Atmospheric versus vegetation controls of Amazonian tropical rain forest evapotranspiration: Are the wet and seasonally dry rain forests any different?

1. Introduction

[2] The seasonality of evapotranspiration (ET) of the Amazon rain forest and its controlling mechanisms has been the subject of controversy for the last two decades. In an early study, Shuttleworth [1988] used a Penman-Monteith model to estimate that evapotranspiration of a forest near Manaus in central Amazon was almost constant throughout the year, with small peaks in March and September, coinciding with periods of increased net radiation. These findings contrast with most subsequent modeling studies that have tended to generate ET annual cycles that follow the precipitation cycle: more ET in the rainy season and less in the dryer austral winter [Nobre et al., 1991; Costa and Foley, 1997; Hahmann and Dickinson, 1997; Werth and Avissar, 2002].

[3] The strong seasonal cycle of Amazonian ET generated by the models is a consequence of soil moisture stress. This is clearly illustrated in two studies by Costa and Foley [1997, 2000], which used the same model but generated radically different seasonal cycles of ET by changing the root depth parameter. In the earlier paper, Costa and Foley [1997] used forest representations with a 2 m deep root system and simulated a wet season peak in ET. In contrast, in their later study Costa and Foley [2000] used 12 m deep roots and simulated a dual-equinox peak of ET, similar to that reported by Shuttleworth [1988]. In another study, Ichii et al. [2007] evaluated the sensitivity of simulated gross primary production (GPP) to different root depths and concluded that only deeply rooted systems can successfully track flux-based GPP seasonality. This divergence in model results is probably attributable to the operation of two different controls on seasonal evapotranspiration. While the dual-equinox peak implies that ET is energy controlled, the wet season peak implies ET is influenced by water-stressed vegetation.

[4] Initial reports from tower-based eddy covariance studies for the Amazon also indicated a wet season evapotranspiration peak. Malhi et al. [2002] measured 1 year of water flux using an eddy covariance system near Manaus and concluded that wet season ET is about 20% higher than dry season ET, the correlation between ET and net radiation being 0.86 in the dry season and 0.94 in the wet season. Vourlitis et al. [2002] analyzed 81 days of eddy covariance ET data for the Sinop site (a transitional forest site in the north of Mato Grosso state, Brazil), reporting wet season ET about 70% higher than the dry season ET. However, their wet season data were limited to only three days.

[5] Werth and Avissar [2004], in their review of Amazon regional ET, concluded that (at that time) there were not enough available ground observations to enrich the discussion and evaluate which was the correct seasonal cycle: strong seasonality due to water stress with a wet season peak or mainly radiation controlled ET with a dual-equinox peak. However, as Costa et al. [2004] pointed out, the eddy covariance data that were rapidly becoming available were indicating that ET in Amazonia displays little seasonality, with reported peaks during the dry season actually higher than during the rainy season. They concluded that, at least in the Caxiuanã sites (∼350 km west of Belém, Melgaço municipality, Pará state), ET is largely controlled by the atmospheric conditions and that control mediated through surface conductance is probably secondary.

[6] Rocha et al. [2004] and Souza-Filho et al. [2005] reported eddy covariance ET results for the Santarém region (Tapajós National Forest, km 83, Pará state) and the Caxiuanã site. In both cases, they found higher values of ET during the dry season, following higher net radiation and vapor pressure deficit. However, Aguiar et al. [2006] analyzed ET from 1999 to 2002 and in 2004 in the Jaru Biological Reserve site (Ji-Paraná, Rondônia state), reporting a reduction of 19.6% in the dry season. Moreover, Hutyra et al. [2007] analyzed 4 years of eddy covariance data at the Santarém km 67 site, finding that rates of ET were inelastic and did not appear to depend on dry season precipitation.

[7] Interestingly, in their study of lowland mixed forest in Borneo, where there is no clear rainfall seasonality, Kumagai et al. [2004] observed that discrepancies between the equilibrium and actual evaporation rates, a measure of water stress, were caused by unpredictable intra-annual dry spells, which reduced transpiration.

[8] More recently, Negrón-Juárez et al. [2007] and Hasler and Avissar [2007] presented the first analyses of eddy covariance data from several rain forest sites in the Amazon. Negrón-Juárez et al. [2007] analyzed 10 sites, concluding that average dry season ET is largely correlated with net surface radiation. Hasler and Avissar [2007] analyzed data from six sites, concluding that in the wet equatorial sites (2°S–3°S), ET increases in the dry season and decreases during the wet season and is in phase with the net radiation cycle. For the seasonally dry tropical sites (9°S–11°S), no clear seasonality could be identified although net radiation and ET are still strongly correlated (r = 0.76–0.92) in the wet season and show decreasing correlations in the dry season (r = 0.00–0.71).

[9] In summary, most previous studies [e.g., Costa et al., 2004; Souza-Filho et al., 2005; Negrón-Juárez et al., 2007; Hasler and Avissar, 2007], with the exception of Malhi et al., [2002], have established the dependence of seasonal ET on net radiation, in particular for wet equatorial sites. This interpretation is supported by a recent review of 21 pan-tropical eddy covariance sites that found that net radiation explained 87% of the variance in monthly evapotranspiration across the sites [Fisher et al., 2009]. Furthermore, the results of Vourlitis et al. [2002], Aguiar et al. [2006], and Hasler and Avissar [2007] suggest there is a difference in the dry season control of ET between the wet equatorial forests and the seasonally dry southern tropical forests. This finding implies it is important to analyze wet equatorial sites and southern tropical sites separately, because the dry season in Amazonia becomes increasingly severe with increasing distance from the equator.

[10] In this study, we test the hypothesis that the factors controlling ET are different in wet equatorial and seasonally dry tropical forests. Specifically, we predict that the biological response of drought-adapted plants in seasonally dry forests will significantly influence ET during the dry season. We evaluate this hypothesis through a comparison of the abiotic and biotic drivers of ET for the wet and dry seasons for five sites spread across the Amazon basin, including three wet equatorial sites (around 2°S–3°S) and two seasonally dry tropical sites (around 11°S).

2. Sites and Data

[11] Data used in this study were collected from five Amazonian rain forest sites situated in different climatic zones (equatorial and tropical) with different degrees of seasonality and forest structure (Figure 1 and Table 1). The three wet rain forest sites are Cuieiras Reserve (km 34, hereafter referred to as the Manaus site), and two sites in the Tapajós National Forest (km 67 and km 83, collectively referred to as the Santarém sites), while the two seasonally dry sites are located farther south in Jaru Biological Reserve (Jaru site) and Fazenda Maracaí (Sinop site).

[12] The Manaus site is located about 60 km northwest of Manaus city in central Amazonia and possesses a flux-tower managed by the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The site is located in the km 34 of the Cuieiras Biological Reserve, a protected area that belongs to Instituto Nacional de Pesquisas da Amazônia (INPA). The vegetation in this site is old-growth closed-canopy terra firme (nonflooded) forest, about 30 m tall with emergent trees reaching 45 m [Araújo et al., 2002]. This site is about 10 km west of the site used by Malhi et al. [2002] for their study.

[13] There are two study sites (Santarém sites) at the Tapajós National Forest (FLONA Tapajós), a primary forest reserve. The reserve is on a broad, flat plateau along the Cuiabá-Santarém highway. The sites located about 67 km and 83 km south of Santarém. Vegetation is typically a tropical humid forest with mostly evergreen and a few deciduous species. Average canopy height is 40 m, with emergent specimens reaching 55 m [Saleska et al., 2003; Goulden et al., 2004].

[14] The Jaru Biological Reserve site (Jaru site) is located about 80 km north of Ji-Paraná, Rondônia [Culf et al., 1996]. The area contains seasonally dry tropical forest with relatively closed canopy structure and emergent trees. Understory vegetation of only a few meters height consists mainly of palms [Rottenberger et al., 2004]. The mean canopy height is 30 m, but the tallest emergent trees reach 44 m [McWilliam et al., 1996].

[15] The Sinop site is an intact transitional tropical forest located about 53 km from Sinop, Mato Grosso state. Although the mean canopy height is only 28 m, the tallest emergent trees can reach 42 m [Priante-Filho et al., 2004]. It should be noted that the Fazenda Maracaí (our Sinop site) has been erroneously labeled as Fazenda Continental in previous literature.

[16] In the wet equatorial sites (Figure 2, thin lines), although precipitation has some seasonality, it is rarely below the annual mean ET line (3.4 mm d⁻¹). In the Jaru and Sinop sites (Figure 2, bold lines), the dry season is more intense, with four to seven months of precipitation below the 3.4 mm d⁻¹ threshold. Temperature at the sites is typical of tropical regions, with monthly means of all sites varying between 21°C and 27°C. Dry season temperatures are slightly higher than the wet season ones.

[17] In all sites, latent heat flux (LE) and sensible heat flux (H) were measured by eddy covariance systems, while an automatic meteorological station measured the surface net radiation (Rn), air temperature, humidity, and wind speed among other variables. Details about the instrumentation for each site may be found in the original publications [Araújo et al., 2002; Saleska et al., 2003; Goulden et al., 2004; Priante-Filho et al., 2004; Aguiar et al., 2006; Hutyra et al., 2007].

[18] Leaf area index (LAI) is an important descriptive variable that provides information about the biological nature of a site and the response of the vegetation to seasonal and annual changes in climatic conditions. Directly measured LAI was only available for the Santarém sites [Malhado et al., 2009], where LAI varied from 5.2 m² m⁻² in January to 4.9 m² m⁻² in May. At Sinop, indirect estimates of LAI through the extinction of photosynthetic photon flux density (PPFD) by the forest canopy indicates a maximum LAI of 5.0 m² m⁻² in February and a minimum of 2.5 m² m⁻² in July [Vourlitis et al., 2004; Sanches et al., 2005]. Moreover, MODIS LAI data for the sites are not particularly reliable, as more than 95% of the data are either missing or retrieved by the less reliable secondary algorithm. Cohen et al. [2006] validated the MODIS LAI collection 4 product against LAI-2000 data for nine sites in the Western Hemisphere, including the tropical rain forest at the km 67 site in Santarém, finding a bias of 0.05 m² m⁻² (highest among nine land cover types analyzed), RMSE of 0.83 m² m⁻² (second highest), and a correlation of only 0.54 (second lowest). Despite the lack of local observations and the limitations of remote sensing, there are indications that the equatorial sites are evergreen, with LAI in the range of 5–6 m² m⁻², while the tropical sites (Jaru and Sinop) are semideciduous.

3. Methods

3.1. Filtering Technique

[19] The input data required to estimate the variables described above are air temperature and humidity, horizontal wind speed, sensible and latent heat flux, atmospheric pressure, and friction velocity. However, eddy covariance data are known to be associated with energy closure problems [Wilson et al., 2002; Fisher et al., 2009], and different authors have come up with different solutions to deal with this problem. For example, Twine et al. [2000] increased both H and LE by the same proportion to match Rn, keeping the Bowen ratio constant. Maayar et al. [2008] recommend that, when used to evaluate land surface model (LSM) performance, measurements of energy fluxes must satisfy the energy budget closure prior to their use in LSM evaluations. Other authors, however, choose not to make any corrections to the eddy covariance data.

[20] Here we use a filtering approach to ensure that all data used are within specified bounds of energy closure. For a specified value of δ, we use only LE data when daily values of LE + H are within the limits Rn(1 − δ) and Rn(1 + δ). Otherwise, all LE data for that specific day are discarded.

[21] Figure 3a illustrates the data availability by site that matches the criteria Rn(1 − δ) < LE + H < Rn(1 + δ) on a daily basis. Data that passes a narrower energy imbalance limit (smaller δ) undoubtedly have better quality, although the number of the data points that pass this threshold would be correspondingly smaller. Conversely, there would be more data points available for analysis when data are not filtered for energy imbalance (δ = infinite), but these data would have no quality assurance. Thus, an operational decision needs to be made between using more data points with unverified energy closure or fewer data points of higher quality.

[22] To make this decision, we plot yearly evapotranspiration totals at all sites against the energy closure limit δ for all sites (Figure 3b). An analysis of this figure indicates that when δ is between 0.2 and 0.4, the calculated ET shows a consistent pattern with little apparent variation (Figure 3b). We therefore conclude that using δ in the range between 0.2 and 0.4 does not significantly bias ET estimates. In contrast, using δ = infinite (no filter applied) causes the yearly ET values to drop in three of the four sites, because many points where LE + H underestimates Rn are being used in the calculation of ET. In addition, using a very strict energy closure criteria (δ = 0.1) significantly reduces the number of data points used (Figure 3a), causing variations in the calculated ET. Based on this analysis, we decided to use the central point of the δ interval that does not bias the results (δ = 0.3) as the filter threshold. In other words, we only used data when daily LE + H was within 30% of the daily Rn, otherwise the entire eddy covariance and Rn data for that day were discarded.

[23] We also analyzed the outliers, i.e., ET estimates that were outside of the range ( − 1.2σ_ET, + 1.2σ_ET), where σ_ET is the standard deviation of ET in a table row, and 1.2 was chosen so that 20% of the points would be outliers (Table 2). Most of the outliers (shaded cells) reside in datasets that were not checked for energy closure (δ = infinite) or where the energy closure filter limit is too rigorous (δ = 0.1), drastically reducing the number of data points available.

Table 2. Annual Means, Seasonal Variability, and Percentage Increase in the Dry Season Value Compared to the Wet Season Value of the Evapotranspiration in Four Amazon Rain Forest Sites, According to the Energy Closure Limit δ^a

Site	Season	ET (mm d−1)
		δ = 0.1	δ = 0.2	δ = 0.3	δ = 0.4	δ = 0.5	No Filter
Manaus	Year	3.73	3.58	3.58	3.52	3.50	2.93
	Wetb	3.62	3.42	3.41	3.38	3.37	2.66
	Dryc	3.84	3.74	3.75	3.67	3.64	3.20
	Increment	6%	9%	10%	9%	8%	20%
Santarém sites	Year	3.73	3.55	3.49	3.44	3.40	3.30
	Wet	3.78	3.45	3.40	3.35	3.32	3.20
	Dry	3.67	3.65	3.59	3.52	3.48	3.40
	Increment	−3%	6%	5%	5%	5%	6%
Jaru	Year	3.56	3.54	3.57	3.53	3.51	3.52
	Wet	3.78	3.77	3.86	3.82	3.81	3.87
	Dry	3.35	3.32	3.27	3.24	3.22	3.18
	Increment	−11%	−12%	−15%	−15%	−15%	−18%
Sinop	Year	3.06	3.09	3.11	3.12	3.10	2.87
	Wet	3.04	3.05	3.04	3.06	3.07	3.25
	Dry	3.08	3.13	3.18	3.18	3.12	2.49
	Increment	1%	3%	5%	4%	2%	−23%

• a Italicized values indicate estimates that lie outside the range ( − 1.2σ_ET, + 1.2σ_ET).
• b Wet season is between November and April.
• c Dry season is between May and October.

[24] We believe that by filtering the data with this energy closure criteria, we maximize the number of data points with acceptable quality, without significantly biasing the results. We should emphasize that if the data are not filtered, a procedure adopted by many authors, much lower estimates of ET are generated, as anticipated from the typical underestimation of eddy covariance energy fluxes.

3.2. Computation of Biotic and Abiotic Components

[25] Evapotranspiration is influenced by four main variables: net radiation available at the surface (Rn), the vapor pressure deficit between the evaporating surface and the atmosphere (VPD), the conductances of the water vapor flow known as aerodynamic conductance (g_a), and surface/stomatal conductance (g_s). Rn, VPD and g_a are the abiotic environmental controls on ET, while g_s is the biological control. To characterize the evapotranspiration process and how this process is controlled in different periods by biotic and environmental factors, we calculate hourly and then monthly averages of surface and aerodynamic conductance. These are analyzed together with other variables measured in each site: net radiation (Rn), evapotranspiration (ET) and vapor pressure deficit (VPD). Each variable was estimated half-hourly or hourly, depending upon data availability. From the 48 half hour period estimates, a typical day was derived for each month. The average of the all hourly averages of the typical day constituted the monthly mean, and in this way, yearly variation could be constructed.

[26] Following Brutsaert [1982, p.112], aerodynamic conductance g_a (m s⁻¹) is calculated using the following equation:

This formulation, that depends on the friction velocity u* and above canopy mean horizontal wind, avoids all the potential errors in computing the roughness length, displacement height, and stability functions. Surface conductance is calculated using the inverted form of the Penman-Monteith equation:

where g_s is the surface conductance (m s⁻¹), LE is the latent heat flux (W m⁻²), ρ_a is the air density (kg m⁻³), c_p is the specific heat of air at constant pressure (J kg⁻¹ °C⁻¹), VPD is in hPa, γ is the psychometric constant (hPa °C⁻¹), H is the sensible heat flux (W m⁻²), and Δ is the slope of the saturation vapor pressure curve (hPa °C⁻¹).

4. Results and Discussion

[27] The monthly results for ET, Rn, VPD, g_a, and g_s for the five sites are shown in Figure 4, while Table 3 summarizes the variables above for the wet (Nov–Apr) and dry (May–Oct) seasons. Data from Santarém sites (km 67 and km 83) are highly concordant, so for the purpose of presentation, they are averaged and presented together. Interestingly, the data for these sites do not overlap as much if the eddy covariance data are not filtered. The Sinop site has operated intermittently in the period of study. Although our average methodology was designed to not bias the results under heavy data missing, in some months it was not possible to define a typical day, and therefore considered the entire month to be missing. In Table 3, sites are arranged from the wettest dry season to the driest dry season. In addition, Table 3 shows the results of a statistical test: seasonal means within each column followed by the same letter are significantly different from each other at the 0.05 significance level, according to the t test.

[28] In three out of the four sites (except Jaru), dry season ET is higher than wet season ET. In these cases ET increases by about 5%–10% in the dry season, while in Jaru it decreases by 15% (Table 3). Increases in ET are not significant at the 5% level, while the decrease in Jaru is significant at the 5% level, according to the t test. The results for the wet equatorial sites support the findings of Rocha et al. [2004], Souza-Filho et al. [2005], Hasler and Avissar [2007], and Rocha et al. [2009]. Moreover, the Jaru data, which depart from the high dry season ET pattern, are similar to those reported by Aguiar et al. [2006] and Rocha et al. [2009].

[29] The long-term data from the Sinop site have been analyzed by Hasler and Avissar [2007] and Rocha et al. [2009]. Here, discrepant results are found: Hasler and Avissar [2007] did not find any seasonality, whereas Rocha et al. [2009] found ET in the dry season 29% less than in the rainy season, while the present study found dry season ET 5% higher than during the wet season. There are two possible explanations for these differences. One possibility is the researchers analyzed different periods of data with different climate conditions. Hasler and Avissar [2007] used data from 1999 to 2003, and Rocha et al. [2009] used data from 1999 and 2001, while the present study used data from 2000 to 2003. Such an explanation, though possible, seems unlikely, and we believe a more robust explanation lies in the filtering technique we applied. If data were not filtered, we would find dry season ET to be 23% lower than wet season ET (Table 2), a result that is similar to Rocha et al. [2009]. However, after applying the δ = 0.3 filter, ET actually increases during the dry season. Again, the increase in the Sinop ET during the dry season is not significant at the 5% level.

[30] Vourlitis et al. [2008] have suggested that the Sinop site seasonality may be affected by deep water reserves, given the lack of available water in the soil surface during the dry season. They speculate that given the relatively shallow depth (3.0–3.6 m) of the water table in this region, the trees are likely to have access to this stable water source during the dry season.

[31] At the wet equatorial sites, there is a general trend of increasing ET toward the end of the dry season (Figure 4a, thin lines). Although small, the net radiation in these sites (Figure 4b, thin lines) is higher during the dry season than during the rainy season, due to less cloudy conditions during the dry season (Figure 4b and Table 3). By contrast, in Jaru and Sinop (bold lines), Rn is relatively constant through the year. Only in Santarém, the increase in Rn during the dry season is significant at the 5% level of significance (Table 3).

[32] As predicted, vapor pressure deficit is larger in the dry season than in the wet season in all sites (Table 3 and Figure 4c). The seasonal amplitude is smaller in Santarém (1.0 hPa) and larger in Sinop (5.8 hPa) possibly because the Sinop site has a significantly drier year-round atmosphere than the other sites. Dry season VPD is significantly higher in Jaru and Sinop (at the 5% level of significance), but not in Manaus and Santarém.

[33] Aerodynamic conductance, dependent on the horizontal wind speed, does not show a significant seasonal variability, with seasonal changes smaller than 8% (Table 3 and Figure 4d). Changes are not significant at the 5% level in any of the sites.

[34] The three controlling variables so far discussed (Rn, VPD and g_a) correspond to the three environmental controls of ET. VPD increases in the dry season in all cases, Rn increases in the dry season in the wet equatorial sites, while g_a is relatively constant throughout the year. The remaining controlling variable, surface conductance (g_s), is the only biotic control on ET, and our analysis indicates important differences in this variable between the wet equatorial and the seasonally dry tropical sites. While the two equatorial wet sites have relatively minor (and not significant) drops in g_s during the dry season (Table 3), the two southern tropical sites show much lower values with a dramatic decrease in g_s (up to 58%) in the dry season (Table 3). This decrease is due to the biological response of the stomata to decreased water availability and indicates that in these sites the control of evapotranspiration in the dry season is primarily biological. The Jaru g_s drop in the dry season is significant at the 5% level. Unfortunately, there are not enough data at Sinop for a formal statistical analysis (only 2 points of data in the wet season), so the significance for g_s and g_a cannot be calculated at this site. Despite this limitation, there is a strong reduction in g_s in both seasonally dry forests sites.

[35] The seasonal change in ET is a combination of the four drivers, Rn, VPD, g_a, and g_s. In the seasonally dry forests they behave in three different ways: (1) Rn and g_a show little variation (<10%) from season to season in both sites, causing weak patterns of seasonal change; (2) g_s decreases by about half, contributing to a decrease in ET; and (3) VPD increases in both sites by over 80%, contributing to an increase in ET. The increase in VPD partially balances the decrease in g_s, avoiding a strong reduction in ET.

[36] A combined analysis of the four controlling variables (Rn, VPD, g_a and g_s) across the four sites indicates a clear distinction between the wet equatorial and the southern seasonally dry tropical sites with respect to the control of ET in Amazonia. Two important research findings support this statement: first, the strong dependence of ET on Rn for the wet equatorial sites, a characteristic not seen in the seasonally dry tropical sites. Second, the observation that while wet equatorial sites do not seem to be water-stressed, the southern tropical sites exhibit different degrees of water stress proportional to the intensity of the dry season. In other words, ET in the wet equatorial sites is almost totally environmentally controlled, while in the seasonally dry tropical sites the biological response of the plants to water stress (e.g., a stomatally mediated reduction in surface conductance) plays an important controlling role.

5. Conclusions

[37] The question originally posed by this study was the following: is evapotranspiration in wet equatorial and seasonally dry tropical rain forests controlled by different factors? The answer is yes, in the sense that in wet equatorial sites evapotranspiration in the dry season is higher than that in the wet season, and surface net radiation is the main controller of evapotranspiration in these sites. Our analyses also indicate that wet equatorial rain forest and the southern seasonally dry tropical rain forest in the Amazon are characterized by different drivers of the seasonality of evapotranspiration. Our key finding is that while evapotranspiration in wet equatorial forests is driven by abiotic environmental factors, in southern seasonally dry tropical forests, seasonal surface conductance (the biotic response of plants to water stress and therefore the biological driver of evapotranspiration) varies by a factor of 2. Identification of these different drivers of evapotranspiration is a major step forward in our understanding of the water dynamics of tropical forests and has significant implications for the future development of vegetation-atmosphere models and land use and conservation planning in the region.

[38] One potential application of these results is to improve the structure and calibration of climate models of the Amazon rain forest. The realization that wet equatorial and tropical rain forests behave differently requires a regionally specific evaluation of model results. Furthermore, although the rain forest clearly has mechanisms to access water deeply stored in the soils, through a deep root system [Nepstad et al., 1994; Hodnett et al., 1996; Negrón-Juárez et al., 2007], hydraulic redistribution [Oliveira et al., 2005], or shallow water table [Vourlitis et al., 2008], it is not correct to assume in models that rain forests are not water stressed, as the southern seasonally dry tropical rain forests do show a relevant degree of water stress. If the wet equatorial forests have similar rooting systems to their tropical counterparts, they would also be water stressed if the dry season were as intense near the equator as it is at 10°S. This sensitivity of wet equatorial rain forests to water stress has already been demonstrated by rainfall exclusion experiments [Nepstad et al., 2002; Meir et al., 2009].

[39] Our research also raises an important question that will need to be answered by future studies: are regional differences in the biological control of evapotranspiration a consequence of dry season duration and intensity only, or does composition and structure of the forest influence observed patterns of seasonality? In other words, are wet equatorial and seasonally dry tropical rain forests different in structural attributes such as root systems, so that the trees of seasonally dry forests are better adapted to water stress than those in wet equatorial forests? If this indeed turns out to be the case, it may have significant implications for the future trajectory of Amazonian vegetation under anthropogenic climate change and, by extension, regional conservation prioritization and policy.