ring width (mm) 3.95 3.45 3.13 SD (mm) 5.45 4.79 7.20 AC1 0.33 0.39 0.07 MSx 0.91 0.9 0.98 MCCP 0.34 0.49 0.44 EPS 0.84 0.86 0.81 PC 1 0.46 0.44 0.50 PC 2 0.12 0.13 0.14 SD, standard deviation; MSx, mean sensitivity of the residual ring width series; MCCP, mean correlation of the common period; EPS, population signal of the residual series; PC 1, the first axis of the principal component analysis; PC 2, the second axis of the principal component analysis View Large Figure 6 View largeDownload slide Correlations of monthly total precipitation and monthly mean temperature with tree-ring index during the growing season of P. pallida. *P < 0.05. The growing season is marked with a horizontal line. Figure 6 View largeDownload slide Correlations of monthly total precipitation and monthly mean temperature with tree-ring index during the growing season of P. pallida. *P < 0.05. The growing season is marked with a horizontal line. Interaction between growth and local climate The tree-growth index was positively correlated with the precipitation in January of the growth year, the highest monthly precipitation of every year, and the precipitation during the ENSO rainfalls (Figure 7). The correlation coefficient was nonsignificant at RIN, was significant at PI (r = 0.40), and was higher at IT (r = 0.67) (Figure 7), showing the importance of the climatic gradient from the coast to inland areas and the differential response of each population to the environmental conditions. Similarly, the tree-growth index correlation with monthly temperature showed significant differences between populations. For instance, there were no significant correlations in the case of RIN. While at PI, it showed a significant relationships with the spring temperature of the previous year (October, r = 0.39; December, r = 0.32). At IT, the tree-growth index was positively correlated with the mean monthly spring temperature of the previous year (October, r = 0.50, November, r = 0.44, December, r = 0.47) and the summer temperature of the current year (January, r = 0.32) (Figure 7). Figure 7 View largeDownload slide Correlations of mean sea temperature in the Niño 1 + 2 region with tree-ring index of P. pallida in each population. *P < 0.05. Figure 7 View largeDownload slide Correlations of mean sea temperature in the Niño 1 + 2 region with tree-ring index of P. pallida in each population. *P < 0.05. Population response to ENSO events The monthly correlation between the tree-growth index and SST index (1 + 2 ENSO) showed no correlation with sea temperature in any month at RIN. Whereas the PI tree-growth index and sea temperature in August (r = 0.38) and September (r = 0.40) were positively correlated. Similarly, tree-growth index was significant for IT in August (r = 0.55), September (r = 0.56), February (r = 0.61) and March (r = 0.53) (Figure 7). Populations RIN and PI showed no significant correlation with the SST in the 1 + 2 or 3.4 ENSO region in any season. While at IT, the tree-growth index was significantly correlated with the mean sea temperature index (1 + 2 ENSO) in the spring of the previous year (r = 0.53) and in the summer of the current year (r = 0.57). Also, for IT the first factor of the PCA was also highly correlated with the sea temperature during these periods; however, this relationship was not significant (r = 0.79 and r = 0.71, respectively; Table 3). Table 3 Pearson correlation coefficient between PC 1 and the tree-growth index of each population, and the mean sea surface temperature indices from the Niño 1 + 2 and Niño 3.4 geographic areas. ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 *P < 0.05. Table 3 Pearson correlation coefficient between PC 1 and the tree-growth index of each population, and the mean sea surface temperature indices from the Niño 1 + 2 and Niño 3.4 geographic areas. ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 ENSO sea surface temperature indices Principal component analysis (PC 1) Tree-growth index RIN PI IT RIN PI IT Niño 1 + 2 Winter (t−1) (J–A–Sep) −0.33 −0.68 0.79 0.18 0.36 0.53* Spring (t−1) (O–N–D) −0.45 −0.64 0.7 0.26 0.37 0.58 Summer (E–F–M) −0.47 −0.55 0.71 0.32 0.31 0.67* Autumn (A–M–J) −0.34 −0.48 0.44 0.24 0.29 0.43 Winter (t) (J–A–Sep) −0.04 −0.15 0.04 0.02 0.17 0.21 Niño 3.4 Winter (t−1) (J–A–Sep) −0.29 −0.43 0.51 0.07 0.19 0.41 Spring (t−1) (O–N–D) −0.25 −0.37 0.36 0.05 0.19 0.37 Summer (t) (E–F–M) −0.25 −0.36 0.31 0.09 0.15 0.36 Autumn (t) (A–M–J) −0.07 −0.28 −0.05 0.03 0.07 0.15 Winter (t) (J–A–Sep) 0.19 0.06 −0.45 −0.01 −0.09 −0.18 *P < 0.05. Discussion Our results show that algarrobo has a common dendrochronological signal with distinctive population response. The coastal population does not seem to correlate with any climate indices, while inland population dendrochronological signals are highly correlated with the local climatic conditions and the SST in the Niño 1 + 2 region. Thus, suggesting that the heterogeneous distribution of precipitation along the North Peruvian coast can hinder the effects of the ENSO phenomenon. Prosopis pallida tree growth Despite what previous dendrochronological studies have suggested (Towner, 2002; Speer, 2010), tropical species are able to provide good dendrochronological results. The dendrochronological characteristics of each population were similar to those found in previous studies (López et al., 2005; López et al., 2006). The mean sensitivity values (0.9) were quite high, even in comparison with Prosopis ferox (0.26) (Morales et al., 2001) or Prosopis caldenia (0.40) (Bogino and Jobbágy, 2011), but were similar to those reported in P. pallida by López (2006) (up to 0.85) and Rodríguez (2005) (up to 0.73). Even though this suggests that variation with time is independent of external factors, it has been proposed that sensitivity values are inefficient estimators of the coefficient of variation (Bunn et al., 2013). Our results also show low autocorrelation values and relatively small standard deviations, considering the impact of ENSO events (López et al., 2008), which are also considered good indicators of sensitivity in dendrochronological sequences. The warmer and wetter site IT, showed the lowest autocorrelation and the highest SD. This suggests that annual tree-ring growth differ greatly throughout time and could be particularly more sensitive in inland populations than in coastal sites. The MCCP was relatively low, but in line with other Prosopis sp. like P. ferox (0.33) (Morales et al., 2001), P. caldenia (0.43) (Bogino and Jobbágy, 2011), or even previous P. pallida studies (0.40) (Rodríguez et al., 2005; López et al., 2006) and tropical dendrochronological studies made in Polylepis besseri (0.43) (Gareca et al., 2010), and Machaerium scleroxylon (0.47) (Paredes-Villanueva et al., 2013). The relatively small number of samples did not reduce the significance of our results, considering that similar AC1 and sensitivity values were found by Lopez (2005) for P. pallida. Therefore, the P. pallida populations show a common dendrochronological signal, and it reaches the standard of other tropical dendrochronological studies. The high amount of variance located in the PC 1 suggest most of the variability comes from a common source, the climatic conditions; similar results were found (45 per cent) for Machaerium scleroxylon (Paredes-Villanueva et al., 2013), and P. caldenia (46 per cent) (Bogino and Jobbágy, 2011). Thus, P. pallida potential for the study of ENSO variation through time and its effect on plant ecosystems makes it the right candidate for dendrochronology on the South Pacific coast. Above a certain threshold, a rise in temperature should have a negative effect on tree-ring growth, especially during the dry season (Salazar et al. 2018). IPCC reports suggest that a temperature increase will be linked to an increase in rainfall events and in ENSO frequency, in a long-term future (Bates et al., 2008; Wang et al., 2017). However, this has not been the case yet. The BAI and tree-growth index have shown minimal increases in recent years. This confirms that the climatic conditions along the North Peruvian Pacific coast have become more limiting to growth in the last decade, a time characterized by long periods of drought, an increase in mean temperature, and limited rainfall events (Caycho and Lavado, 2014; Caycho et al., 2016). Similar changes have been registered for other forests of the Pacific coast. In North America, a 4-year drought on the Pacific coast has affected the Californian Mediterranean forest, and represents an exceptionally severe drought in the context of the last millennium (Griffin and Anchukaitis, 2014). Weakening of the East Asian summer monsoon has caused droughts in Northern China (Cai et al., 2015). The source of these long-term arid conditions is a significant increase in SST. The continuous warming of the Pacific Ocean since the 1950s has been detected (Levitus et al., 2000), alongside an increase in the SST in the Indian and Southern Oceans (Gille, 2002). In Peru, the effect of the increasing SST has been a progressive increase in the temperature in the Andes Mountains during the last 50 years, with subsequent increases in the air temperature in inland and higher-elevation locations (Vuille et al., 2015). Thus, our BAI and tree-growth index results show that the warming of the Pacific Ocean has started to threaten algarrobo growth and survival, especially for inland populations such as IT. Differences in growth–climate relationships among populations The correlations between the tree-growth index and climate differed among populations. For instance, there was no correlation between monthly rainfall and tree growth for RIN, while significant correlation with January rainfall from the current year was found for PI and IT. The difference in plant response to rainfall events among populations could be mostly related to the total annual precipitation, which is 4-fold higher at IT than at RIN or PI. This suggests that the annual precipitation events at RIN and PI are not intense enough to generate a physiological response and these populations probably rely on phreatic layers of underground water (Ramawat, 2009), which occur at <5 m depth for RIN and at 10 m for PI (Alvarez et al., 2002, 2004). This is also the case for Prosopis flexuosa in Argentina where the climate–growth relationships also depend on the groundwater depth (Giantomasi et al., 2013). Thus, lowland populations, similar to RIN and PI, are less responsive to rainfall and are influenced more by other growth-control factors like temperature. Climatic conditions play a more important role in Prosopis growth in high-altitude populations, for which precipitation in January is the main source of available water and underground water is less accessible because it is located at a depth of 50-m (Alvarez et al., 2002). Alongside the depth of the underground water, differences among sites in the rate of herbivory could explain growth variability among populations. Precipitation events may increase insect outbreaks and reduce growth, counteracting the effect of increased water availability (Koprowski and Duncker, 2012). During the ENSO wet phase, plant growth can shift into a high biomass state and overcome herbivory (Gutiérrez et al., 2007). There are no studies about insect outbreak in Northern Peru and its spatial variability across the dryland forest. Its effect on tree-ring growth is important (Ferrero et al., 2013), and could be an interesting force that should be look into in the future to understand population differences. On the South American Pacific coast, air temperature and SST are closely related, and they remain low during the dry season due to the Humboldt Current (Yu and Mechoso, 1999). Sea breezes reduce the mean temperature at coastal sites and create a gradient of temperature from the coast to inland sites. The wind field flows east and reduces cloud formation at coastal sites, allowing higher precipitation at inland sites (Mendelssohn and Schwing, 2002). This creates a climatic gradient between the coast and the inland areas in which temperature and precipitation are positively correlated (Rollenbeck et al., 2015). The SST increases in summer, along with the air temperature, and rainfall events occur (a total of 50–250 mm per year), with lower values on the coast (50–100 mm) than in inland territories (200–250 mm). Our results confirm the importance of summer rainfall for algarrobo annual growth but also show that mean monthly temperature could be an indicator of growth at inland sites. These precipitation events are enough to ensure algarrobo establishment, by promoting fast root growth and allowing water extraction from deeper soil layers (Squeo et al., 2007). For P. pallida dryland forest, the mean temperature could be an indicator of positive climatic conditions for growth – stimulating the beginning of the growing season and enhancing tree-ring width – as our results show a significant, positive effect of higher mean monthly temperatures on growth for IT and PI. The role of ENSO events in Prosopis pallida radial growth variability The relationships between the SST and major climatic phenomena – such as the ENSO, the interdecadal Pacific oscillation (IPO), or the Madden–Julian oscillation – indicate that the Pacific Ocean has a great heat capacity and thermal inertia, being a major contributor to the long-term climatic conditions (Cai et al., 2015). The SST has been remarkably useful in the study of the intensity and frequency of the ENSO; specifically, in the 3.4 region of the Pacific Ocean where the first signs of water warming up and flowing from West to East can be recorded and used as an indicator of the ENSO. Thus, the forecasting and monitoring of ENSO events have become relatively easy (Xue et al., 2017); but, regardless of the predictive power of these indices, a rise in the SST in the Pacific Ocean does not necessarily have an impact on plant growth. The ENSO events in 1975, 2005, 2010 and 2016 did not have a significant effect on summer precipitation (Caycho et al., 2016; McPhaden et al., 2014). Accordingly, our results show that the SST of the 3.4 region does not seem to have a significant correlation with P. pallida tree growth. Instead, variation in the SST of the 1 + 2 region, which is geographically closer, seems to be a more reliable indicator of precipitation events and is probably responsible for changes in local conditions. This result sets the limits of the potential dendroarchaeological use of P. pallida, suggesting it may not be a good indicator of SST variation in the central Pacific. Moreover, only the IT population exhibited a high correlation with the SST in the 1 + 2 region of the Pacific Ocean. Other P. pallida populations, such as RIN and PI, clearly benefit from ENSO events but their growth is not limited to extreme rainfall events. Three factors probably contribute to the differential response of the IT population to rainfall events. (1) Close proximity to the Pacific coast increases humidity and reduces air temperature, producing climatic conditions favorable for growth even with minimal rainfall. This allows growth in the RIN and PI populations during the dry phase of the no-ENSO years, while inland populations, like IT, have restricted growth because they are more susceptible to higher temperatures. (2) The increase in air temperature near the Andes Mountains should also play a role, because the climatic conditions in this area are highly correlated with the SST. Thus, higher temperatures at inland and higher-elevation sites, like IT, are the result of a strong vertical stratification of temperature in the atmosphere, which has been described along the Chilean and Peruvian coasts (Vuille et al., 2015). Finally, (3) it is possible that these differences are due to the regional distribution of algarrobo. Marginal populations usually have greater sensitivity to climate and show a strong response to both temperature and precipitation (Cook and Kalriukstis, 1990; de Ridder et al., 2013). The IT population is located farther from the coast and can be considered a marginal population in the P. pallida regional distribution. Therefore, it may be more sensitive to climate (temperature and precipitation) variation. Based on these results, the search for algarrobo timber remains in archeological studies could provide enough dendrochronological data to reconstruct ENSO events from ~2000 years ago. Algarrobo could also be used to study the Medieval Climate Anomaly (MCA), and whether it was a global event or a regional North Atlantic phenomenon. Recent results indicate that MCA and SST data are more likely to be related due to the century-scale variability. The presence of medieval log remains from the Inca periods, alongside seashell and coral remains (Rustic et al., 2015), could aid the elucidation of whether the MCA event had a significant impact in the South Pacific Ocean and whether it interacted with ENSO events. Conclusion Our results show that P. pallida populations on the North Peruvian coast are a good candidate for dendrochronological reconstructions, especially for the detection of current and historical strong climatic events such as the ENSO. Due to the wind field and Humboldt Current dynamic, coastal sites received lower precipitation than inland sites. Despite this, the growth of inland populations showed a closer relationship with summer precipitation and temperature than that of coastal populations. The climatic dependency of inland populations is the result of a deeper groundwater level and high temperatures that create stressful conditions for growth. Similarly, these populations showed higher sensitivity to ENSO events and Ocean surface temperature indicators, such as the 1 + 2 ENSO index, due to their proximity to the Andes Mountains. Acknowledgements We thank the biologist Luis Urbina and the undergraduate students that helped during field data collection. We also acknowledge the scientific support from the University of Córdoba – Campus de Excelencia CEIA3. Conflict of interest statement None declared. Funding This work was funded by Programa Nacional de Innovación para la Competitividad y Productividad (PNICP), also known as INNOVATE Peru (grant number: 404-PNICP-PIBA-2014). 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Google Scholar Crossref Search ADS © Institute of Chartered Foresters, 2018. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Forestry: An International Journal Of Forest ResearchOxford University Press

Published: Dec 1, 2018

References

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