Biology subjects

Laliberte, E.

Publications and source records attributed to Laliberte, E..

14 recordsLinked to original sources

bioRxiv2026

MillionTrees: A global benchmark for tree detection across geography, sensor, and annotation geometry

Airborne tree monitoring using remotely sensed data, combined with the growth of computer vision models for object detection and segmentation, can facilitate broad scale assessment of tree health, ecology and ecosystem function. However, datasets for airborne tree detection have been largely limited to a single geography, resolution, or annotation approach, making it difficult to develop general models and benchmark their utility for broad applications. We compile existing and newly published annotation datasets to create a comprehensive dataset for training and evaluating airborne tree detection in RGB imagery. The MillionTrees dataset has over a million individual trees from a broad range of forest types, tree species, and geographic locations. In addition, it has six million weakly supervised annotations from a range of sources including airborne LiDAR and photogrammetry, arborist surveys, and simulated landscapes. An evaluation standard is provided to facilitate rapid model development. We benchmark several existing tree detection algorithms and demonstrate that while current open-source models do not generalize across the diverse datasets in the benchmark, training on MillionTrees improves both within-domain and out-of-domain performance. Held-out validation datasets using ground truth derived from terrestrial LiDAR paired with overhead RGB show that overfitting to test data is common and hyperparameter tuning is an underappreciated challenge towards a global zero shot tree detection method. We discuss the limitations of the currently available data for airborne RGB tree detection, how to create more robust evaluation data and the need for better understanding of the uncertainty and utility of tree detection from airborne remote sensing.

ecology↗

bioRxiv2025

Seeing the forest and the trees: a workflow for automatic acquisition of ultra-high resolution drone photos of tropical forest canopies to support botanical and ecological studies

Tropical forest canopies contain many tree and liana species, and foliar and reproductive characteristics useful for taxonomic identification are often difficult to see from the forest floor. As such, taxonomic identification often becomes a bottleneck in tropical forest inventories. Here we present a drone-based workflow to automatically acquire large volumes of close-up, ultra-high resolution photos of selected tree crowns (or specific locations over the canopy) to support tropical botanical and ecological studies (https://youtu.be/80goMEifpc4). Our workflow is built around the small, easy-to-use DJI Mavic 3 Enterprise (M3E) drone, which is equipped with a wide-angle and a telephoto camera. On day one, the pilot maps a forest area of up to [~]200 ha with the wide-angle camera to generate a high-resolution digital surface model (DSM) and orthomosaic using structure-from-motion (SfM) photogrammetry. On subsequent days, the pilot acquires close-up photos with the telephoto camera from up to 300 selected canopy trees per day. These close-up photos are acquired from 6 m above the canopy and contain a high level of visual detail that allows botanists to reliably identify many tree and liana species. The photos are geolocated with survey-grade accuracy using RTK GNSS, thus facilitating spatial co-registration with other data sources, including the photogrammetry products. The primary operational challenge of our workflow is the need to maintain RTK corrections with the drone to ensure that close-up photos are acquired exactly at the predefined locations. The maximum operational range we achieved was 3 km, which would allow the pilot to reach any tree within a [~]2800 ha area from the take-off point. Although our workflow was developed to support taxonomic identification of tropical trees and lianas, it could be extended to any other forest or vegetation type to support botanical, phenological, and ecological studies. We provide harpia, an open-source Python library to program these automatic close-up photo missions with the M3E drone (https://github.com/traitlab/harpia). Data/code for peer review statementWe provide harpia, an open-source Python library to program these automatic close-up photo missions (https://github.com/traitlab/harpia). Drone imagery and labelled close-up photo data are not yet publicly available because they were acquired with the goal of publishing benchmark machine learning datasets and models for tree and liana species classification and prior publication of the data would jeopardize this future publication.

ecology↗

bioRxiv2024

Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy

O_LIFoliar functional traits are key drivers of ecological processes in forests. Despite progress in forest foliar trait mapping from imaging spectroscopy, there is a need to build environment-specific, spectra-trait models trained from tree-level measurements to improve the accuracy of local trait maps. C_LIO_LIWe mapped 12 foliar functional traits in a mixed temperate forest using airborne imaging spectroscopy. Top-of-canopy foliar samples from tree crowns (n = 166), representing a total of 16 species, were collected using a drone platform to measure foliar traits for individual trees, from which tree-level crown spectra were also determined. Partial least squares regression (PLSR) models were used to predict foliar traits from tree-level reflectance spectra (400-2400 nm). C_LIO_LIThese models predicted leaf mass per area (LMA), specific leaf area (SLA) and equivalent water thickness (EWT) with high accuracy (R2 > 0.8, %RMSE < 15). Models for pigment, nitrogen and cellulose concentrations showed a moderate performance (R2 = 0.53-0.68, %RMSE = 17.24-21.31). Poorest performance was observed for lignin, carbon, leaf dry mass content (LDMC) and hemicellulose (R2 = 0.24-0.44, %RMSE = 20.67-26.13). C_LIO_LIHigh-resolution (1.25 m pixel-1) foliar trait maps were produced for the entire 16-km2 study area. Our study adds to the extensive research aiming to use remote sensing to monitor forest functional trait biodiversity at larger scales and provides models that capture intraspecific variation across many tree species from a mixed temperate forest in eastern Canada. C_LI

ecology↗

bioRxiv2024

Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease

Insect and pathogen outbreaks have a major impact on northern forest ecosystems. Even for pathogens that have been present in a region for decades, such as beech bark disease (BBD), new waves of mortality are expected in host populations. Hence, there is a need for innovative approaches to monitor their advancement extensively in real-time. Here we test whether airborne hyperspectral imaging - involving data from 344 wavelengths in the visible, near infrared (NIR) and short-wave infrared (SWIR) - can be used to assess beech bark disease severity in southern Quebec, Canada. Field data on disease severity were linked to the airborne hyperspectral data for individual beech crowns. Partial least-squares regression (PLSR) models using airborne imaging spectroscopy data predicted a small proportion of the variance in beech bark disease severity: the best model had an R2 of only 0.10. Wavelengths with the strongest contributions were from the NIR ([~]719 nm) and the SWIR ([~]1287 nm), which may suggest mediation by canopy greenness, water content and canopy architecture. Similar models using hyperspectral data taken directly on individual leaves had no explanatory power (R2 = 0). In addition, airborne and leaf-level hyperspectral datasets were uncorrelated. The failure of leaf-level models suggests that canopy structure was likely responsible for the limited predictive ability of the airborne model. Somewhat better performance in predicting disease severity was found using common band ratios for canopy greenness assessment (the Green Normalized Difference Vegetation Index, gNDVI, the Red-edge Inflexion Point, REIP, and the Normalized Phaeophytinization Index, NPQI); these variables explained up to 19% of the variation in disease severity. Overall, we argue that the complexity of hyperspectral data is not necessary for assessing BBD spread and that spectral data in general may not provide an efficient means of improving BBD monitoring on a larger scale.

plant biology↗

bioRxiv2024

Early Detection of an Invasive Alien Plant (Phragmites australis) Using Unoccupied Aerial Vehicles and Artificial Intelligence

The combination of unoccupied aerial vehicles (UAVs) and artificial intelligence to map vegetation represents a promising new approach to improve the detection of invasive alien plant species (IAPS). The high spatial resolution achievable with UAVs and recent innovations in computer vision, especially with convolutional neural networks, suggest that early detection of IAPS could be possible, thus facilitating their management. In this study, we evaluated the suitability of this approach for mapping the location of common reed (Phragmites australis subsp. australis) within a national park located in southern Quebec, Canada. We collected data on six distinct dates during the growing season, covering environments with different levels of reed invasion. Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Furthermore, a decrease in spatial resolution negatively affected recall (18% decrease between a spatial resolution of 0.15 cm pixel-1 and 1.50 cm pixel-1) but did not have a strong impact on precision (2% decrease). Despite challenges associated with common reed mapping in a post-treatment monitoring context, the use of UAVs and deep learning shows great potential for IAPS detection when supported by a suitable dataset. Our results show that, from an operational point of view, this approach could be an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS.

ecology↗

bioRxiv2023

Coordination among leaf and fine root traits across a strong natural soil fertility gradient

Unravelling how fundamental axes of trait variation correlate among leaves and roots and relate to nutrient availability is crucial for understanding plant distribution. While the leaf trait variation axis is linked to nutrient availability gradients, the response of root trait variation to the same gradients yields inconsistent results. We studied leaf and root trait variation among 23 co-occurring plant species along a 2-million year soil chronosequence to assess how leaf and root traits coordinate and how this resulting joint axis of variation relates to soil fertility. Mycorrhizal association types primarily structured the axes of leaf and root trait variation. However, when considering species abundance, soil nutrient availability was an important driver of trait distribution. Leaves that support rapid growth in younger, fertile soils were associated with roots of larger diameter and arbuscular mycorrhizal colonization. In contrast, leaves that favour nutrient conservation in nutrient-impoverished soil were associated with greater root hair length and phosphorus-mobilizing root exudates. At the species level, the signals deviated from the community-wide results presented above, highlighting the challenge of generalizing a specific set of root trait values that consistently meet the requirements of leaves supporting either rapid growth or survival.

ecology↗

bioRxiv2023

Root phosphatase activity is coordinated with the root conservation gradient across a phosphorus gradient in a lowland tropical forest

Soil phosphorus (P) is a growth-limiting nutrient in tropical ecosystems, driving diverse P-acquisition strategies among plants. Particularly, mining for inorganic P through phosphomonoesterase (PME) activity is essential, given the substantial proportion of organic P in soils. Yet the relationship between PME activity and other P-acquisition root traits remains unclear. We measured root PME activity and commonly-measured root traits, including root diameter, specific root length (SRL), root tissue density (RTD), and nitrogen concentration ([N]) in 18 co-occurring trees across soils with varying P availability to better understand trees response to P supply. Root [N] and RTD were inversely related, and that axis was related to soil P supply. Indeed, both traits correlated positively and negatively to PME activity, which responded strongly to P supply. Conversely, root diameter was inversely related to SRL, but this axis was not related to P supply. Suggesting that limiting similarity influenced variation along the diameter-SRL axis, explaining high local trait diversity. Meanwhile, environmental filtering tended to impact trait values along the root [N]-RTD axis. Overall, P availability indicator traits like PME activity and root hairs only tended to be associated with these axes, highlighting limitations of these axes in describing convergent adaptations at local sites.

plant biology↗

bioRxiv2023

Influence of Temperate Forest Autumn Leaf Phenology on Segmentation of Tree Species from UAV Imagery Using Deep Learning

Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, the influence of leaf senescence in temperate forests on tree species segmentation using a Convolutional Neural Network (CNN) has not yet been evaluated. Here, we acquired high-resolution UAV imagery over a temperate forest in Quebec, Canada on seven occasions between May and October 2021. We segmented and labelled 23,000 tree crowns from 14 different classes to train and validate a CNN for each imagery acquisition. The CNN-based segmentation showed the highest F1-score (0.72) at the start of leaf colouring in early September and the lowest F1-score (0.61) at peak fall colouring in early October. The timing of the events occurring during senescence, such as leaf colouring and leaf fall, varied substantially between and within species and according to environmental conditions, leading to higher variability in the remotely sensed signal. Deciduous and evergreen tree species that presented distinctive and less temporally-variable traits between individuals were better classified. While tree segmentation in a heterogenous forest remains challenging, UAV imagery and deep learning show high potential in mapping tree species. Our results from a temperate forest with strong leaf colour changes during autumn senescence show that the best performance for tree species segmentation occurs at the onset of this colour change.

ecology↗

bioRxiv2022

Predicting leaf traits across functional groups using reflectance spectroscopy

O_LIPlant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. C_LIO_LIWe measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. C_LIO_LIWithin the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2>0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2=0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. C_LIO_LIWe provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world. C_LI

ecology↗

bioRxiv2021

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

O_LIMore than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. C_LIO_LIUsing 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. C_LIO_LIOur pressed-leaf models were best at estimating LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514-0.790; %RMSE = 12.8-19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. C_LIO_LIThese results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the worlds preserved plant collections. C_LI

ecology↗

bioRxiv2021

Ectomycorrhizas accelerate decomposition to a greater extent than arbuscular mycorrhizas in a northern deciduous forest

It has been proposed that ectomycorrhizal (EcM) fungi slow down decomposition by competing with free-living saprotrophs for organic nutrients and other soil resources (known as the "Gadgil effect"), thereby increasing soil carbon sequestration. As such, this Gadgil effect should depend on soil organic matter age and quality, but this remains unstudied. In addition, the Gadgil effect is not expected to occur in arbuscular mycorrhizal (AM) forests since AM fungi cannot access directly nutrients from soil organic matter, yet few direct comparisons between EcM and AM forests have been made. We performed a two-year reciprocal decomposition experiment of soil organic horizons (litter - L, fragmented - F, humic - H) in adjacent temperate deciduous forests dominated by EcM or AM trees. Litterbags were made of different mesh sizes allowing or excluding ingrowth of external fungal hyphae, which are primarily mycorrhizal in these forests other than for the most-recent superficial litter horizon. As expected, organic matter originating from deeper horizons and from EcM forests was of lower quality (e.g. higher lignin to nitrogen ratios) and decomposed more slowly. However, contrary to the Gadgil effect, organic matter exposed to external fungal hyphae (i.e. primarily mycorrhizal) actually decomposed faster in both forest types, and this effect was strongest in EcM forests, particularly in the F horizon. Unexpectedly, organic matter decomposition was faster in EcM than in AM forests, regardless of organic matter origin. Overall, our study reinforces the view that temperate EcM forests store greater amounts of soil organic carbon than AM forests, but suggests that this is due to factors other than the Gadgil effect.

ecology↗

bioRxiv2021

Mycorrhizal dominance reduces forest-tree species diversity

Ectomycorrhizas and arbuscular mycorrhizas, the two most widespread plant-fungal symbioses, are thought to differentially influence tree species diversity, with positive plant-soil feedbacks favoring locally abundant ectomycorrhizal tree species and negative feedbacks promoting species coexistence and diversity in arbuscular mycorrhizal forests. While seedling recruitment studies and cross-biome patterns of plant diversity and mycorrhizal dominance support this hypothesis, it remains to be tested at the forest stand level over continental scales. Here, we analyze [~]85,000 forest plots across the United States to show that both ectomycorrhizal-dominated and arbuscular mycorrhizal-dominated forests show relatively low tree diversity, while forests with a mixture of mycorrhizal strategies support a higher number of tree species. Our findings suggest that mycorrhizal dominance, rather than mycorrhizal type, shapes tree diversity in forests.

ecology↗

bioRxiv2019

Partitioning plant spectral diversity into alpha and beta components

Plant spectral diversity -- how plants differentially interact with solar radiation -- is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma ({gamma}) diversity, into additive alpha (; within communities) and beta ({beta}; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral{gamma} -, {beta}-, and -diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify \"hotspots\" of spectral -diversity within a region, and discover spectrally unique areas that contribute strongly to {beta}-diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.

ecology↗

bioRxiv2017

A climosequence of chronosequences in southwestern Australia

To examine how climate affects soil development and nutrient availability over long timescales, we studied a series of four long-term chronosequences along a climate gradient in southwestern Australia. Annual rainfall ranged from 533 mm to 1185 mm (water balance from -900 mm to +52 mm) and each chronosequence included Holocene ([≤]6.5 ka), Middle Pleistocene (120-500 ka), and Early Pleistocene ([~]2000 ka) dunes. Vegetation changed markedly along the climosequence, from shrubland at the driest site to Eucalyptus forest at the wettest. The carbonate and P content of the parent sand declined along the climosequence, presumably linked to variation in offshore productivity. However, soil development and associated nutrient status followed remarkably consistent patterns along the four chronosequences. Pedogenesis involved decalcification and secondary carbonate precipitation in Holocene soils and leaching of iron oxides from Middle Pleistocene soils, ultimately yielding bleached quartz sands on the oldest soils. Along all chronosequences soil pH and total P declined, while C:P and N:P ratios increased, consistent with the predicted shift from N to P limitation of vegetation during ecosystem development. The expected unimodal pattern of leaf area index was most pronounced along wetter chronosequences, suggesting an influence of climate on the expression of retrogression. The four chronosequences do not appear to span a pedogenic climate threshold, because exchangeable phosphate and base cations declined consistently during long-term pedogenesis. However, the proportion of the total P in organic form was greater along wetter chronosequences. We conclude that soils and nutrient availability on the coastal sand plains of southwestern Australia change consistently during long-term pedogenesis, despite marked variation in modern vegetation and climate. The four chronosequences provide a rare soil-age x climate framework within which to study long-term ecosystem development.

ecology↗