Find research datasets worth reusing

opennotspecifiedAug 2024View details →

The European Vegetation Archive (EVA)

The European Vegetation Archive (EVA) is an initiative of European Vegetation Survey aimed at establishing and maintenance of a single data repository of vegetation-plot observations (i.e. records of plant taxon co-occurrence at particular sites, also called phytosociological relevés) from Europe and adjacent areas and to facilitate the use of these data for non-commercial purposes, mainly academic research and applications in nature conservation and ecological restoration. The initiative follows the EVA Data Property and Governance Rules. It closely cooperates with the Global Index of Vegetation-Plot Databases (GIVD), the Global Vegetation Database (sPlot) and the Plant Trait Database (TRY). http://euroveg.org/eva-database

opencc-by-4.0Apr 2024View details →

Data for: Synergistic mechanisms of plant phosphorus (P) resorption and microbial P-limitation affecting soil P during grassland vegetation succession

Data for: Synergistic mechanisms of plant phosphorus (P) resorption and microbial P-limitation affecting soil P during grassland vegetation succession

opencc-by-sa-4.0Sep 2024View details →

Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)

This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.

opencc-by-4.0Dec 2022View details →

Table 2 in Effects of different combinations of N, P and K at different time interval on vegetative, reproductive, yield and quality traits of mango (Mangifera Indica. L) cv. Dusehri

Table 2. Effect of different fertilizer combinations of N, P and K on reproductive physiology of mango cv. Dusehri.<table><tbody><tr><th>Treatments</th><th>Growth size (mm)</th><th>Total No. of Panicle/Tree</th><th>Total no. of flowers / Panicle</th><th>Sex Ratio (%)</th><th>Fruit Drop (%)</th><th>Fruit Retention (%)</th><th>Total no. of fruit/tree</th><th>Yield (Kg/Tree)</th><th>Fruit Length (cm)</th><th>Fruit Weight (g)</th><th>Pulp Weight (g)</th><th>Stone Weight (g)</th><th>Peel Weight (g)</th><th>TSS (%)</th><th>Total Acidity (%)</th><th>TSS/Acid Ratio</th><th>Vit. C (mg/100 mL)</th><th>Total Sugar (%)</th></tr></tbody><tbody><tr><th>T1 (Control)</th><td>149.34d ± 3.89</td><td>397.67h ± 3.51</td><td>543.21h ± 3.61</td><td>51.17c ± 2.10</td><td>94.85a ± 1.40</td><td>1.83e ± 0.15</td><td>186.72g ± 4.51</td><td>40.01e ± 4.51</td><td>15.4b ± 3.17</td><td>155.15e ± 6.34</td><td>76.30g ± 2.22</td><td>24.14f ± 2.02</td><td>28.61f ± 1.86</td><td>20.29d ± 1.05</td><td>0.52a ± 0.005</td><td>22.43</td><td>31.26f ± 0.92</td><td>14.52c ± 0.25</td></tr><tr><th>T2 (N)</th><td>166.67b ± 4.47</td><td>508.57f ± 4.51</td><td>612.47f ± 4.58</td><td>54.42bc ± 1.05</td><td>94.86a ± 2.41</td><td>5.57d ± 0.57</td><td>213.34f ± 3.06</td><td>52.70cd ± 4.50</td><td>16.4b ± 3.11</td><td>175.50bc ± 5.50</td><td>82.41f ±1.90</td><td>30.04de ± 2.21</td><td>33.70e ± 1.38</td><td>21.06cd ± 0.95</td><td>0.49b ± 0.004</td><td>25.16</td><td>42.22c ± 1.18</td><td>15.24bc ± 0.41</td></tr><tr><th>T3 (P)</th><td>156.26cd ± 4.85</td><td>467.33g ± 4.04</td><td>593.34g ± 3.61</td><td>53.57bc ± 1.01</td><td>91.72ab± 2.51</td><td>8.82bc ± 0.72</td><td>241.40e ± 4.04</td><td>50.14d ± 5.03</td><td>18.3ab ± 2.75</td><td>169.24cd ± 5.41</td><td>85.23f ± 1.96</td><td>28.50e ± 2.00</td><td>35.62de ±1.94</td><td>22.07bc ± 1.10</td><td>0.45c ± 0.005</td><td>29.13</td><td>39.37d ± 0.98</td><td>15.82bc ± 0.29</td></tr><tr><th>T4 (K)</th><td>164.80bc ± 4.95</td><td>634.57c ± 4.51</td><td>730.19c ± 4.56</td><td>57.39bc ± 2.38</td><td>94.26ab ± 1.79</td><td>5.83d ± 0.48</td><td>278.33d ± 3.51</td><td>55.23cd ± 4.50</td><td>19.3ab ± 2.99</td><td>182.01b ± 5.47</td><td>99.92e ± 1.89</td><td>31.26e ± 1.76</td><td>40.84c ± 1.35</td><td>21.41cd ± 1.25</td><td>0.37d ± 0.002</td><td>43.27</td><td>36.62e ± 1.21</td><td>16.89b ± 0.55</td></tr><tr><th>T5 (NP)</th><td>166.48b ± 4.98</td><td>584.47d ± 3.51</td><td>639e.46 ± 4.04</td><td>56.61bc ± 1.59</td><td>93.34ab ± 2.15</td><td>6.92cd ± 0.33</td><td>288.62c ± 4.51</td><td>57.31c ± 5.03</td><td>17.2b ± 3.29</td><td>160.46de ±4.86</td><td>107.34c ± 2.11</td><td>35.63bc ± 2.02</td><td>37.81d ± 1.88</td><td>23.43ab ± 0.93</td><td>0.35d ± 0.001</td><td>40.48</td><td>42.09b ± 0.47</td><td>16.65b ± 0.61</td></tr><tr><th>T6 (NK)</th><td>160.75bc ± 5.05</td><td>684.66b ± 4.50</td><td>810.62b ± 4.59</td><td>60.17b ± 2.53</td><td>90.28cd ± 2.71</td><td>9.74ab ± 0.66</td><td>320.32b ± 3.05</td><td>66.61b ± 4.49</td><td>18.5ab ± 2.73</td><td>180.32b ± 5.35</td><td>118.04b ± 1.94</td><td>37.21b ± 1.81</td><td>46.92b ± 1.65</td><td>23.30ab ± 0.08</td><td>0.32e ± 0.002</td><td>51.28</td><td>51.48b ±1.07</td><td>16.07b ± 0.71</td></tr><tr><th>T7 (PK)</th><td>159.42bc ± 4.98</td><td>559.71e ± 3.49</td><td>701.17d ± 3.61</td><td>55.31bc ± 1.02</td><td>92.96ab ± 2.56</td><td>7.49bcd ± 0.52</td><td>274.37d ± 2.52</td><td>54.85cd ± 4.51</td><td>18.1b ± 3.01</td><td>178.24bc ± 6.53</td><td>103.51d ± 1.79</td><td>33.07cd ± 1.95</td><td>42.14c ± 1.43</td><td>2.35bc ± 0.05</td><td>0.31e ± 0.002</td><td>52.41</td><td>51.55b ± 0.95</td><td>15.01b ± 0.37</td></tr><tr><th>T8 (NPK)</th><td>177.51a ± 4.92</td><td>845.64a ± 3.61</td><td>974.52a ± 4.58</td><td>69.18a ± 2.87</td><td>86.10e ± 2.85</td><td>13.85a ± 0.43</td><td>379.05a ± 3.00</td><td>82.35a ± 3.51</td><td>23.3a ± 3.10</td><td>197.05a ± 5.62</td><td>135.32a ± 2.09</td><td>43.53a ± 2.07</td><td>52.09a ± 1.77</td><td>24.53a ± 0.06</td><td>0.26f ± 0.001</td><td>73.53</td><td>57.63a ± 0.07</td><td>20.48a ± 0.53</td></tr></tbody></table>Values within each column followed by the same letter are not significantly different at P <0.5 level.Values within each column followed by the same letter are not significantly different at P <0.05 level.Values within each column followed by the same letters are not significantly different at P <0.05 level.

Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS

This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.).

Non-native vegetation encroachment drives functional turnover in island nematodes

This community data matrix accompanies the manuscript "Non-native vegetation encroachment drives functional turnover in island nematodes" by Adam Sharp, Margarida Correia, Alan Gray, Rebecca Lawson, Martha Ledger, Noel Tawatao & Thomas Prior.

The golden threat: Solidago invasion alters native plant-pollinator interactions through vegetative structures

This folder includes all files that were used for the article entitled "The golden threat: Solidago invasion alters native plant-pollinator interactions through vegetative structures". It includes: a README file, the input data for the two research question (Q1 and Q2), the RData of the respective fitted models, the PDF of the main text and sup. mat. figues, and the Rscript to reproduce them.

source data of "Enhanced vegetation drought resistance induced by elephant disturbance in Africa"

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →

Determining the Impacts of Stone Quarry Works On Riparian Vegetation of River Molo in Rongai Sub-County, Nakuru County, Kenya

The contribution made by stone quarrying to the Gross Domestic Product (GDP) in many countries globally is enormous. Whether small or large-scale, stone quarrying is inherently disruptive and can cause detrimental effects on the fragile riparian vegetation. This research therefore, aimed at determining the status of riparian vegetation between 2000- 2023 along river Molo and establishing the effects of stone quarry works on riparian vege-tation. A descriptive survey design was adopted where 404 household heads were randomly sampled from a total of 35,545 households in Visoi, Soin and Mosop wards. 15 key informants were purposively sampled for interviews. Questionnaires, interview schedules and Geographical Information System (GIS) and Remote sensing (RS) instruments were used in data collection. The obtained data was analyzed using SPSS (Version 25), ArchMap and remote sensing (time series) to analyze changes in riparian vegetation using the Normalized Difference Vegetation Index (NDVI). The findings revealed a significant decline in riparian vegetation attributed to land use changes driven by poor agricultural practices and quarrying activities. The five purposively selected quarries were found to be located between 49 - 330 meters away from the river, where one was abandoned with no sign of reclamation, thus directly affecting the health and integrity of the riparian vegetation. It was also noted that massive heaping of quarry overburden and deep excavation accelerated felling of trees, reduced natural regeneration and increased soil erosion. These findings will be useful to inform the best quarrying practices to reduce detrimental impacts on riparian vegetation and a foundation for policy development in quarrying sector at county and national level.

opencc-zeroMay 2020View details →

Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context

1. An ever-expanding human population, climatic changes, and the spread of intensive farming practices is putting increasing pressure on agroecosystems and their inherent biodiversity. Non-production vegetation elements, such as woody patches, riparian margins, and restoration plantings, are vital for conserving agroecosystem biodiversity. Further, such elements are key building blocks that are manipulated via land management, thereby influencing the biotic and abiotic processes that underpin functioning agroecosystems. 2. Despite this critical role, there has been a lack of synthesis on which types of vegetation elements drive and/or support ecological processes, and the mechanisms by which this occurs. Using a systematic, quantitative literature review of 342 articles, we asked: what are the effects of non-production vegetation on agroecosystem processes and how are these processes measured within global agroecosystems? 3. Woody patches, hedgerows and borders, riparian margins, and shelterbelts were the most studied types of non-production vegetation. The majority (61%) of studies showed positive effects of non-production vegetation on ecological processes, where the presence, level or rate of the studied process was increased or enhanced. 4. However, four key research gaps were revealed: (1) most studies (83%) used proxies for, instead of direct measurements of, ecosystem processes related to non-production vegetation; (2) study designs used to investigate non-production vegetation effects on ecosystem processes directly were largely limited to observational comparisons of non-production vegetation types, farm-scale vegetation configurations, and different proximities to vegetation in terms of the effect on ecological processes; relatively few studies used manipulative experiments (3) the relatively few studies directly measuring ecosystem processes were dominated by four process categories: invertebrate biocontrol, predator and natural enemy spillover, animal movement, and ecosystem cycling, and (4) the methods used to directly measure non-production vegetation effects comprised a surprisingly limited set of approaches. 5. To fill key research gaps that will inform the use of non-production vegetation to enhance agroecosystem processes, we present a framework for future research that emphasises the need to combine an understanding of human decision making with carefully-designed and targeted investigations into the roles of taxa, ecosystem processes, and landscape heterogeneity related to non-production vegetation, at multiple spatial scales within agroecosystems.

opencc-zeroDec 2017View details →

Data from: Influences of fire–vegetation feedbacks and post-fire recovery rates on forest landscape vulnerability to altered fire regimes

1. In the context of on-going climatic warming, forest landscapes face increasing risk of conversion to non-forest vegetation through alteration of their fire regimes and their post-fire recovery dynamics. However, this pressure could be amplified or dampened, depending on how fire-driven changes to vegetation feed back to alter the extent or behavior of subsequent fires. 2. Here we develop a mathematical model to formalize understanding of how fire–vegetation feedbacks and the time to forest recovery following high-severity (i.e., stand-replacing) fire affect the extent and stability of forest cover across landscapes facing altered fire regimes. We evaluate responses to increasing burn rates while varying the direction (negative vs. positive) of fire–vegetation feedbacks under a continuum of values for feedback strength and post-fire recovery time to determine how interactions among these variables produce thresholds and tipping points in landscape responses to changing fire regimes. 3. Where the early-seral vegetation is less fire-prone than older forests, negative feedbacks limited the reductions in forest cover in response to increased fire frequency or slower forest recovery. By contrast, positive feedbacks (more flammable early-seral vegetation) produced a tipping point beyond which increased burn rates or slower forest recovery drove extensive forest loss. 4. With negative feedbacks, the rates of forest loss and expansion in response to variation in fire frequency were similar. However, where feedbacks are positive, the conversion from predominantly forested to non-forested conditions in response to increased fire frequency was faster than the re-expansion of forest cover following a return to the initial burn rate. Strengthening the positive feedbacks increased this asymmetry. 5. Synthesis. Our analyses elucidate how fire–vegetation feedbacks and post-fire recovery rates interact to affect the trajectories and rates of landscape response to altered fire regimes. We illustrate the vulnerability of ecosystems with positive fire–vegetation feedbacks to climate change-driven increases in fire activity, especially where post-fire recovery is slow. Although negative feedbacks initially provide resistance to forest loss with increasing burn rates, this resistance is eventually overwhelmed with sufficient increases to burn rates relative to recovery times.

opencc-zeroJun 2021View details →

Effects of salt marsh vegetation zonation on carbon and nitrogen cycling in Connecticut

Coastal marshes fringing the Long Island Sound (Connecticut, USA) are dynamic ecosystems positioned at the interface between land and sea, and provide an array of essential ecosystem services to society associated with improved water quality, carbon sequestration, and disturbance regulation. However, these wetlands are increasingly altered by rising seas and invasive species, and have been affected by historical management such as tidal manipulation. We conducted a survey of 20 Connecticut salt marshes (10 tidally restored, 10 unrestricted references) in 2017 to quantify carbon mineralization, denitrification potential, microbial community composition, above and belowground biomass and a suite of sediment characteristics. Carbon density was our only paramenter that differed between unrestricted and tidally restored marshes, but we observed strong differences across vegetation zones, with vegetation being a top predictor of microbial respiration and potential denitrification rates. Based on sea-level rise model projections, the replacement of S. patens by short-form S. alterniflora is expected to be widespread across the Connecticut coastline, decreasing statewide potential denitrification from the low-to-high marsh transitional zone. Our results suggest that changes in vegetation zones can serve as landscape-scale predictors of the rapid changes occurring in salt marshes.

opencc-zeroJun 2021View details →

Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes

Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post-landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood. Here we use a LiDAR-derived canopy height map coupled with a 25-year landslide time series map to examine how landslide, topographic, and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate aboveground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels. We find that age and elevation are the biggest determinants of forest recovery, and that the jump-start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6-37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest aboveground biomass in frequently disturbed landscapes. Synthesis: Using a high-resolution LiDAR dataset and a time-series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post-landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on aboveground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on aboveground biomass, wherein high-wood density species gradually replace light-wooded pioneer species, it overestimates aboveground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that aboveground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR-derived structure alone.

opencc-by-4.0Dec 2020View details →

Sedimentation, sediment grain size, vegetation composition and vegetation characteristics on a salt marsh in nature reserve the Slufter (Wadden island Texel, the Netherlands)

Data on sedimentation, sediment grain size, vegetation composition and vegetation characteristics of a field experiment on a salt marsh in nature reserve the Slufter (Wadden island Texel, the Netherlands). The data is described in the following publication: Baaij, B.M., Kooijman, J., Limpens, J., Marijnissen, R.J.C., van Loon-Steensma, J.M. Monitoring Impact of Salt-Marsh Vegetation Characteristics on Sedimentation: an Outlook for Nature-Based Flood Protection. Wetlands 41, 76 (2021). https://doi.org/10.1007/s13157-021-01467-w A short description of the aim, study design, measurements and data files is given in README.txt.

opencc-zeroJul 2021View details →

Global dryland vegetation memory

1. Vegetation memory describes the effect of antecedent environmental and ecological conditions on the present ecosystem state and has been proposed as an important proxy for vegetation resilience. In particular, strong vegetation-memory effects have been identified in dryland regions, but the factors underlying the spatial patterns of vegetation memory remain unknown. 2. We aim to map the components and drivers of vegetation memory in dryland regions using state-of-the-art climate-reanalysis data and refined approaches to identify vegetation-memory characteristics across dryland regions worldwide. 3. Using a framework which distinguishes between intrinsic and extrinsic ecological memory, we show that: (i) intrinsic memory is a much stronger component than extrinsic memory in the majority of dryland regions; and (ii) climate reanalysis data sets change the detection of extrinsic vegetation memory effects in some global dryland regions. 4. Synthesis. Our study offers a global picture of the vegetation response to two climate forcing variables using satellite data, information which is potentially relevant for mapping components and properties of vegetation responses worldwide. However, the large differences in the spatial patterns in intrinsic vegetation memory in our study compared to previous analyses show the overall sensitivity of this component in particular to the initial choice of extrinsic forcing variables. As a result, we caution against using the oversimplified link between intrinsic vegetation-memory and vegetation recovery rates at large spatial scales.

opencc-by-4.0Jul 2021View details →

Wild Plants Used as Vegetables by Transhumant People Around the Georgia–Turkey Border in the Western Lesser Caucasus

Recent ethnobotanical studies in the Caucasus, mainly in Georgia, reveal the significant ethnobotanical knowledge of local people related to wild edible plants. However, few studies have been conducted in the Lesser Caucasus, West Georgia, and the Turkish Caucasus. is study aims to represent and evaluate the cultural importance of wild vegetable plants and their patterns of use along the Georgia–Turkey border. During the transhumance period in the summers of 2017 and 2018, 104 participants (65 in Turkey and 39 in Georgia) were interviewed using a semistructured questionnaire. e Cultural Importance Index and Relative Frequency of Citation were used to compare the relative importance of species in each region. e use of 83 wild plant species from 23 plant families as vegetables was documented, with 45 species recorded in Georgia and 72 species in Turkey. One-third of the recorded wild plant species and 52 use instances out of 122 species-use combinations were shared on both sides of the border. Women and men had mentioned almost the same number of species, and there was a nonsignificant correlation between plant knowledge and age. Although there were no significant differences in the plant parts used, the way people used plants as vegetables varied significantly across the border. Considering the floral similarity across the border, the number of species used in common and shared vegetable plant knowledge was quite low. ere is not a significant difference between the two countries in terms of the most frequently cited and culturally important species (Rumex, Urtica, and Polygonum spp.). However, the recognition of some of the most important shared species (Heracleum, Chaerophyllum, Arctium, and Campanula spp.) diverged significantly in different administrative regions.

opencc-zeroAug 2021View details →

Single versus repeated human trampling events: Responses of ground vegetation in sub-urban beech forests

Forests provide important ecosystem services and are often the only natural areas that are freely accessible to the public for outdoor recreation. Large numbers of forest visitors can cause severe damage to forest ecosystems, which in turn can affect the ecosystem functioning. We aimed to assess whether experimental short-term and long-term trampling affect the ground vegetation in deciduous forests and its recovery to a different extent. We used a standard experimental trampling procedure to simulate single and repeated human trampling events of different intensity. Experimental trampling of different intensity was conducted on a single day (single trampling) or on five days at intervals of four weeks (repeated trampling) in three suburban beech forests on different soil types. We recorded the cover, height, species density and species composition of the ground vegetation in the trampling lanes. We also assessed the recovery of the ground vegetation one and two years after trampling. Trampling intensity and the type of trampling (single vs. repeated) affected the ground vegetation in the three forests. Vegetation cover, plant height and species density all decreased with increasing trampling intensity, but to a different extent in the three forests. Most vegetation characteristics were more severely affected by repeated trampling than single trampling of equal intensity. A longer trampling period strengthened the effects of trampling on ground vegetation, except in one forest. Two years after experimental trampling, most vegetation characteristics still differed from those of undisturbed ground vegetation

opencc-zeroAug 2021View details →

Yards, block groups, and vegetation cover measures

Residential yards are a significant component of urban socio-ecological systems; residential land covers 11% of the United States and is often the dominant land use within urban areas. Residential yards also play an important role in the sustainability of urban socio-ecological systems, affecting biogeochemical cycles, water, and the climate via individual- and household-level behaviors. Vegetation, such as trees and grasses, are unevenly distributed across front and back yards, and we sought to understand how similar yards are to each other when compared to their neighboring yards and neighborhoods using aerial imagery. There are many ways to measure yard similarity, and we compared several measures to account for different definitions of 'neighborness'. We examined the spatial autocorrelation of several yard vegetation characteristics in both front and backyards in Boston, MA, USA. Our study area included 1,027 Census block groups (sub-neighborhood areas) and 175,576 parcels with matched front-backyard pairings (n = 351,152 yards in total) across Boston's metropolitan area. This data package contains 1) 351,152 yard spatially-referenced yard polygons with five measures of vegetation summarized, 2) the containing block groups, and 3) and *.R script that replicates the analyses reported in Locke, D. H., Ossola, A., Minor, E., & Lin, B. B. (2021). Spatial contagion structures urban vegetation from parcel to landscape. People and Nature, 00, 1–15. <a href="https://doi.org/10.1002/pan3.10254">https://doi.org/10.1002/pan3.10254</a>

opencc-zeroAug 2021View details →

The role of shade in maintaining alternative stable states between open- and closed-canopy vegetation

Fire is commonly identified as strong driver of alternative stable states such as adjacent open- versus closed-canopy vegetation types. The absence of open-canopy species from closed-canopy understoreys, where light availability is low and dynamic, however, suggests shade tolerance is an integral determinant of such vegetation boundaries. While the importance of light dynamics between alternative stable states has been acknowledged, the physiological mechanisms behind sun versus shade-tolerance are unclear. Here we investigated the differences in light interception and carbon assimilation of open- and closed-canopy species, and whether this contributes to the maintenance of alternative stable states. Evergreen forest and fynbos species were grown under three light availability treatments (24%, 54%, 100%) in a glasshouse with their photosynthetic response to continuous and fluctuating light, dark respiration, root respiration, biomass and leaf traits quantified. Fynbos species displayed smaller, thicker, and tightly aggregated leaves compared to forest species. Under low light, fynbos species experienced some mortality, and showed lower light-use efficiency with fluctuating light and lower in situ photosynthetic rates under the 54% light treatment compared to forest species (0.8 versus 4 mmol CO2 m-2 s-1). Synthesis. Contrasting light availability in forest and fynbos selects for dissimilar traits, where fynbos species are unable to efficiently intercept light and maintain a positive carbon balance under low, dynamic light compared to forest species. This shade intolerance is exacerbated by divergent nutrient and fire regimes in forest and fynbos environments, requiring incompatible trait specializations that promote the emergence and maintenance of alternative stable states.