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The Soil Moisture Niche in a Moist Tropical Forest – A Demographic Approach
Abstract and Figures
Water availability affects tree species performance and distributions in tropical forests. However, there are no studies that have measured detailed spatial variation in soil water availability within a tropical forest. This limits our understanding of how water availability shapes the demography and distributions of tree species within tropical forests. In this dissertation, I measured detailed spatial variation in soil water potential (SWP), the relevant measure of water availability for plant performance, in the seasonal tropical moist forest of the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama. In Paper 1, I mapped spatial variation in SWP across the 50-ha plot in various stages of the dry season using information on topography, soil type, dry season intensity and more. In Paper 2, I quantified the soil moisture niches of species in terms of demographic responses (growth and mortality) and species distributions. I related seedling growth and mortality responses to SWP of 62 species to their distributional centre along the SWP gradient, using data from 20 years of annual seedling censuses across 200 seedling census sites. I found that species that grew faster (slow) with increasing SWP were more common on wetter (drier) parts of the SWP gradient. Moreover, wet-distributed species grew faster on the wet side of the SWP gradient than dry-distributed species. Mortality was unrelated to species distributions but decreased strongly with seedling height. These findings indicate that species with a growth advantage with respect to SWP grow faster out of the vulnerable small size ranges, reducing their mortality in later seedling stages and thus shaping species distributions indirectly. This mechanism is a form of niche differentiation that contributes to species coexistence. In Paper 3, I related seedling growth and mortality responses to spatiotemporal variation in water availability with responses to light availability, another highly limiting resource in tropical forests. I found an interspecific trade-off in responses to shade versus inter-annual drought (dry season intensity): species that performed relatively well in the shade performed worse during more severe dry seasons and vice versa. This trade-off enables coexistence, because species are adapted to perform well under either shade or drought. In sum, water availability contributes to the maintenance of the high diversity of tropical forests through hydrological niche differentiation and a trade-off between performance in shade versus drought. Future work can use my SWP maps and species responses to SWP to identify the functional traits that underlie the species responses and improve Dynamic Global Vegetation Models. Finally, my work facilitates the prediction of future species composition, diversity and ecosystem functioning of tropical forests with shifts in rainfall patterns caused by climate change.
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The seasonal growth advantage hypothesis posits that plant species that grow well during seasonal drought will increase in abundance in forests with increasing seasonality of rainfall both in absolute numbers and also relative to co‐occurring plant species that grow poorly during seasonal drought. That is, seasonal drought will give some plant species a growth advantage that they lack in aseasonal forests, thus allowing them attain higher abundance. For tropical forest plants, the seasonal growth advantage hypothesis may explain the distribution of drought‐adapted species across large‐scale gradients of rainfall and seasonality. We tested the seasonal growth advantage hypothesis with lianas and trees in a seasonal tropical forest in central Panama. We measured the dry‐season and wet‐season diameter growth of 1,117 canopy trees and 648 canopy lianas from 2011 to 2016. We also evaluated how lianas and trees responded to the 2015–2016 El Niño, which was the third strongest el Niño drought on record in Panama. We found that liana growth rate was considerably higher during the dry‐season months than the wet‐season months in each of the five years. Lianas achieved one‐half of their annual growth during the 4‐month dry season. By contrast, trees grew far more during the wet season; they realized only one‐quarter of their annual growth during the dry season. During the strong 2015–2016 El Niño dry season, trees essentially stopped growing, whereas lianas grew unimpeded and as well as during any of the previous four dry seasons. Our findings support the hypothesis that seasonal growth gives lianas a decided growth advantage over trees in seasonal forests compared to aseasonal forests, and may explain why lianas peak in both absolute and relative abundance in highly seasonal tropical forests. Furthermore, the ability of lianas to grow during a strong el Niño drought suggests that lianas will benefit from the predicted increasing drought severity, whereas trees will suffer, and thus lianas are predicted to increase in relative abundance in seasonal tropical forests.
Local tree species distributions in tropical forests correlate strongly with soil water availability. However, it is unclear how species distributions are shaped by demographic responses to soil water availability. Specifically, it remains unknown how growth affects species distributions along water availability gradients relative to mortality.
We quantified spatial variation in dry season soil water potential (SWP) in the moist tropical forest on Barro Colorado Island, Panama, and used a hierarchical Bayesian approach to evaluate relationships between demographic responses of naturally regenerating seedlings to SWP (RGRs and first‐year mortality) and species distributions along the SWP gradient for 62 species. We also tested whether species that were more abundant at the wet or dry end of the gradient performed better (a) at their “home end” of the gradient (“best at home” hypothesis) and (b) “at home” compared to co‐occurring species (“home advantage” hypothesis).
Four and five species responded significantly to SWP in terms of growth or mortality respectively. Growth (but not mortality) responses were positively related to species distributions along the SWP gradient; species with a more positive (negative) growth response to SWP were more abundant at higher (lower) SWP, that is, at wetter (drier) sites. In addition, wet distributed species grew faster on the wet end of the SWP gradient than on the dry end (“best at home”) and grew faster on the wet end than dry distributed species (“home advantage”). Mortality rates declined with seedling size for all species. Thus, seedling growth responses to SWP indirectly shaped local species distributions by influencing seedling size and thereby mortality risk.
Synthesis. By demonstrating how growth responses to spatial variation in soil water availability affect species distributions, we identified a demographic process underlying niche differentiation on hydrological gradients in tropical forests. Recognizing the role of these growth responses in shaping species distributions should improve the understanding of tropical forest composition and diversity along rainfall gradients and with climate change.
The recent 2015–2016 El Niño (EN) event was considered as strong as the EN in 1997–1998. Given such magnitude, it was expected to result in extreme warming and moisture anomalies in tropical areas. Here we characterize the spatial patterns of temperature anomalies and drought over tropical forests, including tropical South America (Amazonia), Africa and Asia/Indonesia during the 2015–2016 EN event. These spatial patterns of warming and drought are compared with those observed in previous strong EN events (1982–1983 and 1997–1998) and other moderate to strong EN events (e.g. 2004–2005 and 2009–2010). The link between the spatial patterns of drought and sea surface temperature anomalies in the central and eastern Pacific is also explored. We show that indeed the EN2015–2016 led to unprecedented warming compared to the other EN events over Amazonia, Africa and Indonesia, as a consequence of the background global warming trend. Anomalous accumulated extreme drought area over Amazonia was found during EN2015–2016, but this value may be closer to extreme drought area extents in the other two EN events in 1982–1983 and 1997–1998. Over Africa, datasets disagree, and it is difficult to conclude which EN event led to the highest accumulated extreme drought area. Our results show that the highest values of accumulated drought area over Africa were obtained in 2015–2016 and 1997–1998, with a long-term drying trend not observed over the other tropical regions. Over Indonesia, all datasets suggest that EN 1982–1983 and EN 1997–1998 (or even the drought of 2005) led to a higher extreme drought area than EN2015–2016. Uncertainties in precipitation datasets hinder consistent estimates of drought severity over tropical regions, and improved reanalysis products and station records are required.
This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.
Tree species distribution in lowland tropical forests is strongly associated with rainfall amount and distribution. Not only plant water availability, but also irradiance, soil fertility, and pest pressure covary along rainfall gradients. To assess the role of water availability in shaping species distribution, we carried out a reciprocal transplanting experiment in gaps in a dry and a wet forest site in Ghana, using 2,670 seedlings of 23 tree species belonging to three contrasting rainfall distributions groups (dry species, ubiquitous species, and wet species). We evaluated seasonal patterns in climatic conditions, seedling physiology and performance (survival and growth) over a 2‐year period and related seedling performance to species distribution along Ghana's rainfall gradient. The dry forest site had, compared to the wet forest, higher irradiance, and soil nutrient availability and experienced stronger atmospheric drought (2.0 vs. 0.6 kPa vapor pressure deficit) and reduced soil water potential (−5.0 vs. −0.6 MPa soil water potential) during the dry season. In both forests, dry species showed significantly higher stomatal conductance and lower leaf water potential, than wet species, and in the dry forest, dry species also realized higher drought survival and growth rate than wet species. Dry species are therefore more drought tolerant, and unlike the wet forest species, they achieve a home advantage. Species drought performance in the dry forest relative to the wet forest significantly predicted species position on the rainfall gradient in Ghana, indicating that the ability to grow and survive better in dry forests and during dry seasons may allow species to occur in low rainfall areas. Drought is therefore an important environmental filter that influences forest composition and dynamics. Currently, many tropical forests experience increase in frequency and intensity of droughts, and our results suggest that this may lead to reduction in tree productivity and shifts in species distribution. To answer the question what determines the distribution of tree species in tropical forests, we carried out a reciprocal transplanting experiment in gaps in a dry and a wet forest site in Ghana, using 2,670 seedlings of 23 tree species belonging to three contrasting rainfall distributions groups (dry, ubiquitous, and wet species) and evaluated species performance in response to seasonal atmospheric drought and soil drought. In both forests, dry species, compared to wet species, had significantly higher stomatal conductance and lower leaf water potential, and they also realized a better drought survival and growth in the dry forest. Dry species are therefore not only physiologically more active, but also more drought tolerant, and unlike the wet forest species, they achieve a home advantage in their habitat of origin. Species drought performance in the dry forest relative to the wet forest significantly predicted species position on the rainfall gradient, indicating that species that grow and survive relatively well in dry forests and dry seasons occur in low rainfall areas, and therefore, increasing frequency of drought as a result of global climate change will likely cause a shift in the distribution of wet forest species.
Life‐history theory posits that trade‐offs between demographic rates constrain the range of viable life‐history strategies. For coexisting tropical tree species, the best established demographic trade‐off is the growth‐survival trade‐off. However, we know surprisingly little about co‐variation of growth and survival with measures of reproduction. We analysed demographic rates from seed to adult of 282 co‐occurring tropical tree and shrub species, including measures of reproduction and accounting for ontogeny. Besides the well‐established fast–slow continuum, we identified a second major dimension of demographic variation: a trade‐off between recruitment and seedling performance vs. growth and survival of larger individuals (≥ 1 cm dbh) corresponding to a ‘stature–recruitment’ axis. The two demographic dimensions were almost perfectly aligned with two independent trait dimensions (shade tolerance and size). Our results complement recent analyses of plant life‐history variation at the global scale and reveal that demographic trade‐offs along multiple axes act to structure local communities.
Understanding the underlying dynamics of building energy consumption is the very first step towards energy saving in building sector; as a powerful tool for knowledge discovery, data mining is being applied to this domain more and more frequently. However, most previous researchers focus on model development during the pipeline of data mining, with feature engineering simply being overlooked. To fill this gap, three different feature engineering approaches, namely Exploratory Data Analysis (EDA) as a feature visualization method, Random Forest (RF) as a feature selection method and Principal Component Analysis (PCA) as a feature extraction method, are investigated in the paper. These feature engineering methods are tested with a building energy consumption dataset with 124 features, which describe the building physics, weather condition, and occupant behavior. The 124 features are analyzed and ranked in this paper. It is found that although feature importance depends on specific machine learning model, yet certain features will always dominate the feature space. The outcome of this study favors the usage of effective yet computationally cheap feature engineering methods such as EDA; for other building energy data mining problems, the method proposed by this study has important implications as well because it provides a starting point where efficient feature engineering and machine learning models could be further developed.
Agricultural ecology, or agroecology, deals in general with the structure and function of agroecosystems at different levels of resolution. In this text/reference, the authors describe in terms of agroecology the tropical environments of sub-Saharan Africa, Southeast Asia, and Latin and Central America, focusing on production and management systems unique to each region.
Tropical forests are converted at an alarming rate for agricultural use and pastureland, but also regrow naturally through secondary succession. For successful forest restoration, it is essential to understand the mechanisms of secondary succession. These mechanisms may vary across forest types, but analyses across broad spatial scales are lacking. Here, we analyse forest recovery using 1,403 plots that differ in age since agricultural abandonment from 50 sites across the Neotropics. We analyse changes in community composition using species-specific stem wood density (WD), which is a key trait for plant growth, survival and forest carbon storage. In wet forest, succession proceeds from low towards high community WD (acquisitive towards conservative trait values), in line with standard successional theory. However, in dry forest, succession proceeds from high towards low community WD (conservative towards acquisitive trait values), probably because high WD reflects drought tolerance in harsh early successional environments. Dry season intensity drives WD recovery by influencing the start and trajectory of succession, resulting in convergence of the community WD over time as vegetation cover builds up. These ecological insights can be used to improve species selection for reforestation. Reforestation species selected to establish a first protective canopy layer should, among other criteria, ideally have a similar WD to the early successional communities that dominate under the prevailing macroclimatic conditions.
Within the tropics, the species richness of tree communities is strongly and positively associated with precipitation. Previous research has suggested that this macroecological pattern is driven by the negative effect of water‐stress on the physiological processes of most tree species. This implies that the range limits of taxa are defined by their ability to occur under dry conditions, and thus in terms of species distributions predicts a nested pattern of taxa distribution from wet to dry areas. However, this ‘dry‐tolerance’ hypothesis has yet to be adequately tested at large spatial and taxonomic scales. Here, using a dataset of 531 inventory plots of closed canopy forest distributed across the western Neotropics we investigated how precipitation, evaluated both as mean annual precipitation and as the maximum climatological water deficit, influences the distribution of tropical tree species, genera and families. We find that the distributions of tree taxa are indeed nested along precipitation gradients in the western Neotropics. Taxa tolerant to seasonal drought are disproportionally widespread across the precipitation gradient, with most reaching even the wettest climates sampled; however, most taxa analysed are restricted to wet areas. Our results suggest that the ‘dry tolerance' hypothesis has broad applicability in the world's most species‐rich forests. In addition, the large number of species restricted to wetter conditions strongly indicates that an increased frequency of drought could severely threaten biodiversity in this region. Overall, this study establishes a baseline for exploring how tropical forest tree composition may change in response to current and future environmental changes in this region.
In the past decade, substantial progress has been made in model-based optimization of sampling designs for mapping. This paper is an update of the overview of sampling designs for mapping presented by de Gruijter et al. (2006). For model-based estimation of values at unobserved points (mapping), probability sampling is not required, which opens up the possibility of optimized non-probability sampling. Non-probability sampling designs for mapping are regular grid sampling, spatial coverage sampling, k-means sampling, conditioned Latin hypercube sampling, response surface sampling, Kennard-Stone sampling and model-based sampling. In model-based sampling a preliminary model of the spatial variation of the soil variable of interest is used for optimizing the sample size and or the spatial coordinates of the sampling locations. Kriging requires knowledge of the variogram. Sampling designs for variogram estimation are nested sampling, independent random sampling of pairs of points, and model-based designs in which either the uncertainty about the variogram parameters, or the uncertainty about the kriging variance is minimized. Various minimization criteria have been proposed for designing a single sample that is suitable both for estimating the variogram and for mapping. For map validation, additional probability sampling is recommended, so that unbiased estimates of map quality indices and their standard errors can be obtained. For all sampling designs, R scripts are available in the supplement. Further research is recommended on sampling designs for mapping with machine learning techniques, designs that are robust against deviations of modeling assumptions, designs tailored at mapping multiple soil variables of interest and soil classes or fuzzy memberships, and probability sampling designs that are efficient both for design-based estimation of populations means and for model-based mapping.