Liana Infestation Research Articles

Tree diversity and liana infestation predict acoustic diversity in logged tropical forests

Logged tropical forests can retain a great deal of biodiversity, but there is substantial variation in the type and severity of habitat degradation caused by logging. Logging-induced habitat degradation can vary significantly at fine spatial scales, with differing effects on plant communities and the growth of lianas, which are woody, climbing vines that proliferate in degraded forests and infest trees by climbing onto them and competing for above and below ground resources. The impacts of such fine-scale variation in habitat structure on faunal diversity is relatively poorly known. We recorded soundscapes and variation in local-scale habitat structure in selectively logged and old-growth primary forests in Malaysian Borneo to examine how changes to logged forest structure predict variation in acoustic diversity indices that are known to correlate with biodiversity indices. We show that acoustic indices relating to higher soundscape diversity increase with liana prevalence but decline with tree species richness and are unaffected by the liana load of adult trees. Our results suggest that acoustic data represent a simple, practicable measure for detecting fine-scale patterns of biodiversity response to post-logging habitat structure. Our findings also suggest that retaining many trees lightly infested by lianas in logged forests is the optimal outcome for biodiversity. This emphasises the need for forest restoration that retains some climbers, rather than blanket-cutting of all stems in projects seeking to return post-logging forest communities towards their primary forest state.

Tropical forest lianas have greater non-structural carbohydrate concentrations in the stem xylem than trees.

Lianas (woody vines) are important components of tropical forests and are known to compete with host trees for resources, decrease tree growth and increase tree mortality. Given the observed increases in liana abundance in some forests and their impacts on forest function, an integrated understanding of carbon dynamics of lianas and liana-infested host trees is critical for improved prediction of tropical forest responses to climate change. Non-structural carbohydrates (NSC) are the main substrate for plant metabolism (e.g., growth, respiration), and have been implicated in enabling tree survival under environmental stress, but little is known of how they vary among life-forms or of how liana infestation impacts host tree NSC. We quantified stem total NSC (NSC) concentrations and its fractions (starch and soluble sugars) in trees without liana infestation, trees with more than 50% of the canopy covered by lianas, and the lianas infesting those trees. We hypothesized that i) liana infestation depletes NSC storage in host trees by reducing carbon assimilation due to competition for resources; ii) trees and lianas, which greatly differ in functional traits related to water transport and carbon uptake, would also have large differences in NSC storage, and that As water availability has a significant role in NSC dynamics of Amazonian tree species, we tested these hypotheses within a moist site in western Amazonia and a drier forest site in southern Amazonia. We did not find any difference in NSC, starch or soluble sugar concentrations between infested and non-infested trees, in either site. This result suggests that negative liana impact on trees may be mediated through mechanisms other than depletion of host tree NSC concentrations. We found lianas have higher stem NSC and starch than trees in both sites. The consistent differences in starch concentrations, a long term NSC reserve, between life forms across sites reflect differences in carbon gain and use of lianas and trees. Soluble sugar concentrations were higher in lianas than in trees in the moist site but indistinguishable between life forms in the dry site. The lack of difference in soluble sugars between trees and lianas in the dry site emphasize the importance of this NSC fraction for plant metabolism of plants occurring in water limited environments. Abstract in Portuguese and Spanish are available in the supplementary material.

What makes a good phorophyte? Predicting occupancy, species richness and abundance of vascular epiphytes in a lowland seasonal tropical forest

Epiphytes typically exhibit clustered distribution patterns, but predicting the spatial variation of their distribution at fine scales has long been a challenge. Taking advantage of a canopy crane giving access to 1.1 ha of lowland seasonal rainforest in Yunnan (China), we assess here which factors promote the probability that a given tree hosts epiphytes, and the variation of species richness and abundance of epiphytic spermatophytes and ferns among trees. Variation in epiphyte species richness as a function of host tree size, characteristics of its surrounding environment, topography and microclimatic conditions, were analyzed by Random Forest. Epiphytic spermatophytes and ferns occupied 2.3 and 10.8% of the available host trees, respectively. Significant models predicting which trees are more likely to host epiphytes than others were obtained, indicating that host tree characteristics and their local environment play a significant role in determining which host tree is most likely to be colonized. These models, as well as models for species richness and abundance, however, exhibited a moderate to low accuracy (r2 0.28 and 0.24 and of 0.12 and 0.14 for spermatophyte and fern richness and abundance, respectively). The best predictor of the presence of epiphytes on a tree, of its epiphytic species richness and abundance, was its DBH. In ferns, however, two peaks of species richness were observed, representing shade-loving ferns on small trees and sun-loving ferns on large trees. Microclimatic conditions and light intensity were the second best factor accounting for variation in species richness and abundance among trees. The contribution of liana infestation, host tree identity, and characteristics of neighboring trees were marginal. Our inclusion of a large number of host-tree characteristics and their local environment did not allow for an apparent improvement of model accuracy over studies with a more limited number of predictors, pointing to the role of chance upon tree colonization. Our results confirm the utmost importance of large trees with emergent canopies for the conservation of the epiphytic flora, but also indicate that epiphytic diversity assessments in tropical forests must also include small understorey trees, which should be further considered for conservation. The importance of the micro-climatic conditions that prevail at the level of each individual host tree further points to the necessity of maintaining a buffer zone around large host trees targeted for conservation.

Landscape‐scale drivers of liana load across a Southeast Asian forest canopy differ to the Neotropics

Abstract Lianas (woody vines) are a key component of tropical forests, known to reduce forest carbon storage and sequestration and to be increasing in abundance. Analysing how and why lianas are distributed in forest canopies at landscape scales will help us determine the mechanisms driving changes in lianas over time. This will improve our understanding of liana ecology and projections of tropical forest carbon storage now and into the future. Despite competing hypotheses on the mechanisms driving spatial patterning of lianas, few studies have integrated multiple tree‐level biotic and abiotic factors in an analytical framework. None have done so in the Palaeotropics, which are biogeographically and evolutionarily distinct from the Neotropics, where most research on lianas has been conducted. We used an unoccupied aerial system (UAS; drone) to assess liana load in 50‐ha of Palaeotropical forest canopy in Southeast Asia. We obtained data on hypothesised drivers of liana spatial distribution in the forest canopy, including disturbance, tree characteristics, soil chemistry and topography, from the UAS, from airborne LiDAR and from ground surveys. We integrated these in a comprehensive analytical framework to extract variables at an individual‐tree level and evaluated the relative strengths of the hypothesised drivers and their ability to predict liana distributions through boosted regression tree (BRT) modelling. Tree height and distance to canopy gaps were the two most important predictors of liana load, with relative contribution values in BRT models of 34.60%–45.39% and 7.93%–10.19%, respectively. Our results suggest that taller trees were less often and less heavily infested by lianas than shorter trees, opposite to Neotropical findings. Lianas also occurred more often, and to a greater extent, in tree crowns close to canopy gaps and to neighbouring trees with lianas in their crown. Synthesis. Despite their known importance and prevalence in tropical forests, lianas are not well understood, particularly in the Palaeotropics. Examining 2428 trees across 50‐ha of Palaeotropical forest canopy in Southeast Asia, we find support for the hypothesis that canopy gaps promote liana infestation. However, we also found that liana presence and load declined with tree height, which is opposite to well‐established Neotropical findings. This suggests a fundamental difference between Neotropical and Southeast Asian forests. Considering that most liana literature has focused on the Neotropics, this highlights the need for additional studies in other biogeographic regions to clarify potential differences and enable us to better understand liana impacts on tropical forest ecology, carbon storage and sequestration.

Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds

Lianas are self-supporting systems that are increasing their dominance in tropical forests due to climate change. As lianas increase tree mortality and reduce tree growth, one key challenge in ecological remote sensing is the separation of a liana and its host tree using remote sensing techniques. This separation can provide essential insights into how tropical forests respond, from the point of view of ecosystem structure to climate and environmental change. Here, we propose a new machine learning method, derived from Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting) algorithms, to separate lianas and trees using Terrestrial Laser Scanning (TLS) point clouds. We test our method on five tropical dry forest trees with different levels of liana infestation. First, we use a multiple radius search method to define the optimal radius of six geometric features. Second, we compare the performance of RF and XGBoosting algorithms on the classification of lianas and trees. Finally, we evaluate our model against independent data collected by other projects. Our results show that the XGBoosting algorithm achieves an overall accuracy of 0.88 (recall of 0.66), and the RF algorithm has an accuracy of 0.85 (recall of 0.56). Our results also show that the optimal radius method is as accurate as the multiple radius method, with F1 scores of 0.49 and 0.48, respectively. The RF algorithm shows the highest recall of 0.88 on the independent data. Our method provides a new flexible approach to extracting lianas from 3D point clouds, facilitating TLS to support new studies aimed to evaluate the impact of lianas on tree and forest structures using point clouds.

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity.

Lianas are a key growth form in tropical forests. Their lack of self‐supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta‐analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (−30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana‐specific traits were also responsible for a significant reduction of tree (−19%) and ecosystem (−7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large‐scale impacts of lianas on forest biogeochemical cycles.

Large herbivores suppress liana infestation in an African savanna

African savannas are the last stronghold of diverse large-mammal communities, and a major focus of savanna ecology is to understand how these animals affect the relative abundance of trees and grasses. However, savannas support diverse plant life-forms, and human-induced changes in large-herbivore assemblages-declining wildlife populations and their displacement by livestock-may cause unexpected shifts in plant community composition. We investigated how herbivory affects the prevalence of lianas (woody vines) and their impact on trees in an East African savanna. Although scarce (<2% of tree canopy area) and defended by toxic latex, the dominant liana, Cynanchum viminale (Apocynaceae), was eaten by 15 wild large-herbivore species and was consumed in bulk by native browsers during experimental cafeteria trials. In contrast, domesticated ungulates rarely ate lianas. When we experimentally excluded all large herbivores for periods of 8 to 17 y (simulating extirpation), liana abundance increased dramatically, with up to 75% of trees infested. Piecewise exclusion of different-sized herbivores revealed functional complementarity among size classes in suppressing lianas. Liana infestation reduced tree growth and reproduction, but herbivores quickly cleared lianas from trees after the removal of 18-y-old exclosure fences (simulating rewilding). A simple model of liana contagion showed that, without herbivores, the long-term equilibrium could be either endemic (liana-tree coexistence) or an all-liana alternative stable state. We conclude that ongoing declines of wild large-herbivore populations will disrupt the structure and functioning of many African savannas in ways that have received little attention and that may not be mitigated by replacing wildlife with livestock.

Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

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 (&gt;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.

Estimation of liana infestation above canopy in Bukit Bakar recreational park, Kelantan using an unmanned aerial vehicle

A study on lianas estimation has been conducted in 1 ha plot at Bukit Bakar Recreational Park, Machang, Kelantan. Liana infestation on tree crowns was assessed from above by using an unmanned aerial vehicle (UAV), DJI Phantom-4 Pro which was manually piloted. Liana load carried by trees were determined by calculating the percentage liana cover (%LC) and measuring crown occupancy index (COI). Collection of image data by UAV had been captured and geo-tagged with GPS location. The value of crown occupancy index (COI) depends on the accuracy of the percentage liana cover (%LC). Fourteen host trees infested by lianas were identified. The value COI of 14 lianas-infested trees were in the range between 0 – 2, indicating low and moderate infestation. One point was recorded with very low liana load, nine points with moderate liana and four points with low liana load on tree crowns with 0.76% to 29.9% of liana cover respectively. The moderate and lower values obtained was probably due to the lesser of anthropogenic disturbance and natural disturbance that occurred after 40 years of selective management system logging. The liana infestation was distributed homogeneously. The lower liana COI obtained, indicates the ongoing progress of forest succession in Bukit Bakar that gradually hindered liana proliferation within the proximity. This method could promote the local authority in forestry management particularly in managing the tree crown occupancy of liana in the logged-over forest.

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

AbstractThe 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.

Causes and consequences of liana infestation in southern Amazonia

Abstract Lianas, a key component of tropical forests, can limit growth of trees, impacting both the structure and functioning of forests, and are expected to benefit from fragmentation and potentially from global climatic changes. While it is critical to understand the impacts of liana infestation on contemporary tropical forests across large geographical areas, to date most liana studies have been focussed on single or few sites. We measured and quantified liana infestation of 16,066 trees with diameter ≥10 cm in 27 plots distributed across southern Amazonia, a region characterized by substantial ecological and environmental variation and environmental change. We examined the influence of potential drivers of liana infestation at the plot, species and individual tree level. Additionally, we evaluated the effect of liana infestation on tree growth. More than half of all trees had lianas in their crown. At the plot level, infestation by lianas was driven by forest structure but not by the studied climate or soil fertility variables, though low levels of liana infestation were found in seasonally flooded forests. At the tree level, larger and stouter trees had a greater proportion of their crown infested with lianas. At the species level, trees belonging to intrinsically slow‐growing, dense‐wooded species were more susceptible to liana infestation. Liana infestation had a negative impact on tree growth, with growth of heavily infested trees reduced by 33% compared to non‐infested. The impact of liana infestation on tree growth was strongest for the best‐lit tree crowns, indicating that lianas act to reduce the large competitive advantage that well‐lit trees otherwise hold over their neighbours. Synthesis. Lianas are a pervasive and influential feature of the extensive forests at the southern edge of Amazonia. The degree of liana infestation in forests was closely linked to species‐level variables such as potential growth and wood density as well as the size of the individual tree. The growth of heavily infested trees was particularly restricted by lianas, and especially so for trees growing in otherwise favourable conditions, indicating the potential for lianas not only to reduce forest growth rates overall, but also to modify competitive hierarchies among trees within tropical forests.

BIG TREES, BIG FALL: LARGE-DIAMETER TREES AND THE FATE OF CARBON STOCKS IN ATLANTIC FOREST REMNANTS

Large trees ( i.e . ≥ 50 cm diameter) are known for being more susceptible to stress, and its decline in fragmented forests have been reported as an important cause of carbon loss associated with forest degradation. In this paper, we investigate along edge-interior gradients (0-250 m) the distribution, biomass and health indicators (trunk and branch breaks, partial crown death, fungi infection, termites and liana infestation) of live and standing dead Large Trees (LTs; diameter ≥ 50 cm), in five seasonal Atlantic Forests (34 to 690 ha) of northern Parana state, Brazil. We sampled 118 live LTs in the five forest fragments, where aboveground biomass (AGB) ranged from 22 to 78 Mg/ha, living tree abundance ranged from 8 to 25.6 trees/ha, and standing dead trees ranged from 1 to 4 trees/ha. Larger forest fragments presented more living LTs, which were healthier and contain more biomass than LTs in small forest fragments. We found that edge effects were stronger than size effects for standing dead LT abundance; we found more dead trees up to 200 m from forest edge, independently of fragment size. Almost all living LTs had some health problem. The most frequent health problems were partial crown death, liana and termite infestation. Results suggests also that hard-wooded, slow-growing species tend to be replaced by soft-wooded, gap specialist species, coupled with an overall decline in LT density in small forest fragments, as well as in forests that suffered logging. Long-term prospects for these big carbon stocks are not encouraging, and given the low abundance of LTs in more degraded sites, strategies of intervention in large, century-old individual trees should be considered, such as liana infestation control and edge effect mitigation, in order to avoid further biodiversity and carbon losses.

Canopy Temperature Differences between Liana-Infested and Non-Liana Infested Areas in a Neotropical Dry Forest

Lianas (woody vines) are important non-structural elements of all tropical forests. Current field observations across the Neotropics suggest that liana abundance is rising as a result of forest disturbance, increasing atmospheric CO2, and more frequent extreme climate events. Lianas can cause mechanical stress on their host trees, thus increasing mortality, in addition to potentially reducing carbon storage capacity. Furthermore, previous studies have suggested that liana leaves have an overall higher temperature than tree leaves, which presents the question of whether these differences can be extended from the leaf to the canopy. In this context, the ability to detect these temperature differences from a remote sensing platform has so far not been put into test, despite the importance such knowledge can have in large-scale land surface modeling studies and liana extent monitoring. To partially fill this knowledge gap, we acquired thermal infrared data using an unmanned aerial vehicle (UAV) system over an intermediate tropical dry forest in Costa Rica, Central America. Classification results from a previous study in the same area were used to subset the thermal infrared images into liana-infested areas, non-liana infested areas, and forest gaps. The temperature differences between these three image components were then investigated using the Welch and Games–Howell post-hoc statistical tests. Our results suggest that liana-infested areas have, on average, a statistically significant higher temperature than non-liana infested areas. Shadowed forest gaps, used as reference, have a cooler temperature than forest canopies. Our findings on the temperature differences between liana-infested and non-liana infested areas support previous leaf-level observations and open the door to the use of new approaches for the classification and modeling of liana infestation in tropical ecosystems.

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests.

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana‐specific plant functional type and mechanisms representing liana–tree interactions (such as light competition, liana‐specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta‐analysis. We tested the model for an old‐growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner‐derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC ha−1 year−1, which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above‐ground biomass after 70 years of regrowth by up to 20 tC/ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old‐growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana‐rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

The effects of the invasive Harrisia cactus mealybug (Hypogeococcus sp.) and exotic lianas (Jasminum fluminense) on Puerto Rican native cacti survival and reproduction

Tropical dry forests are threatened by invasive species. In Puerto Rican dry forests, two of these invasive species are the Harrisia cactus mealybug Hypogeococcus sp. (HCM), a member of the H. pungens-complex (Hemiptera: Pseudococcidae), and the liana Jasminum fluminense (Oleaceae). Here, we aim to determine the effects of HCM and J. fluminense on the flower and fruit production of Pilosocereus royenii and on the short-term mortality of the native columnar (P. royenii and Stenocereus fimbriatus), semi-epiphytic (Selenicereus trigonus and Leptocereus quadricostatus), and globular (Melocactus intortus) cacti of Puerto Rico. To assess HCM infestation, we created a numerical infestation index with six categories, ranging from uninfested and healthy, to heavily infested cacti (0–5) based on the number and size of tumors. Flower and fruit production of P. royenii was observed at three sites, while the survival of all cactus species was observed at twelve sites for 2 years. Mortality of all cacti species increased with severe HCM infestation. Mortality was highest in M. intortus, and lowest in St. fimbriatus and L. quadricostatus. Mortality of P. royenii was negatively correlated to precipitation, and positively to HCM and liana infestation. Individuals of P. royenii that were heavily infested with both HCM and lianas were more likely to die than healthy cacti only infested with lianas, while P. royenii flower and fruit production was negatively correlated only with HCM infestation. This suggests that J. fluminense becomes a better competitor during dry periods when P. royenii is infested with HCM. While other members of the H. pungens-complex are considered biocontrol agents in places where columnar cacti are not native, this study demonstrates that HCM species found in Puerto Rican dry forests could potentially decrease the biodiversity of these cacti in their native habitats.

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests

Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.

Liana cover in the canopies of rainforest trees is not predicted by local ground‐based measures

AbstractLianas (woody climbers) are structural parasites of trees that compete with them for light and below‐ground resources. Most studies of liana–tree interactions are based on ground‐level observations of liana stem density and size, with these assessments generally assumed to reflect the amount of liana canopy cover and overall burden to the tree. We tested this assumption in a 1‐ha plot of lowland rainforest in tropical Australia. We recorded 1072 liana stems (≥1 cm diameter at breast height {dbh}) ha−1 across all trees (≥10 cm dbh) on the plot and selected 58 trees for detailed study. We estimated liana canopy cover on selected trees that hosted 0–15 liana individuals, using a 47‐m‐tall canopy crane. Notably, we found no significant correlations between liana canopy cover and three commonly used ground‐based measurements of liana abundance as follows: liana stem counts per tree, liana above‐ground biomass per tree and liana basal area per tree. We also explored the role of tree size and liana infestation and found that larger trees (≥20 cm dbh) were more likely to support lianas and to host more liana stems than smaller trees (≤20 cm dbh). This pattern of liana stem density, however, did not correlate with greater liana coverage in the canopy. Tree family was also found to have a significant effect on likelihood of hosting lianas, with trees in some families 3–4 times more likely to host a liana than other families. We suggest that local ground‐based measures of liana–tree infestation may not accurately reflect liana canopy cover for individual trees because they were frequently observed spreading through neighbouring trees at our site. We believe that future liana research will benefit from new technologies such as high‐quality aerial photography taken from drones when the aim is to detect the relative burden of lianas on individual trees.

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

Abstract 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. 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. 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. 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.

How do lianas and vines influence competitive differences and niche differences among tree species? Concepts and a case study in a tropical forest

AbstractLianas and other climbing plants are structural parasites of trees, generally reducing host tree survival, growth, and reproduction, yet their influences on the outcome of competition among tree species have remained largely unexplored.We propose that there are three distinct components to liana–tree interactions:prevalence, defined as the proportion of infested trees;load, defined as the mean liana cover on infested trees; andtolerance, defined as the effect of a given level of infestation on tree population growth rates. We introduce a new metric that integrates these components, the lianaburden, defined as the total effect of lianas on per capita population growth rates given current prevalence, load, and tolerance. Using these metrics, we quantify variation among 33 co‐occurring tropical tree species in liana–tree interactions and its relation with shade‐tolerance.The focal tree species vary strongly in liana prevalence, load, tolerance, and burden. Interspecific variation in tolerance is the largest contributor to interspecific variation in burden. Species rankings of per capita population growth rates under current liana infestation levels differ somewhat from rankings under liana‐free conditions, and differ strongly from rankings under uniformly high liana infestation. Thus, lianas alter competitive hierarchies to benefit tree species that are relatively tolerant of and/or resistant to lianas. Among the focal tree species, shade‐tolerance is positively correlated with liana tolerance and prevalence, but largely unrelated to load and burden, meaning shade‐tolerance does not predict which species are competitively disadvantaged by lianas. We describe a variety of mechanisms by which lianas may potentially increase or decrease niche differences among tree species, including interactions with spatial and temporal environmental niche partitioning, and potential differences among tree species in relative vulnerability to different liana species.Synthesis. Lianas, like other natural enemies, can in principle alter competitive hierarchies and niche structure of co‐occurring tree species, and our analyses suggest such influences are substantial in our focal tropical tree community and likely many other tree communities as well. Quantifying these effects requires a more comprehensive approach including analyses and modelling of dynamics of liana–tree interactions and their variation with tree and liana species identities.

On the estimation of tree mortality and liana infestation using a deep self-encoding network

Global environmental change leads to the variation in the relative coverage of dead trees, liana-infested and non-liana-infested trees in many tropical forests. Increase in the coverage of lianas had adverse effects on forested ecosystems such as decreasing tree growth rates and increasing tree mortality. This paper proposes a classification framework that integrates unmanned aerial vehicle systems (UAVs)-derived multi-spectral images and a Deep self-encoding network (DSEN) with the goal of monitoring and quantifying the relative coverage of dead trees, liana-infested, and non-liana-infested trees at high spatial scales. Today's UAVs-derived multi-spectral images provide the much necessary high resolution/quality data to monitor ecosystem-level processes at low cost and on demand. On the other hand, DSEN, a state-of-the-art classification approach that uses multiple layers to exploit abstract, invariant features from input data, has been proved to have the ability to acquire excellent results. This new classification framework, implemented at a tropical Dry Forest site in Costa Rica, provided accurate estimations of the relative coverage of dead trees, liana-infested trees, non-liana-infested trees, and non-forests. The approach opens the door to start exploring linkages between a booming UAVS industry and machine learning/Deep learning classifiers.