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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.1 | The reassembly of diverse forests is an important component in the fight against biodiversity loss and climate change [1].Moreover, many countries and organizations have committed to develop large-scale forest landscape restoration projects as part of programmes such as the Bonn Challenge [2] and the Trillion Tree initiative (1t.org).It is therefore imperative that appropriate management interventions be applied and targeted to local conditions [3,4].Natural regeneration is often an optimal strategy to promote the re-establishment of native species [5,6], yet in many degraded landscapes, planting trees is a necessary step in the re-establishment of local biodiversity [7].Within such environments, survival rates can be particularly low in the early stages of tree development, and so species selection can be a key determinant of restoration outcomes [8,9].Identifying mixtures of species that can establish and survive within harsh environmental conditions is critical to ensure effective long-term outcomes of active forest landscape restoration efforts.Moreover, increasing plant survival in restoration projects across the globe is important to ensure that resources are efficiently allocated by countries committed to the UN Decade on Ecosystem Restoration (see https://www.decadeonrestoration.org/).
Despite the importance of appropriate tree species selection for the success of restoration efforts, few studies have characterized the plant traits that are most effective at promoting survival within degraded landscapes.A long history of ecological literature highlights how the performance of plants is contingent upon the expression of traits that can confer a selective advantage under a given set of environmental conditions.A range of morphological, physiological and biochemical functional traits [10] can predict tropical plant growth under different environmental conditions [11,12].Moreover, there have been calls to use functional traits to develop species mixes for tropical forest restoration [9,13].Additionally, integrating local knowledge of species life-history traits (e.g.successional status) when designing restoration interventions can not only improve outcomes, but also ensure that culturally and economically important species are explicitly included in the design process [14].Yet, applying this knowledge to predict the survival of seedlings within a restoration context remains a major challenge as trait expression, survival rates and their interactions can vary significantly at these initial stages of development.In particular, within tropical dry forests-the second largest forested tropical biome [15] and one of the most endangered ecosystems [16]-seedlings are exposed to strong dry seasons within the first year of growth, and survival in the initial years of development is imperative to ensure that restoration is effective in this system [17,18].Globally, tropical dry forests have been extensively deforested [19].It is estimated that 97% of remaining tropical dry forests are threatened by climate change and human activities [20], making them a top-priority for landscape scale restoration efforts.Recent work highlights how physiological leaf traits such as water-use efficiency can be key indicators of seedling survival during initial phases of development in both tropical dry [21] and wet forests [22].As such, the selection of species mixtures with high community-weighted water-use efficiency could potentially improve early survival rates at the community level.Increasing survival at early restoration stages has direct consequences for the development of soil organic matter [23], canopy structure [24] and subsequent recruitment of other native species [25,26].However, by focusing only on the linear correlations with a few plant traits, these studies cannot account for the majority of the variation in species survival rates, or identify the mixtures of trait combinations likely to promote the initial survival of seedlings in the long term.
Given the multitude of approaches that plants use to compete for space, light, water and nutrients within heterogeneous environments, it is rare that any single trait can directly predict tree survival rates in a given location [27].However, certain traits can be indicative of general life-history strategies.In particular, the growth rate of trees is a well-described indicator of changes in community assembly over time, with fast-growing resource-demanding species dominating in early successional stages and being gradually replaced by slower-growing species with more efficient resource-use [28].As such, differences between 'acquisitive' (i.e.typically fast-growing species that maximize resource capture and are sensitive to abiotic stress) and 'conservative' (i.e.typically slow-growing, stress-tolerant species with higher resource-use efficiency) functional strategies (sensu [27]) may provide a useful framework for guiding species selection that leads to high establishment rates of planted seedlings within a restoration context.Indeed, in a tropical dry forest field experiment, Gerhardt [29] highlighted that within-species tree seedling survival increased with higher height increment growth rates.However, although this 'acquisitive' growth strategy may promote tree survival within early stages of succession, rapid growth can often come at the expense of more 'conservative' strategies (e.g. that can be critical for species survival in harsh dry conditions).
In tropical dry forests, tree growth and survival are strongly limited by seasonal water availability [30], and species survival may be tightly linked to the successful development of deep, robust rooting systems [31].However, while below-ground plant traits have been linked with plant vital rates (e.g.survival and growth) across terrestrial ecosystems, these traits are rarely considered when evaluating plant performance, especially in the context of restoration [32].If investment in fast aboveground growth (i.e.acquisitive strategy) comes at the expense of plant investment in thicker absorptive fine roots and deeper supporting root systems (i.e.conservative strategy), then it may potentially limit seedling survival within tropical dry forest.Yet, until now, no study has explored the relative importance of below-ground traits relative to above-ground traits, or the trade-offs between trait combinations, that may be essential for improving species selection to promote increased survival rates within tropical dry forest restoration.
In this study, we examined growth and survival of 14 native tree species (840 seedlings in total) within a tropical dry forest restoration experiment in Costa Rica.These species are common in early successional forests in the region, are inclusive of the most dominant tree families [33] and encompass a wide range of life-history and resource-use traits [34].Across all species, we measured six below-ground and 22 aboveground traits used to place species on leaf, stem and root economic trait spectra so that we could group trait values onto an acquisitive to conservative gradient [35], then examined how interspecific trait variation corresponds to species survival rates over the first 2 years of seedling growth.By examining the interactions between trait complexes, we test the relative importance of above-versus below-ground traits, and examine how interactions between trait combinations mediate seedling survival rates.Last, we investigated if trait syndromes (i.e.fast versus slow [35]) are coordinated across above-and belowground organs, an area of open debate for tree species [36].Ultimately, by taking a broad scope, we aim to identify a clear hierarchy of traits that should be considered in order to promote the survival of planted seedlings within tropical dry forest restoration. | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.2 | Our study was conducted at Estación Experimental Forestal Horizontes (10.712N, 85.594W) in Área de Conservación Guanacaste (ACG) in northwest Costa Rica.This tropical dry forest has a mean annual precipitation of 1730 mm, mean annual temperature of 25°C and a strong five-to six-month dry season from November to May, with little to no rainfall (http://www.investigadoresacg.org/main_eng.html).The study site was previously used for cattle grazing and agricultural crop production for decades, similar to land use in other tropical dry forests across Central America royalsocietypublishing.org/journal/rstb Phil.Trans.R. Soc.B 378: 20210067 [37] and the globe [19].Succession is arrested in the study area, which had been regenerating for approximately 28 years before management intervention, due to intensive use that both compacted the soil and led to nutrient depletion [38].As a result, the study site is dominated by only three tree species, Cochlospermum vitifolium (Bixaceae), Crescentia alata (Bignoniaceae) and Guazuma ulmifolia (Malvaceae) [38], making it particularly species-poor given that more than 60 tree species are observed in nearby forest inventory plots [39]. | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.3 | Here we report new observations of above-and below-ground plant functional traits that complement published data on seedling survival and growth in an experimental species selection trial [38].Our original experiment aimed to determine how above-ground traits and soil amendments can be used to improve restoration outcomes in degraded Vertisols.Vertisols are soils that impede regeneration due to high shrink-swell clay content that leads to cracks in the dry season and flooding in the wet season [40].In brief, in September 2014 we planted approximately 60 individuals of 32 native tropical dry forest tree species (N = 1710 seedlings) common across this landscape [33] and that have a wide range of functional strategies [34].
Seedlings were grown in an on-site nursery, and we accounted for intraspecific variation by collecting seeds across the ACG from at least three trees per species separated by at least 1 km.
Seedlings were planted at 1 × 1 m spacing, with each species planted randomly in rows across four soil amendment (hydrogel, rice hulls, rice hull ash, sand) and two control blocks.Previous results showed that soil amendments did not influence survival or growth of seedlings after 2 years, so we do not consider this factor in the current study.Seedling competition with remnant vegetation (e.g.exotic forage grasses) was minimized by fully clearing around all seedlings with machetes at regular intervals (October and November 2014, and July 2015).We conducted the current study with species (N = 14) that had least three surviving individuals 2 years after planting (table 1).
We leveraged data collected in the original experiment to calculate species-level survival and growth rate for 14 species [38].We made an effort to control for the influence of plant size on calculated species-level growth rates by initiating production of all species in the nursery at the same time.Mean planted seedling heights were similar at the species-level both when initially planted (mean: 29.8 cm; s.d.: 13.7) and after 2 years when harvests were made (mean: 72.9 cm; s.d.: 25.6).As such, we effectively compared growth rates between species computed from similarly sized seedlings.We calculated final species-level survival percentages and average relative height growth rate (RGR; ln(cm) d -1 ) after 2 years (two full growing and dry seasons; September 2014-June 2016) on 60 seedlings per species (N = 840).RGR was calculated using the standard equation: (ln[final height]-ln[initial height])/(final day-initial day) [41] and assumptions for this approach were met (i.e.RGR did not slow or approach an asymptote over time).In July and August of 2016, we measured physiological traits and whole-plant structural characteristics in situ, then harvested both above-and below-ground biomass of two-year-old seedlings of each species (electronic supplementary material, figure S1).All measurements were made on three individuals of each species (N = 42 individuals total; see electronic supplementary material, table S1 for above-and below-ground trait list). | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.4 | Physiological traits related to photosynthesis and water-use were made on individuals in the field prior to destructive harvests.We used an LCi portable photosynthesis system (ADC Bioscientific Ltd, Hoddesdon, UK) to measure maximum photosynthetic capacity at 1200 par (Amax; μmol CO 2 m -2 s -1 ), transpiration (E; mmol m -2 s -1 ) and instantaneous water-use efficiency (WUE; μmol CO 2 mmol H 2 O -1 ).Next, we measured the change in leaf water potential (Ψ) from pre-dawn to midday (Ψ diurnal ; MPa) to place species on a drought-tolerator versus avoider spectrum [42].We measured pre-dawn and mid-day Ψ on two leaves per seedling with a Scholander pressure chamber (1505D, PMS Instruments, Albany, OR, USA), then calculated Ψ diurnal .
Table 1.The 14 focal tropical dry forest tree species from the restoration experiment, their life-history traits, final survival percentages and relative height growth rates (RGR) over 2 years.Trait abbreviations are as follows: dispersal syndrome (W, wind; A, animal), leaf habit (D, deciduous; EG, evergreen), leaf compoundness (S, simple; C, compound).For all harvested seedlings (N = 3 per species), we quantified a suite of above-and below-ground functional traits related to resource acquisition and stress tolerance.This sample size did not allow for intraspecific comparisons; however, we were able to compare how an extensive suite of above-and belowground traits differed concurrently between many focal species using principal component analysis.In the field, we measured the crown radius (cm) by measuring from the seedling stem to the tip of the furthest leaf, then excavated whole seedlings taking care to harvest the entire root system, including all fine roots (roots ≤ 2 mm diameter).During harvests we measured the maximum lateral rooting extent by measuring from the base of each seedling to the end of the furthest excavated fine root tip (root lateral extent; cm) and maximum depth of the deepest fine root (root depth; cm).No harvested seedlings were observed competing for either above-(i.e.no overlapping canopies) or below-ground resources (i.e.no overlapping root structures) with adjacent seedlings.We washed then scanned all fresh fine roots (absorptive firstand second-order roots ≤ 2 mm diameter; functional classification sensu [43]) to calculate fine root traits for each individual with a transparency scanner (Epson Perfection V800, Suwa, Japan; 2-4 images per individual), placing roots in a clear polycarbonate tray filled with water to ensure no root overlap.We used IJ_Rhizo [44] to calculate mean fine root diameter from scanned images.We then dried roots and calculated specific root length (SRL; m g -1 ) and tissue density (RTD, g cm -3 ) using total fine root length and volume calculated from images.
On three fresh leaves per harvested individual (N = 126 leaves total) we measured petiole length (mm) and leaf thickness (mm), then scanned and calculated leaf area (cm 2 ) with IMAGEJ [45].To quantify whole-plant light capture capacity we scanned all fresh leaves of each individual and calculated whole canopy leaf area (total leaf area; cm 2 ) and leaf perimeter (total leaf perimeter; cm).We then dried all leaves at 60°C to constant weight and calculated specific leaf area (SLA; cm 2 g -1 ) and leaf density (g cm -3 ).We bulked these leaves per individual and transported them to the University of Minnesota, where they were ground and analysed for foliar carbon to nitrogen ratio (C : N) and stable isotopes (δ 13 C; ‰).
Last, for each harvested individual, we dried all plant parts (leaves, stems, roots) to constant weight at 60°C and determined their mass (g), then calculated leaf (LMF), stem (SMF) and root (RMF) mass fractions for each seedling.In final individual-level biomass calculations, we included the mass of all leaves and roots used to quantify all traits above.For all species, we compiled a list of life-history traits from a previous study in the region [34]: dispersal syndrome (wind or animal), leaf habit (deciduous or evergreen), leaf compoundness (simple or compound), nitrogen-fixing status (fixer or non-fixer), seed mass (g) and stem wood density (g cm -3 ).We calculated species-level trait means for statistical analyses (electronic supplementary material, table S2). | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.5 | In an effort to consider all above-or below-ground traits simultaneously, we performed principal component analysis, using the prcomp function within R statistical software [46].All variables were centred and scaled relative to their means and variances to facilitate interpretation of principal components.We performed two principal component analyses, one using all above-ground traits, and a second using all below-ground traits.This generated two sets of species-level principal components which reflected variation in above-and below-ground traits, respectively.
We analysed survival rates at the species level using Bayesian phylogenetic generalized linear models (GLM) to account for species relatedness, via the MCMCglmm function in R [47] and a phylogeny built with V.PhyloMaker [48].Tree species survival rates were logit-transformed prior to analysis, as raw proportional data violate the assumption of homoscedasticity [49].To ensure that results were equivalent to using non-transformed survival values, we compared the logit-transformed Bayesian phylogenetic GLMs to beta GLMs and found that model fits and results were almost identical (electronic supplementary material, figure S2 and table S3).To understand how relative growth rate, above-ground traits, below-ground traits and potential interactions influenced species-level survival rates we fit five statistical models.We focused on predicting species' survival rates rather than growth rates as the goal of our study was to develop a framework for restoration species selection that optimizes initial survival of planted seedlings.The first three models were univariate regressions between survival rate and either relative growth rate, the first principal component (PC1) of aboveground trait variation, or PC1 of below-ground trait variation.Then, we considered potential interactions between relative growth rate and trait principal components by fitting models with relative growth rate, PC1 of above-or below-ground trait variation, and their interaction, respectively.
(1) Survival rate ∼ above-ground PC1 (2) Survival rate ∼ below-ground PC1 (3) Survival rate ∼ relative growth rate (RGR) (4) Survival rate ∼ RGR + above-ground PC1 + RGR * aboveground PC1 (5) Survival rate ∼ RGR + below-ground PC1 + RGR * belowground PC1
We explored how additional principal components of aboveand below-ground trait variation influenced survival rates, but these models performed substantially worse than models fit with the first principal component, and so were excluded from further investigation.Additionally, we performed a 'whole-plant traits' PCA including all above-and below-ground traits, however, PC1 of this PCA only described slightly more variation in survival than above-ground traits PC1, and far less variation than the below-ground traits PC1, so it was excluded from analyses.To determine the extent to which traits were coordinated across plant organs, we calculated pairwise Pearson's correlation coefficients (r) with bootstrapped standard errors and p-values (10 000 samples) between all continuous above-and below-ground functional traits. | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.6 | Survival to 2 years varied from 7.8% to 90.1% among species.Overall, relative growth rate was the strongest single predictor of tree species survival rate (R 2 adj ¼ 0:42), indicating that species with faster growth rates typically had higher survival.However, PC1, which captured the greatest amount of variation in either above-or below-ground traits on their own, were poor predictors of tree species survival.However, when relative growth rate and tree species traits were considered in combination (relative growth rate, PC1 of below-ground trait variation and their interaction), variance explained increased by approximately 32% compared to models fit with relative growth rate alone (R 2 adj ¼ 0:73 versus R 2 adj ¼ 0:41, respectively).By contrast, models fit with relative growth rate, PC1 of aboveground trait variation and their interaction slightly decreased model R 2 adj relative to a model fit with relative growth rate alone (adjusted R 2 adj ¼ 0:28, figure 1).To better understand how the PC1s of above-and belowground trait variation are driven by individual traits, we visualized bi-plots of principal component analyses with royalsocietypublishing.org/journal/rstb Phil.Trans.R. Soc.B 378: 20210067 covariate vectors superimposed, as well univariate regressions between four traits that best correlated with PC1 within each analysis (figures 2 and 3; see electronic supplementary material, table S4 for variable contributions to and correlations with PC1).Below-ground trait variation was dominated by a trade-off between either having thick fine roots (high values of fine root diameter) or high biomass allocation to roots (root mass fraction), greater root length per unit root mass (SRL) and higher root tissue density (figure 2).Above-ground trait variation was dominated by a trade-off between low versus high values of total leaf area and perimeter, crown radius and δ 13 C (figure 3).
To evaluate the interaction between below-ground traits and relative growth rates, we used the fitted model to develop an interaction plot that allowed us to vary relative growth rate at fixed levels of below-ground PC1.We varied relative growth rate from its 5% to 95% quantile in the dataset, and then set below-ground trait PC1 at either its 5%, 50% or 95% quantile.The 95% quantile of below-ground trait PC1 reflects thick fine roots and deeper maximum rooting depth, but lower root allocation and root tissue density.We refer to the 95%, 50% and 5% quantiles of PC1 as 'conservative', 'average' and 'acquisitive' below-ground trait syndromes, respectively.We observed that the interaction between relative growth rate and below-ground trait variation has a strong effect on estimated tree species survival rates, so much so that trees with otherwise high relative growth rates have much lower survival rates if those trees also have thin fine roots and high overall biomass allocation to roots (figure 4).
To examine trait coordination between plant organs, we generated a correlation matrix of pairwise Pearson's correlations between above-and below-ground traits (electronic supplementary material, figure S3).Root traits were largely not correlated with above-ground traits; however, some patterns emerged with respect to biomass allocation.Notably, no above-ground traits were correlated with root diameter, and only one above-ground trait (SMF) was correlated with root depth (i.e.species with deeper roots allocated more resources to stems; r = 0.60).In the few cases where SRL and RTD correlated with above-ground traits, relationships were typically negative; however, species with high RTD generally had high stem wood density (r = 0.69).Species that allocated more biomass below-ground (i.e. higher RMF) had acquisitive leaves (i.e. higher SLA; r = 0.66), narrower crowns (r = -0.74)and allocated less resources to leaves overall (i.e.lower LMF, lower total leaf area and perimeter; r of -0.58, -0.61 and -0.65, respectively).The opposite patterns were observed for species with high root lateral extent (i.e.lower SLA, larger crowns, higher total leaf perimeter and area; r of -0.54, 0.78, 0.75 and 0.76, respectively) and these species also allocated more resources to stem biomass (i.e. higher SMF; r = 0.73). | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.7 | The selection of tree species remains a major challenge facing restoration projects in tropical dry forest.Given the harsh growing conditions, characterized by prolonged dry seasons, mortality rates can be exceptionally high in the first 2 years following seedling establishment [38,50].Surviving this initial growth phase is key to ensure effective restoration outcomes.In this analysis, only 34% of planted seedlings survived this initial 2-year period.Yet, survival rates of different tree species ranged from 7.8 to 90.1%, highlighting that effective species selection is necessary to maximize the chances of initial seedling establishment when restoring this ecosystem type.
To explain this variation in survival rates among our species, we explored a range of above-and below-ground traits.The prominent role of relative growth rate in governing initial survival emerged strongly, explaining over 40% of the variation in survival rates alone (figure 1).Specifically, species able to tolerate conditions in this degraded soil and allocate resources to rapid initial height growth consistently established quickly and tended to survive throughout the initial 2-year growth phase.These findings are consistent with evidence from both tree planting efforts [51] and natural regeneration projects [29] in Neotropical tropical dry forests, highlighting that tropical dry forest seedling growth rates can be tightly and positively linked with survival for the first few years of establishment.Whereas the growthmortality trade-off dictates that fast-growing species typically have the highest mortality rates in closed canopy forest [52], in these high light early successional environments this tradeoff does not appear to hold (also see [53]).Therefore, in early stages of tropical dry forest restoration, acquisitive growth strategies clearly promote rapid establishment and seedling survival.This suggests that integrating fast-growing species that persist naturally at early successional stages into planting mixes is a straightforward approach towards improving restoration outcomes on extensively degraded sites where there is little to no extant canopy cover.Moreover, this pattern was not driven by nitrogen-fixing species included in our study, which can have much higher growth and survival than non-fixers in regenerating tropical wet forest [54] and can have higher growth than non-fixers in tropical wet forest restoration plantings [55].Rather, both fixers and non-fixers were integrated along growth and survival gradients, highlighting that species with both strategies should be considered for species mixes.Indeed, ensuring that species mixes containing a wide range of functional groups with appropriate traits for a given situation can not only restore targeted ecosystem services, but also increase forest resilience to ongoing global environmental change [1].
Despite the important role of seedling growth rate in governing survival in this system, several species diverged strongly from this linear relationship, and the inclusion of functional traits considerably improved the capacity to predict overall survival rates across species.However, the importance of different traits varied considerably between above-and below-ground characteristics.Although the inclusion of above-ground traits did not significantly ( p > 0.05) affect the model fit, the inclusion of below-ground traits considerably improved the predictive accuracy of our model.Specifically, the final model including relative growth rate, the PC1 for below-ground root traits and their interaction explained 73% of the variation in survival rates across our study species.While our results indicate that below-ground traits are highly predictive of initial survival rates in this system, survival rates over longer timescales may be dictated by different suites of traits, ontogeny [56] and/or cyclical climate cycles (e.g.El Niño and La Niña).This prominent role of belowground traits in predicting initial species survival is consistent with the idea that root investment is critical for plant survival, especially in regions with limited water availability.Indeed, both broad- [57] and fine-scale [58] studies consistently highlight the importance of root investment within arid and seasonally dry regions, as the root : shoot ratio of plants tends to increase in drier conditions.Moreover, in this tropical dry forest, species that allocated more resources below-ground (i.e. higher RMF) allocated less resources to leaf construction (i.e. higher SLA), crown development and total canopy leaf area (electronic supplementary material, figure S3).This tradeoff is consistent with observations from Panama indicating that seedling growth in tropical dry forest was maximized when more resources were invested in roots versus leaves [59].
The specific nature of the relationships between root traits and species survival can provide mechanistic insights into their effects on seedling survival.Principal Component 1 for the below-ground traits correlates negatively with root depth, root diameter and root lateral extent and positively with RMF, SRL and root tissue density (figure 2).As such, the negative overall relationship between PC1 and species survival indicates that investing in thicker fine roots, and developing deeper and more laterally extensive root structures can promote species survival within the initial years of seedling growth.These patterns appear to be tightly linked to the optimization of resource capture, and observations demonstrate that early successional tropical dry forest trees in Mexico typically have deep root structures that increase water foraging capacity [60].Moreover, all of the species in this study associate with arbuscular mycorrhizal fungi and species with thicker fine roots typically have high mycorrhizal colonization rates [61], which could directly increase root surface area for nutrient and water uptake.Our findings likely hold across the dry tropics in forests dominated by deciduous species.However, in seasonal tropical forests with communities dominated by evergreen species, which inherently allocate more biomass to roots [62], other suites of traits may better differentiate between species when predicting initial seedling survival rates.Additionally, the low sample size of our seedling harvests may have limited our ability to well resolve trait means for specific species, potentially influencing observed trait syndrome×growth rate interactions.However, our initial analysis highlights that integrating below-ground traits into restoration design is an important research direction, and further characterizing below-ground trait variation will only help to improve our understanding of this topic.
We also found a strong interaction between the effects of root investment and vertical growth rate in predicting overall survival rates.That is, fast-growing species had higher survival if they also had thick fine roots and deep root structures (figure 4).This interaction between root traits and growth rate is counterintuitive as these growth traits are often considered to be associated with distinctly opposing growth strategies.Specifically, fast vertical growth is generally considered to be associated with an 'acquisitive' growth strategy, whereas the investment in thick fine roots, and allocating resources to root structure development are often considered to occur in 'conservative' species.Therefore, to maximize initial survival rates in this tropical dry forest region, and in other tropical seasonal forests with strong dry seasons when little to no rain falls for four months or more, it is essential to select species that exhibit relatively acquisitive above-ground growth, with relatively conservative root structures.Thus, assigning binary resource-use strategies (acquisitive versus conservative) at the whole-plant level may be misleading when attempting to determine the drivers of plant performance in a restoration context.Moreover, below-ground traits were largely uncorrelated with or decoupled from above-ground traits in this system (electronic supplementary material, figure S3).This finding supports growing evidence that coordination across above-and below-ground organs in tree species may be limited [36,63].However, there was some indication that wood and root tissue densities were linked across species, a pattern that has also been observed across a broad range of Neotropical tree species [64].Additionally, the traits tightly coupled with seedling survival in below-ground PC1 (root diameter and RMF; figure 2) were only correlated with above-ground traits in one instance.Thus, our findings suggest that despite the difficulty associated with quantifying below-ground traits, investing time into these efforts and improving our capacity to predict species-level trait values out of sample (e.g.[65]) will most likely improve the design of tropical dry forest restorations.Moreover, large-scale forest biodiversity-ecosystem function experiments have demonstrated that maximizing functional diversity in planted forests promotes higher productivity [66] and increases resilience and ecosystem services [67].Thus, ensuring that planted species mixes are highly diverse, in terms of both above-and below-ground functional attributes, is likely to be important, and using planting mixes with high species richness is a strategy demonstrated to meet this goal [68]. | [
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Werden et al_2022_Below-ground traits mediate tree survival in a tropical dry forest restoration.pdf.tei.xml | Below-ground traits mediate tree survival in a tropical dry forest restoration | 0.8 | By highlighting the traits that predict tree seedling survival, our analysis provides a path forward for the selection of species to promote survival in tropical dry forest restoration efforts.Selecting species with fast vertical growth rates has the potential to promote rapid seedling establishment when planted into degraded Vertisols, but this effect is only apparent in species that also invest in thicker fine roots, and more extensive root structures.As such, this analysis highlights the importance of considering below-ground root characteristics, relative to above-ground stem and leaf traits, when predicting seedling survival within these seasonally dry ecosystems.Given the typically low seedling survival rates in tropical dry forests, these insights into species selection may potentially be invaluable for promoting the initial establishment of vegetation.By highlighting the key traits that promote seedling survival, our study can serve as a guideline to select species that can tolerate initial planting conditions in tropical dry forest restoration efforts.Data accessibility.All survival and growth data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.fd57r[69].All functional trait data and code and data to reproduce analyses and figures are available from Figshare [70].
Supplementary material is available online [71]. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.3 | The study site is in the inner southern suburbs of Brisbane, Queensland, Australia and is known locally as Oxley Creek Common (27°32′S 152°59′E, Figure S1).It is situated on an alluvial floodplain along a section of Oxley Creek, approximately 2 km from its confluence with the Brisbane River to the north.Rainfall averages 1,058 mm/ year and is typically summer dominant with extended dry periods in late winter and early spring (July-September).Mean daily minimum and daily maximum temperatures are 14.4°C and 26.4°C, respectively (station number 40,211;Australian Bureau of Meteorology, 2017).
Oxley Creek Common comprises infrequently inundated pasture and parklands on dermosols.Prior to European settlement, the areas adjacent to Oxley Creek most likely supported gallery notophyll vine forest (Regional Ecosystem 12.3.1;Queensland Herbarium, 2018).While the region receives less rainfall than is typically used to define rainforest in the tropics, this type of vegetation is commonly referred to as riparian subtropical rainforest in Australia.
Established in November 2016, the experiment was embedded within a larger project funded by the Australian Government's National Landcare Programme '20 Million Trees Round II'.Overall, the experiment aimed to test whether the long-term cost-effectiveness of forest restoration could be improved by designing planting mixes based on the functional traits of plants. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.5 | A 'trait targets' approach (Laughlin, 2014) was used to generate two experimental assemblages with distinct trait profiles: a fast assemblage comprising more resource-acquisitive species, and a hardy assemblage comprising species with more conservative strategies (see Appendix S1).Each assemblage had 16 species and similar species evenness.Fast and Hardy assemblages each had eight species unique to them and the remaining eight were common to both assemblages, resulting in a total of 24 species.One species (Jagera pseudorhus) could not be sourced in time for planting, leaving 23 species in the present study (Table S1). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.6 | The areas available for planting were mainly confined to a 50 m wide strip of alluvial floodplain (Figure S1) that was previously managed as beef cattle pasture.To capture environmental variation across the site, and variation between grazing units (paddocks), a randomized block design that included 10 blocks distributed along the entire extent of the available planting area was implemented.Each block contained two plots to which the two mixes were randomly assigned.
Each plot was 30 × 30 m with 400 trees planted in a grid at 1.5 m spacing.Paddocks containing plots were first fenced to exclude livestock, slashed approximately 2 months prior to planting and then sprayed with glyphosate 2-4 weeks prior to planting to suppress excessive grass and forb growth, as is standard restoration practice in the region.
Plants were sourced in 50 mm tubestock from a number of local nurseries but varied considerably in size within and among species (from 5 to 65 cm shoot heights, mean = 22 cm).The 400 plants per plot were pre-randomized at a separate location in the days before planting.Planting was undertaken between the 31st of October and 18th of November, 2016 at the beginning of the typical springsummer growing season.Holes were pre-dug using motorized augers and a handful of water crystals were placed in the bottom of each hole at the time of planting and mixed with soil.Each plant was watered-in within an hour of planting.Supplementary watering was provided 1-2 times a week until mid-January 2017.Individual plants in each plot were mapped and their heights measured within 2 weeks of planting. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.7 | The survival of all plants was assessed 3.5 months after planting.
Plants were considered dead if they had no visible living leaves, tips or shoots.The height of living plants was measured as the vertical distance from the ground to the live central apex.The exact number of days between planting and monitoring was recorded for each plot and used to calculate daily growth rates. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.8 | Maximum temperatures on the day of planting were obtained for each plot (Australian Bureau of Meteorology, 2017) to provide a measure of initial heat stress.Four soil samples were collected from each plot (one in the centre of each plot quarter) at a depth of 2-10 cm, making a total of 80 samples.Samples were passed through a 2-mm sieve and sent to the UQ Soil Analysis Laboratory to measure total ammonium and nitrate, plant-available phosphorus and potassium, as well as soil pH and electrical conductivity.Soil cores were taken immediately adjacent to sample locations to measure bulk density and gravimetric water content (GWC).This was undertaken during a dry period from the 4th of June to the 7th of June when the site had not received rainfall for over 2 weeks.Bulk density rings were 8.5 cm deep, with a radius of 6.5 cm, and were 'trimmed' so that the surface of soil was flush with the ring.Soil cores were weighed immediately in the field.Soil cores were then oven-dried for 48 hr at 105°C before being re-weighed.Bulk density (g/cm 3 ) was calculated as the mass of oven-dried soil divided by the volume of the soil at field condition, while the GWC was calculated as the weight of water in the core sample at field condition, divided by the dried soil weight (g/g).
A LiDAR-derived topographical raster was queried to estimate the elevation of individual plants (Source: QLD Government Remote Sensing Centre, Department of Science, Information Technology and Innovation).Plots were located on the raster using GPS points taken at each plot corner.Individual plant elevations were then extracted in using each plant's grid location within plots.From these data, we created two elevation variables.To capture plot-level variation in elevation, we calculated the mean elevation of plants in each plot.We also subtracted the plot-level minimum from each plant's elevation to describe the relative elevation of plants within plots (i.e.whether plants were on mounds or depressions). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.9 | Leaf, stem and root traits were measured on well-watered, sunhardened seedlings of each species.Five replicate seedlings were sampled, except for three species that had four replicate seedlings.
Three new, fully expanded leaves were selected from each seedling for measurement of SLA, leaf dry matter content (LDMC) and lamina area.Leaves with obvious herbivore or pathogen damage were avoided.After being cut from the stem, leaves were immediately weighed and scanned.The one-sided leaf area (mm 2 ) was obtained using ImageJ software, version 2.0.0 (Schneider, Rasband, & Eliceiri, 2012).Area and mass measurements of all leaves included petioles (simple leaves) or petiolules (compound leaves).Leaves were ovendried at 60°C for 72 hr before being reweighed to obtain the leaf dry mass.SLA was calculated as the one-sided leaf area divided by the leaf dry mass (mm 2 /mg), and LDMC as the oven dry mass divided by fresh leaf mass (mg/g).
Wood density was calculated as dry mass of wood per unit of volume (mg/mm 3 ).One wood sample was cut from the base of each seedling (average stem sample diameter of approximately 4 mm).
Secondary phloem and bark were removed before volume was determined via water displacement (Perez-Harguindeguy et al., 2013).
Wood samples were oven-dried at 105°C for 72 hr and then retained at room temperature for 1-2 min before weighing.Total root length and root volume were measured from fresh, washed root material, which was scanned for each seedling and analysed using WinRhizo image analysis software (Regent Instruments Inc.).Following a preliminary root wash, small samples of the finest living roots in each root system were selected and further washed in deionized water.
All effort was made to select absorptive roots; however, some samples may have included structural or transport roots.Roots were scanned at 720 dpi using an EPSON Expression 11000XL LA2500 scanner (EPSON).Based on the estimated mean root diameter for each scanned sample, the overall mean (across species) was 0.3 mm, and species' means ranged from 0.193 for Melaleuca bracteata to 0.505 mm for Toona ciliata.Roots were then oven-dried at 60°C for 48 hr before weighing to obtain their dry mass.Specific root length (SRL) was calculated as root length divided by the root dry mass (m/g) and root tissue density (RTD) was calculated as root dry mass divided by fresh root volume (mg/mm 3 ).
Traits that were positively skewed were log-transformed (SLA and SRL) or square-root-transformed (lamina area) prior to analyses. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.10 | Separate principal components analyses (PCA) were conducted on soil variables and seedling functional traits to assess correlations between variables.The first three components of the soil PCA explained 79% of the measured variation.Soil PC1 (41%) was negatively loaded by pH, and positively loaded by electrical conductance, phosphorus and nitrate, while the soil PC2 (23%) was positively loaded by ammonium and negatively loaded by potassium (Figure S2 and Table S2).Lastly, soil PC3 (15%) was negatively loaded by bulk density and positively loaded by gravimetric water content.The two elevation variables (mean plot elevation and relative elevation) were not included in the soil PCA because they did not directly describe physical or chemical properties of the soil.
The first three components of the functional trait PCA explained 83% of the variation in the seven measured functional traits.Trait PC1 (52%) described overall seedling economics and was positively loaded by log-transformed SLA and log-transformed SRL, and negatively loaded by LDMC, RTD and WD (Figure 1; Table S2).Trait PC2 (18%) was negatively loaded by square-root-transformed lamina | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.11 | Plant performance was measured as the probability of surviving to 3.5 months, and the height growth increments of surviving plants.
Survival (1 = alive, 0 = dead) was modelled using a generalized linear mixed-effect model (GLMM) with binomial errors and logit link function.Random effects were included to account for the spatial nesting of plants within plots, plots within blocks and blocks within paddocks.Species was included as an additional random effect to account for the fact that members of the same species are likely to respond more similarly to each other than members of different groups.
The survival model included both environmental and plant-related variables as fixed effects.Environmental variables included the maximum temperature on the day of planting (herein maximum temperature), the plot-scale average elevation, the relative elevation of each stem within plots and the first and third soil principal components.We did not include soil PC2 because it was strongly correlated with plot-level mean elevation.We also included twoway interactions between maximum temperature and the soil and elevation variables to allow temperature effects on survival to vary depending on soil characteristics or the elevation of seedlings.The plant-related variables included the initial height of seedlings and the three selected functional traits, log-transformed SLA, squareroot-transformed lamina area and leaf structure (compound = 1, simple = 0).Seedling height was square-root-transformed to reduce the influence of occasional very tall seedlings.We also included a quadratic term for seedling height because exploratory plots indicated that height-survival relationships were not necessarily monotonic.Thus, we included square-root-transformed seedling height and seedling height to fit the quadratic relationship.Two-way interactions between seedling height (linear and quadratic terms) and trait variables were included to allow species' traits to modulate the shape of the height-survival relationships for each species.We also included random slopes for species with respect to seedling height (linear and quadratic terms).
Height growth of surviving seedlings was calculated as (height t1i -height t0i )/(t 1i -t 0i ) and expressed as cm/day, where t 0i was the date that seedling i was initially mapped and measured and t 1i is the date that seedling i was re-measured.Plants that were leaning | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.12 | The overall probability of surviving the first 3.5 months was 0.45.The Hardy mix had a slightly higher overall probability of survival (0.46) than the Fast mix (0.42), but this difference was not significant when assessed in a simple binomial GLMM (p = .063).Individual species survival probabilities ranged from 0.11 (Toona ciliata and Toechima tenax) to 0.87 (Acacia leiocalyx), while growth rates ranged from 0.02 (Toechima tenax) to 0.45 cm/day (Acacia leiocalyx; Table S1). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.13 | The survival model had a marginal R 2 of 0.13 (deviance explained by fixed effects alone), indicating that the fixed effects explained (Figure 2a).This interaction indicated that the probability of survival for seedlings planted in compact soils was lower when they were planted on hot days (Figure 3).We found a strong overall hump-shaped (concave quadratic) relationship between the initial height of seedlings and the probability of survival (Figure 2a), and the shape of this relationship was modulated to some extent by functional traits.In particular, interactions between each trait and the quadratic height term were all significant.For species with simple leaves, hump-shaped curves were narrowest for low SLA, small-leaved species and broadest for high SLA, large-leaved species (Figure 4).For species with compound leaves, curves tended to be boarder and have taller optimal heights.To assess how well these fixed-effect relationships matched observed height-survival relationships for each species, we fitted a simple GLMM to each species separately and only included a quadratic height term if it was significant.When modelled separately, only 8 of the 23 species had significant quadratic terms, while most of the remaining species had positive monotonic relationships (Figure 4).
Furthermore, the conditional R 2 (representing deviance explained by both fixed and random effects) was 0.41, much higher than the marginal R 2 .And as such, height-survival relationships fitted using species-specific random effects matched the observed data much more closely than those using fixed effects (Figure 4). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.14 | The marginal R 2 of the growth model was 0.27 (around double that of the survival model despite having fewer terms) and the conditional R 2 was 0.57.The mean elevation of plots was significantly and positively related to height growth (Figure 2b).The interaction between maximum temperature (on planting day) and relative elevation was also significant.This interaction indicated that growth was slower for seedlings planted on hot days, especially if they were planted in high positions within plots, compared to lower positions.
Initial seedling height and log-transformed SLA also significantly influenced height growth (Figure 2b).Acquisitive species predictably had faster growth than conservative species, but no other trait variable was related to growth.The relationship for seedling height was again quadratic, indicating that seedlings with intermediate initial heights grew faster than smaller and larger seedlings. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.15 | Suboptimal outcomes are common in restoration (Hobbs, 2007;Suding, 2011), but are rarely critically evaluated using empirical data or within the context of a randomized experimental design to reveal barriers impeding restoration.We found that survival and growth of planted seedlings were influenced by planting day temperature and soil properties, and depended on initial seedling height and species.
In particular, the overall relationship between initial height and survival was hump-shaped, indicating optimal seedling heights between 25 and 35 cm (for the purpose of surviving hot planting conditions).
Species varied in the shape of their height-survival relationships and
Plot from the survival model showing the interaction between soil PC3 (bulk density) and the maximum temperature on the day of planting.Soil PC3 is plotted against the probability of survival, with separate fitted lines (and associated 95% confidence intervals) for planting days with average maximum temperatures (29.5°C) and those with above average temperatures (32.5°C).
The underlying points and standard error bars shown for each relationship were calculated from 25 bins of observed binary data F I G U R E 4 Fitted relationships between the probability of survival and the initial seeding height (cm) for each of the 23 species examined in this study.Species are located approximately in trait space formed by log-transformed SLA (x-axis) and square-root-transformed lamina area (y-axis) for species with simple leaves (a) and compound leaves (b).Orange curves are predictions from the full survival model using the trait values of each species and fixed-effect model coefficients.All other explanatory variables were held at their mean values when plotting these 'trait-based' predictions.Red curves are species-specific predictions from the full survival model using the trait values of each species and each species' random-effect coefficients.Black curves are relationships fitted to each species separately in simpler GLMMs with square root (initial seedling height) as the sole fixed effect, unless the quadratic height term was significant in which case the quadratic was fitted (as indicated by *).Grey points are observed binary data that have been jittered to show trends.Black points and standard error bars were calculated from seven bins of observed binary data to indicate how the raw data fit each relationship.The solid portions of curves indicate the limits of observed seedling heights for each study species.The grey envelope shows the 25-35 cm height range.The number of observations used for each species is included in parentheses some of this variation was explained by three easy-to-measure functional traits.Growth was predictably faster for species with acquisitive seedling leaf economics.Environmental variables had significant but weaker effects on survival and growth than seedling heights. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.16 | The influence of soil bulk density and micro-topography on early plant performance has been highlighted in previous restoration studies (Cheesman, Preece, Oosterzee, Erskine, & Cernusak, 2018;Douterlungne et al., 2015;Martínez-Garza, Campo, Ricker, & Tobón, 2016;Martínez-Garza et al., 2011).The survival of seedlings in high bulk density soils is likely reduced because compaction can physically restrict root growth (Bassett et al., 2005;Skinner, Lunt, Spooner, & McIntyre, 2009), reduce water infiltration and retention (Chyba, Kroulík, Krištof, Misiewicz, & Chaney, 2014;Ekwue & Harrilal, 2010) and, consequently, limit plant available water (Archer & Smith, 1972).Similarly, seedlings planted on mounds may have reduced water availability as water moves to depressions, a process that is likely exacerbated on high bulk density soils due to reduced infiltration (Ekwue & Harrilal, 2010).It is also well understood that temperature can directly influence the growth and survival of seedlings (Baumber et al., 2017;Close & Davidson, 2003;Dordel, Seely, & Simard, 2011).Importantly, however, effects of these variables might also emerge through interactions.
In this study, the effect of hot planting days on survival depended on soil bulk density.While seedlings were watered multiple times immediately following planting, compact soils could have reduced the ability of seedlings to respond to acute heat stress.It is also possible that the combination of heat and compact soils resulted in shallower planting due to planter fatigue, which would have exposed seedling surface roots to extreme temperatures.Previous studies have identified planter effects, often relating reduced growth or survival to a lack of planting experience (Charles et al., 2018;Close & Davidson, 2003;Vogt, Watkins, Mincey, Patterson, & Fischer, 2015).While planting effects may be possible in our study, we did not record information on planter experience nor changes in planting quality and so cannot explore these factors further.
The growth of surviving seedlings was significantly slower in lower lying plots, most likely due to temporary waterlogging and associated reductions in water absorption, transpiration and growth rates compared to plants in more elevated plots (Delgado, Zúñiga-Feest, & Piper, 2018). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.17 | Seedling attributes are well understood to influence initial growth and survival in revegetation settings (Grossnickle, 2012).Many studies have reported that initial seedling height positively correlates with survival and growth of seedlings.Poor performance of small seedlings is generally attributed to the disturbance of root systems during planting (Close & Davidson, 2003;Martínez-Garza, Bongers, & Poorter, 2013) or suppression by recolonizing weeds (Cordell et al., 2016;Douterlungne et al., 2015).Instead of the increasing monotonic relationships between seedling height and survival reported in other studies (e.g.Kabrick, Knapp, Dey, & Larsen, 2015), we found strong support for an overall hump-shaped relationship (Figures 2a and4), strongly suggesting intermediate 'optimal' seedling heights for surviving extreme planting temperatures.The reduced survival and growth of larger seedlings may, in part, be due to stock being pot-bound, as 'root circling' in pots reduces anchorage as well as nutrient and water uptake (Allen, Harper, Bayer, & Brazee, 2017;South, Harrisa, Barnett, Hainds, & Gjerstad, 2005).Given that all plants were supplied in 50 mm planting tubes, larger seedlings probably also had lower root:shoot ratios, and thus a reduced capacity to supply water to transpiring foliage under stressful, open pasture situations (Close, Beadle, & Brown, 2005;Grossnickle, 2012).
It should be noted that only eight of the 23 species had significant quadratic height-survival relationships when modelled alone.
The strong quadratic relationship in the overall survival model occurred because many species with apparently monotonic relationships only included seedlings that spanned the lower half of the height range.When all species were modelled in a single GLMM, information was borrowed from species that spanned the full height range (the effect of partial pooling in multi-level models; Gelman & Hill, 2007), including a number of species with strong hump-shaped relationships.While it would have been ideal for all species to span a similar height range, sharing of information across species during model fitting provided conservative estimates of optimal heights for all species.
Significant interactions between trait variables and initial seedling heights explained species' differences in optimal seedling heights to some extent.Low SLA species had a narrower range of predicted optimal heights than those with high SLA, as did species with simple leaves compared to those with compound leaves.However, the result for compound leaves was influenced by two species with very low survival probabilities (Toona ciliata and Toechima tenax; Figure 4) and weakened when these species were removed (not shown).In general, trait effects on optimal heights were small in absolute terms, and most species were predicted to have highest survival with initial shoot heights between 25 and 35 cm tall.
Interestingly, species with more conservative traits did not consistently have high survival in this study.This is counter to reported trait-vital rate relationships in natural systems (Anderegg & HilleRisLambers, 2016;Markesteijn, Poorter, Bongers, Paz, & Sack, 2011) and a recent trait-based restoration study in the Australian tropics reporting a positive relationship between seedling survival and wood density (Charles et al., 2018).It is possible that seedling mortality due to planting stress, mishandling or a decline in planting quality (as previously discussed) could have obscured trait-survival relationships.More consistent with theoretical and empirical expectations (Shipley, Vile, Garnier, Wright, & Poorter, 2005), the growth of surviving seedlings was strongly and positively related to SLA.Thus, once seedlings overcome stresses associated with planting, commonly measured functional traits such as SLA are effective at predicting species' performance in restoration settings, especially if traits are measured on seedlings instead of adult plants (Gibert, Gray, Westoby, Wright, & Falster, 2016). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.18 | This study identified the antagonistic effects of planting in compact soils on hot days, as well as the importance of planting seedlings at optimum sizes (25-35 cm tall) to increase survival during the establishment period.Clearly, planting should be avoided on days with high maximum temperatures.However, it is also recognized that forecasts can be uncertain for the lead-times required to make planning decisions, and the ability to modify decisions upon new information can be limited (Hobday, Spillman, Eveson, & Hartog, 2016).For example, if planting is delayed due to extreme conditions, it may not be possible for nurseries to hold stock for long periods, and it may be difficult to reschedule planting or maintenance teams.Even if stock can be held, seedlings will likely develop taller shoots and thus be more susceptible to transplant shock when eventually planted.Due to the complexity of coordinating projects, and with record-breaking temperatures predicted to occur more frequently in Australia (CSIRO & Bureau of Meteorology, 2015) and elsewhere, it is reasonable to believe that many future restoration projects will experience similar conditions to those presented here.This highlights the need to either make better use of evidence-based strategies that improve planting success when environmental conditions are highly uncertain or develop implementation strategies that enable necessary flexibility to delay restoration projects when adverse environmental conditions arise (Hagger, Dwyer, Shoo, & Wilson, 2018). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.22 | We thank the Oxley Creek Catchment Association, and Phil Gunasekara and Chris Jensen in particular, for implementing the project and inviting us to collaborate so enthusiastically.We are also grateful to Carole Bristow, Mary-Lou Simpson and the Friends of Oxley Creek Common for advice and support.Thanks also to the BSU team, Green Army and all those involved in implementing and maintaining the plantings.Finally, thanks to Melissa Fedrigo and John Armston for generously sharing the LiDAR data.AUTH O R S ' CO NTR I B UTI O N S L.S. and J.M.D. designed the experiment, supervised its implementation and collected baseline mapping and plant height data.R.G. conducted all remaining fieldwork with support from L.S. and J.M.D. R.G. and J.M.D. analysed the data.R.G. wrote the first draft of the manuscript with input from L.S. and J.M.D. J.M.D. revised the manuscript for journal submission. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.23 | We thank the Oxley Creek Catchment Association, and Phil Gunasekara and Chris Jensen in particular, for implementing the project and inviting us to collaborate so enthusiastically.We are also grateful to Carole Bristow, Mary-Lou Simpson and the Friends of Oxley Creek Common for advice and support.Thanks also to the BSU team, Green Army and all those involved in implementing and maintaining the plantings.Finally, thanks to Melissa Fedrigo and John Armston for generously sharing the LiDAR data.AUTH O R S ' CO NTR I B UTI O N S L.S. and J.M.D. designed the experiment, supervised its implementation and collected baseline mapping and plant height data.R.G. conducted all remaining fieldwork with support from L.S. and J.M.D. R.G. and J.M.D. analysed the data.R.G. wrote the first draft of the manuscript with input from L.S. and J.M.D. J.M.D. revised the manuscript for journal submission. | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.26 | Data available via the Dryad Digital Repository https ://datad ryad.org/stash/ datas et/doi:10.5061/dryad.3kc37b(Gardiner, Shoo, & Dwyer, 2019).
John M. Dwyer https://orcid.org/0000-0001-7389-5528 | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.27 | Data available via the Dryad Digital Repository https ://datad ryad.org/stash/ datas et/doi:10.5061/dryad.3kc37b(Gardiner, Shoo, & Dwyer, 2019). | [
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Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.28 | John M. Dwyer https://orcid.org/0000-0001-7389-5528 | [
"bulk density",
"land degradation",
"pasture",
"restoration",
"revegetation",
"riparian",
"tubestock"
] |
Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.29 | Additional supporting information may be found online in the Supporting Information section at the end of the article.
How to cite this article: Gardiner R, Shoo LP, Dwyer JM.
Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration.J Appl Ecol.2019;00: 1-11.https ://doi.org/10.1111/1365-2664.13505 | [
"bulk density",
"land degradation",
"pasture",
"restoration",
"revegetation",
"riparian",
"tubestock"
] |
Gardiner et al. - 2019 - Look to seedling heights, rather than functional t.pdf.tei.xml | Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration | 1.30 | Additional supporting information may be found online in the Supporting Information section at the end of the article.
How to cite this article: Gardiner R, Shoo LP, Dwyer JM.
Look to seedling heights, rather than functional traits, to explain survival during extreme heat stress in the early stages of subtropical rainforest restoration.J Appl Ecol.2019;00: 1-11.https ://doi.org/10.1111/1365-2664.13505 | [
"bulk density",
"land degradation",
"pasture",
"restoration",
"revegetation",
"riparian",
"tubestock"
] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.1 | Shade tolerant tree species, also known as non-pioneers, are important elements of mature forest structure and composition, representing more than 80% of tree species in tropical forests (Denslow, 1987;Uhl et al., 1988).This guild recovers at a lower rate along succession because abandoned agricultural land in the tropics frequently lacks seed and seedling banks of shade tolerant species, which are typically of late successional status and display low seed dispersal reach (Martínez-Garza & Howe, 2003;Muñiz-Castro et al., 2012;Suganuma & Durigan, 2015).Since shade tolerant species are more vulnerable to habitat transformation and play a key role in ecosystem functioning, restoration focused on assisting the recovery of this guild in particular is urgently required (Aide et al., 2011;Holl et al., 2000;Martínez-Garza & Howe, 2003).
Tropical forest tree species are arranged along a continuum of shade tolerance (Wright et al., 2003), but most studies contrast the attributes and responses of pioneer versus shade tolerant species, with much still to be understood about the complexity that exists within the shade tolerant functional group (Valladares et al., 2016).
Shade tolerance is associated with a conservative-use strategy, featuring biomass and energy conservation traits that maximize survival under the low light conditions of the forest understorey (Kitajima, 1994;Reich et al., 2003;Veneklaas & Poorter, 1998).While shade tolerant species display high survival in the dark conditions of the forest understorey, experimental essays have shown that shade tolerant transplants can also establish under high irradiance in earlysuccessional environments if they arrive via ecological restoration efforts (Cole et al., 2011;Martínez-Garza et al., 2013;Pedraza & Williams-Linera, 2003).However, an important variation in survival has been found within the shade tolerant group in disturbed habitats (Benítez-Malvido et al., 2005;Camacho-Cruz et al., 2000;Hooper et al., 2002;Martínez-Garza et al., 2005;Muñiz-Castro et al., 2015).
Due to the high species diversity and variation in environmental conditions across altered tropical habitats, the development of criteria to select appropriate species for restoration efforts is central to design more effective interventions.
Plant functional traits have been proposed as a useful tool with which to predict the response of species to environmental conditions in restoration efforts (Martínez-Garza et al., 2005, 2013;Pywell et al., 2003).Based on the functional theory that establishes that species traits reflect resource use and life-history trade-offs, plant species can also be selected according to their functional traits with the aim of restoring ecosystem functions (Ostertag et al., 2015;Reich, 2014;Zirbel et al., 2017).Leaf mass area (LMA) is a descriptor of the efficiency of light capture per unit of carbon invested, and is thus a key trait involved in carbon return and balance that influences whole plant performance (Poorter et al., 2009;Reich, 2014).Leaf dry mass content (LDMC), the ratio of leaf dry mass to fresh mass, is a trait associated with drought resistance, presumably because high fibre content increases the hydraulic integrity of leaf veins, improving tissue resistance to the cell damage caused by severe drought (Kursar et al., 2009;Markesteijn et al., 2011).Plants adapted to shaded habitats tend to exhibit a conservative leaf morphological pattern, including high LMA and LDMC, traits that minimize carbon loss through respiration and maximize tissue defence (Reich et al., 2003;Valladares & Niinemets, 2008).These leaf traits favour persistence under low-resource conditions at the expense of low growth rates (Reich et al., 2003).Plant adaptations for coping with low light conditions are generally incompatible with those required for development under high light conditions and vice versa (Wright et al., 2010;Reich, 2014).However, there is limited information about the performance of shade tolerant planted trees in different disturbance settings and the use of LMA and LDMC as criteria for species-site matching to improve restoration effectiveness in highly diverse tropical landscapes.
In this study, we examined the performance of eight shade tolerant tree species (six endangered) in restoration plantings in various disturbed environments, including pastures, secondary forests and forests subjected to traditional selective logging, in a tropical montane cloud forest (TMCF) landscape.Restoration interventions are required under a variety of conditions because TMCF landscapes are mosaics of mature, secondary and degraded forest and active and abandoned pastures (Aide et al., 2011;CONABIO, 2010;Muñiz-Castro et al., 2012;Pedraza & Williams-Linera, 2003).One of the main causes of TMCF loss has been its transformation to agricultural land (Aide et al., 2011).Such agricultural and livestock production lands are subsequently abandoned, giving rise to secondary forests worldwide (Mulligan, 2011).In remnant forest fragments, unplanned logging, frequently targeting shade tolerant species, contributes to further degradation and the depletion of locally valuable tree species (Ramıŕez-Marcial et al., 2001; Rüger species.Species selection based on LMA could thus improve restoration initiative outcomes: tree species with high LMA present higher survival probability and can be introduced into pastures, secondary forests and selectively logged forests. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.2 | enrichment planting, late successional, leaf dry mass content, leaf mass area, pasture, secondary forest, seedling survival, traditional selective logging et al., 2008;Williams-Linera, 2002).Recruitment of many shade tolerant tree species is limited in such disturbed habitats (Muñiz-Castro et al., 2012;Ortiz-Colin et al., 2017;Toledo-Aceves et al., 2021).For instance, in Mexico, 60% of TMCF tree species face some degree of threat (González-Espinosa et al., 2011) and many of these are shade tolerant species for which no information is available regarding their performance under different altered environments.
We address two questions across the three altered environments:
(a) What are the survival and growth rates of seedling transplants of eight shade tolerant tree species?and (b) can LMA and LDMC be used as predictors of seedling survival and growth among shade tolerant species in restoration plantings?Given that some shade tolerant species can experience photoinhibition under high radiation (Agyeman et al., 1999) and that high air temperature and low water availability, as well as high soil compaction, are characteristic of tropical pastures (Williams-Linera et al., 1998;Loik & Holl, 1999), we expected lower seedling survival and growth in pastures than in the secondary and selectively logged forests.Under the high irradiance conditions of pastures where water deficit can be limiting (Martínez-Garza et al., 2013;Muñiz-Castro et al., 2015), we expected a strong relationship between LDMC and seedling survival.In contrast, relatively high canopy cover is maintained in cloud forests subjected to traditional selective logging (Ortiz-Colin et al., 2017;Toledo-Aceves et al., 2019), as well as in secondary cloud forests (Muñiz-Castro et al., 2012, 2015;Trujillo-Miranda et al., 2018).Given that, under low-resource conditions, conservative resource-use seedling traits can be associated with higher survival (Poorter & Bongers, 2006;Wright et al., 2010;Reich, 2014), we hypothesized that higher LMA will be related to higher seedling survival in the shaded understorey of the secondary and selectively logged forests. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.4 | The study sites are located in the TMCF of central Veracruz, Mexico, a region of high priority for conservation and restoration due to the high concentration of biodiversity and considerable loss of forest cover (CONABIO, 2010).In the study region, TMCF ranges between 1,200 and 2,200 m a.s.l.The climate is mild and humid throughout the year with three seasons: a wet-warm season (June-October), a humid-cool season (November-March), and a short dry-warm season (April-May).Mean annual temperature in the study sites ranges from 14 to 19°C, and total annual precipitation between 1,523 and 1,707 mm (based on interpolations calculated by Cuervo-Robayo et al., 2014).TMCF remnant fragments in this region are immersed in a landscape matrix dominated by agricultural land and agroforestry systems (mainly shade coffee plantations; CONABIO, 2010).
The study sites comprised four pastures, five secondary forests, and three forests subjected to traditional selective logging, which together represent the most common causes of TMCF transformation (Aide et al., 2011;CONABIO, 2010).Field work permits were secured for all sites.These agreements with the landowners were verbal, as is common in the study region.The study did not require ethical approval.Site locations and characteristics are presented in Appendix ST1.The imbalance in the number of sites was due to the limited number that shared similar management and environmental conditions and in which the landowners were interested in maintaining the restoration plantings.In all pastures, cattle had been recently excluded and the vegetation was dominated by the exotic grass Cynodon plectostachyus (Poaceae).The pastures presented isolated trees and shrubs, some of them remnants of the TMCF.Common tree species include Liquidambar styraciflua (Altingiaceae), Quercus spp.(Fagaceae), Vachellia pennatula (Fabaceae), Trichilia havanensis (Meliaceae) and Turpinia insignis (Staphyleaceae).Scattered trees in pastures are a common element in tropical landscapes, and occur in varying densities and spatial arrangements (Prevedello et al., 2018).
The secondary forests, developed in areas previously used as agricultural and agroforestry lands, were between 15 and 40 years of age.Traditional selective logging in the studied sites is of low intensity, focused on the extraction of oaks to produce charcoal and other hard wood species for rural construction needs (Ortiz-Colin et al., 2017;Williams-Linera, 2002), including the species used in this study.
Based on hemispherical photographs and convex densitometer measurements taken at the study sites (see methodology below), the mean percentage of canopy cover in pastures was 35.5 ± 2.41 (± SE; mean values of canopy cover per site are shown in Appendix ST1).In the secondary forests, this value was 90.8 ± 0.3 and in the logged forests it was 95.2 ± 0.2.Important variation in canopy cover occurred within each environment, but particularly in the pastures (minimummaximum: 0%-77%) due to the presence of scattered trees, and in the secondary forests (61%-99%).Lower variation characterized the logged forests (86%-99%).To characterize the soil, six samples were collected across each site and mixed into one compound sample for the following laboratory analysis: pH, soil bulk density, total carbon (C), nitrogen (N) and phosphorus (P) (see Appendix ST1). | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.5 | Eight native cloud forest tree species were used to establish the plantings, all of which were reported as being shade tolerant sensu Swaine and Whitmore (1988) (Table 1).Seedlings were grown in polythene bags (30 × 16 cm) with a mixture of forest soil and fine gravel Seedling survival and height were recorded in all the plants a month after planting and again 3 years after.Relative growth rate (RGR) in height was calculated as follows: RGR = (ln
where H is plant height; t is time, and the initial and final measurements are denoted by subscripts 1 and 2, respectively (Hunt, 1982).
At each site, 10 seedlings per species were selected at random and one mature undamaged leaf (developed after planting) was collected from each seedling (including the petiole) to quantify LMA and LDMC (following Pérez-Harguindeguy et al., 2013).Leaves were collected 1 year after planting, which provided information regarding the plant response to the environment.In a previous study, we measured leaf traits under two levels of shade in controlled conditions for the same set of species, with the exception of J. pyriformis Light affects tree seedling performance and leaf traits (Rüger et al., 2009;Poorter et al., 2009); therefore, for each of the 10 seedlings selected, canopy cover was measured using hemispherical photographs at the time of leaf collection.For this purpose, a camera (Canon Eos rebel XSi) equipped with a fisheye lens was set up at height 1.3 m above the ground on a tripod, levelled and orientated northwards.The photographs were analysed using Gap Light Analyzer (Version 2.0.). | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.6 | A generalized linear model (GLM; binomial family and logit link function) was used to assess the survival of each species as a function of environment.Environment was included as a factor with three categories (pasture, secondary forest, logged forest).The GLMs were performed using the proportion of surviving seedlings per site.A linear mixed effects model (LMM) with a Normal error distribution was used to assess the relative growth rate (RGR) of each species as a function of environment (factor with three categories); site was included as a random factor.
To evaluate the power of the functional traits LMA and LDMC as predictors of seedling survival (binomial response variable), a GLM was used including the three environments, irrespective of tree species.In the first full models, environment was included as a factor with three categories (pasture, secondary forest, logged forest), while LMA or LDMC, canopy cover and seedling initial height were included as covariables.The interactions of functional traits with canopy cover or environment and environment with canopy cover were included.Seedling initial height was included because it can affect the establishment of plantings (Benítez-Malvido et al., 2005) ).In all cases, the step-wise procedure based on the Akaike information criterion (∆AIC > 2) was used for selection of the best fitting model except in the case of tied values, in which case the most parsimonious model was selected (Crawley, 2013).
To evaluate relative growth rate (RGR) as function of the leaf traits (LMA or LDMC), LMM including the three environments were used, irrespective of tree species, in a similar manner to the procedure described above.In these models, site was included as a random factor.Since the interactions environment × LMA and environment × LDMC were significant (p < 0.05; Appendix ST4), a separate model was fitted for each environment in order to better understand the relationships between leaf functional traits and species growth.Models included the leaf traits (LMA or LDMC), canopy cover and their interaction (LMA × Canopy or LDMC × Canopy) in each environment, with site included as a random factor.The best fitting model was selected based on the criteria described above.
All analyses were carried out in R with the MASS and nlme libraries (R Core Team, 2019). | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.8 | Overall, proportion of survival was highest in logged forests (0.875 ± 0.028), followed by secondary forests (0.798 ± 0.034) and finally pastures (0.624 ± 0.055) after 3 years.Six species presented high survival (>0.70) across all the environments (Figure 1).
Survival was significantly lower in the pastures than in the secondary or logged forests in seven species (Figure 1; Appendix ST5).Only
Fraxinus uhdei, Juglans pyriformis and Magnolia vovidesii displayed significantly lower survival in secondary forests than in logged forests (Figure 1).Juglans pyriformis had the poorest performance, with the lowest survival of all species.
Including all species, RGR was highest in the logged forests (0.290 ± 0.01 cm cm -1 year -1 ), intermediate in secondary forests (0.261 ± 0.01 cm cm -1 year -1 ) and lowest in pastures (0.159 ± 0.03 cm cm -1 year -1 ).Significantly lower RGR was displayed in Oreomunnea mexicana in the pastures compared to the secondary and logged forests (Figure 1; Appendix ST5).A tendency for lower RGR in the pastures could be observed for other species but, probably due to high variation within the environments, the differences were not significant.The RGR of J. pyriformis in pastures was not included in the analysis because growth data was available for only three individuals.The average height values reached per species after 3 years are presented in Appendix ST2. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.9 | LMA was a strong predictor of seedling survival in the three environments (Table 2).LDMC was also significantly related to survival (Table 2).
In the three environments, the species with higher LMA displayed higher probability of survival and, those species with higher LDMC also had a higher probability of survival (Figure 2; Appendix SF6).In addition to the functional trait LMA, canopy cover contributed to a better model fit in the pastures and secondary forests (Table 2).However, canopy cover had no significant effect on the logged forest models.For the models that included LMA as a predictor of seedling survival, initial tree height was not significant in the pastures, but contributed to a better model fit in the secondary and logged forests.In the models that included LDMC, tree height also contributed to predicted survival in the two environments.The magnitude of this contribution is much lower compared to that of the functional traits (see regression coefficients in Table 2).Models with LMA as a predictor of survival explained more variation (Deviance: 27%-68%) than models with LDMC (21%-50%).
Selection of models based on ∆AIC can be found in Appendix ST4.
LMA was also a predictor of RGR in the pastures and secondary forests, but not in the logged forests (Table 3).In the pastures, lower RGR was related to higher LMA.In contrast, the opposite relationship was found in the secondary forests (Figure 3).Lower RGR was related to higher LDMC in the pastures but no significant relationship was found in the secondary and logged forests (Table 3).Canopy cover contributed to a better fit for models that included LMA as a predictor of RGR in pastures and logged forests, but had no significant effect on secondary forests (Table 3; Appendix ST4).For models that included LDMC as a predictor of RGR, canopy cover also contributed to a better model fit in the three environments (Table 3; Appendix ST4). | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.10 | Our results regarding the relationship between leaf traits and seedling performance within the shade tolerant functional group support a higher probability of survival in species with higher LMA under different disturbed environments.LDMC is also linked to seedling survival among the shade tolerant species, but to a lower extent than LMA.Overall, the high seedling survival found supports the potential of the studied species for use in restoration plantings, in particular under the canopy of secondary and selectively logged forests.
While conservative leaf attributes were linked to higher survival, this occurred at the expense of low growth under the conditions of the pastures, thus potentially reducing the establishment success of species of high LMA and LDMC in these altered environments. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.11 | The overall survival found after 3 years (76%) surpassed the average survival of 62% reported in a metanalysis of restoration seedling plantings in varied ecosystems (Palma & Laurance, 2015).
Important interspecific variation in transplant performance occurred among environments.However, due to differences among environments in terms of the size of transplants, time of planting and site characteristics (such as orientation, topography, soil conditions and prior land use) comparisons of performance among environments should be treated with some caution.The high variation in survival and growth in the pastures not only denotes the differential responses of the species to the environment, but also highlights the important heterogeneity present within and among the pastures, probably as result of variation in the soil conditions and in grass and canopy cover.Overall lower survival and growth in the pastures, in comparison to the secondary and logged forests, denotes that while most of the shade tolerant species were capable of resistance, they were probably stressed due to the extreme environmental conditions, such as high radiation and temperature accompanied by lower soil water availability, soil compaction, as well as the dominance of exotic grasses as reported for tropical pastures (Benítez-Malvido et al., 2005;Loik & Holl, 1999;Williams-Linera et al., 1998).Plants in high irradiance conditions have lower leaf area and greater investment in roots and stem support in order to meet the greater demand for nutrients and water imposed by greater transpiration (Poorter et al., 2019), although compacted soil can limit root expansion (Alameda & Villar, 2008).
Shade tolerant tree seedlings exposed to high solar radiation can also experience photoinhibition, which involves a reduction F I G U R E 1 Survival proportions (top) and relative growth rate (RGR; bottom) of cloud forest tree seedlings in restoration plantings established in pastures (white), secondary forests (black) and forests subjected to selective logging (grey).Values are mean ± standard error.Different letters denote significant differences within species among environments (p < 0.05).ns, not significant of light use efficiency in photosynthesis (Agyeman et al., 1999;Lovelock et al., 1994).Previous studies also report lower survival of shade tolerant transplants in pastures than in young secondary and mature tropical forests (Alvarez-Aquino et al., 2004;Benítez-Malvido et al., 2005;Muñiz-Castro et al., 2015).Nevertheless, the overall 62% survival recorded in the pastures after 3 years in our study is an indicator of an acceptable restoration success (Palma & Laurance, 2015) in this harsh environment, thus supporting the use of transplants of shade tolerant species in early successional environments to assist the recovery of this vulnerable group and its associated functions.
In the pastures, J. pyriformis in particular displayed very high mortality indicating that this species is unsuitable for these environments.These results confirm the findings of previous experiments in which J. pyriformis attained poor survival in open sites in comparison to seedlings under the canopy of Pinus patula plantations (Avendaño-Yanez et al., 2016).However, they contrast with the c. 60% survival of J. pyriformis reported after 20 months in a recently abandoned pasture (Pedraza & Williams-Linera, 2003).Oreomunnea mexicana and S. contrerasii also presented very low RGR in the pastures.This indicates that, although they were able to persist, the conditions were unfavourable for their development.In contrast, the similar performance across environments in M. vovidesii, Q. germana, Q. sartori and U. mexicana support their potential for establishment in varied disturbed habitats.
The range of conditions presented by the secondary forests is more favourable than those in pastures, where the developing canopy cover reduces intense radiation and buffers temperature and humidity (Alvarez-Aquino et al., 2004;Muñiz-Castro et al., 2015), resulting in higher survival and RGR.However, in comparison to mature forests, secondary forests are highly heterogeneous and have a higher tree density and dominance of the herbaceous strata (Kappelle et al., 1996;Muñiz-Castro et al., 2012;Trujillo-Miranda et al., 2018), factors that could be less favourable for tree early establishment.In the traditional selectively logged forests, the highest survival and RGR values were reached for most species.While lower growth rates could be expected in the low light environment of the forest understorey (Poorter & Bongers, 2006), the overall higher survival and growth rates found in the logged forests denote the high potential for successful establishment for all species under the prevalent conditions, which tend to be comparable to those of mature forests.Oscillations in humidity and temperature are also buffered in the understorey, which can have an important influence on plant carbon balance (Valladares et al., 2016).Interestingly, some species that presented high survival in the shaded logged forests also pre- | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.12 | Our results support that LMA is a strong predictor of seedling performance within the shade tolerant guild in disturbed habitats, ranging from recently abandoned pastures to secondary and selectively logged forests.Despite the smaller range of trait values expressed in the understorey of the selectively logged forests, a strong pattern emerged in which the species of higher LMA displayed higher survival.
In a previous study, LMA was also found to be a strong predictor of seedling survival among 13 shade tolerant tree species along an elevation gradient in the TMCF understory (Toledo-Aceves et al., 2019).
Under the low light conditions in the forest understorey, conservative resource-use seedling traits can be associated with higher survival (Poorter & Bongers, 2006;Reich, 2014).Additionally, our results denote that this conservative leaf trait provides resistance to the seedlings established in the transformed habitats.Indeed, higher LMA was also linked to higher survival in the pastures.Higher LMA is also associated with increased longevity and better protected leaves with lower nutrient concentrations, which lead to higher plant persistence (Poorter et al., 2009), but this leaf investment strategy occurs at the expense of lower growth in the pastures.Leaves with high LMA constrict potential growth under high irradiance due to lower light interception and less efficient carbon gain (Jantse-ten Klooster et al., 2007;Kitajima, 1994).Thus, a negative relationship between LMA and seedling growth rates has been widely documented (Poorter & Bongers, 2006;Poorter et al., 2009;Wright et al., 2010).Overall, our results are in line with previous studies showing the significant role of LMA in plant performance under controlled and natural forest conditions (Kitajima, 1994;Poorter & Bongers, 2006;Wright et al., 2010).
However, most studies have contrasted pioneer versus shade tolerant species, leaving an important gap in the information regarding differential performance within the shade tolerant functional group and, in particular, our results add to previous findings by providing support for the inclusion of shade tolerant species with high LMA in early successional habitats for restoration purposes.
Plastic adjustments in LMA could contribute to acclimatization to contrasting environments.Intraspecific LMA values were higher in the pastures and lower in the secondary forests, with the lowest values occurring in the selectively logged forests.Seedlings in the pastures were continuously exposed to high radiation and it is known that plant individuals exposed to high radiation display increased LMA (Poorter et al., 2009).Under high irradiance, a greater photosynthetic biomass per unit of leaf area enhances photosynthetic interception is augmented by increasing the area per unit of leaf biomass (Gratani et al., 2006;Poorter et al., 2009).While shade tolerant species display low plasticity in shaded environments (Toledo-Aceves et al., 2019;Valladares & Niinemets, 2008), the plastic leaf response to the varying levels of solar radiation present across the disturbed habitats may play a role in their establishment success.
Supporting our prediction, we found a relationship between LDMC and seedling survival under the high irradiance conditions of pastures where water deficit can be limiting, as was reported in non-pioneers planted in a pasture in prior tropical lowland rain forest (Martínez-Garza et al., 2013).LDMC also had a significant relationship with survival in the secondary and logged forests.Higher LDMC is associated with thicker, tougher leaves, which can be better protected from the damage caused by drought and herbivory and is also associated with longer leaf lifespans (Méndez-Alonzo et al., 2012;Pérez-Harguindeguy et al., 2013).Well-defended and long-lived leaves allow plants to maintain a positive carbon balance, enhancing plant survival (Coley & Barone, 1996;Poorter & Bongers, 2006;Reich et al., 2003).
Overall, the models that included LMA accounted for a greater variation in seedling survival, compared to those that included LDMC.LMA can therefore be considered a better predictor of seedling performance for shade tolerant species in cloud forest restoration plantings.
The canopy cover had a stronger influence on seedling survival and growth within the pastures than in the secondary forests, but no effect in the logged forests.Canopy cover plays an important role in the establishment of tree seedlings in early successional habitats.The development of canopy cover as forest succession progresses acts to reduce light availability in the understory, directly affecting tree recruitment (Bazzaz & Pickett, 1980).The absence of or low canopy cover in pastures leads to high levels of irradiance, higher variation in temperature, lower humidity and competition with grasses, which can limit establishment of shade tolerant species.However, our results indicate that most of the species introduced as 1-to 2-year-old seedlings could establish.The presence of scattered trees and shrubs contributes to a high heterogeneity in tropical pastures and can provide favourable conditions as they may act as nurse species and regeneration nuclei (Douterlungne et al., 2015;García-Orth & Martínez-Ramos, 2011).The higher establishment success of the introduced seedlings under the canopy of the secondary forests indicates the occurrence of facilitation by the secondary vegetation of forest development into advanced successional stages (Brown & Lugo, 1990).The small variation in canopy cover in the selectively logged forests may have led to the lack of a significant effect of this structural variable on seedling performance.In the shaded understorey, growth is light limited (Valladares et al., 2016), which could explain the small predictive power of the leaf traits studied on seedling growth in this environment.
Initial seedling height had apparently little influence on survival, which is contrary to that found in previous reports (Charles et al., 2018;Comita et al., 2009).Indeed, height at time of planting had no effect on survival in pastures when considering LMA, and made only a small contribution to explaining survival in secondary and logged forests.The seedlings might have been tall enough to overcome the limiting factors in the studied sites, but we did not further explore the effect of height since the seedlings were all within a small range of this parameter.These results also suggest that other factors are more determinant for the outcome, and support the predictive nature of LMA for tree seedling performance. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.13 | An overarching goal in ecological restoration is to re-establish biodiversity and ecological functions, for which the achievement of Valencia.We thank Victor Vásquez, M.A. García-Hernández, Claudia Gallardo, Javier Tolome, Marichu Peralta, Martín SanGabriel, Carlos Iglesias, Karina Osorio and Carlos Flores for their help in the field, and Graciela Sánchez for format edition.
Rosario Landgrave calculated temperature and precipitation for the study sites and produced graphics for the figures.K. Macmillan edited the text.We thank the anonymous reviewers for their suggestions to a previous version. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.14 | The authors declare that there is no conflict of interest. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.21 | Grant/Award Number: CB2014-01/238831; Instituto de Ecología A.C., Grant/Award Number: 20030-11218 | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.22 | Grant/Award Number: CB2014-01/238831; Instituto de Ecología A.C., Grant/Award Number: 20030-11218 | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.23 | Data available via the Dryad Digital repository https://doi.org/10.5061/dryad.q2bvq83mj (Toledo-Aceves et al., 2022).
Tarin Toledo-Aceves | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.24 | Data available via the Dryad Digital repository https://doi.org/10.5061/dryad.q2bvq83mj (Toledo-Aceves et al., 2022). | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.25 | Tarin Toledo-Aceves | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.26 | Additional supporting information may be found in the online version of the article at the publisher's website. | [] |
Toledo-Aceves et al. - Leaf functional traits predict shade tolerant tree.pdf.tei.xml | Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings | 2.27 | Additional supporting information may be found in the online version of the article at the publisher's website. | [] |
Charles - 2018 - Plant Functional Traits and Species Selection in T.pdf.tei.xml | Plant Functional Traits and Species Selection in Tropical Forest Restoration | 3.4 | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The data presented here was from research that was funded by the Australian Research Council (LP0989161). | [
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] |
Charles - 2018 - Plant Functional Traits and Species Selection in T.pdf.tei.xml | Plant Functional Traits and Species Selection in Tropical Forest Restoration | 3.5 | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The data presented here was from research that was funded by the Australian Research Council (LP0989161). | [
"functional traits",
"restoration",
"seedling growth",
"succession",
"tropical landscapes"
] |
Charles - 2018 - Plant Functional Traits and Species Selection in T.pdf.tei.xml | Plant Functional Traits and Species Selection in Tropical Forest Restoration | 3.6 | The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Lachlan S. Charles http://orcid.org/0000-0003-0055-2510 | [
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Charles - 2018 - Plant Functional Traits and Species Selection in T.pdf.tei.xml | Plant Functional Traits and Species Selection in Tropical Forest Restoration | 3.7 | The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. | [
"functional traits",
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Charles - 2018 - Plant Functional Traits and Species Selection in T.pdf.tei.xml | Plant Functional Traits and Species Selection in Tropical Forest Restoration | 3.8 | Lachlan S. Charles http://orcid.org/0000-0003-0055-2510 | [
"functional traits",
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"seedling growth",
"succession",
"tropical landscapes"
] |
Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.0 | Ecosystem restoration efforts are carried out by a variety of individuals and organizations with an equally varied set of goals, priorities, resources and time-scales.Once restoration of a degraded landscape or community is recognized as necessary, choosing which species to include in a restoration programme can be a difficult and valueladen process (Fry, Power & Manning 2013;Jones 2013).Species choice in restoration is often carried out with limited ecological information, particularly in regard to species interactions, successional processes and resource-use patterns.Selecting species can be particularly problematic in systems where there is no available baseline data on historical communities, or when restoration to a historic state is not feasible for ecological, logistic or economic reasons.In such cases, it may be preferable to focus on restoring site 'functionality' rather than returning to a historic baseline composition.We present a method for species selection in restoration, based on the collection of plant functional trait data.Using this method, managers can develop species mixtures with desired properties, including expected predictions of interspecific interactions and potential changes in biotic and abiotic conditions.
To illustrate this approach, we present a case study in Hawaiian lowland wet forests (HLWF) in which plant species for a restoration project were chosen based on their functional traits, in order to help land managers achieve their restoration goals while at the same time allowing researchers to better understand invasion resistance and ecosystem functioning.In our case, our choices led to the development of hybrid ecosystems including both native and introduced species.However, the approach that we present is not limited to novel or hybrid ecosystem creation, because the candidate species exam-ined and functional traits measured are determined by the user.
Whereas restoration usually implies a return to historic conditions, there is also growing attention to what some authors have called 'intervention ecology' (sensu Hobbs et al. 2011).This view emphasizes maintaining ecosystem services and functions (Hobbs et al. 2011).The contrast between these frameworks has been widely debated in the literature, and we do not intend to advocate the merit of one view over the other here.Rather, we present the logic behind a functional trait approach, describe why its use is feasible in a Hawaiian lowland forest and present a stepby-step approach to the method that can be applied to a wide variety of ecological systems.While we readily acknowledge that in our study system, we cannot return to a pre-human state, we still consider our approach 'restoration' in a broad sense. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.1 | Functional trait theory holds that characteristics or traits of each species reflect their resource use and life-history trade-offs (Reich 2014).A body of evidence shows that plant traits vary continuously, in predictable ways, along resource availability gradientssuggesting that species' functional traits can be linked to ecosystem properties and to ecosystem services (Lavorel 2013).Many studies have suggested that a robust image of a species' functional profile can be obtained by considering traits related to resource acquisition (e.g.foliar nitrogen, leaf area), resource limitation (e.g.midday leaf water potential, d 13 C-integrated water-use efficiency, leaf mass per area), reproductive investment (e.g.height, seed mass, dispersal type) and resource allocation patterns (e.g.leaf mass per area, specific root length, wood density) (Drenovsky & James 2010;Douma et al. 2012;Sonnier et al. 2012;Fry, Power & Manning 2013).For restoration, selecting species with certain functional trait values can influence species interactions (including competition) and ecosystem properties (Suding et al. 2008).For example, if creating a more fire-tolerant community is necessary, practitioners could select species that increase fine-fuel loads or species whose presence will favour succession from forest to grassland.Other considerations relevant to managers include importance to local wildlife, ability to serve as nurse logs, nitrogen-fixing properties, etc.
Restoration in a Hawaiian contexttesting ideas in a model system
The Hawaiian archipelago offers a unique setting for testing novel approaches to applied ecological questions.On one hand, Hawai'i is considered a model system for ecological research because of the combination of welldefined biotic and abiotic gradients with extreme examples of adaptive radiation (Vitousek 2004).On the other hand, like many isolated oceanic islands, the Hawaiian Islands have been extremely vulnerable to invasion and anthropogenic disturbance.The combination of simplicity and susceptibility presents a distinctive platform for conservation research.
Hawaiian lowland wet forests in particular present serious challenges for restoration and conservation.This forest type only exists as remnant patches, is always populated by invasive plant and animal species, and occurs near human habitation, where non-native propagule pressure is likely to be high (Zimmerman et al. 2008).In such ecosystems, it is important to recognize that not all non-native species are equally problematic.For example, many Polynesian introductions in Hawai'i have persisted in the landscape for up to 1000 years without becoming invasive.In addition to being culturally important, many non-invasive, non-native species have functional trait values, which are not present in the native flora.Breadfruit and coconut are two species that fit both of these criteria: they are not classified as invasive, and they have large leaves and seeds, unlike most native plants.From a restoration perspective, we aimed to test the hypothesis that designing communities with greater diversity of functional trait expression will lead to more invasion-resistant communities (Funk et al. 2008;Drenovsky & James 2010).To do so, we examined the functional traits of species using multivariate analysis; the technique projects each species to a specific x,y location in 'trait space,' which should reflect that species' functional profile.
Our restoration experiment was carried out at the Keaukaha Military Reservation (KMR) within the municipality of Hilo.This site retains a HLWF, which has a canopy that contains significant numbers of native tree species; however, it also has a long history of disturbance, and invasive trees represent up to half of the basal area (Ostertag et al. 2009).A previous removal experiment in this forest has shown that removal of invasive species alone is not enough to get a native forest back, despite its positive influence on seedling recruitment of native species (Cordell et al. 2009;Ostertag et al. 2009).Given current conditions, the forest is poised to lose most of its native species and become like most of the forest patches remaining in the lowlandsalmost exclusively non-native dominated.
The trait-based method we used employs five steps.We illustrate the method's use in HLWF; however, it need not include the use of non-native species, and it is exportable to other ecosystems.
Land managers at KMR are charged with specific targets developed by the US military.These are as follows: (i) conserving and encouraging the regeneration of native biodiversity at the site, (ii) controlling invasive species and (iii) encouraging carbon storage on the landscape.Because restoring this area to an all-native ecosystem is no longer economically feasible, we elected to create hybrid ecosystems.Our experimental communities were assembled using species whose combined functional trait profiles addressed these three management goals to meet restoration objectives of the main stakeholders. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.2 | We had two main considerations for choosing the traits on which to base our species selection.First, we wanted to estimate the 'functional role' or 'strategy' (sensu Reich 2014) of different species within HLWF environments, and secondly, we wanted to identify specific traits that would address our restoration objectives.We first looked at the overall distribution of HLWF species in trait space and then focused on how different species expressed particular traits that were relevant to our restoration objectives.
The functional traits measured (Table 1) were derived from literature and field data.We included traits that are informative in the ecological context of HLWF.For example, knowing that light (rather than nutrient or water availability) is the primary limiting resource for native species in this forest (Ostertag et al. 2009), we included a suite of traits related to light capture (as well as canopy and understorey architecture; including leaf : petiole ratio, plant height and canopy shape).Many of the traits we measured are informative of more than one aspect of a plant species' ecological strategy, and some traits can be used as proxies for others, which are more difficult to measure.For example, we included both maximum plant height and seed mass as proxies for dispersal distance (see Thomson et al. 2011). | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.3 | The potential species pool included native species thought to have been present at the site historically, native species currently found at the site and non-native species already found in the region that are believed to pose low invasion risk.To address the latter, we based our decisions on the Hawaiian Weed Risk Assessment score (Daehler et al. 2004; see http://www.botany.hawaii.edu/faculty/daehler/wra/).In a past study at KMR, woody species richness was nine native species and 10 introduced species (Zimmerman et al. 2008).
Due to a lack of formal records of species occurrence and poor pollen preservation, it is difficult to ascertain which species would have been found historically in any given area of HLWF.Because we suspect that some native species once found at KMR have been locally extirpated, we cast a wide net in order to determine which species could potentially be used for restoring the forest.We included native species with similar environmental requirements that could have occurred in HLWF, based on historical range descriptions by Wagner, Herbst & Sohmer (1999) and previous field experience.
Once the list of potential species was compiled (including 17 non-native and 19 native species), we eliminated species which were too difficult to sample or propagate, and species for which sufficient data were not availablereducing our pool to 16 native species and 15 non-native species.Other limiting factors that practitioners should consider include economics (e.g.cost of seeds, plants, labour or time), logistics (e.g.availability of species, project or budget timelines), resilience to climatic change or disturbance regimes, as well as the goals and expectations of stakeholders. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.4 | We sampled plant traits across the full range of conditions in which HLWF is found regionally in order to account for both site and environmental heterogeneity.In total, we sampled traits at 25 sites throughout east Hawai'i Island, in addition to using existing data from the literature.By far, the most time-consuming and effortconsuming steps in making species choices by using traits are creating the potential species pool and collecting trait data.However, compilations of trait data are increasingly common world-wide.A variety of data sets are available either directly from researchers and agencies involved in the region (e.g. the Australian Virtual Herbarium) or from collaborative data base compilations (e.g.TRY (http://www.try-db.org),DRYAD (http://datadryad.org/) or LEDA (http://www.leda-traitbase.org/LEDAportal/).
Once the trait data were assembled, we grouped the information for each trait into quartiles in order to account for both analytical constraints and the nature of trait data.Because we considered both multiple traits and multiple species, making meaningful comparisons was often challenging.For example, measures of seed mass spanned many orders of magnitude from <0Á001 to 576 g, while the maximum difference in leaf thickness measured no more than 0Á4 mm (Table 1).Furthermore, sometimes it is necessary to compare ratios and categories to numerical data.The use of quartiles places more importance on the relative differences in trait values.This approach emphasizes species which are 'outliers' rather than species that display the 'middle range' (i.e. the most or least effective water use vs. the species whose values are nearest to the group mean).This type of data manipulation is appropriate to the multivariate ordination analysis we later employ. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.5 | We used a principal components analysis (PCA) to show how the selected species are arranged, relative to one another, in trait space and to provide an idea of each spe- cies' functional profile.Other multivariate techniques could be substituted.An important outcome of the PCA was the prominent separation of native and non-native species in distinct areas of the graph, primarily based on leaf mass per area, carbon : nitrogen ratio and foliar nitrogen (Fig. 1a).This result reinforces that species in Hawai'i with different biogeographic origins are functionally divergent.Hawaiian species tend to be conservative in regard to growth and nutrient acquisition, whereas the non-native species tend towards faster life-history strategies.Carbon turnover rates are substantially higher in invasive-dominated communities than they are in native plant communities (Hughes et al. 2014).With this in mind, we ran an additional PCA using only traits associated with carbon cycling (including specific gravity, foliar chemistry and maximum plant height) to find which species from our pool have the greatest likelihood of slow rates of carbon turnover (Fig. 1b).We identified two groups of four 'core species' whose trait profiles indicate either slow or moderate carbon turnover, and then located these core species on the first (general) PCA of all species traits (Fig. 1a).Based on Euclidian distances within the general PCA, we determined a centroid point equidistant from all four species in each group and then selected groups of additional species that were closer (i.e.most similar and potentially most redundant) and furthest (i.e.least similar and potentially most complementary) to each centroid (Fig. 1c).The motivation for this selection was that we are trying to determine whether combinations of species whose traits are more dissimilar (complementary) or similar (redundant) confer greater invasion resistance in the hybrid forests (Funk et al. 2008).
Once species are displayed in trait space, a variety of decisions can be made based on their relative functional profiles.While our process included two PCAs, for some restoration objectives, decisions could be made with only one (Fig. 1a).In our case, pragmatic and logistical concerns were addressed at this point.For example, one of our species was removed, because although it was culturally important, and functionally a good fit, it is unable to maintain itself without direct, ongoing human intervention.Alternatively, when several species proved to be functionally similar, final species choice was based on pragmatic considerations such as seedling cost, availability within given time frames or projected time to maturity.In short, the process allows for an unbiased way to let the data dictate a first step, and then, the practical concerns of practitioners can be layered onto the final species choices. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.6 | To date, multiple studies have evaluated the role of functional traits in ecosystems and/or been carried out in controlled settings and relatively simple ecosystems such as grasslands (Drenovsky & James 2010;Fry, Power & Manning 2013;Lavorel 2013).We show that a trait-based approach can also be used in a tropical forest system.Because the metrics are based on site-specific restoration objectives, this approach is generalizable, flexible and transferable across ecosystems and taxa.Once refined, the approach can be applied with relatively little effort by managers.
We named our project Liko N a Pilina, which translates loosely to 'the budding new relationships', to emphasize the developing associations among the species, and the intertwining of fundamental science questions and the practical needs of land managers faced with a formidable task made more daunting by lack of information on how to achieve their restoration goals.While it is too early to determine if the treatments met our objectives, results 1 year after planting show >90% survival of outplants and increased seedling recruitment by native species.By creating hybrid ecosystems using both native and nonnative species (many of which are culturally significant), the project melds together traditional and contemporary approaches to forest management.This has proved attractive to the general public, school groups and summer programmes, allowing us to tap into an eager set of volunteers.In our experience, the level of community engagement is unusual for science-based restoration experiments.Thus, this new type of restoration can fulfil many goals, providing a rigorous way to choose species for restoration as well as providing a simple framework that is appealing to local communities. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.11 | We thank the Strategic Environmental Research and Development Program for funding (Project RC-2117).Access to field sites was provided by the County of Hawai'i, Division of Forestry and Wildlife and Hawai'i Army National Guard.Jodie Schulten, Amanda Uowolo, Kai McGuire-Turcotte, Chaunda Tactay and Chris Chu assisted with field data collection and laboratory processing.Analyses were conducted by Lucas Mead and Tara Holitzki at the UH Hilo Analytical Laboratory; they were supported in part by National Science Foundation award number EPS-0903833.We thank our partners in the Hawai'i Army National Guard Environmental Office (Angela Kieran-Vast and Craig Blaisdell) and staff at Keaukaha Military Reservation for facilitating the establishment of the Liko N a Pilina project.Karen Holl provided comments on an earlier draft. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.12 | We thank the Strategic Environmental Research and Development Program for funding (Project RC-2117).Access to field sites was provided by the County of Hawai'i, Division of Forestry and Wildlife and Hawai'i Army National Guard.Jodie Schulten, Amanda Uowolo, Kai McGuire-Turcotte, Chaunda Tactay and Chris Chu assisted with field data collection and laboratory processing.Analyses were conducted by Lucas Mead and Tara Holitzki at the UH Hilo Analytical Laboratory; they were supported in part by National Science Foundation award number EPS-0903833.We thank our partners in the Hawai'i Army National Guard Environmental Office (Angela Kieran-Vast and Craig Blaisdell) and staff at Keaukaha Military Reservation for facilitating the establishment of the Liko N a Pilina project.Karen Holl provided comments on an earlier draft. | [
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"Hawaiian lowland wet forest",
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.13 | Data have not been archived because this article does not contain data. | [
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Ostertag et al. - 2015 - Using plant functional traits to restore Hawaiian .pdf.tei.xml | Using plant functional traits to restore Hawaiian rainforest | 4.14 | Data have not been archived because this article does not contain data. | [
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Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.1 | Ecological restoration usually focuses on restoring species composition of pre-disturbance communities (Engst et al. 2016) either by reintroducing targeted native species or by favoring their spontaneous recolonization on degraded sites (Rodrigues et al. 2009;Chazdon & Guariguata 2016).Restoration programs usually rely on the premise that the increased abundance of species typically found in reference ecosystems would support the recovery of key ecosystem services such as nutrient cycling, soil protection, and water supply (SER 2004;Wortley et al. 2013;Laughlin 2014;Kollmann et al. 2016).However, recent metaanalyses on restoration success have demonstrated that either species composition or ecosystem services have not been fully recovered (Crouzeilles et al. 2016;Moreno-Mateos et al. 2017;Shimamoto et al. 2018).Incomplete recovery might be explained by a lack of an explicit approach to restore ecosystem services, which are the benefits people obtain from ecosystems, such as provisioning (e.g.fresh water), supporting (e.g.nutrient cycling), regulating (e.g.climate regulation), and cultural services (e.g.recreation) (MEA 2005).Biodiversity drives ecosystem functions that underlie ecosystem service (MEA 2005) provisioning by influencing ecosystem processes that directly or indirectly affect energy and material flows (Díaz et al. 2015).
Here we consider that every ecosystem function is an ecosystem service by its direct or indirect effects on ecosystem benefits to humans.
If the loss of ecosystem services is a major concern in a world facing severe global changes (MEA 2005;IPBES 2019), services must be explicit targets in restoration programs (Ehrenfeld & Toth 1997;Funk et al. 2008;Montoya et al. 2012;Wortley et al. 2013;Laughlin 2014;Perring et al. 2015;Engst et al. 2016), and not only consequences of interventions guided for achieving other goals.However, a recent literature review found that most (52%) restoration project objectives have not explicitly considered ecosystem services (Kollmann et al. 2016).Although several papers and assessments have emphasized this need (e.g.SER 2004;MEA 2005;Montoya et al. 2012;Brancalion & Holl 2016) and some frameworks linking traits and services have been proposed (Laughlin 2014;Laughlin et al. 2018;Rayome et al. 2019), a clear framework on which services to consider and how to restore them using species traits is still missing (Kollmann et al. 2016), especially for broad-scale restoration.Therefore, the stability of communities over time and the services provided by restored ecosystems to support human well-being, especially under global changes, may be compromised.
Operationalizing ecosystem services as targets for restoration is still a major challenge (Kollmann et al. 2016), and the selection of species to attain target services is not trivial because multiple species may support several ecosystem services (Lavorel & Garnier 2002;Díaz et al. 2007).Knowing which species combination to reintroduce to restore ecosystem services while maintaining a stable restored community is a challenging task (Laughlin 2014;Laughlin et al. 2018).Traditional approaches to consider ecosystem services in restoration programs involve direct measurement of abiotic and biotic components (SER 2004).However, quantifying abiotic features hardly enables a clear association of ecosystem service and species composition, which has led ecologists to look at more meaningful and predictable parameters such as functional traits (Liebsch et al. 2008;Suganuma & Durigan 2015;Laughlin et al. 2017;Brancalion et al. 2018).
A common approach has been to classify species into "functional groups" a priori, and then trust that reintroducing members of each group would restore the targeted ecosystem services (Perring et al. 2012).Nonetheless, a priori grouping of species into "functional groups" has several shortcomings.First, the performance of a species in a given functional group may be context dependent, as the performance of a function may be determined by trait-environment interactions (Brancalion et al. 2019a).Second, a species may belong to more than one functional group, and thereby perform several services in varying degrees (Díaz et al. 2007).Third, the link between species and ecosystem services is mediated by effect traits, that is, those that impact ecosystem processes (Lavorel & Garnier 2002;Violle et al. 2007).Species have multiple effect traits that contribute independently or jointly to ecosystem services (Gamfeldt et al. 2008).Therefore, the effect of multiple species on different ecosystem services is more a multivariate continuum than a scenario with species classified into discrete functional groups.Fourth, by separating species into functional groups a priori, one may at best confirm an arbitrary decision rather than interpret how species relate to functional trait patterns a posteriori, which may provide information on which species contribute more to different ecosystem services.Because of the limitations to operationalize the use of traditional approaches of ecosystem service recovery by ecological restoration, new directions have been proposed in the literature (Funk et al. 2008;Laughlin 2014).
The claim for a new paradigm on ecological restoration demands a comparison on the advantages and disadvantages relative to the traditional paradigm (Table 1).Although restoration ecology studies have increasingly considered ecosystem services, on-the-ground restoration programs often experience difficulty applying biodiversity and ecosystem service theory in concert with the traditional restoration approach (Aerts & Honnay 2011;Kollmann et al. 2016;Naeem 2016).An alternative would be to use species effect traits clearly related to ecosystem services (Lavorel & Garnier 2002;Funk et al. 2008).An advantage of trait-based approaches is that they enable the assessment of the relationship between community assembly and ecosystem functioning (Laughlin 2014).This advantage exists because functional traits are two-sided coins in which "function" can be related either to response to community assembly processes mediated by abiotic and biotic interactions or to effect on ecosystem services (Lavorel & Garnier 2002;Violle et al. 2007;Suding et al. 2008).
Functional traits may be classified into "soft traits," that is, those that are easy and quick to measure, and "hard traits," that is, those that are harder to measure (Hodgson et al. 1999).Hard traits are usually more closely linked to mechanistic processes, but as they are hard to measure for a great number of species, they are usually replaced by soft traits that might be relevant to such processes.For instance, seed mass is a soft trait because it is relatively easy to collect and process seeds to get dry mass values, and at the same time represents correlated functions harder to measure, such as competition versus colonization abilities at initial stages of development (Turnbull et al. 1999).Functional traits may be related to mechanisms of community assembly such as competitive hierarchies (Keddy & Shipley 1989), limiting similarity (MacArthur & Levins 1967) and environmental filtering (Kraft et al. 2015).They can also relate to processes explaining how dominance of traits (i.e.mass ratio hypothesis, Grime 1998;and priority effects, Weidlich et al. 2018) or diversity of traits (i.e.niche complementarity, Tilman et al. 1997) influences ecosystem functioning.While the information on how traits affect ecosystem services relates to the biotic and abiotic components at an ecosystem-level framework, approaching how traits mediate community assembly mechanisms may provide information on community stability and resistance to environmental changes (Laughlin 2014).
The advantages of the trait-based approach for restoration rely, however, on the availability of information on functional traits for the species used in restoration projects (Table 1).Such shortcoming makes the use of a trait-based restoration especially challenging for species-rich tropical ecosystems, because the existing knowledge on functional traits is still concentrated in temperate species (Hortal et al. 2015).Moreover, in tropical regions, there are many taxonomic uncertainties due to high species richness, lack of surveys, and overall low scientific development (Karlsson et al. 2007;Hortal et al. 2015;Wilson et al. 2016).Consequently, there is a lack of basic taxonomic, ecological, and physiological information for a great number of native species in the tropics, making it hard to recompose species composition of originally mega-diverse tropical communities.This knowledge gap is an important barrier for the ambitious restoration programs planned for the upcoming years, which have a clear focus on supporting human well-being through the recovery of ecosystem services in degraded landscapes worldwide, but especially in the tropics (Brancalion et al. 2019b).Synthesizing the existing knowledge on functional trait-based ecological restoration is imperative to transform the potential of this conceptual approach into more successful onthe-ground projects, fostering research to new directions that may better match the demands of restoration practitioners.
The central goal of this article is to assess the progress made in ecosystem service-and trait-based restoration.For this, we systematically reviewed the literature on restoration ecology (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).Although we did this review for all kinds of ecosystems, we had a special focus in tropical ecosystems, which have the most pressing needs to advance with the inclusion of a trait-based approach to guide restoration.Specifically, we answered: (1) is there an increasing trend of published studies on ecological restoration which evaluate ecosystem services and functional traits?(2) how often have ecosystem services and functional traits been used as targets for ecological restoration in general and across continents?(3) are there biases related to target organism, ecosystem type, and geography in the literature towards some ecosystem service type and functional trait?(4) what are the most common approaches of using ecosystem services and functional traits in restoration: a priori or a posteriori?(5) which functional traits are used to evaluate different ecosystem services in ecological restoration?(6) what are the existing trait-based frameworks to target ecosystem services in restoration?After answering these questions, we summarized possible connections between simple-to-measure plant functional traits and ecosystem services and discussed the feasibility of applying trait-based frameworks widely in the tropics, considering both the basic limitations such as the lack of trait information or species in nurseries and the concentration of the >140 million hectares of restoration committed to as part of the Bonn Challenge and the New York Declaration on Forests in tropical countries. | [
"ecosystem function",
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"functional restoration",
"reassembly",
"restoration target",
"trait-based restoration Conceptual Implications"
] |
Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.2 | We systematically reviewed the ISI Web of Science database for papers published between 2007 and 2017.We chose 2007 as a start because a widely accepted concept of functional traits had been published that year (Violle et al. 2007).We followed the PRISMA protocol (http://www.prisma-statement.org/) for paper search and data collection standardization (Supplement S1).We looked for articles and reviews within the categories "Ecology," "Biodiversity conservation," and "Forestry."
We made a general search of papers for obtaining overall patterns within the restoration ecology field.We used the following keywords related to ecological restoration in the title of the papers: restor* OR reforest* OR recover* OR regenerat* OR reintroduc* OR refaunat*.The general search resulted in Table 1.The traditional approach for ecological restoration is based on species composition of the original community, often informed by reference ecosystems, and does not use species traits, while the trait-based approach relies upon theories of community assembly and biodiversity and ecosystem functioning, using functional traits that inform about species coexistence and species contribution to ecosystem services.7,362 papers potentially on restoration.We obtained the information on the number of papers on ecosystem service and functional trait per country by using ISI Web of Science summarizing tools.In order to map the general trend of publication on functional restoration across continents, we refined this general search by using the following sets of keywords related to services or traits in the topic of papers (title, abstract, and keywords): function* OR service* OR guild* and trait* OR attribute*.We used each set of keywords at a time to filter only studies on services or traits, and together to obtain studies that evaluated both subjects simultaneously (by connecting keywords sets with an "AND").This last refined search (i.e.resulting from searching service-related AND trait-related keywords within the general search) resulted in 337 papers, which were used in the following steps of the methods for obtaining more detailed information (hereafter, "specific search").
We obtained the full text of 334 out of the 337 papers from the specific search: 327 in English, 4 in Portuguese, and 3 in Spanish.We evaluated these 334 papers to identify those about restoration, that is, those with the aim of reestablishing, by means of re-introduction or natural recovery of native species, a preexisting community entirely or partially lost due to anthropogenic causes.We consider as anthropogenic causes of a native community entire or partial loss phenomena (e.g.land use change, biological invasions).We did not include papers that studied the reintroduction of only one species with no focus on native species regeneration; assessed the recovery of a community after a natural disturbance (e.g.hurricane); aimed to assess restoration success by interviewing local human populations; aimed to spatially prioritize areas for restoration; or did not evaluate biological parameters.
We identified 265 papers on restoration (Supplement S2), which we screened to check: whether the study discussed or analyzed ecosystem services, and if the use of services was a priori (services used as targets in restoration) or a posteriori (services monitored in restored communities); whether the study discussed or analyzed functional traits, and if the use of traits was a priori (traits used to target services in restoration) or a posteriori (traits used to monitor restored communities); what ecosystem services were assessed; what functional traits were assessed; whether the study was carried in the tropics or elsewhere; the country of the study ecosystem; whether the focus ecosystem was terrestrial or aquatic; what was the specific terrestrial ecosystem type (Olson et al. 2001); what organism was used in restoration or was assessed during natural recovery; and whether the study proposed explicit and generalizable trait-based framework to select species for targeting ecosystem services in restoration (i.e. a framework that might be applied in different ecosystems).
Ecosystem services followed the classification by MEA (2005).We considered functional traits sensu Violle et al. (2007), that is, morphological, physiological, or phenological characteristics of organisms that impact species fitness and ecosystem processes and expanded on that definition by also considering life history and performance traits.Plant functional traits were classified into leaf economics spectrum, stem, root (including P and N acquisition strategy), flower (including pollination syndrome), diaspore (including dispersal syndrome, dispersal ability), whole-plant (maximum height, crown architecture), life history (life form, lifespan, carnivory), and performance traits (survival rate, growth rate, reproduction rate, physiological rate, competitive ability, stress tolerance, disturbance resistance/resilience, Grime's competitive/stress-tolerant/ruderal ecological strategies, total/aboveground/ belowground biomass, vegetation strata, shade tolerance).We did not consider as functional traits general grouping of species based on habitat use (e.g.pioneer vs. non-pioneer) or mixtures of life forms with phylogeny (e.g.graminoids).For additional information screened from papers, see Supplement S3.
In order to test whether the rate of accumulated number of all papers on restoration (from general search; n = 7,362) differed from the rate of accumulated number of papers on restoration that also evaluated ecosystem services, functional traits, and both (from the specific search) as n Service = 135, n Trait = 169, and n S + T = 76, respectively, along time, we fitted generalized linear models (GLMs) using function "glm" of the software R (R Core Team 2019).We considered number of papers in each category as the response and year of publication as the predictor variable.We also fitted generalized nonlinear models (GNMs) with an exponential mathematical function using the "gnm" function of R package "gnm," but the linear models presented best fitting as shown by the Akaike information criterion: GLM All = 872.6,GNM All = 1882.4;GLM Service = 92.5,GNM Service = 122.9;GLM Trait = 96.2,GNM Trait = 129.7;GLM S + T = 73.8,GNM S + T = 93.9.We used Poisson distribution (in GLM and GNM) because response variables consisted of count data.
We used t tests to compare GLM slopes, considering the slopes are part of a Gaussian population of slopes of every possible hypothetical models.The t tests compared the slope of the GLM on the number of papers per each category of the specific search and the GLM on all papers, as follows: All versus Service; All versus Traits; and All versus Service + Trait.For computing the t tests, we used the estimate and the standard error of the estimate of the slope from the GLM.
We used Pearson's chi-square with Monte Carlo randomization tests (R function "χ 2 test") to check for dependency of relative proportion of studies about services, traits, or both on ecosystem type; services on ecosystem type and geography (tropical or not); and plant traits on ecosystem service categories.We also used chi-square tests to check for the prevalence of a priori versus a posteriori approaches in studies on services or traits. | [
"ecosystem function",
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] |
Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.3 | There was an increasing trend of publication of papers on restoration (specific search; n = 265) that assessed ecosystem services, functional traits, or both across the time period analyzed (n = 228), which accompanied the trend for the whole area of restoration (general search; n = 7,362) (Fig. 1).The rates of increase in these specific subjects ( Most of the 7,362 papers (from the general search) were published by authors in North America and Europe, indicating the major role of developed countries on the ecological restoration literature (Fig. 2).Among the 7,362 studies, the proportion of papers that mentioned services, traits, or both was nearly constant across continents (Fig. 2).
We identified 228 out of 265 papers (from the specific search) that evaluated either ecosystem services, functional traits, or both.More than half of these papers focused on ecosystem services, whereas two-thirds evaluated functional traits (Table 2).
Most studies involved plants (72%), while 23% studied animals and only 3% micro-organisms, fungi, or algae.Most papers focused on non-tropical ecosystems (68%), terrestrial habitats (69%), and had forests or woodlands as the target habitat (43%).Only 26% targeted open vegetation such as grasslands and savannas, 16% freshwater ecosystems (rivers, streams, lakes, or wetlands), and 7% marine ecosystems (oceans zones, coral reefs, estuaries, and salt marshes).
The relative proportion of studies (from the specific search) on ecosystem services, functional traits, or both depended on the ecosystem type (Fig. 3).Temperate forests and temperate open vegetation were the most common terrestrial ecosystem types in studies that assessed ecosystem services and functional traits, respectively, while tropical forests (dry and moist) were relatively common in studies using functional traits (Fig. 3).Studies integrating both services and traits were more evenly distributed across ecosystem types, being especially common in temperate open vegetation and tropical rainforests (Fig. 3).
Supporting and regulating were the most common ecosystem services evaluated (Fig. 4).All categories of services (supporting, regulating, provisioning, and cultural) were found in both aquatic and terrestrial ecosystems in non-tropical ecosystems, but no papers assessing provisioning or cultural services were found in the tropics (Fig. 4).
Most studies (from the specific search) that focused on ecosystem services assessed them a posteriori (χ 2 = 62.59, p < 0.001), that is, they assessed services after rather than before the beginning of the restoration (Table 2).Similarly, most studies on functional traits assessed them after rather than before the restoration began (χ 2 = 112.51,p < 0.001).Overall, more studies assessed functional traits (64%) than ecosystem services (51%), especially a posteriori (72% and 61%, respectively).One-third of the studies assessed both ecosystem services and functional traits.Only 31 studies (11.7%) evaluated both a priori, thereby using traits to target services before the start of restoration.
All categories of plant functional traits were found in papers (from the specific search) that evaluated regulating or supporting ecosystem services, while only performance and diasporerelated traits were evaluated together with cultural or provisioning services (Fig. 5).Whole-plant, diaspore, and performance traits were the most common plant traits studied (Fig. 5).Nutrient cycling and food web and community dynamics were the most common supporting services evaluated, while climate regulation, erosion regulation, and water cycling were the most common regulating services (Fig. S1).The different categories of functional traits were well distributed in terrestrial and aquatic ecosystems outside and in the tropics (Figs.S2 &S3).
Only 10 out of 265 screened papers (3.8%) presented a clear trait-based framework to target ecosystem services in restoration (Table 3).
We provided a summary of well-established linkages between plant functional traits and target ecosystem services to guide restoration practitioners (Table 4).
See the number of papers per country and journal in Tables S1 andS2. | [
"ecosystem function",
"functional diversity",
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Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.4 | General Trends and Biases in Functional Restoration Literature Our review elucidates that restoration studies taking into account ecosystem services and functional traits have been growing in number at a similar pace to the whole field of restoration.However, we found a series of biases in functional restoration research.If someone were to randomly pick a study from a pool of restoration studies, the selected study would be most likely about restoration of plants in forests outside the tropics in a developed country.Furthermore, there would be a good chance that the study evaluated functional traits, but most likely it would merely monitor trait variation along time with no clear relation to ecosystem services or to prior specific restoration targets.Ecosystem services were more rarely evaluated, and mainly in terrestrial, non-tropical ecosystems.
Although many international restoration commitments have been made by tropical developing countries harboring speciesrich ecosystems, these countries lack restoration ecology studies considering functional traits, and, especially, ecosystem services.Our findings corroborate the observation made by recent studies about the lack of biodiversity studies in tropical ecosystems in developing countries (Wilson et al. 2016;Clarke et al. 2017).Our results indicate that the most concerning region is Africa, with a large area, high biodiversity, serious threats to native ecosystems, high demand on ecosystem services, and few studies.
Table 2. Number of papers on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) that assessed ecosystem services and functional traits (n = 228 out of 265 screened papers; see details on the specific search in the Methods section).Note that all the papers on ecosystem services are under the category "ecosystem services," not only those that studied exclusively services.The same applies for the category "functional traits," and for "a priori" versus "a posteriori" comparisons.Moreover, the category "Ecosystem services + functional traits" represents the intersection (not the union) of studies on services and traits.Therefore, summing up values of the table does not result in 100%.More specifically, our results indicate that, while the number of studies differ substantially, the proportion of studies that consider services, traits, or both is similar between developed (North America, Europe, Australia) and developing regions (South America, Africa).Despite this relative consistency across continents, studies evaluating services were more common in temperate forests, while those assessing traits were more common in temperate grasslands and relatively common in tropical forests.Our results suggest we need more functional restoration studies in the tropics, especially in tropical and subtropical grasslands, savannas, and coniferous forests, but also in tropical forests.These studies should include functional traits, and whenever possible, ecosystem services.As we found no studies evaluating cultural or provisioning services in the tropics, it would be interesting that future studies attempt to assess these kinds of services.
Our finding that most studies used services and traits a posteriori indicates monitoring of services and traits after restoration started is still much more common than their use before the beginning of restoration projects.The use of traits to target services before the start of restoration should be encouraged as a way to pursue the recovery of services in restored ecosystems (Laughlin 2014).Moreover, several of the studies did not evaluate trait functionality properly; they merely used traits as a separate dimension from taxonomic or phylogenetic dimensions of biodiversity without a clear mention to what the function meant.In order to ensure scientific rigor and higher predictability on the success of ecosystem service recovery, it is important that the relation of traits to ecosystem functioning and services be clear upfront in restoration projects.
What Are the Existing Trait-Based Frameworks to Target Ecosystem Services in Restoration?
A common approach among the trait-based frameworks for targeting ecosystem services in restoration was to use the classic theories of community assembly (Brudvig & Mabry 2008;Bochet & García-Fayos 2015), biodiversity and ecosystem functioning (Perring et al. 2012;Mahaney et al. 2015;Ostertag et al. 2015), or both together (Funk et al. 2008;Laughlin 2014).Funk et al. (2008) proposed to select native species with traits similar to traits from invaders for biological control of invaders.Laughlin (2014) proposed a quantitative model to use trait values for targeting ecosystem services in restoration.For instance, species with dense woods and low specific leaf area might be selected to provide the restored community with resistance to future dry conditions; functional trait diversity might also be prioritized.Laughlin's approach was the only to enable the adjustment of species abundances to functional goals of restoration.An important point that only four papers explicitly addressed was the trait-based selection of species from regional and habitat species pool (Brudvig & Mabry 2008;Laughlin 2014;Bochet & García-Fayos 2015;Ostertag et al. 2015).Assessing the functional structure of the habitat species pool at a regional scale may provide alternative candidate species that are functionally similar.Other frameworks focused on selecting species based on functional traits to restore functional redundancy and complementarity in plant-pollinator networks (Devoto et al. 2012), assure primary succession and phytostabilization on soils degraded by mining (Ilunga et al. 2015), and promote natural regeneration under semiarid conditions using nurse species traits (Navarro-Cano et al. 2016).
More recently (beyond our systematic review time frame), novel quantitative trait-based approaches for restoration of ecosystem services have been published.Laughlin et al. (2018) proposed a framework that enables selecting species for restoration to achieve convergent trait value targets and simultaneously maximize functional trait diversity.Rayome et al. (2019) proposed a framework and computer program to select species from regional pools based on the interpretation of multivariate trait patterns, which should enable practitioners selecting functionally redundant or complementary species based on restoration goals.Tsujii et al. (2020) developed a framework that enables selecting species for restoration from species pools to maximize functional richness and redundancy simultaneously as proxies for multiple ecosystem services and resilience with the minimal possible set of species.These recent trait-based frameworks permit the choice between functionally similar species from a previously known (regional or habitat) species pool, which may facilitate practitioners to use species that are available in the market, while assuring that certain ecosystem services will be provided in the restored ecosystem.Nevertheless, we consider we still miss a unified approach that simultaneously enables the trait-based selection of species from regional/habitat species pools and the definition of relative abundances of the selected species to achieve the provisioning of target ecosystem services.
Challenges to Put Trait-Based Frameworks for Restoration in Practice at Broad Scales A major challenge for a trait-based restoration in the tropics (and elsewhere) is the lack of knowledge on functional traits of native species (Aerts & Honnay 2011).Establishing linkages between traits and services is still a major challenge in the ecological literature as a whole and has important implications to restoration ecology.We showed some patterns on how traits and ecosystem services are related in the restoration literature, which might help prioritize future studies.For instance, food provisioning might be targeted in tropical forest restoration, and the edibility of fruits or seeds might be used as a trait to achieve the delivery of this service in the restored ecosystem.In order to help practitioners put in practice a trait-based restoration to recover ecosystem services, we provided a summary of linkages between plant functional traits and target ecosystem services that are more consolidated in the literature.Ideally, functional traits might be available for the whole regional species pool relative to the sites to be restored.Some functional traits, like performance, whole-plant, and diaspore traits, are relatively well studied in the tropics, while others are less studied.In a major compilation of trait data, Petisco-Souza et al. ( 2020) found major gaps of information for key functional traits of 2,236 tree species of the Brazilian Atlantic Forest after searching in global plant trait databases, specialized books, regional datasets, and digitized plant specimens: 88% for seed mass; 85% for specific leaf area; 53% for wood density; and 16% for maximum height.These findings emphasize the importance of more funding for basic biodiversity research in the tropics both to fill biodiversity knowledge shortfalls and to advance scientific understanding on ecosystem functioning.
Taxonomic and functional information needs also to be constantly reviewed and updated to accomplish the goals of functional restoration.A good example of broad-scale standardization of taxonomic nomenclature exists for Brazil: the database of the Brazilian Flora 2020 project (Forzza et al. 2012).This database compiles the accepted scientific names and basic biological information of more than 46,000 species of plants, algae, and fungi and is frequently updated by more than 400 experts (http://ipt.jbrj.gov.br/jbrj; accessed 24 Aug 2020).Considerable effort has already been made in research groups working in tropical countries for gathering functional traits as well, and data have been made available in major databases such as TRY Plant Trait Database (Kattge et al. 2020) and Botanical Information and Ecology Network (Maitner et al. 2018), besides several regional databases.
Ecosystem services and functional traits are increasingly being used in ecological restoration, although too few studies have considered them for ecological restoration in the tropics.Trait-based frameworks are useful for broad-scale restoration in the tropics because they may help circumvent common limitations of restoration in developing megadiverse countries (e.g.lack of saplings or trait information) by enabling considering alternative sets of species of the regional pool leading to similar resolutions of target services.While funding for tropical research is impaired and more complete datasets are not available, we advocate the use of the best existing ecological theory and available data in trait-based models to select species sets that will enable the restoration of ecosystem services.
We advocate that next steps towards a broad-scale functional trait-based restoration in the tropics are to: (1) define priority target ecosystem services for ecological restoration; (2) focus on measuring key functional traits linked to priority ecosystem services based on up-to-date scientific evidence; (3) test whether current restoration programs are reaching the functional composition and structure of reference ecosystems (Rosenfield & Müller 2017); and (4) run trait-based quantitative frameworks to select sets of native species and their abundances for areas that will be subject to restoration. | [
"ecosystem function",
"functional diversity",
"functional restoration",
"reassembly",
"restoration target",
"trait-based restoration Conceptual Implications"
] |
Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.13 | This study was financed by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001postdoctoral fellowship to M.B.C.; CNPq-Brazil productivity grants to M.V.C. (307796/2015-9), P.H.S.B. (304817/2015-5), and R.L. (306694/2018-2); and FAPESP grant to R.R.R. (2013/50718-5) and is a contribution of the INCT in Ecology, Evolution, and Biodiversity Conservation founded by MCTIC/CNPq (grant 465610/2014-5) and FAPEG (grant 201810267000023).The authors thank A. C. Petisco-Souza for providing information on trait knowledge gaps in the Atlantic forest. | [
"ecosystem function",
"functional diversity",
"functional restoration",
"reassembly",
"restoration target",
"trait-based restoration Conceptual Implications"
] |
Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.14 | This study was financed by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001postdoctoral fellowship to M.B.C.; CNPq-Brazil productivity grants to M.V.C. (307796/2015-9), P.H.S.B. (304817/2015-5), and R.L. (306694/2018-2); and FAPESP grant to R.R.R. (2013/50718-5) and is a contribution of the INCT in Ecology, Evolution, and Biodiversity Conservation founded by MCTIC/CNPq (grant 465610/2014-5) and FAPEG (grant 201810267000023).The authors thank A. C. Petisco-Souza for providing information on trait knowledge gaps in the Atlantic forest. | [
"ecosystem function",
"functional diversity",
"functional restoration",
"reassembly",
"restoration target",
"trait-based restoration Conceptual Implications"
] |
Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.15 | The following information may be found in the online version of this article: Supplement S1.Steps followed during the systematic review.Supplement S2.Papers on ecological restoration (2007-2017) retrieved from the systematic review.Supplement S3.Additional information screened from papers on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).Table S1.Rank of countries where studies on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) were carried out.Table S2.Rank of journals that published papers on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). | [
"ecosystem function",
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"reassembly",
"restoration target",
"trait-based restoration Conceptual Implications"
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Carlucci et al. - 2020 - Functional traits and ecosystem services in ecolog.pdf.tei.xml | Functional traits and ecosystem services in ecological restoration | 5.16 | The following information may be found in the online version of this article: Supplement S1.Steps followed during the systematic review.Supplement S2.Papers on ecological restoration (2007-2017) retrieved from the systematic review.Supplement S3.Additional information screened from papers on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).Table S1.Rank of countries where studies on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) were carried out.Table S2.Rank of journals that published papers on ecological restoration (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). | [
"ecosystem function",
"functional diversity",
"functional restoration",
"reassembly",
"restoration target",
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Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.1 | Restoration, in its broadest sense, involves improving conditions at a site to meet desired objectives.Traditionally, improving site conditions has meant an effort to return to a former, less-disturbed state, and much has been learned by examining recovery rates across ecosystem types (e.g., Rey Benayas et al. 2009).Yet in an increasing number of ecosystems, it is not feasible to return to a previous state for reasons that include the lack of reference sites or historical baseline conditions, irreversible climate change, and colonization by highly invasive nonnative species that cannot practically be removed (Hobbs et al. 2014, Zedler et al. 2012).Further, active forms of restoration via planting or encouraging specific species (Holl and Aide 2011) often proceed while information is lacking about species ecology, genetics, physiology, and evolutionary biology (Jones 2013).Choosing plant species for restoration can be a difficult task because it is not always clear which species are the most appropriate to achieve a particular restoration goal.A multivariate approach that allows users to identify a range of species likely to help them meet restoration objectives is one potential solution.Appropriate species chosen based on their life history characteristics can then be combined in a simulated community to see how these species are related to each other in their characteristics. | [
"Functional traits",
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"drought tolerance",
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Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.2 | One major stumbling block in designing restoration plans is deciding which species to use.The motivation for this approach to species choice comes from the desire to merge practical and ecological restoration techniques, as well as the recognition that species choice for restoration can be a difficult and value-laden process.There are practical concerns such as cost, availability of seeds, and ability to propagate that can partially dictate decisions.Yet in many cases, little is known about each species' life history and how each will interact with other species when planted together, particularly if the planting might represent new combinations that are not seen in the field.Situations in which species that do not share an evolutionary history are thrust together provide relevant examples.These new combinations could arise because of invasion by nonnative species, range shifts of species resulting from climate change, or new species distributions resulting from land use activities.
Although "novel ecosystems" (Hobbs et al. 2006) are becoming widespread, there is a very limited understanding in the ecological literature about the long-term implications of new species interactions and their effects on ecosystem functioning. | [
"Functional traits",
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Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.3 | Functional trait-based restoration is based on the principle that ecosystem function depends in part on the expression of various morphological, structural, physiological, or chemical traits of organisms as well as environmental filters and the interaction between traits and environments.Functional traits reflect fundamental life-history and resource-use tradeoffs (Reich 2014).Because these traits vary predictably across environments, it is assumed that they are the products of natural selection.For plants, global datasets show how traits vary continuously along abiotic resource availability gradients and across biomes (Chave et al. 2009, Donovan et al. 2011, Reich 2014, Wright et al. 2005).Evolutionary tradeoffs faced by organisms in resource acquisition (e.g., light, water, and nutrient uptake) and resource processing (e.g., net primary productivity) result in different ways to make a living, termed the "worldwide 'fast-slow' plant economics spectrum" (Reich 2014).Plant species on the slow end of the spectrum have low rates of resource acquisition and processing, which requires leaf, stem, and root traits that are more conservative and efficient in resource use than plant species on the fast end of the spectrum.Being a slow species is advantageous under low-resource conditions because resource conservation traits enhance survival, but being a slow species can be a drawback under higher resource conditions.In a given biome, there is selection for trait convergence, but within a more localized community, it is likely that interspecific competition ensures that species differ along the slow-fast continuum (Reich 2014).Thus, at the community and ecosystem levels, consideration of functional trait values can help explain the distribution of species, the assembly of communities, and the rate of ecosystem processes (Reich 2014;Reich et al. 1999Reich et al. , 2003)).
At the community level, the functional trait profiles of species can be represented by functional diversity.Simply put, functional diversity is a way to define diversity of species traits within a community or ecosystem, encompassing metrics that focus on the magnitude, variation, and dissimilarity in species' functional traits (Schleuter et al. 2010).Considering functional diversity rather than species diversity may be a more promising approach for addressing questions of how species influence the structure and function of ecosystems (Laureto et al. 2015) or community assembly (Bhaskar et al. 2014).Therefore, selecting species for restoration projects that have a specific set of trait values should influence competitive interactions, resource availability, and ecosystem structure and functioning.Ideally, these functional traits should be easily defined and measured, so that the approach is transportable and flexible, and the predicted successional outcome of restoration can be tested (Ostertag et al. 2015).For example, selecting species with a broad range of functional traits (i.e., low niche overlap or inversely high functional divergence) may preclude exotic species from invading if their functional trait values are already represented in the community (Funk et al. 2008).
Because functional traits differ among species and environments in predictable ways, they can be linked to ecosystem properties and used in restoration to achieve specific objectives in ecosystem functioning (Funk et al. 2008).For example, the growth and recruitment of species with certain functional traits could be selected for by choosing species that facilitate plant and animal recruitment.If the objective is to build a community that will be less likely to burn, one could choose species with traits such as high leaf water content and low levels of volatile compounds.
Although most studies attempting to link traits to ecosystem properties have been carried out in relatively simple systems, the field can be expanded to incorporate increasingly complex systems with higher species and life form diversity.The Restoring Ecosystem Services Tool (REST) program allows the user to design new simulated communities to make some assessments about which combinations of species may be best for specific restoration goals.REST has some data incorporated into it, yet allows users to enter their own species list and trait data.This strategy for species selection is generalizable and flexible, allowing users to choose the species and desired functional outcomes, while acknowledging limiting factors such as economics (e.g., cost of seed/plants, labor, time); logistics (e.g., availability of species, project or budget timelines); and predictability of climate or disturbance regimes, as well as the goals and expectations of stakeholders.The choice of species for restoration objectives is not limited to the scores from their traits alone, but could also incorporate other aspects, such as maintaining a diverse and resilient community that fosters the desired environmental outcomes.REST can be used iteratively; e.g., it can be reset and run again after removing species to continually refine choices. | [
"Functional traits",
"ecosystem services",
"land management",
"drought tolerance",
"fire tolerance",
"successional facilitation",
"carbon storage"
] |
Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.4 | Another difficult decision is the choice of traits.In part, restoration goals determine the traits that should be of interest to consider for a particular restoration project.For example, if the aim is to build an ecosystem that is tolerant of fires, traits such as bark thickness and leaf water content may be of interest.In general, there are six traits that the literature suggests are helpful in the attempt to understand life histories of various species (box 1).
These traits appear often in global analyses (Adler et al. 2014, Kunstler et al. 2016, van Bodegom et al. 2014).If you have no prior plant functional trait knowledge, examining these six traits will provide a good foundation.When you chose a restoration goal in the REST program, it will populate with a list of suggested functional traits that might be linked to your restoration needs. | [
"Functional traits",
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"land management",
"drought tolerance",
"fire tolerance",
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Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.6 | • Foliar Nitrogen (resource acquisition)
• Seed mass (reproductive investment and dispersal)
• Specific leaf area (resource allocation)
• Wood density (resource allocation)
• Leaf lifespan (resource allocation)
• Maximum plant height (dispersal) | [
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"carbon storage"
] |
Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.7 | The Hawaiian archipelago has many extremes of biological invasion, one of which is the remnant Hawaiian lowland wet forest (Zimmerman et al. 2008) that led to the development of REST.Approximately half the flora in Hawaii is not native (Wagner et al. 1999), and a number of invaders have been shown to have strong ecosystemlevel effects on carbon and nitrogen cycling and native biological diversity (e.g., Hughes and Denslow 2005, Litton et al. 2006, Vitousek and Walker 1989).A combination of events has led to systematic alteration of low-elevation lands, including (1) small-scale clearing and burning for agriculture and housing by Hawaiians prior to European contact (Kirch 2002); (2) large-scale clearing for sugarcane agriculture (Cuddihy and Stone 1990); (3) planting and aerial seeding of nonnative trees by territorial foresters, stemming from their lack of understanding about native forest function (Woodcock 2003); and (4) intentional and accidental introduction of many alien plants and animals that benefited from a mild climate, limited interspecific competition, and enemy release (Denslow 2003).The result is a series of communities dominated by mixtures of species that share no evolutionary history, and which contain high proportions of nonnative species classified as invasive.
REST is a culmination of more than a decade of research conducted in a heav- | [
"Functional traits",
"ecosystem services",
"land management",
"drought tolerance",
"fire tolerance",
"successional facilitation",
"carbon storage"
] |
Rayome et al. - Restoring Ecosystem Services Tool (REST) a Comput.pdf.tei.xml | Restoring Ecosystem Services Tool (REST): a program for selecting species for restoration projects using a functional-trait approach | 6.8 | In these highly altered habitats, we have no clear historical guide to what species should be planted to achieve traditional restoration goals, and it has become clear that maintaining these forests as all-native species assemblages is unsustainable in terms of labor, logistics, and cost (Cordell et al. 2009, Ostertag et al. 2009).
Functional trait-based restoration can involve the use of species not originally found in a given site-including exotic species (Ewel andPutz 2004, Schlaepfer et al. 2011)-guiding the biodiversity toward more favorable species assemblages.The application of functional trait theory in restoration and management is an exciting new approach that can be used to understand the persistence of species and ecosystems as well as build model communities with desired ecosystem functions. | [
"Functional traits",
"ecosystem services",
"land management",
"drought tolerance",
"fire tolerance",
"successional facilitation",
"carbon storage"
] |
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