107 research outputs found

    A Reply to Verbeeck and Kearsley: Addressing the Challenges of Including lianas in Global Vegetation Models

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    Verbeeck and Kearsley (1) rightfully point out that global vegetation models would greatly benefit from implicitly including the effects of lianas. Recent experimental evidence that lianas substantially reduce the capacity of tropical forests to uptake and store carbon is compelling (2, 3). Furthermore, lianas are increasing relative to trees rapidly in many neotropical forests (4), which will further change the way that forests uptake, cycle, and store carbon

    Response to Verbeeck and Kearsley: addressing the challenges of including lianas in global vegetation models

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    Verbeeck and Kearsley (1) rightfully point out that global vegetation models would greatly benefit from implicitly including the effects of lianas. Recent experimental evidence that lianas substantially reduce the capacity of tropical forests to uptake and store carbon is compelling (2, 3). Furthermore, lianas are increasing relative to trees rapidly in many neotropical forests (4), which will further change the way that forests uptake, cycle, and store carbon

    Lianas reduce carbon accumulation and storage in tropical forests

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    Tropical forests store vast quantities of carbon, account for a third of the carbon fixed by photosynthesis, and are a major sink in the global carbon cycle. Recent evidence suggests that competition between lianas (woody vines) and trees may reduce forest-wide carbon uptake. However, estimates of the impact of lianas on carbon dynamics of tropical forests are crucially lacking. Here, we used a large-scale liana removal experiment and found that, three years after liana removal, lianas reduced net above-ground carbon uptake (growth and recruitment minus mortality) by ~76% per year, mostly by reducing tree growth. The loss of carbon uptake due to liana-induced mortality was 4-times greater in the control plots were lianas were present, but high variation among plots prevented a significant difference among the treatments. Lianas altered how aboveground carbon was stored. In forests where lianas are present, the partitioning of forest aboveground net primary production is dominated by leaves (53.2% compared to 39.2% in liana-free forests) at the expense of woody stems (from 28.9% compared to 43.9%), resulting in a more rapid return of fixed carbon to the atmosphere. After three years of experimental liana removal, our results clearly demonstrate large differences in carbon cycling between forests with and without lianas. Combined with the recently reported increases in liana abundance, these results indicate that lianas are an important and increasing agent of change in the carbon dynamics of tropical forests

    Effect of lianas on forest-level tree carbon accumulation does not differ between seasons: Results from a liana removal experiment in Panama

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    1. Lianas are prevalent in Neotropical forests, where liana-tree competition can be intense, resulting in reduced tree growth and survival. The ability of lianas to grow relative to trees during the dry season suggests that liana-tree competition is also strongest in the dry season. If correct, the predicted intensification of the drying trend over large areas of the tropics in the future may therefore intensify liana-tree competition, resulting in a reduced carbon sink function of tropical forests. However, no study has established whether the liana effect on tree carbon accumulation is indeed stronger in the dry than in the wet season. 2. Using six years of data from a large-scale liana removal experiment in Panama, we provide the first experimental test of whether liana effects on tree carbon accumulation differ between seasons. We monitored tree and liana diameter increments at the beginning of the dry and wet season each year to assess seasonal differences in forest-level carbon accumulation between removal and control plots. 3. We found that median liana carbon accumulation was consistently higher in the dry (0.52 Mg C ha-1 yr-1) than the wet season (0.36 Mg C ha-1 yr-1), and significantly so in three of the years. Lianas reduced forest-level median tree carbon accumulation more severely in the wet (1.45 Mg C ha-1 yr-1) than the dry (1.05 Mg C ha-1 yr-1) season in all years. However, the relative effect of lianas was similar between the seasons, with lianas reducing forest-level tree carbon accumulation by 46.9% in the dry and 48.5% in the wet season. 4. Synthesis: Our results provide the first experimental demonstration that lianas do not have a stronger competitive effect on tree carbon accumulation during the dry season. Instead, lianas compete significantly with trees during both seasons, indicating a large negative effect of lianas on forest-level tree biomass increment regardless of seasonal water stress. Longer dry seasons are unlikely to impact liana-tree competition directly; however, the greater liana biomass increment during dry seasons may lead to further proliferation of liana biomass in tropical forests, with consequences for their ability to store and sequester carbon

    A view from above: Unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies

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    1. Tropical forests store and sequester large quantities of carbon, mitigating climate change. Lianas (woody vines) are important tropical forest components, most conspicuous in the canopy. Lianas reduce forest carbon uptake and their recent increase may, therefore, limit forest carbon storage with global consequences for climate change. Liana infestation of tree crowns is traditionally assessed from the ground, which is labour intensive and difficult, particularly for upper canopy layers.2. We used a lightweight unmanned aerial vehicle (UAV) to assess liana infestation of tree canopies from above. It was a commercially available quadcopter UAV with an integrated, standard three‐waveband camera to collect aerial image data for 150 ha of tropical forest canopy. By visually interpreting the images, we assessed the degree of liana infestation for 14.15 ha of forest for which ground‐based estimates were collected simultaneously. We compared the UAV liana infestation estimates with those from the ground to determine the validity, strengths, and weaknesses of using UAVs as a new method for assessing liana infestation of tree canopies.3. Estimates of liana infestation from the UAV correlated strongly with ground‐based surveys at individual tree and plot level, and across multiple forest types and spatial resolutions, improving liana infestation assessment for upper canopy layers. Importantly, UAV‐based surveys, including the image collection, processing, and visual interpretation, were considerably faster and more cost‐efficient than ground‐based surveys. 4. Synthesis and applications. Unmanned aerial vehicle (UAV) image data of tree canopies can be easily captured and used to assess liana infestation at least as accurately as traditional ground data. This novel method promotes reproducibility of results and quality control, and enables additional variables to be derived from the image data. It is more cost‐effective, time‐efficient and covers larger geographical extents than traditional ground surveys, enabling more comprehensive monitoring of changes in liana infestation over space and time. This is important for assessing liana impacts on the global carbon balance, and particularly useful for forest management where knowledge of the location and change in liana infestation can be used for tailored, targeted, and effective management of tropical forests for enhanced carbon sequestration (e.g., REDD+ projects), timber concessions, and forest restoration

    Lianas in gaps reduce carbon accumulation in a tropical forest

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    Treefall gaps are the “engines of regeneration” in tropical forests and are loci of high tree recruitment, growth, and carbon accumulation. Gaps, however, are also sites of intense competition between lianas and trees, whereby lianas can dramatically reduce tree carbon uptake and accumulation. Because lianas have relatively low biomass, they may displace far more biomass than they contribute, a hypothesis that has never been tested with the appropriate experiments. We tested this hypothesis with an 8-yr liana removal experiment in central Panama. After 8 years, mean tree biomass accumulation was 180% greater in liana-free treefall gaps compared to control gaps. Lianas themselves contributed only 24% of the tree biomass accumulation they displaced. Scaling to the forest level revealed that lianas in gaps reduced net forest woody biomass accumulation by 8.9% to nearly 18%. Consequently, lianas reduce whole-forest carbon uptake despite their relatively low biomass. This is the first study to demonstrate experimentally that plant–plant competition can result in ecosystem-wide losses in forest carbon, and it has critical implications for recently observed increases in liana density and biomass on tropical forest carbon dynamics

    Editorial: Lianas, ecosystems, and global change

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    Lianas (woody vines) are an abundant and diverse plant group in tropical ecosystems (Gentry, 1991; Dewalt et al., 2014). While they enhance forest canopy connectivity and provide food and shelter for tropical fauna (Yanoviak and Schnitzer, 2013; Schnitzer, 2018), lianas also intensely compete with trees for resources, and hence negatively influence a wide range of tropical ecosystem processes (van der Heijden et al., 2013), such as regeneration (Schnitzer et al., 2000; PĂ©rez-Salicrup, 2001), tree reproduction (GarcĂ­a LeĂłn et al., 2018), and carbon storage and sequestration (van der Heijden et al., 2015).Although the knowledge on lianas has developed significantly since Darwin's initial work on climbing plants (Darwin, 1865), studies in tropical forests still overwhelmingly focus on trees (da Cunha Vargas et al., 2020). This special issue brings together a collection of papers that provide new insights into the diversity of lianas, their impact on the ecosystem, and their relationships with climate

    Lianas decelerate tropical forest thinning during succession

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    The well-established pattern of forest thinning during succession predicts an increase in mean tree biomass with decreasing tree density. The forest thinning pattern is commonly assumed to be driven solely by tree-tree competition. The presence of non-tree competitors could alter thinning trajectories, thus altering the rate of forest succession and carbon uptake. We used a large-scale liana removal experiment over 7years in a 60- to 70-year-old Panamanian forest to test the hypothesis that lianas reduce the rate of forest thinning during succession. We found that lianas slowed forest thinning by reducing tree growth, not by altering tree recruitment or mortality. Without lianas, trees grew and presumably competed more, ultimately reducing tree density while increasing mean tree biomass. Our findings challenge the assumption that forest thinning is driven solely by tree-tree interactions; instead, they demonstrate that competition from other growth forms, such as lianas, slow forest thinning and ultimately delay forest succession

    Detection of spatial and temporal patterns of liana infestation using satellite-derived imagery

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    Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally

    Remote sensing liana infestation in an aseasonal tropical forest:addressing mismatch in spatial units of analyses

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    The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management
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